2 2 natural experiments y y 1 y 0 y 1 y 0 y 1 y 0 counterfactual y 1 y 0 d treatment indicator d = 1 (treatment group) d = 0 control group average tre
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1 12 1 Heckman and Smith (1998) Lee(2005) Cameron and Tridevi (2005, Chapter 25) Wooldridge (2003, Chapter 18) 1
2 2 2 natural experiments y y 1 y 0 y 1 y 0 y 1 y 0 counterfactual y 1 y 0 d treatment indicator d = 1 (treatment group) d = 0 control group average treatment effect : ATE AT E = E(y 1 y 0 ) (average treatment effect on the treated: ATT) AT T = E(y 1 y 0 d = 1) = E(y 1 d = 1) E(y 0 d = 1)
3 3 E(y 0 d = 0) y 0 d AT T AT T E(y 1 d = 1) E(y 0 d = 0) AT E AT E AT E = P (d = 1)E(y 1 y 0 d = 1) + P (d = 0)E(y 1 y 0 d = 0) E(y 0 d = 1) E(y 1 d = 0) y 1 d AT E AT T AT E = AT T E(y 1 d = 1) E(y 0 d = 0) (1) 1 (2) 3 1 Differences-in-Differences (DID) 1 Cameron and Trivedi (2005,25
4 4 b(=before) a(=after) T E Before-After(BA) T E i = E i (y 1a d = 1) E i (y 1b d = 1) = BA i T E j = E j (y 0a d = 0) E j (y 0b d = 0) = BA j 2 DID DID = T E i T E j = BA i BA j = E i (y 1a y 1b d = 1) E i (y 0a y 0b d = 0) BA i BA j DID y Ashenfelter s dip y y y i0a = x iβ + γy ib + δ a + ε ia y i1a = x iβ + γy ib + δ a + αd ia + ε ia δ a time drift α α DID 2 Cross-Section CS T E = E(y 1a d = 1) E(y 0a d = 0) = CS y 0a d = 0 y 0a d = first difference:fd DID FD
5 5 y 1b d = 1 y 0b d = 0 CS = DID y i0 = x iβ 0 + u i0 y i1 = x iβ 1 + u i1 d i = z iγ + ε i d i d i = { 1 iff d i > 0 0 iff d i 0 E(u 1 x, z) = E(u 0 x, z) = 0 y i1 E[y i0 d i = 1] = y i1 x iβ 0 + σ 0ε φ(z i γ) (1 Φ(z i γ)) E[y i1 d i = 1] E[y i0 d i = 1] = x i(β 1 β 0 ) + (σ 0ε σ 01ε ) φ(z i γ) Φ(z i γ) (σ 0ε σ 01ε ) φ(z i γ) Φ(z i γ) exact matching propensity score 3 [Pr[d i = 1 x]] 3 Rosenbaum and Rubin (1983)
6 6 propensity score caliper matching 4 5 M = 1 N T i1 i {d=1}[y w(i, j)y j0 ] j w(i, j) = 1 0 < w(i, j) 1 N T (1) nearest-neighbor matching i A i (x) = {j min j x i x j } 6 (2) (kernel matching) w(i, j) = K(x j x i )/ N ic j=1 K(x j x i ) K (3) stratification matching) propensity score (4) radius matching A i (p(x)) = {p j p i p j < r} propensity score r 4 Lalonde (1986) Dehejia and Wahba (1999, 2002) Cameron and Trivedi (2005, Chapter 25) 4 5 Abadie et al (2004) Becker and Ichino (2002) Becker and Caliendo (2007) 6
7 7 the National Supported Work (NSW) 1970 NSW Lalonde(1986) Panel Study of Income Dynamics (PSID) % 30% (RE78) treatment-control comparison $ control function estimator OLS RE78 i = x iβ + αd i + u i 2 α = $ before-after comparison(ba) RE78 RE75 1 $6349-$1532=$ differencesin-differences(did) RE75 RE78 $ $ =$ , DID $4817-$2491=$2326 OLS RE it = φ + δd78 it + γαd i + αd78 it d i + u i D d = t = 1975, 1978
8 8 α DID 3 propensity score $995 Cameron and Trivedi (2005, p.895 $1000-$ STATA Lalonde(1986) Cameron /faculty/cameron nswpsid.da1 Cameron and Trivedi (2005, Chapter 25) MMA25P1TREATMENT.DO set more off using nswpsid.da1 /*Data Generation*/ 1974 U U75
9 9 drop U74 U75 gen U74 = cond(re74 == 0, 1, 0) gen U75 = cond(re75 == 0, 1, 0) gen AGESQ = AGE*AGE gen EDUCSQ = EDUC*EDUC gen NODEGREE = 0 replace NODEGREE = 1 if EDUC < 12 gen RE74SQ = RE74*RE74 gen RE75SQ = RE75*RE75 gen U74BLACK = U74*BLACK gen U74HISP = U74*HISP sum AGE EDUC NODEGREE BLACK HISP MARR U74 U75 RE74 RE75 RE78 TREAT AGESQ EDUCSQ RE74SQ RE75SQ U74BLACK U74HISP /* */ bysort TREAT: sum AGE EDUC NODEGREE BLACK HISP MARR U74 U75 RE74 RE75 RE78 TREAT AGESQ EDUCSQ RE74SQ RE75SQ U74BLACK /* Treatment-control comparison */ regress RE78 T regress RE78 TREAT, robust /* Control function estimator */ regress RE78 TREAT AGE AGESQ EDUC NODEGREE BLACK HISP RE74 RE75 regress RE78 TREAT AGE AGESQ EDUC NODEGREE BLACK HISP RE74 RE75, robust * gen TAGE = TREAT*AGE gen TAGESQ = TREAT*AGESQ gen TEDUC = TREAT*EDUC gen TNODEGREE = TREAT*NODEGREE gen TBLACK = TREAT*BLACK gen THISP = TREAT*HISP gen TRE74 = TREAT*RE74 gen TRE75 = TREAT*RE75
10 10 regress RE78 TREAT AGE AGESQ EDUC NODEGREE BLACK HISP RE74 RE75 TAGE TAGESQ TEDUC TNODEGREE TBLACK THISP TRE74 TRE75 /* Differences-in-differences*/ gen id = n label variable id id gen EARNS1 = RE75 gen EARNS2 = RE78 reshape long EARNS, i(id) j(year) gen dyear2 = 0 replace dyear2 = 1 if year==2 gen Tdyear2 = TREAT*dyear2 regress EARNS Tdyear2 TREAT dyear2 regress EARNS Tdyear2 TREAT dyear2, robust /* 2 Before-after comparison*/ regress EARNS Tdyear2 if TREAT==1 regress EARNS Tdyear2 if TREAT==1, robust /* Propensity score */ logit TREAT AGE AGESQ EDUC EDUCSQ MARR NODEGREE BLACK HISP RE74 RE75 RE74SQ RE75SQ U74BLACK predict PSCORE propensity score sum PSCORE if TREAT==1 scalar PTMIN = r(min) scalar PTMAX = r(max) sum PSCORE if TREAT==0 scalar PCMIN = r(min) scalar PCMAX = r(max) drop if PSCORE < PTMIN drop if PSCORE < PCMIN drop if PSCORE > PTMAX drop if PSCORE > PCMAX sum PSCORE gen PSCORESQ = PSCORE*PSCORE regress RE78 TREAT PSCORE PSCORESQ *Propensity score 10
11 11 global cut1 = 0.1 global cut2 = 0.2 global cut3 = 0.3 global cut4 = 0.4 global cut5 = 0.5 global cut6 = 0.6 global cut7 = 0.7 global cut8 = 0.8 global cut9 = 0.9 gen STRATA = 1 replace STRATA = 2 if PSCORE > $cut1 & PSCORE <= $cut2 replace STRATA = 3 if PSCORE > $cut2 & PSCORE <= $cut3 replace STRATA = 4 if PSCORE > $cut3 & PSCORE <= $cut4 replace STRATA = 5 if PSCORE > $cut4 & PSCORE <= $cut5 replace STRATA = 6 if PSCORE > $cut5 & PSCORE <= $cut6 replace STRATA = 7 if PSCORE > $cut6 & PSCORE <= $cut7 replace STRATA = 8 if PSCORE > $cut7 & PSCORE <= $cut8 replace STRATA = 9 if PSCORE > $cut8 & PSCORE <= $cut9 replace STRATA = 10 if PSCORE > $cut9 tab STRATA T tab STRATA TREAT, sum(age) nostand nofreq tab STRATA TREAT, sum(educ) nostand nofreq tab STRATA TREAT, sum(marr) nostand nofreq tab STRATA TREAT, sum(nodegree) nostand nofreq tab STRATA TREAT, sum(black) nostand nofreq tab STRATA TREAT, sum(hisp) nostand nofreq tab STRATA TREAT, sum(re74) nostand nofreq tab STRATA TREAT, sum(re75) nostand nofreq tab STRATA TREAT, sum(u74black) nostand nofreq bysort STRATA: oneway EDUC T #delimit ; global sum = 0 ; /* Sums the estimate of interest over strata ; global sumwgt = 0 ; /* Sums the number of treated obs over strata */ global count = 0 ; /* This gives the number of Strata used */ global numcut = 10; global XLIST AGE AGESQ EDUC NODEGREE BLACK HISP RE74 RE75;
12 12 forvalues i = 1/$numcut { ; global addon = 0 ; /* Within strata estiamte of interest */ global tobs = 0 ; /* Within strata number of treated obs */ capture { ; quiet regress RE78 TREAT $XLIST if STRATA == i ; global addon = b[treat] ; quiet summarize TREAT if TREAT==1 & STRATA== i ; global tobs = result(1) ; * # of treatment observations ; } ; di i estimate = $addon Top cut = ${cut i } #treat obs = $tobs ; if $addon = 0 { ; global sum = $sum + $addon * $tobs ; global sumwgt = $sumwgt + $tobs ; global count = $count + 1 ; } ; } ; #delimit cr ; Prppensity score di $sum / $sumwgt Count = $count [1] 2005) [2] Abadie, Alberto, Drukker, David, Herr, Jane Leber, and Imbens, Guido W.(2004) Implementing Matching Estimators for Average Treatment Effects in Stata, The Stata Journal, 4(3), pp [3] Becker, Sascha O. and Ichino, Andrea.(2002) Estimsation of Average Treatment Effects Based on Propensity Scores, The Stata Journal, 2(4), pp [4] Becker, Sascha O. and Caliendo, Marco. (2007) mhbounds-sensitivity Analysis for Average Treatment Effects, The Stata Journal, 7(1), pp [5] Cameron, A.C. and Trivedi, P.K.(2005) Microeconometrics: Methods and Applications, Cambridge University Press.
13 13 [6] Dehejia, R.H.and Wahba,S.(1999) Reevaluating the Evaluation of Training Programs, Journal of the American Statistical Association, 94, pp [7] Dehejia, R.H. and Wahba, S.(2002) Propensity Score-Matching Methods for Nonexperimental Causal Studies, The Review of Economics and Statistics, 84(1), pp [8] Heckman, J.J. and Robb, R.(1985) Alternative Methods for Estimating The Impact of Interventions, in J. Heckman and B. Singer (eds), Longotudinal Analysis of Labor Market Data, Cambridge University Press. [9] Heckman, J.J. and Smith, J.A.(1998) Evaluating the Welfare State, in Strøm, S.(ed) Econometrics and Economic Theory in the 20th Century: The Ragner Frisch Centennial Symposium, Cambridge University Press., pp [10] Holland, P.W.(1986) Statistics and Causal Inference, Journal of the American Statistical Association, 81, pp [11] Lalonde, R.(1986) Evaluating the Econometric Evaluations of Training Programs with Experimental Data, American Economic Review, 76, pp [12] Lee, Myoung, Jae. (2005) Micro-Econometrics for Policy, Programm, and Treatment Effects, Oxford University Press. [13] Moffitt, R.(1991) Program Evaluation with Nonexperimental Data, Evaluation Review, 15., pp [14] Rosenbaum, P. and Rubin, D.B.(1983) The Central Role of Propensity Score in Observational Studies for Causal Effects, Biometrika, 70, pp [15] Rubin, D.B.(1974) Estimating Causal Effects of Treatments in Randomized and Nonrandmized Studies, Journal of Education Psychology, 66, pp [16] Winkelmann, Rainer and Boes, Stefan.(2006) Analysis of Microdata, Springer. [17] Wooldridge, Jeffrey. M.(2003) Econometric Analysis of Cross Section and Panel Data, The MIT Press
14 表 1 変数の定義と平均値の比較 変数 定義 全体処理群対照群 Mean Mean Mean AGE 年齢 EDUC 教育年数 NODEGREE 教育年数が12より小さいダミー BLACK 黒人ダミー HISP ヒスパニックダミー MARR 結婚ダミー U 年の失業ダミー U 年の失業ダミー RE 年のドル価値で1974 年の実質賃金 RE 年のドル価値で1975 年の実質賃金 RE 年のドル価値で1978 年の実質賃金 TREAT 処理群 =1 対照群 = AGESQ 年齢の二乗 EDUCSQ 教育年数の二乗 RE74SQ 1982 年のドル価値で1974 年の実質賃金の二乗 5.21E E E+08 RE75SQ 1982 年のドル価値で1975 年の実質賃金の二乗 5.11E E E+08 U74BLACK 黒人で1974 年に失業しているダミー U74HISP ヒスパニックで1974 年に失業しているダミー Sample Size
15 表 2 職業訓練の賃金効果の推定 Dependent Variable: RE78 Treatment-control comparison Coefficient Robust z-ratio Control function estimator Coefficient Robust z-ratio Before-after comparison Coefficient Robust z-ratio Differences-indifferences Coefficient Robust z-ratio TREAT AGE AGESQ EDUC NODEGREE BLACK HISP RE RE Tdyear dyear _cons Number of observation R-squared Root MSE
16 表 3 Propensity Score のためのロジット推定 Dependent Variable: TREAT Coefficient Robust z-ratio AGE AGESQ EDUC EDUCSQ MARR NODEGREE BLACK HISP RE RE RE74SQ RE75SQ U74BLACK _cons Number of observation Log Likelihood LR chi2(13) Prob>chi2 Pseudo R
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