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.

/pdf/econometric-analysis-of-panel-data-46ld72a5p1.pdf

Econometric Analysis of Panel Data

世界経済・社会統計 = World development indicators

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Examining the correlation between trade and income cannot identify the direction of causation between the two. Countries’ geographic characteristics, however, have important effects on trade, and are plausibly uncorrelated with other determinants of income. This paper therefore constructs measures of the geographic component of countries’ trade, and uses those measures to obtain instrumental variables estimates of the effect of trade on income. The results provide no evidence that ordinary least-squares estimates overstate the effects of trade. Further, they suggest that trade has a quantitatively large and robust, though only moderately statistically significant, positive effect on income. (JEL F43, 040)

/pdf/does-trade-cause-growth-532nt8hsja.pdf

Does Trade Cause Growth

We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

https://www.econstor.eu/bitstream/10419/81822/1/02-28.pdf

Economic growth and income inequality

The evidence from individual cases and from cross-country analysis supports the view that globalization leads to faster growth and poverty reduction in poor countries. To determine the effect of globalization on growth, poverty, and inequality, the authors first identify a group of developing countries that are participating more in globalization. China, India, and several other large countries are part of this group, so well over half the population of the developing world lives in these globalizing economies. Over the past 20 years, the post-1980 globalizers have seen large increases in trade and significant declines in tariffs. Their growth rates accelerated between the 1970s and the 1980s and again between the 1980s and the 1990s, even as growth in the rich countries and the rest of the developing world slowed. The post-1980 globalizers are catching up to the rich countries, but the rest of the developing world (the non-globalizers) is falling further behind. Next, the authors ask how general these patterns are, using regressions that exploit within-country variations in trade and growth. After controlling for changes in other policies and addressing endogeneity with internal instruments, they find that trade has a strong positive effect on growth. Finally, the authors examine the effects of trade on the poor. They find little systematic evidence of a relationship between changes in trade volumes (or any other measure of globalization they consider) and changes in the income share of the poorest-or between changes in trade volumes and changes in household income inequality. They conclude, therefore, that the increase in growth rates that accompanies expanded trade translates on average into proportionate increases in incomes of the poor. Absolute poverty in the globalizing developing economies has fallen sharply in the past 20 years. The evidence from individual cases and from cross-country analysis supports the view that globalization leads to faster growth and poverty reduction in poor countries.

/pdf/trade-growth-and-poverty-12dp2l8wvf.pdf

Trade, growth, and poverty*

The authors present the latest update of their aggregate governance indicators, together with new analysis of several issues related to the use of these measures. The governance indicators measure the following six dimensions of governance: (1) voice and accountability; (2) political instability and violence; (3) government effectiveness; (4) regulatory quality; (5) rule of law, and (6) control of corruption. They cover 209 countries and territories for 1996, 1998, 2000, 2002, and 2004. They are based on several hundred individual variables measuring perceptions of governance, drawn from 37 separate data sources constructed by 31 organizations. The authors present estimates of the six dimensions of governance for each period, as well as margins of error capturing the range of likely values for each country. These margins of error are not unique to perceptions-based measures of governance, but are an important feature of all efforts to measure governance, including objective indicators. In fact, the authors give examples of how individual objective measures provide an incomplete picture of even the quite particular dimensions of governance that they are intended to measure. The authors also analyze in detail changes over time in their estimates of governance; provide a framework for assessing the statistical significance of changes in governance; and suggest a simple rule of thumb for identifying statistically significant changes in country governance over time. The ability to identify significant changes in governance over time is much higher for aggregate indicators than for any individual indicator. While the authors find that the quality of governance in a number of countries has changed significantly (in both directions), they also provide evidence suggesting that there are no trends, for better or worse, in global averages of governance. Finally, they interpret the strong observed correlation between income and governance, and argue against recent efforts to apply a discount to governance performance in low-income countries.

/pdf/governance-matters-iv-governance-indicators-for-1996-2004-1ybeh8tgzn.pdf

Governance matters IV : governance indicators for 1996-2004

The authors present estimates of six dimensions of governance covering 199 countries and territories for four time periods: 1996, 1998, 2000, and 2002. These indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 25 separate data sources constructed by 18 different organizations. The authors assign these individual measures of governance to categories capturing key dimensions of governance and use an unobserved components model to construct six aggregate governance indicators in each of the four periods. They present the point estimates of the dimensions of governance as well as the margins of errors for each country for the four periods. The governance indicators reported here are an update and expansion of previous research work on indicators initiated in 1998 (Kaufmann, Kraay, and Zoido-Lobat 1999a,b and 2002). The authors also address various methodological issues, including the interpretation and use of the data given the estimated margins of errors.

/pdf/government-matters-iii-governance-indicators-for-1996-2002-5fnk2bezp5.pdf

Government Matters III : Governance Indicators for 1996-2002

Institutions, trade, and growth ☆

This paper reports on the latest update of the Worldwide Governance Indicators (WGI) research project, covering 212 countries and territories and measuring six dimensions of governance between 1996 and 2007: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The latest aggregate indicators are based on hundreds of specific and disaggregated individual variables measuring various dimensions of governance, taken from 35 data sources provided by 32 different organizations. The data reflect the views on governance of public sector, private sector and NGO experts, as well as thousands of citizen and firm survey respondents worldwide. The authors also explicitly report the margins of error accompanying each country estimate. These reflect the inherent difficulties in measuring governance using any kind of data. The authors also briefly describe the evolution of the WGI since its inception, and show that the margins of error on the aggregate governance indicators have declined over the years, even though they still remain non-trivial. The authors find that even after taking margins of error into account, the WGI permit meaningful cross-country comparisons as well as monitoring progress over time. In less than a decade, a substantial number of countries exhibit statistically significant improvements in at least one dimension of governance, while other countries exhibit deterioration in some dimensions. These aggregate indicators, spanning more than a decade, together with the disaggregated individual indicators, are available at www.govindicators.org.

Governance matters VII: aggregate and individual governance indicators 1996-2007

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