Gender inequality in labour force participation: an empirical investigation.
Sabir, Muhammad
One of the main caveats of Pakistan's economic development
history is the persistence gender inequality with respect to almost all
socioeconomic indicators. One of the possible explanations of these
gender gaps is discrimination in various socio-economic spheres
including labour market. An attempt is made in this paper to investigate
labour market discrimination in Pakistan by focusing on three aspects
including labour force participation, employed and employment formal
sector by applying a nested logit model. The results show that women are
highly disadvantaged in labour market reflected through estimated
probabilities in 2012-13. These inequalities are attributed to
multifaceted discriminatory factors that prevail in the society
including labour market and not to less human capital among women as
compared to men. It is hypothesised that once these discriminatory
factors are eliminated from the society women labour force participation
will increase in Pakistan.
JEL Classification: J7, J15, J16, J42
Keywords: Labour Market Discrimination
1. INTRODUCTION
Economic growth and development of the nations largely depend on
the quantity and quality of their labour force. In Pakistan, a sizeable
segment of population is considered as out of labour force. For
instance, the overall labour force participation rate for the age 15
years and above remained roughly in the range of 49 percent to 53
percent during 1974-75 to 2012-13. This means that of the total
population in 2012-13, aged 15 years and above, 53 percent is
economically active or part of labour force whereas 47 percent is
economically inactive or out of labour force. And more than 75 percent
of the women population is considered as economically in-active. In
addition, the labour market statistics show that a smaller proportion of
women than men, age 15 years and above, are employed. The unemployment
rate among women is higher than men. One of the possible explanations of
this gender gap is gender discrimination in the labour market.
In this context, this paper aims to analyse the behaviour of female
and male in labour force participation by empirically investigating the
determinants of labour force participation, and access to paid job for
both female and male. It also shed light on occupational gender
inequalities. It is believed that these types of analyses help designing
better policies to increase employment opportunities for both females
and males. They also facilitate suggesting various practical measures
that can be incorporated in gender sensitised employment policies that
in turn could lead towards greater labour force participation.
The rest of the paper is as follows: Section 2 presents the trend
in labour force of Pakistan; Section 3 gives the both the theoretical
and empirical review of literature on gender discrimination in the
labour market; Section 4 describes the empirical strategy employed in
the paper; Section 5 gives the estimated results and Section 7 concludes
the paper by mentioning some relevant policy implications.
2. TREND IN LABOUR FORCE
The section presents sex disaggregated trend in labour force of
Pakistan for the period 1974-75 to 2012-13.
2.1. Magnitude of Labour Force
Total labour force of Pakistan, aged 15 years and above,
constitutes 19.2 million in 1974-75 of which 1.3 million were women and
17.9 million were men. In 2012-13 it increased to 59.1 million of which
13.3 million were women and 45.7 million were men (Chart 1). This
indicates that male labour force dominates over the female labour force.
However, it is worth mentioning that in 1974-75 women labour force
constitutes less than one-tenth of men labour force whereas in 2012-13
this proportion gone up to more than one-fourth.
2.2. Labour Force Participation Rate
The refined Labour Force Participation rate (LFP) is the ratio of
labour force (employed and unemployed but seeking work) to the
population of respective age cohort. It is therefore, a key determinant
of the currently active population or an indicator of the magnitude of
the supply of labour in the economy and a crucial component of long term
economic growth. (1) The LFP rate can be used as an essential tool in
designing employment policies as well as of human resource development
and training policies.
In Pakistan, the overall LFP rate remained roughly in the range of
49 percent to 53 percent during 1974-75 to 2012-13. This means that of
the total population in 2007-08, aged 15 years and above, 53.1 percent
was economically active or part of labour force whereas 47.5 percent was
economically inactive or out of labour force.
As for the LFP rate by gender, the participation rate of men
declined from 87.2 percent in the 1974-75 to 83.6 percent in 1990-91
while remained more than 80 percent during the 1990s, in the 2000s and
2010s. As against, the LFP rate of women persistently rose from 7
percent in 1974-75 to 24.3 percent 2012-13. Apparently, this indicates
that the overall gender gap in labour force participation rates has
tended to reduce in Pakistan. However, it is still distressing that of
the total female population 15 years and above only 24 percent is part
of labour force compared to 81 percent of their male counterpart.
2.3. Employed Labour Force
Chart 3 gives the share of male and female in the employed labour
force of Pakistan. According to this Chart, 93.2 percent of the total
employed persons were male and only 6.8 percent were female in 1974-75.
With time, the share of women in employed labour force has increased
while that of male has declined. In 2012-13, female constitute 21.7
percent and male constitute 78.3 percent of the total employed persons
in Pakistan.
3. GENDER GAPS IN LABOUR MARKET: A REVIEW OF LITERATURE
Gender discrimination in the labor market is a complex subject and
theories explaining this discrimination can be classified into two broad
categories: feminist theories largely directed towards the
"Devaluation Hypothesis" and neo-classical human capital
theories leading towards "The Specialised Human Capital
Hypothesis."
3.1. Feminist Theories and the Devaluation Hypothesis
Feminist theories emphasise that women's disadvantaged
position in the labor market is caused by, and is a reflection of
patriarchy as well as the subordinate position of women in society and
in the family. In other words, the role of gender stereotypes held by
employers and societies at large affect differential occupational
attainment of men and women. These theories predict that women gravitate
towards occupations that are most consistent with their
"female" characteristics e.g. caring, nurture [Anker (1998)].
Moreover, feminists argue that occupations classified as "female
occupations" tend to receive substantially lower wages than male
occupations. This wage penalty on female occupations is thought to be a
form of sex discrimination. The assignment of lower wages to occupations
done mostly by women may also reflect a culture of discrimination
against women's work. Feminists tend to believe that occupations
with more female workers, on average, command lower wages than
comparable occupations with more male participants. This theory is
referred to as the Devaluation Hypothesis [Ruijter and Huffman (2003);
Cohen and Huffman (2003) and Tam (1997)].
3.2. Neo-Classical Theories and the Specialised Human Capital
Hypothesis
Emergence of non-competing groups in the labor market in the 1880s
set the theme for occupational specialisation while creating gender
segmentation in the economic system. The Specialised Human Capital
Hypothesis based on two basic ideas of human capital theory can be used
to explain gender inequality in the market [Becker (1975)]. First,
investment in any human capital is costly and thus has to be compensated
to ensure its adequate supply. Just as employers have to compensate for
workers' investments in general human capital is required for their
work. They also have to compensate for workers' investments in
specialised human capital. Second, the wage premium for specialised
human capital depends on the supply and demand for that particular kind
of specialised human capital. The supply and demand for a skill are
contingent on a wide range of factors. The investment cost of a skill is
often an important factor [Tam (1997)].
3.3. Empirical Findings of Earlier Research
Tam (1997) examines the Devaluation and the Specialised Human
Capital Hypotheses to explain the wage effects of occupational sex
composition in the United State by using data of Population Survey. His
findings entails that differences in the length of specialised training
across occupations and industries, together with a few demographic and
human capital attributes, were able to completely explain most of the
sex composition effects among women and men and whites and blacks. The
central results are difficult to reconcile with the Devaluation
Hypothesis but are consistent with the Specialised Human Capital
Hypothesis. However, the issue turns to access to education and training
opportunities by women.
Semyonov and Frank (1998) in their analysis of data on 56 countries
show that measures of nominal segregation are not equivalent to measures
of hierarchical inequality. They further argue that occupational
segregation should not be equated with occupational inequality. Findings
are illustrated by means of two summary indices SEGR (nominal
segregation) and ORDI (ordinal status inequality).
Manpower Research and Statistics Department, Singapore (2000)
conducted a study of occupational segregation to determine the extent to
which women and men are employed in different occupations and changed
over time in Singapore. Moreover, they examined the degree to which
women have entered traditionally "male" occupations and vice
versa. They also discussed causes for occupation segregation and
computed two summary statistics to highlight gender disaggregation i.e.
the Index of Dissimilarity (ID) and the Marginal Matching (MM) Index.
Blackburn, Brooks, and Jarman (2005) discussed the effect of
standardisation on the measurement of segregation in 16 developed
countries with different occupational sample sizes. They established an
inverse relation between horizontal and vertical segregation
illustrating that increases (decreases) in vertical segregation bring
decreases (increases) in horizontal segregation.
Chzhen (2006) explores the role of labor market discrimination in
determining occupational distributions of men and women in Europe. Using
data from the eighth wave (2001) of the European Community Household
Panel (ECHP), the paper documents the degree of occupational segregation
in a sample of three Western European countries with different
occupational sex segregation regimes namely Denmark, Germany and the
United Kingdom (UK). The result shows that labor market discrimination
appears to play the largest role in Germany, though the overall degree
of discrimination does not vary substantially across the three
countries.
4. EMPIRICAL STRATEGY
The empirical strategy adopted in this study consists of estimating
a nested logit model of labour market participation. In this model, the
labour market outcomes can be divided into a three-level possibility
framework. The first level consists of the possibilities of whether or
not a person is part of the labour force. The second level is a
possibility of employed and unemployed for those who decide to
participate in labour market and leaves the nonparticipants as they are.
The third level possibilities distinguish between formal, informal and
agriculture for those who are employed leaving the other branches
unchanged (Box 1).
[ILLUSTRATION OMITTED]
The computation of above mentioned possibility framework requires
the following three distinct steps. In the first step a logit model for
labour force participation is estimated by using a dichotomous variable
having value 1 for either employed or unemployed and zero for all
others. In the second step, another logit model for employed is
estimated by using a dichotomous variable having value 1 for employed
and zero for unemployed. Finally, a multi-logit model for occupational
choices is estimated by using a variable having value zero for
agriculture, 1 for informal and 2 for formal sector. This three step
estimation is applied separately for all male and female aged 15 years
and above in 2012-13. Heckman procedure is used to avoid selectivity
bias.
In line with economic theory, a set of explanatory variables are
used in the estimation of above mentioned empirical framework. These
include a set of educational dummies indication various highest level of
educational attainments. It also includes demographic variables like age
and square of age, marital status and family size together with regional
dummies. In order to get an idea of reservation wage family income is
also included in the analysis (complete list of variable is available in
Appendix).
Micro-dataset of Pakistan Labor Force Surveys 2012-13 is employed
for empirical investigation. The survey collects comprehensive
information on various activities of workers. The information about
employment status and distribution of employed labour force by
occupation categories, gender and regions is particularly important for
this study. A comparison of LFS with other data sources shows the
superiority of LFS because of greater internal and external
consistencies [Zeeuw (1996)]. For the purpose of our analysis we
restrict our sample to persons of 15 to 65 years of age in both years.
5. ESTIMATED RESULTS
This section presents the results of the estimated three step
nested logit framework discussed in empirical strategy. These logit
equations were regressed on a set of independent variables like age (a
proxy for experience), education level, household size, marital status,
urban etc by using micro-datasets of Labour Force Survey (LFS) 2012-13.
Based on these estimated equations, three sets of probabilities
including labour force participation, employment and formal, informal
and agriculture were estimated each with respect to education levels by
gender. Almost all variables included in the analysis are statically
significant and have expected signs (Table A2).
The aim of estimation of three step nested logit framework is to
compute the probabilities of various outcome with respect to educational
attainment, therefore, the regression estimates, using the logit and
multi-logit modes, are provided in the Appendix.
5.1. Probabilities of Labour Force Participation
Table 1 shows the resulting probabilities of LFP. and Not
Economically Active (NEA) women and men (15 years and above) with
respect to level of education for 201213. These probabilities show three
patterns: (1) probabilities of LFP are increasing with the level of
education in women and U-shaped (decreasing till intermediate and then
increasing) in men, (2) technical education plays a vital role in LFP,
reflected through higher probabilities both in male and female, and (3)
there are significant differences in probabilities among men and women
with same level of education.
It is ten times more likely that a woman with primary education
would be NEA as compared to a primary pass man. Similarly, it is less
likely that a woman primary education find a place in labour market
compared to a woman with graduate or post graduate levels of education
(Table 1). For instance, in the case of a woman having only primary
education, the chances of being NEA is more than 90 percent. For women
with postgraduate degrees however it declines to around 50 percent. This
is indicative that investing in female education has a positive impact
on labour force participation.
This trend is not pronounced in men as in women. In men,
probability of labour force participation is 0.90 at primary level,
which is relatively higher and then it declined to 0.73 at intermediate
and then subsequently increased to 0.88 at post graduate level. This
trend shows that primary, matric and post graduate are terminal
education levels where large number of men dropout from education and
join labour force, while intermediate and graduate level of educations
are not terminal level where a sizeable portion of educated men prefer
to continue their education.
Moreover, the probability of a woman or man with technical training
being part of the labour force holds more significance than the
probability of education levels in both men and women. For example, the
probability of a woman with technical education being a part of the
labour force is almost one-third, while for males it is more than 98
percent. This is greater than the rest of the education categories
combined except post graduation in case of women. Finally, the
probability of a man being part of the labour force as compared to a
woman with same level of education is higher in all education levels. In
case of women with only primary or matric education, the chances of
being NEA is more than 90 percent, while for men with only primary
education the chances of being NEA is slightly less than 10 percent.
5.2. Probabilities of Access to Paid Jobs
Table 2 gives the gender disaggregated probabilities of being
employed and unemployed with respect to level of education in 2012-13.
These probabilities reveal two important messages: (1) the probabilities
for being employed are higher for men as compared to women at every
level of education, (2) unemployment in both male and female increased
with increasing levels of education and peaked at post graduate level in
men and at graduate level in women afterward it decline. Moreover,
chances of being unemployed with technical education is low in both men
and women. Moreover, probability of being employed is highest at post
graduate and technical education in women while for men at primary and
technical education level.
What explains the differences in probabilities of employment? There
are three possible explanations: (1) vertical segmentation in labour
market, (2) different reservation wages for men and women, and (3)
higher demand for low skilled labour force.
The argument of vertical segmentation in labour market explains
that men and women are working in different occupations, which require
different level of education attainment and skills. It can be said from
the pattern of probabilities that labour market in Pakistan creates
greater job opportunities for women in elementary occupations or
occupations with higher level of education. As a consequence, there are
higher probabilities of getting job for women with primary level of
education or post graduate level of education. This also explains the
low LFP at mid-level of education. The relatively high unemployment at
mid-level education discouraged other women to enter in the labour
force.
The reservation wage argument explains that women with graduate and
post graduate level of education might have higher reservation wage--the
lowest wage rate at which a worker would be willing to accept a
particular type of job--as compared to men with same level of education.
This argument is based on the assumption that women's reservation
wage depends on her marital status and family earnings. This implies
that reservation wage of a woman belonging from a family having low
income would be low as compared to the reservation wages of a woman
belonging from a family having high income.
The third explanation is linked to macroeconomic environment. With
low economic growth and decline private investment as a percentage of
GDP created less opportunities for highly qualified and skilled men and
women. Consequently, the probabilities are higher at either primary
level or with technical education.
5.3. Probabilities of Gender Inequalities in Formal Job
Table 3 shows the computed probabilities for women and men by three
broad categories: namely agriculture, informal and formal sectors with
their associated levels of education in 2012-13. The probabilities that
women work in agriculture sector is higher compare to men at all level
of education. Moreover, among women probability of being working in
agriculture sector is high with primary and matric educational levels
and it further increases with technical education. In contrast,
probabilities of being in agriculture is lower compared to other sectors
within male sample and it declining with increase in level of education.
There is less than five percent chances that an employed man with
graduate or postgraduate level of education working in agriculture
sector.
Another striking finding is that probability of a man working is
informal sector is high compared to women at all level of education.
However, probabilities of working in informal sector decline with
increase in education level both in women and men. Finally, there is
mixed pattern in probabilities working in formal sector. These
probabilities vary among both men and women depending on their levels of
education, for instance, chances of women working in formal sectors
increase with levels of education and are highest among women with post
graduate degrees and professional education. Moreover, the probability
that a woman would be working in a formal sector is high for
intermediate and postgraduate levels compared to men with similar
qualifications. This is largely because women with intermediate and
postgraduate levels of education are generally employed in education
institutions.
6. CONCLUSION AND RECOMMENDATIONS
The role of labour market in attracting both female and male
workers and providing decent jobs to them is a complex matter and
requires empirical investigation in both developed and developing
countries including Pakistan. An attempt is made in this paper to
investigate this issue in a comprehensive manner by focusing on three
aspects including labour force participation, access to paid jobs and
inequality in accessing formal jobs for 2012-13 by applying a nested
logit model. The result shows that women are highly disadvantaged in
labour force participation reflected through estimated probabilities.
This is attributed not to less human capital among women as compared to
men but to unobservable factors called discriminatory factors. It is
hypothesised that once these unobservable factors are eliminated from
society, women labour force participation as well as overall labour
force participation will increase in Pakistan.
A prime reason of less participation of women in labour force is
their less chances of being employed and has higher chances of
unemployment if participating in labour force activities. This
discourages women to actively participate in labour market. Moreover,
they have fewer chances to get into jobs in formal sector with less than
postgraduate level of education as compared to men.
In order to improve labour force participation in Pakistan, the
following policy measures are recommended.
* Increase in female and male education which plays a positive role
in attracting both sexes into labour force. Therefore, greater
investment in education is needed, with other gender friendly measures
and through gender responsive budgeting.
* In order to provide more opportunities to women in formal sector,
a tax credit can be provided to women employees.
* There should be an equal-employment opportunities policy aimed at
tackling direct or indirect gender discrimination, equal opportunities
policy aimed at encouraging women to have continuous employment
patterns, without discouraging men, and de-segregating employment by
gender; and wage policies aiming at reducing wage inequality and
improving the remuneration of low-paid and/or female-dominated jobs.
Muhammad Sabir <muhammadsabir@spdc.org.pk,
muhammadsabir@hotmail.com> is Principal Economist, Social Policy and
Development Centre (SPDC), Karachi.
APPENDIX
Table A1
Definitions of Variables
Variable Description
Dependent Variables
LFP value 1 for those who are either employed or
unemployed otherwise 0
Employed value 1 for employed otherwise 0
Economic Sectors value 0 for working in agriculture, 1 for working
in informal sectors and 2 for working in formal
sector
Explanatory Variables
age Age in years
[Age.sup.2] Square of Age
Never Married value 1 for never married otherwise 0
Married value 1 for married otherwise 0
Widowd value 1 for widowed otherwise 0
num_infant Number of infant in a household
fhh value 1 for female headed household otherwise 0
hh_size Number of person in a household
Urban value 1 if living in urban area, otherwise 0
Punjab value 1 for all household in Punjab otherwise 0
Sindh value 1 for all household in Sindh otherwise 0
KP value 1 for all household in Khyber Pakhtunkhwa
otherwise 0
Primary value 1 if the highest level of education is
primary, otherwise 0
Matric value 1 if the highest level of education is
matric, otherwise 0
Intermed value 1 if the highest level of education is
intermediate, otherwise 0
Graduate value 1 if the highest level of education is
graduation, otherwise 0
Post_pro value 1 if the highest level of education is
either post graduation or professional
education, otherwise 0
tech_train value 1 for the person having technical trainings
otherwise 0
hhinc_fem Total household earnings excluding female earnings
Female value 1 for female otherwise 0
Table A2
Estimated Results of Logit Models for Labour Force Participation:
2012-13
Female Sample Male Sample
Coefficient Std. Error Coefficient Std. Error
age 0.1108 0.008 0.3977 0.012
age2 -0.0013 0.000 -0.0052 0.000
married 0.1979 0.093 0.8467 0.135
never_married 0.6842 0.110 -0.6200 0.160
num_infant -0.1472 0.028 -0.0668 0.033
fhh 0.0655 0.094
urban 1.2140 0.044 0.2711 0.049
punjab 1.6344 0.074 0.0975 0.077
sindh 1.0805 0.078 0.2511 0.084
kp 0.5784 0.079 -0.5060 0.079
primarym -0.6467 0.054 -0.8130 0.065
matric -0.6173 0.075 -1.2526 0.074
interned -0.3032 0.102 -1.8949 0.090
graduate 0.3383 0.101 -1.3841 0.122
post_pro 1.7079 0.121 -0.7302 0.181
tech_train 0.7489 0.058 1.3348 0.103
_cons -5.3787 0.198 -3.6407 0.285
Pseudo R2 0.112 0.391
Number of obs 64,964 68433
Table A3
Estimated Results of Logit Models for Employed: 2012-13
Female Sample Male Sample
Coefficient Std. Error Coefficient Std. Error
age -0.0838 0.047 0.088996 0.008411
age2 0.0006 0.001 -0.00117 9.57E-05
never_married -1.3258 0.227
married 1.438457 0.059815
fhh 0.5893 0.333
urban -1.0473 0.440 0.434404 0.041824
punjab -2.8976 0.660 -0.18918 0.06346
sindh -2.1741 0.513 0.191841 0.068633
kp -2.1591 0.376 -0.5487 0.068329
primarym 0.1861 0.278 -0.07686 0.054006
matric -0.3521 0.287 -0.52204 0.059816
interned -1.0597 0.249 -0.82258 0.07392
graduate -2.4064 0.234 -0.98627 0.080718
post_pro -3.3983 0.579 -1.04945 0.091313
tech_train -1.4422 0.295 0.372889 0.060923
mills -1.9574 0.425
_cons 11.6216 2.303 0.940695 0.150813
Pseudo R2 0.170 0.105
Number of obs 12573 54,740
Table A4
Estimated Results of Multi-Logit Models for Economic Sector 2012-13
Female Sample Male Sample
Variables Coefficient Std. Error Coefficient Std. Error
Informal Agriculture
age -0.1647 0.0320 -0.0677 0.0046
age2 0.0020 0.0004 0.0011 0.0001
fhh 0.5863 0.2371
urban -4.3144 0.3906 2.9317 0.0358
punjab -3.8497 0.5400 -0.5978 0.0363
sindh -3.6143 0.4148 -0.0160 0.0384
kp -2.1651 0.3020 -1.1075 0.0431
primarym 1.5689 0.2365 -0.4034 0.0284
matric 1.6628 0.2756 -0.5589 0.0394
intermed 1.5771 0.3622 -0.6566 0.0639
graduate 0.8703 0.3379 -0.9785 0.0957
post_pro 0.5613 0.7336 -1.0067 0.1471
tech_train 1.6474 0.2389 -2.1721 0.0585
mills -1.9917 0.3596
_cons 10.5645 1.7137 -0.9371 0.0928
Formal Formal
age 0.1090 0.0676 0.1097 0.0061
age2 -0.0011 0.0009 -0.0012 0.0001
fhh 0.5744 0.3976
urban -3.2811 0.5869 0.0989 0.0271
punjab -3.7676 0.8584 -0.6741 0.0382
sindh -3.7413 0.6784 -0.4597 0.0398
kp -1.7608 0.5014 -0.8873 0.0448
primarym 2.1598 0.4540 0.2864 0.0365
matric 3.8740 0.4171 1.0040 0.0385
intermed 5.3714 0.3829 1.6245 0.0465
graduate 4.3237 0.4024 2.2116 0.0506
post_pro 5.7700 0.8386 2.9020 0.0635
tech_train 0.9909 0.4052 -0.2047 0.0336
mills -1.4444 0.5583
_cons 1.0239 2.7794 -3.3941 0.1179
Pseudo R2 0.4151 0.2524
Number of obs 11,184 51,863
REFERENCES
Albrecht, et al. (2003) Is there a Glass Ceiling in Sweden? Journal
of Labor Economics 21, 145-77.
Anker, Richard (1998) Gender and Jobs: Sex Segregation of
Occupations in the World. Geneva: ILO.
Bayard, et al. (2003) New Evidence on Sex Segregation and Sex
Differences in Wages from Matched Employer-Employee Data. Journal of
Labor Economics 21:4, 887-922.
Becker, G.S. (1957) The Economics of Discrimination. Chicago: The
University of Chicago Press.
Becker, G.S. (1975) Human Capital: A Theoretical and Empirical
Analysis with Special Reference to Education. Chicago: University of
Chicago Press.
Blackburn , R. M., J. Jarman and B. Brooks (2000) The Puzzle of
Gender Segregation and Inequality; A Cross-National Analysis. European
Sociological Review 16: 2, 119-35.
Blackburn, R. and J. Jarman (2005) Gendered Occupations: Exploring
the Relationship Between Gender Segregation and Inequality. (GeNet
Working Paper 5). ESRC Gender Equality Network, University of Cambridge
at http://www.genet.ac.uk/workpapers/GeNet2005p5.pdf
Blau, Francine, Patricia Simpson, and Deborah Anderson (1998)
Occupational Progress? Trends in Occupational Segregation in the United
States Over the 1970s and 1980s. (NBER Working Paper 6716) at
http://www.nber.org/papers/w6716
Chzhen, Y. (2006) Occupational Gender Segregation and
Discrimination in Western Europe, Centre for Research in Social Policy
(CRSP), Loughborough University, UK.
Cohen, Philip N. and Matt L. Huffman (2003) Individuals, Jobs, and
Labor Markets: The Devaluation of Women's Work. American
Sociological Review 68:3, 443-63.
de Ruijter, Judith and Matt Huffman (2003) Gender Composition
Effects in the Netherlands: A Multilevel Analysis of Occupational Wage
Inequality. Social Science Research 32, 312-334.
de-Zeeuw,-Maarten (1996) The Household Integrated Economic Survey
of Pakistan 1990-91: Internal and External Consistency. The Pakistan
Development Review 35: 1, 71-84.
FBS (Various Issues) Labour Force Survey. Federal Bureau of
Statistics, Government of Pakistan.
Heckman, J.J. (1979) Sample Selection Bias as a Specification
Error. Econometrica 47, 153-162.
Huber, P. J. (1967) The Behaviour of Maximum Likelihood Estimates
under Nonstandard Conditions. In Proceedings of the Fifth Berkley
Symposium on Mathematical Statistics and Probability. Berkley, CA:
University of California Press. 1, 221-23.
Manpower Research and Statistics (2000) Occupation Segregation: A
Gender Perspective. April 2000 Research Brief, Manpower Research and
Statistics Department, Ministry of Manpower, Singapore
http://www.gov.sg/mom/ manpower/manrs/manrs5.htm
OECD (2002) Manual for the PISA 2000 Database. Paris: OECD.
Semyonov, M. and Frank L. Jones (1998) Dimensions of Gender
Occupational Differentiation in Segregation and Inequality: A
Cross-National Analysis. Social Indicators Research 46, 225-247, 1999,
Kluwer Academic Publishers, the Netherlands.
Siddique, et al. (2006) Gender and Empowerment, Evidence from
Pakistan. Pakistan Institute of Development Economics, Islamabad.
Tam, Tony (1997) Sex Segregation and Occupational Gender Inequality
in the United States: Devaluation or Specialized Training? American
Journal of Sociology 102, 1652-1692.
Comments
Paper dedicated a significant part to literature review, which is
definitely a good thing. All most all the literature review revolve
around the two popular hypotheses of sociological literature i.e.,
"Devaluation Hypothesis" and "The Specialised Human
Capital Hypothesis" the devaluation hypothesis asserts that no
economic factors can fully explain the sex composition effects because
cultural bias against women's labour overrides market
considerations. By contrast, the specialised human capital hypothesis
asserts that the same worker is expected to receive different wages
because of their gender. However, the empirical strategy doesn't
include cultural factors as suggested by devaluation hypothesis or wage
differentials as recommended by the specialised human capital
hypothesis.
Further 1 have few minor comments on empirical specification:
Overall wage income of other household members is used as proxy for
reservation wage. Here my concern is this proxy do not take account the
skill level of women, unless we consider the skill level this seems a
very weak proxy.
Table 3, 4 and 5 provide the gender based difference of
probabilities to get a job, and to get a job in particular occupation
etc. The difference of coefficient is not enough to draw any conclusion,
I feel you should also provide the statistical significance of
differences.
The nine major occupational categories are further categorised as
low, mid and high occupations, the criteria is not clear in the text.
Why inverse mills are included in only female equation and not in
male equation. I think unobservable factors effect both male and female
as suggested by Heckman (1979).
Finally, in female participation equations a variable female headed
household is dropped, if this is for purpose like identification, this
is an important variable and shouldn't be dropped in participation
equation.
Asma Hyder
National University of Science and Technology (NUST), Islamabad.
(1) The employed include those who are in paid employment as well
as those who are unpaid family helpers.
Table 1
Probabilities of Labour Force Participation by Level of Education and
Gender based on Logit Regression--2012-13
Female Sample Male Sample
Level of Education LFP NEA LFP NEA
Primary 0.0938 0.9062 0.9022 0.0978
Matric 0.0912 0.9088 0.8498 0.1502
Intermediate 0.1164 0.8836 0.7350 0.2650
Graduate 0.1968 0.8032 0.8110 0.1890
Post Graduate 0.4871 0.5129 0.8877 0.1123
Technical Training 0.2605 0.7395 0.9806 0.0194
Table 2
Probabilities of Access to Paid Jobs by Level of Education and Gender
based on Logit Regression--2012-13
Female Sample Male Sample
Level of Education Employed Unemployed Employed Unemployed
Primary 0.8711 0.1289 0.9621 0.0379
Matric 0.7938 0.2062 0.9452 0.0548
Intermediate 0.7757 0.2243 0.9256 0.0744
Graduate 0.7754 0.2246 0.9133 0.0867
Post Graduate 0.8553 0.1447 0.9070 0.0930
Technical Training 0.9156 0.0844 0.9735 0.0265
Table 3
Probabilities of Economic Sectors by Level of Education and Gender
based on Multi-Logit Regression--2012-13
Level of Education Agriculture Informal Formal Sum
For Female Sample
Primary 0.5743 0.3478 0.0779 1.0000
Matric 0.3901 0.2910 0.3189 1.0000
Intermediate 0.1789 0.1278 0.6933 1.0000
Graduate 0.3644 0.1305 0.5051 1.0000
Post Graduate and 0.1499 0.0395 0.8106 1.0000
Professional
Technical Training 0.5852 0.3860 0.0288 1.0000
For Male Sample
Primary 0.1296 0.6387 0.2317 1.0000
Matric 0.0862 0.5472 0.3666 1.0000
Intermediate 0.0590 0.4054 0.5356 1.0000
Graduate 0.0310 0.2931 0.6759 1.0000
Post Graduate and 0.0162 0.1780 0.8058 1.0000
Professional
Technical Training 0.0293 0.7899 0.1808 1.0000
Difference (Female--Male)
Primary 0.4447 -0.2909 -0.1538 0.0000
Matric 0.3039 -0.2562 -0.0477 0.0000
Intermediate 0.1199 -0.2776 0.1577 0.0000
Graduate 0.3334 -0.1626 -0.1708 0.0000
Post Graduate and 0.1337 -0.1385 0.0048 0.0000
Professional
Technical Training 0.5559 -0.4039 -0.1520 0.0000
Chart 1: Labour Force of Pakistan (Million)
Million
Both Sexes Female Male
1974-75 19.2 1.3 17.9
1978-79 22.6 2.7 19.9
1982-83 24.8 2.7 22.1
1986-87 27.4 3.3 24.0
1990-91 29.7 4.1 25.6
1994-95 32.3 4.1 28.2
1999-00 38.5 6.1 32.4
2005-06 47.4 9.3 38.1
2009-10 50.6 11.2 39.4
2012-13 59.1 13.3 45.7
Source: Estimates based on Labour Force Survey (various issues).
Note: Table made from bar graph.
Chart 2: Labour Force Participation Rate (percent)
Both Sexes Male Female
1974-75 49.3 87.2 7.0
1978-79 51.2 86.6 12.6
1982-83 49.7 85.1 11.3
1986-87 49.9 84.4 12.7
1990-91 49.7 83.6 13.9
1994-95 48.4 82.3 12.7
1999-00 50.4 83.2 16.3
2005-06 53.0 84.0 21.0
2009-10 53.4 81.6 24.1
2012-13 53.1 81.1 24.3
Source: Federal Bureau of Statistics, Labour Force Survey.
Note: Table made from bar graph.
Chart 3: Share of Female and Male in Employed Labour Force (percent)
Male Female
1974-75 6.8 93.2
1978-79 11.3 88.7
1982-83 11.1 89.0
1986-87 12.4 87.5
1990-91 12.0 87.9
1994-95 11.6 88.4
1999-00 14.3 85.7
2005-06 18.9 81.1
2009-10 21.2 78.8
2012-13 21.7 78.3
Source: Federal Bureau of Statistics, Labour Force Survey.
Note: Table made from bar graph.
