We adopt a quantile regression framework, which then uses different quantile spreads to analyse the conditional inequality using the data drawn from the Labour Force Surveys over the 1990 to 2003 time--period.
Also of interest is the ability of QR to track changes over time in quanitle-based measures of conditional inequality. Before analysing changes over time, the paper describes the overall conditional inequality using six different models.
In the different industry sector, service sector found to have upward line in conditional inequality, which also shows the increasing trend over the year compare to all other industries.
For, different level of education, conditional inequality has increased for both upper half and lower half of the distribution and the increase in conditional inequality is much higher for person who has done matriculation or intermediate or having degree or postgraduate degree compare to have less education or no education.
For female, the conditional inequality increase is slightly more compare to increase for male but the inequality remain higher for female compare to male.
The conditional inequality estimates for different provinces is depicted in Figure 7 where Punjab and Balochistan found to have highest increase in conditional inequality over the year, from 1.09 to 1.17 and from 0.64 to 0.79 over 1990 to 1996 and then to 1.28 and to 0.85 for year 1996 to 2003, respectively.
As depicted in table, Punjab has the highest conditional inequality across all the quantiles while Balochistan has the lowest conditional inequality in all quantiles spread compare to other provinces.
Figure 4 shows the conditional inequality at different level of education for male and female as well as for urban and rural area.
The overall figure shows that conditional inequality increasing in the upper half as well as lower half of the distribution.