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DECRG Course Poverty and Inequality Analysis Module 6: Poverty Impacts of Policies and Programs

DECRG Course Poverty and Inequality Analysis Module 6: Poverty Impacts of Policies and Programs. Ex-Ante Evaluation of Policy Reforms Using Behavioral Models Francisco H. G. Ferreira. I. What are “ex-ante evaluations”?.

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DECRG Course Poverty and Inequality Analysis Module 6: Poverty Impacts of Policies and Programs

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  1. DECRG CoursePoverty and Inequality AnalysisModule 6: Poverty Impacts of Policies and Programs Ex-Ante Evaluation of Policy Reforms Using Behavioral Models Francisco H. G. Ferreira

  2. I. What are “ex-ante evaluations”? • An “ex-ante evaluation” is a comparison of a (counterfactual) prediction of the outcomes of a program, with the (actual) absence of the program. • Compare how an ex-post evaluation approximates the counterfactual... • With how an ex-ante evaluation does it:

  3. I. What are “ex-ante evaluations”? • In the absence of actual data on program participants, ys must be simulated. • Simulation requires a model • Simple arithmetic simulations • Behavioral (partial equilibrium) simulations • General equilibrium models • Models may also be structural or reduced-form, provided reduced form does not change under the new policy and one is not interested in the deep structural parameters.

  4. II. What are they for? • To provide an estimate (or prediction) of the impact of programs that do not yet exist. • To compare predicted impacts (and costs) for various alternative designs (even for an existing program). • They are complementary to (not substitute for) ex-post evaluations.

  5. III. Implementation: A Five-Step Process • Step 1: Identify a well-defined, tractable policy reform question. • Step 2: Write the simplest economic model able to capture the factors that are likely to determine the agents’ response to the policy reform. • Step 3: Find a data set that contains reliable information on the variables that need to be included in the model. • Step 4: Estimate the model on the data set. • Step 5: Simulate the policy reform using the empirical estimate of the model.

  6. IV. Two applications from developing countries • Conditional Cash Transfers: Brazil’s Bolsa Escola Program (Bourguignon, Ferreira, Leite WBER 2003) • Employment Guarantee Scheme in India (Murgai, Ravallion 2005, World Bank Policy Research w.p.)

  7. Bolsa Escola Program in Brazil (Bourguignon, Ferreira, Leite)

  8. 1. The policy reform question • How would the introduction of a conditional cash transfer perform with respect to its twin stated objectives: the reduction of current and future poverty? • Are the school enrollment incentives built into CCTs effective? (Do households change their behavior in response to the program?) • What is the impact of the program on current poverty and/or inequality?

  9. The policy reform question: The Bolsa Escola Program • Means-test: income per capita less than R$90 (50% of the 1999 minimum wage) • Conditionality : 6-15 year-olds must • Be enrolled in school. • Attend at least 85% of classes. • Transfer : R$15 per child in school • Limit : R$45 per household • Monitoring at the local and federal levels • Introduced in July 2001. No ex-post evaluations by 2003.

  10. 2. The Model • For simplicity, we make four key simplifying assumptions • Gloss over debate on who makes the child’s occupational decision. • Adult behaviour unaffected by child-level variables • Sibling interactions ignored • Household composition exogenous.

  11. 2. The Model • Child’s occupational choice: (0) Not going to school (paid or unpaid work); (1) Going to school and paid work; (2) Going to school and no paid work Ui(0)= Zi.0 + 0.(Y-i + yj0)+ vi0 Ui(1)= Zi.1 + 1.(Y-i + yj1) + vi1 Ui(2)= Zi.2 + 2.(Y-i + yj2)+ vi2

  12. 2. The Model • Child i’s Contribution to Household Income in state j = 0, 1 or 2: yi0 = wi ; yi1 = Mwi ; yi2 = Dwi with wi = market earnings: Log wi = Xi . + m*Ind(Sj=1) + ui andM = Exp (m)

  13. 2. The Model Household (kid) i chooses the alternative j that yields the highest utility Ui(j): Ui(0)= Zi.0 + 0.Y-i + 0.wi+ vi0 Ui(1)= Zi.1 +1.Y-i + 1.wi + vi1 Ui(2)= Zi.2 + 2.Y-i + 2.wi + vi2 with 0 = 0 ; 1 = 1M; 2 = 2 D

  14. 3. The Data • Pesquisa Nacional por Amostra de Domicílios (PNAD, 1999). • Approx. 60,000 households. • Representative of country, except for rural areas of Acre, Amazonas, Pará, Rondônia and Roraima. • Labor status questions asked if age >= 10. • Enrolment (but no attendance) questions. • Reasons to suspect income variables in rural areas (see FLN, 2003)… but staple hh survey in Brazil. • Bolsa Escola Program not in effect!

  15. 4. Estimation (by age) • If vij are i.i.d. and have a double exponential distribution, then this discrete choice model can be estimated as a multinomial logit: • Earnings estimation: Log wi = Xi . + m*Ind(Sj=1) + ui

  16. 4. Descriptive Statistics and Estimation Results

  17. 5. The Simulation Stage Introducing the State-ConditionalTransfer:

  18. 5. Simulation Results. • 40% currently not enrolled would have the incentive to change status and enroll • Impact on children currently working is smaller • Impacts stronger for the poor (means test)

  19. No conditionality vs. Bolsa Escola: conditionality is key • Impact quite sensitive to changes in T, the transfer amount • Impact less sensitive to changes in the means test Y0

  20. Conclusions • Bolsa Escola was rather effective in inducing additional enrollment (60% of poor kids out of school enroll in simulation) • Role of conditionality is key: substitution rather than income effect. • Likely to INCREASE number of children studying and working. (School duration?) • The program’s impact on current poverty and inequality is modest, due largely to low transfer amounts.

  21. Conclusions (ctd.) • Impact on poverty appears to be elastic w.r.t. transfer amount, but less w.r.t. level of means-test. 5. Dynamics: See Attanasio, Meghir, Santiago (2005), IFS, and Todd, Wolpin (AER, 2006) for dynamic simulations using Progresa in Mexico. • Use the randomized experiment to validate dynamic structural models and simulate alternative designs

  22. Guaranteed Living Wage in India (Murgai, Ravallion)

  23. Setting: • The agricultural wage rate is known to be a significant predictor of the rural poverty rate. • Is a “living wage” a feasible anti-poverty program? • Enforcement problems with minimum wages in an underdeveloped rural economy. • Government as an employer of last resort? • In 2005, an Employment Guarantee Scheme was under consideration by the Government of India.

  24. 1. Policyquestions How much impact on poverty can be achieved by a guaranteed wage sufficient to reach the poverty line? Would such a policy be cost effective, relative to an untargeted allocation of the same public expenditure?

  25. 2. Model for casual labor • If employment by the scheme is guaranteed, the EGS effectively establishes a lower bound to the wage distribution. • Impact would then be found amongst workers whose: • (actual or imputed) wage rate is below the EGS wage floor, and • who have labor market characteristics consistent with doing casual labor

  26. LD LS 2. Underlying assumptions: • Assume a perfectly inelasticlabor demand (matters for predicting program costs.) • Ignore benefits from any assets created by scheme.

  27. 2. Model for casual labor (reduced form, parametric) Wages for casual labor: Selection into casual labor: correct for selection through standard Heckman selection correction • Impute predicted wages for those not working • Account also for the possibility of unemployment in the absence of the EGS Probit for unemployment: Expected wage rate in casual labor:

  28. 2. Gains from a guaranteed wage rate • For those already working as casual workers: • For those induced to switch to casual work by EGS (those with a predicted prob. of participation>1/2):

  29. 2. Impacts on poverty Post-EGS counterfactual consumption of household j is: Compare pre- and post-ESG distributions, under different wage schemes.

  30. 2. Cost for the Government • Supply of casual labor • Cost to the government s is the scheme’s labor share of the total cost

  31. 3. Data • Schedule 10, 55th round NSS (1999-00) • 61,000 households; 178,000 adults (15 to 59 years) from the 15 major states • Potential beneficiaries: all adults who are likely to gain from an increase in casual labor market wages, • directly (as EGS participants), or • indirectly due to an increase in wages paid in the market as a whole. • The beneficiaries need not be currently in the labor force: • 65% sample adults in the labor force, 24% in casual wage labor

  32. 4. Simulated impact of 2 ESG wages: • Anchor the EGS wage rate to the official poverty line (Daily wage rate per working adult around 40 would allow to reach poverty line) • Existing (State-level) minimum wages (adjusted for average working hours) around 50

  33. 4. Compare it to alternative anti-poverty scheme: Uniform (untargeted) allocation of the same gross budget, net of administrative costs. • Per-capita consumption of each household rises by: • k share of admin. Cost in the budget • m population size

  34. 5. Results: incidence of gains • Estimated wage elasticity using different EGS wage rates: overall estimate 0.19 • Individual most likely to gain are: • Those already employed as casual laborers • Those induced to switch likely to be less educated, more likely to be from SC/ST • Given the self-targeting mechanism: gains larger for the poorest quintile (1/2 likely to have gain > 0)

  35. 5. results: simulated absolute gains (ATE) from guaranteed minimum wage

  36. 5. Minimum wage scheme vs. uniform transfers: % gains largest for the poor EGS Uniform unconditional transfer

  37. 5. What about impact on poverty?ESG wage around 40: • Cost estimated to be around 3.7% GDP wage around 50: • Cost estimated to be around 5% GDP

  38. 5. What about impact on poverty?budget-neutral uniform transfer Impacts on poverty are larger!

  39. ESGbetter targeted (higher % gains for the poor), but comes with extra costs • forgone income of participants • non-wage costs government (supervision, materials) • Uniform transfer gives same amount to everybody.Untargeted, but lower efficiency costs

  40. 5. Results: underlying assumptions/caveats • Simulated the effect of an employment guarantee scheme, under different wage levels, assuming government absorbs all excess supply at the EGS wage. • With rationing, most likely lose spillover effects to non-participants, assignment more prone to bureaucratic manipulation • Assumed that work requirement binding for participants. Caveat on local capacity on monitoring • Cost implications sensitive to assumptions on LD • Did not consider way program is financed (taxation, cutting other programs). Different welfare implications for the poor.

  41. Conclusions • Ex-ante evaluations: combine micro-econometric estimation of economic models and simulation procedures. • Tool to predict impact of hypothetical programs, or to simulate the likely effect of alternative program designs. • Complementary to ex-post evaluations • Need for testing / benchmarking models.

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