International Migration and Remittances: International Migration and Remittances:

Assessing the Impact on Rural Households Assessing the Impact on Rural Households

in El Salvadorin El Salvador

by

Amy Damon

SSEF

July, 2008

Source: http://www.elsalvador.com/noticias/2006/01/12/portada/img/portada3.jpgSource: WDI, 2007

Remittances as a % of GDP 2006

Guatemala 10.2%

Honduras 19.4%

Mexico 2.9%

Nicaragua 12.2%

Panama 0.9%

El Salvador 18.1%

Regional Importance of RemittancesRegional Importance of Remittances

Percent of Households that Receive

Remittances by Municipality, 2004

Source: EHPM 2001 – 2004, Chapter 5 UNDP Human Development Report El Salvador

Previous Literature: Migration TheoryPrevious 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.

Research QuestionsResearch 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?

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

Empirical Model for Migration DecisionResearch Question 1

The equation used to predict migration is:

xit 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

itiitit xY *

Empirical Results For Determinants of Migration

Explanatory Variables Random Effects Probit Estimates

% of households that receive remittances 0.016***

Distance to Paved Road from the HH (in km) 0.001

Age of HH Head 0.064***

Age of HH Head Squared -0.001***

Number of Senior Citizen Present in HH 0.457***

Land Area (in Ha) 0.021*

Value of Livestock holdings/1000 0.043

Constant -2.618***

Total Sample over 4 years 1303

Number of Households in each year 449

Standard errors excluded for presentation – see paper.

* significant at 10%; ** significant at 5%; *** significant at 1%

Explaining Remittances (with panel data)

Remittance Equation:

Jit = Xitα1 + Zitα1 + εit

Xit is a set of household characteristics that influence the level of remittances

Zit is the wage rate and the unemployment rate in the destination U.S.A. city

εit is a normally distributed error term

Estimation Procedures: (1) Household Fixed Effects Model (2) Heckman Model

Empirical Results – Explaining Remittance Amounts   Household Fixed Effects Heckman

  Regression Selection Equation

Unemployment rate in destination city -9,455.365* -4029.88  

US Wage in destination city 8.60*** 8.01***  

Age of HH Head 86.7 23.9  

Age of HH Head Squared -0.81 -0.21  

Dependency Ratio -121.84 -46.62  

Number of Senior Citizen Present in HH 9.03 25.22  

Female Headed HH 803.30** 550.58***  

Number of HH Members -89.89 -48.81  

Number of Children Present in HH 138.48 59.24  

Land Area in HA 8.16 13.87  

Value of Livestock holdings/1000 -28.953* -12.11  

ES wage -66.85 46.32  

ES transfers 0.065* 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

Observations 502 1528 1528

Number of Households 268    

Diagnostics      

LR test of independent equations (prob > chi2)     0.0004

* significant at 10%; ** significant at 5%; *** significant at 1%

Explaining Remittances 2002 Cross-Section

• Objective: to look at gender and relationship to the household head effects using 2002 data

The Remittance equation is:

Ji = α1wusai + α1Nusa

i + α2Xi + ui

Xi is a set of household characteristics

Estimation Procedure:• Heckman Selection Model

Cross-Sectional Remittance Results-2002  (1) OLS (2) OLS

US Wage 7.564 10.859**

US Unemployment Rate 38.365 62.777

Migrant is a Female (=1 if migrant is female) 312.806 768.446**

Migrant is Son of HH Head 1,229.727***  

Migrant is Daughter of HH Head 1,445.923***  

Migrant is Brother of HH Head 243.409  

Migrant is Sister of HH Head 1,469.658***  

Migrant is HH Head 2,553.755***  

Age of HH Head -18.257* -7.328

Dependency Ratio 326.056 -681.660*

Number of Senior Citizen Present in HH -290.915 -35.73

Female Headed HH -143.23 781.652***

Number of HH Members 109.53 -3.406

Number of Children Present in HH -80.238 143.834

Constant -2,161.75 -2,093.93

Observations 413 413

R-squared 0.26 0.05

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.

Labor and Migration: Theoretical Model

)];();([);();( 2211 ZCUZCUZCUZCU FFMMFFMM },,,,{ BKMRC ttt

it

MMusa

FFF CTNwBMwGKKRfrKC 111110111 )1(),,(

MMusa

FFF CTNwMwGKKKRfBrrKC 2222210222 )1(),,()1(

FFF MRT 111

FFF MRT 222

Max

subject to:

)( 11 BB

In the credit constrained version

Comparative Static ResultsComparative Static ResultsHow choice variables change with an increase in remittances

No Credit Constraint

Consumption

On-Farm Work

Off-Farm Work

Capital

Credit Constrained

01 usdw

dC

01 usdw

dR

01 usdw

dM

01 dJ

dC

01 dJ

dR

01 dJ

dM

01 usdw

dK 01 dJ

dK

Labor Supply EstimationLabor Supply EstimationThe labor supply equation of interest is:

Hit: measure of (change in) labor hours Xit: set of household demographic change variables Jit: (change in) predicted level of remittances a household receives Migr: (change in) predicted migration εi: aggregate error term assumed to be white noise But…..Mig and Jit 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)

1,1,31,211,1, tttittittittjt MigrJXH

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

Total Labor and On-Farm Labor First Differences Model On-Farm Work

  Total Hours Total Farm Hours Female Male Child Hired

Remittances 0.588 0.02 -0.008 0.042 0.035 -0.051

Migration Status -46.583 2,090.687*** 226.989* 994.693** 206.615* 707.411

Land Area -1.003 18.909 1.194 7.298 -0.017 10.731

No. of Senior Citizens in HH 39.345 -162.803 -20.836 -4.925 -24.796 -117.417

Female Head Status -1,493.411* -663.814 64.344 -347.248 -48.853 -333.75

Number of HH Members 340.032** 119.556 0.334 92.435 0.201 29.511

No. of HH Children 105.903 -183.797 -39.579 -45.614 1.964 -107.867

Livestock Value -0.099*** -0.067*** 0 -0.003 0.003 -0.067***

Dependency Ratio -86.563 -82.855 -8.73 -114.268 3.475 42.416

ES Wage 148.476** 5.312 -4.424 -1.525 1.133 9.721

ES Transfers -0.07 -0.028 -0.006 -0.016 -0.007 0.001

Constant -642.775 -506.308** -72.225* -286.165* -71.476* -81.982

Observations 180 181 180 181 181 181

* significant at 10%; ** significant at 5%; *** significant at 1%

Off-Farm Work Results First Differences Model Female Male Child

Remittances 0.192 0.444 0.06

Migration Status -531.47 -1,981.864** 678.682

Land Area 25.817 -42.047 5.79

No. of Senior Citizens in HH 185.444* 107.268 110.804

Female Head Status 219.067 -1,017.054* -253.132

Number of HH Members 35.717 181.948 65.19

No. of HH Children 128.106 131.897 -30.431

Livestock Value -0.003 -0.021 -0.005

Dependency Ratio -121.874 -105.934 -197.56

ES Wage 50.032** 86.483** 35.955

ES Transfers -0.017 -0.033 -0.014

Constant 171.401 -161.757 20.976

Observations 181 181 181

* significant at 10%; ** significant at 5%; *** significant at 1%

Other Work  Non-Agricultural Self-Employment Housework

 First Differences Model Female Male  

Remittances -0.019 -0.046 0.138

Migration Status 282.747 -160.807 1,923.663**

Land Area 0.485 -3.946 -1.95

No. of Senior Citizens in HH -32.564 32.569 -47.074

Female Head Status -49.612 109.035 -540.245

Number of HH Members -50.53 13.215 139.699

No. of HH Children -0.185 2.201 -51.773

Livestock Value -0.008 0.002 -0.017

Dependency Ratio 146.236 1.249 -256.25

ES Wage 12.935 12.126 23.937

ES Transfers -0.002 0.009 -0.031

Constant -60.699 42.013 -607.293**

Observations 181 181 181

* significant at 10%; ** significant at 5%; *** significant at 1%

Question 3.

How are agricultural production activities affected by migration and remittances?

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?

TheoryTheoryTheoretical 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.

Empirical Approach

• Risk is measured by coefficient of variation (CV) for crop and livestock revenue:

• And explained using household characteristics and remittances (migration):

CV

tjtjtj RCV 21X

σ is standard deviation of total farm revenue for farm j across four years of data

μ is the mean of total farm revenue

 Explanatory Variables Remittances Migration

Remittances / 1000 0.272  

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 -0.138 0.086

Female headed household 0.049 0.297***

Age of household head -0.004 -0.006

Number of household members 0.054 0.005

Number of household children -0.13 -0.021

Dependency Ratio 0.242 -0.016

Salvadoran agricultural wage rate 0.003 0.029**

Minutes to a paved road 0 -0.001

Constant 1.139*** 1.514***

Observations 248 391

R-squared   .13 

* significant at 10%; ** significant at 5%; *** significant at 1%

IV Regression explaining agricultural revenue coefficient of variation

Explaining acreage decisions

titititijt RYXA 431

α is the household fixed effect,

Xit is a vector of household demographic characteristics,

Yit is total land area,

Rit is remittances (replaced by the dichotomous variable, MIGRit, in the migration version of this regression),

εt is an independently distributed error term

Estimated using a household fixed effects model instrumenting for migration and remittances.

  Land use in hectares

 House

lot PastureFallow/Forest Cultivation

Basic grains Coffee

Other cash crops

Migrant Status (0/1) 0.494** 0.191 -0.023 -0.278 1.461*** 0.24 -1.226**

Total Land Area (ha) 0.002 0.468*** 0.291*** 0.078*** 0.025***0.005*

* 0.038***

Number of HH Senior Citizens 0.032 -0.08 0.138 0.061 -0.106 -0.018 0.074

Female Headed HH -0.083 -0.062 0.238 -0.331 -0.449*** -0.081 0.084

Number of HH Members 0.016 -0.052 0.072 0.024 0.077*** 0.013 -0.052

Number of HH Children -0.018 0.033 -0.02 0.008 -0.062** -0.011 0.041

Value of Total Livestock 0 0*** 0 0 0** 0 0***

Salvadoran Wage Rate -0.002 -0.032 0.01 0.012 0.009 0 -0.004

Constant -0.174 -0.09 -0.719 0.434 -0.57** -0.083 0.895***

Observations 1279 1727 1727 1727 1727 1727 1727

Number of households 449 449 449 449 449 449 449

Fixed-effects instrumental variable regression explaining land use by migration status.

* significant at 10%; ** significant at 5%; *** significant at 1%

Asset Holdings and Land Rental Markets

 Land Area

Land Rented In

Land Rented Out

Migrant Status 3.675** 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 Head 0.005 0.015 -0.001

Age of the HH Head 0.024 0.007 -0.007

Number of HH members 0.197 0.053* 0.067

Number of HH children -0.489* -0.091 -0.219*

Dependency Ratio 1.360* 0.195 0.699**

Constant -1.661 -0.566* -0.535

Observations 1253 1253 1253

Number of households 448 448 448

* significant at 10%; ** significant at 5%; *** significant at 1%

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.