percent higher earnings, while a 1.0 percent standard deviation increase in the math test is associated with 7.6 percent higher earnings. Neal and Johnson (1996) use the National Longitudinal Survey of Youth to estimate the effect of students’ scores on the Armed Forces Qualification Test (AFQT) taken at age 15-18 (adjusted for age when the test was taken) on their earnings at age 26-29. They find that a 1.0 standard deviation increase in scores is associated with about 20 percent higher earnings for both men and women. Based on these three studies, a plausible assumption is that a 1.0 standard deviation increase in either math or reading scores is associated with about 8 percent higher earnings.

Of course, noncognitive skills imparted through schooling are also an important component of human capital (see Heckman and Rubinstein, 2001). Unfortunately, measures of noncognitive abilities lag behind available measures of cognitive abilities. In principle, however, the Garrison and Krueger approach could be extended to include noncognitive abilities. If cognitive and noncognitive abilities are correlated, then to some extent the cognitive test measures will reflect noncognitive skills.19

Jorgenson and Fraumeni essentially look at the earnings gains due to additional years of schooling, whereas the other researchers discussed above look at earnings gains due to achievement test score gains. Hansen et al. (2004) provide something of a potential bridge between the Jorgenson and Fraumeni method and this approach, as they estimate the effects of additional years of schooling on cognitive test scores. They find that a 1-year increase in schooling is associated with a 0.17 standard deviation increase in the average person’s score on the Armed Forces Qualifying Test.

Housing Value Approach

Hedonic housing price models provide another possible technique for estimating the output of the education sector, or at least the output of public primary and secondary education. This approach uses differences in housing prices across areas to estimate the value that parents place on school quality, as indicated by test scores. An often-cited study in this genre, Black (1999) exploits the natural experiment created by different school catchment areas, looking at differences in the values of otherwise similar houses on either side of the border between school zones with different test scores. This approach provides an estimate of the latent price placed on student achievement in the housing market. The close proximity of the houses helps to control for unobserved variables. Her results indicate that parents are willing to pay 2.5 percent more for a house in a district with test scores that are 5 percent higher. Unfortunately, she does not have data on the


See Bowles et al. (2001) for evidence that individual characteristics unrelated to cognitive ability—and perhaps unrelated to productivity—are related to earnings.

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