by RJ Barro · Cited by 501 — tend to catch up to rich ones — with extensions that emphasize government term growth effects from legal and educational institutions, size of government,
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Education and Economic Growth Robert J. Barro 1Since the late 1980s, much of the attention of macroeconomists has focused onlong-term issues, notably the effects of government policies on the long-term rate of economic growth. This emphasis reflects the recognition that the difference between prosperity and poverty for a country depends on how fast it grows over the long term. Although standard macroeconomic policies are important for growth, other aspects of fipolicyfl Š broadly interpreted to encompass all government activities that matter for economic performance Š are even more significant. This paper focuses on human capital as a determinant of economic growth. Although human capital includes education, health, and aspects of fisocial capital,fl the main focus of the present study is on education. The analysis stresses the distinction between the quantity of education Š measured by years of attainment at various levels Š and the quality Š gauged by scores on internationally comparable examinations. The recognition that the determinants of long-term economic growth were the central macroeconomic problem was fortunately accompanied in the late 1980s by important advances in the theory of economic growth. This period featured the development of fiendogenous-growthfl models, in which the long-term rate of growth was determined within the model. A key feature of these models is a theory of technological progress, viewed as a process whereby purposeful research and application lead over time 1 Harvard University. This research has been supported, in part, by the National Science Foundation. I
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2to new and better products and methods of production and to the adoption of superiortechnologies that were developed in other countries or sectors. One major contributor in this area is Romer (1990).Shortly thereafter, in the early 1990s, there was a good deal of empirical estimation of growth models using cross-country and cross-regional data. This empirical work was, in some sense, inspired by the excitement of the endogenous-growth theories. However, the framework for the applied work owed more to the older, neoclassical model, which was developed in the 1950s and 1960s (see Solow 1956, Cass 1965, Koopmans 1965, the earlier model of Ramsey 1928, and the exposition in Barro and Sala- i-Martin 1995). The framework used in recent empirical studies combines basic features of the neoclassical model Š especially the convergence force whereby poor economies tend to catch up to rich ones Š with extensions that emphasize government policies and institutions and the accumulation of human capital. For an overview of this framework and the recent empirical work on growth, see Barro (1997). The recent endogenous-growth models are useful for understanding why advanced economies Š and the world as a whole Š can continue to grow in the long run despite the workings of diminishing returns in the accumulation of physical and human capital. In contrast, the extended neoclassical framework does well as a vehicle for understanding relative growth rates across countries, for example, for assessing why South Korea grew much faster than the United States or Zaire over the last 30 years. Thus, overall, the new and old theories are more complementary than they are competing. appreciate the assistance with the education data provided by my frequent co-author, Jong-Wha Lee.
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31. Framework for the Empirical Analysis of Growth The empirical framework derived from the extended neoclassical growth model can be summarized by a simple equation: (1) Dy = F(y, y*) where Dy is the growth rate of per capita output, y is the current level of per capita output, and y* is the long-run or target level of per capita output. In the neoclassical model, the diminishing returns to the accumulation of capital imply that an economy ™sgrowth rate, Dy, is inversely related to its level of development, as represented by y. In equation (1), this property applies in a conditional sense, that is, for a given value of y*. This conditioning is important because the variables y and y* tend to be strongly positively correlated across countries. That is, countries that are observed to be rich (high y) tend also to be those that have high long-run target levels of per capita output (high y*). In a setting that includes human capital and technological change, the variable y would be generalized from the level of per capita product to encompass the levels of physical and human capital and other durable inputs to the production process. These inputs include the ideas that underlie an economy™s technology. In some theories, the growth rate, Dy, falls with a higher starting level of overall capital per person but rises with the ratio of human to physical capital. For a given value of y, the growth rate, Dy, rises with y*. The value y* depends, in turn, on government policies and institutions and on the character of the national
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4population. For example, better enforcement of property rights and fewer market distortions tend to raise y* and, hence, increase Dy for given y. Similarly, if people are willing to work and save more and have fewer children, then y* increases, and Dy rises accordingly for given y. In practice, the determinants of y* tend to be highly persistent over time. For example, if a country maintains strong institutions and policies today, then it is likely also to maintain these tomorrow. In this model, a permanent improvement in some government policy initially raises the growth rate, Dy, and then raises the level of per capita output, y, gradually over time. As output rises, the workings of diminishing returns eventually restore the growth rate, Dy, to a value consistent with the long-run rate of technological progress (which is determined outside of the model in the standard neoclassical framework). Hence, in the very long run, the impact of improved policy is on the level of per capita output, not its growth rate. But since the transitions to the long run tend empirically to be lengthy, the growth effects from shifts in government policies persist for a long time. 2. Empirical Findings on Growth and Investment across Countries A. Empirical Framework The findings on economic growth reported in Barro (1997) provide estimates for the effects of a number of government policies and other variables. That study applied to roughly 100 countries observed from 1960 to 1990. The sample has now been extended to 1995 and has been modified in other respects, as detailed below.The framework includes countries at vastly different levels of economic development, and places are excluded only because of missing data. The attractive
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5feature of this broad sample is that it encompasses great variation in the policies and other variables that are to be evaluated. In fact, my view is that it is impossible to use the experience of one or a few countries to get an accurate empirical assessment of the long- term growth effects from legal and educational institutions, size of government, monetary and fiscal policies, and other variables.There are a number of drawbacks from using the full sample with its great heterogeneity of experience. One problem involves the measurement of variables in a consistent and accurate way across countries and over time. Less developed countries tend, in particular, to have a lot of measurement error in national-accounts and other data. In addition, it may be difficult to implement functional forms for models of economic growth that work satisfactorily over a wide range of economic development. Given these problems, the use of the broad panel relies on the idea that the strong signal from the diversity of the experience dominates the noise. To get some perspective on this issue, the empirical analysis includes a comparison of results from the broad country panel with those obtainable from sub-sets of rich or OECD countries.2The other empirical issue, which is likely to be more important than measurement error, is the sorting out of directions of causation. The objective is to isolate the effects of alternative government policies on long-term growth. But, in practice, much of the government ™s behavior Š including its monetary and fiscal policies and its political stability Š is a reaction to economic events. For most of the empirical results, the 2 Whereas researchers and policymakers in OECD countries are often skeptical about the value of including information on developing countries, researchers and policymakers from development institutions and poor countries are often doubtful about the use of incorporating data from the rich countries. The first position, which relies on issues about data quality and modeling consistency, seems more defensible than the second. If one is interested in recipes for development, then one surely ought to include in the sample the countries
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6labeling of directions of causation depends on timing evidence, whereby earlier values of the explanatory variables are thought to influence subsequent economic performance. However, this approach to determining causation is not always valid. The empirical work considers average growth rates and average ratios of investment to GDP over three decades, 1965-75, 1975-85, and 1985-95. 3 In one respect, this long-term context is forced by the data, because many of the determining variables considered, such as school attainment and fertility, are measured at best over five-year intervals. Data on internationally comparable test scores are available for even fewer years. The low-frequency context accords, in any event, with the underlying theories of growth, which do not attempt to explain short-run business fluctuations. In these theories, the exact timing of response Š for example, of the rate of economic growth to a change in a public institution Š is not as clearly specified as the long-run response. Therefore, the application of the theories to annual or other high-frequency observations would compound the measurement error in the data by emphasizing errors related to the timing of relationships. Table 1 shows panel regression estimates for the determination of the growth rate of real per capita GDP.4 Table 2 shows parallel estimates for the determination of the ratio of investment (private plus public) to GDP. Estimation is by three-stage least squares, using lags of the independent variables as instruments Š see the notes to Tables that have managed to develop. 3 For investment, the third period is 1985-92. 4 The GDP figures in 1985 prices are the purchasing-power-parity adjusted, chain-weighted values from Summers and Heston, version 5.6. These data are available on the Internet from the National Bureau of Economic Research. See Summers and Heston (1991) for a general description of their approach. Real investment (private plus public) is also from this source.
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8the investment flow that connects the stock of attainment to subsequent stocks. Theresulting data set included information for most countries on school attainment at various levels over five-year intervals from 1960 to 1990. The data set has recently been revised and updated; see Barro and Lee (2000) for details. The new data set includes actual figures for 1995 and projections to 2000. The fill-in part of the computational procedure has also been improved. One revision is to use gross enrollment figures (enrollment for students of all ages at a given level of schooling) adjusted to delete class repeaters, rather than either gross figures (which overstate schooling rates because of repeaters) or net figures (which consider only students of the customary age for each level of schooling). The problem with the net figures is that they create errors when students start school at ages either earlier or later than the customary ones. Another revision is that we now consider changes over time in a country ™s typical duration of each level of education. Puzzling discrepancies exist between our data, based primarily on U.N. sources, and the figures provided by the OECD for some of the OECD countries (see OECD 1997, 1998a, 1998b). Table 3 compares our data (denoted Barro-Lee) with those provided by the OECD for OECD and some developing countries. The table shows the distribution of highest levels of school attainment among the adult population in recent years Š 1995for our data and 1997 or 1998 for the OECD (1996 for their data on the developing countries).One difference is that our figures cover the standard UNESCO categories of no schooling, primary schooling, some secondary schooling, complete secondary schooling,
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9and tertiary schooling. 5 We then compute average years of schooling at all levels by multiplying the percentages of the population at each level of schooling by the country ™saverage duration of school at that level. The OECD categories are below upper secondary, upper secondary, and tertiary. We believe that the first OECD category would correspond roughly to the sum of our first three categories. However, this approximation is satisfactory only if the OECD ™s conceptof upper secondary attainment corresponds closely to the U.N. concept of complete secondary attainment. The OECD also reports figures on average years of schooling at all levels, but we are uncertain about how these numbers were calculated.For many countries, the correspondence between the Barro-Lee and the OECD data is good. But, for several countries, the OECD data indicate much higher attainment at the upper secondary level and above Š Austria, Canada, Czech Republic, France, Germany, Netherlands, Norway, Switzerland, and the United Kingdom. The source of the difference, in many cases, is likely to be the distinction between some and complete secondary schooling. The OECD classification probably counts as upper secondary many persons whom the U.N. ranks as less than complete secondary. The treatment of vocational education is particularly an issue here. Another source of discrepancy is that our figures refer to persons aged 25 and over, whereas the OECD data are for persons aged 25 to 64. Since secondary and tertiary attainment have been rising over time, this difference would tend to make the OECD figures on upper secondary and tertiary attainment higher than our corresponding numbers. Further research is warranted to pin 5 Our data also distinguish partial from complete primary education, but that distinction is not made in Table 3. The primary schooling data in the table refer to the percent of the population for whom some level of primary schooling is the highest level attained.
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10down the exact relation between the Barro-Lee and OECD data. See de la Fuente and Domenech (2000) for additional discussion.C. Basic Empirical Results Before focusing on the results for human capital, it is worthwhile to provide a quick summary of the results for the other explanatory variables. a. The Level of Per Capita GDP. As is now well known, the simple relationacross a broad group of countries between growth rates and initial levels of per capita GDP is virtually nil. However, when the policy and other independent variables shown in column 1 of Table 1 are held constant, there is a strong relation between the growth rate and level of per capita GDP. The estimated coefficients are significantly positive for log(GDP) and significantly negative for the square of log(GDP). These coefficients imply the partial relation between the growth rate and log(GDP) as shown in Figure 1. 6 This relation is negative overall but is not linear. For the poorest countries contained in the sample, the marginal effect of log(GDP) on the growth rate is small and may even be positive. The estimated regression coefficients for log(GDP) and its square imply a positive marginal effect for a level of per capita GDP below $580 (in 1985 prices). This situation applies mainly to some countries in Sub Saharan Africa. 6 The variable plotted on the vertical axis is the growth rate net of the estimated effect of all explanatory variables aside from log(GDP) and its square. The value plotted was also normalized to make its mean value zero.
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11For the richest countries, the partial effect of log(GDP) on the growth rate is strongly negative at the margin. The largest magnitude (corresponding to the highest value of per capita GDP in 1995) is for Luxembourg Š the GDP value of $19,794implies a marginal effect of -0.059 on the growth rate. The United States has the next largest value of GDP in 1995 ($18,951) and has an estimated marginal effect on the growth rate of -0.058. These values mean that an increase in per capita GDP of 10% implies a decrease in the growth rate on impact by 0.6% per year. However, an offsetting force is that higher levels of per capita GDP tend to be associated with more favorable values of other explanatory variables, such as more schooling, lower fertility, and better maintenance of the rule of law. Overall, the cross-country evidence shows no pattern of absolute convergence Šwhereby poor countries tend systematically to grow faster than rich ones Š but doesprovide strong evidence of conditional convergence. That is, except possibly at extremely low levels of per capita product, a poorer country tends to grow faster for given values of the policy and other explanatory variables. The pattern of absolute convergence does not appear because poor countries tend systematically to have less favorable values of the determining variables other than log(GDP). In the panel for the investment ratio in column 1 of Table 2, the pattern of estimated coefficients on log(GDP) is also positive on the linear term and negative on the square. These values imply a hump-shaped relation between the investment ratio and the starting level of GDP Š the relation is positive for per capita GDP below $3,800 andthen becomes negative.
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