This chapter explares the power of personality traits both as predictors and as causes of academic and economic success, health, and criminal activity. Measured personality is interpreted as a construct derived from an economic model of preferences, constraints, and information. Evidence is reviewed about the "situational specificity" of personality traits and preferences. An extreme version of the situationist view claims that there are no stable personality traits or preference parameters that persons carry across different situations. Those who hold this view claim that personality psychology has little relevance far economics. The biological and evolutionary origins of personality traits are explored. Personality measurement systems and relationships among the measures used by psychologists are examined. The predictive power of personality measures is compared with the predictive power of measures of cognition captured by IQ and achievement tests. For many outcomes, personality measures are just as predictive as cognitive measures, even after controlling for family background and cognition. Moreover, standard measures of cognition are heavily influenced by personality traits and incentives. Measured personality traits are positively correlated over the life cycle. However, they are not fixed and can be altered by experience and investment. Intervention studies, along with studies in biology and neuroscience, establish a causal basis for the observed effect of personality traits on economic and social outcomes. Personality traits are more malleable over the life cycle compared with cognition, which becomes highly rank stable around age 10. Interventions that change personality are promising avenues for addressing poverty and disadvantage.
For ninety-eight countries in the period 1960-85, the growth rate of real per capita GDP is positively related to initial human capital (proxied by 1960 school-enrollment rates) and negatively related to the initial (1960) level of real per capita GDP. Countries with higher human capital also have lower fertility rates and higher ratios of physical investment to GDP. Growth is inversely related to the share of government consumption in GDP, but insignificantly related to the share of public investment. Growth rates are positively related to measures of political stability and inversely related to a proxy for market distortions.
Barro (1991) and others find that growth and schooling are highly correlated across countries, with each additional year of 1960 enrollment associated with about .6% per year faster growth in per capita GDP from 1960 to 1990. In a model with finite-lived individuals who choose schooling, schooling can influence growth, but also faster technology-driven growth can induce more schooling by raising the effective rate of return on investment in schooling. We consider a variety of evidence to determine the strength of these channels, with two main findings. First, faster-growing countries have at most modestly flatter cross-sectional experience-earnings profiles, consistent with a minority role for the channel from schooling to growth. Second, we calibrate the model using evidence from the labor literature and employ UNESCO attainment data to construct schooling going back well before 1960. We find the channel from schooling to growth to be too weak to generate even half of Barro's coefficient under a range of plausible parameter values. The reverse channel from expected growth to schooling, in contrast, is capable of explaining the empirical relationship. We conclude that the evidence favors a dominant role for the reverse channel from growth to schooling.
This paper develops a measure of investment in education from the literacy level of labour market entrants, using the 1994 International Adult Literacy Survey.
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.
Direct measures of labor-force quality from international mathematics and science test scores are strongly related to growth. Indirect specification tests are generally consistent with a causal link: direct spending on schools is unrelated to student performance differences; the estimated growth effects of improved labor-force quality hold when East Asian countries are excluded; and, finally, home-country quality differences of immigrants are directly related to U.S. earnings if the immigrants are educated in their own country but not in the United States. The last estimates of micro productivity effects, however, introduce uncertainty about the magnitude of the growth effects.
Existing growth research provides little explanation for the very large differences in long-run growth performance across OECD countries. We show that cognitive skills can account for growth differences within the OECD, whereas a range of economic institutions and quantitative measures of tertiary education cannot. Under the growth model estimates and plausible projection parameters, school improvements falling within currently observed performance levels yield very large gains. The present value of OECD aggregate gains through 2090 could be as much as $275 trillion, or 13.8% of the discounted value of future GDP for plausible policy changes. Extensive sensitivity analyses indicate that, while different model frameworks and alternative parameter choices make a difference, the economic impact of improved educational outcomes remains enormous. Interestingly, the quantitative difference between an endogenous and neoclassical model framework – with improved skills affecting the long-run growth rate versus just the steady-state income level – matters less than academic discussions suggest. We close by discussing evidence on which education policy reforms may be able to bring about the simulated improvements in educational outcomes.
We provide evidence that the robust association between cognitive skills and economic growth reflects a causal effect of cognitive skills and supports the economic benefits of effective school policy. We develop a new common metric that allows tracking student achievement across countries, over time, and along the within-country distribution. Extensive sensitivity analyses of cross-country growth regressions generate remarkably stable results across specifications, time periods, and country samples. In addressing causality, we find, first, significant growth effects of cognitive skills when instrumented by institutional features of school systems. Second, home-country cognitive-skill levels strongly affect the earnings of immigrants on the U.S. labor market in a difference-in-differences model that compares home-educated to U.S.-educated immigrants from the same country of origin. Third, countries that improved their cognitive skills over time experienced relative increases in their growth paths. From a policy perspective, the shares of basic literates and high performers have independent significant effects on growth that are complementary to each other, and the high-performer effect is larger in poorer countries.
A panel data approach is advocated and implemented for studying growth convergence. The familiar equation for testing convergence is reformulated as a dynamic panel data model and different panel data estimators are used to estimate it. The main usefulness of the panel approach lies in its ability to allow for differences in the aggregate production function across economies. This leads to results that are significantly different from those obtained from single cross-country regressions. In the process of identifying the individual 'country effect,' the point where neoclassical growth meets development economics can also be seen.
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.
This paper considers the prospects for constructing a neoclassical theory of growth and international trade that is consistent with some of the main features of economic development. Three models are considered and compared to evidence: a model emphasizing physical capital accumulation and technological change, a model emphasizing human capital accumulation through schooling, and a model emphasizing specialized human capital accumulation through learning-by-doing.
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 the author, the estimated impact of growth of human capital on conventional nonregression growth accounting measures of total factor productivity is large, strongly significant, and negative. 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, the author 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: 1) that schooling creates no human capital. 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; 2) that the marginal returns to education are falling rapidly where demand for educated labor is stagnant. 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; and 3) that the institutional environments in many countries have been sufficiently perverse that the human capital accumulated has been applied to activities that served to reduce economic growth. In other words, 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.
This paper outlines a theoretical framework for thinking about the role of human capital in a model of endogenous growth. The framework pay particular attention to two questions: What are the theoretical differences between intangibles like education and experience on the one hand, and knowledge or science on the other? and How do knowledge and science actually affect production? One implication derived from this framework is that the initial level of a variable like literacy may be important for understanding subsequent growth. This emphasis on the level of an input contrasts with the usual emphasis from growth accounting on rates of change of inputs. The principal empirical finding is that literacy has no additional explanatory power in a cross-country regression of growth rates on investment and other variables, but consistent with the model, the initial level of literacy does help predict the subsequent rate of investment, and indirectly, the rate of growth.
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