Pacifico, Daniele (2009): Modelling Unobserved Heterogeneity in Discrete Choice Models of Labour Supply.
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The aim of this paper is to analyse the role of unobserved heterogeneity in structural discrete choice models of labour supply for the evaluation of tax-reforms. Within this framework, unobserved heterogeneity has been estimated either parametrically or nonparametrically through random co- efficient models. Nevertheless, the estimation of such models by means of standard, gradient-based methods is often difficult, in particular if the number of random parameters is high. Given the relative big set of pa- rameters that enter in labour supply models, many researchers have to reduce the role of unobserved heterogeneity by specifying only a small set of random coefficients. However, this simplification affects the estimated labour supply elasticities, which then might hardly change when unob- served heterogeneity is considered in the model. In this paper, we present a new estimation method based on an EM algorithm that allows us to fully consider the effect of unobserved heterogeneity nonparametrically. Results show that labour supply elasticities do change significantly when the full set of coefficients is assumed to be random. Moreover, we analyse the behavioural effects of the introduction of a working-tax credit scheme in the Italian tax-benefit system and show that the magnitude of labour supply reactions and post-reform income distribution do change signifi- cantly when unobserved heterogeneity is fully considered.
|Item Type:||MPRA Paper|
|Original Title:||Modelling Unobserved Heterogeneity in Discrete Choice Models of Labour Supply|
|Keywords:||Labour supply, discrete choice model, latent class models, EM algorithm, mixed logit, random coefficients, working tax credit|
|Subjects:||H - Public Economics > H3 - Fiscal Policies and Behavior of Economic Agents > H31 - Household
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
H - Public Economics > H2 - Taxation, Subsidies, and Revenue > H24 - Personal Income and Other Nonbusiness Taxes and Subsidies
J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J22 - Time Allocation and Labor Supply
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities
|Depositing User:||Daniele Pacifico|
|Date Deposited:||07. Dec 2009 00:24|
|Last Modified:||18. Feb 2013 20:01|
Baldini, M., and E. Ciani (2009): “Gli effetti distributivi delle principali riforme del sistema di tax-benefit italiano nel primo anno della XVI legislatura,” CAPPaper, 68.
Baldini, M., and D. Pacifico (2009): “The recent reforms of the Italian personal income tax: distributive and efficiency effects,” Rivista Italiana degli Economisti, 1, 191–218.
Bargain, O. (2007): “On modelling household labor supply with taxation,” Working Paper No.07/11.
Bhat, C. (1997): “An endogenous segmentation mode choice model with an application to intercity travel,” Transportation Science, 31(1), 34–48. (2000): “Flexible model structures for discrete choice analysis,” Handbook of transport modelling, 1, 71–90.
Blundell, R., A. Duncan, J. McCrae, and C. Meghir (2000): “The labour market impact of the working families tax credit,” Fiscal Studies, 21(1), 75–104.
Blundell, R., and T. MaCurdy (1999): “Labor supply: A review of alternative approaches,” Handbooks in Economics, 5(3 PART A), 1559–1696.
Brewer, M., A. Duncan, A. Shephard, and M. Suarez (2006): “Did working families’ tax credit work? The impact of in-work support on labour supply in Great Britain,” Labour Economics, 13(6), 699–720.
Chiappori, P., and I. Ekeland (2006): “The micro economics of group behavior: general characterization,” Journal of Economic Theory, 130(1), 1–26.
Creedy, J., and G. Kalb (2005): “Discrete hours labour supply modelling: specification, estimation and simulation,” Journal of Economic Surveys, 19(5), 697.
Creedy, J., G. Kalb, and R. Scutella (2006): “Income distribution in discrete hours behavioural microsimulation models: An illustration,” Journal of Economic Inequality, 4(1), 57–76.
Dempster, A., N. Laird, D. Rubin, et al. (1977): “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.
Flood, L., and N. Islam (2005): “A Monte Carlo evaluation of discrete choice labour supply models,” Applied Economics Letters, 12(5), 263–266.
Greene, W. (2001): “Fixed and Random Effects in Nonlinear Models,” Working Papers.
Greene, W., and D. Hensher (2003): “A latent class model for discrete choice analysis: contrasts with mixed logit,” Transportation Research Part B, 37(8), 681–698.
Haan, P. (2006): “Much ado about nothing: Conditional logit vs. random coefficient models for estimating labour supply elasticities,” Applied Economics Letters, 13(4), 251–256.
Haan, P., and A. Uhlendorff (2007): “Intertemporal Labor Supply and Involuntary Unemployment,” Institute for the Study of Labor (IZA).
Heckman, J. (1974): “Shadow prices, market wages, and labour supply,” Econometrica, 42(4), 679–694.
Heckman, J., and B. Singer (1984): “A method for minimizing the impact of distributional assumptions in econometric models for duration data,” Econometrica, 52(2), 271–320.
Keane, M., and R. Moffitt (1998): “A structural model of multiple welfare program participation and labor supply,” International Economic Review, pp. 553–589.
McFadden, D. (1973): “Conditional logit analysis of qualitative choice models,” Frontiers of Econometrics, ed. P. Zarembka. New York: Academic Press.
McFadden, D., and K. Train (2000): “Mixed MNL models for discrete responses,” Journal of Applied Econometrics, 15(5), 447–470.
Roeder, K., K. Lynch, and D. Nagin (1999): “Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology.,” Journal of the American Statistical Association, 94(447), 766–767.
Swait, J. (1994): “A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data,” Journal of Retailing and Consumer Services, 1(2), 77–89.
Thompson, B., and L. Daniel (1996): “Factor analytic evidence for the construct validity of scores: A historical overview and some guidelines.,” Educational and Psychological Measurement, 56(2), 197–208.
Train, K. (2008): “EM Algorithms for Nonparametric Estimation of Mixing Distributions,” Journal of Choice Modelling, 1(1).
Van Soest, A. (1995): “Structural models of family labor supply: a discrete choice approach,” The Journal of Human Resources, 30(1), 63–88.
Vermeulen, F., O. Bargain, M. Beblo, D. Beninger, R. Blundell, R. Carrasco, M. Chiuri, F. Laisney, V. Lechene, N. Moreau, et al. (2006): “Collective models of labor supply with nonconvex budget sets and nonparticipation: A calibration approach,” Review of Economics of the Household, 4(2), 113–127.
Wrohlich, K. (2005): “Labor Supply and Child Care Choices in a Rationed Child Care Market,” Institute for the Study of Labor (IZA).
Wu, C. (1983): “On the convergence properties of the EM algorithm,” The Annals of Statistics, 11(1), 95–103.
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