The Participation of Married Women in Iranian Labor Market: Logit non-linear modeling

Subject Areas : Labor and Demographic Economics

Z. Sarani ¹ (دکتری اقتصاد)
B. Keshte gar ² (استادیار اقتصاد دانشگاه زابل)
Gh. Keshavarz hadad ³ (دانشیار دانشگاه صنعتی شریف)

Received: 2014-06-19 Accepted : 2014-08-27 Published : 2015-02-01

Keywords: J21, J16, C14,

Abstract :

Abstract In the present paper, logit non-linear massive model is presented by using the maximum likelihood method for binary model of Iranian female labor participation based on income-expenditure data of the households in 2006. In logit non-linear model, a continuous mathematical function like power, exponential, polynomial and logarithmic are used for independent variables such as husband income, education, woman age, wealth and the number of children above and under 6 years old. Non-linear equation of married women participation based on econometric comparison standards like White Test and Lagrange coefficient statistics is compared with parametric and non-parametric Logit models. The results of modeling represent that the selection of an appropriate mathematical function can provide a suitable flexibility in binary modeling and so increase the ability and decrease the errors of the modeling compared to parametric and non-parametric logit models. Also, this kind of modeling approach has a variance homogeneity as well as non-parametric model, but here there is less error in modeling.

References:

منابع

-        سالنامه آماری کشور سال 1388، مرکز آمار ایران

-        کشاورز حداد، غلامرضا، باقری قنبرآبادی، مرتضی (1390). تحلیل احتمال مشارکت زنان شهری و روستایی ایران در بازار کار با استفاده از روش های اقتصادسنجی پارامتریک و ناپارامتریک. مجلهتحقیقاتاقتصادی، 97: 151-174.

-         Assaad, R., & El-Hamidi, F. (2002). Female labor supply in Egypt: Participation and Hours of Work. In Human Capital: Population economics in The Middle East. Ismail Sirageldin (Ed.).  London: I.B. Tauris

-         Bera, A. K., & Bilias, Y. (2001). Rao’s Score, Neyman’s C (α) and Silvey’s LM tests: An Essay on Historical Developments and Some New Results. Journal of StatisticalPlanning and Inference, 97:9-44.

-         Berulava, G., Chikava, G., 2012. The determinants of household labor supply in Georgia, France and Romania: a comparative study. Eurasian Journal of Business and Economics, 5 (9), 141-164.

-         Bianco, A.M., & Martínez, E. (2009). Robust testing in the logistic regression model. Computational Statistics and Data Analysis, 53: 4095-4105.

-         Chiappori, P.A. (1988). Rational household labor supply. Econometrica, 56: 63-89.

-         Chiappori, P.A. (1992). Collective labor supply and welfare. Journal of Political Economy, 100: 437-67.

-         Cynthia, B.L., & Beth, T.N. (1979). Theeconomics of six differentials. New York, Columbia University Press.

-         Donni O., & Moreau, N. (2007). Collective labor supply: A single-equation model and some evidence from French data. The Journal of Human Resources, XLII: 214-246.

-         Durbin J., & Watson, G.S., (1971). Testing for serial correlation in least squares regression III. Biometrika 58, 1–19.

-         Fortin, B., & Lacroix, G. (1997). A test of neoclassical and collective models of household labor supply. Economic Journal, 107: 933-955.

-         Frolich, M. (2006). Non-parametric regression for binary dependent variables.Econometrics Journal, 9: 511–540.

-         García, J., & María J. S. (2002). Female Labor Supply in Spain: The Importance of Behavioural Assumptions and Unobserved Heterogeneity Specification. Department of Pampeu Fabra.

-         Gerfin, M. (1996) .Parametric and semi-parametric estimation of the binary response model of labor market participation. Journal of Applied Econometrics, 11:321–39.

-         Gong, X., & van Soest, A. (2000). Family structure and female labour supply in Mexico City. IZA Discussion, Paper No: 214.

-         Killingworth, M.R., & Heckman, J. (1986). Female labor supply: a survey, chapter 2 in orley Ashenfelter and Richard Layard eds, Handbook of Labor Economics. Vol. 1: New York: Elsevier Science Publishers Bv, 103-204.

-         Leckner, M. (1991). Testinglogit models in practice. Empirical Economics, 16(2), 177-198.

-         Maalouf, M., & Trafalis, T.B. (2011). Robust weighted kernel logistic regression in imbalanced and rare events data. Computational Statistics and Data Analysis, 55:168-183

-         Mincer, J. (1962). Labor Force Participation of Married Women: A Study of Labor Supply. Aspects of Labor Economics, Princeton, N.J.: National Bureau of Economic Research, Princeton University Press, 63-105.

-         Nawata, K. (1995). Estimation of the sample-selection models by the maximum likelihood estimator and Heckman’s two-step estimator. Economic Letter, 45:33–40.

-         Racine, J., & Li, Q. (2004). Nonparametric estimation of regression functions with both categorical and continuous data. Journal of Econometrics, 119: 99–130.

-         White, H. (1982). Maximum likelihood estimation of misspecified Models. Econometrica, 50:1-25

-         Willmott, C.J., & Matsuura, K. (1998). On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. International Journal of Geographical, 86: 121-136.