Remittances, education and health in Sub-Saharan Africa

Amega, Komla Article Remittances, education and health in Sub-Saharan Africa Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Amega, Komla (2018) : Remittances, education and health in Sub-Saharan Africa, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 6, Iss. 1, pp. 1-27, https://doi.org/10.1080/23322039.2018.1516488 This Version is available at: https://hdl.handle.net/10419/245162 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 Cogent Economics & Finance ISSN: (Print) 2332-2039 (Online) Journal homepage: https://www.tandfonline.com/loi/oaef20 Remittances, education and health in Sub-Saharan Africa Komla Amega | To cite this article: Komla Amega | (2018) Remittances, education and health in Sub-Saharan Africa, Cogent Economics & Finance, 6:1, 1516488, DOI: 10.1080/23322039.2018.1516488 To link to this article: https://doi.org/10.1080/23322039.2018.1516488 © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 24 Sep 2018. Submit your article to this journal Article views: 2649 View related articles View Crossmark data Citing articles: 2 View citing articles GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Remittances, education and health in Sub-Saharan Africa Komla Amega 1 * Abstract: This study investigates the effects of remittances on education and health outcomes using a 5-year interval data on 46 Sub-Saharan African (SSA) countries from 1975 to 2014. Employing system GMM, remittances were found to significantly improve education and health in SSA. It was also established that improving education impacts positively on health and the reverse was also true. Subjects: African Studies; Economics and Development; Economics Keywords: remittances; education; health; Sub-Saharan Africa 1. Introduction 1.1. Background of the study A sizeable proportion of the Sub-Saharan Africa (SSA) population is living outside their country of origin. The stock of migrants in 2013 was estimated at 23.2 million or 2.5% of the total population, with South Africa, Cote D’Ivoire, Uganda, Nigeria and Ethiopia being the top five countries that recorded the highest migration stock by mid-year 2017 (World Bank, 2016a; United Nations, 2017). Data presented by the United Nations (UN) depict a consistent rise in the total migrant stock from 2000 to 2017. The pattern has been the same for specifically male and female migrants. Nonetheless, the total numbers for the male migrants are slightly higher than their female counterparts (Figure 1). The numbers for total migrant stock recorded in the region between 1990 and 2017 increased but an opposite picture was portrayed when considering migrant stock as a proportion of the [SSA] population. Simply, it means the rise in migrant stock was outweighed by the “speedy”rise in the region’s population, given the year under review. Thus although the numbers for the total migrant stock keep increasing, the migrant stock as a percentage of the population is reducing. Migrant ABOUT THE AUTHOR Komla Amega holds an MSc degree in economics from the University of the West Indies, St Augustine Campus, Trinidad and Tobago. He had his undergraduate studies at the University of Ghana, where he obtained a BA degree in economics with geography and resource development. He has experience in undertaking academic, market and social researches. His research interest covers education, health, behavioral economics and policy analysis. PUBLIC INTEREST STATEMENT Significant proportions of the population from Sub-Saharan Africa are found in places other than their country of origin. They do migrate for varied reasons and one of such is to search for ‘better’ opportunities. These migrants sometimes send monies (remittances) back home to families and friends. And the remittances are spent on various items including food, education, health, purchases of land and payment of loans. This paper thus investigates whether the remittances contribute significantly to the development of education and health in Sub-Saharan Africa. This will be an attempt to explore alternative income sources necessary to deepen investment in education and health in the region Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Received: 05 June 2018 Accepted: 22 August 2018 First Published: 31 August 2018 *Corresponding author: Komla Amega, Economics, University of the West Indies, Trinidad and Tobago E-mail: [email protected] Reviewing editor: Francesco Tajani, University of bari, Italy Additional information is available at the end of the article Page 1 of 27 stock as a percentage of population recorded in 2017 was as a result lower than the figure recorded in 1990 (Figures 2and 3). The growth of population has been high but steady. The growth of the migrant stock has been sharp comparatively, but consistently below that of the population growth (Figure 3). Basically, several migrants are between the ages of 20 and 39, followed by the below 15-years- of-age group. Time after time, relatively fewer migrants are beyond the ages of 60 (Figures 4–6). These possibly make one presume the likely motives behind the majority of such migrations. That is, the dependent age groups likely moving for family visitations whereas the vibrant working age group likely moving to search for “better”opportunities. Migration in SSA is characterized by intra-regional and international migrations, the former being dominant. Preferably, the author calls it the ‘within migration’ 1 and the ‘between migration’ 2 Ratha et al. (2011) report about two-thirds of the entire migration in SSA is intraregional. This translates into over 60, percentage-wise. Specifically, accounts on intra-regional 0 2,000,000 4,000,000 6,000,000 8,000,000 14,000,000 12,000,000 10,000,000 1990 1995 2000 2005 2010 2015 2017 Numbers Years International migrant stock, male and female, SSA, 1990-2017. Male Female Figure 1. International Migrant Stock, Male and Female, SSA, 1990–2017. Source: Author’s based on UN data (2017). 0 0.5 1 1.5 2 2.5 3 3.5 1990 1995 2000 2005 2010 2015 2017 Percentages Years International migrant stock as a percentage of population, SSA. Male Female Figure 2. International migrant stock as a percentage of population, SSA. Source: Author’s based on UN data (2017). Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 2 of 27 migration were 63.2%, 63.0% and 65.6% in 2005, 2010 and 2013, respectively (World Bank, 2008,2011,2016). Intra-regional migration is most common in poorer countries from the region, notably because of insufficient financial resources to travel long distances to other continents. Also, they may lack adequate education and skills needed to be successful in “rich countries’labour markets”(Ratha et al. 2011). Therefore, majority of them move to other SSA countries that have relatively larger and diversified economies like South Africa, Cote D’Ivoire and Nigeria. These are mainly influenced by cultural and language linkages, income variations, geographic closeness, environmental reasons, political instability in home countries and political stability in host countries (Gonzalez-Garcia, Hitaj, Viseth, & Yenice, 2016;Ruyssen&Rayp, 2014). The dominant intra-regional migration corridors outlined in descending order for 2013 were Burkina Faso-Cote D’Ivoire; Zimbabwe-South Africa; Cote D’Ivoire-Burkina Faso; Somalia- Kenya; Somalia-Ethiopia; Sudan-South Sudan; Mali-Cote D’Ivoire; Mozambique-South Africa; and 0.8 -2.2 0.8 1.9 6.5 14.69 14.15 14.22 14.55 14.59 -4 -2 0 2 4 6 8 10 12 14 16 1990-1995 1995-2000 2000-2005 2005-2010 2010-2015 % change Years Percentage change (growth) in migrant stock and population, SSA, 1990-2015. % chan g e in mi g rant stock, total % chan g e in p o p ulation Figure 3. Percentage change (growth) in migrant stock & population, SSA, 1990–2015. Source: Author’s based on UN data (2017). 0-4 15-19 30-34 45-49 60-64 75+ Ages International migrant stock by age and sex, SSA, 2000. Male Female Figure 4. International migrant Stock by age and sex, SSA, 2000. Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 3 of 27 Lesotho-South Africa (World Bank, 2016a). Nine of these corridors also made it to the top 10 entire migration (both intra-regional and international) corridors in 2013. This further stresses the dominant nature of intra-regional migration in the region. Migration outside the region although low is, however, picking up sharply, mostly to the Organization for Economic Co-operation and Development (OECD) countries. The stock of SSA migrants outside the region in 2013 stood at 6.6 million, 2½ times larger the number recorded in 1990. The main driver of migration outside the region is an economic motive. Differences in per capita income between SSA and OECD countries largely tend to push labor to the latter region. The composition of SSA migrants to foreign countries has also changed. There has been a decline in the percentage that moved as refugees, due to a decline in wars in SSA. In 2013, the number of migrants who moved for economic reasons had increased to 90% from 40% in 1990 (Gonzalez- Garcia et al., 2016). 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+ Age International migrant stock by age and sex, SSA, 2017. Male Female Figure 6. International migrant stock by age and sex, SSA, 2017. Source: Author’s based on UN data (2017). 0-4 15-19 30-34 45-49 60-64 75+ Age International migrant stock by age and sex, SSA, 2010. Male Female Figure 5. International migrant Stock by age and sex, SSA, 2010. Source: Author’s based on UN data (2017). Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 4 of 27 Figure 7and Table 1present a pictorial view of migration from SSA to other identified destinations both within and outside the region. Remittances are sent by emigrants to families and friends back home. This has been a substantial source of income for homes in developing countries and specifically Sub-Saharan countries. Statistics from the World Bank (2017) revealed remittances almost doubled between 2005 and 2015. In 2014, US$ 36.9 billion was received as remittances in the region and rose to US$ 39.8 billion in 2015. These represented 2.16% and 2.59% of GDP for the respective years (World Bank, 2017). Remittances received in US dollars depicted an upward trend between 1990 and 2015. But 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2005 2010 2013 Percent Years Emigration from SSA to identified destinations, 2005-2013. high income OECD high income non-OECD intra-regional(within SSA) other develo p in g countries unidentified Figure 7. Emigration from SSA to identified destinations, 2005–2013. Source: Author’s based on UN data (2017). Table 1. Top 10 emigration countries in SSA and their top five destination countries for 2013, in descending order Emigration countries Top five destination countries Somalia Kenya, Ethiopia, Yemen, Libya, Djibouti. Burkina Faso Cote D’Ivoire, Ghana, Mali, Niger, Italy. Sudan South Sudan, Saudi Arabia, United Arab Emirates, Chad, Kuwait. The Democratic Republic of Congo Congo, Rwanda, Uganda, Burundi, France. Nigeria USA, UK and Northern Ireland, Cameroon, Italy, Cote D’Ivoire. Cote D’Ivoire Burkina Faso, Liberia, France, Mali, Italy Zimbabwe South Africa, The UK and Northern Ireland, Malawi, Australia, Botswana. Mali Cote D’Ivoire, Nigeria, France, Gabon, Niger. South Africa The UK and Northern Ireland, Australia, USA, New Zealand, Canada. South Sudan Chad, Ethiopia, Uganda, Sudan, Kenya. Source: World Bank (2016a). Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 5 of 27 for the more than proportionate rise in GDP, remittance as a percentage of GDP decreased between 2005 and 2010 (World Bank, 2017) (Figures 8and 9). The various sources of remittances to the region in 2010 are also presented in Figure 10 (extracted from Ratha et al., 2011, p. 53). Regional comparison of remittances sent and received in monetary terms showed low figures for SSA, for the average period 1990 to 2015. In relation to remittances sent, SSA recorded the second to last, after South Asia under the year under consideration. They [SSA] also recorded the least remittances received over the same period (Figure 11). Nonetheless, the growth of remittances received in the region has been positive with a shock in the year 2000–2005 (Figure 12). So basically, remittances received in SSA continually increase but are still low compared to other regions. 3 Nigeria was the highest recipient of remittances in 2015, valued US$ 21.1 billion, representing more than half of the total remittance received in the region. Ghana and Senegal followed with remittances received valued at US$ 5.0 billion and US$1.6 billion, respectively. Looking at remittances as a percentage of GDP however, put Liberia, Comoros and The Gambia ahead with 31.1%, 22.8% and 19.2% of GDP, respectively, in 2015 (World Bank, 2017). This is explained by the low GDP 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 1990 1995 2000 2005 2010 2015 Remittance (US $) Years Figure 8. Remittances received in current US$, SSA. Source: Author’s based on World Bank Data (2017). 0 0.5 1 1.5 2 2.5 3 3.5 1990 1995 2000 2005 2010 2015 )PDGfo%(ecnattimeR Years Figure 9. Remittances received as a percentage of GDP, SSA. Source: Author’s based on World Bank Data (2017). Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 6 of 27 Figure 10. Sources of remittances to SSA, 2010. Source: Ratha et al. (2011,p. 53). 0 10 20 30 40 50 60 70 80 90 East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa South Asia Sub-Saharan Africa US$, billions Regions Remittances sent and received, various regions, 1990-2015 average Remitances, p aid(US$ bllions) Remittances, received(US$ billions) Figure 11. Remittances sent and received, various regions, 1990–2015 average. Source: Author’s based on World Bank data (2017). Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 7 of 27 2015). They raise consumption and expenditure on health, education, and nutrition which contribute to long-term productivity (Mohapatra & Ratha, 2011). Attempts to empirically understand the effect of remittances on health and education have yielded mixed results. Numerous studies have found that remittances improve health outcomes such as infant mortality and child health. However, the effect on educational outcomes—school enrollment—is debatable. While some have found that remittances increase completion rate, others have argued otherwise (Adams, 2011). 2.1. Remittances and education The level of one’s education is likely to influence decisions to migrate and eventually remit. This shows how education may have an influence on remittances. On the other hand, remittances from migrants are also invested in education, yielding benefits. This paper seeks to examine the latter case, that is, how remittances affect education. Zhunio, Vishwasrao, and P. (2012) conducted a cross-country study on 69 low- and middleincome countries. Their results showed remittances through its effect on private educational spending, have a higher effect on educational outcomes than public expenditure on education. Also, the effect of remittances at the secondary level was higher than that of the primary level. Using two-stage least squares (2SLS) estimation and calculating for elasticity, they found that 1% rise in real remittances per capita leads to 0.12% rise in the share of students registered in the secondary school and 0.09% rise in primary completion rate. The Hausman–Taylor estimates also showed a positive relationship between secondary school enrollment, primary school completion rate (coefficients of 2.352 and 2.931, respectively) and real remittances per capita. The effect of remittances on primary school enrollment was positive but insignificant. Research by Amakom and Iheoma (2014) on 18 Sub-Saharan countries using 2SLS method found primary school enrollment and secondary school enrollment rose by 4.2% and 8.8%, respectively, for every 10% rise in remittances. This confirmed studies by Zhunio et al. (2012) who argued the impact on secondary school enrollment was greater than primary school enrollment. Lu and Treiman (2007) argued that in South Africa, children from remittance recipient households [Blacks] were 30% more likely to have some secondary education compared to non-migrat- ing households without remittances. Remittance recipient households were also 73% likely to have some secondary education and 130% likely to have educational levels beyond secondary school. In Ghana, it was found the probability a child enrolls in primary school rises by 13% if a household’s status moves from a non-international remittance recipient one to an international 5 remittance recipient one. Similarly, the likelihood of attending a secondary school rises by 54% for the same household status change. Households headed by females were also more likely to invest remittances in children’s education than those headed by men (Gyimah-Brempong & Asiedu, 2009,2015). A percentage increase in the fraction of remittance-receiving households in Mexico reduces children illiteracy by close to 3 percentage points. Considering remittances impacts on education for different age categories, he established schooling for under-age-five group rises by 11% for a 1% rise in receiving remittances. However, remittances reduced school attendance among teenagers between 15 and 17 years and had no significant impact among children between 6 to 14 years (Lopez-Cordova, 2006). In contrast, McKenzie and Rapoport (2011) conclude in their research that remittances had negative effects on school attendance and attainment in rural Mexico for boys between 12 and 18 years and girls between 16 and 18 years. This is as a result of children having to take up roles of Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 14 of 27 migrated adults, lack of effective supervision of children, and incentive for older children to also migrate and find low skill jobs. Finally, Cattaneo (2010) finds remittances have no significant results on education in Albania. This he argued may be due to the low perceived returns to education in the region because of the low quality of education, and directives from remittance senders to use the money on specific allocations instead of education. 2.2. Remittances and health A study by Zhunio et al. (2012) found a percentage rise in real remittances per capita raises life expectancy by 0.03% and reduces infant mortality by 0.15%, for 69 low- and middle-income countries. Another cross-country study on 84 countries by Chauvet, Gubert, and Mesplé-Somps (2009) revealed remittances reduced both infant 6 and child 7 mortality, but the reduction was higher for the richest households compared to the poorest households. In SSA, Amakom and Iheoma (2014) using 2SLS estimation found 10% rise in remittances increase life expectancy at birth by 1.2% on average. This impact was larger than those caused by public health expenditure per capita; which was 0.5% on average for every 10% rise in public health expenditure per capita. Duryea et al. (2005) based on population census data examined how international remittances impact on infant mortality in Mexico. They employed 2SLS and instrumented remittances with historic state-level rates of migration in Mexico and distance to the US border. They established international remittances reduce infant mortality in the first month of life, but this is only significant in large urban areas and not rural communities (Cited by Adams, 2011). Further studies in Mexico disaggregated the impacts into an immediate and long run. It was argued that children’s health generally declines during the first year of their parents’migration but improves in later years. The immediate effect was due to the initial family disruption and psychological instability (Kanaiaupuni and Donato, 1999; Cited by UNICEF, 2013). Lopez-Cordova also estimated using instrumental variables and 2SLS based on municipal-level data from Mexico. He instrumented remittances with rainfall concentration and distance to Guadalajara (located in Central Mexico). He established a percentage increase in the fraction of remittance-receiving households lowers deaths of infants by 1.2 lives (2006). In Nigeria, the probability of a child dying reduces by 1.66% for every percentage increase in remittances (Ifeyinwa, 2010). In Jamaica, however, a joint research by UNICEF and the Government of Jamaica found no significant differences in the health outcomes of children from remittance-recipient households and those that are not. This was in spite of increased health expenditure in remittance-recipient households (UNICEF and PIOJ, 8 2009; Cited by UNICEF, 2013). 3. Methodology 3.1. Economic model A review of the literature depicts portions of remittances are spent on education and health, among other goods and services. An assumption in microeconomic utility maximization theory is local non-satiation that explains even a little change in the consumption of goods and services are preferred. However, the resources available at a time become a limitation to our desires. This paper follows the resource constraint model under the assumptions that parents wish to spend on their children’s education and health up to a desirable level, out of altruism, but are faced with resource constraints. Remittance, a source of income to households, when received relaxes the constraint [limited income] and makes it possible to spend on children’s education, health and other goods. This is expressed as follows: Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 15 of 27 ●Assume a household maximizes utility by spending on education and health (human capital), and all other goods subject to a budget constraint. ●Then Utility = U(X,Z) such that Y≥pxX+pzZ;(1) where U = utility X = quantity of education and health demanded Z = quantity of all other goods demanded Px = price of education and health Pz = price of all other goods pxX + pzZ = expenditure on education and health, and all other goods Y = total income = remittance income + non-remittance income The relation in Equation (1) shows households would like to maximize their utility from the consumption of education and health, and all other goods but they are restricted by their income (Y). This is typical for the SSA region because of the high incidence of poverty. An inflow of remittance (an alternative source of income) increases total income making more resources available for families to spend on goods and services. Hence, all things equal, remittances are expected to improve education and health outcomes. In detail, remittances can be argued to alleviate the barriers that force children into work instead of school at earlier ages. As such it is expected school enrollments increase especially at the secondary level that is costlier and not compulsory. Moreover, households can afford medicines, healthy foods and access health care that will lead to a reduction in deaths and higher life expectancies. At the macro level, remittances can be used to finance the construction of schools and health facilities, generating positive benefits. 3.2. Econometric specification and estimation The interest is to analyze the effects of remittances on specific education and health outcomes. The econometric specification for estimation is as follows: Outcome ίt =β j Remit it +β k X ίt +γ ί +Ф t +ε ίt ; where ●Outcome = education and health outcomes; ○Education outcomes = primary enrollment (gross), secondary enrollment (gross), tertiary enrollment (gross). ○Health outcomes = Infant mortality, adult mortality, survival to age 65, life expectancy. ●Remit ίt = real remittance per capita received by country ίat time t. ●X ίt = characteristics of country ίat time t. ○These characteristics include GDP per capita, public expenditure on education and health, primary completion rate, unemployment rate, rural population, physicians, the percentage of population aged 25–24 years with completed TE, net migration rates, income differences between OECD and SSA countries, and dependency ratio. Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 16 of 27 ●γ ί represents country-fixed effect, to control for country-specific time-invariant characteristics that affect education, health and remittances like culture, colonial ties. ●Ф t represents time-fixed effect, to control for shocks at specific times, for example, the financial crises in 2008; wars that happened in some SSA countries over the years. ●ε ίt represents the standard error term. ●β j, β k are coefficients. An issue with estimating remittances is the endogeneity problem. Considering education, health and remittances variables, there is the tendency for this problem due to reverse causality. In as much as remittances may influence the level of education and health demanded, education and health can also influence remittances. The level of one’s education is likely to induce migration and thus remittances. A healthy person, on the other hand, has the ability to migrate, work for more hours and remit. Thus, the stock of education and health may influence remittances received. As a result of the endogeneity problem, estimating with ordinary least-squares (OLS) may yield biased and inconsistent results. A plausible approach to resolve this issue is the use of generalized method of moments (GMM) (Baltagi Badi, 2015). This research specifically uses system GMM. They are designed for “small T, large N”panels. That is panel data with few time periods and large cross-sectional dimension (groups). Relative to OLS, GMM produces consistent results (Baltagi Badi, 2015). Also unlike difference GMM, system GMM includes the level equation, thus generating additional instruments that “can dramatically improve efficiency”(Roodman, 2006). 3.3. Diagnostic checks In all the estimations, the AR(1), AR(2) and Hansen test were employed. These are used to check autocorrelation and overall exogeneity of instruments, respectively. 4. Presentation and discussion of results 4.1. Data The data for the empirical estimation cover 46 SSA countries from the period 1975 to 2014 obtained from the World Bank’s World Development Indicators. Data on net migration rates were obtained from the United Nations Population Division (World Population Prospects, the 2017 Revision). Descriptive statistics of the various variables are presented in Table 3. 4.2. Limitations There are two main limitations to this research. Firstly, is underreporting of actual remittances and secondly missing data. Figures on remittances reported by the World Bank only captured those sent through official channels. Meanwhile, significant portions of these inflows are through informal or unofficial sources that are not recorded. The remittance inflows data reported by countries thus tend to be higher than the World Bank figures. For example, the central bank of Ghana reported US$ 1.6 billion remittances to the country in 2009; over 10 times the US$ 114 million figure reported by the International Monetary Fund (IMF) and captured by the World Bank data. In the same year, Ethiopia reported about US$ 700 million compared to US$ 353 million by IMF. These tend to underestimate the marginal impacts of remittance (Ratha et al., 2011). The other limitation is missing data. Data on a number of variables were not reported by some countries for some periods. Countries like Central African Republic, Chad, The Democratic Republic of Congo, Liberia and Zambia had a little report on remittances and other variables. To maximize the number of observations, the 5-year interval data on all variables were used. This minimized the missing data problem. Notwithstanding, the results were robust. Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 17 of 27 Table 3. Descriptive statistics Variable Description Obs Mean Std. dev. Primary enrollment (% gross) The ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education. 412 78.65358 22.63036 Secondary enrollment the ratio of total enrollment (% gross) Regardless of age, to the population of the age group that officially corresponds to the level of education. 413 38.01903 21.28837 Tertiary enrollment (% gross) Population of the age group that officially corresponds to the level of education. 414 2.98926 4.647189 Infant mortality The number of infants dying before reaching one year of age, per 1,000 live births in a given year. 85.28841 37.74776 Adult mortality The probability of dying between the ages of 15 and 60. 414 347.6619 95.89038 Survival to age 65 Survive to age 65.the percentage of a cohort of newborn infants that would 414 47.776 10.65119 Life expectancy The number of years a newborn infant would live. 414 53.05347 7.442735 Real remittance per capita Personal transfers and compensation of employees, to the population, in real terms. 370 18.2462 160.8305 Public education expenditure General government expenditure on education (current, capital, transfers) expressed as a percentage of GDP 413 2.55258 3.382456 Public health expenditure Recurrent and capital spending on health from government, expressed as a percentage of GDP. 414 1.377533 1.620088 GDP per capita (constant 2000) Gross domestic product divided by midyear population. 414 1543.824 2701.061 Primary completion rate Repeaters) in the last grade of primary education, regardless of age, divided by population at the entrance age for the last grade of primary education. 414 38.90771 31.19203 Unemployment rate The share of the labor force that is without work but available for and seeking employment 414 5.114976 7.327603 Rural population People living in rural areas as defined by national statistical offices. 414 8,958,292 1. 42e+ 07 Physicians (per 1000 people) Generalist and specialist medical practitioners. 413 0.095028 0.148601 25–34 years with completed Percentage of population aged 25–34 with (completed) tertiary schooling. Tertiary education 414 52.12802 59.45642 Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 18 of 27 Table 4. Correlation matrixes between real remittance per capita, education and health outcomes Infant Adult Survival Life Primary Secondary Tertiary Real remittance Mortality mortality to age 65 expectancy enrollment enrollment enrollment per capita Infant mortality 1.0000 Adult mortality 0.3836 1.0000 Survival to age 65 –0.6698 –0.8798 1.0000 Life expectancy –0.7673 –0.7737 0.9579 1.0000 Primary enrollment –0.4713 –0.0983 0.3421 0.4332 1.0000 Secondary enrollment –0.5287 –0.1410 0.4031 0.5020 0.5429 1.0000 Tertiary enrollment –0.4203 –0.2904 0.4643 0.4932 0.3168 0.6095 1.0000 Real remittance per capita –0.1337 –0.1762 0.2108 0.1997 0.0392 0.1872 0.2422 1.0000 Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 19 of 27 4.3. Presentation of results Real remittance per capita correlates positively with all the educational outcomes. But the higher the educational level, the higher the correlation. Real remittance per capita also correlates inversely with infant and adult mortalities, but positively correlated with survival to age 65 and life expectancy (Table 4). A further test of stationarity was conducted on all variables. It was revealed that all variables except the rural population were stationary at levels. The rural population was only stationary after first difference (Table 5). There are two separate tables for outcomes. Table 6presents results for educational outcomes, while Table 7presents that of health outcomes. For each table, there are two columns; system GMM (1) and system GMM (2). Column (1) of each table captures estimations based on using internal instruments. That is, instrumenting remittances with its lags. For column (2) of each table, which is our interest, external instruments are employed in addition to the internal instruments to instrument remittance. These external instruments included net migration rates, dependency ratio and the difference in GDP per capita between individual SSA countries and OECD as a group. These variables were selected as the instrument because they tend to drive the inflow of remittances. Net migration rates comprise of immigration and emigration rates in an area. In SSA majority of the emigrants from a particular country end up as immigrants in other SSA countries. With the focus of this study on the whole region, the net migration rate was preferred to net outward migration [which only captures emigration] which has been used in other studies as instruments. A significant source of remittances to SSA also comes from OECD countries. Thus, the income of the host country [region] significantly affects remittances to SSA (Singh, Haacker, & Lee, 2009). Ideally, people may tend to remit more if the difference between what they receive in their host countries is slightly higher than what their beneficiaries receive back home. The dependency ratios of remittance senders back home also to an extent determine how much is being Table 5. Stationarity tests for various variables Variable Panel means Drift term Cross– sectional means Lags P-value (inverse normal—Z) Primary enrollment Included Included Included 0 0.0000*** Secondary enrollment Included Included Included 0 0.0000*** Tertiary enrollment Included Included Removed 0 0.0068*** Infant mortality Included Included Included 0 0.0000*** Adult mortality Included Included Included 0 0.0000*** Survival to age 65 Included Included Included 0 0.0005*** Life expectancy Included Included Included 0 0.0001*** Public education expenditure Included Included Included 0 0.0000*** Public health expenditure Included Included Included 0 0.0000*** GDP per capita (constant 2000) Included Included Included 0 0.0000*** Primary completion Included Included Included 0 0.0000*** Unemployment rate Included Included Included 0 0.0000*** Rural population Included Included Removed 1 0.0001*** 25–34 years with tertiary education Included Included Included 0 0.0000*** Physicians Included Included Included 0 0.0000*** Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 20 of 27 Table 6. Real remittance per capita and education System GMM, (1) (internal instruments) System GMM, (2) (internal & external instruments) Primary enrollment Secondary enrollment Tertiary enrollment Primary enrollment Secondary enrollment Tertiary enrollment Primary enrollment, t–1 0.263** 0.311** Secondary enrollment, t –1 0.253** 0.338*** Tertiary enrollment, t–1 0.498* 0.546** Real remittance per capita, t–1 0.008 0.070*** 0.025*** –0.071 0.065*** 0.022*** Public education expenditure, t–1 0.546** 0.493 –0.007 0.540* 0.553* –0.018 GDP per capita (cons), t– 1 0.001 0.0003 –0.0001 0.001 0.00004 –0.0001 Infant mortality, t–1–0.157* –0.178*** –0.009 –0.133 –0.101*** –0.011 Unemployment rate, t– 1 -0.176 0.206 -0.007 -0.261 -0.058 -0.005 Rural population, t–1 1.3e–07 1.3e–07* 2.9e–08* 1.4e–07 8.2e–08 2.3e–08 25-34 years with completed tertiary education, t–1 0.052* 0.025 0.012*** 0.056 0.040** 0.011 ** Year FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes AR(1) test, p-value 0.039 0.003 0.086 0.045 0.002 0.092 AR(2) test, p-value 0.478 0.544 0.566 0.436 0.454 0.533 Hansen test, p-value 0.855 0.859 0.593 0.605 0.449 0.466 Observations 321 323 324 321 323 324 *, ** and *** represent significance at 10%, 5% and 1%, respectively. Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 21 of 27 Table 7. Real remittance per capita and health System GMM, (1) (internal instruments) System GMM, (2) (internal & external instruments) Infant Adult Survival Life Infant Adult Survival Life Mortality Mortality To age 65 Expectancy Mortality Mortality To age 65 Expectancy Infant mortality, t–3 0.392*** 0.385*** Adult mortality, t–3 0.323*** 0.355*** Survival to age 65, t–4 0.243 0.292** Life expectancy, t–4 0.306** 0.326*** Real remittance per capita, t –1 –0.017*** –0.238*** 0.035*** 0.019*** –0.017*** –0.198*** 0.033*** 0.017*** Public health expenditure, t– 1 –0.174 3.126 –0.366 –0.031 –0.200 4.419 –0.396 –0.117 GDP per capita (cons), t–1–0.0003 0.0005 0.00007 0.0002 –0.0004 –0.001 0.00007 0.0001 Primary completion rate, t–1–0.168*** 0.010 0.004 0.009 –0.168*** 0.065 0.004 0.006* Unemployment rate, t–1 0.411** 3.316 –0.276 –0.184 0.404* 3.493 –0.364** –0.195* Rural population, t–1 1.9e–08 2.6e–07 –8.7e–09 –9.3e–09 3.5e–08 4.7e–07 –3.1e–08 –3.2e–08 Physicians, t–1–13.18** –59.02** 7.198** 4.534*** –13.26** –30.55 9.111** 5.869** Year FE Yes Yes Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes Yes Yes AR(1) test, p-value 0.001 0.092 0.001 0.001 0.001 0.025 0.003 0.005 AR(2) test, p-value 0.618 0.132 0.211 0.844 0.625 0.132 0.306 0.751 Hansen test, p-value 0.544 0.379 0.548 0.397 0.658 0.288 0.414 0.326 Observations 260 260 220 220 260 260 220 220 *, ** and *** represent significance at 10%, 5% and 1%, respectively. Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 22 of 27 sent. As already established, a majority of the migrants from SSA fall within the active working age group and as such relations left back home have some expectations in terms of financial support. The higher the number of people depending on migrants, the higher the expected amount to remit, all things being equal. These three variables are hence used as external instruments to predict remittances. The results from Tables 6and 7 show the coefficients of estimation and the significance at 10, 5 and 1% level for all education and health outcomes under consideration. Real remittance per capita impacts significantly on all health outcomes and on two out of three educational outcomes. The focus of discussion of the results is on column (2) of each Table 6. 4.4. Discussion on findings Enrollments in secondary and TE increase when real remittance per capita increases. The effect is stronger at the secondary level than at the tertiary level. Likely, the relatively high demand for and to a large extent the “necessary”nature of secondary school will mean high motivation for parents to invest in education at that level for any alternative income received such as remittances. TE on the other hand may be seen as “luxury”for some families and as such if remittance senders do not give specific directives as to what to use remittance for, say invest in TE, then one may expect the impact of remittance on tertiary enrollment to be relatively low. The effect of real remittance per capita on primary enrollment was insignificant and negatively signed. In terms of significance, this result was similar to Zhunio et al. (2012) who also found no significant relationship between real remittance per capita and primary enrollment. However, their research had a positively signed coefficient for the variable of interest —real remittance per capitaon primary enrollment. Conversely, Amakom and Iheoma (2014)had positive and significant results for primary enrollment on [18] SSA countries. The negatively insignificant results in this paper can be likened to the absence of mostly male adults, who are usually the migrants. The result is less control or enforcement on younger children to attend school. The interest of younger children to willingly attend school is low compared to older children. As such, the absence of a parent [and mostly men who happen to the family heads] to exert some control and properly monitor younger children may raise the latter’s desire to snub schooling. Education at the primary level is also virtually free, which implies enrollment at that level may not necessarily depend on income constraints but rather factors such as ethnicity, family’s educational history and parents’value they place on education. Contrary to results from Zhunio et al. (2012) and Amakom and Iheoma (2014), public education expenditure appeared to significantly improve primary and secondary enrollments. It does seem that households are motivated to send their children to school if governments invest more in education at that level. This may be that the specific areas governments’expenditures are directed significantly affect households’income. Thus, households are willing to release their wards from engaging in other incomegenerating activities and attend school when governments spend more on education. Moreover, the effect of public education expenditure was higher than that of real remittance per capita. Reducing infant mortality increases enrollment, significantly at the secondary level. For the percentage of the population aged 25–34 with completed TE, increasing this number translates into higher secondary and tertiary schools enrollments. Majgaard and Mingat (2012)usedthe percentage of the population aged 25–34 with completed TE as a proxy to measure the supply of highly skilled labor and found that it was significant in improving tertiary enrollment in SSA. Using the same variable, this research found similar results for not only tertiary enrollment but also secondary enrollment. This may be due to: firstly, completing TE may guarantee higher paid jobs to assist dependants. Secondly, these highly educated people may be more motivated to advise children and parents -that is if they are not parents themselves––to equally go to school based on their personal educational experiences. Thus, one can only expect a rise in the proportion of educated people to improve subsequent educational outcomes. Unemployment rates had expected signs but were not significant in all cases. Reduction in unemployment will mean more available jobs for parents, guardians and elder brothers, which eventually translate into more money available to fund the education of their dependents. Amega, Cogent Economics & Finance (2018), 6: 1516488 https://doi.org/10.1080/23322039.2018.1516488 Page 23 of 27