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Determinants of rural youth migration throughout the developing world

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PIM Webinar with Dr. Alan de Brauw, IFPRI. More information and recording available at http://bit.ly/2Wozd2i

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Determinants of rural youth migration throughout the developing world

  1. 1. Motivation: Voluntary migration plays central role in development ▪ Countries with higher GDP have lower share of labor in agriculture oMigrants may go to either urban or rural areas
  2. 2. Illustration: GDP and share of labor in agriculture ALB ARG ARM AUS AUT AZE BEL BGR BIH BLR BLZ BMU BOL BRA BRB BRN BTN CAN CEB CHE CHL CHN CIV COL CRI CUB CYP CZE DEU DNK DOM DZA EAP EASECA ECS ECU EGY EMU ESP EST ETH EUU FINFRAGBR GEO GMB GRC GTM HIC HND HRVHUN IBD IDN IRL IRN ISL ISR ITA JAM JPN KAZ KGZ KOR LAC LCA LCN LKA LTE LTU LUX LVA MAC MAR MDA MDG MDV MEA MEX MKD MLT MNA MNE MNG MUS MYS NAC NAM NLD NOR NZLOED PAN PER PHL POL PRT PRY PSE PST QAT ROU RUS RWA SAU SLV SRB SUR SVK SVN SWE SYC TEA TEC THA TLA TMN TTO TUN TUR TZA UGA UKR UMC URY USA VEN VNM WSMXKX ZAF ZMB ZWE 468 1012 Log,GDPpercapita 0 20 40 60 80 Share of Workforce in Agriculture Source: World Development Indicators (2016)
  3. 3. Rural population share, 1996-2015 0 10 20 30 40 50 60 70 80 90 100 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 RuralPopulationShare Brazil Mexico China Indonesia Nigeria Pakistan India Vietnam+Bangladesh Ethiopia Source: World Development Indicators (2016)
  4. 4. Motivation: Voluntary migration plays central role in development ▪ Countries with higher GDP have lower share of labor in agriculture oMigrants may go to either urban or rural areas ▪ International migration more complicated (from rural perspective), but… oMany relatively small countries rely on remittances for a substantial share of GDP (e.g. Nepal) oInternational migration quite important to some large economies (Bangladesh, Pakistan, Philippines, Mexico) oInternational migrant origin often from rural areas (de Brauw, 2019)
  5. 5. Remittances as a Share of GDP Country Population Est. Rural Share of Population Remittances /GDP Nepal 28.5 m 81.4 31.7 Liberia 4.5 m 50.3 31.2 Tajikistan 8.5 m 73.2 28.8 Kyrgyz Republic 5.9 m 64.3 25.7 Haiti 10.7 m 41.4 25.0 El Salvador 6.1 m 33.3 16.6 Senegal 15.1 m 56.3 11.9 Albania 2.9 m 42.6 9.2 Bangladesh 161 m 65.7 7.9 Morocco 34.3 m 39.8 6.9 Source: World Development Indicators (2016)
  6. 6. Outline ▪ Why study youth migration? ▪ Description of Data Used in the Talk ▪ What factors lead to internal migration by youth? oDo individual, household, or village characteristics matter more in predicting different types of migration? oHow much do youth differ from young adults (aged 25-34)? ▪ Summary
  7. 7. How large is the role rural youth play in migration? ▪ Rural youth (aged 15- 24) are in the process of making many life decisions: o When to leave school, whether to marry (and to whom), etc. ▪ Whether or not to migrate, either for labor or other reasons, and where, are key decisions Photo: ILO in Asia and the Pacific
  8. 8. ▪ Availability of Panel Data, some of which that tracks migrants, much greater than in the past o LSMS ISA, but also o IFPRI panel surveys in South Asia o IFLS ▪ Requirement for Inclusion: Can track individuals in panel over time o Can track migrants as a flow rather than as a stock Country Surveyed Years Bangladesh 2011-2, 2015 Indonesia 2007-8, 2014 Nigeria 2013, 2016 Pakistan 2012, 2014 Tanzania 2008, 2013 Opportunity for migration research
  9. 9. Research questions ▪ Are there consistent determinants of migration among youth (aged 15- 24) across this set of quite variable countries? o Are there consistent individual, household, or village level characteristics that predict later migration? ▪ How does migration among youth differ from young adults (aged 25- 34)? ▪ How prevalent are various types of migration? Do migrants go to rural/urban/international destinations? Photo: Asian Development Bank
  10. 10. Data issues ▪ I use a consistent set of variables across surveys o Difficult, for example, to come up with consistent asset indicators ▪ How to treat age? o I follow people who “should” be aged 15-24 (or 25-34) by the final survey used rather than examining 15-24-year-olds at baseline o Rationale: Substantial number of 15-24-year-olds at baseline are still in school, and may migrate after they are youth ▪ Similarly, how to treat education? o Use education in final survey, since many of the younger respondents are still in school
  11. 11. How do we define migration? ▪ Want to use as broad a definition as possible (not just migration for work) ▪ Migrating for any reason has economic consequences oRosenzweig and Stark (1989) show economic rationale to migrate for marriage oBeegle et al. (2011) show migration for any reason is correlated with higher consumption ▪ Two definitions used in this talk oBroad- any reason oNarrow- Eliminates migration reported for marriage or to join family
  12. 12. (Broad) Youth migration rates by country
  13. 13. Are youth more likely to migrate than young adults? 0 5 10 15 20 25 30 Bangladesh Indonesia Nigeria Pakistan Tanzania PercentofSampleMigrating 15-24 25-34
  14. 14. Gender of youth migration, by broad and narrow definition 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Bangladesh Indonesia Nigeria Pakistan Tanzania ShareofMigrants,Female Broad Narrow
  15. 15. ▪ Data are sparse, but o Substantial migration that is rural-rural (typically for family reasons) o Difficult to study flows of international migration (even in Bangladesh, which did not capture locations for all migrants, only labor migrants) o Substantial rural-urban migration to secondary cities International Migrants Rural-Urban Migrants Rural-Rural Migrant Rural-Urban, Major City Indonesia 0.020 0.028 0.153 0.011 Pakistan 0.011 0.135 0.020 Tanzania - 0.098 0.108 N/A Where do youth migrants go?
  16. 16. REGRESSION RESULTS ▪ Regress an indicator variable for someone who migrates by the second period on… oIndividual characteristics (age at baseline, gender, level of education measured in final survey) oHousehold characteristics (primarily baseline household consumption) oVillage characteristics (population density, land per capita) ▪ Idea is to tease out the most important characteristics for specific types of migration
  17. 17. Individual determinants, broad migration, youth Bangladesh Indonesia Nigeria Pakistan Tanzania Gender (1=male) -0.155*** 0.041*** -0.095*** -0.071*** -0.087*** (0.017) (0.016) (0.015) (0.015) (0.017) Age 0.007* 0.015*** 0.014*** 0.019*** 0.010*** (0.004) (0.002) (0.002) (0.003) (0.004) Less than Primary School 0.012 0.168*** -0.003 -0.014 -0.075** (0.026) (0.059) (0.022) (0.024) (0.030) Primary School Complete 0.062** 0.111** -0.014 -0.026 -0.027 (0.030) (0.050) (0.023) (0.018) (0.030) Secondary School Complete 0.185*** 0.165*** 0.027 -0.033 0.084* (0.041) (0.049) (0.030) (0.026) (0.045) More than Secondary School 0.190*** -0.058 -0.122*** 0.895*** (0.054) (0.060) (0.040) (0.062)
  18. 18. Bangladesh Indonesia Nigeria Pakistan Tanzania Gender (1=male) -0.155*** 0.041*** -0.095*** -0.071*** -0.087*** (0.017) (0.016) (0.015) (0.015) (0.017) Age 0.007* 0.015*** 0.014*** 0.019*** 0.010*** (0.004) (0.002) (0.002) (0.003) (0.004) Less than Primary School 0.012 0.168*** -0.003 -0.014 -0.075** (0.026) (0.059) (0.022) (0.024) (0.030) Primary School Complete 0.062** 0.111** -0.014 -0.026 -0.027 (0.030) (0.050) (0.023) (0.018) (0.030) Secondary School Complete 0.185*** 0.165*** 0.027 -0.033 0.084* (0.041) (0.049) (0.030) (0.026) (0.045) More than Secondary School 0.190*** -0.058 -0.122*** 0.895*** (0.054) (0.060) (0.040) (0.062) Individual determinants, broad migration, youth
  19. 19. Bangladesh Indonesia Nigeria Pakistan Tanzania Gender (1=male) 0.123*** 0.065*** 0.021** 0.038*** -0.009 (0.011) (0.011) (0.010) (0.007) (0.013) Age 0.009*** 0.010*** 0.008*** 0.004*** 0.010*** (0.002) (0.002) (0.002) (0.001) (0.003) Less than Primary School 0.001 0.055 0.020 0.010 -0.055** (0.022) (0.044) (0.013) (0.010) (0.023) Primary School Complete -0.000 0.049 0.013 0.009 -0.069*** (0.022) (0.040) (0.013) (0.006) (0.023) Secondary School Complete 0.074** 0.104** 0.049** 0.020* -0.050 (0.033) (0.040) (0.022) (0.011) (0.036) More than Secondary School -0.003 0.000 -0.032*** 0.898*** (0.042) (0.047) (0.009) (0.036) Individual determinants, narrow migration, youth
  20. 20. Individual determinants, narrow migration, young adults Bangladesh Indonesia Nigeria Pakistan Tanzania Gender (1=male) 0.100*** 0.065*** 0.058*** 0.025*** 0.001 (0.013) (0.008) (0.018) (0.007) (0.019) Age -0.003 -0.003** -0.003 -0.001 0.005 (0.002) (0.002) (0.002) (0.001) (0.004) Less than Primary School 0.019* -0.000 0.008 0.003 0.016 (0.011) (0.037) (0.014) (0.012) (0.024) Primary School Complete 0.001 -0.008 -0.019 0.016* 0.043* (0.015) (0.036) (0.016) (0.008) (0.022) Secondary School Complete 0.034 -0.011 -0.009 0.020* 0.117** (0.022) (0.035) (0.019) (0.012) (0.048) More than Secondary School 0.070 -0.028 0.049 0.059 0.614*** (0.071) (0.037) (0.035) (0.073) (0.125)
  21. 21. Differences, youth versus young adults ▪ Migration flows heavily influenced by gender o Males more likely to leave for non-family reasons ▪ Age matters among youth, not among young adults ▪ Education matters more for youth than for young adults ▪ Substantial heterogeneity across countries Photo: ILO in Asia and the Pacific
  22. 22. Is there a relationship between youth migration and household consumption at baseline?
  23. 23. Summary: Household characteristics ▪ Not much correlation with household demographic composition or household head characteristics oIn some countries there is a positive correlation between household size and broad (not narrow) youth migration (Bangladesh and Indonesia) ▪ If anything, negative correlation between migration and consumption expenditures oWait, doesn’t the literature tell us about credit constraints (e.g. McKenzie, 2017)? ▪ Also find no evidence of relationship between migration and relative deprivation with this estimation framework (e.g. Stark and Taylor, 1991)
  24. 24. ▪ No distinct patterns between narrow migration and village characteristics ▪ Potential explanation - high returns to migration outstrip anything possible on the farm or near farm in these contexts Village Land per Capita Share, HHs with Off- Farm Work Log, Population Density Bangladesh -0.018 (0.110) -0.063** (0.027) Indonesia -0.011 (0.010) -0.021 (0.027) -0.023** (0.005) Nigeria -0.002 (0.014) 0.011 (0.019) -0.003 (0.004) Pakistan -0.030** (0.013) -0.038 (0.026) Tanzania -0.003 (0.020) -0.028 (0.042) Correlations with village characteristics
  25. 25. Conclusion ▪ Migration to secondary cities appears quite important to youth ▪ Individual characteristics– notably education– important determinant of migration o Different than among older (25-34 year old) migrants ▪ No (or negative!) correlations between initial household welfare and youth migration o Notably this talk only used representative surveys o Credit constraints vs. migration may exist but may be more pronounced in less densely populated areas or for rural-urban migration o Then doesn’t show up in this type of data ▪ Migration is quite heterogenous across countries and more high quality data would shed more light on migrants’ experiences
  26. 26. References De Brauw, Alan, 2019. “Migration out of Rural Areas, and its Implications for Rural Livelihoods,” Annual Review of Resource Economics, forthcoming. McKenzie, David, 2017. Poverty, Inequality, and International Migration: Insights from 10 years of migration and development conferences, Revue d'economie du developpement 25: pp. 13-28. Stark, Oded, and J. Edward Taylor, 1991. “Migration Incentives, Migration Types: The Role of Relative Deprivation,” Economic Journal 101 (408): 1163-1178. Clemens, Michael, Cindy Huang, Jimmy Graham, and Kate Gough, 2018. Migration is What You Make It: Seven Policy Challenges That Turned Challenges into Opportunities. Center for Global Development: Washington, DC. Bazzi S., Gaduh A., Rothenberg A., Wong M. 2016. Skill Transferability, Migration, and Development: Evidence from a Resettlement Program in Indonesia. American Economic Review 106(9): 2658-2698.
  27. 27. Thank you! Q&A Recordingofthiswebinarwillbeavailableonthewebinarpagesoonafterthelifeevent:http://bit.ly/2Wozd2i PIMWebinarsarchive:https://pim.cgiar.org/resource/webinars/ Visitwww.pim.cgiar.orgformoreinformationaboutourwork. Email: a.debrauw@cgiar.org Twitter: @adebrauw

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