A Case Study of Pakistan Determinants of Female Labour Force in South Asia Interaction terms : Taking into consideration the possibility of the impact of education categories to vary depending upon gender so interaction terms were also added to the probit model to draw better comparisons. For instance, the term female_primary variable would allow us to compare women with primary levels of education directly with males with no formal education. Such interaction terms can clearly highlight the gender impact on LFP based on levels of education. 6.1.3. Probit Model LFP= Bo+ B 1 PF+ B 2 EA+ u BO is a constant term representing the predicted probability of participating if personal factors and educational attainment are zero B 1 and B 2 are the coefficient that measures the effects of PF and EA on LFP PF is a vector of variables including gender female, age, sector of employment, location, household size and marital status EA is a vector for educational attainment levels which includes primary, secondary and tertiary levels of education U is the residual term showing the difference between estimated LFP and the observed LFP 6.1.4. Empirical Results This section discusses the findings of the study. The focus of this research was to resolve the unexpected relationship between education and LFP and if empirically illiterate women have higher LFP than people with some levels of education in Pakistan. The results of probit regression are tabulated below listing the coefficients and standard errors of all the variables used in the model. Table 1: Results of the Probit model, 2017-18 VARIABLES Age Age Squared (1) LFP 0.103*** (0.00147) -0.00119*** (1.78e-05) 38