Conditional Cash Transfers, Adult Work Incentives, and Poverty

Abstract

Conditional cash transfer (CCT) programmes aim to alleviate poverty through monetary and in-kind benefits, as well as reduce future incidence of poverty by encouraging investments in education, health and nutrition. The success of CCT programmes at reducing poverty depends on whether, and the extent to which, cash transfers affect adult work incentives. In this paper we examine whether the PROGRESA programme of Mexico affects adult participation in the labour market and overall adult leisure time, and we link these effects to the impact of the programme on poverty. Utilising the experimental design of PROGRESA's evaluation sample, we find that the programme does not have any significant effect on adult labour force participation and leisure time. Our findings on adult work incentives are reinforced further by the result that PROGRESA leads to a substantial reduction in poverty. The poverty reduction effects are stronger for the poverty gap and severity of poverty measures.

Acknowledgements

The authors are grateful to two anonymous referees for constructive comments, Claudia Aburto for excellent research assistance and to Alessandro Tarozzi for useful suggestions. The findings, interpretations and conclusions in this paper are entirely those of the authors and they do not necessarily reflect the view of the World Bank.

Notes

1. In most CCT programmes, the eligibility status of beneficiary households is, in theory, re-examined every few years. For example, in the PROGRESA programme of Mexico, the eligibility status of households was supposed to be reviewed within three years after a household's entry into the programme. In fact, more than five years elapsed before any effort was made to revise the list of beneficiaries.

2. Skoufias (2005) provides a detailed discussion of PROGRESA, the evaluation design and a summary of the impacts of the programme estimated by a large team of researchers.

3. The average monthly transfers during the 12 month period from November 1998 to October 1999 are around 197 pesos per beneficiary household per month (expressed in November 1998 pesos). The calculation of this average includes households that did not receive any benefits due to non-adherence to the conditions of the programme, or delays in the verification of the requirements of the programme or in the delivery of the monetary benefits. On average, households receive 99 pesos for food support, and 91 pesos for the educational grant. For more details, see Hoddinott and Skoufias (2004).

4. Before the establishment of PROGRESA, previous government interventions in the areas of education, health and nutrition in the rural sector of the country consisted of many programmes each intervening separately in health, education or nutrition with little prior coordination or consideration of the potential synergies that could result from a better coordinated and simultaneous intervention.

5. Behrman and Todd (1999) conducted a careful investigation of the extent to which the selection of localities into treatment and control groups can be considered as random. Their analysis did not reveal any significant differences between village means for more than 300 variables in treatment and controls.

6. For more details see Skoufias (2005).

7. The March 1998 baseline survey was not used because it did not include household income and labour supply information for adults.

8. Note that control households started receiving cash benefits in December 1999. Households are first incorporated into PROGRESA, meaning that they are given all the necessary forms and informed of all the programme requirements. A few months later, the cash benefits are sent out by the PROGRESA administration headquarters.

9. Behrman and Todd (1999) state in their investigation of whether assignment to treatment and control groups can be considered random that formal tests of equality between the distributions of various characteristics generally do not reject the hypothesis when the test is performed on locality means. However, when the test is performed on household level data, they find many more rejections of the null than would be expected by chance given standard significance level. This motivates the choice of including controls in our regressions and employ a 2DIF approach.

10. In preliminary analysis, we considered separating non-salaried workers between self-employed workers and unpaid family workers. Nevertheless, the proportions of individuals participating in each of these activities are quite small for all age groups, and the distinction between these activities is often blurred so that we prefer to aggregate these groups in the impact analysis.

11. This is the poor status after the densifcacion, the revision of the eligibility that raised the number of households eligible for the programme from 52 to 78 per cent. It has to be noticed that the fraction of households that actually ended up receiving the PROGRESA cash transfers during the two-year interval covered by the evaluation sample is just under 65 per cent, due to administrative errors and delays in the final registration of beneficiary households.

12. The complete set of parameter estimates is available directly from the authors upon request.

13. The estimates reported were obtained using the ‘dprobit’ command in STATA v7.0. They can be easily converted into percentage changes or elasticities by dividing the marginal effect by the pre-programme level, both reported in Table 2.

14. Activities are: Working for salary or wage, in own business or family land; Attending school; Doing homework after school; Community work; Voluntary work for neighbors or other relatives; Purchasing food or other products for the household (HH); Sewing, making clothes for HH members; Taking HH members to school, clinic, or work; Cleaning house; Washing and ironing clothes for HH members; Preparing food; Fetching water, firewood or throwing out trash; Taking care of animals; Taking care of small children, elderly and sick; Making HH repairs; Transportation time to work, school, market and so forth; Other activities.

15. Skoufias and Parker (2001) and Schultz (2004) study the impact of PROGRESA on the work time and school attendance of school-age children. In particular, Skoufias and Parker (2001) find significant increases in the school attendance of boys and girls that are accompanied by significant reductions in the participation of boys and girls in work activities.

16. Note, however, that the results on leisure do not necessarily suggest that there has been no reallocation of time between work activities for adults. For instance, there may have been a substitution towards more time in domestic work and less time in market work. (for results on this issue see Parker and Skoufias, 2000).

17. Our measure of income is based on reported income, with this bringing into the picture all the possible biases due to misreporting (especially underreporting) of income. As stated above, eligibility for the programme was decided based on socioeconomic data collected in the 1997 baseline household census (ENCASEH) before the start of the programme. From our knowledge, households were not told that the ENCASEH information was going to be used to discriminate eligibility. In addition to this, households did not have to apply for the programme but this was universally given to all households identified as eligible in the treatment localities. This suggests that there might not be a clear and strong incentive to misreport income. In general, evidence on this issue is scarce due to the severity of data requirements. Martinelli and Paker (2006) study this issue with data from the urban evaluation sample of Oportunidades; it has to be noticed that the rural and urban component of Progresa have quite different characteristics: for example, households need to apply for the programme in the urban version and find that while underreporting is widespread, over reporting is also common in goods that may have a ‘status’ value.

18. The absence of reliable information on household consumption prior to the start of the programme precluded the use of consumption as a measure of the poverty impacts of the programme. For more details on the consumption impacts of PROGRESA see Hoddinott and Skoufias (2004).

19. We do not consider value of leisure in our definition of income (that is, our income is not full income). Our definition of income is consistent with that used for deciding eligibility for the programme. It is important to stress that the effect of the programme on poverty may be depending upon the definition of income; for example, PROGRESA would have a priori a positive effect on poverty in case we employed a full income approach (and so we considered the value of leisure).

20. Many studies have considered whether the introduction of public transfers affects private transfers among the households targeted by the public scheme. For example, Cox and Jimenez (1990) argue that public cash transfers may reduce the amount of private transfers to low-income households so that the net-income effect may be significantly less than the value of the public transfer. Albarran and Attanasio (2003) study this issue with PROGRESA data and find that the programme does crowd our private transfers: both the likelihood of receiving a transfer and the amount received conditional on receiving private transfers are significantly and negatively affected by the programme.

21. FGT poverty measures are related to stochastic dominance. In particular, first-order stochastic dominance (SD) implies that all P(α) for α > 0 are robust to the choice of the poverty line; the same applies for all α > 1 for second-order SD and for all α > 2 for third-order SD.

22. Along similar lines, β₀ + β _R2 is the poverty rate in control localities in round 2 and β₀ + β _T  + β _R2 + β _TR2 is the poverty rate in treatment localities in the same round.

23. We have also estimated the impact of the programme by symmetrically trimming the top and bottom five per cent of the sample of observations in each round so as to eliminate extreme outliers from the sample. Using the trimmed sample resulted in slightly lower impacts of the programme on poverty. Overall, however, the estimates of the impact of the programme on poverty did not change the results presented above in any substantial manner, which implies that the role of outlier observations in income is trivial.

24. The estimated CDF gives the P(0) for any level of income. Poverty deficit curve is defined as the area under the CDF up to some poverty line. If the poverty deficit curve of one distribution lies above the poverty deficit curve of another, the first distribution will always have more poverty according to the poverty-gap measure, P(1).