Remittances and manufacturing sector growth in sub-saharan Africa Emmanuel K.K. Lartey Getachew Nigatu Abstract This paper utilizes data for sub-saharan African countries to analyze the link between remittances and the growth of manufacturing value added, and explores whether the quality of institutions and exchange rate policy matter for the dynamics of the relationship. The findings suggest that the level of financial development influences the effect remittances have on manufacturing output in a positive manner, given that when financial development is accounted for, the standalone effect of remittances is negative, but the effect of the interaction between remittances and financial development is strong and positive. The findings also show that while the effect of exchange rate flexibility on the dynamics between remittances manufacturing value added may be weakly positive, the estimated effect of remittances at the average level of exchange rate flexibility is insignificant. In addition, the results reveal that improvement in the business environment seems to matter for the performance of the manufacturing sector, but it is not significant to the dynamics between remittances and manufacturing output. The development of the financial sector, on that account, emerges as an important factor influencing the impact of remittances on the growth of the manufacturing sector in sub-saharan countries. JEL Classification: F24 F41 O11 Keywords: remittances, manufacturing, financial development, exchange rate regimes, institutions The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Economic Research Service or the U.S. Department of Agriculture or the World Bank. All errors are ours. The World Bank, 1818 H St. NW, Washington, DC 20433, and Department of Economics, California State University, Fullerton, 800 N. State College Blvd, Fullerton, CA 92834. Market and Trade Economics Division, Economic Research Service/USDA, 355 E Street SW, Washington D.C. 20024 1
1 Introduction Industrialization and the concomitant reallocation of resources from agriculture to manufacturing remains one of the essential sources of sustained productivity growth, having powered countries in Europe and the United States into high income countries, and made it possible for countries like Japan, South Korea and Taiwan, among others, to catch up with western countries. Countries in East Asia also successfully transformed their economies from agrarian to manufacturing during the 1970s and 1980s. Yet, the economies of sub-saharan African countries continue to have limited diversification, typically specializing in either agriculture or mining activities with the highest share of employment in the agriculture sector (IMF, 2017). Structural change has featured prominently in the debate on growth strategies in sub- Saharan African countries, and since these countries are among the poorest in the world, there is potential for enormous gains from structural transformation (McMillan, Rodrik and Verduzco-Gallo, 2014). The process of structural change has typically been characterized by a hump-shaped path for manufacturing output, and which can be expected for developing countries as well. However, the turning point is occurring at much lower levels of income for developing countries, with the decline in manufacturing occurring at levels of income that are a fraction of those at which advanced economies started to deindustrialize. Thus, developing countries are transitioning into service economies earlier than the norm dictates, a phenomenon that is referred to as premature deindustrialization (Rodrik, 2016). The potential for productivity growth, it has been argued, is higher in manufacturing than other sectors. Figure 1 depicts the relationship between GDP growth and growth of the manufacturing sector in sub-saharan Africa. The transfer of resources from low productivity sectors such as traditional agriculture or informal services to high productivity and dynamic sectors such as manufacturing yields a structural change bonus. To the extent that the manufacturing sector tends to be technologically dynamic and has the capacity to absorb unskilled labor, early deindustrialization would dampen the prospects of economic growth and development in sub-saharan Africa. Developing countries have experienced a 2
decline in manufacturing shares in both employment and real value added since the 1980s. For countries in the sub-region that are trapped in poverty, the assertion has been rendered, that the prospects for the growth of their economies depends critically on fostering new manufacturing industries (Rodrik, 2016). The question has been raised, however, about the impact of resource endowment on the process of industrialization, given the potential for resource booms to generate sustained real exchange rate appreciation that could hamper manufacturing growth along with the growth of the tradable sector more generally in resource rich countries. A related issue that has also attracted some attention is the potential impact of remittances on the manufacturing sector through the well-documented Dutch disease phenomenon. Remittances to developing countries have been growing in the last decade, reaching $431.6 billion in 2015, which represents an increase of 0.4 percent over $430 billion in 2014 (World Bank, 2017). The growth pace of remittances has diminished since the global financial crises, yet remittances remain an important component in total international financial flows to developing countries, and currently, are more than three times the size of official development assistance and have become the second largest source of external finance behind foreign direct investment (FDI). The magnitude of remittance flows to developing countries has given rise to questions pertaining to the undesirable consequences they tend to be associated with. In particular, remittances could cause a real exchange rate appreciation and loss of international competitiveness, and eventually lead to resource reallocation away from the manufacturing sector and towards real estate and other nontradables that tend to experience rising prices in the face of capital inflow, which could potentially hurt economic growth (Amuedo-Dorantes and Pozo, 2004; Lartey et al, 2012). This paper aims to analyze empirically, the dynamics between remittances and the manufacturing sector in sub-saharan African countries. Remittances are allocated towards financing the consumption of goods and services or the accumulation of capital directly through investments or indirectly through savings. Thus, to the extent that remittances have the 3
potential to trigger resource reallocation effects in favor of the nontradable sector, these financial inflows could, therefore, either foster or harm the performance of the manufacturing sector depending on their end use. The macroeconomic impact of remittances has also been found to depend on certain underlying characteristics of the recipient economies. For instance Lartey (2013) finds that the impact of remittances on the growth rate of GDP increases with the level of development of the financial sector. It is conceivable then, that the quality of institutions, for exmaple, the financial system, would impact how remittances affect the manufacturing sector; to the extent that they create an environment that channels remittances towards productive investments, including financing of firms in the manufacturing sector. The paper, therefore, also addresses whether the level of financial development, exchange rate policy, and institutions responsible for regulatory efficiency as captured by an indicator for business environment, influence the dynamics between remittances and growth of manufacturing output. The lack of diversification of exports in sub-saharan African countries renders their economies vulnerable to adverse external shocks, volatile export earnings and macroeconomic instability. Moreover, the concentration of economic activity in agriculture and other primary sectors, which possess limited scope for productivity growth has led to limited and less broad-based growth for the sub-region. Given that remittances have become an important component of financial flows to the sub-region, understanding the dynamics between remittances and the manufacturing sector would necessarily be critical to policies on diversification and how to promote productivity growth. 2 Related Literature The existing literature provides evidence on desirable as well as undesirable effects of remittances in recipient economies. Adams and Page (2005) and Acosta et al. (2009) find that remittances are associated with lower poverty indicators and high growth rates. Others, like 4
Gupta et al. (2009) also find that remittances have contributed towards smoothing household consumption, and to investment in human capital. There is also evidence that there is a positive relationship between remittances and growth, and that there exists an investment channel through which remittances affect growth (Lartey, 2013). On the contrary, other studies have shown that remittances may be harmful to the long-run growth prospects of recipient economies through a decline in labor supply and labor market participation rate, as well as an appreciation of the real exchange rate, and the associated detrimental effect on the tradable sector, otherwise known as the Dutch disease phenomenon (Acosta et al., 2009). Lartey et al. (2012), using data for 109 developing and transition countries, show that an increase in remittances in emerging economies is associated with Dutch disease effects, in that it generates an appreciation of the real exchange rate and resource reallocation that favors the nontradable sector at the expense of tradable goods. The study also produced additional findings that suggest that these effects operate stronger under fixed exchange rate regimes. Rajan and Subramanian (2011) examine the effects of aid on the growth of manufacturing, and find that foreign aid inflows have adverse effects on the competitiveness of the recipient country, captured by the lower relative growth rate of exportable industries. Dzansi (2013) finds a positive effect of remittances on manufacturing growth, based on data for a sample of 40 remittance-recipient countries. Rodrik (2008) shows that overvaluation of the real exchange hinders economic growth in the longrun in developing countries in particular, due to the fact that the tradables sector in these countries suffers disproportionately from weak institutions and market failures. This suggests that the quality of institutions could influence the impact of remittances on the manufacturing sector, to the extent that remittances have the potential to cause the real exchange rate to appreciate. The impact of institutional quality on economic growth has been analyzed in a number of studies; the results generally showing that the growth performance 5
of countries is poor where institutional quality is low. 1 Arguably, good institutions would ensure the efficient allocation of financial resources towards investments that would foster long-run growth. Moreover, under good institutions, uncertainty about the private sector tends to diminish, which enhances the investment climate thereby facilitating growth in general, as well as the performance of the manufacturing sector. This paper contributes to the literature by analyzing the relationship between remittances and manufacturing growth in sub-saharan Africa. Although it is in the spirit of some of the aforementioned studies, the focus on sub-saharan Africa represents a critical point of departure, particularly given the renewed interest in the question of whether industrialization can be a path out of poverty for the sub-region in the wake of the declining and low commodity prices in recent years. Moreover, we assess the effect of the interaction between institutional quality and remittances on manufacturing. In particular, we focus on the impact on manufacturing, of the interaction between remittances and exchange rate regimes, as well as the interaction with indicators of the quality of the business environment and the level of financial development. The remainder of the paper is organized as follows. Section 3 presents the underlying theoretical framework for the analysis; section 4 discusses the data and methodology; section 5 analyzes the results and section 6 presents some concluding remarks. 3 Theoretical Framework The Salter-Swan-Corden-Dornbusch model provides the theoretical framework that underpins empirical models used to analyze the impact of capital inflows on sectoral dynamics and the real exchange rate in developing economies. The model captures the transmission mechanism by which an increase in capital inflows (remittances) could cause a real exchange rate appreciation which tends to be detrimental to the tradable sector; and serves as the underlying analytical framework for the empirical analysis in this paper. A higher real household 1 See Acemoglu, Johnson and Robinson (2001; 2002) for details. 6
income via an increase in remittances generates an expansion in aggregate demand, which for exogenously given prices of tradable goods, culminates in higher relative prices of nontradable goods (spending effect), which further leades to movement of resources toward this sector away from the tradable sector (resource movement effect). The increase in the relative price of nontradable goods implies an appreciation of the real exchange rate. Acosta et al (2009) develop a microfounded dynamic stochastic general equilibrium model that presents an additional transmission mechanism, such that the increase in household income results in a decrease in the labor supply. In the context of the set-up, remittances may increase the reservation wage of recipients, which could exert pressure on wages to increase, and given fixed world prices, could further lead to a contraction of the tradable sector due to higher production costs. Although the spending effect and resource movement effect both serve as indicators of the existence of the Dutch disease phenomenon, our focus in this paper is to examine the impact of remittances on the output of the manufacturing sector via the resource movement effect. 4 Data and Methodology 4.1 Description of the data The data set consists of an unbalanced panel of annual observations from 1990 to 2015 for 35 sub-saharan African countries, and is obtained from different sources. The data for manufacturing-value added, remittances, GDP per capita, trade openness, foreign direct investment, level of domestic credit are from the World Bank; data for indexes of the quality of institutions come from the Heritage Foundation; and exchange rate data are obtained from Ilzetzki, Reinhart, and Rogoff (2017). 2 Table 1 presents some summary statistics for the main variables employed in the analysis. 2 The details on the variables and respective data sources, and the list of countries are provided in the appendix. 7
During the period covered by the study, remittances accounted for about 4 percent of GDP, i.e. about US$30 per capita in the sub-region. The global remittance per capita during the same period is estimated at about US$32 (World Bank, 2017). The average remittances to sub-saharan African countries over the period was about US$0.5 billion; the highest recipient is Nigeria with US$21 billion. The least populous Cabo Verde is the highest recipient in per capita terms ($386) and, as a share of GDP, Lesotho recorded the highest ratio at 71.7 percent, and the average for SSA is 3.73 percent. Foreign direct investment (FDI), in comparison, is estimated at 3.24 percent of GDP in sub-saharan Africa (SSA). The average GDP per capita for the period was about US$1743 for SSA, making it less than one-sixth of the global average. However, the estimated average masks the significant variation in income across countries ranging from the lowest income country, Liberia with GDP per capita of US$160, to the highest, Gabon, a crude-oil exporter with US$11,906. In terms of relative contribution of the other sectors, value added by the service sector accounted for the largest share of GDP in SSA, as the agriculture sector which employs the majority of sub-saharan Africans, accounted for only 27 percent of GDP (World Bank, 2017) and manufacturing accounted for about 11.3 percent of GDP. Figure 1 suggests that there is a slightly negative correlation between the growth rate of the manufacturing sector the growth rate of remittances. SSA countries exhibit a lower but improving level of economic and other institutions. With the average economic and business freedom index of 54.93 and 56.22 percent, respectively. The average level of domestic credit offered to private sector as a % of GDP is 18.4 percent, while average level of government spending as % GDP reach 74.7 percent. SSA countries also involve in trade at a limited scale with average trade openness measured at 68.3 percent of GDP, and trade freedom index of 4.1. Most of the countries of SSA also practices a fixed or pegged (42 percent of them) or managed pegged exchange rate regimes. 8
Figure 1: Average manufacturing and remittance growth rate in SSA, 1990-2015 4.2 Model specification and estimation The relationship between remittances and the manufacturing sector is formally analyzed by specifying a dynamic panel model that is estimated using the generalized method of moments (GMM) system estimator, tailored to deal with potential endogeneity in all explanatory variables and particularly with the introduction of the lagged dependent variable as an explanatory variable. 3 The dynamic equation is represented by an autoregressive-distributed lag model of the form, y i,t y i,t 1 = (ϕ 1)y i,t 1 + β x it + α i + ε it. (1) This is a dynamic model for y it, where y i,t 1 is the one period lag of y it, x it is a vector of other explanatory variables, β(l) is a vector of associated polynomials in the lag operator, α i is a 3 The GMM system estimator has been applied extensively and discussed in a number of studies. See Blundell and Bond (1998) for details on the system GMM estimators. 9
country specific effect which is unobserved, and ε it is an error term. The dependent variable is manufacturing value added as a percentage of GDP, and the main explanatory variables in the baseline specification include remittances, exchange rate regime index, foreign direct investment (FDI), government spending as a percentage of GDP and an interaction term for remittances and the exchange rate regime index. The interaction term is introduced to analyze the extent to which exchange rate flexibility influences the impact of remittances on manufacturing value added. In addition, other policy and institutional quality variables are subsequently incorporated into the model specification. 4 An identification problem emerges where an explanatory variable is correlated with the error term. For instance, following the reasoning in Lartey et al. (2012), it is plausible that in the presence of risk-sharing strategies between migrants and family members, a recession in the recipient economy would simultaneously affect manufacturing output and increase remittances from the migrants. Also, a reverse causality identification problem may be present, such that a well-performing manufacturing sector would attract remittances for investment purposes. Thus, we estimate all equations using the GMM system estimator, which estimates the model and its first-differenced version as a system of equations. If the instruments are valid, the GMM coefficient captures the immediate impact of the isolated exogenous component of the covariates on the dependent variable. 5 The GMM system estimator allows for the use of either lagged differences and lagged levels of the explanatory variables as instruments for endogenous variables. The validity of the lagged differences of the explanatory variables as instruments occurs under two conditions: 1) the differences of the explanatory variable and the errors are uncorrelated, and 2) there is no serial correlation in the errors. Since the validity of instruments determine 4 For all estimations, we use a logarithmic transformation of all variables, except the exchange rate regime index. We apply the two-step system GMM estimation technique based on Arellano and Bover (1995), and Blundell and Bond (1998). 5 We abstract from a detailed description of the estimator since it has been widely used in several studies (See Arellano and Bover (1995) and Blundell and Bond (1998) for details on the GMM system estimator). Nonetheless, it noteworthy that some issues remain with this estimation technique. For instance, a test for overidentifying restriction might have low power, while weak instruments can arise when the variances of the individual heterogeneity and idiosyncratic errors are the same (see Bun and Windmeijer (2010) for details). 10
whether the GMM estimator is consistent or not, we employ two specification tests to address these issues namely, a test of over-identifying restrictions and a test for second-order serial correlation in the error term. The standard Sargan test of overidentifying restrictions has a null hypothesis that the instruments are overall valid. The Arellano and Bond s (1991) test for second-order serial correlation has a null hypothesis that there is no second-order serial correlation in the differenced error term (the residual of the equation in differences). It should be noted that first-order correlation is expected in the differenced equation even if the error term is uncorrelated, unless it follows a random walk. In contrast, the presence of second-order correlation indicates serial correlation of the error term and that it follows a moving average process of at least order one. 5 Results 5.1 Exchange rate regimes, remittances and manufacturing The preliminary results presented in Table 2 show that without considering the interaction between remittances and the exchange rate regime index, there is a positive effect of remittances on manufacturing output, althought the standalone effect of remittances is positive and significant in columns (1), (2) and (6). Specifications (2) and (5) introduce exchange rate regime as a dummy variable (equal to 1 for fixed exchange rate regime) whereas (3) and (6) feature the exchange rate regime index; interacting each of these variables with remittances in each case. The results indicate that the interaction term is significant in column (6) only, the coefficient being negative. The estimates further indicate that the nonlinear combination of remittances and the interaction term is positive and significant in column (5) only. Another set of regressions introduces trade policy-related covariates into the initial model specification and the results are given in Table 3. The standalone impact of remittances is positive and significant in specifications (1), (5) and (6) whereas the interacterm term for remittances and exchange rate regime is negative and significant in specifications (5) and (6). 11
This suggests that a more flexible exchange rate regime diminishes the positive impact of remittances on the manufacturing sector. Based on specification (5), the effect of remittances on the growth of manufacturing value added estimated at the mean level of exchange rate regime is 0.007. This estimate implies that a percentage point increase in remittances as share of GDP at the mean value will add 0.007 to manufacturing value added as a share of GDP. 6 Additional specifications that introduce an indicator for the level of financial development are estimated and the results are presented in columns (7) and (8) of Table 3. These estimates present some interesting observations. First, the coefficient on the financial development indicator (credit to private sector as share of GDP) has a positive and statistically significant coefficient. Second, the standalone coefficient for remittances bears a negative sign, the interaction with exchange rate regime turns positive, but the impact of remittances estimated at the mean level of the exchange rate regime index is statisticially insignificant, once the level of financial development is accounted for. This, potentially, reflects the importance of the role of the financial system in mobilizing remittances for investment purposes, a hypothesis that is further explored next. 5.2 Institutions, remittances and manufacturing The next set of estimations assesses the extent to which the quality of institutions influences the way remittances affects output in the manufacturing sector. We introduce two indexes, an indicator for business freedom and another for financial development, as well as an interaction of remittances and each of the indicators, while allowing for some variation in the specifications. The results, which are presented in Table 4, show that the standalone coefficieint on remittances in negative and statistically significant only in the specifications with the financial development indicator. The business freedom index has a positive and 6 The negative standalone effect of exchange rate flexibility on manufacturing is, potentially, capturing the impact of rising input costs for manufacturing firms. Alternatively, it could be reflecting the implications for competitiveness due to appreciation of the exchange rate following inflow of remittances. 12
significant coefficient, but the interaction with remittances is statistically insignificant. 7 The interaction with the financial development index is positive and statistically significant, with the estimated impact of remittances at the mean level of financial development being positive. The estimate of the nonlinear combination implies that a percentage point increase in remittances (as a percentage of GDP) will increase the growth of manufacturing value added (as a percentage of GDP) by a value between 0.4 and 0.6 when estimated at the mean level of financial development. The initial positive standalone coefficient of remittance, thus, potentially captures the impact of remittances through alleviation of credit constraints, given that controlling for the level of financial development results in a negative sign on the standalone coefficient on remittances, whereas the interaction of remittances and financial development has a positive effect. Table 5 presents results for the same set of specifications in Table 4, but based on the onestep GMM estimator. The estimates are generally consistent with the results shown in Table 4, in that the standalone coefficient on remittances is negative and statistically significant, whereas the effect of remittances estimated at the mean level of financial development is positive and statistically significant. The findings, thus suggest that policies enhancing financial development work to absorb remittances into the financial system channeling these financial resources towards productive uses, including manufacturing activities. This implies that, in the absence of policies that promote financial development, potential Dutch disease consequences of remittances exist, which is consistent with the findings in Lartey (2013). 5.3 Robustness analysis We conduct additional robustness analysis by using non-overlapping 5-year averages of the data and applying both the two-step and one-step GMM estimators; the results of which are presented in Table 6 and Table 7 respectively. 8 Estimates for the business freedom index 7 The sign on the coefficient for the interaction term is sensitive to the control variables that enter the regressions. 8 Taking 5-year averages leads to the loss of observations and variability in the data. 13
remain statistically insignificant, whereas estimates for the financial development indicator are positive and statistically significant in Table 6, columns (6) and (7); and Table 7, column (7). In addition, while the estimates for both the standalone effect of remittances on one hand, and the nonlinear combination of remittances and the interaction with financial development on the other hand, are not statistically significant in Table 6, the estimates in Table 7 are consistent with those in Table 5. In particular, the estimated impact of remittances at the mean level of financial development is positive, the coefficient being bigger, and between 0.78 and 1.02. This confirms the assessment that there is a positive effect of the interaction between remittances and financial development on manufacturing value added (as a share of GDP), such that the size of this effect could be up to about 1 percentage point, for a percentage point increase in remittances (as a share of GDP). Furthemore, we obtain the longrun elasticity capturing the responsiveness of the growth of the manufacturing sector to an increase in remittances, the estimates of which are given in Table 8. The elasticity is computed using estimates from all specifications in Tables 2 through 7. 9 The results, which reflect the standalone impact of remittances on the growth of manufacturing value added, show that when the level of financial development is not accounted for, remittances have a positive effect on manufacturing. In contrast, the coefficient is larger and bears a negative sign when financial development is incorporated into the specification. The impact of remittances on manufacturing in the longrun, as suggested by the estimates, could be negative in the absence of an enabling environment such as one in the form of an improved financial system. 6 Conclusion This paper assessed the impact of remittances on the output of the manufacturing sector in sub-saharan African countries, and the relevance of exchange rate policy and institutional 9 The longrun elasticity is computed as follows: β REM(LR) = [ βrem(sr) (1 β LMV A ) ], where β LMV A is the coefficient estimate on the lagged dependent variable. 14
quality to the dynamics of the relationship. Foremost, the main findings indicate that the standalone effect of remittances on manufacturing is negative, but when interacted with financial development, the effect is strong and positive, suggesting that as the level of financial development increases, an increase in remittances has a positive effect on manufacturing. Secondly, the findings show that when the level of financial development is accounted for, the effect of exchange rate flexibility on the dynamics between remittances manufacturing value added is weakly positive, whereas the estimated effect of remittances at the average level of exchange rate flexibility is insignificant. Thirdly, while an improvement in the business environment seems to matter for the the performance of the manufacturing sector, it is not significant to the dynamics between remittances and manufacturing output. The development of the financial sector, thus, emerges as an important factor influencing the dynamics of the relationship between remittances and the growth of the manufacturing sector in sub-saharan countries. The potentially detrimental impact of remittances on the manufacturing sector via the Dutch disease mechanism, which is well-documented, therefore, could be minimized and possibly reversed under policies that foster the development of the financial sector, since such policies may serve to harness remittances for investment finance to alleviate the financial constraints that plague small and medium-scale enterprises in sub-saharan Africa. 15
References [1] Acemoglu, D., S. Johnson and J. Robinson (2001), The Colonial Origins of Comparative, Development: An Empirical Investigation, American Economic Review 91:1369-1401. [2] Acemoglu, D., S. Johnson and J. Robinson (2002), Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution, Quarterly Journal of Economics, 117:1231-1294. [3] Acosta, P., Lartey, E.K.K., and Mandelman, F. (2009). Remittances and Dutch Disease. Journal of International Economics 79, 102-116. [4] Adams, R. and Page, J. (2005). Do International Migration and Remittances Reduce Poverty in Developing Countries? World Development 33(10), 1645 1669. [5] Amuedo-Dorantes, C. and Pozo, S. (2004). Workers Remittances and the Real Exchange Rate: A Paradox of Gifts. World Development 32(8), 1407-1417. [6] Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics 68(1), 29-51. [7] Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87(1), 115-143. [8] Dzansi, James. (2013). Do remittance inflows promote manufacturing growth? The annals of regional science 51(1), 89-111. [9] Gupta, S., Pattillo, C., and Wagh, S. (2009). Effect of Remittances on Poverty and Financial Development in Sub-Saharan Africa. World Development 37, 104-115. [10] Heritage Foundation (2017). 2017 Index of Economic Freedom.The heritage Foundation, Washington DC. 16
[11] IMF (2017). Sub-Saharan Africa Restarting the Growth Engine. The Regional Economic Outlook, World Bank, Washington DC. [12] Ilzetzki, E., Reinhart, C.M, and Rogoff, K.S.(2017). Exchange Arrangements Entering the 21st Century: Which Anchor Will Hold? National Bureau of Economic Research (NBER) Working Paper No. 23134. [13] Lartey, Emmanuel K.K. (2013). Remittances, investment and growth in sub-saharan Africa. The Journal of International Trade & Economic Development 22(7), 1038-1058. [14] Lartey, Emmanuel K.K. (2008). Capital Inflows, Dutch Disease Effects and Monetary Policy in a Small Open Economy. Review of International Economics 16, 971-989. [15] Lartey, E.K.K. Pablo A. Acosta and Mandelman, Federico S. (2012). Remittances, exchange rate regimes and the Dutch disease: a panel data analysis. Review of International Economics 20(2), 377-395. [16] McMillan, Margaret, Dani Rodrik, and Inidgo Verduzco-Gallo.(2014). Globalization, structural change, and productivity growth, with an update on Africa. World Development 63, 11-32. [17] Rajan, Raghuram G., and Arvind Subramanian. (2011). Aid, Dutch disease, and manufacturing growth. Journal of Development Economics 94(1), 106-118. [18] Rodrik, Dani (2008). The Real Exchange Rate and Economic Growth. Brookings Papers on Economic Activity 2, 365-412. [19] Rodrik, Dani.(2016). Premature deindustrialization. Journal of Economic Growth 21(1), 1-33. [20] United Nations (2017). Industrial Development Organization: Databases. Vienna, Austria. 17
[21] World Bank (2017). World development indicators. World Bank, Washington DC. 18
Table 1: Summary statistics Variable Mean Std. Min. Max. No. of Dev. Observation Business freedom 56.22 11.5 23.4 85.00 687 Private sector credit 18.40 19.7 0.8 151.07 741 Exchange rate regime index 2.11 1.3 1 6 910 Exchange rate regime dummy 0.42 0.50 0 1 910 FDI 3.24 4.89-8.6 41.8 903 GDP per capita 1743 2278 160 11907 910 Government spending 74.7 17.07 0 97.6 687 MVA 11.3 7.0 1.6 39.5 839 Remittances 3.73 7.6 0 71.7 829 Trade freedom 4.1 0.30 2.5 4.9 684 Trade openness 68.3 29.3 11.1 172.5 848 Note: definition for each variable and sources of data is given in Appendix Table A2. 19
Table 2: Remittances and manufacturing: Role of exchange rate regimes (1) (2) (3) (4) (5) (6) MVA(t-1) 0.029*** 0.019** 0.019* 0.018** -0.013-0.011 (0.004) (0.009) (0.010) (0.007) (0.025) (0.023) Remittances 0.004*** 0.005* 0.002 0.004 0.008 0.010** (0.002) (0.003) (0.004) (0.004) (0.006) (0.005) GDP per capita -0.352*** -0.355*** -0.332*** -0.046* -0.060-0.085* (0.014) (0.017) (0.040) (0.023) (0.042) (0.047) GDP per capita (t-1) 0.132*** 0.170*** 0.106*** -0.332*** -0.299*** -0.289*** (0.019) (0.037) (0.019) (0.024) (0.037) (0.034) Government spending 0.001 0.002* 0.004** (0.001) (0.001) (0.002) FDI -0.014*** -0.014*** -0.012*** (0.002) (0.002) (0.003) ERR 0.017*** -0.011* 0.003-0.014 (0.005) (0.006) (0.006) (0.009) Remit*ERR -0.001-0.001 0.000-0.009*** (0.003) (0.003) (0.004) (0.004) [Remit + Remit*ERR] 1 0.005 0.002 0.008* 0.005 (0.004) (0.004) (0.005) (0.003) Observations 700 700 700 551 551 551 Instruments 71 73 73 66 68 68 AR(2) test 0.733 0.793 0.810 0.323 0.618 0.565 Sargan test 0.999 0.999 0.999 0.999 0.999 0.999 Notes: Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01. Dependent variable is growth rate of manufacturing value added as % of GDP (MVA). GDP per capita is expressed in growth rate. ERR is exchange rate regime; (2) and (5) are specified using ERR dummy, (3) and (6) using ERR index. 1 is estimated at mean value of ERR (=0.42) for (2) and (5) and (=2.11) for (3) and (6). AR(2) test is Arellano-Bond test for second-order serial correlation; Sargan test is for over-identifying restrictions; Both tests show probability values. Estimations are performed using two-step system estimator with maximum of 2 periods lag of instruments. 20
Table 3: Remittances and manufacturing: Role of exchange rate regimes and institutions (1) (2) (3) (4) (5) (6) (7) (8) MVA (t-1) 0.026*** 0.020 0.040* 0.006 0.042** -0.014 0.025 0.069 (0.009) (0.013) (0.024) (0.026) (0.017) (0.043) (0.019) (0.050) Remittances 0.007** 0.004 0.004 0.003 0.018*** 0.010** -0.010** -0.006 (0.003) (0.006) (0.007) (0.004) (0.004) (0.005) (0.005) (0.011) GDP per capita -0.087*** -0.065** -0.061-0.070-0.085** -0.104** -0.122-0.228*** (0.032) (0.032) (0.044) (0.050) (0.036) (0.050) (0.095) (0.084) GDP per capita (t-1) -0.390*** -0.365*** -0.398*** -0.375*** -0.359*** -0.330*** -0.193** -0.238*** (0.032) (0.027) (0.039) (0.047) (0.043) (0.046) (0.087) (0.076) Government spending 0.004-0.013** -0.004-0.012-0.000-0.007* -0.020* 0.001 (0.006) (0.006) (0.005) (0.009) (0.007) (0.004) (0.011) (0.009) FDI -0.013*** -0.012*** -0.015*** -0.012*** -0.013*** -0.010** -0.019*** -0.017*** (0.003) (0.002) (0.003) (0.002) (0.003) (0.004) (0.005) (0.004) Trade openness -0.005 0.002 0.005 0.043** (0.007) (0.005) (0.008) (0.017) Trade freedom 0.013*** 0.013* 0.015*** 0.037*** (0.005) (0.007) (0.005) (0.007) Private sector credit -0.037* -0.055*** (0.020) (0.017) ERR 0.022** -0.005-0.040*** -0.035*** 0.018-0.010 (0.011) (0.010) (0.012) (0.011) (0.023) (0.018) Remit*ERR -0.002 0.001-0.019*** -0.011*** 0.018** 0.003 (0.007) (0.003) (0.005) (0.004) (0.007) (0.011) [Remit + Remit*ERR] 1 0.004 0.003 0.007** 0.004 0.000-0.005 (0.005) (0.004) (0.003) (0.004) (0.003) (0.005) Observations 519 549 519 549 519 549 446 476 Instruments 67 67 69 69 69 69 70 70 AR(2) test 0.505 0.327 0.374 0.434 0.391 0.697 0.882 0.962 Sargan test 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999 Notes: Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01. Dependent variable is growth rate of manufacturing value added as % of GDP (MVA). GDP per capita is expressed in growth rate. ERR is exchange rate regime; (3) and (4) are specified using ERR dummy, and (5)-(8) using ERR index. 1 is estimated at mean value of ERR (=0.42) for (3), (4) and (=2.11) for (5)-(8). AR(2) test is Arellano-Bond test for second-order serial correlation; Sargan test is for over-identifying restrictions; Both tests show probability values. Estimations are performed using two-step system estimator with maximum of 2 periods lag of instruments. 21
Table 4: Remittances and manufacturing: Role of institutions (1) (2) (3) (4) (5) (6) MVA (t-1) -0.005 0.005 0.013 0.066* 0.059 0.006 (0.033) (0.028) (0.046) (0.038) (0.052) (0.026) Remittances 0.026 0.041-0.009-0.074*** -0.067*** -0.087*** (0.043) (0.060) (0.095) (0.010) (0.017) (0.014) GDP per capita -0.092** -0.089* -0.065-0.139* -0.101-0.174 (0.039) (0.051) (0.054) (0.072) (0.097) (0.124) GDP per capita (t-1) -0.351*** -0.341*** -0.331*** -0.211*** -0.265*** -0.302*** (0.026) (0.037) (0.082) (0.062) (0.037) (0.078) Government spending -0.022*** -0.021** -0.030** 0.022*** 0.020** -0.001 (0.007) (0.010) (0.015) (0.004) (0.008) (0.013) FDI -0.014*** -0.014*** -0.017*** -0.012*** -0.011*** -0.015*** (0.002) (0.002) (0.005) (0.004) (0.004) (0.005) Trade openness -0.013 0.022 (0.012) (0.020) ERR -0.016 0.005-0.006-0.001 (0.014) (0.021) (0.012) (0.026) Business freedom 0.026*** 0.027** 0.041*** (0.007) (0.011) (0.015) Rem*Business -0.006-0.010 0.004 (0.011) (0.014) (0.023) [Remit + Remit*Business] 1-0.297-0.503 0.242 (0.569) (0.723) (1.219) Private sector credit -0.036*** -0.033*** -0.045*** (0.008) (0.011) (0.014) Remit*Credit) 0.028*** 0.026*** 0.037*** (0.005) (0.007) (0.006) [Remit + Remit*Credit] 1 0.433*** 0.405*** 0.588*** (0.074) (0.120) (0.099) Observations 551 551 519 478 478 446 Instruments 68 69 70 68 69 70 AR(2) 0.565 0.485 0.662 0.306 0.350 0.746 Sargan test 0.999 0.999 0.999 0.999 0.999 0.999 Notes: Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01. Dependent variable is growth rate of manufacturing value added as % of GDP (MVA). ERR is exchange rate regime index; 1 is estimated at mean value of business freedom index (=56.22) and domestic credit to private sector as % of GDP (= 18.40). Estimations are performed using two-step system estimator with maximum of 2 periods lag of instruments. 22
Table 5: Remittances and manufacturing: Role of institutions (1) (2) (3) (4) (5) (6) MVA (t-1) 0.026 0.032 0.038 0.020 0.022 0.026 (0.069) (0.072) (0.070) (0.072) (0.076) (0.074) Remittances 0.057 0.054 0.025-0.065* -0.064* -0.064* (0.149) (0.152) (0.181) (0.034) (0.036) (0.036) GDP per capita -0.070-0.093-0.131-0.031-0.035-0.131 (0.193) (0.200) (0.207) (0.196) (0.202) (0.224) GDP per capita (t-1) -0.382* -0.377* -0.439** -0.285-0.281-0.347 (0.222) (0.214) (0.203) (0.238) (0.230) (0.227) Government spending -0.021-0.021-0.026 0.027* 0.029* -0.025 (0.033) (0.034) (0.048) (0.016) (0.015) (0.042) FDI -0.015-0.015-0.013-0.018-0.018-0.022 (0.012) (0.012) (0.014) (0.013) (0.012) (0.015) Trade openness -0.011 0.050 (0.066) (0.049) ERR -0.029-0.026-0.012-0.001 (0.040) (0.044) (0.063) (0.066) Business freedom 0.025 0.029 0.044 (0.035) (0.036) (0.058) Remit*Business -0.014-0.014-0.005 (0.037) (0.038) (0.045) [Remit + Remit*Business] 1-0.754-721 -0.249 (1.921) (1.963) (2.328) Private sector credit -0.048* -0.049* -0.042 (0.027) (0.025) (0.027) Remit*Credit 0.022* 0.022 0.025* (0.013) (0.014) (0.014) [Remit + Remit*Credit] 1 0.339* 0.334 0.398* (0.074) (0.216) (0.217) Observations 551 551 519 478 478 446 Instruments 68 69 70 68 69 70 AR(2) 0.226 0.242 0.433 0.595 0.624 0.924 Notes: Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01. Dependent variable is growth rate of manufacturing value added as % of GDP (MVA). ERR is exchange rate regime index; 1 is estimated at mean value of business freedom index (=56.22) and domestic credit to private sector as % of GDP (= 18.40). Estimations are performed using one-step system estimator with maximum of 2 periods lag of instruments. Sargan test statistic is unavailable for one-step (robust) estimator in Stata. 23
Table 6: Remittances and manufacturing: Role of institutions (1) (2) (3) (4) (5) (6) MVA (t-1) 0.128*** 0.167*** 0.169*** 0.141*** 0.231*** 0.241** (0.048) (0.061) (0.065) (0.040) (0.073) (0.105) Remittances -0.165-0.231-0.307-0.135-0.132-0.093 (0.396) (0.392) (0.362) (0.084) (0.087) (0.078) GDP per capita -0.568*** -0.884*** -0.874*** -0.430* -0.707*** -0.739*** (0.202) (0.222) (0.191) (0.248) (0.265) (0.279) GDP per capita (t-1) 0.419*** 0.439*** 0.186 0.524** 0.454* 0.205 (0.157) (0.157) (0.171) (0.268) (0.251) (0.252) Government spending -0.007 0.044 0.004-0.055-0.056 0.054 (0.078) (0.075) (0.127) (0.037) (0.047) (0.062) FDI -0.018-0.018 0.011-0.014-0.008 0.017 (0.013) (0.016) (0.014) (0.013) (0.019) (0.014) Trade openness -0.131** -0.118* (0.057) (0.068) ERR -0.259*** -0.262*** -0.428*** -0.480*** (0.092) (0.097) (0.120) (0.138) Business freedom -0.033-0.044 0.141 (0.074) (0.068) (0.103) Remit*Business freedom 0.040 0.055 0.076 (0.097) (0.095) (0.088) [Remit + Remit*Business] 1 2.065 2.861 3.962 (5.012) (4.911) (4.552) Private sector credit 0.063 0.111** 0.114*** (0.048) (0.053) (0.041) Remit*Credit 0.038 0.040 0.033 (0.026) (0.025) (0.023) [Remit + Remit*Credit] 1 0.599 0.633 0.532 (0.409) (0.393) (0.366) Observations 115 115 110 99 99 94 Instruments 15 16 17 15 16 17 AR(2) 2.223 0.191 0.150 0.234 0.174 0.158 Sargan test 0.098 0.077 0.138 0.234 0.359 0.316 Notes: Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01. Dependent variable is growth rate of manufacturing value added as % of GDP (MVA). ERR is exchange rate regime index; 1 is estimated at mean value of business freedom index (=58.89) and domestic credit to private sector as % of GDP (= 19.13). Estimations are performed using two-step system estimator with maximum of 2 periods lag of instruments, and based on non-overlapping 5-year average of the data. 24
Table 7: Remittances and manufacturing: Role of institutions (1) (2) (3) (4) (5) (6) MVA(t-1) 0.146* 0.182 0.148 0.167* 0.224* 0.147 (0.081) (0.114) (0.115) (0.100) (0.127) (0.141) Remittances 0.408 0.172 0.019-0.205* -0.176* -0.159* (0.511) (0.487) (0.388) (0.120) (0.106) (0.096) GDP per capita -0.775*** -1.001*** -0.894*** -0.788** -0.900** -0.681 (0.280) (0.388) (0.323) (0.336) (0.383) (0.428) GDP per capita (t-1) 0.504 0.532 0.370 0.784 0.691 0.638 (0.489) (0.552) (0.518) (0.782) (0.761) (0.721) Government spending 0.018 0.035 0.052-0.106** -0.097 0.077 (0.086) (0.094) (0.146) (0.052) (0.061) (0.153) FDI -0.002-0.003 0.009 0.003-0.002 0.009 (0.022) (0.031) (0.022) (0.025) (0.041) (0.019) Trade openness -0.130-0.164 (0.100) (0.166) ERR -0.259* -0.269-0.360** -0.327 (0.156) (0.187) (0.180) (0.250) Business freedom -0.081-0.077 0.054 (0.092) (0.103) (0.127) Remit*Business) -0.102-0.045-0.009 (0.124) (0.116) (0.094) [Remit + Remit*Business] 1-5.304-2.368-4.464 (6.422) (6.009) (4.891) private sector credit 0.083 0.101 0.108* (0.064) (0.078) (0.064) Remit*Credit) 0.064* 0.056 0.049* (0.037) (0.034) (0.028) [Remit + Remit*Credit] 1 1.016* 0.888* 0.784* (0.594) (0.545) (0.443) Observations 115 115 110 99 99 94 Instruments 15 16 17 15 16 17 AR(2) 0.185 0.167 0.154 0.206 0.167 0.172 Notes: Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01. Dependent variable is growth rate of manufacturing value added as % of GDP (MVA). ERR is exchange rate regime index; 1 is estimated at mean value of business freedom index (=58.89) and domestic credit to private sector as % of GDP (= 19.13). Estimations are performed using one-step system estimator with maximum of 2 periods lag of instruments, and based on non-overlapping 5-year average of the data. Sargan test statistic is unavailable for one-step (robust) estimator in stata. 25
Table 8: Long-run coefficients (C1) (C2) (C3) (C4) (C5) (C6) Table 2 0.005*** 0.005* 0.002 0.004 0.008 0.010** (0.009) (0.063) (0.643) (0.240) (0.132) (0.043) Table 3 0.007** 0.004 0.004 0.003 0.019*** 0.010** (0.024) (0.507) (0.526) (0.437) (0.000) (0.019) Table 4 0.026 0.042-0.009-0.08*** -0.072*** -0.087*** (0.541) (0.493 ) (0.923) (0.000 ) (0.000) (0.000) Table 5 0.059 0.056 0.026-0.067** -0.066* -0.066* (0.700) (0.723) (0.888) (0.037) (0.053) (0.056) Table 6-0.189-0.277-0.369-0.157-0.172-0.122 (0.679) (0.559) (0.408) (0.124) (0.160) (0.283) Table 7 0.478 0.210 0.023-0.246-0.227-0.186 (0.432) (0.726) (0.961) (0.103) (0.143) (0.151) P-values in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 Table A1: Sub Saharan African countries included in this study Benin Ghana Nigeria Botswana Guinea Rwanda Burkina Faso Guinea-Bissau Senegal Burundi Kenya Sierra Leone Cabo Verde Lesotho South Africa Cameroon Madagascar Sudan Congo, Rep. Malawi Swaziland Cote d Ivoire Mauritius Tanzania Djibouti Mozambique Togo Ethiopia Namibia Uganda Gabon Niger Zambia Gambia Zimbabwe 26
Table A2: List of variables, definitions and data sources Variable name Definitions Source Business freedom (Business) Business freedom is an overall indicator of the efficiency of government regulation of business. Heritage Foundation Private sector credit (Credit) Domestic credit to private sector (% of GDP). World Bank Exchange regime index rate (ERR) Based on the coarse classification ranging from 1 to 6. Ilzetzki, Reinhart and Rogoff (2017) Exchange regime dummy rate (ERR) Based on the coarse classification code 1=1 (for peg exchange rate regimes), and other codes 2-6= 0. Ilzetzki, Reinhart and Rogoff (2017) FDI Foreign Direct Investment net inflows (% of GDP) which is investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. World Bank GDP per capita Gross Domestic Products per capita (constant 2010 US$). World Bank Government spending Government spending freedom is an index that considers the level of government expenditures as a % of GDP. Heritage Foundation MVA Manufacturing-value added (% of GDP). World Bank Remittances (Remit) Trade openness Trade freedom Personal remittances received (% of GDP) is personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households expressed in terms of the receiving country GDP. Trade (% of GDP) is the sum of exports & imports of goods and services measured as a share of GDP. Trade freedom is a composite measure of the absence of tariff and non-tariff barriers that affect imports and exports of goods and services. World Bank World Bank Heritage Foundation 27