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Education and Economic Growth in Historical Perspective

David Mitch, University of Maryland Baltimore County

In his introduction to the Wealth of Nations, Adam Smith (1776, p. 1) states that the proportion between the annual produce of a nation and the number of people who are to consume that produce depends on “the skill, dexterity, and judgment with which its labour is generally applied.” In recent decades, analysts of economic productivity in the United States during the twentieth century have made allowance for Smith’s “skill, dexterity, and judgment” of the labor force under the rubric of labor force quality (Ho and Jorgenson 1999; Aaronson and Sullivan 2001; DeLong, Goldin, and Katz 2003). These studies have found that a variety of factors have influenced labor force quality in the U.S., including age structure and workforce experience, female labor force participation, and immigration. One of the most important determinants of labor force quality has been years of schooling completed by the labor force.

Data limitations complicate generalizing these findings to periods before the twentieth century and to geographical areas beyond the United States. However, the rise of modern economic growth over the last few centuries seems to roughly coincide with the rise of mass schooling throughout the world. The sustained growth in income per capita evidenced in much of the world over the past two to two and a half centuries is a marked divergence from previous tendencies. Kuznets (1966) used the phrase “modern economic growth” to describe this divergence and he placed its onset in the mid-eighteenth century. More recently, Maddison (2001) has placed the start of sustained economic growth in the early nineteenth century. Maddison (1995) estimates that per capita income between 1520 and 1992 increased some eight times for the world as a whole and up to seventeen times for certain regions. Popular schooling was not widespread anywhere in the world before 1600. By 1800, most of North America, Scandinavia, and Germany had achieved literacy rates well in excess of fifty percent. In France and England literacy rates were closer to fifty percent and school attendance before the age of ten was certainly widespread, if not yet the rule. It was not until later in the nineteenth century and the early twentieth century that Southern and Eastern Europe were to catch up with Western Europe and it was only the first half of the twentieth century that saw schooling become widespread through much of Asia and Latin America. Only later in the twentieth century did schooling begin to spread throughout Africa. The twentieth century has seen the spread of secondary and university education to much of the adult population in the United States and to a lesser extent in other developed countries.[2] However, correlation is not causation; rising income per capita may have contributed to rising levels of schooling, as well as schooling to income levels. Thus, the contribution of rising schooling to economic growth should be examined more directly.

Estimating the Contribution of the Rise of Mass Schooling to Economic Growth: A Growth Accounting Perspective

Growth accounting can be used to estimate the general bounds of the contribution the rise of schooling has made to economic growth over the past few centuries.[3] A key assumption of growth accounting is that factors of production are paid their social marginal products. Growth accounting starts with estimates of the growth of individual factors of production, as well as the shares of these factors in total output and estimates of the growth of total product. It then apportions the growth in output into that attributable to growth in each factor of production specified in the analysis and into that due to a residual that cannot otherwise be explained. Estimates of how much schooling has increased the productivity of individual workers, combined with estimates of the increase in schooling completed by the labor force, yield estimates of how much the increase in schooling has contributed to increasing output. A growth accounting approach offers the advantage that with basic estimates (or at least possible ranges) for trends in output, labor force, schooling attainment, and preferably capital stock and factor shares, it yields estimates of schooling’s contribution to economic growth. An important disadvantage is that it relies on indirect estimates at the micro level for how schooling influences productivity at the aggregate level, rather than on direct empirical evidence.[4]

Back-of-the-envelope estimates of increases in income per capita attributable to rising levels of education over a period of a few centuries can be obtained by considering possible ranges of levels of schooling increases as measured in average years of schooling along with possible ranges of rates of return per year of schooling, in terms of the percentage by which a year of schooling raises earnings and common ranges for labor’s share in national income. By using a Cobb-Douglas specification of the aggregate production function with two factors of production, labor and physical capital, one can arrive at the following equation for the ratio between final and initial national income per worker due to increases in average school years completed between the two time periods:

1) (Y/L)1/ (Y/L)0 = ( (1 + r )S1 - S0 )α

Where Y = output, L = the labor force, r = the percent by which a year of schooling increases labor productivity, S is the average years of schooling completed by the labor force in each time period, α is labor’s share in national income, and the subscripts 0 and 1 denote the initial and final time period over which the schooling changes occur.[5] This formulation is a partial equilibrium one, holding constant the level of physical capital. However, the level of physical capital should be expected to increase in response to improved labor force quality due to more schooling. A common specification of a growth model that allows for such responses of physical capital implies the following ratio between final and initial national income per worker (see Lord 2001, 99-100):

2) (Y/L)1/ (Y/L)0 = ( (1 + r )S1 - S0 )

The bounds on increases in years of schooling can be placed at between zero and 16, that is, between a completely unschooled and presumably illiterate population to one in which a college education is universal. As bounds on returns to increasing earnings per year of schooling, one can employ Krueger and Lindahl’s (2001) survey of results from recent estimates of earnings functions, which finds that returns range from 5 percent to 15 percent. The implications of varying these two parameters are reported in Tables 1A and 1B. Table 1A reports estimates based on the partial equilibrium specification holding constant the level of physical capital in equation 1). Table 1B reports estimates allowing for a changing level of physical capital as in equation 2). Labor’s share of income has been set at a commonly used value of 0.7 (see DeLong, Goldin and Katz 2003, 29; Maddison 1995, 255).

Table 1A
Increase in per Capita Income over a Base Level of 1 Attributable to Hypothetical Increases in Average Schooling Levels — Holding the Physical Capital Stock Constant

Percent Increase in Earnings per Extra Year of Schooling
Increase in Average
Years of Schooling
5% 10% 15%
1 1.035 1.07 1.10
3 1.11 1.22 1.34
6 – illiteracy to
universal grammar school
.23 1.49 1.80
12 – illiteracy to
universal high school
1.51 2.23 3.23
16 – illiteracy to
universal college
1.73 2.91 4.78

Table 1B
Increase in per Capita Income over a Base Level of 1 Attributable to Hypothetical Increases in Average Schooling Levels — Allowing for Steady-state Changes in the Physical Capital Stock

Percent Increase in Earnings per Extra Year of Schooling
Increase in Average
Years of Schooling
5% 10% 15%
1 1.05 1.10 1.15
3 1.16 1.33 1.52
6 – illiteracy to
universal grammar school
1.34 1.77 2.31
12 – illiteracy to
universal high school
1.79 3.14 5.35
16 – illiteracy to
universal college
2.18 4.59 9.36

The back-of-the-envelope calculations in Tables 1A and 1B make two simple points. First, schooling increases have the potential to explain a good deal of estimated long-term increases in per capita income. With the average member of an economy’s labor force embodying investments of twelve years of schooling and a moderate ten-percent rate of return per year of schooling and no increase in the capital stock, at least 17 percent of Maddison’s eight-fold increase in per capita income can be accounted for (i.e. 1.23/7) by rising schooling. Indeed, a 16 year schooling increase allowing for steady-state capital stock increases and at 15 percent per year return overexplains Maddison’s eight-fold increase (8.36/7). After all, if schooling has had substantial effects on the productivity of individual workers, if a sizable share of the labor force has experienced improvements in schooling completed and with labor’s share of output greater than half, then the contribution of rising schooling to increasing output should be large.

Second, the contribution of schooling increases that have actually occurred historically to per capita income increases is more modest accounting for at best about one fifth of Maddison’s one-fold increase. Thus an increase in average years of schooling completed by the labor force of 6 years, roughly that entailed by the spread of universal grammar schooling, would account for 19 percent (1.31/7) of an eight-fold per capita output increase at a high 15 percent rate of return allowing for steady state changes in the physical capital stock (Table 1B). And at a low 5 percent return per year of schooling, the contribution would be only 5 percent of the increase (0.34/7). Making lower-level elementary education universal would entail increasing average years of schooling completed by the labor force by 1 to 3 years; in most circumstances this is not a trivial accomplishment as measured by the societal resources required. However, even at a high 15 percent per year return and allowing for steady state changes in the capital stock (Table 1B), the contribution of a 3 year increase in average years of schooling would only account for 7 percent (0.52/7) of Maddison’s eight-fold increase.

How do the above proposed bounds on schooling increases compare with possible increases in the physical capital stock? Kendrick (1993, 143) finds a somewhat larger growth rate in his estimated human capital stock than in the stock of non-human capital for the U.S. between 1929 and 1969, though for the sub-period 1929-48, he estimates a slightly higher growth rate for the non-human capital stock. In contrast, Maddison (1995, 35-37) estimates larger increases in the value of non-residential structures per worker and in the value of machinery and equipment per worker than in years of schooling per adult for the U.S. and the U.K. between 1820 and 1992. For the U.S., he estimates that the value of non-residential structures per worker rose by 21 times and the value of machinery and equipment per worker rose by 141 times in comparison with a ten-fold increase in the years of schooling per adult between 1820 and 1992. For the U.K., his estimates indicate a 15 fold increase in the value of structures per worker and a 97 fold increase in value of machinery and equipment per worker in contrast with a seven-fold increase in average years of schooling between 1820 and 1992. It should be noted that these estimates are based on cumulated investments in schooling to estimate human capital; that is, they are based on the costs incurred to produce human capital. Davies and Whalley (1991, 188-189) argue that estimates based on the alternative approach of calculating the present value of future earnings premiums attributable to schooling and other forms of human capital yield substantially higher estimates of human capital due to capturing inframarginal returns above costs accruing to human capital investments. For the growth accounting approach employed here, the cumulated investment or cost approach would seem the appropriate one. Are there more inherent bounds on the accumulation of human capital over time than non-human capital? One limit on the accumulation of human capital is set by how much of one’s potential working life a worker is willing to sacrifice for purposes of improving education and future productivity. This can be compared with the corresponding limit on the willingness to sacrifice current consumption for wealth accumulation.

However, this discussion makes no explicit allowance for changes over time in the quality of schooling. Improvements in teacher training and teacher recruitment along with ongoing curriculum developments among other factors could lead to ongoing improvements over time in how much a year of school attendance would improve the underlying future productivity of the student. Woessmann (2002) and Hanushek and Kimcoe (2000) have recently argued for the importance of allowing for variation in school quality in estimating the impact of cross national variation in human capital levels on economic growth. Woessmann (2002) makes the suggestion that allowing for improvements in the quality of schooling can remove the upper bounds on schooling investment that would be present if this was simply a matter of increasing the percentage of the population enrolled in school at given levels of quality. While there would seem to be inherent bounds on the proportion of one’s life that one is willing to spend in school, such bounds would not apply to increases in expenditures and other means of improving school quality.

Expenditures per pupil appear to have risen markedly over long periods of time. Thus, in the United States, expenditure per pupil in public elementary and secondary schools in constant 1989-90 dollars rose by over 6 times between 1923-24 and 1973-74 (National Center for Educational Statistics, 60). And in Victorian England, nominal expenditures per pupil in state subsidized schools more than doubled between 1870 and 1900, despite falling prices (Mitch 1982, 204). These figures do not control for the rising percentage of students enrolled in higher grade levels (presumably at higher expenditure per student), general rises in living standards affecting teachers’ salaries and other factors influencing the abilities of those recruited into teaching. Nevertheless, they suggest the possibility of sizable improvements over time in school quality.

It can be argued that implicitly allowance is made for improvements in school quality in the rate of return imputed per year of schooling completed on average by the labor force. Insofar as schools became more effective over time in transmitting knowledge and skills, the economic return per year of schooling should have increased correspondingly. Thus any attempt to allow for school quality in a growth accounting analysis should be careful to avoid double counting school quality in both school inputs and in returns per year of schooling.

The benchmark for the impact of increases in average levels of schooling completed in Table 1 are Maddison’s estimates of changes in output per capita over the last two centuries. In fact, major increases in schooling levels have most commonly been compressed into intervals of several decades or less, rather than periods of a century or more. This would imply that the contribution to output growth of improvements in labor force quality due to increases in schooling levels would have been concentrated primarily in periods of marked improvement in schooling levels and would have been far more modest during periods of more sluggish increase in educational attainment. In order to gauge the impact of the time interval over which changes in schooling occur on growth rates of output, Table 2 provides the change in average years of schooling implied by some of the hypothetical changes in average levels of schooling attainment reported in Table 1 for various time periods.

Table 2

Annual Change in Average Years of Schooling per Adult per Year Implied by Hypothetical Figures in Table 1

Time period over which increase occurred
Total Increase in
Average Years of
Schooling per Adult
5 years 10 years 30 years 50 years 100 years
1 0.2 0.1 0.033 0.02 0.01
3 0.6 0.3 0.1 0.06 0.03
6 1.2 0.6 0.2 0.12 0.06
9 1.8 0.9 0.3 0.18 0.09

Table 3 translates these rates of schooling growth into output growth rates using the partial equilibrium framework of equation 1) using a value for the share of labor of 0.7 as above. The contribution of schooling to growth rates of output and output per capita can be calculated as labor’s share times the percentage return per year of schooling on earnings times the annual increase in average years of schooling.

Table 3A
Contribution of Schooling for Large Increases in Schooling to Annual Growth Rates of Output

Length of time for schooling increase 6 year rise in average years of schooling 6 year rise in average years of schooling 9 year rise in average years of schooling 9 year rise in average years of schooling
5% return 10 % return 5 % return 10% return
30 years 0.7% 1.4% 1.05% 2.1%
50 years 0.42% 0.84% 0.63% 1.26%

Table 3B
Contribution of Schooling for Small to Modest Increases in Schooling to Annual Growth Rates of Output

Length of time for schooling increase 1 year rise in average years of schooling 1 year rise in average years of schooling 3 year rise in average years of schooling 3 year rise in average years of schooling
5 % return 10 % return 5% return 10% return
5 years 0.7% 1.4% 2.1% 4.2%
10 years 0.35% 0.7% 1.05% 2.1%
20 years 0.175% 0.35% 0.525% 1.05%
30 years 0.12% 0.23% 0.35% 0.7%
50 years 0.07% 0.14% 0.21% 0.42%
100 years 0.035% 0.07% 0.105% 0.21%

The case of the U.S. in the twentieth century as analyzed in DeLong, Goldin and Katz (2003) offers an example of how apparent limits or at least resistance to ongoing expansion of schooling have lowered the contribution of schooling to growth. They find that between World War I and the end of the century, improvements in labor quality attributable to schooling can account for about a quarter of the growth of output per capita in the U.S. during this period; this is similar in magnitude to Denison’s (1962) estimates for the first part of this period. This era saw the mean years of schooling completed by age 35 increased from 7.4 years for an American born in 1875 to 14.1 years for an American born in 1975 (DeLong, Goldin and Katz 2003, 22). However, in the last two decades of the twentieth century the rate of increase of mean years of schooling completed leveled off and correspondingly the contribution of schooling to labor quality improvements fell almost in half.

Maddison (1995) has compiled estimates of the average years of schooling completed for a number of countries going back to 1820. It is indicative of the sparseness of schooling completed by adult population estimates that Maddison reports estimates for only 3 countries, the U.S., the U.K., and Japan, all the way back to 1820. Maddison’s figures come from other studies and their reliability warrants further critical scrutiny than can be accorded them here. Since systematic census evidence on adult educational attainment did not begin until the mid-twentieth century, estimates of labor force educational attainment prior to 1900 should be treated with some skepticism. Nevertheless, Maddison’s estimates can be used to give a sense of plausible changes in levels of schooling completed over the last century and a half. The average increases in years of schooling per year for various time periods implied by Maddison’s figures are reported in Table 4. Maddison constructed his figures by giving primary education a weight of 1, secondary education a weight of 1.4, and tertiary, a weight of 2 based on evidence on relative earnings for each level of education.

Table 4
Estimates of the Annual Change in Average Years of Schooling per Person aged 15-64 for Selected Countries and Time Periods

Country 1913-1973 1870-1973 1870-1913
U.S. 0 .112 0.107 0.092
France 0.0783
Germany 0.053
Netherlands 0.064
U.K. 0.0473 0.0722 0.102
Japan 0.112 0.106 0.090

Source: Maddison (1995), 37, Table 2-3

Table 5
Annual Growth Rates in GDP per Capita

Region 1820-70 1870-1913 1913-50 1950-73 1973-92
12 West European Countries 0.9 1.3 1.2 3.8 1.8
4 Western Offshoots 1.4 1.5 1.3 2.4 1.2
5 South European Countries n.a. 0.9 0.7 4.8 2.2
7 East European Countries n.a. 1.2 1.0 4.0 -0.8
7 Latin American Countries n.a. 1.5 1.9 2.4 0.4
11 Asian Countries 0.1 0.7 -0.2 3.1 3.5
10 African countries n.a. n.a. 1.0 1.8 -0.4

Source: Maddison (1995), 62-63, Table 3-2.

In comparing Tables 2 and 4 it can be observed that the estimated actual changes in years of schooling compiled by Maddison (as well as the average over 55 countries reported by Lichtenberg (1994) for the third quarter of the twentieth century) fall within a lower bound set in the hypothetical ranges of a 3 year increase in average schooling spread over a century and an upper bound set by a 6 year increase in average schooling spread over 50 years.

Equations 1) and 2) above assume that each year of schooling of a worker has the same impact on productivity. In fact it has been common to find that the impact of schooling on productivity varies according to level of education. While the rate of return as a percentage of costs tends to be higher for primary than secondary schooling, which is in turn higher than tertiary education, this reflects the far lower costs, especially lower foregone earnings, of primary schooling (Psacharopolous and Patrinos 2004). The earnings premium per year of schooling tends to be higher for higher levels of education and this earnings premium, rather than the rate of return as a percentage costs, is the appropriate measure for assessing the contribution of rising schooling to growth (OECD 2001). Accordingly growth accounting analyses commonly construct schooling indexes weighting years of schooling according to estimates of each year’s impact on earnings (see for example Maddison 1995; Denison 1962). DeLong, Goldin and Katz (2003) use chain weighted indexes of returns according to each level of schooling. A rough approximation of the effect of allowing for variation in economic impact by level of schooling in the analysis in Table 1 is simply to focus on the mid-range 10 percent rate of return as an approximate average of high, low, and medium level returns.[6]

The U.S. is notable for rapid expansion in schooling attainment over the twentieth century at both the secondary and tertiary level, while in Europe widespread expansion has tended to focus on the primary and lower secondary level. These differences are evident in Denison’s estimates of the actual differences in educational distribution between the United States and a number of Western European countries in the mid-twentieth century (see Table 6).

Table 6

Percentage Distributions of the Male Labor Force by Years of Schooling Completed

Years of School Completed United States 1957 France 1954 United Kingdom 1951 Italy 1961
0 1.4 0.3 0.2 13.7
1-4 5.7 2.4 0.2 26.1
5-6 6.3 19.2 0.8 38.0
7 5.8 21.1 4.0 4.2
8 17.2 27.8 27.2 8.1
9 6.3 4.6 45.1 0.7
10 7.3 4.1 8.4 0.7
11 6.0 6.5 7.3 0.6
12 26.2 5.4 2.5 1.8
13-15 8.3 5.4 2.2 3.0
16 or more 9.5 3.2 2.1 3.1

Source: Denison (1967), 80, Table 8-1.

Some segments of the population are likely to have much greater enhancements of productivity from additional years of schooling than others. Insofar as the more able benefit from schooling compared to the rest of the ability distribution, putting substantially greater relative emphasis on expansion of higher levels of schooling could considerably augment growth rates over a more egalitarian strategy. This result would follow from a substantially greater premium assigned to higher levels of education. However, some studies of education in developing countries have found that they allocate a disproportionate share of resources to tertiary schooling at the expense of primary schooling, reflecting efforts of elites to benefit their offspring. How this has impeded economic growth would depend on the disparity in rates of return among levels of education, a point of some controversy in the economics of education literature (Birdsall 1996; Psacharopoulos 1996).

While allocating schooling disproportionately towards the more able in a society may have promoted growth, there would have been corresponding losses stemming from groups that have been systematically excluded or at least restricted in their access to education due to discrimination by factors such as race, gender and religion (Margo 1990). These losses could be attributed in part to the presence of individuals of high ability in groups experiencing discrimination due to failure to provide them with sufficient education to properly utilize their talents. However, historians such as Ashton (1948, 15) have argued that the exclusion of non-Anglicans from English universities prior to the mid-nineteenth century resulted in the channeling of their talents into manufacturing and commerce.

Even if returns have been higher at some levels of education than others, a sustained and substantial increase in labor force quality would seem to entail an egalitarian strategy of widespread increase in access to schooling. The contrast between the rapid increase in access to secondary and tertiary schooling in the U.S. and the much more limited increase in access in Europe during the twentieth century with the correspondingly much greater role for schooling in accounting for economic growth in the U.S. than in Europe (see Denison 1967) points to the importance of an egalitarian strategy in sustaining ongoing increases in aggregate labor force quality.

One would expect on increase in the relative supply of more schooled labor to lead to a decline in the premium to schooling, other things equal. Some recent analyses of the contribution of schooling to growth have allowed for this by specifying a parametric relationship between the distribution of schooling in an economy’s labor force and its impact on output or on a hypothesized intermediary human capital factor (Bils and Klenow 2000).[7]

Direct empirical evidence on trends in the premium to schooling is helpful both to obviate reliance on a theoretical specification and to allow for factors such as technical change that may have offset the impact of the increasing supply of schooling. Goldin and Katz (2001) have developed evidence on trends in the premium to schooling over the twentieth century that have allowed them to adjust for these trends in estimating the contribution of schooling to economic growth (DeLong, Goldin and Katz 2003). They find a marked fall in the premium to schooling, roughly falling in half between 1910 and 1950. However, they also find that this decline in the schooling premium was more than offset by their estimated increase over this same period in years of schooling completed by the average worker of 2.9 years and hence that on net schooling increases contributed to improved productivity of the U.S. workforce. They estimate increases of 0.5 percent per year in labor productivity due to increased educational attainment between 1910 and 1950 relative to the average total annual increase in labor productivity of 1.62 percent over the entire period 1915 to 2000. For the period since 1960, DeLong, Goldin and Katz find that the premium to education has increased while the increase in educational attainment at first increased and then declined. During this latter period, the increase in labor force quality has declined, as noted above, despite a widening premium to education, due to the slowing down in the increase in educational attainment.

Classifying the Range of Possible Relationships between Schooling and Economic Growth

In generalizing beyond the twentieth-century U.S. experience, allowance should be made both for the role of influences other than education on economic growth and for the possibility that the impact of education on growth can vary considerably according to the historical situation. In fact to understand why and how education might contribute to economic growth over the range of historical experience, it is important to investigate the variation in the impact of education on growth that has occurred historically. In relating education to economic growth, one can distinguish four basic possibilities.

The first is one of stagnation in both educational attainment and in output per head. Arguably, this was the most common situation throughout the world until 1750 and even after that date characterized Southern and Eastern Europe through the late nineteenth century, as well as most of Africa, Asia, and Latin American through the mid-twentieth century. The qualifier “arguably” is inserted here, because this view of the matter almost surely makes inadequate allowance for the improvements in informal acquisition of skills through family transmission and direct experience as well as through more formal non-schooling channels such as guild-sponsored apprenticeships, an aspect to be taken up further below. It also makes no allowance for the possible long-term improvements in per capita income that took place prior to 1750 but have been inadequately documented. Still focusing on formal schooling as the source of improvement in labor force, there is reason to think that this may have been a pervasive situation throughout much of human history.

The second situation is one in which income per capita rose despite stagnating education levels; factors other than improvements in educational attainment were generating economic growth. England during its industrial revolution, 1750 to 1840 is a notable instance in which some historians have argued that this situation prevailed. During this period, English schooling and literacy rates rose only slightly if at all, while income per capita appears to have risen. Literacy and schooling appears to have been of little use in newly created manufacturing occupations such as in cotton spinning. Indeed, literacy rates and schooling actually appears to have declined in some of the most rapidly industrializing areas of England such as Lancashire (Sanderson 1972; Nicholas and Nicholas 1992). Not all have concurred with this interpretation of the role of education in the English industrial revolution and the result depends on how educational trends are measured and how education is specified as affecting output (see Laqueur; Crafts 1995; Mitch 1999). Moreover this makes no allowance for the role of informal acquisition of skills. Boot (1995) argues that in the case of cotton spinners, informal skill acquisition with experience was substantial.

The simplest interpretation of this situation is that other factors contributed to economic growth other than schooling or human capital more generally. The clearest non-human capital explanatory factor would perhaps be physical capital accumulation; another might be foreign trade. However, if one turns to technological advance as a driving force, then this gives rise to the possibility that human capital — at least broadly defined — was if not the underlying force at least a central contributing factor to the industrial revolution. The argument for this possibility is that the improvements in knowledge and skills associated with technological advance are embodied in human agents and hence are forms of human capital. Recent work by Mokyr (2002) would suggest this interpretation. Nevertheless, the British industrial revolution does remain as a prominent instance in which human capital conventionally defined as schooling stagnated in the presence of a notable upsurge in economic growth. A less extreme case is provided by the post-World War II European catch-up with the United States, as Denison’s (1967) growth accounting analysis indicates that this occurred despite slower European increases in educational attainment due to other factors offsetting this. Historical instances such as that of the British industrial revolution call into question the common assumption that education is a necessary prerequisite for economic growth (see Mitch 1990).

The third situation is one in which rising educational attainment corresponds with rising rates of economic growth. This is the situation one would expect to prevail if education contributes to economic productivity and if any negative factors are not sufficient to offset this influence. One sub-set of instances would be those in which very large and reasonably compressed increases in the educational attainment of the labor force occurred. One important example of this is the twentieth century U.S., with the high school movement followed by increases in college attendance, as noted above. Another would be those of certain East Asian economies since World War II, as documented in the growth accounting analysis by Young (1995) of the substantial contributions of their rising educational attainment to their rapid growth rates. Another sub-set of cases corresponding to more modest increases in schooling can be interpreted as applying either to countries experiencing schooling increases focussed at the elementary level, as in much of Western Europe over the nineteenth century. The so-called literacy campaigns, as in the Soviet Union and Cuba (see Arnove and Graff eds. 1987) in the early and mid-twentieth century with modest improvements in educational attainment over compressed time periods of just a few decades could also be viewed as fitting into this sub-category. However, whether there were increases in output per capita corresponding to these more modest increases in educational attainment remains to be established.

The fourth situation is one in which economic growth has stagnated despite the presence of marked improvements in educational attainment. Possible examples of this situation would include the early rise of literacy in some Northern European areas, such as Scotland and Scandinavia, in the seventeenth and eighteenth centuries (see Houston 1988; Sandberg 1979) and some regions of Africa and Asia in the later twentieth century (see Pritchett 2001). One explanation of this situation is that it reflects instances in which any positive impact of educational attainment is small relative to other influences having an adverse impact. But one can also interpret it as reflecting situations in which incentive structures direct educated people into destructive and transfer activities inimical to economic growth (see North 1990; Baumol 1990; Murphy, Shleifer, and Vishny 1991).

Cross-country studies of the relationship between changes in schooling and growth since 1960 have yielded conflicting results which in itself could be interpreted as supporting the presence of some mix of the four situations just surveyed. A number of studies have found at best a weak relationship between changes in schooling and growth (Pritchett 2001; Bils and Klenow 2000); others have found a stronger relationship (Topel 1999). Much seems to depend on issues of measurement and on how the relationship between schooling and output is specified (Temple 2001b; Woessmann 2002, 2003).

The Determinants of Schooling

Whether education contributes to economic growth can be seen as depending on two factors, the extent to which educational levels improve over time and the impact of education on economic productivity. The first factor is a topic for extended discussion in its own right and no attempt will be made to consider it in depth here. Factors commonly considered include rising income per capita, distribution of political power, and cultural influences (Goldin 2001, Lindert 2004, Mariscal and Sokoloff 2000, Easterlin 1981; Mitch 2004). The issue of endogeneity of determination has often been raised with respect to the determinants of schooling. Thus, it is plausible that rising income contributes to rising levels of schooling and that the spread of mass education can influence the distribution of political power as well as the reverse. While these are important considerations, they are sufficiently complex to warrant extended attention in their own right.[8]

Influences on the Economic Impact of Schooling

Insofar as schooling improves general human intellectual capacities, it could be seen as having a universal impact irrespective of context. However, Rosenzweig (1995; 1999) has noted that the even the general influence of education on individual productivity or adaptability depend on the complexity of the situation. He notes that for agricultural tasks primarily involving physical exertion, no difference in productivity is evident between workers according to education levels; however, in more complex allocative decisions, education does enhance performance. This could account for findings that literacy rates were low among cotton spinners in the British industrial revolution despite findings of substantial premiums to experience (Sanderson 1972; Boot 1995). However, other studies have found literacy to have a substantial positive impact on labor productivity in cotton textile manufacture in the U.S., Italy, and Japan (Bessen 2003; A’Hearn 1998, Saxonhouse 1977) and have suggested a connection between literacy and labor discipline.

A more macro influence is the changing sectoral composition of the economy. It is common to suggest that the service and manufacturing sector have more functional uses for educated labor than the agricultural sector and hence that the shift from agriculture to industry in particular will lead to greater use of educated labor and in turn to require more educated labor forces. However, there are no clear theoretical or empirical grounds for the claim that agriculture makes less use of educated labor than other sectors of the economy. In fact, farmers have often had relatively high literacy rates and there are more obvious functional uses for education in agriculture in keeping accounts and keeping up with technological developments than in manufacturing. Nilsson et al (1999) argue that the process of enclosure in nineteenth-century Sweden, with the increased demands for reading and writing land transfer documents that this entailed, increased the value of literacy in the Swedish agrarian economy. The findings noted above that those in cotton textile occupations associated with early industrialization in Britain had relatively low literacy rates is one indication of the lack of any clear cut ranking across broad economic sectors in the use of educated labor.

Changes in the organization of decision making within major sectors as well as changes in the composition of production within sectors are more likely to have had an impact on demands for educated labor. Thus, within agriculture the extent of centralization or decentralization of decision making, that is the extent to which farm work forces consisted of farmers and large numbers of hired workers or of large numbers of peasants each with scope for making allocative decisions, is likely to have affected the uses made of educated labor in agriculture. Within manufacturing, a given country’s endowment of skilled relative to unskilled labor has been seen as influencing the extent to which openness to trade increases skill premiums, though this entails endogenous determination (Wood 1995).

Technological advance would have tended to boost the demand for more skilled and educated labor if technological advance and skills are complementary, as is often asserted.

However, there is no theoretical reason why technology and skills need be complementary and indeed concepts of directed technological change or induced innovation would suggest that in the presence of relatively high skill premiums, technological advance would be skill saving rather than skill using. Goldin and Katz (1998) have argued that the shift from the factory to continuous processing and batch production associated with the shift of power sources from steam to electricity in the early twentieth century lead to rising technology skill complementarity in U.S. manufacturing. It remains to be established how general this trend has been. It could be related to the distinction made between the dominance in the United States of extensive growth in the nineteenth century due to the growth of factors of production such as labor and capital and the increasing importance of intensive growth in the twentieth century. Intensive growth is often associated with technological advance and a presumed enhanced value for education (Abramovitz and David 2000). Some analysts have emphasized the importance of capital-skill complementarity. For example, Galor and Moav (2003) point to the level of the physical capital stock as a key influence on the return to human capital investment; they suggest that once physical capital stock accumulation surpassed a certain level, the positive impact of human capital accumulation on the return to physical capital became large enough that owners of physical capital came to support the rise of mass schooling. They cite the case of schooling reform in early twentieth century Britain as an example.

Even sharp declines in the premiums to schooling do not preclude a significant impact of education on economic growth. DeLong, Goldin and Katz’s (2003) growth accounting analysis for the twentieth century U.S. makes the point that even at modest positive returns to schooling on the order of 5 percent per year of schooling, with large enough increases in educational attainment, the contribution to growth can be substantial.

Human Capital

Economists have generalized the impact of schooling on labor force quality into the concept of human capital. Human capital refers to the investments that human beings make in themselves to enhance their economic productivity. These investments can take on many forms and include not only schooling but also apprenticeship, a healthy diet, and exercise, among other possibilities. Some economists have even suggested that more amorphous societal factors such as trust, institutional tradition, technological know how and innovation can all be viewed as forms of human capital (Temple 2001a; Topel 1999; Mokyr 2002). Thus broadly defined, human capital would appear as a prime candidate for explaining much of the difference across nations and over time in output and economic growth. However, gaining much insight into the actual magnitudes and the channels of influence by which human capital might influence economic growth requires specification of both the nature and determinants of human capital and how human capital affects aggregate production of an economy.

Much of the literature on human capital and growth makes the implicit assumption that some sort of numerical scale exists for human capital, even if multidimensional and even if unobservable. This in turn implies that it is meaningful to relate levels and changes of human capital to levels of income per capita and rates of economic growth. Given the multiplicity of factors that influence human knowledge and skill and in turn how these influence labor productivity, difficulties would seem likely to arise with attempts to measure aggregate human capital similar to those that have arisen with attempts to specify and measure the nature of human intelligence. Woessmann (2002, 2003) provides useful surveys of some of the issues involved in attempting to specify human capital at the aggregate level appropriate for relating it to economic growth.

One can distinguish between approaches to the measurement of human capital that focus on schooling, as in the discussion above, and those that take a broader view. Broad view approaches try to capture all investments that may have improved human productivity from whatever source, including not just schooling but other productivity enhancing investments, such as on-the-job training. The basic premise of broad view approaches is that for an aggregate economy, the income going to labor over and above what that labor would earn if it were paid the income of an unskilled worker can be viewed as human capital. This measure can be constructed in various ways including as a ratio using unskilled labor earnings as the denominator as in Mulligan and Sala-I-Martin (1997) or using the share of labor income not going as compensation for unskilled labor as in Crafts (1995) and Mitch (2004). Mulligan and Sala-I-Martin (2000) point to some of the major index number problems that can arise in using this approach to aggregate heterogeneous workers.

Crafts and Mitch find that for Britain during its late eighteenth and early nineteenth century industrial revolution between one-sixth and one-fourth of income per capita can be attributed to human capital measured as the share of labor income not going as compensation for unskilled labor.

One approach that has been taken recently to estimate the role of human capital differences in explaining international differences in income per capita is to consider changes in immigrant earnings between origin and destination countries along with differences between immigrant and native workers in the destination country. Olson (1996) suggested that the large increase in earnings of immigrants commonly observed in moving from a low income to a high income country points to a small role for human capital in explaining the wide variation in per capita income across countries. Hendricks (2002) has used differences between immigrant and native earnings in the U.S. to estimate the contribution of otherwise unobserved skill differences to explaining differences in income per capita across countries and finds that they account for only a small part of the latter differences. Hendricks’ approach raises the issue of whether there could be long-term increases in otherwise unobserved skills that could have contributed to economic growth.

The Informal Acquisition of Human Capital

One possible source of such skills is through the informal acquisition of human capital through on-the-job experience. Insofar as work has been common from early adolescence onwards, the issue arises of why the aggregate stock of skills acquired through experience would vary over time and thus influence rates of economic growth. Some types of on-the-job experience which contribute to economic productivity, such as apprenticeship, may entail an opportunity cost and aggregate trends in skill accumulation will be influenced by societal willingness to incur such opportunity costs.

Insofar as schooling continues through adolescence, this can interfere with the accumulation of workforce experience. DeLong, Goldin and Katz (2003) note the tradeoff between rising average years of schooling completed and decreasing years of labor force experience in influencing labor force quality of the U.S. labor force in the last half of the twentieth century. Connolly (2004) has found that informal experience played a relatively greater role in Southern economic growth than for other regions of the United States.

Hansen (1997) has also distinguished the academically-oriented secondary schooling the United States developed in the late nineteenth and early twentieth century from the vocationally-oriented schooling and apprenticeship system that Germany developed over the same time period. Goldin (2001) argues that in the United States the educational system developed general abilities suitable for the greater opportunities for geographical and occupational mobility that prevailed there, while specific vocational training was more suitable for the more restricted mobility opportunities in Germany.

Little evidence exists on whether long-term trends in informal opportunities for skill acquisition have influenced growth rates. However, Smith’s (1776) view of the importance of the division of labor in influencing productivity would suggest that the impact of trends in these opportunities may well have been quite sizable.

Externalities from Education

Economists commonly claim that education yields benefits to society over and above the impact on labor market productivity perceived by the person receiving the education. These benefits can include impacts on economic productivity, such as impacts on technological advance. They can also include non-labor market benefits. Thus McMahon (2002, 11) in his assessment of the social benefits of education includes not only direct effects on economic productivity but also impacts on a) population growth rates and health b) democratization, political stability, and human rights, c) the environment, d) reduction of poverty and inequality, e) crime and drug use, and f) labor force participation. While these effects may appear to involve primarily non-market activity and thus would not be reflected in national output measures and growth rates, factors such as political stability, democratization, population growth, and health have obvious consequences for prospects for long-term growth. However, allowance should be made for the simultaneous influence of the distribution of political power and life expectancy on societal investments in schooling.

For the period since 1960, numerous studies have employed cross country variation in various estimates of human capital and income per capita to directly estimate the impact of human capital on levels of income per capita and growth. A central goal of many such estimates is to see if there are externalities to education on output over and above the private returns estimated from micro data. The results have been conflicting and this has been attributed not only to problems of measurement error but also to differences in specification of human capital and its impact on growth. There does not appear to be strong evidence of large positive externalities to human capital (Temple 2001a). Furthermore, McMahon (2004) reports some empirical specifications which yield substantial indirect long-run effects.

For the period before 1960, limits on the availability of data on schooling and income have limited the use of this empirical regression approach. Thus, any discussion of the impact of externalities of education on production is considerably more conjectural. The central role of government, religious, and philanthropic agencies in the provision of schooling suggests the presence of externalities. Politicians and educators more frequently justified government and philanthropic provision of schooling by its impacts on religious and moral behavior than by any market failure resulting in sub-optimal provision of schooling from the standpoint of maximizing labor productivity. Thus, Adam Smith in his discussion of mass schooling in The Wealth of Nations, places more emphasis on its value to the state in enhancing orderliness and decency while reducing the propensity to popular superstition than on its immediate value in enhancing the economic productivity of the individual worker.

The Impact of the Level of Human Capital on Rates of Economic Growth

The approaches considered thus far relate changes in educational attainment of the labor force to changes in output per worker. An alternative, though not mutually exclusive, approach is to relate the level of educational attainment of an economy’s labor force to its rate of economic growth. The argument for doing so is that a high but unchanging level of educational attainment should contribute to growth by facilitating creativity, innovation and adaptation to change as well as facilitating the ongoing maintenance and improvement of skill in the workforce. Topel (1999) has argued that there may not be any fundamental difference between the two types of approach insofar as ongoing sources of productivity advance and adaptation to change could be viewed as reflecting ongoing improvements in human capital. Nevertheless, some empirical studies based on international data for the late twentieth century have found that a country’s level of educational attainment has a much stronger impact on its rate of economic growth than its rate of improvement in educational attainment (Benhabib and Spiegel 1994).

The paucity of data on schooling attainment has limited the empirical examination of the relationship between levels of human capital and economic growth for periods before the late twentieth century. However, Sandberg (1982) has argued, based on a descriptive comparison of economies in various categories, that those with high levels of schooling in 1850 subsequently experienced faster rates of economic growth. Some studies, such as O’Rourke and Williamson (1997) and Foreman-Peck and Lains (1999), have found that high levels of schooling and literacy have contributed to more rapid rates of convergence for European countries in the late nineteenth century and at the state level for the U.S. over the twentieth century (Connolly 2004).

Bowman and Anderson (1963), a much earlier study based on international evidence for the mid-twentieth century, can be interpreted in the spirit of relating levels of education to subsequent levels of income growth. Their reading of the cross-country relationship between literacy rates and per capita income at mid-twentieth-century was that a threshold of 40 percent adult literacy was required for a country to have a per capita income above 300 1955 dollars. Some have ahistorically projected back this literacy threshold to earlier centuries although the Bowman and Anderson proposal was intended to apply to mid-twentieth century development patterns.

The mechanisms by which the level of schooling would influence the rate of economic growth are problematic to establish. One can distinguish two general possibilities. One would be that higher levels of educational attainment facilitate adaptation and responsiveness to change throughout the workforce. This would be especially important where a large percentage of workers are in decision making positions such as an economy composed largely of small farmers and other small enterprises. The finding of Foster and Rosenzweig (1996) for late twentieth century India that the rate of return to schooling is higher during periods of more rapid technological advance in agriculture would be consistent with this. Likewise, Nilsson et al (1999) find that literacy was important for nineteenth-century Swedish farmers in dealing with enclosure, an institutional change. The other possibility is that higher levels of educational attainment increase the potential pool from which an elite group responsible for innovation can be recruited. This could be viewed as applying specifically to scientific and technical innovation as in Mokyr (2002) and Jones (2002) — but also to technological and industrial leadership more generally (Nelson and Wright 1992) and to facilitating advancement in society by ability irrespective of social origins (Galor and Tsiddon 1997). Recently, Labuske and Baten (2004) have found that international rates of patenting are related to secondary enrollment rates.

Two issues have arisen in the recent theoretical literature regarding specifying relationships between the level of human capital and rates of economic growth. First, Lucas (1988) in an influential model of the impact of human capital on growth, specifies that the rate of growth of human capital formation depends on initial levels of human capital, in other words that parents’ and teachers’ human capital has a direct positive influence on the rate of growth of learners’ human capital. This specification of the impact of the initial level of human capital allows for ongoing and unbounded growth of human capital and through this its ongoing contribution to economic growth. Such ongoing growth of human capital could occur through improvements in the quality of schooling or through enhanced improvements in learning from parents and other informal settings. While it might be plausible to suppose that improved education of teachers will enhance their effectiveness with learners, it seems less plausible to suppose that this enhanced effectiveness will increase unbounded in proportion to initial levels of education (Lord 2001, 82).

A second issue is that insofar as higher levels of human capital contribute to economic growth through increases in research and development activity and innovative activity more generally, one would expect the presence of scale effects. Economies with larger populations holding constant their level of human capital per person should benefit from more overall innovative activity simply because they have more people engaged in innovative activity. Jones (1995) has pointed out that such scale effects seem implausible if one looks at the time series relationship between rates of economic growth and those engaged in innovative activity. In recent decades the growth of the number of scientists, engineers, and others engaged in innovative activity has far outstripped the actual growth of productivity and other indicators of direct impact on innovation. Thus, one should allow for diminishing returns in the relationship between levels of education and technological advance.

Thus, as with schooling externalities, considering the impact of levels of education on growth offers numerous channels of influence leaving the challenge for the historian of ascertaining their quantitative importance in the past.

Conclusion

This survey has considered some of the basic ways in which the rise of mass education has contributed to economic growth in recent centuries. Given their potential influence on labor productivity, levels and changes in schooling and of human capital more generally have the potential for explaining a large share of increases in per capita output over time. However, increases in mass schooling seem to explain a major share of economic growth only over relatively short periods of time, with a more modest impact over longer time horizons. In some situations, such as the United States in the twentieth century, it appears that improvements in the schooling of the labor force have made substantial contributions to economic growth. Yet schooling should not be seen as either a necessary or sufficient condition for generating economic growth. Factors other than education can contribute to economic growth and in their absence, it is not clear that schooling in itself can contribute to economic growth. Moreover, there are likely limits on the extent to which average years of schooling of the labor force can expand, although improvement in the quality of schooling is not so obviously bounded. Perhaps the most obvious avenue through which education has contributed to economic growth is by expanding the rate of technological change. But as has been noted, there are numerous other possible channels of influence ranging from political stability and property rights to life expectancy and fertility. The diversity of these channels point to both the challenges and the opportunities in examining the historical connections between education and economic growth.

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[1] I have received helpful comments on this essay from Mac Boot, Claudia Goldin, Bill Lord, Lant Pritchett, Robert Whaples, and an anonymous referee. At an earlier stage in working through some of this material, I benefited from a quite useful conversation with Nick Crafts. However, I bear sole responsibility for remaining errors and shortcomings.

[2] For a detailed survey of trends in schooling in the early modern and modern period see Graff (1987).

[3] See Barro (1998) for a brief intellectual history of growth accounting.

[4] Blaug (1970) provides an accessible, detailed critique of the assumptions behind Denison’s growth accounting approach and Topel (1999) provides a further discussion of the problems of using a growth accounting approach to measure the contribution of education, especially those due to omitting social externalities.

[5] By using a Cobb-Douglas specification of the aggregate production function, one can arrive at the following equation for the ratio between final and initial national income per worker due to increases in average school years completed between the two time periods, t = 0 and t =1:

Start with the aggregate production function specification:

Y = A K(1-α) [(1+r)S L]α

Y/L = A (K/L)(1-α) [(1+r)S L/L]α

Y/L = A (K/L)(1-α) [(1+r)S]α

Assume that the average years of schooling of the labor force is the only change between t = 0 and t =1; that is, assume no change in the ratio of capital to labor between time periods. Then the ratio of the income per worker in the later time period to the earlier time period will be:

(Y/L)1/ (Y/L)0 = ( (1 + r )S1- S0 )α

Where Y = output, A = a measure of the current state of technology, K = the physical capital stock, L = the labor force, r = the percent by which a year of schooling increases labor productivity, S is the average years of schooling completed by the labor force in each time period, α is labor’s share in national income, and the subscripts 0 and 1 denote initial and final time periods.

As noted above, the derivation above is for a partial equilibrium change in years of schooling of the labor force holding constant the physical capital stock. Allowing for physical capital stock accumulation in response to schooling increases in a Solow-type model implies that the ratio of final to initial output per worker will be

(Y/L)1/ (Y/L)0 = ( (1 + r )S1 - S0 ) .

For a derivation of this see Lord (2001, 99-100). Lord’s derivation differs from that here by specifying the technology parameter A as labor augmenting. Allowing for increases in A over time due to technical change would further increase the contribution to output per worker of additional years of schooling.

[6]To take a specific example, suppose that in the steady-state case of Table 1B, a 5 percent earnings premium per year of schooling is assigned to the first 6 years of schooling, i.e. primary schooling, a 10 percent earnings premium per year is assigned to the next 6 years of schooling, i.e. secondary schooling, and a 15 percent earnings premium per year is assigned to the final 4 years of schooling, that is college. In that case, the impact on steady state income per capita compared with no schooling at all would be (1.05)6x(1.10)6x(1.15)4 = 4.15, compared with the 4.59 in going from no schooling to universal college at a 10 percent rate of return for every year of school completed.

[7] Denison’s standard growth accounting approach assumes that education is labor augmenting and, in particular, that there is an infinite elasticity of substitution between skilled and unskilled labor. This specification is conventional in growth accounting analysis. But another common specification in entering education into aggregate production functions is to specify human capital as a third factor of production along with unskilled labor and physical capital. Insofar as this is done with a Cobb-Douglas production function specification, as is conventional, the implied elasticity of substitution between human capital and either unskilled labor or physical capital is unity. The complementarity between human capital and other inputs this implies will tend to increase the contribution of human capital increases to economic growth by decreasing the tendency for diminishing returns to set in. (For a fuller treatment of the considerations involved see Griliches 1970, Conlisk 1970, Broadberry 2003). For an application of this approach in a historical growth accounting exercise, see Crafts (1995), who finds a fairly substantial contribution of human capital during the English industrial revolution. For a critique of Crafts’ estimates see Mitch (1999).

[8] For an examination of long-run growth dynamics with schooling investments endogenously determined by transfer-constrained family decisions see Lord 2001, 209-213 and Rangazas 2000. Lord and Rangazas find that allowing for the fact that families are credit constrained in making schooling investment decisions is consistent with the time path of interest rates in the U.S. between 1870 and 1970.

Citation: Mitch, David. “Education and Economic Growth in Historical Perspective”. EH.Net Encyclopedia, edited by Robert Whaples. July 26, 2005. URL http://eh.net/encyclopedia/education-and-economic-growth-in-historical-perspective/