The Impact of Workers’ Remittances on Economic Development in Sub-Saharan African Countries

Abstract

This study attempts to investigate the impact of remittances on economic development in sub-Saharan Africa. Annual data from the top ten remittance-receiving countries in terms of GDP percentage from 1980 to 2019. We consider economic development in three dimensions, which include economic growth proxied by per capita GDP, household consumption expenditure (HHC), and human capital (HDI). The three dimensions of economic development are as stated below: The development of economic growth is employed because it is the main measurement of the general health of an economy. When GDP growth goes up, the economy is thought to be doing well; when it slows down, the economy is thought to be not doing well (Haldane, A, Bank of England Chief Economist). It is one of the main criteria the World Bank employs to classify countries in terms of development (GNI per capita). The development of human capital was chosen since it is a notable driver of economic growth through its impact on productivity (Economic Development in Africa Report, 2018). Yang (2006) states that human capital can be an important driver in building a considerably competitive economy. This study employs HDI as a proxy for human capital. The development of household consumption expenditure was chosen because an estimated 75 percent of remittance is used for consumption and the remaining 25 percent is used for socioeconomic production (Ponsot et al., 2017). From the economic literature, it is acknowledged that consumption is a unit of economics that drives the activities of an economy because of its substantial contribution to aggregate demand, which eventually leads to economic growth and a reduction in unemployment, with the possibility of the benefits from its spillover to the reduction in poverty. Therefore, all three indicators will help capture development, especially in the case of Sub-Saharan Africa. Our research contributes to the existing research in four different ways: Our research contributes to the existing research in four different ways: First, to our knowledge, this is the first study to look at how remittances impact the economic development of the top ten countries in terms of remittances received as a percentage of GDP in Sub-Saharan Africa. Most empirical studies on the topic utilize a panel data model as their empirical framework, which is mostly generalized (Tchamanbe & Miamo, 2012; Mlambo & Kapingura, 2020; Yiheyis & Woldemariam, 2020; Larsson & Ångman, 2014; Adenutsi & Ahortor, 2010). Therefore, this study employs a time series model that is an autoregressive distributed lag (ARDL) model for each country. Based on this, we can draw policy recommendations for each country studied. Furthermore, Toda Yamamoto was conducted to increase the robustness of our work. Second, on economic growth overall, the results of the aforementioned research, such as Fayissa and Nsiah (2010), Balde (2009), Atanda and Charles (2014), and Didia et (2018), are undetermined. The findings vary depending on the variables selected, country, methodology, and time criteria. From the aforementioned research above, there is a shortage of literature on remittances to the top ten nations that receive the most remittances in terms of GDP in Sub-Saharan Africa. The few studies that utilized panel data made it difficult to determine the impact on a specific country because it is usually generalized. Therefore, this research is carried out at the country level using the ARDL model, which is a time series model, to determine the impact in each country. Third, in terms of household consumption expenditure, the existing empirical studies show a mixed relationship between remittance and household consumption, according to the literature review, Musakwa and Odhiambo (2020) found insignificant results, while Akpa (2018) and Ajefu and Ogebe (2020) found positive results, and lastly, Musakwa and Odhiambo (2020) found negative results. With regard to Sub-Saharan Africa (SSA), only six works are found. Due to the inconclusive existing studies and the insufficient literature on SSA countries, it is critical to look into how remittances affect household consumption expenditure in Sub-Saharan Africa. Among them, only two are on SSA in general, including Ajefu and Ogebe (2020) and Adelowokan et al. (2020), but they employed panel data. Therefore, our study contributes to the existing study by employing the ARDL method to determine each of the top ten highest receiving countries in terms of GDP. Fourth, the most crucial components of human capital, according to the aforementioned research analysis, are education and health. Only one study, Seyedsoorosh (2018), used HDI as a proxy for human capital. Seyedsoorosh (2018) concentrates on a panel of 122 developing countries, while our study will concentrate on Sub-Saharan Africa. Despite the fact that there have been a few studies on SSA, such as Adenutsi (2009, 2010), Huay et al. (2019), Umar (2021), and Yiheyis and Woldemariam (2021), they did not use the HDI as a proxy for human capital and dealt with panel data. Thus, difficult to determine the impact of individual countries. As a result, our research investigates the impact of remittances on human capital of the top 10 individual remittance-receiving countries in Sub-Saharan Africa by employing the ARDL model. Autoregressive Distributed Lag (ARDL) model was employed to estimate the model. On economic growth out of 10 countries tested, remittance had a positive and statistically significant relationship with economic growth in 2 countries, remittance had a statistically significant and negative relation between remittance and economic growth in 6 countries and the rest had no impact. On household consumption, remittance had a positive and statistically significant relationship in 4 countries. A negative and significant relationship was found in 3 countries and the remaining 3 were insignificant. On human capital Remittance has a positive and statistically significant sign in 3 out of 10 countries. 2 countries out of 10 had a negative impact on human capital while the remaining 5 had an insignificant result.