Special Edition-07.pdf - Lahore School of Economics

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214 Mehak Ejaz Table-4: Results of Rural Areas Variable Description Probit Logit Coefficients Marginal Effects Coefficients Marginal Effects Constant -1.756* -0.376 -3.039 -0.352 Age of the female 0.013* 0.003 0.024 0.003 Married=1,Otherwise0 -0.138* -0.030 -0.275 -0.032 Years of Schooling 0.014* 0.003 0.032 0.004 No. of working people in family 0.483* 0.103 0.923 0.107 Family size -0.123* -0.026 -0.258 -0.030 Nuclear=1,Otherwise 0 0.097* 0.021 0.223 0.026 Own Agricultural asset 0.003* 0.001 0.004 0.000 Ownership of cattle 0.001* 0.000 0.002 0.000 If own Car, Motorcycle, Cycle=1,Otherwise 0 Weighted Index of home appliances If female head=1,otherwise 0 Infant+ children per female Infant+ children per female squared 0.099* 0.021 0.163 0.019 -0.206* -0.044 -0.367 -0.043 0.481* 0.103 0.955 0.111 0.341* 0.073 0.687 0.080 -0.041* -0.009 -0.081 -0.009 No. of observations 72099 72099 R 2 0.2302 0.2413 Scaled R 2 0.1782 0.1875 Fraction of Correct Predictions 0.8489 0.8506 *, **, *** presents significance at 1%, 5%, and 10% level respectively
Determinants of Female Labor Force Participation in Pakistan 215 V. Empirical Findings The empirical model highlights the major determinants of female labor force participation (LFP) in Pakistan. Further, in order to give due consideration to regional heterogeneity, the model is estimated for the rural and urban areas as well. A number of potential variables are included in the model on the basis of theoretical models that have been discussed in detail in the section on the literature review. In order to improve the model specification, region specific variables relating to the rural and urban areas are incorporated. Estimated parameters and mean probability derivatives of the Probit and Logit model for the overall model are reported in Table-2.2. The probability derivatives indicate the change in probability on account of a one-unit change in the given independent variable after holding all the remaining variables constant at their mean. Female characteristics such as age, marital status, years of schooling, and household characteristics such as the number of working people in the family, nuclear/extended family, ownership of vehicle, female headed household, family size, and availability of home appliances are the significant determinants of female labor force participation in Pakistan. The sample consists of females of the age cohort 15-49 years. The coefficient of age for the Probit and Logit model reflects that with an increase in age, there is a greater likelihood that a female will enter the labor market. The dummy variable that takes the value of 1 for married and 0 otherwise proves that the significance of marital status affects the LFP. The results indicate that if a woman is married, there is less probability that she will enter the labor force. In Pakistan, married women are less likely to be involved in income generating activities due to their preferences for household activities. Education is also a very important factor in determining the probability that a female would enter the labor force, since education plays an important role in deciding whether to work or not by enhancing job prospects. Empirical studies found that for women, greater educational attainment leads to greater participation in the labor force, but also increases the productivity. As the years of schooling increase, the probability of women’s participation in the labor force also increases. Its coefficient is statistically significant. The results suggest that a female that is educated, unmarried and between the ages of 15 and 49 would have the greatest chance of being part of the labor force. In order to understand the participation decisions of women, household characteristics of the female are also included in the model.
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Special Edition ISSN 1811-5438 THE
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THE LAHORE JOURNAL OF ECONOMICS Con
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Monetary, Fiscal and Financial Sect
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Monetary and Fiscal Policies 45 sys
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Monetary and Fiscal Policies 51 The
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Monetary and Fiscal Policies 63 Tax
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