Econometric Analysis of Cross Section and Panel Data - Free

17 Sample Selection, Attrition, and Stratified Sampling
17.1 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 17.1 (Saving Function): Suppose we wish to estimate a saving function for
all families in a given country, and the population saving function is
saving ¼ b 0 þ b 1 income þ b 2 age þ b 3 married þ b 4 kids þ u
ð17:1Þ
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 17.2 (Truncation Based on Wealth): We are interested in estimating the
e¤ect of worker eligibility in a particular pension plan [for example, a 401(k) plan] on
family wealth. Let the population model be
wealth ¼ b 0 þ b 1 plan þ b 2 educ þ b 3 age þ b 4 income þ u
ð17:2Þ
where plan is a binary indicator for eligibility in the pension plan. However, we can
only sample people with a net wealth less than $200,000, so the sample is selected on
the basis of wealth. As we will see, sampling based on a response variable is much
more serious than sampling based on an exogenous explanatory variable.