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Disentangling the Impact of International Migration on Food and Nutrition Security of Left-Behind Households: Evidence from Bangladesh

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Abstract

This paper explores the linkages between international migration and household food and nutrition security (FNS). First, building on existing literature, we discuss the main microeconomic channels through which international migration may affect household FNS. Second, taking Bangladesh as a case study, we estimate the overall impact of international migration on the FNS of left-behind households. Third, by disentangling the overall effect, we assess the importance of the various microeconomic channels that link international migration to household FNS. The empirical results suggest that international migration has a positive impact on the quantity, quality and variety of food consumed by left-behind households. Our findings also suggest that international migration might be considered among the possible drivers of the so-called Bangladesh paradox, i.e. the exceptional progress in health and nutrition achieved by the country during a period of relatively poor economic performance.

Résumé

Cet article étudie les liens entre la migration internationale et la sécurité alimentaire et nutritionnelle (SAN) des ménages. Premièrement, en s’appuyant sur la littérature existante, nous examinons les principaux canaux microéconomiques par lesquels les migrations internationales peuvent avoir une influence sur la SAN des ménages. Deuxièmement, en prenant le Bangladesh comme étude de cas, nous estimons l’impact global de la migration internationale sur la SAN des ménages les plus démunis. Troisièmement, en distinguant l’effet global, nous évaluons l’importance des différents canaux microéconomiques qui relient la migration internationale à la sécurité alimentaire des ménages. Les résultats empiriques suggèrent que la migration internationale a un impact positif sur la quantité, la qualité et la variété des aliments consommés par les ménages les plus démunis. Nos résultats suggèrent également que la migration internationale pourrait être considérée comme l’un des facteurs possibles du prétendu “paradoxe du Bangladesh”, à savoir les progrès exceptionnels réalisés par le pays en matière de santé et de nutrition pendant une période de performance économique relativement médiocre.

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Notes

  1. The per capita caloric intake gap, which stood at 200 kcal/day in the early 1990s, disappeared by the second half of the 2000s, and the proportion of undernourished people rapidly declined from 37% to 16%. Similarly, the prevalence of stunted and underweight children fell by more than one-third, child mortality dropped by two-thirds, and life expectancy at birth increased by about 10 years (World Bank 2018).

  2. For instance, in a context such as the over-populated rural Bangladesh, which has only recently approached the Lewis turning point (Zhang et al. 2014), reducing the number of household members at home may increase per capita food availability (especially for large subsistence farming households).

  3. A more detailed discussion of household self-selection can be found in Sect. 4.4.

  4. The treatments ‘migrants+returnees’, ‘remittances+returnees’ and ‘migrants+remittances+returnees’ include only 64 households and are not considered in the analysis.

  5. However, these items are relatively few and represent a negligible share of the total caloric intake. For instance, on average, eggs represent only 0.44% of daily caloric intake.

  6. Let us take, for instance, a treated unit with an estimated propensity score (ps) of 0.10 and two potential matches, (a) and (b), with an estimated ps of 0.08 and 0.12, respectively. Since the algorithms usually available in statistical packages (e.g., NN, radius, and kernel) perform matching on the linear distances between the scores, (a) and (b) would be erroneously considered as ‘equally close’ matches, even though, because of the non-linearity of the ps, (b) should be considered closer. By linearizing the distances on which matching is performed, the lps addresses this issue; indeed, in the example above, (b) is correctly identified by the algorithm as a better match than (a).

  7. However, as a robustness check, we report in the Appendix (Table 12) the estimates of the overall impact of international migration obtained including regional dummy variables (panel A) and the regional food poverty line (panel B) among the matching covariates. The results do not change significantly.

  8. The number of possible treatment effects is given by \(P(2,n)=k!/(n-2)!\), where n is the number of treatment states.

  9. The empirical literature on migration has usually taken the household structure as exogenous with the exception of newly born members. Yet, as recently pointed out by Bertoli and Murard (2019), this may not always be the case. Indeed, using a panel of Mexican households, they found that migrant-sending households are more likely to receive a new member in the months following the migration episode. They also point out, however, that controlling for this issue is difficult because of the way standard survey questions are formulated, therefore calling for a revision of household questionnaires. While we acknowledge that endogenous household recomposition may introduce a bias into our estimates, we also argue that our only option is to stick with the exogeneity assumption because of the lack of specific survey questions in the HIES 2010. Otherwise, we should drop all the variables that are associated with characteristics of the household members.

  10. In the case of migrant households, the dummies take a value of 1 only if the business was already running before migration.

  11. The reason for choosing the nearest neighbour is mainly practical: this estimator attaches integer weights to matched units and therefore it makes easier to handle the control group.

  12. The p-value of the test is 34.1%. As a further check, we also dropped all the households that reported internal migrants and re-estimated the effect of international migration on household FNS, obtaining not statistically different results.

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Acknowledgements

We would like to thank the two anonymous referees for their careful reading of our paper, constructive comments and helpful suggestions.

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  1. Department of Economics and Management, University of Florence, Firenze, Italy

    Donato Romano & Silvio Traverso

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Appendix

Appendix

See Tables 9, 10, 11, and 12.

Table 9 Within-stratum probit regressions
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Table 10 Probit regressions for multiple treatment analysis
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Table 11 ATT of international migration (alternative matching estimators)
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Table 12 ATT of international migration (alternatives to stratification)
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Romano, D., Traverso, S. Disentangling the Impact of International Migration on Food and Nutrition Security of Left-Behind Households: Evidence from Bangladesh. Eur J Dev Res 32, 783–811 (2020). https://doi.org/10.1057/s41287-019-00240-4

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