Do remittance inflows reduce growth instability in Sub-Saharan Africa?

Do remittance inflows reduce growth instability in Sub-Saharan Africa? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Do remittance inflows reduce growth instability in Sub-Saharan Africa? Relwendé SAWADOGO, Gervasio SEMEDO, Drissa SAWADOGO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6682234/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Remittances have become one of the most important sources of development finance in developing countries, particularly in sub-Saharan Africa. They provide a stable source of household income for consumption and can therefore play a role in stabilizing economic cycles. Indeed, migrant remittance flows can alleviate economic growth instability by easing constraints on domestic resources and indirectly increase growth instability through real exchange rate appreciation. In this context, this paper investigates the impact of remittance inflows on economic growth in a sample of 45 Sub-Saharan African countries from 1980–2019. We use system-GMM to control the presence of endogeneity from the dynamic effects of remittances. The empirical results show that remittance inflows negatively and significantly affect economic growth instability. The results are robust to several robustness checks. The results confirm the hypothesis that migrant remittances are a stabilising source of macroeconomic shocks. Sub-Saharan African countries should strengthen policies to attract remittances to finance their development strategy. JEL classification : C23 E32, F24, O55 Business and commerce/Economics Social science/Development studies Remittance inflows Growth instability Sub-Saharan Africa Panel data system-GMM 1. Introduction Remittances have become in recent decades one of the important sources of external financing in developing countries, particularly in Sub-Saharan Africa. Remittance flows to low, and middle-income countries rose from around US $ 176 billion in 1980 to US $ 568 billion in 2021 (World Bank's World Development Indicators, WDI database). During the same period, in Sub-Saharan Africa (SSA), the amount increased from US $ 1.39 billion to about US $ 49 billion, representing a growth of 34%. Thus, remittances represent a substantial share of international capital flows and could play a significant role in stabilizing SSA countries’ economies. According to literature, there are three main reasons justifying migrant remittances: i) the "insurance" ground that remittances are considered as potential sources of income to insure households against external shocks (Eversole & Johnson, 2014 ; Edward et al., 1996 ; Stark, Edward, & Shlomo, 1986), ii) the motive of "personal interest", which covers investment or corporate objectives as well as personal consumption (Agunias, 2006 ), and iii) the motive of "altruism", which implies that migrants send funds because of emotional ties with countries of origin (Karpestam, 2013 ). Do migrant remittances affect the growth instability in SSA? At the macroeconomic level, remittance flows can play a stabilizing role in economic growth instability by improving the current account balance (Bugamelli & Paterno, 2011 ), and by offsetting the domestic investment deficit of recipient countries (Jidoud, 2015 ). They can support the consumption of households from which migrants come (Combes & Ebeke, 2011 ) and also constitute a source of savings and poverty reduction (Adams & Page, 2005 ). At the microeconomic level, previous literature has argued that remittances help recipient households build resilience and that labour migration is a strategy adopted to reduce family vulnerability in terms of income, food, health and other human insecurities (Mohapatra et al., 2009 ; Yang & Choi, 2007 ). At the microeconomic level, previous literature has argued that remittances help households build resilience and that labour migration is a strategy adopted to reduce family vulnerability in terms of income, food, health, and other human insecurities (Mohapatra et al., 2009 ; Yang & Choi, 2007 ). However, remittance flows can be a source of instability for economic growth since they can increase inflation and demotivate participation in the labour market (Adeniyi et al., 2019 ). Thus, remittance flows could lead to an appreciation of the real exchange rate and ultimately instability in economic growth. Developing economies are particularly vulnerable to negative macroeconomic shocks such as natural disasters, financial crises, conflicts, or terrorism (IMF, 2005 ; Jidoud, 2015 ). If natural disasters tend to degrade economic activity in developing countries, it is perhaps precisely because these countries do not have an insurance system like developed countries (Bugamelli & Paterno, 2011 ; Chami et al., 2012 ; Craigwell et al., 2010 ). Thus, migrants’ income transfers could play this insurance role and enable the economy to better absorb shocks by smoothing consumer spending (Bugamelli & Paterno, 2011 ; Combes & Ebeke, 2011 ; Keho, 2017 ). In this context, the ability of migrant remittances to smooth economic fluctuations depends in particular on the reasons why these remittances are made (Adeniyi et al., 2019 ). If the transfers are made according to an altruistic motive, they should be contracyclic with the economic activity in the country of origin (Chami et al., 2012 ). In this case, remittances act as automatic stabilizers by dampening macroeconomic fluctuations (Bugamelli & Paterno, 2011 ; Chami et al., 2012 ). Moreover, if migrant remittances are made according to an investment motive, they should be procyclical, because they should decrease when the economy of the migrants' home country experiences a slowdown in economic activity (Bugamelli & Paterno, 2011 ). Finally, an increase in the income of emigrants should encourage them to transfer more financial resources to their country of origin in the form of consumption exemptions (Chami et al., 2006 ). Many empirical studies focused on the relationship between remittances and the instability of economic growth in developed and developing countries. For example, Spatafora ( 2005 ) showed that remittances have negative and significant effects on the volatility of overall consumption and investments both in the total sample and in countries dependent on remittances. remittances. In the same line, Bugamelli & Paternò ( 2008 ), argued that remittances have a reducing effect on growth volatility in a sample of emerging and developing countries. Furthermore, the work of Adeniyi et al. ( 2019 ); Chami et al. ( 2012 ); Combes & Ebeke ( 2011 ), and Bugamelli & Paterno ( 2011 ) also showed that remittances negatively influence the volatility of output growth or household consumption, thus affirming the stabilizing effect of migrant remittances. Finally, Lim & Khun ( 2022 ) show that remittances in the presence of labour migration harm the commercial sector of the developing economy, leading to a contraction in aggregate output in developing countries using a macro-dynamic model of two small open economies. Despite this attention to the stabilizing effects of remittances on the instability or volatility of economic growth, empirical research has generally focused on developed or emerging countries (Adeniyi et al., 2019 ; Barajas et al., 2009 ; Bugamelli & Paterno, 2011 ). Empirical studies based on developing countries have focused on emerging economies (Adeniyi et al., 2019 ; Bugamelli & Paterno, 2011 ), while other studies have used mixed country panels (Chami et al., 2008 ; Lim & Khun, 2022 ). These works do not provide specific information on the impact of remittances on the instability of economic growth in SSA. This article examines the stabilizing effects of remittances on economic growth instability in a panel of 45 SSA countries over the period 1980–2019. It makes two contributions to the growing empirical literature on the relationship between remittances and growth instability. First, to the best of our knowledge, this paper is the first to examine exclusively the impact of remittances on growth instability in a large sample of SSA countries. It focuses on the SSA region because this developing region is more dependent on external resources and experiences many episodes of economic instability. According to Arezki & Brückner ( 2012 ), remittance flows can constitute an important external source of development financing for SSA economies and help stabilize negative macroeconomic shocks. Second, we use an econometric technique robust to endogeneity while controlling the effect of other determinants of the instability of economic growth. The generalized method of moments (system-GMM) is used to deal with possible endogeneity problems of our variables. Indeed, most existing macroeconomic studies such as as Azam & Gubert ( 2006 ), and Gupta, Pattillo, & Wagh ( 2009 ) on African countries did not examine the endogeneity problems that are very likely to exist in these studies. Thus, by addressing endogeneity issues using the system-GMM technique at the macro level, this article provides a solid foundation for evidence-based policymaking on the determinants of macroeconomic instability in SSA countries. We structure the rest of the paper as follows. Section 2 reviews the existing literature covering both the direct and indirect effects of remittances on economic growth instability. Section 3 describes the data and the empirical model, while Section 4 analyses the empirical results. Section 5 presents the conclusion and policy implications. 2. Literature Review The vast literature on the relationship between migrant flows and the volatility of economic growth can be divided into two broad categories of empirical studies, in addition to theoretical debates. According to the liberal approach, migration is considered a process reinforcing the optimal allocation of production factors contributing to maximizing gains (Eggoh et al., 2019 ). In contrast, the new economics of labour migration (NELM) argues that migration plays an important role in providing a potential source of investment capital, particularly when credit markets are imperfect, as is the case in many developing countries (Stark, 1996 ; Taylor & Wyatt, 1996 ). In addition, various authors in the literature identified several factors likely to influence growth volatility. These include exogenous shocks (Calderón & Liu, 2003 ; Easterly et al., 1993 ; Matteo & Francesco, 2011), the institutional environment (Acemoglu et al., 2003 ; Rodrik, 1998 ), and remittance flows (Abdih et al., 2012 ; Bugamelli & Paterno, 2011 a; Chami et al., 2012 ; Craigwell et al., 2010 ) The first category includes work that has established a negative relationship between remittances and growth volatility. These suggest that migrant remittances help mitigate growth volatility and promote economic stability. Indeed, (Bugamelli & Paterno, 2011 ), in their paper on 60 emerging and developing countries, show that large flows of migrant remittances can significantly influence the volatility of economic growth. In their view, the characteristics associated with the scale of remittances can effectively smooth consumption and investment, thereby promoting economic stability. As a result, remittances are perceived as financial flows capable of mitigating negative production shocks (Craigwell et al., 2010 ). In other words, remittances could, according to these authors, help to mitigate the negative effects of macroeconomic instability by providing external financial resources to households and businesses. Works such as those by Combes & Ebeke, ( 2011 ) examined the effects of remittances on consumption instability in a panel of 89 developing countries over the period 1975–2004. Their findings highlight three distinct conclusions. First, remittances significantly reduce the instability of household consumption. Secondly, remittances act as a hedge against risks when all other sources of consumption instability are controlled, notably agricultural shocks and discretionary fiscal policies. Third, the risk-hedging role of remittances is more important in countries with a low level of financial development. Furthermore, in research conducted on 70 migrant remittance-receiving countries using the GMM estimator, Chami et al.( 2008) concluded that migrant remittances hurt the volatility of output growth. These results not only support the idea that migrant remittances stabilize production but also confirm the findings of previous studies. In addition, several other studies reinforce the idea that remittances negatively influence economic growth. Research by Woodruff & Zenteno ( 2007 ) and Yang ( 2008 ) converge on three significant conclusions. Firstly, remittances help alleviate credit constraints on household incomes, potentially stimulating entrepreneurial activity and private investment. Secondly, an increase in investment by one household could lead to an increase in income for other households. Finally, a larger remittance could help improve a country's credit rating, providing another avenue for increasing investment in physical and human capital, and encouraging economic growth. The second category of literature concerns work that argues that migrant remittances have a positive impact on growth volatility. According to Chami et al. ( 2012 ), the presence of these remittances constitutes additional evidence of macroeconomic openness, and insofar as these flows are exogenous and volatile, they could induce instability in the recipient economy, comparable to that observed in terms of trade and capital flows. Thus, the works of Ahmed, ( 2000 ); and Kageyama ( 2008 ) argue that migrant remittances could hinder growth and thus accentuate its volatility. Indeed, if these remittances are mainly directed towards non-productive consumption rather than productive investment, they could compromise economic growth and stability. Moreover, large remittance flows could lead to what is known as "Dutch disease", as suggested by Amuedo-Dorantes & Pozo ( 2004 ). Furthermore, Chami et al. ( 2006 ) and Barajas et al., ( 2009 ) showed that remittances negatively impact economic growth. One of the common points in all of this work is that they use very heterogeneous samples from different regions. Such a specification does not allow for the specific effects of countries with similar characteristics, such as those in sub-Saharan Africa. Thus, in this article, we complement the previous literature and examine the relationship between remittances and economic growth instability in a sample made up exclusively of Sub-Saharan African countries. This is justified by the fact that remittances are one of the main sources of external financing for SSA countries. In addition, we renew the literature on the role of remittances as a stabilizing factor for shock absorption in low-income countries. 3. Data and empirical strategy 3.1. Sample and data The study covers 45 countries of Sub-Saharan African countries from 1980 to 2019. 1 . We averaged our sample into 5-year non-overlapping periods to mitigate short-term fluctuations (1980–1984, 1985–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014, 2015–2019). This type of data is preferable to pure time-series or cross-sectional data, as it marries possible inter-temporal dynamics and important cross-country variation. Furthermore, five-year averaging is likely to minimize non-systematic errors in the data. Thus, the calculation of averages aims to reduce the temporal dimension and the number of missing data to improve the efficiency of the system-GMM used. The selection of countries is based on the availability of data. The data come from many sources. The dependent variable measures economic instability and is defined by the rolling standard deviation of the real consumption per capita growth estimated over 5 years. It was calculated from the growth rate of GDP per habitat and is drawn from World Development Indicators (WDI). The choice of the standard deviation as an indicator of the instability of economic growth is justified by the fact that previous works, such as those of Acemoglu et al., ( 2003 ); and Ramey & Ramey ( 1995 ), which also proposed the standard deviation of the growth rate of GDP per capita as a measure of the instability of economic growth, have found that the standard deviation of the growth rate of GDP per capita is a good indicator of the instability of economic growth. The explicative variable is measured by the ratio of remittances to GDP. Moreover, to test robustness, we had real remittances per capita as an alternative measure of the explanatory variable. It was calculated from the ratio between the nominal value of remittances and the total population. They also come from WDI. The selection of control variables aligns with established literature, recognizing these factors as pivotal determinants of economic growth instability. For instance, we include GDP per capita instability measured by the standard deviation of GDP per capita, initial GDP per capita in logarithm, inflation instability defined by the standard deviation of, the ratio of bank-provided private sector credit to GDP, trade measured by the sum of exports and imports to GDP, financial openness measured by the Chinn & Ito ( 2008 ) and ratio of government consumption to GDP). These variables stemmed from the World Bank’s World Development Indicators, except the financial openness index which is from Chinn and Ito ( 2008 ). This choice is supported by studies such as Ramey & Ramey ( 1995 ), which show that instability of GDP per capita and initial GDP per capita is negatively associated with growth and innovation volatility. Also, inflation instability and private sector credit to GDP provided by banks have been used as determinants of economic growth instability by Adeniyi et al. ( 2019 ); Combes & Ebeke ( 2011 ); and Ahamada & Coulibaly ( 2011 ). Mireku et al. ( 2017 ) found that both long and short-run economic growth volatility is positively affected by changes in trade openness, while financial openness contributes negatively to economic growth volatility in the short run. In addition, Yuan et al. ( 2022 ) also show that financial openness leads to an increase in output volatility in China. Details of variables, calculations, sources, and data description are presented in Table 1 . Table 1 Descriptive statistics of variables . Variable Source Obs Mean Std. dev. Min Max GDP per capita growth. instability Calculation of authors from WDI 350 3.768 4.142 0.1609 53.500 Remittances (% of GDP) WDI 293 4.946 18.220 0.0019 201.618 Log (nominal remittances per capita) Calculation of Author’s from WDI 293 10.652 2.497 2.394529 15.998 GDP per capita instability Calculation of authors from WDI 351 111.7544 265.180 2.172656 3230.833 Log (Initial GDP per capita) WDI 351 6.952 1.029 5.036322 9.812 Inflation instability Calculation of authors from WDI 350 53.671 609.952 0.16043 11161.55 Private credit ratio (% of GDP) WDI 271 19.361 28.951 1.7815 353.535 Trade openness WDI 327 69.214 34.629 12.876 229.637 Government consumption (% of GDP) WDI 318 15.430 7.881 0 74.270 Financial openness Chinn & Ito ( 2008 ) 343 1.2587 1.093 0.0895 4.359 3.2. Econometric specification To analyze the impact of remittance flows on economic growth in SSA, we draw on the work of Adeniyi et al. ( 2019 ); Ahamada & Coulibaly( 2011); Chami et al. ( 2012 ); Ramey & Ramey ( 1995 ), etc. Although these previous works have examined the relationship between remittances, financial development, and economic growth volatility, they are limited in scope because they do not show the specific effects of remittance inflows with countries that share common characteristics. In this context, to show how remittances affect economic growth instability in SSA, the empirical model estimates remittances and other controlling factors on economic growth instability. Given the potential inertia of the variables, particularly the instability of economic growth, we use a dynamic specification. Thus, given the strong inertia of the instability of economic growth, the dynamic specification is given in Eq. (1): $$\:{Instability}_{i,t}^{g}=\alpha\:+\delta\:{Instability}_{i,t-1}^{g}+\beta\:{REM}_{i,t}+\theta\:{X}_{i,t}^{{\prime\:}}+{\vartheta\:}_{i}+{\phi\:}_{t}+{\epsilon\:}_{i,t}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ Where \(\:{Instability}_{,t}^{g}\) measures economic instability, \(\:{REM}_{i,t}\) . is the ratio of remittances to GDP. \(\:{X}_{i,t}^{{\prime\:}}\) stands for control variables. \(\:{\vartheta\:}_{i}\) was introduced to consider unobserved and time-invariant national characteristics that could be correlated with economic instability. \(\:{\phi\:}_{t}\) to control for time-varying. \(\:{\epsilon\:}_{i,t}\) is a standard error term. \(\:i\) and \(\:t\) represent the country and the nonoverlapping 5-year period, respectively. The presence of endogenous variables such as remittance inflows (Adeniyi et al., 2019 ; Chami et al., 2012 ; Combes & Ebeke, 2011 ) and the autoregressive term as an explanatory variable inevitably leads to endogeneity problems. The most appropriate estimator to account for these potential endogeneity problems and the dynamic effects of remittances is the system-GMM estimator of Blundell & Bond ( 1998 ). This estimator is preferred for two reasons. Firstly, the OLS estimator is inconsistent when the one-period lagged dependent variable, correlated with the error term, is introduced with country-fixed effects (Nickell, 1981 ). Secondly, the system-GMM estimator controls for the potential endogeneity of the explanatory variables due to any measurement errors, reverse causality or omission of pertinent variables. Thus, if an increase in economic growth instability leads to an increase or decrease in remittances, the OLS estimator for \(\:\beta\:\) is biased due to simultaneity. In terms of measurement error, the volume of a country's remittance flows is derived from World Bank and IMF estimates, which are mainly based on funds repatriated through official channels, both by them and by the banks, whereas in SSA informal transfers account for a significant share that is not recorded in the balance of payments. According to Irving et al. ( 2010 ), and The World Bank ( 2011 ), these unofficial transfers could therefore be underestimated by up to 50% in official figures. This situation suggests that the measurement of remittance inflows may be subject to error. To consider these sources of endogeneity, we employ a dynamic model that estimates the level of economic growth volatility on its lagged value and a set of control variables including remittances. Hence, we therefore use the System-GMM estimator developed by Blundell & Bond ( 1998 ) to address these issues. Indeed, for this estimator, the instrumentation procedure has been performed to limit the problem of too many instruments (Roodman, 2009 ). We use also the two-step GMM procedure with Windmeijer's (2005) finite-sample correction. To validate the econometric technique, we use the Hansen test and the Arellano and Bond test to verify that there is an absence of second-order correlation and the presence of first-order correlation. 4. Empirical results We examine the empirical results, which are presented in Table 2 . The basic empirical model, valuing using OLS with country fixed effects (columns 1 and 2) and the system-GMM (columns 3 and 4), included the lagged value of the dependent variable. Diagnostic test results reveal that all models are well specified. The Hansen test does not reject the validity of the instruments (Hansen test p-values ≥ 10), nor is the absence of second-order serial correlation rejected (AR (2) p-values ≥ 10). The presence of too many instruments can seriously weaken and bias Hansen's overidentification restrictions test and, therefore, the general rule is to use fewer instruments than countries (Roodman, 2009 ). In all the regressions (columns 3 to 4), the number of countries exceeds the number of instruments, indicating that there is no problem with instrument proliferation. Furthermore, the coefficient of the lagged dependent variable is significant (column 1) thus confirming an inertia effect and the use of a dynamic panel. The empirical evidence supports our hypothesis. We find that the remittances significantly reduce the economic growth instability in SSA. Indeed, the coefficients associated with remittance inflows are negative and strongly significant at the 5 per cent level in Table 2 , columns (2–4). More precisely, a one standard deviation increase in remittances leads to a decline in growth instability by 0.06 point percentage (column 4) 2 . Thus, the results confirm that remittances act as automatic stabilizers by dampening macroeconomic fluctuations in SSA countries. These results suggest that unlike other foreign capital flows (FDI and external debt), which are procyclical in developing countries (Contessi et al., 2013 ; Kaminsky et al., 2005 ), remittance inflows constitute a more stable source of external financing for the economy, with the potential to stabilize the instability of economic growth in SSA. The negative influence of remittance inflows on economic growth instability is in line with Spatafora ( 2005 ), Craigwell et al., ( 2010 ), Chami et al., ( 2012 ) and Adeniyi et al. ( 2019 ) which also found a significant relationship between remittances and consumption. However, it should be noted that our work differs from precious works from the fact that we selected SSA countries that are more dependent on remittances. Turning now to the control variables, we have found that they are all consistent with the empirical literature (Adeniyi et al. 2019 ; Combes & Ebeke, 2011 ; Mireku et al., 2017 ). Thus, GDP per capita instability and inflation instability have a positive and significant effect on economic growth instability, while initial GDP per capita reduces economic growth instability. For example, a one-point increase in the GDP per capita instability, and inflation instability leads to an increase in the volatility of economic growth of 0.0111 and 0.175 points percentage respectively (column 4). The effect of other control variables is not significant. Table 2 Baseline Results: Remittances and Growth Instability Dependent variable: GDP per capita growth. instability (1) (2) (3) (4) Fixed effects System-GMM Capita growth Instability, t-1 0.113* (0.0663) -0.0429 (0.0803) 0.478*** (0.171) -0.0276 (0.141) Remittances (% of GDP) -0.0127* (0.00658) -0.274** (0.110) -0.00935** (0.00362) -0.0669** (0.0313) GDP per capita instability 0.0169*** (0.00392) 0.0116*** (0.00251) 0.0229*** (0.00323) 0.0111*** (0.00344) Log (Initial GDP per capita) -3.001*** (0.762) -5.025*** (1.370) -2.560*** (0.477) -1.318*** (0.375) Inflation instability 0.00154*** (0.000461) 0.0724*** (0.0199) 0.00111 (0.00153) 0.175** (0.0800) Private credit ratio (% of GDP) 0.00367 (0.0304) -0.00548 (0.00811) Trade openness -0.00977 (0.0113) 0.00397 (0.00715) Government consumption (% of GDP) 0.0154 (0.0299) 0.0203 (0.0201) Financial openness 0.329 (0.329) -0.100 (0.279) Constant 22.14*** (5.192) 37.12*** (9.094) 16.51*** (3.051) 9.699*** (2.651) Observations 262 185 262 185 R-squared 0.380 0.233 Number of countries 45 40 45 40 AR(1): p-value 0.001 0.066 AR(2): p-value 0.687 0.234 Hansen p-value 0.528 0.342 Instruments 28 36 Note: the estimation method is a two-step System-GMM with Windmeijer's (2005) small sample robust correction. Robust standard errors are in parentheses. *** p < 0.01, significant at 1%, ** p < 0.05 significant at 5%, * p < 0.1 significant at 10%. To check the robustness of the results, we use an alternative measure of independent variable by remittance inflows per capita. The results are presented in Table 3 . Robustness test results corroborate those found in preliminary analyses confirming the hypothesis that remittance inflows reduce the economic growth instability in SSA. The coefficients associated with remittances are negative and statistical in all columns (1–4) and confirm the extern source of stabilisation of the remittances in SSA. The effects of the control variable are qualitatively unchanged. Table 3 Remittances and growth instability: alternative measures of remittances Dependent variable: GDP per capita growth instability (1) (2) (3) (4) Fixed effects System-GMM Capita growth. Instability, t-1 0.0770 (0.0653) -0.0466 (0.0766) 0.166 (0.192) -0.214 (0.145) log (Nominal remittances per capita) -0.305** (0.133) -0.326** (0.149) -0.680*** (0.241) -0.629** (0.241) GDP per capita instability 0.0164*** (0.00403) 0.0109*** (0.00242) 0.0204*** (0.00410) 0.00941*** (0.00237) Log (Initial GDP per capita) -2.846*** (0.730) -4.190*** (1.175) -3.176*** (0.581) -1.800*** (0.374) Inflation instability 0.00150*** (0.000419) 0.0633*** (0.0178) 0.00128 (0.00155) 0.0611** (0.0263) Private credit ratio (% of GDP) 0.00514 (0.0297) 0.00594 (0.0104) Trade openness -0.00730 (0.0129) -0.00296 (0.00688) Government consumption (% of GDP) -0.00285 (0.0295) -0.0150 (0.0353) Financial openness 0.0956 (0.344) -0.296 (0.296) Constant 24.48*** (5.460) 34.66*** (8.489) 30.11*** (5.836) 22.53*** (4.926) Observations 262 185 262 185 R-squared 0.396 0.226 Number of countries 45 40 45 40 AR(1): p-value 0.015 0.030 AR(2): p-value 0.609 0.226 Hansen p-value 0.432 0.497 Instruments 28 39 Note: the estimation method is a two-step System-GMM with Windmeijer's (2005) small sample robust correction. Robust standard errors are in parentheses. *** p < 0.01, significant at 1%, ** p < 0.05 significant at 5%, * p < 0.1 significant at 10%. 5. Concluding remarks Our paper extends the literature by examining the impact of remittances on economic growth instability. Unlike previous literature (Abdih et al., 2012 ; Adeniyi et al., 2019 ; Bugamelli & Paterno, 2011 ; Craigwell et al., 2010 , etc.) which examined the effect of remittances on macroeconomic instability in samples from both developing countries and some sub-Saharan African countries. Our study exclusively focused on Sub-Saharan African countries. Using a sample exclusively made up of 45 SSA countries during the period 1980–2019 and considering the possible endogeneity problem, the empirical results suggest that remittances on average play a stabilizing role in chocs of economic growth. Specifically, a one standard deviation increase in remittances leads to a decline in economic growth instability by 0.06 point percentage. Remittances provide a more stable source of external financing for the economy, with the potential to stabilize the instability of economic growth in SSA. The findings renew the debate on the stabilizing role of remittances in times of economic instability. They suggest that countries that receive more migrant remittances have an additional source of stabilizing the chocs of instability. In consequence, It would therefore be good for SSA countries to continue to promote policies that make remittances attractive to their expatriate citizens. Specifically, they should set up investment facilitation programs for emigrants and reduce the cost of these transfers. This paper has focused on examining the impact of remittance flows on the instability of economic growth, without examining the impact of other external financial flows such as FDI and external debt, and this is its main limitation. Furthermore, at the level of instability analysis, it did not take into account other macroeconomic instability variables such as consumption, tax revenues and public expenditure in the analysis. Therefore, it would be interesting to extend this study to these external flows and other factors of macroeconomic instability. Furthermore, at the level of instability analysis, other macroeconomic instability variables such as consumption, tax revenues and public expenditure were not included in the analysis. It would therefore be interesting to extend this study to include these external flows and other factors of macroeconomic instability. Declarations Funding This work was not supported by any funding Disclosure statement No potential conflict of interest was reported by the author(s). Disclosure of interest No potential conflict of interest was reported by the author(s) Data availability statement The data that support the findings of this study are available from the corresponding author, upon reasonable request. Author Contribution R. S.: Writing – original draft, Data curation, Visualization, Validation, Software, Supervision, Software, Methodology, ConceptualizationG. S.: Writing – original draft, Data curation, Visualization, Validation, Software, Supervision, , ConceptualizationD. S.: Writing –review– original draft, Validation, Investigation, Formal analysis, Data curation, ConceptualizationFunding sourcesThis research did not receive any specific grant from fundingagencies in the public, commercial, or not-for-profit sectors.Declaration of competing interestThe authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influencethe work reported in this paper References Abdih Y, Chami R, Dagher J, Montiel P (2012) Remittances and institutions: Are remittances a curse? World Dev 40(4):657–666. https://doi.org/10.1016/j.worlddev.2011.09.014 Acemoglu D, Johnson S, Robinson J, Thaicharoen Y (2003) Institutional causes, macroeconomic symptoms: Volatility, crises and growth. J Monet Econ 50(1):49–123. https://doi.org/10.1016/S0304-3932(02)00208-8 Adams RH, Page J (2005) Do international migration and remittances reduce poverty in developing countries? 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Economica 78(311):480–500 Bugamelli M, Paternò F (2008) Output Growth volatility and remittances (673; Working Paper) Calderón C, Liu L (2003) The direction of causality between financial development and economic growth. J Dev Econ 72(1):321–334. https://doi.org/10.1016/S0304-3878(03)00079-8 Chami R, Barajas A, Cosimano T, Fullenkamp C, Gapen M, Montiel P (2008) The macroeconomic consequences of remittances. In Ocassional Papet (Vol. 256). https://doi.org/10.1016/j.jinteco.2018.01.010 Chami R, Cosimano TF, Gapen MT (2006) Beware of Emigrants Bearing Gifts: Optimal Fiscal and Monetary Policy in the Presence of Remittances (WP/06/61; Working Paper). https://doi.org/10.5089/9781451863215.001 Chami R, Hakura DS, Montiel PJ (2012) Do Worker Remittances Reduce Output Volatility in Developing Countries ? Volatility in Developing Countries ? J Globalization Dev 3(1). https://doi.org/10.1515/1948-1837.1151 Chinn MD, Ito H (2008) A New Measure of Financial Openness. J Comp Policy Analysis: Res Pratice 10(3):309–322. https://doi.org/10.1080/13876980802231123 Combes JL, Ebeke C (2011) Remittances and Household Consumption Instability in Developing Countries. World Dev 39(7):1076–1089. https://doi.org/10.1016/j.worlddev.2010.10.006 Contessi S, Li L, Russ K (2013) Bank vs. Bond Financing Over the Business Cycle. Economic Synopses 2013(31). https://doi.org/10.20955/es.2013.31 Coulibaly A, Yogo UT (2020) The path to shared prosperity: Leveraging financial services outreach to create decent jobs in developing countries. Economic Modelling , 87 (August 2018), 131–147. https://doi.org/10.1016/j.econmod.2019.07.013 Craigwell R, Jackman M, Moore W (2010) Economic volatility and remittances. Int J Dev Issues 9(1):25–42. https://doi.org/10.1108/14468951011033789 De S, Islamaj E, Kose MA, Reza Yousefi S (2016) Remittances over the business cycle: Theory and evidence. In Economic Notes (Vol. 48, Issue 3). https://doi.org/10.1111/ecno.12143 Easterly W, Kremer M, Pritchett L, Summers LH (1993) Good policy or good luck? Country growth performance and temporary shocks. J Monet Econ 32(3):459–483. https://doi.org/10.1016/0304-3932(93)90026-C Eggoh J, Bangake C, Semedo G (2019) Do remittances spur economic growth? Evidence from developing countries. J Int Trade Econ Dev 28(4):391–418. https://doi.org/10.1080/09638199.2019.1568522 Eversole R, Johnson M (2014) Migrant remittances and household development: an anthropological analysis. Dev Stud Research: Open Access J 1(1):1–15. https://doi.org/10.1080/21665095.2014.903808 Gupta S, Pattillo CA, Wagh S (2009) Effect of Remittances on Poverty and Financial Development in Sub-Saharan Africa. World Dev 37(1):104–115. https://doi.org/10.1016/j.worlddev.2008.05.007 IMF (2005) World Economic Outlook, April 2005: globalization and external imbalances. In World Economic and Financial Surveys (Issue September 2002, pp. 109–156) Irving J, Mohapatra S, Ratha D (2010) Migrant Remittance Flows. Findings from a Global Survey of Central Banks (No. 194; Bank Working Paper) Edward J, Joaquín T, Graeme A, Ali H, Douglas K S., M., Adela P (1996) International Migration and Community Development. Popul Index 62(3):397–418 Jidoud A (2015) Remittances and Macroeconomic Volatility in African Countries (IMF Working Paper; WP/15/49) Kageyama A (2008) Extent of poverty alleviation by migrant remittances in Sri Lanka. South Asia Res 28(1):89–108. https://doi.org/10.1177/026272800702800105 Kaminsky GL, Reinhart CM, Végh CA (2005) When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies. In NBER Macroeconomics Annual (Vol. 19, Issue April). https://doi.org/10.1086/ma.19.3585325 Karpestam P and N. G. F. A (2013) Economic perspectives on migration. In: Gold SJ, Nawyn SJ (eds) The Routledge International Handbook of Migration Studies. Routledge Keho Y (2017) Effect of remittances on household consumption in African and Asian countries: A quantile regression approach. Econ Bull 37(3):1753–1767 Lim S, Khun C (2022) Macroeconomic impacts of remittances: A two-country, two-sector model. Journal of Macroeconomics , 73 (May 2021), 1–24. https://doi.org/10.1016/j.jmacro.2022.103443 Mireku K, Animah Agyei E, Domeher D (2017) Trade openness and economic growth volatility: An empirical investigation. Cogent Econ Finance 5(1). https://doi.org/10.1080/23322039.2017.1385438 Mohapatra S, Joseph G, Ratha D (2009) Remittances and natural disasters: Ex-post response and contribution to ex-ante preparedness. In Policy Research Working Paper (Vol. 4972). https://doi.org/10.1007/s10668-011-9330-8 Nickell S (1981) Biases in dynamic models with fixed effects. Econometrica 49(6):1417–1426. https://doi.org/10.1016/0165-1765(88)90046-8 Ramey G, Ramey A, V (1995) Cross-country evidence on the link between inflation volatility and growth. Am Econ Rev 85(5):1138–1151. https://doi.org/10.1080/000368498324931 Ramey G, Ramey V (1995) Cross-Country Evidence on the Link Between Volatility and Growth. Am Econ Rev 85(5):1138–1151. https://doi.org/10.3386/w4959 Rodrik D (1998) Why do more open economies have bigger governments? J Polit Econ 106(5):997–1032. https://doi.org/10.1086/250038 Roodman D (2009) A Note on the Theme of Too Many Instruments. Oxf Bull Econ Stat 71(1):0305–9049. https://doi.org/10.1111/j.1468-0084.2008.00542.x Spatafora N (2005) Worker’s remittances and economic development. In World Economic Outlook: Globalization and External Imbalances (pp. 69–107). International Monetary Fund Stark O (1996) On the microeconomics of return migration. In: M. LP (ed) (Ed.), Trade and development:: Essays in Honour of Jagdish Bhagwati. Pa, London, pp 32–41. https://doi.org/10.1007/978-1-349-25040-0_3 UK Stark O, Edward J, T., Shlomo. Remittances and Inequality Author (s):, Stark O (1986) J. Edward Taylor and Shlomo Yitzhaki Published by : Wiley on behalf of the Royal Economic Society Stable URL : http://www.jstor.org/stable/2232987 Accessed : 14-06-2016 06 : 16 UTC. The Economic Journal , 96 (383), 722–740 Taylor JE, Wyatt TJ (1996) The shadow value of migrant remittances, income and inequality in a household-farm economy. J Dev Stud 32(6):899–912. https://doi.org/10.1080/00220389608422445 The World Bank (2011) Migration and Remittances Factbook 2011 Second Edition Windmeijer F (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. J Econ 126(1):25–51. https://doi.org/10.1016/j.jeconom.2004.02.005 Woodruff C, Zenteno R (2007) Migration networks and microenterprises in Mexico. J Dev Econ 82(2):509–528. https://doi.org/10.1016/j.jdeveco.2006.03.006 Yang D (2008) International migration, remittances and household investment: Evidence from Philippine migrants’ exchange rate shocks. Econ J 118(528):591–630. https://doi.org/10.1111/j.1468-0297.2008.02134.x Yang D, Choi HJ (2007) Are remittances insurance? Evidence from rainfall shocks in the Philippines. World Bank Economic Rev 21(2):219–248. https://doi.org/10.1093/wber/lhm003 Yuan S, Wu Z, Liu L (2022) The effects of financial openness and financial efficiency on Chinese macroeconomic volatilities. North Am J Econ Finance 63(January):101819. https://doi.org/10.1016/j.najef.2022.101819 Footnotes The list of countries is presented in Table A- 1 in the Appendix. This number is obtained by multiplying the coefficient estimated by the mean of GDP per capita growth volatility, and then dividing by the standard deviation of GDP per capita growth volatility. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6682234","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469204040,"identity":"8a741220-3266-4e70-8166-d48d7c98b3e3","order_by":0,"name":"Relwendé SAWADOGO","email":"data:image/png;base64,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","orcid":"","institution":"Institut Burkinabè des Arts et Métiers (IBAM), Université Joseph Ki-Zerbo, Burkina Faso","correspondingAuthor":true,"prefix":"","firstName":"Relwendé","middleName":"","lastName":"SAWADOGO","suffix":""},{"id":469204042,"identity":"1b2e5865-2830-4371-8811-e0ee108cdba9","order_by":1,"name":"Gervasio SEMEDO","email":"","orcid":"","institution":"Laboratoire d’Economie d’Orléans (LEO), Université de Tours","correspondingAuthor":false,"prefix":"","firstName":"Gervasio","middleName":"","lastName":"SEMEDO","suffix":""},{"id":469204045,"identity":"b7fcb691-a4d3-464f-a3cc-d53ebdd573de","order_by":2,"name":"Drissa SAWADOGO","email":"","orcid":"","institution":"Institut Burkinabè des Arts et Métiers (IBAM), Université Joseph Ki-Zerbo, Burkina Faso","correspondingAuthor":false,"prefix":"","firstName":"Drissa","middleName":"","lastName":"SAWADOGO","suffix":""}],"badges":[],"createdAt":"2025-05-16 16:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6682234/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6682234/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84578694,"identity":"76d2a11c-56e0-4564-8f3e-06d0ab6b499c","added_by":"auto","created_at":"2025-06-13 17:49:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":878508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6682234/v1/b1212e37-ce94-4b4a-a7d6-5ebfe5e7f3b0.pdf"},{"id":84577905,"identity":"88949faf-6afd-4f6c-820d-e45a408206df","added_by":"auto","created_at":"2025-06-13 17:33:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14103,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6682234/v1/3871c36a1afeb19d260a11da.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do remittance inflows reduce growth instability in Sub-Saharan Africa?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRemittances have become in recent decades one of the important sources of external financing in developing countries, particularly in Sub-Saharan Africa. Remittance flows to low, and middle-income countries rose from around US\u003cspan\u003e$\u003c/span\u003e 176\u0026nbsp;billion in 1980 to US\u003cspan\u003e$\u003c/span\u003e 568\u0026nbsp;billion in 2021 (World Bank's World Development Indicators, WDI database). During the same period, in Sub-Saharan Africa (SSA), the amount increased from US\u003cspan\u003e$\u003c/span\u003e 1.39\u0026nbsp;billion to about US\u003cspan\u003e$\u003c/span\u003e 49\u0026nbsp;billion, representing a growth of 34%. Thus, remittances represent a substantial share of international capital flows and could play a significant role in stabilizing SSA countries\u0026rsquo; economies. According to literature, there are three main reasons justifying migrant remittances: i) the \"insurance\" ground that remittances are considered as potential sources of income to insure households against external shocks (Eversole \u0026amp; Johnson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Edward et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Stark, Edward, \u0026amp; Shlomo, 1986), ii) the motive of \"personal interest\", which covers investment or corporate objectives as well as personal consumption (Agunias, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and iii) the motive of \"altruism\", which implies that migrants send funds because of emotional ties with countries of origin (Karpestam, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDo migrant remittances affect the growth instability in SSA? At the macroeconomic level, remittance flows can play a stabilizing role in economic growth instability by improving the current account balance (Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and by offsetting the domestic investment deficit of recipient countries (Jidoud, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). They can support the consumption of households from which migrants come (Combes \u0026amp; Ebeke, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and also constitute a source of savings and poverty reduction (Adams \u0026amp; Page, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). At the microeconomic level, previous literature has argued that remittances help recipient households build resilience and that labour migration is a strategy adopted to reduce family vulnerability in terms of income, food, health and other human insecurities (Mohapatra et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yang \u0026amp; Choi, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). At the microeconomic level, previous literature has argued that remittances help households build resilience and that labour migration is a strategy adopted to reduce family vulnerability in terms of income, food, health, and other human insecurities (Mohapatra et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yang \u0026amp; Choi, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, remittance flows can be a source of instability for economic growth since they can increase inflation and demotivate participation in the labour market (Adeniyi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, remittance flows could lead to an appreciation of the real exchange rate and ultimately instability in economic growth.\u003c/p\u003e \u003cp\u003eDeveloping economies are particularly vulnerable to negative macroeconomic shocks such as natural disasters, financial crises, conflicts, or terrorism (IMF, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jidoud, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). If natural disasters tend to degrade economic activity in developing countries, it is perhaps precisely because these countries do not have an insurance system like developed countries (Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chami et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Craigwell et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Thus, migrants\u0026rsquo; income transfers could play this insurance role and enable the economy to better absorb shocks by smoothing consumer spending (Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Combes \u0026amp; Ebeke, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Keho, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this context, the ability of migrant remittances to smooth economic fluctuations depends in particular on the reasons why these remittances are made (Adeniyi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). If the transfers are made according to an altruistic motive, they should be contracyclic with the economic activity in the country of origin (Chami et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this case, remittances act as automatic stabilizers by dampening macroeconomic fluctuations (Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chami et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, if migrant remittances are made according to an investment motive, they should be procyclical, because they should decrease when the economy of the migrants' home country experiences a slowdown in economic activity (Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Finally, an increase in the income of emigrants should encourage them to transfer more financial resources to their country of origin in the form of consumption exemptions (Chami et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany empirical studies focused on the relationship between remittances and the instability of economic growth in developed and developing countries. For example, Spatafora (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) showed that remittances have negative and significant effects on the volatility of overall consumption and investments both in the total sample and in countries dependent on remittances. remittances. In the same line, Bugamelli \u0026amp; Patern\u0026ograve; (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), argued that remittances have a reducing effect on growth volatility in a sample of emerging and developing countries. Furthermore, the work of Adeniyi et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Chami et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); Combes \u0026amp; Ebeke (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and Bugamelli \u0026amp; Paterno (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) also showed that remittances negatively influence the volatility of output growth or household consumption, thus affirming the stabilizing effect of migrant remittances. Finally, Lim \u0026amp; Khun (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) show that remittances in the presence of labour migration harm the commercial sector of the developing economy, leading to a contraction in aggregate output in developing countries using a macro-dynamic model of two small open economies.\u003c/p\u003e \u003cp\u003eDespite this attention to the stabilizing effects of remittances on the instability or volatility of economic growth, empirical research has generally focused on developed or emerging countries (Adeniyi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Barajas et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Empirical studies based on developing countries have focused on emerging economies (Adeniyi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), while other studies have used mixed country panels (Chami et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lim \u0026amp; Khun, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These works do not provide specific information on the impact of remittances on the instability of economic growth in SSA.\u003c/p\u003e \u003cp\u003eThis article examines the stabilizing effects of remittances on economic growth instability in a panel of 45 SSA countries over the period 1980\u0026ndash;2019. It makes two contributions to the growing empirical literature on the relationship between remittances and growth instability. First, to the best of our knowledge, this paper is the first to examine exclusively the impact of remittances on growth instability in a large sample of SSA countries. It focuses on the SSA region because this developing region is more dependent on external resources and experiences many episodes of economic instability. According to Arezki \u0026amp; Br\u0026uuml;ckner (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), remittance flows can constitute an important external source of development financing for SSA economies and help stabilize negative macroeconomic shocks. Second, we use an econometric technique robust to endogeneity while controlling the effect of other determinants of the instability of economic growth. The generalized method of moments (system-GMM) is used to deal with possible endogeneity problems of our variables. Indeed, most existing macroeconomic studies such as as Azam \u0026amp; Gubert (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and Gupta, Pattillo, \u0026amp; Wagh (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) on African countries did not examine the endogeneity problems that are very likely to exist in these studies. Thus, by addressing endogeneity issues using the system-GMM technique at the macro level, this article provides a solid foundation for evidence-based policymaking on the determinants of macroeconomic instability in SSA countries.\u003c/p\u003e \u003cp\u003eWe structure the rest of the paper as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the existing literature covering both the direct and indirect effects of remittances on economic growth instability. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the data and the empirical model, while Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e4\u003c/span\u003e analyses the empirical results. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the conclusion and policy implications.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe vast literature on the relationship between migrant flows and the volatility of economic growth can be divided into two broad categories of empirical studies, in addition to theoretical debates. According to the liberal approach, migration is considered a process reinforcing the optimal allocation of production factors contributing to maximizing gains (Eggoh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, the new economics of labour migration (NELM) argues that migration plays an important role in providing a potential source of investment capital, particularly when credit markets are imperfect, as is the case in many developing countries (Stark, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Taylor \u0026amp; Wyatt, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In addition, various authors in the literature identified several factors likely to influence growth volatility. These include exogenous shocks (Calder\u0026oacute;n \u0026amp; Liu, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Easterly et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Matteo \u0026amp; Francesco, 2011), the institutional environment (Acemoglu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rodrik, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and remittance flows (Abdih et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003ea; Chami et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Craigwell et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe first category includes work that has established a negative relationship between remittances and growth volatility. These suggest that migrant remittances help mitigate growth volatility and promote economic stability. Indeed, (Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), in their paper on 60 emerging and developing countries, show that large flows of migrant remittances can significantly influence the volatility of economic growth. In their view, the characteristics associated with the scale of remittances can effectively smooth consumption and investment, thereby promoting economic stability. As a result, remittances are perceived as financial flows capable of mitigating negative production shocks (Craigwell et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In other words, remittances could, according to these authors, help to mitigate the negative effects of macroeconomic instability by providing external financial resources to households and businesses. Works such as those by Combes \u0026amp; Ebeke, (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) examined the effects of remittances on consumption instability in a panel of 89 developing countries over the period 1975\u0026ndash;2004. Their findings highlight three distinct conclusions. First, remittances significantly reduce the instability of household consumption. Secondly, remittances act as a hedge against risks when all other sources of consumption instability are controlled, notably agricultural shocks and discretionary fiscal policies. Third, the risk-hedging role of remittances is more important in countries with a low level of financial development.\u003c/p\u003e \u003cp\u003eFurthermore, in research conducted on 70 migrant remittance-receiving countries using the GMM estimator, Chami et al.( 2008) concluded that migrant remittances hurt the volatility of output growth. These results not only support the idea that migrant remittances stabilize production but also confirm the findings of previous studies. In addition, several other studies reinforce the idea that remittances negatively influence economic growth. Research by Woodruff \u0026amp; Zenteno (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Yang (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) converge on three significant conclusions. Firstly, remittances help alleviate credit constraints on household incomes, potentially stimulating entrepreneurial activity and private investment. Secondly, an increase in investment by one household could lead to an increase in income for other households. Finally, a larger remittance could help improve a country's credit rating, providing another avenue for increasing investment in physical and human capital, and encouraging economic growth.\u003c/p\u003e \u003cp\u003eThe second category of literature concerns work that argues that migrant remittances have a positive impact on growth volatility. According to Chami et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the presence of these remittances constitutes additional evidence of macroeconomic openness, and insofar as these flows are exogenous and volatile, they could induce instability in the recipient economy, comparable to that observed in terms of trade and capital flows. Thus, the works of Ahmed, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); and Kageyama (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) argue that migrant remittances could hinder growth and thus accentuate its volatility. Indeed, if these remittances are mainly directed towards non-productive consumption rather than productive investment, they could compromise economic growth and stability. Moreover, large remittance flows could lead to what is known as \"Dutch disease\", as suggested by Amuedo-Dorantes \u0026amp; Pozo (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Furthermore, Chami et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Barajas et al., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) showed that remittances negatively impact economic growth.\u003c/p\u003e \u003cp\u003eOne of the common points in all of this work is that they use very heterogeneous samples from different regions. Such a specification does not allow for the specific effects of countries with similar characteristics, such as those in sub-Saharan Africa. Thus, in this article, we complement the previous literature and examine the relationship between remittances and economic growth instability in a sample made up exclusively of Sub-Saharan African countries. This is justified by the fact that remittances are one of the main sources of external financing for SSA countries. In addition, we renew the literature on the role of remittances as a stabilizing factor for shock absorption in low-income countries.\u003c/p\u003e"},{"header":"3. Data and empirical strategy","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Sample and data\u003c/h2\u003e\n \u003cp\u003eThe study covers 45 countries of Sub-Saharan African countries from 1980 to 2019.\u003csup\u003e1\u003c/sup\u003e. We averaged our sample into 5-year non-overlapping periods to mitigate short-term fluctuations (1980\u0026ndash;1984, 1985\u0026ndash;1989, 1990\u0026ndash;1994, 1995\u0026ndash;1999, 2000\u0026ndash;2004, 2005\u0026ndash;2009, 2010\u0026ndash;2014, 2015\u0026ndash;2019). This type of data is preferable to pure time-series or cross-sectional data, as it marries possible inter-temporal dynamics and important cross-country variation. Furthermore, five-year averaging is likely to minimize non-systematic errors in the data. Thus, the calculation of averages aims to reduce the temporal dimension and the number of missing data to improve the efficiency of the system-GMM used.\u003c/p\u003e\n \u003cp\u003eThe selection of countries is based on the availability of data. The data come from many sources. The dependent variable measures economic instability and is defined by the rolling standard deviation of the real consumption per capita growth estimated over 5 years. It was calculated from the growth rate of GDP per habitat and is drawn from \u003cem\u003eWorld Development Indicators\u003c/em\u003e (WDI). The choice of the standard deviation as an indicator of the instability of economic growth is justified by the fact that previous works, such as those of Acemoglu et al., (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e); and Ramey \u0026amp; Ramey (\u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e), which also proposed the standard deviation of the growth rate of GDP per capita as a measure of the instability of economic growth, have found that the standard deviation of the growth rate of GDP per capita is a good indicator of the instability of economic growth. The explicative variable is measured by the ratio of remittances to GDP. Moreover, to test robustness, we had real remittances per capita as an alternative measure of the explanatory variable. It was calculated from the ratio between the nominal value of remittances and the total population. They also come from WDI.\u003c/p\u003e\n \u003cp\u003eThe selection of control variables aligns with established literature, recognizing these factors as pivotal determinants of economic growth instability. For instance, we include GDP per capita instability measured by the standard deviation of GDP per capita, initial GDP per capita in logarithm, inflation instability defined by the standard deviation of, the ratio of bank-provided private sector credit to GDP, trade measured by the sum of exports and imports to GDP, financial openness measured by the Chinn \u0026amp; Ito (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) and ratio of government consumption to GDP). These variables stemmed from the World Bank\u0026rsquo;s World Development Indicators, except the financial openness index which is from Chinn and Ito (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThis choice is supported by studies such as Ramey \u0026amp; Ramey (\u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e), which show that instability of GDP per capita and initial GDP per capita is negatively associated with growth and innovation volatility. Also, inflation instability and private sector credit to GDP provided by banks have been used as determinants of economic growth instability by Adeniyi et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Combes \u0026amp; Ebeke (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e); and Ahamada \u0026amp; Coulibaly (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Mireku et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that both long and short-run economic growth volatility is positively affected by changes in trade openness, while financial openness contributes negatively to economic growth volatility in the short run. In addition, Yuan et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) also show that financial openness leads to an increase in output volatility in China. Details of variables, calculations, sources, and data description are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive statistics of variables\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita growth. instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalculation of authors from WDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemittances (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201.618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog (nominal remittances per capita)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalculation of Author\u0026rsquo;s from WDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.394529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalculation of authors from WDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.7544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.172656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3230.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog (Initial GDP per capita)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.036322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflation instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalculation of authors from WDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e609.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11161.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate credit ratio (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e353.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrade openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment consumption (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChinn \u0026amp; Ito (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Econometric specification\u003c/h2\u003e\n \u003cp\u003eTo analyze the impact of remittance flows on economic growth in SSA, we draw on the work of Adeniyi et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Ahamada \u0026amp; Coulibaly( 2011); Chami et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e); Ramey \u0026amp; Ramey (\u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e), etc. Although these previous works have examined the relationship between remittances, financial development, and economic growth volatility, they are limited in scope because they do not show the specific effects of remittance inflows with countries that share common characteristics. In this context, to show how remittances affect economic growth instability in SSA, the empirical model estimates remittances and other controlling factors on economic growth instability. Given the potential inertia of the variables, particularly the instability of economic growth, we use a dynamic specification. Thus, given the strong inertia of the instability of economic growth, the dynamic specification is given in Eq.\u0026nbsp;(1):\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{Instability}_{i,t}^{g}=\\alpha\\:+\\delta\\:{Instability}_{i,t-1}^{g}+\\beta\\:{REM}_{i,t}+\\theta\\:{X}_{i,t}^{{\\prime\\:}}+{\\vartheta\\:}_{i}+{\\phi\\:}_{t}+{\\epsilon\\:}_{i,t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Instability}_{,t}^{g}\\)\u003c/span\u003e\u003c/span\u003emeasures economic instability, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{REM}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e. is the ratio of remittances to GDP. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i,t}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e stands for control variables. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\vartheta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e was introduced to consider unobserved and time-invariant national characteristics that could be correlated with economic instability. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\phi\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e to control for time-varying. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e is a standard error term. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e represent the country and the nonoverlapping 5-year period, respectively.\u003c/p\u003e\n \u003cp\u003eThe presence of endogenous variables such as remittance inflows (Adeniyi et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chami et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Combes \u0026amp; Ebeke, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) and the autoregressive term as an explanatory variable inevitably leads to endogeneity problems. The most appropriate estimator to account for these potential endogeneity problems and the dynamic effects of remittances is the system-GMM estimator of Blundell \u0026amp; Bond (\u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). This estimator is preferred for two reasons. Firstly, the OLS estimator is inconsistent when the one-period lagged dependent variable, correlated with the error term, is introduced with country-fixed effects (Nickell, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e). Secondly, the system-GMM estimator controls for the potential endogeneity of the explanatory variables due to any measurement errors, reverse causality or omission of pertinent variables. Thus, if an increase in economic growth instability leads to an increase or decrease in remittances, the OLS estimator for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is biased due to simultaneity. In terms of measurement error, the volume of a country\u0026apos;s remittance flows is derived from World Bank and IMF estimates, which are mainly based on funds repatriated through official channels, both by them and by the banks, whereas in SSA informal transfers account for a significant share that is not recorded in the balance of payments. According to Irving et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), and The World Bank (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), these unofficial transfers could therefore be underestimated by up to 50% in official figures. This situation suggests that the measurement of remittance inflows may be subject to error. To consider these sources of endogeneity, we employ a dynamic model that estimates the level of economic growth volatility on its lagged value and a set of control variables including remittances. Hence, we therefore use the System-GMM estimator developed by Blundell \u0026amp; Bond (\u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e) to address these issues. Indeed, for this estimator, the instrumentation procedure has been performed to limit the problem of too many instruments (Roodman, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). We use also the two-step GMM procedure with Windmeijer\u0026apos;s (2005) finite-sample correction. To validate the econometric technique, we use the Hansen test and the Arellano and Bond test to verify that there is an absence of second-order correlation and the presence of first-order correlation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Empirical results","content":"\u003cp\u003eWe examine the empirical results, which are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The basic empirical model, valuing using OLS with country fixed effects (columns 1 and 2) and the system-GMM (columns 3 and 4), included the lagged value of the dependent variable. Diagnostic test results reveal that all models are well specified. The Hansen test does not reject the validity of the instruments (Hansen test p-values\u0026thinsp;\u0026ge;\u0026thinsp;10), nor is the absence of second-order serial correlation rejected (AR (2) p-values\u0026thinsp;\u0026ge;\u0026thinsp;10). The presence of too many instruments can seriously weaken and bias Hansen\u0026apos;s overidentification restrictions test and, therefore, the general rule is to use fewer instruments than countries (Roodman, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). In all the regressions (columns 3 to 4), the number of countries exceeds the number of instruments, indicating that there is no problem with instrument proliferation. Furthermore, the coefficient of the lagged dependent variable is significant (column 1) thus confirming an inertia effect and the use of a dynamic panel.\u003c/p\u003e\n\u003cp\u003eThe empirical evidence supports our hypothesis. We find that the remittances significantly reduce the economic growth instability in SSA. Indeed, the coefficients associated with remittance inflows are negative and strongly significant at the 5 per cent level in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, columns (2\u0026ndash;4). More precisely, a one standard deviation increase in remittances leads to a decline in growth instability by 0.06 point percentage (column 4)\u003csup\u003e2\u003c/sup\u003e. Thus, the results confirm that remittances act as automatic stabilizers by dampening macroeconomic fluctuations in SSA countries.\u003c/p\u003e\n\u003cp\u003eThese results suggest that unlike other foreign capital flows (FDI and external debt), which are procyclical in developing countries (Contessi et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kaminsky et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), remittance inflows constitute a more stable source of external financing for the economy, with the potential to stabilize the instability of economic growth in SSA. The negative influence of remittance inflows on economic growth instability is in line with Spatafora (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), Craigwell et al., (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), Chami et al., (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Adeniyi et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) which also found a significant relationship between remittances and consumption. However, it should be noted that our work differs from precious works from the fact that we selected SSA countries that are more dependent on remittances.\u003c/p\u003e\n\u003cp\u003eTurning now to the control variables, we have found that they are all consistent with the empirical literature (Adeniyi et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Combes \u0026amp; Ebeke, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mireku et al.,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, GDP per capita instability and inflation instability have a positive and significant effect on economic growth instability, while initial GDP per capita reduces economic growth instability. For example, a one-point increase in the GDP per capita instability, and inflation instability leads to an increase in the volatility of economic growth of 0.0111 and 0.175 points percentage respectively (column 4). The effect of other control variables is not significant.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Results: Remittances and Growth Instability\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDependent variable: GDP per capita growth. instability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSystem-GMM\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCapita growth Instability, t-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113*\u003c/p\u003e\n \u003cp\u003e(0.0663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0429\u003c/p\u003e\n \u003cp\u003e(0.0803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478***\u003c/p\u003e\n \u003cp\u003e(0.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0276\u003c/p\u003e\n \u003cp\u003e(0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRemittances (% of GDP)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0127*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.00658)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.274**\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.110)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.00935**\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.00362)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0669**\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.0313)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0169***\u003c/p\u003e\n \u003cp\u003e(0.00392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0116***\u003c/p\u003e\n \u003cp\u003e(0.00251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0229***\u003c/p\u003e\n \u003cp\u003e(0.00323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0111***\u003c/p\u003e\n \u003cp\u003e(0.00344)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog (Initial GDP per capita)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.001***\u003c/p\u003e\n \u003cp\u003e(0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.025***\u003c/p\u003e\n \u003cp\u003e(1.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.560***\u003c/p\u003e\n \u003cp\u003e(0.477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.318***\u003c/p\u003e\n \u003cp\u003e(0.375)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflation instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00154***\u003c/p\u003e\n \u003cp\u003e(0.000461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0724***\u003c/p\u003e\n \u003cp\u003e(0.0199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00111\u003c/p\u003e\n \u003cp\u003e(0.00153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175**\u003c/p\u003e\n \u003cp\u003e(0.0800)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate credit ratio (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00367\u003c/p\u003e\n \u003cp\u003e(0.0304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00548\u003c/p\u003e\n \u003cp\u003e(0.00811)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrade openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00977\u003c/p\u003e\n \u003cp\u003e(0.0113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00397\u003c/p\u003e\n \u003cp\u003e(0.00715)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment consumption (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0154\u003c/p\u003e\n \u003cp\u003e(0.0299)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0203\u003c/p\u003e\n \u003cp\u003e(0.0201)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003cp\u003e(0.329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003cp\u003e(0.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.14***\u003c/p\u003e\n \u003cp\u003e(5.192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.12***\u003c/p\u003e\n \u003cp\u003e(9.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.51***\u003c/p\u003e\n \u003cp\u003e(3.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.699***\u003c/p\u003e\n \u003cp\u003e(2.651)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(1): p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2): p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHansen p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: the estimation method is a two-step System-GMM with Windmeijer\u0026apos;s (2005) small sample robust correction. Robust standard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, significant at 1%, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant at 5%, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 significant at 10%.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo check the robustness of the results, we use an alternative measure of independent variable by remittance inflows per capita. The results are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Robustness test results corroborate those found in preliminary analyses confirming the hypothesis that remittance inflows reduce the economic growth instability in SSA. The coefficients associated with remittances are negative and statistical in all columns (1\u0026ndash;4) and confirm the extern source of stabilisation of the remittances in SSA. The effects of the control variable are qualitatively unchanged.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRemittances and growth instability: alternative measures of remittances\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDependent variable: GDP per capita growth instability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSystem-GMM\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCapita growth. Instability, t-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0770\u003c/p\u003e\n \u003cp\u003e(0.0653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0466\u003c/p\u003e\n \u003cp\u003e(0.0766)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003cp\u003e(0.192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.214\u003c/p\u003e\n \u003cp\u003e(0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog (Nominal remittances per capita)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.305**\u003c/p\u003e\n \u003cp\u003e(0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.326**\u003c/p\u003e\n \u003cp\u003e(0.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.680***\u003c/p\u003e\n \u003cp\u003e(0.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.629**\u003c/p\u003e\n \u003cp\u003e(0.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0164***\u003c/p\u003e\n \u003cp\u003e(0.00403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0109***\u003c/p\u003e\n \u003cp\u003e(0.00242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0204***\u003c/p\u003e\n \u003cp\u003e(0.00410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00941***\u003c/p\u003e\n \u003cp\u003e(0.00237)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog (Initial GDP per capita)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.846***\u003c/p\u003e\n \u003cp\u003e(0.730)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.190***\u003c/p\u003e\n \u003cp\u003e(1.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.176***\u003c/p\u003e\n \u003cp\u003e(0.581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.800***\u003c/p\u003e\n \u003cp\u003e(0.374)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflation instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00150***\u003c/p\u003e\n \u003cp\u003e(0.000419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0633***\u003c/p\u003e\n \u003cp\u003e(0.0178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00128\u003c/p\u003e\n \u003cp\u003e(0.00155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0611**\u003c/p\u003e\n \u003cp\u003e(0.0263)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate credit ratio (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00514\u003c/p\u003e\n \u003cp\u003e(0.0297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00594\u003c/p\u003e\n \u003cp\u003e(0.0104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrade openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00730\u003c/p\u003e\n \u003cp\u003e(0.0129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00296\u003c/p\u003e\n \u003cp\u003e(0.00688)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment consumption (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00285\u003c/p\u003e\n \u003cp\u003e(0.0295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0150\u003c/p\u003e\n \u003cp\u003e(0.0353)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0956\u003c/p\u003e\n \u003cp\u003e(0.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003cp\u003e(0.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.48***\u003c/p\u003e\n \u003cp\u003e(5.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.66***\u003c/p\u003e\n \u003cp\u003e(8.489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.11***\u003c/p\u003e\n \u003cp\u003e(5.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.53***\u003c/p\u003e\n \u003cp\u003e(4.926)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(1): p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2): p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHansen p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: the estimation method is a two-step System-GMM with Windmeijer\u0026apos;s (2005) small sample robust correction. Robust standard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, significant at 1%, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant at 5%, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 significant at 10%.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5. Concluding remarks","content":"\u003cp\u003eOur paper extends the literature by examining the impact of remittances on economic growth instability. Unlike previous literature (Abdih et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Adeniyi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bugamelli \u0026amp; Paterno, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Craigwell et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, etc.) which examined the effect of remittances on macroeconomic instability in samples from both developing countries and some sub-Saharan African countries. Our study exclusively focused on Sub-Saharan African countries. Using a sample exclusively made up of 45 SSA countries during the period 1980\u0026ndash;2019 and considering the possible endogeneity problem, the empirical results suggest that remittances on average play a stabilizing role in chocs of economic growth. Specifically, a one standard deviation increase in remittances leads to a decline in economic growth instability by 0.06 point percentage. Remittances provide a more stable source of external financing for the economy, with the potential to stabilize the instability of economic growth in SSA.\u003c/p\u003e \u003cp\u003eThe findings renew the debate on the stabilizing role of remittances in times of economic instability. They suggest that countries that receive more migrant remittances have an additional source of stabilizing the chocs of instability. In consequence, It would therefore be good for SSA countries to continue to promote policies that make remittances attractive to their expatriate citizens. Specifically, they should set up investment facilitation programs for emigrants and reduce the cost of these transfers.\u003c/p\u003e \u003cp\u003eThis paper has focused on examining the impact of remittance flows on the instability of economic growth, without examining the impact of other external financial flows such as FDI and external debt, and this is its main limitation.\u003c/p\u003e \u003cp\u003eFurthermore, at the level of instability analysis, it did not take into account other macroeconomic instability variables such as consumption, tax revenues and public expenditure in the analysis. Therefore, it would be interesting to extend this study to these external flows and other factors of macroeconomic instability. Furthermore, at the level of instability analysis, other macroeconomic instability variables such as consumption, tax revenues and public expenditure were not included in the analysis. It would therefore be interesting to extend this study to include these external flows and other factors of macroeconomic instability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was not supported by any funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR. S.: Writing \u0026ndash; original draft, Data curation, Visualization, Validation, Software, Supervision, Software, Methodology, ConceptualizationG. S.: Writing \u0026ndash; original draft, Data curation, Visualization, Validation, Software, Supervision, , ConceptualizationD. S.: Writing \u0026ndash;review\u0026ndash; original draft, Validation, Investigation, Formal analysis, Data curation, ConceptualizationFunding sourcesThis research did not receive any specific grant from fundingagencies in the public, commercial, or not-for-profit sectors.Declaration of competing interestThe authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influencethe work reported in this paper\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdih Y, Chami R, Dagher J, Montiel P (2012) Remittances and institutions: Are remittances a curse? 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North Am J Econ Finance 63(January):101819. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.najef.2022.101819\u003c/span\u003e\u003cspan address=\"10.1016/j.najef.2022.101819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe list of countries is presented in Table A-\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the Appendix.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e This number is obtained by multiplying the coefficient estimated by the mean of GDP per capita growth volatility, and then dividing by the standard deviation of GDP per capita growth volatility.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Remittance inflows, Growth instability, Sub-Saharan Africa, Panel data, system-GMM","lastPublishedDoi":"10.21203/rs.3.rs-6682234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6682234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRemittances have become one of the most important sources of development finance in developing countries, particularly in sub-Saharan Africa. They provide a stable source of household income for consumption and can therefore play a role in stabilizing economic cycles. Indeed, migrant remittance flows can alleviate economic growth instability by easing constraints on domestic resources and indirectly increase growth instability through real exchange rate appreciation. In this context, this paper investigates the impact of remittance inflows on economic growth in a sample of 45 Sub-Saharan African countries from 1980–2019. We use system-GMM to control the presence of endogeneity from the dynamic effects of remittances. The empirical results show that remittance inflows negatively and significantly affect economic growth instability. The results are robust to several robustness checks. The results confirm the hypothesis that migrant remittances are a stabilising source of macroeconomic shocks. Sub-Saharan African countries should strengthen policies to attract remittances to finance their development strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classification : C23\u003c/strong\u003e \u003cstrong\u003eE32, F24, O55\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Do remittance inflows reduce growth instability in Sub-Saharan Africa?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 17:33:33","doi":"10.21203/rs.3.rs-6682234/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-11T10:45:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T16:03:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T13:39:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250936437112010625757681446518955084039","date":"2025-08-07T05:58:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142929781148959748334442081067543699785","date":"2025-07-26T13:22:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230247919257023630563132094645047143906","date":"2025-06-21T07:01:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T11:41:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T08:36:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T14:15:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T11:07:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-05-16T15:59:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9e2ac5c-eff2-47f7-ad27-3425336471d1","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":49815921,"name":"Business and commerce/Economics"},{"id":49815922,"name":"Social science/Development studies"}],"tags":[],"updatedAt":"2026-05-06T21:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 17:33:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6682234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6682234","identity":"rs-6682234","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}