Schooling and Economic Growth: What Have We Learned?

Abstract

This paper explains why different studies present widely-varying estimates of the effect of increased schooling on national income. It shows that when correctly-interpreted, these studies support the hypothesis that a one-year increase in average schooling attainment raises national income directly by about 10% and indirectly by about 19%. The increases in national income [2008, 2012a, and 2012b] present statistical results showing that differences in students' scores on international tests of science and mathematics explain three times the variation in GDP/capita growth rates explained by differences in adults' average schooling attainment. They also show that when the effect of average test scores and average attainment are examined together, differences in test scores explain most of the variation in growth rates and differences in schooling do not explain any of this variation. They conclude that more schooling does not reliably raise students' cognitive skills and that increases in cognitive skills at ages 9 to 15, not increases in schooling, cause economic growth [Hanushek and Woessmann, 2008].But other studies of the effect of schooling on national income come to completely different conclusions. Gennaioli, La Porta, Lopez-de-Silanes, and Shleifer [2013] examine the relationship between average income/adult and average schooling attainment in 1569 world regions that account for 97 percent of world GDP. They find that differences in schooling can explain 58 percent of the differences in income/capita across these regions, far more than any other single factor. When they estimate the effect of multiple factors on income, they find that the level of schooling is by far the most important factor. Each additional year of schooling is associated with a 26% increase in income/capita. This enormous inconsistency in the estimated effect of increased schooling on national income across studies is not a new phenomenon. Krueger and Lindahl [2001] investigated the causes of this inconsistency in their comprehensive review of the earlier empirical literature. They found that the estimated effect of schooling depends on the structure of the income model used in each analysis. They showed that the estimates are very different if the physical capital stock is included or excluded from the model, or if the effect of changes in schooling are examined prior to or during the growth period.They also found that the estimated effect of schooling is minimal if it is examined using poorly-measured schooling data over short periods, or if the mathematical relationship between the measures of income and schooling is not correctly specified.The more recent literature continues to provide widely-varying estimates of the effect of additional schooling on national income. But I show in this article that these differences actually are consistent because they are entirely explained by differences in the structure of the growth model used to estimate the effect of schooling in the different studies. As a consequence, there ...

Perceptions and Opinions of Graduates about the Effects of Open and Distance Learning in Turkey

“…Breton (2003) claims that between 1950 and 1985, for 61 countries, national outputs increased due to increases in the productivity of education due to investments in education. Breton (2014) shows that an increase in average education by one year causes an increase in national income of 10% directly and 19% indirectly. In addition, there are interesting results from studies that imply primary enrollment to be more effective for emerging markets and higher education to be more effective for developed countries; as Mingat and Tan (1996).…”

Economic Efficiency of Public Secondary Education Expenditure: How Different are Developed and Developing Countries?

“…However, to be more precise in terms of the degree of inefficiency between countries, it is necessary to estimate efficiency scores. Table 3 shows the naïve 11 efficiency scores for a core group of 37 countries using public and private expenditure per secondary student as inputs. Three elements should be considered in their analysis: The first is the peers used in the efficiency score calculation for each country; these are those countries that use around the same level of inputs and obtain better output results.…”