clear
set seed 2
// Create data set with experimental conditions
input str4 educ str6 sex str9 treatmentstr str4 health1 number
Low Male Exposed Poor 333
Low Male Exposed Good 917
Low Male Unexposed Poor 1000
Low Male Unexposed Good 2750
Low Female Exposed Poor 500
Low Female Exposed Good 3250
Low Female Unexposed Poor 167
Low Female Unexposed Good 1083
High Male Exposed Poor 83
High Male Exposed Good 542
High Male Unexposed Poor 584
High Male Unexposed Good 3791
High Female Exposed Poor 125
High Female Exposed Good 1750
High Female Unexposed Poor 208
High Female Unexposed Good 2917
end
expand number // Create full number of cases
// Transform strings to numerical variables
generate loeduc = (educ == "Low")
generate female = (sex == "Female")
generate treatment = (treatmentstr == "Exposed")
generate good1 = (health1 == "Good")
label define loeduc 0 "High" 1 "Low"
label val loeduc loeduc
label define female 0 "Male" 1 "Female"
label val female female
label define treatment 0 "Unexposed" 1 "Exposed"
label val treatment treatment
label define health 0 "Poor" 1 "Good"
label val good1 health
// Simulate outcome variable
generate good2 = good1
replace good2 = 1 if good1 == 0 & loeduc == 1 & (runiform() <= .05)
replace good2 = 1 if good1 == 0 & loeduc == 0 & (runiform() <= .20)
replace good2 = 1 if good1 == 0 & treatment == 1 & (runiform() <= .30)
label val good2 health
// Transform some more and clean up
generate poor2 = (good2 == 0)
generate poor1 = (good1 == 0)
drop educ sex treatmentstr health1 number good1 good2
// Table 1-ish
table poor2, by(loeduc female treatment) contents(freq)
// 1) Regression adjustment
logit poor2 treatment female if loeduc == 0, or
logit poor2 treatment female if loeduc == 1, or
logit poor2 treatment##loeduc female##loeduc, or
// 2) Propensity score matching
teffects nnmatch (poor2 female) (treatment) if loeduc == 0
teffects nnmatch (poor2 female) (treatment) if loeduc == 1
// 3) Difference in difference
// Transform to long format
gen id = _n
reshape long poor, i(id) j(year)
logit poor treatment##c.year if loeduc == 0, or
logit poor treatment##c.year if loeduc == 1, or
logit poor loeduc##treatment##c.year, or
// 4) Fixed effects model
replace treatment = 0 if year == 1
xtset id year
xtreg poor treatment year if loeduc == 0, fe
xtreg poor treatment year if loeduc == 1, fe
xtreg poor treatment##loeduc year##loeduc, fe
Aug 2, 2017
Analyzing natural policy experiments
Hu et al. 2017 simulate data of a natural policy experiment and show how to analyze the data with regression adjustment, propensity score matching, difference-in-differences, and fixed effects regression. (The paper also includes IV, regression discontinuity, and interrupted time series, but does not describe the data created for these analyses.)
