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

Using 6 longitudinal data sets, the authors estimate links between three key elements of school readiness--school-entry academic, attention, and socioemotional skills--and later school reading and math achievement In an effort to isolate the effects of these school-entry skills, the authors ensured that most of their regression models control for cognitive, attention, and socioemotional skills measured prior to school entry, as well as a host of family background measures Across all 6 studies, the strongest predictors of later achievement are school-entry math, reading, and attention skills A meta-analysis of the results shows that early math skills have the greatest predictive power, followed by reading and then attention skills By contrast, measures of socioemotional behaviors, including internalizing and externalizing problems and social skills, were generally insignificant predictors of later academic performance, even among children with relatively high levels of problem behavior Patterns of association were similar for boys and girls and for children from high and low socioeconomic backgrounds

/pdf/school-readiness-and-later-achievement-3b0grfzdeu.pdf

School Readiness and Later Achievement

Part 1 Unemployment in the model of balanced growth: the labour market long-run equilibrium and balanced growth adjustment dynamics. Part 2 further ananlysis of the labour market: search intensity and job advertising.

Equilibrium Unemployment Theory

This article examines the extent to which racial differences in socio-economic status (SES), social class and acute and chronic indicators of perceived discrimination, as well as general measures of stress can account for black-white differences in self-reported measures of physical and mental health. The observed racial differences in health were markedly reduced when adjusted for education and especially income. However, both perceived discrimination and more traditional measures of stress are related to health and play an incremental role in accounting for differences between the races in health status. These findings underscore the need for research efforts to identify the complex ways in which economic and non-economic forms of discrimination relate to each other and combine with socio-economic position and other risk factors and resources to affect health.

/pdf/racial-differences-in-physical-and-mental-health-ko7zhxxpt8.pdf

Racial Differences in Physical and Mental Health

A simple supply and demand framework is used to analyze changes in the U. S. wage structure from 1963 to 1987. Rapid secular growth in the demand for more-educated workers, "more-skilled" workers, and females appears to be the driving force behind observed changes in the wage structure. Measured changes in the allocation of labor between industries and occupations strongly favored college graduates and females throughout the period. Movements in the college wage premium over this period appear to be strongly related to fluctuations in the rate of growth of the supply of college graduates.

/pdf/changes-in-relative-wages-1963-1987-supply-and-demand-58fnb54smr.pdf

Changes in Relative Wages, 1963–1987: Supply and Demand Factors

What is the value of an education? Volume 4 of the Handbooks in the Economics of Education combines recent data with new methodologies to examine this and related questions from diverse perspectives. School choice and school competition, educator incentives, the college premium, and other considerations help make sense of the investments and returns associated with education. Volume editors Eric A. Hanushek (Stanford), Stephen Machin (University College London) and Ludger Woessmann (Ifo Institute for Economic Research, Munich) draw clear lines between newly emerging research on the economics of education and prior work. In conjunction with Volume 3, they measure our current understanding of educational acquisition and its economic and social effects. It is the winner of a 2011 PROSE Award Honorable Mention in Economics from the Association of American Publishers. It demonstrates how new methodologies are yielding fresh perspectives in education economics. It presents topics and authors whose data and conclusions attest to the globalization of research. It complements the policy and social outcomes themes of volume 3.

Handbook of the economics of education

There have been several studies of the demand for education as an investment good1 which generally take input and product prices as given and concentrate on computing (internal) rates of return to investment in schooling. Although these estimates usually indicate returns that are high by most standards, there is considerable variation, both through time and space, which points to the need for a clearer understanding of the underlying factors affecting profitability of investment in people. For such an analysis, education must be viewed not only as an investment but also as a factor of production. In this paper, I consider the question: Why has the incentive been maintained for a relative expansion in the supply of skilled labor in the United States? Three alternative explanations are considered, and one is pursued with an empirical analysis of factors determining relative wages among skill classes in agriculture. As we would expect for any factor of production, the evidence suggests that the return to education is affected by factor ratios, but ratios do not tell the whole story. In agriculture, much of the "leverage" distinguishing college graduates from less schooled persons has its roots in technical change as reflected in the level of research activity. Thus the incentive for acquiring a college education is based on dynamical considerations of changing technology; and if technology becomes stagnant, this incentive is reduced and may disappear. Convincing evidence of the maintained incentive for acquiring schooling is found in Gary Becker's (1964) estimates of private rates of return which are reproduced in table 1. When these rates of return are compared to the

Education in Production

Although surveys show that traditional orderings of average wage—i.e., higher earnings with higher schooling and concave age-wage profiles—have not changed during the past three decades, the actual size of the wage differentials measured by education or by work experience has varied from peak to trough by a factor of two-to-one. The patterns are not monotone, but there is a trend toward increased skill premiums. We first examine the structure of wages among white men distinguished by age and schooling for the period from 1963 to 1989. We then compare shifts in the distribution of wages and employment among the age x schooling categories to show in reference to a stable demand structure that employment alone cannot account for observed changes in relative wages. Finally, we describe the characteristics required of candidate demand shifters and offer examples using linear trend, business cycle shocks, and recent patterns of deficits in international trade.

The Structure of Wages

The arrival of the post--World War II baby boom cohorts in the job market raises many questions of effects associated with a rapidly declining average age of the labor force. This paper first summarizes 1967-75 wage behavior, showing that relative wages between schooling groups have not changed for prime-aged workers, but there is some evidence, for new job-market entrants, that wages of more educated workers have fallen relative to wages of less educated workers. However, changes among schooling groups are small in comparison to those between new entrants and peak earners within schooling group. The evidence is very direct: as work-experience distributions shifted toward increased proportions of young workers, their relative wages fell. After examining a career-phase model in which workers at different phases are imperfect substitutes, estimates of empirical relationships between cohort size and wages are presented. The main result is that income-depressant effects of (own) cohort size decline over the c...

/pdf/effects-of-cohort-size-on-earnings-the-baby-boom-babies-1sl306qxsi.pdf

Effects of Cohort Size on Earnings: The Baby Boom Babies' Financial Bust

The "human capital earnings function," in which earnings are expressed as a quadratic in potential experience, is probably the most widely accepted empirical specification in economics. In spite of its widespread acceptance, the human capital earnings function provides a very poor approximation of the true empirical relationship between earnings and experience. The standard formulation understates early career earnings growth by about 30%-50% and overstates midcareer growth by 20%-50%. However, simple alternative specifications that fit the data are available.

Finis Welch

Papers

Handbook of the economics of education

Education in Production

The Structure of Wages

Effects of Cohort Size on Earnings: The Baby Boom Babies' Financial Bust

Empirical Age-Earnings Profiles