metadata: authors: 0: author: Bossavie,Laurent Loic Yves
1: author: Gorlach,Joseph-Simon
2: author: Ozden,Caglar
3: author: Wang,He
count: Bangladesh
docna: 0: docna: Temporary Migration for Long-term Investment
docty: Policy Research Working Paper
subtopic: Employment and Unemployment,Migration and Development,Trade and Services,Private Sector Development Law,Private Sector Economics,Marketing,Labor Markets
teratopic: Poverty Reduction,International Economics and Trade,Law and Development,Private Sector Development,Social Protections and Labor
abstracts: cdata!: In the presence of credit constraints,
            temporary migration abroad provides an effective strategy
            for workers to accumulate savings to finance self-employment
            when they return home. This paper provides direct evidence
            of this link and its effects on workers’ employment
            trajectories by using a new, large-scale survey of temporary
            migrants from Bangladesh. It constructs and estimates a
            dynamic model that establishes connections between asset
            accumulation and credit constraints, and, thus, between
            workers’ migration and self-employment decisions.
            Interlinked impacts also emerge from simulations of three
            key policy interventions that target migration costs or
            domestic credit constraints for entrepreneurship. Lowering
            migration costs increases emigration, reduces the age at
            which workers depart, and reduces the duration of their time
            abroad, which together lead to higher savings and domestic
            self-employment. Reducing the interest rate for
            entrepreneurial loans reduces migration and savings levels,
            undercutting the positive effects on business creation at
            home. Correcting workers’ inflated perceptions about
            overseas earnings potential reduces emigration rates and
            durations, triggering a decrease of both repatriated savings
            and self-employment in Bangladesh. The findings, which have
            implications for migrant-sending countries, highlight the
            need for policies to take into account the linkages between
            migration and self-employment decisions.
geo_reg_key: 517198,517191,517212
repnme: repnme: Temporary Migration for Long-term Investment
display_title: Temporary Migration for Long-term Investment
listing_relative_url: /research/2021/07/33301915/temporary-migration-long-term-investment
topicv3: Private Sector Development m1326060 1921,Temporary Movement of Labor m1326476 783,Self-Employment m1504000 2698,Emigration m1504169 2844,Entrepreneurship m1582903 4424
publishtoextweb_dt: 2021-07-28T00:00:00Z
historic_topic: Poverty Reduction,International Economics and Trade,Law and Development,Private Sector Development,Social Protections and Labor
origu: DECRG: Trade & Intl. Integration (DECTI)
no_of_pages: 75
author_keys: 0: author_key: 1239893
1: author_key: 658703
model_extractions: Policy Research Working Paper 9740 Temporary Migration for Long-term Investment _Laurent Bossavie Joseph-Simon Görlach Çağlar Özden He Wang_ Social Protection and Jobs Global Practice & Development Research Group July 2021
Policy Research Working Paper 9740

# **Abstract**

In the presence of credit constraints, temporary migration abroad provides an effective strategy for workers to accumulate savings to finance self-employment when they return home. This paper provides direct evidence of this link and its effects on workers’ employment trajectories by using a new, large-scale survey of temporary migrants from Bangladesh. It constructs and estimates a dynamic model that establishes connections between asset accumulation and credit constraints, and, thus, between workers’ migration and self-employment decisions. Interlinked impacts also emerge from simulations of three key policy interventions that target migration costs or domestic credit constraints for entrepreneurship. Lowering migration costs increases emigration, reduces the age at which workers depart, and reduces the duration of their time abroad, which together lead to higher savings and domestic self-employment. Reducing the interest rate for entrepreneurial loans reduces migration and savings levels, undercutting the positive effects on business creation at home. Correcting workers’ inflated perceptions about overseas earnings potential reduces emigration rates and durations, triggering a decrease of both repatriated savings and self-employment in Bangladesh. The findings, which have implications for migrant-sending countries, highlight the need for policies to take into account the linkages between migration and self-employment decisions.

This paper is a product of the Social Protection and Jobs Global Practice and the Development Research Group, Development Economics.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www. worldbank.org/prwp. The authors may be contacted at lbossavie@worldbank.org, josephsimon.goerlach@unibocconi.it, cozden@worldbank.org, and hwang21@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Temporary Migration for Long-term Investment<sup>∗</sup>

Laurent Bossavie<sup>1</sup> , Joseph-Simon G ̈orlach<sup>2</sup> , C ̧aglar Ozden<sup> ̈1</sup> , and He Wang<sup>1</sup>

> 1World Bank

> 2Bocconi University, CReAM, IGIER, IZA and LEAP

**JEL codes:** J61, D15, F22, O13

**Key words:** Temporary migration, entrepreneurship, credit constraints, developing countries, migration costs

> ∗We would like to thank Nelly Elmallakh and participants at the 13th Migration and Development conference, the Economics of Migration online seminar series, Migrations and Organizations Conference at the University of Pennsylvania, Social Protection and Labor Workshop at the World Bank and colleagues at Bocconi University for helpful comments and suggestions. We are also very thankful to Rubaba Anwar for providing excellent field coordination support during the implementation of the survey in Bangladesh, and to the Bangladesh Bureau of Manpower, Employment and Training for granting clearance to carry out the Bangladesh Return Migrant Survey (BRMS). Funding for this research was provided by the Rapid Social Response Multi-Donor Trust Fund, the DEC Research Support Budget (RSB), Knowledge for Change Program (KCP) and the Multi-Donor Trust Fund for International Trade within the World Bank.
# **1 Introduction**

Temporary migration is part of life in many regions of the world. One of the most prominent examples is the migration of millions of mostly low-skilled workers from South and Southeast Asia to oil-rich Persian Gulf countries. Every year these workers depart with temporary employment visas and short-term contracts. Once their contracts expire, workers are no longer allowed to stay, and they return home (OECD 2008; World Bank 2018). Academic and policy research on the subject has largely focused on how these migrants increase their current income and support their families at home while they are abroad.<sup>1</sup> In this paper, we examine temporary migration from a different angle: We argue and present compelling evidence from Bangladesh that such temporary migration episodes are an integral part of workers’ savings and investment strategies implemented over a lifetime, with important ramifications on the overall economic outcomes of origin countries.

Answering questions on temporary labor migration decisions and related economic outcomes requires a dynamic framework that accounts for individuals’ intertemporal optimization decisions.<sup>2</sup> Data are needed to reveal migrants’ complete labor market trajectories over time and across borders, and to show their investment and savings behavior. Standard national surveys and administrative data sources do not provide these data, as they focus on current labor market outcomes, but do not ask detailed questions about workers’ employment and financial histories. Thus, static analyses based on these data sources are unable to identify the dynamic linkages between key decisions: whether, when, and where to migrate; how long to stay; how much to save; what employment options to pursue back at home; and whether migration provides the means to overcome credit constraints, as we argue is the case in Bangladesh.

In this paper, we identify and examine the implications of these linkages by estimating a dynamic model of decisions on emigration, return, and self-employment. We then use the estimated model to evaluate the intertwined effects that arise from key changes in migration or domestic lending policies. Specifically, we examine the effects likely to emerge as a result of a decrease in migration costs, a decrease in interest rates for self-employment loans, or the provision of more accurate information on overseas earnings to would-be migrants. Our simulations show that a reduction in migration fees, or an increase in wages earned overseas raises emigration, but decreases its duration while increasing overall savings and entrepreneurship rates in Bangladesh. We further show that core development policies, such as loan subsidies for entrepreneurs, can trigger a decrease in the extent and duration of

> 1See Mobarak, Sharif, and Shrestha (2020) for a recent study on temporary migrants to Malaysia and the evidence on the impact on their families.

> 2See Dustmann and G ̈orlach (2016) for a review of the literature.

2
migration, undercutting the incentive to migrate and to accumulate savings. Moreover, we show that providing accurate information on wage levels in destination countries can reduce both emigration rates and durations, leading to lower levels of repatriated savings and less domestic business creation after return.

Our model is closely tailored to the institutional context. Temporary migration is pervasive in Bangladesh, and domestic economic activity strongly depends on migrants’ remittances and repatriated savings. Our model captures the key determinants that together influence the decisions about whether to emigrate, the age of departure, the destination, the duration, and the employment decision upon return. Demand for migrant labor in the main destination countries varies with oil revenues, which in turn limits individual migration opportunities. In addition, labor contracts may be canceled prematurely, creating income and employment risks for migrants. Migration choices are further influenced by pre-migration employment outcomes, migration fees and other costs, access to credit, and the costs and returns of entrepreneurship. For instance, the duration of a worker’s migration is affected by migration costs and the extent to which these are covered on credit, but also by the level of assets required to start better-paying self-employment activities in Bangladesh. In turn, credit conditions influence a worker’s ability to access self-employment in Bangladesh, but also the incentive to migrate, and the amount of savings repatriated. Thus, cost-benefit analyses of implemented policies must account for potentially offsetting changes in migration, savings and investment behavior.

Our main data source is a new and unique survey of returning migrants that was specifically developed to answer these questions. The Bangladesh Return Migrant Survey (BRMS) was designed with the idea that temporary migration is an integral part of workers’ life-cycle career. Hence, it provides very detailed information on the entire employment and migration history of a sample of 5,000 migrant workers who had returned to Bangladesh at the time of the data collection. The survey is one of the very few comprehensive surveys on temporary migrants globally, and to the best of our knowledge, it is the first of its kind to have been conducted in a country in which emigration is based almost exclusively on regular temporary work contracts.<sup>3</sup> The dataset includes detailed information on migrants’ personal and family backgrounds, their labor market outcomes before migration, their expectations prior to departure about earning and saving prospects abroad, migration expenditures, sources of income and savings, employment histories at the foreign destination, and labor market activities and earnings after their return to Bangladesh.

> 3For less regulated contexts, in which emigration is more often undocumented, the Mexican Migration Project (MMP) and the Egypt Labor Market Survey (ELMS) have a similar design. While the Egyptian survey covers all workers in the country and is not specifically focused on temporary migrants, it includes a module on past migration of household members currently living in the Arab Republic of Egypt.

3
We complement the data from the BRMS with nationally representative household and firm surveys. Our modeling of both emigration and return choices accounts for selection into the sample of return migrants. The Bangladeshi Household Income and Expenditure Survey (HIES), which provides information on both non-migrants and current migrants, allows us to model the initial emigration decision. We make labor demand an explicit part of our model to account for the fact that emigration is restricted by the requirement to obtain a work visa. We document and utilize the fact that the volume of Bangladeshi emigration strongly depends on oil prices.<sup>4</sup> All major destination countries for Bangladeshi migrants are oil exporters, and we show that migrant numbers track fuel revenues very closely. Subject to the probability of obtaining a work visa and to not being dismissed in the destination country, migrant supply depends on individuals’ unobserved location preferences, which govern emigration and migration duration choices. We identify these preference parameters by fitting our structural model to data on both emigration rates and the time migrants spend abroad.

Our original data and analysis provide novel and direct evidence on the linkages between self-employment and migration. Comparing (within individual) activities before and after migration, we document a strong shift into self-employment upon return. Our survey directly asks how self-employment has been financed. Around half of the return migrants in our sample report that savings from abroad were their main source of financing. Migration-related expenses in Bangladesh, while typically lower than the capital needed for self-employment, are nevertheless substantial. We find that while low-skilled workers can often borrow to pay for migration expenses, they can rarely borrow to finance self-employment activities. One explanation is that migrant workers hold a formal job contract that makes loan repayment more secure and shirking more difficult compared to a loan repayment that depends on entrepreneurial profits. The credit market can be thought of as segmented, with a clear distinction between migration loans and entrepreneurship loans, with higher or even prohibitive interest rates for the loans supporting entrepreneurship. We document a causal, positive effect of earnings accumulated abroad and duration of migration on the probability of self-employment after return.

Why is self-employment, then, so attractive for low-skilled workers? Even though it has been argued that self-employment is a last resort for workers in some developing-country contexts, evidence from Bangladesh indicates that the vast majority of the self-employed have voluntarily chosen this activity (Gutierrez, Kumar, Mahmud, Munshi, and Nataraj 2019).

> 4The positive association between oil prices and emigration to the six member countries of the Cooperation Council of the Arab States of the Gulf (also known as the Gulf Cooperation Council, GCC) has also been documented by Wahba (2015) for Egyptian migrants.

4
While informal wage employment tends to be readily available for workers, self-employment often generates higher income. However, it also requires accumulating sufficient assets in the presence of tight credit constraints.<sup>5</sup> To motivate our focus on self-employment as an objective of many migrants from Bangladesh, we also show that alternative mechanisms, such as asset accumulation for marriage, are not supported by our data.

We use the estimated structural model to evaluate the effects of three core policy parameters on temporary migration and self-employment outcomes. First, we consider a subsidy that lowers the cost of migration. In our dynamic model, this policy intervention not only raises the prevalence of emigration, but also shifts it to younger ages, and reduces the time migrants spend abroad. On the one hand, an earlier return implies lower savings. On the other, conditional on the level of repatriated assets, emigration at a younger age and with an earlier return implies a longer payoff period that makes entrepreneurial investments more profitable for younger people. We find that the net effect of these mechanisms is a sizable increase in overall repatriated savings and business creation. Second, we examine a loan subsidy that reduces the interest rate for entrepreneurial loans. While this naturally makes self-employment a more attractive and accessible option, the policy’s effect is undermined by an endogenous adjustment in migration behavior. Specifically, we estimate that cutting the current entrepreneurial lending rate in half would lower the emigration rate by 5.4 percent, and reduce the average migration duration by 6.4 percent. Taken together, these imply a decrease of 8.4 percent in asset repatriation, limiting the positive effect of lower interest rates for entrepreneurial loans on business creation. Finally, we investigate an information treatment that aligns individuals’ expectations about their foreign earnings potential with the average earnings for different migrant groups observed in our data. Our model predicts that information about the true expected wages paid at a destination decreases emigration by a sizable 13 percent. An implication from this finding is that the interests of individuals and policy makers may not align in such a situation; individuals naturally benefit from making from better-informed decisions, but policy makers aiming to boost business creation may, in fact, favor a situation in which individuals overestimate foreign wages because it encourages emigration and asset formation. In sum, our joint modeling of migration and entrepreneurship decisions highlights the importance of these endogenous responses, which need to be accounted for in the cost-benefit analyses of different policy interventions.

The conditions that characterize the migration experience for workers in Bangladesh differ markedly from those that characterize migration from Mexico to the United States, and from Africa and the Middle East to Europe – the regions that are the focus of much of the existing

> 5Self-employment options in our context primarily consist of relatively simple occupations such as owning a small store, driving a taxi, or running a small business.

5
research. Migration from Bangladesh is almost exclusively regular and temporary, mediated by officially registered agencies. Entry into the main destination countries in the Persian Gulf and Southeast Asia is strictly conditional on holding an employment contract. Due to tight enforcement and severe punishments, overstays and undocumented stays are very rare.<sup>6</sup> If an employment contract is renewed, migrants may extend their stay, but, otherwise they must return home. Temporary migrants from Bangladesh, like most migrants from other countries in South and Southeast Asia, also pay high upfront fees to the intermediary agents who match workers with employers in destination countries. We show in this paper that these costs play a critical role in the timing and duration of emigration, and, subsequently, in the self-employment patterns in Bangladesh.

The relationship between migration and self-employment under credit constraints has been investigated by Rapoport (2002) and Djaji ́c (2010) at a theoretical level. We build on their insights, and extend the empirical literature on temporary migration and entrepreneurship in several ways. First, we model emigration, the destination choice, and return migration jointly with the self-employment decision, accounting for financial constraints. Among the earlier and influential papers that link migration to entrepreneurship, McCormick and Wahba (2001) focus on emigration from the Arab Republic of Egypt, while Dustmann and Kirchkamp (2002) focus on migrants who returned from Germany to Turkey. Piracha and Vadean (2010) investigate the relation for Albania; Wahba and Zenou (2012) and Mah ́e (2019) again for Egypt; and Br ̈uck, Mah ́e, and Naud ́e (2018) for the Kyrgyz Republic. Several papers aim at isolating the causal relationships between migration and financial investments. These include Mesnard (2004) for Tunisia; Yang (2006), Yang (2008), and Khanna, Theoharides, and Yang (2020) for the Philippines; Woodruff and Zenteno (2007) for Mexico; and Batista, McIndoe-Calder, and Vicente (2017) for Mozambique.<sup>7</sup> Whereas most papers restrict attention to the link between return migration and entrepreneurship in the country of origin, Wahba (2015) accounts for the selection of migrants from the nonmigrant population and for the selectivity of returns. We draw on the insights developed in this literature, and extend them. Our paper is the first to directly estimate a dynamic model of the relationships between migration and self-employment, and to explicitly account for credit constraints, migration costs, and the risks of lower-than-expected earnings abroad, as well as for the risk of exogenous contract termination.

Methodologically, our paper contributes to the literature that uses dynamic life-cycle

> 6Southeast Asian countries such as Singapore apply very strict deportation measures to address overstays. Among the GCC countries, overstays are punished by imprisonment in Saudi Arabia and the United Arab Emirates.

> 7Also see Wahba (2014), Rapoport and Docquier (2006) and Naud ́e, Siegel, and Marchand (2017) for related surveys.

6
models to examine the determinants and effects of international migration. Previous papers that have estimated such models to study temporary migration include Kirdar (2012), Kleemans (2015), Girsberger (2017), Lessem (2018), G ̈orlach (2020), and Adda, Dustmann, and G ̈orlach (forthcoming). This structural economics literature so far has not examined the role of entrepreneurship and self-employment in migration decisions.<sup>8</sup> Our context of temporary, formal migration that is strictly conditional on an employment contract differs from the contexts of prior work in this literature; nevertheless, this is the norm shared by many other sending countries in South and Southeast Asia. Our paper, therefore, provides new insights on the dynamics of migration of millions of low-skilled workers from similar lowerincome origin countries where migration is highly regulated, and self-employment represents an attractive option over informal wage jobs.

The paper is organized as follows: Section 2 provides the contextual background on temporary labor migration from Bangladesh. Section 3 presents the new dataset and descriptive statistics for our sample. Section 4 introduces a set of stylized facts that inform and motivate the dynamic model laid out in Section 5. Section 6 discusses estimation and identification of this model. Section 7 presents our results. Section 8 concludes.

# **2 Background and Context**

Bangladesh is one of the main countries of origin of low-skilled labor migrants. It ranked fifth worldwide, with an estimated 7.8 million workers working abroad in 2018, the year before our survey was conducted (Ahmed and Bossavie 2021). In relative terms, the incidence of migration among the working-age population is also high. As of 2017, about 13 percent of the total working-age male population of Bangladesh (ages 15-64) was currently or in the past employed overseas. Oil-exporting Gulf Cooperation Council (GCC) member countries are the main destinations for these migrants. More than 750,000 workers have emigrated annually in recent years, and over time the magnitude of migration outflows has risen steadily with the labor demand in the main destination countries. Though long-term trends show that emigration from Bangladesh is rising, there is noticeable year-to-year volatility. Figure 1 shows that these variations are closely tied to fluctuating oil prices. Fuel exports are the single-most important source of income and a major determinant of labor demand in the destination countries of most Bangladeshi migrants. Although emigration correlates with oil prices, wages do not (Figure 1). This indicates that labor demand poses the binding constraint, whereas supply at prevailing wage differences between destinations and Bangladesh – is rather elastic a feature we exploit in our estimation of the model (see Section 5).

> 8See Dustmann and G ̈orlach (2016) for a survey of the structural literature on temporary migration.

7
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tries (GCC members and Southeast Asian countries), emigration from Bangladesh is temporary by design. Acquisition of citizenship or permanent residency in the destination countries is effectively impossible, irrespective of migrants’ occupation, education, nationality, or duration of stay (Wahba 2015; Fargues 2011; Fargues and De Bel-Air 2015). Low-skilled migrants’ stay is strictly conditional on a valid employment contract. These contracts are typically of fixed duration and tied to a specific employer, but contract renewals are possible to extend duration, again conditional on the continued agreement of the employer. The initial duration of the work permit for low-skilled migrants is one year in Malaysia, and two years in Singapore, and, in both countries, these can be renewed for a total duration of up to 10 years. There is no explicit cap on total duration of stay for migrants in the GCC countries. However, because their residence at the destination is conditional on holding a contract, migrants cannot stay after they have retired. Similarly, a job loss or the expiration of the contract means mandatory and automatic return to the home country. Furthermore, lowskilled migrants are almost never allowed to take their families with them. This generates additional costs to migrating, and further incentives to return.

In addition to the high levels of international migration from Bangladesh, internal ruralurban migration is also widespread. Although transportation costs have been shown to be important (Bryan, Chowdhury, and Mobarak 2014), the financial constraints for overseas migrants are considerably tighter, and complemented by strictly enforced legal restrictions.

# **3 Data**

## **3.1 The Bangladesh Return Migrant Survey (BRMS)**

The primary data source in this paper is a newly collected dataset with unique information on full employment histories, migration expenditures, expectations and demographic characteristics of migrants. The Bangladesh Return Migrant Survey (BRMS), conducted by the World Bank in 2019, consists of a sample of 5,000 temporary migrants who had returned from employment overseas to Bangladesh at the time of the survey. It is one of the largest datasets on temporary migration conducted to date and, to the best of our knowledge, it is the first of its kind in a country from where migration is almost exclusively based on temporary worker contracts. It also is the first comprehensive survey on temporary migrants who have returned to Bangladesh, one of the major migrant sending countries globally. The survey was designed for an analysis of the economic activity of recently returned, temporary migrants in rural and semi-urban areas of Bangladesh. It covers all districts in the country.<sup>11</sup>

> 11Bangladesh consists of eight divisions which are divided into 64 administrative districts.

9
Eligibility for the BRMS was restricted to migrants who had returned from employment overseas to Bangladesh since 2010. This restriction was put in place to reduce possible issues that might arise regarding the accuracy of migrants’ recollection of past migration experiences, and, at the same time, to allow for some variability in the timing of return of the migrants. The sampling frame used for the survey was the Bangladesh Census of Population and Housing for 2011. The survey used a two-stage, stratified-cluster sample design: In the first sampling stage, a total of 250 unions throughout Bangladesh were randomly selected from the rural and semi-urban unions of the census. In the second sampling stage, households with returned migrants were randomly selected from a listing carried out in each selected union to identify households with return migrants.

The dataset has several features that enable us to fill important data gaps identified in prior work. First, the survey was designed with the understanding that temporary migration decisions are part of a life-cycle-optimization process by the workers. As a result, the survey includes detailed retrospective questions on the entire employment histories of migrants both in Bangladesh (before and after the migration) and while they were abroad. The data allow us to construct the full employment trajectories of the workers in the sample with detailed information on each migration episode, including destination country, dates and duration of stay, labor market outcomes (such as wages and occupation), and the reasons for returning.

The survey records the costs of each migration episode, finely disaggregated by cost categories and the source of financing. Such detailed data are rarely collected in household surveys that include migration modules.<sup>12</sup> Those expenses play a critical role in migration decisions in the case of Bangladesh and other South Asian countries, where these costs are known to be very high relative to workers’ earnings (International Labour Organization 2015; Farole, Cho, Bossavie, and Aterido 2017).<sup>13</sup> The survey asks workers about the expectations that they held prior to their migration experience regarding labor market outcomes in the destination countries, such as wages and savings. As a result, we can compare their expectations with actual outcomes. Borjas and Bratsberg (1996) postulate that lower-than-expected earnings may be an important reason for return migration from the United States, and McKenzie, Gibson, and Stillman (2013) provide empirical evidence on over-optimistic expectations for migrants from Tonga. Even

> 12One exception is the Labor Market Panel Survey for Egypt, one of the few labor market surveys in sending countries that include a detailed module on past migration, and collects information on intermediary fees paid by migrants. Migration from Egypt, however, is distinct from the migration from Bangladesh in that the former includes a larger share of undocumented migrants, many of them headed to European countries.

> 13In a recent study, Mobarak, Sharif, and Shrestha (2020) examine a government-mediated program between Bangladesh and Malaysia where the migration costs are significantly lower. The migrants are chosen via lottery which allows the authors to address the selection problems directly and show how migration improves the welfare of the families. The authors attribute the gains, at least in part, to the low fees.

10
though a person’s recall might be imperfect, our data are the first that enables to link premigration expectations to realized earnings on an individual level for temporary migrants.

Finally, the survey collects additional information on migrants’ most-recent migration episode, such as remittances sent, monthly expenses and savings, professional and personal difficulties encountered, and overall impressions. The survey includes a detailed module on demographic characteristics, enterprises and assets for the household.

# **3.2 The Household Income and Expenditure Survey (HIES)**

Because the BRMS only covers households with return migrants, we complement it with a nationally representative household survey that samples both non-migrants and current international migrants. The Household Income and Expenditure Survey (HIES, 2016-2017 wave) was designed to be representative at the national and division levels. The HIES collects detailed data on each household member, including labor market outcomes such as employment status, earnings, and sector of employment. The data are comparable to the labor market information captured by the BRMS. Information on non-migrants and current international migrants is collected through an “absentee module” answered by the household members who stayed in Bangladesh. The HIES also includes information on the debt and assets of the household, as well as detailed expenditure information that allows us to calculate household consumption and savings rates.

The Census of Population and Housing 2011 was used as the sampling frame for both the HIES and the BRMS. While the HIES covers both rural and urban areas, the BRMS sampling was designed to capture a representative sample of recently returned migrants in rural and semi-urban areas of Bangladesh. We thus restrict the HIES sample to rural and semi-urban areas in all of our comparisons. The HIES was carried out two years earlier than the BRMS.<sup>14</sup> This is an advantage in our context as current migrants in 2016 are likely to be more comparable to the return migrants captured in 2019 by the BRMS. Indeed, among the BRMS respondents, the median returned migrant had been back in Bangladesh for two years. Ahmed, Ahmed, Bossavie, Ozden,<sup> ̈</sup> and Wang (2020) show that the sample composition of temporary migrants in the HIES and the BRMS are very similar, and that the characteristics and destinations of migrants in the two surveys are also aligned with those in administrative data covering the entire population of temporary migrants from Bangladesh.<sup>15</sup>

> 14The Bangladesh HIES was carried between April 2016 and March 2017.

> 15The Bureau of Manpower, Employment and Training in Bangladesh publishes aggregate data on the number, composition and destinations of legal migrants from Bangladesh by year of departure.

11
Table 1: Summary statistics

||B|RMS|||HIES||
|---|---|---|---|---|---|---|
||Return|migrants|Non-m|igrants|Curren|t migrants|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|
|**_Socio-econmonic characteristics_**|||||||
|Male_†_|0.96|0.20|0.48|0.50|0.96|0.19|
|Rural/Semi-urban|0.99|0.10|0.95|0.22|0.99|0.11|
|Age currently<br>|37.9|8.2|35.3|11.4|34.1|8.8|
|Age category<br>|||||||
|_18-24_|0.03|0.17|0.21|0.41|0.14|0.34|
|_25-34_|0.35|0.48|0.28|0.45|0.39|0.49|
|_35-44_|0.39|0.49|0.25|0.43|0.32|0.47|
|_45-54_|0.20|0.40|0.19|0.39|0.13|0.34|
|_55-59_|0.03|0.17|0.07|0.25|0.02|0.14|
|Years of schooling|6.6|4.0|5.6|5.0|7.3|3.6|
|Education level|||||||
|_Illiterate_<br>|0.16<br>|0.37<br>|0.30|0.46<br>|0.08|0.26<br>|
|_Some primary (1-5)_<br>|0.24|0.43|0.25|0.44|0.26|0.44|
|_Some secondary (6-9)_|0.36|0.48|0.21|0.41|0.38|0.48|
|_Above some secondary (10-12)_<br>|0.22|0.41|0.17|0.37|0.25|0.43|
|_Tertiary (16+)_|0.02|0.15|0.06|0.24|0.03|0.16|
|Married currently|0.88|0.32|0.77|0.42|||
|Married before migration|0.58|0.49|||||
|**_Migration costs_**|||||||
|Total costs, 2010 constant dollar|4,203|2,121|||||
|Costs by usage, 2010 constant dollar|||||||
|_Intermediary fees_<br>|2,318|2,195|||||
|_Visa/Passport_|998|1,544|||||
|_Government fees_|51|224|||||
|_Other costs_|746|1,386|||||
|Fraction of migrants who borrowed|0.56|0.50|||||
|Share of costs fnanced by borrowing*|0.76|0.30|||||
|**_Stay abroad_**|||||||
|Destination country|||||||
|_GCC**_|0.74|0.44|||0.62|0.49|
|_Southeast Asia_|0.17|0.38|||0.21|0.41|
|_Other country_|0.08|0.28|||0.17|0.38|
|Age at departure|28.5|7.2|||28.1|8.1|
|Duration of stay at destination|6.5|5.6|||6.3|5.2|
|Return earlier than planned|0.25|0.43|||||
|Annual income, 2010 constant dollar|5,299|4,703|||||
|**_After return/Currently_**|||||||
|Age at return|34.9|8.1|||||
|Years since return|2.7|2.6|||||
|Employment status currently***|||||||
|_Not working_|0.17|0.38|0.16|0.36|||
|_Waged worker_|0.29|0.45|0.56|0.50|||
|_Self-employed_|0.54|0.50|0.28|0.45|||
|Annual income, 2010 constant dollar|1,280|499|1,163|1,174|||
|Observations|4,|709|47,|137||3,571|

_Note:_ Sample is restricted to males aged 18-59. _†_ Here, the mean is the share of males in the full sample. *Conditional on having taken a loan to migrate. **For columns 3 to 6, the GCC does not include Bahrain, which is coded as “others” in the original HIES data. ***These statistics exclude individuals who have been back in Bangladesh for less than a year at the time of the survey. 12
# **3.3 Descriptive Statistics**

Table 1 reports the summary statistics of the BRMS sample of return migrants.<sup>16</sup> A clear majority of migrants are men. Women represent only 4 percent of returnees in the survey sample; this level is consistent with statistics from administrative data from the Bureau of Manpower, Employment and Training (BMET). The low level of female migration can be explained by the low labor-force participation of women in Bangladesh, and by difficult work conditions in the destination countries (International Labour Organization 2015; Farole, Cho, Bossavie, and Aterido 2017). In addition, there is social pressure on women to stay behind because they bear household responsibilities, and because low-skilled temporary migrants are not allowed to take their families with them. Given the small share of female migrants in the sample, combined with the fact that post-migration patterns are likely to differ between genders, we restrict our working sample to men.

Most temporary migrants in the sample have some secondary schooling, while 17 percent have never attended school and only 2 percent have some tertiary education. The average years of schooling in the male sample of return migrants is 6.5 years, which is low compared to average educational attainment across the world (Barro and Lee 2013). Returning migrants have a higher level of schooling than the non-migrant, male, working-age population (5.8 years), but their schooling levels are lower than those of migrants who are currently overseas (7.6 years) according to the HIES 2016. These differences are largely explained by the older age of returnees compared to current migrants and increasing education levels across cohorts.

About 75 percent of temporary migrants in the sample have returned from GCC countries. Saudi Arabia, with about 25 percent of migrants, is the largest destination and closely followed by the United Arab Emirates. Malaysia represents 13 percent of temporary migrants, and is the largest destination in Southeast Asia. Only a small minority of migrants in our sample were employed in other high-income countries with Singapore as the leading destination.

Return migrants in the sample have been back, on average, for 2.7 years; with a median time since return of two years. The mean current age in the sample is 38, with average departure and return ages of 29 and 35, respectively. Therefore, most workers do not migrate overseas immediately upon entering the labor force, but often work for more than a decade before going abroad.

We observe substantial variation in the duration of migration episodes in our sample (Table 1). The median and mean duration of stay in all destinations are 4.7 years and 6.5 years, respectively. Close to a quarter of migrants stayed abroad for less than two years,

> 16For a detailed description of the BRMS and its sample characteristics, see Ahmed, Ahmed, Bossavie,  ̈Ozden, and Wang (2020).

13
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to cover the start-up expenses of self-employment and entrepreneurial activities. Second, prior to their move abroad, migrants have self-employment rates that are very similar to those of non-migrants across all ages (blue and red lines in Figure 6). These two groups of workers may differ in various dimensions, yet their propensity to become self-employed is almost identical until after the migration experience. Third, and most importantly for this paper, the rate of self-employment of return migrants is considerably higher than the rate among non-migrants, and the rate for the same migrants prior to their migration experience. This difference emerges at any age (green line versus the red and blue lines). The gap in selfemployment rates between return migrants and non-migrants is largest at younger ages.<sup>17</sup> For instance, 65 percent of 30-year-old return migrants in our sample are self-employed, compared to 28 percent of workers of the same age who have not migrated. While this difference has been pointed out in different contexts, our data facilitate a comparison not only with nonmigrants, but also with migrants’ own activity prior to migration. This within-individual comparison strengthens the case that migration is instrumental in business creation in the origin country. Across all ages, the rates of self-employment for return migrants are high, reaching levels of between 60 and 70 percent. The gap in self-employment between those who did and did not migrate narrows with age, but it never closes. Data from the BRMS support the pattern. They show that over 90 percent of the enterprises owned by former migrants were established after their return from overseas work. In other words, self-employment patterns are not driven by other forces, such as returning migrants taking over the family businesses. This descriptive evidence highlights the role played by temporary migration in accelerating transitions of workers into self-employment. Faster asset accumulation is the main channel behind this observation (see Section 4 for detail).

While self-employment can be a last resort for workers in certain contexts (Gindling and Newhouse 2014), evidence from Bangladesh indicates that the vast majority of the selfemployed choose this type of work for other reasons. Gutierrez, Kumar, Mahmud, Munshi, and Nataraj (2019) report that 82 percent of the self-employed state that the ability to work independently and the possibility of earning higher incomes are the main reasons behind their decisions. The same study also finds that the self-employed stay in the same activity much longer than casual laborers and wage employees in the private sector. Self-employment thus appears to be an “absorbing state” for many workers in Bangladesh.

> 17Figure 6 includes both dependent and self-employed workers in agriculture. Excluding that sector slightly reduces the self-employment rate but otherwise the pattern carries over.

18
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specifies a wage and initial duration for their employment. Although workers face some uncertainty as to the monetary wage actually paid (as opposed for instance to benefits in kind that may be subtracted), such contracts send a strong signal to lenders that the migrant will have the requisite income to pay back the loan. In addition, migrants cannot settle permanently in these countries, and they almost never migrate with their families – effectively guaranteeing that they will return home. By contrast, earnings from selfemployment are more uncertain and riskier. The likelihood of defaulting on a loan to finance a new business is therefore higher than for a loan to finance migration. Second, the agency problem faced by lenders is more pronounced in the case of entrepreneurship. Migrants’ earnings abroad are easy to verify and quantify because of the formal nature of migration arrangements. By contrast, it is difficult for lenders to verify self-employment earnings, which tend to have a high informal share due to the nature of these ventures (such as small shops). The markets for migration loans and entrepreneurship loans can thus be thought of as two separate credit markets, in which interest rates on entrepreneurship loans are significantly higher, and possibly prohibitive.

# **Stylized Fact 3:** **_Duration of stay overseas increases with migration costs and wages._**

There is a strong, positive association between migration costs, wages abroad, and the duration of stay at the destination country. Figure 10 shows that, first, migrants who pay higher upfront migration expenses tend to stay longer at the destination. This finding is intuitive if workers are migrating with the aim of achieving a minimum level of net savings during the migration episode. Holding everything else equal, an increase in migration costs raises the length of stay required to recover the upfront expenses and to reach the desired level of savings.

Second, migrants who earn higher wages at the destination country also stay there longer. The literature on the relationship between migrants’ wages and duration of stay suggests that this relationship could go in either direction. On the one hand, higher wages can increase incentives to stay longer to maximize lifetime earnings. On the other hand, migrants with a target level of savings can reach this target earlier when they earn higher wages. Figure 10 indicates that the first channel tends to prevail, particularly when upfront costs are high. We do not find strong evidence in the data that the risk of forced return due to visa issues or contract termination decreases for migrants who earn higher wages. Nevertheless, we account in the model for the possibility that the risk of forced return differs for higherskilled migrants.

23
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which are obtained by multiplying a worker’s monthly wage in the destination country in PPP Bangladeshi takas by the duration of stay abroad.<sup>22</sup> Columns (1) and (4) of Table 2 report the estimates for the coefficient of interest, _β_ , when Equation 1 is estimated using a simple OLS estimation. Both columns show that there exists a positive and statistically significant correlation between total earnings abroad and the likelihood of self-employment after return, both in the full sample and among working individuals. The OLS coefficients, however, may not be interpreted as causal because total earnings at destination are likely to be endogenous to the desire to become self-employed after return. In particular, the optimal migration duration and the occupational choice after return may be determined simultaneously (Dustmann and Kirchkamp 2002).

To address this endogeneity, we instrument total earnings overseas by exploiting specific characteristics of the Bangladeshi migration context, which centers on providing labor for oil-exporting countries. Specifically, we use the growth rate in oil prices, interacted with the oil dependency of the GDP of the destination country. The oil-price growth rate is calculated as the ratio of oil prices in the year when the individual returned to Bangladesh over the price during the year when the individual left Bangladesh. The destination-specific, oil-GDP dependency is measured by the mean of the annual oil rent over GDP in a destination country for all years from 1996 to 2017. Formally, the instrumental variable for total earnings abroad, denoted _Zidt_ , is constructed as: The intuition behind this instrumental-variable strategy is that exogenous fluctuations in oil prices affect migrants’ duration of stay by increasing demand for immigrant labor, and thus increasing the likelihood that their labor contracts will be extended. The power of the instrument thus resides in the fact that oil prices affect migrants’ total earnings through adjustments in the demand for labor, rather than the wages paid for labor. Indeed, we do not observe any visible association between overseas wages and oil prices; yet, demand for foreign labor appears very responsive to oil prices (Figure 1). Such adjustments through quantities as opposed to wages (of immigrant labor) in response to GDP shocks in destination countries has also been confirmed by McKenzie, Theoharides, and Yang (2014) for migration from the Philippines. Importantly, since we condition on full sets of year and destination effects, only the interaction of oil-price growth and the share of oil revenue in GDP is used for

> 22The PPP rate half way through the migration episode is used to convert foreign currency to Bangladeshi takas.

26
Table 2: Earnings abroad and self-employment after return, reduced-form estimation

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Full sample||Wor|king individuals|only|
||OLS|2SLS|First Stage|OLS|2SLS|First Stage|
|Dependent Variable|Self-employed|Self-employed|ln(Cum.Earn.)|Self-employed|Self-employed|ln(Cum.Earn.)|
|ln(Cum. Earning abroad)|0.031***|0.112***||0.040***|0.110**||
||(0.008)|(0.032)||(0.009)|(0.047)||
|Oil price growth _×_ Oil rents/GDP|||0.855***|||0.824***|
||||(0.119)|||(0.112)|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Year of return FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Origin division FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Destination country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|F-statistics of excluded instruments||51.9|||54.3||
|Observations|3354|3354|3354|2775|2775|2775|

_Note:_ * p _<_ 0.10, ** p _<_ 0.05, * p _<_ 0.01. Standard errors clustered at the destination-origin level are reported in parentheses. The sample is restricted to males between the ages 18 to 59, and excludes individuals who have been back in Bangladesh for less than a year. Control variables include age and squared age at the time of survey, educational attainment dummies, and a dummy for self-employment prior to migration. Oil-price growth is the ratio of the oil price at the time of return to Bangladesh over the one at the time of departure from Bangladesh.
One possible identification issue regarding the exclusion restriction may be that fluctuations in oil prices directly affect the relative attractiveness of wage and self-employment activities in the home country (El-Mallakh and Wahba 2021). This might occur, for instance, when wage and self-employment work are concentrated in different sectors in Bangladesh. Two arguments mitigate this concern: First, our estimation conditions on a full set of year effects, so that only the interaction with destination country-specific oil shares in GDP provide the identifying variation.

Second, in our sample, the majority (two-thirds) of returned migrants living in rural and semi-urban areas work either in small-scale agriculture or retail businesses. According to the 2016 industry-level Input-Output tables for Bangladesh, both sectors have a very small share of their input values coming from “Coke or refined Petroleum”.<sup>23</sup> We do, however, observe a slightly higher importance of the transportation sector for the self-employed (21 percent) compared to wage workers (15 percent) in the sample. Since transportation is more dependent on oil-derived inputs compared to other sectors, we also run our IV estimation on a sample that excludes people employed in transportation. The results of this robustness check are reported in Table A2, and show that the IV coefficients are virtually identical when the transportation sector is excluded.

We next investigate changes in the share of self-employment among non-migrants over time, and a possible association with fluctuations in oil prices. We explore these potential connections to provide further evidence that oil prices do not directly affect the relative attractiveness of self-employment and wage work. We use several waves of the Bangladesh Labor Force Survey, which is nationally representative and available at several points in time between 2000 and 2017. We focus on workers who have not migrated overseas. As shown in Figure A1, the fraction of those who did not migrate and who are self-employed in rural and semi-urban areas (the areas covered by the BRMS) is fairly stable over time. In short, we do not observe any noticeable association between the share of self-employment in Bangladesh and large fluctuations in oil prices over time.

As the instrument primarily operates through its effect on the duration of stay abroad, another potential threat is that duration of stay may affect self-employment odds after return in ways that extend beyond effects on total earnings abroad. This could occur, for example, if a longer duration abroad reduces the likelihood of finding a wage job back in Bangladesh, due to a loss of social networks and contacts with local employers. However, as we have previously discussed, self-employment is typically preferred by workers in Bangladesh. Therefore, it is unlikely that workers who spent a longer time abroad would turn to self-employment because they encounter more frictions in finding a wage job. To provide further evidence on this,

> 23The ratio is around 1.2 percent in agriculture and 0.2 percent in retail.

28
we also check whether there are systematic differences in employment spells according to the duration of stay abroad of return migrants. As shown in Figure A3, we do not find any systematic association between duration of stay overseas and duration of unemployment after return. These results do not seem compatible with the hypothesis that return migrants who stayed overseas longer are more likely to opt for self-employment because they cannot access wage employment back home.

Columns (2) and (5) of Table 2 display our estimation results using this instrumentalvariable approach. The first-stage coefficient of the oil-price instrument reported in columns (3) and (6) has the expected sign: A positive growth rate in oil prices during the migrant’s stay abroad increases the total value of earnings overseas in local currency. The coefficient is statistically significant at the one percent level, and the F-statistic for the excluded instrument suggests a strong first stage. The estimated effect of cumulative earnings overseas on the likelihood to become self-employed after return is sizable, with a semi-elasticity of around 0.1. As shown in Table A2, these findings are virtually unchanged when we exclude return migrants who are employed in the transportation sector, which uses a higher fraction of oil-derived inputs than other sectors.

These stylized facts jointly suggest that a key driver of temporary migration from Bangladesh is the desire to work overseas to accumulate sufficient financial savings to fund self-employment upon return. In other contexts, human capital accumulation abroad has been pointed out as an important factor for temporary migration and post-return decisions (McCormick and Wahba 2001). Consistent with this, a wage premium for returning migrants has been estimated, for instance, for migrants from Hungary (Gang and Yun 2000), Albania (De Coulon and Piracha 2005), West-Africa (De Vreyer, Gubert, and Robilliard 2010), Mexico (Lacuesta 2010; Reinhold and Thom 2013), Romania (Ambrosini, Mayr, Peri, and Radu 2015) and Egypt (Marchetta 2012; Wahba 2015; El-Mallakh and Wahba 2017; El-Mallakh and Wahba 2021).

In the context of migration from Bangladesh, however, human capital accumulation is unlikely to be the main motivation. The vast majority of migrants from Bangladesh are employed in low-skilled jobs in the destination countries. These occupations generally rely on physical strength, providing limited opportunity for learning and human capital accumulation. In addition, the occupational patterns of individuals before, during, and after migration do not seem to be compatible with the human-capital accumulation hypothesis (Table A3). For example, while two-thirds of temporary migrants in the sample were employed in the construction sector at their destination abroad, but only 10 percent after returning home (Panel A). In the HIES data, this ratio is only slightly lower (7 percent) for those who did not migrate. In addition, 24 percent of the returning migrants were employed in construction 29
_prior_ to their migration, suggesting that experience accumulation occurs beforehand. The ratio of migrants employed in construction, however, is significantly lower after return. Likewise, only 14 percent of return migrants had worked in retail or agriculture while overseas; by contrast, more than two-thirds of migrants were employed in one of these two sectors when they returned to Bangladesh. Furthermore, migrants who were employed in the construction sector overseas do not transition into that same sector at significantly higher rates after return, compared to individuals who were employed in transport and utility sectors (panel B of Table A3). Similarly, temporary migrants employed in transport and utility sectors while abroad do not disproportionately transition into that sector after return. Overall, descriptive evidence on sectoral patterns lends little supports for human-capital accumulation as a driver of temporary migration patterns from Bangladesh.

Another possible motivation for temporary migration from low-income countries is the improvement of marriage market prospects through asset accumulation overseas. The marriage and migration patterns of temporary migrants in the BRMS sample, however, do not seem compatible with this hypothesis. Appendix Figure A2 depicts the shares of married individuals before and after migration, at a given age. If migrants were moving overseas to improve their marriage prospects, one would expect the share of those temporary migrants who are married after migration to be higher than the share who are married before departure. However, as shown in Figure A2, the share of individuals who are married before and after migration, at any given age, is virtually the same.<sup>24</sup> This descriptive evidence does not support the idea that improvement of marital prospects is a major mechanism driving temporary migration decisions.

> 24Unreported results show that the same holds when we disaggregate the sample by education groups. These results are available upon request.

30
# **5 Model**

Motivated by the above evidence, this section presents a dynamic model that captures emigration, return migration, and wage and self-employment decisions of temporary migrant workers across multiple destinations and time periods. Each individual _i_ in the model is identified by his education level _ei_ and age _ait_ at time _t_ .<sup>25</sup> While education is fixed over the lifetime of the individual, age increases by one in each time period (year). Every individual makes several interrelated decisions in each period _t_ . First, he chooses a location _lit ∈{B, M, O, Q, SA, UAE}_ to live and work in that period. The individual has the option of staying in Bangladesh ( _B_ ), or choosing to work in one of five foreign destinations. These are, in alphabetical order, Malaysia ( _M_ ), Oman ( _O_ ), Qatar ( _Q_ ), Saudi Arabia ( _SA_ ) and the United Arab Emirates ( _UAE_ ), and together account for the destinations of 82 percent of all migrant workers in our sample. Modeling the choice between alternative destinations is important because, for example, when conditions in one country deteriorate, migrant flows divert elsewhere (Bertoli, Moraga Fern ́andez-Huertas, and Ortega 2013).

If the individual migrant already resides in a foreign destination, _d ∈{M, O, Q, SA, UAE}_ , he chooses whether to extend the stay for another period at the same location, or to return to Bangladesh.<sup>26</sup> As it is rare among Bangladeshi migrants workers, we rule out the possibility of moving directly from one foreign destination to another.

The second critical decision of the individual is about his employment type, denoted by _sit ∈{S, W }_ where the option _S_ indicates self-employment, and _W_ indicates wage employment. Emigration, return or self-employment decisions have a financial component. If a wage worker in Bangladesh decides to emigrate to a foreign destination, or switch to selfemployment, then he has to undertake an investment which needs to be financed via personal savings, borrowing, or a combination of both. If the individual stays in his current location and employment, he continues to allocate that period’s income between savings and consumption, which, in turn, determine his stock of assets and future investment options. We now describe each of these decisions and variables in more detail.

## **5.1 Employment, Assets, Costs and Constraints**

**Wage employment** in any given location _l_ yields labor income _wit_<sup>_l_=</sup><sup>_wl_(</sup><sup>_ei, ait, α_</sup> _it_<sup>_l,W_) which</sup> is a function of the education level _ei_ , current age _ait_ , and unobserved random factors _αit_<sup>_l,W_</sup> _∼_

> 25We use male pronouns to refer to the migrants because more than 95 percent of those who work abroad are men, as seen in Table 1.

> 26We use the phrase “destination” and superscript _d_ to refer to the five foreign countries that host a large majority of the migrants. The phrase “location” and the superscript _l_ refer to any one of these countries, as well as Bangladesh. In other words _d ∈{M, O, Q, SA, UAE}_ and _l ∈{B, M, O, Q, SA, UAE}_ .

31
_N_ (0 _, σl,W_<sup>2).</sup> When working in one of the foreign destinations, _d ∈{M, O, Q, SA, UAE}_ , migrant workers face an exogenous risk that their contract will be terminated, in which case, they will be forced to return to wage employment in Bangladesh. The probability of termination and forced return, _δit_<sup>_d_=</sup><sup>_δd_(</sup><sup>_ei, ait, yit, fuelRevdt_)isspecifictothedestination</sup> and, again, depends on an individual’s age and education level, as well as on the time since migration to the destination country, denoted by _yit_ , and that destination’s revenues from fuel exports. In parallel, wage workers in Bangladesh also face a risk of unemployment, which we denote by _δit_<sup>_B_=</sup><sup>_δB_(</sup><sup>_ei, ait_).Migrationdurationnaturallydoesnotenterintothis</sup> expression since the individual is at home. Individuals are assumed to be risk-neutral, utility maximizers, and we adjust wages in Bangladesh by these employment probabilities.

In principle, wages in both Bangladesh and foreign destinations are equilibrium outcomes. Yet, Figure 1 shows that the real wages paid to Bangladeshi migrants in our sample vary neither with international oil prices nor with the scale of migration, possibly due to highly elastic immigrant labor supply. Instead, wages are quite stable over the time period we consider. As such, we can rule out any major effects of migration on wages in destination countries. Within Bangladesh itself, migrants are a much smaller fraction of the workforce than in destination countries. Furthermore, estimates in the literature suggest that the wage effects of emigration are generally small. Dustmann, Frattini, and Rosso (2015), for instance, estimate a wage elasticity of 0.02 for the effect of emigration on wages in Poland.<sup>27</sup> Thus, we also disregard equilibrium wage effects in Bangladesh, which we believe to be of second order relative to the mechanisms investigated in the paper.

Our data suggest that potential migrants tend to overestimate their future, foreign wages prior to their emigration. In line with this observation, we assume the emigration decision is based on destination-specific _expected_ wages, denoted by _w_ ̃ _it_<sup>_d_=E[ ̃</sup><sup>_wd|ei, ait_].However,once</sup> they are abroad and start to work, migrants realize their _actual_ wages for period _t_ , denoted as _wit_<sup>_d_.Allfuturedecisionsarethenbasedonthelatter.</sup>

**Self-employment** is an option for wage workers who are in Bangladesh and it requires an initial investment _Ce_<sup>_I_.Weletthisinvestmentvaryacrosseducationgroupstoaccount</sup> for the heterogeneity of businesses operated by individuals with different education levels. In return, self-employment generates profits _πit_ = _π_ ( _ei, ait, αit_<sup>_S_)perperiod,whichagainvary</sup> with the education level and the age of the individual, as well as with unobserved factors given by _αit_<sup>_S∼N_(0</sup><sup>_, σ_</sup> _S_<sup>2).Becausetheoverwhelmingmajorityofmigrantsinoursurveyare</sup>

27Overall, however, there is little consensus on the size of the effect of emigration on wages, with estimates ranging from small, positive effects when estimated for different member countries of the Organisation for Economic Co-operation and Development (OECD) by Docquier, Ozden, and Peri (2014) to negative effects estimated by Aydemir and Borjas (2007).

32
contract wage workers, we assume that the self-employment option is available only when workers are in Bangladesh.

**Migration costs and labor demand at destination** are critical in shaping emigration decisions and their timing. The main reason for emigration for most migrants is the ability to earn higher wages. However, emigration requires an upfront payment of fees and expenses, which we denote by _Cit_<sup>_d_=</sup><sup>_Cd_(</sup><sup>_ei, ait_).Theamountisspecifictodestinationcountry</sup><sup>_d∈_</sup> _{M, O, Q, SA, UAE}_ and dependent on the individual’s age and education level. While they are abroad, migrants further suffer from an education and destination-specific disutility _ηe_<sup>_d_</sup> in each period, arising from their separation from family and friends who stay in the home country.

Migration is also constrained by skill-specific labor demand in destination countries. As documented in Figure 1, aggregate migration is strongly related to the price of oil, which is the main source of revenue in all major destination countries. We account for this dependence by specifying a function that relates revenues from oil to the share of individuals with education-level _e_ who can locate a job and obtain a work visa in destination _d_ , conditional on having the desire and financial ability to move there. The probability _λ_<sup>_d_</sup> _et_<sup>ofsecuringa</sup> visa is related to the level of fuel revenues _fuelRevdt_ in destination _d_ and year _t_ through the relationship ln( _λ_<sup>_d_</sup> _et_<sup>)=</sup><sup>_φd_</sup> _e_<sup>+</sup><sup>_ψ_ln(</sup><sup>_fuelRevdt_)).28Figure1alsoshowsthattherealwages</sup> paid to migrants do not track oil prices, but are roughly constant over the time period we consider, in spite of the strong variation in oil prices.

**Credit constraints and asset accumulation** link self-employment and migration decisions. Both self-employment and emigration to a foreign country generate higher incomes (in expectation), but require upfront investments. Individuals can finance these investment amounts from their current savings, or through borrowing or a combination of the two options. The level of private assets accumulated by an individual at time _t_ is denoted by _Ait_ , and yields real interest at a rate _rA_ . Assets _Ait_ can have a negative value, implying that the individual is in debt, in which case a higher lending rate _rL > rA_ accrues.

Many migrants finance their trip through loans, but not all individuals may have access to credit. We assume that an individual with education level _e_ can cover migration fees and expenses through credit with probability _pe_ . Without access to credit, migration costs have to be fully financed from individual savings. Hence, with probability 1 _− pe_ , migration to destination _d_ requires that _Ait ≥ C_<sup>_d_</sup> ( _ei, ait_ ). In the case of investment for self-employment, we

> 28Fuel revenues are measured in USD, deflated to 2010 prices, and we restrict realizations of _λdet_<sup>to</sup> [0 _,_ 1]. Importantly, as for the case of Filipino migrants analyzed by Bertoli, Fern ́andez-Huertas Moraga, and Keita (2017), labor demand across the main destinations for Bangladeshi migrants is correlated, and our specification of _λ_<sup>_d_</sup> _et_<sup>allowsforthis.</sup>

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assume that a maximum of 50 percent of the investment costs can be covered via borrowing. This ratio is based on the guidelines of the Microcredit Regulatory Authority (MRA) in Bangladesh, and reflects the credit constraints faced by entrepreneurs as previously discussed (see also Battaglia, Gulesci, and Madestam 2021). Becoming self-employed requires that _Ait ≥_ 0 _._ 5 _Ce_<sup>_I_.Weobservetheshare</sup><sup>_q_ofindividualswhoinheritedabusiness,andinthe</sup> model let this fraction of individuals start their working life as entrepreneurs without having to accumulate the upfront investment cost _Ce_<sup>_I_.</sup> The savings rate is given by _ρ_<sup>_l_</sup> , which is the share of income saved when the individual is in location _l_ . In our calibration, we take this parameter directly from the data. Subject to the inequality constraints above, the stock of assets of an individual then accumulates according to the following equation: In this expression, _ρ_<sup>_l_</sup> _i_<sup>isthelocation-specificsavingsrate,</sup><sup>_sit_istheindicatorvariable</sup> on whether the individual is self-employed ( _sit_ = _S_ ) or a wage worker ( _sit_ = _W_ ), _C_<sup>_d_</sup> is the destination-specific migration fee, and _Ce_<sup>_I_istheeducationspecificinvestmentcostfor</sup> self-employment. If his asset level is negative (because he is still repaying a loan), then the individual pays interest at a rate _r_ = _rL_ . If the individual has a positive level of assets, then he earns returns at the rate of _r_ = _rA < rL_ . Hence, an individual will use all of his savings before borrowing to finance self-employment or migration expenses. The expression 1[ _lit−_ 1 = _B∩lit_ = _d_ ] is an indicator variable that is equal to 1 if an individual is in Bangladesh in period _t −_ 1 and migrates to destination country _d_ at the beginning of period _t_ . Therefore, the individual has to pay the migration fee _C_<sup>_d_</sup> ( _ei, ait_ ) for that destination. Similarly, the expression 1[ _sit−_ 1 = _W ∩ sit_ = _S_ ] defines an indicator variable that is equal to one if an individual is a wage worker in period _t −_ 1, becomes self-employed in period _t_ , and has to pay the self-employment investment cost _Ce_<sup>_I_.Finally,weassumethatindividualsownaninitial</sup> stock _Ae,_ 0 of assets, depending on their education level _e_ at the beginning of their working life.

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# **5.2 Welfare**

**Utility** in each period consists of three components: (i) Consumption, _cit_ , is given by income minus savings. The income might come from wage employment abroad or in Bangladesh (given by _wit_<sup>_l_forlocation</sup><sup>_l_),orfromtheprofitsfromself-employment,</sup><sup>_πit_.Ashare</sup><sup>_ρl_</sup> _i_<sup>of</sup> this income is saved. (ii) Individuals experience an education- and location-specific disutility from migration, _ηe_<sup>_l_.ThisisequaltozeroiftheindividualislivinginBangladesh,butmay</sup> take other values if he is in a foreign country. (iii) There are transitory taste shocks _ε_<sup>_l_</sup> _it_<sup>fora</sup> location _l_ , as well as for sectoral choices (when in Bangladesh), denoted by _ε_<sup>_s_</sup> _it_<sup>.Consumption</sup> is given by

In this expression, 1[ _sit_ = _S_ ] is an indicator variable that is equal to 1 if the individual is self-employed as explained in equation (3). 1[ _sit_ = _W_ ] is the corresponding indicator variable for wage employment. With consumption defined as above, the flow utility for individual _i_ in period _t_ at location _l_ is where 1[ _lit_ = _B_ ] indicates whether the individual is in Bangladesh. Wages earned in foreign destinations and the corresponding consumption are transformed accounting for price level differences between destinations and Bangladesh. This is in line with most families staying behind, and financing consumption through remittances sent by the migrant. It also implies that a higher purchasing power of the destination country’s currency in Bangladesh facilitates faster asset accumulation and impacts migration duration (Yang 2006; Yang 2008; Akay, Brausmann, Djaji ́c, and Kırdar forthcoming; Albert and Monras 2020).

Individuals maximize their discounted expected lifetime utility over the sequences of location and self-employment choices, and subject to the evolution of the vector of state variables Ω _it_ = _{t, ei, ait, Ait, sit, lit, yit, αit_<sup>_S, α_</sup> _it_<sup>_W, εs_</sup> _it_<sup>_, εl_</sup> _it_<sup>_}_asexplainedabove.Themaximized</sup> lifetime value at time _t_ is, then, given by where individuals live until period _T_ .

**Value functions** depend on location and employment choices. We first define _Wit_<sup>_B,W_</sup> , which denotes the value for a wage worker who is currently in Bangladesh and faces proba-

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bility _λ_<sup>_d_</sup> _et_<sup>thathecanobtainaworkvisaforforeigndestination</sup><sup>_d_.Allvaluesinthesection</sup> depend on the vector of state variables Ω _it_ , which for better readability we omit from the notation. With probability _pe_ , agents have access to a migrant loan, and their value includes the choice between all locations In this expression, the first term is the value in Bangladesh. The subsequent terms are sums of values in each destination and Bangladesh, weighted by the probability of being able to migrate to that destination. If the agent has no credit access (with probability 1 _− pe_ ), his value is given by (7) only if _Ait ≥ C_<sup>_d_</sup> ( _ei, ait_ ). Instead, when assets cannot cover the cost of migration, the agent cannot migrate, and the value is simply given by _Wit_<sup>_B,W_</sup> = _Vit_<sup>_B_.</sup> The value in Bangladesh, _Vit_<sup>_B_, depends on the decision between wage and self-employment,</sup> which are subject to the individual’s asset level. If an individual has enough assets _Ait_ to meet the investment cost, _Ait ≥_ 0 _._ 5 _Ce_<sup>_I_,hehastheoptiontobecomeself-employed.Ifhe</sup> does not have enough assets, an individual will continue as a wage worker. The value of being in Bangladesh is, therefore, given by where _Vit_<sup>_B,W_</sup> and _Vit_<sup>_B,S_</sup> denote values of wage and self-employment at home, respectively. The value attributed to wage employment in Bangladesh is, in turn, given by whereas, the value for self-employment is We assume that self-employed individuals do not migrate (because they would have to 36
leave their businesses behind), and that they stay self-employed from that period forward. This is supported by our data, which show that only a small fraction of the respondents report being self-employed at the time of their migration. It also is consistent with findings for other developing countries, see Giambra and McKenzie (2021). Accordingly, the continuation value in equation (9) is given by _Wit_<sup>_B,W_</sup> +1<sup>,since wage workers can change occupations and locations,</sup> and their continuation value involves this optimizing decision. By contrast, the continuation value in equation (10) is given by _Vit_<sup>_B,S_</sup> +1<sup>,asindividualsstayself-employed.</sup>

**The expected value before migration** to destination _d_ depends on (potentially biased) wage expectations. This wage, which individuals expect to obtain if they were to move from Bangladesh to destination _d_ at time _t_ , is

In this expression, as a reminder, _w_ ̃ _it_<sup>_d_=E[ ̃</sup><sup>_wd|ei, ait_]isthewagethatpotentialmigrants</sup> expect to earn before migration takes place, and _ηe_<sup>_d_is the education specific disutility.Finally,</sup> _W_ ̃ _it_<sup>_d_</sup> +1<sup>isthe(expected)continuationvalue</sup><sup>_before_themigrantarrivesindestination</sup><sup>_d_and</sup> observes the true wage. It is defined as

where _δit_<sup>_d_istheprobabilityoflosingthejobatthedestinationandbeingforcedtoreturnto</sup> Bangladesh. Having defined all of its components, _l_<sup>_∗_</sup> is the optimal location decision that maximizes the expression in equation (7) and gives _Wit_<sup>_B,W_</sup> .

**The value after migration** to destination _d_ depends on the realized wage _wit_<sup>_d_,and</sup> enters into the return migration decision. It is given by

where continuation values _after_ migration to destination _d_ (when the actual wage is observed) is given by 37
# **6 and Estimation**

Several components of our model are directly observed in the data. These include the cost _Cit_<sup>_d_ofmigrationtodestination</sup><sup>_d_,earnings</sup><sup>_w_</sup> _it_<sup>_l_andprofits</sup><sup>_πit_byindividualcharacteristics</sup> at location _l_ , saving rates _ρ_<sup>_l_</sup> , and earnings expected prior to emigration, _w_ ̃ _it_<sup>_d_.Furthermore,</sup> the survey contains information on the reason for return migration, including whether a work contract has been terminated. We use this information to compute the corresponding probabilities _δit_<sup>_d_withineachgroupofmigrants.AppendixB,andinparticularTablesA4</sup> and A5 provide further details. In the Bangladesh Informal Firms Survey 2010, we observe the share _q_ of businesses that are inherited. Finally, _rA_ and _rL_ are, respectively, the interest rates earned on savings, or paid on migrant loans; these are obtained from Mallick (2012) and Berg, Emran, and Shilpi (2013).

We estimate the remaining structural parameters of the model with by method of simulated moments, minimizing the distance between informative moments computed for a population of agents simulated from the model, and the counterpart of these moments observed in the data. In total, we jointly estimate 52 parameters pertaining to the demand in each destination country for workers of a given education level (parameters determining the share _λ_<sup>_d_</sup> _et_<sup>ofwould-bemigrantswhoobtainaworkvisa),agents’destination-education-</sup> specific disutility from migration _ηe_<sup>_d_andtheirinitialstockofassets</sup><sup>_Ae,_0,theshare</sup><sup>_pL_</sup> _e_<sup>of</sup> would-be migrants who can finance migration on credit, as well as the cost _Ce_<sup>_I_ofsetting</sup> up a business. The latter three sets of parameters are education-specific, and identified by having the model match the observed asset level, the fraction of migrants who report having financed their migration on credit, and the self-employment share in our sample. The remaining parameters are identified by observed migration patterns. Note that the intensity and distribution of emigration from Bangladesh to different destination countries are affected both by labor demand _λ_<sup>_d_</sup> _et_<sup>andbyagents’preferences</sup><sup>_η_</sup> _e_<sup>_d_.Todisentanglethetwoeffects,we</sup> target both emigration shares and migration durations conditional on having migrated to a given destination. The disutility _ηe_<sup>_d_from staying abroad determines both the emigration and</sup> the migration duration decision. Motivated by Figure 1, the share _λ_<sup>_d_</sup> _et_<sup>primarilyaffectsemi-</sup> gration. The termination or expiration of work permits is observed directly and accounted for through _δit_<sup>_d_.</sup> Building on the evidence reported in Figure 1, we let _λ_<sup>_d_</sup> _et_<sup>vary with destination countries’</sup> fuel revenues. We proceed under the following assumptions: (i) Fuel revenues are exogenous; (ii) The labor supply of Bangladeshi migrants always exceeds labor demand in destination countries (or is very elastic, as suggested by the wage trend depicted in Figure 1); (iii) The elasticity of the probability of locating a job in a destination with respect to fuel revenues 38
equals the elasticity of migration with respect to fuel revenues in a destination; that is, the relationship between fuel revenues and migrant demand can be identified as an estimate of _ψ_ in an estimating equation We thus feed a regression estimate of _ψ_ into the structural model, and estimate intercepts _φ_<sup>_d_</sup> _e_<sup>foreachmigrantgroupbylettingthemodelmatchtherespectiveobservedemigration</sup> rate. The vector ( _ψ, φ_<sup>_M_</sup> 1<sup>_, ..., φUAE_</sup> 4 ) then parameterizes _λ_<sup>_d_</sup> _et_<sup>,theprobabilityoflocatinga</sup> foreign job. Estimating a life-cycle model requires an assumption on agents’ expectations about the paths for fuel revenues in destination countries over time. We fit quadratic trends to fuel revenues in the main destination countries over the 2000-2017 period, and assume that agents’ expectations are based on these trends.

Taken together, We estimate the 52-element structural parameter vector **_θ_** _≡_ ( _η_ 1<sup>_M, η_</sup> 2<sup>_M, ...,_</sup> _η_ 1<sup>_O, ..., η_</sup> 4<sup>_UAE_</sup> _, φ_<sup>_M_</sup> 1<sup>_, ..., φUAE_</sup> 4 _, A_ 1 _,_ 0 _, ..., A_ 4 _,_ 0 _, C_ 1<sup>_I, ..., C_</sup> 4<sup>_I, p_1</sup><sup>_, ..., p_4)</sup><sup>_′_ofthemodelinSection5byus-</sup> ing method of simulated moments, which minimizes the distance between moments **_m_** _m_ simulated from the model, and the corresponding data moments **_m_** _d_ from the HIES and BRMS samples.<sup>29</sup> Specifically, we minimize the estimation criterion _crit_ ( **_θ_** ) = ( **_m_** _d −_ **_m_** _m_ ( **_θ_** ))<sup>_′_</sup> **_W_** ( **_m_** _d −_ **_m_** _m_ ( **_θ_** )) _,_ where **_W_** is a diagonal weighting matrix with the inverse variances of the data moments on the diagonal.

The moments targeted are listed in Appendix Table A6, and Figure 12 shows the model’s good overall fit. The figure plots moments simulated by the model against their empirical counterparts. For better visibility, we use log scales, and indicate the different groups of moments in the graph. The precise magnitudes for all moments are shown in Appendix tables A7-A9.

> 29The simulation draws individuals from the sample-specific, age-education distribution, and mirrors the empirical sampling scheme of the BRMS in selecting former (simulated) migrants who have returned since 2010.

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<!-- Start of picture text --> 107<br>x assets by education,<br>3 ©<br>[e)) eae<br>—2 ~~" migration duration<br>0<br>2 o by education<br>5 10 andsharesshares borrowingself-employed for migration {a5 (e) and destination<br>= each by education 2<br>fe)<br>=<br>_@) CO<br>2 <<br>& 1 02 6 emigration rates by education and destination<br>O<br>aD<br>10° 107 10°" 10° 10" 10?<br>observed moments (log scale)<br><!-- End of picture text -->
Table 3: Structural parameter estimates: initial stock of assets, investment costs, and creditaccess parameters

|Parameter|Point estimate|Standard error|Identifying moment|
|---|---|---|---|
|_A_<sup>0</sup><br>1|5390.5|(2048.2)|stock of assets, education level 1|
|_A_<sup>0</sup><br>2|5012.9|(203.6)|stock of assets, education level 2|
|_A_<sup>0</sup><br>3|7891.4|(206.5)|stock of assets, education level 3|
|_A_<sup>0</sup><br>4|9756.1|(246.0)|stock of assets, education level 4|
|_C_<sup>_I_</sup><br>1|31.53|(4.43)|share self-employed, education level 1|
|_C_<sup>_I_</sup><br>2|29.65|(2.95)|share self-employed, education level 2|
|_C_<sup>_I_</sup><br>3|35.40|(0.29)|share self-employed, education level 3|
|_C_<sup>_I_</sup><br>4|32.73|(6.20)|share self-employed, education level 4|
|_p_1|0.543|(0.046)|share borrowing, education level 1|
|_p_2|0.572|(0.048)|share borrowing, education level 2|
|_p_3|0.771|(0.026)|share borrowing, education level 3|
|_p_4|0.980|(0.066)|share borrowing, education level 4|

_Note:_ Asymptotic standard errors are in parentheses. Assets and investment costs are denoted in 1,000 PPP adjusted USD. Education levels 1-4 refer to illiterate, some primary, some secondary and high school degree, respectively.

We show the larger subsets of parameters pertaining to preferences ( _ηe_<sup>_d_) and foreign labor</sup> demand ( _φ_<sup>_d_</sup> _e_<sup>) graphically in Figure 13.30The left panel of Figure 13 shows that utility losses</sup> _ηe_<sup>_d_frommigrationarelargestforless-skilledmigrantsinOman.Notethattheselossesare</sup> conditional on wages and job loss risk at a destination. Given the assumed utility function, the absolute magnitude of _ηe_<sup>_d_correspondstoforgoneutilityinthousandsofPPP-adjusted</sup> dollars. The displayed estimates are the values for _ηe_<sup>_d_thatmakethemodelmatchobserved</sup> emigration rates and migration duration across education groups and destination countries.

> 30The full list of estimates, their standard errors, and the moment most directly contributing to identification of each parameter are relegated to Appendix tables A10 and A11.

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# **7 Policy Simulations**

Our model links a worker’s decisions about where to migrate, when to leave, and how long to stay to his financial decisions on self-employment after returning back home. We use the estimated model to evaluate three core policy interventions pertaining to the conditions under which migration and self-employment decisions are made. First, we consider cuts to the cost of migration. Second, we examine a subsidy that reduces the interest rate for self-employment loans. Finally, we investigate an information treatment that aligns individuals’ (overly optimistic) expectations about their foreign earnings potential with the average earnings observed in our data.

Our model is flexible regarding the direction of the effects. For instance, lower costs of migration, our first policy intervention, make migration more attractive but the impact on self-employment is not clear. On the one hand, we may observe less self-employment in Bangladesh as workers favor migration as a means to achieve higher incomes. On the other hand, a rise in repatriated savings may contribute to more financing of entrepreneurial activities. Mobarak, Sharif, and Shrestha (2020), who examined the outcomes for Bangladeshi agricultural workers who went to Malaysia under a specific government-mediated migration program, document a negative short-run net effect of emigration on entrepreneurial activity while migrants are abroad. Anelli, Basso, Ippedico, and Peri (2020) present similar evidence for emigration from Italy. Our simulations predict that a decline in migration costs not only affects the emigration decision, but also its timing and the duration of the employment period abroad. We show that once we account for return migration, these changes lead to sizable increases in repatriated savings and business creation upon return.

Similarly, lower interest rates on entrepreneurship loans may discourage emigration because workers simply prefer to borrow more instead of accumulating savings abroad to raise the capital they need. Alternatively, improved credit conditions may lead workers who originally considered self-employment out of their reach to decide to migrate, as a way to save to become entrepreneurs after their return. Our results show that, while a decline in interest rates naturally makes self-employment more attractive, the policy’s impact is actually undercut by an endogenous adjustment in migration behavior. This opposing effect needs to be accounted for in a cost-benefit analysis.

There are also opposing effects from our third policy intervention, the information treatment regarding expected wages abroad. Individuals naturally benefit from better-informed decisions because the migrants with overly optimistic wage expectations do not migrate with the intervention. As a result, the level of disappointment and premature returns decline. At the same time, the overall levels of savings and business creation in Bangladesh also de43
cline. In conclusion, our policy simulations clearly show that understanding the extent and direction of these potential responses to policy measures are critical to identifying the likely impact of any intervention.

# **7.1 Lowering of Migration Costs**

Costs of migration receive significant attention, especially from policy makers. For many low-skilled temporary migrants from poor countries, the inability to pay these expenses is the main impediment to taking advantage of significantly higher wages abroad. The average migrant in our sample paid an amount that is equivalent to about three years of earnings in Bangladesh. Even if borrowing is an option, high rates of interest further increase the burdens for migrants. Lowering migration costs, either by limiting the rent-seeking behavior of intermediary firms, or through migration subsidies is one of the most frequently demanded policy interventions by migrants, policy advocates and academics (World Bank 2018).

Our model shows that an immediate effect of lowering of the cost of migration would be an increase in emigration, and a shortening of the duration of migration episodes. The graphs in Panel A of Figure 14 show the effects that lowering migration costs by half would have on emigration rates over the life cycle of individuals with different education levels. Because liquidity constraints are the most binding for younger workers, the expansionary effect is stronger for them. For example, reducing the cost of emigration by half leads the annual emigration rate to increase from 0.5 percent to 0.8 percent for 25-year-old workers without secondary education. In addition, the peak migration age drops from 33 to 30, largely because workers need less personal savings to pay for fees. Similarly, under the same policy intervention of reducing fees, the annual emigration rate for 25-year-old workers with some secondary education increase from 1.6 percent to 1.9 percent. There is minimal impact for either education group after age 40. Finally, we find that the overall impact on the migration rate is an increase of 29 percent (9 percent) over the lifetime of a worker with less than (at least some) secondary education.

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<!-- Start of picture text --> 2 Less than secondary<br>1.8<br>16<br>xe<br>‘o 14<br>Ba<br>gs<br>om 1.2<br>2©4 28.9% rise in emigration rate<br>5 0.8<br>cod eects.<br>20.6 ecortT S<br>< -- sS<br>o oc<br>O4- -<br>0.2; |- = baseline<br>——~ halved migration cost<br>0<br>20 25 30 35 40 45 50<br>age<br><!-- End of picture text -->

<!-- Start of picture text --> 2 At least some secondary<br>1.8 |<br>16 wl<br>xe ‘ NS 8.6% rise in emigration rate<br>‘o 14 ‘ ‘\<br>= 4<br>§ ‘ ‘ NS<br>oi2f 7 NS<br>D© 4<br>508 .<br>cod<br>20.6<br><<br>ol<br>0.4<br>0.2;|- — baseline<br>——— halved migration cost<br>0<br>20 25 30 35 40 45 50<br>age<br><!-- End of picture text -->

<!-- Start of picture text --> 0.14 Less than secondary<br>— — baseline<br>\ ———~ halved migration cost, all migrants<br>0.12 \ ss halved migration cost, original migrants<br>\ *<br>5 ‘ a<br>xs)=©= 0.080.06 Vue AwAsYa‘Cann 6.8%migrationot reductionduration. in<br><<br>0.04 ‘<br>,<br>0.02 \ ‘ .<br>x<br>~~ .<br>0 =~. 1 .<br>0 5 10 15 20<br>years abroad<br><!-- End of picture text --> <!-- Start of picture text --> 0.12 At least some secondary<br>J - — baseline<br>J ———~ halved migration cost, all migrants<br>0.1 J s+ halved migration cost, original migrants<br>0.08 wed’ i \\<br>sf 4 \ 10.5% reduction in<br>6.S©s 0.06 ’ migrationyoyho"\ oy duration<br>{4<br>0.04 \<br>yO<br>) \<br>0.02. \osAN<br>“Dy .<br>s ~<br>0 ><br>0 5 10 15 20<br>years abroad<br><!-- End of picture text --> <!-- Start of picture text --> 250 Asset repatriation<br>o -~<br>& 200 Mone’<br>s 8 a oe<br>@ 3 150 gu os,<br>a fu a,<br>ks) fa e,<br>~o fou<br>QaDn fyff es%<br>0SOe ffA °<br>2 oof<br>& 50K 7<br>= Y - - baseline<br>a ———~ halved migration cost, all migrants<br>“ «halved migration cost, original migrants<br>0<br>25 30 35 40 45 50<br>age<br><!-- End of picture text --> <!-- Start of picture text -->
1.2 Business creation<br>S 1<br>5 fe ee<br>o o ae Ses,<br>5 2 , ee<br>o : rom<br>5 f / se<br>=wo 0.4 fiFi , ‘<br>rd3 i:a ‘<br>< A 7<br>6 fos<br>0.2 ff - - baseline<br>¢ ———~ halved migration cost, all migrants<br>wad “ «halved migration cost, original migrants<br>0<br>25 30 35 40 45 50<br>age<br><!-- End of picture text -->
<!-- Start of picture text --> 3.4 Domestic expenditure<br>_ uec oct ~.<br>a 3 ¢<br>oo “7<br>ao>BH5529 ae < ’<br>o iS} t “<br>ba 3 Y<br>528 a<br>seo ¢<br>oc o<br>a B 2.7 ¢ 4<br>3D 67<br>ecce LZ A<br>© S26 °<br>Ee0T ge<br>7 - — baseline<br>———~ halved migration cost<br>2.5<br>25 30 35 40 45 50<br>age<br><!-- End of picture text --> <!-- Start of picture text --> 0.35 Welfare ><br>;<br>03 — halved migrationigrati cost ;J welfare gain. of<br>] , 0.056 std. dev.<br>|<br>0.25 1t)|<br>q<br>> 02 p<br>@ /<br>5 y<br>70.15 J<br>y<br>0.1 y<br>/<br>‘<br>0.05<br>0 :<br>-6 -4 -2 0 2 4<br>welfare (standardized to baseline)<br><!-- End of picture text -->
Our results show that the combination of (i) lower initial migration costs, (ii) the resulting increased migration of younger workers, and (iii) shorter duration of migration episodes leads to higher level of accumulated savings, with important implications for business creation in Bangladesh. Panel C of Figure 14 illustrates these effects. The left figure shows a pronounced shift with higher levels of accumulated and repatriated assets at younger ages when the migration cost is halved. Original and new migrants, however, show significant behavioral differences in this respect. Original migrants (dotted line) return earlier, and, thus, they return with a lower level of assets. Once we account for new migrants (solid line), the level of repatriated assets per capita of the Bangladeshi population rises significantly. In addition, these assets are repatriated at younger ages.

Together, the increase in emigration at younger ages and the ensuing rise in repatriated assets early in life are the main reasons why we observe an expansion of self-employment and entrepreneurship. The graph on the right in Panel C of Figure 14 shows that the same 50 percent decrease in migration costs raises business creation, particularly for younger workers. For example, the annual business creation rate increases from 0.7 percent to 1.1 percent for 35-year old workers, the median age for returning migrants. We see that about half of the expansion is due to increased investment by existing migrants, who return earlier in life. The other half derives from new migrants. The overall effect is a sizeable boost in business creation and self-employment.

– All of these effects of lower migration expenses younger migrants, shorter migration – periods, faster accumulation of savings, higher and earlier entry into self-employment jointly contribute to significant welfare gains. We observe a rise in domestic expenditure in Bangladesh (net of migration expenses) over individuals’ life cycles, as shown in the left figure of Panel D of Figure 14. The overall effect is a gain of 0.06 of a standard deviation in the lifetime welfare across the population, including non-migrants. We should note, however, that these gains arise at the higher end of the welfare distribution (right figure of Panel D). There is minimal change on the left tail of the welfare distribution because the very poor still cannot afford to migrate and then move to self-employment, even with a 50 percent decrease in migration costs. We show that it is the middle- and upper-middle-income groups who manage to take advantage of lower migration costs, migrate in higher numbers, become self-employed and realize higher incomes.<sup>32</sup>

> 32Note that welfare gains are lower than income gains, since agents in our model suffer a utility loss while working abroad.

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# **7.2 Decrease in Lending Rates**

Credit constraints faced by workers who would like to seek self-employment and entrepreneurial opportunities are among the main motivations to migrate. Working abroad for higher wages (and under rather demanding conditions), saving some of these extra earnings, and bringing them home enable workers to finance self-employment activities upon their return. A policy-relevant question is what happens to migration, savings, and self-employment levels if domestic credit constraints were to be eased. We model such policy interventions through a 50 percent reduction in the interest rate faced by the workers on self-employment loans.

As expected, an improvement in the credit conditions for self-employment mitigates emigration pressures. As Figure 15 shows, a 50 percent cut in the lending rate on loans to finance a business impacts migration duration and asset repatriation. Panel A reveals that such a policy leads the overall emigration rate to decrease by 5.4 percent, distributed rather evenly across age groups. At the same time, migration duration shrinks by 6.4 percent. In sum, lower borrowing rates for entrepreneurship leads to a decline of migration at both the extensive and intensive margins. A situation in which fewer migrants stay abroad for shorter periods of time, in turn, implies a lower level of repatriated assets. The left figure of Panel B shows that assets repatriated by migrant workers decline by 8.4 percent, and that this effect is stronger for workers over age 35. The endogenous adjustment in migration behavior is due to the fact that financing self-employment through loans has now become less expensive.

These effects are likely to limit part of the positive, direct effect of lower interest rates on business creation. The right figure in Panel B of Figure 15 shows that without any adjustment in migration, the 50 percent cut in lending rates on business loans would boost the annual business creation rate for a 35-year-old worker from 0.7 percent to about 1 percent. The counteracting effect of less migration and lower levels of repatriated savings instead only leaves a net effect of 0.8 percent for workers at that age. Across the life cycle, this accumulates to a 2 percentage point increase in the self-employment rate in Bangladesh. These patterns demonstrate the importance of a joint consideration of migration and selfemployment decisions when designing policies aiming to either foster entrepreneurial activity, or to address international labor migration.

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expectations is on the extensive margin (fewer migrants) than the intensive margin (shorter episodes). The resulting decline in repatriated savings (left figure in Panel B of Figure 16), in turn, leads to a decline in the rate at which new businesses are created, which accumulates to a 0.8 percentage point reduction in self-employment over the life cycle (see the right figure in Panel B). Whereas individual welfare unambiguously benefits from the better choices agents can make under more accurate information about foreign earnings, overall business creation is actually enhanced by individuals’ optimistic expectations.

# **8 Conclusions**

For millions of low-skilled workers from countries in South and Southeast Asia, temporary migration to the high-income countries in the Persian Gulf and East Asia is a critical part of their working lives. Immigrant workers not only earn significantly higher wages while working abroad, but they also accumulate savings that they use to finance self-employment – and entrepreneurial activities upon their return. Workers’ decisions about whether and when to emigrate, where to go abroad, how long to stay, how much to save, and what kind – employment to seek upon their return are intricately linked, as our paper demonstrates.

This paper differs from the previous migration literature in two key respects. First, most papers in the migration literature focus on the wage and labor market gains during the migration episode. Largely due to data constraints, these studies tend to employ cross-sectional analyses to answer their questions. By contrast, our paper highlights the interdependence between workers’ decisions at each stage of the life cycle, and the dynamic effects of temporary migration on workers’ economic activity upon return. Second, the literature usually focuses on the role that economic conditions at the immigration destination play in determining immigrants’ decisions about when to return to their home countries. Our paper takes a different approach to the migration experience by modeling the central role played by self-employment aspirations upon return. These are important distinctions from the previous work. A joint, dynamic analysis of migration and self-employment decisions is crucial. For example, policies that aim to overcome financial constraints to self-employment might become less effective because endogenous adjustments in migration behavior may offset part of the intended on business creation.

Indeed, we show that this is the case. The data that underlie our findings come from a unique survey, _the Bangladesh Return Migrant Survey_ (BRMS), which was specifically designed to investigate the patterns and determinants of the temporary migrants’ lifetime employment trajectories, earnings, and welfare. We use these data to construct a dynamic framework that models the tightly intertwined nature of pre-migration labor market out51
comes, the timing and duration of migration episodes, workers’ savings levels, and their post-return employment outcomes. We show how the savings accumulated abroad enable workers to overcome the credit constraints they face for entrepreneurship at home. We estimate our dynamic model using data from the BRMS and the nationally representative Household Income and Expenditure Survey. Using our estimated model, we then explore the effects of changes in exogenous parameters, such as the cost of migration, and the interest rates on loans. We examine how these changes simultaneously affect migration and investment decisions, and lifetime welfare.

Our analysis of simulated policy changes shows that a decline in migration costs would lead to a rise in emigration and a reduction in the duration of migration which, in turn, contribute to an increase in savings and self-employment. By contrast, a reduction in interest rates for entrepreneurial investment reduces migration and savings levels, undercutting part of the positive effect on self-employment. We find that providing workers with information that corrects widely held, inflated expectations about earnings abroad would reduce the rate and duration of emigration. The result is a decline in repatriated savings and in the rate at which new businesses are created. Though individual welfare benefits from betterinformed choices, overall business creation is reduced by lowering of workers’ exaggerated expectations. Throughout these simulations, we observe changes in both the extensive and intensive margins of migration and investment decisions.

These results confirm that policy makers must take such dynamic externalities into account when designing their policy interventions. Given Bangladesh’s similarities with other migrant sending, lower income countries, the findings of this paper have relevance for lowskilled temporary migrant workers from many countries of origin.

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