Treatment Effects in Sample Selection Models and Their Nonparameric Estimation
2008/06/17
研討會日期 : 2008-06-17
時間 : 00:00
主講人 : Myoung-jae Lee
地點 : B棟110室
演講者簡介 : Professor Myoung-jae Lee 為 Ph.D. in Economics，University of Wisconsin-Madison (1989)。
目前為Professor，Korea University。
其主要研究領域為Econometrics and Statistics及Labor and Health Economics。
演講摘要 : In a sample-selection model with the ‘selection’ variable Q and the ‘outcome’ variable Y*, Y* is observed only when Q = 1; i.e., Y ≡ QY* is observed along with Q. For a treatment D that affects both Q and Y*, three treatment effects are of interest: ‘participation’ (i.e., selection) effect of D on Q, ‘visible performance’ (i.e., observed outcome) effect of D on Y, and ‘invisible performance’ (i.e., latent outcome) effect of D on Y*. The visible performance effect combines the participation and invisible performance effects, and can be decomposed into the effect on ‘the always-participants’, the effect on ‘the compliers’ and so on. This paper shows under what conditions the three effects are identified, respectively, with the three corresponding mean differences of Q, Y, and Y|Q = 1 (i.e., Y*|Q = 1) across the control (D = 0) and treatment (D = 1) groups. With identification issue settled, nonparametric estimators for the effects are proposed. The estimators under a two-sample framework take the form of two-sample U-statistics of order (1,1), and have three advantages. First, there is no need to decide the number of matched observations as in the usual matching methods. Second, there is a built-in protection against the problem of ‘non-overlapping supports’ across the control and treatment groups. Third, over-sampling of the control/treatment group is allowed. A real-data illustration of job-training effects on female work-hours is provided.
