**Table 2.**

*Correlation coefficients analysis.*

*Peripheral Territories, Tourism, and Regional Development*

2014 465263 (2.6)

2019 493223 (1.2)

2014 97276 (−0.8)

2019 90851 (−1.3)

2014 2270

2019 2630

2014 55497 (2.9)

2019 60436 (1.8)

2014 238950 (1.9)

2019 256716 (1.5)

2014 0.26

2019 0.33

2014 7278

2019 7356

2014 5898

2019 4708

*Descriptive analysis of municipal regions in Gauteng Province.*

(3.6)

(5.4)

(5.9)

(0.2)

(−0.1)

(−4.0)

*Source: [37]. Note: () brackets contain annual growth percentages between observations.*

(4.3)

(3.2)

GDP at constant prices (R million)

Population density (People per sqkm)

Jobs in the tourism

Disposable income (R million)

International tourism trips as a ratio to total trips

Tourism spending per capita

Tourism GDP per

capita

sector

**Variables Year COJ COT EKR SDM WRDM Total** 

302464 (4.2)

321164 (1.3)

95823 (0.8)

90325 (−1.2)

30903 (3.4)

37722 (4.4)

170929 (3.3)

185860 (1.8)

> 0.24 (4.0)

0.30 (5.0)

7022 (7.9)

7165 (0.4)

5681 (1.2)

4527 (−4.1)

GDP per capita 2009 101171 92190 59140 42686 57163 80489

386 (4.1) 1375

445 (3.1) 1562

2009 412238 250160 176621 38031 45544 922596

43682 (2.9)

44852 (0.5)

45353 (1.3)

43132 (−0.9)

> 190 (1.8)

> 208 (1.9)

6098 (7.6)

6298 (0.7)

35471 (3.8)

39085 (2.1)

0.25 (5.0)

0.32 (5.6)

2657 (10.1)

2717 (0.45)

> 2131 (2.9)

1687 (−4.2)

43870 (−0.7)

42004 (−0.9)

52188 (−1.7)

47211 (−1.9)

7984 (0.43)

> 9117 (2.8)

33702 (1.9)

36034 (1.4)

> 0.32 (5.6)

0.39 (4.4)

3559 (10.6)

3986 (2.4)

2628 (0.9)

2210 (−3.2)

164 (1.4) 569 (3.6)

176 (1.5) 649 (2.8)

208049 (3.6)

215681 (0.7)

61371 (0.8)

57141 (−1.4)

2009 1871 321 1178 174 153 483

(3.3)

(2.7)

32730 (4.1)

38591 (3.6)

146191 (2.7)

157971 (1.6)

2009 0.22 0.20 0.21 0.20 0.25 0.21

0.27 (5.7)

0.34 (5.2)

2009 5613 5043 3818 1769 2324 4483

5385 (8.2)

5730 (1.3)

2009 5934 5374 4030 1857 2513 5376

4307 (1.4)

3583 (−3.4)

2009 217674 146803 128552 29777 30830 553637

2009 48465 26388 27212 4415 7815 114295

**Gauteng**

1065330 (3.1)

1116927 (1.1)

> 80965 (0.1)

76040 (−1.2)

133213 (3.3)

132155 (2.8)

625246 (2.6)

675669 (1.6)

0.25 (3.8)

0.32 (5.6)

6151 (7.5)

6360 (0.7)

5198 (−0.7)

4558 (−2.5)

**110**

**Table 1.**

coefficient are jobs in the tourism sector and international trips of 0.97, followed by jobs in the tourism sector and disposable income with a coefficient of 0.95.

#### **4.2 Econometric analysis**

**Table 3** illustrates the results obtained from the unit root testing. Unit root tests are the first step before cointegration estimations are completed. The reason for this important step is to prevent the use of non-stationary variables, as this may produce spurious results [41]. Unit root tests also assist in the selection of the final long-run estimation model. This analysis results reveal that all variables are non-stationary at levels I(0), while all variables become stationary at 1st difference. Based on the unit root test results, it could be concluded that the Fisher - Johansen panel cointegration test should be utilised to assess the long-run relationships between the variables as all variables are stationary at the same level or at 1st difference.

The next step in the process was to determine the direct nexus between the independent variables and GDP per capita, which was used as a proxy for economic development. The Fisher - Johansen panel cointegration test was utilised as this test is most suitable when all variables are of order I(1). The results are presented in **Table 4**. The Fisher - Johansen cointegration test is used to test for long-run cointegration. [6] define cointegration as the systematic co-movement between variables in the long-run. The results indicate that the Trace and Max-Eigen tests provide evidence of a cointegrating relationship between the variables, at a 1 percent significance level. It could be concluded that the results obtained from the Fisher - Johansen cointegration test confirm a long-run equilibrium relationship between tourism and economic growth. The subsequent step is to determine the exact impact of tourism on economic growth in the study region.

The Fisher - Johansen test indicates a long-run relationship between the variables. This relationship needs to be confirmed, and the strength of the various relationships between the variables need to be established via a regression analysis with coefficients. For this purpose, the two types of estimation methods utilised are the Fully Modified Ordinary Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS) models. A consideration of various forms of residual-based panel method results indicate that these models generally out-perform singleequation estimation techniques [39]. Firstly, the results of both the FMOLS and DOLS models are listed in **Table 5**. With LGDPC as the dependent variable, the FMOLS model results indicate that all of the independent variables are significant and positive predictors of GDP per capita (LGDPC) at a significance level of 1%,


**113**

*An Assessment of the Impact of the Tourism Sector on Regional Economic Development…*

**None** 185.6 0.0001\* 121.40 0.0004\* **At most 1** 93.04 0.0003\* 44.46 0.0007\* **At most 2** 57.05 0.0005\* 49.02 0.0000\* **At most 3** 19.89 0.0303 14.47 0.1527

**Method Variables Coefficient Std. Error t-statistic Prob. FMOLS** LGVAT 0.8796 0.0854 10.2886 0.0001\*

**DOLS** LGVAT 0.8609 0.1315 6.5432 0.0001\*

LJOBST 0.2328 0.0535 4.3463 0.0003\* LSPENDT 0.4473 0.0862 5.1881 0.0002\* LINTTT 0.0993 0.0805 1.2328 0.2212

LJOBST 0.2963 0.1014 2.9219 0.0139\* LSPENDT 0.1740 0.1589 1.0952 0.2968 LINTTT 0.4196 0.0999 4.2001 0.0015\*

**Probability Fisher Stat. (from** 

**max-eigen test)**

**Probability**

**Fisher Stat. (from trace test)**

except for international tourist trips (LINTTT). GVA in tourism (LGVAT) has the highest coefficient of 0.88, meaning that a 1% increase in LGVAT could increase by an increase of 0.88% in GDP per capita. Spending on tourism (LSPENDT) has the second-highest coefficient of 0.45, followed by the number of jobs in tourism

Similar results have been estimated using the DOLS method. The only major difference between the two methods is that only spending on tourism (LSPENDT) is not a significant predictor of GDP per capita for the DOLS model. At the same time, international tourist trips are, however, a significant predictor. Similar coefficients were estimated for both models. This analysis's results are that all the variables could be accepted as significant predictors of GDP per capita. Paci and Marrocu [20] found similar results in that both domestic and international tourism positively influences regional economic growth. Alberti and Giusti [22] also found a positive relationship but added that if all regional role players collaborate, it could lead to

**Table 6** presents the results of the pairwise Granger-causality tests indicating short-run relationships. The purpose of the Granger causality test is to determine which variable causes changes to any of the other variables in the model [42]. The focus of the analysis is on the dependent variable, namely GDP per capita. The results indicate that changes in GVA in tourism impact GDP per capita, while GDP per capita does cause changes in both jobs in tourism and spending in tourism. No causality was detected between GDP per capita and international tourist trips. Causality between other variables excluding the official dependent variable allows

*DOI: http://dx.doi.org/10.5772/intechopen.95810*

*indicates variables are statistically significant at 1% and \*\* at 5%.*

*indicates variables are statistically significant at 1% and \*\* at 5%.*

*Independent variable: LGVAT; LJOBST; LSPENDT; LINTTT.*

*The Fisher - Johansen panel cointegration test.*

**Hypothesised No. of CE(s)**

*\**

*\**

**Table 5.**

*Dependent variable: LGDPC.*

*FMOLS and DOLS results.*

**Table 4.**

(LJOBST) with a coefficient of 0.23.

more competitiveness in regional tourism.

*Null hypothesis: Unit root.*

*\* indicates 1% statistically significant.*

*\*\*indicates 5% statistically significant.*

*Source: own elaboration.*

*An Assessment of the Impact of the Tourism Sector on Regional Economic Development… DOI: http://dx.doi.org/10.5772/intechopen.95810*


#### **Table 4.**

*Peripheral Territories, Tourism, and Regional Development*

**4.2 Econometric analysis**

coefficient are jobs in the tourism sector and international trips of 0.97, followed by

**Table 3** illustrates the results obtained from the unit root testing. Unit root tests are the first step before cointegration estimations are completed. The reason for this important step is to prevent the use of non-stationary variables, as this may produce spurious results [41]. Unit root tests also assist in the selection of the final long-run estimation model. This analysis results reveal that all variables are non-stationary at levels I(0), while all variables become stationary at 1st difference. Based on the unit root test results, it could be concluded that the Fisher - Johansen panel cointegration test should be utilised to assess the long-run relationships between the variables as

The next step in the process was to determine the direct nexus between the independent variables and GDP per capita, which was used as a proxy for economic development. The Fisher - Johansen panel cointegration test was utilised as this test is most suitable when all variables are of order I(1). The results are presented in **Table 4**. The Fisher - Johansen cointegration test is used to test for long-run cointegration. [6] define cointegration as the systematic co-movement between variables in the long-run. The results indicate that the Trace and Max-Eigen tests provide evidence of a cointegrating relationship between the variables, at a 1 percent significance level. It could be concluded that the results obtained from the Fisher - Johansen cointegration test confirm a long-run equilibrium relationship between tourism and economic growth. The subsequent step is to determine the exact impact of tourism on economic growth in the study region. The Fisher - Johansen test indicates a long-run relationship between the variables. This relationship needs to be confirmed, and the strength of the various relationships between the variables need to be established via a regression analysis with coefficients. For this purpose, the two types of estimation methods utilised are the Fully Modified Ordinary Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS) models. A consideration of various forms of residual-based panel method results indicate that these models generally out-perform singleequation estimation techniques [39]. Firstly, the results of both the FMOLS and DOLS models are listed in **Table 5**. With LGDPC as the dependent variable, the FMOLS model results indicate that all of the independent variables are significant and positive predictors of GDP per capita (LGDPC) at a significance level of 1%,

**Variables Levin, Lin and Chu test Im, Pesaran and Shin W-stat ADF - Fisher Chi-square test**

LGDPC 0.1036 0.0001\* 0.1515 0.0001\* 0.1534 0.0002\* LGVAT 0.1394 0.0008\* 0.7478 0.0001\* 0.8529 0.0003\* LJOBST 0.0305\*\* 0.0001\* 0.7798 0.0003\* 0.8116 0.0001\* LSPENDT 0.9484 0.0045\* 0.9478 0.0175\*\* 0.9723 0.0074\* LINTTT 0.0005\* 0.0427\*\* 0.3174 0.01311\*\* 0.5237 0.0031\*

**I (0) I (1) I (0) I (1) I (0) I (1)**

jobs in the tourism sector and disposable income with a coefficient of 0.95.

all variables are stationary at the same level or at 1st difference.

**112**

**Table 3.**

*\**

*Null hypothesis: Unit root.*

*Source: own elaboration.*

*Panel unit root tests: P-values.*

*indicates 1% statistically significant. \*\*indicates 5% statistically significant.* *The Fisher - Johansen panel cointegration test.*


*\* indicates variables are statistically significant at 1% and \*\* at 5%.*

*Dependent variable: LGDPC. Independent variable: LGVAT; LJOBST; LSPENDT; LINTTT.*

#### **Table 5.**

*FMOLS and DOLS results.*

except for international tourist trips (LINTTT). GVA in tourism (LGVAT) has the highest coefficient of 0.88, meaning that a 1% increase in LGVAT could increase by an increase of 0.88% in GDP per capita. Spending on tourism (LSPENDT) has the second-highest coefficient of 0.45, followed by the number of jobs in tourism (LJOBST) with a coefficient of 0.23.

Similar results have been estimated using the DOLS method. The only major difference between the two methods is that only spending on tourism (LSPENDT) is not a significant predictor of GDP per capita for the DOLS model. At the same time, international tourist trips are, however, a significant predictor. Similar coefficients were estimated for both models. This analysis's results are that all the variables could be accepted as significant predictors of GDP per capita. Paci and Marrocu [20] found similar results in that both domestic and international tourism positively influences regional economic growth. Alberti and Giusti [22] also found a positive relationship but added that if all regional role players collaborate, it could lead to more competitiveness in regional tourism.

**Table 6** presents the results of the pairwise Granger-causality tests indicating short-run relationships. The purpose of the Granger causality test is to determine which variable causes changes to any of the other variables in the model [42]. The focus of the analysis is on the dependent variable, namely GDP per capita. The results indicate that changes in GVA in tourism impact GDP per capita, while GDP per capita does cause changes in both jobs in tourism and spending in tourism. No causality was detected between GDP per capita and international tourist trips. Causality between other variables excluding the official dependent variable allows


#### **Table 6.**

*Granger causality tests.*

for interesting results. Changes or increasing GVA in the tourism sector cause changes in tourism jobs and not vice versa, so new value-adding products and services, in this case, cause more jobs in the sector. Also, bi-directional causality relationships exist between spending in the tourism sector and GVA in tourism; between international tourism trips and GVA in tourism; spending and jobs in the tourism sector; international tourist trips and tourism; and between international tourist trips and spending in tourism. Mishra et al. [30] found similar results whereby tourism activities Granger-cause changes in regional economic growth.

Lastly, the econometric model is tested in terms of stability using residual diagnostics. In addition to the various aforementioned statistical procedures, diagnostic statistics were used to determine whether the residuals were distributed normally. Three residual diagnostic tests were performed, namely the Jarque-Bera normality test, the serial correlation test, and a heteroscedasticity test. To achieve this, the histogram of the residuals device was used. The histogram of residuals and the Jarque-Bera statistic shows that the data are normally distributed, and the results gained are valid. In terms of the serial correlation, both tests had AC values above 0.5, suggesting no autocorrelation between the variables. The results further suggested that there was no conditional heteroscedasticity among the variable.

#### **5. Conclusion and recommendations**

This study's primary objective was to assess and evaluate the impact of the tourism sector on the regional economy of the Gauteng Province in South Africa. Research on

**115**

*An Assessment of the Impact of the Tourism Sector on Regional Economic Development…*

the impact of tourism on regional economies is relatively limited if compared to other economic sectors. One of the reasons for this situation is that data on the tourism sector is limited as it is not counted as a formal economic sector. Only sections of the industry are counted in detail, such as the hospitality industry. The tourism sector cuts across many formal economic sectors making the quantification thereof difficult. Data on a provincial or regional level are even more limited than on a national level. The objectives of this study were achieved via both descriptive and econometric data analysis. The study's main results indicate that the tourism sector has a significant positive impact on economic growth in the Gauteng provincial region. This study's outcome is important as it reduces the uncertainty surrounding tourism and its impact on regional economies. Results indicate that tourism could contribute significantly to economic growth per capita in the study region. A 1% increase in the tourism sector's gross value-added activities could lead to between a 0.86 to 0.89 percent increase in GDP per capita. This indicates that tourism does have the potential to decrease unemployment and further contribute to alleviating poverty and improv-

For tourism to make even greater contributions to regional economies, close cooperation is required between key regional role players such as the business community via business chambers, provincial and local government, and local communities. An effective regional tourism organisation that has as its goals as sustainable tourism development is important. It could help promote the region, initiate new regional projects, share information, and improve coordination among industry leaders. Also, cooperation between the public and private sectors could ensure natural environment protection, leading to a more attractive and marketable region. As with most research studies, this paper also had a few limitations. The findings of this study are based upon the results in the Gauteng region, which consists of a range of municipal regions. These sub-regions differ in terms of the level of development but forms a coherent entity. This region is a leading economic region in South Africa but not in tourism as it is located in-land. Results from this region may differ from results of coastal regions such as the Western Cape or Kwa-Zulu Natal regions. Data sets per municipal area were also only available only from 2000 up to 2019 but provided sufficient data for the analysis. However, the listed limitations allow for future research studies such as the comparison of in-land and coastal regions or regions with established tourism sectors versus regions where tourism has not been developed.

This study indicates that the tourism sector could even play a critical economic developmental role in regions that are not primarily focused on the tourism sector. For tourism to be a regional economic driver, a relatively clean environment is a requirement, as well as a diversified tourist product offering or tourism sector complexity. This relates to sustainable traditional economic sectors such as the mining and manufacturing sectors in collaboration with the tourism sector. South Africa and its regions and provinces have unlimited potential within the tourism sector due to its history, cultural diversity, rich biodiversity, and natural beauty. The tourism sector should be the main industry to revitalise the ailing economy.

*DOI: http://dx.doi.org/10.5772/intechopen.95810*

ing living standards for people in local regions.

#### *An Assessment of the Impact of the Tourism Sector on Regional Economic Development… DOI: http://dx.doi.org/10.5772/intechopen.95810*

the impact of tourism on regional economies is relatively limited if compared to other economic sectors. One of the reasons for this situation is that data on the tourism sector is limited as it is not counted as a formal economic sector. Only sections of the industry are counted in detail, such as the hospitality industry. The tourism sector cuts across many formal economic sectors making the quantification thereof difficult. Data on a provincial or regional level are even more limited than on a national level. The objectives of this study were achieved via both descriptive and econometric data analysis.

The study's main results indicate that the tourism sector has a significant positive impact on economic growth in the Gauteng provincial region. This study's outcome is important as it reduces the uncertainty surrounding tourism and its impact on regional economies. Results indicate that tourism could contribute significantly to economic growth per capita in the study region. A 1% increase in the tourism sector's gross value-added activities could lead to between a 0.86 to 0.89 percent increase in GDP per capita. This indicates that tourism does have the potential to decrease unemployment and further contribute to alleviating poverty and improving living standards for people in local regions.

For tourism to make even greater contributions to regional economies, close cooperation is required between key regional role players such as the business community via business chambers, provincial and local government, and local communities. An effective regional tourism organisation that has as its goals as sustainable tourism development is important. It could help promote the region, initiate new regional projects, share information, and improve coordination among industry leaders. Also, cooperation between the public and private sectors could ensure natural environment protection, leading to a more attractive and marketable region.

As with most research studies, this paper also had a few limitations. The findings of this study are based upon the results in the Gauteng region, which consists of a range of municipal regions. These sub-regions differ in terms of the level of development but forms a coherent entity. This region is a leading economic region in South Africa but not in tourism as it is located in-land. Results from this region may differ from results of coastal regions such as the Western Cape or Kwa-Zulu Natal regions. Data sets per municipal area were also only available only from 2000 up to 2019 but provided sufficient data for the analysis. However, the listed limitations allow for future research studies such as the comparison of in-land and coastal regions or regions with established tourism sectors versus regions where tourism has not been developed.

This study indicates that the tourism sector could even play a critical economic developmental role in regions that are not primarily focused on the tourism sector. For tourism to be a regional economic driver, a relatively clean environment is a requirement, as well as a diversified tourist product offering or tourism sector complexity. This relates to sustainable traditional economic sectors such as the mining and manufacturing sectors in collaboration with the tourism sector. South Africa and its regions and provinces have unlimited potential within the tourism sector due to its history, cultural diversity, rich biodiversity, and natural beauty. The tourism sector should be the main industry to revitalise the ailing economy.

*Peripheral Territories, Tourism, and Regional Development*

**Null Hypothesis: Obs F-Statistic Prob.** LGVAT does not Granger Cause LGDPC 110 7.4393 0.0010\* LGDPC does not Granger Cause LGVAT 0.1007 0.9043 LJOBST does not Granger Cause LGDPC 110 0.5872 0.5577 LGDPC does not Granger Cause LJOBST 7.4212 0.0010\* LSPENDT does not Granger Cause LGDPC 85 0.8280 0.4406 LGDPC does not Granger Cause LSPENDT 10.4710 9.E-05\* LINTTT does not Granger Cause LGDPC 85 0.8497 0.4313 LGDPC does not Granger Cause LINTTT 1.9399 0.1504 LJOBST does not Granger Cause LGVAT 110 0.2084 0.8122 LGVAT does not Granger Cause LJOBST 14.7989 2.E-06\* LSPENDT does not Granger Cause LGVAT 85 7.2153 0.0013\* LGVAT does not Granger Cause LSPENDT 11.0183 6.E-05\* LINTTT does not Granger Cause LGVAT 85 7.3938 0.0011\* LGVAT does not Granger Cause LINTTT 5.4569 0.0060\* LSPENDT does not Granger Cause LJOBST 85 4.6972 0.0118\* LJOBST does not Granger Cause LSPENDT 6.7106 0.0020\* LINTTT does not Granger Cause LJOBST 85 14.4359 4.E-06\* LJOBST does not Granger Cause LINTTT 7.1791 0.0014\* LINTTT does not Granger Cause LSPENDT 85 11.1663 5.E-05\* LSPENDT does not Granger Cause LINTTT 10.7987 7.E-05\*

for interesting results. Changes or increasing GVA in the tourism sector cause changes in tourism jobs and not vice versa, so new value-adding products and services, in this case, cause more jobs in the sector. Also, bi-directional causality relationships exist between spending in the tourism sector and GVA in tourism; between international tourism trips and GVA in tourism; spending and jobs in the tourism sector; international tourist trips and tourism; and between international tourist trips and spending in tourism. Mishra et al. [30] found similar results whereby tourism activities Granger-cause changes in regional economic growth. Lastly, the econometric model is tested in terms of stability using residual diagnostics. In addition to the various aforementioned statistical procedures, diagnostic statistics were used to determine whether the residuals were distributed normally. Three residual diagnostic tests were performed, namely the Jarque-Bera normality test, the serial correlation test, and a heteroscedasticity test. To achieve this, the histogram of the residuals device was used. The histogram of residuals and the Jarque-Bera statistic shows that the data are normally distributed, and the results gained are valid. In terms of the serial correlation, both tests had AC values above 0.5, suggesting no autocorrelation between the variables. The results further suggested that there was no conditional heteroscedasticity among the variable.

This study's primary objective was to assess and evaluate the impact of the tourism sector on the regional economy of the Gauteng Province in South Africa. Research on

**114**

*\**

**Table 6.**

*Granger causality tests.*

**5. Conclusion and recommendations**

*Rejection of null hypothesis at 5% significance level.*

*Peripheral Territories, Tourism, and Regional Development*

## **Author details**

Daniel F. Meyer College of Business and Economics, University of Johannesburg, South Africa

\*Address all correspondence to: dfmeyer@uj.ac.za

© 2021 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.

**117**

*An Assessment of the Impact of the Tourism Sector on Regional Economic Development…*

Middle East Journal of Scientific Research. 2011;10(1), 28-32.

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*DOI: http://dx.doi.org/10.5772/intechopen.95810*

[1] Cooper C. Essentials of tourism. SAGE Publications Limited; 2020.

[2] Todaro MP, Smith SC. Economic development. 12th ed. Edinburgh Gate:

Pearson Education Ltd; 2015.

10.1177/0885412203260306.

2007.

[4] Ashley C, De Brine P, Wilde H. The role of the tourism sector in expanding economic opportunity. Corporate Social Responsibility Initiative Report. Cambridge, MA: Harvard University;

[5] Butler G, Rogerson CM. Inclusive local tourism development in South Africa: Evidence from Dullstroom. Local Economy. 2016;31(1), 264-281. DOI: 10.1177/0269094215623732.

[6] Seghir GM, Mostéfa B, Abbes SM, Zakarya GY. Tourism spendingeconomic growth causality in 49 countries: A dynamic panel data approach. Procedia Economics and Finance. 2015;23, 1613-1623. DOI: 10.1016/S2212-5671(15)00402-5.

[7] UNWTO (The United Nations World Tourism Organization). Why tourism? 2016. Available from: http:// www2.unwto.org/content/why-tourism.

[8] Meyer DF, Meyer N. The role and impact of tourism on local economic development: A comparative study. African Journal for Physical, Health Education, Recreation and Dance.

[9] Samini AJ, Sadeghi S, Sadeghi S. Tourism and economic growth in developing countries: P-VAR approach.

[Accesses: 2020-04-12].

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[3] Harrill R. Residents' attitudes toward tourism development: A literature review with implications for tourism planning. Journal of Planning Literature. 2004;18(3), 251-266. DOI:

**References**

*An Assessment of the Impact of the Tourism Sector on Regional Economic Development… DOI: http://dx.doi.org/10.5772/intechopen.95810*

#### **References**

*Peripheral Territories, Tourism, and Regional Development*

**116**

**Author details**

Daniel F. Meyer

College of Business and Economics, University of Johannesburg, South Africa

© 2021 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: dfmeyer@uj.ac.za

provided the original work is properly cited.

[1] Cooper C. Essentials of tourism. SAGE Publications Limited; 2020.

[2] Todaro MP, Smith SC. Economic development. 12th ed. Edinburgh Gate: Pearson Education Ltd; 2015.

[3] Harrill R. Residents' attitudes toward tourism development: A literature review with implications for tourism planning. Journal of Planning Literature. 2004;18(3), 251-266. DOI: 10.1177/0885412203260306.

[4] Ashley C, De Brine P, Wilde H. The role of the tourism sector in expanding economic opportunity. Corporate Social Responsibility Initiative Report. Cambridge, MA: Harvard University; 2007.

[5] Butler G, Rogerson CM. Inclusive local tourism development in South Africa: Evidence from Dullstroom. Local Economy. 2016;31(1), 264-281. DOI: 10.1177/0269094215623732.

[6] Seghir GM, Mostéfa B, Abbes SM, Zakarya GY. Tourism spendingeconomic growth causality in 49 countries: A dynamic panel data approach. Procedia Economics and Finance. 2015;23, 1613-1623. DOI: 10.1016/S2212-5671(15)00402-5.

[7] UNWTO (The United Nations World Tourism Organization). Why tourism? 2016. Available from: http:// www2.unwto.org/content/why-tourism. [Accesses: 2020-04-12].

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*Peripheral Territories, Tourism, and Regional Development*

Development: Empirical Evidence. Innovative Issues and Approaches in Social Sciences. 2012;5(2), 6-20.

[26] Surugiu C, Surugiu MR. Is the tourism sector supportive of economic

Romanian tourism. Tourism Economics.

[27] Fundeanu DD. Innovative regional cluster, model of tourism development. Procedia Economics and Finance.

[28] Gunderson RJ, Ng PT. Analysing the effects of amenities, quality of life attributes and tourism on regional economic performance using regression quantiles. Journal of Regional Analysis and Policy. 2005;35(1), 1-22. DOI:

growth? Empirical evidence on

2013;19(1), 115-132.

2015;23, 744-749.

10.22004/ag.econ.132298.

2011;18(4), 518-527.

[29] Klytchnikova I, Dorosh P. Tourism sector in Panama: Regional economic impacts and the potential to benefit the poor. Natural Resources Forum. 2013;37(2), 70-79. DOI: 10.1111/1477-8947.12019.

[30] Mishra PK, Rout HB, Mohapatra SS. Causality between tourism and economic growth: Empirical evidence from India. European Journal of Social Sciences.

[31] Wen-li ZHOU. A review on impact of tourism on economic growth. Economic Geography. 2011;8, 1-12.

Agricultural Science. 2011;3(1), 212-223.

[32] He LH, Zheng XG. Empirical analysis on the relationship between tourism development and economic growth in Sichuan. Journal of

[33] Yang Y, Fik TJ, Altschuler B. Explaining regional economic multipliers of tourism: does crossregional heterogeneity exist? Asia Pacific Journal of Tourism Research. 2018;23(1), 15-23. DOI: 10.1080/10941665.2017.1394335

2009;30(5), 759-770. DOI: 10.1016/j.

[18] Milne S, Ateljevic I. Tourism, economic development and the global-local nexus: Theory embracing complexity. Tourism Geographies

[19] Bellini N, Grillo F, Lazzeri G, Pasquinelli C. Tourism and regional

[20] Paci R, Marrocu E. Tourism and regional growth in Europe. Papers in Regional Science. 2014;93, S25-S50.

economic resilience from a policy perspective: Lessons from smart specialisation strategies in Europe. European Planning Studies. 2017;25(1), 140-153. DOI: 10.1080/09654313.2016.1273323

DOI: 10.1111/pirs.12085.

ccs.2012.11.003

DOI: 10.1002/jtr.646

[21] Dana LP, Gurau C, Lasch F.

Entrepreneurship, tourism and regional development: a tale of two villages. Entrepreneurship & Regional

Development. 2014;26(3-4), 357-374. DOI: 10.1080/08985626.2014.918182.

[22] Alberti FG, Giusti JD. Cultural heritage, tourism and regional competitiveness: The Motor Valley cluster. City, Culture and Society. 2012;3(4), 261-273. DOI: 10.1016/j.

[23] Cortes-Jimenez I. Which type of tourism matters to the regional economic growth? The cases of Spain and Italy. International Journal of Tourism Research. 2008;10(2), 127-139.

[24] Vieira AC, Santos LD. Tourism and regional development: a spatial econometric model for Portugal at municipal level. FEP Working Papers 589, Universidade do Porto, Faculdade

de Economia do Porto; 2017.

[25] Petrevska B, Manasieva Gerasimova V. Tourism in Regional

tourman.2008.11.014

2001;3(4), 369-393.

**118**

[35] Cresco S, Querini G. The role of tourism in sustainable economic development. European Regional Science Association. 2003;1(1), 1-13.

[36] WTTC (World Travel and Tourism Council). Understanding the critical issues for the future of travel and tourism, March 2017. London: WTTC; 2017.

[37] Global Insight. Data analysis. 2020. Available from: https://g-insight. org/what/#data-analysis [Accessed: 2020-05-20].

[38] Gujarati DN, Porter DC. Essentials of econometrics. 4th ed. New York: McGraw-Hill Education; 2010.

[39] Pedroni P. Panel cointegration: asymptotic and finite sample properties of fooled time series tests with an application to the PPP hypothesis. Economic Theory. 2004;20, 597-625.

[40] Brooks C. Introductory Econometrics for Finance. Cambridge publishers; 2014.

[41] Ogbokor C.A. Foreign trade and economic growth in Namibia: A time series analysis. Vanderbijlaprk, NWU. (Thesis – PhD); 2015.

[42] Rivera MA. The synergies between human development, economic growth, and tourism within a developing country: An empirical model for Ecuador. Journal of Destination Marketing & Management. 2017;6(3), 221-232. DOI: 10.1016/j. jdmm.2016.04.002.

*Edited by Rui Alexandre Castanho, Gualter Couto and Rossana Santos*

Limited land and resources, along with the overexploitation of tourism and multiple other factors, make peripheral and ultra-peripheral territories relevant cases for studying governance and sustainable development. This book presents case studies of European and Mediterranean regions to study regional development and territorial sustainability, strategic planning, and territorial management and governance. Written by experts in the field, the chapters contained herein provide the reader with a deep understanding, from several perspectives, of the dynamics, challenges, and opportunities of tourism in these specific territories.

Published in London, UK © 2021 IntechOpen © Sherry Epley / iStock

Peripheral Territories, Tourism, and Regional Development

Peripheral Territories,

Tourism, and Regional

Development

*Edited by Rui Alexandre Castanho,* 

*Gualter Couto and Rossana Santos*