Handbook of labor economics. Vol. 5 / Christian Dustmann and Thomas Lemieux.

Format:

Book

Author/Creator:

Dustmann, Christian, author.

Lemieux, Thomas, author.

Series:

Handbooks in economics ; Volume 5.

Handbooks in Economics Series ; Volume 5

Language:

English

Subjects (All):

Labor economics.

Physical Description:

1 online resource (862 pages)

Edition:

First edition.

Place of Publication:

Amsterdam, Netherlands : Elsevier, [2024]

Summary:

Volumes 5 and 6 of the Handbook of Labor Economics will systematically review the research topics, empirical findings, and methods that constitute frontier research in the field.

Contents:

Front Cover

Introduction of the Series

Handbook of Labor Economics

Copyright

Contents

List of Contributor

Preface

Chapter 1: Instrumental variables with unobserved heterogeneity in treatment effects

1 Introduction

2 Background

2.1 IV in a nutshell

2.2 Why is there unobserved heterogeneity in treatment effects?

2.3 From the classical linear IV model to potential outcomes

2.4 Selection models

2.5 Full exogeneity

2.6 Target parameters

2.7 Testability

3 Reverse engineering: interpreting linear estimators

3.1 Estimators, estimands, and weak causality

3.2 Binary treatment, binary instrument, no covariates

3.3 Multivalued instruments

3.4 Violations of monotonicity

3.5 Multiple instruments

3.6 Ordered, cardinal treatments

3.7 Unordered or non-cardinal treatments

3.8 Covariates

3.8.1 Controlling for covariates nonparametrically

3.8.2 Controlling for covariates linearly

3.8.3 Level-dependence caused by covariates

3.8.4 Weighting expression for linear IV under rich covariates

3.8.5 Monotonicity-correct first stage specifications

3.8.6 Specification considerations with covariates

3.9 Summary of reverse engineering

4 Forward engineering: estimating target parameters

4.1 Assuming away the problem

4.2 Estimating LATEs and ACRs in the presence of covariates

4.2.1 Propensity score weighting

4.2.2 Double robustness and machine learning

4.2.3 Empirical illustration

4.3 Marginal treatment effects

4.3.1 Definitions

4.3.2 Motivation

4.3.3 A linear regression formulation

4.3.4 Identification

4.3.5 Unstratified regressions and local instrumental variables

4.3.6 Estimation and inference

4.3.7 Applications and uses of marginal treatment effects

4.4 Binary treatments when monotonicity is violated

4.5 Ordered treatments.

4.5.1 Threshold-crossing with multiple treatments

4.5.2 A linear regression formulation

4.5.3 Continuous treatments

4.5.4 Selection models that do not allow for heterogeneity

4.6 Unordered treatments

4.7 No selection model

4.7.1 Manski-Robins and IV intersection bounds

4.7.2 Empirical illustration

4.7.3 The role of a selection model

4.8 Summary of forward engineering

5 Recommendations for practice

5.1 Step 1: Assess the likely role of UHTE

5.2 Step 2: Reverse engineer with caution

5.3 Step 3: Forward engineer estimates of interpretable target parameters

6 Conclusion

Appendix A Potential outcomes or latent variables? It's just notation ...

Appendix B Definition of a weakly causal estimand

Appendix C Deriving the average causal response and an alternative decomposition

Appendix D Estimating the average causal response with covariates

Appendix E Derivations for marginal treatment effects

E.1 Derivations of weighting expressions

E.2 The normal selection model

E.3 Saturated MTR specifications reproduce the LATE

References

Chapter 2: Firm wage effects

1 Background

2 What sorts of firms pay high wages?

2.1 Productivity, worker flows, and firm size

2.2 Entry, reallocation, and dynamics

2.3 Sorting, outsourcing, and displacement

2.4 Industry structure and amenities

3 The AKM model

3.1 An edgy interpretation of firm effects

3.1.1 Firm effects as restricted edge effects

3.1.2 Estimators

3.1.3 Combination weights and smoothing

3.2 Evaluating the AKM restrictions

3.2.1 Visualizing goodness of fit

3.2.2 Accounting for noise

3.3 Causality

3.3.1 Indirect contrasts and spanning trees

3.3.2 Restricting selection

4 Variance decomposition

4.1 Limited mobility bias

4.2 Cross-fitting and bias correction

4.2.1 Leave-out connectedness.

4.2.2 Bounding and imputation

4.2.3 An empirical example

4.3 Clustering approaches

4.4 How variable are worker and firm effects?

5 Regressing firm effects on observables

5.1 One step vs two

5.2 Variance estimation

5.3 Revisiting the firm size wage premium

6 Hiring origins and state dependence

6.1 Structural interpretation

6.2 Testable restrictions

6.3 It ain't where you're from, it's where you're at

6.4 Information and conduct

7 Conclusion

Appendix: Covariance between person and firm effects

Chapter 3: Empirical Bayes methods in labor economics

2 Empirical Bayes basics

2.1 An empirical Bayes recipe

2.1.1 Step 2: Deconvolution

2.1.2 Step 3: Shrinkage

2.1.3 Recap: A three-step EB recipe

2.2 Gains from shrinkage

2.2.1 MSE improvements in the normal/normal model

2.2.2 The James/Stein theorem

2.2.3 Compound decision problems

2.3 Practical shrinkage issues

2.3.1 Distributions of true parameters, unbiased estimates, and posterior means

2.3.2 Shrinkage and regression

2.3.3 Long regression or mean residuals? Correlated versus uncorrelated random effects

2.4 Generalizations of linear shrinkage

2.4.1 Adding covariates

2.4.2 Multivariate EB

2.4.3 Combining estimators

2.5 EB decision rules

2.6 Precision-dependence

2.6.1 Testing and modeling precision-dependence

2.6.2 Variance-stabilizing transformations

2.6.3 Noisy standard errors

2.7 Connections to machine learning

2.8 Linear shrinkage application: school value-added in Boston

3 Non-parametric empirical Bayes

3.1 Bias-corrected variance estimation

3.1.1 Unbiased estimation of the mixing variance

3.2 Non-parametric priors and posteriors

3.2.1 Non-parametric maximum likelihood

3.2.2 Log-spline deconvolution

3.2.3 Non-parametric posteriors.

3.2.4 Incorporating precision-dependence

3.3 Partial identification

3.4 EB for multiple testing: large-scale inference

3.4.1 An EB approach to FDR control

3.5 Ranking problems

3.6 Compound decisions and shrinkage strategies

3.7 Non-parametric EB application: firm-level labor market discrimination

3.7.1 Distributions of discrimination

3.7.2 Posterior predictions of discrimination

3.7.3 Multiple testing to detect discrimination

3.7.4 Contrasting shrinkage approaches

4 Conclusion

Chapter 4: Minimum wages in the 21st century

1 Introduction and overview

2 A brief history of minimum wages

2.1 The rationale for minimum wage policies

2.2 The minimum wage debate

3 The wage and employment effects of minimum wages

3.1 Wages, employment and labor demand

3.2 Empirical methods to study the impact of minimum wage policies

3.2.1 Exploiting local variation in the level of minimum wages

3.2.2 Other considerations for the comparability of treatment and control groups

3.2.3 Methods to estimate the overall effect of the policy

3.2.4 Exploiting nation-wide variation in the level of minimum wages

3.2.5 Exploiting minimum wage exemptions

3.3 Review of the evidence on employment effects

3.3.1 Estimates across all studies

3.3.2 Estimates for broad groups

3.3.3 Heterogeneity of employment effects

3.4 Effect on total hours

4 Margins of adjustment

4.1 Review the evidence on various margins of adjustment

4.1.1 Non-compliance

4.1.2 Amenities

4.1.3 Substitution with other inputs

4.1.4 Firm-entry and exit

4.1.5 Migration and participation

4.1.6 Wage retrenchment of higher-skilled workers

4.1.7 Output prices and consumers reactions

4.1.8 Input prices and rent

4.1.9 Profits

4.1.10 Worker turnover and reduction in training costs

4.1.11 Productivity.

4.2 Summary of evidence on margins of adjustment

4.3 Modeling implications and open questions

5 Inequality, distributional implications, and downstream effects

6 Minimum wages in developing countries

7 Conclusion and future directions

Appendix A Additional results

Appendix B Bias from heterogeneous pre-existing trends: a simulation study

Appendix C Data sources for cross-country Kaitz indices

Appendix D Constructing historical QCEW restaurant data

Appendix E Construction of 60 state-level minimum wage events

Appendix F Construction of probability groups using demographic predictors

Further reading

Aaronson and French, 2007D.AaronsonE.FrenchProduct market evidence on the employment effects of the minimum wageJ. Labor. Econ.2512007167200Aaronson, D. and E. French (2007) ''Product market evidence on the employment effects of the minimum wage,'' J. Labor. Econ., 25 (1), 167-200.Gibbons and Roberts, 2015R.GibbonsJ.RobertsOrganizational economicsEmerg. Trends Soc. Behav. Sci.2015American Cancer Society115Gibbons, R. and J. Roberts (2015) ''Organizational economics,'' Emerg. Trends Soc. Behav. S

Chapter 5: The micro and macro economics of short-time work

2 Overview of STW schemes

2.1 The spread of STW since the 1920s

2.2 STW and other workforce retention measures

2.3 The design of STW schemes

3 STW take-up

3.1 Take-up by type of workers and firms

3.2 Take-up in large recessions

3.3 Take-up outside of large recessions

3.4 Take-up and labor market regulation

3.5 STW design and administrative capacity

4 The theoretical models of STW

4.1 Normative approach

4.2 Positive approach

5 The efficiency of STW

5.1 The social willingness to pay for STW

5.1.1 The impact on short-time users

5.1.2 The impact on short-time non-users.

5.2 The Net public cost of STW.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9780443297656

0443297657

9780443297649

0443297649

OCLC:

1484073804