Print page

Active Labor Market Policies

Edited by Robert J. LaLonde, Professor, Irving B. Harris Graduate School of Public Policy Studies, University of Chicago, US
This timely two-volume set brings together seminal works on active labour market policies. Topics covered in this collection include econometric policy evaluation, social experiments, regression discontinuity designs, evaluations of active labour market policies and ending with final conclusions on evaluating the evaluations.

Along with an original introduction by Professor LaLonde, this in-depth collection will be an invaluable source of reference for academics, scholars and practitioners.
Two volume set
Extent: 1,984 pp
Hardback Price: $1080.00 Web: $972.00
Publication Date: 2016
ISBN: 978 1 78347 988 7
Availability: In Stock
$0.00

Buy the E-Book @ paperback price

Join our mailing list

  • Economics and Finance
  • Labour Economics
  • Politics and Public Policy
  • Public Policy
  • Social Policy and Sociology
  • Labour Policy
This timely two-volume set brings together seminal works on active labour market policies. Topics covered in this collection include econometric policy evaluation, social experiments, regression discontinuity designs, evaluations of active labour market policies and ending with final conclusions on evaluating the evaluations.

Along with an original introduction by Professor LaLonde, this in-depth collection will be an invaluable source of reference for academics, scholars and practitioners.
75 articles, dating from 1983 to 2014
Contributors include: A. Abadie, J. Angrist, D. Card, J. Heckman, J. Horowitz, G. Imbens, M. Lechner, D. Lee, C. Manski
Contents:

Acknowledgements

Introduction Robert Lalonde

PART I ECONOMETRIC POLICY EVALUATION
1. Anders Björklund and Robert Moffitt (1987), ‘The Estimation of Wage Gains and Welfare Gains in Self-Selection Models’, Review of Economics and Statistics, 69 (1), February, 42–49

2. James J. Heckman, Jeffrey Smith and Nancy Clements (1997), ‘Making the Most Out Of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts’, Review of Economic Studies, 64 (4), October, 487–535

3. James Heckman and Salvador Navarro-Lozano (2004), ‘Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models’, Review of Economics and Statistics, 86 (1), February, 30–57

4. James J. Heckman and Edward Vytlacil (2005), ‘Structural Equations, Treatment Effects, and Econometric Policy Evaluation’, Econometrica, 73 (3), May, 669–738

5. Edward Vytlacil (2002), ‘Independence, Monotonicity, and Latent Index Models: An Equivalence Result’, Econometrica, 70 (1), January, 331–41

6. J.P. Florens, J.J. Heckman, C. Meghir and E. Vytlacil (2008), ‘Identification of Treatment Effects Using Control Functions in Models with Continuous, Endogenous Treatment and Heterogeneous Treatment Effects’, Econometrica, 76 (5), September, 1191¬–206

7. Pedro Carneiro, James J. Heckman and Edward Vytlacil (2010), ‘Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin’, Econometrica, 78 (1), January, 377–94

8. Jeffrey M. Woolridge (1997), ‘On Two Stage Least Squares Estimation of the Average Treatment Effect in a Random Coefficient Model’, Economics Letters, 56 (2), October, 129–33

9. Whitney K. Newey (2009), ‘Two-step Series Estimation of Sample Selection Models’, Econometrics Journal, 12 (S1), January, S217–S219

10. Jinyong Hahn and Geert Ridder (2013), ‘Asymptotic Variance of Semiparametric Estimators with Generated Regressors’, Econometrica, 81 (1), January, 315–40

PART II SOCIAL EXPERIMENTS
11. James J. Heckman (1996), ‘Randomization as an Instrumental Variable’, Review of Economics and Statistics, 78 (2), May, 336–41

12. Joel L. Horowitz and Charles F. Manski (2000), ‘Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data’, Journal of the American Statistical Association, 95 (449), March, 77–84

13. James Heckman, Hidehiko Ichimura, Jeffrey Smith and Petra Todd (1998), ‘Characterizing Selection Bias Using Experimental Data’, Econometrica, 66 (5), September, 1017–98

PART III METHOD OF MATCHING ESTIMATORS
14. Paul R. Rosenbaum and Donald B. Rubin (1983), ‘The Central Role of the Propensity Score in Observational Studies for Causal Effects’, Biometrika, 70 (1), April, 41–55

15. Donald B. Rubin and Neal Thomas (1996), ‘Matching Using Estimated Propensity Scores: Relating Theory to Practice’, Biometrics, 52 (1), March, 249–64

16. James J. Heckman, Hidehiko Ichimura and Petra E. Todd (1998), ‘Matching as an Econometric Evaluation Estimator’, Review of Economic Studies, 65 (2), April, 261–94

17. Jinyong Hahn (1998), ‘On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects’, Econometrica, 66 (2), March, 315–31

18. Keisuke Hirano, Guido W. Imbens and Geert Ridder (2003), ‘Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score’, Econometrica, 71 (4), July, 1161¬–89

19. Alberto Abadie and Guido W. Imbens (2006), ‘Large Sample Properties of Matching Estimators for Average Treatment Effects’, Econometrica, 74 (1), January, 235–67

20. Alberto Abadie and Guido W. Imbens (2008), ‘On The Failure of the Bootstrap for Matching Estimators’, Econometrica, 76 (6), November, 1537–57
21. Alberto Abadie and Guido W. Imbens (2011), ‘Bias-Corrected Matching Estimators for Average Treatment Effects’, Journal of Business and Economic Statistics, 29 (1), January, 1–11

PART IV IV AND LATE ESTIMATORS
22. James Heckman (1997), ‘Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations’, Journal of Human Resources, 32 (3), Summer, 441–62

23. Guido W. Imbens (2004), ‘Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Survey’, Review of Economics and Statistics, 86 (1), February, 4–29

24. Guido W. Imbens and Joshua D. Angrist (1994), ‘Identification and Estimation of Local Average Treatment Effects’, Econometrica, 62 (2), March, 467–75

25. Joshua D. Angrist and Guido W. Imbens (1995), ‘Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity’, Journal of the American Statistical Association, 90 (430), June, 431–42

26. Joshua D. Angrist, Guido W. Imbens and Donald B. Rubin (1996), ‘Identification of Causal Effects Using Instrumental Variables’, Journal of the American Statistical Association, 91 (434), June, 444–55

27. James J. Heckman, Sergio Urzua and Edward Vytlacil (2006), ‘Understanding Instrumental Variables in Models with Essential Heterogeneity’, Review of Economics and Statistics, LXXXVIII (3), August, 389–432

28. Whitney K. Newey and James L. Powell (2003), ‘Instrumental Variable Estimation of Nonparametric Models’, Econometrica, 71 (5), September, 1565–78

PART V REGRESSION DISCONTINUITY DESIGNS
29. Jinyong Hahn, Petra Todd and Wilbert Van der Klaauw (2001), ‘Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design’, Econometrica, 69 (1), January, 201–9

30. David S. Lee and David Card (2008), ‘Regression Discontinuity Inference with Specification Error’, Journal of Econometrics, 142 (2), February, 655¬–74

31. David S. Lee and Thomas Lemieux (2010), ‘Regression Discontinuity Designs in Economics’, Journal of Economic Literature, 48 (2), June, 281–355

32. Justin McCrary (2008), ‘Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test’, Journal of Econometrics, 142 (2), February, 698–714

PART VI DIFFERENCE-IN-DIFFERENCES, INVERSE PROBABILITY WEIGHTING AND THE MIXING PROBLEM
33. Marianne Bertrand, Esther Duflo and Sendhil Mullainathan (2004), ‘How Much Should We Trust Differences-in-Differences Estimates?’, Quarterly Journal of Economics, 119 (1), February, 249¬–75

34. Charles F. Manski (1997), ‘The Mixing Problem in Programme Evaluation’, Review of Economic Studies, 64 (4), October, 537–53

35. Jeffrey M. Woolridge (2007), ‘Inverse Probability Weighted Estimation for General Missing Data Problems’, Journal of Econometrics, 141 (2), December, 1281–301

PART VII DYNAMIC TREATMENT EFFECTS AND DURATION MODELS
36. Jaap H. Abbring and Gerard J. Van den Berg (2003), ‘The Nonparametric Identification of Treatment Effects in Duration Models’, Econometrica, 71 (5), September, 1491–517, Corrigendum

37. James J. Heckman and Salvador Navarro (2007), ‘Dynamic Discrete Choice and Dynamic Treatment Effects’, Journal of Econometrics, 136 (2), February, 341–96

38. Richard K. Crump, V. Joseph Hotz, Guido W. Imbens and Oscar A. Mitnik (2009), ‘Dealing with Limited Overlap in Estimation of Average Treatment Effects’, Biometrika, 96 (1), March, 187–99




Volume II

Acknowledgements

Introduction An Introduction by the Editor appears in Volume I

PART I SURVEYS OF EVALUATIONS OF ACTIVE LABOR MARKET POLICIES
1. David Friedlander, David H. Greenberg and Philip K. Robins (1997), ‘Evaluating Government Training Programs for the Economically Disadvantaged’, Journal of Economic Literature, XXXV (4), December, 1809–55

2. David Card, Jochen Kluve and Andrea Weber (2010), ‘Active Labour Market Policy Evaluations: A Meta-Analysis’, Economic Journal, 120, 548, November, F452–F477

PART II EVALUATIONS OF SOCIAL EXPERIMENTS
3. Howard S. Bloom, Larry L. Orr, Stephen H. Bell, George Cave, Fred Doolittle, Winston Lin and Johannes M. Bos (1997), ‘The Benefits and Costs of JTPA Title II-A Programs: Key Findings from the National Job Training Partnership Act Study’, Journal of Human Resources, 32 (3), Summer, 549–76

4. Jere R. Behrman, Susan W. Parker and Petra E. Todd (2011), ‘Do Conditional Cash Transfers for Schooling Generate Lasting Benefits?: A Five-Year Follow-up of PROGRESA/Oportunidades’, Journal of Human Resources, 46 (1), Winter, 93–122

5. Peta Z. Schochet, John Burghardt and Sheena McConnell (2008), ‘Does Job Corps Work? Impact Findings from the National Job Corps Study’, American Economic Review, 98 (5), December, 1864–86

PART III NON-EXPERIMENTAL EVALUATIONS
6. Michael Lechner (1999), ‘Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification’, Journal of Business and Economic Statistics, 17 (1), January, 74–90

7. Markus Frölich, Almas Heshmati and Michael Lechner (2004), ‘A Microeconometric Evaluation of Rehabilitation of Long-Term Sickness in Sweden’, Journal of Applied Econometrics, 19 (3), May/June, 375–96 ]

8. Michael Gerfin and Michael Lechner (2002), ‘A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland’, Economic Journal, 112 (482), October, 854–93

9. James J. Heckman and Paul A. LaFontaine (2006), ‘Bias-Corrected Estimates of GED Returns’, Journal of Labor Economics, 24 (3), July, 661–700
10. Louis Jacobson, Robert Lalonde and Daniel G. Sullivan (2005), ‘Estimating the Returns to Community College Schooling for Displaced Workers’, Journal of Econometrics, 125 (1-2), March-April, 271–304


PART IV HETEROGENEITY IN TREATMENT EFFECTS
11. Michael Lechner (2002), ‘Program Heterogeneity and Propensity Score Matching: An Application to the Evaluation of Active Labor Market Policies’, Review of Economics and Statistics, 84 (2), May, 205–20

12. Pedro Carneiro, James J. Heckman and Edward J. Vytlacil (2011), ‘Estimating Marginal Returns to Education’, American Economic Review, 101 (6), October, 2754–81

PART V EVALUATIONS USING IV, RD DESIGNS, AND MATCHING ESTIMATORS
13. Joshua D. Angrist (1989), ‘Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records’, American Economic Review, 80 (3), June, 313–36

14. Dan A. Black, Jeffrey A. Smith, Mark C. Berger and Brett J. Noel (2003), ‘Is the Threat of Reemployment Services More Effective Than the Services Themselves? Evidence from Random Assignment in the UI System’, American Economic Review, 93 (4), September, 1313–27

15. Wilbert Van der Klaauw (2002), ‘Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach’, International Economic Review, 43 (4), November, 1249–87

16. James J. Heckman, Hidehiko Ichimura and Petra E. Todd (1997), ‘Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme’, Review of Economic Studies, 64 (4), October, 605–54

17. Martin Huber, Michael Lechner and Conny Wunsch (2013), ‘The Performance of Estimators Based on the Propensity Score’, Journal of Econometrics, 175 (1), July, 1–21

18. Michael Lechner and Conny Wunsch (2013), ‘Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables’, Labour Economics, 21, April, 111–21

19. Matias Busso, John DiNardo and Justin McCrary (2014), ‘New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators’, Review of Economics and Statistics, 96 (5), December, 885–97

PART VI ACCOUNTING FOR DROPOUTS, ASHENFELTER’S DIP AND PERFORMANCE STANDARDS
20. James Heckman, Jeffrey Smith and Chrsitopher Taber (1998), ‘Accounting for Dropouts in Evaluations of Social Programs’, Review of Economic and Statistics, LXXX (1), February, 1–14

21. James J. Heckman and Jeffrey A. Smith (1999), ‘The Pre-Programme Earnings Dip and the Determinants of Participation in a Social Programme. Implications for Simple Programme Evaluation Strategies’, Economic Journal, 109 (457), July, 313–48

22. James J. Heckman, Carolyn Heinrich and Jeffrey Smith (2002), ‘The Performance of Performance Standards’, Journal of Human Resources, 37 (4), Autumn, 778–811

PART VII THE EFFECT ON DURATIONS OF UNEMPLOYMENT AND EMPLOYMENT
23. John C. Ham and Robert J. Lalonde (1996), ‘The Effect of Sample Selection and Initial Conditions in Duration Models: Evidence from Experimental Data on Training’, Econometrica, 64 (1), January, 175–205

24. Curtis Eberwein, John C. Ham and Robert J. Lalonde (1997), ‘The Impact of Being Offered and Receiving Classroom Training on the Employment Histories of Disadvantaged Women: Evidence from Experimental Data’, Review of Economic Studies, 64 (4), October, 655–82

25. Gerard J. van den Berg, Bas van der Klaauw and Jan C. van Ours (2004), ‘Punitive Sanctions and the Transition Rate from Welfare to Work’, Journal of Labor Economics, 22 (1), January, 211–41

26. Jaap H. Abbring, Gerard J. van den Berg and Jan C. van Ours (2005), ‘The Effect of Unemployment Insurance Sanctions on the Transition Rate from Unemployment to Employment’, Economic Journal, 115 (505), July, 602–30

27. Barbara Sianesi (2004), ‘An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s’, Review of Economics and Statistics, 86 (1), February, 133–55

28. Peter Fredriksson and Per Johansson (2008), ‘Dynamic Treatment Assignment: The Consequences for Evaluations Using Observational Data’, Journal of Business and Economic Statistics, 26 (4), October, 435¬–45

PART VIII EVALUATING THE EVALUATIONS
29. Daniel Friedlander and Philip K. Robins (1995), ‘Evaluating Program Evaluations: New Evidence on Commonly Used Nonexperimental Methods’, American Economic Review, 85 (4), September, 923–37

30. Rajeev H. Dehejia and Sadek Wahba (1999), ‘Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs’, Journal of the American Statistical Association, 94 (448), December, 1053–62

31. Rajeev H. Dehejia and Sadek Wahba (2002), ‘Propensity Score-Matching Methods for Nonexperimental Causal Studies’, Review of Economics and Statistics, 84 (1), February, 151–61

32. Juan Jose Diaz and Sudhanshu Handa (2006), ‘An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator: Evidence from Mexico’s PROGRESA Program’, Journal of Human Resources, 41 (2), Spring, 319–45

33. Stevem Glazerman, Dan M. Levy and David Myers (2003), ‘Nonexperimental versus Experimental Estimates of Earnings Impacts’, Annals of the American Academy of Political and Social Science, 589, September, 63–93

34. Charles Michalopoulos, Howard S. Bloom and Carolyn J. Hill (2004), ‘Can Propensity-Score Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs?’, Review of Economics and Statistics, 86 (1), February, 156–79

35. Jeffrey A. Smith and Petra E. Todd (2005), ‘Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?’, Journal of Econometrics, 125 (1-2), March-April, 305–53

36. Elizabeth Ty Wilde and Robinson Hollister (2007), ‘How Close Is Close Enough? Evaluating Propensity Score Matching Using Data from a Class Size Reduction Experiment’, Journal of Policy Analysis and Management, 26 (3), Summer, 455–77