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Why are some countries so much richer than others? Development Accounting is a first-pass attempt at organizing the answer around two proximate determinants: factors of production and efficiency. It answers the question "how much of the cross-country income variance can be attributed to differences in (physical and human) capital, and how much to differences in the efficiency with which capital is used?" Hence, it does for the cross-section what growth accounting does in the time series. The current consensus is that efficiency is at least as important as capital in explaining income differences. I survey the data and the basic methods that lead to this consensus, and explore several extensions. I argue that some of these extensions may lead to a reconsideration of the evidence.
The study focused on a cross-section of countries observed in the mid-1990s, so the conclusions from that effort are beginning to be a bit dated. In addition, signi cant revisions of the data underlying the 2005 paper have been published. Last but not least, in the intervening years become aware of ways in which the original methodology can be usefully improved and extended. Hence the present update and upgrade of the original paper. This paper focuses on data (mostly) from 2005 and improves on the original methodology in several dimensions. Development accounting compares differences in income per worker between developing and developed countries to counter-factual differences attributable to observable components of physical and human capital. Such calculations can serve a useful preliminary diagnostic role before engaging in deeper and more detailed explorations of the fundamental determinants of differences in income per worker. The research and policy agenda would then have to focus on technology, allocative efficiency, competition, and other determinants of the efficient use of capital. However because of limitations in thecoverage of the test results, author also present results where human capital is only measured from years of schooling and health. It turns out that, at least in my preferred calibration, the addition or omission of cognitive skills (as measured by test scores) does not greatly affect the quantitative results.
The paper presents evidence that the contribution of differences in total factor productivity (TFP) to income differences across countries steadily increased between 1970 and 2000. We verify that our finding is neither imputable to measurement errors in input factors nor dependent on the assumption of factor neutral differences in technology. We conclude that theories explaining cross-country income differences based on institutions or on forces that are constant over time, such as geography or legal origin, should be reconsidered in the light of their consistency with the rise of the explanatory power of TFP.
This paper measures the role of quality-adjusted education in accounting for cross- country differences in income per worker. The returns to schooling of immigrants to the United States are used as a measure of their source-country education quality. Returns are available for 130 countries and vary by up to an order of magnitude between developed and developing countries. A model shows why the returns to schooling of immigrants and not other wage statistics are an appropriate measure of education quality. The model is consistent with the relationships between education quality, average school attainment, and the returns to schooling for immigrants and non-migrants. Calibrating the model, or augmenting a Bils and Klenow (2000)-style accounting exercise to account for education quality, yields large results. Quality- adjusted schooling is found to account for 38-42% of the income difference between the richest and poorest quintiles of countries, as opposed to the 21-24% in the current literature that accounts only for years of schooling.
"We investigate, using firm level data for 79 developed and developing countries, whether differences in the allocation of resources across heterogeneous plants are a significant determinant of cross-country differences in income per worker. For this purpose, we use a standard version of the neoclassical growth model augmented to incorporate monopolistic competition among heterogeneous firms. For our preferred calibration, the model explains 58% of the log variance of income per worker. This figure should be compared to the 42% success rate of the usual model"--National Bureau of Economic Research web site
"... Papers presented at a conference held at the Stouffer Wailea Hotel, Maui, Hawaii, January 6-7, 1989. ... part of the Research on Taxation program of the National Bureau of Economic Research." -- p. ix.
The 32nd issue of the International Productivity Monitor is a special issue produced in collaboration with the OECD. All articles published in this issue were selected from papers presented at the First Annual Conference of the OECD Global Forum on Productivity held in Lisbon, Portugal, July ...
We investigate, using plant-level data for 79 developed and developing countries, whether differences in the allocation of resources across heterogeneous plants are a significant determinant of cross-country differences in income per worker. For this purpose, we use a standard version of the neoclassical growth model augmented to incorporate monopolistic competition among heterogeneous plants. For our preferred calibration, the model explains 58% of the log variance of income per worker. This figure should be compared to the 42% success rate of the usual model.