Human Capital Policy - Klenow ((FREE))
Externalities play a central role in most theories of economic growth. We argue that international externalities, in particular, are essential for explaining a number of empirical regularities about growth and development. Foremost among these is that many countries appear to share a common long run growth rate despite persistently different rates of investment in physical capital, human capital, and research. With this motivation, we construct a hybrid of some prominent growth models that have international knowledge externalities. When calibrated, the hybrid model does a surprisingly good job of generating realistic dispersion of income levels with modest barriers to technology adoption. Human capital and physical capital contribute to income differences both directly (as usual), and indirectly by boosting resources devoted to technology adoption. The model implies that most of income above subsistence is made possible by international diffusion of knowledge.
Human Capital Policy - Klenow
Low output per worker goes together with low levels of schooling, cognitive test results, and health indicators, leading naturally to the conjecture that low levels of human capital are responsible for low levels of income. This conjecture has contributed to motivate several decades of international focus on policies aimed at increasing the quantity and quality of schooling, as well as improving overall health, in low-income countries. (Needless to say improvements education and health are desirable in their own right, quite apart from their impact on income per capita.)
Over the last twenty years several researchers have developed and refined a set of tools, now known as development accounting (DA), which has allowed, among other things, a quantitative evaluation of the hypothesis that human capital is a major driver of the large gaps in income we observe around the world. Time and again contributions in this line of research find a relatively modest role for human capital. [E.g., Klenow and Rodriguez-Clare (1997), Hall and Jones (1999), Caselli (2005)]
But most DA calculations are based on a counterfactual model of aggregate human capital where skilled and unskilled workers are perfect substitutes. So if some workers become skilled, this has no impact on the marginal productivity of other participants in the labour force. A long labour economics tradition, however, shows convincingly that workers with different skills are imperfect substitutes. [E.g., Hamermesh (1993), Katz and Autor (1999), Ciccone and Peri (2005)] Hence, if a group of workers becomes skilled, this has implications for the marginal productivity of workers who remain unskilled as well as workers who were already skilled. The measurement of aggregate human capital should take this into account.
The upshot is that when workers with different educational attainment are imperfect substitutes, cross-country differences in skilled-bias efficiency are large for plausible values for the elasticity of substitution. The role of human capital for cross-country income differences varies enormously with the interpretation of these differences in skilled-bias efficiency. In particular, if one is willing to assume that all of the skill bias of efficiency reflects the human capital of more educated workers, then one can convince oneself that underdevelopment is entirely a human capital issue.
On the other hand, the role of human capital for cross-country income differences is diminished to the extent that skilled-biased efficiency also reflects technology, institutions, and other features of the economic environment. In the limit, if all of the cross-country variation in the skill bias of efficiency is due to the economic environment, then allowing for imperfect substitution implies that human capital explains even less of the variation in income than was originally thought in the DA literature.
In conclusion, both the back-of-the-envelope calculations in Caselli and Ciccone (2018) and the systematic empirical work of Rossi, strongly suggest that differences in skill premia across countries (after controlling for the relative supply of skills) are not due to differences in human capital embodied in skilled workers. Rather, they are more likely to be due to differences in country-specific technological and institutional environments. In turn, this means that the consideration of imperfect substitutability per se does not weaken (indeed, probably strengthens) the conclusions from the existing body of development-accounting research, namely that human capital appears to be a comparatively minor contributing factor to cross-country differences in income.[*]
These data from previous studies demonstrate a substantial gap in the formation of human capital: students are in school, but do not learn enough. Closing this gap is an important priority for economic development. Several studies have suggested that when human capital is measured by schooling, it does not deliver the returns predicted by growth models. However, when measured by learning, human capital is more strongly associated with growth3,23,24.
To date, much of the effort to measure learning has focused on high-income countries. This limitation is due to the absence of comparable measures of learning in low- and middle-income countries. Existing measures exclude a considerable portion of the global distribution, in particular countries with the most potential to gain from the accumulation of human capital.
Data harmonization efforts such as the one described in this Article serve the dual purpose of compiling the best available data at a given point in time and motivating additional data collection. Thus, they set in motion a cycle that can continually improve learning data over time. For example, in the most recent release of the World Bank human capital index, 20 new countries participated in learning assessments for the first time, enabling their inclusion in subsequent versions of this database.
A large number of studies in the development accounting literature have explored the relative contribution of human capital to cross-country income differences. However, the results have been inconclusive, in part owing to difficulties in measuring human capital. Although direct measures of years of schooling exist, the quality of schooling has been harder to measure.
Approaches to estimate the quality of schooling across countries have relied on differences in Mincerian wage returns5,6, immigrant returns7 and cross-country skill premia8. However, these approaches face several challenges, including the need to make assumptions about the substitutability of skilled and unskilled workers9. The challenges in measuring quality have contributed to substantial variation in estimates of the role of human capital in accounting for cross-country differences in income, ranging from nearly all to potentially none5,6,7,8,9,10,11,12,13.
The average relationship between learning and income masks significant heterogeneity across countries (Extended Data Table 2). We find that human capital explains less than two-fifths of cross-country income differences among low-income countries, but more than half among high-income countries. We find even larger differences across regions. For example, when measured by schooling, human capital accounts for 54% of cross-country income differences in advanced economies and only 4% in sub-Saharan Africa. When we include learning, this gap widens to 86% in advanced economies but only 10% in sub-Saharan Africa. This substantial heterogeneity reveals the importance of including a global distribution of countries covering multiple stages of economic development when examining the role of human capital.
Therefore, our measure of human capital appears to have a stronger relationship with economic growth, both individually and jointly. This is probably because alternative measures of human capital rely largely on years of schooling and omit learning. However, the use of these alternative measures remains standard practice, in part because these data have the broadest coverage. By constructing learning data across 164 countries, we fill a key gap: broad coverage over nearly two decades and a measure of human capital with strong links to economic development.
The contribution of human capital to cross-country income differences is analysed in studies of development accounting. We follow the literature5,6,41 and begin with a standard aggregate production function in its per-capita form:
In 1960, 94 percent of doctors were white men, as were 96 percent of lawyers and 86 percent of managers. By 2008, these numbers had fallen to 63, 61, and 57 percent respectively. Skilled occupations have become more equally distributed across race and gender, as have earnings within occupations. The result is arguably better allocation of talent and human capital investment, potentially accounting for 15 to 20 percent of U.S. economic growth over the last fifty years.
The difficulty for economic growth appears to lie in translating this rising education into rising productivity. Doing so is not just a matter of restoring growth, though that is an essential task, but to alleviate acute social pressures arising from youth unemployment fuelled by rapid population growth. For quite a while these employment problems were solved by accumulation of physical capital bought with oil. With that option no longer viable, solutions for human resource problems must be found elsewhere, particularly in the markets for labor and human capital.
Education is an economic good because it is not easily obtainable and thus needs to be apportioned. Economists regard education as both a consumer and capital good, because it offers utility (satisfaction) to a consumer and also serves as an input to develop the human resources necessary for economic and social transformation. The focus on education as a capital good related to the concept of human capital, which emphasizes that the development of skills is an important factor in production activities. It is widely accepted that education creates improved citizens and helps to upgrade the general standard of living in a society. The increased faith in education as an agent of change in many developing countries, has led to heavy investments in it. The pressure for higher education in many developing countries has undoubtedly been helped by public perception of financial reward from pursuing such education. There is belief that expanding educational opportunities and access promotes economic growth. 041b061a72