The Impact of Education on Economic Growth Theory, Findings, and Policy Implications potx

Learning through observation is a mechanism by which such educational benefits may be spread within a community.6 To quantify the private rate of return to education, we may regress indi

The Impact of Education on Economic Growth

Theory, Findings, and Policy Implications

Brian G Dahlin Duke University

I Introduction

In June of 2002, President Bush announced a doubling of funds for the African Education Initiative Total U.S spending on basic education in Africa will total $630 million over the next five years Motivation for such an increase lies in the belief that the education of children in developing countries “is key to future economic growth and lasting democracy, leading to greater stability and improved standards of living.”1 Many growth models include education and offer predictions as to the implications of education policy changes on macroeconomic performance Some empirical analyses of the growth rate of real per capita GDP in the U.S suggest that years of secondary and higher

schooling contribute positively toward economic growth.2 Such research is of particular importance as developed nations continue taking a more active role in the development of third-world nations, as growth models offer predictions useful in aiding policy decisions

III Overview

Economics offers a variety of theories and models relating education to economic growth Education increases an individual’s earning potential, but also produces a “ripple effect” throughout the economy by way of a series of positive externalities Katharina Michaelowa of the Hamburg Institute for International Economics diagrams the impact of education at both micro and macro levels as follows:

Source: Michaelowa, Katharina (2000) “Returns to Education in Low Income Countries: Evidence for Africa.”

http://www.hwwa.de/Projects/Res_Programmes/RP/Development_Processes/VfS_EL_2000_Rev2.pdf

Direct and indirect effects of education are shown in the above diagram Key

assumptions underlying the diagram are: 1) education results in learning – it is not merely

a “signal” of worker quality (see section V for more on signaling); 2) demand within the economy is sufficient to consume higher levels of output resulting from productivity gains; 3) monetary and fiscal policy are sufficiently responsive to meet the demands of a growing economy (to prevent deflation, the money supply grows at a rate equal to the growth rate of GDP)

Direct effects of education such as increased individual wages follow from the assumption that education results in learning that increases a worker’s productivity If workers are paid the value of their marginal product, it follows that better-educated workers should earn higher wages

Externalities and other indirect effects related to education,

health, and population growth:

™ higher educ attainment and achievement of children

™ better health and lower mortality of children

™ better individual health

™ lower number of births

Lower population growth and better health of population (and labor force)

Education Increased earnings (higher productivity)

Increased earnings of neighbors Participation in the labor force

Higher growth

Increased labor force

Figure 1

In addition to the direct effects of education, a number of indirect effects have emerged in the literature.3 Studies have found a “positive effect of [a] mother’s

schooling on her children’s health in developing countries.”4 Healthier children may be more productive than unhealthy children and the result may be higher performance in school Similarly, better-educated parents tend to make more informed decisions with regard to family planning – the result being smaller family sizes.5 Smaller family size enables more parental involvement in each child’s education (as parents’ time is scarce) Increased parental involvement in a child’s education may enable the child to perform better in school and encourage him or her to pursue additional years of education

An individual’s choice to pursue further education may improve the earnings of his or her neighbors Michaelowa offers the example of an educated farmer who

implements new agricultural techniques Neighbors may observe the new methods used

by the educated farmer and imitate them Learning through observation is a mechanism

by which such educational benefits may be spread within a community.6

To quantify the private rate of return to education, we may regress individuals’ incomes on their level of education and other characteristics.7 Linking a nation’s growth

6 Foster, Andrew and Rosenzweig, Mark (1995) “Learning by Doing and Learning from Others: Human

Capital and Technical Change in Agriculture.” Journal of Political Economy, v.103, No 6, pp 1176-1209

7 Here we refer to “education” as a quantifiable individual characteristic – methods used to quantify various aspects of education are discussed in section IV

rate of GDP to its stock of human capital is more difficult.8 Some empirical studies find human capital to be positively related to the growth rate of GDP; other studies find the linkage to be insignificant.9

Some disagreement in the results of empirical studies arises from different

measures of education and different definitions of human capital Before reviewing the literature on education and economic growth, we discuss methods used to measure

education

IV The Measurement of Education

An ideal measure of an individual’s education should capture several components, including the number of years spent in school, the quality of the schooling, the nature of the curriculum, and the student’s effort Creating a measure that accurately quantifies these components is difficult Of these components, an individual’s years of schooling is the only directly observable characteristic We may indirectly measure aspects such as educational quality and individual ability and effort through standardized tests; however, there is disagreement regarding the reliability of such tests.10

In microeconomic analysis that studies the variation in wages as a function of education, individuals’ years of schooling is frequently used as an independent variable This method has advantages in that such data are readily available in developed countries,

8 “Human capital” has many interpretations and is discussed in greater detail in section IV

9 Positive effects were found in the following studies: Mankiw, Romer, and Weil (1992), Levine and Renelt (1992), Barro (1991) Insignificant effects were found in the following studies: Pritchett (1997), Islam (1995), Caselli, Esquivel, and Lefort (1996)

10 The existence of an industry focused on standardized test preparation, racial disparities in test scores, and concerns over test-retest reliability have led to criticism of the use of standardized tests in recent years For further information, see:

Gordon, Edmund (1995) “Toward an Equitable System of Educational Assessment.” Journal of Negro

Education, Vol 64, No 3, pp 360-372

but it does not account for differences in the quality or type of education received

Alternatively, individuals may be classified by highest degree completed This measure also has problems; for example, an individual nearly finished with college is counted as a high school graduate

In macroeconomic analysis, economists often include a variable for human

capital Because human capital encompasses a range of characteristics such as education, work experience, and health, it is extremely difficult to directly measure human capital.11 Any measure of a country’s aggregate human capital must have the following

characteristics: 1) it must be comparable across countries; 2) it must address the broad range of criteria that comprise human capital; 3) it must include elements of human capital for which data are available or estimable

An extensive literature discusses, proposes, and computes measures of human capital.12 As the workforce’s education is a key component of an economy’s human capital, average years of education within the workforce may serve as a component of an estimate of an economy’s human capital The use of averages, however, hides the

distribution of educational attainment, which may affect an economy’s growth potential

An economy in which most individuals have a basic level of schooling may grow faster than one in which a minority of individuals have advanced educations while the

remainder of the population has little to no education – as positive household-level

externalities of education benefit a greater number of people in the former case

11 Shupp, Frank “Income distribution and endogenous growth: A review with an application to South Africa.” http://www.uct.ac.za/org/saner/doc_f/sanewp13.zip

12 For examples of various measures of human capital, see the following:

Abowd, John, et al (Aug 2002) “The Measurement of Human Capital in the U.S Economy.”

Jeong, Byeongju (Feb 2001) “Measurement of Human Capital Input across Countries: A New Method and Results.”

In estimating an economy’s human capital, corrections for differences in

educational quality again raise difficulties Suggested quantitative measures of quality include “costs per student, library expenditures, number of earned doctorates among faculty and administrators … [and] student-faculty ratios.”13 No consensus exists

regarding the ideal combination of such measures in the formation of an index of

educational quality For example, a recent study found that per-pupil spending is a poor proxy for and index of school quality.14 Alone, none of these measures provides much insight into the quality of education – a low student-faculty ratio, for instance, says nothing about faculty’s ability to teach

Techniques used to measure the education of individuals and the aggregate human capital of an economy are imperfect Disagreement among researchers as to the “best” measure of various aspects of education and human capital makes it more difficult to compare the findings of empirical studies to determine the true impact of education on individuals’ incomes and economies’ growth rates

V Microeconomic Theory

Microeconomic analysis attempts to determine the effect of education on an individual’s wage People invest in education up to the point where the marginal cost of additional education equals its marginal benefit As an investment in human capital, a year of schooling produces a financial return by raising an individual’s income once he or

she enters the workforce Following is a model that considers education to be an

investment in human capital

The Mincerian Wage Equation:

The Mincerian wage equation is a popular model for analyzing how an

individual’s education and experience affect his or her wage A basic assumption of the model is that all years of education generate an equal rate of return to the student – that

is, kindergarten is just as important as a year of college This assumption implies a linear relationship between the log of earnings and the number of years of education.15 Second,

we assume that the cost of an additional year of education equals the lost wages one might earn in that time Finally, no accounting exists in this model for the quality of education received

Since this model views education as an investment in individual human capital, individuals choose how many years of schooling to pursue with the goal of maximizing

the present value of lifetime earnings Mathematically, agents choose s, (the number of

years of education) to maximize: 16

1

) (

The interest rate is denoted as r The objective function represents the present value of

lifetime income The first term in the objective function captures the present value of an

15 Krueger, Alan and Lindahl, Mikael (December 2001) “Education for Growth: Why and for Whom?”

Journal of Economic Literature, Vol XXXIX pp 1101-1136

16 Wagstaff, Adam (2001) “Deriving the Mincerian Earnings Function.” University of Sussex

http://www.sussex.ac.uk/economics/ma_micro/lec4.doc pp 48-54

individual’s income while he or she is a student If we assume that students could only

have earned income had they not been in school, γ becomes zero and this first term may

be ignored The second summation in the objective function represents the discounted value of lifetime earnings from the time the agent begins employment until the end of the

planning horizon, denoted as L Income in period τ is determined by M τ (s), a function of

education, experience, and ability (see derivation in next section) An understanding of

M τ (s) is crucial to understanding the Mincerian model As s represents the agent’s years

of schooling, M(s) must be increasing in s The equation for M τ (s) contains a second term, g(τ - s), which captures the effect of experience, (τ – s), on a worker’s wage in period τ The function g(.) is non-increasing in s, as less schooling leads to greater work experience in any given period τ

+++

r

s L g r

g r

g r

s M PV

1

)(

1

)2(1

)1(1

)(

Rewriting (3) with summation notation results in the following:

∑−

= ++

i g r

s M PV

1 (1 )

)()

1(

)(

s L G

1 (1 )

)(,

17 We follow the derivation outlined in Wagstaff (see previous footnote) that offers an excellent, though more technical discussion of the Mincerian wage equation

)(

r s L G r

s M

If we assume that r is fairly large, and that s is small compared to L (meaning that

education carries a high rate of return, and that the fraction of our lives spent in school versus that of work is small), the correlation between G(L−s,r) and s is assumed to be

minimal Thus, lnG(L−s,r) may be regarded as a constant and we may rewrite the relationship expressed in (7) as below

Individuals with higher ability may have a higher marginal benefit to additional schooling

in terms of generating income Ability differences can cause the present value of lifetime income to vary across individuals (since investment decisions are made at the margin, we expect higher-ability individuals to invest more in education) Solving (8) for lnM(s)and replacing lnPV with ε (an error term that captures individual differences in the

present value of lifetime income that are consequences of ability differences) we obtain:

ε++

=constant rs s

18 The approximation ln( 1 + )r = r increases in accuracy as r → 0 It remains a close approximation for

values of r such that r < 0 2 (interest rates beyond this magnitude are unusual)

develop a wage equation that relates a worker’s wage in any period τ to his or her years

of schooling, ability, and work experience Equation (2) expresses the wage in period τ

as a function of the starting salary M (s) and work experience Having solved

forlnM(s), it is straightforward to use the relationship expressed in equation (9) with the

structure of equation (2) to find a wage equation for any period τ

Taking the log of equation (2), we see that:

)(ln)(ln)(

Finally, substituting (9) into (10) for lnM(s), we have:

ετ

Equation (11) determines income in period τ as a function of years of education, years of

work experience, and ability

Interpretation and Empirical Evidence:

The slope, r, in equation (11) represents the rate of return to a year of education

According to this model, education adds to a student’s knowledge and human capital, thereby allowing him or her to find higher-paying employment upon entering the

workforce In contrast to the Mincerian model, Spence’s signaling model considers the possibility that education is purely a signal of ability.19 Spence’s model assumes that education adds nothing to an individual’s human capital; rather, the educational system serves as a filter through which the most able students pass As a result, the possession of more education “signals” a worker’s quality in the job market While there are various

19 Spence, Michael (Aug 1973) “Job Market Signaling.” The Quarterly Journal of Economics, Vol 87,

No 3 pp 355-374

interpretations of education’s effect on an individual’s human capital, Krueger and

Lindahl note that “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.”20

Most researchers agree that Mincerian estimates of the return to investment in education tend to underestimate (or at the very least not overestimate) its true value This tendency toward downward-biased estimates is in part the result of two sources of

simultaneity bias within the Mincerian model.21

First, since the error term reflects individual ability, it is positively correlated with

an individual’s choice of years of schooling Second, individuals make their choice of schooling based on the knowledge of the earnings function.22 Both cases are violations

of the OLS assumption that the independent variable (years of schooling) is exogenously determined Researchers attempt to correct this problem through the use of instrumental variable techniques Harmon and Walker propose to “rely on exogenous changes in the educational distribution of individuals caused by the raising of the minimum school-leaving age … to provide instruments for schooling.”23 Their work, as well as that of

20 Krueger, Alan and Lindahl, Mikael (December 2001) “Education for Growth: Why and for Whom?”

Journal of Economic Literature, Vol XXXIX pp 1101-1136

21 Criticism of the Mincerian wage equation regarding the difficulty of overcoming simultaneity bias is primarily a matter of statistics; the equation itself is not invalidated, rather, simultaneity bias makes the accurate estimate of the private rate of return to education more challenging

22 Wagstaff, Adam (2001) “Deriving the Mincerian Earnings Function.” University of Sussex

http://www.sussex.ac.uk/economics/ma_micro/lec4.doc pp 48-54

23 Harmon, Colm and Walker, Ian (Dec 1995) “Estimates of the Economic Return to Schooling for the

United Kingdom.” The American Economic Review, Vol 85, No 5 pp 1278-1286

their contemporaries, supports the notion that the true return to education may be twice that found through OLS estimation of the Mincerian wage equation.24

An ongoing examination of the rates of return to education throughout the world has been published throughout recent decades by George Psacharopoulos, applying the Mincerian model to the data of 61 countries.25 His methodology does not account for simultaneity bias; rather, standard OLS is applied (likely underestimating the rate of return to education, as discussed above) However, if we are only interested in the relative differences between rates of return to education across countries, not the explicit values of the returns themselves, Psacharopoulos’ results remain valuable

Psacharopoulos’ findings are summarized in the following statements: 26

• The rate of return tends to be higher in low-income countries

• Primary education makes the most valuable contribution to an individual’s

expected income in developing countries

• The rate of return declines with the level of schooling and the country’s per capita income

• Investment in girls’ education tends to yield a higher rate of return than

investment in boys’ education

• Among those in the labor force, the return to educated people is generally higher

in the private, competitive sectors than in the public sector

24 For further reference, see Card (1993), Butcher and Case (1994), Ashenfelter and Krueger (1994), and Ashenfelter and Zimmerman (1993) Variations of the IV technique are applied, the conclusions of which support Harmon and Walker’s suggestion that the OLS estimate is downward biased

25 Psacharopoulos, George (1985) “Returns to Education: A Further International Update and

Implications.” The Journal of Human Resources, Vol 20, No 4 pp 583-604

(Psacharopoulos updated his study using more recent data in 1994, the results of which are consistent with those of his previous research.)

26 Ibid

These findings support diminishing individual rates of return to education Students in developing countries have, on average, fewer years of schooling than their counterparts in developed nations This lack of education among students in poor

countries suggests these students should receive higher returns to education

Psacharopoulous’ empirical results support this intuition, as the “returns to any level of education are highest in Africa and lowest in the advanced industrial countries.”27 Similar intuition may explain the higher rate of return to girls’ education Particularly in developing countries, girls’ education lags well behind that of boys For example,

Burkina Faso, Chad, and Niger had girl-to-boy ratios at the primary school level of approximately 2:3 during the last decade.28 Worse, Afghanistan’s ratio is less than 1:2 over the same period

Policy Implications:

The Mincerian model seeks to find the private rate of return to education, not its social rate of return It is important to distinguish between the two, as the latter provides

a measure of the aggregate return to investment in education From a policy perspective,

we may be more interested in the social rate of return to education, as it considers effects

of education on society that cannot be estimated through individual wage equations Examples of such effects are: 1) lower government expenditures on health and human

27 Psacharopoulos, George (1985) “Returns to Education: A Further International Update and

Implications.” The Journal of Human Resources, Vol 20, No 4 pp 583-604

28 United Nations Department of Economic and Social Affairs

http://millenniumindicators.un.org/unsd/mi/mi_results.asp?row_id=611

Girl-to-boy ratios are more disparate at secondary and higher levels of education in developing nations