Human capital interventions Design and evaluation

1. Human capital interventionsDesign and evaluation

2. Introduction • In this lecture we will be looking at human capital interventions: • Education • Nutrition • Health • Why are they important? • Externalities in HK and the role of HK in the growth process • Imperfections in credit markets • ‘Imperfect’ Altruism and ‘excessive discounting’ • We will also be looking at the issue of evaluating the effect of specific interventions.

3. The evaluation problem: • Main problem is evaluating • We observe: • When can we say that:

4. The evaluation problem • Assignment of the program is not necessarily random. • Assignment of or participation to the program can be related to the outcome of interest. • We need either something that mimics a random assignment or an explicit model of participation and its relationship to the outcome of interest

5. Main approaches: • Randomized trials • Selection on observables • Linear controls and OLS • Propensity Score Matching • Difference in difference methods • Selection on unobservables • Instrumental Variables • Control functions • Structural models

6. Randomized trials • This is the easiest solution, when available. • Compare treatment and control individuals to get the effect as they are ‘identical’. • Issues: randomization across individuals or across areas? • Problems: • Political impopolarity • Difficulty in extrapolation • Contamination of controls • Availability of alternative programs • Anticipation effects

7. Selection on observables • Assumption is that conditional on a set of observables program assignment or participation is random. • Two possible strategies: • Parametric (linear) dependence • Non- Parametric.

8. Selection on observables: parametric dependence

9. Selection on observables: • Flexible dependence of Y on X: compare treatment and control individuals for the same value of X. • But if the dimension of X is large this can be difficult and requires a very large sample. • Propensity Score Matching provides the solution. • It has been shown that instead of conditioning on all X, one can condition on the probability of receiving the treatment given the X

10. Propensity score matching • Two steps: • Estimate the probability of receiving the treatment as a function of X (logit, probit) • Compare treated individuals to control individuals that have similar P(X). • Different methodologies: • Nearest neighbour • Splines • Kernel methods • Common support problems • The method allows for heterogeneous effects: we compute the average treatment effect

11. Difference in difference methods • Assumption of PSM is that there are no unobserved characteristics that are related to outcome of interest. • If there are, this can be problematic. • However, diff in diff can solve this problem if the unobserved factor is constant • … and if we have data before and after the program for treatment and control. Y(i,t) = e T + bt + gt + u

12. Example 1: gT =gC: e=YT2-YC2

13. Example 2: gT ≠ gC: YT2-YC2= e+gT-gC YT1-YC1 = gT-gC e= (YT2-YC2 )– (YT1-YC1)

14. Difference in difference methods • To the previous equations we can add control variables. • We can effectively combine PSM methods and diff in diff methods. • The crucial assumption is that of common trends and constant unobserved differences.

15. Selection on unobservables • We model the participation process. • We need a variable that affects participation (or program assignment) and does not affect outcomes • This assumption is not testable. • IV and control methods

16. PROGRESA • Progresa is a large welfare program in rural Mexico. • It has three components: health, nutrition and education. • We will focus on the education component • The program is now very large: it covers around 50000 villages and 11 million people. It is worth about 0.2% of Mexican GDP • It targets poor villages and the poorest households within each village. • All grants are given to the mothers. Most grants are conditional on some type of behaviour

17. Bimonthly amount of monetary supports (Mexican Pesos) Type of Benefit 1998(1st semester) 1998 2nd semester 1999 (1st semester) 1999 (2nd semester) Nutrition support 190 200 230 250 Primary 3 130 140 150 160 4 150 160 180 190 5 190 200 130 250 6 260 270 300 330 Secondary First year Boys 380 400 440 480 Girls 400 410 470 500 Second year Boys 400 420 470 500 Girls 440 470 520 560 Third year Boys 420 440 490 530 Girls 480 510 570 610 Maximum support 1,170 1,250 1,390 1,500 The education component of PROGRESA Bi-monthly grants

18. Bimonthly Payments per Family Minimum 250 Average per family 504 Average per family with children in school 750 Maximum 1500 Source: PROGRESA ( July-December 1999) In Mexican Pesos

19. The evaluation sample • When the programme was started, the administration decided to use a randomized sample to evaluate it. • In 1997 a sample of 506 villages was chosen and 186 were randomized out of the programme for two years. • In March 1998 a first survey was collected in all villages • In June 1998 the programme was started, except in the control villages • In November 1998, March 1999 and November 1999 additional waves were collected in all villages • In December 1999 the Programme was started in all villages

20. How good is the randomization? • Overall the randomization seems reasonably good • Behrman and Todd(2000)

21. Comparing treatment and control • If we are only interested in the effect of the program on enrolment (or enrolment of children of a given age or a given level of schooling)… • … and if we think that announcement effects on controls are not important… • … and if we think that the randomization is appropriate…. • We can compute the effect of the program comparing enrolment in treatment and control villages after the implementation of the program

22. A structural model to estimate the effect of PROGRESA • As an alternative (or a complement) to the use of diff in diff (or simple diff) we can also try to use a structural model of individual behaviour • We can use the variation induced by the program to estimate a more flexible model • Advantages: • Ability to extrapolate • Emphasis on mechanisms • Ability to allow for anticipation effects • Disadvantages: • Lots of assumptions!

23. The model

24. The model • The model is dynamic for two reasons: • ‘habits’ : school today affects utility of school tomorrow. • Finite number of years in which children can go to school. Going to school today buys the possibility of reaching high grades. • Initial education stock is modelled as a order probit which depends on a number of variables + lagged school availability • The last variable is crucial for identification.

25. Value functions

26. Modelling Progresa • We can easily insert the grant in the framework above, changing the value of going to school • Notice that the presence of a randomized experiment allows us to estimate a different coefficient on the grant and on the wage using exogenous variation.

27. Assumptions • Children cannot go to school past age 18. • The value at age 18 for each completed grade is observed and known with certainty • VT(i)=c*/(1-d*exp(i)) • We estimate c and d • The only uncertainty comes from costs and from passing the grade • Each child decides independently (we ignore siblings)

28. Assumptions (ctd) • Treatment villages are assumed to believe the program is permanent • We use different assumptions about when the program is implemented in the control villages • 1 year • 2 years • never

29. Assumptions (ctd) • No liquidity constraints • No uncertainty on returns to education (but they are estimated) • No uncertainty about own ability

30. Notice that assuming an extreme value distribution for residuals and zero discount factor we get a logit. • We make a slightly more general assumption about unobserved heterogeneity: extreme value conditional on type. L types each observed with probability πl • Similar assumption is made on heterogeneity in slopes. • Maximum likelihood estimation

31. Measurements • Village specific costs of going to school • primary and secondary • Distance and travel time • Direct costs • Village specific children wage (log linear in age and grade) • Background variables

32. Sample selection and specification • We select boys • We select children older than 9 • We use parental background (education, ethnicity) and state of residence as cost variables • We use costs of secondary and distance to secondary as direct costs of going to school • Discount factor

36. Simulations • We can now simulate various versions of the model and estimate the effect of the program as well as that of different programs. • Also study heterogeneity and other issues