tailieunhanh - Book Econometric Analysis of Cross Section and Panel Data By Wooldridge - Chapter 17

Sample Selection, Attrition, and Stratified Sampling Introduction Up to this point, with the exception of occasionally touching on cluster samples and independently pooled cross sections, we have assumed the availability of a random sample from the underlying population. | Sample Selection Attrition and Stratified Sampling Introduction Up to this point with the exception of occasionally touching on cluster samples and independently pooled cross sections we have assumed the availability of a random sample from the underlying population. This assumption is not always realistic because of the way some economic data sets are collected and often because of the behavior of the units being sampled random samples are not always available. A selected sample is a general term that describes a nonrandom sample. There are a variety of selection mechanisms that result in nonrandom samples. Some of these are due to sample design while others are due to the behavior of the units being sampled including nonresponse on survey questions and attrition from social programs. Before we launch into specifics there is an important general point to remember sample selection can only be an issue once the population of interest has been carefully specified. If we are interested in a subset of a larger population then the proper approach is to specify a model for that part of the population obtain a random sample from that part of the population and proceed with standard econometric methods. The following are some examples with nonrandomly selected samples. Example Saving Function Suppose we wish to estimate a saving function for all families in a given country and the population saving function is saving b0 b1income b2 age fipnarried b4kids u where age is the age of the household head and the other variables are self-explanatory. However we only have access to a survey that included families whose household head was 45 years of age or older. This limitation raises a sample selection issue because we are interested in the saving function for all families but we can obtain a random sample only for a subset of the population. Example Truncation Based on Wealth We are interested in estimating the effect of worker eligibility in a particular .