In the Jorgenson and Fraumeni approach, lifetime income includes both market and nonmarket components. Given the substantial amount of time that individuals spend outside of the market, even excluding sleep, the value of nonmarket activities can be several times larger than the value of market activities. Jorgenson and Fraumeni make the strong assumption that education increases productivity in all nonmarket activities, except for the ten hours a day assumed to be devoted to self-maintenance, by the same amount that it increases market productivity. They refer to the assigned value of the time spent in nonmarket activities as nonmarket income. Assigning a uniform increase in value to a full fourteen hours per day of educated individuals’ time is somewhat controversial. An alternative would be to assume that, beyond its effect on market productivity, education raises the value only of nonmarket time in which individuals are engaged in productive activity, thereby excluding time devoted to leisure. One also might want to allow for the possibility that the effect of education on nonmarket productivity varies depending on the task involved; as discussed elsewhere in this report, there are certain tasks for which the productivity advantage enjoyed by more educated individuals seems likely to be relatively small. Other researchers who have estimated the nonmarket return to education report substantially smaller figures than do Jorgenson and Fraumeni.18
The studies that have estimated incremental earnings as a function of improvements in test scores focus on annual market earnings, rather than lifetime market and nonmarket income. Murnane et al. (1995), for example, estimate that male high school seniors who scored one standard deviation higher on the basic math achievement test in 1980 earned 7.7 percent higher earnings 6 years later, based on data from the High School and Beyond Survey; the comparable figure for females was 10.9 percent. Because this study also controls for students’ eventual educational attainment, any effect of cognitive ability as measured by test scores on educational attainment is not counted as a gain from higher test scores.
Currie and Thomas (1999) use the British National Child Development Study to examine the relationship between math and reading test scores at age 7 and earnings at age 33. Estimating a multiple regression of earnings on both test score variables, they find that students who score in the upper quartile of the reading exam earn 20 percent more than students who score in the lower quartile of that exam; similarly, students in the top quartile of the math exam earn another 19 percent more than those in the bottom quartile of that exam. Assuming normality, the average student in the top quartile scores about 2.5 standard deviations higher than the average student in the bottom quartile, so their results imply that a 1.0 standard deviation increase in reading test performance is associated with 8.0