1. 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

2. I.What are “ex-ante evaluations”? 1.An “ex-ante evaluation” is a comparison of a (counterfactual) prediction of the outcomes of a program, with the (actual) absence of the program. a.Compare how an ex-post evaluation approximates the counterfactual... b.With how an ex-ante evaluation does it:

3. I.What are “ex-ante evaluations”? 2.In the absence of actual data on program participants, y s must be simulated. 3.Simulation requires a model Simple arithmetic simulations Behavioral (partial equilibrium) simulations General equilibrium models 4.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? 1.To provide an estimate (or prediction) of the impact of programs that do not yet exist. 2.To compare predicted impacts (and costs) for various alternative designs (even for an existing program). 3.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? 1.Are the school enrollment incentives built into CCTs effective? (Do households change their behavior in response to the program?) 2.What is the impact of the program on current poverty and/or inequality?

9. 1. 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 U i (0) = Z i.  0 +  0.(Y -i + y j0 ) + v i0 U i (1) = Z i.  1 +  1.(Y -i + y j1 ) + v i1 U i (2) = Z i.  2 +  2.(Y -i + y j2 ) + v i2

12. 2. The Model Child i’s Contribution to Household Income in state j = 0, 1 or 2: y i0 = w i ; y i1 = Mw i ; y i2 = Dw i with w i = market earnings: Log w i = X i.  + m*Ind(S j =1) + u i and M = Exp (m)

13. 2. The Model Household (kid) i chooses the alternative j that yields the highest utility U i (j): U i (0) = Z i.  0 +  0.Y -i +  0.w i + v i0 U i (1) = Z i.  1 +  1.Y -i +  1.w i + v i1 U i (2) = Z i.  2 +  2.Y -i +  2.w i + v i2 with  0 =  0 ;  1 =  1 M;  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 v ij 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 w i = X i.  + m*Ind(S j =1) + u i

16. 4. Estimation (by age) Usual estimation of ML generates estimates of differences γ j -γ 0, α j -α 0, β j -β 0 only (j= 1,2). Since income variables are asymmetric across occupational alternatives, this is insufficient. (Need all three αs). But:

17. 4. Descriptive Statistics and Estimation Results

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

22. 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)

23. 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 Y 0

25. Conclusions 1.Bolsa Escola was rather effective in inducing additional enrollment (60% of poor kids out of school enroll in simulation) 2.Role of conditionality is key: substitution rather than income effect. 3.Likely to INCREASE number of children studying and working. (School duration?) 4.The program’s impact on current poverty and inequality is modest, due largely to low transfer amounts.

26. Conclusions (ctd.) 4.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

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

28. 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.

29. 1. Policy questions 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?

30. 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

31. Assume a perfectly inelastic labor demand (matters for predicting program costs.) Ignore benefits from any assets created by scheme. LDLD LSLS 2. Underlying assumptions:

32. 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:

33. 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):

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

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

36. 3. Data Schedule 10, 55 th 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

37. 4. Simulated impact of 2 ESG wages: 1.Anchor the EGS wage rate to the official poverty line (Daily wage rate per working adult around 40 would allow to reach poverty line) 2.Existing (State-level) minimum wages (adjusted for average working hours) around 50

38. 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

39. 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)

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

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

42. 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

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

44. ESG better 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

45. 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 L D Did not consider way program is financed (taxation, cutting other programs). Different welfare implications for the poor. 5. Results: underlying assumptions/caveats

46. 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.