Final
Push or Pull Factors: Determinants of Female Labour Force
Participation in Bangladesh

Research Report prepared forCase Study: International Political Economy
Submitted To:
Professor Nobuhiro Hiwatari
Graduate School of Public Policy
The University of Tokyo

Prepared By:
Mst Morium Khatun
Student ID: 51-148213
MPP/IP, 1st Year Student

Graduate School of Public Policy (GraSSP)The University of Tokyo
Tokyo 113-0033, Japan
July 2015

Contents
Section 1: Introduction ............................................................................................................... 1
Section 2: Literature review....................................................................................................... 3
Section 3: Methodology of the Study ...................................................................................... 5
3.1 The Economic Theory ................................................................................................................... 5
3.2 Model Specification ...................................................................................................................... 6
3.3 Variable specification and expected sign .................................................................................. 7

Section 4: Data and Regression results and findings........................................................... 8
4.1 Date and variable ........................................................................................................................ 10
4.2 Estimation results: Evidence from LFS 2010 ........................................................................... 12
4.3 Estimation results: Evidence from LFS urban surbey 2013 ................................................. 12

Section 5: Summary and Conclusions ................................................................................... 13
References ................................................................................................................................................. 16
Appendix ................................................................................................................................................... 19

Acronyms and Abbreviations
BBS

Bangla Bureau of Statistics

GDP

Gross Domestic Product

LFS

Labour Force Survey

LFPR

Labour Force Participation rate

HDR

Human Development Report

SME

Small and Medium Enterprise

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Push or Pull Factors: Determinants of Female Labour Force
Participation in Bangladesh
Mst Morium Khatun
Abstract: Female can play an important role by contributing to household income, adding to the
supply of labour for economic activities, and above all by empowering women in the society.
Despite the advances in female educational attainment and the expansion of the market economy,
however, female labour force participation rates are still low in comparison to the rates of theirmale counterparts and others developing countries. Recent past, urban female participation also
slowed down. This paper, which seeks to examine the deterministic factors of female
participation and also investigates the reasons behind the urban slowed down. Data for the study
were extracted from the 2010 Labour Force Survey of Statistics Bangladesh and also 357
individual urban female interviews. Logistic regression modeling was used to estimate theinfluence of education on labour force participation, and the results show that there is anassociation between the level of education status and FLFP. Wage earners or low paid household
heads strongly motivates their spouses to participate in the labour market, compared salaried orhigh earners household heads. Number of infants creates obstacles to participate. Therefore,
government can focus on improving female education, increase or establish childcare or daycare
centers especially in the rural area and increase female safety both in office and roads.

1. Introduction
In developing economies, women play an important role by contributing to householdincome, adding to the supply of labour for economic activities, and above all byempowering women (Rahman, R.I. 2013). As the country’s development process shifting
up the women participation rate shows declining trends from increasing trends
experienced during early development-trend in women’s employment hypothesizes a Ushaped relationship between economic growth and women’s employment (Bosereup,
1970, 1990; Psacharopoulos and Tzannatos, 1989, 1991; Schultz, 1990). The growth ofthe modern sectors like education, finance creates more opportunities for female which
increases the labour force participation, while as the wage increases the households has
more wealth than before which increases the reservation wage to join in the labour
market.
The national labour force statistics showed that male and female labour force
participation has increasing trends (Table 1.1) and also stated that female LFPR went!

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through an incessant rise during 1991 to 2010. During the latest sub-period, that is 2006
to 2010, female LFPR increased from 26.1 to 36.0 percent, which was faster thancompared to other neighboring countries (Rahman, R.I. 2013). Although the trend
showing the improvement of the labour market situation especially for women, but stillaround two-third females are outside from the economic activity. The country will notachieve faster economic development without major proportion of female in the labour
force.
Table 1.1: LFPR in Bangladesh (15+ years)
LFS year

Female

Male

Total

1991-92
14.0
86.2
51.2
1995-96
15.8
87.0
52.0
1999-00
23.9
84.0
54.9
2002-03
26.1
87.4
57.3
2005-06
29.2
86.8
58.5
2010
36.0
82.5
59.3
Source: Bangladesh Bureau of Statistics (BBS), Labour Force Survey, LFS (various
years)The disaggregated picture of rural and urban LFPR can help realize associations between
LFPR and growth of urbanization. Table 1.2 presents the proportion of rural and urban
LFPR. Female LFPR in both rural and urban areas have increased during 1992 to 2010.
Table 1.2: FLFPR by area in Bangladesh
LFS year

Rural

Urban

1995-96

17.4

20.5

1999-00

23.1

26.5

2002-03

25.6

27.4

2005-06

29.8

27.4

2010

36.4

34.5

Source: BBS, LFS (various years)!

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Female LFPR in urban areas were 20.5 and 34.5 per cent in 1996 and 2010 respectively.
In these years, female LFPR in rural areas has risen from 17.4 to 36.4 percent. Urban andrural FLFPR are, thus, quite close and both have risen over the last 20 years. The recent
years rural LFPR exceeds up compared to urban, while the earlier years the pattern was
the reverse. There are two noticeable points: firstly, rural LFPR increasing speeds were
faster than urban, and secondly, rural LFPR exceeds urban area. The question is why inrural areas LFPR increases rapidly and also why urban LFPR slowed down.
Given the background and context mentioned above, it would be useful to examine thefactors that influence female labour force participation and employment. This could help
further appropriate policy initiatives to speed up the female LFPR. The purpose of thepresent study is to enhance the understanding why urban LFPR becomes slowed down?
As the urbanization growing faster as the economic development, it can provide insightmaterials for policy initiatives to speed up urban female employment.
The remainder of the paper is structured as follows. Section 1 presents the backgroundand motivation for the study. Section 2 reviews the major existing studies on female
labour force participation (LFP) in Bangladesh and other developing countries. Section 3
presents the economic theory: how individual decide to join labour force or enjoying
leisure, and also model specification. Section 4 provides data on the labour force
participation rate (LFPR) in Bangladesh and explaining estimated results. Section 5
provides an analysis of the policy environment for women’s employment and makessome suggestions for improvement in that regard. It also provides a summary of findings
and concluding observations.
2. Literature review: determinants of female labour force participation in
Bangladesh
Factors influencing female labour force participation have been usually conceptualized as
supply side determinants. In the context of Bangladesh where the underemployment ishigh and social factors plays vital role, so both push and pull factors are expected to link!

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with female employment. Kabeer (2012) highlights the role of such social factors in thefemale labour market and states that women’s participation in the labour market is often
not her own decision. As a result of strong patriarchy in Bangladesh society, malemembers of the family usually dictate or guide such a decision. Family’s male members,society’s attitude and established norms important deterministic factor for such decision.
Bridges et al. (2011) examines the factors influencing female LFPR, and showed thepositive association between severity of the poverty and the probability of female
employments. They also found that female come from extreme poor households have asignificantly higher probability of participation compared to non-poor; while foundinsignificant difference between moderate poor and non-poor group.
Amin (2005) has argued that due to home-based economic activities has pushed thefemale labour force participation in Bangladesh. He also found that female-headedhouseholds, smaller family size, lower educational attainment, living in urban areas,
lower levels of household wealth and microcredit have a positive impact on participation.
Moreover, the data set does not have information on self/family employment, which is alarger component of female employment and the study cannot provide insights into its
determinants.
Rahman, R.I. (2006) examine the deterministic factor of female employment, and foundthat women as head, female with higher education, residence in urban area have apositive impact on participation. On the other hand, land ownership; low level of
education, marital status, having infants, and having educated household head have anegative impact. Rahman, R.I. (2006) found that the rapid expansion of micro finance inrural areas has supported women’s employment in poultry and livestock; while urban
employment are dependent on ready-made garments only. In the urban area, working
hours, safety and health in the work place, freedom of association and collective
bargaining remain also act as a constraint to increase participation. The author suggested
increase the availability and accessibility of education and skill training, productive assets
like land and credit beyond microcredit, and services of various government institutions.

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The level of education increases the opportunity cost of not participating, and this implies
higher education postulates higher probability of participation in the labour market(Khandker, 1987). He also found husband’s education has a negative effect and increasein female wage reduces the probability of women’s leisure. Thus, the author suggested
policy interventions to raise women’s wages would have a large positive effect on
women’s LFP. Khandker (1988) reports results for women’s home production labour
input. Women’s education, land holding, and husband’s assets have negative effects.
Predicted female wage has a positive effect, which is difficult to explain.
3. Methodology of the Study
3.1 The Neoclassical Model of Allocation of Time or labour-leisure choice model
The model of labour-leisure choice, which is an extension of the utility maximization
problem of consumer theory; its analyzes how individuals make choices in deciding how
they will spend a fixed amount of time. In the simplest model, an individual has two uses
for his/her time, either working in the labour market at a real wage rate of W per hour, or“leisure” (George Borjas, 2012:Labor Economics 6th Edition). The amount of bothconsumed will depend on the individual’s market wage (W), personal preferences, andthe non-labour income (V) that person enjoys.
The individual’s utility function will be:
U = f(C, L) …………………………………….(1)These constraints can be written as the following:
Time constraint: L + H = T …………………………………….(2)Budget constraint: C = (W * H) + V ……………………………..(3)We can rewrite (2) and (3): C = W (T- L) + V……………………(4)!

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Setting up the Lagrangian expression to represent the individual’s utility maximization
problem yields= U (C, L) + λ {[W (T – L) + V] – C}
The first order conditions for a maximum are

An increase in W, holding income constant, makes leisure more expensive. An increase
in V will cause an increase in leisure time and a decrease in the hours worked, and viceversa. This theory has been successfully used to explain women’s labour force
participation: she will be willing to participate in the labour force if the reservation wage
is greater than or equal to the market wage --that is, if W*≥ W. The theory suggests that
number of kids below 5 years and elderly persons, number of earner in the households,
and marital status increases the reservation wages.
3.2 Model Specification
To examine the determinants of female labour force participation, we will deal with thedependent variable in this study is a dichotomous variable (0, 1). Given the nature of thedependent variable, Y, which takes value Y=1 if the respondent (female) is in theworkforce; and takes value Y=0 if the female is not in the workforce, the two models,
Probit and Logit models, can be used. Both of these models provide a prediction for the
probability that a female with a given set of characteristics is in employment/workforce.
However, since logistic model is easier to understand and uses a standard form ofanalysis, logistic regression model is used in this study.

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In the logistic model with more than one independent variable, the model can be writtenas (Mon, Myat, 2000):
Prob [Female in workforce] =11+ e-zwhere, Z is a linear function of the explanatory variables. If X1, X2……… Xk represent
various characteristics of the household and female respondent, then “Z” equation would
be as follows: Z = βo + β1 X1 + β2 X2 +………..…..+ β k X kEach βi can be shown to be∂ log (odds ratio)= − βi∂xi![!"#$%"!!"!!"#$%"#&']
where odds ratio is define as: ![!"#$%"!!!"!!"!!"#$%"#&']=! !!

Therefore, βi provides a measure of change in the logarithm of the odds ratio of thechance of a female working to not working.
3.3 Variable specification and expected sign
Age is a crucial deterministic factor for women’s participation in the labour force isusually viewed as a positive feature. Female participation varies significantly as age
distribution (Durand, John D., 1975). The influence of age may be non-linear, that is,may decline after some threshold has been reached that’s why quadratic term also
included.
The marital status is one of the major influential deterministic factors on female
participation in the labour force as it gives additional responsibility compared tounmarried female (Mon, Myat, 2000). Being married has a negative effect and this result
may have been influenced by the fact that paid employment dominated by the RMG
sector requires long hours of work. In the contrary, they also found there is a growing
acceptance of outside employment among young unmarried workers (Bridges et al.,2011).

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The female LFP rate reaches a peak before the beginning of childbirth and a few years
after the period of childbearing and declines during the child-rearing period (Mon, Myat,
2000). Presence of young children has a positive effect on self-employment and anegative effect on wage employment (Bridges et al., 2011).
Education can have an important positive impact on female LFPR as it increases theopportunity and productivity. Some literatures suggested that some cases influence can bein either direction or non-linear, i.e. do not necessarily improve women’s opportunities
for meaningful economic participation (Standing 1978; Smock 1981; Jones1984). Higher
level of education will expand the opportunity of good jobs, while low education force ina trap to become unemployment.
Urban areas has more opportunity for employment, so urban dummy expected positiveeffects. Having more non-labour income increase intercept so it discourage employment.
4. Date and Estimated Results
4.1 Data and Variables
To find the deterministic factors of female participation in the labour market, andexamine the reasons behind the recent slowed down of urban female participations; weused two types of data sets. For a better understanding of the determinants of female
labour force participation, logistic regressions have been estimated on the basis of datafrom LFS 2010. All individuals aged 15 and above and not currently studying in any
educational institutions have been included in the sample. LFS survey consists a total of
58,297 females and 57,525 males in the sample. We only consider the female
respondents, as sample size 58,297. In addition, we also collected 357 individual
structured interviews in Dhaka City in 2013, to understand the current employment
situation and future prospects.
A number of potential variables for inclusion in the logistic regression as suggested
theoretical models that explain female participation in the labour force. The following
table describes the notation and dummies, which used in the regression:!

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Table 4.1: Notation and Description of variables in Logistic Regression model
Variable(s)Base for dummy variables
AGE
Age
SQAGE
Square of age
MAR_D
Married dummy
Not currently married
NOC5Y
No. of children <5 years in household
NOME
No. of male earners in household
NEARNR- No. of non-earning males inNhousehold
ASSET_2 No land, other asset
Asset 1 (No land or non-land asset)
ASSET_3 Small land owned
ASSET_4 Larger land
URBAN_D Urban dummy
Rural
EDUC_D2 Education dummy 2 (primary)No education

EDUC_D3
Education Education dummy 3 (secondary)dummy 3
EDUC_D4

Education dummy 4 (above
secondary)Household head education
attainment years)
Household head status dummy (non- HSTAT-D1 (agriculture-selfHSTAT_D2
agriculture, wage employment)
employed)
HEDUY

REL

Whether head of households

Not

Some observations: Urban Survey Data 2013
Marital status has adverse effects on female employment due to family responsibility, i.e.
childcare, house works and etc (Appendix Table 1). Education has positive effects on
employment but on the lower and upper level. May be urban areas generates fewer jobs
for secondary and college graduates (Appendix Table 2).

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No education and with low level of educated females are employed in low paid jobs likegarments and factories. Interestingly, they are less constraint to participate in the job
market compared to rest. Mid-level educated (secondary and college graduates) female
more supposed to be housewives, compared to lower and upper educated females. Morethan 50% of undergraduates and 36% graduates females are not employed in the job
markets, and they are housewives, which is a concern issue (Appendix Table 3).
The survey results suggested female are less came to job market if she belongs tohouseholds with secured jobs and stable income earners like salaried and business.
Female are more supposed to come to job market if she came from households where
household earnings source are not secured and stable like garments/factory workers, petty
professions (Appendix Table 4).

4.2 Regression Results: Evidence from Labour Force Survey, 2010
Most of the results of the logit regression analysis presented in Table 4.2 are inconformity with a priori expectations. Indicators of family responsibility have significant
negative coefficients-married, number of children aged 5 years or less, number of maledependents above age five etc. Among human capital variables, age has a non-linear
effect, first positive and then negative.
Education has a positive impact. Four levels of education, with ‘no education’ as basecase, have gradually rising positive coefficients. Education raises productivity and,
thereby, raises wage/salary and through its substitution effect, raises female LFPR.
Among the family characteristics, number of male earners has a significant negative
coefficient.

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Family asset has positive influence. Compared to the base case of households ‘with noland or non-land asset’ women in households with own land assets have a significantly
higher probability of labour force participation. However, ownership of some land raises
the probability of female LFP, but in the highest land ownership group, it is insignificant,which may be due to use of more hired labour and prestige considerations.
Table 4.2: Results of logit regression: Determinants of probability of female labour force
participation (FP) and salaried (SP)Independent'variables'
Age'
Square'age'
Marital_d'No'of'kids<5'years'
No'of'male'earner'
No'of'nonearner'male'
Asset_d2'
Asset_d3'
Asset_d4'
Urban_d'
HH'head'
Education_d2'
Education_d3'
Education_d4'
Husband'edu'
Husband'wage'earner'
Pseudo'R2'
Wald'chi2(16)'Prob>chi2'
Log'pseudolikelihood'
Observation'log(FP/I)FP)+
Marginal+Effect+
0.027917***'=0.000464***'=0.110999***'=0.033719***'0.015207***'=0.073678***'0.012783***'0.049450***'=0.042829'
0.020085***'=0.020158*'
0.010206*'
0.037621***'0.057568***'0.011978***'=0.270196***'0.1190'4831.9'0.0000'=33982.53'
58297'log(SP/I)SP)+
Marginal+Effect+
0.000448***'=0.000012***'=0.017466***'=0.003933***'=0.002023***'=0.000950*'=0.001865*'
0.003532***'=0.008531***'0.014648***'0.013875***'0.001599*'
0.006842***'0.007052***'0.002052***'0.017469***'0.1995'2744.8'0.0000'=5959.18'
58297'!

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Table 4.3: Results of logit regression: Determinants of probability of female labour force
participation (FP) and salaried (SP) employment by rural-urban
Independent'variables'''
Age'
Square'age'
Marital_d'No'of'kids<5'years'
No'of'male'earner'
No'of'nonearner'male'
Asset_d2'
Asset_d3'
Asset_d4'
HH'head'
Education_d2'
Education_d3'
Education_d4'
Husband'edu'
Husband'wage'earner'
Pseudo'R2'
Wald'chi2(16)'Prob>chi2'
Log'pseudolikelihood'
Observation'log(FP/I5FP)'
Rural'
Urban'
0.02915***' 0.02513***'50.00047***' 50.00045***'50.09346***' 50.16727***'50.02882***' 50.05869***'0.01654***'0.00474'
50.06149***' 50.11113***'0.02024***' 50.04900***'0.05048***' 0.03592***'0.06508'
50.06412***'50.03188***' 0.00611***'0.01356***'0.01691'
0.03515***' 0.06944***'0.05237***' 0.07688***'0.01353***' 0.01069***'50.27527***' 50.28067***'0.1191'0.1362'3461.1'1321.5'0.0000'0.0000'526900.29'
55959.18'
46053'
12244'log(SP/I5SP)'
Rural'
Urban'
0.00063***'0.00078'
50.00001***' 50.00003***'50.01168***' 50.05329***'50.00143***' 50.02119***'50.00084'
50.01018***'50.00003'
50.00628**'50.00128'50.01387**'
50.00069'
0.02439***'50.00044'
50.02858***'0.00660'
0.04889***'0.00330***'0.00070'0.00582***' 0.02054***'0.00444***' 0.02874***'0.00205***' 0.00444***'0.01011***' 0.04707***'0.1615'0.1619'1292.14'761.81'0'0'53116.21'
52716.03'
46053'
12244'!

The interesting observation from regression Table 4.3 is that number of earner variable
has positive for rural but negative for urban participation. Another things, asset has no
significant impact on rural salaried but has for urban female participation.
4.3 Regression Results: Evidence from Urban Survey Data
Marital status and having kids has significantly negative effects on the labour force
participation. In the urban areas, we have very limited and expensive childcare/baby carecenters, so lots of female left jobs after becoming mother.

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Table 4.4: Results of logit regression from urban survey data: Determinants of female
labour force participation
Logistic regression
Log pseudolikelihood = -176.55053

Number of obs
Wald chi2(10)Prob > chi2
Pseudo R2

=
=
=
=

333
43.19
0.00000.1845-----------------------------------------------------------------------------|
Robustlfp |Coef.
Std. Err.
zP>|z|[95% Conf. Interval]-------------+---------------------------------------------------------------age | -.0109867.0176524
-0.62
0.534
-.0455849.0236115
marital_d | -2.767991
1.105473
-2.50
0.012
-4.934679-.6013028
kids_5yrs | -.7595198.2918497
-2.60
0.009
-1.331535-.1875048
educ level |.6091554.1483378
4.11
0.000.3184186.8998921
hhsex_d |.7389839.4123335
1.79
0.073-.0691749
1.547143
m_earner |.2318931.2272891
1.02
0.308-.2135853.6773715
hh_educ | -.4740789.1527748
-3.10
0.002
-.773512-.1746458
hhoccp_d2 |.50234.6620740.76
0.448-.7953011
1.799981
hhoccp_d3 |1.810041.6711519
2.70
0.007.4946076
3.125475
hhoccp_d4 |.3110695.6017840.52
0.605-.8684055
1.490545_cons |
1.355826
1.386896
0.98
0.328-1.362441
4.074093------------------------------------------------------------------------------

Educational level has significantly positive effects on labour force participation, which
was same as expected. Sex of the household heads also has positive effects on female
employment, in absence of male household heads, and number of male earners has week
positive effects. The level of education of household heads has strong negative effects on
female employment or job market participation, which is unexpected. The plausible
reasons might be, they are employed in secured and high salaried jobs, which influenced
the female members not to participate in the job markets.
The survey results also suggested that garments workers allow their spouses significantly
to enter job markets compared to wage earners, but salaried and businessman husband orfather are not strongly motivate theirs spouse/daughter to enter job.
In the urban area, the working-women facing problem after having baby, due to very
limited childcare centers and lack of trusted home servant; as a consequences some ofthem quit from jobs. The job market in Bangladesh has producing few part-time jobs forstudents, married women so even at some point of time female when they wanted tocome back but could not find suitable jobs.

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In addition to regression results, the transportation in the urban area is not female
friendly. Dhaka is the one of the worst traffic congestion city and the public
transportation is very limited compared to demand. Another thing, very few offices haschildcare facilities which also one of the major obstacles for working mothers.
5. Summaries and Conclusion
The findings common to a number of earlier studies on determinants of female LFPR in
Bangladesh have shown that: having a small child, more assets and husband’s education
reduces the probability of female employment. Education and family asset are expected
to raise women’s participation in both rural and urban areas. This result also implies thesame as theory; these factors increased the reservation wage so lots of female in the urban
areas stay at home rather participate.
The urban survey results suggested that garments workers or low earning husbands orhousehold heads allow their spouses significantly to enter job markets compared to wage
earners; since its pretty difficult for them to survive properly by household head income.
In the contrary, the reverse scenario seen to the household where household head aresalaried and businessman, then they are not strongly motivate theirs spouse/daughter toenter job.
The policy makers showed emphasis on the female education till undergraduate level,
and also create more public or public-private childcare or day care centers, which
promote more mothers to keep in the job markets. Policy makers can increase/establishedgovernment supported or subsidized private childcare or baby care for employed mothers.
Unavailability of enough public transport for female also discourage middle classfamilies to allow their spouse to enter into job markets. One plausible solution would be
providing enough female buses or public transport in the urban areas. To reduce thetraffic congestions more female friendly public transportation can provide more safety forworking women. To speed up female job market participation in urban areas we have tocreate jobs for secondary and college graduates; i.e. part-time jobs.

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On the other hand, government can provides more SME loans female entrepreneur, sothat they can run their own business, which will more job opportunity for more females.
The vocational education can help to create more entrepreneurs.
Limitations of the study
Due to time and financial constraints, only 357 women could be interviewed in the urban
areas in addition to the LFS national data. Although just 357 respondents cannot
adequately represent the social life of all women in urban, it is expected that the survey
results will be representative of the total female population in Urban areas. The survey
data may also be subject to response errors and biases. These errors and biases may
occur from both interviewers and respondents.
Some data like number of childcare in the urban areas, statistics of female public
transportation and also percentage of public or private office having childcare or daycare
facilities could not collect by the short time. Also those data are not readily available innational statistics, which is the one of the weak points of this study.
Notwithstanding these limitations, the survey has provided some very useful indicators offemale status and a key satisfactory model, which identifies important determinants offemale employment.

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Appendix Tables

Table 1: Marital status of the sample respondents
Marital status
Un-married
MarriedWidowed/divorced
Total

Not in Job
0.45
97.7
1.80
220

In Job
18.2
65.6
16.0
137

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Table 2: Level of education of the respondents
Level ofeducation
No education
Primary
incomplete
Primary completed
SecondaryHigher secondary
Undergraduate
Graduate/Higher
Total

Not in Job

In Job

5.45

6.6

11.8

16.8

20.9
20.4
20.9
10.9
9.5
220

13.8
10.2
8.03
17.5
27
137

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Table 3: Respondents Level of education and occupational choice!

18!

Table 4: Household head occupational status and female employments!

19!