Productivity in education: the quintessential upstream industry.

• Author: Hoxby, Caroline M.

1. Education as an Upstream Industry

   Hardly a day goes that we do not hear about the way today's economy relies on human skills. We may be justifiably dubious about hyperbolic statements: Not everyone in America works in the "New Economy," a knowledge-based service-sector industry, such as software design. Nevertheless, America is, with every passing year, increasingly reliant on industries that use workers who have formal education, training, and the skills that allow a person to adapt rapidly to changing demands. Manufacturing has become substantially more oriented toward specialized production that produces customized products. There are many advantages of having an economy dominated by skill-oriented industries: They generate high per-capita incomes, and they tend to be environmentally "friendly." An upstream industry is any industry that produces inputs for other industries that are closer to the product market. Yet, when the words "upstream industry" appear, steel or petrochemicals often spring to mind. However, the sector that produces education is probably the upstream industry on which skill-oriented economies most rely.

   Many economic models link education to a country's growth. Nevertheless, thinking about the education sector as an upstream industry generates a new perspective on the education-growth relationship. We know that downstream industries locate where there is a relative abundance of the inputs on which they disproportionately rely. If the United States is to have future growth based on skilled industries, it must maintain its relative abundance of human capital.

   Human capital is made, not naturally endowed, so in the long run, it will be relatively abundant where the education sector has high productivity--that is, where schools efficiently transform inputs into skill. For years, America did have a high-productivity elementary and secondary education sector: Compared to other countries, America spent only moderately on education, and yet its population completed an unusually large number of years of education. For more than thirty-five years, however, the extensive margin (more years of elementary and secondary education per person) has been exhausted in the United States. Moreover, on the extensive margin, all of the developed world has caught up or more than caught up to the United States (Organization for Economic Cooperation and Development [OECD] 2003). Achievement per dollar spent has thus become the key measure of productivity in the primary and secondary (K-12) education sector.

2. Measuring the Productivity of Public Primary and Secondary Schools

   Measuring productivity in education is somewhat difficult. Indeed, measuring productivity in any industry is somewhat difficult. Education is so important, however, that we must try seriously to measure it. I will start with time-series data from the United States, which can give us the range of productivity that the sector has displayed in the last few decades. Then I will compare the United States with OECD and other countries in a recent year. This comparison will give us the range of productivity that exists in competing countries.

   Even with all of the bells and whistles that can be included, this will not be an empirical exercise with the exactitude beloved by today's applied microeconomists. It is, however, sufficiently accurate to motivate the remaining discussion.

   One measures the productivity of the education industry by dividing its outputs by its inputs. The inputs are relatively easy: Per-pupil spending is the best available measure. The outputs we can measure are limited, especially if we want to look at schools' productivity now. Test scores are available relatively quickly; lifetime earnings are not. Also, it has proven very difficult for researchers to identify how primary and secondary educational inputs contribute to later earnings, partly because of self-selection into further education and partly because many factors other than education affect later earnings. Thus, I will use test scores rather than earnings as the measure of output. Fortunately, the test scores of the sort I use have been shown to be strong predictors of later wages, employment, and other outcomes. (1) I will use test scores that are representative of the whole population of potential students, not merely self-selected students who have chosen to remain in school past some common stopping point.

   Let us first consider the United States, where I can make solid computations for the last three decades. Since 1970, American students have taken a series of tests called the National Assessment of Educational Progress (NAEP) that are specifically designed to track achievement from year to year. The NAEP is administered to very large samples of American students in a few grades. (2)

   Figure 1 shows performance on the NAEP from 1971 to 2000 (U.S. Department of Education 2003e). I have normalized the 1970 scores to zero. For ease of interpretation, the height of the vertical axis is one standard deviation. That is, a student's score would have to rise from the very bottom of the figure to the very top of the figure if he were to move one standard deviation on the test. Clearly. regardless of the subject or grade we examine, performance has been flat.

   [FIGURE 1 OMITTED]

   One might worry that the flat test scores are the result of better schools working on students with worse sociodemographics. The data suggest that this worry is unnecessary. If one weights a student's scores by the number of students who were in his family background cell in 1970, one gets the line labeled "Using 1970 Socio-Demographics" in Figure 2. (3) If one weights a student's scores by the number of students who were in his family background cell in 2000, one gets the line labeled "Using 2000 Socio-Demographics" in Figure 2. (Notice that I have normed achievement so that actual achievement in 1970 is always zero.) If you have eagle eyes, you may be able to see that year 2000 sociodemographics are slightly more favorable than 1970 sociodemographics. This is largely a result of the growth of family income in the United States. Nevertheless, the message of Figure 2 is that sociodemographics cannot easily be made to account for flatness of achievement.

   [FIGURE 2 OMITTED]

   Achievement may have been quite flat, but the same cannot be said for inputs into public primary and secondary education in the United States. Figure 3 shows per-pupil spending in public schools from 1970 to 2000 (U.S. Department of Education 2003c). I have used the Consumer Price Index (CPI) to put dollars of the day into real 2003 dollars (U.S. Department of Labor 2004). In the figure, per-pupil spending rises from $4800 in 1970 to $9230 in 2000.

   [FIGURE 3 OMITTED]

   By dividing the test scores from Figure 2 by per-pupil spending from Figure 3, one gets Figure 4, which gives us estimates of public schools' productivity over time. From 1970 to 2000, productivity fell from 58 to 30 national percentile rank points per $1000. This near-halving of productivity is a decline so substantial that it is hard to ignore.

   [FIGURE 4 OMITTED]

   One might criticize the computations shown in Figure 4 because they take no account of Baumol's "cost disease" argument for why productivity falls in nontraded service industries (Baumol 1967). He argued that the labor in nontraded service industries such as education does not enjoy productivity increases over time, whereas the labor in traded industries is combined more efficiently with other inputs (including human inputs) over time, and the labor in goods-producing industries benefits more from productivity-enhancing technology. Yet, he points out, education must hire people who could work in industries where productivity is rising, so the education industry must pay more and more with each passing year in order to hire workers of the same quality. According to Baumol's argument, the education industry's failing productivity may be beyond our control, an inevitable result of rising productivity elsewhere.

   We can take Baumol's argument seriously by assuming that the education sector would have had to pay its workers more (in real terms) with each passing year in order to hire people with the same skills. For instance, we can use an index based on the earnings of professional women in the United States (those with a professional degree such as attorneys, physicians, and masters of business administration) instead of the Consumer Price Index to put per-pupil spending into today's dollars (U.S. Department of Labor 2003). In other words, we will show the productivity of schools over time, holding them harmless for the increasing cost of hiring good teachers. When I use professional women's earnings to generate an index, I am being too generous to schools. That is, I am overadjusting for the rising costs that schools face. The overadjustment occurs because college-educated labor is not the only input into education. Many inputs such as classroom materials and equipment have not had their costs rise significantly over time. I am treating schools as though the costs of all their inputs rose with professional women's earnings. Also, professional women's earnings rose more steeply than the cost of hiring the same quality teacher. This is because I use earnings rather than hourly wages. Professional women worked more and more hours each year, so their earnings grew faster than their wages. Teachers have not worked longer hours with each year. Finally, professional women's earnings rose more steeply than those of constant-quality college-graduate women. This is because professional women are among the most skilled workers in the economy, and the most skilled workers have had earnings gains relative even to other college graduates. In short, I deliberately, substantially overcorrect for Baumol's argument and for other influences on women's earnings opportunities.

   Figure 5 shows that, even with this...