Regional Specific Issues of Foreign Direct Investment and Divestment

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

International Management. 2011; 4(5). DOI: 10.1504/EJIM.2010.034963

[25] Maček, A., Ovin, R., Divjak, M, Skoko H., and Horvat, T. Foreign Direct Investments' Openness in Local Communities – The case of Slovenia and Serbia. Economic Research. 2020. 10.1080/1331677X.2020.1819848

[26] Arte, P. An investigation into the impact of cross-national distance on foreign divestment. Acta Wasaensia. 2018; 401. Univesrity of Vaasa.

Management. Saint Petersburg.

pdf. [Accessed 12.12.2020]

jmacro.2006.02.004

[Accessed 12.12.2020]

[Internet]. 2016. Available from: https:// core.ac.uk/download/pdf/217178973.

[18] Basu, P., & Guariglia, A. Foreign direct investment, inequality, and growth. Journal of Macroeconomics. 2007; 29(4), 824-839. DOI:10.1016/j.

[19] Tandon, Y. M&A is takeover of firms in north, economies in south [online]. Geneva: Third world network [Internet]. 2000. Available from: http:// www.twnside.org.sg/title/firms.htm.

[20] Tsang, M. and Hauck, D. Stock Markets Contract as M&A Overtakes Equity Sales [Internet]. 2007. Available from: http://www.bloomberg.com/apps/ news?pid=newsarchive&sid=a7IeW7Bt

[21] Wyplosz, C. How Risky is Financial Liberalization in the Developing Countries? CEPR Discussion Paper. University of Geneva [online]. 2001. [Available from: http://www.cepr. org/pubs/dps/DP2724.asp. [Accessed

[22] Borga, M., Ibarlucea-Flores, P. and Sztajerowska, M. (2020). Divestments By Multinational Enterprises Accessed in December, 2020: https://www.oecd. org/investment/Divestments-bymultinational-enterprises-Investment-

[24] Ovin, R., Maček, A. How beneficial are inward C-B M&A for European countries? European Journal of

Policy-Insights.pdf. [Accessed

[23] Forbes. Ranked: The 25 Smartest Countries in The World. [Internet]. 2020. Available from: https://www.forbes.com/sites/ duncanmadden/2019/01/11/rankedthe-25-smartest-countries-in-theworld/?sh=5c77e7e163f7. [Accessed

D0dQ. [Accessed 12.12.2020]

12.12.2020]

12.12.2020].

12.12.2020]

**24**

**27**

inflation [2].

**Chapter 3**

Hotel Sector

*Mohamed Salem*

**Abstract**

**1. Introduction**

Identifying Location Drivers and

Barriers of FDI Determinants in

MENA Countries: Undertaking

The study aims to examine the location drivers and barriers influencing the foreign direct investment (FDI) in the hotel sector in selected Middle Eastern and North African (MENA) countries. Data of study variables was selected from fDi Intelligence, Euromonitor International, World Economic Forum, and Datamonitor. Findings indicated a significant correlation of investor, quality, rule and law, infrastructure quality, corruption, politics, government effect, gross domestic product (GDP) growth, total tax rate, and real export GDP with FDI. However, FDI inflows were significantly determined by the level of investment freedom, investor protection, and political stability. The study concluded that investment freedom, market

size, and stability of the country revealed the anticipated signs.

**Keywords:** barriers, drivers, FDI determinants, hotel sector, MENA countries

Middle Eastern and North African (MENA) countries consist of a group of Middle Eastern and North African countries that are characterized as economically diverse regions. Among MENA countries, gross domestic product (GDP) per capita differs significantly from Qatar (46,598 US\$) with the highest per capita income to Sudan, which has the lowest per capita income (719 US\$). Egypt, Iran, and Turkey are the countries with the largest populations among MENA countries in terms of population size. However, GDP rate of Turkey is the largest, whereas Bahrain has the smallest GDP in terms of economic size. On the contrary, Jordan, Bahrain, Sudan, and Lebanon have the highest net of foreign direct investment (FDI) inflows, while Yemen, Syria, and Iran have the lowest net of FDI inflows in terms of GDP percentage [1]. A major challenge for resource-poor countries is represented from high inflation, importing meaningful accounts of fuel and food, while major resources of rich countries in the region are lacking. In addition, Turkey, Sudan, and Iran comprise the highest consumer price, whereas Bahrain, Saudi Arabia, Libya, and Morocco accounted the lowest consumer price in terms of the rate of

Financial sector development is important for the expansion and development of real estate and hotel sectors to improve inward FDI in developing and emerging

## **Chapter 3**

## Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries: Undertaking Hotel Sector

*Mohamed Salem*

## **Abstract**

The study aims to examine the location drivers and barriers influencing the foreign direct investment (FDI) in the hotel sector in selected Middle Eastern and North African (MENA) countries. Data of study variables was selected from fDi Intelligence, Euromonitor International, World Economic Forum, and Datamonitor. Findings indicated a significant correlation of investor, quality, rule and law, infrastructure quality, corruption, politics, government effect, gross domestic product (GDP) growth, total tax rate, and real export GDP with FDI. However, FDI inflows were significantly determined by the level of investment freedom, investor protection, and political stability. The study concluded that investment freedom, market size, and stability of the country revealed the anticipated signs.

**Keywords:** barriers, drivers, FDI determinants, hotel sector, MENA countries

## **1. Introduction**

Middle Eastern and North African (MENA) countries consist of a group of Middle Eastern and North African countries that are characterized as economically diverse regions. Among MENA countries, gross domestic product (GDP) per capita differs significantly from Qatar (46,598 US\$) with the highest per capita income to Sudan, which has the lowest per capita income (719 US\$). Egypt, Iran, and Turkey are the countries with the largest populations among MENA countries in terms of population size. However, GDP rate of Turkey is the largest, whereas Bahrain has the smallest GDP in terms of economic size. On the contrary, Jordan, Bahrain, Sudan, and Lebanon have the highest net of foreign direct investment (FDI) inflows, while Yemen, Syria, and Iran have the lowest net of FDI inflows in terms of GDP percentage [1]. A major challenge for resource-poor countries is represented from high inflation, importing meaningful accounts of fuel and food, while major resources of rich countries in the region are lacking. In addition, Turkey, Sudan, and Iran comprise the highest consumer price, whereas Bahrain, Saudi Arabia, Libya, and Morocco accounted the lowest consumer price in terms of the rate of inflation [2].

Financial sector development is important for the expansion and development of real estate and hotel sectors to improve inward FDI in developing and emerging markets. The most important determinants of FDI are the real estate market, market liquidity, market maturity and transparency, and institutional real estate market size [3]. Other drivers to FDI include economic and demographic factors, institutional factors, infrastructure quality, and sociocultural factors in real estate. In contrast, data availability, trading, currency, liquidity, portfolio construction, tax, and fund structuring and trading are the barriers experienced by real estate and hotel sectors [4]. However, major motivation behind the selection of real estate and hotel sectors of MENA was the paucity of empirical evidence in this region. In addition, there is very scarce academic literature in the body of context which is entirely associated with average FDI determinants. Exploratory evidence has shown interest regarding investment in specific MENA countries [5]. The availability of data in the selected countries is another influential factor.

Initially, the real estate sector is segregated into four asset classes, which include residential, industrial, retail, and office. In contrast, hotels have not been considered as a commercial real estate asset class for assorted reasons including complexity for a quick exit strategy; lack of understanding of the industry by investors, resulting from unstable cash flows; and volatility when compared to other property assets [6]. Hotel investors have different motives and barriers when venturing into these sectors. For example, hotel investors are more anxious toward the progression of the tourism industry. Government sectors are also seeking to attract capital so that they can design policies to improve and stimulate the entire investment environment and FDI, to enlarge their economies.

Previously, FDI flows were comparatively scarce in the MENA region, as compared to the European Union (EU) and other emerging and developing countries [7]. An important challenge can be experienced from several features of the MENA countries for the inward FDI performance. It is a fact that this region is highly fastened on oil, which deteriorates the economic foundation, has a high unemployment rate, has a high population growth, and portrays a deteriorated regional integration and the financial and capital markets persevere undeveloped [8]. In addition, the weight of the state in the country is still high, where the literature stresses the low rates of return on human and physical capital, the underdevelopment of physical infrastructure, and the lack of transparency in spite of the privatizations in the last years [9].

The examination of MENA institutional systems emerges to be specifically influential since a substantial number of these economies have been experiencing intense economic and institutional reforms [10]. In addition, trade relations are encouraged by the Euro-Mediterranean Partnership agreement along with the developed reduction of trade barriers. Some economies have created special regimes and liberalized investment regulatory framework for FDI. Tax and custom duty breaks, capital market reform programs, and lowering ownership limitations are included in reforms [11]. It is essential to study this subject considering the facts and the comparatively sparse empirical research on FDI in MENA countries. Therefore, this study aims to examine the location drivers and barriers influencing the FDI in real estate and hotel sectors in MENA countries.

This study has presented its novelty in different ways. Firstly, it has selected eight real estate and hotel markets in MENA countries and collected time series data during 2003–2009. The rationale behind the selection of this time period is that it provides the adequate analysis of financial development factors. These traditional factors are no longer sufficient to explain the FDI alteration; however, the quality of economic freedom is increasingly integrated into the direction of investors' choices with increasing wave of globalization. In addition, suitable techniques are applied in this study to estimate the models based on a pooled tobit model. Secondly, this study has provided multidimensional evidence on the impact of location drivers

**29**

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries…*

and barriers on FDI in hotel sectors practically. Moreover, Dunning's ownership, location, and internalization (OLI) paradigm is selected as a theoretical background to demonstrate the behavior of hotel foreign investors toward the selected MENA markets. Lastly, the application of economic models takes place in the emerging market. The key themes from the models are comprehensively used to cover political, sociocultural, and economic variables. This study has tried to aid governments of MENA countries to understand the drivers and barriers to sectoral-associated FDI in these markets and assist governments for reconsidering their policies by endowing specific recommendations to foreign investment policymakers.

This paper is organized as follows. The empirical literature associated with FDI and institutions, highlighting the research on the MENA countries, is reviewed in Section 2. The data utilized in the empirical study is presented in Section 3 along with some descriptive statistics on the institutional and economic variables in the MENA region. The econometric approach is presented in Section 4 along with the

discussion of results. Section 5 presents the overall summary of this paper.

**2. Theoretical approach to location drivers and barriers of FDI**

The internationalization and traditional trade theories are embraced by the eclectic or OLI paradigm and systematized the advantages for firms that operate internationally [12, 13]. There are several benefits in selecting FDI when there are correspondingly ownership benefits such as location advantages, ownership advantages, and internationalization advantages. The significance of a firm owning assets is concerned in the ownership advantage such as exclusive productive procedures, patents, management skills, and pioneering technologies that can generate

Location is considerable when a firm acquires from its presence in a predefined market by promoting from circumstances such as lower production, transport costs, access to protected markets, special tax regimes, and lower risks [14]. Internationalized operations, which allow a reduction in transaction costs related with risks of managing technology, can reduce market imperfections such as the imbalance of international resources allocation [12]. Therefore, the selection of a specific location is based on particular conditions that are in its preference [15]. The core objective of eclectic paradigm of Dunning to the literature was to bring forward the several complementary theories, which help in identifying a series of variables that reflect the activities of multinational enterprises [14]. The emphasis of this approach is to implement these variables for trading, for the international organization of production, and for international production. It shows that three modes of internationalization can be covered within the same

The OLI framework of Dunning was extended by Holsapple et al. [16] into the subject of international real estate investments. They claim that international real estate investments were hybrids of portfolio investments and direct FDI. The portfolio P subparadigm in the framework was included in the extended OLI framework for allowing the disadvantage of being international in an international environment to be comprehensively implemented. The modified framework divides

*DOI: http://dx.doi.org/10.5772/intechopen.93290*

advantages in the future [14].

analytical framework [15].

**3.1 Ownership-specific advantages**

**3. Related literature**

## *Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries… DOI: http://dx.doi.org/10.5772/intechopen.93290*

and barriers on FDI in hotel sectors practically. Moreover, Dunning's ownership, location, and internalization (OLI) paradigm is selected as a theoretical background to demonstrate the behavior of hotel foreign investors toward the selected MENA markets. Lastly, the application of economic models takes place in the emerging market. The key themes from the models are comprehensively used to cover political, sociocultural, and economic variables. This study has tried to aid governments of MENA countries to understand the drivers and barriers to sectoral-associated FDI in these markets and assist governments for reconsidering their policies by endowing specific recommendations to foreign investment policymakers.

This paper is organized as follows. The empirical literature associated with FDI and institutions, highlighting the research on the MENA countries, is reviewed in Section 2. The data utilized in the empirical study is presented in Section 3 along with some descriptive statistics on the institutional and economic variables in the MENA region. The econometric approach is presented in Section 4 along with the discussion of results. Section 5 presents the overall summary of this paper.

## **2. Theoretical approach to location drivers and barriers of FDI**

The internationalization and traditional trade theories are embraced by the eclectic or OLI paradigm and systematized the advantages for firms that operate internationally [12, 13]. There are several benefits in selecting FDI when there are correspondingly ownership benefits such as location advantages, ownership advantages, and internationalization advantages. The significance of a firm owning assets is concerned in the ownership advantage such as exclusive productive procedures, patents, management skills, and pioneering technologies that can generate advantages in the future [14].

Location is considerable when a firm acquires from its presence in a predefined market by promoting from circumstances such as lower production, transport costs, access to protected markets, special tax regimes, and lower risks [14]. Internationalized operations, which allow a reduction in transaction costs related with risks of managing technology, can reduce market imperfections such as the imbalance of international resources allocation [12]. Therefore, the selection of a specific location is based on particular conditions that are in its preference [15]. The core objective of eclectic paradigm of Dunning to the literature was to bring forward the several complementary theories, which help in identifying a series of variables that reflect the activities of multinational enterprises [14]. The emphasis of this approach is to implement these variables for trading, for the international organization of production, and for international production. It shows that three modes of internationalization can be covered within the same analytical framework [15].

## **3. Related literature**

## **3.1 Ownership-specific advantages**

The OLI framework of Dunning was extended by Holsapple et al. [16] into the subject of international real estate investments. They claim that international real estate investments were hybrids of portfolio investments and direct FDI. The portfolio P subparadigm in the framework was included in the extended OLI framework for allowing the disadvantage of being international in an international environment to be comprehensively implemented. The modified framework divides

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

data in the selected countries is another influential factor.

ronment and FDI, to enlarge their economies.

zations in the last years [9].

markets. The most important determinants of FDI are the real estate market, market liquidity, market maturity and transparency, and institutional real estate market size [3]. Other drivers to FDI include economic and demographic factors, institutional factors, infrastructure quality, and sociocultural factors in real estate. In contrast, data availability, trading, currency, liquidity, portfolio construction, tax, and fund structuring and trading are the barriers experienced by real estate and hotel sectors [4]. However, major motivation behind the selection of real estate and hotel sectors of MENA was the paucity of empirical evidence in this region. In addition, there is very scarce academic literature in the body of context which is entirely associated with average FDI determinants. Exploratory evidence has shown interest regarding investment in specific MENA countries [5]. The availability of

Initially, the real estate sector is segregated into four asset classes, which include residential, industrial, retail, and office. In contrast, hotels have not been considered as a commercial real estate asset class for assorted reasons including complexity for a quick exit strategy; lack of understanding of the industry by investors, resulting from unstable cash flows; and volatility when compared to other property assets [6]. Hotel investors have different motives and barriers when venturing into these sectors. For example, hotel investors are more anxious toward the progression of the tourism industry. Government sectors are also seeking to attract capital so that they can design policies to improve and stimulate the entire investment envi-

Previously, FDI flows were comparatively scarce in the MENA region, as compared to the European Union (EU) and other emerging and developing countries [7]. An important challenge can be experienced from several features of the MENA countries for the inward FDI performance. It is a fact that this region is highly fastened on oil, which deteriorates the economic foundation, has a high unemployment rate, has a high population growth, and portrays a deteriorated regional integration and the financial and capital markets persevere undeveloped [8]. In addition, the weight of the state in the country is still high, where the literature stresses the low rates of return on human and physical capital, the underdevelopment of physical infrastructure, and the lack of transparency in spite of the privati-

The examination of MENA institutional systems emerges to be specifically influential since a substantial number of these economies have been experiencing intense economic and institutional reforms [10]. In addition, trade relations are encouraged by the Euro-Mediterranean Partnership agreement along with the developed reduction of trade barriers. Some economies have created special regimes and liberalized investment regulatory framework for FDI. Tax and custom duty breaks, capital market reform programs, and lowering ownership limitations are included in reforms [11]. It is essential to study this subject considering the facts and the comparatively sparse empirical research on FDI in MENA countries. Therefore, this study aims to examine the location drivers and barriers influencing

This study has presented its novelty in different ways. Firstly, it has selected eight real estate and hotel markets in MENA countries and collected time series data during 2003–2009. The rationale behind the selection of this time period is that it provides the adequate analysis of financial development factors. These traditional factors are no longer sufficient to explain the FDI alteration; however, the quality of economic freedom is increasingly integrated into the direction of investors' choices with increasing wave of globalization. In addition, suitable techniques are applied in this study to estimate the models based on a pooled tobit model. Secondly, this study has provided multidimensional evidence on the impact of location drivers

the FDI in real estate and hotel sectors in MENA countries.

**28**

ownership advantage dimension of Dunning into two subparadigms so that ownership is equivalent to ownership and portfolio in the ownership portfolio location framework.

Holsapple et al. [16] asserted that investors must assess both ownership and portfolio benefits when selecting on an investment in an international country. Further, it was demonstrated that the ownership advantages as the advantages possessed by enterprises in their host countries are transferable into international countries. It has been asserted that ownership must be taken into consideration where ownership is the advantage possessed by local enterprises to operate in the host country and claim that global investors must take into account the fixed costs of operating in a foreign environment.

## **3.2 Location-specific advantages**

Location considerations are apparently at the core of FDI in real estate. It has been explained that real estate actors are interested in specific countries and majorly relied on a greater extent on the type of direct and indirect barriers, experienced by market actors in host countries [17, 18]. D'Arcy [18] has claimed that the business culture, institutional environment, and regulatory barriers are essential aspects when developing strategies for internationalization. Location advantages need investors for asking the where question in the OPLI extended framework in order to explore the factors such as monetary policies, host country political risks, and laws and fiscal policies. Holsapple et al. [16] claimed that location benefits must be estimated alongside the recurring costs of being international such as differential treatment in the host country or operating a long distance from the investment. Holsapple et al. [16] argued that enterprises can simply obtain passive interests in current real estate assets in the host country if they depend on portfolio advantages and the location selection is less essential.

## **3.3 Internalization-specific advantages**

The internationalization advantage can be considered as an approach to exploit ownership by not contracting the related development activity but by objectively following it and maintaining control over it. It will be more advantageous for enterprises possessing ownership advantages to own the investment itself as compared to sell, franchise, or lease the advantage for foreign firms situated in the host country [16]. Internationalization of real estate activities is the process for determining the organizational mode by which stakeholders select to transfer capital across boundaries and intangible assets. Those intangible assets may entail human and management expertise, the reputation and knowledge of the internationalizing firm [18]. The capital transfer can be initiated and offered either from equity or debt positions, along with the financial structure related to the predefined transfer [18].

## **3.4 Empirical review**

The location served as the central point for several researches, where it is generally highlighted as a motive for FDI. The significance of the location in FDI has been substantially supplemented by a number of studies [19–21]. Despite the immense work on the phenomena, the determination of the core location drivers for the FDI remained unknown. The study by O'brien and Williams [22] stated that the globalization and the liberalization of the national economic relations impact the significance of location which works as an important determinant for FDI. This aligns with the study of Mao and Yang [23], claiming the significance of one determinant

**31**

trade policy.

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries…*

the markets, taxes, and wages and degree of business regulations [24].

as policy incentive serve as the major drivers of location for FDI.

the advantage which the location offers serves as a catalyst for the FDI.

key drivers for the internationalization of Spanish hotel chains.

Snyman and Saayman [32] highlighted the characteristics of 42 host countries which influence the FDI in hotel and tourism industries. The study highlighted that political stability, health, safety, and infrastructure, i.e., airports and roads, along with factors related to cost and skills, as well as market sizes such as international tourism demand and GDP, are the main indicators of FDI location. Similarly, Brida et al. [33] highlighted the size and the past internationalization experience act as

Phung [34] highlighted the locations' market size, trade openness, and macroeconomic stability as the prime variables for FDI. These three variables have been supported by various empirical studies, which focused on the concept of FDI [30, 34]. The explanation behind was provided by Crescenzi and Petrakos [35] stating that the investor is concerned with the return, which is in direct relation with the host country customer base size, the availability of the resources, and the implementation of

The labor market size and its low acquisition are also regarded as the location advantages for various developing countries. Phung [34] stressed upon these factors particularly for the developing countries since it is immobile as well as region-specific. The labor incentivizes the resources for investors as they are able to locate their function in the host country lowering their cost of production. In the hospitality sector, the estimation of labor force has found to be momentous in terms of the participation made by labor, its growth, and population stock [36, 37]. Wild and Wild [38] highlighted that due to the availability of the cheap labor in Mexico,

may vary with time, as its importance declines with increase in significance of

Theoretically, the selection of the location for the FDI has been promoted by various studies. For example, Mao and Yang [23] highlighted that FDI emerges as a consequence of the broad strategy formulated by the corporation in relation to the investment. It is based on maximizing profit while simultaneously perpetuating its global outreach. This is evident from the success of the United Kingdom, India, and Mexico for drawing hotel FDI in the periods 2005–2011 with respect to their size of

For Bayraktar [25], location serves as the main determinant for the FDI in terms of its investment decisions. The location drivers include land area, per capita income of the state, labor conditions, its production capacity, transportation, taxes, expenditures, as well as its agglomeration [26]. The review of the study by Yin et al. [27] illuminated that the conventional location theory, new location theory, and institutional environment regarding the labor cost, infrastructure, and market size as well

Omoniyi and Omobitan [28] stated that the flow of FDI points toward the activities adopt with an intent to expand their profitability and competitive prospects. The activities carried out by these foreign firms are reflected as the strategies which overcome the economic gap and prevail in the domestic capital of the developing countries, simulating their economic growth. Lien and Filatotchev [29] argued that the FDI investment in terms of location is conditioned to the state capital, operations involved for its regulation, as well as parameters laid out for its repatriation of the profit and capital. Ma and Raimondos [30] further asserted that since the foreign firms are profit-oriented, therefore, the first priority is to assess return capacity of the state regardless of its host country social conditions. Location, where the possibility of capital loss prevails, is usually neglected by the firms irrespective of the industry. Falk [24] supplemented that the FDI decision is significantly related to the advantage, which the location offers to the firm. This is further corroborated by the research of Al-Shammari, Al-Halaq, and Al-Shammari [31], which adds that

*DOI: http://dx.doi.org/10.5772/intechopen.93290*

another determinant.

## *Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries… DOI: http://dx.doi.org/10.5772/intechopen.93290*

may vary with time, as its importance declines with increase in significance of another determinant.

Theoretically, the selection of the location for the FDI has been promoted by various studies. For example, Mao and Yang [23] highlighted that FDI emerges as a consequence of the broad strategy formulated by the corporation in relation to the investment. It is based on maximizing profit while simultaneously perpetuating its global outreach. This is evident from the success of the United Kingdom, India, and Mexico for drawing hotel FDI in the periods 2005–2011 with respect to their size of the markets, taxes, and wages and degree of business regulations [24].

For Bayraktar [25], location serves as the main determinant for the FDI in terms of its investment decisions. The location drivers include land area, per capita income of the state, labor conditions, its production capacity, transportation, taxes, expenditures, as well as its agglomeration [26]. The review of the study by Yin et al. [27] illuminated that the conventional location theory, new location theory, and institutional environment regarding the labor cost, infrastructure, and market size as well as policy incentive serve as the major drivers of location for FDI.

Omoniyi and Omobitan [28] stated that the flow of FDI points toward the activities adopt with an intent to expand their profitability and competitive prospects. The activities carried out by these foreign firms are reflected as the strategies which overcome the economic gap and prevail in the domestic capital of the developing countries, simulating their economic growth. Lien and Filatotchev [29] argued that the FDI investment in terms of location is conditioned to the state capital, operations involved for its regulation, as well as parameters laid out for its repatriation of the profit and capital. Ma and Raimondos [30] further asserted that since the foreign firms are profit-oriented, therefore, the first priority is to assess return capacity of the state regardless of its host country social conditions. Location, where the possibility of capital loss prevails, is usually neglected by the firms irrespective of the industry. Falk [24] supplemented that the FDI decision is significantly related to the advantage, which the location offers to the firm. This is further corroborated by the research of Al-Shammari, Al-Halaq, and Al-Shammari [31], which adds that the advantage which the location offers serves as a catalyst for the FDI.

Snyman and Saayman [32] highlighted the characteristics of 42 host countries which influence the FDI in hotel and tourism industries. The study highlighted that political stability, health, safety, and infrastructure, i.e., airports and roads, along with factors related to cost and skills, as well as market sizes such as international tourism demand and GDP, are the main indicators of FDI location. Similarly, Brida et al. [33] highlighted the size and the past internationalization experience act as key drivers for the internationalization of Spanish hotel chains.

Phung [34] highlighted the locations' market size, trade openness, and macroeconomic stability as the prime variables for FDI. These three variables have been supported by various empirical studies, which focused on the concept of FDI [30, 34]. The explanation behind was provided by Crescenzi and Petrakos [35] stating that the investor is concerned with the return, which is in direct relation with the host country customer base size, the availability of the resources, and the implementation of trade policy.

The labor market size and its low acquisition are also regarded as the location advantages for various developing countries. Phung [34] stressed upon these factors particularly for the developing countries since it is immobile as well as region-specific. The labor incentivizes the resources for investors as they are able to locate their function in the host country lowering their cost of production. In the hospitality sector, the estimation of labor force has found to be momentous in terms of the participation made by labor, its growth, and population stock [36, 37]. Wild and Wild [38] highlighted that due to the availability of the cheap labor in Mexico,

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

framework.

of operating in a foreign environment.

and the location selection is less essential.

**3.3 Internalization-specific advantages**

**3.2 Location-specific advantages**

ownership advantage dimension of Dunning into two subparadigms so that ownership is equivalent to ownership and portfolio in the ownership portfolio location

Holsapple et al. [16] asserted that investors must assess both ownership and portfolio benefits when selecting on an investment in an international country. Further, it was demonstrated that the ownership advantages as the advantages possessed by enterprises in their host countries are transferable into international countries. It has been asserted that ownership must be taken into consideration where ownership is the advantage possessed by local enterprises to operate in the host country and claim that global investors must take into account the fixed costs

Location considerations are apparently at the core of FDI in real estate. It has been explained that real estate actors are interested in specific countries and majorly relied on a greater extent on the type of direct and indirect barriers, experienced by market actors in host countries [17, 18]. D'Arcy [18] has claimed that the business culture, institutional environment, and regulatory barriers are essential aspects when developing strategies for internationalization. Location advantages need investors for asking the where question in the OPLI extended framework in order to explore the factors such as monetary policies, host country political risks, and laws and fiscal policies. Holsapple et al. [16] claimed that location benefits must be estimated alongside the recurring costs of being international such as differential treatment in the host country or operating a long distance from the investment. Holsapple et al. [16] argued that enterprises can simply obtain passive interests in current real estate assets in the host country if they depend on portfolio advantages

The internationalization advantage can be considered as an approach to exploit ownership by not contracting the related development activity but by objectively following it and maintaining control over it. It will be more advantageous for enterprises possessing ownership advantages to own the investment itself as compared to sell, franchise, or lease the advantage for foreign firms situated in the host country [16]. Internationalization of real estate activities is the process for determining the organizational mode by which stakeholders select to transfer capital across boundaries and intangible assets. Those intangible assets may entail human and management expertise, the reputation and knowledge of the internationalizing firm [18]. The capital transfer can be initiated and offered either from equity or debt positions, along with the financial structure related to the predefined transfer [18].

The location served as the central point for several researches, where it is generally highlighted as a motive for FDI. The significance of the location in FDI has been substantially supplemented by a number of studies [19–21]. Despite the immense work on the phenomena, the determination of the core location drivers for the FDI remained unknown. The study by O'brien and Williams [22] stated that the globalization and the liberalization of the national economic relations impact the significance of location which works as an important determinant for FDI. This aligns with the study of Mao and Yang [23], claiming the significance of one determinant

**30**

**3.4 Empirical review**

various technology- and capital-rich investors in the United States shifted to Mexico for maximizing their profitability.

Another possible driver of location was highlighted by Bénassy-Quéré et al. [39]. According to them, the variation in terms of charged tax with regard to the offered good or service significantly impacts the flow of FDI in a state. The benefits in terms of agglomeration are further supplemented by Lien and Filatotchev [29] to improve the FDI flow in the country. Another study stated that when the location is successful in attracting FDI, it paves the path and serves as a catalyst for improving future FDI. These are similar to the stated results of Phung [34] highlighting the positive link of Japanese's manufacturing plants in the United States to agglomeration when the location is being chosen.

Dunning and Lundan [14] also laid out factors in relation to the policy framework incorporating the specific policies related to the country FDI. Considering the model for general policy, it is suggested that the host country business environment should be stable in terms of its economy and political settings as well as social conditions. Reflecting upon the literature further highlighted various barriers for FDI in correspondence to the hospitality industry. Paudel and Tiwari [40] stated that the delay of approval in terms of FDI hotel serves as a major hindering block for the country hospitality sector. Evaluating the hotel and tourism industries, Bissoon [41] reported that inadequate guidelines in terms of tourism policy also impact the FDI flow in a country, particularly for its hospitality sector. Inadequate support from the regulatory institutes is also reported as the barrier for FDI.

Hayakawa et al. (2011) researched 93 countries constituting 63 developing countries and showed that the instability of the politics hinders the capability of the country for FDI inflow. Another research of Topal [42] concerning the developing country further highlighted that the reduction of the economic and political risks in terms of capital loss improves the country hotel FDI flow.

The reduction and restriction in FDI are inclusive of various factors such as legislative and regulatory frameworks, bureaucracy, protection of the investors' finances, and restrictions on the foreign ownership [43]. Another research of Azémar and Desbordes [44] proposed that regulation in the product market of the host country, which may induce additional costs for businesses, serves as an FDI barrier for their entry.

## **4. Methods**

The study adopted a quantitative causal research design to identify and examine the location drivers and barriers influencing FDI in real estate and hotel sectors. The data was collected from eight MENA countries, which include Algeria, Egypt, Morocco, Qatar, Saudi Arabia, Turkey, Tunisia, and the United Arab Emirates (UAE) during 2003–2009 (i.e., prior the Arab spring). The selection of the host countries was indicated from the availability and accessibility of the data. Moreover, the econometric analysis was conducted for both time series and crosssectional data using the pooled tobit model technique.

The core purpose of this study is to examine the location drivers and barriers affecting real estate and hotel sector FDI location in the MENA countries. The study has employed a panel data, which is an authentic modeling strategy including both cross-sectional and time series analyses over a short period as selected in this study. The important characteristic of panel data that differentiates it from a cross section is the same as cross-sectional units followed over a predefined time period and allowed a study of the significance of lags in behavior or the outcomes of decisionmaking [45]. This information can be substantial as the number of economic

**33**

**Table 1.**

*Unit root test results.*

*\*\*\*Significant at 1%, \*significant at 10%.*

**5. Results**

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries…*

policies can be anticipated to have an effect merely after some time has passed. Each independent variable is lagged once a year, considering the likely form of a cause and effect relationship. Pooled tobit, fixed effect, and random effect models and pooled ordinary least squares (POLS) were anticipated to a balanced panel of appropriate data for testing the effect of the selected location drivers and barriers. The nominal FDI flows measure the dependent variable real estate FDI as provided by the FDI market database. The level of real estate investment in each country is normalized by distributing real estate FDI by the nominal GDP of the country. This allows the author for adjusting the level of investment for the size of the economy of each country. This modification facilitates for more direct comparisons between MENA countries as the size of the GDP for each country is potentially appropriate for the extent of real estate FDI received by each country. Euromonitor

Spurious results are produced by regressions performed on nonstationary time series variables. It is therefore essential for confirming that variables are stationary, which indicates that the mean and variance and probability distribution do not change over time and do not follow any patterns. An autoregressive model was used to estimate whether a time series variable is nonstationary based on a unit root test. The commonly used root tests such as Phillips-Perron and augmented Dickey-Fuller (ADF) tests lack coerciveness to differentiate the unit root from stationary alternatives. The conventional ADF-type tests of unit root further experience from the issue of low strength in order to reject the null hypothesis of stationarity of the series, particularly for short-span data. The study variables which include real visitor export, tourist arrival, gross domestic product, overall quality of infrastructure, total tax rates, corruption, government effectiveness, regulatory quality, rule of law, voice and accountability, levels of investors protection, and levels of human development were also treated as independent variables. The data for these variables were selected from fDi Intelligence, Euromonitor International, World Economic Forum, and Datamonitor.

This section is divided into two major results: unit root test and panel estimation. **Table 1** has summarized the results of the unit root test based on the propositions of Levin, Lin, and Chu (LLC) test. Findings have confirmed that the null

**Variables LLC test results Conclusion** HFDIGDP −16.5351\*\*\* I (0) HUMANDEVELOPMENT −1.29020\* I (0) INFRAQUAL −4.32190\*\*\* I (0) INVFREEDOM −3.55790\*\*\* I (0) REALVEXPOTGDP −2.44533\*\*\* I (0) RGDPGROWTH −6.12141\*\*\* I (0) TAGROWTH −5.95415\*\*\* I (0) TOTALTAXRATE −3.04086\*\*\* I (0) PROTECTINVESTOR −3.55790\*\*\* I (0)

hypothesis is rejected for all series investigated at their levels.

*DOI: http://dx.doi.org/10.5772/intechopen.93290*

International was used to derive the nominal GDP data.

## *Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries… DOI: http://dx.doi.org/10.5772/intechopen.93290*

policies can be anticipated to have an effect merely after some time has passed. Each independent variable is lagged once a year, considering the likely form of a cause and effect relationship. Pooled tobit, fixed effect, and random effect models and pooled ordinary least squares (POLS) were anticipated to a balanced panel of appropriate data for testing the effect of the selected location drivers and barriers.

The nominal FDI flows measure the dependent variable real estate FDI as provided by the FDI market database. The level of real estate investment in each country is normalized by distributing real estate FDI by the nominal GDP of the country. This allows the author for adjusting the level of investment for the size of the economy of each country. This modification facilitates for more direct comparisons between MENA countries as the size of the GDP for each country is potentially appropriate for the extent of real estate FDI received by each country. Euromonitor International was used to derive the nominal GDP data.

Spurious results are produced by regressions performed on nonstationary time series variables. It is therefore essential for confirming that variables are stationary, which indicates that the mean and variance and probability distribution do not change over time and do not follow any patterns. An autoregressive model was used to estimate whether a time series variable is nonstationary based on a unit root test. The commonly used root tests such as Phillips-Perron and augmented Dickey-Fuller (ADF) tests lack coerciveness to differentiate the unit root from stationary alternatives. The conventional ADF-type tests of unit root further experience from the issue of low strength in order to reject the null hypothesis of stationarity of the series, particularly for short-span data.

The study variables which include real visitor export, tourist arrival, gross domestic product, overall quality of infrastructure, total tax rates, corruption, government effectiveness, regulatory quality, rule of law, voice and accountability, levels of investors protection, and levels of human development were also treated as independent variables. The data for these variables were selected from fDi Intelligence, Euromonitor International, World Economic Forum, and Datamonitor.

## **5. Results**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

regulatory institutes is also reported as the barrier for FDI.

terms of capital loss improves the country hotel FDI flow.

sectional data using the pooled tobit model technique.

for maximizing their profitability.

tion when the location is being chosen.

barrier for their entry.

**4. Methods**

various technology- and capital-rich investors in the United States shifted to Mexico

Another possible driver of location was highlighted by Bénassy-Quéré et al. [39]. According to them, the variation in terms of charged tax with regard to the offered good or service significantly impacts the flow of FDI in a state. The benefits in terms of agglomeration are further supplemented by Lien and Filatotchev [29] to improve the FDI flow in the country. Another study stated that when the location is successful in attracting FDI, it paves the path and serves as a catalyst for improving future FDI. These are similar to the stated results of Phung [34] highlighting the positive link of Japanese's manufacturing plants in the United States to agglomera-

Dunning and Lundan [14] also laid out factors in relation to the policy framework incorporating the specific policies related to the country FDI. Considering the model for general policy, it is suggested that the host country business environment should be stable in terms of its economy and political settings as well as social conditions. Reflecting upon the literature further highlighted various barriers for FDI in correspondence to the hospitality industry. Paudel and Tiwari [40] stated that the delay of approval in terms of FDI hotel serves as a major hindering block for the country hospitality sector. Evaluating the hotel and tourism industries, Bissoon [41] reported that inadequate guidelines in terms of tourism policy also impact the FDI flow in a country, particularly for its hospitality sector. Inadequate support from the

Hayakawa et al. (2011) researched 93 countries constituting 63 developing countries and showed that the instability of the politics hinders the capability of the country for FDI inflow. Another research of Topal [42] concerning the developing country further highlighted that the reduction of the economic and political risks in

The reduction and restriction in FDI are inclusive of various factors such as legislative and regulatory frameworks, bureaucracy, protection of the investors' finances, and restrictions on the foreign ownership [43]. Another research of Azémar and Desbordes [44] proposed that regulation in the product market of the host country, which may induce additional costs for businesses, serves as an FDI

The study adopted a quantitative causal research design to identify and examine the location drivers and barriers influencing FDI in real estate and hotel sectors. The data was collected from eight MENA countries, which include Algeria, Egypt, Morocco, Qatar, Saudi Arabia, Turkey, Tunisia, and the United Arab Emirates (UAE) during 2003–2009 (i.e., prior the Arab spring). The selection of the host countries was indicated from the availability and accessibility of the data. Moreover, the econometric analysis was conducted for both time series and cross-

The core purpose of this study is to examine the location drivers and barriers affecting real estate and hotel sector FDI location in the MENA countries. The study has employed a panel data, which is an authentic modeling strategy including both cross-sectional and time series analyses over a short period as selected in this study. The important characteristic of panel data that differentiates it from a cross section is the same as cross-sectional units followed over a predefined time period and allowed a study of the significance of lags in behavior or the outcomes of decisionmaking [45]. This information can be substantial as the number of economic

**32**

This section is divided into two major results: unit root test and panel estimation. **Table 1** has summarized the results of the unit root test based on the propositions of Levin, Lin, and Chu (LLC) test. Findings have confirmed that the null hypothesis is rejected for all series investigated at their levels.



**Table 2.**

**35**

**Correlation**

HFDIGDP TOTALTAXRATE

REALVEXPOTGDP

TAGROWTH RGDPGROWTH

HUMAN DEVELOPMENT

INFRAQUAL

CORRUP GOVEFFECT

POLITIC PROTECTINVESTOR

REGQUALT

RULELAW VACCOUNT INVFREEDOM

0.0499 *\*\*\*, \*\*, and \* denote significant at 1%, 5%, and 10%, respectively.*

**Table 3.**

*Correlation matrix for dependent, independent, and control variables for hotel FDI panel.*

0.42\*\*\*

0.242\*

−0.014

−0.222\*

0.37\*\*\*

−0.41\*\*\*

0.1588

−0.0778

−0.39\*\*\*

−0.257\*

−0.328\*\*

−0.23\*

0.38\*\*\*

1

−0.0169

0.47\*\*\*

0.0386

−0.092

0.1126

0.1773

0.1028

0.2106

−0.1433

−0.0341

−0.294\*\*

−0.48\*\*\*

−0.114

1

−0.359\*\*\*

0.372\*\*\*

0.1862

−0.49\*\*\*

0.44\*\*\*

0.1839

0.1495

−0.311\*\*

0.253\*

0.65\*\*\*

−0.1255

0.2084

−0.34\*\*\*

0.328\*\*

1

−0.71\*\*\*

0.1007

0.016

0.1623

−0.50\*\*\*

0.1924

0.340\*\*

0.51\*\*\*

0.52\*\*\*

0.0136

1

−0.1763

−0.50\*\*\*

0.1196

−0.0285

0.0114

−0.336\*\*

−0.47\*\*\*

−0.0958

−0.1573

1

0.281\*\* 0.427\*\*\*

−0.54\*\*\*

0.0918

−0.068

0.48\*\*\*

−0.38\*\*\*

0.47\*\*\*

0.2075

0.329\*\*

1

−0.296\*\*

0.122

−0.024

0.07

−0.44\*\*\*

0.1852

0.0543

1

0.321\*\* 0.369\*\*\*

−0.35\*\*\*

0.41\*\*\*

0.0389

0.1669

−0.267\*\*

0.244\*

1

−0.241\*

0.2213

−0.064

0.44\*\*\*

−0.0025

1

0.238\* −0.1117

0.52\*\*\*

−0.0933

−0.061

−0.049

1

−0.304\*\*

−0.0962

−0.067

1

−0.0342

−0.2168

−0.0358

1

0.323\*\*

0.064

1

−0.395\*\*\*

1

1

**1**

**2**

**3**

**4**

**5**

**6**

**7**

**8**

**9**

**10**

**11**

**12**

**13**

**14**

**15**

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries…*

*DOI: http://dx.doi.org/10.5772/intechopen.93290*

*Descriptive statistics for variables employed in the hotel FDI panel.*


**Table 3.** *Correlation matrix for dependent, independent, and control variables for hotel FDI panel.*

## *Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries… DOI: http://dx.doi.org/10.5772/intechopen.93290*

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

**34**

**HFDIGDP** 

**TOTAL TAX** 

**REALVEXPOTGDP** 

**TAGROWTH** 

**RGDPGROWTH\_1**

**HUMAN** 

**INFRAQUAL\_1**

**DEVELOPMENT** 

**(−1)**

0.766104 0.758000 0.910000 0.583000 0.094394 −0.188557

1.993324

48 **RULELAW** 

**VACCOUNT** 

**INVFREEDOM** 

**(−1)**

**(−1)**

**(−1)**

0.732639 0.791667 1.000000

0.333333 0.139611 −1.120847 4.047233

48

48

48

3.382113

2.134145

−0.908529

0.243874

0.104689

13.62491

0.062500

30.00000

0.472500

70.00000

0.323250

50.00000

0.304635

46.25000

48

3.214573

0.135323

0.112819

0.230400

0.796320

0.493200

0.490380

**(−1)**

**(−1)**

**RATE (−1)**

**(−1)**

Mean Median Maximum Minimum

Std. Dev. Skewness

Kurtosis Observations

48 **CORRUP** 

**GOVEFFECT** 

**POLITIC** 

**(−1)**

**(−1)**

**(−1)**

Mean Median Maximum Minimum

Std. Dev. Skewness

Kurtosis Observations

**Table 2.**

*Descriptive statistics for variables employed in the hotel FDI panel.*

48

48

48

2.418904

7.339100

2.152811

0.397863

1.312546

0.206238

0.077095

0.086807

0.147541

0.250000

0.250000

0.250000

0.500000

0.750000

0.775000

0.333333

0.500000

0.462500

0.348611

0.520833

0.508854

48

48

3.817879

1.606463

1.860656

1.315563

−0.022776

0.705193

0.008347

23.16453

0.063158

0.000000

11.30000

0.001485

0.033068

76.90000

0.183237

0.004323

44.85000

0.033075

0.007495

40.54792

0.063554

9.871250 7.600000 35.95000 −18.00000

10.67751 0.412802 3.810357

48 **PROTECTINVESTOR** 

**(−1)**

4.612500 5.000000 5.700000 3.000000 0.935045 −0.699379

1.983307

48

5.856083 5.328500 20.83500 0.128000 3.467567 2.029346 9.235464

48 **REGQUALT** 

**(−1)**

0.731061 0.727273 0.954545 0.500000 0.142151 0.040110 1.998622

48


### **Table 4.**

*Correlation matrix (dependent vs. all independent variables) for hotel FDI panel.*

## **5.1 Unit root test**

The unit root test results indicated that the model can be anticipated regardless of any differenced variables. All the variables are stationary at level I (1), so that they are included in their actual form.

## **5.2 Panel model estimation**

The descriptive statistics and correlation matrix have been used to calculate the absolute values of the variables in the panel model estimation (**Tables 2** and **3**).

Hotel FDI inflows have zero values, making the POLS, RE, and FE biased and inconsistent with respect to the pooled tobit test ([46], p. 616). **Table 4** shows the correlation matrix between dependent and independent variables for hotel FDI panel. Results indicate a significant correlation of investor, quality, rule and law, infrastructure quality, corruption, politics, government effect, GDP growth, total tax rate, and real export GDP with FDI.

**Table 5** summarizes the results of pooled tobit regression for investigating the hotel barriers and determinants. From the findings, it is emphasized that the hotel FDI inflows are not significantly determined by control of corruption, regulatory quality, voice and accountability levels, government effectiveness, and rule of law. However, FDI inflows are significantly determined by the level of investment freedom, investor protection, and political stability (**Table 5**).

## **6. Discussion**

The study shows an insignificant effect of control of corruption, regulatory quality, voice and accountability levels, government effectiveness, and rule of law

**37**

**HFDI/GDP**

Constant HFDIGDP (−1)

TOTALTAXRATE (−1)

REALVEXPOTGDP (−1)

TAGROWTH (−1)

RGDPGROWTH\_1

HUMANDEVELOPMENT (−1)

INFRAQUAL\_1

CORRUP (−1) GOVEFFECT (−1)

POLITIC (−1) PROTECTINVESTOR (−1)

REGQUALT (−1)

RULELAW (−1)

VACCOUNT (−1)

INVFREEDOM (−1)

No. of observation

Left censored obs

Uncensored obs

Log likelihood

LR chi square

48

5 43 150.5809 26.86752

27.27717

28.05343

29.93649

30.93959

26.87071

28.9005

28.90633

32.08509

150.7858

151.1739

152.1154

152.617

150.5825

151.5974

151.6004

153.1897

43

43

43

43

43

43

43

43

5

5

5

5

5

5

5

5

48

48

48

48

48

48

48

48

0.025201\*\*

0.006230

0.006678 0.009782

0.013991

0.017354\*

−0.00805\*\*

−0.000622

−0.014402

0.016666

0.000241\*\*

0.005784

−0.001205

−0.006250

0.006244

0.007213

0.003966

0.022551\*

0.025477\*\*

0.03073\*\*\*

0.031677\*\*

0.023776\*\*

0.02503\*\*

0.026764\*\*

0.027620\*\*

0.017054\*\*

**1** −0.015938 0.036498 −0.000181\*\*\*

0.055010\*\*\*

3.71E-05 0.000621\*\*

0.000591\*\*

0.000605\*\*

0.000530\*

0.000483\*

0.000619\*\*

0.000607\*\*

0.000495\*

0.000596\*\*

3.81E-05

3.10E-05

6.12E-05

5.61E-05

3.58E-05

3.62E-05

2.11E-05

1.31E-05

0.05024\*\*\*

0.05439\*\*\*

0.05448\*\*\*

0.046490\*\*\*

0.055139\*\*\*

0.071612\*\*\*

0.05693\*\*\*

0.037851\*\*

−0.00017\*\*\*

−0.00018\*\*\*

−0.0001\*\*

−0.00025\*\*\*

−0.00018\*\*

−0.00023\*\*\*

−0.0002\*\*\*

−0.00021\*\*\*

0.024437

0.002213

−0.012777

−0.049757

0.036856

−0.024509

−0.010593

−0.069064

−0.019560\*

−0.026673\*

−0.026624\*\*

0.038978

−0.015241

−0.005379

−0.018302\*

−0.025179\*\*

**2**

**3**

**4**

**5**

**6**

**7**

**8**

**9**

**Pooled tobit**

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries…*

*DOI: http://dx.doi.org/10.5772/intechopen.93290*


## *Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries… DOI: http://dx.doi.org/10.5772/intechopen.93290*

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

**Correlation HFDIGDP** HFDIGDP (−1) 0.391034 TOTALTAXRATE (−1) −0.418039\*\*\* REALVEXPOTGDP (−1) 0.345872\*\*\* TAGROWTH (−1) 0.082825 RGDPGROWTH\_1 0.270806\*\* HUMANDEVELOPMENT (−1) −0.056115 INFRAQUAL\_1 0.355238\*\*\* CORRUP (−1) 0.359171\*\*\* GOVEFFECT (−1) 0.236522\* POLITIC (−1) 0.478399\*\*\* PROTECTINVESTOR (−1) −0.381454\*\*\* REGQUALT (−1) 0.335257\*\*\* RULELAW (−1) 0.240289\* VACCOUNT (−1) 0.010185 INVFREEDOM (−1) 0.080180

The unit root test results indicated that the model can be anticipated regardless of any differenced variables. All the variables are stationary at level I (1), so that

The descriptive statistics and correlation matrix have been used to calculate the absolute values of the variables in the panel model estimation (**Tables 2** and **3**). Hotel FDI inflows have zero values, making the POLS, RE, and FE biased and inconsistent with respect to the pooled tobit test ([46], p. 616). **Table 4** shows the correlation matrix between dependent and independent variables for hotel FDI panel. Results indicate a significant correlation of investor, quality, rule and law, infrastructure quality, corruption, politics, government effect, GDP growth, total

**Table 5** summarizes the results of pooled tobit regression for investigating the hotel barriers and determinants. From the findings, it is emphasized that the hotel FDI inflows are not significantly determined by control of corruption, regulatory quality, voice and accountability levels, government effectiveness, and rule of law. However, FDI inflows are significantly determined by the level of investment

The study shows an insignificant effect of control of corruption, regulatory quality, voice and accountability levels, government effectiveness, and rule of law

freedom, investor protection, and political stability (**Table 5**).

**36**

**6. Discussion**

**5.1 Unit root test**

**Table 4.**

they are included in their actual form.

*\*\*\*, \*\*, and \* denote significant at 1%, 5%, and 10%, respectively.*

*Correlation matrix (dependent vs. all independent variables) for hotel FDI panel.*

tax rate, and real export GDP with FDI.

**5.2 Panel model estimation**


## **Table 5.**

*Determinants of hotel FDI (pooled tobit).*

**39**

2003 to 2016.

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries…*

but a significant effect of level of investment freedom, investor protection, and political stability on hotel FDI inflows. The findings indicate a positive but insignificant effect of corruption on the FDI inflows, which reveals that the role of corrup-

Also, a negative and insignificant effect of regulatory quality on hotel FDI in MENA countries at 5% level of significance is identified, indicating it an unimportant determinant for hotel-related FDI decisions. Findings further indicated a negative and insignificant effect of rule of law on hotel FDI in MENA countries at 5% level of significance, indicating it an unimportant determinant for hotel-related FDI decisions. However, the findings have shown a positive but insignificant effect of voice and accountability on hotel FDI in MENA countries at 5% level of significance, indicating it an unimportant determinant for hotel-related FDI decisions. In contrast, findings provided a positive and significant effect of level of investment freedom on hotel FDI in MENA countries at 5% level of significance, referring it an

In this regard, Falk [24] indicated a positive but insignificant effect of corruption and tax rates on the hotel FDI projects in 104 host countries from 2005 to 2011. Nguyen et al. [47] have indicated a positive and significant impact of corruption on the FDI inflows in the service sector in Vietnam. The study has observed that the extent of corruption in Vietnam is lagging behind the country in terms of market institutions and the legal systems. In addition, Shah and Azam [48] also found the insignificant influence of corruption index on FDI inflows in MENA countries from

Similarly, the present study showed a positive and insignificant effect of government effectiveness on hotel FDI, which indicates an unimportant determinant in hotel FDI decisions. In this regard, Shah and Afridi [49] have found a significant impact of government effectiveness on hotel FDI in SAARC countries from 2006 to 2014. Subramanian and Subramanian [50] showed a significant impact on government effectiveness in the service sector in India. It further indicated that the steps attempted by the government are effective in short run but can be successful in the long run if exporters concentrate on value addition, which offsets the rising domestic interest rates, and market development and calculated measures of restrictions are taken. A positive and significant impact of political stability has been found on hotel

FDI inflows, which shows that political stability acts as a core determinant in attracting hotel-associated investments in specific markets. Mao and Yang [23] found a significant and positive impact of political stability on FDI inflows in Chinese domestic hotels. It further provided significant spillovers in domestic hotels of Eastern and Western China. Findings provided in the study of Tekin [51] indicated the negative and indirect effect of political instability on FDI inflows of Russian and Turkish tourism industries. Maclean et al. [52] outlined that macropolitical instability universalizes the growth of multinational hotel industry. The study provided that the postwar globalization and its associated discourses demon-

The panel model estimation showed a negative but significant effect of the strength of investor on the hotel FDI inflows at the 5% level of significance. There is empirical evidence in the hotel industry which shows a positive relationship between investors and FDI inflows. In addition, the study of Nam [53] indicated a significant effect of the strength of investors on the hotel FDI inflows by revealing a positive association between private and public hotels. The interest of investors toward the FDI inflows in Cambodian hotels is developed from the value-added benefits of each type of hotel investment. Kumar [54] on the other hand outlined a positive and significant impact of the strength of investors toward maintaining

*DOI: http://dx.doi.org/10.5772/intechopen.93290*

tion toward hotel FDI decisions is not critical.

ineffective determinant for hotel-related FDI decisions.

strate the ideology of the hotel industry.

budget hotels and quality of hotel services.

## *Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries… DOI: http://dx.doi.org/10.5772/intechopen.93290*

but a significant effect of level of investment freedom, investor protection, and political stability on hotel FDI inflows. The findings indicate a positive but insignificant effect of corruption on the FDI inflows, which reveals that the role of corruption toward hotel FDI decisions is not critical.

Also, a negative and insignificant effect of regulatory quality on hotel FDI in MENA countries at 5% level of significance is identified, indicating it an unimportant determinant for hotel-related FDI decisions. Findings further indicated a negative and insignificant effect of rule of law on hotel FDI in MENA countries at 5% level of significance, indicating it an unimportant determinant for hotel-related FDI decisions. However, the findings have shown a positive but insignificant effect of voice and accountability on hotel FDI in MENA countries at 5% level of significance, indicating it an unimportant determinant for hotel-related FDI decisions. In contrast, findings provided a positive and significant effect of level of investment freedom on hotel FDI in MENA countries at 5% level of significance, referring it an ineffective determinant for hotel-related FDI decisions.

In this regard, Falk [24] indicated a positive but insignificant effect of corruption and tax rates on the hotel FDI projects in 104 host countries from 2005 to 2011. Nguyen et al. [47] have indicated a positive and significant impact of corruption on the FDI inflows in the service sector in Vietnam. The study has observed that the extent of corruption in Vietnam is lagging behind the country in terms of market institutions and the legal systems. In addition, Shah and Azam [48] also found the insignificant influence of corruption index on FDI inflows in MENA countries from 2003 to 2016.

Similarly, the present study showed a positive and insignificant effect of government effectiveness on hotel FDI, which indicates an unimportant determinant in hotel FDI decisions. In this regard, Shah and Afridi [49] have found a significant impact of government effectiveness on hotel FDI in SAARC countries from 2006 to 2014. Subramanian and Subramanian [50] showed a significant impact on government effectiveness in the service sector in India. It further indicated that the steps attempted by the government are effective in short run but can be successful in the long run if exporters concentrate on value addition, which offsets the rising domestic interest rates, and market development and calculated measures of restrictions are taken.

A positive and significant impact of political stability has been found on hotel FDI inflows, which shows that political stability acts as a core determinant in attracting hotel-associated investments in specific markets. Mao and Yang [23] found a significant and positive impact of political stability on FDI inflows in Chinese domestic hotels. It further provided significant spillovers in domestic hotels of Eastern and Western China. Findings provided in the study of Tekin [51] indicated the negative and indirect effect of political instability on FDI inflows of Russian and Turkish tourism industries. Maclean et al. [52] outlined that macropolitical instability universalizes the growth of multinational hotel industry. The study provided that the postwar globalization and its associated discourses demonstrate the ideology of the hotel industry.

The panel model estimation showed a negative but significant effect of the strength of investor on the hotel FDI inflows at the 5% level of significance. There is empirical evidence in the hotel industry which shows a positive relationship between investors and FDI inflows. In addition, the study of Nam [53] indicated a significant effect of the strength of investors on the hotel FDI inflows by revealing a positive association between private and public hotels. The interest of investors toward the FDI inflows in Cambodian hotels is developed from the value-added benefits of each type of hotel investment. Kumar [54] on the other hand outlined a positive and significant impact of the strength of investors toward maintaining budget hotels and quality of hotel services.

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

**38**

**HFDI/GDP** Prob>Chi square

*\*, \*\*, and \*\*\* denote significance at 10%, 5%, and 1%, respectively.*

**Table 5.**

*Determinants of hotel FDI (pooled tobit).*

**1** 0.0004

0.0006

0.0005

0.0002

0.0001

0.0007

0.0003

0.0003

0.0001

**2**

**3**

**4**

**5**

**6**

**7**

**8**

**9**

**Pooled tobit**

## **7. Limitations**

The study presented several limitations based on the model and findings. Firstly, it used data on an aggregate level, which augments the possibility that some information is lost during the data collection and transformation process. Secondly, only annual time series data is used to compute the panel models. Thirdly, data has been extracted from 2003 to 2009, which indicates doubts whether a vigorous econometric analysis can be conducted. It was also not possible for expanding more on the qualitative data end since the study period was based on only 6 years. However, this is an opportunity for future researchers to collect more primary data from policymakers, hotel markets intermediaries, and foreign investors. Lastly, public and private agencies have been used as potential sources to measure the accuracy of the data and, therefore, challenge the accuracy of such data. However, findings obtained through such data have provided significant empirical evidence for hotel investors and markets.

## **8. Conclusion**

The study was aimed to investigate the location drivers and barriers of FDI determinants in MENA countries within the hotel sectors. In this regard, findings obtained from the econometric analysis of hotel FDI inflows have shown that hotel sector-specific variables and country-specific factors are influencing the FDI inflows in MENA countries, though the findings of the study are somewhat unsupportive. For instance, hotel FDI values are insignificant for FDI flows for the selected MENA countries, whereas investment freedom, market size, and stability of the country revealed the anticipated signs. In addition, the study showed an opposite sign of the investor protection, indicating that hotel investors are reducing such risks significantly and accomplishing a high extent of control through specific contractual agreements.

It further indicated three common barriers, which include taxation, level of investment freedom, and political instability. These barriers have explained why MENA countries attract hotel FDI at the least extent than to other countries at a similar stage of development. The level of investment freedom is found to be a significant and important barrier in explaining hotel-related FDI. Terrorism, political instability, and violence are shown as important barriers in restricting MENA countries toward hotel FDI inflows. Lastly, taxation also restricts hotels in MENA countries to attract toward FDI inflows.

## **9. Recommendations**

Several recommendations are proposed for future research in this context. Firstly, it is recommended that the current research should be extended to investigate the influence of FDI determinants on economic growth of MENA countries. It will be of beneficial interest to indicate the significance of hotel sector to explain the wider economy and whether governments are making significant efforts to acquire explicit advantage for hotel sector as part of their economic growth. Secondly, it is recommended to undertake different regions or countries such as South East Asia or Eastern Europe. This may include hotel-related FDIs. Data sources could vary though, especially for FDI-related variables. Concerning independent variables, the current study already tried pragmatically a number of variables for the first time and thus suggests that variables from this study as well as other variables will be interesting to be empirically tested.

**41**

**Author details**

Mohamed Salem

Australian College of Kuwait, Kuwait City, Kuwait

provided the original work is properly cited.

\*Address all correspondence to: msalem@ack.edu.kw

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries…*

*DOI: http://dx.doi.org/10.5772/intechopen.93290*

*Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries… DOI: http://dx.doi.org/10.5772/intechopen.93290*

## **Author details**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

provided significant empirical evidence for hotel investors and markets.

The study was aimed to investigate the location drivers and barriers of FDI determinants in MENA countries within the hotel sectors. In this regard, findings obtained from the econometric analysis of hotel FDI inflows have shown that hotel sector-specific variables and country-specific factors are influencing the FDI inflows in MENA countries, though the findings of the study are somewhat unsupportive. For instance, hotel FDI values are insignificant for FDI flows for the selected MENA countries, whereas investment freedom, market size, and stability of the country revealed the anticipated signs. In addition, the study showed an opposite sign of the investor protection, indicating that hotel investors are reducing such risks significantly and accomplishing a high extent of control through specific

It further indicated three common barriers, which include taxation, level of investment freedom, and political instability. These barriers have explained why MENA countries attract hotel FDI at the least extent than to other countries at a similar stage of development. The level of investment freedom is found to be a significant and important barrier in explaining hotel-related FDI. Terrorism, political instability, and violence are shown as important barriers in restricting MENA countries toward hotel FDI inflows. Lastly, taxation also restricts hotels in MENA

Several recommendations are proposed for future research in this context. Firstly, it is recommended that the current research should be extended to investigate the influence of FDI determinants on economic growth of MENA countries. It will be of beneficial interest to indicate the significance of hotel sector to explain the wider economy and whether governments are making significant efforts to acquire explicit advantage for hotel sector as part of their economic growth. Secondly, it is recommended to undertake different regions or countries such as South East Asia or Eastern Europe. This may include hotel-related FDIs. Data sources could vary though, especially for FDI-related variables. Concerning independent variables, the current study already tried pragmatically a number of variables for the first time and thus suggests that variables from this study as well as other variables will be

The study presented several limitations based on the model and findings. Firstly, it used data on an aggregate level, which augments the possibility that some information is lost during the data collection and transformation process. Secondly, only annual time series data is used to compute the panel models. Thirdly, data has been extracted from 2003 to 2009, which indicates doubts whether a vigorous econometric analysis can be conducted. It was also not possible for expanding more on the qualitative data end since the study period was based on only 6 years. However, this is an opportunity for future researchers to collect more primary data from policymakers, hotel markets intermediaries, and foreign investors. Lastly, public and private agencies have been used as potential sources to measure the accuracy of the data and, therefore, challenge the accuracy of such data. However, findings obtained through such data have

**7. Limitations**

**8. Conclusion**

contractual agreements.

**9. Recommendations**

countries to attract toward FDI inflows.

interesting to be empirically tested.

**40**

Mohamed Salem Australian College of Kuwait, Kuwait City, Kuwait

\*Address all correspondence to: msalem@ack.edu.kw

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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112-121

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[1] Binkhamis M. Barriers and Threats to Foreign Direct Investment (FDI) in Saudi Arabia: A Study of Regulatory, Political and Economic Factors. PhD Thesis. De Montfort University; 2016

**References**

[2] Miniesy RS, Elish E. Is MENA different? An investigation of the host country determinants of Chinese Outward foreign direct investment. In Economic Research Forum Working Papers (No. 1024). RePEc (Research

Papers in Economics); 2016

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2016;**34**(2)116-142

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[4] Anyanwu JC, Yameogo ND. What drives foreign direct investments into West Africa? An empirical investigation. African Development Review. 2015;**27**(3):199-215. DOI:

[5] Gharaibeh AMO. The determinants of foreign direct investment-empirical evidence from Bahrain. International Journal of Business and Social Science.

[6] Salem M, Baum A. Determinants of foreign direct real estate investment in selected MENA countries. Journal of Property Investment & Finance.

[7] Hisarciklilar M, Kayam S, Kayalica M. Locational Drivers of FDI in MENA Countries: A Spatial Attempt. 2006

[8] Chauvin N. FDI Flows in the MENA Region: Features and Impacts. Institute for Emerging Market Studies (IEMS), Moscow School of Management. Vol. 13.

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OECD Publishing; 2010

2002. p. 108

International Journal of Contemporary

[34] Phung H. Determinants of FDI into Developing Countries. Mark A Endowment Summer Research fund in

[35] Crescenzi R, Petrakos G. The European Union and its neighboring countries: The economic geography of trade, foreign direct investment and development. Environment and Planning C: Government and Policy. 2016;**34**(4):581-591. DOI: 10.1177/0263774x16642640

[36] Shahmoradi B, Baghbanyan M. Determinants of foreign direct investment in developing countries: A panel data analysis. Asian Economic and

Financial Review. 2011;**1**(2):49

[37] Williams K. Foreign direct investment in Latin America and the Caribbean: An empirical analysis. Latin American Journal of Economics. 2015;**52**(1):57-77. DOI: 10.7764/

[38] Wild and Wild. International Business: The Challenges of Globalization. 6th ed. Pearson Education Limited: Essex; 2012

[39] Bénassy-Quéré A, Fontagné L, Lahrèche-Révil A. How does FDI react to corporate taxation? International Tax and Public Finance. 2005;**12**(5):583-603.

DOI: 10.1007/s10797-005-2652-4

[41] Bissoon O. Corporate social

[40] Paudel RC, Tiwari GP. Barriers and solution for better investment climate in Nepal. International Journal of Humanities and Social Science. 3. 2018

responsibility in Mauritius: An analysis of annual reports of multinational hotel groups. Asian Journal of Sustainability and Social Responsibility. 2018;**3**(1):2. DOI: 10.1186/s41180-017-0017-4

laje.52.1.57

Hospitality Management. 2015;**27**(5):1024-1047

Economics; 2016

**44**

[52] Maclean M, Harvey C, Suddaby R, O'Gorman K. Political ideology and the discursive construction of the multinational hotel industry. Human Relations. 2018;**71**(6):766-795. DOI: 10.1177/0018726717718919

[53] Nam S. Assessing the impacts of foreign direct investment (FDI) on local skills development: The hotel industry in Siem reap [doctoral dissertation]. Cambodia: Auckland University of Technology; 2018

[54] Kumar K. Determinants of growth and challenges in hotel industry: A study of budget and luxury segments of hotel business in India. Clear International Journal of Research in Commerce & Management. 2016;**7**(3):24-31. DOI: 10.4172/2169-0286.1000169

**47**

**Chapter 4**

**Abstract**

Behavior of Global Investors

in the Industry Level

*Ignatius Roni Setyawan*

the best for the portfolios.

**1. Introduction**

in Five ASEAN Stock Markets

When the capital markets in ASEAN are integrated, global investors can still pursue the benefits of international diversification more than in the country level but in also in the industry level. The intended international diversification is diversification between industries. To implement this diversification between industries, measurement tools are needed to determine the benefits of international diversification directly. The intended instrument tool is a correlation which in this study uses country level correlation and industry level correlation. In order for these two correlations to be effective, it is necessary to make a hypothesis test to find if there is a difference in the level of integration between country and industry levels in ASEAN. To analyze industry level correlations, Equally Weighted and Value Weighted estimation procedures are required to test the construction of industry sector sample data according to GICS. The results show that there are differences in the level of integration between country and industry levels in ASEAN and the implication that the Indonesian capital market provide the greatest benefits and global investors could utilize all GICS industrial sectors as a reliable portfolio. The practical implications of these final result is choosing countries and industries are

**Keywords:** inter-industry diversification benefit in ASEAN, country and industrial

Until now ASEAN stock exchanges are still a mainstay of potential portfolios for global investors. The main consideration is due to the higher yield offered than other regional exchanges. The reason for this high yield offer is due to high banking interest rates in ASEAN. In the development of international diversification studies, it turns out that global investors are also concerned with aspects of the industry besides of course high returns. The classic study from Roll [1] states that when the benefits of international diversification cannot be achieved due to the process of increasing the integration of capital markets in one region, the choice of diversification between industries becomes more relevant. This is based on the idea that the process of capital market integration is more rapid at the country level and will be less rapid at the industry level. Several other researchers stated that industrial factors are more non-systematic than state factors. Since the

level correlation, Global Industry Classification Standard, global investors

## **Chapter 4**

## Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level

*Ignatius Roni Setyawan*

## **Abstract**

When the capital markets in ASEAN are integrated, global investors can still pursue the benefits of international diversification more than in the country level but in also in the industry level. The intended international diversification is diversification between industries. To implement this diversification between industries, measurement tools are needed to determine the benefits of international diversification directly. The intended instrument tool is a correlation which in this study uses country level correlation and industry level correlation. In order for these two correlations to be effective, it is necessary to make a hypothesis test to find if there is a difference in the level of integration between country and industry levels in ASEAN. To analyze industry level correlations, Equally Weighted and Value Weighted estimation procedures are required to test the construction of industry sector sample data according to GICS. The results show that there are differences in the level of integration between country and industry levels in ASEAN and the implication that the Indonesian capital market provide the greatest benefits and global investors could utilize all GICS industrial sectors as a reliable portfolio. The practical implications of these final result is choosing countries and industries are the best for the portfolios.

**Keywords:** inter-industry diversification benefit in ASEAN, country and industrial level correlation, Global Industry Classification Standard, global investors

## **1. Introduction**

Until now ASEAN stock exchanges are still a mainstay of potential portfolios for global investors. The main consideration is due to the higher yield offered than other regional exchanges. The reason for this high yield offer is due to high banking interest rates in ASEAN. In the development of international diversification studies, it turns out that global investors are also concerned with aspects of the industry besides of course high returns. The classic study from Roll [1] states that when the benefits of international diversification cannot be achieved due to the process of increasing the integration of capital markets in one region, the choice of diversification between industries becomes more relevant. This is based on the idea that the process of capital market integration is more rapid at the country level and will be less rapid at the industry level. Several other researchers stated that industrial factors are more non-systematic than state factors. Since the classic study of Roll [1] states that industrial factors have an important role in the effectiveness of international diversification, many researchers such as Ratner and Leal [2], Richard [3], Hwang and Sitorus [4], Do et al. [5], and Chen et al. [6] began to use industry data such as Global Industry Classification Standard (GICS). According to Menchero and Morozov [7] GICS has been more globalized than other industry standards since it was handled by Morgan Stanley Capital International (MSCI).

This study intends to measure the benefits of international diversification on the ASEAN stock exchanges by using unconditional correlations of the return of the five market indices in ASEAN including KLCI (Malaysia), STI (Singapore), Thailand (SET), Philippines (PSI), and IHSG (Indonesia), respectively, with the return of MSCI. The type of unconditional correlation of each index with MSCI is seen to be stronger than the correlation between the indexes of each ASEAN country itself and the VAR and VECM cointegration analysis that has been used by Endri [8], Robiyanto and Ernayani [9], and other researchers. The advantage of this correlation is that it is able to regulate the magnitude of the correlation number lower than the correlation among ASEAN countries' own indexes. The low magnitude of correlation supports the potential benefits of international diversification in ASEAN. VAR and VECM cointegration analysis is only able to prove the presence or absence of capital market integration and is still unable to calculate the benefits of international diversification.

What is new in this study is the use of unconditional level industry correlations in each ASEAN country. The use of industry level correlations to reaffirm the argument of Click and Plummer [10] about international diversification between industries is more effective in ASEAN than international diversification between countries. This is because ASEAN capital markets have been integrated since the last 20 years. Industry unconditional level correlations are calculated in equally weighted (EW) and value-weighted (VW), both with local currency (LOC) and USD referring to the correlation estimation procedure from Kim [11]. The use of EW and VW will be able to produce different levels of correlation between industries in the five ASEAN exchanges and will further determine the level of benefit of diversification among different industries. The use of EW and VW is also in line with the research conducted by Vo et al. [12], which is based on the research of Nguyen [13].

Thus the main problem of this study is "there are still differences in the level of integration of capital markets at the country and industry levels in the five ASEAN capital markets." The different levels of integration show the different benefits of international diversification at the country and industry levels. This issue is representative of studies from Setyawan [14] and Setyawan and Wibowo [15] which emphasize the importance of correlation as a measure of capital market integration as well as the benefits of international diversification rather than cointegration analysis, namely, vector autoregression (VAR) and vector error correction model (VECM).

The reason for setting the observation period from 2006 to 2009 in this study is because it is related to my study period in a doctoral program several years ago, at which time I was asked by my supervisor to calculate the variable of level of intraindustry competition as measured by the entropy index of Ruefli [16]. The use of the entropy index in the study of capital market integration is to my knowledge that I have just done it and it must be admitted that it has made an extraordinary contribution because in empirical testing in Setyawan and Wibowo [15], the entropy index variable has a significant influence on the level of capital market integration both by using unconditional correlation by Pearson and dynamic conditional

**49**

will be obtained.

*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level*

correlation by Engle [17]. My study in this book chapter is actually the initial part of my dissertation research [14]. The calculation of correlations between industries by utilizing the GICS database is very useful for the calculation of the entropy index. The entropy index calculation requires creating a mock database in GICS to be effective (see Setyawan [14] and Setyawan and Wibowo [15]). So the time to

**2.1 Benefits of international diversification with industrial level correlations**

The benefits of international diversification for industry level correlations are measured by Pearson correlation referring to the studies of Vo et al. [12], Nguyen [13], Setyawan [14], Luzey and Zhang [18], and Dutt and Mihov [19]. They formu-

ρ(Rijt, Rwt) = Rijt and Rwt correlation with unconditional correlation (Pearson)

First, calculate Rijt, which is the difference in the close price indices of industry i for each country j when t and t-1. Since the period t is monthly in 1 year, then it should also be the dividend factor of each company (1, 2, 3, 4, 5 … n) incorporated

Second, calculate Rwt, the difference between the MSCI international index at

Third, do the correlation calculation process between Rijt and Rwt with σ as the

Technically measuring industrial level unconditional correlation (UCC) is used in the EW and VW categories. Kim's study [11] provides a measure of stock portfolio returns for one industry sector in the United States Industrial Classification (USIC) category. Determination of EW is done by utilizing the multiplier 1/N for <sup>∑</sup> Rij, while the determination of VW is done by using the multiplier factor Rijt \* Xi, where Xi is the proxy market capitalization of an industry.

This market capitalization calculation will have an impact as an industry effect on the determinant return model, whereas fixing the local and USD exchange rates at the industrial level UCC justifies whether the effect of exchange rates on international diversification between countries will also apply to diversification between industries. This is because a study from Eun and Rescnick [20] found that when the local exchange rate is converted to USD, the correlation between Rijt and Rwt will weaken. Based on formula 1 and Kim's [11] and Vo et al. [12] studies, the unconditional

industry level correlations such as EW can be measured with this formula:

<sup>ρ</sup>[E(Rijt )EW, Rwt] = Cov [E(Rijt )EW, Rwt] \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ σ E(Rijt )EW <sup>∗</sup> <sup>σ</sup>Rwt

σRijt = standard deviation of Rijt (industrial return i in country j at time t) σRwt = Rwt standard (international index return (world) w at time t)

Cov Rijt \_

, Rwt σRijt ∗ σRwt

(1)

(2)

*DOI: http://dx.doi.org/10.5772/intechopen.91308*

**2. Theoretical review**

where:

\*

time t and t-1.

calculate the entropy index will be very long and tiring.

late the unconditional level industry correlations, namely:

Some steps to determine ρ (Rijt, Rwt) are as follows:

in industry i also included in the calculation component of Rijt.

standard deviation or variant root for Rijt and Rwt, respectively.

ρ(Rijt, Rwt )=

Cov Rijt, Rwt = Rijt and Rwt covariance

= multiplication symbol (sign)

correlation by Engle [17]. My study in this book chapter is actually the initial part of my dissertation research [14]. The calculation of correlations between industries by utilizing the GICS database is very useful for the calculation of the entropy index. The entropy index calculation requires creating a mock database in GICS to be effective (see Setyawan [14] and Setyawan and Wibowo [15]). So the time to calculate the entropy index will be very long and tiring.

## **2. Theoretical review**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

International (MSCI).

international diversification.

[12], which is based on the research of Nguyen [13].

classic study of Roll [1] states that industrial factors have an important role in the effectiveness of international diversification, many researchers such as Ratner and Leal [2], Richard [3], Hwang and Sitorus [4], Do et al. [5], and Chen et al. [6] began to use industry data such as Global Industry Classification Standard (GICS). According to Menchero and Morozov [7] GICS has been more globalized than other industry standards since it was handled by Morgan Stanley Capital

This study intends to measure the benefits of international diversification on the ASEAN stock exchanges by using unconditional correlations of the return of the five market indices in ASEAN including KLCI (Malaysia), STI (Singapore), Thailand (SET), Philippines (PSI), and IHSG (Indonesia), respectively, with the return of MSCI. The type of unconditional correlation of each index with MSCI is seen to be stronger than the correlation between the indexes of each ASEAN country itself and the VAR and VECM cointegration analysis that has been used by Endri [8], Robiyanto and Ernayani [9], and other researchers. The advantage of this correlation is that it is able to regulate the magnitude of the correlation number lower than the correlation among ASEAN countries' own indexes. The low magnitude of correlation supports the potential benefits of international diversification in ASEAN. VAR and VECM cointegration analysis is only able to prove the presence or absence of capital market integration and is still unable to calculate the benefits of

What is new in this study is the use of unconditional level industry correlations in each ASEAN country. The use of industry level correlations to reaffirm the argument of Click and Plummer [10] about international diversification between industries is more effective in ASEAN than international diversification between countries. This is because ASEAN capital markets have been integrated since the last 20 years. Industry unconditional level correlations are calculated in equally weighted (EW) and value-weighted (VW), both with local currency (LOC) and USD referring to the correlation estimation procedure from Kim [11]. The use of EW and VW will be able to produce different levels of correlation between industries in the five ASEAN exchanges and will further determine the level of benefit of diversification among different industries. The use of EW and VW is also in line with the research conducted by Vo et al.

Thus the main problem of this study is "there are still differences in the level of integration of capital markets at the country and industry levels in the five ASEAN capital markets." The different levels of integration show the different benefits of international diversification at the country and industry levels. This issue is representative of studies from Setyawan [14] and Setyawan and Wibowo [15] which emphasize the importance of correlation as a measure of capital market integration as well as the benefits of international diversification rather than cointegration analysis, namely, vector autoregression (VAR) and vector error correction

The reason for setting the observation period from 2006 to 2009 in this study is because it is related to my study period in a doctoral program several years ago, at which time I was asked by my supervisor to calculate the variable of level of intraindustry competition as measured by the entropy index of Ruefli [16]. The use of the entropy index in the study of capital market integration is to my knowledge that I have just done it and it must be admitted that it has made an extraordinary contribution because in empirical testing in Setyawan and Wibowo [15], the entropy index variable has a significant influence on the level of capital market integration both by using unconditional correlation by Pearson and dynamic conditional

**48**

model (VECM).

## **2.1 Benefits of international diversification with industrial level correlations**

The benefits of international diversification for industry level correlations are measured by Pearson correlation referring to the studies of Vo et al. [12], Nguyen [13], Setyawan [14], Luzey and Zhang [18], and Dutt and Mihov [19]. They formulate the unconditional level industry correlations, namely: Cov Rijt \_

$$\varrho\left(\mathbf{R}\_{\text{jft}}, \mathbf{R}\_{\text{wt}}\right) = \frac{\text{Cov } \mathbf{R}\_{\text{jft}}, \mathbf{R}\_{\text{wt}}}{\sigma \mathbf{R}\_{\text{jft}} \* \sigma \mathbf{R}\_{\text{wt}}} \tag{1}$$

where:

ρ(Rijt, Rwt) = Rijt and Rwt correlation with unconditional correlation (Pearson) Cov Rijt, Rwt = Rijt and Rwt covariance

σRijt = standard deviation of Rijt (industrial return i in country j at time t) σRwt = Rwt standard (international index return (world) w at time t)

\* = multiplication symbol (sign)

Some steps to determine ρ (Rijt, Rwt) are as follows:

First, calculate Rijt, which is the difference in the close price indices of industry i for each country j when t and t-1. Since the period t is monthly in 1 year, then it should also be the dividend factor of each company (1, 2, 3, 4, 5 … n) incorporated in industry i also included in the calculation component of Rijt.

Second, calculate Rwt, the difference between the MSCI international index at time t and t-1.

Third, do the correlation calculation process between Rijt and Rwt with σ as the standard deviation or variant root for Rijt and Rwt, respectively.

Technically measuring industrial level unconditional correlation (UCC) is used in the EW and VW categories. Kim's study [11] provides a measure of stock portfolio returns for one industry sector in the United States Industrial Classification (USIC) category. Determination of EW is done by utilizing the multiplier 1/N for <sup>∑</sup> Rij, while the determination of VW is done by using the multiplier factor Rijt \* Xi, where Xi is the proxy market capitalization of an industry.

This market capitalization calculation will have an impact as an industry effect on the determinant return model, whereas fixing the local and USD exchange rates at the industrial level UCC justifies whether the effect of exchange rates on international diversification between countries will also apply to diversification between industries. This is because a study from Eun and Rescnick [20] found that when the local exchange rate is converted to USD, the correlation between Rijt and Rwt will weaken.

Based on formula 1 and Kim's [11] and Vo et al. [12] studies, the unconditional industry level correlations such as EW can be measured with this formula:

$$\begin{aligned} \text{Sensitivity level correlations such as EW can be measured with this formula:}\\ \varrho \left[ \mathbf{E} (\mathbf{R\_{ijt}})\_{\text{EW},\mathbf{R\_{wt}}} \right] = \frac{\text{Cov} \left[ \mathbf{E} (\mathbf{R\_{jt}})\_{\text{EW},\mathbf{R\_{wt}}} \right]}{\sigma \mathbf{E} (\mathbf{R\_{jt}})\_{\text{EW}\*} \sigma \mathbf{R\_{wt}}} \end{aligned} \tag{2}$$

will be obtained.

## **2.2 Benefits of international diversification with country level correlations**

As suggested by Nguyen [13] and Vo et al. [12], when applied at the country level, the formulation ρ (Rijt, Rwt) in part 1 has been modified, namely, the component Rijt to Rj (state market index return j at time t). The importance of estimation ρ (Rjt, Rwt) is to prove the hypothesis of differences in the level of integration at the country level. Unlike the Rijt which is composed of stock portfolios according to the EW and VW categories, it is not the case for Rjt, so it is formulated as follows: Cov Rjt \_

$$\varrho\left(\mathbf{R}\_{\text{jts}}, \mathbf{R}\_{\text{wt}}\right) = \frac{\text{Cov } \mathbf{R}\_{\text{jts}}, \mathbf{R}\_{\text{wt}}}{\sigma \mathbf{R}\_{\text{jt}} \* \sigma \mathbf{R}\_{\text{wt}}} \tag{3}$$

Based on formulas 1, 2, and 3, it can be concluded that the lower the value of covariance between Rijt and Rwt as well as Rjt and Rwt, the lower the correlation between industry level and country level. Where this will mean the higher the benefits of international diversification that occurs both diversification between industries and between countries.

## **3. Research method**

## **3.1 Unit of analysis and data**

This study uses analysis units of several market indexes in five ASEAN stock exchanges consisting of KLCI (Malaysia), STI (Singapore), IHSG (Indonesia), SET (Thailand), and PSI (Philippines). From the five market indices, each return will be estimated which will be correlated with the MSCI return index to determine the degree of integration in the ASEAN capital market with the MSCI index. The type of correlation, the Pearson correlation, is unconditional because it can eliminate the pattern of volatility clustering between the indexes of five ASEAN countries and MSCI. The pattern of volatility clustering tends to lead to high levels of correlation which will actually reduce the benefits of international diversification.

The index data of the five ASEAN countries were taken from Bloomberg from January 1, 2006, to December 31, 2009, while the MSCI index data was taken from the MSCI website www.mscibarra.com for the period January 1, 2006, to December 31, 2009 as well. MSCI industry data consists of 10 industrial sectors, namely, oil and gas (OG), industrial goods (IG), basic materials (BM), consumer goods (CG), health care (HC), financial institution (FI), service goods (SG), technologies (TC), property and real estate (PR), and utilities and telecommunication (UT), which are taken from www.mscibarra.com, and an index of each industry is derived from the construction of company data as a member of the industry. As stated at the beginning of writing this book chapter, the selection of 2006–2009 data regarding my dissertation research period which has the main interest proves the effectiveness of the entropy index as the main determinant of the level of capital market integration in ASEAN. The study results will complement the various findings from the Hwang and Sitorus study (2014), namely, the consistency of the use of 10 types of industries in the GICS database by equally weighted and value-weighted for the estimation of industry level correlations.

## **3.2 Analysis tool**

Referring to the problem that there are still differences in the level of integration of capital markets at the country and industry levels in the five ASEAN capital

**51**

*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level*

markets, the analysis tool is the F-test (ANOVA) with specifications according to

a.H0: μ1 = μ2 = μ3 = μ4 = μ5 (μ is the average level of integration at country

H1: one or more of the μ is different or not the same as the other μ.

c.If H1 is accepted, it means that there is a difference in the level of

2. The H0 test at the industry level integration i = 1.2, up to 10 for the 10 GICS

H1: one or more of the μ is different or not the same as the other μ.

integration of 10 GICS industries in 5 ASEAN countries.

**4.1 Benefits of international diversification with country level correlations**

has the most isolated nature compared to 4 other ASEAN countries.

In the section below, the unconditional correlation values vary between each index return of ASEAN countries and MSCI returns. The analysis in **Table 1** is done in local currencies and USD. Based on the observation in **Table 1**, it appears that Indonesia has the weakest negative correlation, which indicates the greatest benefit of international diversification. On the contrary, for the Philippines, even though it has a correlation of close to zero, it cannot be categorized as providing the benefit of a large international diversification considering that the Philippines capital market

Based on **Table 1**, it can be stated generally that unconditional correlations in five ASEAN countries between the index returns of each country and MSCI returns are still much lower than the correlation returns between index pairs of each ASEAN country which confirm the potential gap of diversification benefits internationally in ASEAN that can be utilized by global investors. Based on the opinions of Piumsombun [21], Hwang and Sitorus [4], and Do et al. [5], the ASEAN countries which have smaller values of standard deviations than that of the mean for each unconditional correlation will have the potential benefits of international diversification. If you see **Table 1**, the ASEAN countries are Singapore (LOC and

b.Level of significance: 1-α = 95%, and F-test is done with the inference process; if F-test > F-table or p-value <0.05, then H0 is rejected.

c.If H1 is accepted, it means that there are differences in the level of sectoral

a.H0: μ1 = μ2 = μ3 = μ4 = μ5 … .. = μ10 (average industrial level integration

integration in the ASEAN exchange.

b.Level of significance: 1-α = 95%, and F-test is done with the inference process; if F-test > F-table or p-value <0.05, then H0 is rejected.

1.H0 test at country level integration j = 1, 2, 3, 4, 5 for the five ASEAN

*DOI: http://dx.doi.org/10.5772/intechopen.91308*

Setyawan [14] as follows:

countries, namely:

industries, namely:

level).

**4. Result analysis**

level).

*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level DOI: http://dx.doi.org/10.5772/intechopen.91308*

markets, the analysis tool is the F-test (ANOVA) with specifications according to Setyawan [14] as follows:

	- a.H0: μ1 = μ2 = μ3 = μ4 = μ5 (μ is the average level of integration at country level).

H1: one or more of the μ is different or not the same as the other μ.

	- a.H0: μ1 = μ2 = μ3 = μ4 = μ5 … .. = μ10 (average industrial level integration level).

H1: one or more of the μ is different or not the same as the other μ.


## **4. Result analysis**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

ρ(Rjt, Rwt )=

industries and between countries.

**3. Research method**

**3.1 Unit of analysis and data**

estimation of industry level correlations.

**2.2 Benefits of international diversification with country level correlations**

As suggested by Nguyen [13] and Vo et al. [12], when applied at the country level, the formulation ρ (Rijt, Rwt) in part 1 has been modified, namely, the component Rijt to Rj (state market index return j at time t). The importance of estimation ρ (Rjt, Rwt) is to prove the hypothesis of differences in the level of integration at the country level. Unlike the Rijt which is composed of stock portfolios according to the EW and VW categories, it is not the case for Rjt, so it is formulated as

Based on formulas 1, 2, and 3, it can be concluded that the lower the value of covariance between Rijt and Rwt as well as Rjt and Rwt, the lower the correlation between industry level and country level. Where this will mean the higher the benefits of international diversification that occurs both diversification between

This study uses analysis units of several market indexes in five ASEAN stock exchanges consisting of KLCI (Malaysia), STI (Singapore), IHSG (Indonesia), SET (Thailand), and PSI (Philippines). From the five market indices, each return will be estimated which will be correlated with the MSCI return index to determine the degree of integration in the ASEAN capital market with the MSCI index. The type of correlation, the Pearson correlation, is unconditional because it can eliminate the pattern of volatility clustering between the indexes of five ASEAN countries and MSCI. The pattern of volatility clustering tends to lead to high levels of correlation

The index data of the five ASEAN countries were taken from Bloomberg from January 1, 2006, to December 31, 2009, while the MSCI index data was taken from the MSCI website www.mscibarra.com for the period January 1, 2006, to December 31, 2009 as well. MSCI industry data consists of 10 industrial sectors, namely, oil and gas (OG), industrial goods (IG), basic materials (BM), consumer goods (CG), health care (HC), financial institution (FI), service goods (SG), technologies (TC), property and real estate (PR), and utilities and telecommunication (UT), which are taken from www.mscibarra.com, and an index of each industry is derived from the construction of company data as a member of the industry. As stated at the beginning of writing this book chapter, the selection of 2006–2009 data regarding my dissertation research period which has the main interest proves the effectiveness of the entropy index as the main determinant of the level of capital market integration in ASEAN. The study results will complement the various findings from the Hwang and Sitorus study (2014), namely, the consistency of the use of 10 types of industries in the GICS database by equally weighted and value-weighted for the

Referring to the problem that there are still differences in the level of integration of capital markets at the country and industry levels in the five ASEAN capital

which will actually reduce the benefits of international diversification.

Cov Rjt \_

, Rwt σRjt ∗ σRwt

(3)

**50**

**3.2 Analysis tool**

follows:

## **4.1 Benefits of international diversification with country level correlations**

In the section below, the unconditional correlation values vary between each index return of ASEAN countries and MSCI returns. The analysis in **Table 1** is done in local currencies and USD. Based on the observation in **Table 1**, it appears that Indonesia has the weakest negative correlation, which indicates the greatest benefit of international diversification. On the contrary, for the Philippines, even though it has a correlation of close to zero, it cannot be categorized as providing the benefit of a large international diversification considering that the Philippines capital market has the most isolated nature compared to 4 other ASEAN countries.

Based on **Table 1**, it can be stated generally that unconditional correlations in five ASEAN countries between the index returns of each country and MSCI returns are still much lower than the correlation returns between index pairs of each ASEAN country which confirm the potential gap of diversification benefits internationally in ASEAN that can be utilized by global investors. Based on the opinions of Piumsombun [21], Hwang and Sitorus [4], and Do et al. [5], the ASEAN countries which have smaller values of standard deviations than that of the mean for each unconditional correlation will have the potential benefits of international diversification. If you see **Table 1**, the ASEAN countries are Singapore (LOC and


**Table 1.**

*Unconditional correlation country level (2006–2009: weekly).*

USD), Indonesia (USD), and Thailand (LOC). The comparison between standard deviations and the mean of each pair of correlations is actually identical to the Sharpe ratio formula used to measure portfolio performance [see Chen et al. [6]]. One form of practical implication is the comparison between mean and standard deviation which refers to Sharpe ratio as the benchmark for benefits of international diversification [see Piumsombun [21], Hwang and Sitorus [4], and Do et al. [5]].

## **4.2 Benefits of international diversification with industrial level correlations**

As seen in **Table 2**, all the standard deviations for each type of correlation EW-LOC, VW-LOC, EW-USD, and VW-USD have values below the mean related to the 10 GICS industry sectors. These results confirm the argumentation that diversification between industries in ASEAN is more effective than diversification between countries and supports the study of Ratner and Leal [2], Richard [3], Hwang and Sitorus [4], and Do et al. [5] that for the 2006–2009 period, the correlation between the index returns of each ASEAN country and MSCI's high returns will lead to the more important industry effects than the country effect for global investors. But what's interesting in **Table 2** is the higher unconditional correlation of some sectors such as basic materials, financial institution, and property and real estate.

According to Kim [11] referred to by Piumsombun [21], Hwang and Sitorus [4], and Chen et al. [6], the cause of the high correlation of the three sectors is the specification of the EW-LOC, VW-LOC, EW-USD, and VW-USD, as follows:


**53**

*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level*

**dev**

**Mean Std.** 

**EW-LOC VW-LOC EW-USD VW-USD**

0.57 0.06 0.56 0.03 0.61 0.08 0.58 0.03

0.54 0.05 0.57 0.04 0.54 0.06 0.59 0.05

0.33 0.15 0.34 0.11 0.38 0.14 0.38 0.10

0.38 0.09 0.34 0.09 0.43 0.07 0.39 0.11

0.58 0.07 0.55 0.09 0.56 0.08 0.57 0.08

0.52 0.04 0.49 0.09 0.57 0.06 0.55 0.06

**Mean Std.** 

**dev**

**Mean Std.** 

**dev**

**Mean Std.** 

Oil and gas (code 10) 0.46 0.09 0.41 0.11 0.50 0.11 0.43 0.09

Health care (code 35) 0.43 0.06 0.41 0.07 0.44 0.08 0.43 0.07

Technology (code 45) 0.43 0.05 0.39 0.08 0.53 0.05 0.41 0.08 Utilities (code 50) 0.41 0.10 0.35 0.10 0.41 0.09 0.39 0.10

**dev**

According to Menchero and Morozov [7], market capitalization of the three sectors also contributed to the high correlation in addition to the number of industry members in GICS. Referring to **Table 2**, the practical implication is that global investors can choose the basic material industry (code 15) and industrial goods (code 20) because both have very low standard deviation values among other industries. The basis of this selection method refers to study from Hwang and Sitorus [4]

**4.3 Comparing benefits of international diversification (country level** 

**Table 3** shows the results of the F-test to prove whether there are differences in the level of integration at the country level in ASEAN, which at the same time prove the presence or absence of differences in the benefits of international diversification between countries. After calculating the F-test (ANOVA), the F-calculated value of 28.643 is greater than the F-table of 2381. The F-test results showed H1 was accepted, namely, there were still differences in the level of integration in ASEAN. The test results above support the study findings of Ratner and Leal [2], Richard [3], Hwang and Sitorus [4], and Do et al. [5] about the still relevant differences in the level of integration in ASEAN which proves the potential benefits of international diversification for global investors. Furthermore, when testing with USD currency conversion, the F-calculated value is 41.905 which is greater than the

In addition to reaffirming H1's acceptance in Section 3.2.1, it also supports the argument of Eun and Rescnick [20] that the USD value factor also plays an additional contribution to the benefits of international diversification if global investors are able to carry out a good hedging strategy, namely, entry to countries in ASEAN

and Do et al. [5], Nguyen [13], and Vo et al. [12].

Unconditional correlation *for industry level (five ASEAN countries).*

F-count when using the local currency which is 28.643.

**correlation)**

*DOI: http://dx.doi.org/10.5772/intechopen.91308*

**Industry sector (GICS code)**

Basic material (code 15)

Industrial goods (code 20)

Service goods (code 25)

Consumer goods (code 30)

Financial institution (code 40a)

Property and real estate (code 40b)

**Table 2.**

*Sources: Adaption from Setyawan [14].*


*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level DOI: http://dx.doi.org/10.5772/intechopen.91308*

## **Table 2.**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

*Source: Setyawan [14]; Rjt, market index return of each country; Rwt,* return *MSCI.*

*Unconditional correlation country level (2006–2009: weekly).*

USD), Indonesia (USD), and Thailand (LOC). The comparison between standard deviations and the mean of each pair of correlations is actually identical to the Sharpe ratio formula used to measure portfolio performance [see Chen et al. [6]]. One form of practical implication is the comparison between mean and standard deviation which refers to Sharpe ratio as the benchmark for benefits of international diversification [see Piumsombun [21], Hwang and Sitorus [4], and Do et al. [5]].

**ASEAN countries (pair of correlation: Rjt, Rwt) Unconditional (local) Unconditional (USD)**

Singapura ρ(RSTI, RMSCI) 0.325 0.215 0.316 0.224 Malaysia ρ(RKLCI, RMSCI) 0.181 0.250 0.105 0.256 Indonesia ρ(RIHSG, RMSCI) −0.132 0.252 −0.259 0.235 Thailand ρ(RSET, RMSCI) 0.280 0.237 0.230 0.253 Philippines ρ(RPSI, RMSCI) 0.078 0.230 0.008 0.238

**Mean Std. dev Mean Std. dev**

**4.2 Benefits of international diversification with industrial level correlations**

As seen in **Table 2**, all the standard deviations for each type of correlation EW-LOC, VW-LOC, EW-USD, and VW-USD have values below the mean related to the 10 GICS industry sectors. These results confirm the argumentation that diversification between industries in ASEAN is more effective than diversification between countries and supports the study of Ratner and Leal [2], Richard [3], Hwang and Sitorus [4], and Do et al. [5] that for the 2006–2009 period, the correlation between the index returns of each ASEAN country and MSCI's high returns will lead to the more important industry effects than the country effect for global investors. But what's interesting in **Table 2** is the higher unconditional correlation of some sectors

such as basic materials, financial institution, and property and real estate.

According to Kim [11] referred to by Piumsombun [21], Hwang and Sitorus [4], and Chen et al. [6], the cause of the high correlation of the three sectors is the specification of the EW-LOC, VW-LOC, EW-USD, and VW-USD, as follows:

• EW-LOC = Rijt correlation (industry return i in country j at t) and Rwt [MSCI return (w) on t] equally weighted; local exchange rate with the formula ρ [E (Rijt) EW-LOC, Rwt] = Cov [E (Rijt) EW-LOC, Rwt]/σ E (Rijt) EW-LOC x σRwt

• VW-LOC = Rijt correlation (industry return i in country j in t) and Rwt [MSCI return (w) on t] in a value-weighted manner; local exchange rate with the formula ρ [E (Rijt) VW-LOC, Rwt] = Cov [E (Rijt) VW-LOC, Rwt]/σ E (Rijt) VW-LOC x σRwt.

• EW-USD = Rijt correlation (industry return i in country j to t) and Rwt [MSCI return (w) on t] equally weighted; USD exchange rate with the formula ρ [E (Rijt) EW-USD, Rwt] = Cov [E (Rijt) EW-USD, Rwt]/σ E (Rijt) EW-USD x σRwt

• VW-USD = Rijt correlation (industry return i in country j in t) and Rwt [MSCI return (w) on t] in a value-weighted manner; the USD exchange rate with the formula ρ [E (Rijt) VW-USD, Rwt] = Cov [E (Rijt) VW-USD, Rwt]/σ E (Rijt)

**52**

**Table 1.**

VW-USD x σRwt

Unconditional correlation *for industry level (five ASEAN countries).*

According to Menchero and Morozov [7], market capitalization of the three sectors also contributed to the high correlation in addition to the number of industry members in GICS. Referring to **Table 2**, the practical implication is that global investors can choose the basic material industry (code 15) and industrial goods (code 20) because both have very low standard deviation values among other industries. The basis of this selection method refers to study from Hwang and Sitorus [4] and Do et al. [5], Nguyen [13], and Vo et al. [12].

## **4.3 Comparing benefits of international diversification (country level correlation)**

**Table 3** shows the results of the F-test to prove whether there are differences in the level of integration at the country level in ASEAN, which at the same time prove the presence or absence of differences in the benefits of international diversification between countries. After calculating the F-test (ANOVA), the F-calculated value of 28.643 is greater than the F-table of 2381. The F-test results showed H1 was accepted, namely, there were still differences in the level of integration in ASEAN.

The test results above support the study findings of Ratner and Leal [2], Richard [3], Hwang and Sitorus [4], and Do et al. [5] about the still relevant differences in the level of integration in ASEAN which proves the potential benefits of international diversification for global investors. Furthermore, when testing with USD currency conversion, the F-calculated value is 41.905 which is greater than the F-count when using the local currency which is 28.643.

In addition to reaffirming H1's acceptance in Section 3.2.1, it also supports the argument of Eun and Rescnick [20] that the USD value factor also plays an additional contribution to the benefits of international diversification if global investors are able to carry out a good hedging strategy, namely, entry to countries in ASEAN


**Table 3.**

*F-test for comparison of international diversification benefit at country level.*

that are experiencing a strengthening (appreciation) of their local currency against the USD. Instead, this global investor will opt out of countries in ASEAN which are currently experiencing a depreciation of their local currency against the USD. This hedging pattern is recommended by Samsi [22] and Omay and Iren [23].

Returning to **Table 3**, global investors continue to target Indonesia as their portfolio target. This is because Indonesia has a negative correlation when using local currency and USD. The nature of the correlation is negative because Indonesia has the highest yields in ASEAN (see Aggarwal et al. [24]). The Malaysian stock exchange is not a global investor portfolio target because the standard deviation is higher than the mean. The results of this test support the findings of Omay and Iren [23]. Malaysia is still not a mainstay portfolio since local authorities are still so restrictive.

## **4.4 Comparing benefits of international diversification (industry level correlation)**

The purpose of the analysis in **Tables 4** and **5** is to find out whether there are differences in the level of integration at the industry level in ASEAN. The difference in the level of integration determines the benefits of diversification between industries referring to Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14], and Setyawan and Wibowo [15].

Based on the F-test results in **Table 4** panel A, it is evident that there are differences in the level of integration of the industry level in ASEAN. These results support the findings of Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14], and Setyawan and Wibowo [15]. If considered in panel A, all industries have the potential benefits of diversification, referring to Chen et al. [6], namely, the value of standard deviation is smaller than that of the mean.

When analyzing the currency to the USD, H1 is still accepted, that is, there are still differences in the level of integration of the industrial level in ASEAN, with an F-count value of 1.578, significant at the 5% level. These results again support

**55**

Wibowo [15].

**Table 4.**

tion in ASEAN.

*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level*

Basic material 0.568 0.062 Industrial goods 0.541 0.051 Service goods 0.332 0.149 Consumer goods 0.377 0.090 Health care 0.431 0.063 Financial institution 0.576 0.072 Property and real estate 0.519 0.043 Technology 0.430 0.049 Utilities 0.414 0.101

**Industry sector Mean Std. dev F-test**

Oil and gas 0.459 0.091 1.980\*\*\*

Oil and gas 0.507 0.106 1.578\*\*

Basic material 0.609 0.078 Industrial goods 0.541 0.056 Services goods 0.382 0.137 Consumer goods 0.427 0.075 Health care 0.442 0.082 Financial institution 0.556 0.077 Property and real estate 0.575 0.065 Technology 0.531 0.055 Utilities 0.412 0.089

*Source: Adaptation from Setyawan [14] and Setyawan and Wibowo [15].*

Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14], and Setyawan and

*F-test for comparison of international diversification benefit at industry level (correlation model EW).*

The next analysis is to change the EW proxy to VW, which includes the market capitalization factor of each industry in calculating the correlation according to Kim [11]. Based on **Table 5** panels A and B, the results of the F-test still receive H1 in Section 3.2.2, namely, F-arithmetic of 1.571 and 1.591, which is significant at the 5% level. These results reaffirm support for Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14], and Setyawan and Wibowo [15], namely, the wide availability of the potential benefits of industrial diversifica-

Referring to the study of Ratner and Leal [2] and Hwang and Sitorus [4], if a comparison of panels A and B of **Table 6** is done above, then the entire industry has a standard deviation value smaller than that of the mean. In addition, it can be seen that the industrial sector which has the biggest contribution is in industry diversification. When referring to panels A and B, the basic material industry shows the biggest contribution. This can be seen from the value of the smallest standard deviation. If it is discussed that the industrial sector is not contributing, it can be

seen in panel A that is Goods Services and in panel B is Consumer Goods.

*DOI: http://dx.doi.org/10.5772/intechopen.91308*

**Panel A: correlation model of EW-LOC**

**Panel B: correlation model of EW-USD**


## *Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level DOI: http://dx.doi.org/10.5772/intechopen.91308*

## **Table 4.**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

KLCI and MSCI 0.181 0.250 IHSG and MSCI −0.132 0.252 SET and MSCI 0.280 0.237 PSI and MSCI 0.078 0.230

KLCI and MSCI 0.105 0.256 IHSG and MSCI -0.259 0.235 SET and MSCI 0.230 0.253 PSI and MSCI 0.008 0.238

*Source: Adaptation from Setyawan [14] and Setyawan and Wibowo [15].*

*F-test for comparison of international diversification benefit at country level.*

**Panel A: local currency**

**Panel B: USD currency**

*\*\*\*Significant at the 1% level.*

**Table 3.**

**Pair of correlation Mean Std. dev F-test**

STI and MSCI 0.325 0.215 28.643\*\*\*

STI and MSCI 0.316 0.224 41.905\*\*\*

that are experiencing a strengthening (appreciation) of their local currency against the USD. Instead, this global investor will opt out of countries in ASEAN which are currently experiencing a depreciation of their local currency against the USD. This

Returning to **Table 3**, global investors continue to target Indonesia as their portfolio target. This is because Indonesia has a negative correlation when using local currency and USD. The nature of the correlation is negative because Indonesia has the highest yields in ASEAN (see Aggarwal et al. [24]). The Malaysian stock exchange is not a global investor portfolio target because the standard deviation is higher than the mean. The results of this test support the findings of Omay and Iren [23]. Malaysia is still not a mainstay portfolio since local authorities are still so

hedging pattern is recommended by Samsi [22] and Omay and Iren [23].

**4.4 Comparing benefits of international diversification (industry level** 

The purpose of the analysis in **Tables 4** and **5** is to find out whether there are differences in the level of integration at the industry level in ASEAN. The difference in the level of integration determines the benefits of diversification between industries referring to Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14],

Based on the F-test results in **Table 4** panel A, it is evident that there are differences in the level of integration of the industry level in ASEAN. These results support the findings of Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14], and Setyawan and Wibowo [15]. If considered in panel A, all industries have the potential benefits of diversification, referring to Chen et al. [6], namely, the

When analyzing the currency to the USD, H1 is still accepted, that is, there are still differences in the level of integration of the industrial level in ASEAN, with an F-count value of 1.578, significant at the 5% level. These results again support

value of standard deviation is smaller than that of the mean.

**54**

restrictive.

**correlation)**

and Setyawan and Wibowo [15].

*F-test for comparison of international diversification benefit at industry level (correlation model EW).*

Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14], and Setyawan and Wibowo [15].

The next analysis is to change the EW proxy to VW, which includes the market capitalization factor of each industry in calculating the correlation according to Kim [11]. Based on **Table 5** panels A and B, the results of the F-test still receive H1 in Section 3.2.2, namely, F-arithmetic of 1.571 and 1.591, which is significant at the 5% level. These results reaffirm support for Roll [1], Hwang and Sitorus [4], Do et al. [5], Setyawan [14], and Setyawan and Wibowo [15], namely, the wide availability of the potential benefits of industrial diversification in ASEAN.

Referring to the study of Ratner and Leal [2] and Hwang and Sitorus [4], if a comparison of panels A and B of **Table 6** is done above, then the entire industry has a standard deviation value smaller than that of the mean. In addition, it can be seen that the industrial sector which has the biggest contribution is in industry diversification. When referring to panels A and B, the basic material industry shows the biggest contribution. This can be seen from the value of the smallest standard deviation. If it is discussed that the industrial sector is not contributing, it can be seen in panel A that is Goods Services and in panel B is Consumer Goods.


## **Table 5.**

*F-test for comparison of international diversification benefit at industry level (correlation model EW).*


### **Table 6.**

*The names of countries and industries that should be selected in this study portfolio.*

Then overall I can show the practical implications, namely, which industry sector and which countries will be the mainstay portfolio in **Table 6** (sourced from **Tables 3**–**5** previously). The number of countries and industries in the mainstay portfolio is at least two, confirming Hwang and Sitorus [4], Do et al. [5], Nguyen [13], and also Vo et al. [12]. In **Table 6** I set three of them.

The portfolio above is arranged from top to bottom as a priority portfolio. Thus the best portfolio by country is Indonesia while for the industrial sector; we can look for industrial goods and basic material.

**57**

**Author details**

Ignatius Roni Setyawan

provided the original work is properly cited.

Faculty of Economics and Business, Tarumanagara University, Jakarta, Indonesia

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: ignronis@gmail.com; ign.s@fe.untar.ac.id

*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level*

This study succeeded in proving the difference in the level of integration between country and industry levels in ASEAN with unconditional correlation (Pearson). In testing the differences in the level of integration at the country level, Indonesia shows the nature of negative correlations that ensure there are potential benefits of international diversification for global investors. Furthermore, in testing differences in the level of integration at the industry level by using the correlation of EW-LOC, EW-USD, VW-LOC, and VW-USD, it has been found that the potential benefits of international diversification between industries have a smaller value of standard deviation than that of the mean. Both of these test results prove the importance of correlation as a measure of capital market integration as well as

This study also yields practical implications that by distinguishing the benefits of international diversification between countries and industries, which countries and industries can be found as a mainstay portfolio. Although this study has not succeeded in determining the name of one industry in one country. This is due to the limitations of the GICS database. Estimation only of one industry in one

Besides the limitation above, this study has the disadvantage of not using correlation for integration models such as the VAR and VECM cointegration models. Future research can use unconditional and conditional correlations as endogenous variables in the integration equation model in ASEAN with the SUR (Seemingly Unrelated Regression) technique with the data from 2012–2020. This is for capturing some structural break data that occurred, namely, the continued impact of the US subprime mortgage global financial crisis, the emergence of Bitcoin and many

kinds of crypto currencies, and impacts of the US and China trade war.

*DOI: http://dx.doi.org/10.5772/intechopen.91308*

**5. Conclusion**

international diversification.

country is still rarely done.

*Behavior of Global Investors in Five ASEAN Stock Markets in the Industry Level DOI: http://dx.doi.org/10.5772/intechopen.91308*

## **5. Conclusion**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

**Panel A: correlation model of VW-LOC**

**Panel B: correlation model of VW USD**

**Industry sector Mean Std. dev F-test**

Oil and gas 0.412 0.107 1.571\*\*

Oil and gas 0.422 0.096 1.591\*\*

Basic material 0.561 0.026 Industrial goods 0.570 0.043 Services goods 0.338 0.113 Consumer goods 0.340 0.096 Health care 0.415 0.068 Financial institution 0.553 0.087 Property and real estate 0.487 0.089 Technology 0.392 0.080 Utilities 0.353 0.105

Basic material 0.584 0.029 Industrial goods 0.595 0.052 Services goods 0.381 0.103 Consumer goods 0.399 0.116 Health care 0.428 0.073 Financial institution 0.566 0.083 Property and real estate 0.549 0.060 Technology 0.418 0.083 Utilities 0.389 0.108

Then overall I can show the practical implications, namely, which industry sector and which countries will be the mainstay portfolio in **Table 6** (sourced from **Tables 3**–**5** previously). The number of countries and industries in the mainstay portfolio is at least two, confirming Hwang and Sitorus [4], Do et al. [5], Nguyen

**LOC USD EW-LOC EW-USD VW-LOC VW-USD**

*F-test for comparison of international diversification benefit at industry level (correlation model EW).*

Malaysia Malaysia Technology Technology Health care Health care

Property and real estate

material

Industrial goods

Basic material

Industrial goods

Indonesia Indonesia Industrial goods Industrial goods Basic

real estate

*The names of countries and industries that should be selected in this study portfolio.*

The portfolio above is arranged from top to bottom as a priority portfolio. Thus the best portfolio by country is Indonesia while for the industrial sector; we can

[13], and also Vo et al. [12]. In **Table 6** I set three of them.

**Country level correlation Industry level correlation**

*Sources: Adaptation from Setyawan [14] and Setyawan and Wibowo [15]*

Philippines Philippines Property and

look for industrial goods and basic material.

**56**

**Table 6.**

**Table 5.**

This study succeeded in proving the difference in the level of integration between country and industry levels in ASEAN with unconditional correlation (Pearson). In testing the differences in the level of integration at the country level, Indonesia shows the nature of negative correlations that ensure there are potential benefits of international diversification for global investors. Furthermore, in testing differences in the level of integration at the industry level by using the correlation of EW-LOC, EW-USD, VW-LOC, and VW-USD, it has been found that the potential benefits of international diversification between industries have a smaller value of standard deviation than that of the mean. Both of these test results prove the importance of correlation as a measure of capital market integration as well as international diversification.

This study also yields practical implications that by distinguishing the benefits of international diversification between countries and industries, which countries and industries can be found as a mainstay portfolio. Although this study has not succeeded in determining the name of one industry in one country. This is due to the limitations of the GICS database. Estimation only of one industry in one country is still rarely done.

Besides the limitation above, this study has the disadvantage of not using correlation for integration models such as the VAR and VECM cointegration models. Future research can use unconditional and conditional correlations as endogenous variables in the integration equation model in ASEAN with the SUR (Seemingly Unrelated Regression) technique with the data from 2012–2020. This is for capturing some structural break data that occurred, namely, the continued impact of the US subprime mortgage global financial crisis, the emergence of Bitcoin and many kinds of crypto currencies, and impacts of the US and China trade war.

## **Author details**

Ignatius Roni Setyawan Faculty of Economics and Business, Tarumanagara University, Jakarta, Indonesia

\*Address all correspondence to: ignronis@gmail.com; ign.s@fe.untar.ac.id

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Foreign Direct Investment Perspective through Foreign Direct Divestment*

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[12] Vo DH, Pham TN, Pham TTV, Truong LM, Nyuyen TC. Risk, return and portfolio optimization for various industries in the ASEAN region. Borsa Istanbul Review. 2019;**19**(2):132-138

[13] Nguyen TL. Diversification and bank efficiency in six ASEAN countries. Global Finance Journal. 2018;**37**:57-78

[14] Setyawan IR. Pengaruh Tingkat Kompetisi Intra Industri dan Market Openness Level terhadap Tingkat Integrasi Lima Pasar Modal di ASEAN. Disertasi Program Pasca Sarjana Ilmu Manajemen FE-UI; 2011

[15] Setyawan IR, Wibowo B. Determinant of capital market integration: The Case of ASEAN and implication to China. In: Foo CT, editor. Finance and Strategy Inside China. Springer; 2019. pp. 91-111. DOI:

10.1007/978-981-13-2841-1\_8

1990. pp. 33-47 and 154-155

[16] Ruefli TW. Ordinal Time-Series Analysis: Methodology and Applications in Management Strategy and Policy. London, England: Quorum Books Inc.;

[17] Engle R. Dynamic conditional correlation: A simple class of

multivariate generalized autoregressive conditional heteroscedasticity models. Journal of Business & Economic Statistics. 2002;**20**(3):339-350

[18] Luzey BM, Zhang QY. Does cultural distance matter in international stock

Economics. 2005;**16**(1):5-28

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[3] Richard A. Big fish in small ponds: The trading behavior and price impact of foreign investors in Asian emerging equity markets. Journal of Financial and Quantitative Analysis.

[4] Hwang P, Sitorus RE. A study of financial integration and optimal diversification strategy in ASEAN equity market. Journal of Economic Integration. 2014;**29**(3):496-519

[5] Do HQ, Bhatti MI, Konya L. On ASEAN capital market and industry integration: A review. Corporate Ownership and Control. 2016;**2**(1):8-23

[6] Chen MP, Chung DT, Lin YH. Assessing international financial integration: Do industry and firmspecific characteristic matter? Evidence from the Japanese Market. Economic

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[7] Menchero J, Morozov A. The Relative Strengths of Industry and Country Factors in Global Equity Markets, *Research Insight* from MSCI BARRA (www.msci.com); 2011. pp. 1-20

[8] Endri. Integrasi Pasar Saham ASEAN dengan Menggunakan Pendekatan Structural VAR, Disertasi Program Pasca Sarjana Ilmu Manajemen FE-UI;

[9] Robiyanto R, Ernayani R. Capital market integration in some ASEAN countries revisited. Jurnal Manajemen.

stock market indices. Journal of Finance. 1992;**47**(1):3-41

[2] Ratner M, Leal RPC. Sector integration and the benefits of global diversifications. Multinational Finance

Journal. 2005;**9**(3/4):237-269

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**References**

**61**

**Chapter 5**

**Abstract**

Diaspora Investment to Help

Achieve the SDGs in Africa:

financing mix, especially as part of 'blended finance' packages.

Goals (SDGs), affordable housing in Africa

investment vehicles targeting diaspora investors.

**1. Introduction**

**Keywords:** diaspora investment, development finance, Sustainable Development

This chapter examines trends in diaspora investment, and in particular how such investments can drive socio-economic growth and development in countries of origin, as well as in countries of transit and destination. It argues that the African diaspora, in addition to being recognised as the source of significant resource flows to countries of origin or heritage through remittances, should also be seen—and be encouraged to see themselves—as significant social investors in African development in their own right. This in turn creates potential opportunities for governments, financial institutions, and the private sector to harness and scale up these investments for economic growth, especially in Africa's emerging and frontier markets, by developing and deploying a combination of policy frameworks and

This is even more critical in supporting the implementation of the SDGs on the continent. The projected shortfall in development financing needed to achieve the Sustainable Development Goals globally, is estimated at around \$30 bn USD per annum. This means that innovative approaches to development financing are

Governments and the private sector have traditionally viewed the diaspora as both ongoing providers of financial capital at the micro level, and, as consumers. While recognition of the diaspora's role in 'doing development' has grown, and the diaspora are increasingly seen as important development stakeholders, they are still not viewed as significant social investors by governments, the private sector, or indeed the diaspora themselves. This represents a missed opportunity for harnessing and seeking to scale up diaspora investments for socio-economic growth, especially given the gap in financing available to deliver the Sustainable Development Goals (SDGs). This chapter offers an overview of diaspora investment types and forms, dividing these into four main types, namely: diaspora philanthropy, diaspora remittances, diaspora direct investment (DDI), and diaspora Portfolio Investment (DPI). It argues that governments, financial institutions, the private sector, and the diaspora themselves should view diaspora investments as part of the development

Prospects and Trends

*Paul Asquith and Stella Opoku-Owusu*

## **Chapter 5**

## Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends

*Paul Asquith and Stella Opoku-Owusu*

## **Abstract**

Governments and the private sector have traditionally viewed the diaspora as both ongoing providers of financial capital at the micro level, and, as consumers. While recognition of the diaspora's role in 'doing development' has grown, and the diaspora are increasingly seen as important development stakeholders, they are still not viewed as significant social investors by governments, the private sector, or indeed the diaspora themselves. This represents a missed opportunity for harnessing and seeking to scale up diaspora investments for socio-economic growth, especially given the gap in financing available to deliver the Sustainable Development Goals (SDGs). This chapter offers an overview of diaspora investment types and forms, dividing these into four main types, namely: diaspora philanthropy, diaspora remittances, diaspora direct investment (DDI), and diaspora Portfolio Investment (DPI). It argues that governments, financial institutions, the private sector, and the diaspora themselves should view diaspora investments as part of the development financing mix, especially as part of 'blended finance' packages.

**Keywords:** diaspora investment, development finance, Sustainable Development Goals (SDGs), affordable housing in Africa

## **1. Introduction**

This chapter examines trends in diaspora investment, and in particular how such investments can drive socio-economic growth and development in countries of origin, as well as in countries of transit and destination. It argues that the African diaspora, in addition to being recognised as the source of significant resource flows to countries of origin or heritage through remittances, should also be seen—and be encouraged to see themselves—as significant social investors in African development in their own right. This in turn creates potential opportunities for governments, financial institutions, and the private sector to harness and scale up these investments for economic growth, especially in Africa's emerging and frontier markets, by developing and deploying a combination of policy frameworks and investment vehicles targeting diaspora investors.

This is even more critical in supporting the implementation of the SDGs on the continent. The projected shortfall in development financing needed to achieve the Sustainable Development Goals globally, is estimated at around \$30 bn USD per annum. This means that innovative approaches to development financing are

required more now than ever before. The Addis Ababa Action Agenda on financing the SDGs, agreed in 2015, is explicit that governments, civil society, and the private sector will all have to contribute more resources to achieve these goals.

At the same time, developing countries are seeking to raise investment finance to meet growing infrastructure, energy, and other needs in national development planning. The challenges to doing so in frontier and emerging economies are all the greater given current market and investment trends. There is a lot of innovation in diaspora interventions with potential for significant results. This chapter argues that more focus on diaspora investors and diaspora investment could yield significant results for Africa's emerging markets and for helping achieve the SDGs.

## **2. Methodology**

The research methodology involved extensive review and analysis of corporate, financial, legal, institutional, and academic literature. This included African financial institutions, innovative finance schemes, and social enterprise structures. Research consultation questions were used to guide face-to-face and telephone interviews and discussions with practitioners, policymakers, and potential diaspora investors in Europe and Africa. The review was also informed by internal AFFORD reports and data, drawing on its experience of delivering diaspora enterprise and finance initiatives in Africa over the last decade.

People consulted included officials of the African Union and the World Bank. Participation in forums such as the Global Forum on Migration and Development (GFMD) in Marrakech in December 2018 enabled consultations with development and investment policymakers and practitioners from government, multilateral, diaspora, media, money transfer, and other organisations.

## **3. Defining diaspora investors and investments**

A range of definitions have been proposed for the term diaspora, and for what constitutes diaspora investment. Definitions of diaspora can vary and may also be contested by some groups. However, for the purposes of this chapter, the African Union (AU) definition is used whereby:

*'the African Diaspora consists of peoples of African origin living outside the continent, irrespective of their citizenship and nationality and who are willing to contribute to the development of the continent and the building of the African Union' [1]*

According to Plaza and Ratha, the size of the African diaspora totalled over 30.6 million in 2011 [2]. However, this number is a significant underestimate as it only counts the total foreign-born population and therefore excludes second-, third-, and subsequent-generation migrants who in many cases maintain ties to their country of origin and/or heritage. It also excludes the millions from the historic Atlantic Diaspora, which the AU includes in its definition of the diaspora and as part of its concept of a sixth region.

As Oviatt and McDougall argue, due to the sentimental attachment to their countries of origin, diaspora transnationalism initiates investments; connects home and host countries in political, economic, and social issues; creates jobs; and provides business, technical, and technological information about host countries to their country of origin [3]. Moreover, such investments need not be necessarily for

**63**

flows [9].

[5, 6].1

**4.1 Diaspora remittances**

tances to Africa can be as high as \$200 bn USD [8].<sup>2</sup>

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends*

financial return but also for socio-economic improvement and feelings of duty and

Faal [4] provides a detailed breakdown of diaspora investment types and forms,

 There are some rare exceptions to this though, such as Rwanda's Agaciro Fund [5], which has relied on philanthropic donations from the diaspora, and is being developed as a sovereign wealth fund with a strong investment element.

Diaspora remittances have been the subject of numerous studies at the macro and micro levels, and there is strong evidence that remittances are a major engine for development in Africa. According to the World Bank in 2019, formal remittances to Africa reached \$86 bn USD [7]. Of this, 70% was received by Egypt, Nigeria, and Morocco. In the case of Nigeria, the amount remitted in 2018 (\$22 bn USD) was larger than the entire federal budget that year (\$18 bn USD). For five countries, formal remittances alone account for over 10% of GDP, namely, Comoros, The Gambia, Lesotho, Cape Verde, and Liberia. In addition to formal channels, migrants and the diaspora still use unregistered and informal channels to send money to Africa. They also send in-kind remittances. If funds sent through formal, informal, and in-kind remittances are taken into account, it is estimated that annual remit-

The World Bank notes that remittance transaction costs for Africa are the highest in the world at 9% as compared to the global average cost of 7%. Remittance costs within Africa are particularly high, the highest costs being transactions

<sup>1</sup> Faal ([4], pp. 22–37) notes that he main forms of diaspora philanthropy are: Direct donations to civil society, religious, social and community appeals and schemes; Collective remittances channelled through Home Town, Community and Alumni Associations (HCA) and diaspora networks; and national Trust Funds such as the Rwanda Agaciro Development Fund (ADF) set up in 2012, which had an asset value of about USD43m in 2016, and the Ethiopia Diaspora Trust Fund (EDTF) which was set up in August 2018 and had raised about USD4m from 20,000 people in 70 countries by April 2019. <sup>2</sup> The Lead Economist on Migration and Remittances at the World Bank, Dilip Ratha [8], has stated that "unrecorded flows through informal channels are believed to be at least 50% larger than recorded flows." The SSRC reported that informal remittances are estimated to vary from 35% to 250% of formal

dividing these into four main types, namely: diaspora philanthropy, diaspora remittances, diaspora direct investment (DDI), and Diaspora Portfolio Investment (DPI), mirroring the established distinctions between FDI and FPI [4]. Although the former of these, diaspora philanthropy, is an important form of financial contribution to development, it falls out of the scope of this chapter as it does not fit the strict definition of financial investment in that no profit or financial return is expected or received by the diaspora or migrants or who make such contributions

Diaspora investors are more likely to invest in their country of origin than non-diaspora investors as they are privy to a more sophisticated understanding of the governance and business in-country, and may therefore have a differing understanding of risk from other investors. Plaza and Ratha characterise this as diaspora investors taking advantage of information asymmetries, and suggest this is an area where diaspora investments should be encouraged so as to take advantage of their

*DOI: http://dx.doi.org/10.5772/intechopen.93129*

obligation to their country of origin [2].

comparative advantage in this regard [2].

**4. Types of diaspora investments**

## *Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends DOI: http://dx.doi.org/10.5772/intechopen.93129*

financial return but also for socio-economic improvement and feelings of duty and obligation to their country of origin [2].

Diaspora investors are more likely to invest in their country of origin than non-diaspora investors as they are privy to a more sophisticated understanding of the governance and business in-country, and may therefore have a differing understanding of risk from other investors. Plaza and Ratha characterise this as diaspora investors taking advantage of information asymmetries, and suggest this is an area where diaspora investments should be encouraged so as to take advantage of their comparative advantage in this regard [2].

## **4. Types of diaspora investments**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

finance initiatives in Africa over the last decade.

diaspora, media, money transfer, and other organisations.

**3. Defining diaspora investors and investments**

Union (AU) definition is used whereby:

*Union' [1]*

concept of a sixth region.

**2. Methodology**

sector will all have to contribute more resources to achieve these goals.

required more now than ever before. The Addis Ababa Action Agenda on financing the SDGs, agreed in 2015, is explicit that governments, civil society, and the private

At the same time, developing countries are seeking to raise investment finance to meet growing infrastructure, energy, and other needs in national development planning. The challenges to doing so in frontier and emerging economies are all the greater given current market and investment trends. There is a lot of innovation in diaspora interventions with potential for significant results. This chapter argues that more focus on diaspora investors and diaspora investment could yield significant results for Africa's emerging markets and for helping achieve the SDGs.

The research methodology involved extensive review and analysis of corporate, financial, legal, institutional, and academic literature. This included African financial institutions, innovative finance schemes, and social enterprise structures. Research consultation questions were used to guide face-to-face and telephone interviews and discussions with practitioners, policymakers, and potential diaspora investors in Europe and Africa. The review was also informed by internal AFFORD reports and data, drawing on its experience of delivering diaspora enterprise and

People consulted included officials of the African Union and the World Bank. Participation in forums such as the Global Forum on Migration and Development (GFMD) in Marrakech in December 2018 enabled consultations with development and investment policymakers and practitioners from government, multilateral,

A range of definitions have been proposed for the term diaspora, and for what constitutes diaspora investment. Definitions of diaspora can vary and may also be contested by some groups. However, for the purposes of this chapter, the African

According to Plaza and Ratha, the size of the African diaspora totalled over 30.6 million in 2011 [2]. However, this number is a significant underestimate as it only counts the total foreign-born population and therefore excludes second-, third-, and subsequent-generation migrants who in many cases maintain ties to their country of origin and/or heritage. It also excludes the millions from the historic Atlantic Diaspora, which the AU includes in its definition of the diaspora and as part of its

As Oviatt and McDougall argue, due to the sentimental attachment to their countries of origin, diaspora transnationalism initiates investments; connects home and host countries in political, economic, and social issues; creates jobs; and provides business, technical, and technological information about host countries to their country of origin [3]. Moreover, such investments need not be necessarily for

*'the African Diaspora consists of peoples of African origin living outside the continent, irrespective of their citizenship and nationality and who are willing to contribute to the development of the continent and the building of the African* 

**62**

Faal [4] provides a detailed breakdown of diaspora investment types and forms, dividing these into four main types, namely: diaspora philanthropy, diaspora remittances, diaspora direct investment (DDI), and Diaspora Portfolio Investment (DPI), mirroring the established distinctions between FDI and FPI [4]. Although the former of these, diaspora philanthropy, is an important form of financial contribution to development, it falls out of the scope of this chapter as it does not fit the strict definition of financial investment in that no profit or financial return is expected or received by the diaspora or migrants or who make such contributions [5, 6].1 There are some rare exceptions to this though, such as Rwanda's Agaciro Fund [5], which has relied on philanthropic donations from the diaspora, and is being developed as a sovereign wealth fund with a strong investment element.

## **4.1 Diaspora remittances**

Diaspora remittances have been the subject of numerous studies at the macro and micro levels, and there is strong evidence that remittances are a major engine for development in Africa. According to the World Bank in 2019, formal remittances to Africa reached \$86 bn USD [7]. Of this, 70% was received by Egypt, Nigeria, and Morocco. In the case of Nigeria, the amount remitted in 2018 (\$22 bn USD) was larger than the entire federal budget that year (\$18 bn USD). For five countries, formal remittances alone account for over 10% of GDP, namely, Comoros, The Gambia, Lesotho, Cape Verde, and Liberia. In addition to formal channels, migrants and the diaspora still use unregistered and informal channels to send money to Africa. They also send in-kind remittances. If funds sent through formal, informal, and in-kind remittances are taken into account, it is estimated that annual remittances to Africa can be as high as \$200 bn USD [8].<sup>2</sup>

The World Bank notes that remittance transaction costs for Africa are the highest in the world at 9% as compared to the global average cost of 7%. Remittance costs within Africa are particularly high, the highest costs being transactions

<sup>1</sup> Faal ([4], pp. 22–37) notes that he main forms of diaspora philanthropy are: Direct donations to civil society, religious, social and community appeals and schemes; Collective remittances channelled through Home Town, Community and Alumni Associations (HCA) and diaspora networks; and national Trust Funds such as the Rwanda Agaciro Development Fund (ADF) set up in 2012, which had an asset value of about USD43m in 2016, and the Ethiopia Diaspora Trust Fund (EDTF) which was set up in August 2018 and had raised about USD4m from 20,000 people in 70 countries by April 2019.

<sup>2</sup> The Lead Economist on Migration and Remittances at the World Bank, Dilip Ratha [8], has stated that "unrecorded flows through informal channels are believed to be at least 50% larger than recorded flows." The SSRC reported that informal remittances are estimated to vary from 35% to 250% of formal flows [9].

originating from South Africa, as high as 18%. The two cheapest intra-African remittance corridors were about 3.5%, these being Senegal-Mali and Cote D'Ivoire-Mali. The cheapest international corridors were about 4%, being France-Cameroon and France-Comoros. It should be noted that SDG Target 10.7 (10.C) states: 'By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per cent' [10].

Formal remittances to Africa, which keep growing, are higher than all other forms of non-trade financial flows, as shown in **Table 1** below:


## **Table 1.**

*Financial inflows to Africa in 2011–2015.*

The World Bank has identified a number of factors that increase the propensity for remittance receivers to invest:


## **4.2 Diaspora Direct Investment (DDI)**

This relates to direct investments whereby the investor has origins or heritage in the foreign country of investment, irrespective of their nationality. The notion of heritage-based African DDI is practically useful because millions of African diasporans are unable to definitely pinpoint their origins to a particular country in Africa. However, unlike FDI flows, which are officially monitored by a range of multilateral institutions, there is a dearth of reliable data on formal DDI flows to Africa, and it is recommended that annual surveys should aim to gather data on levels of DDI to inform investment trends and also policy-making in this area.

**65**

<sup>3</sup> Ref. [4], p. 30.

**Figure 1.**

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends*

The relationship between DDI and diaspora remittances is a complex one, in part due to the fact that DDI (like remittances) includes both formal and informal channels. There is significant evidence that remittances are counter-cyclical, inasmuch as remittance flows tend to increase in times of crisis or conflict, when nondiaspora investors may be looking to exit difficult markets. Certainly, remittances tend to be far less volatile than other inflows such as FDI as **Figure 1** demonstrates. As the African Development Bank [13] notes, 'remittances, over the years, were observed to be more stable than other capital inflows. Moreover, both Newland and Patrick (2004) and Africa Development Bank (2012) underscore that household remittances, if significant and supported by appropriate policies and enabling conducive environment, can generate multiplier effects, which may provide the basis for more sustainable poverty reduction' [14]. It is not yet clear however if diaspora investments beyond remittances display the same tendencies, and there is a need for

Informal DDI, like informal remittances, include investments to unincorporated

Another form of DDI that should be considered is in-kind DDI. While FDI may be used to procure key assets such as machinery or business equipment, DDI is more likely to include in-kind provision both of physical assets and also technical and management skills and experience. Indeed, as Ardovino and Debass argue,

businesses. In Africa, the informal sector forms a significant percentage of the economy, providing up to 65% of all jobs, and it is estimated that investments in the informal sector make up a significant component of diaspora investment activity, often investing in family MSMEs [15]. Moreover, a proportion of remittances are used for formal and informal investments, typically in the MSME and property sectors. According to the World Bank, 20% of all monies remitted are used for such investment purposes; IFAD [16] uses a higher figure, noting that up to 30% of remittances are used for investment purposes, typically in the informal sector [17]. Faal [4] also notes that DDI is 'a vital and significant source of capital in the sector of the self-employed, sole traders, partnerships, unregistered trade agents, occasional and accidental entrepreneurs. Similar to remittances, individual DDIs may be

*DOI: http://dx.doi.org/10.5772/intechopen.93129*

further research and data on this subject.

*Remittances vs. FDI. Source: World Bank [12].*

small, but the aggregate is likely to be very high' [4].3

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends DOI: http://dx.doi.org/10.5772/intechopen.93129*

## **Figure 1.**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

originating from South Africa, as high as 18%. The two cheapest intra-African remittance corridors were about 3.5%, these being Senegal-Mali and Cote D'Ivoire-Mali. The cheapest international corridors were about 4%, being France-Cameroon and France-Comoros. It should be noted that SDG Target 10.7 (10.C) states: 'By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and

Formal remittances to Africa, which keep growing, are higher than all other

**Inflows to Africa ( \$ Billion USD) 2011 2012 2013 2014 2015** Migrant and diaspora remittances 59.6 64.3 63.7 67.2 64.8 Foreign direct investment (FDI) 49.8 49.4 53.1 56.0 51.3 Official development assistance 51.6 51.8 56.8 54.3 51.0 Foreign portfolio investment (FPI) 21.6 34.3 23.0 21.3 15.7

The World Bank has identified a number of factors that increase the propensity

• 'Remittance flows are viewed by the household as transitory rather than permanent and thus should be saved (and invested) rather than spent.

• The sender conditions the remittance on it being spent for particular purposes, which are more likely to involve investment than current consumption.

• The remittance is targeted (or "tagged") to household members more likely to

• Households practice a form of mental accounting with their overall budget, with remittances being disproportionately put in accounts set aside for invest-

• Remittances also have the effect of increasing the likelihood of entrepreneurial

This relates to direct investments whereby the investor has origins or heritage in the foreign country of investment, irrespective of their nationality. The notion of heritage-based African DDI is practically useful because millions of African diasporans are unable to definitely pinpoint their origins to a particular country in Africa. However, unlike FDI flows, which are officially monitored by a range of multilateral institutions, there is a dearth of reliable data on formal DDI flows to Africa, and it is recommended that annual surveys should aim to gather data on levels of DDI to

Examples include education or the purchase of new farm machinery.

use the funds for investment purposes (women rather than men).

enterprises for middle-income households [2].

inform investment trends and also policy-making in this area.

**4.2 Diaspora Direct Investment (DDI)**

eliminate remittance corridors with costs higher than 5 per cent' [10].

forms of non-trade financial flows, as shown in **Table 1** below:

for remittance receivers to invest:

*Financial inflows to Africa in 2011–2015.*

*Source: UNCTAD [11].*

**Table 1.**

ment purposes'.

**64**

*Remittances vs. FDI. Source: World Bank [12].*

The relationship between DDI and diaspora remittances is a complex one, in part due to the fact that DDI (like remittances) includes both formal and informal channels. There is significant evidence that remittances are counter-cyclical, inasmuch as remittance flows tend to increase in times of crisis or conflict, when nondiaspora investors may be looking to exit difficult markets. Certainly, remittances tend to be far less volatile than other inflows such as FDI as **Figure 1** demonstrates.

As the African Development Bank [13] notes, 'remittances, over the years, were observed to be more stable than other capital inflows. Moreover, both Newland and Patrick (2004) and Africa Development Bank (2012) underscore that household remittances, if significant and supported by appropriate policies and enabling conducive environment, can generate multiplier effects, which may provide the basis for more sustainable poverty reduction' [14]. It is not yet clear however if diaspora investments beyond remittances display the same tendencies, and there is a need for further research and data on this subject.

Informal DDI, like informal remittances, include investments to unincorporated businesses. In Africa, the informal sector forms a significant percentage of the economy, providing up to 65% of all jobs, and it is estimated that investments in the informal sector make up a significant component of diaspora investment activity, often investing in family MSMEs [15]. Moreover, a proportion of remittances are used for formal and informal investments, typically in the MSME and property sectors. According to the World Bank, 20% of all monies remitted are used for such investment purposes; IFAD [16] uses a higher figure, noting that up to 30% of remittances are used for investment purposes, typically in the informal sector [17]. Faal [4] also notes that DDI is 'a vital and significant source of capital in the sector of the self-employed, sole traders, partnerships, unregistered trade agents, occasional and accidental entrepreneurs. Similar to remittances, individual DDIs may be small, but the aggregate is likely to be very high' [4].3

Another form of DDI that should be considered is in-kind DDI. While FDI may be used to procure key assets such as machinery or business equipment, DDI is more likely to include in-kind provision both of physical assets and also technical and management skills and experience. Indeed, as Ardovino and Debass argue,

<sup>3</sup> Ref. [4], p. 30.

DDI provides a package that can include both capital investment and technological development ('brain gain') [18]. In addition, DDI can operate as a business catalyst in countries of residence and/or transit, and helps facilitate bilateral trade and development links [18].

Diaspora members already make great use of collective remittances such as those generated by hometown and alumni associations, both for philanthropy, and also for small-scale impact investments in the African social economy; some of this activity therefore extends beyond the philanthropic into definitions of DDI. Moreover, the diaspora pool funds through diaspora investment clubs and business angel networks, which are used to make direct investments in diverse businesses, as well as investments in regulated financial products [19].4 However, the data on the value of DDI from different sources are often lacking and there is scope for multilateral institutions monitoring DDI and FDI to improve measurement of this sort of activity.

## **4.3 Diaspora real estate as DDI**

Acquisition and development of real estate is often the biggest investment that individuals in the diaspora make in their countries of origin or heritage. Diaspora investors may seek to acquire real estate for multiple reasons, ranging from retirement accommodation, use by extended family to rental of residential or commercial units, longer term leasing or outright sale. Furthermore, the diaspora can use such assets as leverage and guarantee for local bank loans to invest in other real estate ventures and in other businesses. However, access to credit for property and real estate can be limited, both in countries of residence and in particular in countries of origin and/or heritage, and there is a potential untapped market for greater provision of mortgages in several African countries [20].5

While there is a debate over the extent to which diaspora real estate can be considered DDI, centring on how such investments are productive, it is clear that much (if not most) of such activity can be classed as such. Indeed, the World Bank has previously undertaken household surveys to estimate the value of remittances spent on real estate that can inform methodologies for assessing levels of real estate DDI in Africa [17].

## **4.4 Diaspora portfolio investments (DPIs)**

FDI is typically contrasted with Foreign Portfolio Investments (FPIs), defined as 'cross border transactions and positions involving debt or equity securities, other than those included in direct investment or reserve assets' [21]. Securities are negotiable and tradable financial instruments representing an ownership position in an asset, be it company stocks and shares, corporate or sovereign bonds or debenture, derivative contracts, or other forms of shareholding or debt.

In comparison to other parts of the world, FPI in Africa is small and relatively underdeveloped. In 2017, the value of global FPI assets was 60 tn \$USD [19]. There are no directly comparable data for Africa, but the value of outstanding African

**67**

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends*

Eurobonds in the same period was USD92 Billion [20]. This indicates that African

Similarly, diaspora investors also invest in what may be termed 'Diaspora Portfolio Investment' (DPI). Terrazas defines DPI as: 'Investments made in the country of origin by a diasporan or groups of diasporans, including (1) the purchase of sovereign bonds issued by the country of origin government, (2) the purchase of equity in companies in the country of origin, (3) investments made in fixed-income or other securities that lend money to firms exclusively in the country of origin, (4) stock purchases in the country of origin, and (5) investments made in mutual funds

Unlike direct investment, ownership of securities does not generally lead to involvement in the management of the enterprise, venture, or asset. It is a form of passive investment, which is one of the reasons why the sector is regulated by public authorities to, among other things, protect investors. In this respect portfolio investment products may be more suitable and less risky for diaspora investors. In the specific case of the African diaspora, DPI also includes portfolio investments made by multigenerational diaspora investors in their country of heritage (rather than just country of birth or parental origin), irrespective of their current nationality. African DPI thus includes investments by African Americans, Afro-Brazilians and other members of the historic Atlantic Diaspora who may not even be able to pinpoint their origins to a particular country in Africa. Diaspora bonds and Diaspora Mutual Funds are therefore important emergent DPI products target-

Neither the World Bank, nor IMF or any other global or regional Multilateral Financial Institution measure and monitor Diaspora Portfolio Investment (DPI) in a structured, standardised, and regular manner. A better understanding of this could provide an opportunity for governments, private sector, and the diaspora to support SDG financing gaps by maximising the contributions and benefits of DPI for social

Several African countries have schemes backed by policy and legislation that offer DDI incentives and tax breaks comparable to those available for FDI. Some countries provide grants, co-finance, and loan guarantees to diaspora investors. For some countries like Gambia and Ghana, the incentive programme is managed by the national inward investment promotion agency and is a variant of the FDI

Some countries set up additional specialist diaspora co-finance and Diaspora Development Funds to stimulate DDI. For Morocco, the 'MDM Invest' programme enables Moroccans Living Abroad to access a grant of up to 5 Million Dirham (over \$500,000 USD) or 10% of costs, for projects implemented in Morocco [24]. For Senegal, the 'FAISE' programme provides low-interest, 5-year loans of up to 15 Million CFA (over \$25,000 USD), with repayment holidays, for diaspora projects

<sup>6</sup> See, inter alia, Gambia: http://giepa.gm/Investment%20and%20Export%20Incentives%20and%20 Support%20to%20MSMEs; Ghana: https://www.gipcghana.com/invest-in-ghana/why-ghana/tax-

investment requirement for diaspora investors to access to incentives.

Typically, such schemes tend to set a lower threshold of cash

*DOI: http://dx.doi.org/10.5772/intechopen.93129*

FPI assets are a fraction of a percent of global assets.

comprised of firms in the country of origin' [22].

ing diaspora investment appetites.

**5. Examples of DDI and DPI programmes**

investment purposes.

**5.1 DDI programmes**

programme [23].6

regime-and-incentives.html.

<sup>4</sup> See inter alia. African Diaspora Network's African Diaspora Symposium https://www.adis2019.com/; Diaspora Investment Club https://diasporainvestmentclub.com/investing-2/investing-in-africa/; Club Efficience https://club-efficience.com/en/efficience-africa-fund-en/; African Business Angel Network https://abanangels.org/the-aban-network/; AFFORD Business Club https://afford-uk.org/what-we-do/ projects/abc-2/

<sup>5</sup> Ref. [4], p. 30.

## *Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends DOI: http://dx.doi.org/10.5772/intechopen.93129*

Eurobonds in the same period was USD92 Billion [20]. This indicates that African FPI assets are a fraction of a percent of global assets.

Similarly, diaspora investors also invest in what may be termed 'Diaspora Portfolio Investment' (DPI). Terrazas defines DPI as: 'Investments made in the country of origin by a diasporan or groups of diasporans, including (1) the purchase of sovereign bonds issued by the country of origin government, (2) the purchase of equity in companies in the country of origin, (3) investments made in fixed-income or other securities that lend money to firms exclusively in the country of origin, (4) stock purchases in the country of origin, and (5) investments made in mutual funds comprised of firms in the country of origin' [22].

Unlike direct investment, ownership of securities does not generally lead to involvement in the management of the enterprise, venture, or asset. It is a form of passive investment, which is one of the reasons why the sector is regulated by public authorities to, among other things, protect investors. In this respect portfolio investment products may be more suitable and less risky for diaspora investors.

In the specific case of the African diaspora, DPI also includes portfolio investments made by multigenerational diaspora investors in their country of heritage (rather than just country of birth or parental origin), irrespective of their current nationality. African DPI thus includes investments by African Americans, Afro-Brazilians and other members of the historic Atlantic Diaspora who may not even be able to pinpoint their origins to a particular country in Africa. Diaspora bonds and Diaspora Mutual Funds are therefore important emergent DPI products targeting diaspora investment appetites.

Neither the World Bank, nor IMF or any other global or regional Multilateral Financial Institution measure and monitor Diaspora Portfolio Investment (DPI) in a structured, standardised, and regular manner. A better understanding of this could provide an opportunity for governments, private sector, and the diaspora to support SDG financing gaps by maximising the contributions and benefits of DPI for social investment purposes.

## **5. Examples of DDI and DPI programmes**

## **5.1 DDI programmes**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

nesses, as well as investments in regulated financial products [19].4

development links [18].

this sort of activity.

DDI in Africa [17].

**4.3 Diaspora real estate as DDI**

sion of mortgages in several African countries [20].5

**4.4 Diaspora portfolio investments (DPIs)**

derivative contracts, or other forms of shareholding or debt.

DDI provides a package that can include both capital investment and technological development ('brain gain') [18]. In addition, DDI can operate as a business catalyst in countries of residence and/or transit, and helps facilitate bilateral trade and

Diaspora members already make great use of collective remittances such as those generated by hometown and alumni associations, both for philanthropy, and also for small-scale impact investments in the African social economy; some of this activity therefore extends beyond the philanthropic into definitions of DDI. Moreover, the diaspora pool funds through diaspora investment clubs and business angel networks, which are used to make direct investments in diverse busi-

data on the value of DDI from different sources are often lacking and there is scope for multilateral institutions monitoring DDI and FDI to improve measurement of

Acquisition and development of real estate is often the biggest investment that individuals in the diaspora make in their countries of origin or heritage. Diaspora investors may seek to acquire real estate for multiple reasons, ranging from retirement accommodation, use by extended family to rental of residential or commercial units, longer term leasing or outright sale. Furthermore, the diaspora can use such assets as leverage and guarantee for local bank loans to invest in other real estate ventures and in other businesses. However, access to credit for property and real estate can be limited, both in countries of residence and in particular in countries of origin and/or heritage, and there is a potential untapped market for greater provi-

While there is a debate over the extent to which diaspora real estate can be considered DDI, centring on how such investments are productive, it is clear that much (if not most) of such activity can be classed as such. Indeed, the World Bank has previously undertaken household surveys to estimate the value of remittances spent on real estate that can inform methodologies for assessing levels of real estate

FDI is typically contrasted with Foreign Portfolio Investments (FPIs), defined as 'cross border transactions and positions involving debt or equity securities, other than those included in direct investment or reserve assets' [21]. Securities are negotiable and tradable financial instruments representing an ownership position in an asset, be it company stocks and shares, corporate or sovereign bonds or debenture,

In comparison to other parts of the world, FPI in Africa is small and relatively underdeveloped. In 2017, the value of global FPI assets was 60 tn \$USD [19]. There are no directly comparable data for Africa, but the value of outstanding African

<sup>4</sup> See inter alia. African Diaspora Network's African Diaspora Symposium https://www.adis2019.com/; Diaspora Investment Club https://diasporainvestmentclub.com/investing-2/investing-in-africa/; Club Efficience https://club-efficience.com/en/efficience-africa-fund-en/; African Business Angel Network https://abanangels.org/the-aban-network/; AFFORD Business Club https://afford-uk.org/what-we-do/

However, the

**66**

projects/abc-2/ <sup>5</sup> Ref. [4], p. 30.

Several African countries have schemes backed by policy and legislation that offer DDI incentives and tax breaks comparable to those available for FDI. Some countries provide grants, co-finance, and loan guarantees to diaspora investors. For some countries like Gambia and Ghana, the incentive programme is managed by the national inward investment promotion agency and is a variant of the FDI programme [23].6 Typically, such schemes tend to set a lower threshold of cash investment requirement for diaspora investors to access to incentives.

Some countries set up additional specialist diaspora co-finance and Diaspora Development Funds to stimulate DDI. For Morocco, the 'MDM Invest' programme enables Moroccans Living Abroad to access a grant of up to 5 Million Dirham (over \$500,000 USD) or 10% of costs, for projects implemented in Morocco [24]. For Senegal, the 'FAISE' programme provides low-interest, 5-year loans of up to 15 Million CFA (over \$25,000 USD), with repayment holidays, for diaspora projects

<sup>6</sup> See, inter alia, Gambia: http://giepa.gm/Investment%20and%20Export%20Incentives%20and%20 Support%20to%20MSMEs; Ghana: https://www.gipcghana.com/invest-in-ghana/why-ghana/taxregime-and-incentives.html.

[25]. Other countries have made it easy for the diaspora to access domestic investment incentives such as loan guarantee schemes.

In addition to state-led DDI incentives, there are a number of diaspora co-financing programmes operating from Europe and North America. These include: 'Diaspora Programme Support' social enterprise grants of \$75,000 USD (Denmark); 'African Diaspora Marketplace' business plan competition prizes of USD70,000 (USA); 'AFFORD Business Centre (ABC)' social enterprise co-finance of \$38,000 uSD; 'PRA/OSIM' enterprise and project co-finance of \$34,000 USD (France); 'Entrepreneurship by Diaspora for Development' technical support (Netherlands); 'MeetAfrica' providing technical assistance for the establishment of diaspora enterprises in Africa (France and Germany) [26].7 A structured DDI measurement methodology therefore needs to map and monitor output from the various diaspora incentive schemes across Africa.

## **5.2 DPI programmes: diaspora bonds and mutual funds**

Diaspora bonds are perhaps the best-known example of DPI programmes. Israel bonds have been issued since 1951, raising approximately \$40bn USD by 2015 to finance a range of infrastructure, security and strategic developments [27]. India issued the \$1.6 bn USD Indian Development Bonds (IDB) in 1991 to address balance of payments crisis; the \$4.2 bn USD Resurgent India Bonds (RIB) in 1998 in response to economic sanctions imposed after nuclear testing; and the \$5.5bn USD India Millennium Deposits (IMD) in 2000 to capitalise on this new source of development finance [23].

African countries have successful experience of launching bonds, as examples across the continent demonstrate, but such bonds have typically been open to all investors, including foreign individuals and institutions. Despite the great potential of diaspora bonds in African markets, only four African countries have ever issued bonds packaged and targeted specifically for the African diaspora. Of the diaspora bonds issued by Ghana, Ethiopia, Kenya, and Nigeria, only the 2014 Bank of Kenya Infrastructure Bond and the 2017 Nigeria Diaspora Bond were fully subscribed [28, 29]. Nevertheless, there is an increasing appetite to develop investment products that can tap into diaspora investments and savings. AFFORD is currently developing a pilot commercial bond in partnership with the e Rwandan Ministry of Finance and Economic Planning that is aimed at financing affordable housing for key workers in Kigali.

Diaspora mutual funds are a distinct form of DPI where investors are exclusively diaspora and friends of the diaspora. These are structured to meet the interests and needs of the diaspora and marketing activities are targeted at existing and new diaspora investors. Continental banks such as Ecobank and national and regional banks like KCB offer mutual fund products to their clients who hold distinctive diaspora bank accounts [30]. As with diaspora bonds, however, there are limited bespoke Diaspora mutual funds available in the African financial marketplace. The literature makes reference to schemes such as the Liberian Diaspora Social Investment Fund, Zambia First Investment Fund, and Rwanda Diaspora mutual fund (RDMF), but these have yet to take off at scale [26].

**69**

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends*

fears of bureaucracy; a lack of partners in the country of origin.

The range of sectors and trends of African diaspora investors are broad, even if a lot of their investment activity is channelled through the informal and MSME sectors through family networks. Nevertheless the impact of this activity extends beyond this to fund expansion of diaspora-based businesses and greenfield ventures; technology, professional and skill-based consultancies and enterprise, and real estate, heritage, tourism, and export sector businesses [4]. Diaspora investments tend to fund services and light industry, rather than manufacturing and heavy industry, and can include in-kind input such as technical skills and plant, machinery, and equipment. Collective remittances are used for diverse ventures and projects, and are channelled to cooperatives, and social and community enterprises. A range of barriers to diaspora investment have been identified by surveys such as those conducted by UNCTAD [11] and Commonwealth Foundation [31]. These include: a lack of knowledge of the country of investment (particularly among 2nd and 3rd generation migrants); fears of corruption, perceived political instability;

Due to such factors, diaspora investment patterns often appear to be conservative (albeit far from risk averse) and mostly channelled via DDI to the informal and MSME sectors through extended family and social networks, rather than towards structured DPI products. However, remittances are an inefficient way of investing capital—the transfer costs to sub-Saharan Africa alone are on average 8.9–9.4% and

The challenge for financial institutions, policymakers, and the private sector alike is to develop DPI products (and suitable policy and business environments) that are sufficiently attractive to encourage diaspora investors to shift away from remittances. This represents a large potential market for suitable DPI products, with a suitable range of entry points and risk profiles for different diaspora investor profiles. Trust in the institutions issuing such products is key to this, as is understanding the size, location, environment, and investor appetites of the diaspora

In this we chapter we have shown how the diaspora continues to act as an important stakeholder in economic growth and development in countries of origin and/or transit, both through remittances but also investment in family businesses and property. Moreover, such financial contributions are mostly channelled to the informal and MSME sectors, which in Africa are the motor of economic growth

The scale and extent of diaspora investments in countries of origin and/or heritage is vast, far outstripping bilateral ODA and indeed FDI in many African countries. Diaspora communities therefore represent increasingly important economic (as well as political) constituencies for governments in countries of origin. As noted above, diaspora investments can help countries overcome the development financing gap and raise additional investment finance to meet growing infrastructure,

Governments and the private sector have traditionally viewed the diaspora as both ongoing providers of financial capital at the micro level, and, as consumers. While recognition of the diaspora's role in 'doing development' has grown, and the diaspora are increasingly seen as important development stakeholders, they are

energy, and other needs in national development planning

*DOI: http://dx.doi.org/10.5772/intechopen.93129*

the return on investment can be low.

communities concerned [32].

and, critically, job creation.

**7. Conclusion**

**6. Harnessing diaspora investment trends**

<sup>7</sup> See, inter alia: Denmark: https://drc.ngo/media/1893463/new-infographics\_4-parts-in-one-file.pdf; USA: http://diaspora.globalinnovationexchange.org/organizations/african-diaspora-marketplace; United Kingdom: http://www.afford-diasporafinance.org/; France: https://www.forim.net/contenu/ praosim-0; Netherlands: https://www.connectingdiaspora.org/ed4d/call-for-business-ideas/; France & Germany: https://www.campusfrance.org/en/MEETAfrica-creation-entreprises

## **6. Harnessing diaspora investment trends**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

of diaspora enterprises in Africa (France and Germany) [26].7

**5.2 DPI programmes: diaspora bonds and mutual funds**

ment incentives such as loan guarantee schemes.

various diaspora incentive schemes across Africa.

development finance [23].

workers in Kigali.

these have yet to take off at scale [26].

[25]. Other countries have made it easy for the diaspora to access domestic invest-

measurement methodology therefore needs to map and monitor output from the

Diaspora bonds are perhaps the best-known example of DPI programmes. Israel bonds have been issued since 1951, raising approximately \$40bn USD by 2015 to finance a range of infrastructure, security and strategic developments [27]. India issued the \$1.6 bn USD Indian Development Bonds (IDB) in 1991 to address balance of payments crisis; the \$4.2 bn USD Resurgent India Bonds (RIB) in 1998 in response to economic sanctions imposed after nuclear testing; and the \$5.5bn USD India Millennium Deposits (IMD) in 2000 to capitalise on this new source of

African countries have successful experience of launching bonds, as examples across the continent demonstrate, but such bonds have typically been open to all investors, including foreign individuals and institutions. Despite the great potential of diaspora bonds in African markets, only four African countries have ever issued bonds packaged and targeted specifically for the African diaspora. Of the diaspora bonds issued by Ghana, Ethiopia, Kenya, and Nigeria, only the 2014 Bank of Kenya Infrastructure Bond and the 2017 Nigeria Diaspora Bond were fully subscribed [28, 29]. Nevertheless, there is an increasing appetite to develop investment products that can tap into diaspora investments and savings. AFFORD is currently developing a pilot commercial bond in partnership with the e Rwandan Ministry of Finance and Economic Planning that is aimed at financing affordable housing for key

Diaspora mutual funds are a distinct form of DPI where investors are exclusively diaspora and friends of the diaspora. These are structured to meet the interests and needs of the diaspora and marketing activities are targeted at existing and new diaspora investors. Continental banks such as Ecobank and national and regional banks like KCB offer mutual fund products to their clients who hold distinctive diaspora bank accounts [30]. As with diaspora bonds, however, there are limited bespoke Diaspora mutual funds available in the African financial marketplace. The literature makes reference to schemes such as the Liberian Diaspora Social Investment Fund, Zambia First Investment Fund, and Rwanda Diaspora mutual fund (RDMF), but

<sup>7</sup> See, inter alia: Denmark: https://drc.ngo/media/1893463/new-infographics\_4-parts-in-one-file.pdf; USA: http://diaspora.globalinnovationexchange.org/organizations/african-diaspora-marketplace; United Kingdom: http://www.afford-diasporafinance.org/; France: https://www.forim.net/contenu/ praosim-0; Netherlands: https://www.connectingdiaspora.org/ed4d/call-for-business-ideas/; France &

Germany: https://www.campusfrance.org/en/MEETAfrica-creation-entreprises

A structured DDI

In addition to state-led DDI incentives, there are a number of diaspora co-financing programmes operating from Europe and North America. These include: 'Diaspora Programme Support' social enterprise grants of \$75,000 USD (Denmark); 'African Diaspora Marketplace' business plan competition prizes of USD70,000 (USA); 'AFFORD Business Centre (ABC)' social enterprise co-finance of \$38,000 uSD; 'PRA/OSIM' enterprise and project co-finance of \$34,000 USD (France); 'Entrepreneurship by Diaspora for Development' technical support (Netherlands); 'MeetAfrica' providing technical assistance for the establishment

**68**

The range of sectors and trends of African diaspora investors are broad, even if a lot of their investment activity is channelled through the informal and MSME sectors through family networks. Nevertheless the impact of this activity extends beyond this to fund expansion of diaspora-based businesses and greenfield ventures; technology, professional and skill-based consultancies and enterprise, and real estate, heritage, tourism, and export sector businesses [4]. Diaspora investments tend to fund services and light industry, rather than manufacturing and heavy industry, and can include in-kind input such as technical skills and plant, machinery, and equipment. Collective remittances are used for diverse ventures and projects, and are channelled to cooperatives, and social and community enterprises.

A range of barriers to diaspora investment have been identified by surveys such as those conducted by UNCTAD [11] and Commonwealth Foundation [31]. These include: a lack of knowledge of the country of investment (particularly among 2nd and 3rd generation migrants); fears of corruption, perceived political instability; fears of bureaucracy; a lack of partners in the country of origin.

Due to such factors, diaspora investment patterns often appear to be conservative (albeit far from risk averse) and mostly channelled via DDI to the informal and MSME sectors through extended family and social networks, rather than towards structured DPI products. However, remittances are an inefficient way of investing capital—the transfer costs to sub-Saharan Africa alone are on average 8.9–9.4% and the return on investment can be low.

The challenge for financial institutions, policymakers, and the private sector alike is to develop DPI products (and suitable policy and business environments) that are sufficiently attractive to encourage diaspora investors to shift away from remittances. This represents a large potential market for suitable DPI products, with a suitable range of entry points and risk profiles for different diaspora investor profiles. Trust in the institutions issuing such products is key to this, as is understanding the size, location, environment, and investor appetites of the diaspora communities concerned [32].

## **7. Conclusion**

In this we chapter we have shown how the diaspora continues to act as an important stakeholder in economic growth and development in countries of origin and/or transit, both through remittances but also investment in family businesses and property. Moreover, such financial contributions are mostly channelled to the informal and MSME sectors, which in Africa are the motor of economic growth and, critically, job creation.

The scale and extent of diaspora investments in countries of origin and/or heritage is vast, far outstripping bilateral ODA and indeed FDI in many African countries. Diaspora communities therefore represent increasingly important economic (as well as political) constituencies for governments in countries of origin. As noted above, diaspora investments can help countries overcome the development financing gap and raise additional investment finance to meet growing infrastructure, energy, and other needs in national development planning

Governments and the private sector have traditionally viewed the diaspora as both ongoing providers of financial capital at the micro level, and, as consumers. While recognition of the diaspora's role in 'doing development' has grown, and the diaspora are increasingly seen as important development stakeholders, they are

still not viewed as significant social investors by governments, the private sector, or indeed the diaspora themselves. This represents a missed opportunity for harnessing and seeking to scale up diaspora investments for socio-economic growth.

This chapter argues that governments, financial institutions, the private sector, and the diaspora should view diaspora investments as part of the development financing mix, especially as part of 'blended finance' packages.

There are a number of ways in which this might be achieved. Firstly, financial institutions and the private sector could continue to develop and market innovative financial products, especially via the use of online platforms, business incubators and accelerators, targeting the diaspora as social investors; examples include diaspora bonds.

Secondly, they could also facilitate the establishing of bank accounts, foreign currency deposit accounts, fixed-term super FX accounts, or allow diaspora the option of holding funds in either foreign currency or domestic currency with greater benefits for domestic currency.

Governments and multilateral financial institutions can develop programmes to encourage diaspora investments, in particular as part of blended finance packages, but also including the use of online platforms, business incubators and accelerators, targeting the diaspora as social investors. For such programmes to be effective, they should also include elements of match funding for diaspora investors who are exploring DDI as part of their businesses.

Remittance transfer costs to Africa remain high, and there is global consensus (as evinced by SDG target 10.c to reduce transfer fees to 3% or less) that governments, multilateral financial institutions, and the private sector should intensify efforts to reduce remittance transfer costs, to meet this SDG target. This action would return millions to senders and recipients, which can be targeted to investment purposes.

There is also a need for governments and multilateral financial institutions to develop methodologies for better monitoring of DDI and DPI flows, including publication of a DDI index. AFFORD is currently developing a more granular methodology of the 2nd edition of an African DDI Index to track diaspora investment trends in the continent and this series will be developed and extended over 2020.

## **Acknowledgements**

The authors would like to thank Subodh Tailor at the University of Edinburgh for his help and support in compiling data for this article.

**71**

**Author details**

Paul Asquith and Stella Opoku-Owusu

provided the original work is properly cited.

\*Address all correspondence to: paul@afford-uk.org

African Foundation for Development (AFFORD UK), London, United Kingdom

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends*

*DOI: http://dx.doi.org/10.5772/intechopen.93129*

## **Conflict of interest**

The authors declare no conflict of interest.

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends DOI: http://dx.doi.org/10.5772/intechopen.93129*

## **Author details**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

financing mix, especially as part of 'blended finance' packages.

diaspora bonds.

ment purposes.

**Acknowledgements**

**Conflict of interest**

greater benefits for domestic currency.

ing DDI as part of their businesses.

still not viewed as significant social investors by governments, the private sector, or indeed the diaspora themselves. This represents a missed opportunity for harnessing and seeking to scale up diaspora investments for socio-economic growth.

This chapter argues that governments, financial institutions, the private sector, and the diaspora should view diaspora investments as part of the development

There are a number of ways in which this might be achieved. Firstly, financial institutions and the private sector could continue to develop and market innovative financial products, especially via the use of online platforms, business incubators and accelerators, targeting the diaspora as social investors; examples include

Secondly, they could also facilitate the establishing of bank accounts, foreign currency deposit accounts, fixed-term super FX accounts, or allow diaspora the option of holding funds in either foreign currency or domestic currency with

Governments and multilateral financial institutions can develop programmes to encourage diaspora investments, in particular as part of blended finance packages, but also including the use of online platforms, business incubators and accelerators, targeting the diaspora as social investors. For such programmes to be effective, they should also include elements of match funding for diaspora investors who are explor-

Remittance transfer costs to Africa remain high, and there is global consensus (as evinced by SDG target 10.c to reduce transfer fees to 3% or less) that governments, multilateral financial institutions, and the private sector should intensify efforts to reduce remittance transfer costs, to meet this SDG target. This action would return millions to senders and recipients, which can be targeted to invest-

There is also a need for governments and multilateral financial institutions to develop methodologies for better monitoring of DDI and DPI flows, including publication of a DDI index. AFFORD is currently developing a more granular methodology of the 2nd edition of an African DDI Index to track diaspora investment trends in the continent and this series will be developed and extended over 2020.

The authors would like to thank Subodh Tailor at the University of Edinburgh

for his help and support in compiling data for this article.

The authors declare no conflict of interest.

**70**

Paul Asquith and Stella Opoku-Owusu African Foundation for Development (AFFORD UK), London, United Kingdom

\*Address all correspondence to: paul@afford-uk.org

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[14] Mugano G. Diaspora investment and African national economies: Case studies. In: Hack-Polay S, editor. African Diaspora Direct Investment: Establishing the Economic and Socio-Cultural Rationale. London: Palgrave; 2018

[15] Leandro M, Andrew J, Mehmet C. The informal economy in Sub-Saharan Africa: Size and determinants. In: IMF Working Paper WP/17/156. Washington, DC: International Monetary Fund; 2017

**73**

pdf

*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends*

difficult times. Journal of International Commerce, Economics and Policy. 2010;**1**(2):251-263. Available from: http://www.dilipratha.com/index\_files/

[24] Morocco: MDM Invest. Available

[26] Central Bank approves 'Diaspora Mutual Fund". New Times. [Internet]. December 2019. Available from: https:// www.newtimes.co.rw/section/read/14617

[27] DCI/Israel Bonds. [Internet]. 2020. Available from: https://israelbonds.com/ About-Us/DCI-Israel-Bonds.aspx

[28] Enders Mira, Development Finance: Untapped Potential [Internet]. March 2020. Available from: https://www. dandc.eu/en/article/diaspora-bondscould-play-bigger-role-development-

[29] World Bank. A Billion Dollar Idea: Leveraging Migration for Financing Development. Washington, DC: World Bank; 2013. Available from: http:// blogs.worldbank.org/peoplemove/files/

[30] Available from: https://ecobank. com/ng/personal-banking/everydaybanking/savings-accounts/diaspora; https://ke.kcbgroup.com/diaspora/

[31] Commonwealth Foundation. Financing the SDGs with. Diaspora Investment: Geneva. 2017;**2018**. Available from: http:// thecommonwealth.org/sites/ default/files/inline/2017\_CW\_ FinancingtheSDGswith

DiasporaInvestment\_REPORT.PDF

finance

special\_topic.pdf

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from: http://www.ccg.ma/en/ votre-projet/mdm-invest

[25] Senegal: Fonds d'Appui à l'Investissement des Sénégalais de l'Exterieur ['Support fund for Senegalese diaspora investment). Available from: http://faise.sn/

*DOI: http://dx.doi.org/10.5772/intechopen.93129*

[16] IFAD. The use of remittances and financial inclusion. 2015. Available from: https://www.ifad. org/documents/38714170/40187309/ gpfi.pdf/58ce7a06-7ec0-42e8-82dc-

Ratha D. Migration and Remittances Household Surveys in Sub Saharan Africa: Methodological Aspects and Main Findings. Washington, DC: World Bank and African Development Bank; 2011. Available from: http://pubdocs. worldbank.org/en/866251444753456291/

[18] Ardovino M, Debass T. Diaspora Direct Investment (DDI): The

Untapped Resource for Development. Washington, DC: United States

Agency for International Development

[19] IMF Coordinated Portfolio Investment Survey (CPIS). [Internet]. 2020. Available from: http://data.imf. org/?sk=B981B4E3-4E58-467E-9B90-

[20] African Eurobonds: what to look for in 2019. Business Telegraph. Jan 2019. Available from: https:// thebusinesstelegraph.com/2019/01/23/ african-eurobonds-what-to-look-

[21] IMF. Balance of Payments and International Investment Position Manual (BPM6), Chapter 6. 2007. Available from: https://www.imf.org/ external/pubs/ft/bop/2007/pdf/chap6.

[22] Terrazas A. Diaspora Investment in Developing and Emerging Country Capital Markets: Patterns and Prospects. Washington, DC: Migration Policy

[23] Ketkar S, Ratha D. Diaspora bonds: Tapping the diaspora during

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*Diaspora Investment to Help Achieve the SDGs in Africa: Prospects and Trends DOI: http://dx.doi.org/10.5772/intechopen.93129*

[16] IFAD. The use of remittances and financial inclusion. 2015. Available from: https://www.ifad. org/documents/38714170/40187309/ gpfi.pdf/58ce7a06-7ec0-42e8-82dcc069227edb79

[17] Plaza S, Navarrete M, Ratha D. Migration and Remittances Household Surveys in Sub Saharan Africa: Methodological Aspects and Main Findings. Washington, DC: World Bank and African Development Bank; 2011. Available from: http://pubdocs. worldbank.org/en/866251444753456291/ Plaza-Navarrete-Ratha-MethodologicalPaper.pdf

[18] Ardovino M, Debass T. Diaspora Direct Investment (DDI): The Untapped Resource for Development. Washington, DC: United States Agency for International Development (USAID); 2009

[19] IMF Coordinated Portfolio Investment Survey (CPIS). [Internet]. 2020. Available from: http://data.imf. org/?sk=B981B4E3-4E58-467E-9B90- 9DE0C3367363

[20] African Eurobonds: what to look for in 2019. Business Telegraph. Jan 2019. Available from: https:// thebusinesstelegraph.com/2019/01/23/ african-eurobonds-what-to-lookfor-in-2019

[21] IMF. Balance of Payments and International Investment Position Manual (BPM6), Chapter 6. 2007. Available from: https://www.imf.org/ external/pubs/ft/bop/2007/pdf/chap6. pdf

[22] Terrazas A. Diaspora Investment in Developing and Emerging Country Capital Markets: Patterns and Prospects. Washington, DC: Migration Policy Institute; 2010

[23] Ketkar S, Ratha D. Diaspora bonds: Tapping the diaspora during difficult times. Journal of International Commerce, Economics and Policy. 2010;**1**(2):251-263. Available from: http://www.dilipratha.com/index\_files/ DiasporaBonds-JICEP.pdf

[24] Morocco: MDM Invest. Available from: http://www.ccg.ma/en/ votre-projet/mdm-invest

[25] Senegal: Fonds d'Appui à l'Investissement des Sénégalais de l'Exterieur ['Support fund for Senegalese diaspora investment). Available from: http://faise.sn/

[26] Central Bank approves 'Diaspora Mutual Fund". New Times. [Internet]. December 2019. Available from: https:// www.newtimes.co.rw/section/read/14617

[27] DCI/Israel Bonds. [Internet]. 2020. Available from: https://israelbonds.com/ About-Us/DCI-Israel-Bonds.aspx

[28] Enders Mira, Development Finance: Untapped Potential [Internet]. March 2020. Available from: https://www. dandc.eu/en/article/diaspora-bondscould-play-bigger-role-developmentfinance

[29] World Bank. A Billion Dollar Idea: Leveraging Migration for Financing Development. Washington, DC: World Bank; 2013. Available from: http:// blogs.worldbank.org/peoplemove/files/ special\_topic.pdf

[30] Available from: https://ecobank. com/ng/personal-banking/everydaybanking/savings-accounts/diaspora; https://ke.kcbgroup.com/diaspora/

[31] Commonwealth Foundation. Financing the SDGs with. Diaspora Investment: Geneva. 2017;**2018**. Available from: http:// thecommonwealth.org/sites/ default/files/inline/2017\_CW\_ FinancingtheSDGswith DiasporaInvestment\_REPORT.PDF

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*Foreign Direct Investment Perspective through Foreign Direct Divestment*

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[10] Sustainable Development Goals: Goal 10: Reduce inequality within and among countries. [Internet]. 2015. Available from: https://www.un.org/ sustainabledevelopment/inequality/

[11] UNCTAD. World Investment Report 2018: Investment and New Industrial Policies. Geneva: United Nations Conference on Trade and Development

[12] World Bank. Migration and Remittances: Recent Developments and Outlook. Migration and Development Brief 31: April 2019. 2019. Available from: https://www. knomad.org/sites/default/files/2019-04/ Migrationanddevelopmentbrief31.pdf

[13] AfDB. Diaspora Bonds: Some Lessons for African Countries, Africa Economic Brief 3 (13). African Development Bank: Abidjan; 2012

[14] Mugano G. Diaspora investment and African national economies: Case studies. In: Hack-Polay S, editor. African Diaspora Direct Investment: Establishing the Economic and Socio-Cultural Rationale. London: Palgrave;

[15] Leandro M, Andrew J, Mehmet C. The informal economy in Sub-Saharan Africa: Size and determinants. In: IMF Working Paper WP/17/156. Washington, DC: International

Monetary Fund; 2017

htm

001CC477EC70/

(UNCTAD); 2018

2018

[1] Sandton Declaration: African Union. 2012. Available from: https://www.gov. za/declaration-global-african-diasporasummit-sandton-johannesburg-south-

[2] Sonia P, Dilip R. Diaspora for Development in Africa (English). Washington, DC: World Bank; 2011. Available from: http:// documents.worldbank.org/ curated/en/389011468191676942/ Diaspora-for-development-in-Africa

[3] Oviatt B, McDougall P. Defining international entrepreneurship and modeling the speed of

[4] Faal G. Strategic, Business and Operational Framework for an African Diaspora Finance Corporation: African Union Legacy Project on Diaspora Investment, Innovative Finance and Social Enterprise in Africa. GK Partners/African Union Commission; 2019. An abridged version is available from: https://au.int/sites/default/files/ documents/37383-doc-adfc\_business\_ framework\_-\_abridged\_version.pdf

[5] International Forum of Sovereign Wealth Funds: Agaciro [Internet]. 2020. Available from: https://www.ifswf.org/

[6] Agaciro Fund [Internet]. 2011. Available from: http://www.agaciro.rw/

[7] World Bank, Migration and Remittances: Recent Developments and Outlook, Migration and Development Brief 31: April 2019. 2019. Available from: https://www. knomad.org/sites/default/files/2019-04/ Migrationanddevelopmentbrief31.pdf

[8] Dilip R. Remittances: Funds for the Folks Back Home [Internet]. 2020.

members/rwanda

internationalization. Entrepreneurship Theory and Practice. 2005;**29**(5):537-553

**References**

africa

**Chapter 6**

**Abstract**

an ongoing basis.

**1. Introduction**

**75**

relations, multi-criteria decision-making

for Vietnam

Applying Consistency Fuzzy

Preference Relations to Select

Direct Investment (FDI) in

*Nguyen Xuan Huynh and Hoang Dinh Phi*

a Strategy that Attracts Foreign

Developing Supporting Industries

The Vietnamese government has been focused on promoting supporting industries, which may provide a "key" solution for sustained development and thereby improve the national welfare. Coincidentally, Vietnam is also focused on an optimal strategy to attract foreign direct investment (FDI that develops a strategy for supporting industries). However, these results have not been achieved due to the weaknesses of low FDI flow, the limited number of capital projects, and the inclusion of smaller enterprises with lower technology into the mix. This negative situation begs the question as to what might be the best strategy for attracting FDI that developmentally supports the Vietnamese industry. As an intended remedy, this inquiry establishes an analytical, hierarchy framework beneficial to the Vietnamese government on a best strategic method for attracting FDI to develop supporting local industries. This study utilizes fuzzy preference relations to improve the decision-making process to be both consistent and effective. The analytical results demonstrate that institutional policies, domestic supply capacity, human resources, and technological development, coupled with innovation, are the key criteria to be considered when selecting a strategy that attracts regular FDI. Furthermore, analytical results presented in this work demonstrate that the best strategies for attracting FDI to Vietnam are those that motivate sustainable economic growth on

**Keywords:** attracting FDI, developing supporting industries, fuzzy preference

those of other Asian countries, comparatively speaking [1]. Some of the major factors leading to the weakness of supporting industries in Vietnam are a distinct

Vietnam's supporting industries are still less developed and less competitive than

[32] Commonwealth Foundation. Understanding the Investment Potential of the Commonwealth Diaspora: Results of the Commonwealth Diaspora Investor Survey. 2018. Available from: https://thecommonwealth.org/sites/ default/files/inline/Understanding%20 the%20Investment%20Potential%20 of%20the%20Commonwealth%20 Diaspora.pdf

## **Chapter 6**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

[32] Commonwealth Foundation. Understanding the Investment Potential

of the Commonwealth Diaspora: Results of the Commonwealth Diaspora Investor Survey. 2018. Available from: https://thecommonwealth.org/sites/ default/files/inline/Understanding%20 the%20Investment%20Potential%20 of%20the%20Commonwealth%20

Diaspora.pdf

**74**

## Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign Direct Investment (FDI) in Developing Supporting Industries for Vietnam

*Nguyen Xuan Huynh and Hoang Dinh Phi*

## **Abstract**

The Vietnamese government has been focused on promoting supporting industries, which may provide a "key" solution for sustained development and thereby improve the national welfare. Coincidentally, Vietnam is also focused on an optimal strategy to attract foreign direct investment (FDI that develops a strategy for supporting industries). However, these results have not been achieved due to the weaknesses of low FDI flow, the limited number of capital projects, and the inclusion of smaller enterprises with lower technology into the mix. This negative situation begs the question as to what might be the best strategy for attracting FDI that developmentally supports the Vietnamese industry. As an intended remedy, this inquiry establishes an analytical, hierarchy framework beneficial to the Vietnamese government on a best strategic method for attracting FDI to develop supporting local industries. This study utilizes fuzzy preference relations to improve the decision-making process to be both consistent and effective. The analytical results demonstrate that institutional policies, domestic supply capacity, human resources, and technological development, coupled with innovation, are the key criteria to be considered when selecting a strategy that attracts regular FDI. Furthermore, analytical results presented in this work demonstrate that the best strategies for attracting FDI to Vietnam are those that motivate sustainable economic growth on an ongoing basis.

**Keywords:** attracting FDI, developing supporting industries, fuzzy preference relations, multi-criteria decision-making

## **1. Introduction**

Vietnam's supporting industries are still less developed and less competitive than those of other Asian countries, comparatively speaking [1]. Some of the major factors leading to the weakness of supporting industries in Vietnam are a distinct

lack of capital, technological innovation, and management skills suitable for development purposes [2]. FDI increases economic growth of recipient countries by bringing physical change through the introduction of infrastructure, advanced technology, and management expertise [3]. It is also considered to increase domestic capital, create employment, raise personal incomes, promote technology, and generate the transfer of skills through foreign technology and know-how to boost host country economies; so, such investment is seen as the engine of economic growth in the long term [4]. Moreover, FDI is an important vehicle for the transfer of technology, as it contributes relatively more to overall growth than what domestic investment may accomplish [5]. Therefore, attracting FDI for developing supporting industries is seen to be the best strategy to solve the problem of muchneeded capital obtainment.

review and to choose a strategy platform which may attract future FDI to develop supporting industries by using the established eight main criteria and three

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

This study uses AHP methodology to perform complicated pairwise comparisons among the criteria at hand. It may take considerable time to obtain a convincing consistency index with such an increasing number of criteria. From that, or because of that, the study uses the consistency fuzzy preference relations (CFPR) model [6, 9–16] useful to calculate the nature of the criteria and the adjacent alternative weighting. These results are utilized to determine the most important

**2.1 Concepts of supporting industry and developing supporting industries**

to signify industries that supply raw materials, parts, and capital goods for assembly-type industries. Currently, the term "supporting industries" is widely used, especially in East Asia. It is interpreted differently in different fields of activities [2, 18, 20]. In Vietnam, the use of supporting industries is defined in accordance with Decision No. 12/2011/QĐ-TTg promulgated by the Prime Minister: "The supporting industries are industries producing materials, spare parts, components, accessories or semi-finished products as the means of the production of final products in production and assembly industries or of consumer products" [21]. Accordingly, Decision No. 1483/QĐ-TTg promulgated by the Prime Minister, on August 26, 2011, stated: "On promulgating a list of supporting industry products that are given priority for development" [22], there are six industries which are identified, including textile-garments, leather shoes, electronic computing,

manufacturing and assembly automobiles, mechanical engineering, and supporting

Supporting industries can play a role in promoting economic growth [7]. A country with competitive supporting industries will contribute to economic development and national welfare even in the long run [6]. It is expressed through the following means: First, the development of competitive supporting industries will cause the dynamic effect of promoting technological innovations and developing human resources, thereby improving national welfare [23]. Second, a country with competitive supporting industries can sustain FDI for final assembly processes relatively longer than a country without competitive supporting industries. And, finally, a country with competitive supporting industries can export manufactured inputs to countries where the final assembly processes are ultimately transferred. Moreover, it should be noted that national industries will benefit most when the domestic supporting industries are able to become globally competitive, although a nation does not need to be competitive in all supporting industries if it has specialization in certain given areas [24]. Therefore, developing countries may wish to establish competitive supporting industries for long-term economic growth.

industry products used for and by high-tech industries.

The term "supporting industries" is derived from Japanese literature in the mid-1980s [2, 17, 18]. It first appeared in a White Paper on Economic Cooperation of the Ministry of International Trade and Industry (MITI) of Japan [19] for the promotion of industrialization as a process and as part of directing the development of small and medium enterprises (SMEs) which were part of the Association of Southeast Asian Nations (ASEAN) country structure, especially in Singapore, Malaysia, Thailand, Indonesia, and the Philippines. The term was officially defined

alternatives posited in this study.

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

**2. Related literature**

**in Vietnam**

**77**

criteria and to select the best strategy for attracting FDI.

In addition, Vietnam is still considered to be a developing country. The Vietnamese government has concentrated on attracting FDI to develop supporting industries which will add to further overall development throughout the nation as a whole [6, 7]. However, the FDI attraction necessary to develop supporting industries in Vietnam is still viewed as an inherent weakness due to low FDI inflows, the noticeably limited number of infrastructure projects, and the proliferation of smaller, lower-technology enterprises that do not significantly contribute to any great extent [6]. Moreover, the General Statistics Office (GSO) of Vietnam [8] states that the total cumulative FDI for developing supporting industries is approximately US \$29.16 billion, accounting for a 46.19% of all FDI projects, and 72.25% of the total FDI value in industries, wherein 15.63% quantity of FDI projects and 16.87% of the total FDI value are shown in supporting industries. Therefore, this study demonstrates the key factors that will have the most important impact to attract FDI to develop supporting industries in Vietnam.

This study concentrates on selecting a workable strategy for attracting FDI to develop supporting industries in Vietnam. Moreover, it should be noted that FDI firms consistently perform better than domestic ones in order to drive the development of Vietnam's supporting industries [2]. It utilizes a theoretical study, and it examines the current situation of developing supporting industries coupled with the reality of attracting FDI to Vietnam. This examination is to be taken together with the results which are concomitant with interviews of government staff and policymakers, economists, foreign investors, and managers from six supporting industries. The results have indicated that there are eight main criteria that influence the attraction of FDI to develop supporting industries [6]; and, there are three alternative strategies applicable to attracting FDI. The eight main criteria are as follows: (1) institutions and policies; (2) human resources (e.g., quantity, salary, education, skill, and morale); (3) infrastructure facilities (e.g., transport, power, information, communication, etc.); (4) domestic supply capability (total value and partition domestic supply chain and the quantity and size of supporting industries firms); (5) market size of supporting industries (i.e., the total consumption of supporting industries products); (6) technological development and innovation; (7) international cooperation and competition; and (8) other criteria (such as environmental policy, culture, tax policy, land support, corruption, etc.). When taken together, there exist various alternative strategy policies for attracting FDI to develop supporting industries, namely: (i) attracting FDI for developing supporting industries, which motivates the economy's sustainable growth; (ii) attracting FDI for developing supporting industries, which increases national competitiveness; and (iii) attracting FDI for developing supporting industries, which stimulates overall national technological development. Based on the results obtained, an analytical, hierarchy framework has been developed to assist the Vietnamese government and involved policy-makers to evaluate the practical influence of those criteria under

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

review and to choose a strategy platform which may attract future FDI to develop supporting industries by using the established eight main criteria and three alternatives posited in this study.

This study uses AHP methodology to perform complicated pairwise comparisons among the criteria at hand. It may take considerable time to obtain a convincing consistency index with such an increasing number of criteria. From that, or because of that, the study uses the consistency fuzzy preference relations (CFPR) model [6, 9–16] useful to calculate the nature of the criteria and the adjacent alternative weighting. These results are utilized to determine the most important criteria and to select the best strategy for attracting FDI.

## **2. Related literature**

lack of capital, technological innovation, and management skills suitable for development purposes [2]. FDI increases economic growth of recipient countries by bringing physical change through the introduction of infrastructure, advanced technology, and management expertise [3]. It is also considered to increase domestic capital, create employment, raise personal incomes, promote technology, and generate the transfer of skills through foreign technology and know-how to boost host country economies; so, such investment is seen as the engine of economic growth in the long term [4]. Moreover, FDI is an important vehicle for the transfer of technology, as it contributes relatively more to overall growth than what domestic investment may accomplish [5]. Therefore, attracting FDI for developing supporting industries is seen to be the best strategy to solve the problem of much-

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

In addition, Vietnam is still considered to be a developing country. The Vietnamese government has concentrated on attracting FDI to develop supporting industries which will add to further overall development throughout the nation as a whole [6, 7]. However, the FDI attraction necessary to develop supporting industries in Vietnam is still viewed as an inherent weakness due to low FDI inflows, the noticeably limited number of infrastructure projects, and the proliferation of smaller, lower-technology enterprises that do not significantly contribute to any great extent [6]. Moreover, the General Statistics Office (GSO) of Vietnam [8] states that the total cumulative FDI for developing supporting industries is approximately US \$29.16 billion, accounting for a 46.19% of all FDI projects, and 72.25% of the total FDI value in industries, wherein 15.63% quantity of FDI projects and 16.87% of the total FDI value are shown in supporting industries. Therefore, this study demonstrates the key factors that will have the most important impact to

This study concentrates on selecting a workable strategy for attracting FDI to develop supporting industries in Vietnam. Moreover, it should be noted that FDI firms consistently perform better than domestic ones in order to drive the development of Vietnam's supporting industries [2]. It utilizes a theoretical study, and it examines the current situation of developing supporting industries coupled with the reality of attracting FDI to Vietnam. This examination is to be taken together with the results which are concomitant with interviews of government staff and policymakers, economists, foreign investors, and managers from six supporting industries. The results have indicated that there are eight main criteria that influence the attraction of FDI to develop supporting industries [6]; and, there are three alternative strategies applicable to attracting FDI. The eight main criteria are as follows: (1) institutions and policies; (2) human resources (e.g., quantity, salary, education, skill, and morale); (3) infrastructure facilities (e.g., transport, power, information, communication, etc.); (4) domestic supply capability (total value and partition domestic supply chain and the quantity and size of supporting industries firms); (5) market size of supporting industries (i.e., the total consumption of supporting industries products); (6) technological development and innovation; (7) international cooperation and competition; and (8) other criteria (such as environmental policy, culture, tax policy, land support, corruption, etc.). When taken together, there exist various alternative strategy policies for attracting FDI to develop supporting industries, namely: (i) attracting FDI for developing supporting industries, which motivates the economy's sustainable growth; (ii) attracting FDI for developing supporting industries, which increases national competitiveness; and (iii) attracting FDI for developing supporting industries, which stimulates overall national technological development. Based on the results obtained, an analytical, hierarchy framework has been developed to assist the Vietnamese government and involved policy-makers to evaluate the practical influence of those criteria under

attract FDI to develop supporting industries in Vietnam.

needed capital obtainment.

**76**

## **2.1 Concepts of supporting industry and developing supporting industries in Vietnam**

The term "supporting industries" is derived from Japanese literature in the mid-1980s [2, 17, 18]. It first appeared in a White Paper on Economic Cooperation of the Ministry of International Trade and Industry (MITI) of Japan [19] for the promotion of industrialization as a process and as part of directing the development of small and medium enterprises (SMEs) which were part of the Association of Southeast Asian Nations (ASEAN) country structure, especially in Singapore, Malaysia, Thailand, Indonesia, and the Philippines. The term was officially defined to signify industries that supply raw materials, parts, and capital goods for assembly-type industries. Currently, the term "supporting industries" is widely used, especially in East Asia. It is interpreted differently in different fields of activities [2, 18, 20]. In Vietnam, the use of supporting industries is defined in accordance with Decision No. 12/2011/QĐ-TTg promulgated by the Prime Minister: "The supporting industries are industries producing materials, spare parts, components, accessories or semi-finished products as the means of the production of final products in production and assembly industries or of consumer products" [21]. Accordingly, Decision No. 1483/QĐ-TTg promulgated by the Prime Minister, on August 26, 2011, stated: "On promulgating a list of supporting industry products that are given priority for development" [22], there are six industries which are identified, including textile-garments, leather shoes, electronic computing, manufacturing and assembly automobiles, mechanical engineering, and supporting industry products used for and by high-tech industries.

Supporting industries can play a role in promoting economic growth [7]. A country with competitive supporting industries will contribute to economic development and national welfare even in the long run [6]. It is expressed through the following means: First, the development of competitive supporting industries will cause the dynamic effect of promoting technological innovations and developing human resources, thereby improving national welfare [23]. Second, a country with competitive supporting industries can sustain FDI for final assembly processes relatively longer than a country without competitive supporting industries. And, finally, a country with competitive supporting industries can export manufactured inputs to countries where the final assembly processes are ultimately transferred. Moreover, it should be noted that national industries will benefit most when the domestic supporting industries are able to become globally competitive, although a nation does not need to be competitive in all supporting industries if it has specialization in certain given areas [24]. Therefore, developing countries may wish to establish competitive supporting industries for long-term economic growth.

As stated, Vietnam is a developing country, and the industrialization and modernization is still progressing [25–28]. Therefore, the Vietnamese government is concentrating on promoting supporting industries wherever possible. This promotion is exemplified by Vietnam's support of industry prospects currently under assessment by Japanese enterprises [18], coupled with various decisions and policies for developing supporting industries such as: Decision No. 34/2007/QD-BCN, promulgated on July 31, 2007, by the Minister of Industry and Trade, which states: "Approving the planning of industrial development supports up to 2010 and vision to 2020" [29]; Decision No. 12/2011/QD-TTg, promulgated on February 24, 2011, by the Prime Minister, which states: "On development policies of some supporting industries" [21]; Decision No. 1843//QD-TTg, promulgated on August 26, 2011, by the Prime Minister, which states: "On promulgating a list of supporting industry products that are given priority for development" [22]; Decision No. 1556/QD-TTg, promulgated on October 17, 2012, by the Prime Minister, which states: "Development of an approval scheme, to help developing small and medium enterprises in supporting industries field" [19]; Decision No. 9028/QD-BCT, promulgated on October 10, 2014, by the Minister of Industry and Trade, which states: "Approval of a master plan for developing supporting industries up to 2020, vision to 2030" [30]; Decree No. 1111/2015/ND-CP, promulgated on November 3, 2015, by the Prime Minister, which states: "On the development of supporting industries" [31]; Decision No. 68/QD-TTg, promulgated on January 18, 2017, by the Prime Minister, which states: "On the approving of the program on development of supporting industries from 2016 to 2025" [32]; Decision No. 10/2017/QD-TTg, promulgated on April 3, 2017, by the Prime Minister, which states: "Promulgating the regulation on management and implementation of the program on development of supporting industries" [33]; and Decision No. 4572/QD-BCT, promulgated on November 7, 2014, by the Minister of Industry and Trade, which states: "Promulgating the regulation on formulation, receipt, appraisal, approval and implementation of schemes under the program on development of supporting industries" [34]. However, Vietnam's supporting industries are still in the very initial stages of development. The reality of supporting industries in Vietnam is that they are significantly lower in developmental status and weak in competitiveness [35]. This is evidenced by the lower proportion of locally finished goods. One recent Vietnamese governmental report [36] and a notice from the General Statistics Office of Vietnam [8] indicate that the proportion of localization in finished products in some supporting industries is 10.5% in manufacturing and assembly of automobiles, 17.2% in electronics, 12.5% in mechanical engineering, 9.5% in textile-garments, and 13.1% in leather shoes. This obviously underdeveloped state of the local supporting industry has negatively resulted in increased production costs, the risk of bigger trade deficits with foreign partners, and a lowered competitiveness of Vietnamese products than regional peers. This is due in large part to the importation of components and spare parts, which continues to be one of the primary factors preventing industrial development and economic growth, leading to increased national welfare. Some of the major factors which have led to overall weakness in Vietnam's supporting industries are a lack of capital expenditure, reduced technological innovation, and paucity of management skills for development [2].

linked to FDI flows: demonstration or imitation; labor mobility; exportation; competition; and backward and forward linkages with domestic firms [38]. These five channels, respectively, match the following situations: (i) the efforts of domestic firms to adopt successful technology used by multinational corporations (MNCs); (ii) the recruitment by domestic firms of workers with MNC experience who are able to use different technologies; (iii) access to large distribution networks and the related gain due to a better knowledge of consumer tastes in foreign markets; (iv) a more efficient usage of existing resources and technology or the incorporation by domestic firms of new technologies in the production process to compete with MNCs; and (v) the relationship between MNCs and domestic firms, where the latter can become suppliers of MNCs (backward linkages) or customers of inter-

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

In addition, the research results produced by international case studies, in terms

Many empirical studies support the theory that MNCs and MNEs tend to have higher productivity than domestic firms located in the same sector, thereby contributing to considerable GDP growth in developing markets. While customers of supporting industries are typically domestic assemblers, it is possible for foreign assemblers to become located in the domestic market and for foreign assemblers to also be located in adjacent foreign countries in the region [37]. It should be noted that foreign assemblers are frequently MNCs and MNEs. More importantly, it seems that developing countries expect that MNCs will have a supposed positive impact on the productivity levels of domestic firms by the potential generation of positive externalities. FDI may generate positive externalities worthy of the productivity growth of domestic suppliers through business relationships with MNCs (known as "backward linkages" afterward) and to increased output and productivity of domestic supporting industries, due to the additional demand and technolog-

ical transfer caused by MNCs. Moreover, if increasing FDI causes positive

through backward linkages, national welfare in FDI host countries will also undoubtedly improve. Finally, developing countries may improve their national welfare through the attraction of FDI, if their supporting industries can obtain positive externalities that far exceed the negative externalities some domestic

externalities for domestic suppliers and concomitantly improves their productivity

The role of FDI in the development of supporting industries is seen as follows: (i) to develop the infrastructure of the industry, paving the way for the development supporting industries; (ii) to expand the market scale; (iii) to create

of the positive impact FDI flows have on the invested country, are related as follows: (i) managing the status of lack of funds and thereby increasing labor productivity, employment, and other production factors; (ii) promoting the growth by increasing total social investment; (iii) keeping the balance of payments; (iv) contributing to diversification of the production structure; (v) employment effects; and (vi) a transfer of technology [2]. The infusion of FDI creates the effect of future production and further investment both before and after subsequent production stages have been initiated. The general situation in many countries, Vietnam included, is that FDI significantly contributes to export growth and job creation, but it does not help to increase the level of national prosperity despite job creation, albeit at minimum wage, in the manufacturing sector. However, FDI flows may have the potential of causing major instability to a given marketplace environment, as follows: pinching domestic manufacturers instead of network cooperation and weakening the overall sense of prosperity through the outward transfer of profits and income to foreign countries. This situation would decidedly be in favor of the investors who make investments through massive incentive programs presented

mediate outputs of MNCs (forward linkages) [37].

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

by host countries [17].

assemblers may yet encounter [2].

**79**

## **2.2 Strategy for attracting FDI for developing supporting industries**

Developing countries may improve national welfare by attracting FDI [37]. It is understood that FDI supports economic growth, increased personal incomes, and leads to a greater rate of employment and technological transfer on a national basis [37]. Moreover, there are five main channels of technological diffusion which are

## *Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

linked to FDI flows: demonstration or imitation; labor mobility; exportation; competition; and backward and forward linkages with domestic firms [38]. These five channels, respectively, match the following situations: (i) the efforts of domestic firms to adopt successful technology used by multinational corporations (MNCs); (ii) the recruitment by domestic firms of workers with MNC experience who are able to use different technologies; (iii) access to large distribution networks and the related gain due to a better knowledge of consumer tastes in foreign markets; (iv) a more efficient usage of existing resources and technology or the incorporation by domestic firms of new technologies in the production process to compete with MNCs; and (v) the relationship between MNCs and domestic firms, where the latter can become suppliers of MNCs (backward linkages) or customers of intermediate outputs of MNCs (forward linkages) [37].

In addition, the research results produced by international case studies, in terms of the positive impact FDI flows have on the invested country, are related as follows: (i) managing the status of lack of funds and thereby increasing labor productivity, employment, and other production factors; (ii) promoting the growth by increasing total social investment; (iii) keeping the balance of payments; (iv) contributing to diversification of the production structure; (v) employment effects; and (vi) a transfer of technology [2]. The infusion of FDI creates the effect of future production and further investment both before and after subsequent production stages have been initiated. The general situation in many countries, Vietnam included, is that FDI significantly contributes to export growth and job creation, but it does not help to increase the level of national prosperity despite job creation, albeit at minimum wage, in the manufacturing sector. However, FDI flows may have the potential of causing major instability to a given marketplace environment, as follows: pinching domestic manufacturers instead of network cooperation and weakening the overall sense of prosperity through the outward transfer of profits and income to foreign countries. This situation would decidedly be in favor of the investors who make investments through massive incentive programs presented by host countries [17].

Many empirical studies support the theory that MNCs and MNEs tend to have higher productivity than domestic firms located in the same sector, thereby contributing to considerable GDP growth in developing markets. While customers of supporting industries are typically domestic assemblers, it is possible for foreign assemblers to become located in the domestic market and for foreign assemblers to also be located in adjacent foreign countries in the region [37]. It should be noted that foreign assemblers are frequently MNCs and MNEs. More importantly, it seems that developing countries expect that MNCs will have a supposed positive impact on the productivity levels of domestic firms by the potential generation of positive externalities. FDI may generate positive externalities worthy of the productivity growth of domestic suppliers through business relationships with MNCs (known as "backward linkages" afterward) and to increased output and productivity of domestic supporting industries, due to the additional demand and technological transfer caused by MNCs. Moreover, if increasing FDI causes positive externalities for domestic suppliers and concomitantly improves their productivity through backward linkages, national welfare in FDI host countries will also undoubtedly improve. Finally, developing countries may improve their national welfare through the attraction of FDI, if their supporting industries can obtain positive externalities that far exceed the negative externalities some domestic assemblers may yet encounter [2].

The role of FDI in the development of supporting industries is seen as follows: (i) to develop the infrastructure of the industry, paving the way for the development supporting industries; (ii) to expand the market scale; (iii) to create

As stated, Vietnam is a developing country, and the industrialization and modernization is still progressing [25–28]. Therefore, the Vietnamese government is concentrating on promoting supporting industries wherever possible. This promotion is exemplified by Vietnam's support of industry prospects currently under assessment by Japanese enterprises [18], coupled with various decisions and policies for developing supporting industries such as: Decision No. 34/2007/QD-BCN, promulgated on July 31, 2007, by the Minister of Industry and Trade, which states: "Approving the planning of industrial development supports up to 2010 and vision to 2020" [29]; Decision No. 12/2011/QD-TTg, promulgated on February 24, 2011, by the Prime Minister, which states: "On development policies of some supporting industries" [21]; Decision No. 1843//QD-TTg, promulgated on August 26, 2011, by the Prime Minister, which states: "On promulgating a list of supporting industry products that are given priority for development" [22]; Decision No. 1556/QD-TTg, promulgated on October 17, 2012, by the Prime Minister, which states: "Development of an approval scheme, to help developing small and medium enterprises in supporting industries field" [19]; Decision No. 9028/QD-BCT, promulgated on October 10, 2014, by the Minister of Industry and Trade, which states: "Approval of a master plan for developing supporting industries up to 2020, vision to 2030" [30]; Decree No. 1111/2015/ND-CP, promulgated on November 3, 2015, by the Prime Minister, which states: "On the development of supporting industries" [31]; Decision No. 68/QD-TTg, promulgated on January 18, 2017, by the Prime Minister, which states: "On the approving of the program on development of supporting industries from 2016 to 2025" [32]; Decision No. 10/2017/QD-TTg, promulgated on April 3, 2017, by the Prime Minister, which states: "Promulgating the regulation on management and implementation of the program on development of supporting industries" [33]; and Decision No. 4572/QD-BCT, promulgated on November 7, 2014, by the Minister of Industry and Trade, which states: "Promulgating the regulation on formulation, receipt, appraisal, approval and implementation of schemes under the program on development of supporting industries" [34]. However, Vietnam's supporting industries are still in the very initial stages of development. The reality of supporting industries in Vietnam is that they are significantly lower in developmental status and weak in competitiveness [35]. This is evidenced by the lower proportion of locally finished goods. One recent Vietnamese governmental report [36] and a notice from the General Statistics Office of Vietnam [8] indicate that the proportion of localization in finished products in some supporting industries is 10.5% in manufacturing and assembly of automobiles, 17.2% in electronics, 12.5% in mechanical engineering, 9.5% in textile-garments, and 13.1% in leather shoes. This obviously underdeveloped state of the local supporting industry has negatively resulted in increased production costs, the risk of bigger trade deficits with foreign partners, and a lowered competitiveness of Vietnamese products than regional peers. This is due in large part to the importation of components and spare parts, which continues to be one of the primary factors preventing industrial development and economic growth, leading to increased national welfare. Some of the major factors which have led to overall weakness in Vietnam's supporting industries are a lack of capital expenditure, reduced technological innovation, and

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

paucity of management skills for development [2].

**78**

**2.2 Strategy for attracting FDI for developing supporting industries**

Developing countries may improve national welfare by attracting FDI [37]. It is understood that FDI supports economic growth, increased personal incomes, and leads to a greater rate of employment and technological transfer on a national basis [37]. Moreover, there are five main channels of technological diffusion which are

conditions for the host country to participate in global production networks, pushing them up to higher-value stage in the value chain; (iv) to implement the international division of labor and human resource development; and (v) to develop and transfer technology to the host country. Aside from the positive impacts, FDI may also negatively impact industry in invested countries, such as (1) pinching domestic manufacturers instead of meeting network needs, as in the case of foreign manufacturers who choose to produce supporting industry products as domestic enterprises, and (2) causing environmental pollution and depleting host country resources due to involvement with supporting industry small- and medium-sized enterprises using outdated technology or with TNCs'strategies which invest overseas solely for purposes of natural resource exploitation.

supporting industries (This is called a "linking effect" toward the inputs that will have an impact leading to the development of one industry with supporting industries in manufacturing intermediate inputs) and (2) attracting FDI in manufacturing industries before entering the component-assembling industry, so that the component manufacturing industry will develop before the assembly sector concerns; or, in other words, the development of a component industry will lead to the development of related assembly industries. As more and more facilities appear in an effort to supply intermediate equipment and materials, goods manufacturers and enterprises will soon have an even greater ability to access raw materials and component sources. Therefore, the average cost for manufacturing and assembling will decrease, and these countries shall continuously attract more multinational

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

companies to set up new assembly plants at those relative locations [2].

invest in Vietnam's supporting industries [6], including:

Because of its status as a developing country, Vietnam has concentrated on attracting FDI to develop supporting industries. However, the attraction of FDI to develop supporting industries in Vietnam is still a weakness due to a deficient quantity of total governmental capital expenditure and qualitative infrastructure projects. The General Statistics Office of Vietnam [9] states that the total cumulative FDI for developing supporting industries is approximately US \$29.7 billion, accounting for 16.8% quantity of FDI projects and 18.3% of the total FDI value in all supporting industries and main industries. This study examines the reality of developing supporting industries and attracting FDI for the development of

supporting industries in Vietnam. The results indicate that there are eight important factors for making investment decisions by foreign investors whenever deciding to

1.*Institutions and state policies*: This all-important factor creates just the right conditions for the development of supporting industries. The factorial impact may be expressed in two possible ways: First, it is the view of the state regarding the development of supporting industries to orient the national industrial development strategy to be consistent with the trend of globalization and international economic integration. The relationships needed to associate

with the international economy must be expanded upon. It must be understood that the mutual assurance of relationships between supporting areas and industrial manufacturing sectors is not to be confined within a single country but within a regional or a global scale. Therefore, a unified view of any development regarding supporting industries is particularly important for national and industrial development of supporting industries to occur. Second, the policy on the development of industry and supporting industries in a country, which may or may not be developed, is largely dependent on the development strategies and policies decided by the state. Therefore, those policies related to promoting supporting industries, such as support for information technology, capital, provisions of association in business, etc., will greatly contribute to the promotional development of supporting industry. On

the other hand, the localization policy; tax policy on importing and

to this issue [40, 41].

**81**

manufacturing semifinished products, both parts and components; the level of state-sponsored investment in scientific research and technology in supporting industry areas; the laws, standards, and technical regulations promulgated on behalf of industries; and diversification of products within the supporting industry networks can be seen to either facilitate or hinder the continued development of supporting industries. This is due in most part to the presence or the lack of a development-oriented perspective of the state as it is related

Accordingly, the importance of competitive supporting industries as partners in MNCs' dynamic technology innovation and their positive roles as the recipients of technology transfer from MNCs has been stressed [24]. Additionally, domestic supporting industries may wish to take advantage of their relative geographical proximity to MNCs for purposes of rapid information flow and technical interchange. Therefore, it is important to note that for developing countries to establish competitive supporting industries, FDI-driven economic growth must occur as a prerequisite. Furthermore, domestic supporting industries are of increasing importance because they may act as a significant factor to attract FDI as well [2]. In a reverse sense, FDI will promote the development of supporting industries. Thus, attracting FDI for the development of supporting industries motivates an economy's sustainable level of growth, thus stimulating the national technological development and increasing national competitiveness on a global scale.

Evaluation criteria useful for analyzing the attraction of FDI for the development of supporting industries may be considered, as follows:

First, FDI inflows on supporting industry: The increase or decrease in FDI flow into supporting industries reflects the attractiveness of supporting industries for FDI enterprises as well as for those countries that receive the FDI. So, the FDI inflows to supporting industries are considered to be part of the capital flow on implementation, the number of projects in each industry, and the scale of FDI enterprises in those supporting industries.

Secondly, technological transfer from FDI enterprises to supporting industry enterprises: It reflects the quality of supporting industries products as well as the ability to receive technological transfer from TNCs, MNCs, and MNEs of domestic enterprises.

Thirdly, the association level between FDI enterprises and domestic enterprises: It expresses a connection between both domestic and FDI enterprises. It should be considered from two aspects: (i) the relationship between supporting industries enterprises with customers and suppliers and (ii) the correlation between supplying resources of internal businesses, importing, and domestic supplying resources.

Fourthly, development of human resources and management skills: It is wholly transferrable through FDI exchange to the host country.

Finally, environmental problems: Strict environmental regulations will nurture technological development and facilitate the creation of a "green" technology market [39].

There are two trends which are known to attract FDI for the development of industry in developing countries. These trends include, but are not limited to, (1) attracting FDI in assembly-related industries before investment in component manufacturing industries since the development of an assembly industry will promote the development of a component manufacturing industry and other

## *Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

supporting industries (This is called a "linking effect" toward the inputs that will have an impact leading to the development of one industry with supporting industries in manufacturing intermediate inputs) and (2) attracting FDI in manufacturing industries before entering the component-assembling industry, so that the component manufacturing industry will develop before the assembly sector concerns; or, in other words, the development of a component industry will lead to the development of related assembly industries. As more and more facilities appear in an effort to supply intermediate equipment and materials, goods manufacturers and enterprises will soon have an even greater ability to access raw materials and component sources. Therefore, the average cost for manufacturing and assembling will decrease, and these countries shall continuously attract more multinational companies to set up new assembly plants at those relative locations [2].

Because of its status as a developing country, Vietnam has concentrated on attracting FDI to develop supporting industries. However, the attraction of FDI to develop supporting industries in Vietnam is still a weakness due to a deficient quantity of total governmental capital expenditure and qualitative infrastructure projects. The General Statistics Office of Vietnam [9] states that the total cumulative FDI for developing supporting industries is approximately US \$29.7 billion, accounting for 16.8% quantity of FDI projects and 18.3% of the total FDI value in all supporting industries and main industries. This study examines the reality of developing supporting industries and attracting FDI for the development of supporting industries in Vietnam. The results indicate that there are eight important factors for making investment decisions by foreign investors whenever deciding to invest in Vietnam's supporting industries [6], including:

1.*Institutions and state policies*: This all-important factor creates just the right conditions for the development of supporting industries. The factorial impact may be expressed in two possible ways: First, it is the view of the state regarding the development of supporting industries to orient the national industrial development strategy to be consistent with the trend of globalization and international economic integration. The relationships needed to associate with the international economy must be expanded upon. It must be understood that the mutual assurance of relationships between supporting areas and industrial manufacturing sectors is not to be confined within a single country but within a regional or a global scale. Therefore, a unified view of any development regarding supporting industries is particularly important for national and industrial development of supporting industries to occur. Second, the policy on the development of industry and supporting industries in a country, which may or may not be developed, is largely dependent on the development strategies and policies decided by the state. Therefore, those policies related to promoting supporting industries, such as support for information technology, capital, provisions of association in business, etc., will greatly contribute to the promotional development of supporting industry. On the other hand, the localization policy; tax policy on importing and manufacturing semifinished products, both parts and components; the level of state-sponsored investment in scientific research and technology in supporting industry areas; the laws, standards, and technical regulations promulgated on behalf of industries; and diversification of products within the supporting industry networks can be seen to either facilitate or hinder the continued development of supporting industries. This is due in most part to the presence or the lack of a development-oriented perspective of the state as it is related to this issue [40, 41].

conditions for the host country to participate in global production networks, pushing them up to higher-value stage in the value chain; (iv) to implement the international division of labor and human resource development; and (v) to develop and transfer technology to the host country. Aside from the positive impacts, FDI may also negatively impact industry in invested countries, such as (1) pinching domestic manufacturers instead of meeting network needs, as in the case of foreign manufacturers who choose to produce supporting industry products as domestic enterprises, and (2) causing environmental pollution and depleting host country resources due to involvement with supporting industry small- and medium-sized enterprises using outdated technology or with TNCs'strategies which invest over-

Accordingly, the importance of competitive supporting industries as partners in MNCs' dynamic technology innovation and their positive roles as the recipients of technology transfer from MNCs has been stressed [24]. Additionally, domestic supporting industries may wish to take advantage of their relative geographical proximity to MNCs for purposes of rapid information flow and technical interchange. Therefore, it is important to note that for developing countries to establish competitive supporting industries, FDI-driven economic growth must occur as a prerequisite. Furthermore, domestic supporting industries are of increasing importance because they may act as a significant factor to attract FDI as well [2]. In a reverse sense, FDI will promote the development of supporting industries. Thus, attracting FDI for the development of supporting industries motivates an economy's sustainable level of growth, thus stimulating the national technological develop-

Evaluation criteria useful for analyzing the attraction of FDI for the develop-

Secondly, technological transfer from FDI enterprises to supporting industry enterprises: It reflects the quality of supporting industries products as well as the ability to receive technological transfer from TNCs, MNCs, and MNEs of domestic

enterprises: It expresses a connection between both domestic and FDI enterprises. It should be considered from two aspects: (i) the relationship between supporting industries enterprises with customers and suppliers and (ii) the correlation between supplying resources of internal businesses, importing, and domestic supplying

Fourthly, development of human resources and management skills: It is wholly

Finally, environmental problems: Strict environmental regulations will nurture

There are two trends which are known to attract FDI for the development of industry in developing countries. These trends include, but are not limited to, (1) attracting FDI in assembly-related industries before investment in component manufacturing industries since the development of an assembly industry will promote the development of a component manufacturing industry and other

technological development and facilitate the creation of a "green" technology

Thirdly, the association level between FDI enterprises and domestic

First, FDI inflows on supporting industry: The increase or decrease in FDI flow into supporting industries reflects the attractiveness of supporting industries for FDI enterprises as well as for those countries that receive the FDI. So, the FDI inflows to supporting industries are considered to be part of the capital flow on implementation, the number of projects in each industry, and the scale of FDI

seas solely for purposes of natural resource exploitation.

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

ment and increasing national competitiveness on a global scale.

ment of supporting industries may be considered, as follows:

transferrable through FDI exchange to the host country.

enterprises in those supporting industries.

enterprises.

resources.

market [39].

**80**

2.*Human resources:* Due to the manpower requirements of supporting industries, human resources are a principal factor maintaining a strong impact on future levels of industrial development and national supporting industries. The criteria of interest for determining human resources include the number, educational background, personal qualifications, absorptive capacity, selfdiscipline, communication skills (including language competency), drive for innovation, and professionalism of the human resources managers who are involved [2, 39, 42].

8.*Other criteria*: The influence of attracting FDI toward the overall development of supporting industries is important. This factor (i.e., environment policy, culture, tax policy, land supporting, corruption, inclusion of MNEs) will lead to a positive ripple effect in the national economy, which, in turn, will lead to further employment opportunities, greater tax digest, and sustainable growth

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

When taken together, there are various alternative strategy policies possible for

Respective of this study, the proposed procedure utilizes the consistent fuzzy preference relations (CFPR) process to select a beneficial strategy for attracting foreign direct investment (FDI). The following section will give a brief description

Herrera-Viedma et al. [9] proposed the consistent fuzzy preference relations methodology in accordance with two preference relations, namely, the multiplica-

A multiplicative preference relations *A* on a set of alternatives X is represented

*aij* � *aji* ¼ 1∀*i*, *j* ∈f g 1, … , *n* (1)

*aij* � *ajk* ¼ a*ik*∀*i*, *j*, *k* ¼ 1, … , *n* (2)

*<sup>i</sup>*¼<sup>1</sup> *pij* implies indifference between xi and

*pij* þ *pji* ¼ 1∀*i*, *j*∈f g 1, … , *n* (3)

<sup>2</sup> indicates that xi is preferred to *xi*, *xi* > *xj*.

the ratio of the preference degree of alternative xi over xj [44, 45]. As *aij* ¼ 1 indicates no difference between xi and xj, *aij* ¼ 9 indicates that xi is strongly pref-

**Definition 3.1.** A reciprocal multiplicative preference relation *<sup>A</sup>* <sup>¼</sup> *aij* � � is

Expert preferences over a set of alternatives where X is denoted by a positive preference relation matrix *P*⊂*X* � *X* with membership function *β<sup>p</sup>* : *X* � *X* ! ½ � 0, 1 ,

xj (xi � xj), pij ¼ 1 indicates that xi is absolutely preferred to xj, pij ¼ 0 indicates

� � indicates the ratio of the preference intensity of alternative

<sup>9</sup> , 9 � �, where aij denotes

tive preference relation and fuzzy preference relation (10)–(16).

by a preference relations matrix *<sup>A</sup>* <sup>⊂</sup><sup>X</sup> � X, *<sup>A</sup>* <sup>¼</sup> *aij* � �, *aij* <sup>∈</sup> <sup>1</sup>

erable to xj. A is assumed to be a multiplicative reciprocal, that is,

attracting FDI to develop supporting industries, namely: (1) attracting FDI for developing supporting industries, which motivates the economy's sustainable growth; (2) attracting FDI for developing supporting industries, which increases national competitiveness; and (3) attracting FDI for developing supporting industries, which stimulates the nation's technological development, leading to

for the host nation [6].

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

significant benefits overall.

**3. Research methodology**

of the suggested CFPR method.

consistent if.

**3.2 Fuzzy preference relation**

xi to that of xj. Moreover, if *pij* <sup>=</sup> <sup>P</sup>*<sup>n</sup>*

xj is absolutely preferred to xi, and pij > <sup>1</sup>

Meanwhile, P is assumed to be an additive reciprocal, that is,

where *pij* ¼ *β<sup>p</sup> xi*, *xj*

**83**

**3.1 Multiplicative preference relations**


*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

8.*Other criteria*: The influence of attracting FDI toward the overall development of supporting industries is important. This factor (i.e., environment policy, culture, tax policy, land supporting, corruption, inclusion of MNEs) will lead to a positive ripple effect in the national economy, which, in turn, will lead to further employment opportunities, greater tax digest, and sustainable growth for the host nation [6].

When taken together, there are various alternative strategy policies possible for attracting FDI to develop supporting industries, namely: (1) attracting FDI for developing supporting industries, which motivates the economy's sustainable growth; (2) attracting FDI for developing supporting industries, which increases national competitiveness; and (3) attracting FDI for developing supporting industries, which stimulates the nation's technological development, leading to significant benefits overall.

## **3. Research methodology**

2.*Human resources:* Due to the manpower requirements of supporting industries, human resources are a principal factor maintaining a strong impact on future levels of industrial development and national supporting industries. The criteria of interest for determining human resources include the number, educational background, personal qualifications, absorptive capacity, selfdiscipline, communication skills (including language competency), drive for innovation, and professionalism of the human resources managers who are

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

3.*Infrastructure facilities:* This remains an important influence needed to attract FDI for the development of strategic investment (SI). Any nation possessing appropriate infrastructure (i.e., transportation, power, information, and communication) conditions has an advantage in attracting FDI to develop supportive industries, while a lack of infrastructure means the opposite is

4.*Domestic supply capability:* In order to ensure the domestic supply of materials, parts, and accessories for production-stage products derived from MNEs, the domestic supply chain (i.e., total value and partition of the domestic supply and the quantity and size of supporting industry firms) must have a huge number of offerings at its disposal, good quality, and cheap prices. It will greatly help MNEs to minimize the costs incurred for transportation and storage while guaranteeing the timely delivery through accurate production

5.*Market size of supporting industries:* Market size (i.e., total consumption of supporting industries products) and outsourced procurement both play a central role in the development of SI. If a market is large enough to attract business participation in the supply of products and services, it will enable MNEs to easily facilitate partnership, make technology transfers possible, and

6.*Technological development and innovation:* As a solid foundation for the

do not apply modern technology and improved techniques in their

7.*International cooperation and competition:* The liberalization of trade and investment, through international forums and region, significantly reduces transaction costs, increases trade, and strengthens national competitiveness and international engagement. Moreover, the level of competition in attracting investment capital between countries is becoming ever more acute as global revenue pools shrink. To enhance competitiveness in attracting FDI, a growing number of countries have tried to adjust their national policies and to improve the local investment climate to become more attractive for foreign investment

development of principal industry, supporting industries require considerable regular investment in terms of modern machinery, capital equipment, and innovative technology. Assemblage enterprises consistently set out many stringent requirements for the technical standards involved in the production of component types and spare parts. Therefore, if the supporting enterprises

manufacturing efforts, they will not be able to create products which match assemblers' exacting standards. At such a time, the assemblers will have to invest in the manufacturing of, or importation of, overseas components and

planning and the timely assembly of MNEs [43].

parts by themselves to meet client needs [2, 18].

establish business linkages [43].

to occur [6].

**82**

involved [2, 39, 42].

inevitable [41].

Respective of this study, the proposed procedure utilizes the consistent fuzzy preference relations (CFPR) process to select a beneficial strategy for attracting foreign direct investment (FDI). The following section will give a brief description of the suggested CFPR method.

Herrera-Viedma et al. [9] proposed the consistent fuzzy preference relations methodology in accordance with two preference relations, namely, the multiplicative preference relation and fuzzy preference relation (10)–(16).

## **3.1 Multiplicative preference relations**

A multiplicative preference relations *A* on a set of alternatives X is represented by a preference relations matrix *<sup>A</sup>* <sup>⊂</sup><sup>X</sup> � X, *<sup>A</sup>* <sup>¼</sup> *aij* � �, *aij* <sup>∈</sup> <sup>1</sup> <sup>9</sup> , 9 � �, where aij denotes the ratio of the preference degree of alternative xi over xj [44, 45]. As *aij* ¼ 1 indicates no difference between xi and xj, *aij* ¼ 9 indicates that xi is strongly preferable to xj. A is assumed to be a multiplicative reciprocal, that is,

$$a\_{ij} \cdot a\_{ji} = \mathbf{1} \forall i, j \in \{1, \ldots, n\} \tag{1}$$

**Definition 3.1.** A reciprocal multiplicative preference relation *<sup>A</sup>* <sup>¼</sup> *aij* � � is consistent if.

$$a\_{ij} \cdot a\_{jk} = \mathbf{a}\_{ik} \forall i, j, k = 1, \ldots, n \tag{2}$$

## **3.2 Fuzzy preference relation**

Expert preferences over a set of alternatives where X is denoted by a positive preference relation matrix *P*⊂*X* � *X* with membership function *β<sup>p</sup>* : *X* � *X* ! ½ � 0, 1 , where *pij* ¼ *β<sup>p</sup> xi*, *xj* � � indicates the ratio of the preference intensity of alternative xi to that of xj. Moreover, if *pij* <sup>=</sup> <sup>P</sup>*<sup>n</sup> <sup>i</sup>*¼<sup>1</sup> *pij* implies indifference between xi and xj (xi � xj), pij ¼ 1 indicates that xi is absolutely preferred to xj, pij ¼ 0 indicates xj is absolutely preferred to xi, and pij > <sup>1</sup> <sup>2</sup> indicates that xi is preferred to *xi*, *xi* > *xj*. Meanwhile, P is assumed to be an additive reciprocal, that is,

$$p\_{\vec{\eta}} + p\_{\vec{\mu}} = 1 \forall i, j \in \{1, \ldots, n\} \tag{3}$$

**Proposition 3.1.** Suppose that this paper has a set of alternatives, *X* ¼ f g *x*1, … , *xn* , and associated with it is a reciprocal multiplicative preference relation *<sup>A</sup>* <sup>¼</sup> *aij* with *aij* <sup>∈</sup> <sup>1</sup> <sup>9</sup> , 9 . Then, the corresponding reciprocal fuzzy preference relation, *<sup>P</sup>* <sup>¼</sup> *<sup>p</sup>*ij , with *<sup>p</sup>*ij <sup>∈</sup>½ � 0, 1 associated with A is given as follows:

$$\mathfrak{g}\_{\mathsf{i}\mathfrak{j}} = \mathfrak{g}\left(a\_{\mathsf{i}\mathfrak{j}}\right) = \frac{1}{2} \cdot \left(\mathbb{1} + \log\_{\mathfrak{g}} a\_{\mathsf{i}\mathfrak{j}}\right) \tag{4}$$

**Proposition 3.4-2.** A fuzzy preference relation *P* ¼ ð *p*ijÞ is consistent if and only if.

**Proposition 3.4-3.** For a reciprocal additive fuzzy preference relation *P* ¼ ð *p*ijÞ,

If the preference matrix contains any values that are not in the interval 0, 1 ½ �, but in an interval ½ � �*a*, 1 þ *a* , being *a*>0, a linear solution is required to preserve the reciprocity and additive transitivity, that is, *F* : ½ �! �*a*, 1 þ *a* ½ � 0, 1 . Therefore, by

��; *<sup>p</sup>*ij <sup>¼</sup> *<sup>j</sup>* � *<sup>i</sup>* <sup>þ</sup> <sup>1</sup>

<sup>∪</sup> *<sup>B</sup>* <sup>∪</sup> <sup>f</sup>1�*p*12, 1 � *<sup>p</sup>*23, … , 1 � *pn*�1*<sup>n</sup>*

2

2

∀*i* ≤*j*≤*k* (11)

∀*i*<*j*< *k* (12)

∀*i* <*j* (13)

�

<sup>2</sup> � *<sup>p</sup>*iiþ<sup>1</sup> � *<sup>p</sup>*<sup>i</sup>þ1iþ<sup>2</sup> � *<sup>p</sup>*<sup>j</sup>‐1j

�

on *X* ¼

; the steps are

(14)

(18)

∪ ¬*B* (16)

2

��� � � � � (15)

*f* : ½ �! �*a*, 1 þ *a* ½ � 0, 1 (17)

*pij* <sup>þ</sup> *pjk* <sup>þ</sup> *pki* <sup>¼</sup> <sup>3</sup>

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

<sup>i</sup>*: pij* <sup>þ</sup> *pjk* <sup>þ</sup> *pki* <sup>¼</sup> <sup>3</sup>

Proposition 3.4, it can construct a consistent fuzzy preference relation P<sup>0</sup>

*<sup>a</sup>* <sup>¼</sup> min *<sup>B</sup>* <sup>∪</sup> *<sup>p</sup>*<sup>12</sup> � , *<sup>p</sup>*23, … , *pn*�1*<sup>n</sup>*

2.The consistent fuzzy preference relation P<sup>0</sup> is obtained as *P*<sup>0</sup> ¼ *f P*ð Þ such that

*f x*ð Þ¼ *<sup>x</sup>* <sup>þ</sup> *<sup>a</sup>* 1 þ 2*a*

As part of this study, 22 government staff members and policy-makers, foreign investors, managers of 6 supporting industries, and economists were interviewed in

Their identifications and their attributes are summarized as follows: *C*1, institutions and policies; *C*2, human resources; *C*3, infrastructure facilities; *C*4, domestic supply capability; *C*5, market size of supporting industries (total consumption of supporting industries' products); *C*6, technological development and innovation;

**4. Framework for selecting a strategy for attracting FDI under**

order to examine the current status of developing supporting industries and

**4.1 Evaluated criteria and framework of the evaluation model**

attracting FDI for developing supporting industries in Vietnam.

*C*7, international cooperation and competition; and *C*8, other criteria.

<sup>f</sup>*x*1, *<sup>x</sup>*2, … , *xn*; *<sup>n</sup>* <sup>≥</sup>2<sup>g</sup> from *<sup>n</sup>* � 1 preference values *<sup>p</sup>*12, � *<sup>p</sup>*23, … , *pn*�1*<sup>n</sup>*

1.Compute the set of preference values B as

*<sup>i</sup>* <sup>&</sup>lt;*<sup>j</sup>* <sup>∧</sup> *<sup>p</sup>*ij <sup>∉</sup> *<sup>p</sup>*<sup>12</sup> � , *<sup>p</sup>*23, … , *pn*�1*<sup>n</sup>*

�

**3.5 Construct a consistency of the fuzzy preference relations**

ii*: pi i*ð Þ <sup>þ</sup><sup>1</sup> <sup>þ</sup> *<sup>p</sup>*ð Þ *<sup>i</sup>*þ<sup>1</sup> ð Þ *<sup>i</sup>*þ<sup>2</sup> <sup>þ</sup> … *<sup>p</sup>*ð Þ *<sup>j</sup>*�<sup>1</sup> *<sup>j</sup>* <sup>þ</sup> *pji* <sup>¼</sup> *<sup>j</sup>* � *<sup>i</sup>* <sup>þ</sup> <sup>1</sup>

the following statements are equivalent:

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

described in the following:

*<sup>P</sup>* <sup>¼</sup> *<sup>p</sup>*<sup>12</sup> � , *<sup>p</sup>*23, … , *pn*�1*<sup>n</sup>*

**multi-criteria decision-making**

*B* ¼ *p*ij, n

**85**

With such a transformation function *g*, this paper can relate the research issues obtained for both kinds of preference relations.

## **3.3 On consistency of the fuzzy preference relations**

**Proposition 3.2.** Let *<sup>A</sup>* <sup>¼</sup> *<sup>a</sup>*ij be a consistent multiplicative preference relation; then the corresponding reciprocal fuzzy preference relations *P* ¼ gð Þ *A* verify additive transitivity property.

**Proof.** For *<sup>A</sup>* <sup>¼</sup> *<sup>a</sup>*ij being consistent, this paper has that *<sup>a</sup>*ij � *ajk* <sup>¼</sup> *aik*∀*i*, *<sup>j</sup>*, *<sup>k</sup>* or equivalently *a*ij � *ajk* � *aki* ¼ 1 ∀*i*, *j*, *k*. Taking logarithms on both sides, it has.

$$
\log\_9 a\_{\vec{\imath}\vec{\jmath}} + \log\_9 a\_{\vec{\jmath}k} + \log\_9 a\_{\vec{\imath}\vec{\imath}} = 0 \forall i, j, k \tag{5}
$$

Adding 3 to both sides and dividing by 2 yields.

$$\frac{1}{2} \cdot \left( \mathbf{1} + \log\_{\mathcal{g}} a\_{\vec{\imath}} \right) + \frac{1}{2} \cdot \left( \mathbf{1} + \log\_{\mathcal{g}} a\_{j\vec{k}} \right) + \frac{1}{2} \cdot \left( \mathbf{1} + \log\_{\mathcal{g}} a\_{k\vec{\imath}} \right) = \frac{3}{2} \forall i, j, k \tag{6}$$

The fuzzy preference relations *<sup>P</sup>* <sup>¼</sup> <sup>g</sup>ð Þ *<sup>A</sup>* , being *<sup>p</sup>*ij <sup>¼</sup> <sup>1</sup> <sup>2</sup> � <sup>1</sup> <sup>þ</sup> log <sup>9</sup>*a*ij , verifies.

$$p\_{\text{ij}} + p\_{jk} + p\_{ik} = \frac{3}{2} \forall i, j, k \tag{7}$$

We conclude that *P* ¼ gð Þ *A* verifies additive transitivity property.

In such a way, this paper considers the following definition of the consistent fuzzy preference relation:

**Definition 3.3.** A reciprocal fuzzy preference relation *P* ¼ ð *p*ijÞ is consistent if.

$$p\_{\rm ij} + p\_{jk} + p\_{ki} = \frac{3}{2} \forall i, j, k = 1, \ldots n \tag{8}$$

In what follows, this paper will use the term additive consistency to refer to consistency for fuzzy preference relations based on the additive transitivity property.

## **3.4 Additive transitivity consistency of the fuzzy preference relations**

**Proposition 3.4-1.** For a reciprocal fuzzy preference relation *P* ¼ ð *p*ijÞ, the following statements are equivalent:

$$\text{i.i.}\ p\_{\text{ij}} + p\_{jk} + p\_{ki} = \frac{3}{2} \forall i, j, k \tag{9}$$

$$\text{iii.}\ p\_{\text{ij}} + p\_{jk} + p\_{ki} = \frac{3}{2} \forall i < j < k \tag{10}$$

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

**Proposition 3.4-2.** A fuzzy preference relation *P* ¼ ð *p*ijÞ is consistent if and only if.

$$p\_{ij} + p\_{jk} + p\_{ki} = \frac{3}{2} \forall i \le j \le k \tag{11}$$

**Proposition 3.4-3.** For a reciprocal additive fuzzy preference relation *P* ¼ ð *p*ijÞ, the following statements are equivalent:

$$\text{i. } p\_{ij} + p\_{jk} + p\_{ki} = \frac{3}{2} \forall i < j < k \tag{12}$$

$$\text{iii.}\ p\_{i(i+1)} + p\_{(i+1)(i+2)} + \dots \\ p\_{(j-1)j} + p\_{ji} = \frac{j-i+1}{2} \\ \forall i < j \tag{13}$$

## **3.5 Construct a consistency of the fuzzy preference relations**

If the preference matrix contains any values that are not in the interval 0, 1 ½ �, but in an interval ½ � �*a*, 1 þ *a* , being *a*>0, a linear solution is required to preserve the reciprocity and additive transitivity, that is, *F* : ½ �! �*a*, 1 þ *a* ½ � 0, 1 . Therefore, by Proposition 3.4, it can construct a consistent fuzzy preference relation P<sup>0</sup> on *X* ¼ <sup>f</sup>*x*1, *<sup>x</sup>*2, … , *xn*; *<sup>n</sup>* <sup>≥</sup>2<sup>g</sup> from *<sup>n</sup>* � 1 preference values *<sup>p</sup>*12, � *<sup>p</sup>*23, … , *pn*�1*<sup>n</sup>* � ; the steps are described in the following:

1.Compute the set of preference values B as

$$B = \left\{ p\_{\vec{\mathbf{j}}}, i < j \land p\_{\vec{\mathbf{j}}} \notin \left\{ p\_{12}, p\_{23}, \dots, p\_{n-1n} \right\} \right\}; \\ p\_{\vec{\mathbf{i}}} = \frac{j - i + 1}{2} - p\_{\vec{\mathbf{i}} + 1} - p\_{\vec{\mathbf{i}} + 1 \mathbf{i} + 2} - p\_{\vec{\mathbf{j}} + 1} \tag{4.4}$$

(14)

$$\mathcal{a} = \left| \min \left\{ B \cup \{ p\_{12}, p\_{23}, \dots, p\_{n-1n} \} \right\} \right| \tag{15}$$

$$P = \{p\_{12}, p\_{23}, \dots, p\_{n-1n}\} \cup B \cup \{1 - p\_{12}, 1 - p\_{23}, \dots, 1 - p\_{n-1n}\} \cup \neg B \tag{16}$$

2.The consistent fuzzy preference relation P<sup>0</sup> is obtained as *P*<sup>0</sup> ¼ *f P*ð Þ such that

$$f: [-a, 1+a] \to [0, 1] \tag{17}$$

$$f(\mathbf{x}) = \frac{\mathbf{x} + a}{\mathbf{1} + \mathbf{2}a} \tag{18}$$

## **4. Framework for selecting a strategy for attracting FDI under multi-criteria decision-making**

## **4.1 Evaluated criteria and framework of the evaluation model**

As part of this study, 22 government staff members and policy-makers, foreign investors, managers of 6 supporting industries, and economists were interviewed in order to examine the current status of developing supporting industries and attracting FDI for developing supporting industries in Vietnam.

Their identifications and their attributes are summarized as follows: *C*1, institutions and policies; *C*2, human resources; *C*3, infrastructure facilities; *C*4, domestic supply capability; *C*5, market size of supporting industries (total consumption of supporting industries' products); *C*6, technological development and innovation; *C*7, international cooperation and competition; and *C*8, other criteria.

**Proposition 3.1.** Suppose that this paper has a set of alternatives,

relation *A* ¼ *aij*

preference relation, *P* ¼ *p*ij

with *aij* ∈ <sup>1</sup>

obtained for both kinds of preference relations.

**Proposition 3.2.** Let *A* ¼ *a*ij

tive transitivity property. **Proof.** For *A* ¼ *a*ij

> <sup>2</sup> � <sup>1</sup> <sup>þ</sup> log <sup>9</sup>*a*ij <sup>þ</sup>

fuzzy preference relation:

following statements are equivalent:

**84**

1

**3.3 On consistency of the fuzzy preference relations**

Adding 3 to both sides and dividing by 2 yields.

1

<sup>2</sup> � <sup>1</sup> <sup>þ</sup> log <sup>9</sup>*ajk* <sup>þ</sup>

The fuzzy preference relations *<sup>P</sup>* <sup>¼</sup> <sup>g</sup>ð Þ *<sup>A</sup>* , being *<sup>p</sup>*ij <sup>¼</sup> <sup>1</sup>

*p*ij ¼ *g a*ij

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

*X* ¼ f g *x*1, … , *xn* , and associated with it is a reciprocal multiplicative preference

<sup>¼</sup> <sup>1</sup>

<sup>9</sup> , 9 . Then, the corresponding reciprocal fuzzy

<sup>2</sup> � <sup>1</sup> <sup>þ</sup> log <sup>9</sup>*a*ij

With such a transformation function *g*, this paper can relate the research issues

then the corresponding reciprocal fuzzy preference relations *P* ¼ gð Þ *A* verify addi-

1

2

equivalently *a*ij � *ajk* � *aki* ¼ 1 ∀*i*, *j*, *k*. Taking logarithms on both sides, it has.

*<sup>p</sup>*ij <sup>þ</sup> *pjk* <sup>þ</sup> *pik* <sup>¼</sup> <sup>3</sup>

In such a way, this paper considers the following definition of the consistent

**Definition 3.3.** A reciprocal fuzzy preference relation *P* ¼ ð *p*ijÞ is consistent if.

2

In what follows, this paper will use the term additive consistency to refer to consistency for fuzzy preference relations based on the additive transitivity property.

**Proposition 3.4-1.** For a reciprocal fuzzy preference relation *P* ¼ ð *p*ijÞ, the

2

2

<sup>i</sup>*: <sup>p</sup>*ij <sup>þ</sup> *pjk* <sup>þ</sup> *pki* <sup>¼</sup> <sup>3</sup>

ii*: <sup>p</sup>*ij <sup>þ</sup> *pjk* <sup>þ</sup> *pki* <sup>¼</sup> <sup>3</sup>

**3.4 Additive transitivity consistency of the fuzzy preference relations**

We conclude that *P* ¼ gð Þ *A* verifies additive transitivity property.

*<sup>p</sup>*ij <sup>þ</sup> *pjk* <sup>þ</sup> *pki* <sup>¼</sup> <sup>3</sup>

, with *p*ij ∈½ � 0, 1 associated with A is given as follows:

be a consistent multiplicative preference relation;

being consistent, this paper has that *<sup>a</sup>*ij � *ajk* <sup>¼</sup> *aik*∀*i*, *<sup>j</sup>*, *<sup>k</sup>* or

log <sup>9</sup>*a*ij þ log <sup>9</sup>*ajk* þ log <sup>9</sup>*aki* ¼ 0∀*i*, *j*, *k* (5)

<sup>2</sup> � <sup>1</sup> <sup>þ</sup> log <sup>9</sup>*aki* <sup>¼</sup> <sup>3</sup>

2

<sup>2</sup> � 1 þ log <sup>9</sup>*a*ij

∀*i*, *j*, *k* (7)

∀*i*, *j*, *k* ¼ 1, … *n* (8)

∀*i*, *j*, *k* (9)

∀*i* <*j*<*k* (10)

∀*i*, *j*, *k* (6)

, verifies.

(4)

## *Foreign Direct Investment Perspective through Foreign Direct Divestment*

*4.2.2 Reciprocal additive consistent fuzzy preference relations for prioritizing the*

AHP separates a complex decision issue into elemental problems to produce a hierarchical model. Each of these preference relations necessitates the completion of

**Definition Intensity of importance**

Fair (F) 1 High (H) 3 Very high (VH) 5 Intermediate values use to present compromise 2, 4

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

<sup>2</sup> judgments for a preference matrix containing n elements. To reduce the judgment times, this study employs the reciprocal additive consistent fuzzy preference relation designed by Herrera-Viedma et al. [9] because it requires only *n* � 1

The procedures of the reciprocal additive consistent fuzzy preference relations

*Ci* ð Þ , *i* ¼ 1, 2, … , *n* in the dimensions of the hierarchy system. The evaluators ð Þ *Ek*, *k* ¼ 1, 2, … , *m* provide the more important of each of the pairs of considered criteria for a set of n-1 preference values *a*12, *a*23, … , *a*ð Þ *<sup>n</sup>*�<sup>1</sup> *<sup>n</sup>*

*C*<sup>1</sup> *C*<sup>2</sup> ⋯ *Cn*�<sup>1</sup> *Cn*

⋮ ⋮ ⋱⋱ ⋮

*x x* ⋯ 1 *ak*

*x x* ⋯ *x* 1

ij denotes the preference intensity toward considered criteria i and j,

which are assessed by evaluator k; *a*ij ¼ 1 indicates no difference between considered criteria i and j; *a*ij ¼ 3, 5, 7, 9 reveals that criterion i is relatively important to

ij into *pk*

<sup>12</sup> ⋯ *x x*

<sup>23</sup> *x x*

*n*�1*n*

<sup>9</sup> indicates that considered criterion i is less important

ij based on the reciprocal transitivity property, as

ij, which can be done via

ij using an interval scale 0, 1 ½ �, and

� �, for

(19)

1.Establish pairwise comparison matrices among all of the criteria

1 *a<sup>k</sup>*

*x* 1 *ak*

*evaluation criteria*

judgments from a set of n elements.

for prioritizing the assessment criteria are given below:

*Linguistic variables for the priority rating of attracting FDI strategy.*

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

*Ak* <sup>¼</sup>

<sup>3</sup> , <sup>1</sup> <sup>5</sup> , <sup>1</sup> <sup>7</sup> , <sup>1</sup>

2.Transform the preference value *a<sup>k</sup>*

then derive the remaining *p<sup>k</sup>*

than criterion j. The sign "x" indicates the remaining *a<sup>k</sup>*

*C*1

*C*2

⋮

*Cn*�<sup>1</sup>

*Cn*

all *<sup>n</sup>*�ð Þ *<sup>n</sup>*þ<sup>1</sup>

**Table 2.**

where *a<sup>k</sup>*

criterion j; and *<sup>a</sup>*ij <sup>¼</sup> <sup>1</sup>

inverse comparison.

follows:

**87**

## **Figure 1.**

*Analytical framework to select a strategy for attracting FDI for Vietnam's supporting industries.*

Following the analytical framework, there are candidate solutions for identifying the strategy useful toward attracting FDI and that will eventually develop supporting industries. These include attracting FDI for developing supporting industries, which motivates the economy's sustainable growth (A1); attracting FDI for developing supporting industries, which increases national competitiveness (A2); and attracting FDI for developing supporting industries, which stimulates the national technological development (A3), respectively. An analytical hierarchy framework based on eight main criteria and three alternatives is established in **Figure 1**.

## **4.2 Hierarchical analytical process for selection of a strategy attracting FDI**

## *4.2.1 Linguistic variables*

This study compares certain pairs of criteria using expressions such as "equally important (EQ)," "moderately important (MO)," "strongly important (ST)," "very strongly important (VS)," and "absolutely important (AB)," using a 5-point Likerttype scale possessing values indicated by actual numbers (see **Table 1**).

Additionally, three linguistic variables, namely, "very high (VH)," "high (H)," and "fair (F)," are used to measure the strategy for attracting FDI to develop supporting industries in Vietnam (see **Table 2**).


## **Table 1.**

*Linguistic terms for priority weights of influential factors to attract FDI.*

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*


**Table 2.**

Following the analytical framework, there are candidate solutions for identifying the strategy useful toward attracting FDI and that will eventually develop supporting industries. These include attracting FDI for developing supporting industries, which motivates the economy's sustainable growth (A1); attracting FDI for developing supporting industries, which increases national competitiveness (A2); and attracting FDI for developing supporting industries, which stimulates the national technological development (A3), respectively. An analytical hierarchy framework based on

eight main criteria and three alternatives is established in **Figure 1**.

*Analytical framework to select a strategy for attracting FDI for Vietnam's supporting industries.*

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

type scale possessing values indicated by actual numbers (see **Table 1**).

supporting industries in Vietnam (see **Table 2**).

*Linguistic terms for priority weights of influential factors to attract FDI.*

and "fair (F)," are used to measure the strategy for attracting FDI to develop

Equally important (EQ) 1 Moderate important (MO) 3 Strongly important (ST) 5 Very strongly important (VS) 7 Absolutely important (AB) 9 Intermediate values between two adjacent judgments 2, 4, 6, 8

*4.2.1 Linguistic variables*

**Figure 1.**

**Table 1.**

**86**

**4.2 Hierarchical analytical process for selection of a strategy attracting FDI**

This study compares certain pairs of criteria using expressions such as "equally important (EQ)," "moderately important (MO)," "strongly important (ST)," "very strongly important (VS)," and "absolutely important (AB)," using a 5-point Likert-

Additionally, three linguistic variables, namely, "very high (VH)," "high (H),"

**Definition Intensity of importance**

*Linguistic variables for the priority rating of attracting FDI strategy.*

## *4.2.2 Reciprocal additive consistent fuzzy preference relations for prioritizing the evaluation criteria*

AHP separates a complex decision issue into elemental problems to produce a hierarchical model. Each of these preference relations necessitates the completion of all *<sup>n</sup>*�ð Þ *<sup>n</sup>*þ<sup>1</sup> <sup>2</sup> judgments for a preference matrix containing n elements. To reduce the judgment times, this study employs the reciprocal additive consistent fuzzy preference relation designed by Herrera-Viedma et al. [9] because it requires only *n* � 1 judgments from a set of n elements.

The procedures of the reciprocal additive consistent fuzzy preference relations for prioritizing the assessment criteria are given below:

1.Establish pairwise comparison matrices among all of the criteria *Ci* ð Þ , *i* ¼ 1, 2, … , *n* in the dimensions of the hierarchy system. The evaluators ð Þ *Ek*, *k* ¼ 1, 2, … , *m* provide the more important of each of the pairs of considered criteria for a set of n-1 preference values *a*12, *a*23, … , *a*ð Þ *<sup>n</sup>*�<sup>1</sup> *<sup>n</sup>* � �, for

$$\mathbf{C}\_1 \ \mathbf{C}\_2 \ \cdots \ \mathbf{C}\_{n-1} \ \mathbf{C}\_n$$

$$\mathbf{C}\_1 \ \begin{bmatrix} \mathbf{1} & a\_{12}^k & \cdots & \mathbf{x} & \mathbf{x} \\\\ \mathbf{x} & \mathbf{1} & a\_{23}^k & \mathbf{x} & \mathbf{x} \\\\ \vdots & \vdots & \ddots & \ddots & \vdots \\\\ \mathbf{C}\_{n-1} \\\\ \mathbf{C}\_n \\\\ \mathbf{x} & \mathbf{x} & \cdots & \mathbf{1} & a\_{n-1n}^k \\\\ \end{bmatrix} \tag{19}$$

where *a<sup>k</sup>* ij denotes the preference intensity toward considered criteria i and j, which are assessed by evaluator k; *a*ij ¼ 1 indicates no difference between considered criteria i and j; *a*ij ¼ 3, 5, 7, 9 reveals that criterion i is relatively important to criterion j; and *<sup>a</sup>*ij <sup>¼</sup> <sup>1</sup> <sup>3</sup> , <sup>1</sup> <sup>5</sup> , <sup>1</sup> <sup>7</sup> , <sup>1</sup> <sup>9</sup> indicates that considered criterion i is less important than criterion j. The sign "x" indicates the remaining *a<sup>k</sup>* ij, which can be done via inverse comparison.

2.Transform the preference value *a<sup>k</sup>* ij into *pk* ij using an interval scale 0, 1 ½ �, and then derive the remaining *p<sup>k</sup>* ij based on the reciprocal transitivity property, as follows:

$$P^k = \frac{C\_1}{2} (\mathbf{l} + \log\_2 A^k) = \frac{C\_2}{2} \begin{bmatrix} 0.5 & p\_{12}^k & x & x \\ x & 0.5 & p\_{23}^k & x \\ \vdots & \vdots & \vdots & \vdots \\ x & x & \dots & 0.5 \end{bmatrix} \tag{20}$$

1.For each considered criteria, the evaluators were asked to choose the best among three attracting FDI strategies for a set of *s* � 1 preference data

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

*rt* represents the performance value assigned by evaluator k to attract

*rt* is transformed within the range <sup>1</sup>

*uk*

3.The opinions of evaluators are then taken to obtain the transformed synthetic

denotes the transformed fuzzy preference value of evaluator k for assessing strategies for attracting FDI r and t in terms of considered criterion i. This study uses the notation of average value to integrate the judgment values of m

4.Following the normalization of the synthetic fuzzy preference rating of the

*urt=* X*s r*¼1 *i*

attracting FDI can be derived for each considered criterion, that is,

*i <sup>β</sup><sup>r</sup>* <sup>¼</sup> <sup>1</sup> *s* � X*s t*¼1

where s represents the number of the strategy for attracting FDI.

indicate the normalized rating of the strategies for attracting FDI u and v with

r with respect to considered criterion i. The desired rating of each strategy for

*β<sup>r</sup>* denotes the average rating of the strategy for attracting FDI

rating of the strategy for attracting FDI for each considered criteria *<sup>i</sup>*

*i urt* <sup>¼</sup> <sup>1</sup> *m* � X*m j*¼1 *i uk*

strategy for attracting FDI for each considered criteria, *<sup>i</sup>*

*i αrt* ¼ *<sup>i</sup>*

respect to considered criterion i, for example,

ð25Þ

*uk rt* in

ð26Þ

*uk rt* which

<sup>5</sup> , 5 � � into *<sup>i</sup>*

*rt* are obtained via the reciprocal

*rt* (27)

*urt* (28)

*αrt* (29)

*αrt* is adopted to

*o*12, *o*23, … , *o*ð Þ *<sup>s</sup>*�<sup>1</sup> *<sup>s</sup>*

where *<sup>i</sup>*

*ok*

2.Next, the preference value *<sup>i</sup>*

evaluators, that is,

5.Consequently, *<sup>i</sup>*

**89**

transitivity property as follows:

� �, for example,

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

FDI strategy r and t based on considered criterion i.

an interval scale 0, 1 ½ �, and the remaining *<sup>i</sup>*

*ok*

where *p*ij ¼ 0*:*5 indicates no difference between criteria i and j, *p*ij ¼ 1 demonstrates that criterion i is absolutely important to criterion j, and *p*ij ¼ 0 illustrates that criterion i is absolutely less important to criterion j. The remaining *pk* ij can be calculated using Eqs. (3) and (13), but in an interval ½ � �*a*, 1 þ *a* , and a transformation function is required to preserve the reciprocity and additive transitivity. The transformation function is

$$f\left(p\_{\vec{\text{ij}}}^k\right) = \frac{p\_{\vec{\text{ij}}}^k + a}{1 + 2 \cdot a} \tag{21}$$

where a denotes the absolute value of the minimum negative value or maximum positive value minus one in this preference matrix.

3.The study pulled the opinions of evaluators to obtain the aggregated weights of the criteria. Moreover, let *pk* ij denote the transformed fuzzy preference value of evaluator k for assessing the criteria i and j. This study uses the notation of the average value to integrate the judgment values of *m* evaluators, namely,

$$p\_{\text{ij}} = \left(p\_{\text{ij}}^1 + p\_{\text{ij}}^2 + \dots + p\_{\text{ij}}^m\right) / m \tag{22}$$

4.Normalizing the aggregated fuzzy preference relation matrices *q*ij is used to indicate the normalized fuzzy preference values of each considered criteria, such as

$$q\_{\text{ij}} = p\_{\text{ij}} / \sum\_{i=1}^{n} p\_{\text{ij}} \tag{23}$$

5.Using the *ϖ<sup>i</sup>* denoting the average priority weight of considered criterion i, the priority of each criterion can be obtained, that is,

$$
\omega \sigma\_i = \frac{1}{n} \cdot \sum\_{i=1}^n q\_{ij} \tag{24}
$$

where n denotes the number of criteria considered.

## *4.2.3 Obtaining the synthetic utility value for a strategy attracting FDI with respect to each criterion*

The evaluators were asked to express their subjective judgments regarding the preference ratings of a strategy for attracting FDI *Ar* ð Þ ,*r* ¼ 1, 2, … , *s* with respect to each considered criteria in linguistic terms.

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

1.For each considered criteria, the evaluators were asked to choose the best among three attracting FDI strategies for a set of *s* � 1 preference data *o*12, *o*23, … , *o*ð Þ *<sup>s</sup>*�<sup>1</sup> *<sup>s</sup>* � �, for example,

$$A\_1 = \begin{bmatrix} A\_1 & A\_2 & \dots & A\_s \\ A\_1 \end{bmatrix}$$

$$A\_i \circ G = \begin{bmatrix} A\_1 \\ \mathbf{x} & \mathbf{1} & \mathbf{e}\_i \mathbf{g}\_{12}^k & \mathbf{x} \\ \vdots & \vdots & \vdots & \mathbf{e}\_i \mathbf{g}\_{(s-1)s}^k \\ \mathbf{x} & \mathbf{x} & \dots & \mathbf{1} \end{bmatrix} \tag{25}$$

where *<sup>i</sup> ok rt* represents the performance value assigned by evaluator k to attract FDI strategy r and t based on considered criterion i.

2.Next, the preference value *<sup>i</sup> ok rt* is transformed within the range <sup>1</sup> <sup>5</sup> , 5 � � into *<sup>i</sup> uk rt* in an interval scale 0, 1 ½ �, and the remaining *<sup>i</sup> uk rt* are obtained via the reciprocal transitivity property as follows:

$$\begin{aligned} A\_1 &= A\_2 & \dots & A\_s\\ A\_1 \mathcal{Q} &= \frac{1}{2} (1 + \log\_{1s} G) = \begin{array}{c} A\_1\\ \vdots\\ \infty \end{array} \begin{array}{ccc} 0.5 & \prescript{}{1}{q}\_{12}^k & \times & \xleftarrow{} \\ \times & 0.5 & \prescript{}{1}{q}\_{23}^k & \times\\ \vdots & \vdots & \vdots & \prescript{}{1}{q}\_{(s-l)s}^k\\ \times & \times & \dots & 0.5 \end{array} \end{aligned} \tag{26}$$

3.The opinions of evaluators are then taken to obtain the transformed synthetic rating of the strategy for attracting FDI for each considered criteria *<sup>i</sup> uk rt* which denotes the transformed fuzzy preference value of evaluator k for assessing strategies for attracting FDI r and t in terms of considered criterion i. This study uses the notation of average value to integrate the judgment values of m evaluators, that is,

$$
\mu\_i u\_{rt} = \frac{1}{m} \cdot \sum\_{j=1}^{m} \mu\_{rt}^k \tag{27}
$$

4.Following the normalization of the synthetic fuzzy preference rating of the strategy for attracting FDI for each considered criteria, *<sup>i</sup> αrt* is adopted to indicate the normalized rating of the strategies for attracting FDI u and v with respect to considered criterion i, for example,

$$
\mu\_{\rm tr} = \mu\_{\rm tr} / \sum\_{r=1}^{s} \mu\_{\rm tr} \tag{28}
$$

5.Consequently, *<sup>i</sup> β<sup>r</sup>* denotes the average rating of the strategy for attracting FDI r with respect to considered criterion i. The desired rating of each strategy for attracting FDI can be derived for each considered criterion, that is,

$$\overline{\beta}\_{i}\overline{\beta}\_{r} = \frac{1}{s} \cdot \sum\_{t=1}^{s} a\_{rt} \tag{29}$$

where s represents the number of the strategy for attracting FDI.

ð20Þ

ij can be

(21)

where *p*ij ¼ 0*:*5 indicates no difference between criteria i and j, *p*ij ¼ 1 demonstrates that criterion i is absolutely important to criterion j, and *p*ij ¼ 0 illustrates

calculated using Eqs. (3) and (13), but in an interval ½ � �*a*, 1 þ *a* , and a transformation function is required to preserve the reciprocity and additive transitivity. The

> ¼ *pk* ij þ *a* 1 þ 2 � *a*

where a denotes the absolute value of the minimum negative value or maximum

3.The study pulled the opinions of evaluators to obtain the aggregated weights

ij <sup>þ</sup> … <sup>þ</sup> *pm*

X*n i*¼1

5.Using the *ϖ<sup>i</sup>* denoting the average priority weight of considered criterion i, the

The evaluators were asked to express their subjective judgments regarding the preference ratings of a strategy for attracting FDI *Ar* ð Þ ,*r* ¼ 1, 2, … , *s* with respect to

� �

4.Normalizing the aggregated fuzzy preference relation matrices *q*ij is used to indicate the normalized fuzzy preference values of each considered criteria,

ij

value of evaluator k for assessing the criteria i and j. This study uses the notation of the average value to integrate the judgment values of *m* evaluators,

ij <sup>þ</sup> *<sup>p</sup>*<sup>2</sup>

*q*ij ¼ *p*ij*=*

*<sup>ϖ</sup><sup>i</sup>* <sup>¼</sup> <sup>1</sup> *n* � X*n i*¼1

ij denote the transformed fuzzy preference

*=m* (22)

*p*ij (23)

*q*ij (24)

that criterion i is absolutely less important to criterion j. The remaining *pk*

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

*f p<sup>k</sup>* ij � �

positive value minus one in this preference matrix.

*<sup>p</sup>*ij <sup>¼</sup> *<sup>p</sup>*<sup>1</sup>

priority of each criterion can be obtained, that is,

where n denotes the number of criteria considered.

*with respect to each criterion*

each considered criteria in linguistic terms.

*4.2.3 Obtaining the synthetic utility value for a strategy attracting FDI*

of the criteria. Moreover, let *pk*

transformation function is

namely,

such as

**88**

## *4.2.4 Obtaining the priority weight for selection*

A preferred value *Ur* for developing supporting industries in Vietnam is obtained by multiplying the priority weights of considered criteria by the ratings of the strategy for attracting FDI. That is,

$$U\_r = \sum\_{i=1}^n \overline{\beta}\_i \cdot \varpi\_i \mathbf{R}\_{\mathbf{u}} = \sum\_{i} \overline{\mathbf{l}}\_{\mathbf{u}} \* \varpi\_{\mathbf{i}} \tag{30}$$

where *ϖ<sup>i</sup>* denotes the aggregated weight of considered criterion i.

## **5. Results**

This study used six supporting industries in Vietnam to serve as examples to demonstrate the efficacy of the theoretical framework proposed in this study. A total of 22 questionnaires were dispatched and effectively returned, with survey candidates including managers from the Vietnamese Local Industry Department and the Vietnamese Foreign Investment Agency, policy-makers, economists, foreign investors, and managers from representatives of the six supporting industries located in Vietnam.

## **5.1 Weighting calculation of the evaluating criteria**

Eight major evaluation criteria are considered as part of the problem of selecting a strategy for attracting FDI considered herein. The pairwise comparisons for these eight criteria are obtained by means of interviews with the assessment representatives involved in this study.

The following examples clarify the computational process used to derive the priority weights using the reciprocal additive consistent fuzzy preference relation approach:


$$\begin{aligned} p\_{12} &= \left(1 + \log\_9 9.0000\right)/2 = 1.0000; \; p\_{23} = \left(1 + \log\_9 5.0000\right)/2 = 0.8662; \\ p\_{34} &= \left(1 + \log\_9 0.2500\right)/2 = 0.1845; \; p\_{45} = \left(1 + \log\_9 7.0000\right)/2 = 0.8662; \\ p\_{56} &= \left(1 + \log\_9 0.3333\right)/2 = 0.2500; \; p\_{67} = \left(1 + \log\_9 7.0000\right)/2 = 0.9428; \\ p\_{78} &= \left(1 + \log\_9 3.0000\right)/2 = 0.7500. \end{aligned}$$

The remaining value then can be calculated using Eqs. (3) and (13) with *p*21, *p*31, *p*81, *p*82, *p*28, etc., being used as examples:

**E1**

**91**

C1 C2 C3 C4 C5 C6 C7 **Table 3.** *The linguistic terms into* 

*corresponding*

 *numbers toward eight factors assessed by evaluators.*

3

 3

 1/2

 2

 1

 1/2

 1

 2

 2

 1

 ½

 3

 2

 2

 1

 1/3

 3

 1

 1/2

 2

 2

 1

 C8

7

 8

 7

 8

 5

 7

 5

 6

 8

 7

 6

 7

 8

 6

 5

 8

 8

 7

 6

 7

 6

 5C7

1/3

 1/2

 1/4

 1/3

 2

 1/3

 1

 1/2

 1

 1/2

 1

 1/3

 12

 1/2

 1/3

 1

 1/2

 2

 1/3

 1

 1/3

 ½

 C6

5

 8

 6

 7

 4

 8

 5

 4

 8

 7

 5

 7

 8

 6

 5

 7

 8

 7

 5

 8

 5

 6C5

1/4

 1/7

 1/4

 1/6

 1/4

 1/7

 1/1

 1/5

 1/4

 1/6

 ¼

 1/5

 1/7

 1/5

 1/4

 1/6

 1/5

 1/5

 1/4

 1/6

 1/5

 1/3

 C4

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

5

 6

 5

 6

 2

 5

 4

 3

 6

 5

 4

 5

 7

 4

 3

 7

 6

 5

 4

 5

 4

 5C3

9

 9

 7

 8

 3

 9

 6

 4

 9

 8

 5

 7

 9

 7

 5

 9

 7

 8

 6

 9

 5

 8C2

**E2**

**E3**

**E4**

**E5**

**E6**

**E7**

**E8**

**E9**

**E10**

**E11**

**E12**

**E13**

**E14**

**E15**

**E16**

**E17**

**E18**

**E19**

**E20**

**E21**

**E22**

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*


**Table 3.**

*The linguistic terms into corresponding numbers toward eight factors assessed by evaluators.*

*4.2.4 Obtaining the priority weight for selection*

the strategy for attracting FDI. That is,

**5. Results**

located in Vietnam.

approach:

**90**

tives involved in this study.

corresponding numbers.

[0, 1], yielding the following values:

*<sup>p</sup>*<sup>78</sup> <sup>¼</sup> <sup>1</sup> <sup>þ</sup> log <sup>9</sup>3*:*<sup>0000</sup> � �*=*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*7500*:*

*p*81, *p*82, *p*28, etc., being used as examples:

A preferred value *Ur* for developing supporting industries in Vietnam is obtained by multiplying the priority weights of considered criteria by the ratings of

where *ϖ<sup>i</sup>* denotes the aggregated weight of considered criterion i.

*<sup>β</sup><sup>r</sup>* � *<sup>ϖ</sup>i*Ru <sup>¼</sup> <sup>X</sup>

This study used six supporting industries in Vietnam to serve as examples to demonstrate the efficacy of the theoretical framework proposed in this study. A total of 22 questionnaires were dispatched and effectively returned, with survey candidates including managers from the Vietnamese Local Industry Department and the Vietnamese Foreign Investment Agency, policy-makers, economists, foreign investors, and managers from representatives of the six supporting industries

Eight major evaluation criteria are considered as part of the problem of selecting a strategy for attracting FDI considered herein. The pairwise comparisons for these eight criteria are obtained by means of interviews with the assessment representa-

The following examples clarify the computational process used to derive the priority weights using the reciprocal additive consistent fuzzy preference relation

1.Based on the interviews taken with the 22 representatives regarding the relative importance of eight aforementioned evaluation criteria, **Table 3** lists the pairwise comparison matrices for a set of *n* � 1 neighboring criteria

2.The assessment of evaluator 1 (E1) may serve as an example, and it is listed in **Table 4**. Also listed are the linguistic terms, which are transferrable into

3.Eq. (4) was used to transform the elements (listed in **Table 4**) into an interval

The remaining value then can be calculated using Eqs. (3) and (13) with *p*21, *p*31,

*<sup>p</sup>*<sup>12</sup> <sup>¼</sup> <sup>1</sup> <sup>þ</sup> log <sup>9</sup>9*:*<sup>0000</sup> � �*=*<sup>2</sup> <sup>¼</sup> <sup>1</sup>*:*0000; *<sup>p</sup>*<sup>23</sup> <sup>¼</sup> <sup>1</sup> <sup>þ</sup> log <sup>9</sup>5*:*<sup>0000</sup> � �*=*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*8662; *<sup>p</sup>*<sup>34</sup> <sup>¼</sup> <sup>1</sup> <sup>þ</sup> log <sup>9</sup>0*:*<sup>2500</sup> � �*=*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*1845; *<sup>p</sup>*<sup>45</sup> <sup>¼</sup> <sup>1</sup> <sup>þ</sup> log <sup>9</sup>7*:*<sup>0000</sup> � �*=*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*8662; *<sup>p</sup>*<sup>56</sup> <sup>¼</sup> <sup>1</sup> <sup>þ</sup> log <sup>9</sup>0*:*<sup>3333</sup> � �*=*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*2500; *<sup>p</sup>*<sup>67</sup> <sup>¼</sup> <sup>1</sup> <sup>þ</sup> log <sup>9</sup>7*:*<sup>0000</sup> � �*=*<sup>2</sup> <sup>¼</sup> <sup>0</sup>*:*9428;

f g *a*12, *a*23, … , *a*<sup>78</sup> with their corresponding numbers.

i

ῑ<sup>u</sup> ∗ ϖ<sup>i</sup> (30)

*Ur* <sup>¼</sup> <sup>X</sup>*<sup>n</sup> i*¼1 *i*

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

**5.1 Weighting calculation of the evaluating criteria**


C1ð Þ 0*:*2022 >C4ð Þ 0*:*1551 >C2ð Þ 0*:*1529 >C6ð Þ 0*:*1227 >C3ð Þ 0*:*1139 >C5ð Þ 0*:*1083 >

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

The results show that the six main assessment attributes are institutions and policies (0.2022), domestic supply capacity (0.1551), human resources (0.1529), technological development and innovation (0.1227), infrastructure facilities

**E1 C1 C2 C3 C4 C5 C6 C7 C8** C1 0.5000 1.0000 1.3662 1.0508 1.4170 1.1670 1.6098 1.8598 C2 0.0000 0.5000 0.8662 0.5508 0.9170 0.6670 1.1098 1.3598 C3 �0.3662 0.1338 0.5000 0.1845 0.5508 0.3008 0.7436 0.9936 C4 �0.0508 0.4492 0.8155 0.5000 0.8662 0.6162 1.0590 1.3090 C5 �0.4170 0.0830 0.4492 0.1338 0.5000 0.2500 0.6928 0.9424 C6 �0.1670 0.3330 0.6992 0.3838 0.7500 0.5000 0.9428 1.1928 C7 �0.6098 �0.1098 0.2564 �0.0590 0.3072 0.0572 0.5000 0.7500 C8 �0.8598 �0.3598 0.0064 �0.3090 0.0572 �0.1928 0.2500 0.5000

**E1 C1 C2 C3 C4 C5 C6 C7 C8** C1 0.5000 0.6834 0.8185 0.7025 0.8372 0.7453 0.9081 1.0000 C2 0.3162 0.5000 0.6347 0.5187 0.6533 0.5614 0.7242 0.8162 C3 0.1815 0.3653 0.5000 0.3840 0.5187 0.4267 0.5896 0.6815 C4 0.2975 0.4813 0.6160 0.5000 0.6347 0.5427 0.7056 0.7975 C5 0.1628 0.3467 0.4813 0.3653 0.5000 0.4081 0.5709 0.6628 C6 0.2547 0.4386 0.5733 0.4573 0.5919 0.5000 0.6628 0.7547 C7 0.0919 0.2758 0.4104 0.2944 0.4291 0.3372 0.5000 0.5919 C8 0.0000 0.1838 0.3185 0.2025 0.3372 0.2453 0.4081 0.5000

**E C1 C2 C3 C4 C5 C6 C7 C8** C1 0.5000 0.6755 0.8141 0.6676 0.8341 0.7828 0.9566 0.9667 C2 0.3245 0.5000 0.6385 0.4921 0.6586 0.6073 0.7811 0.7911 C3 0.1859 0.3615 0.5000 0.3536 0.5201 0.4687 0.6425 0.6526 C4 0.3324 0.5079 0.6464 0.5000 0.6665 0.6151 0.7889 0.7990 C5 0.1659 0.3414 0.4799 0.3335 0.5000 0.4487 0.6225 0.6326 C6 0.2172 0.3927 0.5313 0.3849 0.5513 0.5000 0.6738 0.6839 C7 0.0434 0.2189 0.3575 0.2111 0.3775 0.3262 0.5000 0.5101 C8 0.0333 0.2089 0.3474 0.2010 0.3674 0.3161 0.4899 0.5000 Total 1.8026 3.2068 4.3151 3.1438 4.4756 4.0649 5.4553 5.5360

C7ð Þ 0*:*0738 >C8ð Þ 0*:*0710 *:*

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

**Table 6.**

**Table 5.**

**Table 7.**

**93**

*The transformation matrix of criteria by linear solution.*

*Consistent fuzzy preference relations matrix of criteria E1.*

*Aggregated pairwise comparison matrices of the 22 evaluators.*

## **Table 4.**

*Interval pairwise comparisons of the criteria.*

$$\begin{aligned} p\_{12} &= 1 - p\_{12} = 1 - 1.00000 = 0.00000; \\ p\_{31} &= \frac{3 - 1 + 1}{2} - p\_{12} - p\_{23} = 1.5 - 1.00000 - 0.8662 = -0.3662; \\ p\_{81} &= \frac{8 - 1 + 1}{2} - p\_{12} - p\_{23} - p\_{34} - p\_{45} - p\_{56} - p\_{78} &= \frac{3 - 1 + 1}{2} - p\_{78} + p\_{66} - p\_{78} + p\_{89} + p\_{67} \\ &= 4 - 1.00000 - 0.8662 - 0.1845 - 0.8662 - 0.2500 - 0.9428 - 0.7500 = -0.8598 \\ p\_{82} &= \frac{8 - 2 + 1}{2} - p\_{23} - p\_{34} - p\_{45} - p\_{56} - p\_{67} - p\_{78} &= \frac{3 - 1 + 1}{2} - p\_{78} + p\_{89} + p\_{89} + p\_{78} \\ &= 3.5 - 0.8662 - 0.1845 - 0.8662 - 0.2500 - 0.9428 - 0.7500 = -0.3598 \end{aligned}$$

$$p\_{28} = 1 - p\_{82} = 1 - (-0.3598) = 1.3598;$$

The fuzzy preference relation matrix for the eight evaluation criteria assessed by evaluator 1 is established in **Table 5**.

**Table 5** lists *p*13, *p*14, *p*15, *p*16, *p*17, *p*18, *p*27, *p*28, *p*31, *p*41, *p*47, *p*48, *p*51, *p*61, *p*68, *p*71, *p*72, *p*74, *p*81, *p*82, *p*84, and *p*<sup>86</sup> elements but not in the interval [0, 1]; and thus a linear transformation stated in Eq. (21) is employed to ensure the reciprocity and additive transitivity for the preference relation matrix. **Table 6** lists the transformation matrix.


The priority weight of each evaluation criterion can then be obtained by Eq. (24). The priority weight and rank of each influence is assessed by 22 evaluators as listed in **Table 8**.

The ranks of the evaluation criteria weights thus are substituted as:

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

C1ð Þ 0*:*2022 >C4ð Þ 0*:*1551 >C2ð Þ 0*:*1529 >C6ð Þ 0*:*1227 >C3ð Þ 0*:*1139 >C5ð Þ 0*:*1083 > C7ð Þ 0*:*0738 >C8ð Þ 0*:*0710 *:*

The results show that the six main assessment attributes are institutions and policies (0.2022), domestic supply capacity (0.1551), human resources (0.1529), technological development and innovation (0.1227), infrastructure facilities


**Table 5.**

*p*<sup>21</sup> ¼ 1 � *p*<sup>12</sup> ¼ 1 � 1*:*0000 ¼ 0*:*0000;

*Interval pairwise comparisons of the criteria.*

*Where x is a variable that can be calculated using Eqs. (3) and (13).*

*p*<sup>28</sup> ¼ 1 � *p*<sup>82</sup> ¼ 1 � �ð Þ¼ 0*:*3598 1*:*3598;

evaluator 1 is established in **Table 5**.

derived, as listed in **Table 7**.

Taking *q*<sup>11</sup> as an example:

0.0333) = 0.2774.

as listed in **Table 8**.

**92**

<sup>2</sup> � *<sup>p</sup>*<sup>12</sup> � *<sup>p</sup>*<sup>23</sup> <sup>¼</sup> <sup>1</sup>*:*<sup>5</sup> � <sup>1</sup>*:*<sup>0000</sup> � <sup>0</sup>*:*<sup>8662</sup> ¼ �0*:*3662;

**E1 C1 C2 C3 C4 C5 C6 C7 C8** C1 1.0000 9.0000 x x x x x x C2 x 1.0000 5.0000 x x x x x C3 x x 1.0000 0.2500 x x x x C4 x x x 1.0000 5.0000 x x x C5 x x x x 1.0000 0.3333 x x C6 x x x x x 1.0000 7.0000 x C7 x x x x x x 1.0000 3.0000 C8 x x x x x x x 1.0000

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

¼ 4 � 1*:*0000 � 0*:*8662 � 0*:*1845 � 0*:*8662 � 0*:*2500 � 0*:*9428 � 0*:*7500 ¼ �0*:*8598

¼ 3*:*5 � 0*:*8662 � 0*:*1845 � 0*:*8662 � 0*:*2500 � 0*:*9428 � 0*:*7500 ¼ �0*:*3598

The fuzzy preference relation matrix for the eight evaluation criteria assessed by

**Table 5** lists *p*13, *p*14, *p*15, *p*16, *p*17, *p*18, *p*27, *p*28, *p*31, *p*41, *p*47, *p*48, *p*51, *p*61, *p*68, *p*71, *p*72, *p*74, *p*81, *p*82, *p*84, and *p*<sup>86</sup> elements but not in the interval [0, 1]; and thus a linear transformation stated in Eq. (21) is employed to ensure the reciprocity and additive transitivity for the preference relation matrix. **Table 6** lists the transformation

4.Likewise, the above computational procedures can calculate the fuzzy preference relation matrices of the other 21 evaluators; therefore, using Eq. (22), the aggregated pairwise comparison matrix of 22 evaluators can be

5.Eq. (23) is applied to normalize the aggregated pairwise comparison matrix.

*q*<sup>11</sup> = 0.5000/(0.5000 + 0.3245 + 0.1859 + 0.3324 + 0.1659 + 0.2172 + 0.0434 +

The priority weight of each evaluation criterion can then be obtained by Eq. (24). The priority weight and rank of each influence is assessed by 22 evaluators

The ranks of the evaluation criteria weights thus are substituted as:

;

;

<sup>2</sup> � *<sup>p</sup>*<sup>12</sup> � *<sup>p</sup>*<sup>23</sup> � *<sup>p</sup>*<sup>34</sup> � *<sup>p</sup>*<sup>45</sup> � *<sup>p</sup>*<sup>56</sup> � *<sup>p</sup>*<sup>67</sup> � *<sup>p</sup>*<sup>78</sup>

<sup>2</sup> � *<sup>p</sup>*<sup>23</sup> � *<sup>p</sup>*<sup>34</sup> � *<sup>p</sup>*<sup>45</sup> � *<sup>p</sup>*<sup>56</sup> � *<sup>p</sup>*<sup>67</sup> � *<sup>p</sup>*<sup>78</sup>

*<sup>p</sup>*<sup>31</sup> <sup>¼</sup> <sup>3</sup> � <sup>1</sup> <sup>þ</sup> <sup>1</sup>

**Table 4.**

*<sup>p</sup>*<sup>81</sup> <sup>¼</sup> <sup>8</sup> � <sup>1</sup> <sup>þ</sup> <sup>1</sup>

*<sup>p</sup>*<sup>82</sup> <sup>¼</sup> <sup>8</sup> � <sup>2</sup> <sup>þ</sup> <sup>1</sup>

matrix.

*Consistent fuzzy preference relations matrix of criteria E1.*


### **Table 6.**

*The transformation matrix of criteria by linear solution.*


### **Table 7.**

*Aggregated pairwise comparison matrices of the 22 evaluators.*



(0.1139), and market size of supporting industries (0.1083). Meanwhile, the two least important attributes are international cooperation and competition (0.0738)

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

**5.2 Calculation of the weights for a strategy attracting FDI with respect to**

To determine the priority weight matrix for a strategy to attract FDI with respect to each evaluation criterion, **Table 2** lists the linguistic variables for evaluators. The priority weights of the three attractive FDI strategies are calculated as follows:

1.This study examines the implementation of this strategy for attracting FDI for developing supporting industries; the 22 evaluators are interviewed to assess which path is more likely to occur according to each evaluation criteria. **Table 9** lists the opinions of these 22 evaluators regarding their preference intensities related to the strategy for attracting FDI with respect to each evaluation criterion, and the linguistic term is assigned into the corresponding numbers.

<sup>5</sup> , 5 into the interval [0, 1]. **Table 10** lists the transformed

3.Using Eq. (27), and taking <sup>1</sup>*urt* as an example, the synthetic rating of a strategy

for attracting FDI can be obtained (as listed in **Table 11**), where <sup>1</sup>*urt* represented the transformed fuzzy preference value of 22 evaluators for assessing strategies for r and t for attracting FDI in terms of evaluating criteria 1. Eqs. (28) and (29) can then be employed to normalize and synthesize the fuzzy preference rating of 3 attracting FDI strategies based on eight evaluation criteria. **Table 12** lists the normalized values and priority weights, while **Table 13** lists the normalized values and priority weights of all of the criteria.

Using Eq. (30), the priority weights of the eight evaluation criteria and the priority ratings of three strategies for attracting FDI are given, in addition to the preference weightings of the candidates. They are listed in **Table 13**. The preferred

A1 ¼ 0*:*2022 ∗ 0*:*4405 þ 0*:*1529 ∗ 0*:*4396 þ 0*:*1139 ∗ 0*:*4378 þ 0*:*1551 ∗ 0*:*4427

A2 ¼ 0*:*2022 ∗ 0*:*2441 þ 0*:*1529 ∗ 0*:*2333 þ 0*:*1139 ∗ 0*:*2292 þ 0*:*1551 ∗ 0*:*2238

A3 ¼ 0*:*2022 ∗ 0*:*3155 þ 0*:*1529 ∗ 0*:*3271 þ 0*:*1139 ∗ 0*:*3330 þ 0*:*1551 ∗ 0*:*3335

þ 0*:*1083 ∗ 0*:*4290 þ 0*:*1227 ∗ 0*:*4318 þ 0*:*0738 ∗ 0*:*4366 þ 0*:*0710 ∗ 0*:*4354

þ 0*:*1083 ∗ 0*:*2367 þ 0*:*1227 ∗ 0*:*2236 þ 0*:*0738 ∗ 0*:*2321 þ 0*:*0710 ∗ 0*:*2247

þ 0*:*1083 ∗ 0*:*3343 þ 0*:*1227 ∗ 0*:*3446 þ 0*:*0738 ∗ 0*:*3312 þ 0*:*0710 ∗ 0*:*3399

From **Table 13**, the ranking of alternative solutions is obtained as follows: Alternative A1 (0.4374) > alternative A3 (0.3306) > alternative A2 (0.2320). Evaluators clearly believe that the best policy for creating and implementing a strategy to

weights for the strategy for attracting FDI are calculated as follows:

<sup>2</sup> <sup>1</sup> <sup>þ</sup> log <sup>5</sup>*aij* , to transform the values in

and other criteria (0.0710).

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

**evaluation criteria**

the scale <sup>1</sup>

¼ 0*:*4374

¼ 0*:*2320

¼ 0*:*3306

**95**

preference data.

2.The study uses this function, *pij* <sup>¼</sup> <sup>1</sup>

**5.3 Weighting the selection priorities**

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

(0.1139), and market size of supporting industries (0.1083). Meanwhile, the two least important attributes are international cooperation and competition (0.0738) and other criteria (0.0710).

## **5.2 Calculation of the weights for a strategy attracting FDI with respect to evaluation criteria**

To determine the priority weight matrix for a strategy to attract FDI with respect to each evaluation criterion, **Table 2** lists the linguistic variables for evaluators. The priority weights of the three attractive FDI strategies are calculated as follows:


## **5.3 Weighting the selection priorities**

Using Eq. (30), the priority weights of the eight evaluation criteria and the priority ratings of three strategies for attracting FDI are given, in addition to the preference weightings of the candidates. They are listed in **Table 13**. The preferred weights for the strategy for attracting FDI are calculated as follows:


From **Table 13**, the ranking of alternative solutions is obtained as follows: Alternative A1 (0.4374) > alternative A3 (0.3306) > alternative A2 (0.2320). Evaluators clearly believe that the best policy for creating and implementing a strategy to

**E**

**94**

C1 C2 C3 C4 C5 C6 C7 C8 Total

**Table 8.** *Normalized*

 *matrix of priority weight and rank of influential*

 *factors.*

 1.0000

 1.0000

 1.0000

 1.0000

 1.0000

 1.0000

 1.0000

 1.0000

 8.0000

 1.0000

0.0185

 0.0651

 0.0805

 0.0639

 0.0821

 0.0778

 0.0898

 0.0903

 0.5680

 0.0710

0.0241

 0.0683

 0.0828

 0.0671

 0.0844

 0.0802

 0.0917

 0.0921

 0.5907

 0.0738

0.1205

 0.1225

 0.1231

 0.1224

 0.1232

 0.1230

 0.1235

 0.1235

 0.9818

 0.1227

0.0920

 0.1065

 0.1112

 0.1061

 0.1117

 0.1104

 0.1141

 0.1143

 0.8663

 0.1083

0.1844

 0.1584

 0.1498

 0.1590

 0.1489

 0.1513

 0.1446

 0.1443

 1.2408

 0.1551

0.1032

 0.1127

 0.1159

 0.1125

 0.1162

 0.1153

 0.1178

 0.1179

 0.9114

 0.1139

0.1800

 0.1559

 0.1480

 0.1565

 0.1472

 0.1494

 0.1432

 0.1429

 1.2231

 0.1529

0.2774

 0.2107

 0.1887

 0.2124

 0.1864

 0.1926

 0.1754

 0.1746

 1.6180

 0.2022

 **C1**

**C2**

**C3**

**C4**

**C5**

**C6**

**C7**

**C8**

**Total**

 **Weight**

 **Ranking**

1

3

5

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

2

6

4

7

8


*The*

**E1**

**97**

C1 A1 0.7153 0.8413 0.7153 0.9307 0.7153 1.0000 0.8413 0.9307 0.8413 0.8413 0.9307 0.7153 0.9307 0.5000 0.7153 0.8413 0.8413 0.5000 0.8413 0.9307 0.2847 0.9307 A2

A2 0.2847 0.5000 0.0693 0.7153 0.1587 0.2847 0.5000 0.7153 0.2847 0.7153 0.2847 0.5000 0.5000 0.7153 0.5000 0.2847 0.5000 0.2847 0.1587 0.2847 0.5000 0.1587 A3 C2 A1 0.8413 0.9307 0.7153 0.8413 0.5000 0.8413 0.9307 0.2847 0.9307 0.7153 0.8413 0.7153 0.8413 1.0000 0.8413 0.7153 0.9307 0.7153 1.0000 0.8413 0.9307 0.8413 A2 A2 0.5000 0.2847 0.5000 0.5000 0.2847 0.1587 0.2847 0.5000 0.1587 0.2847 0.5000 0.0693 0.2847 0.7153 0.5000 0.0693 0.7153 0.1587 0.2847 0.5000 0.7153 0.2847 A3 C3 A1 0.9307 0.7153 1.0000 0.8413 0.9307 0.8413 0.8413 0.9307 0.7153 1.0000 0.8413 0.8413 1.0000 0.7153 0.5000 0.8413 0.9307 0.2847 0.9307 0.7153 0.8413 0.7153 A2

A2 0.7153 0.1587 0.2847 0.5000 0.7153 0.2847 0.7153 0.2847 0.5000 0.8413 0.1587 0.2847 0.2847 0.0693 0.2847 0.1587 0.2847 0.5000 0.1587 0.2847 0.5000 0.0693 A3 C4 A1 0.7153 1.0000 0.7153 0.9307 0.7153 1.0000 0.8413 0.9307 0.8413 0.8413 0.9307 0.7153 0.7153 1.0000 0.8413 0.9307 0.2847 0.9307 0.7153 0.8413 0.7153 0.9307 A2 A2 0.1587 0.2847 0.0693 0.7153 0.1587 0.2847 0.5000 0.7153 0.2847 0.7153 0.2847 0.5000 0.1587 0.2847 0.1556 0.2847 0.5000 0.1587 0.2847 0.5000 0.0693 0.7153 A3

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

C5 A1 1.0000 0.7153 0.7153 0.8413 0.7153 0.9307 0.7153 1.0000 0.8413 0.9307 0.8413 0.8413 0.8413 0.5000 0.5000 0.8413 0.9307 0.5000 0.9307 0.7153 0.8413 0.7153 A2 A2 0.7153 0.1587 0.2847 0.5000 0.0693 0.7153 0.1587 0.2847 0.5000 0.7153 0.2847 0.7153 0.5000 0.2847 0.2847 0.1587 0.2847 0.5000 0.1587 0.2847 0.5000 0.0693 A3 C6 A1 0.9307 0.8413 0.8413 0.9307 0.5000 0.8413 0.9307 0.2847 0.9307 0.7153 0.8413 0.7153 0.7153 1.0000 0.9307 0.7153 0.8413 0.7153 0.9307 0.7153 1.0000 0.8413 A2

A2 0.7153 0.2847 0.7153 0.2847 0.2847 0.1556 0.2847 0.5000 0.1587 0.2847 0.5000 0.0693 0.1587 0.2847 0.1587 0.2847 0.5000 0.0693 0.7153 0.1587 0.2847 0.5000 A3 C7 A1 0.9307 0.8413 0.8413 0.5000 0.8413 0.9307 0.2847 0.9307 0.7153 0.8413 0.7153 0.8413 0.7153 0.8413 0.7153 0.9307 0.7153 1.0000 0.8413 0.9307 0.8413 0.8413 A2 A2 0.2847 0.2847 0.5000 0.2847 0.1587 0.2847 0.5000 0.1587 0.2847 0.5000 0.0693 0.5000 0.2847 0.5000 0.0693 0.7153 0.1587 0.2847 0.5000 0.7153 0.2847 0.7153 A3 C8 A1 0.8413 0.9307 0.2847 0.9307 0.7153 0.8413 0.7153 0.9307 0.7153 1.0000 0.8413 0.9307 0.8413 0.9307 0.7153 0.9307 0.7153 1.0000 0.8413 0.8413 0.8413 0.5000 A2 A2 0.0693 0.7153 0.1587 0.2847 0.5000 0.2847 0.5000 0.2847 0.1587 0.2847 0.5000 0.1556 0.2847 0.5000 0.0693 0.7153 0.1587 0.2847 0.5000 0.7153 0.5000 0.2847 A3

**Table 10.** *The transformed*

 *fuzzy preference*

 *value of three attractive FDI strategies.*

**E2**

**E3**

**E4**

**E5**

**E6**

**E7**

**E8**

**E9**

**E10**

**E11**

**E12**

**E13**

**E14**

**E15**

**E16**

**E17**

**E18**

**E19**

**E20**

**E21**

**E22**

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

**E1**

**E2**

**E3**

**E4**

**E5**

**E6**

**E7**

**E8**

**E9**

**E10**

**E11**

**E12**

**E13**

**E14**

**E15**

**E16**

**E17**

**E18**

**E19**

**E20**

**E21**

**E22**


*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

**Table 10.**

*The transformed fuzzy preference value of three attractive FDI strategies.*

**E1**

**96**

C1 A1 2.0000 3.0000 2.0000 4.0000 2.0000 5.0000 3.0000 4.0000 3.0000 3.0000 4.0000 2.0000 4.0000 1.0000 2.0000 3.0000 3.0000 1.0000 3.0000 4.0000 0.5000 4.0000 A2 A2 0.5000 1.0000 0.2500 2.0000 0.3333 0.5000 1.0000 2.0000 0.5000 2.0000 0.5000 1.0000 1.0000 2.0000 1.0000 0.5000 1.0000 0.5000 0.3333 0.5000 1.0000 0.3333 A3 C2 A1 3.0000 4.0000 2.0000 3.0000 1.0000 3.0000 4.0000 0.5000 4.0000 2.0000 3.0000 2.0000 3.0000 5.0000 3.0000 2.0000 4.0000 2.0000 5.0000 3.0000 4.0000 3.0000 A2 A2 1.0000 0.5000 1.0000 1.0000 0.5000 0.3333 0.5000 1.0000 0.3333 0.5000 1.0000 0.2500 0.5000 2.0000 1.0000 0.2500 2.0000 0.3333 0.5000 1.0000 2.0000 0.5000 A3 C3 A1 4.0000 2.0000 5.0000 3.0000 4.0000 3.0000 3.0000 4.0000 2.0000 5.0000 3.0000 3.0000 5.0000 2.0000 1.0000 3.0000 4.0000 0.5000 4.0000 2.0000 3.0000 2.0000 A2 A2 2.0000 0.3333 0.5000 1.0000 2.0000 0.5000 2.0000 0.5000 1.0000 3.0000 0.3333 0.5000 0.5000 0.2500 0.5000 0.3333 0.5000 1.0000 0.3333 0.5000 1.0000 0.2500 A3 C4 A1 2.0000 5.0000 2.0000 4.0000 2.0000 5.0000 3.0000 4.0000 3.0000 3.0000 4.0000 2.0000 2.0000 5.0000 3.0000 4.0000 0.5000 4.0000 2.0000 3.0000 2.0000 4.0000 A2 A2 0.3333 0.5000 0.2500 2.0000 0.3333 0.5000 1.0000 2.0000 0.5000 2.0000 0.5000 1.0000 0.3333 0.5000 0.3300 0.5000 1.0000 0.3333 0.5000 1.0000 0.2500 2.0000 A3 C5 A1 5.0000 2.0000 2.0000 3.0000 2.0000 4.0000 2.0000 5.0000 3.0000 4.0000 3.0000 3.0000 3.0000 1.0000 1.0000 3.0000 4.0000 1.0000 4.0000 2.0000 3.0000 2.0000 A2 A2 2.0000 0.3333 0.5000 1.0000 0.2500 2.0000 0.3333 0.5000 1.0000 2.0000 0.5000 2.0000 1.0000 0.5000 0.5000 0.3333 0.5000 1.0000 0.3333 0.5000 1.0000 0.2500 A3 C6 A1 4.0000 3.0000 3.0000 4.0000 1.0000 3.0000 4.0000 0.5000 4.0000 2.0000 3.0000 2.0000 2.0000 5.0000 4.0000 2.0000 3.0000 2.0000 4.0000 2.0000 5.0000 3.0000 A2 A2 2.0000 0.5000 2.0000 0.5000 0.5000 0.3300 0.5000 1.0000 0.3333 0.5000 1.0000 0.2500 0.3333 0.5000 0.3333 0.5000 1.0000 0.2500 2.0000 0.3333 0.5000 1.0000 A3 C7 A1 4.0000 3.0000 3.0000 1.0000 3.0000 4.0000 0.5000 4.0000 2.0000 3.0000 2.0000 3.0000 2.0000 3.0000 2.0000 4.0000 2.0000 5.0000 3.0000 4.0000 3.0000 3.0000 A2 A2 0.5000 0.5000 1.0000 0.5000 0.3333 0.5000 1.0000 0.3333 0.5000 1.0000 0.2500 1.0000 0.5000 1.0000 0.2500 2.0000 0.3333 0.5000 1.0000 2.0000 0.5000 2.0000 A3 C8 A1 3.0000 4.0000 0.5000 4.0000 2.0000 3.0000 2.0000 4.0000 2.0000 5.0000 3.0000 4.0000 3.0000 4.0000 2.0000 4.0000 2.0000 5.0000 3.0000 3.0000 3.0000 1.0000 A2 A2 0.3333 0.5000 1.0000 0.3300 0.5000 1.0000 0.2500 2.0000 0.3333 0.5000 1.0000 2.0000 0.3333 0.5000 0.2500 2.0000 0.3333 0.5000 1.0000 0.5000 1.0000 0.5000 A3

**Table 9.** *The linguistic term with its* 

*corresponding*

 *number to the priority weight of three attractive FDI strategies.*

**E2**

**E3**

**E4**

**E5**

**E6**

**E7**

**E8**

**E9**

**E10**

**E11**

**E12**

**E13**

**E14**

**E15**

**E16**

**E17**

**E18**

**E19**

**E20**

**E21**

**E22**

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

## *Foreign Direct Investment Perspective through Foreign Direct Divestment*


will involve legal, institutional, and policy-driven changes and improvements con-

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign…*

Domestic supply capacity, human resources, technological development and innovation, infrastructure facilities, and market size of the supporting industries have also received heavier weightings in selecting a strategy for attracting FDI. Notably, international cooperation and competition, along with other outstanding criteria, are not presently taken in terms of seriousness, which will undoubtedly

All survey evaluators agreed that "attracting FDI for developing supporting industries, which motivates the economy's sustainable growth," is the best strategy to pursue for attracting FDI related to the development of Vietnam's supporting industries. This is followed by "attracting FDI for developing supporting industries, which stimulates the national technological development." The statement that ranked last was "attracting FDI for developing supporting industries, which

The multi-criteria decision-making model for selecting a strategy attractive to FDI presented here is clearly applicable to the evaluation process. The proposed strategy also reveals the concerns and preferences of all supporting industries and main industries. The results of this study provide a valuable reference for the Vietnamese government and policy-makers useful to improve institutions and policies, domestic supply capacity, human resources, technological development and innovation, infrastructure facilities, and assistance to improve the environment for investment. The overall purpose is to better attract FDI that will lead to the development of supporting industries and to select the best strategy for attracting future FDI that will also develop the all-important, requisite supporting industries of

Together, based on these available results, we are continuing to produce future research via a large-scale survey in an effort to select a strategy for better develop-

The authors would like to thank the reviewers for their constructive comments

Nguyen Xuan Huynh designed the research and methodology. Nguyen Xuan Huynh and Hoang Dinh Phi collected and analyzed the data. Nguyen Xuan Huynh

and Hoang Dinh Phi wrote and revised the paper and corrected the final

sistent with membership in such global trade organizations.

lead to diminished levels of future FDI.

*DOI: http://dx.doi.org/10.5772/intechopen.90125*

increases national competitiveness."

ing supporting industries in Vietnam.

The author declares no conflict of interest.

**Acknowledgements**

related to this article.

**Conflicts of interest**

**Author contributions**

manuscript.

**99**

Vietnam.

## **Table 11.**

*Aggregated pairwise comparison matrices 22 evaluator of C1.*


## **Table 12.**

*Normalized matrix of priority weight of C1.*


## **Table 13.**

*Normalized matrix of priority weight of all criteria and preference rate of candidates.*

attract FDI for developing supporting industries is one that motivates Vietnam's economy's sustainable growth.

## **6. Conclusions**

This study interviewed approximately 22 policy-makers, economists, and managers to identify their individual prioritization of the goals and assessment criteria discussed above. Based on the opinions of all survey respondents, the following findings were obtained:

"Institutions and policies" is the most important criterion considered by supporting industries in attracting potential FDI. Because Vietnam has joined the AFTA, the WTO, and the CPTPP, the Vietnamese government must now concentrate on building special policies for the promotion of supporting industries, which

## *Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

will involve legal, institutional, and policy-driven changes and improvements consistent with membership in such global trade organizations.

Domestic supply capacity, human resources, technological development and innovation, infrastructure facilities, and market size of the supporting industries have also received heavier weightings in selecting a strategy for attracting FDI. Notably, international cooperation and competition, along with other outstanding criteria, are not presently taken in terms of seriousness, which will undoubtedly lead to diminished levels of future FDI.

All survey evaluators agreed that "attracting FDI for developing supporting industries, which motivates the economy's sustainable growth," is the best strategy to pursue for attracting FDI related to the development of Vietnam's supporting industries. This is followed by "attracting FDI for developing supporting industries, which stimulates the national technological development." The statement that ranked last was "attracting FDI for developing supporting industries, which increases national competitiveness."

The multi-criteria decision-making model for selecting a strategy attractive to FDI presented here is clearly applicable to the evaluation process. The proposed strategy also reveals the concerns and preferences of all supporting industries and main industries. The results of this study provide a valuable reference for the Vietnamese government and policy-makers useful to improve institutions and policies, domestic supply capacity, human resources, technological development and innovation, infrastructure facilities, and assistance to improve the environment for investment. The overall purpose is to better attract FDI that will lead to the development of supporting industries and to select the best strategy for attracting future FDI that will also develop the all-important, requisite supporting industries of Vietnam.

Together, based on these available results, we are continuing to produce future research via a large-scale survey in an effort to select a strategy for better developing supporting industries in Vietnam.

## **Acknowledgements**

The authors would like to thank the reviewers for their constructive comments related to this article.

## **Conflicts of interest**

The author declares no conflict of interest.

## **Author contributions**

Nguyen Xuan Huynh designed the research and methodology. Nguyen Xuan Huynh and Hoang Dinh Phi collected and analyzed the data. Nguyen Xuan Huynh and Hoang Dinh Phi wrote and revised the paper and corrected the final manuscript.

attract FDI for developing supporting industries is one that motivates Vietnam's

*Normalized matrix of priority weight of all criteria and preference rate of candidates.*

**C1 A1 A2 A3** A1 0.5000 0.7775 0.6766 A2 0.2225 0.5000 0.3991 A3 0.3234 0.6009 0.5000 Total 1.0459 1.8784 1.5757

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

**C1 A1 A2 A3 Total Average** A1 0.4781 0.4139 0.4294 1.3214 0.4405 A2 0.2127 0.2662 0.2533 0.7322 0.2441 A3 0.3092 0.3199 0.3173 0.9464 0.3155 Total 3.0000 1.0000

**Weight Priority Weighted rate**

C1 0.2022 0.4405 0.2441 0.3155 0.0891 0.0494 0.0638 C2 0.1529 0.4396 0.2333 0.3271 0.0672 0.0357 0.0500 C3 0.1139 0.4378 0.2292 0.3330 0.0499 0.0261 0.0379 C4 0.1551 0.4427 0.2238 0.3335 0.0687 0.0347 0.0517 C5 0.1083 0.4290 0.2367 0.3343 0.0465 0.0256 0.0362 C6 0.1227 0.4318 0.2336 0.3446 0.0530 0.0274 0.0423 C7 0.0738 0.4366 0.2321 0.3112 0.0322 0.0171 0.0245 C8 0.0710 0.4354 0.2347 0.3399 0.0309 0.0160 0.0241 Total 1.0000 0.4374 0.2320 0.3306

**A1 A2 A3 A1 A2 A3**

This study interviewed approximately 22 policy-makers, economists, and managers to identify their individual prioritization of the goals and assessment criteria discussed above. Based on the opinions of all survey respondents, the following

"Institutions and policies" is the most important criterion considered by supporting industries in attracting potential FDI. Because Vietnam has joined the AFTA, the WTO, and the CPTPP, the Vietnamese government must now concentrate on building special policies for the promotion of supporting industries, which

economy's sustainable growth.

**6. Conclusions**

**Table 13.**

**98**

**Table 11.**

**Table 12.**

*Aggregated pairwise comparison matrices 22 evaluator of C1.*

*Normalized matrix of priority weight of C1.*

findings were obtained:

*Foreign Direct Investment Perspective through Foreign Direct Divestment*

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[16] Wang T-C, Lin Y-L. Applying the consistent fuzzy preference relations to select merger strategy for commercial banks in new financial environments. Expert Systems with Applications.

[17] Ohno K. The Supporting Industry, some Analyses and Consideration.

## **Author details**

Nguyen Xuan Huynh\* and Hoang Dinh Phi Hanoi School of Business and Management, Vietnam National University, Hanoi, Vietnam

\*Address all correspondence to: huynhngx@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign… DOI: http://dx.doi.org/10.5772/intechopen.90125*

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*Foreign Direct Investment Perspective through Foreign Direct Divestment*

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© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

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## *Edited by Anita Maček*

Since the turn of the century, the liberalization of capital markets has caused exponential growth of foreign direct investment (FDI). However, developments in recent years have shown that countries have placed limitations on foreign investors. In addition, dynamic economic developments in the surge of financial and economic crisis and later have clearly exposed the possibility that FDI will change course and result in foreign direct divestment.

This book looks at specific country experiences related to FDI as well as determinants of FDI that could be connected to the new course of divestment.

Published in London, UK © 2021 IntechOpen © MarianVejcik / iStock

Foreign Direct Investment Perspective through Foreign Direct Divestment

Foreign Direct Investment

Perspective through Foreign

Direct Divestment

*Edited by Anita Maček*