Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

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Econometric Analysis of Panel Data

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.

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Why Do Some Countries Produce so Much More Output Per Worker than Others

This paper presents evidence that "social capital" matters for measurable economic performance, using indicators of trust and civic norms from the World Values Surveys for a sample of 29 market economies. Memberships in formal groups—Putnam's measure of social capital—is not associated with trust or with improved economic performance. We find trust and civic norms are stronger in nations with higher and more equal incomes, with institutions that restrain predatory actions of chief executives, and with better-educated and ethnically homogeneous populations.

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Does Social Capital Have an Economic Payoff? A Cross-Country Investigation

In Acemoglu, Johnson, and Robinson, henceforth AJR, (2001), we advanced the hypothesis that the mortality rates faced by Europeans in different parts of the world after 1500 affected their willingness to establish settlements and choice of colonization strategy. Places that were relatively healthy (for Europeans) were—when they fell under European control—more likely to receive better economic and political institutions. In contrast, places where European settlers were less likely to go were more likely to have “extractive” institutions imposed. We also posited that this early pattern of institutions has persisted over time and influences the extent and nature of institutions in the modern world. On this basis, we proposed using estimates of potential European settler mortality as an instrument for institutional variation in former European colonies today. Data on settlers themselves are unfortunately patchy—particularly because not many went to places they believed, with good reason, to be most unhealthy. We therefore followed the lead of Curtin (1989 and 1998) who compiled data on the death rates faced by European soldiers in various overseas postings. 1 Curtin’s data were based on pathbreaking data collection and statistical work initiated by the British military in the mid-nineteenth century. These data became part of the foundation of both contemporary thinking about public health (for soldiers and for civilians) and the life insurance industry (as actuaries and executives considered the

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The Colonial Origins of Comparative Development: An Empirical Investigation

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. In 1988 output per worker in the United States was more than 35 times higher than output per worker in Niger. In just over ten days the average worker in the United States produced as much as an average worker in Niger produced in an entire year. Explaining such vast differences in economic performance is one of the fundamental challenges of economics. Analysis based on an aggregate production function provides some insight into these differences, an approach taken by Mankiw, Romer, and Weil [1992] and Dougherty and Jorgenson [1996], among others. Differences among countries can be attributed to differences in human capital, physical capital, and productivity. Building on their analysis, our results suggest that differences in each element of the production function are important. In particular, however, our results emphasize the key role played by productivity. For example, consider the 35-fold difference in output per worker between the United States and Niger. Different capital intensities in the two countries contributed a factor of 1.5 to the income differences, while different levels of educational attainment contributed a factor of 3.1. The remaining difference—a factor of 7.7—remains as the productivity residual. * A previous version of this paper was circulated under the title ‘‘The Productivity of Nations.’’ This research was supported by the Center for Economic Policy Research at Stanford and by the National Science Foundation under grants SBR-9410039 (Hall) and SBR-9510916 (Jones) and is part of the National Bureau of Economic Research’s program on Economic Fluctuations and Growth. We thank Bobby Sinclair for excellent research assistance and colleagues too numerous to list for an outpouring of helpful commentary. Data used in the paper are available online from http://www.stanford.edu/,chadj.

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Why do Some Countries Produce So Much More Output Per Worker than Others

glance appears to be boring and technical but that in fact turns out to be quite important: the proper interpretation of chain-weighted data.To illustrate, consider this simple question:What is the growth rate of real GDP? Figure 1 displays two different answers to this question for the period 1992 to 1998.The solid line is calculated using a chainweighted index, the method currently used by the U.S. Department of Commerce in the National Income and Product Accounts (NIPA); the dashed line is calculated using a fixed-weighted index, the method NIPA statisticians used up to 1997. According to the chain-weighted measure, the growth rate of real GDP rose to just under 4% by 1997 and 1998.Although this was relatively rapid compared to the growth rates observed earlier in the decade, it pales in comparison to the growth rate calculated using a fixed-weighted measure, which rises sharply after 1995, reaching a rate of 6.6% by 1998. By this measure, the “New Economy” of the late 1990s looks even more remarkable!

Using chain-weighted NIPA data

At least since 1950, the U.S.economy has benefited from increases in both educational attainment and research intensity. Such changes suggest, contrary to the conventional view, that the U.S.economy is far from its steady-state balanced growth path. This paper develops a model in which these facts are reconciled with the stability of av- erage U.S. growth rates over the last century. In the model, long-run growth is driven by the worldwide discovery of new ideas, which in turn is tied to world population growth. Nevertheless, a constant growth path can temporarily be maintained at a rate greater than the long- run rate provided research intensity and educational attainment rise steadily over time. Growth accounting with this model reveals that 30 percent of U.S. growth between 1950 and 1993 is attributable to the rise in educational attainment, 50 percent is attributable to the rise in worldwide research intensity, and only about 10 to 15percent is due to the long-run component of growth related to the increase in world population.

Sources of U.S. Economic Growth in a World of Ideas

July 6, 1999 Research and development (R&D) is a key determinant of long run productivity and welfare. A central issue is whether a decentralized economy undertakes too little or too much R&D. We develop an endogenous growth model that incorporates parametrically four important distortions to R&D: the surplus appropriability problem, knowledge spillovers, creative destruction, and congestion externalities. We show that our model is consistent with the available evidence on R&D, growth, and markups. Calibrating the model to micro and macro data, we find that the decentralized economy typically underinvests in R&D relative to what is socially optimal. The only exceptions to this conclusion occur when both the congestion externality is extremely strong and the equilibrium real interest rate is very high. These results are robust to reasonable variations in model parameters.

Too Much of a Good Thing? The Economics of Investment in R&D"

Top income inequality rose sharply in the United States over the last 35 years but increased only slightly in economies like France and Japan. Why? This paper explores a model in which heterogeneous entrepreneurs, broadly interpreted, exert effort to generate exponential growth in their incomes. On its own, this force leads to rising inequality. Creative destruction by outside innovators restrains this expansion and induces top incomes to obey a Pareto distribution. The development of the world wide web and a reduction in top tax rates are examples of changes that raise the growth rate of entrepreneurial incomes and therefore increase Pareto inequality. In contrast, policies that stimulate creative destruction reduce top inequality. Examples include research subsidies or a decline in the extent to which incumbent firms can block new innovation. Differences in these considerations across countries and over time, perhaps associated with globalization, may explain the varied patterns of top income inequality that we see in the data.

Charles I. Jones

Papers

Using chain-weighted NIPA data

Sources of U.S. Economic Growth in a World of Ideas

Too Much of a Good Thing? The Economics of Investment in R&D"

A Schumpeterian Model of Top Income Inequality

Productivity and Convergence Across U.S. Industries