Seasonal migration within low-income countries

This is a writeup of a shallow investigation, a brief look at an area that we used to decide how to prioritize further research in our work on GiveWell Labs, which is now a separate organization called the Open Philanthropy Project. This report also appears on the Open Philanthropy Project website.

In a nutshell

  • What is the problem? Domestic seasonal migration may carry large benefits for migrants and their families, but some people who might benefit from migration may not be taking part.
  • What are possible interventions? A funder might support or expand the micro-credit or conditional cash transfer migration support programs that have been studied in Bangladesh, or fund more studies of the impact of such programs in different settings to help address the question of generalizability.
  • Who else is working on it? The authors of the one randomized study to date are planning replications in other countries. It is unclear whether the partner organization from the study will be expanding the program studied to other parts of Bangladesh.

Published: April 2013

Table of Contents

What is the problem?

Seasonal migration from rural to urban areas in developing countries may carry large benefits for the migrants and their families under certain circumstances.1 However, some people who might be able to benefit from domestic migration may not be taking part.

We know of one randomized controlled trial on this topic, which has driven our interest in the area. It was conducted in Rangpur, a region of rural Bangladesh that persistently suffers from pre-harvest famines.

A randomized study of incentives to migrate

Bryan, Chowdhury, and Mobarak ran a multi-arm cluster-randomized trial with 100 villages:2

  • 16 villages received information about jobs in potential destination areas, but no subsidies of any kind.3
  • 37 villages were offered cash worth $8.50 (a little more than the cost of round-trip transportation to the destinations) at the origin if they decided to migrate, with another $3 paid if the migrant checked in with the researchers at the destination, and were given the same information as the group above.4
  • 31 villages were offered 0% interest limited-liability loans in the same amounts as the cash transfers, along with the same information.5
  • 16 villages served as the control group.6

Both the 0% interest loans and the conditional cash transfers led to large increases in seasonal migration, while “information only” treatment had no apparent effect,7 leading the authors to combine the cash and credit groups and information only and control groups into “incentivized” and “non-incentivized” groups, respectively, for the remainder of their analysis.8

In the year the study was conducted, incentives led to a 22 percentage point increase in migration on a baseline level of 36% in the non-incentivized group, a 60% increase.9 In the following year, when incentives were no longer provided, migration was still 10 percentage points higher in the villages that had previously received cash or credit incentives. Three years later, an 8 percentage point increase persisted in these villages.10 This increase in seasonal migration led to large and well-identified gains for the families of migrants at the origin, who benefit from remittances:11

  • 37% increase in expenditures on food12
  • 37% increase in total expenditures (including food expenditures)13
  • 38% increase in calories consumed (from a control mean of 2,061/day)14

All of these effects are statistically significant at the 5% level, and persist (statistically significantly, though with attenuation) in the following year even after incentives are removed.15

The authors are unable to estimate a direct financial return on investment from the incentives because individuals who did not migrate typically do not work for wages (instead working in self-employment, agriculture or entrepreneurship, which makes estimating control group income difficult).16 They nonetheless find migrants induced by the experiment earn an average of $105 at the destination, most of which is saved or remitted, potentially suggesting high rates of return to the original $8.50+$3 incentive to migrate.17 Another approach to estimating the financial returns to the migration incentive is to impute such returns from consumption gains: the authors report an increase in consumption at the source worth about $20/family/month,18 though it's unclear whether these results were measured during the famine season and how much they might generalize beyond the month of the survey.19 Finally, non-experimentally comparing earnings between incentivized migrants and non-migrants who earned wages or profits, the authors observe a gap of approximately $36 in income.20 Summarizing across these disparate estimates, the $8.5+$3 investment appears to carry a ~2-6x financial return, not counting any benefits from migration during later seasons.21

The authors argue that it was risk aversion on the part of potential migrants that prevented them from realizing these benefits prior to the intervention.22

Although the returns to induced migration appear to have been large in this case, we are not sure how generalizable the results are, as the study took place in an area that may have been unusually appropriate for the intervention:

  • Despite the frequency of seasonal famines, agricultural laborers from the region of the study may seasonally migrate at lower rates than those from other regions of Bangladesh (though this appears to be contested).23
  • The variation in income and poverty between Rangpur and other areas of Bangladesh appears to be much higher than the variation across seasons within Rangpur, which means that migration may be a more appropriate approach than services like microfinance that facilitate consumption smoothing.24
  • Existing government and NGO efforts in the region focused on direct subsidy programs rather than migration support.25

What are possible interventions?

We see several avenues for a potential funder interested in this area to get involved:

  1. Funding further experimental research on the returns to migration or mechanisms for encouraging migration in other settings. We haven't investigated this issue, but have the impression that such a study might cost on the order of $500,000-$1,000,000.
  2. Supporting or scaling the program in Bangladesh that was studied (including conditional cash transfers or micro-credit loans).
  3. Advocating to countries, funders, or NGOs to scale up the program to other settings.

We don't have a sense of how these opportunities might stack up in cost or likely returns, though our intuition is that supporting further research is likely the appropriate first step.

Who else is working on this?

We spoke with Mushfiq Mobarak, one of the authors of the paper describing the randomized controlled trial in Bangladesh, about what other activities are being undertaken. He reported:26

Replication of Professor Mobarak's study on seasonal migration in Bangladesh

We are pursuing opportunities to replicate our studies in other areas. AusAID has expressed an interest in doing a similar study in Indonesia, and we've had early conversations with a foundation about funding a replication in a couple countries in southern Africa, which also have hunger seasons, though those conversations have been more preliminary...

Scaling within Bangladesh

In Bangladesh, the program from the original study was implemented in partnership with the Palli Karma Shohayok Foundation – the umbrella organization for micro-credit NGOs in Bangladesh. PKSF has a very positive approach to all of this because it has their stamp on it. The microfinance groups are generally busy with their main business of doing microcredit with frequent repayment, which forces borrowers to stay at the origin rather than migrate. The kinds of loan programs we're talking about, with less regular repayment periods, are outside of their regular way of doing business, so I think it will require a little more pushing.

On the government side, I haven't seen as much interest.

Professor Mobarak directed us to some other scholarship on the returns to seasonal migration but was not aware of any other experiments or NGO efforts aimed at supporting domestic seasonal migration in low-income countries.27

Questions for further investigation

Our research in this area has been relatively limited, and many important questions remain unanswered by our investigation.

Amongst other topics, our further research on this cause might address:

  • non-experimental estimates of the returns to seasonal migration from other settings (to see whether they comport with the findings from Bangladesh).
  • financial costs and potential humanitarian benefits of replicating or scaling the research conducted by Bryan, Chowdhury, and Mobarak.
  • the existing level of funding that is available for research in this area.


  • 1

    “We estimate large returns: migration induced by our intervention increases food and non-food expenditures of migrants’ family members remaining at the origin by 30-35%, and improves their caloric intake by 550-700 calories per person per day. On an initial investment of about $6-$8 (the average round-trip cost to a destination), migrants earn $110 on average during the lean season and save about half of that, suggestive of a very high rate of return on investment. Most strikingly, households in the treatment areas continue to migrate at a higher rate even after the incentive is removed. The migration rate is 10 percentage points higher in treatment areas a year later, and this figure drops only slightly to 8 percentage points 3 years later.” Bryan, Chowdhury, and Mobarak 2011, pg 2.

  • 2

    Bryan, Chowdhury, and Mobarak 2011, pgs 9-11.

  • 3

    “A further 16 villages (consisting of another 304 sample households) were placed in a job information only treatment. These households were given information on types of jobs available in four pre-selected destinations, the likelihood of getting such a job and approximate wages associated with each type of job and destination (see Appendix 1 for details).” Bryan, Chowdhury, and Mobarak 2011, pg 10.

  • 4

    “703 households in 37 randomly selected villages were offered cash of 600 Taka (~US$8.50) at the origin conditional on migration, and an additional bonus of 200 Taka (~US$3) if the migrant reported to us at the destination during a specified time period. We also provided exactly the same information about jobs and wages to this group as in the information-only treatment. 600 Taka covers a little more than the average round-trip cost of safe travel from the two origin districts to the four nearby towns for which we provided job information. We monitored migration behavior carefully and strictly imposed the migration conditionality, so that the 600 Taka intervention was practically equivalent to providing a bus ticket.” Bryan, Chowdhury, and Mobarak 2011, pgs 10-11.

  • 5

    “The 589 households in the final set of 31 villages were offered the same information and the same Tk 600 + Tk 200 incentive to migrate, but in the form of a zero-interest loan to be paid back at the end of the monga season. The loan was offered by our partner micro-credit NGOs that have a history of lending money in these villages. There is an implicit understanding of limited liability on these loans since we are lending to the extremely poor during a period of financial hardship. As discussed below, ultimately 80% of households were able to repay the loan. ” Bryan, Chowdhury, and Mobarak 2011, pg 11.

  • 6

    "16 of the 100 study villages (consisting of 304 sample households) were randomly assigned to form a control group." Bryan, Chowdhury, and Mobarak 2011, pg 10.

  • 7

    Bryan, Chowdhury, and Mobarak 2011, Table 2, PDF page 60, shows the migration rate in 2008 as:

    • Cash: 59.0%
    • Credit: 56.8%
    • Information only: 35.9%
    • Control: 36%

  • 8

    "The migration response to the cash and credit incentives are statistically significant relative to control or information, but there is no statistical difference between providing cash and providing credit.13 Since households appear to react very similarly to either incentive, we combine the impact of these two treatments for expositional simplicity (and call it “incentive”) for much of our analysis, and compare it against the combined information and control groups (labeled “non-incentive”)." Bryan, Chowdhury, and Mobarak 2011, pg 13.

    This kind of post-hoc grouping would typically lead us to worry about data-mining, but we don't view it as a particular cause for concern in this case, since the distinction between “incentivized” and “non-incentivized” groups strikes us as intuitively plausible, and collapsing groups together, as opposed to splitting them into subgroups, is typically less of a cause for concern.

  • 9

    “About a third (35.9%) of households in control villages sent a seasonal migrant. Providing households information about wages and job opportunities at the destination had no effect on the migration rate (the difference in point estimate is 0.0% and is quite tightly estimated). Either households already had the information that we made available to them, or the information we made available was not useful or credible. With the $8.50 (+$3) cash or credit treatments, the seasonal migration rate jumps to 59.0% and 56.8% respectively. In other words, incentives induced about 22% of the sample households to send a migrant.12 The migration response to the cash and credit incentives are statistically significant relative to control or information, but there is no statistical difference between providing cash and providing credit.13 Since households appear to react very similarly to either incentive, we combine the impact of these two treatments for expositional simplicity (and call it “incentive”) for much of our analysis, and compare it against the combined information and control groups (labeled “non-incentive”).” Bryan, Chowdhury, and Mobarak 2011, pgs 12-13.

  • 10

    “The lower panel of table 2 compares re-migration rates in subsequent years across the incentive and non-incentive groups. We conducted follow-up surveys in December 2009 and in July 2011 and asked about migration behavior in the preceding lean seasons, but we did not repeat any of the treatments in the villages used for the comparisons in the top half of table 2. Strikingly, the migration rate in 2009 was 10 percentage points higher in treatment villages, and this is after the incentives were removed. Regressions of the re-migration choice (discussed in detail in section 6) shows that if a particular household was induced to migrate in 2008, that roughly doubles the chance (a 45 percentage point effect) that it will send a migrant again in 2009. The July 2011 survey focused on migration during the other (lesser) lean season that coincides with the pre-harvest period for the second (lesser) rice harvest. Even two and a half years later, without any further program or incentive, the migration rate remains 8% higher in the villages randomly assigned to the cash or credit treatment in 2008.” Bryan, Chowdhury, and Mobarak 2011, pg 13.

  • 11
    Bryan, Chowdhury, and Mobarak 2011, Table 4, PDF pg 62, columns 4 and 5. We report results from the authors' instrumental variables estimates with full controls, which are meant to represent the effect of migration on those who the program causes to migrate (as opposed to the average effect across all study participants, including the majority who did not migrate as a result of the program).
  • 12
    • 260.139 takas/person/month increase in food expenditures
    • on a control group mean of 702.9 takas/person/month
    • 260.139/ 702.9 = 37.0%

  • 13
    • 355.115 takas/person/month increase in total expenditures
    • on a control group mean of 954.1 takas/person/month
    • 355.115/954.1 = 37.2%

  • 14
    • 788.118 increase in calories consumed per person per day
    • on a control group mean of 2060.5 calories per person per day
    • 788.118/2060.5 = 38.2%

  • 15

    Bryan, Chowdhury, and Mobarak 2011, Table 4, PDF pg 62, column 6.

  • 16

    “It is difficult to infer the income these migrants would have received had they not migrated, since we do not have comparable measures of wages and earnings for non-migrants (who engage in a variety of agricultural, self-employment and entrepreneurial tasks at the origin).” Bryan, Chowdhury, and Mobarak 2011, pg 19.

  • 17

    “Next we examine the data on migrants’ earnings and savings at the destination to see whether the magnitude of consumption gains we observe at the origin are in line with the amount migrants earn, save and remit. Table 5 shows that migrants earn about $110 (7777 Taka) on average and save about half of that. The average savings plus remittance is about a dollar a day. Remitting money is difficult and migrants carry money back in person, which is partly why we observe multiple migration episodes during the same lean season. Therefore, joint savings plus remittances is the best available indicator of money available for consumption at the origin. The destination data suggest that this amount is about $66 (4600 Taka).
    The “regular” migrants in the control group earn more per episode, save and remit more per day relative to migrants in the treatment group. This is understandable, since the migrants we induce are new and relatively inexperienced in this activity. Even though the induced migrants had lower earning potential, they earned $105 on average and saved and remitted more than half of that, which suggests a very high rate of return on the $8.50 incentive.” Bryan, Chowdhury, and Mobarak 2011, pgs 18-19.

  • 18
    • “In terms of magnitude of effects, monthly consumption expenditures among migrant families increase by about $5 per person, or $20 per household due to induced migration. Our survey only asked about expenditures during the second month of monga, and the modal migrant in our sample had not yet returned from their current migration episode (which includes cases where they may have returned once, but left again). We therefore expect the effects to persist for at least another month, and the total expenditure increase therefore easily exceeds the amount of the treatment ($8.50). Furthermore, if households engage in consumption smoothing, then some benefits may persist even further in the future. In any case, the $8.50 is spent two months prior on transportation costs.” Bryan, Chowdhury, and Mobarak 2011, pg 17.
    • Table 4 (PDF page 62) shows an increase in consumption of 355 takas/person/month. Families average four members (Table 1, PDF page 59), which implies an increase in consumption of approximately 1,400 takas/month. Using the same 600 takas = $8.50 exchange rate, this implies an additional $19.83 in monthly consumption for families with a member induced to migrate. Based on the quoted calculation above, we assume that the authors include the migrants as family members both in calculating the increased per-capita consumption and in calculating family size.

  • 19
    • The authors report, “Our survey only asked about expenditures during the second month of monga, and the modal migrant in our sample had not yet returned from their current migration episode (which includes cases where they may have returned once, but left again). We therefore expect the effects to persist for at least another month.” Bryan, Chowdhury, and Mobarak 2011, pg 17.
    • However, elsewhere they state that the survey was conducted in December 2008, after the monga season ended: “We have 2008 migration data from two follow-up surveys, one conducted immediately after the monga ended (in December 2008), and another in May 2009.”
    • See also Khandker and Mahmud 2012, pg 164, noting “The seasons are boro (March–May), aus (June−August), monga (September−November), and aman (December−February).”

    Since the survey was conducted in the month after monga was over, it's not clear how much its results generalize to consumption during monga itself or to other time periods more broadly.

  • 20
    • “Observed migrant earnings at the destination (100 Taka per day on average) do compare favorably to the earnings of the sub-sample of non-migrants with salaried employment at the origin (65 Taka per day) and to the profits of small-business entrepreneurs at the origin (61 Taka per day). This comparison is on the basis of a selected sample of migrants and non-migrants with employment, but it is informative about the source from which the extra consumption among migrant households is derived.
      While all our data suggest that the extra consumption at the origin is primarily related to migrant earnings, savings and remittance, it is possible that some intensive margin effects (e.g. a switch towards protein or child expenditures) are realized because the husband is away, and there are intra-household gender differences in spending priorities (Thomas, 1994; Duflo, 2003; G. Miller, 2008). However, the intra-household mechanism is unlikely to explain the overall consumption gain (aggregating across food and non-food expenditures). Another possibility is that the overall caloric requirement increases because the migrant works harder, but that also does not directly explain the consumption gain among household members remaining at the origin.” Bryan, Chowdhury, and Mobarak 2011, pgs 19-20.
    • Bryan, Chowdhury, and Mobarak 2011, Table 5, PDF page 63, shows that incentivized migrants earned an average of 96.09 takas/day, and the fine print at the bottom of the table states that “[a]verage migration duration [is] 76 days.” Multiplying these figures implies an average of 7,302 takas of total earnings, which corresponds fairly well to the “total earnings by household” figure of 7,451 takas displayed in the table.
    • Splitting the difference of 65 takas/day and 61 takas/day to get an expected counterfactual average earnings of 63 takas/day and multiplying by the 76 day average duration of migration suggests a counterfactual average earnings of 4,788 takas.
    • 7,302 takas – 4,788 takas = 2,514 takas, which, at $8.5 = 600 takas, is about $36.

  • 21

    The lower bound comes from the $20/family/month estimate, conservatively assuming that the benefits are experienced only in the month of the surveys, while the upper bound comes from the $66 remitted or saved estimate.

  • 22

    “Our theory takes the view that the poor are not able to take advantage of a profitable opportunity because they are “vulnerable” and afraid of losses (Banerjee, 2004). This is closely related to the conceptualization of poverty in several other models (Kanbur, 1979; Kihlstrom & Laffont, 1979; Banerjee & Newman, 1991). The Monga setting therefore provides an opportunity to test the “poverty as vulnerability” theory, and we return to our data and conduct a new round of experiments to test five new implications that are drawn from our model.
    First, households that are close to subsistence – on whom experimenting with a new activity imposes the biggest risk – should start with lower migration rates, but should be the most responsive to our intervention. Second, the incentive should have a larger effect on households that do not have network connections at the destination, because they have more to learn about the destination. Third, households should exhibit learning about migration opportunities and destinations in their subsequent choices on whether and where to re-migrate. Specifically, households with successful outcomes should be more likely to re-migrate, especially to the destinations where we originally induced them to migrate. Fourth, because fear of the disastrous negative outcome is the key aversion preventing migration, offering a limited liability loan to migrate should have a similar effect on migration rates as a conditional grant. Fifth, migration should be more responsive to incentives (e.g. credit conditional on migration) than to unconditional credit, because the latter also improves the returns to staying at home.5 We find support for all of these predictions using our data and a new round of treatments.
    Although we do conduct a new round of experiments to test two of the model’s implications, many of these other results are identified through heterogeneity in treatment effects, and are therefore not experimental. There are legitimate omitted variables concerns with the risk- aversion interpretation we provide of the observation that households that are close to subsistence are more responsive to our incentive. That result could be driven by other characteristics correlated with low income, such as behavioral attributes that make certain households liquidity constrained on a regular basis (Banerjee & Mullainathan, 2010; Duflo et al., 2011). Our claim is not that there are no other possible explanations for our findings, but rather that, taken as a whole, the results are consistent with, and highly suggestive of, the model we propose.” Bryan, Chowdhury, and Mobarak 2011, pgs 4-5.

  • 23
    • “Several puzzling stylized facts about household and institutional characteristics and coping strategies motivate the design of our migration experiments. First, seasonal out-migration from the monga-prone districts appears to be low despite the absence of local non-farm employment opportunities. According to the nationally representative HIES 2005 data, it is more common for agricultural laborers from other regions of Bangladesh to migrate in search of higher wages and employment opportunities, and this is known to be one primary mechanism by which households diversify income sources in India (Banerjee & Duflo, 2007).” Bryan, Chowdhury, and Mobarak 2011, pgs 8-9.
    • However, see Khandker and Mahmud 2012, pgs 103-104, “As discussed in chapter 4 in this volume, seasonal migration was found to be the single most important coping mechanism adopted in response to monga (seasonal hunger) by poor households in the Rangpur region. Of the nearly half million poor households covered by the Institute of Microfinance (InM) baseline survey of 2006, about 35 percent reported having resorted to migration during the monga season. The InM follow- up survey of 2008 found considerable out-migration among the workers of the region in the non-monga seasons as well; furthermore, most of the migration was found to be seasonal or temporary.
      National-level data also clearly show the exceptionally high levels of interdistrict out-migration from the Rangpur region. The findings of the official Agricultural Sample Survey 2005 are particularly revealing. Of all the country’s agricultural workers who worked in agricultural and nonagricultural jobs outside their home districts, those originating from the five districts in Rangpur accounted for nearly one-half and one-quarter, respectively, although the region accounted for only 11 percent of the country’s population (see BBS 2010, 268).2 Although the nature of migration is unknown, most of the migration captured in such a survey is likely to be temporary or seasonal.”
    • The apparent contrast between the migration rates reported by these two sources for the Rangpur region may arise from mere differences across surveys or from Bryan, Chowdhury, and Mobarak's focus on agricultural laborers.

  • 24
    • “Second, inter-regional variation in income and poverty between Rangpur and the rest of the Bangladesh have been shown to be much larger than the inter-seasonal variation within Rangpur (Khandker & Mahmud, forthcoming). This suggests smoothing strategies that take advantage of inter-regional arbitrage opportunities (i.e. migration) rather than inter-seasonal variation (e.g. savings, credit) may hold greater promise. Moreover, an in-depth case-study of the Monga phenomenon (Zug, 2006) explicitly notes that there are off-farm employment opportunities in rickshaw-pulling and construction in nearby urban areas during the monga season. To be sure, Zug (2006) points out that this is a risky proposition for many, as labor demand and wages drop all over rice-growing Bangladesh during that season. However, this seasonality is less pronounced than that observed in Rangpur (Khandker & Mahmud, forthcoming).” Bryan, Chowdhury, and Mobarak 2011, pg 9.
    • See Khandker and Mahmud 2012, pgs 48-49, Figures 3.8-3.9.

  • 25

    “Finally, both government and large NGO monga-mitigation efforts have concentrated on direct subsidy programs like free or highly-subsidized grain distribution (e.g. “Vulnerable Group Feeding,”), or food-for-work and targeted microcredit programs. These programs are expensive, and the stringent micro-credit repayment schedule may itself keep households from engaging in profitable migration (Shonchoy, 2010). There are structural reasons associated with rice production seasonality for the seasonal unemployment in Rangpur, and thus encouraging seasonal migration towards where jobs are appears to be a sensible complementary policy to experiment with.” Bryan, Chowdhury, and Mobarak 2011, pg 9.

  • 26
    GiveWell, "Notes from Phone Conversation with Mushfiq Mobarak (11/08/12).”
  • 27

    Other researchers working on migration issues

      Dean Yang and David McKenzie are doing some experimental research on international migration, which has potentially larger returns than domestic migration. I'm not aware of anyone else doing experiments on domestic migration.

        In the non-experimental realm, Kathleen Beegle and Stefan Dercon found that returns to moving in Tanzania are 36% of consumption, and other researchers have found similar effects in India.

          Other areas that GiveWell should consider exploring

            I think both the practice and research of development might be too focused on rural areas. Poor people do tend live in rural areas and work in agriculture, so it might make sense to focus there, but that takes a static view of the world. It's important to think about how to help people move to opportunities, not just fix their current situations. So I think that one area that might need more work is urban development related to migration.”

              GiveWell, "Notes from Phone Conversation with Mushfiq Mobarak (11/08/12).”