variables

International Research Journal of Finance and Economics

ISSN 1450-2887 Issue 55 (2010)

© EuroJournals Publishing, Inc. 2010

http://www.eurojournals.com/finance.htm

Determinants of Gender Based Wage Discrimination in

Pakistan: A Confirmatory Factor Analysis Approach

Ghulam Yasin

Department of Sociology, Bahauddin Zakariya University, Multan (Pakistan)

Muhammad Ishaque Fani

Department of Pakistan Studies, Bahauddin Zakariya University, Multan (Pakistan)

E-mail: [email protected]

Asif Yaseen

Department of Commerce, Bahauddin Zakariya University, Multan, (Pakistan)

Abstract

This paper is an empirical study of the development of labour market participation

and wage differentials between males and females in Pakistan between 1999 and 2008.

There is little known about the position of women in the labour market of Pakistan. The

purpose of this paper is to investigate the gender based wages differences in Pakistan by

knowing the individual and socio-cultural factors. This has been done by the application of

regression models and earning estimations on the panel data taken from Pakistan Labour

Force Survey for analyzing factors responsible for gender based wages discrimination. This

study employs Oaxaca & Blinder decompositions to measure the effects of wages

discrimination. Mincer earning function is used to estimate the earning equations for males

and females and confirmatory analysis approach exhibits that the adverse treatment of

female labour market participation is the largest identifiable reason why the wage gap is in

the same type of paid employment and it further emanates differences in remunerations.

The empirical findings indicate that individual factors particularly education and labour

market experience are the most important determinants as evident from the decreasing gap

of wage differentials for higher level of education, while organizational factors are assumed

constant for this research. This is concluded that gap is increasing with the passage of time

and causes and extent of gender based wages discrimination in Pakistan’s labour market is

multi-folded. The Government should understand its implications as a major impediment to

resolve unemployment in Pakistan and also discriminatory practices in Pakistan.

Introduction Unemployment in the developed countries in general and in the developing countries in particular has

been a major cause of economic instability and has significantly retarded the growth and development

of such countries. The consequences of unemployment are adverse and lead to social and economic

disaster. It is imperative to overcome unemployment, taking it as an important barrier in the economic

expansion of the country. Promotion of the employment sector not only has a considerable positive

impact on the overall structure of national economy but strengthens the social institutions as well.

Over the last six decades, a series of polices have been made to address the issue of

unemployment in Pakistan. Promoting labour intensive technique through implementation of the

policies pertaining to tax exemptions and facilitating the medium and small industries in the country

International Research Journal of Finance and Economics - Issue 55 (2010) 178

has been one of the key factors to overcome this problem but the issue still remains as intense as ever.

Employment of the labour has never been entirely free of gender discrimination if viewed and analyzed

in the perspective of such policies. These policies designed with a clear manifestation of the gender

discrimination for the labour employment is though a matter of major concern but there are a lot of

other cultural, social and economic constraints upon the female labour force as well.

The socio-cultural constraints and such polices together have a profound impact on the labour

market of Pakistan and have greatly promoted the issue of gender discrimination. As mentioned earlier,

most of the strategies intended for the economic growth and stability in the country, are based on the

promotion of export. The export industries strongly believe in the employment of skilled or highly

skilled labour force. Owing to a number of socio-cultural constraints, a vast majority of the female

labour force is not acquainted up with the new technology and inadequate or no vocational training

results in their inability to meet the present day demands of the labour market. This phenomenon has

supported a sense of hostility against the female labour force in the industries. On top of that, the

meagre wages, as an incentive offered to the female labour has been a major obstacle which

considerably reduces its active participation in the main stream of country's workforce. According to

Human Development Report (1998), in almost all the societies, relative to men, women are

concentrated in low-paying jobs, generally overrepresented in clerical, sales and service occupations,

often work longer hours and much of their work remains undervalued, unrecognized and

unappreciated. It is still an unequal world. The research literature on gender based wage discrimination

has indeed swelled enormously over the past few years with numerous researchers administering the

various models across the world. Interestingly, the conceptualization, measurement and application of

different instruments across government and commercial setting are not bereft of the controversies

either. A careful examination divulges that the factors and the corresponding items are comprehensive

as it appears. The current research work strives to bring to light some of the critical determinants of

gender wages discrimination that has been overlooked in the literature and proposes a comprehensive

model and an instrument framework for measuring gender based wages discrimination to identify and

decompose the factors, which influence the wage structure of the female labour force and encourage

gender bias in the labour market.

Significance of the Study Women though have acquired a great degree of skill pertaining to work and have participated actively

in various professions at all levels, however, wage discrimination predominantly discourages them to

play a significant role to strengthen national economy. According to Human Development Report

(1998), in almost all the societies, relative to men, women are concentrated in low-paying jobs,

generally overrepresented in clerical, sales and service occupations, often work longer hours and much

of their work remains undervalued, unrecognized and unappreciated. It is still an unequal world.

Differences in male-female earning structures has been a subject of discussion since long and

economists have attempted to analyze these issues over a long period of time. One of the most

dominant explanations of these differences is given by Becker (1962) and Mincer (1962), the human

capital theorists who emphasized the role of schooling, training and other productivity-related factors

in bridging up this gap. In another study, Bergman (1974) presented the crowding model suggesting

that the employer decides to hire a woman into an occupation and the employer’s rational decision may

be a discriminatory one, if he uses only a person’s sex to disqualify her from an occupation. Mincer

and Polachek (1974) emphasized on the deterioration of women’s human capital during periods of

intermittency due to child-bearing. Polachek (1981) hypothesized that it is due to these interruptions

that women enter into those occupations where cost of interruption is low. Conversely, England (1982)

has demonstrated that a woman who plans to enter into an intermittent labour market would not gain an

advantage by choosing a traditional female occupation.

179 International Research Journal of Finance and Economics - Issue 55 (2010)

A sufficient literature is available concerning the issue of gender based wage discrimination all

over the world. A number of experts discussed a variety of issues concerning, the labour market

discrimination, the female managers and their wages in especially in the Central Europe and wage

patterns in Segmented Labour Markets (Cain, 1986; Jurajda and Teodora,2006; Taubman and

Michael,1986). Others studies reported the theoretical and empirical work on the gender discrimination

related to the corporate sector (Babcock and Laschever, 2003; Baker and Murphy, 1988; Becker,1985;

Bell, 2005; Bertrand and Hallock, 2001; Black et.al.,2004; Blau and Ferber,1987; Bonin et.al.,1993:

Gneezy et.al. 2003)

Little research has been carried out on male-female earning differences in developing countries,

however, the available data focused upon some significant aspects of the problem coving a wider part

of the developing world e.g. the case South Asian economies, Nepal, and the third world were

explained by various authors (Bardhan and Kalpana,1994; Acharya and Bennett,1982;

Assenmacher,1990). Similarly, the gender discrimination for various labour markets of the under

developed world was extensively described by several authors covering Brazil, Bangla Desh, Africa,

Philippine, Dominican Republic and South Africa and South Asia (Birdsall and Behrman,1991;

Chaudhuri, 1991; Collier, 1994; Folbre, 1984: Finlay,1989: Geisler 1993; Greenhalgh, 1985). The

condition of female labour force in relation to transitional economies in the Indian sub-continent was

illustrated by Ibraz (1993) and Barry (1997) for Pakistan while Kalpagam, (1986) and Mathur, (1994)

detailed the wage implication for India. Ashraf and Ashraf (1993a, 1993b) conducted studies directly

relating to these issues but a number of attributes potentially linked to earning differentials in Pakistan

were unavailable.

This research focuses on some aspects of wage determination and evaluates a number of

possible reasons of wage discrimination, using Labour Force Survey 2007-08 for nine self-representing

cities of Pakistan. This study is also an attempt to understand and suggest measures to bridge up the

existing gaps concerning gender based wage differences in Pakistan. A variety of hypotheses have been

tested applying Oaxaca (1973) and Blinder (1973) standard decompositions and incorporating

occupational attainment model suggested by Brown et. al., (1980) to ascertain the extent to which the

wage offers are sensitive to productivity-related factors and the extent to which it is because of

discrimination. To expand the study, this discriminatory component is further decomposed to analyze

the extent to which discrimination is due to lower positions in the same occupation.

This study is an attempt to analyze quantitatively the extent of gender bias in the labour market

of Pakistan. Important questions which are addressed in the course of study are:

(i) What is the nature of gender discrimination that prevails in the labour market? Is labour market

biased or neutral?

(ii) How are the factors in the labour market related to the productivity of the female labour force?

To what extent, decomposition explains them?

(iii) Is female labour force offered low paid occupations?

Hypotheses Formulation Gender inequality may be an indicator of implied unfair treatment due to different sexes. This can exist

in education, access to work, work processes and work outcomes (Tomaskovic-Devey, 1993).

Extenuating this opinion of gender differences at work, different theoretical perspectives attribute

apparent disparity to disparity in some other fields. This can be explained as human capital theory

suggests gender difference in education is a result of difference in work experiences (Becker, 1993;

Tomaskovic-Devey,1993). Social Theory explains differential involvement in work is an index of role

assumed by different sexes (Tomaskovic-Devey,1993). The significance of gender discrimination in

terms of participation and wage differentials depends on the substantiation of the following hypotheses

concerning inequality


Hypothesis 1

A female is less productive than a male after controlling human capital and social roles.

Hypothesis 2

A female is less likely considered than a male to work for a paid job after controlling human

capital and social roles.

Hypothesis 3

A female has lower earning than has a male after controlling human capital and social roles.

These hypotheses have been tested by applying Oaxaca (1973) and Blinder (1973) standard

decompositions and incorporating occupational attainment model suggested by Brown et. al., (1980) to

ascertain the following matters,

1. The extent to which the wage offers are sensitive to productivity-related factors and the extent

to which it is because of discrimination in Pakistan.

2. The extent to which discrimination is due to lower positions in the same occupation and this

discriminatory component has been further decomposed to expand the study in Pakistan.

Methods The empirical analysis employs the cross sectional data from the Labour Force Survey (LFS), 2007-08,

conducted by the Federal Bureau of Statistics. We have also used different issues of statistical year

book of Pakistan and also reports of Ministry of Production and Ministry of Industries for making

variables data comparable for confirmatory analysis, only those variables which has the same

definition in all these reports have been used for this confirmatory analysis. This survey covers 18,912

households and more than 100,000 individuals of all urban and rural areas of the four provinces of

Pakistan. The entire sample of household has been drawn from 1347 primary sampling units out of

which 660 are urban and 687 are rural. The entire samples of households in Punjab are 8816 whereas

3096 are from urban and 5120 are from rural areas. The data for this study is restricted to eight self-

representing cities of Pakistan including Lahore, Multan, Faisalabad, Sialkot, Rawalpindi, Gujranwala,

Bahawalpur and Sargodha. The reason for this restriction is that we have comparatively rich

information on wages in these cities. Total sample size for these cities is 19,714 in which 10379 are

males and 9335 are females.

Furthermore, the data for present study are further confined to those individual, aged between

14-65 years, for whom wages were reported and for whom we could obtain occupations. It, in turn

reduced our sample size to 3584 individuals, of which 3252 were males and 332 were females,

including only paid employees who worked in public or private sector and received remuneration in

terms of wages, salary, commission, tips, piece rate or pay in kind.

Of our total sample, 91 percent are males and 9 percent are females. The categorization of the

data by occupations reveals that out of 7 occupations, almost 34 percent males are concentrated into

production sector, while 37 percent females are confined to lower level white collar jobs such as clerks.

At the same time, women are under-represented in professional, administration, sales and agriculture

sectors. However, our data set for females are not large enough to produce reliable estimates of

proportion of females in each occupation. So the biases may result from poor measurement of this

variable.

Variables Monthly Earnings

It is defined to include earnings and bonuses of workers evaluated on monthly basis. This variable has

positively skewed score in the distribution so the dependent variable used throughout the analysis was


the natural logarithm of monthly earnings that would be appropriate for analysis to eliminate bias due

to skewed distribution.

The explanatory variables included in the analysis are human capital, marital status, regional

variables and occupational status. The detailed description is as follows,

1. Human Capital variables: These can be classified as follows,

(i) Schooling

In our study, this variable is measured as years of schooling completed. It is expected that its

coefficient will be positively related to earnings through its positive impact on productivity.

(ii) Experience and Experience Square

To capture worker’s post-school investment in human capital through on-the-job training or

learning by doing, we have constructed a potential work experience measure as a residual from current

age, completed years of schooling and six. It is assumed here that schooling starts at the age of six.

(iii) Schooling-Experience Interaction

In order to assess that more educated is more able and would get more on-the-job training, we

include schooling-experience interaction. It would be expected that this interaction term will be steeper

for more educated individual than for less educated.

(iv) Technical Education

Human capital theory also suggests that the more trained individual is the more productive and

hence the coefficient associated with it would be positively related to earnings.

2. Marital Status

It affects labour force participation of males and females differently and hence their earnings.

Married women have a large amount of time which is spent out of the labour force in order to bear and

raise children. However, married males and never-married females would be more motivated as

compared to married females and never-married males similarly widows and divorced females have

more incentive to increase their productivity and earnings.

3. Regional Variables

We have included nine self-representing cities of Pakistan to control the cost of living

differences, opportunities for education and job differences, labour market differences and other

possible differences among different regions of the country. It can be anticipated that there will be

higher earnings for the resident of Karachi and Lahore as composed to those living in other cities.

4. Occupational Status

There are many studies in which differences in occupations contributed to the differences in

earnings. It is argued that women may be concentrated in relatively low paying occupations or in low

paying positions as compared to men. It could be due to both individual characteristics and possibility

of discrimination.

The Model In order to identify and decompose the factors, which influence the wage structure of the female labour

force and foster gender bias in the labour market, we have used Mincer earnings function as its starting

point

lnwi =ai +xiβi +ui

In the first place, the study uses regression analysis with maximum likelihood estimation to

analyze gender participation in paid work. For this we, have applied Oaxaca and Blinder’s (1973)

decomposition technique that requires separate estimation of the wage equation for men and women.

ln wm − ln w f = β m (x m − x f) + [(am − a f) + x f (β m − β f)]

As Oaxaca and Blinder’s (1973) decomposition model does not take into account the wage

structure so we have used wage gap decomposition developed by Juhn et al. (1991). The average

gender wage gap for country a can be written as follow:

D = ln w − ln w = (x − x) β +σ (θ −θ) = ∆x β +σ ∆θ


The gender wage gap is hence decomposed in a part due to human capital differences (x)

between men and women and another part due to differences in the ranking of men and women in the

male residual distribution (if women are located at the top or the bottom of the male wage residual

distribution). This last element can reflect either the gender differences in terms of unobserved

characteristics or the impact of the discrimination against women on the labour market. Jones (1983)

has shown that the discrimination term in the Oaxaca decomposition cannot be decomposed in order to

identify the contribution of each price to this term. This is due to the use of dummy variables in the

wage equation. In order to observe the mediating and moderating effect of occupational variables, we

have used the approach of Brown, Moon and Zoloth (1980) decomposition technique.

The estimation of separate wage equations by gender and the mean characteristics give the

following decomposition:

)(1)(1111

∑∑==

−+−=−n

j

f

j

m

j

m

j

n

j

m

j

f

j

m

j

f

jfm ppwnxxpwnwn β

∑

∑ ∑

=

= =

−+

−+−+

n

j

f

j

m

j

f

n

j

n

j

f

j

f

j

m

j

f

j

m

j

f

j

f

j

aap

ppwnxp

1

1 1

)(

)(1)( ββ

Where f

jp measures the predicted share of women in the jth occupation according to the model

of male predicting occupational distribution. In this model the gender wage gap is decomposition into

five elements: (i) gender differences in individual characteristics, (ii) differences in occupational

segregation between men and the simulated women’s distribution (due to differences in gender

productivity characteristics), (iii) differences in the return of these characteristics, (iv) differences in

occupational segregation between the simulated women’s distribution and the women’s actual

distribution (residual), (iv) differences in unobserved characteristics between men and women and their

prices.

The second and fourth elements of this decomposition are obtained by estimating a reduced

form multinomial logit model of occupational attainments for men. The probability of a male worker i

being in the jth occupation is a function of worker characteristics, z:

∑=

=n

j

m

j

m

i

m

j

m

im

ij

yz

yzp

1

)exp(

)exp(

The estimate of this model predicts f

jp the proportion of women that would be in each

occupation if women were allocated between occupations according to the male occupation attainment

model. This approach supposes that in a world without discrimination, women would be distributed

across occupations according to the male occupational mechanism. This is also important to consider

that all discrimination studies make an implicit assumption as to what earnings would be in the absence

of discrimination. This is called the non-discriminatory market structure. It is important that the

assumed non-discriminatory wage structure is as realistic as possible. Second, assuming a model which

explicitly estimates current total earnings in the economy, allows confirmatory analysis to isolate the

development of macroeconomic changes in the Wage Gap.

Brown at al. (1980) showed that their decomposition is a particular case of the Oaxaca

decomposition where the gender differences in occupational distribution are taken as exogenous and

therefore, part of the explained component. In the Brown et al, decomposition this part is further

decomposed into an explained component and a residual component.

Finally, in order to evaluate the differences in the earning structures of males and females, the

statistical earning function by Mincer (1974) is augmented by other factors earnings of the individuals.


So, we have adopted expanded methods for decompositions to distinguish between the factors affecting

individual’s characteristics and discrimination.

This can be modelled as follows,

(WD) (I)

)β-(βX P)-(PW L -W Lk k

FF

∑ ∑+∝∝=∩∩F

k

M

k

Fkk

F

k

M

kk

FM

(QD) (PD)

)P (PW )βX -X(P k

k

k

M

k

k

kF F

kM

kMF

k

M

−++ ∑∑

(OD)

)P P̂(W k

k

k

M Fk

F −+∑

Brown et.al. (1980) defined (I) and (WD) as unjustified differences in intra-occupation wages,

(PD) as the justifiable intra-occupation wage differential, with (QD) and (OD) as the justifiable and

unjustifiable portions of the inter–occupation wage differentials, respectively. The terms included in

the decomposition are defined below:

)W and WFM

= The grand mean wages for males and females. FM

P and P kk the observed proportion of males and females in occupation k.

X and β̂ , kk∝ = The regression coefficients and mean characteristics respectively are now given

for the kth occupation and superscripts M and F still refer to males and females.

kMW = mean wages for males in occupation k.

PF

k

∧

= the hypothetical proportion of females in the sample who would be in occupation k if

females faced the same occupational allocation mechanism as males.

Final Model Specification The exact specification of the earning function which is adopted in this study for estimation is given

below, dropping the individual subscripts and sex superscripts:

LNWAGE= TECHEDU β EXPSCH β EXPSQ β EXP β SCH β β 543210 +++++

SALE β CLER β ADMN β PROF β DIV β WIDβ MAR β 1211109876 +++++++

GUJ β MUL β RAW β FAS β LAH β AGRI β SERV β 19181716151413 +++++++

BAH β SIA β 2120 ++

Note: The definition of variables is given in Appendix-1

Analytic Procedure This analysis was completed into three steps to examine the mediating and moderating effects

involving interactive variables. First, we studied gender based differences in its standard form. Second,

we introduced a set of predictors of explanatory variables of human capital and social roles and

analyzed the result of this intervention for gender differences. Third, we introduced the term gender

with regional variables and occupational status. In the logistic regression analysis, odds ratios and

partial correlations represented the effects estimated and the pseudo-R 2 represented the goodness of fit

(Norusis, 1994; Veall and Zimmermann, 1991).

The methodological approach, which we have adopted, allows for variation, both in wages and

occupational distribution resulting from differences in productivity related factors, demographic factor,

and occupational attainments. Oaxaca (1973) and Blinder (1973) decomposition analysis was used to


investigate the pattern of earning structures responsible for personal characteristics of the individual

and the discriminatory factors. Furthermore, a multinomial logit model hypothesized the occupational

distribution of females that would exist if they faced the same structure of occupational determination

as males. Consequently, we decomposed the overall wage differential into justifiable and unjustifiable

portions attributable to productivity differences and occupational differences.

Table 1: Distribution of the Sample across Categorical Variables

Variables Total Sample (%) Males (%) Females (%)

Gender 100 91 9

Occupations

Prof 14.6 13.3 1.3

Admin 4.8 4.4 0.4

Clerks 13.3 12.1 1.2

Sale 6.6 6 0.6

Services 23.4 21.3 2.1

Prod 32.9 29.9 3

Agri 4.4 4 0.4

Technical Education

Trained 11.3 10.3 1

Untrained 88.7 80.7 8

Marital Status

Unmarried 32.3 29.4 2.9

Married 65.1 59.2 5.9

Widow 2.1 1.9 0.2

Divorced 0.5 0.4 0.1

Cities

Lahore 37.6 34.2 3.4

Faisalabad 18.4 16.7 1.7

Rawalpindi 8.4 7.6 0.8

Multan 11.3 8.6 2.7

Gujranwala 7.9 7.2 0.7

Sialkot 9.5 8.6 0.9

Bahawalpur 2.3 2.1 0.2

Sargodha 5.6 5.1 0.5

Source: Labour Force Survey, Various Issues upto 2008

This table-1 shows only 1% females have attained technical education while males have very

high percentage (10.30%). Marital data status suggests that the percentage of married male workers

(59.2 percent) is greater than the percentage of married female workers (5.9 percent). The percentage

of unmarried, widowed and divorced females’ workers is also less than that of their respective male

counterparts. The distribution of our sample in terms of cities indicates that the largest proportion of

the sample, about 37.6 percent came from Lahore city, with 34.2 percent and 3.4 percent males and

females respectively. Faisalabad and Multan have the second and third largest proportions of the

sample (18.4 percent and 11.3 percent respectively while the samples from Bahawalpur and Sargodha

are very unrepresentative having only 2.3 percent and 5.6 percent of the total sample, respectively.


Table 2: Descriptive Statistics for Non-categorical Variablesa (Means and Standard Deviation)

b

Variables Total Sample Males Females

Lnwage 7.7

1.7

7.8

1.8

7.6

1.3

Age 33.5

11.2

33.6

11.6

32.96

11.4

Sch 8.4

4.2

8.1

4.1

8.8

3.1

Exp 19.6 8.3

19.2 7.0

17.9 6.5

Expsq 549.5

164.3

552.1

168.7

519.7

161.3

Expsch 125.8

34.5

128.3

44.0

103.5

33.5

Child0-6 .6 .2

.62 .3

.44 .1

Child6-14 .95

.4

.95

.4

.90

.3

Malpres 2.45

.8

2.51

.7

1.87

.7

Bold values represent the mean and italic values represent the Standard Deviation

Source: Labour Force Survey, Various Issues upto 2008 aFor definitions of variables, see Appendix-1

Table-2 reveals the natural logarithm of monthly earnings that is dependent variable. This table

also details out among other features of female workers on average females are younger by 1 year

having obtained 1 more year of schooling, having almost 2 fewer years of experience than their male

counterparts.

Table 3: Mean Values of Log Monthly Earnings (By Occupation and Gender)

Variables Total Sample Males Females

Prof 5.7 5.6 7.0

Admin 6.8 6.8 5.5

Clerks 6.4 6.6 5.2

Sale 7.0 6.8 7.1

Services 6.2 6.9 6.1

Prod 6.3 6.3 6.1

Agri 6.7 6.6 7.0

Source: Labour Force Survey, 2007-2008

Table-3 lists the mean values of log monthly earnings for seven occupation groups. From the

tabulations, it is revealed that the production sector gives the highest mean log wages to males,

approximately 6.9 (Rs. 995), where it is already indicated in Table-1 that males are highly concentrated

in production sector. On the other hand, Table-3 showed mean log wages to females around 5.2 (Rs.

182).While Table-1 provides evidences that most of the females are concentrated into this sector.

Empirical Analysis Results Using the standard method for decomposition, the earning differential is decomposed for the full-scale

wage equation in Table-4 and for personal characteristics wage equation in Table- 5. The results

presented in these two tables show that endowments count even less (approximately zero) and

discrimination differential for even more (Table-4). Table-4 reveals that the first column of the

differential except experience males does not have an advantage in schooling, experience-squared and


technical education. It was confirmed by negative coefficients on these variables. It shows that females

earn 6.83 percent more than males in case of non-discrimination. With respect to the differences in

coefficients, Table-4 shows that experience, technical education, occupations and cities are the main

sources of discrimination accounted for 131.4 percent discrimination. When we do not control for

occupations, our decomposition in Table-5 shows 153.70 percent discrimination implies that the

estimated effects of discrimination are larger than those reported in Table-5 so it may be that the main

way in which women are discriminated is by occupational segregation or within-occupation

discrimination.

Table-4: Decomposition Analysis from Full-Scale Wage Equation

Variable Difference in Endowments

)XX( β̂ M FM

−

Difference in Coefficients

)β̂β̂(X FM −F

Sch -0.06 -0.04

Exp 0.08 0.11

Expsq -0.01 -0.001

Expch -0.02 -0.01

Tech -0.001 0.00

Marital Status 0.01 -0.06

Occupational -.03 0.03

Cities -.001 0.20

Total -0.032 0.229

Male – Female Earnings Differentials

Due to Endowments -0.01 -6.8%

Due to Returns to Explanatory Variables 0.20 131.4%

Intercept Differential -0.05 -24.4%

Total Differential due to Discrimination 0.14 106.8%

Overall Earning Differential 0.13 100%

(Difference in log earnings)

Table-5: Decomposition Analysis from Personal Wage Equation

Variable Difference in Endowments

)XX( β̂ M FM

−

Difference in Coefficients

)β̂β̂(X FM −F

Sch -0.06 0.01

Exp 0.08 0.14

Expsq -0.01 -0.02

Expch -0.02 -0.02

Tech -0.01 -0.01

Marital Status 0.01 -0.07

Cities -0.01 0.23

Total -0.010 0.25

Male – Female Earnings Differentials

Due to Endowments -0.01 (-7.17%)

Due to Returns to Explanatory Variables 0.25 (153.70%)

Intercept Differential -0.10 (-46.42%)

Total Differential due to Discrimination 0.14 (107.17%)

Overall Earning Differential

(Difference in log earnings) 0.138 (100%)

Notes: A ‘+’ sign indicates an advantage for males,

A ‘-‘sign indicates an advantage for females

Further, the estimated results from multinomial logit model are presented separately for males

and females in Table-6 and Table-7 respectively. In the logistic regression production is taken as

comparison group, against which each other group is compared. Among male’s group, a highly

educated individual is more likely to obtain a job in the professional, administration, clerical or


agriculture sector, relative to attaining a job in production sector. Those who have more experience are

more likely to work in the professional group, but experience variables do not show any significance in

deciding entry into other occupations. The more technical education an individual has, the more likely

it is that he will work in production sector as compared to professional, service or agricultural groups.

Marital status does not seem to make any difference to the occupational attainment decision making of

an individual. The results for the dummy variables for the cities suggest that residents from Lahore,

Gujranwala, Sialkot, Multan and Faisalabad are more likely to work in sale, service and agriculture

sector as compared to entry into production sector.

The greater the number of children between zero to six in the family, the greater is the

probability to opt for production sector relative to professionals, administration or clerks. The higher

the number of males in a home, the higher is the probability that an individual will be in sales group,

but it decreases the probability for entering into agriculture sector relative to production group.

Table -6: Results of the Multinomial Logit Occupational

Attainment Model (For Males Only)

Dependent Variable = Prob (one’s occupational attainment)

Variable Prof Admin Cler Sale Serv Agri

Constant -3.58***

(-10.5)

-2.92***

(-6.0)

-1.69

(-1.6)

-2.71***

(5.6)

-0.76***

(-2.9)

-3.76***

(-4.2)

Sch 0.196***

(15.7)

0.98***

(5.9)

0.08***

(6.8)

0.0026

(0.02)

-0.05***

(-6.6)

0.08***

(3.4)

Exp 0.02***

(4.7)

0.01

(0.7)

0.01

(1.4)

0.001

(0.14)

-0.004

(-0.8)

0.002

(1.1)

Tech -0.68***

(-3.54)

0.69***

(2.86)

0.01

(0.01)

0.17

(1.20)

-0.52***

(-3.61)

-0.58**

(-2.00)

Marital Status

Mar -0.05

(-0.4)

0.09

(0.6)

-0.03

(-0.6)

-0.059

(-0.5)

0.08

(1.2)

-0.30

(-1.2)

Wid 0.11

(0.28)

0.70

(1.1)

-0.20

(-0.20)

0.08

(0.04)

-0.77

(-1.5)

-0.78

(-0.7)

Div 0.02

(0.02)

-- -0.27

(-0.20)

-- 0.07

(0.1)

--

Child 0-6 -0.10*

(-1.56)

-0.19**

(-1.9)

-0.05**

(-2.11)

-0.01

(-0.07)

-0.06

(-1.2)

-0.09

(-0.73)

Child 6-14 0.032

(0.67)

0.043

(0.60)

0.07

(1.5)

0.08

(1.2)

0.01

(0.4)

-0.001

(-0.00)

Malpres 0.01

(0.08)

-0.07

(-1.11)

0.008

(0.23)

0.076*

(1.56)

0.0020

(0.2)

-0.07**

(-2.29)

Cities

Lah 0.03

(0.07)

-0.40

(-1.5)

-0.68***

(-2.8)

1.04**

(2.19)

0.69***

(3.2)

1.05**

(2.09)

Fai -0.04

(-0.3)

0.29

(1.12)

-0.27

(-1.43)

1.06**

(2.10)

0.54**

(2.29)

1.48**

(1.91)

Raw 0.16

(0.8)

0.49

(1.43)

-0.39

(-1.4)

1.05*

(1.8)

0.32

(1.24)

1.12

(1.3)

Mul -0.014

(-0.08)

-0.47

(-1.06)

0.10

(-0.62)

1.08**

(2.10)

1.06

(1.46)

0.80

(1.4)

Guj 0.19

(0.75)

0.56

(-1.7)

0.26

(1.06)

1.05**

(2.1)

1.7***

(6.4)

2.39***

(2.7)

Sia 0.83**

(2.11)

0.88

(1.6)

0.13

(0.50)

1.42**

(2.12)

1.01***

(2.9)

2.07**

(2.01)

Bah -0.05

(-0.1)

-0.23

(-0.61)

-0.23

(-1.08)

1.05*

(1.9)

1.56***

(6.1)

1.88**

(2.5)

Sample Size 480 158 355 221 790 128

Pscudo R2

0.07


Notes: Numbers with * are statistically significant at the 10 percent level, with ** at the 5 percent level and *** at the 1

percent level, two tailed test.

Numbers in parentheses are z-values.

Unmarried, Production sector and Sargodha city are reference categories.

Table-7: Results of the Multinomial Logit Occupational

Attainment Model (For Females Only)

Dependent Variable = Prob (one’s occupational attainment)

Variable Prof Admin Cler Sale Serv Agri

Constant -1.73

(1.3)

-24.23***

(-15.5)

-4.93***

(3.5)

-23.33***

(7.4)

-19.37***

(-6.87)

--

Sch 0.036***

(2.67)

0.02

(-0.31)

0.27***

(6.95)

-0.06

(-1.05)

-.088***

(-2.61)

0.12***

(4.5)

Exp 0.03

(1.05)

0.06

(1.50)

0.01

(0.8)

0.02***

(3.45

.043*

(1.71

0.06**

(2.10

Tech -1.9

(-1.50)

-- -1.6***

(-2.82)

-- -- 0.38

(-0.61)

Marital Status

Mar -0.10

(-0.04)

-0.80

(0.7)

0.02

(0.06)

-1.48

(-1.35)

0.04

(0.15)

0.03

(0.61)

Wid -- -1.75

(1.02)

-0.47

(-0.65)

-1.25

(-0.84)

-0.15

(-0.16)

--

Div -- 0.14

(0.11)

-1.5934

(-1.02)

-- -0.1998

(-0.09)

--

Child 0-6 -0.01

(-0.31)

-0.34

(-0.6)

0.19

(0.89)

0.07

(0.5)

-0.13

(-0.94)

-0.19

(-0.38)

Child 6-14 0.07

(0.25)

-0.16

(-0.19)

-.1938

(-1.03)

0.62**

(-2.51)

-0.02

(-0.11)

0.07

(0.3)

Malpres -0.17

(-1.31)

-0.35

(-1.3)

-0.03

(-1.05)

-0.68***

(-2.61)

-0.12

(-1.2)

-.11

(-0.81)

Cities

Lah -0.01

(-0.51)

24.16***

(17.82)

1.01

(1.33)

22.24***

(7.3)

19.34***

(7.2)

20.58***

(17.9)

Fai -0.05

(-0.32)

-- 0.75

(0.67)

23.66***

(8.01)

19.04***

(7.0)

21.01***

(17.01)

Raw -0.42

(-0.39)

22.74***

(12.92)

0.49

(0.37)

22.01***

(6.98)

18.73***

(6.85)

20.02***

(14.6)

Mul -- -- 2.44**

(2.10)

22.38***

(6.98)

20.11***

(7.32)

--

Guj -- -- 3.73

(1.39)

-- -- --

Sia -- -- -- -- -- --

Bah -- -- 0.07

(0.07)

21.88***

(7.42)

18.56***

(6.99)

20.36***

(2.90)

Sample Size 22 15 121 23 41 25

Pseudo R2

0.27

Notes: Numbers with * are statistically significant at the 10 percent level, with ** at the 5 percent level and *** at the 1

percent level, two tailed test.

Numbers in parentheses are z-values.

Unmarried, Production sector and Sargodha city are reference categories.

Moreover, a woman from Lahore will choose administration sale, service or agriculture sector

relative to production while a woman from Faisalabad and Rawalpindi is more likely to enter into

sales, service and agricultural sector. The occupational attainment pattern is almost same for males and

females. The coefficient estimates obtained from the males’ multinomial logit model are used to

predict the hypothetical distribution of females in each occupation.


Table-8 shows females actual occupational distribution, females’ hypothetical distribution and

males’ actual distribution. Hypothetical distribution is estimated to see the difference of their

occupational attainment if females are facing the same structure of occupations as men. This table also

shows that the proportion of females will increase in professionals and administration by considerable

size. And in production, service and clerical group, their proportion will be decreased. As compared to

males, their proportion in professional and administration jobs will increase which implies that more

females will be in high paying jobs. The final section provides us with the comprehensive picture if

male-female earning differentials incorporate occupational attainment. To estimate the extended

version of decomposition, we have estimated separate earning equation for each sex-occupational

group. A summary of these results is presented in Table-9.

Table-8: Occupational Distribution

Occupation Males’ Actual

Distribution

Females’

Distribution

Females’ Hypothetical

Distribution

Value % Value % Value %

Prof 0.04 13.3 0.06 1.3 0.12 23.11

Admn 0.04 4.4 0.03 0.4 0.04 14.66

Cler 0.01 12.1 0.27 1.2 0.17 27.59

Sale 0.06 6.0 0.06 0.6 0.08 8.45

Serv 0.13 21.3 0.03 2.1 0.04 4.77

Prod 0.23 29.9 0.15 3 0.00 11.00

Agri 0.03 4.0 0.07 0.4 0.16 25.65

Source: Calculation of first two columns is based on the observations from the data.

Calculations of third column are based on the estimated results reported in Table-6.

Discussion This paper explains that gender discrimination in participation and wages exist in Pakistan. As a result,

all hypotheses receive consistent support to strengthen the claim of gender discrimination in

participation and wage differentials. The factors of human capital might be more significant than sex

for predicting a working person's earnings. Status composition, marital status and occupation selection

also receive support in various steps of analysis. However, the thesis of homogeneity and bargaining

exert no significant effect on earnings. Although marital status and childrearing roles particularly

reduced a female's access to a paid job and earnings, education if acquired showed the opposite effects

for the female. The findings also show that gender discrimination might apply only for women who

were married and responsible for childrearing. Likelihood to work and self-selection sufficiently

mediate gender difference in earnings. Education and marital status are the most important background

characteristics to predict earnings and work participation respectively. They also reflect the effect of

human capital and social roles. According to human capital and social role theories, women committed

to their families would acquire less skill for work (Eagly 1987; Mincer 1993). Other findings are also

supportive of structural explanation of individual's earnings that depends on characteristics and choice

of the occupation.

This is evident that though female participation is improved yet male participation is increased

faster. The net effect is in favour of males (see the difference between Row 4 & Row 5). Establishing

the reason for wage gap changes can have important policy ramifications. A significant reduction in the

wage gap is attributed to the unexplained part of the earnings estimates (Row 6). So this makes sense

that market is offering better wages to females than their counterparts over a period of time.

Participation estimates convey a far less favourable picture regarding the relative female position.

Overall participation changes increased the wage gap considerably. Explained participation propensity

changes favoured males considerably, while unexplained changes were favoured them a lot less. This

implies whatever it is that makes females observably worse participants than males (one must look at


the participation variables. Finally, changes in macroeconomic conditions introduced by changes in the

non-discriminatory wage structure (Row 7) have worked in favour of females between 1999 and 2008.

Given the limited extent of female employment rate in Pakistan, this is noticed that macroeconomic

changes are working in the right direction regarding participation and the wage gap.

Confirmatory Factors Based Wage Gap decompositions Scenario

Total Wage Gap change 1999-2008 0.0579

Participation stage estimates

1. Changes in unexplained participation 0.1204

2. Changes in unexplained indirect participation -0.0865

3. Changes in explained indirect participation 0.0792

Total Wage Gap change due to participation 0.1080

Earnings stage estimates

4. Changes in male productivity 0.1021

5. Changes in female productivity -0.0649

6. Changes in unexplained earnings -0.0510

7. Changes in the non-discriminatory wage structure -0.0572

Total Wage Gap change due to earnings estimates -0.0823

Furthermore, the results point out that when occupational dummies were not included in the

analysis, the discriminatory component of the total differential increased from 130.12 percent to 151.72

percent. Thus, the way in which occupation was incorporated into the model significantly affected the

discriminatory component. A separate model of occupational attainment was used to predict the

probability of attaining a certain occupation, and we were enabled to calculate a comprehensive

decomposition analysis allowing for within-occupation and occupational segregation in the overall

wage differential. The results of decomposition analysis showed that unjustified differences within-

occupational and accounted for 62.29 percent while occupational segregation showed 34.28 percent

unjustified differences in gender wage gap. The results also manifest that women in Pakistan are not

different in their productivity-related endowment from men and if there is no discrimination, women

earn more as compared to men.

Thus, dissimilarity in attainment of jobs is a remarkable phenomenon between males and

females in Pakistan. It could be both due to differences in employer’s preferences toward women and

due to a lack of product-market competition. If the labour market does not have only limited traditional

occupations for women, it will reduce the degree of gender occupational segregation. Finally, within

occupation, discrimination could be reduced by applying the law ‘equal pay for equal work’.

Policy Suggestions i. Gender Mainstreaming

The advancement of gender equality is manifested in women’s participation in decision making,

transformations in institutions and organizational cultures, and collective actions to rectify the

gendered practices especially in employment and labour market of Pakistan.

ii. Balanced Development

Gender discrimination does not just affect the participation and earnings; it has many other dimensions

as well. Without access to essential infrastructure and services, women will lack human and social

capital to participate in the process of earning or job market. Women may be barred from developing

their capabilities because of social or cultural restrictions. Such restrictions limit their geographical

mobility, entrance in the job market and make it difficult for them to attend school or seek technical

training. Gender discrimination in all its dimensions is declining in most countries of Asia and the

Pacific but it is more widespread and serious in Pakistan and particularly in rural areas than in urban


areas and also wide disparities between regions within the country. Government policies that leave the

allocation of resources to the market and that invest scarce resources in places with the best growth

potential will benefit some segment of the labour force. Owing to the inadequate communication and

social networks, female labour force is disadvantaged when it comes to organization and the

articulation of needs, priorities and preferences through economic processes. This area needs

immediate attention of Government of Pakistan for tapping the potential of females in improved

fashion.

iii. Gender Bias and Reducing Discrimination

Government of Pakistan has been failed to recognize the gender bias which prevails in the social and

economic sectors of Pakistan. Government should take institutional measures to look at the

development of the both genders separately rather than viewing them as closely related. Government

should provide education and training to female labour force. Attempts to develop the female labour

force are not very significant. A lot of exploitation of female labour force can be seen in many sectors.

Better infrastructure, provision of education and training opportunities can improve the present

condition of this segment of the labour force.

iv. Investing in education

The knowledge-based economy is a dynamic call of today. This requires government willingness and

attention to introduce required changes in the curriculum and changes in the attitude and the mindset of

every member of the community. These changes will affect the professionals and later the industrial

workers in the economy; they will eventually have an impact on female population of the economy. All

will face new and rapidly changing technologies that they will have to use in their daily lives and in

whatever sector they are working in to add more value to their products. Investment in education will

be necessary to enhance the competitiveness of the countries.

Further Proposed Study 1. In Pakistan like all other developing countries, Socio-economic research perspective is needed to

explore issues pertaining to individual’s psychological processes, predisposition and preference.

This further study would comprehend issues regarding deliberate choice of disadvantaged

position of target population. For example, a married educated women might prefer child raising

rather doing job so in this case her low involvement and earnings may not indicate gender

discrimination in the labour market. So further research is needed to identify and analyze factors

such as preference, expectation and perception of deprivation and discrimination (Crosby, 1982).

2. All kind of gender discrimination studies assume static relationship among selected variables but

variables may change from time to time so further research is required to identify dynamic causal

processes leading to employment and earnings. Along with cross-sectional studies, longitudinal

studies are required to ascertain reciprocal relationships between earnings and their predictors,

including education, marital status, the type of family, and number of children, is possible.

3. Another proposed area for research is that all existing models overemphasize on individual

characteristics such as education and consider organizational characteristics such as nature of job,

hierarchical position and departmental location either constant or ignoring them completely. But

this is widely seen that discriminate policy is also ensued while assigning job positions to

different genders so the conventional models do not take into account the complete extent of

wages discrimination across genders.


Appendix-1 SCH = years of schooling completed

EXP = experience: AGE-SCH-6

EXPSQ = experience square

TECHEDU = one if worker received technical education, zero otherwise.

MAR = one if individual is married, zero otherwise.

WID = one if individual is widowed, zero otherwise.

DIV = one if individual is divorced, zero otherwise.

(Unmarried individuals are reference category)

PROF = one if individual is a professional, zero otherwise.

ADMN = one if individual is an administrator/ manager, zero otherwise.

CLER = one if individual is clerk or related worker, zero otherwise.

SALE = one if individual is a sales or related worker, zero otherwise.

SERV = one if individual is a services worker, zero otherwise.

AGRI = one if individual is an agricultural worker, zero otherwise.

(Production workers are reference category)

LAH = one if individual is lives in Lahore, zero otherwise.

FAS = one if individual lives in Faisalabad, zero otherwise.

RAW = one if individual lives in Rawalpindi, zero otherwise.

MUL = one if individual lives in Multan, zero otherwise.

GUJ = one if individual lives in Gujranwala, zero otherwise.

SIA = one if individual lives in Sialkot, zero otherwise.

BAH = one if individual lives in Bahawalpur, zero otherwise.

(Sargodha is a reference category)

OLS method is used to estimate the regression coefficient.

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