maría laura alzúa guillermo cruces centro de estudios distributivos, laborales y sociales

Labor supply responses to cash transfer programs

Experimental and non-experimental evidence from Latin America

María Laura AlzúaGuillermo CrucesCentro de Estudios Distributivos, Laborales y SocialesCEDLAS-National University of Plata, Argentina

Laura RipaniInter American Development Bank

April 1st 2009, El Cairo, Egypt

Outline1. Conditional Cash Transfers (CCTs) in Latin

America and the Caribbean (one slide!)

2. Motivation: Why the labor supply of adults? Why is it important?

3. Results: Evidence from experimental settings: Mexico

(PROGRESA), Nicaragua (RPS) and Honduras (PRAF)

Evidence from non-experimental settings: Brazil (Bolsa Familia), Mexico (PROGRESA), Colombia (Familias en Acción)

4. Conclusions

1. Conditional Cash Transfers (CCTs)

in LAC(one slide!)

1. Importance of CCTs in LAC Idea: combine short term income support (poverty alleviation)

with long term goals through conditionalities on human capital accumulation with the multiple objectives of increasing education, improving health and nutrition, and reducing child labor. CCTs work as a combination of already existing components in previous programs like income support, nutrition programs, conditionalities (work requirements), which already existed.

Innovation: original combination of existing factors plus long term and short term objectives.

Many CCTs in the Latin American region and outside of the Region already!! ……..success partly due to credible evaluation and the positive results of Mexico’s PROGRESA/Oportunidades program.

Source: Conditional cash transfers: reducing present and future poverty, World Bank (2009)

2. CCTs in LAC: Why the labor supply of

adults?

Intended objectives and unintended impacts Typical intended outcomes of CCT programs:

More education (increase school enrollment and attendance) Reduce child labor Better health (regular health checkups for children and pregnant

women) Better nutrition (through transfers, training on nutrition issues,

delivery of micronutrients at the time of health checkups, etc.) Income support (transfer helps to reduce poverty (and might

replace foregone income from reduced child labor))

Evaluations: evidence of positive impacts on these outcomes in many programs (not all outcomes in all programs, but still pretty effective).

Changes on labor supply of adults in beneficiary households is not one of the intended outcomes.

Previous evaluations have covered other unintended impacts of CCTs (i.e., subjective well being of women).

Why the labor supply of adults? Theoretical predictions:

Reduction in labor supply (from the increase in unearned income » pure income effect, if leisure is a normal good).

Increase in hours or participation (now individuals can pay the monetary costs of going to work or they can free time from child care at home, because of the increase in kids’ school enrollment and attendance).

Which will prevail is necessarily an empirical matter

Intuition: probably not major impacts because of relatively low level of transfers (10/30% of household income).

Potential impacts in some subgroups. But still important to understand interactions of programs

with autonomous income generation mechanisms.

Why is this important?

In LAC, important for:

Policy: CCT design

Welfare: CCTs complement the labor income in beneficiary households » it is of interest if they actually have an impact on this kind of income…

Previous results

Experimental evaluations – PROGRESA (Mexico): Skoufias and Parker (2000), and Skoufias and Di Maro (2008) find no significant impact on labor supply.

Evidence from non-experimental evaluations: mixed.

3. Results

Evidence from experimental settings

Evidence from experimental settings:programs and data

Countries and programs: Mexico-PROGRESA Nicaragua-RPS Honduras-PRAF

Mexico: Sample of communities (506) and households (24,000) surveyed b/w

Nov-97 and Nov-99 (baseline and three follow-ups) Nicaragua:

Sample of communities (42) and households (20,280), surveyed between 2000 (baseline) and 2001 (follow-up).

Honduras: Sample of communities (70) and households (64,000), baseline (2000)

and follow up (2002). Multiple interventions, but here, only demand side-intervention data and control group.

Experimental setting estimation: conditional DD

The estimation of a standard DD model takes the following form:

where Yist denotes the outcome variable of interest for individual i in group s for time t, Iist is an indicator variable representing treatment status, As and Bt are group and time effects, respectively, Xist is a matrix of individual covariates and εist is an error term.

Under unconfoundedness, the estimate of the program impact is the OLS estimate of β. Estimation is carried out by fixed effects regression depending on the time periods available to the researcher.

ist s t ist st istY A B cX I

Conditioning

Few random assignments are perfect – either the process, or the resulting samples might differ by chance.

Conditioning on observables balances treatment and control groups

Empirical results – PROGRESA (MEXICO)

Unconditional

Sex (ii) + Household Size(iii) + Children

younger than 18(iv) + Interaction

Children/Treatment (v) + Age (vi) + Education

Uncorrected -0.005 -0.005 -0.005 -0.005 -0.005 -0.005 -0.003(.0034)* (.0034)* (.0035)* (.0035)* (.0035)* (.0035)* (0.004)

Clustered -0.005 -0.005 -0.005 -0.005 -0.005 -0.005 -0.003(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

Boostrapped -0.005 -0.005 -0.005 -0.005 -0.005 -0.005 -0.003(0.009) (0.009) (0.008) (0.008) (0.009) (0.008) (0.008)

Observations 259,818 259,420 253,177 253,173 253,173 253,173 190,312

Uncorrected -0.007 -0.007 -0.007 -0.007 -0.007 -0.007 -0.006(.0025)*** (.0025)*** (.0025)*** (.0025)*** (.0025)*** (.0025)*** (.0028)**

Clustered -0.007 -0.007 -0.007 -0.007 -0.007 -0.007 -0.006(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.007)

Boostrapped -0.007 -0.007 -0.007 -0.007 -0.007 -0.007 -0.006(0.007) (0.007) (0.006) (0.006) (0.007) (0.006) (0.005)

Observations 259,818 259,420 253,177 253,173 253,173 253,173 190,312

Uncorrected -0.006 -0.006 -0.006 -0.006 -0.006 -0.007 -0.005(.0025)*** (.0025)*** (.0025)*** (.0025)*** (.0025)*** (.0025)*** (.0028)**

Clustered -0.006 -0.006 -0.006 -0.006 -0.006 -0.007 -0.005(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.007)

Boostrapped -0.006 -0.006 -0.006 -0.006 -0.006 -0.007 -0.005(0.007) (0.007) (0.006) (0.006) (0.007) (0.007) (0.007)

Observations 259,818 259,420 253,177 253,173 253,173 253,173 190,312

DD Estimates (PROGRESA)

Conditional

Year 1

Year 2

Year 3

With cluster corrected or block-bootstrapped standard errors, we find no ‘work disincentive’ effect of the program on average for adults but…

Source: Own calculations based on program evaluation on surveys; Standard errors in parentheses; Bootstrapped errors obtained by CRVE-Block Bootstrap with 100 replications. * Significant at 10%; ** significant at 5%; *** significant at 1%

Empirical results – PROGRESA (MEXICO)Females

Unconditional

(ii) + Household Size(iii) + Children



Uncorrected -0.009 -0.010 -0.010 -0.010 -0.010 -0.004(.005)** (.0051)** (.0051)** (.0051)** (.0051)** (0.006)

Clustered -0.009 -0.010 -0.010 -0.010 -0.010 -0.004(0.015) (0.016) (0.016) (0.016) (0.016) (0.015)

Boostrapped -0.009 -0.010 -0.010 -0.010 -0.010 -0.004(0.012) (0.014) (0.014) (0.012) (0.014) (0.014)

Observations 130,476 127,123 127,121 127,121 127,121 89,815

Uncorrected -0.010 -0.009 -0.009 -0.009 -0.009 -0.009(.0038)*** (.0038)*** (.0038)*** (.0038)*** (.0038)*** (.0044)**

Clustered -0.010 -0.009 -0.009 -0.009 -0.009 -0.009(0.012) (0.012) (0.012) (0.012) (0.012) (0.011)

Boostrapped -0.010 -0.009 -0.009 -0.009 -0.009 -0.009(0.011) (0.011) (0.011) (0.010) (0.011) (0.010)

Observations 130,476 127,123 127,121 127,121 127,121 89,815

Uncorrected -0.018 -0.018 -0.018 -0.018 -0.018 -0.017(.0038)*** (.0038)*** (.0038)*** (.0038)*** (.0038)*** (.0044)***

Clustered -0.018 -0.018 -0.018 -0.018 -0.018 -0.017(.0137)* (.0138)* (.0138)* (.0138)* (.0138)* (.0124)*

Boostrapped -0.018 -0.018 -0.018 -0.018 -0.018 -0.017(.0119)* (.0118)* (.0131)* (.0105)** (.0116)* (.0113)*

Observations 130,476 127,123 127,121 127,121 127,121 89,815

DD Estimates (PROGRESA)Conditional

Year 1

Year 2

Year 3

…we observe a small and statistically significant effect among women when comparing the baseline with the third follow-up round of the evaluation survey


Empirical results – PRAF (HONDURAS)

Unconditional

Sex(ii) + Household

Size(iii) + Children



Uncorrected 0.010 0.010 0.010 0.010 0.010 -0.008 -0.010(0.011) (0.011) (0.011) (0.011) (0.009) (0.012) (0.012)

Clustered 0.010 0.010 0.010 0.010 0.010 -0.008 -0.010(0.018) (0.018) (0.019) (0.019) (0.016) (0.022) (0.022)

Boostrapped 0.010 0.010 0.010 0.010 0.010 -0.008 -0.010(0.009) (0.011) (0.011) (0.011) (0.010) (0.015) (0.014)

Observations 18,965 18,965 18,778 18,778 13,574 13,574 13,509

DD Estimates (PRAF)

Conditional

Females

Unconditional(ii) + Household




Uncorrected 0.010 0.011 0.011 0.011 -0.020 -0.022(0.023) (0.023) (0.023) (0.019) (0.026) (0.026)

Clustered 0.010 0.011 0.011 0.011 -0.020 -0.022(0.028) (0.029) (0.029) (0.025) (0.042) (0.043)

Boostrapped 0.010 0.011 0.011 0.011 -0.020 -0.022(0.013) (0.016) (0.015) (0.014) (0.023) (0.027)

Observations 8,077 7,981 7,981 5,803 5,803 5,776

DD Estimates (PRAF)Conditional

The same lack of effect of the program on the labor supply of adults is evident when breaking down these results by gender. Employment rates are high (91-92 percent) for adult males, and relatively low for adult women (36-33 percent), but the program seems to make no difference in the labor supply of either group.

The estimates of the treatment indicators are small and not significantly different from zero at the standard levels. This result holds for the seven specifications, and for the three estimates of the standard errors.

Source: Own calculations based on program evaluation on surveysStandard errors in parenthesesBootstrapped errors obtained by CRVE-Block Bootstrap with 100 replications* Significant at 10%; ** significant at 5%; *** significant at 1%

Empirical results – RPS (NICARAGUA)


Unconditional

Sex(ii) + Household




Uncorrected -0.028 -0.028 -0.021 -0.021 -0.021 -0.003 -0.003(.0092)*** (.0092)*** (.0097)** (.0097)** (.0097)** -0.013 -0.013

Clustered -0.028 -0.028 -0.021 -0.021 -0.021 -0.003 -0.003(.0187)* (.0187)* (0.018) (0.018) (0.018) (0.027) (0.028)

Boostrapped -0.028 -0.028 -0.021 -0.021 -0.021 -0.003 -0.003(.0113)*** (.0118)*** (.0136)* (.0121)** (.0109)** (0.016) (0.018)

Observations 9,375 9,375 9,309 9,309 9,309 9,309 9,230

DD Estimates (RPS)

Conditional

Females

Unconditional(ii) + Household




Uncorrected -0.062 -0.053 -0.053 -0.053 -0.009 -0.008(.0151)*** (.0159)*** (.0159)*** (.0159)*** (0.021) (0.021)

Clustered -0.062 -0.053 -0.053 -0.053 -0.009 -0.008(.0266)*** (.0259)** (.0259)** (.0259)** (0.036) (0.037)

Boostrapped -0.062 -0.053 -0.053 -0.053 -0.009 -0.008(.0178)*** (.0167)*** (.0199)*** (.0166)*** (0.025) (0.023)

Observations 4,612 4,587 4,587 4,587 4,587 4,543

DD Estimates (RPS)Conditional

The difference for men is small and not significantly different from zero. The difference for women is much larger (6.2 percent for the unconditional estimate) and strongly significant although the effect again becomes much smaller (and not significantly different from zero) once the number of children and the education level are included in the regressions.This effect, however, seems to

vanish when including controls for the age and the education of the individuals in the regression, and the levels of significance are also affected by the inclusion of other controls.

Here, employment is 2.8 percent lower in treatment localities, and this difference is significant at the 1 percent level with the bootstrapped standard errors, and at the 10 percent level with the cluster-adjusted estimates.

So far… The evidence on the impact of the three CCTs

programs on employment rates is mixed at best. It only seems statistically significant for the

medium term round of the PROGRESA evaluation, and for the RPS program in some specifications.

The effect, in both cases, seems to be driven by the labor supply of women, since the differences between the treatment and the controls are never significant for adult men.

Other outcomes The programs, however, might affect not only the

extensive margin of the labor supply but also its intensive margin.

Results on hours of work: PRAF HONDURAS: negative and significant effect of PRAF on hours of work.

These effects are mostly due to a reduction in the hours worked by men. PROGRESA MEXICO: no effect on hours worked when considering all adults.

However, women in the third year follow-up survey (those among whom a negative impact on participation was observed) exhibited a positive difference in hours worked, significant with cluster-adjusted standard error for most specifications.

RPS NICARAGUA: no impact.

Results on occupational choice: This is especially relevant in the case of rural areas in Latin America given the

results of Skoufias et al. (2008), who find that the PAAL program in Mexico induced workers to move away from agricultural work.

PRAF HONDURAS: seems to reduce participation in agricultural activities, with significant impacts among men when not controlling for age and education.

PROGRESA MEXICO and RPS NICARAGUA: no impacts.

Evidence from non-experimental

evaluations

Evidence from non-experimental evaluations

Programs and data

Evidence from non-experimental settings: Brazil-Bolsa Familia Mexico-PROGRESA/Oportunidades Colombia-Familias en Acción

Use of “regular” household surveys, where we can identify beneficiaries. The surveys are: the Encuesta Nacional de Ingreso y Gasto de los Hogares

(ENIGH) from México for the year 2002, the Pesquisa Nacional por Amostra de Domicílios (PNAD) from

Brazil for the year 2004, and the Encuesta de Evaluación de Familias en Acción from

Colombia for the year 2004.

Non-experimental setting estimation:Propensity score matching

Imbens (2008) step-wise procedure to choose the variables of the PS.

NN and Gaussian kernel matching, SE corrected according to Abadie and Imbens (2006).

Empirical results – Oportunidades (MEXICO)Labor Force Participation

Average outcome of the matched TREATED

Average outcome of the matched CONTROL

ATET Observations

All 0.665 0.643 0.022 7655(0.0099)**

Male 0.953 0.943 0.010 3582(0.0067)

Females 0.409 0.370 0.039 3940(0.0130)***

Females without children 0.472 0.367 0.105 920(0.0294)***

Females with children 0.391 0.364 0.028 3016(0.0139)**

Females heads of households 0.740 0.676 0.063 738(0.0344)*



ATET Observations

All 0.674 0.648 0.026 3180(0.0140)*

Male 0.963 0.962 0.001 1478(0.0071)

Females 0.419 0.370 0.049 1689(0.0191)**

Females without children 0.469 0.365 0.105 228(0.0404)***

Females with children 0.411 0.373 0.038 1460(0.0243)

Females heads of households 0.711 0.718 -0.007 263(0.0365)

Nearest Neighbor Matching

Kernel Matching

Source: Own calculations based on household surveys; Standard errors in parentheses* Significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent

The treatment effect on adult labor force participation is positive and statistically significant in all women sub-samples using nearest neighbor matching and for women and women without children when we use kernel matching. The magnitudes of program’s effect are slightly higher using kernel matching.

Empirical results – Oportunidades (MEXICO)Employment (more than 35 hours)



ATETObservation

sAll 0.695 0.700 -0.005 4942

(0.0125)Male 0.858 0.838 0.020 3430

(0.0109)*Females 0.359 0.385 -0.026 1497

(0.0231)Females without children 0.390 0.390 0.000 357

(0.0415)Females with children 0.344 0.375 -0.031 1124

(0.0256)Females heads of households 0.488 0.584 -0.096 525

(0.0329)***



ATETObservation

sAll 0.686 0.693 -0.007 2144

(0.0156)Male 0.851 0.838 0.013 1434

(0.0146)Females 0.347 0.388 -0.041 704


(0.0634)Females with children 0.348 0.383 -0.035 598

(0.0342)Females heads of households 0.575 0.596 -0.021 186

(0.0541)


Kernel Matching

The program reduces employment of women heads of household and the treatment effect is significant at the 1 percent level using nearest neighbor matching.

However, the result is not robust to the matching estimator and employment definition (more than 35 hours versus more than 20 hours): the treatment effect is not statistically different from zero using kernel matching and under the second employment definition (more than 20 hours).




ATET Observations

All 0.832 0.817 0.015 4942(0.0097)

Male 0.955 0.923 0.032 3430(0.0070)***

Females 0.597 0.573 0.023 1497(0.0243)

Females without children 0.646 0.588 0.058 357(0.0481)


Females heads of households 0.757 0.738 0.019 525(0.0351)



ATET Observations

All 0.828 0.827 0.001 2144(0.0120)

Male 0.941 0.940 0.002 1434(0.0095)

Females 0.597 0.590 0.007 704(0.0308)

Females without children 0.623 0.527 0.095 106(0.0538)*




Kernel Matching

On the contrary, the program’s effect is positive and statistically significant for men using both employment definitions and nearest neighbor matching.

Finally, we found a positive effect for the sample of women without children at a much lower significance level.

Empirical results – Oportunidades (MEXICO)Employment (more than 20 hours)


Empirical results – Bolsa Familia (BRAZIL) Labor Force Participation



ATET Observations

All 0.670 0.642 0.028 83906(0.0082)***

Male 0.877 0.847 0.030 39046(0.0088)***

Females 0.485 0.465 0.020 41321(0.0126)






ATET Observations

All 0.677 0.645 0.032 3468(0.0088)***

Male 0.890 0.857 0.033 1619(0.0081)***

Females 0.490 0.462 0.028 1849(0.0138)**





Kernel Matching


The program has a significant and positive impact on adult labor force participation for the whole sample and men using nearest neighbor matching and also for women and women with children when we use kernel matching.

Empirical results – Bolsa Familia (BRAZIL) Employment (more than 35 hours)



ATET Observations

All 0.667 0.716 -0.049 55157(0.0098)***

Male 0.865 0.880 -0.016 34414(0.0097)

Females 0.370 0.462 -0.092 11243(0.0168)***

Females without children 0.537 0.540 -0.003 310(0.1009)

Females with children 0.368 0.456 -0.088 10731(0.0172)***

Females heads of households 0.582 0.671 -0.089 3550(0.0249)***



ATET Observations

All 0.652 0.722 -0.070 2348(0.0103)***

Male 0.851 0.877 -0.026 1442(0.0102)**

Females 0.337 0.464 -0.127 905(0.0160)***



Females heads of households 0.595 0.681 -0.087 365(0.0242)***


Kernel Matching

The effect on employment is negative and highly significant in statistical terms for all sub-samples when using the first definition of employment with the exception of women without children. The impact is more pronounced for women.


Empirical results – Bolsa Familia (BRAZIL) Employment (more than 20 hours)



ATET Observations

All 0.717 0.740 -0.023 66813(0.0089)**

Male 0.884 0.884 0.000 39004(0.0097)

Females 0.487 0.543 -0.055 15122(0.0157)***






ATET Observations

All 0.715 0.747 -0.031 2572(0.0093)***

Male 0.891 0.887 0.004 1517(0.0091)

Females 0.463 0.537 -0.075 1055(0.0166)***





Kernel Matching


When we turn to the second definition of employment we find no program effect over the decision to work of males and a negative impact for women and women with children. So Bolsa Família seems to have a clear disincentive effect over decision to work of women.



ATET Observations

All 0.751 0.762 -0.011 57838(0.0098)

Male 0.944 0.943 0.000 25451(0.0074)

Females 0.554 0.572 -0.018 25182(0.0156)


Females with children 0.547 0.567 -0.020 21899(0.0168)




ATET Observations

All 0.753 0.770 -0.018 5434(0.0091)*

Male 0.945 0.947 -0.002 2792(0.0065)

Females 0.550 0.583 -0.033 2641(0.0168)*


Females with children 0.542 0.576 -0.034 2277(0.0152)**



Kernel Matching

Empirical results – Familias en Accion (COLOMBIA) Labor Force Participation

Contrary to Oportunidades and Bolsa Família, Familias en Acción has a negative impact on labor force participation of women and women with children.


Empirical results – Familias en Accion (COLOMBIA) Employment (more than 35 hours)



ATETObservation

sAll 0.556 0.557 -0.001 35637

(0.0129)Male 0.689 0.687 0.002 22721

(0.0152)Females 0.337 0.320 0.017 12497


(0.0507)**Females with children 0.323 0.316 0.007 10591

(0.0216)Females heads of households 0.530 0.525 0.005 5558

(0.0333)



ATETObservation

sAll 0.564 0.556 0.008 4096

(0.0122)Male 0.687 0.683 0.003 2642

(0.0153)Females 0.341 0.320 0.021 1454


(0.0541)*Females with children 0.329 0.321 0.008 1236

(0.0225)Females heads of households 0.538 0.530 0.008 666

(0.0271)


Kernel Matching

The estimated program effect over employment is positive and statistically significant for women without children using both matching estimators under the first definition of employment …


Empirical results – Familias en Accion (COLOMBIA) Employment (more than 20 hours)



ATET Observations

All 0.863 0.841 0.023 38128(0.0091)**

Male 0.885 0.880 0.005 24010(0.0101)

Females 0.831 0.788 0.043 13001(0.0165)***






ATET Observations

All 0.866 0.845 0.021 4176(0.0085)**

Male 0.885 0.879 0.006 2683(0.0102)

Females 0.831 0.788 0.043 1489(0.0158)***

Females without children 0.815 0.741 0.075 222(0.0420)*




Kernel Matching

… and women with children under the second.

Conclusions

CCTs and the labor supply of adults Results: some non-significant impacts

(experimental evaluations) and some negative impacts (non-experimental).

CCT programs have a very limited impact on employment, when they have an effect at all.

In those cases, the small observed reductions in different measures of labor supply correspond mostly to women.

Further research could concentrate on establishing whether there is indeed a displacement effect, in which work in the market is not substituted by leisure (which would increase the individual’s utility) but by other activities.

CCTs and the labor supply of adults No policy conclusions yet, but if anything, there do not

appear to be major distortions in the labor supply of adults.

Might still be an issue in programs with more significant transfers.

However, important not to rule out the opposite case: transfers and school attendance might mitigate fixed money and time costs of labor participation.

Facilitate labor participation as a program component: job intermediation, training, free nurseries, kindergarden, transport cost subsidies? (Not as work requirements but as services to the poor to facilitate integration into the labor market if so desired).

Thank you!

maría laura alzúa guillermo cruces centro de estudios distributivos, laborales y sociales

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