Impact of foreign direct investments on unemployment in emerging market economies: A co-integration analysis

Bayar, Yilmaz; Sasmaz, Mahmut Unsal Article Impact of foreign direct investments on unemployment in emerging market economies: A co-integration analysis International Journal of Business and Economic Sciences Applied Research (IJBESAR) Provided in Cooperation with: International Hellenic University (IHU), Kavala Suggested Citation: Bayar, Yilmaz; Sasmaz, Mahmut Unsal (2017) : Impact of foreign direct investments on unemployment in emerging market economies: A co-integration analysis, International Journal of Business and Economic Sciences Applied Research (IJBESAR), ISSN 2408-0101, Eastern Macedonia and Thrace Institute of Technology, Kavala, Vol. 10, Iss. 3, pp. 90-96, https://doi.org/10.25103/ijbesar.103.07 This Version is available at: https://hdl.handle.net/10419/185675 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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International Journal of Business and Economic Sciences Applied Research IJBESAR ijbesar.teiemt.gr Impact of Foreign Direct Investments on Unemployment in Emerging Market Economies: A Co-integration Analysis Yilmaz Bayar1, Mahmut Unsal Sasmaz2 1Assoc.Prof., Usak University, Faculty of Economics and Administrative Sciences, Department of Economics, 2 Assist.Prof., Usak University, Faculty of Economics and Administrative Sciences, Department of Public Finance ARTICLE INFO ABSTRACT Article History Received 16 May 2017 Accepted 5 June 2017 Purpose The goal of the paper is to investigate the long run effect of both foreign direct investments and domestic investments on the unemployment in 21 emerging economies over the period 1994-2014. Design/methodology/approach: The effect of domestic and foreign direct investments on unemployment was investigated via panel data analysis. First tests of cross-section dependence and homogeneity were conducted, and then the stationarity of the series was analyzed with Pesaran’s (2007) CIPS unit root test. The long run relationship among the series was examined with Westerlund-Durbin-Hausman’s (2008) co-integration test. Finally, we estimated the long run coefficients with the Augmented Mean Group (AMG) estimator. Findings: The empirical findings revealed a co-integrating relationship among domestic investments, foreign direct investments, and unemployment. Furthermore, foreign direct investment inflows affected the unemployment positively in the long term, but domestic investments affected the unemployment negatively. Originality/value: This study can be considered as one of the early studies researching the long run interaction between domestic investments, foreign direct investments and unemployment for the sample of emerging market economies. Furthermore, the findings are very meaningful for policymakers in the design the economic policies for decreasing unemployment. JEL Classifications C33, E24, F21, F23 Keywords: Gross capital formation, foreign direct investments, unemployment, emerging market economies, panel co-integration analysis ©Eastern Macedonia and Thrace Institute of Technology 1. Introduction Globalisation has accelerated as of mid-1980s, although its past dated back to the Second World War and has had many economic, technological, social and cultural impacts on the societies. One of the most important impacts and causes of globalisation is foreign direct investment (FDI) flows. FDI can be implemented by a new establishment termed green-field investment or by merger and acquisition of a local enterprise termed brown-field investment (Wong and Adams, 2002). Global FDI inflows reached to about $1.871 trillion in 2007, but then significant contractions have been experienced in global FDI inflows due to recent financial crises and amounted to about $1.228 trillion in 2014 as seen in Figure 1. Emerging market economies have experienced a similar trend in FDI inflows and the share of emerging economies in international FDI inflows reached approximately 34.5% in 2014, up from 9.3% in 1990 (UNCTAD, 2016). Figure 1: FDI inflows in the world and emerging market economies (millions of dollars) Source: UNCTAD, 2016 FDI inflows place the economies of host countries at both an advantage and disadvantage; major advantages - 500 000.0 1 000 000.0 1 500 000.0 2 000 000.0 1990 1993 1996 1999 2002 2005 2008 2011 2014 World Emerging Market Economies †Corresponding Author: Yilmaz Bayar E: [email protected] DOI: 10.25103/ijbesar.103.07 International Journal of Business and Economic Sciences Applied Research, Vol. 10, No.3, 90-96 ! ! 91! of FDIs are that they provide capital for productive investments and in turn, foster economic growth, create jobs, particularly FDI in the form of green-field investment, and also contribute to the economy via technology and productivity spillovers and improvements in production and competitiveness. On the other hand, FDIs harm economies by deteriorations in financial stability and inequality and environmental degradation. The goal of this study was to analyze the interaction among unemployment, domestic investments, and foreign direct investments in 21 emerging economies between the 1994 to 2014 period via a WesterlundDurbin-Hausman (2008) co-integration test. In this context, we first review the literature, then give information about data and method. We then present the major results of the empirical application in section 4 and finally conclude the study in section 5. 2. Review of the Literature The considerable increase in global FDI flows have directed scholars to analyze the effect of FDI flows on various macroeconomic and microeconomic indicators such as economic activity, unemployment, tax revenues, environmental degradation, and competitiveness. However, a great majority of the studies have focused on the interplay between FDI flows-economic growth; these studies mostly revealed that FDI inflows have had a positive influence on growth (See Yao (2006), Yucel (2014), Bhattarai (2016)). However, relatively few studies have been carried out to determine the impact of FDI inflows on unemployment and those that did, have reached different mixed findings. A large number of studies revealed that FDI inflows affected unemployment negatively (see Seyf (2000), Craigwell (2006), Karlsson et al. (2009), Balcerzak and Zurek (2011), Carp (2012), Shaari et al. (2012), Mehra (2013), Gocer et al. (2013), Zeb et al. (2014) and Kurtovic et al. (2015)), while relatively fewer studies have discovered a positive relationship through FDI inflows and unemployment (see Mucuk et al. (2013), Bayar (2014)). Furthermore, a considerable number of studies also found no significant relationship between FDI inflows and unemployment (see Chang (2007), Rizvi and Nishat (2009), Aktar et al. (2009), Peker ve Gocer (2010) and Djambaska and Lozanoska (2015)). Table 1: Literature summary Study Country/Country Group and Period Method Impact of FDI on unemployment Seyf (2000) France, Germany, Spain, UK, 1994 Regression Negative Craigwell (2006) 20 Caribbean countries, 1990-2000 Panel data analysis Negative Chang (2007) Taiwan, 1981-2003 VAR analysis No causality Rizvi and Nishat (2009) Pakistan, India and China, 1985-2008 Panel data analysis No significant impact Karlsson et al. (2009) China, 1998-2004 Time series analysis Negative Aktar et al. (2009) Turkey, 2001-2007 VAR analysis No significant impact Peker ve Gocer (2010) Turkey, 2000-2009 ARDL cointegration No significant impact in the long run Pinn et al. (2011) Malaysia, 1970-2007 ARDL cointegration and causality tests One-way causality from FDI to employment Balcerzak and Zurek (2011) Poland, 1995-2009 VAR analysis Negative Carp (2012) Romania, 1991-2010 Regression Negative Shaari et al. (2012) Malaysia, 1980-2010 Regression Negative Yayli ve Deger (2012) 27 developing countries, 1991-2008 Causality analysis One-way causality from FDI to employment Mucuk et al. (2013) 7 countries, 1981-2009 Panel data analysis Positive (Turkey and Argentina), while negative (Thailand) Mehra (2013) India, 1970-2007 Regression Negative Gocer et al. (2013) Turkey, 2000-2011 ARDL cointegration Negative Zeb et al. (2014) Pakistan, 1995-2011 Regression Negative Bayar (2014) Turkey, 2000-2013 ARDL cointegration Positive Kurtovic et al. (2015) 6 Western Balkan countries, 1998-2012 Pedroni and FisherJohansson cointegration tests and Granger causality test Negative Djambaska and Lozanoska (2015) Macedonia 1999-2013 Regression No significant impact International Journal of Business and Economic Sciences Applied Research, Vol. 10, No.3, 90-96 ! ! 92! Study Country/Country Group and Period Method Impact of FDI on unemployment Strat et al. (2015) last 13 EU member countries, 1991-2012 Toda and Yamamoto (1995) causality test Unidirectional causality from FDI inflows to unemployment for 4 countries, opposite oneway causality for 3 countries and no causality for 6 countries 3. Data and Econometric Methodology 3.1. Data The annual values of FDI inflows, gross capital formation, and unemployment were extracted from World Bank (2016a, 2016b and 2016c) data to investigate the relationship among FDI net inflows, unemployment and gross capital formation, as seen in Table 2. The study sample was established taking notice of the MSCI (2016) emerging markets index, but the existence of data limited the sample and study period. The sample included 21 emerging economies (Brazil, Chile, China, Colombia, Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Peru, Philippines, Poland, Qatar, Russia, South Africa, Thailand and Turkey) and the period of study was 1994 to 2014. Table 2: Data description Variables Description Source unemp Unemployment, total (% of total labor force) World Bank (2016a) fdi Foreign Direct Investment, Net Inflows (% of GDP) World Bank (2016b) gcf Gross capital formation (percent of GDP) World Bank (2016c) 3.2. Econometric Methodology First the cross-sectional dependence was investigated with LM adjusted test of Pesaran et al. (2008) considering the equality of time dimension and crosssection dimension (N=T=21) of the dataset. Secondly, homogeneity of the co-integrating coefficients was analyzed with the adjusted delta tilde test established by Pesaran and Yamagata (2008). Thirdly, the stationarity of the series was examined with Pesaran’s (2007) CIPS unit root test, taking notice of cross-sectional dependence. Fourthly, the co-integrating relationship was analyzed using the Westerlund-Durbin-Hausman (2008) co-integration test. Finally, we estimated the long run coefficients with Augmented Mean Group (AMG) estimator (see Eberhardt and Bond (2009), Eberhardt and Teal (2010, 2011)). 3.2.1. Cross-sectional Dependency and Homogeneity Test Cross-sectional dependence indicates that a shock in a cross-sectional unit affects the other cross-sectional units. Therefore, cross-sectional dependence should be considered in the selection of the econometric tests used in the study. The first test, the LM (cross-section dependence LaGrange multiplier) test, investigating cross-sectional dependence was developed by Breusch and Pagan (1980), then Pesaran (2004) developed the LM CD (cross-section dependence) test. However, these two tests may yield biased results when group average equals zero, but individual average is different from zero. Thereupon Pesaran et al. (2008) corrected the biasness by adding the variance and mean to the test statistics. Therefore, Pesaran et al. (2008) cross-sectional dependence test called as !𝐿𝑀$%&.(adjusted LM test) and the test statistic of adjusted LM is calculated as follows (Pesaran et al., 2008): 𝐿𝑀$%& =! 2 𝑁 𝑁 − 1 𝑇 − 𝑘 𝜌0& 1− 𝜇30& 𝑣30& 5 &6078 598 068 !!!!!!(1) The null hypothesis indicates that there is a crosssectional independence among the series, while alternative hypothesis shows that there is cross-sectional dependency. On the other hand, the homogeneity test investigates whether the slope coefficients are homogenous or not (Pesaran and Yamagata, 2008). The homogeneity of the slope coefficients is also important for the selection of unit root, co-integration, and causality tests. The test statistics of delta and adjusted delta tests of Pesaran and Yamagata (2008) are calculated as follows: ∆= √𝑁 𝑁^ −1 𝑆9− 𝑘 /2𝑘 ~𝑋_𝑘^2!!(2) ∆_adj = √𝑁 𝑁^ −1 𝑆9− 𝑘 /v 𝑇, 𝑘 ~𝑁 0,1 !(3)! In the equations numbered (2) and (3), N represents the cross-section dimension, S indicates Swamy test statistic, k shows the number of independent variables, and v(T,k) represents the standard error. Finally, the null hypothesis indicates that the slope coefficients are homogenous, while alternative hypothesis indicates that the slope coefficients are heterogeneous. 3.2.2. CIPS Panel Unit Root Test Pesaran’s (2007) Cross-Sectionally Augmented Dickey Fuller (CADF) test uses the panel regression model in equation (3) and the stationarity of the variables is investigated using the t statistics of the 𝛼80. Furthermore, Pesaran (2007) calls the Cross-Sectionally Augmented IPS (CIPS ) (Im-Pesaran-Shin (2003)) as arithmetic mean of the CADF test statistics, as seen in equation (5). ∆𝑦0M = 𝛼N0 + 𝛼80𝑦0,M98 + 𝛼10𝑦M98 + 𝛼P0∆𝑦M98 + 𝜀0M!!!!(4)!!!!!!!!! 𝐶𝐼𝑃𝑆 =𝐶𝐴𝐷𝐹0 5 068 𝑁!!!!(5)!!!!! International Journal of Business and Economic Sciences Applied Research, Vol. 10, No.3, 90-96 ! ! 93! The null hypothesis of the test indicates that every cross-section of the panel is not stationary. CIPS test has an asymptotically standard distribution and the critical values of the test were tabulated by Pesaran (2006) with use of Monte Carlo Simulation. 3.2.3. Westerlund-Durbin-Hausman (2008) Cointegration Test The Westerlund-Durbin-Hausman (2008) co-integration test considers both cross-sectional dependency and heterogeneity and can be used where the independent variables are different integration levels, on condition that the dependent variable is I(1). The test calculates two different test statistics called as Durbin-Hausman group 𝐷𝐻[ and Durbin-Hausman panel 𝐷𝐻\. The 𝐷𝐻[ statistic is considered when the panel is heterogeneous, while 𝐷𝐻\ test statistic is considered when the panel is homogeneous (Westerlund, 2008) 𝐷𝐻[= 𝑆0 ] 068 ∅_− ∅_ 1𝑒0M98 3 M61 !!!!!!!(6) 𝐷𝐻\= 𝑆]= ∅_− ∅_ 1𝑒0M98 3 M61 ] 068 !!(7) The refusal of the null hypothesis revealed the existence of the co-integrating relationship among the variables. 4. Empirical Analysis 4.1. Cross-sectional Dependency and Homogeneity Test The Pesaran et al. (2008) LM adjusted test was used where the time dimension and cross-sectional dimension both are 21; the test results are presented in Table 3. We rejected the null hypothesis (there is cross-sectional independence) at 5% significance level, because probability values were found to be smaller than 5%. So we discovered a cross-section between the series. Furthermore, the homogeneity of the co-integrating coefficients was investigated by adjusted delta tilde test of Pesaran and Yamagata (2008) and null hypothesis (there is homogeneity) was denied as a result of test results. We therefore concluded that the cointegrating coefficients were heterogenous. Table 3: Results of cross-sectional dependency and homogeneity tests Cross-sectional dependency tests Test Statistic p-value LM (Breusch and Pagan (1980)) 469.7 0.0000 LM adj* (Pesaran et al. (2008)) 28.95 0.0000 LM CD* (Pesaran (2004)) 2.757 0.0058 Homogeneity tests Test Statistic p-value Delta_tilde 11.461 0.000 Delta_tilde_adj 12.899 0.000 *two-sided test 4.2. CIPS Panel Unit Root Test The integration levels of the series was analyzed with Pesaran’s (2007) CIPS (ImPesaran-Shin (2003) unit root test due to the cross-sectional dependence between the variables. The test results can be found in Table 4 and the unemp and gcf were found to be I(1), while fdi was found to be I(0) considering the test results. Table 4: CIPS panel unit root test results Variables Constant Constant + Trend unemp -0.043(0.483) -1.158 (0.124) d(unemp) -7.522 (0.000)*** -5.897 (0.000)*** fdi -3.196 (0.001)*** -1.569 (0.058)* d(fdi) -10.384 (0.000)*** -8.076 (0.000)*** gcf 0.628 (0.735) -0.316 (0.376) d(gcf) -6.934 (0.000)*** -4.262 (0.000)*** * significance at 1% level 4.3. Westerlund-Durbin-Hausman (2008) Cointegration Test The Westerlund-Durbin-Hausman (2008) co-integration test was employed to analyse the long run relationship among unemployment, FDI inflows and gross capital formation, because dependent variable unemp was I(1) and the remaining variables had different integration levels; the findings of the test are shown in Table 5. The group statistic was taken in consideration due to heterogeneity of the cointegrating coefficients. Therefore, the null hypothesis was denied and we revealed a cointegration for some cross-section units. Table 5: Results of Westerlund-Durbin-Hausman (2008) cointegration test Statistic p-value Durbin-Hausman Group Statistic 9.087 0.000 Durbin-Hausman Panel Statistic 5.019 0.000 4.4. Estimation of Co-integrating Coefficients The long run coefficients was estimated by AMG estimator taking notice of cross-sectional dependence and the heterogeneity. These results , displayed in table 6 show that FDI inflows (FDI) affected the unemployment positively in the overall panel, while gross capital formation (GCF) affected the unemployment negatively in the overall panel. However, individual long run coefficients denoted that FDI inflows affected the unemployment negatively in Colombia, Mexico and Russia, while FDI inflows affected the unemployment positively in Brazil, China, Czech Republic, India, Korea, Poland, Thailand and Turkey. Furthermore, FDI inflows had no significant effects on the unemployment in Chile, Egypt, Greece, Hungary, Indonesia, Malaysia, Peru, Qatar, and South Africa. On the other hand, gross capital formation (GCF) affected the unemployment negatively in Brazil, Chile, Colombia, Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Mexico, Peru, Poland, Russia and Turkey, but gross capital formation (GCF) had no significant effects over the unemployment in China, Malaysia, Philippines, Qatar, South Africa and Thailand. Table 6: The cointegrating coefficients International Journal of Business and Economic Sciences Applied Research, Vol. 10, No.3, 90-96 ! ! 94! Country FDI GCF Coefficie nt P value Coefficie nt P value Brazil 0.281392 6 0.065* - 0.380289 0.000** * Chile - 0.027523 7 0.785 - 0.258501 7 0.003** * China 0.198808 2 0.097* 0.011602 4 0.647 Colombi a - 0.471744 3 0.001** * - 0.326620 6 0.000** * Czech Republic 0.280728 3 0.000** * - 0.223080 7 0.001** * Egypt 0.115195 2 0.272 - 0.504683 5 0.000** * Greece 1.156838 0.410 - 1.035121 0.000** * Hungary 0.002368 7 0.922 - 0.558590 9 0.000** * India 0.399807 8 0.028** - 0.056985 7 0.028** Indonesi a 0.478054 1 0.218 - 0.166857 5 0.099* Korea 0.887587 1 0.011** - 0.227004 3 0.000** * Malaysia 0.027213 6 0.777 0.001212 2 0.947 Mexico - 1.075137 0.016** - 0.353486 3 0.020** Peru 0.173671 0.198 - 0.142086 4 0.012** Philippin es - 0.002387 3 0.995 0.205142 8 0.112 Poland 1.132812 0.059* - 1.028136 0.005** * Qatar 0.016780 4 0.691 0.020222 3 0.136 Russia - 0.789516 4 0.020** - 0.334599 7 0.016** South Africa 0.231031 2 0.510 - 0.327502 8 0.217 Country FDI GCF Coefficie nt P value Coefficie nt P value Thailand 0.235786 0.068** 0.007325 6 0.790 Turkey 0.941849 6 0.004** * - 0.281845 5 0.018** Panel 0.199696 0.000* ** - 0.283804 1 0.000* ** 5.Conclusion The significant increases in both-green field and brownfield FDI flows have been experienced globally, and changes in FDI flows have affected many economic indicators such as growth rate of economic activity, unemployment, tax revenues, environmental degradation, and competitiveness. In this study, we researched the long run interaction among domestic investment, foreign direct investments, and the unemployment in emerging markets during 1994-2014 period with Westerlund-Durbin-Hausman (2008) cointegration test. The results indicate that FDI inflows positively affects unemployment in overall panel, as in Mucuk et al. (2013) and Bayar (2014), while gross capital formation negatively affected the unemployment in the overall panel. However, FDI inflows affected the unemployment negatively in Colombia, Mexico and Russia, while FDI inflows affected the unemployment positively in Brazil, China, Czech Republic, India, Korea, Poland, Thailand and Turkey. Furthermore, FDI inflows had no significant effects on unemployment in Chile, Egypt, Greece, Hungary, Indonesia, Malaysia, Peru, Qatar and South Africa. The large part of the empirical literature on the FDIunemployment nexus showed that FDI inflows have negatively impacted unemployment. Therefore, our findings were found to be inconsistent with the general trend in the relevant literature. However, we evaluated that the positive impact of FDI inflows on the unemployment may be a result of the relatively higher share of brown-field investments consisting of mergers and acquisitions in FDI inflows in our sample. Future studies can be conducted to investigate the separate impact of both brown-field investments and green-field investments on the unemployment. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence International Journal of Business and Economic Sciences Applied Research, Vol. 10, No.3, 90-96 ! ! 95! References Aktar, I., Demirci, N.Ozturk, L., (2009), “Can Unemployment be Cured by Economic Growth and Foreign Direct Investment?”, SÜ İİBF Sosyal ve Ekonomik Araştırmalar Dergisi, 17, 453-467. 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