Data And Measures
Data
We compare two datasets from the National Longitudinal Surveys; both are nationally representative samples of young men who were aged 14 to 22 in the first survey year. The National Longitudinal Survey of Young Men (NLSYM) is a sample of young men born between 1944 and 1952 who were surveyed in 1966 and tracked until 1981, reinterviewed yearly except for 1972, 1974, 1977, and 1979. The National Longitudinal Survey of Youth (NLSY) is a sample of young men born between 1957 and 1965 who were surveyed in 1979 and interviewed yearly through 1994. Throughout, we refer to the former as the "original cohort" and to the latter as the "recent cohort." We selected non-Hispanic whites only because attrition among non-whites was extreme in the original cohort. We also excluded the poor white supplemental sample and the military supplemental sample from the recent cohort, as there are no comparable supplemental samples available for the original cohort. These supplemental samples simply provide additional cases for groups already present, so excluding them has no effect on the representativeness of our sample (e.g. in terms of income).[2] The resulting sample sizes are 2,743 and 2,434, respectively, for the original and recent cohorts.
The power of this research design lies in the fact that we observe both cohorts across a full sixteen years, at exactly the same ages, with comparable information on schooling, work history, and job characteristics. The young men are followed from their late-teens to their mid-thirties. Roughly two-thirds of lifetime job changes and wage growth occur during these years (Murphy & Welch, 1990; Topel & Ward, 1992). Thus, findings from these data will capture most of the economic and career mobility that these young adults can expect to achieve over their lifetime. This research design also enables us to isolate the impact of potential differences in the economic context of early career development: the original cohort entered the labor market in the late 1960s at the tail of the economic boom, while the recent cohort entered the labor market in the early 1980s after the onset of economic restructuring.[3]
Measures
Wages are measured as the respondent's hourly wages at his main CPS employer at the date of the interview, which is identified in the same way across both cohorts in all survey years. The wage measure is constructed by the NLS: using direct information if the respondent reported his earnings as an hourly wage, and from questions on the weeks (or months) and hours worked in the last year if the respondent reported in other units. We focus on hourly wages rather than yearly earnings because the latter are confounded by hours and weeks worked and the number of jobs held during the year. The former allows us to more closely approximate the market distribution of wage offers. Analyses are based on the natural log of real wages in 1992 dollars, using the Personal Consumption Expenditure (PCE) deflator.
Instead of wages at one point in time, we focus on total wage growth over time as our primary outcome variable because it is the most fundamental measure of upward mobility. Wage growth is defined as the change in the log wage from ages 16 to 36. Since individuals are observed for only sixteen years and at different ages, we standardize to the above twenty-year span using estimates from a random effects model (see the Appendix). These estimates smooth out short-term fluctuations in wages, so they reflect "permanent," long-term wage growth for individuals.
We base the level of education attained on two measures: (1) the number of years of schooling and (2) the highest degree obtained. Thus, an individual with fifteen years of schooling and no reported associate's or bachelor's degree is coded as "some college experience." We identify six categories: (1) high school dropouts, (2) high school graduates, (3) individuals with some college experience (no degree), (4) two-year college graduates (associate's degrees), (5) bachelor's graduates, and (6) those with master's or higher degrees (for simplicity, we refer to these simply as master's graduates). We also identify whether an individual is working while in school. Due to limitations with the NLSYM, we cannot match the timing of employment and enrollment directly, so we use the following indirect approach. We say that an individual has worked during a survey year if he reports more than twenty-six weeks of work in that year. This work may be part-time or full-time, but by spanning more than half of the year we ensure that there will be some overlap with the regular school year. The individual is coded "working while enrolled" if he is working under this definition and enrolled in the same survey year. With this criterion, an individual who only has a summer job and does not work during the school year is coded as "not working" while in school.
Recall that the original cohort was not surveyed in the years of 1972, 1974, 1977, and 1979. For most of our comparative analyses, we remove the corresponding survey years of 1985, 1987, 1990, and 1992 from the recent cohort in order to avoid a greater probability of observing someone enrolled or working simply because they were interviewed more often in the recent cohort.
After analyzing the sequences of schooling and work over the sixteen-year period in each survey, three distinct "pathways" emerged. If an individual completes all of his schooling in one continuous spell without working, we label that path "exclusive enrollment." Paths with only one continuous education spell, but during which the individual works at least one year are labeled "working while enrolled." The final category captures those who interrupted their education for at least one year and then returned to school. These individuals may have been working, unemployed, or merely out of the labor force, but in all cases they were not in school, and so we label these "interrupted enrollment" (note that almost everyone in this group combined work with school for some of the years in which they were enrolled). In order to identify educational pathways in an unbiased manner, we restricted our sample to individuals who were observed in school during at least one survey year. This means that workers who have already completed their schooling by the start of the NLS survey are excluded. This reduced our sample sizes to 2,178 and 2,063 for the original and recent cohorts, respectively.
It is important that the reader understand the structure of our data. While each respondent is observed across a number of years, our analyses are performed at the person-level. Our variables are therefore time-invariant summaries of each person's entire history of work and education. Consider the two variables that are the focus of this paper: (1) the educational pathways summarize key information about how young adults made the transition into the labor market, and (2) the wage variable summarizes total wage growth over that same time span. Our goal is to describe how these two dimensions are related to one another, how pathways taken affect upward mobility over the long-run. We recognize, of course, that there is feedback between education and wages over time--goals about desired income affect decisions about how much education to attain, initial experiences with low-wage jobs may lead to a return to school, and mediocre performance in school may lead to an exploration of work alternatives. While clearly an important topic, we do not attempt to disentangle this complex chain of decisions. Rather, our interest lies in the educational and working pathways that result from this chain of decisions and their impact on the total amount of upward mobility gained.
[2] Bernhardt, Morris, Handcock, and Scott (1997, 1998b) conducted a series of analyses and established the representativeness of the samples, as well as the impact of differential attrition bias.
[3] An important characteristic of the original cohort is that about one-third of the respondents served in the Vietnam War at some point during the survey years. A majority of the veterans returned to the survey after their military service, however, and at that point behaved much like the general population along key dimensions such as wages (see Bernhardt et al., 1997).