PUSH OR PULL FACTORS:DETERMINANTS OF FEMALELABOR FORCE PARTICIPATION
IN BANGLADESH
Mst Morium Khatun

Justification of the study
Table 1.1: LFPR in Bangladesh (15+ years)
LFS year

Female

Male

Total

1991-92
1995-96
1999-00
2002-03
2005-06
2010

14.0
15.8
23.9
26.1
29.2
36.0

86.2
87.0
84.0
87.4
86.8
82.5

51.2
52.0
54.9
57.3
58.5
59.3

Table 1.2: FLFPR by area in Bangladesh
LFS year
1995-96
1999-00
2002-03
2005-06
2010

Rural
17.4
23.1
25.6
29.8
36.4

Urban
20.5
26.5
27.4
27.4
34.5

Source: BBS, LFS (various years)• Data on female and male LFPR (Table 1.1) show that female LFPR went
through a continuous rise during 1991 to 2010
• Table 1.2 illustrates the increased aggregate female participation was
backed by rural female participation.
• Urban FLFPR becomes slowed down compared to rural areas.

Objectives of the study• Given the background and context mentioned above, it would be
useful to examine the factors that influence female labour force
participation and employment• The purpose of the present study is to enhance the understanding
why urban LFPR becomes slowed down?

Literature review: determinants of female labour force
participation in Bangladesh• Bridges et al. (2011)
 Participation in paid employment: Extreme poor, those with smaller number ofyoung children, unmarried are more likely to participate
 Participation in self-employment (agriculture): Non-poor and young child has apositive impact. Education has no impact.

• Amin (2005) Participation in paid employment: Female-headed households, smallerfamily size, lower educational attainment, living in urban areas, lower levels ofhousehold wealth and microcredit have a positive impact on participation.
• Rahman (2006) Participation in labour force (all types of employment): Women as
head, education SSC+, urban, unmarried have a positive impact. Land ownership,
education lower than SSC, young children, and education of household head have anegative impact.
• Khandker (1988) Women’s home production labour input: Women’s education,husband’s assets and landholding have negative effects; female wage has a positiveeffect.
• Khandker (1987) Participation in cash income earning: Education and female wage havea positive impact; husband’s education has a negative impact.

The Neoclassical Model of Allocation of Time or labor-leisurechoice model• The model of labor-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 labor market at a real wage rate of W per hour, or“leisure”.
• The amount of both consumed will depend on the individual’s market
wage (W), personal preferences, and the nonlabor 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)The Neoclassical Model of Allocation of Time or labor-leisurechoice model (contd.)
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 vice versa.

The Neoclassical Model of Allocation of Time or labor-leisurechoice model (contd.)• This theory has been successfully used to explain women’s labor
force participation: , she will be willing to participate in the labor
force if the reservation wage is greater than or equal to the market
wage --that is, if W*≥ W.
• Literature suggests that no. of kids below 5 years, no of earner inthe households, marital status increases the reservation wages.

Model Specification• To examine the determinants of female labor force participation,
we will deal with the dependent variable in this study is adichotomous variable (0, 1).
• Given the nature of the dependent variable, Y, which takes value
Y=1 if the respondent (female) is in the workforce; 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 thata female with a given set of characteristics is in
employment/workforce.
• However, since logistic model is easier to understand and uses a
standard form of analysis, logistic regression model is used in thisstudy.

Model of Logistic Regression• In the logistic model with more than one independent variable,
the model can be written as:
1
Prob [Female in workforce] =1+ 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 = o + 1 X1 + 2 X2 + …..+  k X k• Therefore, i provides a measure of change in the logarithm of theodds ratio of the chance of a female working to not working.

Notation and Description of variables in Logistic Regression
model
Base for dummy variables

AGE
SQAGE
MAR_D
NOC5Y
NOME
NEARNR-N
ASSET_2
ASSET_3
ASSET_4
URBAN_D

Variable(s)agesquare of age
Married dummy
No. of children <5 years in household
No. of male earners in household
No. of non-earning males in household
No land, other asset
Small land owned
Larger land
Urban dummy

EDUC_D2

Education dummy 2 (primary)No education

Not currently married

Asset 1 (No land or non-land asset)

Rural

EDUC_D3
Education
Education dummy 3 (secondary)dummy 3(secondary)
EDUC_D4
Education dummy 4 (above secondary)HEDUYHSTAT_D2
RELHousehold head education attainment
years)
Household head status dummy(nonagriculture, wage employment)
Whether head of hhs

HSTAT-D1 (agriculture-self-employed)
Not

Data
• Labor Force Survey 2010
To find out the drivers and barriers of LFPR, both in rural and urban areas
Female part of the labor force survey-58297 observations• Urban area survey data 2013
To find the reasons behind why urban women drop out from employment?
We collected 357 individual interviews

Variable specification and expected sign
• Age is a critical variable because young women’s participation in thelabour force is usually viewed as a positive feature. The influence of age
may be non-linear, that is, may decline after some threshold has beenreached that’s why quadratic term also included• Education can have an important positive impact on female LFPR. The
influence can be in either direction or non-linear, i.e. first declining andthen rising.
• Marital status of women is likely to influence the chances of joining thelabour force because marriage reduces her independence to move to alocation away from home.
• Having children in the family can have a similar discouraging effect.
Having children will mean a larger burden to reduce the female labour
supply.
• Urban areas has more opportunity for employment, so urban dummy
expected positive effects.
• Having more non-labor income increase intercept so it discourage
employment.

Table 1.4: Results of logit regression: Determinants of probability offemale 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
Observationlog(FP/I-FP)
Marginal Effect0.027917***-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***0.11904831.90.0000-33982.53
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***
0.19952744.80.0000-5959.18
58297

• Most of the results of the logit regression analysis presented in Table 1.4
are in conformity with a priori expectations.
• Indicators of family responsibility have significant negative coefficientsmarried, 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 base case, have gradually rising positive coefficients.
Education raises productivity and, thereby, raises wage/salary andthrough its substitution effect, raises female LFPR.
• Among the family characteristics, number of male earners has asignificant negative coefficient.
• Family asset has positive influence. Compared to the base case ofhouseholds ‘with no land or nonland asset’ women in households withown 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 1.5: 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
Observationlog(FP/I-FP)
log(SP/I-SP)
Rural
Urban
Rural
Urban
0.02915*** 0.02513*** 0.00063***0.00078-0.00047*** -0.00045*** -0.00001*** -0.00003***-0.09346*** -0.16727*** -0.01168*** -0.05329***-0.02882*** -0.05869*** -0.00143*** -0.02119***0.01654***0.00474-0.00084-0.01018***-0.06149*** -0.11113***-0.00003-0.00628**
0.02024*** -0.04900***-0.00128-0.01387**
0.05048*** 0.03592***-0.00069
0.02439***0.06508-0.06412***-0.00044-0.02858***-0.03188*** 0.00611***0.00660
0.04889***
0.01356***
0.01691
0.00330***0.000700.03515*** 0.06944*** 0.00582*** 0.02054***0.05237*** 0.07688*** 0.00444*** 0.02874***
0.01353*** 0.01069*** 0.00205*** 0.00444***-0.27527*** -0.28067*** 0.01011*** 0.04707***0.11910.13620.16150.16193461.11321.51292.14761.810.00000.000000-26900.29-5959.18-3116.21-2716.03
46053
12244
46053
12244

Urban Survey 2013
Summary Statistics of the data
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

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

• Marital status has adverse
effects on female
employment due to family
responsibility, i.e. childcare, house works and etc.
• Education has positiveeffects on employment buton the lower and upper
level.
• May be urban areas
generates fewer jobs forsecondary and college
graduates

Respondents Level of education and occupational
choice• No education and with low level of educated females are employed in low paid jobs
like garments and factories. Interestingly, they are less constraint to participate in thejob market compared to rest.
• Mid-level educated (secondary and college graduates) female more supposed to behousewife's, compared to lower and upper educated females.
• More than 50% of undergraduates and 36% graduates females are not employed in thejob markets, and they are housewives which is a concern issueHousehold head occupational status and female
employments• 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 came to job market if she came from households
where household earnings source are not secured and stable like garments/factory
workers, petty professions.

Determinants of labor force participation

Implications of the results: Urban Survey 2013
• Marital status and having kids has significantly negative effects on thelabor force participation. In the urban areas, we have very limited andexpensive childcare/baby care centers, so lots of female left jobs after
becoming mother.
• Educational level has significantly positive effects on labor 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 highsalaried jobs, which influenced the female members not to participate in
the job markets.
• The survey results also suggested that garments workers allow theirspouses significantly to enter job markets compared to wage earners, butsalaried and businessman husband or father are not strongly motivate
theirs spouse/daughter to enter job.

Beyond Regression: Female Safety and Childcare
or daycare

In the urban area:
1. the transportation in the urban area is not female friendly. Dhaka is the one of theworst traffic congestion city and the public transportation is very limited compared
to demand.
2. the working-women facing problem after having baby, due to very limited childcare
centers and lack of trusted home servant; as a consequences some of them quit fromjobs.

Possible Solution to speed up female job
participation• National Level: emphasis more female education until undergraduation, also emphasis vocational education• Urban areas: provide more safety environment for female byproviding more female transportations, childcare or day carefacilities, and also create part-time jobs for house-wives andstudents• Rural area: expand more micro-credits, and SME loans to female
entrepreneurs, which could generates more job opportunities forfemalesComments and Suggestions