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Determinants of Child Labour and School Attendance: The Role of Household Unobservables

We develop a random effects multinomial logit model to distinguish between unobserved and observed household characteristics as determinants of child labor and school attendance. Using a semi-parametric approach, the random effect is drawn from a discrete distribution of latent classes of households. The results show that household-level unobserved heterogeneity is substantial. Household-level unobserved heterogeneity swamps observed income and wealth heterogeneity. Households that belong to the class with a high latent propensity to send their children to work are not influenced by marginal changes in the explanatory variables. Households most sensitive to changes in explanatory variables are those with a high propensity to have their children neither in school nor working. Policy interventions and changes in external conditions are likely to produce large changes in the behavior of this group of families.

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Paper provided by Understanding Children's Work (UCW Programme) in its series UCW Working Paper with number 9.

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Date of creation: Dec 2002
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Handle: RePEc:ucw:worpap:9
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  1. Lee, Lung-fei, 2000. "A numerically stable quadrature procedure for the one-factor random-component discrete choice model," Journal of Econometrics, Elsevier, vol. 95(1), pages 117-129, March.
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  3. Peter Jensen & Helena Skyt Nielsen, 1997. "Child labour or school attendance? Evidence from Zambia," Journal of Population Economics, Springer, vol. 10(4), pages 407-424.
  4. FurioCamillo Rosati & Zafiris Tzannatos, 2006. "Child Labour In Vietnam," Pacific Economic Review, Wiley Blackwell, vol. 11(1), pages 1-31, 02.
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  9. Ravallion, Martin & Wodon, Quentin, 2000. "Does Child Labour Displace Schooling? Evidence on Behavioural Responses to an Enrollment Subsidy," Economic Journal, Royal Economic Society, vol. 110(462), pages C158-75, March.
  10. Ranjan, Priya, 2001. "Credit constraints and the phenomenon of child labor," Journal of Development Economics, Elsevier, vol. 64(1), pages 81-102, February.
  11. Keane, Michael, 1993. "Simulation estimation for panel data models with limited dependent variables," MPRA Paper 53029, University Library of Munich, Germany.
  12. Kaushik Basu, 1999. "Child Labor: Cause, Consequence, and Cure, with Remarks on International Labor Standards," Journal of Economic Literature, American Economic Association, vol. 37(3), pages 1083-1119, September.
  13. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 673-748, August.
  14. Pudney, Stephen & Galassi, Francesco L & Mealli, Fabrizia, 1998. "An Econometric Model of Farm Tenures in Fifteenth-Century Florence," Economica, London School of Economics and Political Science, vol. 65(260), pages 535-56, November.
  15. Behrman, Jere R., 1993. "Intrahousehold distribution and the family," Handbook of Population and Family Economics, in: M. R. Rosenzweig & Stark, O. (ed.), Handbook of Population and Family Economics, edition 1, volume 1, chapter 4, pages 125-187 Elsevier.
  16. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
  17. Kim, Byung-Do & Blattberg, Robert C & Rossi, Peter E, 1995. "Modeling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 291-303, July.
  18. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
  19. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-64, May.
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