Do credit risks deter FDI? Empirical evidence from the SAARC countries

Alam, Md. Badrul; Tahir, Muhammad; Ali, Norulazidah Omar Article Do credit risks deter FDI? Empirical evidence from the SAARC countries Journal of Economics, Finance and Administrative Science Provided in Cooperation with: Universidad ESAN, Lima Suggested Citation: Alam, Md. Badrul; Tahir, Muhammad; Ali, Norulazidah Omar (2024) : Do credit risks deter FDI? Empirical evidence from the SAARC countries, Journal of Economics, Finance and Administrative Science, ISSN 2218-0648, Emerald Publishing Limited, Bingley, Vol. 29, Iss. 57, pp. 42-56, https://doi.org/10.1108/JEFAS-09-2021-0191 This Version is available at: https://hdl.handle.net/10419/289673 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Empirical evidence from the SAARC countries Md Badrul Alam Jahangirnagar University, Savar, Bangladesh Muhammad Tahir COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan, and Norulazidah Omar Ali Universiti Brunei Darussalam, Gadong, Brunei Darussalam Abstract Purpose –This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in the existing empirical literature. Design/methodology/approach –To provide a comprehensive understanding of the relationship between credit risk and FDI inflows, the study incorporates all the eight-member economies of the South Asian Association of Regional Cooperation (SAARC hereafter) and analyzes a panel data set, over the period 2011 to 2019, extracted from the World Development Indicators, using the suitable econometric techniques for the efficient estimations of the specified models. Findings –The results indicate a negative and statistically significant relationship between the credit risk of the banking sectors and FDI inflows. Similarly, market size and inflation rate appear to be the two other main factors behind the increasing FDI inflows in the SAARC member economies. Interestingly, the size of the market became irrelevant in attracting FDI inflows when the Indian economy is excluded from the sample due to its higher economic weight. On the other hand, FDI inflows are not dependent on the level of trade openness, with most of the specifications showing either an insignificant or negative coefficient of the variable. Practical implications –The obtained results are unique and robust to alternative methodologies, and hence, the SAARC economies could consider them as the critical inputs in formulating the appropriate policies on FDI inflows. Originality/value –The findings are unique and original. The authors have established a relationship between credit risk and FDI for the first time in the SAARC context. Keywords FDI, Credit risk, Banking sector, SAARC Paper type Research paper 1. Introduction Foreign direct investment (FDI hereafter) helps multinational entities (MNEs hereafter) to penetrate new markets by opening subsidiaries abroad. The MNEs could be attracted by many favourable factors of the emerging economies, for example, the ever-growing local needs, abundant natural resources and cheap labour force, which are a few of the many (Alam et al., 2023). On the contrary, the recipient countries, owing to their capital deficits, are supposed to welcome inward FDI to benefit from its many positive externalities, including JEFAS 29,57 42 JEL Classification —C51, F37, G21 © Md Badrul Alam, Muhammad Tahir and Norulazidah Omar Ali. Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2077-1886.htm Received 23 September 2021 Revised 14 December 2021 6 August 2022 22 February 2023 Accepted 24 May 2023 Journal of Economics, Finance and Administrative Science Vol. 29 No. 57, 2024 pp. 42-56 Emerald Publishing Limited 2077-1886 DOI 10.1108/JEFAS-09-2021-0191 higher economic growth, new employment generations, technological knowledge and forward and backward integration with the mainstream industries (Dao and Ngo, 2022). Given the merits of FDI, the policymakers of the South Asian Association of Regional Cooperation (SAARC) have made several initiatives, including lower tariffs, a stable foreign exchange rate and a supportive environment for cross-border trade (Shah and Khan, 2016). From Figure 1, we see that, in the case of all the major SAARC countries except Nepal, FDI inflows had been increasing from 1999 to 2007 before they plunged in 2008 due to the financial crisis throughout the world. In the 1990s, member countries of the SAARC initiated trade liberalisation policies by promoting privatisation, adopting favourable export policies and opening investment windows for foreign investors. These deregulation strategies resulted in a surge in FDI inflows into the region (Arif et al., 2020). After recovering from the global financial crisis, the countries resumed attracting inward FDIs of 0.5%–1% of their GDPs from 2011 to 2019. Despite the consistent FDI inflows into the SAARC region over the last decade, the member countries should attract more foreign capital to promote economic growth (Jena and Sethi, 2021). According to the previous studies, market size, trade openness, institutional quality, physical infrastructure and inflation rate of the destination countries appear to be the key forces of FDI inflows (Demirhan and Masca, 2008;Rahman, 2016;Asif and Majid, 2018; Mahmood et al., 2019;Iqbal et al., 2019;Uddin et al., 2019;Tahir and Alam, 2022;Shahbaz et al., 2021). However, the studies should have focused on the necessity of the local financial system, thereby ignoring the factors associated with the corporate financial decisions of foreign subsidiaries. Bilir et al. (2019) confirmed that the availability of external debt funding in the local financial markets significantly influences the MNEs to choose the locations for foreign affiliates and determines the types of affiliate sales as well. The study claims that local financial institutions could play a key role by providing working capital financing, thereby facilitating foreign affiliates to export to other countries. In another study, Aggarwal and Kyaw (2008) argue that market-seeking FDI firms are supposed to take loans from local financial institutions owing to some factors in the host countries, including external credit availability, high tax rate and depreciating local currency. The study further posits that FDI firms tend to follow the capital structures of domestic firms operating in the same industries due to similar cash flow patterns. Hence, the FDI firms competing in the local markets will likely access external credits from domestic financial institutions if the local financial system is efficient. –0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 Bangladesh India Nepal Pakistan Sri Lanka Source(s): Own elaboration Figure 1. Inward FDI as a percentage of GDP Credit risks and FDI 43 To have a paradigm shift from the conventional framework of international economics, Foley and Manova (2015) emphasised the necessity of easy access to finance in multinational operations and all other immovable physical capital factors, which could be decisive in opening a foreign subsidiary. However, the credit risk represented by the number of bad loans could hamper the stability of the banking sectors (Priyadi et al., 2021). In another study, Hanifah (2016) argues that a higher level of non-performing loans (NPLs hereafter) erodes the financial condition of individual banks, being a threat to the overall banking sector. To demonstrate the detrimental effect of credit risk, Isaev and Masih (2017) concluded that it could prohibit commercial banks from performing their crucial financing role, making them less capable of facilitating a nation’s economic growth. Given the paramount importance of credit risk reduction, all three Basel Accords have given special attention to minimising NPLs for the soundness of the banking sector. In addition, Berger et al. (2010) argue that banks could be vulnerable to credit risk owing to their less diversification across different industries and sectors. To reduce credit risk, banks of host countries should finance FDI projects serving new industries with innovative products and services. However, the financial instability caused by the high number of NPLs could harm the inbound FDI projects if they want to fulfil their further financing requirements. As inward FDI seems to rely on the sound financial sectors of the recipients, the SAARC countries have been increasing the level of private sector credits over the last decade. However, extending fresh credits could be seriously interrupted as commercial banks need to make loan loss provisions for the existing NPLs from their earnings (Kellard et al., 2022). More importantly, with the elevated level of credit risk, the banking sectors cannot maintain sustainable growth in private sector credits, hampering the friendly investment environment for FDI projects. According to the existing literature, along with the bank-specific factors, some macroeconomic factors, including negative GDP growth, high inflation and high interest rates, could result in higher unemployment and lower levels of income, thereby reducing the borrowers’ability to repay the interest and principles (Anita et al., 2022). In addition, the member countries of the SAARC suffer from credit risk owing to the poor loan assessment policies, contributing to adverse selection of borrowers (Bhowmik and Sarker, 2021). Therefore, it seems reasonable to assume that the substantial amount of NPLs in the SAARC region could harm the banking sector’s primary financing activity, thereby shrinking its ability to extend credits to the high potential foreign subsidiaries and local firms. Foreign investors are likely to prefer domestic financial markets over the global market in borrowing funds to support their further growth in the host country, thereby avoiding exchange rate risk in case of depreciating local currency (Bilir et al., 2019). Mostly, foreign subsidiaries tend to borrow from commercial banks to finance their exports and other credit sales (Nguyen and Rugman, 2015). The exchange rate of local currency may be volatile in the short run. In that case, foreign subsidiaries should prefer the sources of the host countries over the global heads in case of international trade finance (Nguyen and Almod ovar, 2018). Unlike long-term equity investments in foreign subsidiaries, working capital financing seems to be short-term terms extending up to the credit terms of exports. Therefore, foreign subsidiaries could avoid short-term exchange rate risks by borrowing from the financial sectors of the host countries. Since the prevailing credit risk could erode the availability of trade financing of the foreign subsidiaries in SAARC countries, the study opts for filling up the gap by introducing the NPL ratio, which is the prominent measure of credit risk, as one of the decisive forces of the inward FDI inflows into the economic zone. Hence, the researchers predict that the current study will likely make three novel contributions to the existing literature given the negative effects of prevailing credit risk on the availability of working capital financing. Firstly, the study would JEFAS 29,57 44 show how accumulating NPLs could discourage inward FDI. Secondly, it has incorporated all the member countries of the SAARC, thereby focusing on some countries, namely Afghanistan, Bhutan and the Maldives, which the previous relevant studies should have included. Thirdly, the study would provide policymakers with a way out of the sustainable management of inward FDI with the availability of short-term trade financing by reducing credit risk exposure. The remaining portion consists of the following sections: Section 2 illustrates the actual scenario of the credit risk of the SAARC countries. In contrast, Section 3 demonstrates the literature review reflecting the relevant studies. Section 4 discusses the methodology, including the data collection, variables and analytical procedures. Section 5 presents the results with a comparison and contrast with the relevant previous studies. Section 6 represents the discussion reflecting the implications, shortcomings and future research window. Finally, Section 7 illustrates the conclusion section with a summary of the overall study and the research objectives. 2. The shortage of loanable funds in the SAARC countries Figure 2 demonstrates that the banking sectors of the SAARC zone had been suffering from accumulating a sizeable portion of bad loans, representing the high credit risk of the banking industries of the region over the period 2011 to 2019. Some previous studies found that commercial banks in the SAARC region were less diversified in terms of assets and industries, resulting in an elevated level of concentration (Mani, 2016). Hence, the region’s banking sectors could be vulnerable to external adversities, which could enhance credit risk. According to Figure 2, the median NPL ratio of the eight countries had been demonstrating an uprising trend, with the percentage increasing from 4.31% in 2011 to 8.74% in 2019. On the contrary, for the incoming FDI-to-GDP ratio, the figure shows an opposite trend, with the median value of the ratio declining from 1.22% in 2011 to 0.73% in 2019. The percentage of NPLs is much greater in the case of the SAARC member countries than that of other top FDI-receiving Asian countries, namely China, Singapore and Indonesia, whose median values had been 1.46, 1.06 and 2.29%, respectively, over the period 2011 to 2019. The better management of the credit risk of those countries could be a critical factor in attracting the MNEs to open subsidiaries there. Moreover, the availability of financial support and the risk management skills of the banking industries of the economies could provide foreign investors with additional motivation to invest in those countries. The higher 0 1 2 3 4 5 6 7 8 9 10 2011 2012 2013 2014 2015 2016 2017 2018 2019 in percentage (%) NPL FDI inflows Source(s): Own elaboration Figure 2. Median nonperforming loan (NPL) ratio and FDI inflows Credit risks and FDI 45 amount of NPLs could result in a shortage of loanable funds for promising business projects, which the commercial banks could have extended in financing the further expansion of the foreign subsidiaries operating in the region. Due to the abundance of classified loans, commercial banks cannot issue fresh credits to competing business projects. Therefore, the prevailing poor credit risk management in the SAARC region could be a potential underlying reason, making the foreign investors less motivated to open subsidiaries through FDI in the member countries. 3. Literature review 3.1 Determinants of FDI Given the importance of FDI to the host economies, the researchers attempted to explore the factors inducing foreign capital inflows. Demirhan and Masca (2008) endorsed the significance of openness to trade, inflation and GDP in encouraging FDI inflows. The study revealed that per capita GDP and trade flexibility favourably impact FDI inflows, while inflation adversely affects the same. In the case of the Asia-Pacific Economic Cooperation member countries, Rodr ıguez (2008) reinforced the importance of regional trade agreements in attracting FDIs in those countries. To provide evidence from Latin America, Iqbal et al. (2016) examined ten sample countries in the region. The results of the econometric procedure suggest that market size, trade openness and the size of the labour force are favourably connected with inward FDI. However, the study reported consumer price hikes and exports as insignificant in attracting inward FDI. To reveal the relationship between trade flexibility and FDI inflows, Rahman and Grewal (2017) investigated the member economies of the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) zone. The study indicated a significant causality from imports and exports to FDI inflows, meaning that trade openness will likely impact inward FDI positively. In a recent analysis, Gao et al. (2021) highlighted that the real GDP growth rate, inflation measured by Consumer Price Index (CPI), environmental quality and trade accessibility were favourably connected with FDI in the case of Chinese aggregate provincial data. Another strand of literature emphasised the importance of the recipient country’s financial development on incoming FDI. Hermes and Lensink (2003) outlined the impact of economic growth on domestic firms to absorb the technological diffusion of foreign subsidiaries. In another study, Guru and Yadav (2019) argue that financial results are essential to facilitate the economic growth of Brazil, Russia, India, China, South Africa (BRICS) member economies by channelling funds into the productive sectors. In the case of Organisation for Economic Co-operation and Development (OECD) countries and several non-OECD economies, Alfaro et al. (2004) highlighted the necessity of a vibrant financial system to support the higher level of economic affairs associated with growing inward FDI. The study emphasised that a sound financial plan could help foreign subsidiaries operate efficiently in the host country. To provide evidence from the Chinese economy, Xu (2012) revealed that FDI firms could only work efficiently if commercial banks facilitate them by a considerable amount of credit disbursed in the private sector. The study also suggests that credits issued for financing state-owned projects are negatively associated with China’s economic growth, suggesting the necessity of a sound banking sector represented by a high focus on the private sector’s credit growth. Nguyen et al. (2022) recently showed the value of external funding in boosting the inclination of foreign subsidiaries to export. Since the export-oriented foreign subsidiaries should need additional working capital to finance the cross-border trades, the availability of short-term credits in the host countries could be a location-specific advantage for the subsidiaries. The study argues that the availability of bank loans, especially those extended by the host countries’commercial banks, could positively influence the subsidiaries to JEFAS 29,57 46 engage in exports. In another very recent study, Kellard et al. (2022) revealed the negative impact of banking sector credit risks of both the investor and recipient countries on FDI decisions in the case of Euro Areas from 2009 to 2016. According to the study, MNEs may find it much more difficult to support the trade financing of their international subsidiaries due to the credit risk of their home countries. In contrast, the credit risk of the host country could be a locational disadvantage for foreign subsidiaries. The study also argues that foreign subsidiaries will be less interested in operating in countries with high credit risk. Though the previous studies have highlighted the requirements of the host country’s financial development in facilitating the economic activities of FDI projects, the current study found that the impact of credit risk on inward FDI needs to be more researched. The study, according to the researchers, could add to the body of knowledge by demonstrating the significance of credit risk reduction in bolstering the perception of the influence of commercial banks on FDI inflows. Studies on SAARC economies extensively focused on different macroeconomic determinants of FDI, excluding the credit risk management of the banking sectors of those countries. Iqbal et al. (2018) conducted a study to highlight the relevant factors attracting inward FDIs in India and Sri Lanka. The results of the study suggest that the current account balance is positively associated with inward FDIs in the context of India. In contrast, market size positively affects inward FDIs in Sri Lanka. The study also reveals that inflation and trade openness do not impact FDI inflows for both countries. In another study, Rai and Sharma (2020) confirmed the causal associations of FDI inflows with economic size, trade flexibility and political condition in the SAARC zone. The study argues that expanding markets, trade liberalisation policies and political stability are decisive factors in attracting FDI to the region. In the context of Pakistan, Saleem et al. (2020) investigated the impact of some macroeconomic factors, including trade openness, institutional condition and economic growth, on inbound FDI. The results suggest a positive association of the forces with inbound FDI in the country. In another study, Adhikary (2017) highlighted the importance of trade openness and financial sector development in attracting FDIs to several SAARC countries. In the context of Pakistan, Asif and Majid (2018) investigated the forces affecting FDI inflows. The study outlines the implications of institutional quality and GDP in attracting inward FDI. In the same economy, Uddin et al. (2019) verified different indicators of institutional forces on inbound FDI. The findings suggest that regulation positively influences inward FDIs. Nayyar and Mukherjee (2019) tried in a different study to pinpoint the pertinent external and policy-specific elements impacting FDIs coming into India. The authors find that bank-based financial sector development and trade liberalisation positively impact FDI. In another study, Tahir and Alam (2022) verified the importance of economic growth and trade accessibility in attracting inward FDIs in the context of the SAARC zone. The study also confirmed the positive influence of inflation on FDI inflows in the same sample countries. In a very recent study, Alam et al. (2023) reinforced the long-run connection of FDI inflows with flexible trade policies, economic size and inflation after analysing the time series data of Bangladesh. 3.2 Hypothesis development We have developed the following testable hypotheses: 3.2.1 Credit risk and FDI. H1. Credit risk is negatively associated with FDI inflows. 3.2.2 Macroeconomic factors and FDI. H2. Market size positively influences FDI inflows. H3. Trade openness positively impacts FDI inflows. Credit risks and FDI 47 H4. Moderate inflation positively attracts FDI inflows. 4. Method 4.1 Model We have designed Model 1 to show the potential connection between credit risk and FDI inflows in the presence of control variables. FDIit ¼β0þβ1CRit þβ2MSit þβ3OPENit þβ4INFit þUit (1) In Model 1, a net inflow of FDI is the dependent variable. The primary variable of interest is the credit risk represented by the NPL ratio. We captured market size by taking the logarithm of GDP, the standard practice in the existing literature. Similarly, we have used the trade volume-to-GDP ratio for measuring the influence of trade on FDI inflows. Moreover, we used the consumer price index’s growth to quantify the inflation rate’s impact on FDI inflows. 4.2 Data and variables This study seeks to ascertain how credit risk and FDI inflows are associated while controlling for market size, trade openness and inflation. Although other variables, such as institutional quality, infrastructural development and political stability, could have been used as the control variables, we used only the variables found statistically significant in most of the previous studies. To make the model more economical, we did not incorporate the variables found insignificant in the previous studies. In addition, the excluded variables lack uniform proxy indicators, so we did not incorporate them into the study. In addition, the current study could not include credit risk data for Afghanistan due to the data unavailability for the country. In the case of trade openness, it is considered unbalanced panel data owing to the missing observations of Nepal for a few years. From 2011 to 2019, we gathered data on the eight SAARC nations from the World Development Indicators (WDI). We have provided a list of countries in Appendix,Table A1 and information about variables in Table A2. 4.3 Analytical procedure Several efficient econometric tools are available to estimate models on panel data. The models that are most frequently encountered in earlier empirical investigations are the fixed-effect and random-effect estimators (Tahir and Alam, 2022;Asongu et al., 2018). The researchers presume that the former estimator is appropriate for analysing data, mainly when the chances of serial correlation between the disturbance term and independent variables are high. However, the problem with panel data is that by its design, it cannot capture the impact of time-invariant factors such as dummy variables. On the other hand, the latter estimator works better in dealing with time-invariant factors. However, it is unsuitable when regressors and error terms are correlated. Hausman (1978) put forward a tool that efficiently provides the relevancy of using either a fixedor random-effect estimator for the panel data. In the χ 2 testing, rejecting the null hypothesis would suggest using the fixed-effect estimator. On the other hand, using random effects would be recommended, if researchers could not deny the null hypothesis. In our case, the Hausman test provides considerable evidence about using the fixed-effect model, with the rejection of the null hypothesis (Appendix,Table A3). Moreover, results for the redundant test are provided in Table A4,Appendix. In the next step of the analysis, we used generalised least squares (GLS) for estimating models. Researchers consider the GLS-based estimation as an alternative to fixed effects. They could also use the GLS estimator for testing the sensitivity of the static panel data model JEFAS 29,57 48 (Tahir and Alam, 2022). Moreover, the study also employs the two-stage least squares (TSLS) to address the endogeneity problem. The TSLS is preferred over the Generalized Method of Moments (GMM) estimator as the number of cross sections is relatively tiny. Usually, GMM estimation is suitable in the case of large cross sections and short periods. However, in this study, fewer cross sections were used than the number of time periods. Roodman (2009) asserts that the ratio of instruments to cross sections needs to be less than one. We are unable to employ higher order lagged values of the regressors and dependent variables as the instruments due to the minimal number of cross sections, and hence, we could end up with inadequate instruments. Therefore, the study did not use GMM estimation due to insufficient instruments. 5. Results Results are shown in Table 1 in columns 2 and 3, which provide findings from Pooled Least Squares (PLS) and fixed-effect analyses of the entire sample. On the other hand, the last two columns show results without the Indian economy due to its relatively larger size than the remaining SAARC economies. The PLS results indicate that credit risk could positively and significantly influence FDI inflows. Similarly, economic size and trade accessibility have also accelerated the speed of FDI inflows. Moreover, the inflation rate is also positively but insignificantly related to FDI inflows. In column 3, the fixed-effect estimator shows that credit risk is one of the main problems the SAARC economies face in attracting FDI. The coefficient of the NPLs, the proxy of credit risk, is negative and statistically significant. The fixed-effect findings are consistent with the GLS-based conclusion on the connection between credit risk and FDI inflows. The underlying reason behind this inverse association could be the inefficient allocation of loanable funds to unproductive projects, resulting from the poor credit risk management of the countries’ banking sectors. This misallocation of the funds could further restrict the commercial banks from extending loans to the productive sectors, thereby causing the crowding out of the investable funds for the promising business ventures of the foreign affiliates. Consequently, foreign subsidiaries and affiliates will need more external capital to grow to their optimal levels in the domestic market. Besides, domestic firms producing the necessary inputs for the MNEs cannot thrive due to the external financing crisis. Therefore, poor credit risk management could deter the potential FDI inflows in the region by exerting a detrimental Variables Whole sample Without India PLS Fixed effects PLS Fixed effects CRi;t0.065*** (0.019) 0.030** (0.015) 0.777** (0.372) 0.428** (0.209) MSi;t1.366*** (0.058) 0.695** (0.327) 1.821*** (0.328) 0.824* (0.448) OPENi;t2.402*** (0.271) 0.292 (0.530) 10.244*** (1.096) 0.308 (0.820) INFLi;t0.146 (0.107) 0.118*** (0.028) 0.100 (0.306) 0.090* (0.048) C23.911 (2.384) 4.199 (9.433) 84.732 (11.799) 21.690 (12.286) Diagnostics R 2 : 0.912 R 2 : 0.966 R 2 : 0.734 R 2 : 0.854 R 2 (Adj): 0.907 R 2 (Adj): 0.960 R 2 (Adj): 0.716 R 2 (Adj): 0.820 S.E.R: 0.717 S.E.R: 0.469 S.E.R:1.975 S.E.R: 0.506 F-test: 172.606 F-test: 155.536 F-test: 40.212 F-test: 25.288 P(F-test): 0.000 P(F-test): 0.000 P(F-test): 0.000 P(F-test): 0.000 Note(s): The dependent variable is the natural logarithm of FDI inflows. Values in parentheses represent standard errors. * ¼p<0:1;** ¼p<0:05;*** ¼p<0:01, respectively Source(s): Own elaboration Table 1. Regression results Credit risks and FDI 49 Corresponding author Muhammad Tahir can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Cross-section and random-effect testing Summary Statistics D.F Prob “Cross-section random”(whole sample) 10.853 4 0.0283 “Cross-section random”(without India) 8.486 4 0.0753 Source(s): Authors’own calculation using Eviews 10 Cross-section and period fixed-effect testing Statistics D.F Prob Effects test “Cross-section F”10.873 (7.51) 0.000 “Cross-section χ 2 ”64.840 7 0.000 “Period F”0.291 (8.51) 0.965 “Period χ 2 ”3.177 8 0.922 Without India “Cross-section F 16.397 (6,44) 0.000 “Cross-section χ 2 ”73.984 6 0.000 “Period F”0.758 (8,44) 0.640 “Period χ 2 ”8.142 8 0.419 Source(s): Authors’own calculation using Eviews 10 Variables Definition Source FDIi;t“Foreign direct investment, net inflows (% of GDP)”WDI CRi;t“Bank non-performing loans to total gross loans (%)” MSi;t“GDP (constant 2010 US$)” OPENi;t“Trade (% of GDP)” INFi;t“Inflation, consumer prices (annual %)” Source(s): Own elaboration using data of the World Bank Table A3. Hausman test Table A4. Redundant test Table A2. Variables and data sources JEFAS 29,57 56