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Tedeschi, Federico (2008) An "ex ante"evaluation of the effects of reforms to an Italian labour market policy. [Tesi di dottorato]

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Abstract (inglese)

My thesis is about "ex ante" policy evaluation, i.e. the estimate of the impact of a public intervention on a defined outcome measure prior to its implementation.
The policy regime I analyze is an Italian labour market program targeted to dismissed employees, called "Liste di mobilità'' (literally, "Mobility lists'', LM hereafter), which includes both a "passive'' component (monetary benefits to part of the unemployed workers) and an "active'' one (money transfer for the firm hiring them). Length of the period in the LM and entitlement to benefits vary according to the age of the worker and the size of the firm at time of dismissal. However, the amount of the unemployment subsidy (for people entitled to them) is proportional to the last wage earned.
Linked administrative panel data set for the Veneto region (a large region in the Northeastern Italy) are used. Information about people ever entered in the LM (including labour market history, socio-demographic chacteristics, entitlement to receive monetary benefits and characteristics of the firms where they have been employed) is available.
My interest is on the effect of possible changes to the existing policy regime on the probability of re-employment in the 36 months subsequent to enrollment in the LM. Since it deals with potential reforms to the current policy, I need to perform an "ex-ante" policy evaluation.
I will briefly review the job-market economic theory about wage determination, describing why similar workers (from the point of view of attractiveness for firms) may earn different wages.
Basically, I use such random variability in wages, hence in the amount of monetary benefits received, to mimic a variation in the policy regime. If such policy equivalent variation (PEV) exists and workers are otherwise similar, identification of the effects of not-yet-implemented policy reforms is possible.
Specifically, I consider two alternative identification strategies and test their validity. First, I compare individuals basing on the generalized propensity score (an extension of the procedure of propensity score matching or subclassification to non-binary cases; then, I consider the hypothesis that the selection bias pattern is the same in the group of treated and non-treated, using a "difference in differences'' approach.


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Tipo di EPrint:Tesi di dottorato
Relatore:Rettore, Enrico
Dottorato (corsi e scuole):Ciclo 20 > Scuole per il 20simo ciclo > SCIENZE STATISTICHE
Data di deposito della tesi:2008
Anno di Pubblicazione:2008
Parole chiave (italiano / inglese):Ex ante policy evaluation, propensity score, difference in differences, Liste di mobilità, wage dispersion, labour market, monetary benefits, benefit transfer, re-employment
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:840
Depositato il:06 Ott 2008
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Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

1. Abadie A. (2005), “Semiparametric Difference-in-Differences Estimators”,Review of Economic Studies, 72(1), 1-19. Cerca con Google

2. Abowd J. M., Kramarz F., Lengermann P., Roux S.(2003), “Interindustry and Firm-Size Wage Differentials in the United States and France”, Cornell University working paper. Cerca con Google

3. Abowd J. M., Kramarz F., Margolis D.N.(1999), “High Wage Workers and High Wage Firms”, Econometrica, 67, 251-333. Cerca con Google

4. Anastasia B. et al. (2004), “Interazione fra sussidi passivi e incentivi al reimpiego: provenienze ed esiti di lavoratori iscritti nelle Liste di Mobilità”, Rapporto Finale, Venezia: Agenzia Veneto Lavoro (mimeo). Cerca con Google

5. Angrist J. D., Imbens G. W. (1991), “Sources of Identifying Information in Evaluation Models”, Technical Working Paper 117, National Bureau of Economic Research. Cerca con Google

6. Angrist J. D., Imbens G.W. (1994), “Identification and Estimation of Local Average Treatment Effects”,Econometrica, 62(2), 467-476. Cerca con Google

7. Angrist J. D., Imbens G.W. (1995), “Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity”, Journal of the American Statistical Association, 90:430, 431-42. Cerca con Google

8. Angrist J. D., Imbens G.W. (1996),”Identification of Causal effects Using Instrumental Variables”, Journal of Econometrics, 71(1-2), 145-160. Cerca con Google

9. Angrist J. D., Krueger A.B. (1995), “Split-Sample Instrumental Variables Estimates of the Returns to Schooling”, Journal of Business and Economic Statistics, 132, 225-35. Cerca con Google

10. Angrist J.D., Krueger A.B. (2001), “Instrumental variables and the search for identification: From supply and demand to natural experiments”, Journal of Economic Perspectives 15, 69-85. Cerca con Google

11. Angrist J.D., Kuersteiner G.M. (2004), “Semiparametric Causality Tests Using the Policy Propensity Score”,NBER Working Paper No. 10975. Cerca con Google

12. Ashenfelter O. (1978), “Estimating the effect of training programs on earnings “,Review of Economics and Statistics, 60, 47-57. Ashenfelter O., Card, D. (1985): “Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs”,Review of Economics and Statistics, 67, 648-660. Cerca con Google

13. Balke A., Pearl J. (1994), “Counterfactual probabilities: Computational methods, bounds, and applications”, in R. Lopez de Mantaras and D. Poole (Eds.), Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI- 94), Morgan Kaufmann, San Mateo, CA, 46-54, July 29-31, 1994. Cerca con Google

14. Bassmann (1957), “A generalized classical method of linear estimation of coeficients in a structural equation”, Econometrica 25, 77-83. Cerca con Google

15. Bertrand M., Duflo E., Mullainathan S. (2004), “How Much Should We Trust Differences-in-Differences Estimates?”, The Quarterly Journal of Economics, MIT Press, 119(1), 249-275. Cerca con Google

16. Blundell R., Costa Dias M. (2000), “Evaluation methods for non-experimental data”, Fiscal Studies21(4), 427-468. Cerca con Google

17. Blundell R., Costa Dias M. (2002), “Alternative approaches to evaluation in empirical microeconomics”, Portuguese Economic Journal, 1, 91-115. Cerca con Google

18. Burdett K.(1979), “Unemployment Insurance Payments as a Search Subsidy: A Theoretical Analysis”, Economic Inquiry, 17:3, 333-343. Cerca con Google

19. Burdett K., Mortensen D.T.(1998), “Wage Differentials, Employer Size, and Unemployment', International Economic Review, 39 (2), 257-273. Cerca con Google

20. Cahuc P., Postel-Vinay F., Robin J.M., 2006, “Wage Bargaining with On-thejob search: Theory and Evidence”,Econometrica,74(2), 323-64. Cerca con Google

21. Campbell D. T. (1969), “Reforms as Experiments”,American Psychologist, 24, 409-429. Cerca con Google

22. Caruso E., Pisauro G. (2005), “Licenziamenti definitivi o temporanei? Durata della disoccupazione nelle Liste di mobilità tra nuovi e vecchi datori di lavoro”,Politica Economica,21(3), 361-399. Cerca con Google

23. Chamberlain G.(1982), “The General Equivalence of Granger and Sims Causality “,Econometrica, 50(3), 569-582. Cerca con Google

24. Chamberlain, G. (1984), “Panel Data”, in Handbook of Econometrics, Z. Grilliches and M. Intriligator, Volume 2, Amsterdam: Elsevier Science. Cerca con Google

25. Chapin F. S. (1947), “Experimental Designs in Sociological Research”, Harper, New York. Cerca con Google

26. Cochran W. G.(1965), “The planning of observational studies in human populations “,Journal of The Royal Statistical Society, Series A 128, 234-266. Cerca con Google

27. Cochran W. G. (1968),”The effectiveness of subclassification in removing bias in observational studies”, Biometrics 24, 295-313. Cerca con Google

28. Cochran W.G., Rubin D.B. (1973), “Controlling Bias in Observational Studies: A Review”, Sankhya, Series A 35, 417-446. Cerca con Google

29. Cox D.R.(1958), “Planning of Experiments”, New York: John Wiley. Cerca con Google

30. Dee T. S., Fu H. (2003), “Do Charter Schools Skim Students or Drain Resources? “, Economics of Education Review 23, 259-271. Cerca con Google

31. Del Conte M., Devillanova C., Morelli S. (2004), “L'indice OECD di rigidità nel mercato del lavoro: una nota”, Politica Economica,20, 335-356. Cerca con Google

32. DiNardo J., Tobias J. L. (2001), “Nonparametric Density and Regression Estimation “,Journal of Economic Perspectives,15, 11-28. Cerca con Google

33. Durbin, J. (1954), “Errors in Variables”, Review of the International Statistical Institute, 22, 23-32. Cerca con Google

34. Engle R.F., Hendry D.F. and Richard J.F. (1983), “Exogeneity”, Econometrica, 51(2), 277-304. Cerca con Google

35. Florens J.P., Mouchart, M., 1985: “A Linear Theory for Noncausality”, Econometrica, 53(1), 157-75. Cerca con Google

36. Geary R.C. (1949), “Determination of Linear Relations Between Systematic Parts of Variables with Errors of Observations, the Variances of Which are Unknown”, Econometrica,17(1), 30-58. Cerca con Google

37. Goldberger A. S. (1972a), “Structural Equation Methods in the Social Sciences “, Econometrica, 40, 979-1001. Cerca con Google

38. Goldberger, A.S. (1972b), “Selection Bias in Evaluating Treatment Effects: Some Formal Illustrations”, Madison, Wisconsin: University of Wisconsin Press. Cerca con Google

39. Goldberger, A.S. (1972c), “Selection Bias in Evaluating Treatment Effects: The case of interaction”, Madison, Wisconsin: Institute for Research on Poverty. Cerca con Google

40. Granger, C. W. J. (1969), “Investigating causal relations by econometric models and cross-spectral methods”, Econometrica 37, 424-438. Cerca con Google

41. Hahn J., Todd P., Van der Klaauw W. (2001), “Identification and Estimation of Treatment E”ects with a Regression-Discontinuity Design”, Econometrica, 69, 201-209. Cerca con Google

42. Heckmann J.J. (1978), “Dummy Endogenous Variables in a Simultaneous Equation System”, Econometrica, Econometric Society, 46(4), 931-59. Cerca con Google

43. Heckmann J. J., Honoré B. E.(1990), “The Empirical Content of the Roy Model”, Econometrica58(5), 1121-1149. Cerca con Google

44. Heckman J.J., Robb R. (1985), “Alternative methods for evaluating the impact of interventions”, in Longitudinal analysis of labour market data, Wiley, New York. Cerca con Google

45. Heckman J.J., Robb R. (1986), “Alternative methods for solving the problem of selection bias in evaluating the impact of treatments on outcomes”, in: Wainer H, (ed) Drawing inferences from self-selected samples, Springer, Berlin Heidelberg New York. Cerca con Google

46. Hirano K., Imbens G.(2004), “The Propensity Score with Continuous Treatments “, Applied Bayesian Modeling and Causal Inference from Incomplete- Data Perspectives, A. Gelman and X.-L. Meng, New York: Wiley. Cerca con Google

47. Holland P.W. (1986),”Statistics and Causal Inference”, Journal of the American Statistical Association 81, 945-960. Cerca con Google

48. Holland P.W.(1987),”The Role of a Second Control Group in an Observational Study: Comment”,Statistical Science,2(3), 306-308. Cerca con Google

49. Hosoya, Y. (1977), “On the Granger condition for non-causality”, Econometrica 45 7, 1735-1736. Cerca con Google

50. Ichimura H., Taber C.(2000), “Direct Estimation of Policy Impacts”, NBER Technical working paper No. 254. Cerca con Google

51. Ichimura H., Taber C.(2002), “Semiparametric Reduced Form Estimation of Tuition Subsidies”, American Economic Review, 92(2), 286-292. Cerca con Google

52. Ichino P. (2004), “Job security and the value of equality. A tentative law & economics approach to the problem of e”ectiveness of the workers protection against dismissal for economic reasons”, paper presented at the University of Vienna, January 31, 2004 (mimeo). Cerca con Google

53. Imai K., Van Dyk D.A.(2004), “Causal Inference With General Treatment Regimes: Generalizing the Propensity Score”, Journal of the American Statistical Association, 99(467), 854-866. Cerca con Google

54. Imbens G. W. (2000), “The Role of the Propensity Score in Estimating Dose- Response Functions”, Biometrika,87, 706-710. Cerca con Google

55. Imbens G.W., Lemieux T. (2008), “Regression discontinuity designs: A guide to practice”, Journal of Econometrics, 142(2), 615-635. Cerca con Google

56. Joffle M. M., Rosenbaum P. R. (1999), “Propensity Scores”, American Journal of Epidemiology,150, 327-333. Cerca con Google

57. Li Q., Huang C. J., Li D., Fu T.T. (2002), “Semiparametric Smooth Coeffcient Models”, Journal of Business Economic Statistics, 20, 412-422. Cerca con Google

58. Lu B., Zanutto E., Hornik R., Rosenbaum P. R. (2001), “Matching With Doses in an Observational Study of aMedia Campaign Against Drug Abuse”,Journal of the American Statistical Association,96, 1245-1253. Cerca con Google

59. Marschak J.(1953), “Econometric Measurements for Policy and Prediction”, Studies in Econometric Method, edito da W.Hood and T.Koopmans (New York, John Wiley, 1-26). Cerca con Google

60. Martin J.P., Grubb D.(2001), “What works and for whom: A review of OECD countries experience with active labour market policies”, Swedish Economic Policy Review, 8: 9-56. Cerca con Google

61. Meyer B.(1995), “Natural and Quasi-Natural Experiments in Economics”, Journal of Business and Economic Statistics, 13, 151-162. Cerca con Google

62. Mincer J. (1974), “Schooling, Experience and Earnings”, Columbia University Press: New York. Cerca con Google

63. Mortensen D.T. (2000), “Equilibrium unemployment with wage posting: Burdett- Mortensen meet Pissarides”, Panel data and structural labor market models, Bunzel H., B.J. Christensen, P. Jensen, N.M. Kiefer and D.T. Mortensen (eds.), Elsevier, North-Holland. Cerca con Google

64. Mortensen D.T.(2003), “Wage Dispersion- Why Are Similar Workers Paid Differently? “, MIT Press. Cerca con Google

65. Mortensen D.T., Pissarides C.A. (1999), “New developments in models of search in the labor market”, Handbook of Labor Economics, Ashenfelter and Card ed., edition 1, volume 3, chapter 39, pages 2567-2627. Cerca con Google

66. Morgan, M.S. (1990), “The History of Econometric Ideas”, Cambridge: Cambridge University Press. Cerca con Google

67. Mortensen D.T.(1977), “Unemployment Insurance and Job Search Decisions”, Industrial and Labor Relations Review,30(4), 505-517. Cerca con Google

68. Neyman, J.(1923), “On the Application of Probability Theory to Agricultural Experiments”, Essay on Principles, Section 9, translated in Statistical Science, (with discussion), 5(4), 465-480, 1990. Cerca con Google

69. OECD (2004), OECD Employment Outlook 2004, Paris: OECD Publishing. Cerca con Google

70. OECD (2005), OECD Employment Outlook 2005, Paris: OECD Publishing Cerca con Google

71. Paggiaro A., Rettore E., Trivellato U. (2007),”The effect of extended duration of eligibility in an Italian labour market for dismissed workers”, paper presented at the Conference “Labor Market Flows, Productivity and Wage Dynamics: Ideas and Results from Empirical Research on Employer-Employee Linked Longitudinal Databases”, LABORatorio R. Revelli, Fondazione Carlo Alberto, Moncalieri (Torino), September 2007. Cerca con Google

72. Postel-Vinay, F. Robin J.M.(2002), “EquilibriumWage Dispersion withWorker and Employer Heterogeneity”,Econometrica,70(6), 2295-350. Cerca con Google

73. Postel-Vinay F., Robin J.M. (2006), “Microeconometric Search-Matching Models and Matched Employer-Employee Data” in R. Blundell, W. Newey and T. Persson, editors, Advances in Economics and Econometrics, Theory and Applications, Ninth World Congress, Volume 2, Cambridge: Cambridge University Press. Cerca con Google

74. Postel-Vinay F., Turon H. (2005), “On-the-job Search, Productivity Shocks, and the Individual Earnings Process”, CEPR Discussion Paper No. 5593. Cerca con Google

75. Reiersol O. (1945), “Confluence Analysis by Means of Instrumental Sets of Variables”, Arkiv for Matematik, Astronomi och Fysik, 32a:4, 1-119. Cerca con Google

76. Rettore E., Battistin E. (2008), “Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs”, Journal of Econometrics, 142(2), 715-730. Cerca con Google

77. Rogerson R., Shimer R., Wright R., “Search-Theoretic Models of the Labor Market: A Survey”,Journal of Economic Literature, 63(4), 959-988. Cerca con Google

78. Rosenbaum P.R.(1987),”The Role of a Second Control Group in an Observational Study”,Statistical Science,2(3), 292-306. Cerca con Google

79. Rosenbaum P.R., Rubin D.B. (1983), “The Central Role of the Propensity Score in Observational Studies for Causal Effects” Biometrika, 70(1), 41-55. Cerca con Google

80. Royston P., Altman D.G.(1994):”Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with disc.)”, Applied Statistics,43, 429-467. Cerca con Google

81. Rubin, D.B. (1973a),”Matching to remove bias in observational studies”,Biometrics,29,159- 183. Cerca con Google

82. Rubin, D.B. (1973b),”The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies”, Biometrics,29(1), 185-203. Cerca con Google

83. Rubin, D.B. (1974), “Estimating Causal E”ects of Treatments in Randomized and Nonrandomized Studies”, Journal of Educational Psychology,66(5), 688- 701. Cerca con Google

84. Rubin, D.B. (1976a),”Multivariate Matching Methods That are Equal Percent Bias Reducing, I: Some Examples”, Biometrics,32(1), 109-120. Cerca con Google

85. Rubin, D.B. (1976b),”Multivariate Matching Methods That are Equal Percent Bias Reducing, II: Maximums on Bias Reduction for Fixed Sample Sizes”, Biometrics,32(1), 121-132. Cerca con Google

86. Rubin, D.B. (1977), “Assignment to a Treatment Group on the Basis of a Covariate”,Journal of Educational Statistics,2, 1-26. Cerca con Google

87. Rubin, D.B. (1978), “Bayesian Inference for Causal Effects: The Role of Randomization “,Annals of Statistics,6(1), 34-58. Cerca con Google

88. Rubin, D.B.(1979), “Using Multivariate Matched Sampling and Regression Adjustment to Control Bias In Observational Studies”,Journal of the American Statisrical Association, 74(3), 18-328. Cerca con Google

89. Rubin, D.B. (1980a), “Randomization Analysis of Experimental Data: The Fisher Randomization Test. Comment”,Journal of the American Statistical Association,75,371(3), 591-593. Cerca con Google

90. Rubin, D.B. (1980b), “Bias Reduction Using Mahalanobis-Metric Matching”, Biometrics,36(2), 293-298. Cerca con Google

91. Rubin, D.B. (1986), “Statistics and Causal Inference: Comment: Which Ifs Have Causal Answers”, Journal of the American Statistical Association, 81(396), 961-962. Cerca con Google

92. Rubin, D.B. (1990), “Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies”,Statistical Science,5(4), 472-480. Cerca con Google

93. Sargan J. (1958), “The estimation of economic relationships using instrumental variables”,Econometrica 26, 393-415. Cerca con Google

94. Sargan, J. (1959), “The estimation of relationships with autocorrelated residuals by the use of the instrumental variables”, Journal of the Royal Statistical Society, Series B 21, 91-105. Cerca con Google

95. Schivardi F., Torrini R. (2004), “Firm size distribution and employment protection legislation in Italy”, Temi di discussione No. 504, Roma: Banca d'Italia. Cerca con Google

96. Shimer R. (2003), “The Cyclical Behavior of Equilibrium Unemployment and Vacancies: Evidence and Theory”, NBER Working Papers 9536, National Bureau of Economic Research, Inc. Cerca con Google

97. Sims C.A. (1972),”Money, Income, and Causality”, American Economic Review, 62(4), 540-552. Cerca con Google

98. Theil H. (1953), “Repeated Least Squares Applied to Complete Equation Systems “, The Hague: Central Planning Bureau. Cerca con Google

99. Theil H.(1958), “Economic Forecasting and Policy”, North Holland, Amsterdam (1958). Cerca con Google

100. Thistlewaite D.L., Campbell D.T. (1960), “Regression-Discontinuity Analysis: An Alternative to the Ex-Post Facto Experiment”, Journal of Educational Psychology, 51, 309-317. Cerca con Google

101. Todd P.E., Wolpin K.I.(2005), “Ex Ante Evaluation of Social Programs”, PIER Working Paper 06-022. Cerca con Google

102. Trochim W. M. K. (1984), “Research Design for Program Evaluation”, Beverly Hills, CA: Sage Publications. Cerca con Google

103. Trochim W. M. K., Spiegelman C. (1980), “The Relative Assignment Variable Approach to Selection Bias in Pretest-Posttest Designs”,American Statistical Association. Cerca con Google

104. Wright, P.G. (1925), “Corn and Hog Correlations”, US Department of Agriculture Bulletin 1300, January 1925, Washington, DC. Cerca con Google

105. Wright, P.G. (1928), “The Tariff on Animal and Vegetable Oils”, New York: MacMillan. Cerca con Google

106. Yatchew A. (1998), “Nonparametric Regression Techniques in Economics”, Journal of Economic Literature, 36, 669-721. Cerca con Google

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