1. International Migration and Remittances: Assessing the Impact on Rural Households in El Salvador by Amy Damon SSEF July, 2008

2. Source: http://www.elsalvador.com/noticias/2006/01/12/portada/img/portada3.jpg Source: WDI, 2007 Remittances as a % of GDP2006 Guatemala10.2% Honduras19.4% Mexico2.9% Nicaragua12.2% Panama0.9% El Salvador18.1% Regional Importance of Remittances

3. Percent of Households that Receive Remittances by Municipality, 2004 Source: EHPM 2001 – 2004, Chapter 5 UNDP Human Development Report El Salvador

4. Previous Literature: Migration Theory Migration and development – the Harris-Todaro approach: –Two sector model where rural to urban labor migration is a result of expected income differences between two sectors. –Assumes migrants maximize their individual utility by migrating to labor market with highest expected income. The new economics of labor migration (NELM): –Addressed assumption that migration is an individualistic process. –Migration is rational behavior of a group. –Migration is a response not just to wage differentials, but also relative deprivation. –Migration is a function of missing credit and capital markets.

5. Research Questions (1)Which households choose to migrate and what determines remittance amounts? (2)How are household labor decisions affected by migration and remittances? (3)How are agricultural production, crop choice, and agricultural assets affected by the receipt of remittances?

6. Data Four year (1996, 1998, 2000, 2002) panel in El Salvador. Collected by Ohio State University and FUSADES. 450 households that have information for each year. Information on migration, migrants, household characteristics, household production activities, and detailed individual time allocation data. Cumulative attrition rate of 28 percent. Also EHPM data for community level data and CPS data for US wages and unemployment

7. Empirical Model for Migration Decision Research Question 1 The equation used to predict migration is: x it is a set of exogenous community and household characteristics including: (1) % of households that receive remittances in community (2) distance of households from a paved road. (3) other household characteristics Estimation Procedure: –Random effects probit

8. Empirical Results For Determinants of Migration Explanatory VariablesRandom Effects Probit Estimates % of households that receive remittances0.016*** Distance to Paved Road from the HH (in km)0.001 Age of HH Head0.064*** Age of HH Head Squared-0.001*** Number of Senior Citizen Present in HH0.457*** Land Area (in Ha)0.021* Value of Livestock holdings/10000.043 Constant-2.618*** Total Sample over 4 years1303 Number of Households in each year449 Standard errors excluded for presentation – see paper. * significant at 10%; ** significant at 5%; *** significant at 1%

9. Explaining Remittances (with panel data) Remittance Equation: J it = X it α 1 + Z it α 1 + ε it X it is a set of household characteristics that influence the level of remittances Z it is the wage rate and the unemployment rate in the destination U.S.A. city ε it is a normally distributed error term Estimation Procedures: (1) Household Fixed Effects Model (2) Heckman Model

10. Empirical Results – Explaining Remittance Amounts Household Fixed EffectsHeckman RegressionSelection Equation Unemployment rate in destination city-9,455.365*-4029.88 US Wage in destination city8.60***8.01*** Age of HH Head86.723.9 Age of HH Head Squared-0.81-0.21 Dependency Ratio-121.84-46.62 Number of Senior Citizen Present in HH9.0325.22 Female Headed HH803.30**550.58*** Number of HH Members-89.89-48.81 Number of Children Present in HH138.4859.24 Land Area in HA8.1613.87 Value of Livestock holdings/1000-28.953*-12.11 ES wage-66.8546.32 ES transfers0.065*0.071*** Constant-2668.24-1122.88-0.652*** % of households that receive remittances 0.016*** Distance to Paved Road from the HH (in km) 0 Observations5021528 Number of Households268 Diagnostics LR test of independent equations (prob > chi2) 0.0004 * significant at 10%; ** significant at 5%; *** significant at 1%

11. Explaining Remittances 2002 Cross-Section Objective: to look at gender and relationship to the household head effects using 2002 data The Remittance equation is: J i = α 1 w usa i + α 1 N usa i + α 2 X i + u i X i is a set of household characteristics Estimation Procedure: Heckman Selection Model

12. Cross-Sectional Remittance Results-2002 (1) OLS(2) OLS US Wage7.56410.859** US Unemployment Rate38.36562.777 Migrant is a Female (=1 if migrant is female)312.806768.446** Migrant is Son of HH Head1,229.727*** Migrant is Daughter of HH Head1,445.923*** Migrant is Brother of HH Head243.409 Migrant is Sister of HH Head1,469.658*** Migrant is HH Head2,553.755*** Age of HH Head-18.257*-7.328 Dependency Ratio326.056-681.660* Number of Senior Citizen Present in HH-290.915-35.73 Female Headed HH-143.23781.652*** Number of HH Members109.53-3.406 Number of Children Present in HH-80.238143.834 Constant-2,161.75-2,093.93 Observations413 R-squared0.260.05

13. Intuition for Question 2: Work Hours and Remittances If a household operates in a perfectly functioning market environment (complete credit and labor markets: –An increase in remittances will increase consumption –Separability holds (production and consumption decisions are independent of one another) –Remittances will not affect labor allocation outcomes. But if a household is credit constrained: –Migration and remittances may substitute for missing credit or insurance markets. –Separability no longer holds and migration and remittances will impact on-farm and off-farm labor allocation decisions and investment decisions.

14. Labor and Migration: Theoretical Model Max subject to: In the credit constrained version

15. Comparative Static Results Comparative Static Results How choice variables change with an increase in remittances No Credit Constraint Consumption On-Farm Work Off-Farm Work Capital Credit Constrained

16. Labor Supply Estimation The labor supply equation of interest is: H it : measure of (change in) labor hours X it : set of household demographic change variables J it : (change in) predicted level of remittances a household receives Migr: (change in) predicted migration ε i : aggregate error term assumed to be white noise But….. Mig and J it is endogenous so we use an instrumental variable (2sls) approach. Instruments are: (1) % of hh that receive remittances in community (2) in USA wage rate (3) Unemployment rate in USA and (4) Household distance to a paved road Estimation Procedures: First Differences Model (also household fixed effects estimation - see paper)

17. Types of Labor Examined Total Household Labor Total Farm labor On-Farm –Male, Female, Child, Hired Off-Farm Wage Labor –Male, Female, Child Non-agricultural Self-Employment –Male, Female Household Work

18. Total Labor and On-Farm Labor First Differences Model On-Farm Work Total HoursTotal Farm HoursFemaleMaleChildHired Remittances0.5880.02-0.0080.0420.035-0.051 Migration Status-46.5832,090.687***226.989*994.693**206.615*707.411 Land Area-1.00318.9091.1947.298-0.01710.731 No. of Senior Citizens in HH39.345-162.803-20.836-4.925-24.796-117.417 Female Head Status-1,493.411*-663.81464.344-347.248-48.853-333.75 Number of HH Members340.032**119.5560.33492.4350.20129.511 No. of HH Children105.903-183.797-39.579-45.6141.964-107.867 Livestock Value-0.099***-0.067***0-0.0030.003-0.067*** Dependency Ratio-86.563-82.855-8.73-114.2683.47542.416 ES Wage148.476**5.312-4.424-1.5251.1339.721 ES Transfers-0.07-0.028-0.006-0.016-0.0070.001 Constant-642.775-506.308**-72.225*-286.165*-71.476*-81.982 Observations180181180181 * significant at 10%; ** significant at 5%; *** significant at 1%

19. Off-Farm Work Results First Differences ModelFemaleMaleChild Remittances0.1920.4440.06 Migration Status-531.47-1,981.864**678.682 Land Area25.817-42.0475.79 No. of Senior Citizens in HH185.444*107.268110.804 Female Head Status219.067-1,017.054*-253.132 Number of HH Members35.717181.94865.19 No. of HH Children128.106131.897-30.431 Livestock Value-0.003-0.021-0.005 Dependency Ratio-121.874-105.934-197.56 ES Wage50.032**86.483**35.955 ES Transfers-0.017-0.033-0.014 Constant171.401-161.75720.976 Observations181 * significant at 10%; ** significant at 5%; *** significant at 1%

20. Other Work Non-Agricultural Self-EmploymentHousework First Differences ModelFemaleMale Remittances-0.019-0.0460.138 Migration Status282.747-160.8071,923.663** Land Area0.485-3.946-1.95 No. of Senior Citizens in HH-32.56432.569-47.074 Female Head Status-49.612109.035-540.245 Number of HH Members-50.5313.215139.699 No. of HH Children-0.1852.201-51.773 Livestock Value-0.0080.002-0.017 Dependency Ratio146.2361.249-256.25 ES Wage12.93512.12623.937 ES Transfers-0.0020.009-0.031 Constant-60.69942.013-607.293** Observations181 * significant at 10%; ** significant at 5%; *** significant at 1%

21. Question 3. How are agricultural production activities affected by migration and remittances?

22. Intuition and Literature Many studies have examined the relationship between farm income safety nets (migration and remittances) and agricultural outcomes such as cropping patterns (Smith and Goodwin, 1996; and Babcock and Hennessey, 1996). Chavas and Holt (1990) examine how farmers allocate acreage to different crops under risk and find that both risk and wealth are important in corn-soybean acreage decisions. Since migration and remittances are a form of insurance (Stark and Lucas, 1988; Gubert, 2002; Stark and Lucas, 1988; Cox et al., 1998); do migration and remittances affect risk behavior or crop acreage decisions?

23. Theory Theoretical model suggests that the change in land use in response to wealth depends on the risk preferences of the household. –Constant absolute risk aversion implies no change in acreage with a change in wealth –Decreasing absolute risk aversion means they will move into riskier crops as wealth increases –Increasing absolute risk aversion households would move into less risky crops as wealth increases.

24. Empirical Approach Risk is measured by coefficient of variation (CV) for crop and livestock revenue: And explained using household characteristics and remittances (migration): σ is standard deviation of total farm revenue for farm j across four years of data μ is the mean of total farm revenue

25. Explanatory VariablesRemittancesMigration Remittances / 10000.272 Migration Status (0/1) -0.38 Total Land Area (ha)-0.022*-0.028*** Livestock Value-0.095-0.034 Number of household senior citizens-0.1380.086 Female headed household0.0490.297*** Age of household head-0.004-0.006 Number of household members0.0540.005 Number of household children-0.13-0.021 Dependency Ratio0.242-0.016 Salvadoran agricultural wage rate0.0030.029** Minutes to a paved road0-0.001 Constant1.139***1.514*** Observations248391 R-squared.13 * significant at 10%; ** significant at 5%; *** significant at 1% IV Regression explaining agricultural revenue coefficient of variation

26. Explaining acreage decisions α is the household fixed effect, X it is a vector of household demographic characteristics, Y it is total land area, R it is remittances (replaced by the dichotomous variable, MIGR it, in the migration version of this regression), ε t is an independently distributed error term Estimated using a household fixed effects model instrumenting for migration and remittances.

27. Land use in hectares House lotPasture Fallow/ ForestCultivation Basic grainsCoffee Other cash crops Migrant Status (0/1)0.494**0.191-0.023-0.2781.461***0.24-1.226** Total Land Area (ha)0.0020.468***0.291***0.078***0.025***0.005**0.038*** Number of HH Senior Citizens0.032-0.080.1380.061-0.106-0.0180.074 Female Headed HH-0.083-0.0620.238-0.331-0.449***-0.0810.084 Number of HH Members0.016-0.0520.0720.0240.077***0.013-0.052 Number of HH Children-0.0180.033-0.020.008-0.062**-0.0110.041 Value of Total Livestock00***000**00*** Salvadoran Wage Rate-0.002-0.0320.010.0120.0090-0.004 Constant-0.174-0.09-0.7190.434-0.57**-0.0830.895*** Observations12791727 Number of households449 Fixed-effects instrumental variable regression explaining land use by migration status. * significant at 10%; ** significant at 5%; *** significant at 1%

28. Asset Holdings and Land Rental Markets Land Area Land Rented In Land Rented Out Migrant Status3.675**0.652*1.712** Number of Senior Citizens-0.525-0.124-0.215 Female Headed Household-1.654**-0.262*-0.265 Years of Education of the Head0.0050.015-0.001 Age of the HH Head0.0240.007-0.007 Number of HH members0.1970.053*0.067 Number of HH children-0.489*-0.091-0.219* Dependency Ratio1.360*0.1950.699** Constant-1.661-0.566*-0.535 Observations1253 Number of households448 * significant at 10%; ** significant at 5%; *** significant at 1%

29. Concluding Points It is the act of migration rather than remittances that change household behavior. Migrant households allocated their labor back to the farm when they send out a migrant. When female migrants’ wages increase they send more money. Males appear to send less. Migrant households allocated more land to “food security” crops rather than other crops or cash crops. Migrant households do not appear to undertake riskier crops (in terms of revenue). Migrant household have larger land holdings and have larger land areas involved in rental markets.