2. Literature review

October 2003 represented an important milestone in ASEAN economic cooperation. It stimulates FDI inflows by reducing business costs associated with multinational activities in the ASEAN region that has always been a primary objective of the economic cooperation. Strengthening the financial sector with remaining stable economic condition may establish an attractive business environment for multina-

Accounting and Finance - New Perspectives on Banking, Financial Statements and Reporting

The trend of FDI inflows in ASEAN-5 countries fluctuate from year 1980 to 2017 as shown in Figure 1. There is high volatility in FDI inflows after the Asian financial crisis in 1997–1998 that leads FDI inflows dropped for Singapore, Malaysia and Indonesia, but increased for Philippines and Thailand. While Malaysia and

Philippines drop in 2001 in another event of crisis, that is, bubble.com, but the FDI inflows to Singapore, Thailand and Indonesia are increased. The trend of FDI inflows among ASEAN-5 countries are however increases following AEC formation in 2003. The trend of FDI inflows dropped after the global financial crisis in 2007–2008, which affected ASEAN-5 countries, but its effects are delayed for Malaysia and Indonesia. Fluctuation of FDI inflows may reliance on the uncertainty

To materialize the benefit of FDI, thus, the role of financial development was found as an enabler of the FDI performance. There are five major functions of a financial system: producing information and allocating capital; monitoring firms and implementing corporate governance; meliorating risk; pooling the savings; and easing exchange [1]. These functions contribute to the stimulation of economic growth. Thus, the financial institutions and financial markets can exert a strong influence on economic development, where the incremental of economic growth enabling the FDI to perform better. Financial development is discovered as assistance to the FDI especially in technology transfer that needed more capital to

It is important to consider the effect of financial development on FDI inflows are influenced by the quality of financial information. The investors would review the financial strength via the bank's financial report in the host countries. A quality of financial reporting relies on regulatory accounting standards through the International Financial Reporting Standard (IFRS). In general, ASEAN-5 countries already

FDI inflows (% of GDP) in ASEAN-5 countries from 1980 to 2017. Source of data: UNCTAD website

of its enabler such as financial development in ASEAN-5 countries.

tional firms to invest in the ASEAN-5 countries.

funding the technology expenses.

Figure 1.

42

(Accessed on 9 March 2019).

Early theories of the determinants of FDI were encompassed in eclectic approach [2]. The key requirements for FDI as follows: the firm must possess stable specific advantages; the firm must find it beneficial to utilize these advantages directly instead of selling or leasing them (so called as internalization advantages); and the firm must firm must find it profitable to combine these advantages with at least one factor input abroad (so that local production dominates exporting or locational advantages). These advantages include proximity to markets, specialized suppliers, evasion of protective barriers, and factor endowment advantages.

Financial development is found as one of the significant determinants of FDI [3, 4]. The financial markets are measured by the domestic credit provided by banks and domestic credit provided to the private sector as a percentage of GDP [5]. Domestic credit to the private sector refers to the financial resources provided to private sector through loans, purchases of non-equity securities, and trade credits and other receivable accounts. Meanwhile, the domestic credit provided by banks is nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. The other measurement financial development is financial freedom as a catalyst for FDI inflows. Financial freedom is a measurement of banking security as well as independence from government control. The state ownership of banks and other financial institutions is seen as an inefficient burden, and political favoritism has no place in a free capital market [6]. Thus, the financial information quality also affected the investment efficiency because the investors need the information of financial health in the particular host countries [7].

Financial development, as better accounting and disclosure rules and better corporate governance, reduces the spread between domestic and foreign cost of capital [8]. It requires a sound financial reporting system that produces reliable and transparent accounting information for both domestic and foreign investors [9, 10]. Lack of financial reporting systems credibility is likely to have adverse effects on the ability of particular countries to attract foreign investments, because it retards the equity markets development [11]. In fact, the effect of financial development on economic growth, which enhances FDI inflows, is contingent the adoption of financial accounting quality by host countries [12].

The application of IFRS in host countries is considered a way to attract the FDI [13, 14]. IFRS leads to higher earnings quality and more foreign investment [15]. Furthermore, the short-run and long-run causality existed between IFRS adoption and FDI inflows [16]. The quality of financial reporting according to IFRS has potential to enhance transparency that reduces asymmetric information and cost for foreign investors [17]. IFRS adoption requires strong governance and internal controls within a bank to give confidence to the investors resulting in the quality financial information. The effect of regulatory quality is found as an incentive for quality of accounting information and compliance to the IFRS by firms. Hence, the financial statement act as organization's resume that indicate the strength of finance by banks in host countries. Quality of financial statement among bans in host countries will build good reputation that leads the confidence of decision maker from foreign firms. The information of financial development, that is, credit

market, through the financial reporting can reduce the asymmetric information that attracts the FDI inflows into the host countries.

among the ASEAN countries including Malaysia, Thailand, Indonesia, Singapore, and the Philippines. The study covers 38 years for the period of 1980–2017. The sources of the data are World Development Indicators, UNCTAD Database and

Nonlinear Effect of Financial Development and Foreign Direct Investment in Integration…

The last few decades, the ASEAN-5 economies have witnessed an increasing economic freedom and financial integration implies a strong interdependence between these countries. To measure the existence of economic integration among ASEAN-5 countries, the cross-sectional dependency (CD) test is used for all variables [9]. The existence of cross-sectional dependency among ASEAN-5 countries are proven in Table 1 indicated by the p-value of CD statistics which are lower than 0.01 for all variables that against the null hypotheses of cross-sectional independence among countries, CD N(0,1). Consumer price index is the highest absolute correlation among ASEAN-5 countries at 0.976, means the changes of price of one country closely affected price the other countries. Meanwhile, liquid liabilities are the highest absolute correlation in ASEAN-5 region among other proxies for financial development. This may involve the integrated economic process especially when the countries are neighbors. Furthermore, the cross-sectional dependence can arise for several reasons, such as spatial spillovers, financial contagion, and socioeconomic interactions [19].

Table 2 shows the descriptive statistics of the variables. Jarque-Bera for normality test shows that all variables are not normally distributed. The median for

Variable CD test Breusch-Pagan LM test Absolute correlation Foreign direct investment inflows 2.833\*\*\* 17.103\* 0.193 Domestic credit to private sector 10.776\*\*\* 134.678\*\*\* 0.553 Liquid liabilities 15.234\*\*\* 239.532\*\*\* 0.781 Private credit by deposit money 11.116\*\*\* 146.326\*\*\* 0.570 Real GDP per capita 17.571\*\*\* 313.179\*\*\* 0.901 Consumer Price Index 19.020\*\*\* 361.854\*\*\* 0.976

FDI DCPS LL PCDM RGDPPC CPI

Minimum 2.583 9.681 10.400 6.490 1230.840 5.554 25% quantile 0.979 34.884 39.500 33.940 1880.193 46.614 Median 2.269 75.908 72.295 74.725 3571.915 71.617 75% quantile 5.058 106.363 105.290 102.210 8635.566 95.117 Maximum 26.084 166.504 136.63 163.210 55,235.500 142.182 St. deviation 5.841 40.852 35.270 40.304 13,085.87 32.874 Skewness 1.997 0.230 0.062 0.193 2.066 0.248 Kurtosis 6.389 1.875 1.733 1.782 6.224 2.237 Jarque-Bera 217.175 11.701 12.827 12.924 217.441 6.566 Probability 0.000 0.003 0.002 0.002 0.000 0.038

Financial Structure Dataset.

DOI: http://dx.doi.org/10.5772/intechopen.86104

\*\*\*Significant at 1% level. \* Significant at 10% level.

Result of cross-sectional dependency test for ASEAN-5 countries.

Table 1.

Table 2.

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Descriptive statistics of variables.

The development of strong financial market can increase an economy's ability to absorb and efficiently manage FDI inflows and take advantage of potential FDI benefits [18]. Although recent studies discover that financial development influences FDI performance to be realized, the long run relationship between the variables including FDI, financial development and macroeconomic variables need adequately addressed in the field of study. This study is therefore attempting to contribute to the existing literature in the dimensions of nonlinearity and crosssectional dependency dimensions. The investigation on the effects of financial development on the FDI inflows employs both linear and quadratic models in the estimations. Incorporating the cross-sectional dependency due to economic integration, financial openness, economic freedom and spillover effects among ASEAN-5 countries over the period 1980–2017, the panel cointegration for second generation is used and the long-run coefficient estimated by considering the cross-sectional dependence in this study.
