This paper presents empirical estimates of human-capital augmented growth equations for a panel of 21 OECD countries over the period 1971-98. It uses an improved dataset on human capital and a novel econometric technique that reconciles growth model assumptions with the needs of panel data regressions. Unlike several previous studies, our results point to a positive and significant impact of human capital accumulation to output per capita growth. The estimated long-run effect on output of one additional year of education (about 6 per cent) is also consistent with microeconomic evidence on the private returns to schooling. We also found a significant growth effect from the accumulation of physical capital and a speed of convergence to the steady state of around 15 per cent per year. Taken together these results are not consistent with the human capital augmented version of the Solow model, but rather they support an endogenous growth model ŕ la Uzawa-Lucas, with constant returns to scale to "broad" (human and physical) capital.
This Paper presents a new set of data on human capital. It is constructed so as to stay as close as possible to the censuses compiled by national, OECD or UNESCO sources. We then use these data to test a model that embeds the Mincerian approach to human capital into the Mankiw, Romer and Weil version of the neo-classical model. We find that the model performs extremely well. Physical and human capital appears to carry social returns that are essentially identical to the private ones.
We construct a revised version of the Barro and Lee (1996) data set for a sample of OECD countries using previously unexploited sources and following a heuristic approach to obtain plausible time profiles for attainment levels by removing sharp breaks in the data that seem to reflect changes in classification criteria. It is then shown that these revised data perform much better than the Barro and Lee (1996) or Nehru et al. (1995) series in a number of growth specifications. We interpret these results as an indication that poor data quality may be behind counterintuitive findings in the recent literature on the (lack of) relationship between educational investment and growth. Using our preferred empirical specification, we also show that the contribution of TFP to cross-country productivity differentials is substantial and that its importance relative to differences in factor stocks increases over time.
Growth economists have spent more than forty years slowing chipping away at the Solow residual, largely by attributing increasingly larger chunks of it to investment in human capital. A few years ago we were reasonably certain that this was the way to go. But an increasing number of studies seem to be telling us that the effect of schooling variables on productivity vanishes when we turn to what seem to be the appropriate econometric techniques for the purpose of estimating growth equations. Should we take these results at face value? Before we do so and abandon the only workable models we have, it seems sensible to search for ways to reconcile recent empirical findings with some kind of plausible theory. In this paper we argue that we can make a fair amount of progress in this direction by combining two ingredients: better data on human capital, and a further extension of the human capital-augmented neoclassical model that allows for cross-country productivity differentials and for technological diffusion.
We construct estimates of educational attainment for a sample of OECD countries using previously unexploited sources. We follow a heuristic approach to obtain plausible time profiles for attainment levels by removing sharp breaks in the data that seem to reflect changes in classification criteria. We then construct indicators of the information content of our series and a number of previously available data sets and examine their performance in several growth specifications. We find a clear positive correlation between data quality and the size and significance of human capital coefficients in growth regressions. Using an extension of the classical errors in variables model, we construct a set of meta-estimates of the coefficient of years of schooling in an aggregate Cobb-Douglas production function. Our results suggest that, after correcting for measurement error bias, the value of this parameter is well above 0.50.
This paper summarizes and tries to reconcile evidence from the microeconometric and empirical macro growth literatures on the effect of schooling on income and GDP growth. Much microeconometric evidence suggests that education is an important causal determinant of income for individuals within countries. At a national level, however, recent studies have found that increases in educational attainment are unrelated to economic growth. This discrepancy appears to be a result of the high rate of measurement error in first-differenced cross-country education data. After accounting for measurement error, the effect of changes in educational attainment on income growth in cross-country data is at least as great as microeconometric estimates of the rate of return to years of schooling. Another finding of the macro growth literature--that economic growth depends positively on the initial stock of human capital--is not robust when the assumption of a constant-coefficient model is relaxed.
Empirical results on the role of education on economic growth are significantly influenced by the education measure used. This paper analyses the measurement error in education induced by the use of the perpetual inventory method in Barro and Lee data. It is shown that there is a systematic difference between census data and constructed data for education. Moreover, this systematic difference interferes with the estimated impact of education on economic growth. We conclude that changes in education have both, contemporaneous, and long-run effects. While the former is relatively small, the latter although significantly large, takes too long to be in place. We also achieve a better reconciliation between the results associated with data based on different time spans.
This paper studies the puzzling lack of correlation between income and schooling in macro regressions. It is argued that the root of the puzzle is threefold. First, there is a problem of a proper definition of the way in which years of schooling should enter in a production function. Second, collinearity between physical and human capital stocks seriously undermines the ability of educational indicators to display any significance in growth regressions. And third, failure to cope with measurement error and endogeneity produces biased estimates. After dealing with these problems, the neoclassical approach to human capital is strongly supported by the data.
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