Determinants of Foreign Direct Investment in Developing Countries A Focus on Sub Saharan Africa Countries and the Role of Public Governance International Master in Public Finance – 2019/2020 Alessia DE SANTO, Camilla FIORINA Table of contents 1. INTRODUCTION AND LITERATURE REVIEW 3 2. PUBLIC GOVERNANCE AND FDI 5 3. DATA AND METHODOLOGY 6 4. DESCRIPTIVE STATISTICS 9 5. MODEL SPECIFICATION 14 6. RESULTS 16 6.1 Sub Saharan countries 18 7. CONCLUSION 21 APPENDIX A 23 REFERENCES 25 2 1. Introduction and literature review Foreign Direct Investments are a global phenomenon which has displayed remarkably constant growth over the last decades. As reported by Buchanan et al. (2012) the worldwide flow of FDI has grown by 19% between 1996 and 2006, which is more than double than the 8% growth in the international trade of goods and services. Developing countries have been increasing recipient of FDI. While multinational companies benefit from lower production costs, developing countries are eager to attract FDI inflows, as they play an important role in nourishing their growth. First, they represent a vital source of funds which creates job opportunities and increases income. Moreover, productive activity from multinational enterprises often brings technical and managerial expertise, boost R&D activity and facilitate the spread of new technologies, contributing in the reduction of the technological gap between developed and developing economies. This has led most of developing country to undertake steps to attract high FDI inflows. However, developing countries differ widely in their ability to attract FDI. The favorite destination for FDI in the latest decades has been BRIC countries (Brazil, Russia, China and India) which are very dynamic economies and have been displaying very high growth rates since the 1990s. In particular, China has been the major FDI recipient in the last 30 years, followed by India, which according to UNCTAD (2007) is the second most preferred FDI destination. Countries from Sub Saharan Africa, which are traditionally at the bottom of investors’ preferences according to the UNCTAD, have registered a more than six-fold increase in FDI inflow since 1996 (Buchanan et al., 2012). The region’s FDI to GDP ratio has improved significantly in the current century: Azemar and Desbordes (2009) report that, between 1984 and 2004, the median FDI to GDP ratio was less than 1% in the SSA region, compared to almost 2% in South East Asia and Latin America. In contrast, we find that over the period 1999-2014, median FDI values represented around 3,8% of GDP, overcoming the median value for Asian economies – of about 3,2%. Despite this substantial growth, the SSA region still lags behind in terms of quantity of FDI received compared to the rest of the world (Figure 1). While many scholars have been investigating on the basic determinants of FDI, no consensus seems to have been reached, meaning that there is not a universally accepted set of variables which can be considered as the true determinants of FDI, especially for developing countries. As reported by Kumari and Sharma (2017), the existing literature has considered as the determinants of FDI volumes mainly the following set of variables: market size, trade openness, infrastructure, return on investment, real labor cost, human capital, exchange rate, inflation, political risk and government incentives. Most of the studies have found that market size, trade openness and infrastructure play a positive role in attracting flows of FDI (Na and Lightfoot (2006); Quazi (2007); Hoang and Goujon (2014)). Others, as Buchanan et al. (2012), find evidences in support of the centrality of quality of institutions in attracting FDI, concluding that the traditional policy recommendation of “offering the correct macroeconomic environment” would be ineffective without adequate institutional reforms. 3 Figure 1 - net FDI inflow (1998-2014) Source: World Bank Development Indicators. See table 1 in Appendix A for the complete list of countries included in the analysis. Only a limited number of studies has investigated the sources of the high disparity between SSA countries and the rest of the world. Asiedu (2002, 2006) analyzed a panel of 34 SSA countries over the period 1980-200 and confirmed the importance of market size, infrastructure, trade openness and institutional framework. Azemar and Desbordes (2009) grouped all the previously mentioned variables under four indicators of public governance; they perform a panel analysis over a sample of 70 developing countries and conclude that reasons for the gap in FDI inflows between SSA countries and other regions can be mostly find in market size and low human capital accumulation. To be able to draw this conclusion, they first estimate a fixed effect panel model over the entire sample and include a second stage of the analysis where they decompose the sources of the FDI gap between SSA region and the rest of developing countries. Following a similar methodology to that of Azemar and Desbordes (2009), this paper provides an analysis of the main determinants of FDI based on a more recent dataset, covering the period 19982014 for 66 developing countries. While the study is performed over a sample including developing countries from all regions, the focus will be, as in Azemar and Desbordes, on investigating the main factors that drive the disparity of SSA countries in inbound FDI volumes. Because of the important improvement in FDI volumes directed toward SSA countries, we believe that it would be meaningful to perform an analysis of FDI determinants over a more recent period of time, to investigate for possible new dynamics compared to those highlighted in Azemar and Desbordes and the previous literature. The remainder of this paper will be composed of the following sections. Section 2 details the four dimensions of public governance and how they can impact the level of FDI inflow into developing countries. Section 3 presents the database on which the analysis is performed. Section 4 includes a graphical representation of the data and the related descriptive statistics. Section 5 define the econometric model and section 6 report the estimation’s results. Section 7 concludes. 4 2. Public governance and FDI The OECD defines public governance as “the formal and informal arrangements that determine how public decisions are made and how public actions are carried out, from the perspective of maintaining a country’s constitutional values when facing changing problems and environments”. Public governance plays and important role in influencing the investment decisions since it is fundamental in designing the necessary rules that will make a country institution trustworthy. Azemar and Desbordes (2009) distinguish four dimensions of public governance at a macroeconomic level: the degree of democracy, provision of public good, macroeconomic policies and security of property right. The following section is dedicated to detail these different dimensions and discuss their expected effect on FDI level. The first dimension which is taken into account is the degree of democracy, a form of government where, thanks to free and competitive elections and the existence of check and balances mechanisms, governors are elected by citizen and directly accountable to them. Level of democracy is expected to have a positive effect in attracting foreign capital. First, democratic states offer more both political and economic stability – in that, for example, they allow citizens to express dissatisfaction by denying votes to politicians instead than through the use of violence. Moreover, from an ethical point of view, multinational corporations prefer to invest in democratic countries, in order not to compromise their image. These hypotheses are empirically supported. A number of studies (e.g. Busse (2004) and Jakobsen & De Soysa (2006)) demonstrate that, since the beginning of 1980s, countries which ensure higher levels of political and civil freedom to their citizens attract higher FDI flow. Provision of public good is a central dimension as it includes public supply of infrastructure, which previous literature proved to be an important determinant of FDI: better infrastructures provide more investment opportunities for companies and facilitate their operations, making them more eager to invest in a country which offers a dense network of infrastructures and good telephone or internet coverage. Moreover, governments are the main provider of health and education, thus determining the level of human capital of a country. As previously mentioned, higher average human capital often corresponds to larger FDI inflows. Indeed, as higher human capital improves productivity, it lowers unit labor costs, therefore decreasing productions costs for multinationals. Section 1 mentioned some econometric studies which report evidences of a positive and significant effect of infrastructure and human capital in determining FDI volume into developing countries. The ability of governments to maintain a favorable and stable macroeconomic environment - mainly by the use of efficient and sound macroeconomic policies - can play a major role in attracting the attention of foreign investors. Indeed, volatile inflation and exchange rates or high debt to GDP ratios increase uncertainty over the feature and consequently investment cost, making companies desist from investing. Degree of market openness can as well have an impact on inflow of foreign funds, as it facilitates exports and imports for companies that delocalize their production process in different countries – as it is often the case for multinationals. A number of studies have proven empirically that multinational companies favor stable countries to direct their FDI. Among others, Cevis and Camurdan, (2007) find a negative and significant impact of inflation on inbound FDI in developing countries. Asiedu (2002) shows that a positive and significant relation exists between FDI and degree of market openness. 5 The last dimension identified by Azemar and Desbordes (2009) is the capacity of ensuring security of property rights and contracts, included in the long term, which is fundamental in that it guarantees to companies the appropriation of gains on their investments. The main tool through which government can ensure property rights and enforce contracts is the rule of law and its “monopoly on legitimate violence”. However, some external events as wars can compromise this ability, thus being negatively associated with the level of FDI. Some empirical studies, as Globerman and Shapiro (2002), provide evidences that FDI inflows are higher in countries with a strong rule of law. 3. Data and methodology The analysis is performed over 66 developing countries pertaining to the period 1998-2014. The database includes 21 Sub Saharan Africa (SSA) countries, 12 Asian and Pacific countries, 18 Latin American and Caribbean countries (LAC) and 15 other countries1. The complete list of countries can be found in appendix A (table A1). The level of Foreign Direct Investment in each country is proxied by the net FDI inflow obtained from the World Bank Development Indicators2. It is composed by the sum of equity capital, reinvestment of earnings and other capital injected into the domestic economy by foreign investors. Data are converted from current USD to constant USD (base=2010) by multiplying the former by the ratio of constant 2010 USD GDP to current USD GDP. In this way, the evolution of net FDI inflow only reflects changes in the level of FDI and not in the price level. In light of the previously discussed literature, different variables have been considered as possible determinants of net FDI inflow in developing countries. A negative FDI dummy, which takes the value of 1 if the FDI net flow is negative in a certain year, is included to take into account the potential negative signal this may send to investors. Per capita and total GDP (in constant 2010 USD dollars) are included to account for the market size of the economy. The sign of their effects on FDI flow is expected to be positive, since a greater market size offers more investment and operating opportunities. Data are taken from the World Bank Development Indicators. As mentioned in the first section of this work, this work considers four dimensions of Public Governance as determinants of FDI inflow: degree of democracy, public good provision, macroeconomic stability and security of property rights. The first dimension, degree of democracy, is proxied by 3 variables: - The degree of Civil Liberties (extracted from the Freedom House indicator) which consists on scores given to countries on the basis of Freedom of Expression and Belief, Associational and Organizational Rights, Rule of Law and Personal Autonomy and Individual Rights. Countries Countries are grouped by geographical region according to the World Bank classification. Asian countries include country from South Asia and East Asia and Pacific regions. Group “other” includes countries from Europe and Central Asia, Middle East and North Africa. 2 https://databank.worldbank.org/source/world-development-indicators 1 6 - - obtaining a score of 1 or 2 are considered free; 3,4 or 5 partly free; from 6 to 7 not free. Data are available on the Freedom House website3. The POLITY2 index, which defines on a scale from -10 (hereditary monarchy) to 10 (complete democracy) the concomitant democracy and autocracy qualities of national governing institutions. Data are obtained from the Polity™ IV project database4, developed by the Center for Systematic Peace The POLCONIII index measures the degree of political constraint by estimating the feasibility of policy change. The variable is constructed by Henisz (2002) by taking into account the number of independent branches of government with veto power over policy change, and the extent of the political alignment of the members of the executive and legislative branches. An updated version of the index is available on the Wharton University of Pennsylvania website5. In order to avoid possible multicollinearity between these 3 variables, and to account for possible nonlinear effects of democracy on level of FDI, 6 dummies variables are created - following Azemar and Desbordes (2009). A medium civil liberty dummy takes value 1 when Civil Liberties score is included between 3 and 5, while when it is equal to 1 or 2, a high civil liberty dummy takes value 1. The same method applies for POLITY2 and POLCONIII indexes. They are rescaled from 1 to 7 as the degree of Civil Liberties, so that when their values are greater than 3 and lower than 66, a medium electoral democracy dummy and a medium political constraint dummy – respectively – take value of 1. When their values are included between 6 and 7, a high electoral democracy dummy and a high political constraint dummy, respectively, take value of 1. To account for the role of public good provision in attracting FDI, proxies for health, education and level of infrastructure are included in the database. Data on life expectancy at birth – as a proxy for health – are obtained from the World Bank Indicators, where it is measured as the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Level of education is proxied by average years of schooling for people aged 25 or older, taken from the Human Development Reports of the United Nations Development Programme, which is able to provide a wide range of observations, gathering values from Barro and Lee (2018), the UNESCO Institute for Statistics (2019) and ICF Macro Demographic and Health Surveys, UNICEF Multiple Indicator Cluster Surveys and OECD (2018). Finally, because of unavailability of data on most measures of infrastructure for SSA countries – as per capita electrical consumption or per capita stock of fixed telephone mainlines – the number of fixed telephone subscriptions is used as a proxy for the level of infrastructure. Data are available on the World Bank Development Indicators. Nevertheless, this is an imperfect measure of the level of infrastructure for the last years of the sample, since the spread of mobile phones as the main communication tool has significantly reduced their use, including in developing countries. However, as displayed in the World Bank, the average number of fixed telephone users worldwide has been growing until 2010, meaning that an hypothetical problem exists only for the last 4 years of study; moreover, it could be suggested that https://freedomhouse.org/report/freedom-world/2020/leaderless-struggle-democracy (Country and Territory Ratings and Statuses, 1973-2020) 4 https://www.systemicpeace.org/inscrdata.html 5 https://mgmt.wharton.upenn.edu/faculty/heniszpolcon/polcondataset/ (2017 Data Release) 6 the POLITY2 and the POLCONIII indexes are continuous, while the Civil Liberty score can only take integer values. 3 7 in developing countries the spread of mobile phones to the detriment of landline phones has occurred somewhat later than the world average, further reducing the margin of error. Macroeconomic stability is measured by the level of Inflation, Foreign Debt Position and Openness of the economy. As Azemar (2009) reports, stable and low inflation can be considered as a measure of the capacity of governments to manage the economy, thus creating a stable and safe economic environment, which attracts investments. Data on inflation, measured as the annual percentage change in average Consumer Prices Index, come from the International Monetary Fund7. The same holds for Foreign Debt position, thus a low debt/GNI ratio is expected to have a positive impact on FDI inflow. Data on external debt stocks as a % of GNI are available on the World Bank Development Indicators. Trade openness, intended as the importance that nondomestic transaction cover in a country’s economy, is expected to provide more investment opportunities to MNEs, thus to have a positive impact on FDI inflow. As in previous studies (Kumary and Sharma, 2017) the standard trade "#$ openness ratio ! %&' ( is used as a proxy for the degree of economy’s openness. In conclusion, security of property rights is assumed to depend on the rule of law of countries and the probability of conflict. The strengthen of the rule of law is measured by the ICRG law and order index, which measures on a scale from 1 (low) to 6 (high) the strength and impartiality of the legal system - the “Law” component – and the popular observance of the law – the “Order” component (The PRS group). Data on ICRG law and order index are available on Wharton University of Pennsylvania website8. Moreover, the probability of occurrence of a conflict can jeopardize security of property rights. To account for this eventuality, two dummies are created, according to the UCDP/PRIO9 definition of conflict intensity. The dummy minor conflict takes value 1 if a conflict involving between 25 and 999 battle-related deaths has occurred in a given year, while the dummy war takes value of 1 if at least 1,000 battle-related deaths are recorded in a given year. Data are available on the UCDP Dataset Download Center10. When possible, data are taken in the natural logarithmic form. This allows for an easier interpretation of the coefficients and for a linearization of the observations, thus reducing the impact of extreme values. In addition, the explanatory variables are lagged by one year with respect to the dependent variable, net FDI inflow11. This solution, previously used by Azemar and Desbordes (2009), allows to reduce possible endogeneity bias and makes the model more realistic in that it takes into account the fact that companies decide where to direct their investments based on past information. The database includes some missing values (see Figure 2). However, since they represent a very small percentage of total observation and are randomly distributed in the sample, they are labelled as “NA” and kept in the database, which will therefore result in an unbalanced one. https://www.imf.org/external/datamapper/PCPIPCH@WEO/OEMDC/ADVEC/WEOWORLD https://mgmt.wharton.upenn.edu/faculty/heniszpolcon/polcondataset/ (2017 Data Release) 9 Uppsala Conflict Data Program (Uppsala University) and International Peace Research Institute (Oslo) 10 https://ucdp.uu.se/downloads/index.html#armedconflict 11 This is performed directly in the database used for the econometric estimation (see Appendix B2). Observations of the dependent variable are taken from 1999 to 2014, while observations of the explanatory variable cover the period 1998-2013. 7 8 8 Missing values in the net FDI inflows observations are due to the logarithm transformation12: the negative values are excluded in order to obtain the logarithm of net FDI inflows. The presence of negative FDI inflow is taken into account anyway by the negative FDI dummy. Figure 2 – Distribution of the missing values in the database 4. Descriptive statistics Before proceeding with the econometric analysis of the relationship between the determinants described above and the level of net FDI inflow, a descriptive overview of the data will be given in this section to provide a more complete illustration of the database. Since the focus of the analysis is to investigate on the FDI inflows weaknesses in Sub Saharan Countries with respect to the rest of developing countries, the descriptive statistics and the graphic analysis will be mainly performed over the groups of countries that have been specified at the beginning of section 3. We begin with a graphic visualization of variables which allow us to understand both their evolution overtime and their spread. The use of a boxplot helps to visualize the distribution of the dependent variable and to check for the presence of outliers. Figure 3 displays boxplots for the dispersion of net FDI inflows in each of the four regions. Observations seem to be symmetrically distributed around the mean, although LAC countries present a slight left skewed distribution while SSA countries are slightly skewed to the right. The region with the highest number of outliers is Sub Saharan Africa, where 4 countries display much lower FDI volumes than the others – which is coherent with the skewedness of the 12 Values of net FDI inflow for Myanmar (1998,1999) are NA because of missing values in the deflator for converting current prices in constant prices. 9 distribution. Dispersion of data is quite homogeneous in the four groups, with countries from the AP and LAC region displaying a slightly higher variability. Figure 3 - Boxplots, net FDI inflow by region Panel A of figure 4 plots the evolution overtime of the FDI inflows in all countries included in the sample. The logarithm form allows to smooth extreme observations and highlights an average growth in FDI level in all regions. Moreover, the regional grouping allows to notice that FDI inflows into SSA countries are much lower with respect to the other regions of the world, which, on average, present more similar level of FDI. Panel B takes a closer look to average trends for each region. The growth trend was more stable in the first half of the period considered, while it slowed down starting from 2007, in conjunction with the global financial crises. Even if disparity between SSA countries and the other regions remains significant, it is possible to notice a reduction of this gap during the whole study period, in particular with respect to LAC countries. Sub Saharan countries experienced a quite important growth in the inflow of FDI in the sixteen years considered; moreover, while other regions reported sharp declines in FDI levels between 2007 and 2010, the amount of FDI injected into this region continued to grow, which further contributed to cover the distance. Table 1 reports the regional averages of all variables over the period 1998-2014. It allows to get a general idea of the existing equilibriums concerning the determinants of FDI under consideration. The first thing which can be noticed is that SSA countries rank last in the majority of fields. In particular, an important disparity with respect to the other developing countries, can be found in the size of markets, as proxied by GDP and GDP pc. Moreover, SSA countries record the worst performance also in the dimensions related with the provision of public goods, namely life expectancy, average years of schooling and fixed telephone subscribers. The latter, in particular, which proxied the level of infrastructure, is much lower than the average of the other regions. 10 Figure 4 - Net FDI inflows Panel A –net FDI inflows trend in each country, 1998-2014 Panel B – Average net FDI inflow trend by region, 1998-2014 11 Table 1 - Mean of all variables, by region, 1998-2014 FDI (net inflow) Presence of negative FDI GDP GDP pc Political Constraints Civil Rights Political Rights Life Expectancy Av. Years of Schooling Fixed Telephone Subscribers Inflation Foreign Debt Market Openness Law and order index Presence of Minor Conflict Presence of War SSA AP LAC Other 19.72 0.03 23.49 6.95 0.25 3.97 2.33 4.01 1.39 11.8 8.12 3.68 -0.55 1.04 0.17 0.05 21.67 0.04 25.58 7.37 0.29 4.13 2.67 4.22 1.79 14.93 7.75 3.58 -0.45 1.13 0.39 0.07 21.03 0.03 24.62 8.28 0.36 3.1 7.17 4.27 1.91 14.04 8.71 3.75 -0.54 0.84 0.06 0.02 21.5 0 24.79 8.27 0.26 4.12 1.61 4.26 2.17 14.73 11.86 3.83 -0.23 1.34 0.13 0.02 *All variables are in log, with the exception for Inflation, Political Constraints, Civil Rights and Political rights. Concerning the degree of democracy, SSA region performs slightly better. Although the political constraints on governors and the political rights of citizens are low in SSA countries, for what concerns the civil liberties of their citizens they outperform Asian countries, as well as countries which are included in the “Other” category (which covers eastern Europe, north Africa and middle East). The region which performs better in terms of degree of democracy is Latin America and the Caribbean, which ranks first in all three indexes. Asian economies offer, on average, the most favorable economic environment. SSA economies display the lowest degree of market openness, while the highest average inflation and foreign debt is recorded in developing countries from eastern Europe, north Africa and middle east. Finally, the highest incidence of wars and minor conflicts can be found in Asia and the Pacific, followed by SSA countries Additional useful information can be obtained by analyzing the variability of data. Total variance in panel data can be decomposed in two different kind of variability: between variability – that is the sample variance between countries – and within variability – which instead is intended as the sample variance within countries, along the years of study. Since the dataset for this study is unbalanced, the computation of the share of within and between variability of variables must performed over a balanced subsample. For this purpose, 11 countries have been excluded13. Since the majority of missing values come from the fact that negative FDI values have been excluded in order to transform the net inflow of FDI in logarithm, it is preferable to measure the variability of the net FDI inflow in level. This translates in a slightly higher percentage of within variability, as the logarithmic form smooth fluctuations of variables over time. 13 Argentina, Armenia, Azerbaijan, Belarus, Gabon, Guinea Bissau, Guyana, Madagascar, Myanmar, Nicaragua and Vietnam. 12 Therefore, we believe that it is more methodologically coherent to compute the variability composition using the data in level for all variables. To present a more complete analysis, table 2A in the appendix reports the composition of variance of the logarithmic form of explanatory variables. As depicted in Table 2, the dependent variable and the majority of the other variables displays a higher degree of between variability. These variables include GDP, GDP per capita, life expectancy, average years of schooling, fixed telephone subscribers and the degree of market openness. Only inflation and the level of foreign debt present a higher degree of within variability. The risk is that those variables will not have any significant impact in explaining the variation of FDI inflows. However, net inflow of FDI still presents a degree of within variability (equal to 15% of total variability), so it is possible that this fraction of variability is explained by the evolution in price levels and the amount of foreign debt. Table 2 – Total, Between and Within Variability Net FDI Inflow GDP GDP pc Life Expectancy Av. Years of Schooling Fixed Telephone Subscribers Inflation Foreign Debt Trade Openness Law and Order index Total Variability % Between % Within 5.60987899379975e+23 5.18954593097873e+26 8817281380.51 69249.51 6380.26 1292587940688366080 205171.9 1348247.15 74.69 966.49 0.85 0.85 0.94 0.91 0.95 0.9 0.29 0.41 0.84 0.84 0.15 0.15 0.06 0.09 0.05 0.1 0.71 0.59 0.16 0.16 Before proceeding with the choice of the econometric model, it is useful to check for possible multicollinearity issues in the correlation matrix (Figure 5). In general, it does not report very high correlation across the explanatory variables, with the exception of telephone subscriptions, which is very highly correlated with GDP. This could rise some problems when performing the estimation for their coefficients. However, due to the impossibility to substitute the proxy for infrastructure, and to the fundamental role that GDP has in explaining FDI patterns, none of the variables could be dropped. 13 Figure 5 - Correlation Matrix 5. Model Specification In order to build a suitable model for the purpose of our analysis, we started from the following unobserved effects model: 𝑦*+ = 𝜇* + 𝛽𝑥*+ + 𝜖*+ Even if this kind of model assumes that 𝛽 coefficients are the same across all units, it differs from the pooling model in that it allows for a certain degree of heterogeneity. This is made possible by assuming that the standard error term 𝜀*+ has two components: 𝜇* which is specific to each unit and 𝜖*+ , called “idiosyncratic” error term, which is assumed to be uncorrelated from the set of explanatory variables, as the standard error term. Fixed effect models assume that the unit specific error terms is correlated with the regressors. To be able to perform an OLS estimation on this regression, the error is treated as standard set of n parameters to be estimated, which translates in a model where the 𝛼 coefficient is specific to each country and is represented by 𝜇* . On the other hand, random effects model assume that the unit specific error term is uncorrelated with the set of regressor as well as the standard error terms 𝜀*+ Fixed effects model seems suitable for the purpose of our analysis, since it is performed over a sample including exclusively developing country. Since units are not randomly selected, it is very likely that the individual component of the error term is not independent from the regressors. Some works from the previous literature (e.g. Azemar and Desbordes (2009)) support this hypothesis, by making use of fixed effect model for a similar kind of analysis. 14 Once the set entire of regressor is included, the following model is obtained: 𝐿𝑛(𝐹𝐷𝐼<=+ )+* = 𝜇* + 𝛽? 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝐹𝐷𝐼*+G? + 𝛽H ln (𝐺𝐷𝑃)+G? + 𝛽M ln (𝐷𝑒𝑔𝑟𝑒𝑒 𝑜𝑓 𝑑𝑒𝑚𝑜𝑐𝑟𝑎𝑐𝑦)+G? * * + 𝛽T ln(𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛)+G? + 𝛽W ln(𝐿𝑖𝑓𝑒 𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦)+G? * * + 𝛽Y ln(𝑇𝑒𝑙𝑒𝑝ℎ𝑜𝑛𝑒 𝑠𝑢𝑏𝑠𝑐𝑟𝑖𝑏𝑒𝑟𝑠)+G? + 𝛽_ (𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛)+G? * * + 𝛽` ln(𝐹𝑜𝑟𝑒𝑖𝑔𝑛 𝐷𝑒𝑏𝑡)+G? + 𝛽a ln(𝑇𝑟𝑎𝑑𝑒 𝑂𝑝𝑒𝑛𝑛𝑒𝑠)+G? + 𝛽?c ln(𝐼𝑐𝑟𝑔)+G? + 𝜀*+ * * * As mentioned in section 3, the degree of democracy is captured through six dummies: two of them account for the degree of civil liberties (medium and high civil liberties), other two represent the presence of a more or less democratic regime (medium and high political rights) and the last two indicate the feasibility of policy change (medium or high political constraints). As already explained, variables have been lagged by one year and when possible, variables have been taken in the natural logarithm form. An individual (country) specific fixed effect is chosen to account for heterogeneity between developing countries; this enables to account for factors specific to each country that can be considered constant over time and disentangle their effects from the effect of time varying factors. For developing countries, there might be some time constant factors which can be correlated with other explanatory variables and could affect the estimation of the FDI determinants. For example, many developing countries are rich of natural resources, which might have an important role in attracting FDI and are very likely to be constant over 16 years covered by our study. To confirm the choice of the individual fixed effect model we performed both the Fisher and the Hausman test. The Fisher test confirmed that the fixed model was to be preferred to the simple pooled regression due to the presence of significant individual effects. The Hausman test allowed us to compare the Fixed effect model and the Random effect. Again, the result was in favor of the former regression. Table 3 reports the results from the two tests Table 3 – Results of Fisher and Hausman test F test for individual effects data: lnfdinet ~ negfdi + lngdp + lnlifexp + lneduc + lnteleph + inflation + ... F = 10.704, df1 = 64, df2 = 933, p-value < 2.2e-16 alternative hypothesis: significant effects Hausman Test data: lnfdinet ~ negfdi + lngdp + lngdppc + lnlifexp + lneduc + lnteleph + chisq = 45.25, df = 17, p-value = 0.0002235 alternative hypothesis: one model is inconsistent ... 15 6. Results Results from implementing the individual fixed effect model are showed in Table 4. Column (1) and (2) show the differences in terms of significance of the coefficients when using normal standard errors or robust standard errors. As it is possible to see in the first column, our results are partly in line with the traditional literature: trade openness and market size are the main determinants of the flows of FDI, whereas the proxy for infrastructure appear to be only mildly relevant. In accordance with the more recent researches, other factors result relevant. The level of public good provision is highly significant and have a positive sign in both life expectancy and education. The political structure result to have an impact only when the political rights are classified as “high”. Finally, civil freedom of citizens seems to be a relevant element: both medium and civil rights resulted significant. When analyzing the results with coefficients robust to heteroskedasticity and serial correlation, many variables lose their significance. The most traditional determinants however, namely GDP and trade openness, remain significant, at 1 and 0.1 per cent respectively. The fact that a larger market size should attract FDI flows is quite straightforward. First of all, the GDP is an indicator of the degree of development, therefore, since investors will invest in a country where the perceived profitability of their projects is secured, they will take into account the signal transmitted by GDP in this regard. Moreover, a larger market size implies, usually, lower distribution cost (if of course the production and distribution facilities are located where the majority of the customers will be located) and a greater chance of obtaining better and more specialized inputs. Trade openness covers a crucial role for developing countries since most of them do not have access to international capital markets. Due to this issue they often implement several trade policies to ease the inflows of capital through FDI and the effect of these policies might be captured by the significance of this indicator. However, as suggested by various previous works (Rodriguez and Rodrik, 2000; Asiedu,2006) the interpretation of this coefficient should be done with care since policymakers do not directly control the volume of trade; therefore - when the objective of the analysis is policy prescription - different measure of openness, that can be considered as directly influenced by politicians, might result more useful. The dummy that indicate the presence of negative FDI stays significant also in the heteroskedastic robust specification. Past studies investigated the phenomenon of agglomeration in foreign investments. Wheeler and Mody (1992), found that US investments into a country are strongly conditioned by existing stock of FDI in that country. This could be explained by the presence agglomeration benefits: some industries rely heavily on the proximity of the suppliers for intermediate inputs, therefore the presence of an existing network could create a certain persistence in the investors preferences. Moreover, the possibility of a learning experience is also a plausible explanation. Kogut and Chang (1996) argue that obtaining a greater familiarity with operating in a country and a better knowledge of the specific opportunities of a region might create commitment in the investors. Both these explanation lead to the conclusion that the presence of negative inflows of FDI can have a negative impact on future investment, which is confirmed by our results. 16 Also, the strength of the law system is proven to play a positive role in influencing FDI flows in developing countries. As mentioned before, this is possibly due to the fact that foreign investors demand the protection of their property rights in order to secure the returns from their investment. Low security in this field will reduce the feasibility of economic activity and the scope for market transactions. In addition, Table 4 shows a positive and significant impact on the dependent variable of higher level of human rights and citizens freedom. This is in line with the part of the literature that proved that countries which ensure higher levels of political and civil freedom to their citizens attract higher FDI flows. In contrast, and as expected, the presence of both minor conflicts and wars (as defined in section 3) has a negative effect on FDI, even if it is not strongly significant. A higher incidence of war and conflicts sends a strong signal to investors regarding the instability of the country, in fact it is often linked with poor governance and a weak protection of property right. Surprisingly, when using heteroskedastic robust errors, the three dimensions which proxy provision of public good – life expectancy, average years of schooling and fixed telephone subscribers - lose their significance. At odd with the literature, the infrastructure system does not appear to matter. However this could be explained by three different reasoning. First, the limited time period that has been taken into account could affect the estimation since infrastructures seems to be a stable variable over time. Second, the measure of infrastructure that was chosen (Fixed Telephone Subscribers) resulted highly correlated with the GDP since usually larger and richer countries are characterized by more physical infrastructure. In order to understand this issue, we implemented our model in the absence of GDP and our infrastructure proxy resulted statistically significant and positive14. Lastly, as already mentioned in Section 3, this measure has some flaws since the spread of mobile phones as the main communication tool has significantly reduced the use of fixed ones including in developing countries; according to the World Bank, the average number of fixed telephone users worldwide has been growing until 2010. The use of alternative variables would have been useful, for example the number of telephone lines instead of the number of subscribers would probably be a better source of information but several problems were encountered when trying to gather these data. The coefficients in Table 4 are the expression of the direct effects on FDI of the chosen variables. However, it is meaningful to underline that they leave out the interaction of these various factors, which is an important element to take into account, especially for countries which want to take steps to attract more FDI. An example of this interplay could be the link between the level of democracy of a country and the provision of public goods, or the one between conflicts and the quality of infrastructures. Indeed, it could be possible that territorial conflicts destroy physical infrastructure and also that war contribute to the deterioration of public governance. In this regards, the main risk is that some of the effects of the FDI determinants might be underestimated. 14 Results of this specification are reported in Appendix A, table A3. 17 Table 4 - Determinants of FDI in developing countries Dependent variable: Net FDI inflow Negative FDI GDP Life expectancy Education Fixed telephone subscriptions Inflation Foreign Debt (% GNI) Trade openness ICRG law and order Medcivrights Highcivrights Medpolconst Highpolconst Medpolrights Highpolrights Minor conflict War Observations R2 Adjusted R2 F Statistic (df = 17; 933) Signif. codes default (1) robust (2) -0.856*** (0.179) 0.547** (0.170) 2.837** (1.016) 0.581 (0.421) 0.155 . (0.082) -0.002 (0.002) -0.141* (0.062) 0.635*** (0.142) 0.365* (0.149) 0.370* (0.175) 0.435* (0.216) -0.026 (0.070) 0.248 (0.156) 0.074 (0.123) 0.327* (0.163) -0.229* (0.110) -0.304 . (0.168) -0.856* (0.380) 0.547* (0.258) 2.837 (2.127) 0.581 (0.595) 0.155 (0.130) -0.002 (0.003) -0.141 (0.108) 0.635** (0.200) 0.365* (0.159) 0.370* (0.148) 0.435* (0.207) -0.026 (0.119) 0.248 (0.189) 0.074 (0.179) 0.327 (0.215) -0.229 . (0.130) -0.30 . (0.171) 1,016 0.302 0.241 23.800** 1,016 0.302 0.241 23.800** 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘1 6.1 Sub Saharan countries Previous section presented an analysis of the general determinants of FDI in developing country. However, the previous econometric model does not give any information about the sources of disparity in FDI levels in SSA countries. This section enriches our analysis by filling this gap. As suggested by other studies (Azemar and Desbordes (2009); Fernandez-Arias and Montiel, 2001), the specificity of the determinants of FDI flows in sub-Saharan countries can be examined with an inter-regional study. 18 Differences among various regions considered can be in fact analyzed using the following decomposition15: ddddd ddddd 𝑙 ij 𝑛𝐹𝐷𝐼 −j 𝑙𝑛𝐹𝐷𝐼 eef jjj jkj jj jlh = fm+nop q*rr=s=<m= wv x 𝛽t𝑙𝑛𝑋 −j𝑙𝑛𝑋 ij jjdddddd jeef jkj jjjl y*z=G{os|*<} q*rr=s=<m= + dddddd t𝐶 − 𝐶wv x eef ij jkjjl e+snm+nsop q*rr=s=<m= + t𝜀 ddddd −jl 𝜀wx eef v ij jkj •<=€•po*<=q q*rr=s=<m= The “actual difference” term is the simple average difference in volume of net FDI in region j (which corresponds alternately to all non-SSA developing countries) and in SSA, in the period considered (1999-2014). On the right-hand side of the equation, and (i = country i) are respectively the coefficients and the country specific fixed effects estimated in the previous model (taking into account all the four groups) and are the time-varying FDI determinants. The last term corresponds to differences in aggregate FDI residuals and can be interpreted as a measure of the performance of the benchmark region, that is, whether it has been higher or lower than expected. dddddeef − 𝑙𝑛𝐹𝐷𝐼 dddddh . To obtain the share of each components we divided each term by 𝑙𝑛𝐹𝐷𝐼 Results of the decomposition are displayed in Table 5. A significant part of the inter-regional gap seems to be explained by the market size, which points out to the big differences, in terms of economic growth, that still exists between SSA and other developing countries. It is noteworthy, indeed, that SSA countries are reporting major delays in terms of industrial revolution, which instead, has interested many other developing countries, and provides many investment opportunities besides the mere exploitation of natural resources. In line with the literature the good provision of public goods (Education, Health and Infrastructure) seems to explain a consistent part – more than half – of the unattractiveness of SSA countries for foreign investors with respect to all the other groups considered. Indeed, SSA countries still lag well behind the other developing countries in both these 3 indicators, as confirmed by the descriptive statistics in section 4. In particular, an higher incidence of infectious disease as HIV and Malaria, has a considerable negative impact on citizens of SSA countries, which, together with modest educational attainment, lowers their productivity importantly. The differences in the degree of democracy and in macroeconomic stability have instead a little role according to our computations: on average they account respectively for less than 5% and less than 10% of the difference in FDI levels. This is coherent with findings from section 4: SSA countries’ performance in those field is more or less in line with that of the other developing countries. In addition, also the security of property rights explains only a very small part of the divergencies. Finally, the contribution of structural factors (due to the country specifics characteristics) seems to be crucial, particularly with respect to LAC countries. On average, country specific effects seem to explain at least half of the attractiveness of SSA countries for foreign investors. As already mentioned, this could be attributed to the fact that SSA region is one of the richest areas of the world in term of natural resources, especially diamonds, oil, natural gas and metals. Obviously, the presence of these important resources represents a major investment opportunity, especially for developed country that lack instead from this kind of richness. 15 This decomposition has been performed using both R and Excel. Computation are reported in Appendix D. 19 However, when studying these results, it clearly appears that the unexplained portion is quite important and therefore further researches might be necessary, for example by including more precise proxies for some indicators, as the level of infrastructure or the degree of market openness. An interesting extension could be to include tax rates on the activity of foreign companies operating on the national territory. Indeed, as reported by the OECD (2008)16 volumes of FDI are sensitive to taxation and many countries, especially developing ones, are trying to offer competitive tax environments in the attempt to increase the FDI inflow toward their economies. Unfortunately, due to unavailability of data, we weren’t able to include level of taxation among the regressors. Table 5 - Inter-regional differences in FDI volume, relative contribution of single determinants (%) 16 OECD Observer , 2008. “Tax Effects on Foreign Direct Investment” 20 7. Conclusion This work aimed at understanding the determinants of the location of FDI in developing countries and, in particular, to investigate the source of disparity in the ability of attracting FDI that concerns countries located in Sub Saharan regions. For this purpose, it uses an unbalanced panel dataset including 66 developing countries and covering the period 1998-2014 The results of our estimation confirm that the size of the markets plays a major role in attracting FDI inflow. This confirm the idea that a greater market size offers more investment and operating opportunities. Moreover, the presence of negative FDI in the previous year seems to send a significant negative signal to investor, which prefer to direct their capital elsewhere. Following the work of Azemar and Desbordes (2009) and Asiedu (2006), we investigated the role of public governance in FDI dynamics. This has been done analyzing several dimensions of the governance of a country: the degree of democracy, the provision of public goods, the macroeconomic stability and the possibility of ensuring protection of property rights. Among these dimensions, the degree of democracy and in particular of civil liberties (the guarantee of freedom of expression and human and civil rights) appears to promote FDI in all developing counties. Also, the strengthen of the rule of law, which represent the impartiality of the legal system and its observance, plays an important role in our estimations, confirming previous literature results (among others Globerman and Shapiro, 2002). In addition, low frequency of conflict, which together with rule of law enhance security of property right, contributed in attracting foreign capital. Surprisingly, once robust errors are taken into account, the three dimensions of public good provision, namely life expectancy, average years of schooling and fixed telephone subscribers, lose their significance. However, we believe that this issue can be related to the specification of our model rather than to an actual independence between those variable and level of FDI. In particular, as already mentioned, the use of fixed telephone subscribers as a proxy for infrastructure is quite imprecise and leads to some problems at the level of econometric estimation. Importance of public goods provision is indeed confirmed in the second step of the analysis. To fully understand the relatively poor FDI performance of SSA countries we implemented an inter-regional decomposition that allowed us to examine the relative contribution of each determinant to the FDI gap between SSA e non-SSA regions. What emerged is that the differences can largely be explained by insufficient provision of public goods in terms of health and education, as well as market size. However, these deficiencies can be also indirectly related to country specific conditions, as natural resources provision, that can promote but also undermine the choice of FDI investors towards SSA countries. On the one hand, richness in terms of resources can be an important element in favor of investment from the perspective of MNEs. On the other, the greater is the role for these exogenous factors in attracting FDI, the smaller results the share coming from policy efforts undertaken by the country. The empirical work for understanding the determinants of FDI in developing countries and in particular in Africa should be continued to better disentangle country specific effects from other influences. This could be crucial for the implementation of adequate policies in developing countries that aim at a stronger integration in the international market. Moreover, this kind of analysis might not give a complete overview of the most important determinants of FDI since the interaction between the various factors is left out and could bring to an underestimation of the impact of various determinants. 21 Despite its limitation, some useful policy recommendation can be draw from this study. Developing countries should focus their policy attention in developing their market size and making regulations more international trade friendly. Moreover, reinforcing security of property rights might be very useful especially for LAC countries which lag well behind the average of other regions in this field. Concerning SSA countries, although they are importantly improving their ability in attracting foreign capital, they should focus their attention on expanding their markets to open new opportunities to foreign investors, and improve the provision of public good, especially for what concerns health and infrastructures. 22 Appendix A Table A1 - Countries in the sample, by region SSA AP LAC Other Botswana Bangladesh Argentina Albania Cameroon China Bolivia Armenia Congo, Rep. India Brazil Azerbaijan Ethiopia Indonesia Colombia Belarus Gabon Mongolia Costa Rica Bulgaria Ghana Myanmar Dominican Republic Egypt, Arab Rep. Guinea Pakistan Ecuador Jordan Kenya Papua New Guinea El Salvador Kazakhstan Philippines Guatemala Moldova Malawi Sri Lanka Guyana Morocco Mali Thailand Haiti Romania Niger Vietnam Honduras Russian Federation Nigeria Jamaica Tunisia Senegal Mexico Turkey Sierra Leone Nicaragua Ukraine South Africa Paraguay Madagascar Sudan Peru Tanzania Venezuela, RB Togo Uganda Zambia Table A2 - Variability of variables in their logarithmic form Ln GDP Ln GDP pc Ln Life Expectancy Ln Av. Years of Schooling Ln Fixed Telephone Subscribers Ln Foreign Debt Ln Trade Openness Ln Law and Order index Total Variability % Between % Within 2894.19 949.96 18.42 226.55 4278.76 415.19 164.93 119.11 0.98 0.97 0.89 0.96 0.97 0.48 0.82 0.79 0.02 0.03 0.11 0.04 0.03 0.52 0.18 0.21 23 Table A3 - Determinants of FDI in developing country excluding GDP Dependent variable: Net FDI Inflow Negative FDI Life expectancy Education Fixed telephone subscriptions Inflation Foreign Debt (% GNI) Trade openness ICRG law and order Medcivrights Highcivrights Medpolconst Highpolconst Medpolrights Highpolrights Minor conflict War Observations R2 Adjusted R2 F Statistic (df = 16; 934) default (1) robust (2) -0.862*** (0.179) 4.109*** (0.941) 1.105** (0.390) 0.211** (0.080) -0.003 (0.002) -0.149* (0.062) 0.614*** (0.142) 0.364* (0.149) 0.385* (0.176) 0.497* (0.217) -0.035 (0.071) 0.273. (0.157) 0.045 (0.124) 0.313. (0.163) -0.207. (0.110) -0.324. (0.169) -0.862* (0.376) 4.109* (1.937) 1.105* (0.560) 0.211. (0.119) -0.003 (0.003) -0.149 (0.112) 0.614** (0.205) 0.364* (0.157) 0.385** (0.149) 0.497* (0.216) -0.035 (0.124) 0.273 (0.191) 0.045 (0.189) 0.313 (0.226) -0.207 (0.129) -0.324. (0.167) 1,016 0.295 0.234 24.398*** 1,016 0.295 0.234 24.398*** Appendix B1 – Database reporting non-lagged data Appendix B2 – Database reporting lagged data Appendix C1 – R work file, descriptive statistic Appendix C2 – R work file, regressions Appendix D – Excel file and R work file, decomposition 24 References Asiedu, E. 2006. “Foreign direct investment in Africa: The role of natural resources, market size, government policy, institutions and political instability”. The World Economy, Vol. 29(1), pp. 6377. Azémar, C. & Desbordes, R. 2009. "Public Governance, Health and Foreign Direct Investment in Sub-Saharan Africa". Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 18(4), pages 667-709, August. Buchanan, B. G. & Le, Q. V. & Rishi, M. 2012. 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