The counterfactual decomposition technique popularized by Blinder (1973, Journal of Human Resources, 436–455) and Oaxaca (1973, International Economic Review, 693–709) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. This article summarizes the technique and addresses several complications, such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new command called oaxaca is introduced, and examples illustrating its usage are given.

https://journals.sagepub.com/doi/pdf/10.1177/1536867X0800800401

The Blinder–Oaxaca decomposition for linear regression models

One lingering puzzle is why voluntary contribu tions to public goods decline over time in experimental and real-w orld settings. We show that the decl ine of cooperation is driven by individual preferences for im perfect conditional cooperation. Many people’s desire to contribute less than oth ers, rather than changing beliefs of what others will contr ibute over time or people’s heterogeneity in preferences makes voluntary cooperation fragile. Universal free riding thus even tually emerges, despite the fact tha t m ost peop le a re not selfis h. (JEL C91, C72, H41, D64)

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Social Preferences, Beliefs, and the Dynamics of Free Riding in Public Goods Experiments

Although most studies underscore institutional change as replacement of one dominant logic for another and assume that professions are guided by a single logic, professions that operate in multiple...

/pdf/institutional-logics-and-institutional-pluralism-the-76e53om5hk.pdf

Institutional Logics and Institutional Pluralism: The Contestation of Care and Science Logics in Medical Education, 1967–2005:

Impacts of positive and negative corporate social responsibility activities on company performance in the hospitality industry.

Agriculture and fishing are the primary source of almost all our food, as well as of many other products, so remain vital activities even though their share in the economy is small and in continuous decline. Partly for this reason public policy in these areas is almost entirely integrated at European level in the Common Agricultural Policy (CAP) and Common Fisheries Policy (CFP). Forestry policies are also heavily affected by EU-level decision-making. As agriculture dominates in terms of direct contribution to GDP and numbers of people engaged in it, as well as accounting for the largest amount of public support expenditure, it is agriculture that will receive the greatest attention here.

Agriculture, Forestry and Fisheries

Chapter 1: The Nature and Scope of Econometrics Part I: The Linear Regression Model Chapter 2: Basic Ideas of Linear Regression Chapter 3: The Two-Variable Model: Hypothesis Testing Chapter 4: Multiple Regression: Estimation and Hypothesis Testing Chapter 5: Functional Forms of Regression Models Chapter 6: Dummy Variable Regression Models Part II: Regression Analysis in Practice Chapter 7: Model Selection: Criteria and Tests Chapter 8: Multicollinearity: What Happens if Explanatory Variables are Correlated? Chapter 9: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? Chapter 10: What Happens if Error Terms are Correlated? Part II: Advanced Topics in Econometrics Chapter 11: Simultaneous Equation Models Chapter 12: Selected Topics in Single-Equation Regression Models Appendices Introduction: Basics of Probability and Statistics Appendix A: Review of Statistics: Probability and Probability Distributions Appendix B: Characteristics of Probability Distributions Appendix C: Some Important Probability Distributions Appendix D: Statistical Inference: Estimation and Hypothesis Testing Appendix E: Statistical Tables Appendix F: Computer Output of EViews, Minitab, Excel, and STATA

/pdf/essentials-of-econometrics-122fsq0s99.pdf

Essentials of Econometrics

Basic Econometrics, 4th Edition

PART I: THE LINEAR REGRESSION MODEL CHAPTER 1: The Linear Regression Model: An Overview CHAPTER 2: Functional Forms of Regression Models CHAPTER 3: Qualitative Explanatory Variables Regression Models PART II: CRITICAL EVALUATION OF THE CLASSICAL LINEAR REGRESSION MODEL CHAPTER 4: Regression Diagnostic I: Multicollinearity CHAPTER 5: Regression Diagnostic II: Heteroscedasticity CHAPTER 6: Regression Diagnostic III: Autocorrelation CHAPTER 7: Regression Diagnostic IV: Model Specification Errors PART III: REGRESSION MODELS WITH CROSS-SECTIONAL DATA CHAPTER 8: The Logit And Probit Models CHAPTER 9: Multinomial Regression Models CHAPTER 10: Oridinal Regression Models CHAPTER 11: Limited Dependent Variable Regression Models CHAPTER 12: Modeling Count Data: The Poisson and Negative Binomial Regression Models PART IV: TOPICS IN TIME SERIES ECONOMETRICS CHAPTER 13: Stationary and Nonstationary Time Series CHAPTER 14: Cointegration and Error Correction Models CHAPTER 15: Asset Price Volatility: The ARCH and GARCH Models CHAPTER 16: Economic Forecasting CHAPTER 17: Panel Data Regression Models CHAPTER 18: Survival Analysis CHAPTER 19: Stochastic Regressors and the Method of Instrumental Variables

Damodar Gujarati

Papers

Essentials of Econometrics

Basic Econometrics, 4th Edition

Econometrics by Example

Student solutions manual for use with basic econometrics

Basic econometrics / Damodar N. Gujarati)