The Nature of econometrics and Economic data observation. When econometric methods are used to analyze time series data, the data should be stored in chronological order The variable avgmin refers to the average minimum wage for the year, avgcov is the average coverage rate( the percentage of workers covered by the minimum wage law), unemp is the unemployment rate, and gnp is the gross national product. We will use these data later in a time series analysis of the effect of the minimum wage or Pooled cross sections Some data sets have both cross-sectional and time series features. For example, suppose at two cross-sectional household surveys are taken in the United States, one in 1985 and one in 1990. In 1985, a random sample of households is surveyed for variables such as income, savings, family size, and so on. In 1990, a new random sample of households is taken using the same survey questions. In order to increase our sample size, we can form a pooled cross section by combining the two years. Because random samples are taken in each year, it would be a fluke if the same household appeared in the sample during both years. ( The size of the sample is usually very small compared with the num- ber of households in the United States. This important factor distinguishes a pooled cross section from a panel data set. Pooling cross sections from different years is often an effective way of analyzing the effects of a new government policy. The idea is to collect data from the years before and after a key policy change. As an example, consider the following data set on hous- ing prices taken in 1993 and 1995, when there was a reduction in property taxes in 1994. Suppose we have data on 250 houses for 1993 and on 270 houses for 1995. One way to store such a data set is given in Table 1.4 Observations I through 250 correspond to the houses sold in 1993, an rations rresp 270 houses sold in 1995. While the re store the data turns out not to be crucial, keeping track of the year for each obser- vation is usually very important. This is why we enter year as a separate variable a pooled cross section is analyzed much like a standard cross section, except that we often need to account for secular differences in the variables across the time. In fact in addition to increasing the sample size, the point of a pooled cross-sectional analysis is often to see how a key relationship has changed over time Panel or Longitudinal Data A panel data (or longitudinal data) set consists of a time series for each cross- sectional member in the data set. As an example, suppose we have wage, education, and employment history for a set of individuals followed over a ten-year period. Or we might collect information, such as investment and financial data, about the same set of firms over a five-year time period. Panel data can also be collected on geographical its. For example, we can collect data for the same set of counties in the United States on immigration flows, tax rates, wage rates, government expenditures, etc, for the years 1980,1985,and1990 The key feature of panel data that distinguishes it from a pooled cross section is the fact that the same cross-sectional units(individuals, firms, or counties in the aboveobservation. When econometric methods are used to analyze time series data, the data should be stored in chronological order. The variable avgmin refers to the average minimum wage for the year, avgcov is the average coverage rate (the percentage of workers covered by the minimum wage law), unemp is the unemployment rate, and gnp is the gross national product. We will use these data later in a time series analysis of the effect of the minimum wage on employment. Pooled Cross Sections Some data sets have both cross-sectional and time series features. For example, suppose that two cross-sectional household surveys are taken in the United States, one in 1985 and one in 1990. In 1985, a random sample of households is surveyed for variables such as income, savings, family size, and so on. In 1990, a new random sample of households is taken using the same survey questions. In order to increase our sample size, we can form a pooled cross section by combining the two years. Because random samples are taken in each year, it would be a fluke if the same household appeared in the sample during both years. (The size of the sample is usually very small compared with the num￾ber of households in the United States.) This important factor distinguishes a pooled cross section from a panel data set. Pooling cross sections from different years is often an effective way of analyzing the effects of a new government policy. The idea is to collect data from the years before and after a key policy change. As an example, consider the following data set on hous￾ing prices taken in 1993 and 1995, when there was a reduction in property taxes in 1994. Suppose we have data on 250 houses for 1993 and on 270 houses for 1995. One way to store such a data set is given in Table 1.4. Observations 1 through 250 correspond to the houses sold in 1993, and observations 251 through 520 correspond to the 270 houses sold in 1995. While the order in which we store the data turns out not to be crucial, keeping track of the year for each obser￾vation is usually very important. This is why we enter year as a separate variable. A pooled cross section is analyzed much like a standard cross section, except that we often need to account for secular differences in the variables across the time. In fact, in addition to increasing the sample size, the point of a pooled cross-sectional analysis is often to see how a key relationship has changed over time. Panel or Longitudinal Data A panel data (or longitudinal data) set consists of a time series for each cross￾sectional member in the data set. As an example, suppose we have wage, education, and employment history for a set of individuals followed over a ten-year period. Or we might collect information, such as investment and financial data, about the same set of firms over a five-year time period. Panel data can also be collected on geographical units. For example, we can collect data for the same set of counties in the United States on immigration flows, tax rates, wage rates, government expenditures, etc., for the years 1980, 1985, and 1990. The key feature of panel data that distinguishes it from a pooled cross section is the fact that the same cross-sectional units (individuals, firms, or counties in the above Chapter 1 The Nature of Econometrics and Economic Data 10 14/99 4:34 PM Page 10