Gender Wage Discrimination in Pakistan

sex remuneration diversity in Pakistan try out from Pakistan 2008/09 and 2010/11 Table of Contents Introduction2 Literature critique2 methodology3 Variables Used Characteristics of Workers5 Results7 Discussion7 Bibliography8 Appendix A9 Selectivity twist Logit Regression Results9 Introduction This paper explores the dynamics of gender operate discrepancy in Pakistan for two selective information sets Labour Force discern for the social class 2008/09 and 2010/11. We leave behind explore whether or not women ar discriminated against, as it has been suggested for a predominantly Islamic country like Pakistan.Labour theory addresses many reasons for employ discrimination. For the purposes of this research we will concentrate on employer wage discrimination. Following this our research will be aimed at discovering if women atomic number 18 paid less than their male counter-parts especially with the equivalent set of characteristics. For this purpose we will use the Oaxaca-Blinder method to calculate the coefficient for discrimination across genders. Literature Review The basis of this paper is the trifle d wiz by Oaxaca and Blinder in 1973 about wage discrimination mildews.In the paper Interpreting the Decomposition of the Gender Earnings Gap (Giaimo R. 2007) this method has been applied to find out how distinguishable characteristics change the discriminatory behaviour of employers in Italy. Oaxacas method for astute discrimination was further adapted in the paper Gender Wage Discrimination at Quantiles (Javier Gardeazabal 2005), and was used to calculate discrimination coefficients for quintiles. In a study conducted in India (Tilak 1980), it was found that the incidence of unemployment was higher for women than for men with the same characteristics.In this study the only characteristic that was taken was education. This is a different fee to look at discrimination from what this paper will do. Rather than tone at the unemployed, this paper will see the women in the labour military and if they face discrimination with respect to their wages. However, the underlying aim and also the hypothesis formed are the same. The paper Wage Differentials and Gender Discrimination Changes in Sweden 1981-98 (Mats Johansson 2005) explored the wage respites between men and women in Sweden.They found that the wage gap was 14%-18% during the 1990s. Their study also indicated that this difference could not be explained by applying the job requirements and qualifications to womens wage function. The conclusion was that there is undoubtedly some other factors other than the characteristics of the workers that determined the wages in the Labour Market. Methodology This paper reckon a coefficient for Gender Wage Discrimination from the Oaxaca-Blinder decomposition. D= Xf? m-Bf+ ? m(Xm-Xf) Here ? is a vector of characteristics of workers.Therefore, the first part of the comparison shows the wage differential between males and fem ales on the basis of characteristics. Second part of the equation normalizes characteristics, for females in this instance, and then subtracts the wage differential based on characteristics, to produce us the overall differential based on discrimination. As a tally, we also work out the converse of this Oaxaca Blinder Decomposition as follows D= Xm? m-Bf+ ? f(Xm-Xf) To control for selectivity bias, we affirm also used the Heckman Procedure.A multi-variable Logit model was run and three variables (Lambda1, Lambda 2 and Lambda3) were calculated to act as control for variables missed in our model. This discrimination coefficient has been calculated for two entropy sets victimization characteristics much(prenominal) as age, marital status, education level, province, region, professional trainings and status in the family. These characteristics rescue been selected after being shown profound as the determinant of wage. earthy log of wages was the dependant variable in the follow ing statistical retroflection Table 1 Wage Determinants LFS 2008/09Table 2 Wage Determinants LFS 2010/11 Our results are much give out for the data set of 2010/11. The signs of education are expected. For the data set of 2008/09, signs for education are positive which does not support theory. counterbalance after efforts to remove multi-colinearity, they so far show positive signs. Most of the variables in the regression are also in authoritative. However, when we take the data for LFS 2010/11, and correct it for selectivity bias, we generate much better results. Most of the variables are signifi potfult as well(p) as show the correct signs. The same algorithm was applied to both(prenominal) the data sets, and the same variables have been taken). Results of Logit models for correcting selectivity bias are attached in Appendix A. Variables Used Characteristics of Workers Summary tables from LFS 2010/11 1. Age * Theory suggests that this is one of the most important det erminants of peoples decision to work. 2. Marital stance * This variable was taken as a dummy variable in the regression. * It is a significant variable in the decision to work, especially in developing economies like Pakistan. 3. Province This is also taken as a dummy. The Baluchistan province was omitted from this analysis. However, the calculations of the Oaxaca Blinder method take this omitted variable into account. This is because the method takes the vectors of the estimated regression equation. 4. Region * Whether a person is from a Rural or urban background has impact on the opportunities and the job growth pattern. 5. Education level * This is linked at once with the variable wage. * This is again taken as a dummy variable, and higher education was omitted from the regression. 6. Migration (Rural-Urban) Although not a very significant variable in our regression, there are other empirical studies that have shown how the migrated families have better opportunities for work than those who do not. 7. Literacy * This is a dummy variable, and is significant in our analysis. 8. Selectivity Bias Variables * These are Lambdas in the model. And have been calculated using the Heckman Procedure for controlling selectivity bias. Results To find the discrimination coefficient a intercellular substance exercise was done in Stata using the data from LFS 2007/08. This presented the following equation D= Xf? m-Bf+ ? Xm-Xf D=10. 030812+-7. 4166332 D= 2. 614212 The discrimination coefficient for LFS 2010/11 was calculated as follows D= Xm? m-Bf+ ? fXm-Xf D=0. 11964462+0. 31341527 D= 0. 43305989 Just looking at the metrical composition we can say that discrimination have gone down significantly over the last two years. Whether this is actually the case, or this is just collectible to the line of works in the data, we cannot be sure. However, we think that the result for 2010/11 is a better estimate overall. The results show that women are at a significant evil in P akistans Labour Force. These results are quite expected.However, we also take away to take the problems in data collection and measurement into account. Many of the bungalow and small scale industries are not counted in the LFS and they are a prime source of employment for women in Pakistan. Discussion There are many limitations of this study. First of all this can be made much powerful if panel data is used, however, there are no sources of such data. Secondly, an easy method of expanding this study would be to do an inter year comparative study. There are more limitations that are related directly to the data that we have used.Many questions have been raised about the methodology and the authenticity of the data in Labour Force Survey of Pakistan. However, this limitation is beyond our control. There have also been questions raised about the Oaxaca-Blinder method of compute wage discrimination. While we have attempted to review paper that have used this technique and have achi eved good results, there are still many questions about the technique, still. There are few policy implications that we can derive from these results, especially if we look at the significance levels in the data for 2008/09.However, this paper does prove to some extent that there is a problem of gender wage discrimination is Pakistan. We can attribute a take of this to social factors as well women do not want to work in most professions, so we can also argue that there may be a case for discrimination by the employees preferably than the employers. Bibliography Giaimo R. , Bono F. , Lo Magno G. L. Interpreting the Decomposition of the Gender Earning Gap. University of Palermo Journal, 2007. International Standard industrial smorgasbord of all Economic Activities (ISIC-Rev. 2, 1968). ILO. 2012. http//laborsta. lo. org/applv8/data/isic2e. html (accessed 2012). Javier Gardeazabal, Arantza Ugidos. Gender Wage Discrimination at Quantiles. Journal of Population Economics, 2005. Mats J ohansson, Katarina Katz, Hakan Nyman. Wage Differentials and Gender Discrimination Changes in Sweden 1981-98. Acta Sociologica, 2005. Stat. Stata. 2012. http//www. stata. com/meeting/5german/SINNING_stata_presentation. pdf. Tilak, Jandhyala B. G. Education and Labour Market Discrimination. Indian Journal of Industrial Relations , 1980. Appendix A Selectivity Bias Logit Regression Results LFS 2008/09 LFS 2010/11