Heckit模型.pptVIP

Heckit Method Outlines: 1.Introduction to the Sample Selection Problem 2.Describe the Heckit Model 3.Apply Heckit method to the example of “Wage Offer for Married Women” Introduction to Sample Selection Problem (background) Mostly, we assumed the availability of a random sample from the population. But random samples are not always available. Sample Selection Problem: the observed sample is not random sample but systematically chosen from the population. Cause of nonrandom sample selection: --sample design; --the behavior of the units being sampled ( including nonresponse on survey questions and attrition from social programs); Introduction to Sample Selection(examples) Examples (by survey design): 1. saving function If we only have access to a survey that included families whose household head was 45 years of age or older. We can obtain random sample only for a subset of the population since we are interested in the saving function for all families. 2. truncation based on wealth (choice-based sampling) If we can only sample people with a net wealth less than $200,000, the sample is selected on the basis of wealth (response variable). Introduction to Sample Selection(examples) Incidental truncation : do not observe y because of the outcome of another variable. Leading sample as wage offer function: consider a wage offer equation for people of working age. in the work force: wage offer observed out of the work force: wage offer unobserved (wage offer) is missing as a result of the outcome of another variable, labor force participation. Introduction to Sample SelectionProblem(the case bias doesn’t exist) =When is OLS on the selected sample consistent? The population model is if we can observe y and x, we can simply use OLS, The model we estimated is selection indicator s=1 if we