variables
TRANSCRIPT
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
International Research Journal of Finance and Economics - Issue 55 (2010) 180
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
181 International Research Journal of Finance and Economics - Issue 55 (2010)
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
International Research Journal of Finance and Economics - Issue 55 (2010) 182
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.
183 International Research Journal of Finance and Economics - Issue 55 (2010)
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
International Research Journal of Finance and Economics - Issue 55 (2010) 184
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.
185 International Research Journal of Finance and Economics - Issue 55 (2010)
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
International Research Journal of Finance and Economics - Issue 55 (2010) 186
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
187 International Research Journal of Finance and Economics - Issue 55 (2010)
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
International Research Journal of Finance and Economics - Issue 55 (2010) 188
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.
189 International Research Journal of Finance and Economics - Issue 55 (2010)
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
International Research Journal of Finance and Economics - Issue 55 (2010) 190
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
191 International Research Journal of Finance and Economics - Issue 55 (2010)
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.
International Research Journal of Finance and Economics - Issue 55 (2010) 192
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.
References [1] Male-Female labour market participation and wage differentials in Greece C. N. Kanellopoulos
K. G. Mavromaras February 1999 CENTRE OF PLANNING AND ECONOMIC RESEARCH
No 70
[2] Government of Pakistan (2006). Labor Force Survey 2005-06, Federal Bureau of Statistics,
Statistics Division, Islamabad, Pakistan.
[3] Acharya M. and L. Bennett (1982) Women and the subsistence sector: Economic participation
and household decision making in Nepal. World Bank Working Paper no. 526.
[4] Aftab K (1990) Growth of the Informal Sector Firms: Lessons From Experience, Quaid-i-Azam
University and Freiderich Ebert Stiftung National Workshop on the Informal Sector.
[5] Albrecht, J., Bjorklund, A., and S. Vroman (2003) Is There a Glass Ceiling in Sweden? Journal
of Labour Economics, 21 (1), 145-177.
[6] Anderson, S and Patrick Francois (2001) Wage and Employment Differentials by Gender in
LDCs Center, Tilburg University (Preliminary Draft)
[7] Arrow, K. (1972) Models of job discrimination and some mathematical models of race in the
labour market. Racial Discrimination in Economic Life, A. Pascal (ed.) Lexington Books,
Lexington, Mass. p.83-102 and p.187-204.
[8] Ashraf, Javed and B. Ashraf (1993b) An Analysis of the Male-Female earning Differential in
Pakistan. Pakistan Development Review, 32:4, Winter, 895-904.
[9] Bergmann, B., (1974) Occupational Segregation, Wages and Profits when Employers
Discriminate by Race or Sex. Eastern Economic Journal, I(April-July), 103-110.
193 International Research Journal of Finance and Economics - Issue 55 (2010)
[10] Bergmann, Barbara. (1989) Does the Labour Market for Women’s Labor Need Fixing? Journal
of Economic Perspectives 3 (Winter 1989): 43-60.
[11] Blau F D and M A Ferber. (1987) “Discrimination: Empirical Evidence from the United States”
American Economic Review, 77(2): 316-320.
[12] Blau, Francine D. and Lawrence M. Khan. (1996) Wage structure and Gender Earnings
Differentials: An international comparison. Economica 63 (May 1996): S29-S62.
[13] Blau, F. and M. Ferber (1987) Discrimination: Empirical Evidence from the United States.
American Economics Review, 77(2), 316-320.
[14] Blinder, A. S. 1973. "Wage Discrimination: Reduced Form and Structural Estimates." Journal
of Human Resources 8(4):436-55. Boston, T. D., 1990.
[15] Blinder, Alan S. (1973) Wage Discrimination: Reduced Form and Structural Estimates. Journal
of Human Resources 8 (Autumn 1973): 436-455.
[16] Cagatay, Nilufer and Sule Ozler (1995) Feminization of the Labor Force: The Effects of Long-
Term Development and Structural Adjustment. World Development, 23(11):1883-1894.
[17] Cain, G. (1986) The economic analysis of labour market discrimination: A survey. In
Handbook of Labour Economics Volume 1, O. Ashenfelter and R. Layard (eds.), Elsevier
Science Publisher, Amsterdam, p.693-785.
[18] Chau-Kiu Cheung Gender Differences in Participation and Earnings in Hong Kong Journal of
Contemporary Asia, Vol. 32 No. 1 (2002)
[19] Chenng, Chau-kiu and Kwan-kwok Leung (1995) "Democratic Predispositions Among College
Students in Hong Kong" Journal of Personality and Social Behavior, 10(2):363-378.
[20] Chenng, Chau-kiu (1998) "Impacts of Classes on Hong Kong People's Well-being," Human
Relations,51(1):89-119.
[21] Chaudhry, M.A. and Ghulam Mustafa Chaudhry (1992) Trends of Rural Employment and
Wages in Pakistan. Pakistan Development Review. Vo. 31, No. 4. pp. 803-812.
[22] Collier, P. (1994) A theoretical framework and the Africa experience, in labour Markets in an
Era of adjustment. Vol. 1 edited by S. Horton, R. Kanbur and D. Mazumdar, The World Bank,
Washington, D.C. p. 279-307. 21
[23] Dayioglu, M. (2002) A comparative study on the labor market experience of Turkish women at
two time periods: 1988, 1994. Mimeo, 37 pp. (August).Economic Survey 1996-97, Statistical
Supplements.
[24] Galiani and Pablo Sanguinetti (2000) Wage inequality and trade liberalization: Evidence from
Argentina1 Sebastian Universidad Torcuato Di Tella
[25] Gannon, Brenda, Robert Plasman, François Rycx and Ilan Tojerow. (2004) Inter industry wage
differentials and the gender wage gap: Evidence from European countries. PiEP Working
paper. The London School of Economics, 2004.
[26] Gneezy, U., Niederle, M., and A. Rustichini (2003) Performance in Competitive Environments:
Gender Differences. Quarterly Journal of Economics, 118 (3), 1049 - 1074.
[27] Goldin, C. (1990) Understanding the gender gap: An economic history of American women,
New York, Oxford University Press.
[28] Greenhalgh, S. (1985) Sexual stratification: the other side of growth with equity. East Asia.
Population and Development Review, 11(2), 265-314.
[29] Gregg, P. and S. Machin (1993) Is the Glass Ceiling Cracking? Gender Compensation,
Differentials and Access to Promotion among UK Executives. University College London,
Discussion Paper 94-05, July.
[30] Grimshaw, Damian, Jill Rubery and Hugo Figueiredo. (2002) The Gender Pay Gap and Gender
Mainstreaming Pay Policy in EU Member States. European Expert Group on Gender and
Employment Report to the Equal Opportunities Unit, DG Employment (November 2002).
International Research Journal of Finance and Economics - Issue 55 (2010) 194
[31] Hartmann, H. (1976) Capitalism, patriarchy, and job segregation by sex. In Women and the
Workplace. ed. M. Blaxall and B. Reagan, University of Chicago Press, Chicago, 137-69.
[32] Heckman, J.J., H. Ichimura and P. Todd (1997) Matching as an Econometric Evaluation
Estimator: Evidence from Evaluating a Job Training Program. Review of Economic Studies
(64), 605-654.
[33] Heckman, J.J., H. Ichimura and P. Todd (1998) Matching as an Econometric Evaluation
Estimator. Review of Economic Studies (65), 261-294.
[34] Heckman, J.J., R. LaLonde, and J. Smith (1999) The Economics and Econometrics of Active
Labour Market Programs. Handbook of Labor Economics, III, ed. by O. Ashenfelter and D.
Card, 1865-2097, Elsevier.
[35] Heckman, Lochner, and Todd (2003) Fifty Years of Mincer Earnings Regressions. Working
Paper 9732, National Bureau of Economic Research.
[36] Hirashima, S. (1978) The Structure of Disparity in Developing Agriculture: A Case Study of
the Pakistan Punjab, Tokyo, Institute of Developing Economics.
[37] Jurajda, Stepan and Teodora Paligorova (2006) Female Managers and their Wages in Central
Europe, DISCUSSION PAPER SERIES IZA DP No. 2303 Forschungsinstitut zur Zukunft der
Arbeit Institute for the Study of Labou.r
[38] Hussain, Syed Mushtaq. (1971) Projections of Manpower Requirements in Pakistan: A
Methodological Note. Ronald G. Ridker and Harold Lubell. Eds. Employment and
Unemployment Problems of the Near East and South Asia, Delhi: Vikas Publications, pp. 384-
433.
[39] Ibraz, T. (1993) The cultural context of women’s productive invisibility: a case study of a
Pakistani Village. Pakistan Development Review, 32(1), 101-125.
[40] Jolliffe, D. and N.F. Campos (2005) Does Market Liberalization Reduce Gender
Discrimination? Econometric Evidence from Hungary, 1986-1998. Labour Economics, 12 (1),
1-22.
[41] Jurajda S. (2005) Gender Segregation and Wage Gap: An East-West Comparison. Journal of
the European Economic Association, Papers and Proceedings, 3 (2-3), 598-607.
[42] Kanellopoulos C N (1982) ‘Male – Female Pay Differentials in Greece’, Greek Economic
Review, 2: 222-241.
[43] Kalpagam, U. (1986) Gender in Economics: The Indian perspective. Economic and Political
weekly, Vol. 21, No. 43, p. WS-59-66.
[44] Khoo, S.E., P.C. Smith and J.T. Fawcett. (1984) Migration of Women to Cities: The Asian
Situation in Comparative Perspective. International Migration Review, 18(4): 1247- 1263.
Winter.
[45] Kidd, Michael and Michael Shannon. (1996) Does the level of Occupation Aggregation Affect
Estimates of Gender Wage Gap? Industrial and Labor Relations Review 49 (January 1996):
317-329.
[46] Long, J. Scott (1997)Regression Models for Categorical and Limited Dependent Variables
(Thousand Oaks, CA: Sage).
[47] Mujahid, G.B.S., (1978) A Note on Measurement of Poverty and Income Inequalities in
Pakistan: Some Observations on Methodology. Pakistan Development Review, Vol.XVII,
Autumn 1978.
[48] Nadvi K. (1990) Multiple Forms of Subcontracting Arrangements: Implications for the Growth
of the Informal Manufacturing Sector, Quaid-i-Azam University and Freiderich Ebert Stiftung
National Workshop on the Informal Sector of Pakistan.
[49] Nadvi K. and Schmitz H., (1993) Industrial Clusters in Less Developed Countries: Review of
Experiences and Research Agenda, IDS Discussion Paper. 166
195 International Research Journal of Finance and Economics - Issue 55 (2010)
[50] Naqvi, Syed Nawab Haider and A. R. Kemal. (1994) Structural Adjustment, Privatisation and
Employment in Pakistan. In Rizwanul Islam, ed., Social Dimensions of Economic Reforms in
Asia. New Delhi: International Labour Organisation, South Asia Multidisciplinary Team
(SAAT).
[51] Ngo, Hang-yue (1992) "Employment Status of Married Women in Hong Kong, Sociological
Perspectives”, 35(3):475-488.
[52] Nopo, H. (2004) Matching as a Tool to Decompose Wage Gaps. IZA Discussion Paper No.
981.
[53] Norris, M. (1992) The impact of development on women: A specific-factors analysis. Journal
of Development Economics. Vol.38, p. 183-201.
[54] Oaxaca, R. (1973) Male-Female Differentials in Urban Labour Markets. International
Economic Review, 14, 693-709.
[55] Oaxaca, R.L. and M.R. Ransom (1994) Discrimination and Wage Decomposition. Journal of
Econometrics, 61, 5-22.
[56] Oaxaca, Ronald and Michael Ransom. (1998) Calculation of Approximate Variance for the
Wage ecomposition Differential. Journal of Economic and Social Measurement 24 (1998): 55-
61.
[57] Ogloblin, C.G. (1999) The Gender Earnings Differential in the Russian Transition Economy.
Industrial and Labour Relations Review, 52(4), 602-627.
[58] Orazem, Peter and Vodopivec, Milian (1998) Male-Female Differences in Labor Market
Outcomes, During the Early Transition to Market: The Cases of Estonia and Slovenia,
[59] Paternostro Stefano and David E. Sahn (1998) Wage Discrimination and Gender
Discrimination in a Transition Economy: The Case of Romania.
[60] Polachek, Solomon W. (1981) Occupational self-selection: A Human Capital Approach to Sex
Differences in Occupation Structure. Review of Economics and Statistics, 63:1, 60-69.
[61] Psachoropoulos, G. and Z. Tzannatos (1989) Female labour force participation: An
International Perspective, World Bank Research Observer 4, 2, July, p. 187-201.
[62] Rosenbaum, P. and D. Rubin (1985) Constructing a Control Group Using Multivariate Matched
Sampling Methods that Incorporate the Propensity Score. The American Statistician, 39 (1), 33-
38.
[63] Schmidt, Peter and Robert P. Strauss. (1975) The Prediction of Occupation Using Multiple logit
Models. International Economic Review 16 (June 1975): 471-486.
[64] Sen, A. (1987) Gender and Cooperative Conflicts. Harvard University Discussion paper no.
1342, Cambridge, Mass.
[65] SPDC. (1997a). Integrated Social Policy and Macro Economic Planning Model, Karachi: Social
Policy and Development Centre, June, Processed.
[66] SPDC. (1997b). Review of the Social Action Program, Karachi: Social Policy and
Development Centre, June, Processed.
[67] Suen, Wing. (1997) Decomposing Wage Residuals: Unmeasured Skill or Statistical Artifact?
Journal of Labor Economics 15 (July 1997): 555-566.
[68] Tarver, James D. (1964) Occupational Migration Differentials Social Forces, Vol. 43, No. 2.
(Dec., 1964), pp. 231-241. Stable URL: http://links.jstor.org/sici?sici=0037-
7732%28196412%2943%3A2%3C231%3AOMD%3E2.0.CO%3B2-S
[69] Waterston, Albert. (1963) Planning in Pakistan, Washington, DC: The Economic Development
Institute.
International Research Journal of Finance and Economics - Issue 55 (2010) 196
[70] Watkins, J.F., T.R. Leinbach and K.F. Falconer (1993) Women, family, and work in Indonesian
Transmigration. Journal of Developing Areas 27, 377-398.
[71] World Bank. (1987) 2 volumes. Pakistan Financial Sector Review (in 2 volumes), Report No.
7049-PAK, Washington, DC: The World Bank, December 11, Processed. 168
[72] World Bank, (1991) Pakistan: Current Economic Situation and Prospects. Washington, D.C.:
World Bank. (1997) World Development Report 1997: The State in a Changing World,
Baltimore, Maryland: Published for the World Bank by Oxford University Press.
[73] Yasin, G (2006) Micro Finance as construct of social change: A case study of Khushali Bank,
Multan. Journal of Research (Humanities), Vol. 26, B. Z. University, Multan
[74] Zaman, Arshad. (1988) Rural Employment Strategies and Policies in Pakistan. Pakistan
Manpower Review, Vol. XIV, No. 1, pp. 11-57.