A Case Study of Pakistan Determinants of Female Labour Force in South Asia blocks, consisting of 200 to 250 households on average. Enumeration blocks are considered primary sampling units(PSUs) and are selected based on probability proportional to size(PPS) method of sampling. Households within sample PSUs are taken as secondary sampling units(SSUs) with sixteen from rural and twelve from urban PSUs selected using a systematic sampling technique with a random start. The LFS is representative at the regional, i.e. rural and urban level, and it is possible to get representative figures for the provinces and Pakistan in general. The LFS has been specifically designed to capture key characteristics of Pakistan’s labour force. It includes information at the household and individual level such as gender, age, relationship with household head, education levels, hours worked in both primary and subsidiary sectors and occupations, wages, and average monthly income earned. 6.1.2. Description of variables To determine the extent to which educational attainment affects the FLFP in Pakistan while considering other socio-economic and cultural factors, this report uses cross-sectional data with a total number of 191,229 observations. Below is a brief discussion of the different variables used in this model. Female : The main emphasis of this study is to show how there are disaggregated impacts of educational attainment on LFP based on gender, so the main variable of interest is this gender variable. This categorical variable is converted into a dummy variable which takes the value of 1 for female and 0 for male. There are about 73% of male respondents compared to 27% of females in the survey. LFP: The dependent variable, in this case, is the lfp and it is categorised into individuals who are part of the labour force(employed and unemployed) or not part of the labour force. Statistics reveal that almost 46% of the respondents are part of the LF while 54% are not part of the LF. Education: As discussed in the theory section, one of the most important determinants of LFP is Human Capital. Due to conflicting literature on the effect of different levels of education on LFP, it is significant to categorize the education variable. In addition, as it is quite unrealistic for the impact of an additional year of education on the probability of LFP to remain constant across different years of education, hence the education variable is split into multiple dummy variables namely no education, primary education, secondary education, higher education using no education as a reference 36