Abstract
Research has insufficiently addressed the role of financial inclusion in migration decisions. Financial inclusion empowers people and provides the means to improve their own lives. We explore this relationship using data from the 2014 and 2017 waves of the Gallup World Poll Survey, which includes the first global measure of individual-level financial inclusion. Using a series of binary-choice models with sample selection, we find that financial inclusion meaningfully affects both intentions to migrate and preparations to do so. The likelihood of migration intentions and preparations increase with having an account, access to a debit card and the ability to make internet payments and to send and receive remittances. Saving for educational purposes, the ability to take out a loan, and savings via a savings club are also critical factors in converting migration intentions to preparations. Results show heterogeneous effects across rural and urban locations. Our findings imply that financial inclusion increases respondents’ ability to finance and save for migration costs and may increase their ability to utilize social networks for migration purposes. This has potentially important policy implications for policymakers seeking to achieve the Sustainable Development Goals, specifically targets 1.4, 8.10, and 10.7.
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Notes
We do not use 2011 data since the 2014 and 2017 waves asked far more detailed questions about the characteristics of respondents’ financial access.
GWP also includes territories such as Northern Cyprus and Palestine, but for the sake of this paper we refer to them as countries.
In some of the largest countries more interviews take place. For example, in 2014 researchers interviewed 3,000 and 5000 individuals in India and China, respectively.
Appendix A presents a list with all countries in our data set.
Household income is initially reported in the individual’s local currency (Gallup 2016). Local currency is then converted to international dollars using the World Bank’s PPP (2011) private consumption conversion factor to make household income comparable across countries. Respondents who have difficulty answering the income question are presented a set of ranges in local currency and are asked which group they fall into (Gallup 2016). This measure also relies on multiple imputation methodology to replace missing values.
The country-fixed effects specification (including a dummy variable for each country) allows us to control for the cross-sectional variation related to unobserved heterogeneity associated with individuals in each country. However, fixed effects probit models may produce biased estimates and standard errors due to the incidental parameter problem (i.e., in nonlinear models, as the number of groups increases towards infinity, the number of estimated parameters increases at the same rate, which may produce inconsistent estimates). However, consistent estimates depend on a sufficiently large number of observations per origin country (Ruyssen & Salomone, 2018). While no consensus exists as to a definitive number of observations that reliably overcomes the incidental parameter problem, the heuristic is generally 50 observations per group. In our sample, there are an average of 1,900 migration intentions and 25 migration preparations per origin country.
The index is the mean of valid items multiplied by 100 and is only calculated if respondents had more than 7 out of 10 valid scores. For more information on the construction of the EB index see Gallup (2016).
Sample weights provided by Gallup are used to estimate all descriptive statistics but are not used in the regression analyses. In the regressions we do not include the weights because the weights are constructed using our control variables. Gallup uses population statistics to weight the data by gender, age, and when possible, education or socioeconomic status (Gallup, 2016). When using these control variables in a regression, the survey weights become redundant and worse, may add statistical imprecision (Solon et al. 2015).
We tested the estimated coefficients and found evidence that the covariates varied significantly by rurality, justifying the location-separated analysis.
Further discussion on the descriptive statistics can be found in Appendix D.
Results are available upon request from the authors. Our results are robust to using only the first principal component or equally weighting the six principal components in the index.
The effect of having an account could be related to sending and receiving remittances, which may reduce the importance of the effect of having an account on migration decisions. We find a small, significant correlation between having an account and sending (ρ = 0.239) and receiving (ρ = 0.132) remittances.
References
Amemiya T (1984) Tobit models: a survey. J Econ 24(1–2):3–61
Baborska R, Hernandez E, Magrini E, Morales-Opazo C (2017) The impact of financial inclusion on rural food security experience: a perspective from low-and middle-income countries. Mimeo.
Banerjee B (1983) Social networks in the migration process: empirical evidence on chain migration in India. J Dev Areas 17(2):185–196
Beine M, Parsons C (2015) Climatic Factors as Determinants of International Migration. Scandinavian J Econ 117(2):723–767
Beine M, Salomone S (2013) Network effects in international migration: education versus gender. Scand J Econ 115(2):354–380
Beine M, Docquier F, Özden Ç (2011) Diasporas. J Dev Econ 95(1):30–41
Beine M, Docquier F, Özden Ç (2015) Dissecting network externalities in international migration. J Demogr Econ 81(4):379–408
Bekaert E, Ruyssen I, Salomone S (2021) Domestic and international migration intentions in response to environmental stress: A global cross-country analysis. J Demogr Econ 87:383–436
Belot MV, Hatton TJ (2012) Immigrant Selection in the OECD. Scand J Econ 114(4):1105–1128
Bertoli S, Ruyssen I (2018) Networks and migrants’ intended destination. J Econ Geogr 18(4):705–728
Borjas G (1987) Self-Selection and the Earnings of Migrants. Am Econ Rev 77(4):531–553
Castelli F (2018) Drivers of migration: why do people move? J Travel Med 25(1):1–7
Cattaneo C, Peri G (2016) The migration response to increasing temperatures. J Dev Econ 122:127–146
Chort I (2014) Mexican migrants to the US: what do unrealized migration intentions tell us about gender inequalities? World Dev 59:535–552
Demirgüç-Kunt A, Klapper LF, Singer D, Van Oudheusden P (2015) The global findex database 2014: Measuring financial inclusion around the world. World Bank Policy Res Work Pap (7255)
Dimova R, Wolff F-C (2015) Remittances and chain migration: longitudinal evidence from Bosnia and Herzegovina. J Dev Stud 51(5):554–568
United Nations, Population Division (2016) International migration report 2015: Highlights (Vol. ST/ESA/SER.A/375)
Docquier F, Rapoport H (2012) Globalization, brain drain, and development. J Econ Literature 50(3):681–730
Docquier F, Lohest O, Marfouk A (2007) Brain drain in developing countries. World Bank Econ Rev 21(2):193–218
Docquier F, Peri G, Ruyssen I (2014) The Cross-country determinants of potential and actual migration. Int Migr Rev 48(S1):S37–S99
Docquier F, Tansel A, Turati R (2020) Do Emigrants self-select along cultural traits? Evidence from the MENA countries. Int Migr Rev 54(2):388–422
Dustmann C, Okatenko A (2014) Out-migration, wealth constraints, and the quality of local amenities. J Dev Econ 110:52–63
Ferraro PJ, Miranda JJ (2014) The performance of non-experimental designs in the evaluation of environmental programs: a design-replication study using a large-scale randomized experiment as a benchmark. J Econ Behav Organ 107:344–365
Gallup (2016) Worldwide research methodology and codebook
Gammage S, Stevanovic N (2016) Gender, migration, work and care deficits: what role is there for the SDGs (pp. 26–28). Presented at the background paper prepared for the United Nations women expert group meeting on Women’s economic empowerment in the changing world of work. Geneva.
Grogger J, Hanson GH (2011) Income maximization and the selection and sorting of international migrants. J Dev Econ 95(1):42–57
Heckman J (1979) Sample selection bias as a specification error. Econom J Econom Soc 153–161
Kanaiaupuni SM (2000) Reframing the migration question: An analysis of men, women, and gender in Mexico. Soc Forces 78(4):1311–1347
Karlan D, Ratan AL, Zinman J (2014) Savings by and for the poor: a research review and agenda. Rev Income Wealth 60(1):36–78
Kugler M, Rapoport H (2011) Migration, FDI, and the Margins of Trade. CID Working Paper, No. 222
Leeves G (2009) Migrations plans and received remittances: Evidence from Fiji and Tonga. Int Migr Rev 43:160–177
Manchin M, Orazbayev S (2018) Social networks and the intention to migrate. World Dev 109:360–374
McKenzie D, Rapoport H (2007) Network effects and the dynamics of migration and inequality: theory and evidence from Mexico. J Dev Econ 84(1):1–24
Meng C-L, Schmidt P (1985) On the cost of partial observability in the bivariate probit model. Int Econ Rev, 71–85
Munshi K, Rosenzweig M (2009) Why is mobility in India so low? Social insurance, inequality, and growth. National Bureau of Economic Research
Muralidharan K, Niehaus P, Sukhtankar S (2016) Building state capacity: evidence from biometric smartcards in India. Am Econ Rev 106(10):2895–2929
Nikolova M, Graham C (2015) In transit: The well-being of migrants from transition and post-transition countries. J Econ Behav Organ 112:164–186
Polgreen L, Simpson N (2011) Happiness and international migration. J Happiness Stud 12:819–840
Ruyssen I, Salomone S (2018) Female migration: A way out of discrimination? J Dev Econ 130:224–241
Schiff M, Morrison AR (2007) The international migration of women. World Bank Publications
Smith MD, Floro MS (2020) Food insecurity, gender, and international migration in low-and middle-income countries. Food Policy 91:101837
Smith MD, Wesselbaum D (2022) Food insecurity and international migration flows. Int Migr Rev 56(2):615–635
Solon G, Haider SJ, Wooldridge JM (2015) What Are We Weighting For? J Hum Resour 50(2):301–316
Stuart G (2016) Government-to-person transfers: on-ramp to financial inclusion? Center for Financial Inclusion, Washington, DC
Stuart EA (2010) Matching methods for causal inference: A review and a look forward. Stat Sci Rev J Inst Math Stat, 25(1):1
Udry C (1994) Risk and insurance in a rural credit market: An empirical investigation in northern Nigeria. Rev Econ Stud 61(3):495–526
Udry C (1990) Credit markets in Northern Nigeria: Credit as insurance in a rural economy. The World Bank Economic Review, 251–269
Van Dalen H, Groenewold G, Fokkema T (2005a) Remittances and their effect on emigration intentions in Egypt. Morocco Turkey Popul Stud 59(3):375–392
Van Dalen H, Groenewold G, Fokkema T (2005b) Out of Africa: What drives the pressure to emigrate? J Popul Econ 18:741–778
World Bank Group (2017). Good practices for financial consumer protection, 2017 Edition. World Bank, Washington, DC
Zimmerman JM, Baur S (2016) Understanding how consumer risks in digital social payments can erode their financial inclusion potential. CGAP Brief, Consultative Group to Assist the Poor, Washington, DC
Acknowledgements
We would like to thank two anonymous referees for helpful comments and suggestions. We are grateful to Gallup, Inc. and The World Bank, for access to the Gallup World Poll and Financial inclusion data. We also wish to thank Chris Barrett for comments and suggestions. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official NOAA or U.S. Government determination or policy.
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Smith, M.D., Wesselbaum, D. Financial inclusion and international migration in low- and middle-income countries. Empir Econ 65, 341–370 (2023). https://doi.org/10.1007/s00181-022-02331-4
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DOI: https://doi.org/10.1007/s00181-022-02331-4