Gender wage disparity and economic prosperity in Pakistan

Figures & data

Table 1. Provincial trends

PUNJAB	2000–2005	2006–2010	2011–2015	2016–2020
GPI (growth)	2.85	2.53	2.84	2.89
LFPRT	33.37	34.12	34.67	34.55
Average Male Wages(in thousand Rupees)	5567	8520	12,385	18,563
Average Female Wages(in thousand Rupees)	4563	6899	10,490	13,437
SINDH
GPI (growth)	2.88	2.54	2.62	2.69
LFPRT	26.54	28.37	30.28	30.58
Average Male Wages(in thousand Rupees)	7693	10,159	14,406	20,456
Average Female Wages(in thousand Rupees)	6537	9867	12,201	18,537
KPK
GPI (growth)	2.82	2.47	2.73	2.77
LFPRT	24.6	26.57	25.43	24.72
Average Male Wages(in thousand Rupees)	5948	8319	13,665	18,367
Average Female Wages(in thousand Rupees)	5367	6899	10,490	13,437
BALUCHISTAN
GPI (growth)	2.78	2.41	2.64	2.58
LFPRT	24.53	26.54	25.17	23.82
Average Male Wages(in thousand Rupees)	7545	11,897	16,754	21,853
Average Female Wages(in thousand Rupees)	6437	9772	13,542	19,675

Source: Labor Force Survey, State Bank of Pakistan and Global Data Lab

Table 2. Variable description

Variables	Indicator	Unit	Data source	References
Economic prosperity	Gross national income per capita (GNI) calculated as aggregated provincial per capita income of four provinces	US dollars	Global Data Lab (GDL). GDL collected the data from various surveys that are conducted in Pakistan like Household Income Expenditure Survey (HIES), Multiple Indicator Cluster Surveys (MICS), and Labor Force Survey (LFS)	Kennedy et al. (2017)
Wage Gap	Wage difference is measured by the ratio of female wages to male wages.	Ratio	Labor Force Survey of Pakistan. The study included data on wages in major occupational groups from agricultural, industrial, and services sectors for both male and female workers.^1	Bintoro (2021), Kennedy et al. (2017) and Sabir and Aftab (2007)
Human capital	It is an index of education, health, and living standard	Percentage	GDL	Wang et al. (2018) and Smits and Permanyer (2019)
Labor force participation	Labor force participation measures the number of people (aged 15 and above) available for work, as a percentage of the total working age population.	Percentage	LFS	Yakubu et al. (2020)
Development expenditure	Total development expenditures of the government	Growth rate	State Bank of Pakistan (SBP) publications	Ejaz et al. (2017)

Table 3. Unit root test

variables	levin, lin and chu		lm, pesaran and shin		adf-fisher
	Level	First Difference	Level	First Difference	Level	First Difference
lep	0.4909	0.0094	0.2292	0.0000	0.0012	0.0000
gwg	0.0630	0.0000	0.0000	0.0000	0.0048	0.0000
tde	0.0025	0.0007	0.0001	0.0000	0.034	0.0000
hdi	0.0029	0.0000	0.0043	0.0000	0.0063	0.0001
lfprt	0.5799	0.0000	0.6358	0.0000	0.2745	0.0747

Source: Authors’ calculations. Values in the table are p-values

Table 4. Lag selection

Model	Lag Specification	p-value
1	1,1	2.969
2	1,2	3.001
3	1,3	3.096
4	1,4	3.030

Authors’ own calculations

Table 5. Hausman test

Ho: Difference in coefficients not systematic
Chi^2 (7)	9.02
p-value	0.0606
Decision	Reject Ho
Conclusion	PMG model is good

Source: Authors’ own calculations

Table 6. Co integration results

Tests	Statistics	p-value
Modified dickey fuller	−3.4504	0.0003
Dickey fuller	−4.2181	0.0000
Augmented dickey fuller	−2.8083	0.0025
Unadjusted modified dickey fuller	−8.1924	0.0000
Unadjusted dickey fuller	−5.5498	0.0000

Source: Authors’ calculations

Table 7. Empirical results

Variables	PMG	MG	DFE
Long Run
Gwg	−0.042* (0.000)	−0.004 (.886)	−0.027** (0.037)
Lfprt	.092* (0.000)	−11.68 (0.402)	0.034 (0.327)
hdi	0.135* (0.004)	0.0817 (0.511)	0.054 (0.346)
Tde	.0185* (0.000)	.0022 (0.848)	0.132* (0.007)
Short Run
Error Correction Term	−.71* (0.001)	−.99* (0.002)	−.89* (0.000)
d.gwg	.01174 (0.171)	.006 (0.685)	−0.157** (0.065)
d.lfprt	−.01269 (0.680)	.034 (0.179)	−0.030 (0.233)
d.hdi	−.05174 (0.675)	−.172*** (0.092)	−0.024 (0.73)
d.tde	−.00556* (0.003)	−.0019 (0.179)	−0.006*** (0.070)
Constant	−18.869 (0.001)	−0.6055 (0.445)	−0.997 (0.741)

Authors’ own calculations

Table 8. Diagnostic test

Problems	Test	p-value
Heteroscedasticity	Breusch-Pagan/Cook-Weisberg test	0.9809
Multicollinearity	Variance inflation factor	1.22
Auto correlation	Breusch-Pagan LM test	0.1599

Source: Authors’ calculations

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