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Program evaluation, Spring 2010 Džanić Alen Levina Olga

Income Gains to the Poor from Workfare Estimates for Argentina’s Trabajar Program Jyotsna Jalan and Martin Ravallion. Program evaluation, Spring 2010 Džanić Alen Levina Olga. Trabajar Program.

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Program evaluation, Spring 2010 Džanić Alen Levina Olga

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  1. Income Gains to the Poor from WorkfareEstimates for Argentina’s Trabajar ProgramJyotsna Jalan and Martin Ravallion Program evaluation, Spring 2010 Džanić Alen Levina Olga

  2. Trabajar Program • Who: Least skilled unemployed workers, preferably the heads of household, without any other income sources. • What: Participants receive a monthly benefit below the minimum wage from government. During that period they had to work 20-40 hours per week on communitarian projects at public or non-profit organizations • Why: Act as a short-term safety net and increase employability among the least skilled workers

  3. Data • Two household surveys: • Program participants • National Sample Survey Done by government’s statistics office (INDEC) using the same questionnaire, same interviewing teams and at approximately same time • National Sample Survey (EDS): • Covers population in localities with 5000 or more residents • Such localities totaled to 420 and represented 96% of the urban and 84% of the total population • 114 localities were sampled • Used to construct control group • Program Participants Survey • In 1997 300 projects in localities from • EDS sample were randomly selected • and 50 projects for replacement purposes • Focus on participants who received wages • from Trabajar in August 1997 which is • 80% of the sample • With this restriction total number of • Participants is 2802.

  4. Method of Estimating the Gains from Workfare • Using the two sets of data N program participants are matched with comparison group of non-participants from the population • Matching performed conditioning on P(X) P(X)=Prob(D=1 X) • The propensity score is calculated for each observation using standard logit models • The matching estimators used: • Nearest neighbour • Average income of the closest 5 matched non-participants and compares this to the participants income • Kernel weighted estimator • After matching, the comparison group of nearest neighbors drawn from the national sample has a mean score of 0.394 (0.253), very close to that of the Trabajar sample 0.405 (0.266)

  5. Net income gains from the program using different estimators

  6. Percentage of participant housholds using different estimator

  7. Net income gains from the program

  8. Net income gains for female participants

  9. Income gains for 15-24 years of age

  10. Income gains for 25-64 years of age

  11. Mean income gain plotted against pre-intervention income

  12. Impact estimator biasedness due to selection on unobservables • Yi=α + βPi+ γRi + δZi + υi Z– vector of control variables Yi – income of the houshold i Ri – residuals from the participation model Selection bias is indicated if we can reject the null that γ=0 β, the coefficient on participation was 154.358 (t=5) γ, the coefficient on the residual was 4.064 (t=0.4) Therefore, selection bias on an unobservables is not an important concern.

  13. Conclusions • Average gains are very similar between men and women, but are higher for younger workers. • Higher female participation would not enhance average income gains,and the distribution of the gains would worsen. • Higher participation by the young would raise average gains, but also worsen the distribution. • After matching, tests suggests that selectivity bias (due to unobservables) is a negligible problem.

  14. Evaluation IDid program really help the participants? • Decline in unemployment and reduction of poverty in late 1990s has been presented by government as evidence of the positive income and employability effects of program. Paper confirms it. • Short term-beliveable, but long term? Did the program make participants more competitive on the job market or just offered them short-term help? • With data for only 1997-1998 not much can be claimed about long term effects.

  15. Evaluation IIIs the sample adequate? • Authors randomly chose 350 out of 16 000 projects in Trabajar program (2.19%) • Projects on average employed 20-30 people. Taking the lower limit, projects from sample might employ around 7000 people. • Some participants were dropped from the random sample becouse their address could not be found or they stopped responding. Total 2802 were included (40%)

  16. Evaluation III • Program run by government institutions, not independent bodies • Jobs given to people inside the political party, friends and family of local politicians?

  17. Evaluation IV Given the available data, we do not believe some extreme improvements could be done, but results might be questionable

  18. ???

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