The Nature of econometrics and Economic data Cross-Sectional data A cross-sectional data set consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time. Sometimes the data on all units do not correspond to precisely the same time period. For example several families may be surveyed during different weeks within a year. In a pure cross section analysis we would ignore any minor timing differences in collecting the data. If a set of families was surveyed during different weeks of the same year, we would still view this as a cross-sectional data set An important feature of cross-sectional data is that we can often assume that they have been obtained by random sampling from the underlying population. For exam- ple, if we obtain information on wages, education, experience, and other characteristics by randomly drawing 500 people from the working population, then we have a random sample from the population of all working people. Random sampling is the sampling scheme covered in introductory statistics courses, and it simplifies the analysis of cross sectional data. A review of random sampling is contained in Appendix C. Sometimes random sampling is not appropriate as an assumption for analyzing cross-sectional data. For example, suppose we are interested in studying factors that influence the accumulation of family wealth. We could survey a random sample of fam- ilies, but some families might refuse to report their wealth. If, for example, wealthier families are less likely to disclose their wealth, then the resulting sample on wealth is ot a random sample from the population of all families. This is an illustration of a sam- ple selection problem, an advanced topic that we will discuss in Chapter 17 Another violation of random sampling occurs when we sample from units that ar large relative to the population, particularly geographical units. The potential problem in such cases is that the population is not large enough to reasonably assume the obser vations are independent draws. For example, if we want to explain new business activ- ity across states as a function of wage rates, energy prices, corporate and property tax rates, services provided, quality of the workforce, and other state characteristics, it is unlikely that business activities in states near one another are independent. It turns that the econometric methods that we discuss do work in such situations, but they some- times need to be refined. For the most part, we will ignore the intricacies that arise in analyzing such situations and treat these problems in a random sampling framework, even when it is not technically correct to do so. Cross-sectional data are widely used in economics and other social sciences. In eco- omics, the analysis of cross-sectional data is closely aligned with the applied micro- economics fields, such as labor economics, state and local public finance, industrial organization, urban economics, demography, and health economics. Data on individu- als, households, firms, and cities at a given point in time are important for testing micro- economic hypotheses and evaluating economic policies The cross-sectional data used for econometric analysis can be represented and stored in computers. Table 1. 1 contains, in abbreviated form, a cross-sectional data set on 526 working individuals for the year 1976.(This is a subset of the data in the file WAGEl.RAW)The variables include wage(in dollars per hour), educ(years of educa- tion), exper (years of potential labor force experience), female(an indicator for gender), and married(marital status). These last two variables are binary(zero-one)in natureCross-Sectional Data A cross-sectional data set consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time. Sometimes the data on all units do not correspond to precisely the same time period. For example, several families may be surveyed during different weeks within a year. In a pure cross section analysis we would ignore any minor timing differences in collecting the data. If a set of families was surveyed during different weeks of the same year, we would still view this as a cross-sectional data set. An important feature of cross-sectional data is that we can often assume that they have been obtained by random sampling from the underlying population. For exam￾ple, if we obtain information on wages, education, experience, and other characteristics by randomly drawing 500 people from the working population, then we have a random sample from the population of all working people. Random sampling is the sampling scheme covered in introductory statistics courses, and it simplifies the analysis of cross￾sectional data. A review of random sampling is contained in Appendix C. Sometimes random sampling is not appropriate as an assumption for analyzing cross-sectional data. For example, suppose we are interested in studying factors that influence the accumulation of family wealth. We could survey a random sample of fam￾ilies, but some families might refuse to report their wealth. If, for example, wealthier families are less likely to disclose their wealth, then the resulting sample on wealth is not a random sample from the population of all families. This is an illustration of a sam￾ple selection problem, an advanced topic that we will discuss in Chapter 17. Another violation of random sampling occurs when we sample from units that are large relative to the population, particularly geographical units. The potential problem in such cases is that the population is not large enough to reasonably assume the obser￾vations are independent draws. For example, if we want to explain new business activ￾ity across states as a function of wage rates, energy prices, corporate and property tax rates, services provided, quality of the workforce, and other state characteristics, it is unlikely that business activities in states near one another are independent. It turns out that the econometric methods that we discuss do work in such situations, but they some￾times need to be refined. For the most part, we will ignore the intricacies that arise in analyzing such situations and treat these problems in a random sampling framework, even when it is not technically correct to do so. Cross-sectional data are widely used in economics and other social sciences. In eco￾nomics, the analysis of cross-sectional data is closely aligned with the applied micro￾economics fields, such as labor economics, state and local public finance, industrial organization, urban economics, demography, and health economics. Data on individu￾als, households, firms, and cities at a given point in time are important for testing micro￾economic hypotheses and evaluating economic policies. The cross-sectional data used for econometric analysis can be represented and stored in computers. Table 1.1 contains, in abbreviated form, a cross-sectional data set on 526 working individuals for the year 1976. (This is a subset of the data in the file WAGE1.RAW.) The variables include wage (in dollars per hour), educ (years of educa￾tion), exper (years of potential labor force experience), female (an indicator for gender), and married (marital status). These last two variables are binary (zero-one) in nature Chapter 1 The Nature of Econometrics and Economic Data 6 14/99 4:34 PM Page 6