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Maria D. Fitzpatrick The B. E. Journal of Economic Analysis and Policy 2008

Starting School at Four: the Effect of Universal Pre-Kindergarten on Children’s Academic Achievement. Maria D. Fitzpatrick The B. E. Journal of Economic Analysis and Policy 2008.

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Maria D. Fitzpatrick The B. E. Journal of Economic Analysis and Policy 2008

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  1. Starting School at Four: the Effect of Universal Pre-Kindergarten on Children’s Academic Achievement Maria D. Fitzpatrick The B. E. Journal of Economic Analysis and Policy 2008

  2. Outline1. What effects of child care? (literature)2. The difference in difference framework3. The article’s estimation strategy & results3.1. Simple D-i-D 3.2. DDD 3.3. With synthetic control group4. Discussion

  3. What input? preschool in the US • Preschool in the US: • Kindergarten • Pre-K • Head Start • Experiments: Abecedarian, Perry

  4. What outcomes ? • Child development • Language abilities • Socialization, behavior ( tests) • School preparedness: later test scores • Even later: wages, crime, # of arrests … • Labor supply of mothers

  5. Literature • Experiments: Abecedarian, Perry • Cost: • between 15 000 and 40 000 USD /year • vs. 4000 for universal Pre-K • Head Start : heterogeneous in practice  effects not that clear = trade-off between intensive targeted care and smaller-scale universal care?

  6.  Articles to be reviewed • Cascio (JHR 2009): DiD, timing of free Kindergarten / maternal employment • Fitzpatrick (JoLE 2010): same theme, Regression Discontinuity Design • Cascio & al (QJE 2010) : effect of financial incentives / desegregation: : 2SLS + DD • Heckman & al (JPE 2010) rate of return to the HighScope Perry Preschool Program revisited (experimental design)

  7. 2. The D-in-D method

  8. The “difference-in-difference” method • Intuition: • Comparing “before” and “after” situation of the treated  bias (many confounding factors) • Comparing “after” situation of treated and control  bias (composition effect)  rather look at change in thedifference in outcome between treated and control

  9. The “difference-in-difference” method • Example (Wooldridge): impact of the building of an incinerator in 2005 on house prices in neighborhood • Define T=1 as “the house lies within 5 miles of the incinerator” • Estimating price2006= 0+ 1(T=1) + uyields, for example, = -30 000 (euros) = price T,2006 – price C,2006 = • Can you conclude that the building of the incinerator decreased neighborhood house prices by 30 000 € on average?

  10. The “difference-in-difference” method • You need price data from before construction was even announced • Estimating price2002 = 0+ 1(T=1) + u yieldsdvd = -15 000 = price T,2002 – price C,2002 = • This shows that prices were already lower in the neighborhood where the incinerator would be built • The D-i-D estimator simply combines these 2 elements of information to estimate the causal impact of T

  11. The “difference-in-difference” method • Average increase in the difference in prices between houses located near the incinerator (Near=1 : treated group) and those located far (Control) • This means we can easily derive and test whether it is significantly different from 0 by estimating which equation ? (priceC,2006 – priceT,2006) – (priceC,2002 – priceT,2002)

  12. The “difference-in-difference” method price = 0 + 1.Near+ 0.date2 + 1.Near*date2 + u [where date2 is a variable that =1 if t = 2006] • What do 0, 0, 1 represent, in this model ?

  13. The “difference-in-difference” method price = 0 + 1.Near+ 0.date2 + 1.Near*date2 + u b

  14. price = 0 + 1.Near+ 0.date2 + 1.Near*date2 + u 1 measures the difference in average price change “difference”: between the neighborhood where the incinerator was built and the rest of the town “change”: between date 1 (2002) and date 2 (2006) b The “difference-in-difference” method

  15. The “difference-in-difference” method price = 0 + 1.Near+ 0.date2 + 1.Near*date2 + u

  16. The “difference-in-difference” method Price 0 • 1 • 1 • 0 0 1 2002 2006

  17. The “difference-in-difference” method • In general: estimate y = 0 + 0.date2 + 1. (T=1)+ 1. (T=1)*date2 + controls + u • The treatment effect is given by coefficient 1 • It’s an ATE: Average Treatment Effect

  18. The “difference-in-difference” method • Crucial assumption: • in the absence of treatment, the difference (once observables have been controlled for) between T & C would have remained constant = counterfactual

  19. The “difference-in-difference” method • Same as: nothing but the treatment had an impact on T’s outcome trend but not on C’s • Under this assumption, the entire shift away from the common trend can be attributed to the treatment • If something else happens at the same time that could affect T & C differently, DD won’t identify causal effect

  20. 3. The paper’s strategy

  21. The evaluated policy • Fall 1993: Georgia institutes lottery to fund targeted preschool • Fall 1995: too much money  preschool program becomes universal • Share of Georgian 4-year-olds enrolled: 8% (1993)  50% (1996)

  22. The evaluated policy

  23. The evaluated policy • Treatment on the treated? No • ATE of the availability of Pre-K • “Intention to treat” • What outcomes to consider? • Test scores in 4th grade • Grade retention

  24. The evaluated policy

  25. Important checks 1) Take-up and crowd out • If all increase is made of children new to preschool, total enrolment will have increased • If increase = only “crowd out” from children previously attending private preschool, no increase in total enrolment 2) Trends in Head Start • A pretty similar program  if changes at the same time, confounding factor

  26. Important checks (1/2)

  27. Important checks (2/2)

  28. Estimation • The basic D-i-D model : • Where : • Yijt is test score of pupil i in school j on year t • Statei and yeart are state and year fixed effects • Georgia*after takes the value 1 if student 1 belongs to a cohort having had access to universal pre-K • Who are we comparing to whom?

  29. Estimation • Potential bias if composition of test-taking population changed in GA relative to other states over time  added controls : • Where : • Xijt is a vector of characteristics of pupil i in school j on year t • Zjt is a vector of school j characteristics on year t

  30. 4. Results

  31. Estimation strategy

  32. Estimation strategy

  33. Results

  34. A DDD specification • Check for further confounding factors: add 8th graders to regression • The model becomes

  35. A DDD specification • The estimated coeff before the Georgia*after*fourth dummy is [(GA 4th after - other States 4th after)- (GA 4th before - other States 4th before)] - [(GA 8th after- other States 8th after) - (GA 8th before - other States 8th before)]

  36. A DDD specification • If something else than the policy at hand (universal Pre-K) caused school results of Georgian kids to shift away from those of other states, the D-i-D on 8th graders will catch that shift • Then the difference between that shift and the one observed on 4th graders would be the policy effect

  37. A DDD specification • Assumption here = ? • Shift between GA & other states not caused by the program = same for 4th & 8th graders • Put differently: other factors (not the program) affected 4th & 8th graders in the same way

  38. The “synthetic control” method • Abadie, Diamond, Hainmueller (2007): aggregrated data (e.g. at region level...) • One unit is treated • There are several control units

  39. The “synthetic control” method • Instead of choosing one control group they construct a counterfactual outcome as a linear combination of all non treated outcomes • Each control unit is weighted by its distance to the treated unit (according to several predictors of the outcome)

  40. The ADH method • Minimize distance between GA “before” and “before” synthetic control made of all other states (k=2 to N) with weights wk • = minimize distance between and • Uses both y and X

  41. The ADH method Treatment effect can then be estimated the usual D-i-D way If is small enough, treatment effect is estimated by

  42. The ADH method • Here: a “placebo DD test” with treatment between 1996 and 2000 yields an “effect” • Math scores decreased by 1.7% of an SD in GA relative to other states • Idea: compare GA to states that show the same trend before treatment • Even better: compare GA to the linear combination of states that best fits pre-treatment trend

  43. Results

  44. Results

  45. Results • Statistically insignificant though positive state-wide effects  heterogeneous effects?

  46. Results

  47. Discussion • Effects not very large, not always statistically significant • What could cause this even if pre-K had an effect on school preparedness? 12% in Head Start, 15 to 35% already in some non-subsidized preschool  many “treated” in the control group Only “intent to treat” effect  assuming all increase = new participants, ATT = much stronger

  48. Discussion • Seems that benefits are more prominent for some groups : rural, non-white • Cost-benefit analysis: better grades  better wages  more taxes • but huge costs ($300M) outweigh benefits (<$50M) • Public policy recommendation: target publicly funded pre-K programs

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