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An independently pooled cross section is obtained by sampling randomly from a large population at different points in time (usually, but not necessarily, different years). For instance, in each year,we can draw a random sample on hourly wages, education, experience, and so on, from the population of working people in the United States. Or, in every other year, we draw a random sample on the selling price, square footage, number of bathrooms, and so on,of houses sold in a particular metropolitan area. From a statistical standpoint, these data sets have an important feature: they consist of independently sampled observations.This was also a key aspect in our analysis of cross-sectional data: among other things,it rules out correlation in the error terms for different observations.
A panel data set, while having both a cross-sectional and a time series dimension, differs in some important respects from an independently pooled cross section. To collect panel data—sometimes called longitudinal data—we follow (or attempt to follow)the same individuals, families, firms, cities, states, or whatever, across time. For example, a panel data set on individual wages, hours, education, and other factors is collected by randomly selecting people from a population at a given point in time. Then, these same people are reinterviewed at several subsequent points in time. This gives us data on wages, hours, education, and so on, for the same group of people in different years.
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