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Q1: Is Education an engine of Growth? (Meier and Rauch 2000) :1
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InputDate: 7/30/2006
Reference: Meier, Gerald M. and James E. Rauch. 2000. Overview: Education. Leading Issues in Economic Development (seventh edition) Oxford University Press
A: Education is thought to be the most important determinant of the economic growth. Many high growth Asian economies are also known as the countries which put strong emphasis on education from their early stages of development. During the industrialization process based on borrowed technologies, primary and secondary education are thought to be more important growth factors than tertiary education. However, in the era of ?gknowledge economy,?h it is widely considered that the higher education has become more important growth factor.
Long-standing debate on the growth impact of education
However, there were long-standing debate as to the economic impact of education on growth. Psacharopoulos (1991) surveyed the literature on the micro and macro economic impact of the education. He wrote, ?geconomic historian took a stab at the matter by taking a much longer-term view than sophisticated statistical analysis permits. Thus it has been established that bout of long-term economic growth were preceded by increase in the population?fs literacy level. The example of Japan and Korea are the classic cases in which an educated population base has provided the necessary infrastructure for industrial advances to take place at a late date (see Saxonhous 1977 and Easterlin 1981)?h Schults (1961) and Denison (1967) used the growth accounting framework to find a significant contribution of education expenditure for the growth.

Role of education in high growth of East Asian countries
?gAsian Miracle?h study (The World Bank 1993) also emphasized the role of education in the high growth performance of East Asian countries and they concluded that these countries?f focus on basic education rather than higher education produced more impact on growth performance.
Critiques of the role of education on growth
On the other hand, Pretchett (1996) examined cross-national data on economic growth rates and showed that increases in educational capital resulting from improvements in the educational attainment of the labor force have had no positive impact on the growth rate of output per worker. In fact, contends Pritchett, the estimated impact of growth of human capital on conventional non-regression growth accounting measures of total factor productivity is negative and strongly significant. Benhabib and Spiegel (1994) used the regression treating human capital (accumulation of past education investment) as another factor of production and it is not statistically significant. But when he insert the initial income level as an explanatory variable, human capital becomes an significant explanatory value. This means a great role of education in the catching-up process.

Reference:
Pretchett, Lant. Where Has All the Education Gone? The World Bank Policy Research Working Paper 1581

Benhabib, Jess, and Mark M. Spiegel. 1994. The Role of Human Capital in Economic Development: Evidence from Aggregate Cross-Country Data, Journal of Monetary Economics 34 (October 1994): 143-151, 158-161.

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Q2: Is Human Capital engine of growth through innovation? :1.1
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InputDate: 8/12/2007
Reference: Vandenbussche, Jerome, Philippe Aghion and Costas Meghir. 2006. "Growth, distance to frontier and composition of human capital" J Econ Growth (2006) 11: 97?127
A: Bandenbussche, Agion and Meghir (2006) contributes to two different strands of literature. First, it complements previous theoretical and empirical work on the link between the level of education and growth. This link was emphasized in the work by Nelson and Phelps (1966), who argued that a more educated labor force would adopt new technologies faster. It was given complementary theoretical support by the new endogenous growth theories (Aghion & Howitt, 1992, Romer, 1990) who described human capital as the engine of growth through innovation.
Distinction of skilled and unsklled labour
Closer to our work, Grossman and Helpman (1991) show that the skill composition of the labor force matters for the amount of innovation in the economy. In particular, they obtain that an increase in the stock of skilled labor is growth-enhancing while an increase in the stock of unskilled labor can be growth-depressing. The papr goes beyond their analysis by studying how the impact of the composition of human capital on growth depends on the distance to the technological frontier.
Schooling together with technology gap leads to higher growth
This technological view of human capital received empirical support in the work of Benhabib and Spiegel (1994), Barro and Sala-i-Martin (1995) and Barro (1998), all of whom showed that both the initial schooling level and its interaction with a measure of the technology gap with the frontier were positively associated with subsequent growth. Their work focused on large cross-country datasets and did not address how the effect of different types of education varies with the level of development, which is the real focus of our paper.
Krueger and Lindahl(2001) found that the effect of education was highly heterogeneous
However, as mentioned at the beginning of this introduction, Krueger and Lindahl (2001) found that the effect of the initial level of education was highly heterogeneous between rich countries (including OECD members), low-income and middle-income countries, and that it was surprisingly not positive in the richest countries of their sample. The possibility that human capital might play a different role at different stages of development has not often been addressed in the empirical growth literature. Apart from Krueger and Lindahl (2001), evidence of heterogeneous effects has been provided by Durlauf and Johnson (1995), and evidence of non-linearities by Kalaitzidakis et al. (2001) but was informed by little theoretical analysis. We enrich the existing empirical literature by providing evidence that education has a heterogeneous effect even among the OECD group of countries, i.e. that it is crucial to distinguish between the two margins of primary/secondary versus tertiary educational attainment and that the latter type of education is a source of economic divergence.
Bils and Klenow (2000) argued the large correlations reflected reverse causality
Bils and Klenow (2000) argued that most of the positive relationship between initial schooling level and subsequent growth in large cross-country datasets reflected reverse causality. While we do not dispute that expected future growth impacts schooling decisions, our use of panel data estimation techniques and instrumentation minimize the impact of this reverse channel on our reported estimates. Moreover, as documented by Krueger and Lindahl, there is no positive relationship to explain in OECD countries, at least if one assumes, as Bils and Klenow do, that all types of human capital are perfect substitutes in contributing to productivity improvements. Our empirical analysis shows that this assumption is unwarranted in rich countries.
Reference:
Nelson, R., & Phelps, E. (1966). Investment in humans, technological diffusion and economic growth. American Economic Review, 56, (1/2), 69?75.
Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323?351.
Romer, P. (1990). Endogenous technological change. Journal of Political Economy, 98, S71?S102
Grossman, G., & Helpman, E. (1991). Innovation and growth in the global economy. Cambridge, MA: MIT Press.
Benhabib, J., & Spiegel, M. (1994). The role of human capital in economic development: Evidence from aggregate cross-country data. Journal of Monetary Economics, 34(2), 143?174.
Krueger, A., & Lindahl, M. (2001). Education for growth: Why and for whom? Journal of Economic Literature, 39, 1101?1136.
Bils, M., & Klenow, P. (2000). Does schooling cause growth? American Economic Review, 90, 1160?1183.
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Q3: What is the microeconomic analysis of the rate of return to education? :2
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InputDate: 8/13/2007
Reference: KRUEGER, ALAN B. and MIKAEL LINDAHL. 2001. "Education for Growth: Why and For Whom?" Journal of Economic Literature Vol. XXXIX (December 2001) pp. 1101?1136
A: There are two strand of economic analysis of the impact of education: one is the microeconomic analysis to estimate the rate of return to education, and the other is the macroeconomic analysis using county-level educational indicators and GDP growth. In this question, we use Crueger and Lindahl (2001) to introduce what is the micro analysis of education.
People with longer education has hidden characteristics for higher ability
Since at least the beginning of the century, economists and sociologists have sought to estimate the economic rewards individuals and society gain from completing higher levels of schooling. It has long been recognized that workers who attended school longer may possess other characteristics that would lead them to earn higher wages irrespective of their level of education. If these other characteristics are not accounted for, then simple comparisons of earnings across individuals with different levels of schooling would overstate the return to education.
Ability bias was examined extensively
Early attempts to control for this gability biash included the analysis of data on siblings to difference-out unobserved family characteristics (e.g., Donald Gorseline 1932), and regression analyses which included as control variables observed characteristics such as IQ and parental education (e.g., Griliches and William Mason 1972). This literature is thoroughly surveyed in Griliches (1977), Sherwin Rosen (1977), Robert Willis (1986), and David Card (1999). We briefly review evidence on the Mincerian earnings equation, emphasizing recent studies that exploit exogenous variations in education in their estimation.
Reference:
Gorseline, Donald E. 1932. The Effect of Schooling upon Income. Bloomington: U. Indiana Press.
Griliches, Zvi and William M. Mason. 1972. gEducation, Income, and Ability,h J. Polit. Econ. 80:3, Part II, pp. S74?S103.
Griliches, Zvi. 1977. gEstimating the Returns to Schooling: Some Econometric Problems,h Econometrica 45:1, pp. 1?22.
Rosen, Sherwin. 1977. gHuman Capital: A Survey of Empirical Research,h in Researchin Labor Economics. R. Ehrenberg, ed. Greenwich, CT: JAIPress.
Willis, Robert J. 1986. gWage Determinants: A Survey and Reinterpretation of Human Capital Earnings Functions,h in Handbook of Labor Economics. Orley A. Ashenfelter and Richard Layard, eds. Amsterdam: North Holland.
Card, David. 1999. gThe Causal Effect of Schooling on Earnings,h in Handbook of Labor Economics. Orley Ashenfelter and David Card, eds. Amsterdam: North Holland.
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Q4: What is Mincerial Wage Equation, and how to calculate a rate of return on Education? :2.1
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InputDate: 8/13/2007
Reference: KRUEGER, ALAN B. and MIKAEL LINDAHL. 2001. "Education for Growth: Why and For Whom?" Journal of Economic Literature Vol. XXXIX (December 2001) pp. 1101?1136
A: There are many empirical studies to calculate the rate of return on education. These studies are based on the microeconomic data, and a theoretical framework called "Mincerial Wage Equation" which was developed by Mincer (1974). Let us learn the logic of this equation summarised by Krueger and Lindahl (2001).
Mincer's formulation
Mincer (1974) showed that if the only cost of attending school an additional year is the opportunity cost of studentsf time, and if the proportional increase in earnings caused by this additional schooling is constant over the lifetime, then the log of earnings would be linearly related to individualsf years of schooling, and the slope of this relationship could be interpreted as the rate of return to investment in schooling.4 He augmented this model to include a quadratic term in work experience to allow for returns to on-the-job training, yielding the familiar Mincerian wage equation:

ln Wi = a0 + a1*Si + a2*Xi + a3*Xi^2 + ai, (1)

where ln Wi is the natural log of the wage for individual i, Si is years of schooling, Xi is experience, Xi 2 is experience squared, and ai is a disturbance term.
a1 represents the discount rate
With Mincerfs assumptions, the coefficient on schooling, a1, equals the discount rate, because schooling decisions are made by equating two present value earnings streams: one with a higher level of schooling and one with a lower level. An attractive feature of Mincerfs model is that time spent in school (as opposed to degrees) is the key determinant of earnings, so data on years of schooling can be used to estimate a comparable return to education in countries with very different educational systems.
Providing good fits
Equation (1) has been estimated for most countries of the world by OLS, and the results generally yield estimates of a1 ranging from .05 to .15, with slightly larger estimates for women than men (see George Psacharopoulos 1994). The log-linear relationship also provides a good fit to the data. It is apparent that the semi-log specification provides a good description of the data even in countries with dramatically different economic and educational systems.
What does the positive slope mean?
Much research has addressed the question of how to interpret the education slope in equation (1). Does it reflect unobserved ability and other characteristics that are correlated with education, or the true reward that the labor market places on education? Is education rewarded because it is a signal of ability (Michael Spence 1973), or because it increases productive capabilities (Becker 1964)? Is the social return to education higher or lower than the coefficient on education in the Mincerian wage equation? Would all individuals reap the same proportionate increase in their earnings from attending school an extra year, or does the return to education vary systematically with individual characteristics?

education is not merely a proxy for unobserved ability
Definitive answers to these questions are not available, although the weight of the evidence clearly suggests that education is not merely a proxy for unobserved ability. For example, Griliches (1977) expected positive ability bias in the return to education, gThe implied net bias is either nil or negativeh once measurement error in education is taken into account.
Reference:
Mincer, Jacob. 1974. Schooling, Earnings, and Experience. NY: Columbia U. Press.
Psacharopoulos, George. 1994. gReturns to Investment in Education: A Global Update,h World Devel. 22:9, pp. 1325?43.
Spence, A. Michael. 1973. gJob Market Signaling,h Quart. J. Econ. 87:3, pp. 355?74.
Becker, Gary S. 1964. Human Capital. NY: Columbia U. Press.
Griliches, Zvi. 1977. gEstimating the Returns to Schooling: Some Econometric Problems,h Econometrica 45:1, pp. 1?22.
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Q5: What is the rate of return on education and how to measure it? (Psacharopoulos 1995) :2.2
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InputDate: 7/30/2006
Reference: Psacharopoulos, George. 1995. The Profitability of Investment in Education: Concepts and Methods
A: This paper reviews the basic concept of the profitability of investment in education and enumerates the various techniques that have been used in the literature to estimate the rate of return to investment in education. The various estimating techniques are illustrated by using household survey data from Venezuela and Guatemala. The paper also reviews the controversies that have appeared in the literature regarding the use of rates of return to investment in education for designing educational policy.
Education as an investment in human capital
The early 1960s witnessed what has been described in the economics literature as the "human investment revolution in economic thought" (Bowman 1966). Expenditures on education, whether by the state or households, have been treated as investment flows that build human capital (see Schultz 1961; Becker 1964). Once education is treated as an investment, the immediate natural question is: what is the profitability of this investment in order to compare it to alternatives? Such comparison can provide priorities for the allocation of public funds to different levels of education, or can explain individual behavior regarding the demand, or lack of demand, for particular levels or types of schooling.

In the three decades that followed the human investment revolution in economic thought, hundreds of estimates have been made on the profitability of investment in education in all parts of the World and for all levels and types of schooling and training (for a review, see Psacharopoulos 1994).
Basic concepts
The costs and benefits of education investments can be analyzed in the same way that these are calculated for other types of projects. In education, a series of expenditures occur during school construction and while students are in school, and benefits are expected to accrue over the life-cycle of the graduates. For establishing education investment priorities at the margin, the net present value or internal rate of return of the prospective operation can be computed. The discussion below focuses on the rate of return in order to ease comparisons with other projects.

The internal rate of return of an education project can be estimated from either the private or the social point of view. The private rate of return is used to explain the demand for education. It can also be used to assess the equity or poverty alleviation effects of public education expenditures, or the incidence of the benefits of such expenditure. The social rate of return summarizes the costs and benefits of the educational investment from the state's point of view, i.e., it includes the full resource cost of education, rather than only the portion that is paid by the recipient of education.

Private Rate of Return
The costs incurred by the individual are his/her foregone earnings while studying, plus any education fees or incidental expenses the individual incurs during schooling. Since education is mostly provided free by the state, in practice the only cost in a private rate of return calculation is the foregone earnings. The private benefits amount to what a more educated individual earns (after taxes), above a control group of individuals with less education. "More" and "less" in this case usually refers to adjacent levels of education, e.g., university graduates versus secondary school graduates. The private rate of return to an investment in a given level of education in such a case can be estimated by finding the rate of discount (r) that equalizes the stream of discounted benefits to the stream of costs at a given point in time.

In addition, there may be no need to estimate a rate of return to justify investment in basic education ?E it is taken for granted that the literacy of the population is a goal that stands on its own merits for a variety of reasons other than economic considerations. However, as one climbs the educational ladder and schooling becomes more specialized, it is imperative to estimate the costs and benefits of postprimary school investments, especially those in the vocational track of secondary education and higher education.
Social Rate of Return
The main computational difference between private and social rates of return is that, for a social rate of return calculation, the costs include the state's or society's at large spending on education. Hence, in the above example, Cu would include the rental of buildings and professorial salaries. Gross earnings (i.e., before taxes and other deductions) should be used in a social rate of return calculation, and such earnings should also include income in kind where this information is available.

A key assumption in a social rate of return calculation is that observed wages are a good proxy for the marginal product of labor, especially in a competitive economy using data from the private sector of the economy. Civil service pay scales are irrelevant for a social rate of return calculation, although they may be used in a private one.

The "social" attribute of the estimated rate of return refers to the inclusion of the full resource cost of the investment (direct cost and foregone earnings). Ideally, the social benefits should include non-monetary or external effects of education (e.g., lower fertility or lives saved because of improved sanitation conditions followed by a more educated woman who never participates in the formal labor market). Given the scant empirical evidence on the external effects of education, social rate of return estimates are usually based on directly observable monetary costs and benefits of education (but see Summers 1992).

Since the costs are higher in a social rate of return calculation relative to the one from the private point of view, social returns are typically lower than a private rate of return. The difference between the private and the social rate of return reflects the degree of public subsidization of education.
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Q6: What are the findings on the rates of return on education? (Psacharopoulos 1994) :2.3
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InputDate: 7/30/2006
Reference: Psacharopoulos, Goerge. 1991. The Economic Impact of Education: Lessons for Policymakers. San Francisco: ICS Press. Reprinted in Meier, Gerald M. and James E. Raich. "leading Issues in Economic Devlopment" seventh edition. pp223-226
A: Hundreds of studies have been conducted in the past thirty years on the profitability of investment in education in a large number of countries across the dimensions cited above (for a summary see Psacharopoulos 1985)
Rate of Return on Education much Higher in Developing Countries
The first notable result of the application of the rate of return studies to education is that the rates are not far off the yield of more conventional investments. The return to investment in education in advanced industrial countries are roughly the same as those of invstments in physical capital. By contrast, the return to education in developing countries stand at a much higher level relative to industrial countries. This reflects both the continuing scarcity of human capital in poorer countries and barriers to the allocation of funds to human capital investment, so that the return to any kind of capital (physical or human) equalize at the margin.
Return on Primary Education higher than secondary, or university education
A typical pattern, found since the early days of rate of return estimation in education, is that returns decline by level of schooling.Thus, returns to primary education are higher relative to returns to secondary education, and the latter are higher than returns to university education. This finding, corroborated in studies after study, has fundamental policy implications.
Gap between Social and Private rate of returns
Another results worth noting is the difference between social and private rates of return. Because of the public subsidization of education in all parts of the world, private rates are typically several percentage points higher than social rates of return. By definition, the cost in a private rate-of-return estimation refers only to what individual pays out of his or her pocket, whereas the cost in a social rate of return estimation refers to the full resource cost of osmeone attending school. The distortion incurred by the public subsidization of education means that, in some instances, individuals will find it profitable to pursue education to a given level whereas, from the point of view of society, this investment is not profitable.
Maximum distortion existin University Edication
The maximum distortion between the private and the social rate of return refers to education at the unversity level. This level is more heavily subsidized in most countries relative to any other level.
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Q7: What are the contraversies around rate of return studies? (Psacharopoulos 1995) :2.4
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InputDate: 7/30/2006
Reference: Psacharopoulos, George. 1995. The Profitability of Investment in Education: Concepts and Methods
A: .
screening hypothesis
Perhaps the most debated hypothesis in the economics of education is the one referring to the so-called "screening hypothesis," namely that earnings differences might be due to the superior ability of the more educated, rather than to their extra education. Among the several tests reported in the literature, the one by Ashenfelter and Krueger (1994) using pairs of twins as units of observation deserves mention because of the quasi-experimental "design" of the sample: twins who were separated early in life and received different amounts of education were observed. The authors found no bias in the estimated returns to schooling. On the contrary, they found that measurement errors in self-reported schooling differences resulted in a substantial underestimation from conventionally-estimated returns to investment in education.
Higher rate of return was found on the sophsticated estimates
The crux of the matter is that the undisputable and universal positive correlation between education and earnings can be interpreted in many different ways. As Ashenfelter (1991) put it, the causation issue on whether education really affects earnings can only be answered with experimental data generated by exposing at random different people to various amounts of education. Given the fact that moral and pragmatic considerations prevent the generation of such pure data, researchers will have to make do with indirect inferences or natural experiments. Three recent papers report the results of using natural experiments in order to asses the effect of selectivity bias on the returns to education. One example of such a natural experiment was carried out with identical twins who received different amounts of education (as to control for differences in genetic ability). In fact, Angrist and Krueger (1992) found that a rate of return to the extra years of schooling was 10 percent higher than conventional rate of return estimates. Angrist and Krueger (1991) found a very similar rate of return to investment in education to the one conventionally estimated.
Impactof sociaoeconomic background
Another debated issue in the literature has been the role of socioeconomic background. Card and Krueger (1992) find that, holding school quality constant, there is no evidence that parental income or education affects state-level returns to education. But Neuman (1991), using Israeli data, found that the returns to schooling are higher to those coming from more favorable socioeconomic backgrounds.
Impact of gender
When the sample is split by gender, typically the returns to female education are higher than those for males. It should be remembered that such calculations are based on the observed wages of women who are working in the labor market. Several other women have chosen to work at home, tacitly placing a higher value on their household-activities time than on market wages. In addition, the truncation of women's earnings' samples leads to classic econometric biases documented by Heckman (1979). In recent work, correction for selectivity bias does not appear to change significantly the returns on investment in women's education (see Psacharopoulos and Tzannatos 1992). However, the fact remains that rates of return for women do not take into account household production.
Whether wage differences reflect productivity differences
Regarding the "earnings-reflect-productivity" assumption, the returns in the private/competitive sector of the economy are higher than for those who work in the public/non-competitive sector. Dabos and Psacharopoulos (1991) analyzed the earnings of Brazilian males in 1980 and found sizeable returns to education across labor market "segments," especially among rural workers and the self-employed. This finding was upheld even after correcting for dependent variable selectivity bias regarding who enters a particular economic sector.

Perhaps the best and most cited finding in this area refers to agricultural production. Jamison and Lau (1982) found that, other things being equal, four years of education for farmers translates to a nearly 10 percent increase in physical agricultural output.

On the issue of whether or not earnings really reflect productivity, Chou and Lau (1987) repeated the Jamison and Lau (1982) production function methodology for Thailand and upheld the results. They found that one additional year of schooling adds about 10 percent to farm output. In East Asia, for example, one additional year of education contributed over three percent to real GDP. (See also Azhar (1991) reporting similar results for Pakistan.)
Reference:
Ashenfelter, Orley and A. Krueger. 1994. "Estimates of the Economic Return of Schooling from a New Sample of Twins." American Economic Review (December).
Jamison, D.T. and L. Lau. 1982. Farmer Education and Farm Efficiency. Baltimore: Johns Hopkins University Press.
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Q8: Why is Private Returns to Education higher than Social rate of Return? :2.5
FAQID:1011

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InputDate: 8/13/2007
Reference: KRUEGER, ALAN B. and MIKAEL LINDAHL. 2001. "Education for Growth: Why and For Whom?" Journal of Economic Literature Vol. XXXIX (December 2001) pp. 1101?1136
A:
Sometimes social return is higher because of education's externality
The social return to education can, of course, be higher or lower than the private monetary return. The social return can be higher because of externalities from education, which could occur, for example, if higher education leads to technological progress that is not captured in the private return to that education, or if more education produces positive externalities, such as a reduction in crime and welfare participation, or more informed political decisions.
Social return could be lower if education is a credential
The former is more likely if human capital is expanded at higher levels of education while the latter is more likely if it is expanded at lower levels. It is also possible that the social return to education is less than the private return. For example, Spence (1973) and Fritz Machlup (1970) note that education could just be a credential, which does not raise individualsf productivities. It is also possible that in some developing countries, where the incidence of unemployment may rise with education (e.g., Mark Blaug, Richard Layard, and Maureen Woodhall 1969) and where the return to physical capital may exceed the return to human capital (e.g., Arnold Harberger 1965), increases in education may reduce total output.
Education has other social impacts than monetary rewards
It should also be noted that education may affect national income in ways that are not fully measured by wage rates. For example, particularly in developing countries, education is negatively associated with womenfs fertility rates and positively associated with infantsf health (see Paul Glewwe 2000). In addition, education is positively associated with labor force participation; most of the micro human capital literature uses samples that consist of those in the labor force, so this effect of education is missed.
Micro analysis tend to cover more on private monetary return
A potential weakness of the micro human capital literature is that it focuses primarily on the private pecuniary return to education rather than the social return. The possibility of externalities to education motivates much of the macro growth literature, to which we now turn. Micro-level empirical analysis is less well suited for uncovering the social returns to education.
Reference:
Spence, A. Michael. 1973. gJob Market Signaling,h Quart. J. Econ. 87:3, pp. 355?74.
Machlup, Fritz. 1970. Education and Economic Growth. Lincoln: U. Nebraska Press.
Blaug, Mark; Richard Layard and Maureen Woodhall. 1969. The Causes of Graduate Unemployment in India. London: Allen Lane, Penguin Press.
Harberger, Arnold. 1965. gInvestment in Men Versus Investment in Machines: The Case of India,h in Education and Economic Development. C. Arnold Anderson and Mary Jean Bowman, eds. Chicago: Aldine Publishing, pp. 11? 50.
Glewwe, Paul. 2000. gSchools, Skills and Economic Development: Evidence, Gaps and Research Prospects,h mimeo, World Bank.
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Q9: Why is Private Returns to Education higher than Social rate of Return? :2.5
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InputDate: 8/13/2007
Reference: KRUEGER, ALAN B. and MIKAEL LINDAHL. 2001. "Education for Growth: Why and For Whom?" Journal of Economic Literature Vol. XXXIX (December 2001) pp. 1101?1136
A:
Sometimes social return is higher because of education's externality
The social return to education can, of course, be higher or lower than the private monetary return. The social return can be higher because of externalities from education, which could occur, for example, if higher education leads to technological progress that is not captured in the private return to that education, or if more education produces positive externalities, such as a reduction in crime and welfare participation, or more informed political decisions.
Social return could be lower if education is a credential
The former is more likely if human capital is expanded at higher levels of education while the latter is more likely if it is expanded at lower levels. It is also possible that the social return to education is less than the private return. For example, Spence (1973) and Fritz Machlup (1970) note that education could just be a credential, which does not raise individualsf productivities. It is also possible that in some developing countries, where the incidence of unemployment may rise with education (e.g., Mark Blaug, Richard Layard, and Maureen Woodhall 1969) and where the return to physical capital may exceed the return to human capital (e.g., Arnold Harberger 1965), increases in education may reduce total output.
Education has other social impacts than monetary rewards
It should also be noted that education may affect national income in ways that are not fully measured by wage rates. For example, particularly in developing countries, education is negatively associated with womenfs fertility rates and positively associated with infantsf health (see Paul Glewwe 2000). In addition, education is positively associated with labor force participation; most of the micro human capital literature uses samples that consist of those in the labor force, so this effect of education is missed.
Micro analysis tend to cover more on private monetary return
A potential weakness of the micro human capital literature is that it focuses primarily on the private pecuniary return to education rather than the social return. The possibility of externalities to education motivates much of the macro growth literature, to which we now turn. Micro-level empirical analysis is less well suited for uncovering the social returns to education.
Reference:
Spence, A. Michael. 1973. gJob Market Signaling,h Quart. J. Econ. 87:3, pp. 355?74.
Machlup, Fritz. 1970. Education and Economic Growth. Lincoln: U. Nebraska Press.
Blaug, Mark; Richard Layard and Maureen Woodhall. 1969. The Causes of Graduate Unemployment in India. London: Allen Lane, Penguin Press.
Harberger, Arnold. 1965. gInvestment in Men Versus Investment in Machines: The Case of India,h in Education and Economic Development. C. Arnold Anderson and Mary Jean Bowman, eds. Chicago: Aldine Publishing, pp. 11? 50.
Glewwe, Paul. 2000. gSchools, Skills and Economic Development: Evidence, Gaps and Research Prospects,h mimeo, World Bank.
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Q10: What are the roles of Human Capital for Growth? (Yusuf and Stiglitz 2002) :3
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InputDate: 7/16/2006
Reference: Yusuf, Shahid and Joseph E. Stiglitz. Development Issues: Settled and Open. Frontiers of Development Economics. Edited by Gerald M. Mier and Joseph E. Stiglitz. World Bank And Oxford University Press
A: Although a precise answer to this question has eluded the most diligent researchers, nearly 40 years of increasingly refined analysis has established the primary importance of capital accumulation and factor productivity that arise from research, learning, technological change, and improvements in the quality of labor.
Contribution of Human Capital
The contribution of human capital, viewed by many as one of the principal drivers of growth in East Asia, remains contested (Pritchett (1997), and even the role of capital is periodically challenged. But such skepticism cannot dislodge the body of evidence, backed by intuition, that links economic growth with investment and gains in productivity (Oulton 1997). Thus, promoting investmetn in equipment and infrastructure and encouraging the accumulation of usable knowledge through a variety of channels are among the central tenets of development. Because investment must, to a large extent, be financed from domestic resources, raising the level of saving is integral to a progrowth strategy.

Reference:
Pritchett, Lant. 1997. Where has all the Education Gone?. Policy Research Working Paper 1581. Policy Research Department, World Bank, Washington, D.C.
Oulton, Nicholas. 1997. Total Factor Productivity and Growth and the Role of Externalities. National Institute Economc Review 162 (October): 99-111.
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Q11: What is Human Capital and how has the concept developed? :3.1
FAQID:1013

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InputDate: 8/13/2007
Reference: KRUEGER, ALAN B. and MIKAEL LINDAHL. 2001. "Education for Growth: Why and For Whom?" Journal of Economic Literature Vol. XXXIX (December 2001) pp. 1101?1136
A: Neo-classical growth theory started by Slow/Swan does not have explicit explanation of the role of education or human capital. However, when many exconomis tried to explain technological change as an "endogenous" variable, they come to deal with the concept of human capital. Here, Krueger and Lindahl (2002) explain how the caman capital concept has been developed.
Two objectives of macro analysis of education
Two issues have motivated the use of aggregate data to estimate the effect of education on the growth rate of GDP.
First, the relationship between education and growth in aggregate data can generate insights into endogenous growth theories, and possibly allow one to discriminate among alternative theories.
Second, estimating relationships with aggregate data can capture external returns to human capital that are missed in the microeconometric literature.
Two types of endogenous growth models
Human capital plays different roles in various theories of economic growth. In the neoclassical growth model (Robert Solow 1956), no special role is given to human capital in the production of output. In endogenous growth models human capital is assigned a more central role. Aghion and Howitt (1998) observe that the role of human capital in endogenous growth models can be divided into two broad categories. The first category broadens the concept of capital to include human capital. In these models sustained growth is due to the accumulation of human capital over time (e.g., Hirofumi Uzawa 1965; Robert Lucas 1988). The second category of models attributes growth to the existing stock of human capital, which generates innovations (e.g., Paul Romer 1990a) or improves a country's ability to imitate and adapt new technology (e.g., Richard Nelson and Edmund Phelps 1966). This, in turn, leads to technological progress and sustained growth. The observation that an individual's productivity can be affected by the human capital in the economy is also prominent in early work on the economics of cities by Jane Jacobs (1969).
Lucus Model of Human Capital
In Lucas's model the aggregate production function is assumed to be:

y = Ak^a*(uh)^1 − a*(ha)^a, where y is output, k is physical capital, u is the fraction of time devoted to production (as opposed to accumulating human capital), h is the human capital of the representative agent, and ha is the average human capital in the economy. Taking logs and differentiating with respect to time establishes that the growth of output depends on the growth of physical capital and the accumulation of human capital. If a > 0 there are positive externalities to human capital. It is further assumed that human capital grows at the rate:

d log(h) ⁄ dt = a(1 − u),

where 1 ? u is the time devoted to creating human capital and a is the maximum achievable growth rate of human capital. In steady state, output and human capital grow at the same rate, and depend on a and the determinants of the equilibrium value of u. Sustained growth arises because there are constant returns in the production of human capital in this model.
Romer assumes Human Capital to produce "innovation"
In Romer's (1990) model, the production function for a multi-sector economy is:

Y=Hy^a*L^aIntegral(X(i)^(1-a−a))dt

where Hy is the human capital employed in the non-R&D sector and L is labor. Physical capital is disaggregated into separate inputs, denoted X(i), which are used in the production of Y. Note that the "capital stock" depends on the technological level, A. Capital is disaggregated in this way because for each capital good there is a distinct monopolistically competitive firm. Technological progress evolves as:

d log(A) ⁄ dt = cHA, where HA is the human capital employed in the R&D sector. If more human capital is employed in the R&D sector, technological progress and the production of capital are greater. This, in turn, generates faster output growth. In steady state, however, the rate of growth equals the rate of technological progress, which is a linear function of the total human capital in both sectors.
Romer tested the two models empirically
It should be emphasized that the different roles played by human capital in these two classes of models generate testable implications. The growth of human capital in the Lucas model should affect output growth, while the stock of human capital in the Romer model should affect growth. An early test of these implications is provided by Romer (1990b), who regressed the average annual growth of output per capita between 1960 and 1985 on the literacy rate in 1960 and the change in the literacy rate between 1960 and 1980, holding the initial level of GDP per capita and share of GDP devoted to investment constant. He found evidence that the initial level of literacy, but not the change in literacy, predicted output growth. Romer noted that in this model investment could reflect the rate of technological progress, so the effects of the level and change of literacy are hard to interpret when investment is also held constant. When the investment rate was dropped from the growth equation, however, the change in literacy was still statistically insignificant.
Reference:
Solow, Robert M. 1956. gA Contribution to the Theory of Economic Growth,h Quart. J. Econ. 70:1, pp. 65?94.
Aghion, Phillippe and Peter Howitt. 1998. Endogenous GrowthTh eory. Cambridge, MA: MIT Press.
Uzawa, Hirofumi. 1965. gOptimum Technical Change in an Aggregative Model of Economic Growth,h Int. Econ. Rev. 6:1, pp. 18?31.
Lucas, Robert. 1988. gOn the Mechanics of Economic Development,h J. Monet. Econ. 22:1, pp. 3?42.
Romer, Paul. 1990a. gEndogenous Technological Change,h J. Polit. Econ. 89:5, pp. S71?S102.
Nelson, Richard R. and Edmund S. Phelps. 1966. gInvestment in Humans, Technological Diffusion, and Economic Growth,h Amer. Econ. Rev. 56:1/2, pp. 69?75.
Jacobs, Jane. 1969. The Economy of Cities. NY: Random House.
Romer, Paul. 1990b. gHuman Capital and Growth: Theory and Evidence,h Carnegie-Rochester Conf. Ser. Public Pol. 32:0, pp. 251?86.
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Q12: Where Has All the Education Gone? (Pretchett, 1996) :4
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InputDate: 7/16/2006
Reference: Pretchett, Lant. Where Has All the Education Gone? The World Bank Policy Research Working Paper 1581
A: Cross-national data on economic growth rates show that increases in educational capital resulting from improvements in the educational attainment of the labor force have had no positive impact on the growth rate of output per worker. In fact, contends Pritchett, the estimated impact of growth of human capital on conventional nonregression growth accounting measures of total factor productivity is large, strongly significant, and negative.
Three explanation for negative impsct of education on growth
Needless to say, this at least appears to contradict the current conventional wisdom in development circles about education's importance for growth. After establishing that this negative result about the education-growth linkage is robust, credible, and consistent with previous literature, Pritchett explores three possible explanations that reconcile the abundant evidence about wage gains from schooling for individuals with the lack of schooling impact on aggregate growth:
That schooling creates no human capital.
The first explanation is that schooling may not actually raise cognitive skills or productivity but schooling may nevertheless raise the private wage because to employers it signals a positive characteristic like ambition or innate ability.
That the marginal returns to education are falling rapidly where demand for educated labor is stagnant
The second explanation is that expanding the supply of educated labor where there is stagnant demand for it causes the rate of return to education to fall rapidly, particularly where the sluggish demand is due to limited adoption of innovations.
That the institutional environments in many countries have been perverse to economic growth
Third explanation is that possibly education does raise productivity, and there is demand for this more productive educated labor, but demand for educated labor comes from individually remunerative but socially wasteful or counterproductive activities - a bloated bureaucracy, for example, or overmanned state enterprises in countries where the government is the employer of last resort - so that while individuals' wages go up with education, output stagnates, or even falls.
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Q13: Does Human Capital actually increase growth? (Benhabib, and Spiegel. 1994) :4.2
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InputDate: 7/16/2006
Reference: Benhabib, Jess, and Mark M. Spiegel. 1994. The Role of Human Capital in Economic Development: Evidence from Aggregate Cross-Country Data, Journal of Monetary Economics 34 (October 1994): 143-151, 158-161.
A: How does human capital or the educational attainment of the labor force affect the output and the growth of an economy?
Two ways to handle human capital
A standard approach is to treat human capital or the average year of schooling of the labor force, as an ordinary input in the production function. The recent work of Mankiw, Romer and Weil(1992) is in this tradition.

An alternative approach, associated with endogenous growth theory, is to model technological progress, or the growth of total factor productivity, as a function of the level of education or human capital.
Insignificant and negative effect of human capital on growth
This paper uses estimates of physical capital and human capital stocks to examine cross-country evidence on the determinants of economic growth. We begin with estimation of a standard Cobb-Dougluas production function in which labor and human and physical capital enter as factors of production. Our findings shed some doubt on the traditional role given to human capital in the development proccess as a separate factor of production. In our first set of results, we find that human capital growth has an insignificant, and usually negative effect in explaining per capita income growth.This result is robust to a number of alternative specifications and data sources, as well as to the possibility of bias which is encountered when regressions per capita income growth on accumulated factor of production.
Human capital directly affect productivity
Nontheless. human capital accumulation has long been stressed as a prerequisite for economic growth. As pointed out by Nelson and Phelps (1966), by treating human capital simply as another factor in growth accounting we may be misspecifyng its role. Below, we introduce an alternative model which allows human capital levels to directly affect aggregate factor productivity through two channels:
Human Capital increases capacity for innovation and catch-up
Following Romer (1990a), we postulate that human capital may directly influence productivity by determining the capacity of nations to innovate new technologies suited to domestic production.

Furthermore, we adopt the Nelson and Phelps (1996) model to allow human capital levels to affect the speed of technological catch-up and diffusion. We assume that the ability of a nation to adopt and implement new technology from abroad is a function of its domestic human capital.
Human capital did have a big role
Under certain conditions growth rate may differ across countries for a long time due to differences in levels of human capital stocks.
Second, a country which lies below the "leader nation" in technology, but possesses a higher human capital stock, will catch up and overtake the leader in a finite time period.
Third, the country with the highest stock of human capital will always eventually emerge as the technological leader nation in finite time and maintain its leadership as long as its human capital advantage is sustained.
Reference:
Mankiw,Gregory, David Romer, and David Weil. 1992. A contribution to the empirics of economic growth, Quarterly Journal of Econommics 106, 407-437.
Nelson, Richard and Edmund Phelps. 1966. Investment in humans, technological diffusion, and economic growth, American Economic Review: Papers and Proceedings 61, 61-75
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Q14: What is the impact of primary and secondary education to the economic growth? (Kusakabe 2005) :4.3
FAQID:705

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InputDate: 7/30/2006
Reference: Kusakabe, Motoo. 2005. ICT and National Innovation System: Is ICT Engine of Growth? Presentation at International Workshop on Workforce Development For Knowledge Economy, 7-13 September 2005, Seoul, Korea
A: Education is thought to be the most important determinant of the economic growth. Many high growth Asian economies are also known as the countries which put strong emphasis on education from their early stages of development.
Is primary education more important for lower income countries?
During the industrialization process based on borrowed technologies, primary and secondary education are thought to be more important growth factors than tertiary education. However, in the era of ?gknowledge economy,?h it is widely considered that the higher education has become more important growth factor. Let us see the statistical evidence of whether there have been such changes in importantce of higher education.

Primary education had a mixed impact on growth
The figure above shows correlations between School Enrollment, Primary (Net) and the per capita GDP growth rates in the succeeding decades. Primary education enrollment in higher-income countries during 1980s had a significant impact on the growth rates in these countries during 1990s. Also to a lesser degree, in lower-income countries, primary education had a fairly high correlation with the growth until the most recent period. In recent period, in higher-income countries, primary education seems to have lost its impact on the growth rate in the succeeding period, mainly due to the fact that most of these countries already achieved universal education and primary education became no longer a differentiating factor for the growth. Why education indicators have not always significant correlation with growth? Education is effective only if other factors, such as a good job opportunity exists for school graduates, economic incentive system exists to reward better education, etc.
Secondary education has increased impact on growth
The figure above shows correlations between school enrollment ratio for secondary education (net) and the per capita GDP growth rates in the succeeding decades. It shows clearly that the secondary education has been a significant determinants of the economic growth for higher-income countries and becomes increasing important growth factor for lower-income countries. Particularly in 1990s correlations between secondary education in lower-income countries and growth in 2000s became as high as 54.6%, which is much higher than that of primary education. It suggests the importance of human capacity to cope with more sophisticated tasks in these countries.

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Q15: What is the impact of tertiary education on economic growth? :4.4
FAQID:706

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InputDate: 7/30/2006
Reference: Kusakabe, Motoo. 2005. ICT and National Innovation System: Is ICT Engine of Growth? Presentation at International Workshop on Workforce Development For Knowledge Economy, 7-13 September 2005, Seoul, Korea
A: Tertiary education has been considered as having the lowest rate of return and lowest correlation with economic growth. Until recently, developing community, including the World Bank, has tried to shift the focus of the educational budget to primary education from tertiary education, as in many developing countries universities tend to be created as the simbol of national prestage and often considered as wasting money for irrelevant education. Has the tertiary education become relevant to growth in lower income countries, as the global economy becomes, so-called knowledge economy?
Tertiary education had a mixed impact on growth
The figure above shows the correlations between school enrollment, tertiary, (gross) to the per capita GDP growth rates in the succeeding decades. Tertiary education has only a negligible or even negative impact on the economic growth of the succeeding decades until 1990s when it became a significant growth factor for the lower-income countries. This is a remarkable finding in the sense that until recently, developing community, including the World Bank, has tried to shift the focus of the educational budget to primary education from tertiary education, as in many developing countries universities were considered as wasting money for irrelevant education and to raise the national prestige. It seems that this diagnosis might have been true until 1980s. But in 1990s, tertiary education became a significant determinant of the growth rates in the succeeding period.
Should Lower income countries prepare for knowledge economy?
It is premature to judge whether this result is one of the signal that even in lower-income countries, transition to a ?gknowledge economy?h becomes the necessity for their growth, and educational system has to respond to the needs to produce more sophisticated and innovative personnel required to change their country to knowledge economy.

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Q16: How was education becomes the priority of the World Bank under the Washington Consensus? :5
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InputDate: 7/30/2006
Reference: Rose, Pauline. 2006. From Washington to Post-Washington Consensus: The triumph of Human Capital. The development Economics after the Washington Consensus, Jomo & Fine ed., Zed Books, London
A: Human capital took two decades to achieve prominence within the World Bank, finally coming into its own from the early 1980s. After cautious adoption of education using a manpower planning approach, analysis of rate of return to education became influential by the 1980s.
rate of return concepts decided the priorities in education sector
The 1980 World Development Report highlighted the direct productivity benefit of primary education, drawing in particular on Psacharopoulos's studies of international comparisons of rates of returnto education. Investment in primary education, particularly of girls, was promoted on the basis that their estimated rates of return to education were the highest, and the difference between socail and private returns the greatest, at that level. Rates of return to investment in education were also estimated to be above 10% yardstick used as the opportunity cost of capital in developing countries (Psachalopoulos and Woodhall 1985).
World Bank niglected social aspects of education
Furthermore, the narrow focus on economic aspects risks neglecting important features of education. As Lauglo (1996) notes, the 1995 World Bank review of education ignored the moral and social impacts of education, both in terms of the problems that schools might generate, as well as thier potential for remedying the social dislocations of modernization and restoring social cohesion.
Harms of over-emphasizing the rate of return
The process of teaching and learning, which transform inputs into outputs, tend to remain outside the scope of theBank's approach to education, leaving the black box of educational provisionfirmly shut, as discussed below. In addition, emphsis on rates of return to education, with their measurement of returns focusing on years of schooling, has served to form attention on the importance of increasing the quantity of education provided, potentially at the expense of quality, as experiences from developing countries such as Malawi indicate (Rose 2003).
Human capital approach and the Washington Consensus were not coincidental
It is not coincidental that the ascendancy of human capital, in conjunction with the application of rates of return, corresponded with the advent of the Washington Consensus. Human capital provided the opportunity for the neoliberaagenda to be applied to education, allowing the World Bank to continue its involvement and even increase its influence in the education sector, despite austerity package adopted as part of structural adjustent programmes.

Reference:
Lauglo, Jon. 1996. Banking on Education and the Use of Research: A Critique of World Bank Priorities and Strategies for Education. International Journal of Educational Development, 16(3): 221-33.
Rose, Pauline. 2003. From the Washington to Post-Washington Consensus: The influence of International Agendas on Education Policy and Practice inMalawi. Globalization, Education and Society, 1(1):67-86
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Q17: Is General Education better than Skill-specific Education? :5.2
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InputDate: 8/12/2007
Reference: Krueger, Dirk and Krishna B. Kumar. 2004. Skill-Specific rather than GeneralEducation: A Reason for US-Europe Growth Differences?
A: Kumar (2004) develope a model of technology adoption and economic growth in which households optimally obtain either a concept-based, "General" education or a skill-specific, "vocational" education. Genarl education is costly to obtain, but enables workers to operate new production technologies. Firsms weigh thecost of adopting and operating new technologies against increased profits and optimally choose the level of adoption. Kumar shows that an economy whose policies favour vocational education will grow slower in equilibrium than one that favour general education. More importantly, the gap between their growth rates will increase with growth rate of available technology.

By characterizing the optimal Ramsey education policy he also demonstrate that the optimal subsidy for general education increases with the growth rate of available technology. His theory suggests that European education policies that favoured specialized, vocational education might have worked well, both in terms of growth rates and welfare, during the 1960s and 1970s when available technologies changed slowly. However, in the information age of the 1980s and 1990s when new technologies emerged at a more rapid pace, they might have contributed to an increased gap relative to the United States.
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