• Almas, I., 2012. International Income Inequality: Measuring PPP Bias by Estimating Engel Curves for Food. American Economic Review, 102(2), pp.1093–1117. Available at: Link.
    • Abstract

      Purchasing power-adjusted incomes applied in cross-country comparisons are measured with bias. This paper estimates the purchasing power parity (PPP) bias in Penn World Table incomes and provides corrected incomes. The bias is substantial and systematic: the poorer a country, the more its income tends to be overestimated. Consequently, international income inequality is substantially underestimated. The methodological contribution is to exploit the analogies between PPP bias and the bias in consumer price index (CPI) numbers. The PPP bias and subsequent corrected incomes are measured by estimating Engel curves for food, an established method of measuring CPI bias.

  • Barro, R.J. & Sala-i-Martin, X., 1992. Convergence. Journal of Political Economy, 100(2), pp.223–251. Available at: Link.
    • Abstract

      A key economic issue is whether poor countries or regions tend to grow faster than rich ones: are there automatic forces that lead to convergence over time in the levels of per capita income and product? The authors use the neoclassical growth model as a framework to study convergence across the forty-eight contiguous U.S. states. They exploit data on personal income since 1840 and on gross state product since 1963. The U.S. states provide clear evidence of convergence, but the findings can be reconciled quantitatively with the neoclassical model only if diminishing returns to capital set in very slowly. Copyright 1992 by University of Chicago Press.

  • Barro, R.J. & Lee, J.W., 2013. A new data set of educational attainment in the world, 1950–2010. Journal of Development Economics, 104(C), pp.184–198. Available at: Link.
    • Abstract

      Our panel data set on educational attainment has been updated for 146 countries from 1950 to 2010. The data are disaggregated by sex and by 5-year age intervals. We have improved the accuracy of estimation by using information from consistent census data, disaggregated by age group, along with new estimates of mortality rates and completion rates by age and education level. We compare the estimates with our previous ones (Barro and Lee, 2001) and alternative measures (Cohen and Soto, 2007). Our estimates of educational attainment provide a reasonable proxy for the stock of human capital for a broad group of countries and should be useful for a variety of empirical work.

  • Barro, R.J. & Sala-i-Martin, X., 1991. Convergence across States and Regions. Brookings Papers on Economic Activity, 22(1), pp.107–182. Available at: Link.
    • Abstract

      No abstract is available for this item.

  • Baumol, W.J. & Bowen, W.G., 1965. On the Performing Arts: The Anatomy of Their Economic Problems. The American Economic Review, 55(1/2), pp.495–502. Available at: Link.
  • Baumol, W.J., 2012. The Cost Disease: Why Computers Get Cheaper but Healthcare Doesn’t, New Haven, CT: Yale University Press.
  • Baumol, W.J., 1967. Macroeconomics of Unbalanced Growth: The Anatomy of Urban Crisis. The American Economic Review, 57(3), pp.415–426. Available at: Link.
  • Bernard, A.B. & Jones, C.I., 1996. Comparing Apples to Oranges: Productivity Convergence and Measurement across Industries and Countries. American Economic Review, 86(5), pp.1216–38. Available at: Link.
    • Abstract

      This paper examines the role of sectors in aggregate convergence for fourteen OECD countries during 1970-87. The major finding is that manufacturing shows little evidence of either labor productivity or multifactor productivity convergence, while other sectors, especially services, are driving the aggregate convergence result. To determine the robustness of the convergence results, the paper introduces a new measure of multifactor productivity which avoids many problems inherent to traditional measures of total factor productivity when comparing productivity levels. The lack of convergence in manufacturing is robust to the method of calculating multifactor productivity. Copyright 1996 by American Economic Association.

  • Bond, S., Leblebicioglu, A. & Schiantarelli, F., 2009. Capital Accumulation and Growth: A New Look at the Empirical Evidence.
    • Abstract

      Using annual data for 75 countries in the period 1960-2000, we present evidence of a positive relationship between investment as a share of GDP and the long-run growth rate of GDP per worker. This result is robust for our full sample and for the sub-sample of non-OECD countries, but not for the sub-sample of OECD countries. Our analysis controls for time-invariant country-specific heterogeneity in growth rates, and for a range of time- varying control variables. We also address endogeneity issues, allow for heterogeneity across countries in model parameters, and for cross-section dependence.

  • Caselli, F., 2005. Accounting for Cross-Country Income Differences. In P. Aghion & S. Durlauf, eds. Handbook of Economic Growth. North-Holland.
  • Deaton, A. & Heston, A., 2010. Understanding PPPs and PPP-based National Accounts. American Economic Journal: Macroeconomics, 2(4), pp.1–35.
    • Abstract

      We provide an overview of the theory and practice of constructing PPPs. We focus on four practical areas: how to handle international differences in quality; the treatment of urban and rural areas of large countries; how to estimate prices for government services, health, and education; and the effects of the regional structure of the latest International Comparison Program for 2005. We discuss revisions of the Penn World Table, and their effects on econometric analysis, and include health warnings. Some international comparisons are close to impossible, even in theory, and in others, the practical difficulties make comparison exceedingly hazardous.

  • Durlauf, S., Johnson, P.A. & Temple, J.R.W., 2005. Growth Econometrics. In P. Aghion & S. Durlauf, eds. Handbook of Economic Growth. Amsterdam: Elsevier, pp. 555–677.
  • Eberhardt, M., Helmers, C. & Strauss, H., 2013. Do Spillovers Matter When Estimating Private Returns to R&D? The Review of Economics and Statistics, 95(2), pp.436–448. Available at: Link.
    • Abstract

      A large body of literature estimates private returns to R&D adopting the Griliches knowledge production framework, which ignores the potential impact of spillovers on consistent estimation. Using a panel of twelve manufacturing industries across ten OECD economies, we investigate whether ignoring spillovers leads to bias in the estimated private returns to R&D. We compare results from a common factor framework, which accounts for spillovers and other unobserved shocks, to those from a standard Griliches approach. Our findings confirm that conventional estimates conflate own-R&D and spillover effects, implying that spillovers cannot be ignored even when the interest lies exclusively in evaluating private returns to R&D. \copyright 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

  • Eberhardt, M. & Teal, F., 2013. No Mangos in the Tundra: Spatial Heterogeneity in Agricultural Productivity Analysis. Oxford Bulletin of Economics and Statistics, 75(6), pp.914–939.
  • Eberhardt, M. & Teal, F., 2013. Structural Change and Cross-Country Growth Empirics. World Bank Economic Review, 27(2), pp.229–271. Available at: Link.
    • Abstract

      One of the most striking features of economic growth is the process of structural change whereby the share of agriculture in GDP decreases as countries develop. The cross-country growth literature typically estimates an aggregate homogeneous production function or convergence regression model that abstracts from the process of structural change. In this paper, we investigate the extent to which assumptions about aggregation and homogeneity matter for inferences regarding the nature of technology differences across countries. Using a unique World Bank dataset, we estimate production functions for agriculture and manufacturing in a panel of 40 developing and developed countries for the period from 1963 to 1992. We empirically model dimensions of heterogeneity across countries, allowing for different choices of technology within both sectors. We argue that heterogeneity is important within sectors across countries implying that an analysis of aggregate data will not produce useful measures of the nature of the technology or productivity. We show that many of the puzzling elements in aggregate cross-country empirics can be explained by inappropriate aggregation across heterogeneous sectors. Copyright 2013, Oxford University Press.

  • Feenstra, R.C., Inklaar, R. & Timmer, M.P., 2015. The Next Generation of the Penn World Table. American Economic Review, 105(10), pp.3150–82. Available at: Link.
    • Abstract

      We describe the theory and practice of real GDP comparisons across countries and over time. Version 8 of the Penn World Table expands on previous versions in three respects. First, in addition to comparisons of living standards using components of real GDP on the expenditure side, we provide a measure of productive capacity, called real GDP on the output side. Second, growth rates are benchmarked to multiple years of cross-country price data so they are less sensitive to new benchmark data. Third, data on capital stocks and productivity are (re)introduced. Applications including the Balassa-Samuelson effect and development accounting are discussed. (JEL C43, C82, E01, E23, I31, O47)

  • Gollin, D., 2002. Getting Income Shares Right. Journal of Political Economy, 110(2).
    • Abstract

      Many widely used economic models implicitly assume that income shares should be identical across time and space. Although time series data from industrial countries appear consistent with this notion, cross-section data generally appear to contradict the assumption of constant income shares. A commonly used calculation suggests that labor shares of national income vary from about 0.05 to about 0.80 in international cross-section data. This paper suggests, however, that this widely used approach underestimates the labor income of the self-employed and other proprietors. Several adjustments for calculating labor shares are identified and compared. All of them yield data that appear broadly consistent with the hypothesis that labor shares for most countries fall in the range of 0.65 to 0.80

  • Gordon, R.J., 2016. The Rise and Fall of American Growth, Princeton University Press. Available at: Link.
  • Hall, R. & Jones, C.I., 1999. Why Do Some Countries Produce So Much More Output per Worker than Others? Quarterly Journal of Economics, 114(1), pp.83–116.
    • Abstract

      Output per worker varies enormously across countries. Why? On an accounting basis our analysis shows that differences in physical capital and educational attainment can only partially explain the variation in output per worker-we find a large amount of variation in the level of the Solow residual across countries. At a deeper level, we document that the differences in capital accumulation, productivity, and therefore output per worker are driven by differences in institutions and government policies, which we call social infrastructure. We treat social infrastructure as endogenous, determined historically by location and other factors captured in part by language.

  • Hall, R.E., 1988. The Relation between Price and Marginal Cost in U.S. Industry. Journal of Political Economy, 96(5), pp.921–47. Available at: Link.
    • Abstract

      An examination of data on output and labor input reveals that some U.S. industries have marginal cost well below price. The conclusion rests on the finding that cyclical variations in labor input are small compared with variations in output. In booms, firms produce substantially more output and sell it for a price that exceeds the costs of the added inputs. This paper documents the disparity between price and marginal cost, where marginal cost is estimated from annual variations in cost. It considers a variety of explanations of the findings that are consistent with competition, but none is found to be completely plausible. Copyright 1988 by University of Chicago Press.

  • Hall, R.E., 1989. Invariance Properties of Solow’s Productivity Residual, National Bureau of Economic Research. Available at: Link.
    • Abstract

      In 1957, Robert Solow published a paper that provided the theoretical foundation for almost all subsequent work on productivity measurement. Although most applications of Solow’s method have measured trends over fairly long time periods, the method also has important uses at higher frequencies. Under constant returns to scale and competition, the Solow residual measures the pure shift of the production function. Shifts in product demand and factor supplies should have no effect on the residual. Tests of this invariance property show that it fails in a great many industries. Though other explanations may deserve some weight, it appears that the leading cause of the failure of invariance is increasing returns and market power. The empirical findings give some support to the theory of monopolistic competition.

  • Hendricks, L., 2002. How Important Is Human Capital for Development? Evidence from Immigrant Earnings. The American Economic Review, 92(1), pp.pp. 198–219. Available at: Link.
    • Abstract

      This paper offers new evidence on the sources of cross-country income differences. It exploits the idea that observing immigrant workers from different countries in the same labor market provides an opportunity to estimate their human-capital endowments. These estimates suggest that human and physical capital account for only a fraction of cross-country income differences. For countries below 40 percent of U.S. output per worker, less than half of the output gap relative to the United States is attributed to human and physical capital. (JEL O15, O41, F22)

  • Heston, A., Summers, R. & Aten, B., 2009. Penn World Table Version 6.3, Center for International Comparisons of Production, Income, and Prices at the University of Pennsylvania.
  • Hsieh, C.-T., 2002. What Explains the Industrial Revolution in East Asia? Evidence from the Factor Markets. The American Economic Review, 92(3), pp.502–526. Available at: Link.
    • Abstract

      This paper presents dual estimates of total factor productivity growth (TFPG) for East Asian countries. While the dual estimates of TFPG for Korea and Hong Kong are similar to the primal estimates, they exceed the primal estimates by 1 percent a year for Taiwan and by more than 2 percent for Singapore. The reason for the large discrepancy for Singapore is because the return to capital has remained constant, despite the high rate of capital accumulation indicated by Singapore’s national accounts. This discrepancy is not explained by financial market controls, capital income taxes, risk premium changes, and public investment subsidies.

  • Hsieh, C.-T. & Klenow, P.J., 2010. Development accounting. American Economic Journal: Macroeconomics, 2(1), pp.207–223.
    • Abstract

      Researchers have made much progress in the past 25 years in accounting for the proximate determinants of income levels: physical capital, human capital, and Total Factor Productivity (TFP). But we still know little about why these factors vary. We argue that TFP exerts a powerful influence on output not only directly, but also indirectly, through its effect on physical and human capital accumulation. We discuss why TFP varies across countries, highlighting misallocation of inputs across firms and industries as a key determinant.

  • Hsieh, C.-T. & Klenow, P.J., 2007. Relative Prices and Relative Prosperity. The American Economic Review, 97(3), pp.562–585. Available at: Link.
    • Abstract

      The positive correlation between real investment rates and real income levels across countries is driven largely by differences in the price of investment relative to output. The high relative price of investment in poor countries is due to the low price of consumption goods in those countries. Investment prices are no higher in poor countries. Thus, the low real investment rates in poor countries are not driven by high tax or tariff rates on investment. Poor countries, instead, appear to be plagued by low efficiency in producing investment goods and in producing consumer goods to trade for them.

  • Imbs, J. & Wacziarg, R., 2003. Stages of Diversification. The American Economic Review, 93(1), pp.pp. 63–86. Available at: Link.
    • Abstract

      This paper studies the evolution of sectoral concentration in relation to the level of per capita income. We show that various measures of sectoral concentration follow a U-shaped pattern across a wide variety of data sources: countries first diversify, in the sense that economic activity is spread more equally across sectors, but there exists, relatively late in the development process, a point at which they start specializing again. We discuss this finding in light of existing theories of trade and growth, which generally predict a monotonic relationship between income and diversification.

  • Islam, N., 1995. Growth Empirics: A Panel Data Approach. The Quarterly Journal of Economics, 110(4), pp.1127–1170. Available at: Link.
    • Abstract

      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," we can also see the point where neoclassical growth empirics meets development economics.

  • Jaffe, A.B., 1986. Technological Opportunity and Spillovers of R&D: Evidence from Firms’ Patents, Profits, and Market Value. American Economic Review, 76(5), pp.984–1001. Available at: Link.
    • Abstract

      This paper quantifies the effects of exogenous variations in the state of technology (technological opportunity) and of the R&D of other firms (spillovers of R&D) on the productivity of firms’ R&D. The R&D productivity is increased by the R&D of "technological neighbors," though neighbors’ R&D lowers the profits and market value of low-R&D-intensity firms. Firms are shown to adjust the technological composition of their R&D in response to technological opportunity. Copyright 1986 by American Economic Association.

  • Johnson, S. et al., 2009. Is Newer Better? Penn World Table Revisions and Their Impact on Growth Estimates.
    • Abstract

      This paper sheds light on two problems in the Penn World Table (PWT) GDP estimates. First, we show that these estimates vary substantially across different versions of the PWT despite being derived from very similar underlying data and using almost identical methodologies; that this variability is systematic; and that it is intrinsic to the methodology deployed by the PWT to estimate growth rates. Moreover, this variability matters for the cross-country growth literature. While growth studies that use low-frequency data remain robust to data revisions, studies that use annual data are less robust. Second, the PWT methodology leads to GDP estimates that are not valued at purchasing power parity (PPP) prices. This is surprising because the raison d’être of the PWT is to adjust national estimates of GDP by valuing output at common international (PPP) prices so that the resulting PPP-adjusted estimates of GDP are comparable across countries. We propose an approach to address these two problems of variability and valuation.

  • Klenow, P.J. & Rodriguez-Clare, A., 1997. The Neo-Classical Revival in Growth Economic: Has it Gone Too Far? B. Bernanke & J. Rotemberg, eds. NBER Macroeconomics Annual, 12.
  • Kuznets, S., 1973. Modern Economic Growth: Findings and Reflections. American Economic Review, 63(3), pp.247–58. Available at: Link.
  • Lagakos, D., 2016. Explaining Cross-Country Productivity Differences in Retail Trade. Journal of Political Economy, 124(2), pp.579–620. Available at: Link.
    • Abstract

      Many macroeconomists argue that productivity is low in developing countries because of frictions that impede the adoption of modern technologies. I argue that in the retail trade sector, developing countries rationally choose technologies with low measured labor productivity. My theory is that the adoption of modern retail technologies is optimal only when household ownership of complementary durable goods, such as cars, is widespread. Because income is low in the developing world, households own few such durables. The theory implies that policies that increase measured retail productivity do not necessarily increase welfare.

  • Lagakos, D. et al., 2012. Experience Matters: Human Capital and Development Accounting.
    • Abstract

      Using recently available large-sample micro data from 36 countries, we document that experience- earnings profiles are flatter in poor countries than in rich countries. Motivated by this fact, we conduct a development accounting exercise that allows the returns to experience to vary across countries but is otherwise standard. When the country-specific returns to experience are interpreted in such a development accounting framework – and are therefore accounted for as part of human capital – we find that human and physical capital differences can account for almost two thirds of the variation in cross-country income differences, as compared to less than half in previous studies.

  • Maddison, A., 2010. Historical Statistics of the World Economy, 1-2008 A.D.
  • Mankiw, N.G., Romer, D. & Weil, D.N., 1992. A Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics, 107(2), pp.pp. 407–437. Available at: Link.
    • Abstract

      This paper examines whether the Solow growth model is consistent with the international variation in the standard of living. It shows that an augmented Solow model that includes accumulation of human as well as physical capital provides an excellent description of the cross-country data. The paper also examines the implications of the Solow model for convergence in standards of living, that is, for whether poor countries tend to grow faster than rich countries. The evidence indicates that, holding population growth and capital accumulation constant, countries converge at about the rate the augmented Solow model predicts.

  • McMillan, M., Rodrik, D. & Verduzco-Gallo, Íñigo, 2014. Globalization, Structural Change, and Productivity Growth, with an Update on Africa. World Development, 63, pp.11–32. Available at: Link.
  • Pritchett, L., 1997. Divergence, Big Time. Journal of Economic Perspectives, 11(3), pp.3–17. Available at: Link.
    • Abstract

      Historical data are unnecessary to demonstrate that perhaps the basic fact of modern economic history is massive absolute divergence in per capita income across countries. A plausible lower bound on per capita income can be combined with estimates of its current level in the poorer countries to place an upper bound on long-run income growth. Between 1870 and 1990, the ratio of richest to poorest countries’ income increased from roughly 9 to 1 to 45 to 1, the standard deviation of (natural log) per capita income doubled, and the average income gap between the richest and all other countries grew nearly tenfold from 1,286 to 12,000.

  • Putterman, L. & Weil, D.N., 2010. Post-1500 Population Flows and the Long-Run Determinants of Economic Growth and Inequality. Quarterly Journal of Economics, 125(4), pp.1627–1682.
    • Abstract

      We construct a matrix showing the share of the year 2000 population in every country that is descended from people in different source countries in the year 1500. Using the matrix to adjust indicators of early development so that they reflect the history of a population’s ancestors rather than the history of the place they live today greatly improves the ability of those indicators to predict current GDP. The variance of the early development history of a country’s inhabitants is a good predictor for current inequality, with ethnic groups originating in regions having longer histories of organized states tending to be at the upper end of a country’s income distribution.

  • Rodrik, D., 2016. Premature deindustrialization. Journal of Economic Growth, 21(1), pp.1–33. Available at: Link.
    • Abstract

      I document a significant deindustrialization trend in recent decades that goes considerably beyond the advanced, post-industrial economies. The hump-shaped relationship between industrialization (measured by employment or output shares) and incomes has shifted downwards and moved closer to the origin. This means countries are running out of industrialization opportunities sooner and at much lower levels of income compared to the experience of early industrializers. Asian countries and manufactures exporters have been largely insulated from those trends, while Latin American countries have been especially hard hit. Advanced economies have lost considerable employment (especially of the low-skill type), but they have done surprisingly well in terms of manufacturing output shares at constant prices. While these trends are not very recent, the evidence suggests both globalization and labor-saving technological progress in manufacturing have been behind these developments. The paper briefly considers some of the economic and political implications of these trends. Copyright Springer Science+Business Media New York 2016

  • Rodrik, D., 2013. Unconditional Convergence in Manufacturing. Quarterly Journal of Economics, 128(1), pp.165–204.
    • Abstract

      Unlike economies as a whole, manufacturing industries exhibit unconditional convergence in labor productivity. The paper documents this finding for 4-digit manufacturing sectors for a large group of developed and developing countries over the period since 1990. The coefficient of unconditional convergence is estimated quite precisely and is large, at 3.0-5.6 percent per year depending on the estimation horizon. The result is robust to a large number of specification tests, and statistically highly significant. Because of data coverage, these findings should be as viewed as applying to the organized, formal parts of manufacturing.

  • Rodrik, D., 1999. Where Did All the Growth Go? External Shocks, Social Conflict, and Growth Collapses. Journal of Economic Growth, 4(4), pp.385–412.
    • Abstract

      This article argues that domestic social conflicts are a key to understanding why growth rates lack persistence and why so many countries have experienced a growth collapse since the mid-1970s. It emphasizes, in particular, the manner in which social conflicts interact with external shock on the one hand, and the domestic institutions of conflict-management on the other. Econometric evidence provides support for this hypothesis. Countries that experienced the sharpest drops in growth after 1975 were those with divided societies (as measured by indicators of inequality, ethnic fragmentation, and the like) and with weak institutions of conflict management (proxied by indicators of the quality of governmental institutions, rule of law, democratic rights, and social safety nets).

  • Sala-i-Martin, X., 2006. The World Distribution of Income: Falling Poverty and... Convergence, Period. The Quarterly Journal of Economics, 121(2), pp.351–397. Available at: Link.
    • Abstract

      We estimate the World Distribution of Income by integrating individual income distributions for 138 countries between 1970 and 2000. Country distributions are constructed by combining national accounts GDP per capita to anchor the mean with survey data to pin down the dispersion. Poverty rates and head counts are reported for four specific poverty lines. Rates in 2000 were between one-third and one-half of what they were in 1970 for all four lines. There were between 250 and 500 million fewer poor in 2000 than in 1970. We estimate eight indexes of income inequality implied by our world distribution of income. All of them show reductions in global inequality during the 1980s and 1990s.

  • Sala-i-Martin, X., Doppelhofer, G. & Miller, R.I., 2004. Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach. The American Economic Review, 94(4), pp.813–835. Available at: Link.
    • Abstract

      This paper examines the robustness of explanatory variables in cross-country economic growth regressions. It introduces and employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates by averaging OLS coefficients across models. The weights given to individual regressions have a Bayesian justification similar to the Schwarz model selection criterion. Of 67 explanatory variables we find 18 to be significantly and robustly partially correlated with long-term growth and another three variables to be marginally related. The strongest evidence is for the relative price of investment, primary school enrollment, and the initial level of real GDP per capita.

  • Sala-i-Martin, X.X., 1996. The Classical Approach to Convergence Analysis. Economic Journal, 106(437), pp.1019–1036. Available at: Link.
    • Abstract

      The concepts of sigma-convergence, absolute beta-convergence and conditional beta-convergence are discussed in this paper. The concepts are applied to a variety of data sets that include a large cross-section of 110 countries, the subsample of OECD countries, the states within the United States, the prefectures of Japan, and regions within several European countries. Except for the large cross-section of countries, all data sets display strong evidence of sigma-convergence and absolute beta-convergence. The cross-section of countries exhibits sigma-divergence and conditional beta-convergence. The speed of conditional convergence, which is very similar across data sets, is close to 2 percent per year. Copyright 1996 by Royal Economic Society.

  • Syverson, C., 2011. What Determines Productivity? Journal of Economic Literature, 49(2), pp.326–365.
    • Abstract

      Economists have shown that large and persistent differences in productivity levels across businesses are ubiquitous. This finding has shaped research agendas in a number of fields, including (but not limited to) macroeconomics, industrial organization, labor, and trade. This paper surveys and evaluates recent empirical work addressing the question of why businesses differ in their measured productivity levels. The causes are manifold, and differ depending on the particular setting. They include elements sourced in production practices – and therefore over which producers have some direct control, at least in theory – as well as from producers’ external operating environments. After evaluating the current state of knowledge, I lay out what I see are the major questions that research in the area should address going forward.

  • Tabarrok, A. & Goldschlag, N., 2015. Is Regulation to Blame for the Decline in American Entrepreneurship?, George Mason University Working Paper. Available at: Link#.
  • Weil, D.N., 2007. Accounting for the Effect of Health on Economic Growth. The Quarterly Journal of Economics, 122(3), pp.pp. 1265–1306. Available at: Link.
    • Abstract

      I use microeconomic estimates of the effect of health on individual outcomes to construct macroeconomic estimates of the proximate effect of health on GDP per capita. I employ a variety of methods to construct estimates of the return to health, which I combine with cross-country and historical data on height, adult survival rates, and age at menarche. Using my preferred estimate, eliminating health differences among countries would reduce the variance of log GDP per worker by 9.9 percent and reduce the ratio of GDP per worker at the 90th percentile to GDP per worker at the 10th percentile from 20.5 to 17.9. While this effect is economically significant, it is also substantially smaller than estimates of the effect of health on economic growth that are derived from cross-country regressions.

  • Young, A., 1995. The Tyranny of Numbers: Confronting the Statistical Realities of the East Asian Growth Experience. Quarterly Journal of Economics, 110(3), pp.641–80.
    • Abstract

      This paper documents the fundamental role played by factor accumulation in explaining the extraordinary postwar growth of Hong Kong, Singapore, South Korea, and Taiwan. Participation rates, educational levels, and (excepting Hong Kong) investment rates have risen rapidly in all four economies. In addition, in most cases there has been a large intersectoral transfer of labor into manufacturing, which has helped fuel growth in that sector. Once one accounts for the dramatic rise in factor inputs, one arrives at estimated total factor productivity growth rates that are closely approximated by the historical performance of many of the OECD and Latin American economies. While the growth of output and manufacturing exports in the newly industrializing countries of East Asia is virtually unprecedented, the growth of total factor productivity in these economies is not.