Abstract
Prior research on trends in educational inequality has focused chiefly on changing gaps in educational attainment by family income or parental occupation. In contrast, this contribution provides the first assessment of trends in educational attainment by family wealth and suggests that we should be at least as concerned about growing wealth gaps in education. Despite overall growth in educational attainment and some signs of decreasing wealth gaps in high school attainment and college access, I find a large and rapidly increasing wealth gap in college attainment between cohorts born in the 1970s and 1980s, respectively. This growing wealth gap in higher educational attainment co-occurred with a rise in inequality in children’s wealth backgrounds, although the analyses also suggest that the latter does not fully account for the former. Nevertheless, the results reported here raise concerns about the distribution of educational opportunity among today’s children who grow up in a context of particularly extreme wealth inequality.
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Notes
Although it is not the aim of this contribution to assess whether the association between family wealth and children’s education is causal, it is worth nothing that Lovenheim’s evidence on the causal relationship between housing wealth and college entry is an important advance in the literature, especially in the context of continuing debates about the causal influences of family income (see, e.g., Cameron and Taber 2004; Mayer 1997).
The net cost of attending college (i.e., tuition/fees/board minus all financial aid and tax credits) has risen less steeply than sticker prices but still profoundly. In the 25 years between 1990 and 2015, the average net cost of attendance at public four-year colleges rose by 84 % (while the sticker price rose by 111 %); the corresponding increase at private four-year colleges was 39 % (sticker price rose by 78 %) (Ma et al. 2015).
The PSID has been conducted biannually since 1997, so I assess educational attainment at ages 20 and 25 if surveyed in that year but at adjacent ages (older, if available) otherwise. Online Resource 1, section 1, provides an overview of the different measurement years for each birth cohort. It also details how birth cohorts were differently affected by the 1997 PSID sample reduction but shows that the conclusions presented here do not appear to be substantially influenced by it.
To best capture the economic conditions in which the child grows up, I define family wealth as a characteristic of the household in which the child resides at ages 10–14, irrespective of the household’s structure. A different measurement approach would instead link children to the wealth reports of their parents, which, for nonintact families, can provide additional information on the wealth of nonresident parents (an alternative approach that could also be applied in studies focused on family income but typically is not). However, this information is available for only a selective set of cases in which the nonresident parent continues to be interviewed as a PSID respondent. In addition, it is debatable whether and how a nonresident parent’s wealth should be taken into account. Including the wealth of a “truly absent” parent may induce as much measurement error as failing to include the wealth of a nonresident parent with continued parenting commitments (undivided by new parenting commitments to stepchildren). In other work on the intergenerational effects of wealth (Pfeffer and Killewald 2017), we tested the sensitivity of results to these two distinct measurement approaches and concluded that they do not yield substantively different findings.
Stability analyses based on linear probability models are presented in Online Resource 1, section 3.
Based on the Current Population Survey March Supplement, I estimate a college graduation rate for comparable individuals—specifically, individuals who are heads of households and age 25 in survey years 1995 through 2009—of 28 %, compared with 27.2 % in the analytic sample used here.
Here, the lowest group contains those whose parents do not own a home (home value of zero), about 30 % of the sample, while the second lowest group (about 10 % of the sample) consists of children from owned homes valued at most about $64,000 (see Table 4, Appendix 1). The remaining groups are standard quintiles (20 % each).
This comparative assessment could be influenced by differences in the measurement error present in the income and wealth measures. Although separate assessments of the quality of PSID’s income and wealth measures do exist (with generally positive conclusions; see Gouskova and Schoeni 2007; Pfeffer et al. 2016), it is difficult to draw firm conclusions about the relative degree of measurement error in these two variables. However, most researchers would probably be ready to assume more measurement error in wealth than in income, submitting that it may be more difficult to capture (e.g., when held in complex financial products) and more difficult for the respondent to recall and estimate (e.g., paycheck information is recent, but home valuation may not be). If this assumption is correct, the estimated size of the wealth coefficients relative to that of the income coefficients would be underestimated, making for a conservative assessment of the relative importance of wealth.
For an explanation of why statistical significance tests should be based on estimates of discrete change, see Long and Freese (2014:297).
Stability analyses reported in Online Resource 1, section 4, further reinforce the contrast between stagnating college persistence rates among the bottom three quintiles and sharply increasing rates among the top quintile.
Stability analyses based on linear probability models (see Online Resource 1, section 3) reveal only one notable difference: an even more pronounced increase in the growth of college attainment among children from the top wealth quintile; the main conclusion about growing wealth gaps in higher educational attainment based on average marginal effects from logistic regression models, as presented here, thus appears to be conservative.
Note that distributions that include negative values, as is the case for wealth, can produce a Gini coefficient above 1.0.
Additional analyses reported in Online Resource 1, section 5, reveal that the Gini coefficient of nonhousing net worth (net worth excluding home equity) followed a similarly sharp increase, now reaching an astounding level of 0.98 (but see also the previous footnote).
The specific model used here has been calibrated to provide the best empirical fit (discussed further below; see also Online Resource 1, section 6).
Furthermore, in the context of the specific model applied here, I also need to assume that the parameterization of the model remains equally valid—that is, the absolute thresholds chosen for the spline knots that were drawn based on the earlier cohort remain equally useful for the later cohort.
Later ages would introduce endogeneity concerns because college attendance is expected to affect family wealth.
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Acknowledgments
This work was supported by an award from the Spencer Foundation (Grant No. 201300139) and the Russell Sage Foundation. The collection of data used in this study was partly supported by the National Institutes of Health (Grant No. R01HD069609) and the National Science Foundation (Grant No. 1157698). Any opinions expressed are those of the author alone and should not be construed as representing the opinions of the funding agencies. Earlier versions of this paper were presented at meetings of the Population Association of America, the American Sociological Association, and the Research Committee on Social Inequality and Mobility (RC28). I thank Sheldon Danziger, Thomas DiPrete, Alexandra Killewald, and Robert Schoeni for helpful comments on an earlier version. A replication package containing the data and code used for this article is available through the PSID Public Data Extract Repository at https://doi.org/10.3886/E101105V1.
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Appendices
Appendix 1
Additional Tables
Appendix 2
Effects of the Great Recession
Here I discuss how the vast changes in families’ wealth during the Great Recession may have contributed to the changing wealth gaps in education presented here. Of course, the potential effect of the Great Recession on inequality in educational outcomes does not alter the description of trends provided here, but understanding whether these trends may be driven by a period effect rather than reflect a broader secular trend is of interest.
I first summarize findings on how the wealth distribution shifted during the last decades and then locate the two birth cohorts studied here within this timeline. I then hypothesize the ways in which the pre- and post-recession periods may have affected the observed trends.
Last, I note the results of a stability analysis that partly responds to a measurement concern related to wealth fluctuation around the Great Recession.
The Great Recession and Wealth
Pfeffer and Schoeni (2016) documented that wealth inequality has been rising for decades, particularly since the early 2000s and in the run-up to the Great Recession: relative increases at higher points in the distribution outpaced increases at lower points. Beginning with the crash of the housing market in late 2007, the Great Recession exerted a tremendous and lasting impact on the wealth distribution among U.S. households. Wealth grew even more unequal as lower points in the distribution incurred larger relative losses that were also more sustained through at least 2013. With that, the Great Recession’s effects on wealth inequality extended far beyond its official end date of June 2009 (as set by the National Bureau of Economic Research).
The Great Recession intersects with the birth cohorts assessed here. For Cohort 1, born in the 1970s, educational attainment was assessed before the recession (given that they turned 25 between 1994 and 2004). In contrast, substantial macroeconomic fluctuation coincided with the educational career of some members of Cohort 2, born in the 1980s, who graduated from high school and, in most cases, made their college enrollment decision before the recession (having turned age 18 between 1998 and 2007), but some of those who ended up going to college or entertained the decision at a later point did so during the recession (having turned age 25 between 2005 and 2014).
Potential Effects of the Great Recession on Wealth Gaps in College Outcomes
Given this timing, the Great Recession’s effects should be concentrated on the college outcomes of the second birth cohort, possibly in two ways.
First, the run-up period to the recession positively affected their college-going as the emerging housing bubble reduced credit constraints for college access (see Lovenheim 2011). This influx of home equity would have been most consequential for the homeowning middle class, whose wealth is chiefly concentrated in their homes, pushing toward a reduction of wealth gaps in college access compared with the earlier cohort. Conversely, the same students who enrolled in college thanks to an influx in home equity were negatively affected by the bursting of the housing market bubble and the ensuing loss of available finances to sustain their further college careers (Johnson 2012), implying that decreasing gaps in college access may not have translated into decreasing gaps in attainment. At the same time, wealthier households were less affected by the recession because their wealth is typically less concentrated in housing and instead also includes significant financial wealth. After a period of substantial fluctuation, however, the stock market rebounded much more quickly than the housing market did, translating into less pronounced and less prolonged wealth losses at the top. These trends may have preserved the advantage of the wealthiest students in terms of college persistence and attainment.
Second, as is typical in any recession (Leslie and Brinkman 1987), the Great Recession may have driven more students into college to avoid weak labor markets (Long 2014), potentially contributing to smaller gaps in college access. However, to the extent that students who were induced to enroll in college only by a recession are less prepared for college or less motivated, corresponding increases in persistence and attainment may not follow.
In sum, broad economic forces at work before and during the Great Recession may have helped narrow the wealth gap in college access between the two cohorts studied here while maintaining or even increasing gaps in persistence. This possible direction of influence, of course, is very much in line with the actual trends documented earlier.
Stability Analyses
The tremendous fluctuation in parental wealth before and during the recession may also be considered a measurement challenge (and opportunity for further research) when studying wealth gaps in educational outcomes. In line with my analytical aims, I use a stability analysis that accounts for the fact that the wealth position of children during childhood (ages 10–14, as measured here) may differ from family wealth assessed closer to children’s college enrollment decision.
Reanalyses of all presented models of college access and graduation based on wealth measures at age 18Footnote 16 produce substantively equivalent results (see Online Resource 1, section 4). As a reminder, wealth measures at age 18 were assessed before the Great Recession for all included individuals, so this stability analysis captures wealth fluctuation induced by the run-up to the recession.
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Pfeffer, F.T. Growing Wealth Gaps in Education. Demography 55, 1033–1068 (2018). https://doi.org/10.1007/s13524-018-0666-7
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DOI: https://doi.org/10.1007/s13524-018-0666-7