Sample Selection: Heckman’s Model and Method Presenters: Satya Prakash Enugula Andrew Wendel Cagla Yildirim.

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Presentation transcript:

Sample Selection: Heckman’s Model and Method Presenters: Satya Prakash Enugula Andrew Wendel Cagla Yildirim

Selection bias and Sample selection bias  Selection bias: Proper randomization is not in place  Sampling bias: Systematic error because of the non-random sample  There is no selection problem if:  Unmeasured factors uncorrelated  Explanatory variables controlled Reference:

Heckman’s method for sample selection model Also known as “Heckit Model” Corrects the issue of the data being nonrandom. Heckman’s correction; Involves normality assumption Provides a test for sample selection bias Provides a formula for bias corrected model Reference:

What is the selection problem? We want to estimate the determinants of wage offers for woman but….. We have access to wage observations for only those who work People who work are selected non-randomly Therefore; Estimating the determinants of wages from the our sample involves bias This is where Heckman’s correction takes place. Reference:

Wages, Effects of Education and Utility Reference: Peter H. Westfall, Horn Professor of Statistics, Texas Tech University

Utility Education Linear relationship between utility and education 60 Reference: Peter H. Westfall, Horn Professor of Statistics, Texas Tech University

Density Salary The distribution when education level is 1 Reference: Peter H. Westfall, Horn Professor of Statistics, Texas Tech University

Density Salary What if we take out the people who choose not to work? Reference: Peter H. Westfall, Horn Professor of Statistics, Texas Tech University

Women’s wage Education Linear relationship between women’s wage and education Reference: Peter H. Westfall, Horn Professor of Statistics, Texas Tech University

The Heckman Model estimation by Maximum Likelihood Estimator : A sample selection model always involves two equations: The regression equation considering mechanisms determining the outcome variable. The selection equation considering a portion of the sample whose outcome is observed and mechanisms determining the selection process. Reference :

Assumptions of the model: Reference :

Cont. Reference :

Log Likelihood Function : : Reference :

Log Likelihood Function (cont.): Reference :