Four Facts About Human Capital

This paper synthesizes what we have learned about human capital into four stylized facts. Fact 1: Human capital explains a substantial share of the variation in labor earnings within and across countriesThe bottom line is that naïve cross-sectional comparisons and studies with strong quasi-experimental research designs yield very similar estimates of the economic return to education. Overall, studies that identify returns to education using instrumental variables, regression discontinuity, and other quasi- experimental approaches yield estimates of an additional year of education ranging between 6 and 18 percent, with a median in the 10-12 percent range. This is slightly higher than the 10 percent return from a “naïve” Mincer model, most likely because of some combination of measurement error and higher returns for marginal students (Card 1999). Across all OECD countries, the median earnings premium for a four-year college / tertiary education is 52 percent, or roughly 13 percent per year of education Table 1 uses data from the 1979 National Longitudinal Survey of Youth (NLSY79), which tracks a cohort of youth ages 14 to 22 in 1979 as they progress through the labor market. To estimate returns to education over the life-course, I compute the average inflation-adjusted hourly wage for individuals between the ages of 25 and 54 over multiple observations, and then regress log average hourly wages on years of education, race and gender indicators, and cognitive ability as measured by adolescent scores on the Armed Forces Qualifying Test (AFQT). Column 1 shows that the average return to a year of education over an individual’s prime working years is 10.9 percent. The R-squared of this regression is 30 percent. Controlling for AFQT to account for “ability bias” reduces the coefficient on years of education to 7.2 percent and increases the R-squared of the regression to 35 percent. Column 3 shows the average return for different levels of educational attainment, with less than high school as the left-out category. High school graduates and four-year college graduates earn an average of 13 percent and 48 percent higher wages than those with less than a high school education, respectively.These results are similar in magnitude to the quasi-experimental studies discussed above and to naïve cross-sectional estimates from other data sources. Basic measures of human capital such as education and cognitive ability can explain at least one-third of the variation in wages in a recent cohort of US workers. However, one-third is probably a lower bound for the impact of human capital on earnings, for three reasons. First, the calculation here does not include variation in education quality between workers with the same level of attainment. Quality adjustment is particularly important, because nearly all expansion of US postsecondary education over the last few decades has occurred within less-selective institutions. Carneiro and Lee (2011) estimate that the college premium would have grown an additional 30 percent between 1960 and 2000 if pre-college education quality were held constant for the marginal college graduate. Second, several studies find a larger role for human capital when it is measured in a way that includes education but also other attributes. For example, Smith et al. (2019) study the impact of owner death or retirement on private pass-through businesses and find that 75 percent of profits are attributable to the owner’s human capital, rather than physical or financial assets. Card et al. (2018) and Song et al. (2019) decompose the variance of earnings in matched employer-employee data and find that “worker effects” account for 40 percent of the variance in earnings in West Germany and 50 percent in the US respectively. Because worker effects are invariant to firm pay premia and occupational shifts by construction, we can reasonably consider them an estimate of workers’ human capital. I think the contribution of signaling is probably small, for two reasons. First, many studies find positive returns to education even when no degree or credential is earned. This is important because signaling theory requires employers to observe the signal, and most people don’t report years of education on a resume. For example, studies of compulsory schooling compare groups of students who all seek to drop out as soon as they can, but some are required to stay in school longer based on when they were born during a calendar year. Many of the youth staying in school for an extra year do not end up obtaining a high school degree at all - they drop out in 11th grade rather than 10th grade. Nonetheless, such studies show that additional education leads to gains in earnings.A school construction program in Indonesia studied by Duflo (2001) mostly worked by increasing primary school enrollment, not receipt of degrees—but still led to later gains in wages. Aryal, Bhuller and Lange (2022) cleverly exploit the differential observability of compulsory schooling laws across regions in Norway to separate returns to human capital from signaling, and find that human capital accounts for 70 percent of the private return to secondary school education. empirical support for signaling theory is scant. Clark and Martorell (2014) find no difference in earnings between high school students who barely pass or fail an exit exam, implying that there is no signaling value of a high school diploma.Some studies do find that the return to education decreases over time as employers learn workers’ true ability, which is a testable implication of the signaling model Yet a similar test of the employer learning model in a more recent cohort finds that the return to education does not diminish with experienceFact 2: Human capital investments have high economic returns throughout childhood and young adulthood. There is strong evidence supporting value of skill investments in early childhood.Perhaps best-known are two randomized evaluations of preschool interventions from the 1960s: the High/Scope Perry Preschool Project and the Carolina Abecedarian Project. These studies are from decades ago, involving small-scale and intensive interventions for highly disadvantaged families, and thus their results may not generalize to larger and more recent programs. However, several studies of more recent preschool interventions also find substantial impacts —although the evidence also provides a puzzle. Pre-K programs often provide only a short-term boost to test scores that fades out in a few years. Yet they have longer-run impacts on important life outcomes such as high school graduation and college attendance, as well as non-educational metrics like reductions in crime and teen pregnancy and improved health later in life (for example, Ludwig and Miller 2007, Deming 2009, Gray-Lobe, Pathak and Walters 2021). In addition, there are many other methods of early childhood investment: pre-natal care, early child health care, food and nutrition support, home visits to encourage practices like breast-feeding and smoking cessation, and others. In recent essays in this journal, Aizer, Hoynes, and Lleras-Muney (2022) describe the evidence of long-term benefits from policy interventions affecting low-income children like Medicaid and food stamps, while Wust (2022) presents the evidence from the Nordic countries about the benefits of universal provision of early childhood investments in pre-natal care, health care at time of birth, and early childhood health care. Again, any benefit-cost analysis of these programs needs to take a long-term view, because many of these benefits only become apparent later in life. In sum, the evidence suggests that human capital investments are, at least in rough terms, equally productive between the ages of 0 and 25. Fact 3: The technology for producing foundational skills such as numeracy and literacy is well- understood, and resources are the main constraint. The overall picture is that some specific input investments work very well, but many do not, and it is often hard to predict ahead of time. I interpret the evidence as follows. First, at least in the United States, increased school spending is productive on the margin. Increasing school spending from current levels would produce substantially more human capital and may even pay for itself in the long run. The technology for producing basic math and literacy skills in school-aged children is fairly well-understood. Smaller class sizes, better school facilities, and more instructional time all have reliable impacts on the development of foundational academic skills. The inputs with the best track record of effectiveness - high-dosage tutoring, extra instructional time, personalization and teaching to the right level - mostly deliver to students “more of the same”, rather than reinventing the learning process. Second, while sharpening incentives works in some contexts, achievement gains are often short-lived and there is not much evidence of long-run benefits. An important caveat is that resources plus incentives appear to be more effective than resources alone. Third, simply giving schools money - and allowing them to spend it flexibly - may be a more reliable way to increase human capital than pinning our collective hopes on any particular “silver bullet” approach that all schools would be required to follow.This makes education experts queasy, and rightfully so. In a perfect world, increases in resources are combined with transparency and accountability for results. Yet the evidence suggests that “helicopter drops” of money are spent well enough to be worth the investment, at least in developed countries and in schools with strong internal accountability. However, just because school spending is economically productive on the margin does not mean that the money is spent optimally. It can be simultaneously true that school spending is productive and that much of it is wasted. We can probably always do better, and so innovation and experimentation are critical for increasing the productivity of human capital investments. Fact 4: Higher-order skills such as problem-solving and teamwork are increasingly economically valuable, and the technology for producing them is not well understood

David Demings, “Four Facts About Human Capital,” National Bureau Of Economic Research, June 2022, https://www.nber.org/papers/w30149