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Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants
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Insurance Motives to Remin: Evidence from a Matched Sample of Ethiopian Internal Migrants

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Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI), Seventh International Conference on Ethiopian Economy, June 24, 2010

Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI), Seventh International Conference on Ethiopian Economy, June 24, 2010

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  • Point 2: Mention that were times when entire households had left and we followed those who had moved for economic reasons according to a PA official. Also mention that we had a 77 percent success rate excluding international migrants. 16 percent of our households with migrants had migrants abroad.
  • Point 1: Every village has at least one migrant household, but there’s quite a bit of heterogeneity in migration rates which cannot entirely be explained by access to roads or travel time to the nearest city. Point 1: This makes especially sense when you break the rates by location type. The predominant for of migration is rural to rural (64 percent), then rural-urban (31 percent), so it may not necessarily be important to have access to the nearest city if networks matter more. We find in another paper that networks are a strong determinant of migration in this sample.
  • Demographic composition is consistent with who was targeted in the tracking study; individuals motivated to leave to primarily seek employment elsewhere.
  • Very few differences. Remitters are wealthier, larger household size, also primarily dependent on farming.
  • Remitters appear wealthier and older than non-migrants. They tend to have more people in the household (vulnerability?) However, less literate. If you also compare non-remitting migrants to non-migrants, you confirm that there seems to be a positive selection among remitters, and negative selection of migrant non-remitters.
  • Two Dependent Variables (Remittance amount and Remit or Not). Four models (OLS, Tobit, LPM, Probit). RHS: Individual migrant characteristics X, Source household Characteristics H, Destination household Characteristics D, Migrant shocks, Source household Shocks. Dummies for regional origin, location, time of move. Standard errors clustered by the origin neighborhood.
  • -Males give more but no evidence that they remit more or less -Occupation matters, pretty much everyone gives more than farmers except for civil servants. -We also control for age and education but there is not significance on these parameters, likely due to the lack of variation in our sample at least on the age variable.
  • Potential Precautionary Savings story when look at parameters on land per daughter variable. Competition for resources may increase incentives to remit. Destination household characteristics don’t have significant effect on remittance behavior.
  • Migrant shock consistent with precautionary savings and self-insurance motive. No evidence of trying to insure the family.
  • Risk aversion: could be picking up shocks matter to the risk averse rather than to remittance behavior.
  • Transcript

    • 1. Insurance Motives to Remit: Evidence from a Matched Sample of Ethiopian Internal Migrants Alan de Brauw (IFPRI) Valerie Mueller (IFPRI) Tassew Woldehanna (Addis Ababa University) Ethiopian Economic Association Meetings June 25, 2010
    • 2. Motivation
    • 3. Objectives <ul><li>Explain low remittance rates by examining what motivates migrants to remit </li></ul><ul><li>Matched migrant sample </li></ul><ul><ul><li>We know migrant’s exposure to shocks, his demographic and economic situation </li></ul></ul><ul><ul><li>We know source household’s exposure to shocks and their demographic and economic conditions over time </li></ul></ul><ul><ul><li>We have information from all stakeholders that stand to benefit from migration </li></ul></ul>
    • 4. Data <ul><li>Ethiopian Rural Household Survey (ERHS) </li></ul><ul><ul><li>Multi-topic survey </li></ul></ul><ul><ul><li>Spans 15 villages from 1994-2009 </li></ul></ul><ul><ul><li>We use 2004-5 and 2009 rounds which includes 3 additional villages (18 villages total) </li></ul></ul><ul><li>Migrant Tracking Survey, 2009 </li></ul><ul><ul><li>Based on 2004-5 Roster </li></ul></ul><ul><ul><li>Tracked migrants </li></ul></ul><ul><ul><ul><li>Ages 10+ moved to another PA for 3 mos. </li></ul></ul></ul><ul><ul><ul><li>Left for economic reasons (includes schooling if individual now works elsewhere) </li></ul></ul></ul><ul><ul><ul><li>Relative of household head </li></ul></ul></ul><ul><ul><li>15 % of households had tracked migrant (313 migrants) </li></ul></ul>
    • 5. Migration Prevalence from ERHS villages
    • 6. Migrant’s Characteristics % % Male 62.0 Amhara 30.4 Ages 19-40 yrs 83.40 Oromo 20.0 Single 57.2 Tigrayan 12.5 <5 years ed 17.9 Orthodox christian 53.0 5-8 years ed. 30.4 Protestant 26.8 9+ years ed. 35.1 Muslim 15.3
    • 7. Occupations of Migrants Prior to migration Post- migration Farm worker 43.0 14.1 Daily laborer 3.5 23.3 Domestic work/housekeeper 9.6 12.8 Self-employed 5.1 16.6 Teacher 1.6 12.1 Student 32.4 0.6 Other salaried employment 1.3 11.2 Other/unemployed 3.5 9.3 Migrants 312 313
    • 8. Migrant households Source: ERHS 2004-5 and 2009 Remit Do not remit T stat Tropical livestock units, 2009 6.22 4.82 1.78* Females (16-40 years), 2004-5 1.20 0.95 1.92* Males (>40 years), 2004-5 0.71 0.64 1.93* Females (>40 years), 2004-5 0.86 0.72 2.12** Head’s primary occupation is farming, 2004-5 0.80 0.68 2.11**
    • 9. Remitters and Non-Migrant households Source: ERHS 2004-5 and 2009 Non-migrants Remitting migrants T stat Tropical livestock units, 2009 3.28 4.22 -1.86* Males (<=15 years), 2009 0.96 0.72 2.46** Females (<=15 years), 2004-5 0.90 1.23 -2.13** Males (16-40 years), 2004-5 0.90 1.27 -2.66*** Females (16-40 years), 2004-5 0.97 1.20 -1.84* Males (>40 years), 2004-5 0.50 0.73 -3.55*** Females (>40 years), 2004-5 0.50 0.80 -4.82*** Hh head’s age, 2004-5 50.18 54.19 -3.06*** Literate head, 2009 0.50 0.38 2.04**
    • 10. Motives to Remit Literature <ul><li>Hoddinott (1994) finds competition and promise of bequests increases incentive to remit in Kenya </li></ul><ul><li>De la Briere et al. (2002) find women and males without migrant siblings remit to insure family while all are motivated to remit as an investment in future inheritance in DR </li></ul><ul><li>Amuedo-Dorantes and Pozo (2006) find migrants remit to self-insure (against own risk and precautionary savings motive) in Mexico </li></ul><ul><li>Osili (2007)also find precautionary savings motive to remit in Nigeria. Skilled more altruistic. </li></ul>
    • 11. Conceptual Framework Source: Amuedo-Dorantes and Pozo 2006. Self-insurance (Precautionary Saving) Responsive to the immigrant’s rising exposure to risk in the host community Family-provided Insurance Responsive to the immigrant’s rising exposure to risk in the host community Remittances Sent to finance family’s consumption in home community Sent to accumulate assets in home community Altruism Non-responsive to the immigrant’s rising exposure to risk in the host community
    • 12. Empirical Model
    • 13. Regression Results (Migrant Variables) Includes age and education categorical dummy variables. OLS Remittances Coeff. Tobit Remittances ME LPM Remits Coeff. Probit Remits ME Male 108.4* 197.2 0.0106 -0.00410 Daily laborer 97.24 666.4** 0.187* 0.271* Domestic 249.9** 989.3*** 0.302** 0.402** Trader 56.73 583.6* 0.198* 0.286* Teacher 361.8** 1285*** 0.436*** 0.553*** Civil servant -40.94 149.3 0.0759 0.121 Food seller 319.5 1103** 0.188 0.337 Health worker 619.0* 1934*** 0.610** 0.656*** Administrative 323.5 1332*** 0.557*** 0.636*** Other 138.0* 809.4** 0.239** 0.337**
    • 14. Regression Results (Hh variables) OLS Remittances Coeff. Tobit Remittances ME LPM Remits Coeff. Probit Remits ME Source hh Adult daughters 16.51 43.19 -0.0152 -0.00688 Adult sons -13.76 -45.84 -0.0158 -0.0192 Land per son -7.861 -27.78 -0.00920 -0.0112 Land per daughter 54.31 178.7* 0.0584** 0.0677** Livestock -4.859 -2.194 0.00288 0.00329 Sole migrant -156.5*** -380.0** -0.108 -0.112 Destination hh HH size -24.85 -69.78 -0.0399 -0.0446 Relatives 21.45 -39.97 0.00345 -0.0108
    • 15. Regression Results (Shock variables) OLS Remittances Coeff. Tobit Remittances ME LPM Remits Coeff. Probit Remits ME Individual Migrant shock Migrant reports 2001 (EC) food price rise 14.46 213.2 0.152** 0.176*** Source hh shock Hh reports 2000 (EC) drought -82.76 -184.4 -0.0469 -0.0455 Migrants 289 289 289 293 R-squared 0.16 0.04 0.28 0.25
    • 16. Empirical Challenges <ul><li>Omitted variable bias </li></ul><ul><ul><li>Unobservables at the individual level affect remittance behavior </li></ul></ul><ul><li>Selection bias </li></ul><ul><ul><li>Remitters different than non-remitters </li></ul></ul><ul><ul><li>Remitters may be more risk averse </li></ul></ul><ul><li>Future work will consist of matching individual migrant data with individual migrants and non-migrants in 2004-5 and 2009 ERHS surveys to address two issues </li></ul>
    • 17. Discussion <ul><li>Remitters appear to be positively selected which could explain low remittance rates </li></ul><ul><li>Incentives to remit follow self-insurance/ precautionary savings motive </li></ul><ul><li>Low remittance rates suggest benefits from migration likely to come from migrant freeing up resources </li></ul>

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