Role of Gender Disaggregate Statistics in Urban Planning and Development in Pakistan

• Published in: Promoting Statistical Practice and Collaboration in Developing Countries pp. 407 - 422
• Main Authors: Naz, Lubna, Umair, Muhammad
• Format: Book Chapter
• Language: English
• Published: CRC Press 2022
• Edition: 1
• Subjects: Female Urban Workers; Small Urban Centers; Undergraduate Statistics Programs; Exponential Ratio Type Estimator; Data Set; Vice Versa; Large Urban Centers; Gender Wage Gap; HPD Interval; Complex Sample Design; Data Science Practice; Lower Gender Wage Gap; Gender Wage Differentials; Pennsylvania State University; Statistical Consulting Centers; Gender Inequality; Undergraduate Statistics Students; Urban Labor Market; Kernel Density Estimate; Unexplained Component; Higher Gender Wage Gaps; Gender Wage Equality; Wage Gap; Autoregressive Distributed Lag Model; Blinder Oaxaca Decomposition; HPD
• ISBN: 103219555X; 9781032195551; 1032198508; 9781032198507
• DOI: 10.1201/9781003261148-35

Gender disaggregation in statistical information on wages, job skills, and occupational choices is necessary to formulate policies to reduce gender gaps in the labor market, particularly in developing countries. In Pakistan, previous research has focused on analyzing the gender wage gap at the national level without using a complex sampling design for the survey. This study fills the gap in two ways. First, it analyzes gender-disaggregated statistics on wages and occupations across small and large urban centers. Second, it uses the gender inequality measurement techniques, i.e., Blinder Oaxaca Decomposition, to quantify the gender wage gaps and compare the gender wage differentials for small and large cities. The study used a complex sample design of a nationally representative survey, the Labor Force Survey (LFS), 2014-2015. Results show a lower gender wage gap in small-sized cities (8%) than prevails in larger cities (31%). The gender gap tends to be more common in the lower tiers of the urban labor market than managerial ranks, and the result holds irrespective of the location of the job. The results highlight the significance of gender disaggregate statistics to monitor progress on the following specific Sustainable Development Goals (SDGs): (1) to eliminate gender discrimination against vulnerable groups by implementing national legislation and international conventions (SDG 5), (2) to reduce the gender wage gap in cities through the identification of loopholes, mainly the lack of gender mainstreaming in work-related policies (SDG 10), and (3) to formulate proactive employment policies to tackle the challenges of urbanization (SDG 11) by increasing employment opportunities and reducing discrimination in small urban centers (Sustainable Kindly check and confirm. Development Goals (SDGs) https://www.un.org/development/desa/disabilities/envision2030.html). This study sets new directions for future research. Gender disaggregation in statistical information on wages, job skills, and occupational choices is necessary to formulate policies to reduce gender gaps in the labor market, particularly in developing countries. This chapter aims to analyze and compare the gender wage differentials in the large and small urban centers of Pakistan. The wage gap across gender is one of the challenging issues for governments and policymakers. For the descriptive analysis and econometric estimations, the study employed the nationally representative data set for the labor market in Pakistan, the LFS, 2014-2015. In the urban labor markets, female workers earned 24% lower than their male counterparts in 2014-2015. It analyses and compares the gender wage differentials in the large and small urban centers of Pakistan employing LFS 2014-2015. The increase in female labor force participation and increased educational opportunities for females will reduce the gender wage gap.