What Does Increased Economic Inequality Imply about the Future Level and Dispersion of Human Capital

Does the persistent increase in economic inequality among families and geographic areas have implications for the levels of educational attainment of children who have experienced this development? In this study, we consider this question using longitudinal data on about 1,200 children from the Panel Study of Income Dynamics. We observe these children over a period of at least 20 years, and statistically estimate the relationship of the family, regional, and neighborhood factors that exist while they are growing up to the educational choices that they make as young adults. We examine three educational attainments of these children: (1) completed years of schooling, (2) graduation from high school, and (3) college attendance. The four economic variables that we use to reflect observed changes in economic inequality are (1) family income (relative to needs), (2) family net worth (wealth), (3) a state measure of inequality (the Gini coefficient), and (4) a measure of the accessibility of higher education (the cost of tuition at state colleges). We use the coefficient estimates from our stylized model of children’s educational attainments together with adjusted values of the economic variables reflecting increased inequality to simulate the effects of increased economic inequality on youths’ schooling attainments. We measure the effects of increased economic inequality on the full distribution of educational attainments, and calculate changes in both the average level and the dispersion of educational attainment of the next generation of youths. We find that increased family and geographic economic inequality have small positive effects on the average level of schooling of this generation, but also significantly increase the inequality of their schooling attainments. What Does Increased Economic Inequality Imply about the Future Level and Dispersion of Human Capital? The past three decades have witnessed substantial growth in economic inequality among families and the neighborhoods in which they live. For example, between 1973 and 1998, the Gini coefficient of family income (reflecting both labor earnings and public transfers) rose 21 percent (see Haveman et al., 2004). Over the same period, inequality in both male and female labor earnings increased by nearly 30 percent. The urban neighborhoods in which many poor and minority children live have declined (Jargowsky, 1997), and since 1970 both affluent and poor families have become more concentrated in high-income and poor neighborhoods (Massey, 1996). Though of substantial concern, the implication of these trends in inequality for the development and attainment of both rich and poor children is both unknown and little studied (Jencks and Mayer, 1990). In this paper, we study the relationship between these increases in family and geographic economic inequality and the educational attainments of young people—their years of completed schooling, the probability that they will graduate from high school, and the probability they will undertake postsecondary schooling. We relate observed changes in family and geographic economic inequality to both the average level and the inequality of their schooling attainments. We measure the changes in family and geographic economic inequality during the years when they were growing up (ages 2–15), and their schooling attainments when they are young adults (age 25). Our analysis rests on two primary conjectures. First, reflecting the diversity of speculations regarding the effects of increased environmental and peer inequality on youth attainments, we have no firm expectation regarding the direction of the impact of increased economic inequality on the level of educational attainment. Second, reflecting the extensive literature on the intergenerational transmission See Adler et al., 1994; Marmot et al., 1991; Diez-Roux et al., 1997; Kawachi and Kennedy, 1997; Jencks and Mayer, 1990. 2 of economic status, we expect increases in family and geographic economic inequality when children are growing up to be positively related to disparities in their ultimate schooling attainments. Mayer (2001) stands as the only empirical exploration of the relationship between family and geographical inequality while children are growing up and attainment patterns in young adulthood. Using data from the Panel Study of Income Dynamics (PSID), she estimated the relationship between state-level economic inequality during adolescence and educational attainment, distinguishing impacts on children from highand low-income families. The state-specific Gini coefficient of family income at ages 12–15 was estimated using decennial census data, interpolated linearly to obtain a measure for each annual observation. Mayer’s control variables included estimated measures of returns to education, region, year, percentage of the state population that was African-American or Hispanic, state-specific mean household income and unemployment rate, log of family income, and a measure of state-specific economic segregation. Mayer found a positive relationship between within-state inequality and both overall educational attainment and inequality in educational attainment. The relationship between the level of educational attainment and state income inequality reflects a greater probability that youths enter college by age 23 in states with more income inequality. Variables reflecting changes in returns to education, parental income, and state-level investments in education are shown to increase youths’ educational attainment more among higher-income families than other families. Our study addresses a similar question, but uses a quite different approach. As we have described, Mayer provided reduced-form estimates of the relationship between parental income inequality (at the See Haveman and Wolfe, 1994, 1995; McLanahan and Sandefur, 1994; Haveman et al., 2001. Mayer’s sample of children consists of all observations in the data set at ages 12–14 and ages 20 and 23 (when the relevant educational outcome data were available). Measures of within-state economic inequality and control variables were obtained when the respondents were between the ages of 12 and 14. Mayer considered four educational outcomes: the probability of high school completion by age 20; the probability of beginning college by age 23; the probability of completing four years of postsecondary education by age 23; and the number of years of completed schooling by age 23. High-(low-)income families were defined as families above (below) the median level of family income when children were aged 12–14. 3 state level) and the level and dispersion of youths’ educational attainment. In our approach, we first directly alter family indicators of economic status (e.g., income and assets) and state measures of income inequality and access to higher education to reflect the increased dispersion in these variables. We then simulate the effect on the average level and the dispersion of youths’ educational attainments attributable to this increased economic inequality. Our simulated individual educational attainments reflect estimated coefficients from models of attainment that include family economic status, state-specific inequality, and indicators of access to schooling and an extensive number of control variables. Mayer measured family and state characteristics only during the child’s adolescent years; we measure these characteristics at nearly all of the child’s ages until age 16. Our estimates also reflect the child’s experiences in child care, the characteristics of the neighborhoods in which the child grew up, and a direct measure of the statespecific cost of postsecondary schooling. I. RESEARCH STRATEGY AND DATA We view the schooling choices of youths to be related to family circumstances and neighborhood environment. We estimate the relationship between educational attainment and family and geographic economic variables (measured during ages 2–15) using a large sample of youths. The family and economic variables in our sparse model are those that our review of an extensive body of research has consistently identified as being significantly related to schooling outcomes (Haveman et al., 2001). Our basic data set consists of 29 years of information on 2,609 children from the Panel Study of Income Dynamics (PSID). We selected children who were born from 1966 to 1970, and followed them from 1968, the first year of the PSID (or their year of birth, if later) until 1999. Only those individuals Haveman and Wolfe (1994, 1995) discuss the implications of this framework for empirical estimation. See also Haveman, Wolfe, and Wilson (forthcoming). 4 who remained in the survey until they reached age 21 are included. Educational outcomes were measured at age 25 (although to limit missing data, some respondents’ outcomes were measured as late as age 29). After omitting observations for which information on core variables is missing, we have a sample of 1,210 children. These data contain extensive longitudinal information on the status, characteristics, and choices of family members, family income, living arrangements, neighborhood characteristics, and background characteristics such as race, education, and location for each individual. In order to make comparisons of individuals with different birth years, we index the time-varying data elements in each data set by age. All monetary values are expressed in 1993 dollars, using the Consumer Price Index for all items. We merged census tract (neighborhood) information describing the percentages of residents that are high school dropouts and that have low incomes from the 1970 and 1980 censuses with our PSID data. The census data are matched to the specific location of the children in our sample for each year from 1968 to 1985. Finally, we merged onto our data estimates of the Gini coefficient on family income in the Some persons observed did not respond in an intervening year but reente