Following in their footsteps: an analysis of the impact of successive migration on rural household welfare in Ghana

Internal migration is a common and sizeable phenomenon in many developing countries. An estimated 740 million people live outside their region of birth (Bell and Muhidin 2009). Differences in regional economic performance induce people to leave poorer areas and move to those where more and better opportunities are located. In Ghana, around 35% of people in the population Census of 2010 had moved from their place of birth to another location within the country (Ghana Statistical Service 2013a). Many people move from poorer to richer regions, some move with the whole household and others send a member of the household (Litchfield and Waddington 2003; Molini et al. 2016).

Internal migration plays an important role in poverty reduction and economic development at the individual, household and macroeconomic level. On the one hand, it contributes to structural change in the country when rural workers move into non-agricultural work in urban areas (Harris and Todaro 1970). On the other hand, migration of a household member can insure the sending household against income shocks in the origin. Such insurance can prevent households from falling into poverty. Moreover, the income earned by the migrant member can raise consumption levels at home or even pay for investments in profitable technologies (Stark and Bloom 1985). Additionally, geographic mobility offers young people the possibility to advance in their education and gain new skills if their origins do not provide these opportunities.

Because of its size and relevance for economic development, economists study internal migration, but data limitations and methodological issues remain a challenge. One focus of research is the question whether and how internal migration affects households at origin. This paper contributes to this strand of the literature. We investigate the impact of having a new migrant on the welfare of origin households conditional on their prior migration experience.

The engagement in migration of some village or community members has been shown to significantly reduce migration costs for later migrants from that same network and to improve the probability of successful job search at destination (McKenzie and Rapoport 2007; Munshi 2003). Households themselves can gain migration experience through the engagement of their members in migration, increasing the household’s participation in future migration (Bryan et al. 2014). Therefore, the focus on new migrants is relevant for a setting in which households have several migrant members who move at different points in time. This is revealed by the data available in this paper. We use primary data from a new two-wave household panel survey conducted in Ghana in 2013 and 2015. The surveys were designed with the goal to collect as much information as possible about migration.

The impact of migration on household welfare is often drawn from comparisons between households with and without migrants. The econometric challenge this poses is of unobserved heterogeneity. There are unobservable factors that determine both the fact that a household has a migrant and the outcome of interest, for example household income. Any result from a simple comparison of households with and without migrants would be biased. Our analysis uses a baseline of households that all have prior migration experience and data on new migrants in the intervening period in order to reduce selection bias. We apply entropy balancing weights (Hainmueller 2012), similar to matching methods, and exploit the panel nature of our data to overcome remaining selection and omitted variable bias. The outcome variable of interest capturing household welfare is a housing quality index constructed using multiple correspondence analysis (MCA). We use housing quality as our measure of welfare as there is strong body of evidence that suggests that remittances are often invested in the improvement of housing (e.g. Kagochi and Kiambigi 2012, Osili 2004, Durand et al. 1996).

Because there is little existing evidence on the consequences of idiosyncratic migration experience of households, we first describe migrants and their households in our data to explore the dynamic patterns of migration. A comparison of the new migrants to those migrants who left the household earlier documents that new migrants are from a younger generation within households, such as children or grandchildren of the head. Their migration costs are lower, possibly due to family networks and the households’ prior engagement in migration. From these observations, we derive hypotheses for the impact assessment. Then, we estimate how the welfare of households with a new migrant changes compared to those without, conditional on the fact that all households have previously had a migrant. We analyse whether there are heterogeneous effects by sex of the migrant, by reason for migration (family or work) and by destination (within or across regional borders).

We find no effect of sending a new migrant on the change in the housing quality index of origin households compared to those households who do not engage further in migration in the same period. This result is robust to variations in the construction of the index, the matching method applied and community shocks. Our interpretation is that the returns to migration might not show after the short period of our study. Households in our sample use their savings to finance migration. They hence do not experience a drop in their housing quality index. However, they also do not experience an increase in their housing quality index since the new migrant left. This could be, on the one hand, due to their use of savings to cover migration costs instead of investing into more assets and, on the other hand, because new migrants send only rarely and low remittances. We further suggest that due to prior engagement in migration our sample of households does not experience an initial decline in welfare. This could be caused by the migration costs or the loss in labour due to a member leaving (Taylor and Lopez-Feldman, 2010). We document however that migration costs for new migrants are smaller than for prior migration, which indicates that migration experience at the household level reduces the costs of migration. In addition, prior to their move, new migrants are either in school or doing unpaid work. It is thus less likely that their migration implies a loss in labour income for the household.

This study builds on earlier work where we employed propensity score matching at baseline to estimate the welfare effect of migration (Egger and Litchfield 2017). In contrast to the matching approach, entropy balancing weights allow us to further control for time-varying characteristics. In addition, we previously adopted factor analysis to construct the housing quality index, while MCA fits the data better. Extending from that previous work, we provide a more comprehensive discussion of the sensitivity of results and explore possible channels of the results in the data.

The paper is structured as follows. In the next section, we discuss the literature on impacts of migration on households left behind with respect to methodological challenges, knowledge gaps and evidence for our context. This is followed by the analytical framework for this study in Section 3. Then, we present the data used for the analysis (Section 4) followed by a description of the migrants, migrant households and their prior migration experience (Section 5). In Section 6, we explain the methodology to estimate the impact of sending a new migrant on the welfare of origin households. In Section 7, we provide the results and robustness checks. Section 8 concludes.

The research interest of this paper is the short-term relationship between having a new migrant and the welfare of origin households in rural Ghana conditional on prior migration experience. Many studies explored the more general question looking at the impact of having a migrant or not on some measure of well-being of the origin household. Antman (2012) and Mendola (2012) review this evidence. Both summarize mixed results from the literature. The following examples illustrate the inconclusive findings.

Empirical evidence from China by De Brauw and Giles (2012) documents an increase in consumption growth as well as ‘increased accumulation of housing welfare and consumer durables’ (p.3). Quisumbing and McNiven (2010) consider the impact of migration and remittances on assets, consumption and credit constraints in the rural Philippines. They find that a larger number of migrant children reduces the values of non-land assets and total expenditures per adult equivalent in the origin households. However, remittances have a positive impact on housing, consumer durables, non-land assets and total (per adult equivalent) and educational expenditures. They find no effect on status of credit constraint. Mendola (2008) finds an increase in investments in agricultural production among the left behind households in Bangladesh with international migrants, but she does not find an effect of internal migration. Taylor and López-Feldman (2010) provide evidence of a positive effect of migration to the USA on land productivity of migrant-sending families in Mexico. They also document an increase in per capita income via remittances. Damon (2010) finds only weak increases in asset accumulation in El Salvador and no impact of migration and remittances on investments in agricultural production.

Only a few studies consider migration experience at the household level. De Brauw and Harigaya (2007) and De Brauw (2010) provide evidence about the impact of seasonal migration on household welfare or agricultural production in Vietnam. While seasonal migration is most likely a repeated event, the authors do not specifically account for the repetition and potential learning process of the household. Bryan et al. (2014) conduct a randomized control trial in a region in Bangladesh that is seasonally affected by famine to understand underused seasonal migration. Their intervention was a cash transfer to vulnerable households conditioned to finance seasonal migration of one household member. The results show significant improvements of consumption levels for the treated households. According to the authors’ model, migration results in success or failure in terms of finding a job at destination and sending remittances. Households learn from this experience, and it predicts their future engagement in migration. Further evidence for the role of migration experience within the family is provided by Giulietti et al. (2014). The authors develop a model that differentiates between ‘weak’ and ‘strong’ network ties and their role for migration decisions. Their findings suggest that networks at community level (weak ties) and prior migration of a family member (strong ties) act complementary, but weak ties have a higher impact on the migration decision. No further analysis is conducted to investigate how such different networks might influence migration and household outcomes.

In Ghana, internal migration is highly correlated with economic conditions (Castaldo et al. 2012). Many researchers have studied the impacts of migration in Ghana with mixed results due to the different data sources used and different methodologies applied (Ackah and Medvedev 2010; Adams 2006; Adams et al. 2008; Adams and Cuecuecha 2013; Litchfield and Waddington 2003; Mahé and Naudé 2016; Molini et al. 2016).

What gives rise to these mixed results? Theoretical models such as from the New Economics of Labour Migration (NELM, Stark and Bloom 1985) cannot predict the direction of the impact of migration on origin households. The reason for this is that the impact depends on counteracting factors. For example, De Brauw and Harigaya (2007) model the impact of migration on consumption growth and find that the impact depends on both the loss of farm production incurred by migration and the increase in consumption due to remittance receipt as well as the costs of moving. One explanation for mixed results therefore is that the counteracting factors of costs and rewards to migration materialize at different speeds (Taylor and López-Feldman 2010). The loss of labour is felt immediately as are the costs of moving. The returns to migration in the form of remittances contribute to higher consumption levels. They delay, however, until the migrant has arrived at the destination, found a job and earned enough income to send some of it back home. It might take even longer for remittances to accumulate enough to invest in assets. Other aspects that contribute to the mixed results are the different data, definitions for migration and methodologies used. Migrants, or migrant households, are not a random sample of the population, but observable and unobservable factors determine their participation in migration. These factors can affect the outcomes of interest at the same time. In addition, the outcome itself can affect the migration decision. This is especially an issue in cross-sectional data.

This study contributes to the understanding of internal migration in Ghana and its consequences for origin households by using novel data. We exploit the panel nature of the data and apply a new method from the evaluation literature to reduce concerns of bias. We condition the analysis on prior migration experience. Thus, we contribute to the literature aiming to understand whether households learn from migration and what the implications are for future migration at household level and consequent welfare impacts, specifically for investments in housing quality.

This paper investigates whether having a new migrant is related to a change in the welfare of the migrant’s household at origin conditional on migration experience. The analysis is set in two periods, baseline and follow-up. All households have at least one member who is a migrant in the baseline period. Thus, they have previously engaged in migration, which we define as ‘migration experience’. A migrant is defined in the surveys as a member of the household who is currently absent, left at least 3 months ago, but not more than 5 years.

This household has five members. At baseline, member A and member D are away as migrants. In the follow-up period, member A is still away as a migrant, while member D has returned to the household. Now, member E is away as a migrant. If we were to compare only the total number of migrants away, we would see no difference between these two periods for this household. However, member D might have returned with money for the household and will now contribute again to the household production (farm or business), and he or she potentially returned with new skills that could improve the returns to her or his labour. At the same time, for member E to migrate, the household had to incur some costs, maybe by selling assets or using savings. These factors have different impacts on the household welfare, so we focus on new migrants instead of the total number of migrants. Thus, this example household would be defined as a household with migration experience and a new migrant. Member E would be this new migrant.

Different aspects determine the impact of having a new migrant. Firstly, migration is costly and can initially lead to a decline in welfare due to the costs incurred as well as the loss in labour. Secondly, migration is beneficial when migrants send money back to their origin household and thus create another source of income. Thirdly, migration can be beneficial for the migrant him or herself directly. There might be more and better opportunities to earn an income or pursue further education at destination than at origin. Moreover, the household has one member less to care for and it might derive utility from the fact that the migrant can find a better livelihood somewhere else.

However, it is not clear in which direction the effect should work and which factor dominates. The aforementioned factors work in different directions. Additionally, in our specific case, households have migration experience at baseline before they have a new migrant, which can influence the effect. While sending a new migrant can incur costs, these might be lower conditional on prior migration experience of the household.

Following this discussion, we look at the impact of sending a new migrant conditional on migration experience. The sample is therefore first restricted only to households with migration experience at baseline. Then, households are assigned to a group called ‘treated’ and another one named ‘control’. Households are in the treated group if they have at least one new migrant between the two periods. The remaining households without a new migrant between the two periods are in the control group.² This definition implies that households can have more than one new migrant and they can have several baseline migrants. Our sample is restricted to those households whose new migrants were present members of the household in the baseline period.

The data used for this analysis is a household survey collected in April/May 2013 and again of the same households in April/May 2015.³ In this way, the households are interviewed during the same season to avoid issues of seasonality between survey waves. We call this data set MooP Ghana panel study from here on.

In the first wave, around 1400 households were surveyed, and in the second wave, the team was able to follow up with around 1100 of them.⁴ Based on the 2010 Ghana Population and Housing Census (Ghana Statistical Service 2013), five major source regions of migration were identified as survey regions, the Northern region, the Upper East, Upper West, Brong Ahafo and Volta region. Consequently, the household sample is not nationally representative. In a two-stage stratified sampling design, enumeration areas (EA) were randomly selected to be proportional to the number of migrants from each region. Within each EA, households were then randomly selected from a list categorized by migrant and non-migrant households, oversampling households with migrants. With this approach, we achieved a feasible sample of migrants, which in most nationally representative surveys remains difficult.

The questionnaire was directed at the household head and asked about the demographics of each household member, their education and employment status, as well as their migration history. Migrants are members who are currently not living in the household and who have been away for at least 3 months, but less than 10 (in 2013) or 5 years (in 2015). Sixty percent of households in the treatment group for this analysis have only one new migrant, 25% have two and the remaining 15% have three or more new migrants in the study period.

After cleaning the data and making sure that the main variables of interest are available for all households in both survey waves, we are left with a balanced panel of 960 household-year observations. One hundred thirty-one migrant households are in the treated group, and 349 in the control group.

New migrants are on average younger, and relatively more of them are single. They are from a younger generation within the household, often sons or daughters of the household head or even from the third generation. Relatively more of the new migrants have no or only primary education compared to baseline migrants.

Figure 1 presents the modes of financing migration. The most common way to finance migration in 2013 was savings, indicating that migration is an investment under credit constraints. Savings are also the most common source of financing migration for new migrants in 2015. The category ‘Others’ is in most cases money from a parent and in some cases from the migrant her or himself. Other modes of financing appear less important.

These observations suggest that costs can be reduced through migration experience within the household. This is not owed to a general decline in migration costs. Looking only at migrants who moved in 2015 from a household without any migrants in 2013, we find that they pay around 203 Ghanaian Cedi, similar to the average moving costs of baseline migrants. Despite lower costs of migration, the new migrants moved relatively more often to another region in Ghana than remaining in their own district or region which normally is associated with higher moving costs.

This paper documents the dynamic nature within households of internal migration in rural Ghana. Using a new panel dataset from 2013 and 2015, we show that many households with migrants at the baseline sent a new migrant by 2015. Looking more closely at these migrants and their households, we provide an insight into the nature of such successive migration. Within the same household, migrants move for different reasons and at different times and their connection with the origin household differs as well.

This motivates the question of how households with prior migration experience are affected if they have a new migrant. There are hypotheses for positive, negative or no effect due to the variety of factors involved and their counteracting impacts.

We find that having a new migrant does not have an impact on the housing quality index of origin households compared to those without a new migrant. We suggest that this is partially due to the stable nature of such an index over the short period of our analysis. In order to identify an impact, the households in our sample would have needed to invest in their housing to different extent between treated and control group. However, their investment priorities might lie somewhere else, for example, in their farm or business. Previous studies have found that remittances are invested in better housing whereas we do not find this for successive migrants, suggesting that behaviour differs. One insight from the literature is that older migrants are more likely to send remittances home to be used for housing improvements (Osili 2004) and that this may be in anticipation of a return home. Our sample of first migrants are older at the time of survey than successive migrants, so our results may further reflect different intentions surrounding return.

Another insight we gain is that new migrants pay relatively less for their migration than baseline migrants. This indicates that migration becomes cheaper with the migration experience of the household so that a negative effect of migration incurred by moving costs might not materialize in this case. Furthermore, we observed that new migrants are in many aspects different from baseline migrants. Among the differences are for example the fact that new migrants are from a younger generation, coming straight from school and often not sending any remittances or only for special occasions. This also supports the zero effect we find for the housing quality index. Households with prior migration experience might not send a new migrant in expectation of future remittances and income diversification. Instead, the new migrants might move primarily to improve their own situation.

These unanswered hypotheses point at the limitations of this study. The effect we estimate is that of only 2 years or less since a new migrant left the household. The comparison of studies using longitudinal data from longer periods with those of short periods indicates that the positive returns to migration might only present itself after a certain period (Davis et al. 2010; Taylor and Lopez-Feldman, 2010). More data collection is required to confirm our results over the longer run.