Migrant Remittances and Food Security in Sub-Saharan Africa: The Role of Income Classifications

Abstract

The pervasiveness of food insecurity in sub-Saharan Africa and other developing regions has resulted in increased emigration not only to wealthier countries within the continent but also to developed countries elsewhere in the world. A growing body of research has examined the welfare implications of remittances from international migrants for families left behind. A strand of that literature focuses on the association between international remittances and household food security. We contribute to this body of work by examining the variability of this relationship across three groupings of African countries, based on the World Bank's income classifications. Using data from the Afrobarometer Surveys, our results from an instrumental variable ordered probit regressions reveal that international remittances are positively and significantly correlated with household food security for all three country groupings. After correcting for endogeneity, we find that remittance-receiving households were 83.59 percent, 72.66 percent, and 26.06 percent more likely to report having never gone without enough food to eat in low-income, lower-middle income, and upper-middle income countries in sub-Saharan Africa, respectively. These findings suggest that central governments and policymakers in Africa should reform public policy in a way that strengthens the effectiveness and efficiency of international remittances transfer to reduce food insecurity across the continent.

Keywords

food security income classifications migrant remittances sub-Saharan Africa

Introduction

Despite numerous global and individual country efforts, malnutrition is still a challenge in many developing economies (FAO 2016). Malnutrition and inadequate access to food remain very high in sub-Saharan Africa, Latin America, and parts of Asia (FAO 2013). Within Sub-Saharan Africa, a consequence of malnutrition and inadequate food supply is that a large number of people seek better economic opportunities in developed countries (World Bank 2018), relatively wealthier countries within the African continent (Regmi, Paudel, and Bhattarai 2020; Abel and Sander 2014), or relatively more developed cities within their home countries (Krokfors 1995). As a result of increased out-migration in developing countries, migrant remittances have grown significantly within the past decade, driven by both internal and international migration (Masron and Subramaniam 2018). Recently, surveys on migration and remittances have revealed that 85 percent of Ethiopians, 68 percent of Ugandans, and 66 percent of Kenyans have migrated in pursuit of better living conditions, resulting in an increase in food security for household members left behind (World Bank 2018).

In response to the negative repercussions of food insecurity, the second Sustainable Development Goal adopted in 2015 by United Nations member-states seeks to “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” (United Nations Environment Programme 2015), and in the past decade, major international institutions have lent their support to promoting migration and remittances as potential tools to reduce poverty and promote sustainable human development (World Bank 2009; United Nations Development Programme 2009). Nonetheless, as of 2015, access to enough food remained a challenge for about 795 million people globally (Food and Agriculture Organization 2015). Currently, about 822 million people are said to be undernourished around the world and unable to live active and healthy lives (FAO, ECA & AUC 2020). In Africa, food insecurity and undernourishment have remained great challenges since the 2007–2008 global food crises (Deaton and Lipka 2015), and two-fifths of the continent's population lived in poverty, as of 2013 (World Bank 2013). Out of 256 million Africans with inadequate access to food, 239 million reside in sub-Saharan Africa (FAO, ECA & AUC 2020). Thus, the prevalence of malnourishment in Africa continues to rise, particularly in sub-Saharan Africa, as illustrated in Figure 1 (FAO 2015). In this context, migrant remittances have become a key component of households’ livelihood and survival strategies (Mabrouk and Mekni 2018).

Figure 1.

Undernourishment Prevalence. Source: FAO (2015).

Food insecurity contributes to sub-Saharan Africa's social, economic, and political instability (Deaton and Lipka 2015; Rena 2005), as well as to the movement of people within and beyond the sub-region (Adepoju 2010). As more people migrate, remittances, in the form of money, goods, skills, knowledge, attitudes, mindsets, and social capital, are repatriated (Levitt 1998). Migrant remittances to developing countries have continued to increase significantly since 1995, surpassing official development assistant and placing second only to foreign direct investment (Masron and Subramaniam 2018). For instance, remittances to developing countries in 2015, although the lowest since the 2007–2008 global financial crises, still amounted to about $441 billion, excluding unrecorded flows via informal channels (World Bank 2016).

In sub-Saharan Africa, the flow of remittances reached a total of $38.4 billion in 2017 (World Bank 2017). Large proportions of remittances received in Ghana and Nigeria are channeled into domestic consumptions, ameliorating hunger (Quartey 2006b; Fonta et al. 2015). According to Sulemana, Bugri Anarfo, and Quartey (2019), the frequent receipt of international remittances in sub-Saharan Africa improves households’ food security. Corroborating this finding, Chukwuone et al. (2008), in an analysis involving data from a 2004 Nigerian national living standard survey, showed that more than 70 percent of international remittances received in Nigeria were channeled into households’ consumption and expenses, significantly reducing poverty. Similarly, household welfare variables in a 2011 survey were shown to be significantly affected by the receipt of remittances in Ethiopia (Andersson 2012). Previously, Quartey (2006b), employing Ghana's living standard survey conducted between 1991 and 1999, also revealed that the receipt of remittances substantially improved and cushioned household welfare against economic shocks, except among crop farmers.

Within the literature on migration and remittances, studies that examine the nexus between migrant remittances and food security in Sub-Saharan Africa are largely single-country studies (e.g., Babatunde and Martinetti 2011; Abadi et al. 2013; Regmi and Paudel 2016; Atuoye et al. 2017; Mabrouk and Mekni 2018) that do not allow for the generalization of findings. A few studies have examined the relationship between remittances and food security beyond individual country-level analysis (e.g., Sulemana, Bugri Anarfo, and Quartey 2019; Ebadi, Ahmadi, and Melgar-Quiñonez 2020) but do not take into consideration the varying income levels of countries in sub-Saharan Africa, where six out of the world's 10 most unequal countries reside (Byanyima 2014).

Therefore, employing Round 6 (conducted in 2015) of the Afrobarometer survey that involved 32 sub-Saharan African countries and covered 48,000 respondents, this article contributes to the literature on international migration and remittances by seeking answer to the following research question: Do income inequalities among sub-Saharan African countries influence the relationship between migrant remittances and food security in the region? Categorizing countries in sub-Saharan Africa into three income groupings according to the World’s Bank 2018 income classifications,¹ the results from an instrumental variable (IV) ordered probit regression reveal that international remittances are positively and significantly correlated with households’ food security at the 0.1 percent level for all three income groupings. Controlling for endogeneity, the analysis reveals a much stronger correlation (83.59 percent) for low-income countries and weaker correlations (26.06 percent) for upper-middle income countries. These findings suggest that remittances resulting from international migration substantially mitigate household members left behind against food insecurity, especially in low-income and lower-middle income countries in sub-Saharan Africa.

Table 1.

Country Groupings by World Bank 2018 Income Classifications.

Upper-middle income countries
Botswana	Gabon	Mauritius
Namibia	South Africa
Lower-middle income countries
Cameroon	Cape Verde	Cote d'Ivoire
Ghana	Kenya	Lesotho
Nigeria	São Tomé and Príncipe	Sudan
Swaziland	Zambia,
Low-income countries
Benin	Burkina Faso	Burundi
Guinea	Liberia	Madagascar
Malawi	Mali	Mozambique
Niger	Senegal	Sierra Leone
Tanzania	Togo	Uganda
Zimbabwe

Migration Dynamics in Sub-Sahara Africa

Spatial mobility is a basic historical and social aspect of life in Africa, where evidence suggests that the human race itself started and migrated outward (Adepoju 2011). Today, Africa has the world's highest volume of migration, both voluntary and involuntary (United Nation 2017). What makes the continent different from other regions is that most international migration in Africa is intra-continental and composed of regional movements by nomads, refugees, seasonal labor migrants, and undocumented migrants (IOM 2019). In recent decades, deteriorating political, social, economic, and ecological conditions across the African continent have brought about changes in migration's dynamics, direction, and pattern, thereby compelling movements abroad (IOM 2020). As these various crises have intensified, migration outflow has increased in both effect and size (IOM 2020).

Particularly in sub-Saharan Africa, the international migrant stock at mid-years for both sexes witnessed an increment from 14.69 million in 1990 to about 22.98 million in 2017 (United Nations 2017). About 2.6 million sub-Saharan Africans were said to be involuntary migrants in 2018, due to drought, cyclones, floods, and wars (IDMC 2019). Rapid population growth, coupled with political instability, escalating ethnic conflict, persistent economic decline, and poverty, have continued to shape international migration within and from the sub-region (FAO, ECA & AUC 2020; IOM 2020). In addition, international trends, such as regional integration, globalization, political transformation, the entry of multinational companies in search of cheap labor, and network formation continually influence migration in sub-Saharan Africa (Shimeless 2010).

Migration is commonly used by Africans as a survival strategy to pursue economic mobility and to supplement their household income or resources (Quartey 2006b). Households normally choose and invest in a family member who has the potential to generate income and send remittances (Chukwuone et al. 2008), and for families compelled by economic hardships, international migration has become a key coping mechanism to ensure survival (Adepoju 2011). Furthermore, migrant remittances have played a central role in the development of many countries across sub-Saharan Africa, where remittances financed more than 50 percent of foreign exchange earnings in Lesotho, 70 percent of total commodity export earnings in Sudan, and 80 percent of current account deficit in Botswana (De Haan 2000). In addition, migrants have served as a source of labor on many coffee and cocoa plantations in Côte d'Ivoire and Ghana and in many mining and agriculture fields in Gabon (Adepoju 2000; Adepoju 2011).

Globally, about 247 million people are residing outside their origin countries (World Bank 2017). International migration continues to grow in recent times and is a major feature in many countries and a salient tool for globalization (Dao et al. 2021). In 2017, approximately 420,000 more sub-Saharan African migrants were residing in Europe (4.15 million) than in 2010 (3.73 million), and the sub-Saharan African population also increased in the United States from 1.22 million in 2010 to 1.55 million in 2017 (United Nations 2017). In a Pew Research poll conducted in 2018, 75 percent of Ghanaians expressed passion to leave their home country for other parts of the world, with 42 percent actually taking concrete steps to exit in the next five years (Pew Research Centre 2018). The burgeoning number of people exiting sub-Saharan Africa leads to increased remittances to family members left back home (World Bank 2019). However, many countries in sub-Saharan Africa still face food insecurity, hunger, and diseases (Flahaux and De Haas 2016). Research into migration in sub-Saharan Africa is growing (United Nations 2017), and this literature shows migration's centrality to addressing the development inequality in sub-Saharan Africa. This article adds to this growing body of work by examining the heterogeneous income levels among sub-Saharan African countries and their impacts on migrant remittances and food security.

Literature Review on Remittances and Food Security

There is substantial scholarly debate about the extent to which migrant remittances promote the development agenda of migrants’ home countries (Adams and Page 2005; Gorton, Bullen, and Mhurchu 2010; Adams 2011; Ngoma and Ismail 2013). Some researchers view remittances as significant drivers of economic development, poverty alleviation, and human capital development in migrant-sending countries (Fajnzylber and Humberto Lopez 2008; Eversole and Shaw 2010; Adams 2011; Combes and Ebeke 2011; Ratha et al. 2011; De and Ratha 2012; Orozco and Ellis 2014; Crush and Caesar 2018) and as a reliable source of international funds for Sub-Saharan Africa's economic growth and development relative to other international inflows (Anyanwu and Erhijakpor 2010; Ouedraogo, Sourouema, and Zahonogo 2018). Other researchers, however, contend that migrant remittances inflict adverse repercussions on migrant-sending countries by weakening institutional capacity, increasing dependency, and not necessarily contributing to economic growth and development (Azam and Gubert 2006; Rao and Hassan 2011; Abdih et al. 2012; Rao and Hassan 2012).

Africa continues to receive substantial inflows of remittances from abroad amid rising food insecurity and undernourishment (Verpoorten et al. 2013; FAO 2015; Mabrouk and Mekni 2018). Empirical studies that have examined the remittances-food-security nexus in Africa have acknowledged remittances’ importance in mitigating food insecurity among receiving households (FAO 2008; Karamba, Quiñones, and Winters 2011; Zaman and Akbar 2013). Several other studies have also revealed that remittances alleviate poverty among receiving households in Africa (Babatunde and Martinetti 2011; Mohapatra, Joseph, and Ratha 2012; Abadi et al. 2013; Generoso 2015; Atuoye et al. 2017).

In Ethiopia, Abadi et al. (2013) note a positive relationship between migrant remittances and household food security, based on data from 300 households. This finding was corroborated by Generoso (2015), whose literature review showed that remittances had a significant and affirmative effect on food security in rural Mali. Generoso further noted that remittances reduced the adverse repercussions of intra-annual rainfall variability on food security, since they were used to purchase food. Babatunde and Martinetti (2011) investigated migrant remittances’ effect on household food security and nutrition in Nigeria and found that remittances enhanced nutrition in remittance-receiving households. Together, these findings suggest an affirmative link between migrant remittances and nutrition, as remittances increase households’ income and access to food, positioning households receiving remittances as better off in terms of income levels, assets, and calorie supply than households not receiving remittances.

While the literature on migrant remittances and food security shows that migrant remittances have a significant and affirmative effect on food security, the positive link between migrant remittances and food security has been shown to be adversely affected by the presence of underdeveloped agricultural sector, rapid population growth, political instability, and natural disasters (United Nations, 2012). Given that there is income inequality among sub-Saharan African countries (Gimba, Seraj, and Ozdeser 2021; Stein 2011) and that such inequality can affect economic growth (UNDP 2017; Sulemana, Doabil, and Anarfo 2019; Bah, Abdulwakil, and Azam 2020), the income variability among sub-Saharan Africa countries could also influence the relationship between migrant remittances and food security.

Data and Methods

We utilize data from the Afrobaromoter Surveys, a nonpartisan research network that aims to tap into public attitudes in Africa toward governance, democracy, economic conditions, and other factors (Mattes 2008). Currently, data for six rounds of the Afrobarometer Survey are publicly available, and Round 7 surveys are ongoing. For the publicly available data, the survey question on international remittances, on which we focus here, was asked in only Rounds 4, conducted in 20 countries between 2008/2009, and Round 6, conducted in 36 countries between 2014/2015. Since Round 6 is more recent, covers more countries, and contains almost twice the number of observations as Round 4, we chose Round 6 for our empirical analysis. Although Round 6 interviews were conducted in 36 African countries, our analysis is based on data from 32 sub-Saharan Africa countries, due to the exclusion of Tunisia, Morocco, Egypt, and Algeria that formed part of the surveyed countries but do not form part of sub-Saharan Africa.

Furthermore, because we are interested in whether the magnitude of the association between international remittances and food security varied by income classification, we split the surveyed countries in three groups. As shown in Table 1, 16 countries were in the low-income category, compared to 11 and 5 in the lower-middle income and upper-middle income country categories, respectively. The only sub-Saharan African country classified as high-income is Seychelles, which is not covered in the Afrobarometer Surveys. After excluding observations with missing responses, the total number of observations used in the analysis was 48,081.

Dependent Variable

The dependent variable in our analysis is the food security status (FSS) of the respondent's household. Although food security has been measured in diverse ways (e.g., Abadi et al. 2013; IFPRI 2020), we follow the precedent of a strand of the literature on international remittances and food security that operationalizes food security by asking respondents how often they or anyone in their family had gone without enough food to eat over a period, usually a year (e.g., Mpesi and Muriaas 2012; Verpoorten et al. 2013; Sulemana, Bugri Anarfo, and Quartey 2019). Thus, our dependent variable is based on responses to the question: “Over the past year, how often, if ever, have you or anyone in your family: Gone without enough food to eat?” (0 = Never, 1 = Just once or twice, 2 = Several times, 3 = Many times, 4 = Always). Responses were reverse-coded so that households indicating higher values were relatively more food secure.

Independent Variables

For migrant remittances, we relied on the survey question: “How often, if at all, do you or anyone in your household receive money remittances from friends or relatives living outside of the country?” (5 = At least once a month, 4 = At least every three months, 3 = At least every six months, 2 = At least once a year, 1 = Less than once a year, 0 = Never). We created “remittances” equal to 1 if the respondent indicated having received international remittances at least once a year or more and 0 otherwise. This construction is consistent with how previous studies have operationalized remittances (e.g., Verpoorten et al. 2013; Ivlevs, Nikolova, and Graham 2019; Sulemana, Bugri Anarfo, and Quartey 2019; Sulemana, Doabil, and Anarfo 2019). We controlled for other covariates of food security established in the literature on migration and food security, including demographic characteristics like age, gender, and education (Kuwornu, Suleyman, and Amegashie 2013; Kassie, Ndiritu, and Stage 2014; Generoso 2015; Regmi and Paudel 2016; Regmi and Paudel 2017), unemployment (Loopstra and Tarasuk 2013; Etana and Tolossa 2017), asset ownership (Guo 2011), access to electricity (Habtewold 2018) and pipe water (Iram and Butt 2004), and rural versus urban residence (Verpoorten et al. 2013).

Empirical Models

Following McKelvey and Zavoina (1975), we model the individual's household FSS as a function of their receipt or otherwise of migrant remittances, their demographic characteristics, and other covariates. A household's FSS can be transformed into an unobserved latent index $F S S_{i c}^{*}$ that is assumed to be normally distributed with mean μ_i and variance 1 (McKelvey and Zavoina 1975; Regmi and Paudel 2017). Prior studies have shown that international remittances may be endogenous to households’ food security situation (Gupta, Pattillo, and Wagh 2009; Babatunde and Martinetti 2011; Regmi and Paudel 2017). For instance, Gupta, Pattillo, and Wagh (2009) argued that it is unlikely that the association between food security and remittances is unidirectional. Because poverty strongly influences food security (Adams and Page 2005), countries with high food insecurity may be more likely to produce migrants (Rademacher-Schulz, Schraven, and Mahama 2014; Warner and Afifi 2014). Hence, our empirical approach follows the following framework:
$F S S_{i c}^{*} = β_{0} + β_{1} R_{i c} + X_{i c}^{'} γ + δ_{c} + ε_{i c}$
(1)
$R_{i c} = α_{0} + α_{1} Z_{i c} + X_{i c}^{'} θ + v_{i c}$
(2)
where R indicates whether the respondent or someone in their household had received international remittances in the past year, X is a vector of control variables, Z is an instrumental variable for remittances, $β_{0}$ and $α_{0}$ are intercepts, $β_{1}$ and $α_{1}$ are the slope parameters for remittances and the instruments, respectively, and $γ$ and $θ$ are vectors of coefficients for the control variables in the structural and reduced form equations, respectively. Additionally, $δ$ is the country-specific dummy variable that controls for country heterogeneities, i and c index the respondent and country, respectively, and $ε$ and v are the error terms. The observed FSS is related to the unobserved FSS* as follows:
$F S S_{i} = {\begin{matrix} 0 i f - \infty \leq F S S_{i}^{*} \leq τ_{0} \\ 1 i f τ_{0} \leq F S S_{i}^{*} \leq τ_{1} \\ 2 i f τ_{1} \leq F S S_{i}^{*} \leq τ_{2} \\ \begin{matrix} 3 i f τ_{1} \leq F S S_{i}^{*} \leq τ_{3} \\ 4 i f τ_{3} \leq F S S_{i}^{*} \end{matrix} \end{matrix}$
(3)
where 0, 1, 2, 3, and 4 are the food security categories and $τ_{i}$ denotes FSS thresholds.

Because the dependent variable is ordinal, we, first, ran ordered probit regressions for the models (e.g., McKelvey and Zavoina 1975; Wooldridge 2010; Greene 2012). For the potential endogeneity problem, we implemented an IV ordered probit (IV-ordered probit) estimation procedure, using the conditional mixed process (cmp) command, a user-written command in STATA (Roodman 2011). The cmp framework works like seemingly unrelated regressions (SUR) but allows for binary, ordinal, categorical, or censored variables (Roodman 2011). An instrument for remittances is suitable if it is correlated with remittances (i.e., relevance) but uncorrelated with the error term in equation (1). In other words, the instrument itself should not cause food insecurity but should affect food security through remittances (i.e., exclusion restriction). We employed the IV ordered probit approach because, unlike most other adjustment approaches such as stratification, mixing, and multiple regression, IV ordered probit has the advantage of being able to account for all confounders, including unobserved ones such as propensity ratings (Hanck et al. 2020). This method is used to approximate causal inferences and to assess exogenous treatment variance (Hanck et al. 2020). IVs are used in the IV ordered probit technique in multiple regression to assess an exogenous part of the variability from an endogenous predictor. In other words, this strategy helps us use only the portion of the predictor variance that is “arguably random,” that is, unrelated to unobservable variables that influence both the predictor and the outcome. Researchers may use this procedure to estimate the causal relationship between the result and the predictor (Stock and Yogo 2004; Hanck et al. 2020). However, finding such an instrument has proven to be cumbersome, as has been noted in similar studies (e.g., Pfutze 2012; Ivlevs, Nikolova, and Graham 2019; Sulemana, Doabil, and Anarfo 2019).

Adams and Page (2005) have argued that countries with high levels of political instability are more likely to produce more migrants. Building on this argument, Sulemana et al. (2019) contend that people who believe that their country is moving in the wrong direction may be more likely to emigrate. Similarly, with the question, “Would you say that the country is going in the wrong direction or going in the right direction?,” we created an instrument for remittances equal to 1 for respondents that felt that their country was moving in the wrong direction and 0 otherwise to the question. If this instrument is valid, it practically suggests that the F-statistic in the first stage regression will be higher than 10 (Stock and Yogo 2004).

Results and Discussion

As noted earlier, our overarching objective is to explore whether there are variations in the magnitude of the association between international remittances and food security across income groups in Sub-Saharan Africa, based on national income. In what follows, we discuss the results of our econometric analysis. At the outset, we note that the results presented here do not establish causation between international remittances and food security; rather, they provide conditional correlations between them. Table 2 reports the summary statistics for the independent variables by income classification. Respondents’ average age was 37 for low-income countries, about 36 for lower-middle income countries, and 38 for upper-middle income countries. Half of respondents in each country grouping were female, while their educational attainments varied considerably. About 42 percent of respondents in low-income countries had primary education or less, but the corresponding statistics for lower-middle income and upper-middle income countries were 22.88 percent and 13.25 percent, respectively. Additionally, 31.41 percent of respondents had attained secondary-school education in low-income countries, compared to 51.67 percent for lower-middle income and 64.92 percent for upper-middle income countries. Only 5.17 percent, 11.43 percent, and 13.69 percent of respondents had attained university education in low-income, lower-middle income, and upper-middle income countries, respectively.

Table 2.
Summary Statistics for the Independent Variables by Income Classification Group.

Low-income Lower-middle income Upper-middle income Pooled sample

Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Remittances 0.1531 0.3601 0.1991 0.3994 0.0773 0.2671 0.1574 0.3642

Age 37.6098 14.4880 35.9776 14.3302 38.0126 14.8881 37.1165 14.5183

Female 0.5039 0.5000 0.4995 0.5000 0.5017 0.5000 0.5021 0.5000

Primary or less 0.4263 0.4946 0.2283 0.4197 0.1325 0.3391 0.3156 0.4648

Secondary 0.3141 0.4642 0.5167 0.4997 0.6492 0.4773 0.4325 0.4954

University 0.0517 0.2214 0.1143 0.3182 0.1369 0.3437 0.0856 0.2797

Unemployed 0.2084 0.4062 0.2678 0.4428 0.2890 0.4533 0.2405 0.4274

Asset ownership 1.7857 1.2006 2.3949 1.1108 2.6938 1.0881 2.1270 1.2102

Electric connection 0.3306 0.4704 0.7332 0.4423 0.8479 0.3592 0.5438 0.4981

Access to pipe water 0.4368 0.4960 0.6544 0.4756 0.8554 0.3517 0.5727 0.4947

Urban 0.3061 0.4609 0.4573 0.4982 0.5381 0.4986 0.3918 0.4882

Note: the number of observations was 24,648 for low-income countries, 16,295 for the lower-middle income countries, 7,138 for the upper-middle income countries, and N = 48,081 for the pooled sample.

Interestingly, the data show a higher proportion of respondents being unemployed in upper-middle income countries (28.90 percent) than in lower-middle income countries (26.78 percent) and low-income countries (20.84 percent). Asset ownership was higher in upper-middle income and lower-middle income countries than in low-income countries.² The data also show significant variations in access to electricity and pipe water across country groupings. While only 33.06 percent of respondents in low-income countries had electricity connection, about 73.32 percent of respondents in lower-middle countries and 84.79 percent of respondents in upper-middle income countries had electricity connection. Also, 43.68 percent of respondents in low-income countries reported having access to pipe water, compared to 65.44 percent for lower-middle income countries and 85.54 percent for upper-middle income countries. Finally, while the majority (53.81 percent) of respondents in upper-middle income countries resided in urban areas, that figure was lower in both low-income countries (30.61 percent) and lower-middle income countries (45.73 percent). The proportion of respondents who had received international remittances in the last year was highest for lower-middle income countries (19.91 percent), compared to 15.31 percent and 7.73 percent in low-income and upper-middle income countries, respectively.

Figure 2 reports the distribution of responses to the food security question. Less than 5 percent of respondents for each country grouping reported that they “always” went without enough food to eat. However, almost 15 percent of respondents in low-income countries and almost 8 percent of respondents in lower-middle income and upper-middle income countries reported that they went without enough food to eat “many times” over the past year. Additionally, 22.26 percent of the respondents reported they had gone without enough food “several times” in low-income countries, compared to 16.09 percent in lower-middle income countries and 14.47 percent in upper-middle income countries. This trend suggests that food insecurity is more severe in low-income countries than in lower-middle and upper-middle income countries, a finding supported by the proportion of respondents who indicated that they had never gone without enough food to eat (45.75 percent of respondents in low-income countries, 57.98 percent of respondents in lower-middle income countries, and 62.27 percent of respondents in upper-middle countries).

Figure 2.
Distribution of Responses to the Question “Over the past year, how often, if ever, have you or anyone in your family: gone without enough food to eat?” by Country Groupings.

To explore whether households that received remittances were more likely to report being food secure, we performed cross tabulations for remittance receipt and FSS. The results, presented in Table 3, show that respondents who had never received international remittances were disproportionately more likely to indicate having gone without food for all income classification categories. Yet, among those who received remittances, it cannot be concluded unambiguously that they more frequently were more food secure.³

Table 3.
Cross-Tabulation for Remittances Receipts and Food Security status.

How often gone without food

Low-income

How often received remittances Never Just once or twice Several times Many times Always Total

Never 78.65% 80.46% 84.14% 87.49% 84.34% 81.61%

Less than once a year 3.25% 3.43% 3.24% 2.11% 2.47% 3.08%

At least once a year 4.31% 4.38% 4.17% 2.47% 3.33% 3.99%

At least every six months 2.83% 2.80% 2.72% 2.61% 2.96% 2.77%

At least every three months 10.95% 8.93% 5.72% 5.33% 6.91% 8.55%

Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Lower-middle-income

How often received remittances Never Just once or twice Several times Many times Always Total

Never 74.82% 77.30% 78.91% 82.44% 84.23% 76.66%

Less than once a year 3.34% 4.12% 3.09% 3.11% 4.10% 3.42%

At least once a year 4.81% 4.69% 4.04% 3.57% 3.15% 4.54%

At least every six months 3.68% 3.74% 4.39% 3.57% 2.21% 3.77%

At least every three months 13.35% 10.15% 9.57% 7.30% 6.31% 11.61%

Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Upper-middle-Income

How often received remittances Never Just once or twice Several times Many times Always Total

Never 89.76% 90.36% 90.42% 90.50% 91.45% 90.03%

Less than once a year 2.27% 2.29% 2.32% 2.05% 0.85% 2.24%

At least once a year 2.45% 2.29% 3.29% 1.86% 4.27% 2.54%

At least every six months 1.26% 1.19% 1.16% 0.56% 0.85% 1.18%

At least every three months 4.25% 3.88% 2.81% 5.03% 2.56% 4.02%

Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Table 4 reports the ordered probit and IV-ordered probit regression results for the three country groupings by income classification. In the odd-numbered models, we regressed food security on the independent variables while excluding country fixed effects. We, then, repeated the exercise but included the country fixed effects and reported the results in the even-numbered models. In both instances, the results were identical, even though the coefficient on remittances increases for the models in which we controlled for country fixed effects. In the ordered probit models, we found a positive and statistically significant association between remittances and FSS for low-income and lower-middle income countries at the 0.1 percent level. The ordered probit regression results for upper-middle income countries (Models 9 and 10) did not show a statistically significant association between remittances and food security. The marginal effects for Models 2, 6, and 8 are reported in Tables A, B, and C in the Supplemental Appendix. The results show that, relative to households that did not receive international remittances within the reference period, those who received remittances were about 5.70 percent and 3.27 percent more likely to report having never gone without enough food to eat in low-income and lower-middle income countries, respectively. These respondents were also significantly less likely to report having gone without enough food to eat “just once or twice,” “several times,” “many times,” and “always” in those groups of countries.

Table 4.
Ordered Probit and IV-Ordered Probit Regression Results for the Association Between Remittances and Household Food Security.

Low-income countries Lower-middle income Upper-middle income

Ordered probit IV-ordered probit Ordered probit IV-ordered probit Ordered probit IV-ordered probit

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12

Remittances 0.1083*** 0.1584*** 2.6879*** 2.7159*** 0.0768** 0.0915*** 2.3559*** 2.4134*** −0.0306 0.1075 3.5913*** 3.5713***

(0.0206) (0.0211) (0.0328) (0.0278) (0.0238) (0.0243) (0.0545) (0.0374) (0.0544) (0.0565) (0.1225) (0.1565)

Age −0.0155*** −0.0148*** −0.0040*** −0.0036*** −0.0291*** −0.0308*** −0.0101*** −0.0087*** −0.0071 −0.0132* −0.0015 −0.0040

(0.0025) (0.0025) (0.0009) (0.0009) (0.0032) (0.0033) (0.0020) (0.0017) (0.0050) (0.0052) (0.0015) (0.0023)

Age²/100 0.1373*** 0.1201*** 0.0344*** 0.0274** 0.2704*** 0.2819*** 0.0929*** 0.0793*** 0.0616 0.1195* 0.0130 0.0366

(0.0277) (0.0280) (0.0094) (0.0086) (0.0354) (0.0361) (0.0196) (0.0168) (0.0550) (0.0567) (0.0160) (0.0236)

Female 0.0503*** 0.0466** 0.0136** 0.0118** 0.0111 0.0130 0.0043 0.0041 −0.0311 −0.0741* −0.0103 −0.0260

(0.0148) (0.0149) (0.0047) (0.0044) (0.0186) (0.0188) (0.0069) (0.0058) (0.0289) (0.0300) (0.0088) (0.0140)

Secondary 0.1843*** 0.2031*** 0.0569*** 0.0556*** 0.1441*** 0.1804*** 0.0537*** 0.0558*** 0.1999*** 0.3251*** 0.0559* 0.1065*

(0.0175) (0.0184) (0.0094) (0.0094) (0.0215) (0.0218) (0.0115) (0.0110) (0.0404) (0.0420) (0.0245) (0.0438)

University 0.3432*** 0.4477*** 0.1000*** 0.1197*** 0.3022*** 0.3557*** 0.1079*** 0.1104*** 0.2403*** 0.6080*** 0.0669* 0.1993*

(0.0381) (0.0390) (0.0179) (0.0202) (0.0353) (0.0369) (0.0213) (0.0208) (0.0573) (0.0612) (0.0305) (0.0803)

Unemployed −0.1964*** −0.1504*** −0.0580*** −0.0391*** −0.2201*** −0.1815*** −0.0831*** −0.0550*** −0.3529*** −0.3226*** −0.0945* −0.1045*

(0.0177) (0.0181) (0.0096) (0.0075) (0.0208) (0.0214) (0.0149) (0.0109) (0.0323) (0.0333) (0.0382) (0.0423)

Asset ownership 0.2059*** 0.1936*** 0.0595*** 0.0526*** 0.1372*** 0.1487*** 0.0504*** 0.0447*** 0.2906*** 0.1689*** 0.0799* 0.0554*

(0.0072) (0.0074) (0.0086) (0.0078) (0.0095) (0.0097) (0.0086) (0.0077) (0.0155) (0.0163) (0.0316) (0.0223)

Electric connection 0.0429* 0.0813*** 0.0107 0.0257*** 0.2126*** 0.1718*** 0.0803*** 0.0490*** 0.2062*** 0.2257*** 0.0629* 0.0722*

(0.0195) (0.0204) (0.0059) (0.0066) (0.0235) (0.0262) (0.0151) (0.0112) (0.0437) (0.0473) (0.0271) (0.0323)

Access to pipe 0.1291*** 0.1263*** 0.0332*** 0.0326*** 0.0808*** 0.1169*** 0.0212* 0.0339*** 0.3402*** 0.0529 0.0891* 0.0148

water (0.0169) (0.0186) (0.0068) (0.0069) (0.0213) (0.0231) (0.0086) (0.0089) (0.0419) (0.0453) (0.0370) (0.0160)

Urban 0.0120 0.0193 0.0119 0.0090 −0.0209 −0.0795*** −0.0046 −0.0222** −0.3053*** −0.0063 −0.0761* 0.0034

(0.0205) (0.0210) (0.0061) (0.0059) (0.0210) (0.0217) (0.0078) (0.0075) (0.0326) (0.0376) (0.0315) (0.0124)

Country fixed effects No Yes No Yes No Yes No Yes No Yes No Yes

LR Chi²/Wald Chi²
Log likelihood
N 2386.37***
−32369.275
24,648 3750.00***
−31687.462
24,648 45484.73***
−41938.229
24,648 46699.60***
−41265.866
24,648 1205.80***
−18642.325
16,295 2079.33***
−18205.559
16,295 24294.82***
−26701.494
16,295 28945.51***
−26232.100
16,295 1077.60***
−7404.8575
7,138 2227.25***
−6830.0316
7,138 12754.95***
−8082.2103
7,138 12155.93***
−7518.079
7,138

Note: * p < 0.05, ** p < 0.01, *** p < 0.001. Standard errors are in parentheses.

The second stage IV-ordered probit regression results are reported in Models 3 and 4 for low-income countries, in Models 7 and 8 for lower-middle income countries, and in Models 11 and 12 for upper-middle income countries. All regressions reveal a statistically significant association between migrant remittances and food security at the 0.1 percent level. The marginal effects for Models 4, 8, and 12 are also shown in Tables A, B, and C in the Supplemental Appendix for low-income, lower-middle income, and upper-middle income countries, respectively. After correcting for endogeneity, we found that remittance-receiving households were 83.59 percent more likely to report that they had never gone without enough food to eat in low-income countries. 72.66 percent of respondents in lower-middle income countries and 26.06 percent of respondents in upper-middle income countries were more likely to report that they had never gone without enough food to eat. The F-statistics in the first stage regressions were all higher than 10, suggesting that the IV for remittances is valid (Stock and Yogo 2004).⁴ The control variables had their expected signs and statistical significance. Hence, for brevity and economy of space, we do not discuss them here.

Prior studies have shown that food insecurity afflicts many countries, regardless of their income classification. For instance, Gorton, Bullen, and Mhurchu (2010, 1), in a literature review, noted that food insecurity is pervasive on a global scale: “29% amongst low-income households in the United Kingdom (UK), 20% of households with children in New Zealand, 15% in Canada, 11% in the United States, and 5% in Australia.” Following the above, our results suggest a positive association between international remittances and food security for all three country groupings in sub-Saharan Africa. Nevertheless, the extent of this association varies across these country groupings, with the strongest association occurring in low-income countries. This result is evidenced by the findings that (1) the association between international remittances and food security was statistically insignificant for upper-middle income countries for the ordered probit models and (2) there were relatively higher marginal effects for low-income countries compared to lower-middle income and upper-middle income countries for the IV-ordered probit regressions.

There are several plausible explanations for the positive association between remittances and household food security in lower-income and upper-middle income countries. Because poverty is a major determinant of food insecurity (Regmi and Paudel 2016), the receipt of remittances could improve the household food insecurity situation by augmenting household incomes. In addition to mostly accruing to low-income households, remittances have been shown to help such families rise in income quintiles within their country (De and Ratha 2012). Therefore, it may be that remittances supplement household incomes and, thus, improve remittance-receiving households’ FSS. Additionally, studies have shown that remittances alleviate households’ economic hardship in times of shocks (Quartey 2006a; Quartey 2006b; Munyegera and Matsumoto 2016; Regmi and Paudel 2017) and serve as capital for investing in education, health, small-scale businesses, and livestock farming (Diatta and Mbow 1999; Sofranko and Idris 1999; Amuedo-Dorantes and Pozo 2006). Hence, it may be that returns from these investments help households better their economic lot and, thereby, improve their FSS.

Conclusion and Policy Implications

Food insecurity has deleterious consequences for human health and well-being (Dunifon and Kowaleski-Jones 2003; Cook et al. 2004; Seligman et al. 2007; Sulemana and James 2019), and the incidences of poverty and food insecurity in developing countries have led to an increasing proportion of their citizens emigrating to developed countries in pursuit of better economic opportunities (World Bank 2018). As a result, international remittances to Africa have not only increased significantly (Ratha, 2005; Anyanwu and Erhijakpor 2010; Masron and Subramaniam 2018) but also become a crucial source of family income and development finance (Mabrouk and Mekni 2018). Previous studies on migration and remittances have shown that international remittances positively influence recipient households’ livelihoods (FAO 2008; Karamba, Quiñones, and Winters 2011; Zaman and Akbar 2013; Generoso 2015; Atuoye et al. 2017). In particular, a strand of the literature on remittances and food security has documented that remittance-receiving households report significantly better household FSS than their non-receiving counterparts (Babatunde and Martinetti 2011; Generoso 2015; Regmi and Paudel 2016, 2017; Mabrouk and Mekni 2018; Sulemana, Bugri Anarfo, and Quartey 2019).

We found some evidence that international remittances are positively and significantly correlated with household food security at the 0.1 percent level for all three country groupings investigated here. For instance, relative to households that did not receive international remittances over the past year, those who received remittances were about 5.70 percent in low-income countries and 3.27 percent in lower-middle income countries more likely to report having never gone without enough food to eat. Furthermore, after correcting for endogeneity, our results revealed that remittance-receiving households were 83.59 percent more likely to report that they had never gone without enough food to eat in low-income countries. 72.66 percent of respondents in lower-middle income countries and 26.06 percent of respondents in upper-middle income countries were more likely to report that they had never gone without enough food. Accordingly, we conclude that while international remittances are correlated with food security for all three country groupings, the extent of the correlation is much stronger for low-income countries than for lower-middle and upper-middle income countries in sub-Saharan Africa. Thus, our findings demonstrate that increased remittances could support the achievement of the second Sustainable Development Goal, especially for low-income sub-Saharan African countries. Because food security is complexly related to peace, conflict, and security (FAO 2016), improving food security in the region via migrant remittances could ultimately help reduce extremism and radicalism surfacing in parts of the sub-region (Abdih et al. 2012).

The findings presented here point to two key policy recommendations. First, central governments and policymakers in Africa and the world at large should focus on improving the effectiveness and efficiency of international remittances transfer to reduce food insecurity in the continent. Such policies should aim at strengthening the continent's financial systems in a bid to make international remittance transfers easier, especially in low-income and middle-income countries. Africa is the most expensive continent to send money to and within, and as of the fourth quarter of 2018, the average cost of international remittances to Africa was 9 percent, compared to the global average of 7 percent (Natali and Isaacs 2020). Therefore, African policymakers should prioritize easing the difficulties and costs involved in remitting to help alleviate food insecurity across the continent. Second, international migration remains an area of concern in Africa (IOM 2019). Many sub-Saharan Africans continue to perish in the desert and in the Mediterranean Sea in attempts to seek economic asylum in others parts of the globe, especially Europe (Dünnwald, 2011). Research has shown that the restrictions of African migrants to Europe do not correspond to less Africans out-migration but, rather, to more unauthorized migration and fewer returns (Beauchemin, Flahaux, and Schoumaker 2020). Since migrant remittances are an essential ingredient for food security in sub-Saharan Africa and, more importantly, in lower-income and lower-middle income countries, central governments and policymakers should work toward policies and multilateral agreements that ease restrictions on migration and provide protection for migrants against violence, inhumane, and barbaric treatment while abroad.

This article is not without limitations. First, there are several other household-level variables (i.e., marital status, household structure, etc.) that could be incorporated into our analyses to strengthen our results. However, because such pieces of information were not captured by the Afrobarometer Surveys, we could not include them in our empirical analyses. Second, an ideal dataset for our analyses would have information on household incomes and monetary values of actual remittances received. The Afrobarometer Surveys, however, ask respondents about neither their household income nor their income from international remittances. Third, a legitimate argument is that our analyses should be limited to responses from individuals in informed positions about the overall household's food security situation and remittance receipt (i.e., the household head).⁵ However, we could not incorporate this informed position about food security because the surveys were designed to fulfil a gender quota by alternating interviews between men and women without identifying them as household heads or otherwise. These limitations affect our results in terms of the efficiencies and consistency of the parameter estimates, but those results can still be of use as a tool for policy purposes. A direction for future research would be to explore a measure of international remittances that captures migrants’ cash, goods, and services received back home (Crush and Caesar 2016).

Supplemental Material

sj-docx-1-mrx-10.1177_01979183221107925 - Supplemental material for Migrant Remittances and Food Security in Sub-Saharan Africa: The Role of Income Classifications

Supplemental material, sj-docx-1-mrx-10.1177_01979183221107925 for Migrant Remittances and Food Security in Sub-Saharan Africa: The Role of Income Classifications by Iddisah Sulemana, Ebenezer Bugri Anarfo, and Louis Doabil in International Migration Review

	Low-income	Lower-middle income	Upper-middle income	Pooled sample
Variable	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.
Remittances	0.1531	0.3601	0.1991	0.3994	0.0773	0.2671	0.1574	0.3642
Age	37.6098	14.4880	35.9776	14.3302	38.0126	14.8881	37.1165	14.5183
Female	0.5039	0.5000	0.4995	0.5000	0.5017	0.5000	0.5021	0.5000
Primary or less	0.4263	0.4946	0.2283	0.4197	0.1325	0.3391	0.3156	0.4648
Secondary	0.3141	0.4642	0.5167	0.4997	0.6492	0.4773	0.4325	0.4954
University	0.0517	0.2214	0.1143	0.3182	0.1369	0.3437	0.0856	0.2797
Unemployed	0.2084	0.4062	0.2678	0.4428	0.2890	0.4533	0.2405	0.4274
Asset ownership	1.7857	1.2006	2.3949	1.1108	2.6938	1.0881	2.1270	1.2102
Electric connection	0.3306	0.4704	0.7332	0.4423	0.8479	0.3592	0.5438	0.4981
Access to pipe water	0.4368	0.4960	0.6544	0.4756	0.8554	0.3517	0.5727	0.4947
Urban	0.3061	0.4609	0.4573	0.4982	0.5381	0.4986	0.3918	0.4882

	How often gone without food
Low-income
How often received remittances	Never	Just once or twice	Several times	Many times	Always	Total
Never	78.65%	80.46%	84.14%	87.49%	84.34%	81.61%
Less than once a year	3.25%	3.43%	3.24%	2.11%	2.47%	3.08%
At least once a year	4.31%	4.38%	4.17%	2.47%	3.33%	3.99%
At least every six months	2.83%	2.80%	2.72%	2.61%	2.96%	2.77%
At least every three months	10.95%	8.93%	5.72%	5.33%	6.91%	8.55%
Total	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%
Lower-middle-income
How often received remittances	Never	Just once or twice	Several times	Many times	Always	Total
Never	74.82%	77.30%	78.91%	82.44%	84.23%	76.66%
Less than once a year	3.34%	4.12%	3.09%	3.11%	4.10%	3.42%
At least once a year	4.81%	4.69%	4.04%	3.57%	3.15%	4.54%
At least every six months	3.68%	3.74%	4.39%	3.57%	2.21%	3.77%
At least every three months	13.35%	10.15%	9.57%	7.30%	6.31%	11.61%
Total	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%
Upper-middle-Income
How often received remittances	Never	Just once or twice	Several times	Many times	Always	Total
Never	89.76%	90.36%	90.42%	90.50%	91.45%	90.03%
Less than once a year	2.27%	2.29%	2.32%	2.05%	0.85%	2.24%
At least once a year	2.45%	2.29%	3.29%	1.86%	4.27%	2.54%
At least every six months	1.26%	1.19%	1.16%	0.56%	0.85%	1.18%
At least every three months	4.25%	3.88%	2.81%	5.03%	2.56%	4.02%
Total	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%

	Low-income countries	Lower-middle income	Upper-middle income
Remittances	0.1083***	0.1584***	2.6879***	2.7159***	0.0768**	0.0915***	2.3559***	2.4134***	−0.0306	0.1075	3.5913***	3.5713***
	(0.0206)	(0.0211)	(0.0328)	(0.0278)	(0.0238)	(0.0243)	(0.0545)	(0.0374)	(0.0544)	(0.0565)	(0.1225)	(0.1565)
Age	−0.0155***	−0.0148***	−0.0040***	−0.0036***	−0.0291***	−0.0308***	−0.0101***	−0.0087***	−0.0071	−0.0132*	−0.0015	−0.0040
	(0.0025)	(0.0025)	(0.0009)	(0.0009)	(0.0032)	(0.0033)	(0.0020)	(0.0017)	(0.0050)	(0.0052)	(0.0015)	(0.0023)
Age²/100	0.1373***	0.1201***	0.0344***	0.0274**	0.2704***	0.2819***	0.0929***	0.0793***	0.0616	0.1195*	0.0130	0.0366
	(0.0277)	(0.0280)	(0.0094)	(0.0086)	(0.0354)	(0.0361)	(0.0196)	(0.0168)	(0.0550)	(0.0567)	(0.0160)	(0.0236)
Female	0.0503***	0.0466**	0.0136**	0.0118**	0.0111	0.0130	0.0043	0.0041	−0.0311	−0.0741*	−0.0103	−0.0260
	(0.0148)	(0.0149)	(0.0047)	(0.0044)	(0.0186)	(0.0188)	(0.0069)	(0.0058)	(0.0289)	(0.0300)	(0.0088)	(0.0140)
Secondary	0.1843***	0.2031***	0.0569***	0.0556***	0.1441***	0.1804***	0.0537***	0.0558***	0.1999***	0.3251***	0.0559*	0.1065*
	(0.0175)	(0.0184)	(0.0094)	(0.0094)	(0.0215)	(0.0218)	(0.0115)	(0.0110)	(0.0404)	(0.0420)	(0.0245)	(0.0438)
University	0.3432***	0.4477***	0.1000***	0.1197***	0.3022***	0.3557***	0.1079***	0.1104***	0.2403***	0.6080***	0.0669*	0.1993*
	(0.0381)	(0.0390)	(0.0179)	(0.0202)	(0.0353)	(0.0369)	(0.0213)	(0.0208)	(0.0573)	(0.0612)	(0.0305)	(0.0803)
Unemployed	−0.1964***	−0.1504***	−0.0580***	−0.0391***	−0.2201***	−0.1815***	−0.0831***	−0.0550***	−0.3529***	−0.3226***	−0.0945*	−0.1045*
	(0.0177)	(0.0181)	(0.0096)	(0.0075)	(0.0208)	(0.0214)	(0.0149)	(0.0109)	(0.0323)	(0.0333)	(0.0382)	(0.0423)
Asset ownership	0.2059***	0.1936***	0.0595***	0.0526***	0.1372***	0.1487***	0.0504***	0.0447***	0.2906***	0.1689***	0.0799*	0.0554*
	(0.0072)	(0.0074)	(0.0086)	(0.0078)	(0.0095)	(0.0097)	(0.0086)	(0.0077)	(0.0155)	(0.0163)	(0.0316)	(0.0223)
Electric connection	0.0429*	0.0813***	0.0107	0.0257***	0.2126***	0.1718***	0.0803***	0.0490***	0.2062***	0.2257***	0.0629*	0.0722*
	(0.0195)	(0.0204)	(0.0059)	(0.0066)	(0.0235)	(0.0262)	(0.0151)	(0.0112)	(0.0437)	(0.0473)	(0.0271)	(0.0323)
Access to pipe	0.1291***	0.1263***	0.0332***	0.0326***	0.0808***	0.1169***	0.0212*	0.0339***	0.3402***	0.0529	0.0891*	0.0148
water	(0.0169)	(0.0186)	(0.0068)	(0.0069)	(0.0213)	(0.0231)	(0.0086)	(0.0089)	(0.0419)	(0.0453)	(0.0370)	(0.0160)
Urban	0.0120	0.0193	0.0119	0.0090	−0.0209	−0.0795***	−0.0046	−0.0222**	−0.3053***	−0.0063	−0.0761*	0.0034
	(0.0205)	(0.0210)	(0.0061)	(0.0059)	(0.0210)	(0.0217)	(0.0078)	(0.0075)	(0.0326)	(0.0376)	(0.0315)	(0.0124)
Country fixed effects	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
LR Chi²/Wald Chi² Log likelihood N	2386.37*** −32369.275 24,648	3750.00*** −31687.462 24,648	45484.73*** −41938.229 24,648	46699.60*** −41265.866 24,648	1205.80*** −18642.325 16,295	2079.33*** −18205.559 16,295	24294.82*** −26701.494 16,295	28945.51*** −26232.100 16,295	1077.60*** −7404.8575 7,138	2227.25*** −6830.0316 7,138	12754.95*** −8082.2103 7,138	12155.93*** −7518.079 7,138

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Iddisah Sulemana

Louis Doabil

Notes

References

1.
Abadi N. Techane A. Tesfay G. Maxwell D. Vaitla B. . 2013. “The impact of remittances on household food security: A micro perspective from Tigray, Ethiopia.” Department of National Resource Economics and Management, Mckelle University, Ethiopia.

2.
Abdih Y. Chami R. Dagher J. Montiel P. . 2012. “Remittances and Institutions: Are Remittances a Curse?” World Development 40(4):657–66. doi:10.1016/j.worlddev.2011.09.014

3.
Abel G. J. Sander N. . 2014. “Quantifying Global International Migration Flows.” Science 343(6178):1520–2. doi:10.1126/science.1248676

4.
Adams R. H. Jr 2011. “Evaluating the Economic Impact of International Remittances on Developing Countries Using Household Surveys: A Literature Review.” Journal of Development Studies 47(6):809–28. doi:10.1080/00220388.2011.563299

5.
Adams R. H. Jr Page J. . 2005. “Do International Migration and Remittances Reduce Poverty in Developing Countries?” World Development 33(10):1645–69. doi:10.1016/j.worlddev.2005.05.004

6.
Adepoju A. 2000. “Regional Integration, Continuity and Changing Patterns of Intra-Regional Migration in Sub-Saharan Africa.” In International Migration into the 21st Century: Essays in Honour of Reginald Appleyard, edited by Siddique M. A. B. . Aldershot, UK: Edward Edgar Publishing.

7.
Adepoju A. 2010. International Migration Within, to and from Africa in a Globalised World. Accra: Sub-Saharan Publishers.

8.
Adepoju A. 2011. “Reflections on International Migration and Development in sub-Saharan Africa.” African Population Studies 25(2): 298–319. doi:10.11564/25-2-233

9.
Amuedo-Dorantes C. Pozo S. . 2006. “Remittance Receipt and Business Ownership in the Dominican Republic.” World Economy 29(7):939–56. doi:10.1111/j.1467-9701.2006.00830.x

10.
Andersson L. 2012. “Migration, Remittances and Household Welfare in Ethiopia.” UNU-MERIT Working Paper #2014-004.

11.
Anyanwu J. C. Erhijakpor A. E. . 2010. “Do International Remittances Affect Poverty in Africa?” African Development Review 22(1):51–91. doi:10.1111/j.1467-8268.2009.00228.x

12.
Atuoye K. N. Kuuire V. Z. Kangmennaang J. Antabe R. Luginaah I. . 2017. “Residential Remittances and Food Security in the Upper West Region of Ghana.” International Migration 55(4):18–34. doi:10.1111/imig.12310

13.
FAO, ECA , and AUC . 2020. Africa Regional Overview of Food Security and Nutrition 2019. Accra: FAO. Retrieved from doi:10.4060/CA7343EN

14.
Azam J. P. Gubert F. . 2006. “Migrants’ Remittances and the Household in Africa: A Review of Evidence.” Journal of African Economies 15(suppl_2):426–62. doi:10.1093/jae/ejl030

15.
Babatunde R. O. Martinetti E. C. . 2011. “Impacts of Migrant Remittances on Food Security and Nutrition of Farming Households in Kwara State, Nigeria.” Paper presented at the International Conference ‘Shocks in Developing Countries,’ 30 June–1 July, Paris.

16.
Bah M. M. Abdulwakil M. M. Azam M. . 2020. “Income Heterogeneity and the Environmental Kuznets Curve Hypothesis in sub-Saharan African Countries.” Geojournal 85: 617–28. doi:10.1007/s10708-019-09985-1

17.
Beauchemin C. Flahaux M.-L. Schoumaker B. . 2020. “Three sub - Saharan Migration Systems in Times of Policy Restriction.” Comparative Migration Studies 8: 19. doi:10.1186/s40878-020-0174-y

18.
Byanyima W. 2014. “Growth in Africa Undermined by Rapidly Increasing Inequality.” OXFAM International.

19.
Chukwuone N. A. Okpukpara B. C. Amaechina E. Enebeli-Uzor S. E. Iyoko E. . 2008. “Do remittances have any effect on poverty in Nigerai? Evidence from instrumental variable estimation.” UNU - WIDER Conference on Frontiers of Poverty Analysis, Helsinki.

20.
Combes J. L. Ebeke C. . 2011. “Remittances and Household Consumption Instability in Developing Countries.” World Development 39(7):1076–89. doi:10.1016/j.worlddev.2010.10.006

21.
Cook J. T. Frank D. A. Berkowitz C. Black M. M. Casey P. H. Cutts D. B. Heeren T. . 2004. “Food Insecurity is Associated with Adverse Health Outcomes among Human Infants and Toddlers.” The Journal of Nutrition 134(6):1432–8. doi:10.1093/jn/134.6.1432

22.
Crush J. Caesar M. . 2016. Food remittances and food security in Africa. SAMP Migration Policy Series No. 72.

23.
Crush J. S. Caesar M. S. . 2018. “Food Remittances and Food Security: A Review.” Migration and Development 7(2):180–200. doi:10.1080/21632324.2017.1410977

24.
Dao T. H. Docquier F. Maurel M. Schaus P. . 2021. “Global Migration in the Twentieth and Twenty - First Centuries: The Unstoppable Force of Demography.” Review of World Economics 157: 417–49. doi:10.1007/s10290-020-00402-1

25.
De P. K. Ratha D. . 2012. “Impact of Remittances on Household Income, Asset and Human Capital: Evidence from Sri Lanka.” Migration and Development 1(1):163–79. doi:10.1080/21632324.2012.719348

26.
Deaton B. J. Lipka B. . 2015. “Political Instability and Food Security.” Journal of Food Security 3(1):29–33. doi:10.12691/jfs-3-1-5

27.
De Haan A. 2000. Migrants, Livelihoods, and Rights: The Relevance of Migration in Development Policies. Social Development Working Paper. 4. London: Department for International Development.

28.
Diatta M. A. Mbow N. . 1999. “Releasing the Development Potential of Return Migration: The Case of Senegal.” International Migration 37(1):243–66. doi:10.1111/1468-2435.00072

29.
Dunifon R. Kowaleski-Jones L. . 2003. “The Influences of Participation in the National School Lunch Program and Food Insecurity on Child Well-Being.” Social Service Review 77(1):72–92. doi:10.1086/345705

30.
Dunnwald S . 2011. “On Migration and Security: Europe Managing Migration from Sub-Saharan Africa.” Cardernos De Estudos Africanos: 103–28.

31.
Ebadi N. Ahmadi D. Melgar-Quiñonez H. . 2020. “Domestic and International Remittances and Food Security in sub-Saharan Africa.” Remittances Review 5(1):37–54. doi:10.33182/rr.v5i1.842

32.
Etana D. Tolossa D. . 2017. “Unemployment and Food Insecurity in Urban Ethiopia.” African Development Review 29(1):56–68. doi:10.1111/1467-8268.12238

33.
Eversole R. Shaw J. . 2010. “Remittance Flows and Their use in Households: A Comparative Study of Sri Lanka, Indonesia and the Philippines.” Asian and Pacific Migration Journal 19(2):175–202. doi:10.1177/011719681001900201

34.
Fajnzylber P. Humberto Lopez J. . 2008. Remittances and Development: Lessons from Latin America. Washington, DC: World Bank.

35.
FAO. 2013. The State of Food Insecurity in the World the Multiple Dimensions of Food Security. Rome: Food and Agriculture Organization of the United Nations.

36.
FAO. 2015. FAOSTAT: Crops Processed, available http://www.fao.org/faostat/en/#data/QD. (Accessed October 31, 2018).

37.
FAO. 2016. Food security, Nutrition and Peace. Proceedings of the United Nations Security Council Meeting: New York, USA.

38.
FAO (Food and Agriculture Organization). 2008. The State of Agricultural Commodity Markets: High Food Prices and the Food Crisis: Experiences and Lessons Learned. Rome: Food and Agriculture Organization of the United Nations.

39.
Flahaux M.-L. De Haas H. . 2016. “African Migration: Trends, Patterns, Drivers.” Comparative Migration Studies 4(1):1–25.

40.
Fonta W. M. Ayuk E. T. Chukwu O. J. Onyukwu E. O. Agu C. C. Umenwa O. I. . 2015. “Dynamics of Remittance Utilization by Nigerian Households.” Progress in Development Studies 15(4):343–57. doi:10.1177/1464993415592742

41.
Generoso R. 2015. “How do Rainfall Variability, Food Security and Remittances Interact? The Case of Rural Mali.” Ecological Economics 114: 188–98. doi:10.1016/j.ecolecon.2015.03.009

42.
Gimba O. J. Seraj M. Ozdeser H. . 2021. “What Drives Income Inequality in sub - Saharan Africa and its sub - Regions?An Examination of Long-run and Short-run Effects.” African Development Review 33(4): 729–41.

43.
Gorton D. Bullen C. R. Mhurchu C. N. . 2010. “Environmental Influences on Food Security in High-Income Countries.” Nutrition Reviews 68(1):1–29. doi:10.1111/j.1753-4887.2009.00258.x

44.
Greene W. H. 2012. Econometric Analysis. 7th ed. Upper Saddle River: Prentice Hall.

45.
Guo B. 2011. “Household Assets and Food Security: Evidence from the Survey of Program Dynamics.” Journal of Family and Economic Issues 32(1):98–110. doi:10.1007/s10834-010-9194-3

46.
Gupta S. Pattillo C. A. Wagh S. . 2009. “Effect of Remittances on Poverty and Financial Development in sub-Saharan Africa.” World Development 37(1):104–15. doi:10.1016/j.worlddev.2008.05.007

47.
Habtewold T. M. 2018. “Determinants of Food Security in the Oromiya Region of Ethiopia.” In Economic Growth and Development in Ethiopia. Perspectives on Development in the Middle East and North Africa (MENA) Region, edited by Heshmati A. Yoon H. , 39–65. Singapore: Springer.

48.
Hanck C. Arnold M. Gerber A. Schmelzer M. . 2020. Introduction to Econometrics with R. Essen, Germany: University of Duisburg-Essen.

49.
IDMC (International Displacement Monitoring Centre ). 2019. Global Report on International Displacement. Geneva: IDMC.

50.
IFPRI (International Food Policy Research Institute). 2020. 2020 Global Food Policy Report: Building Inclusive Food Systems. Washington, DC: International Food Policy Research Institute. Retrieved from doi:10.2499/9780896293670

51.
IOM. 2020. “World Migration Report 2020.” UN Migration. Retrieved from http://www.iom.int/wmr.

52.
IOM (International Organization for Migration). 2019. “Africa Migration Report: Challenging the Narative.” UN Migration.

53.
Iram U. Butt M. S. . 2004. “Determinants of Household Food Security: An Empirical Analysis for Pakistan.” International Journal of Social Economics 31(8):753–66. doi:10.1108/03068290410546011

54.
Ivlevs A. Nikolova M. Graham C. . 2019. “Emigration, Remittances, and the Subjective Well - Being of Those Behind.” Journal of Population Economics 32(1): 113–51 doi:10.1007/s00148-018-0718-8

55.
Karamba W. R. Quiñones E. J. Winters P. . 2011. “Migration and Food Consumption Patterns in Ghana.” Food Policy 36(1):41–53. doi:10.1016/j.foodpol.2010.11.003

56.
Kassie M. Ndiritu S. W. Stage J. . 2014. “What Determines Gender Inequality in Household Food Security in Kenya? Application of Exogenous Switching Treatment Regression.” World Development 56: 153–71. doi:10.1016/j.worlddev.2013.10.025

57.
Krokfors C. 1995. “Poverty, Environmental Stres and Culture as Factors in African Migration.” In The Migration Experience in Africa, edited by Institute T. N. , 55–64. Uppsala, Sweden.

58.
Kuwornu J. K. Suleyman D. M. Amegashie D. P. . 2013. “Analysis of Food Security status of Farming Households in the Forest Belt of the Central Region of Ghana.” Russian Journal of Agricultural and Socio-Economic Sciences 3(1):39–61.

59.
Levitt P. 1998. “Social Remittances: Migration Driven Local - Level Forms of Culture Diffusion.” International Migration Review 32(4):926–48. doi:10.1177/019791839803200404

60.
Loopstra R. Tarasuk V. . 2013. “Severity of Household Food Insecurity is Sensitive to Change in Household Income and Employment status among low-Income Families.” Journal of Nutrition 143(8):1316–23. doi:10.3945/jn.113.175414

61.
Mabrouk F. Mekni M. M. . 2018. “Remittances and Food Security in African Countries.” African Development Review 30(3):252–63. doi:10.1111/1467-8268.12334

62.
Masron T. A. Subramaniam Y. . 2018. “Remittance and Poverty in Developing Countries.” International Journal of Development Issues 17(3):305–25. doi:10.1108/IJDI-04-2018-0054

63.
Mattes R. 2008. “The Material and Political Bases of Lived Poverty in Africa: Insights from the Afrobarometer.” In Barometers of Quality of Life Around the Globe, 161–85. Dordrecht: Springer. doi:10.1007/978-1-4020-8686-1_7

64.
McKelvey R. D. Zavoina W. . 1975. “A Statistical Model for the Analysis of Ordinal Level Dependent Variables.” Journal of Mathematical Sociology 4(1):103–20. doi:10.1080/0022250X.1975.9989847

65.
Mohapatra S. Joseph G. Ratha D. . 2012. “Remittances and Natural Disasters: Ex-Post Response and Contribution to Ex-Ante Preparedness.” Environment, Development and Sustainability 14(3):365–87. doi:10.1007/s10668-011-9330-8

66.
Mpesi A. M. Muriaas R. L. . 2012. “Food Security as a Political Issue: The 2009 Elections in Malawi.” Journal of Contemporary African Studies 30(3):377–93. doi:10.1080/02589001.2012.689624

67.
Munyegera G. K. Matsumoto T. . 2016. “Mobile Money, Remittances, and Household Welfare: Panel Evidence from Rural Uganda.” World Development 79: 127–37. doi:10.1016/j.worlddev.2015.11.006

68.
Natali C. Isaacs L. . 2020. “Remittances to and from Africa.” In Africa Migration Report: Challenging the Narrative, edited by Adepoju A. Fumagalli C. Nyabola N. , 117–31. Addis Ababa, Ethiopia: International Organization for Migration(IOM).

69.
Ngoma A. Ismail N. . 2013. “Do Migrant Remittances Promote Human Capital Formation? Evidence from 89 Developing Countries.” Migration and Development 2: 106–16. doi:10.1080/21632324.2013.786883

70.
Orozco M. Ellis C. . 2014. “ Impact of remittances in developing countries .” In A new Perspective on Human Mobility in the South, edited by Anich R. Crush J. Melde S. Oucho J. , 89–118. London and Geneva: Springer and IOM.

71.
Ouedraogo R. Sourouema W. S. Zahonogo P. . 2018. “Capital Inflows and Exports Diversification in Sub-Saharan Africa During the MDGs Era.” African Development Review 30(1):1–18. doi:10.1111/1467-8268.12308

72.
Pew Research Centre. 2018. At Least a Million Sub-Saharan Africans Moved to Europe Since 2010. Retrieved June 15, 2022. https://www.pewresearch.org/global/2018/03/22/at-least-a-million-sub-saharan-africans-moved-to-europe-since-2010/

73.
Pfutze T. 2012. “Does Migration Promote Democratization? Evidence from the Mexican Transition.” Journal of Comparative Economics 40(2):159–75. Retrieved from doi:10.1016/j.jce.2012.01.004

74.
Quartey P. 2006b. “The impact of migrant remittances on household welfare in Ghana.” AERC research paper 158. African Economic Research Consortium, Nairobi.

75.
Quartey P. 2006a. Migrant Remittances and Household Welfare in Times of Macro-Volatility: The Case of Ghana (No. 69). Legon: Institute of Statistical, Social & Economic Research, University of Ghana.

76.
Rademacher-Schulz C. Schraven B. Mahama E. S. . 2014. “Time Matters: Shifting Seasonal Migration in Northern Ghana in Response to Rainfall Variability and Food Insecurity.” Climate and Development 6(1):46–52. doi:10.1080/17565529.2013.830955

77.
Rao B. B. Hassan G. M. . 2011. “A Panel Data Analysis of the Growth Effects of Remittances.” Economic Modelling 28(1-2):701–9. doi:10.1016/j.econmod.2010.05.011

78.
Rao B. B. Hassan G. M. . 2012. “Are the Direct and Indirect Growth Effects of Remittances Significant?” The World Economy 35(3):351–72. doi:10.1111/j.1467-9701.2011.01399.x

79.
Ratha D. 2005. “Workers' Remittances: An Important and Stable Source of External Development Finance.” In Remittances Development Impact and Future Prospects, edited by Maimbo S. Ratha D. , 19–51. Washington DC: The World Bank.

80.
Ratha D. Mohapatra S. Ozden C. Plaza S. Shaw W. Shimeles W. . 2011. Leveraging Migration for Africa: Remittances, Skills, and Investments. Washington, DC: World Bank.

81.
Regmi M. Paudel K. P. . 2016. “Impact of Remittance on Food Security in Bangladesh.” In Food Security in a Food Abundant World: An Individual Country Perspective, edited by Schmitz A. Kennedy P. L. Schmitz T. G. , 145–58. Bingley: Emerald Group Publishing.

82.
Regmi M. Paudel K. P. . 2017. “Food Security in a Remittance-Based Economy.” Food Security 9(4):831–48. doi:10.1007/s12571-017-0705-z

83.
Regmi M. Paudel K. P. Bhattarai K. . 2020. “Migration Decisions and Destination Choices.” Journal of the Asia Pacific Economy 25(2):197–226. doi:10.1080/13547860.2019.1643195

84.
Rena R. 2005. “Challenges for Food Security in Eritrea: A Descriptive and Qualitative Analysis.” African Development Review 17(2):193–212. doi:10.1111/j.1017-6772.2005.00112.x

85.
Roodman D. 2011. “Fitting Fully Observed Recursive Mixed-Process Models with cmp.” Stata Journal 11(2):159–206. doi:10.1177/1536867X1101100202

86.
Seligman H. K. Bindman A. B. Vittinghoff E. Kanaya A. M. Kushel M. B. . 2007. “Food Nutrition Examination Survey (NHANES) 1999–2002.” Journal of General Internal Medicine 22(7):1018–23. doi:10.1007/s11606-007-0192-6

87.
Shimeless A. 2010. “Migration Patterns, trends and Policy Issues in Africa.” Working Paper Series No.119.

88.
Sofranko A. J. Idris K. . 1999. “Use of Overseas Migrants’ Remittances to the Extended Family for Business Investment: A Research Note.” Rural Sociology 64(3):464–81. doi:10.1111/j.1549-0831.1999.tb00362.x

89.
Stein H. 2011. “World Bank Agricultural Policies, Poverty and Income Inequality in sub - Saharan Africa.” Cambridge Journal of Regions, Economy and Society 4(1):79–90. doi:10.1093/cjres/rsq023

90.
Stock J. H. Yogo M. . 2004. “ Testing for Weak Instruments in Linear IV Regression .” In Identification and Inference for Econometric Models: Essays in Honor of Thomas J. Rothenberg, edited by Andrews D. W. K. Stock J. H. . Cambridge: Cambridge University Press.

91.
Sulemana I. Bugri Anarfo E. Quartey P. . 2019. “International Remittances and Household Food Security in sub-Saharan Africa.” Migration and Development. 8.2: 264–80. doi:10.1080/21632324.2018.1560926

92.
Sulemana I. Doabil L. Anarfo E. B. . 2019. “International Remittances and Subjective Wellbeing in sub-Saharan Africa: A Micro-Level Study.” Journal of Family and Economic Issues, 40.3: 524–39. doi:10.1007/s10834-019-09615-5

93.
Sulemana I. James H. S. . 2019. “The Association Between Food Insecurity and Subjective Wellbeing in sub-Saharan Africa.” International Journal of Happiness and Development 5(3): 201–24.

94.
Sulemana I. Nketiah-Amponsah E. Codjoe E. A. Nyarko-Andoh J. A. . 2019. “Urbanization and Income Inequality in sub-Saharan Africa.” Sustainable Cities and Societies 48 (101544): 1-8.

95.
UNDP. 2017. Income Inequality Trends in sub-Saharan Africa: Divergence, Determinants and Consequences. New York: United Nations Development Programme.

96.
UNDP (United Nations Development Programme). 2009. Overcoming Barriers: Human Mobility and Development. New York: United Nations Development Programme.

97.
United Nations. 2017. “Trends in International Migrant Stock. The 2017 Revisions.” Population Division, Department of Economic and Social Affairs.

98.
United Nations . 2012. The Millennium Development Goals Report 2012. New York: United Nations.

99.
United Nations Environment Programme. 2015. “Sustainable Development Goals.” United Nations Environment Programme, Annual Report 2015, Nairobi, Kenya.

100.
Verpoorten M. Arora A. Stoop N. Swinnen J. . 2013. “Self-reported Food Insecurity in Africa During the Food Price Crisis.” Food Policy 39: 51–63. doi:10.1016/j.foodpol.2012.12.006

101.
Warner K. Afifi T. . 2014. “Where the Rain Falls: Evidence from 8 Countries on how Vulnerable Households use Migration to Manage the Risk of Rainfall Variability and Food Insecurity.” Climate and Development 6(1):1–17. doi:10.1080/17565529.2013.835707

102.
Wooldridge J. M. 2010. Econometric Analysis of Cross Section and Panel Data. 2nd ed. Cambridge: MIT.

103.
World Bank. 2009. World Development Report 2009: Reshaping Economic Geography. Washington, DC: World Bank.

104.
World Bank. 2013. World Development Indicators 2013. Washington DC: World Bank.

105.
World Bank. 2016. Migration and Remittances Factbook 2016. Washington DC: World Bank.

106.
World Bank. 2017. Migration and Development Brief. Washinton, DC: World Bank.

107.
World Bank. 2018. “Migration and Remittances Surveys.” Retrieved from http://microdata.worldbank.org/index.php/catalog/mrs.

108.
World Bank. 2019. Leveraging Economic Migration for Development: A Brief for the World Bank Board. Washington DC 20433: World Bank.

109.
Zaman H. Akbar M. I. . 2013. “Exploring non-Traditional Sources of Development Finance: The Case of Remittance in Bangladesh.” Progress in Development Studies 13(2):105–16. doi:10.1177/1464993412466504

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	Low-income countries				Lower-middle income				Upper-middle income
	Ordered probit		IV-ordered probit		Ordered probit		IV-ordered probit		Ordered probit		IV-ordered probit
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
Remittances	0.1083***	0.1584***	2.6879***	2.7159***	0.0768**	0.0915***	2.3559***	2.4134***	−0.0306	0.1075	3.5913***	3.5713***
	(0.0206)	(0.0211)	(0.0328)	(0.0278)	(0.0238)	(0.0243)	(0.0545)	(0.0374)	(0.0544)	(0.0565)	(0.1225)	(0.1565)
Age	−0.0155***	−0.0148***	−0.0040***	−0.0036***	−0.0291***	−0.0308***	−0.0101***	−0.0087***	−0.0071	−0.0132*	−0.0015	−0.0040
	(0.0025)	(0.0025)	(0.0009)	(0.0009)	(0.0032)	(0.0033)	(0.0020)	(0.0017)	(0.0050)	(0.0052)	(0.0015)	(0.0023)
Age²/100	0.1373***	0.1201***	0.0344***	0.0274**	0.2704***	0.2819***	0.0929***	0.0793***	0.0616	0.1195*	0.0130	0.0366
	(0.0277)	(0.0280)	(0.0094)	(0.0086)	(0.0354)	(0.0361)	(0.0196)	(0.0168)	(0.0550)	(0.0567)	(0.0160)	(0.0236)
Female	0.0503***	0.0466**	0.0136**	0.0118**	0.0111	0.0130	0.0043	0.0041	−0.0311	−0.0741*	−0.0103	−0.0260
	(0.0148)	(0.0149)	(0.0047)	(0.0044)	(0.0186)	(0.0188)	(0.0069)	(0.0058)	(0.0289)	(0.0300)	(0.0088)	(0.0140)
Secondary	0.1843***	0.2031***	0.0569***	0.0556***	0.1441***	0.1804***	0.0537***	0.0558***	0.1999***	0.3251***	0.0559*	0.1065*
	(0.0175)	(0.0184)	(0.0094)	(0.0094)	(0.0215)	(0.0218)	(0.0115)	(0.0110)	(0.0404)	(0.0420)	(0.0245)	(0.0438)
University	0.3432***	0.4477***	0.1000***	0.1197***	0.3022***	0.3557***	0.1079***	0.1104***	0.2403***	0.6080***	0.0669*	0.1993*
	(0.0381)	(0.0390)	(0.0179)	(0.0202)	(0.0353)	(0.0369)	(0.0213)	(0.0208)	(0.0573)	(0.0612)	(0.0305)	(0.0803)
Unemployed	−0.1964***	−0.1504***	−0.0580***	−0.0391***	−0.2201***	−0.1815***	−0.0831***	−0.0550***	−0.3529***	−0.3226***	−0.0945*	−0.1045*
	(0.0177)	(0.0181)	(0.0096)	(0.0075)	(0.0208)	(0.0214)	(0.0149)	(0.0109)	(0.0323)	(0.0333)	(0.0382)	(0.0423)
Asset ownership	0.2059***	0.1936***	0.0595***	0.0526***	0.1372***	0.1487***	0.0504***	0.0447***	0.2906***	0.1689***	0.0799*	0.0554*
	(0.0072)	(0.0074)	(0.0086)	(0.0078)	(0.0095)	(0.0097)	(0.0086)	(0.0077)	(0.0155)	(0.0163)	(0.0316)	(0.0223)
Electric connection	0.0429*	0.0813***	0.0107	0.0257***	0.2126***	0.1718***	0.0803***	0.0490***	0.2062***	0.2257***	0.0629*	0.0722*
	(0.0195)	(0.0204)	(0.0059)	(0.0066)	(0.0235)	(0.0262)	(0.0151)	(0.0112)	(0.0437)	(0.0473)	(0.0271)	(0.0323)
Access to pipe	0.1291***	0.1263***	0.0332***	0.0326***	0.0808***	0.1169***	0.0212*	0.0339***	0.3402***	0.0529	0.0891*	0.0148
water	(0.0169)	(0.0186)	(0.0068)	(0.0069)	(0.0213)	(0.0231)	(0.0086)	(0.0089)	(0.0419)	(0.0453)	(0.0370)	(0.0160)
Urban	0.0120	0.0193	0.0119	0.0090	−0.0209	−0.0795***	−0.0046	−0.0222**	−0.3053***	−0.0063	−0.0761*	0.0034
	(0.0205)	(0.0210)	(0.0061)	(0.0059)	(0.0210)	(0.0217)	(0.0078)	(0.0075)	(0.0326)	(0.0376)	(0.0315)	(0.0124)
Country fixed effects	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
LR Chi²/Wald Chi² Log likelihood N	2386.37*** −32369.275 24,648	3750.00*** −31687.462 24,648	45484.73*** −41938.229 24,648	46699.60*** −41265.866 24,648	1205.80*** −18642.325 16,295	2079.33*** −18205.559 16,295	24294.82*** −26701.494 16,295	28945.51*** −26232.100 16,295	1077.60*** −7404.8575 7,138	2227.25*** −6830.0316 7,138	12754.95*** −8082.2103 7,138	12155.93*** −7518.079 7,138