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

This paper proposes three principles for users and producers of governance indicators that both summarize the challenges in measurement and suggest ways forward: (1) all governance indicators have measurement error, (2) there are no silver bullets, and (3) the links from governance to development outcomes are complex. An overarching message is that alternative governance indicators should be viewed as complements rather than substitutes.

On Measuring Governance: Framing Issues for Debate

This paper documents inequality in health and education outcomes by constructing an index of human capital disaggregated by quintiles of socioeconomic status (SES) for a sample 51 mostly low- and middle-income countries. The index measures the expected future human capital of children born today, following the methodology of the World Bank Human Capital Index that was launched in October 2018. Within-country disparities in human capital outcomes across SES quintiles are large, accounting for roughly one-third of the total variation. On average, human capital outcomes increase with income at roughly the same rate across socio-economic groups within countries as they do across countries.

A Socioeconomic Disaggregation of the World Bank Human Capital Index

The World Development Report (WDR) 2002 is about building market institutions that promote growth and reduce poverty, addressing how institutions support markets, what makes institutions work, and how to build them. This theme is a natural a natural continuation of the WDR 2000/01, which discussed the central role of markets in the lives of poor people, leaving important issues for the WDR 2003, which will focus on the development of human, natural, and environmental capital. This report emphasizes on building institutions to support the development of markets, and its main messages address supplying effective institutions, and creating the demand for them, through: a design that complements institutions, human capabilities, and available technologies; innovations that identify institutional successes, and weaknesses, by experimenting with, and recognizing local conditions, and differences, ranging from social norms, to geography; a connection of communities of market players, by opening information flows, and trade; and, the promotion of competition among jurisdictions, firms, and individuals. The report is an introspective analysis about how do institutions support markets, growth, and poverty reduction, defining institutional work as a channel of information about market conditions, goods, and participants; as a mean to enforce property rights, and contracts; and, as a tool to increase competition in markets. The report offers lessons derived from experience in institutional evolution, and, although it does not address all possible institutional problems, it does focus on sets of institutions from many fields, to show that the framework, and messages can be applied regardless of the sector.

Informe sobre el desarrollo mundial 2002 : instituciones para los mercados

Scholars, policymakers, aid donors, and aid recipients acknowledge the importance of good governance for development. This understanding has spurred an intense interest in more refined, nuanced, and policy-relevant indicators of governance. In this paper we review progress to date in the area of measuring governance, using a simple framework of analysis focusing on two key questions: (i) what do we measure? and, (ii) whose views do we rely on? For the former question, we distinguish between indicators measuring formal laws or rules'on the books', and indicators that measure the practical application or outcomes of these rules'on the ground', calling attention to the strengths and weaknesses of both types of indicators as well as the complementarities between them. For the latter question, we distinguish between experts and survey respondents on whose views governance assessments are based, again highlighting their advantages, disadvantages, and complementarities. We also review the merits of aggregate as opposed to individual governance indicators. We conclude with some simple principles to guide the refinement of existing governance indicators and the development of future indicators. We emphasize the need to: transparently disclose and account for the margins of error in all indicators; draw from a diversity of indicators and exploit complementarities among them; submit all indicators to rigorous public and academic scrutiny; and, in light of the lessons of over a decade of existing indicators, to be realistic in the expectations of future indicators.

Governance indicators: where are we, where should we be going ?

Several detailed cross-country datasets measuring specific policy indicators relevant to business regulation and government integrity have been developed in recent years. The promise of these indicators is that they can be used to identify specific reforms that policymakers and aid donors can target in their efforts to improve the regulatory and institutional environment. Doing so, however, requires evidence on the partial effects of the many specific policy choices reflected in such datasets. In this paper we use Bayesian model averaging (BMA) to document the cross-country partial correlations between detailed policy indicators and several measures of regulatory and institutional outcomes. We find major instability in the set of policy indicators identified by BMA as important partial correlates of similar outcomes: specific policy indicators that matter for one outcome are, on average, not important correlates of other closely-related outcomes. This finding illustrates the difficulties in using highly-specific policy indicators to identify reform priorities using cross-country data.

Can specific policy indicators identify reform priorities

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