female labor force participation in afghanistan: a case ... · female labor force participation in...

Ghulam Reza Paikar

Female labor force participation in Afghanistan:

A case study from Mazar-e-Sharif city

Volume | 048 Bochum/Kabul | 2018 www.development-research.org | www.afghaneconomicsociety.org

Female labor force participation in Afghanistan: A case study from Mazar-e-Sharif city

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Female labor force participation in Afghanistan: A case study

from Mazar-e-Sharif city

Ghulam Reza Paikar

Abstract

Afghanistan is a country with the lowest rate of female labor force participation. There are several

demographic and socio-economic factors preventing women from participating in the labor

market. This case study investigated the factors influencing female labor force participation in

Afghanistan with empirical evidence from Mazar-e-Sharif city. An attempt was made to ascertain

the determinants of the types of economic activities in which women engage. The data for this

case study consists of information from 504 women and their households that were collected

through a field survey. Binomial and multinomial logistic regressions were used for the data

analysis. In this case study, we conclude that the educational attainment of women has a positive

impact on the probability that they will work in the labor market. In particular, a woman with a

bachelor’s degree has a higher chance of potential earnings in the labor market. Marital status,

family size, employment status of the husbands and GDP per capita of the household have

negative impacts on the probability that women will work in the labor market.

Keyword list

Female labor force, labor market, labor supply, logit model, multinomial logistic regression,

Mazar-e-Sharif

Description of Data

There is limited research on the present topic. An attempt was made to investigate the factors

determining female employment status in Afghanistan. Mazar-e-Sharif, a city of Balkh province,

was chosen as the study area. This is a large city with 11 districts (Nahiya) and from the total

population of this province 31.7 percent live in Mazar-e-Sharif city. In this populated city, only 10.7

percent of female residents are economically active, while 87.7 percent of female residents do

not work (UNFPA, 2016).

The primary data for this case study were collected through interviews with women who

live in Mazar-e-Sharif city. The information were recorded through a reasonably extensive

questionnaire with 504 female residents and their household members. The questionnaire

contains both qualitative and quantitative information about a woman’s household and partner

characteristics, and in case of married women, information about her children.


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The survey sample consists of 308 economically-active female residents and 196

households. Economically-active female residents were categorized into three sub-samples in

relation to their employment status. The first category is “salary-employed” and includes women

who are officially employed in public or private organizations like schools and hospitals. The

second category is “wage-employed” and consists of women who are employed in small and

medium businesses like tailoring, embroidery, carpet weaving, handicraft industries and other

industries. These women receive daily and monthly payments for seasonal work, according to the

market situation of the firms. The third category is “self-employed” and includes women who own

cloth stores, fast food restaurants, grocery shops, handicrafts stores and handicraft industries,

beauty salons and so forth. Economically-active women were selected randomly. The procedure

of random sampling was carried out according to the following steps. First, a list of schools and

hospitals was provided by the president of education and the president of public health of Balkh

province. Next, six schools and two hospitals (public and private) were selected randomly using

a spreadsheet. Salary-employed women were interviewed randomly through the lists of school

teachers and hospital nurses and doctors provided by the selected schools and hospitals. The

same procedure was applied for self-employed and wage-employed women. The lists for these

two categories were provided by the Afghanistan Chamber of Commerce and Industries (ACCI)

of Balkh province.

There are no lists available on non-working women or women who are underemployed,

so these respondents were targeted using household surveys. The household sample contains

information on women who are underemployed or economically active in different types of

businesses. The household survey followed a “lottery sampling method”. In total, there are 11

districts and approximately 274 alleys. Four districts (Nahiya) and eight alleys (Gozar) were

selected by lottery, then the households were selected by lottery using a list of every alleys’ chief

or leader (Kalanter-e-Gozar).

This research aims to explore several aspects of female labor force participation to

determine the factors influencing female employment. It considers the effects of different

explanatories including demographic factors (e.g., age, education level of the female, education

level of the female’s spouse, head of the household, marital status, number of children and family

size) and socio-economic factors (e.g., per capita income of the household, family assets, spouse

participation in economic activities, non-economic factors like caregiving and household

responsibilities, religious beliefs and Afghan traditional norms).

Table 1 presents the percentages by age group of economically-active women calculated

from the survey data. As shown in Table 1, a high percentage of young women contribute to the

labor market.


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Table 1: Age group percentage of employed women

Age group Percentage

16 – 25 33.08%

26 – 35 32.57%

36 – 45 20.10%

46 – 55 12.21%

56 – 65 2.04%

Total 100%

Source: Calculated from Mazar-e-Sharif survey data

Literature review and statement of the problem

The economic development of any country depends on the quality and participation of its human

capital. In any country, women constitute half of the human capital. Therefore, women’s

participation in economic activities and in the labor market foster robust growth and development,

an argument which was first introduce by Mincer (1962) and Becker (1965).

The participation of women in economic activities can be influenced by many factors,

especially by a country’s changing structures. In several studies, it has been shown that the

participation of women in the labor market is U-shaped during the transition of an economy from

agriculture to industrialization. At the time when agriculture dominates the economic activities,

despite high fertility rates and low levels of education among women, more women participate in

the labor market. However, this participation rate lowers when the structure of an economy

transitions from agriculture to industrialization (Goldin, 1994). At the early stages, when the

economic structure changes from agriculture to industrialization, women are unable to take

advantage of the job opportunities in the formal sector of the labor market. This is because the

fertility rate is higher and women are less educated. In addition, female labor force participation

can be influenced by social norms and culture as the society is more traditional at the early stages

of economic transition from agriculture to industrialization.

Low female labor force participation and the underutilization of women in the labor market

are a key problem and issue of discussion among researchers. In general, the main causes of

low female labor force participation are due to influential demographic factors and economic

characteristics such as age, schooling, work experience, number of children, family size, market

earnings of men, market earnings of women and so on (see, e.g., Gronau, 1973; Smith, 1973).

The female labor supply has been explained in several labor supply models. In these

models, it is generally assumed that females utilize their time having children, generating income

and pursuing leisure activities. However, all of these choices cost time. Leisure and children also


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cost money, while income can only be earned by doing work. Mincer’s (1962) analysis found that

women preferred the level of work time on the basis of permanent income they earned from

performing economic activities. His research reported that the level of income negatively affects

the time allocated between familial responsibilities and market activities and has a direct impact

on leisure time. Mincer (1962) also observed that the level of female earnings in the labor market

has a strong positive impact on the female labor supply, while the number of children has a

negative impact on women’s labor supply during their lives. Based on the theory of the allocation

of time, a woman allocates her time between household responsibilities and market activities in

order to maximize her utility function. This theory provides a basis for the household production

model. It describes how a family allocates their time in order to maximize their utility. For example,

a woman’s educational attainment will create benefits for both the labor market and the home.

Labor market benefits consist of increased earning potential. No market benefits can be obtained

from either home-based or social activities (Becker, 1965).

Highly educated women in an extended family and with low monthly income are more

likely to participate in the labor market. Women with less education and who have households

with a greater number of workers and more financial assets are less likely to participate in the

labor market (Hafeez & Ahmed, 2002). In addition, the overall increase in family productivity and

family earnings has a negative income effect on female participation in the labor market (Gaddis

& Klasen, 2013). However, job opportunities or the accessibility of fitting jobs also matter to the

female labor supply. A woman can earn income by only doing work to pay. Suitable work

environments can be provided by the accessibility of appropriate work and the opportunity to

obtain such jobs. The limitations for female employment are related to discrimination in the labor

market and also by restrictions on budget and time (Vlasblom & Schippers, 2004).

Despite these determinants, women’s participation in the labor force is much more

dependent on a country’s noneconomic (cultural) characteristics than is men’s participation in the

labor force. Countries with strong religious views about women’s social behavior have the lowest

participation of women in the labor force. An empirical analysis found that the coefficient on

Muslim, Hindu, and Catholic religions was negative and highly significant to female participation

in the labor force (Psacharopoulos & Tzannatos, 1989).

Afghanistan is a challenging country for women. Women in a country like Afghanistan

comprise the most deprived stratum of society. A large proportion of women do not have access

to educational attainment due to cultural and social restrictions. In the most traditional regions,

girls are not allowed to go to school, or they can only go to school for the elementary level. It is

believed that women can only take part in household chores and babysitting. Consequently, there

is a considerable percentage of illiterate women in Afghanistan which makes them unable to take

part in the labor market. Even if a woman wanted to take part in economic activities, she would

be exposed to sexual harassment in either the work environment or on her way to work. In the


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field of women’s economic activities, social and cultural stigma work against their participation in

the labor market.

The current situation for Afghan women must be seen in terms of the impact of more than

30 years of widely destructive civil conflict, an often patriarchal and conservative society, and

since the fall of the communist regime, a process of discrimination sanctioned by successive

power holders. The government following the Taliban regime, along with the contributions of

international agencies and donors, has started to present opportunities for Afghan women so that

they can reclaim their rights as active participants in the governance as well as the rehabilitation

and reconstruction of Afghanistan. Substantial achievements have been made as schools for girls

have reopened, young women are enrolling in universities and women are being employed as

teachers, doctors and civil servants. Despite these achievements, there are still a substantial

proportion of women who are unemployed. From 8.5 million active labor force participants, the

unemployment rate for male Afghans stood at 22.6 percent in 2013/14, while the rate for female

unemployment stood two and half times higher (Bank, 2017).

The main goal of this study is to estimate the determinants of female employment using

survey data from Mazar-e-Sharif city, Balkh province, in order to answer the specific question:

“what factors influence women’s decisions to participate in the labor market?”

Field research design and methods of data gathering

To determine the key factors influencing women’s decisions to participate in the labor market, we

use survey data from Mazar-e-Sharif city. Every respondent answered “Yes=1” if they participate

in economic activities and “No=0” if they do not participate in economic activities in response to

the question as to whether or not they attend the labor market. The dependent variable in this

present study is binary or dichotomous. To analyze the binary dependent variable, logistic

regression is normally used for determining the probability of the dependent variable (McCullagh

& Nelder, 1989). The function for logit is as follows:

𝐹(𝜋) = 𝑙𝑜𝑔{𝜋|(1 − 𝜋)}

Where “𝜋” is the probability that a person works in the labor market or not.

For a linear logistic regression, if the covariate takes the values 𝑋1, 𝑋2, … , 𝑋𝑛 , the

operational function looks as follows:

𝑙𝑜𝑔 (𝜋

1 − 𝜋) = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ + 𝛽𝑛𝑋𝑖

To study the effects of a unit change in 𝑋1 and the increasing of log odds by 𝛽1, the derivative of

𝜋 with respect to 𝑋1 is:


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𝜕𝜋

𝜕𝑋1= 𝜋(1 − 𝜋)𝛽1

To understand the participation of women with different employment statuses, the multinomial

logit regression is used, and the function is as follows:

𝑃𝑟𝑜𝑏 (𝑌 = 1) = 𝑒−𝛽𝑘𝑥𝑖

1 + ∑ 𝛽𝑘𝑥𝑖𝑗𝑘−1

𝑃𝑟𝑜𝑏 (𝑌 = 0) =1

1 + ∑ 𝑒𝛽𝑘𝑥𝑖𝑗𝑘−1

Where 𝛽𝑘= coefficients and 𝑥𝑖= independent variables.

Binomial logit estimates the determinants of female labor force participation and the

probability of being an active female worker. The multinomial deals with the analysis of

determinants of women as salaried workers, wage workers or self-employed workers. Table 2

contains all variables with their definitions, which includes both bivariate and multinomial logistic

regression.

Table 2: Definition of variables

Acronym Definition of the variables

Age Women’s age in years

Educ Women’s education in years

PEL 1 if a woman has primary education level, 0 otherwise

SEL 1 if a woman has secondary education level, 0 otherwise

HEL 1 if a woman has high school education level, 0 otherwise

BEL 1 if a woman has bachelor education level, 0 otherwise

MS 1 if a woman is married, 0 otherwise

CH Number of total children

CH1 Number of children below 6 years old

CH2 Number of children who are between 6 and 15 years old

H_educ Husband’s education in years

H_emp 1 if a woman’s husband is employed, 0 otherwise

HHS Total household size

HM_emp Household members employed

HM_unemp Household member unemployed

GDP per capita of

household Total income of household divided by total household size

W_emp 1 if a woman is economically active, 0 otherwise

WS_emp 1 if a woman is salary employed, 0 otherwise

WS1_emp 1 if a woman is self-employed, 0 otherwise

WW_emp 1 if a woman is wage employed, 0 otherwise


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The data analysis for this case study follows both quantitative and qualitative approaches.

Table 3 presents some parameters such as the mean and standard deviation of the sample

characteristics. The data show that women who are more educated are more likely to have a

salary employment status. These women have bachelor-level educations on average, while

women with a self-employment status are less educated. It may seem that a woman needs to

have expert in economic activities in order to work in the labor market. The data show that on

average, a married woman has one child who is younger than six years of age and one child who

is between six to 15 years of age. The standard deviation of the GDP per capita of the household

for salary-, self- and wage-employment statuses are much higher than the GDP per capita of the

household for under-employed women.

Table 3: Descriptive statistic of the survey data

Characteristics

Salary-

employed

mean

(std. deviation)

Self-employed

mean

(std. deviation)

Wage-employed

mean

(std. deviation)

Households

mean

(std. deviation)

Over all

mean

(std. deviation)

Age 33.87

(9.89)

29.54

(9.81)

34.31

(11.34)

33.84

(11.5)

33.07

(10.97)

Educ 15.47

(1.66)

9.62

(5.758)

8.94

(6.59)

7.73

(6.6)

9.99

(6.45)

PEL .03

(.17) .15

(.356)

.09

(.288)

.06

(.240)

.09

(.283)

SEL .05

(.22) .09

(.285)

.07

(.259)

.11

(.310)

.05

(.216)

HEL .11

(.31) .36

(.483)

.19

(.391)

.17

(.375)

.21

(.411)

BEL .49

(.501) .24

(.426)

.26

(.441)

.31

(.462)

.50

(.501)

MS

.73

(.44)

.51

(.502)

.53

(.50)

.84

(.410)

.69

(.47)

CH 1.74

(1.80)

1.40

(1.85)

1.36

(1.78)

2.01

(1.8)

1.70

(1.83)

CH1 .69

(.84)

.34

(.69)

.40

(1.03)

.66

(.9)

.55

(.89)

CH2 .81

(1.34)

.72

(1.07)

.66

(1.08)

.84

(1.3)

.77

(1.23)

H_educ 8.78

(7.14)

4.23

(5.97)

3.61

(6.00)

6.20

(6.92)

5.84

(6.84)

H_emp .54 .38 .33 .71 .53


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(.50) (.488) (.47) (.45) (.5)

HHS 6.83

(2.91)

6.16

(2.91)

5.45

(1.97)

5.93

(2.25)

6.08

(2.53)

HM_emp 2.32

(.94)

2.33

(1.19)

2.48

(1.12)

2.07

(1.12)

2.26

(1.11)

HM_unemp 3.93

(2.80)

3.47

(2.38)

2.47

(1.81)

2.56

(1.8)

3.02

(2.26)

GDP per capita of

household times

by 1000 AFNs

17.19

(28.39)

17.74

(28.55)

17.62

(28.70)

5.67

(6.06)

5.58

(5.48)

W_emp 1.00

(.00)

1.00

(.00)

1.00

(.00)

.44

(.49)

.78

(.41)

N 107 102 99 196 504

Source: Estimates by author using survey data from Mazar-e-Sharif city (2018)

As noted above, Afghanistan has a high female unemployment rate. This implies that

there are some underlying factors influencing women’s economic activities. In this survey,

respondents reacted to the barriers affecting female labor force participation in Afghanistan. A

higher percentage of respondents reacted to “insecurity” as the reason for their unemployment,

while sexual harassment on the street and sexual harassment at work were also identified as

the main problems related to female unemployment in Mazar-e-Sharif city. Figure 1 shows the

percentages of women’s reactions to a set of choices offered as the main causes of female

unemployment in Mazar-e-Sharif city.

Figure 1: Reasons for unemployment

18.18

40.91

53.64 50.91

63.64

9.09

76.36

54.55

0

10

20

30

40

50

60

70

80

90


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Results

The results of this case study, using both binomial and multinomial logistic regression for the data

analysis of 504 surveys in the sample, appear in Table 4 and Table 5. The output of logistic

regression estimates the probability which is used to infer the degree of confidence that the

predicted value can be the actual value with a given input of X. The binomial logistic regression

expresses the probability of a woman being employed in certain economic activities or not, as a

function of a set of predictor variables like family size, GDP per capita of household, partner’s

employment status, education level, number of children and other variables as displayed in Table

2. In comparison, the multinomial logistic regression expresses the probability of the categorical

dependent variable which is the different employment statuses of women, given a set of

independent variables. It estimates the effects of predictor variables on whether a woman is

salary-employed, wage-employed or self-employed.

Table 4 presents the binomial logistic estimates for women’s employment in diverse types

of jobs. As Table 4 shows, there is a positive and highly significant correlation between a woman’s

education and the probability that she participates in the labor market. If a woman’s education

increases by one year, the probability that she works (e.g., in a firm as a wage employee)

increases by 31 percent. Interestingly, the analysis found that the coefficient of a woman’s marital

status is negatively correlated and highly significant. This finding is in line with the empirical study

of Grossbard-Shechtman (1984). The decision to marry can be translated into labor market terms

because marriage is seen as an exchange of household labor supply. The exchange reflects the

change in behavior of women for some activities like cooking and other chores around the house

and taking care of children in comparison to the time when women are single. Traditionally, men

demand women’s household labor and women supply the household labor. Thus, this may

negatively affect the participation of women in the labor market. On the other hand, marriage can

have an advantage on labor decision-making where family members match their labor market

participation so that the partners benefit from their comparative advantage, which increases the

labor market returns. For example, one partner may be engaged in economic activity and the

other may be engaged in house work. In the case of Afghanistan, women are culturally and

religiously more likely to participate in house work. That is why the husband’s employment has a

negative and significant correlation on the probability of women’s labor force participation in this

case study. However, the association between participating in the labor market and marital status

is a controversial issue to some researchers. A recent study found that mothers are highly

competent and committed to their jobs (Benard & Correll, 2010).

The family size is negatively significant to the probability that a woman participates in the

labor market. This means that in larger households there will be more supporting hands which


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reduces the likelihood that women will participate in the labor market. However, the correlation is

insignificant for diverse types of woman’s jobs (salary-, wage- and self-employment).

Women in extended family with greater financial resources are less likely to participate in

the labor market. As the per capita income of the household increases, the probability of women’s

participation in the labor market decreases.

Table 4: Binomial logistic analysis for the sample data

VARIALBLES

Salary-

employed

coefficient

Self-

employed

coefficient

Wage-

employed

coefficient

Underemplo

yed

coefficient

Overall

employment

coefficient

Educ 1.244***

(.374)

.263***

(.051)

.313***

(.065)

.163***

(.027)

.156***

(.021)

MS -3.578*

(1.910)

-1.427*

(.677)

-2.316*

(1.111)

-.625

(.499)

-1.359***

(.477)

H_emp -3.937*

(1.684)

-.360

(.602)

-1.082

(.819)

-.971***

(.374)

-.975***

(.360)

HHS -.289

(.213)

-.037

(.158)

-.043

(.166)

.023

(.118)

-.179*

(.078)

GDP per capita

of household

-.310***

(.082)

-.230***

(.042)

-.426***

(.079)

-.040

(.033)

-.013

(.024)

Constant -4.546

(3.283)

2.034*

(.907)

4.973***

(1.371)

-.907*

(.407)

2.193***

(.552)

Nagelkerke R

square 0.86 0.73 0.82 0.35 0.36

Mean & std.dev

of dependent

Variable

0.49

(0.5)

.48

(.501)

.47

(.501)

.44

(.498)

.78

(.415)

N 217 212 209 196 504

Source: Estimates by author using SPSS statistical software Note: Numbers in parentheses represent std. error; *** significant at 1% level; ** significant at 5% level; * significant at 10% level

This section focuses on the different employment statuses of women. For the determinants

of female labor force participation in different types of employment such as salary-employed, self-

employed and wage-employed, multinomial logistic regression was applied and the results are

presented in Table 5. The first two columns represent women’s salaried employment and self-

employment in relation to wage employment, the second two columns represent self-employment

and wage employment in relation to salary employment, and the last two columns represent salary

and wage employment in relation to self-employment.


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The coefficient of marital status is significantly positive with salary employment in relation

to wage employment and self-employment, while this variable shows an inverse relation to self-

employment and wage employment in relation to salary employment. Primary and high school

education levels are positively related to the probability that a woman works in her own business,

with the reason being that women with low education levels may not be able to work in high-profile

jobs. In contrast, bachelor-level education is positively significant to salary employment, yet this

education level has a negative association with wage and self-employment. This means that

women with a bachelor’s degree have a greater chance to obtain permanent or high-profile jobs

in the labor market. An increase in family size reduces the probability of women to be wage-

employed compared to salary and self-employment, because the greater demands of home-

based activities mean that women may not have the time to work in the labor market. On the other

hand, family size is positively related to salary employment and self-employment. Women with

higher earning potential in the labor market may help the family economically. There are

significant positive and negative constants in multinomial logistic regression.

Table 5: Multinomial logistic regression analysis of the sample data

VARIALBLE

S

Salary

employment

status in

relation to

wage

employment

coefficient

Self-

employment

status in

relation to

wage

employment

coefficient

Self-

employment

status in

relation to

salary

employment

coefficient

Wage

employment

status in

relation to

salary

employment

coefficient

Salary

employment

status in

relation to self-

employment

coefficient

Wage

employment

status in

relation to self-

employment

coefficient

MS 1.383*

(.544)

-.145

(.507)

-1.528*

(.595)

-1.383*

(.544)

1.528*

(.595)

.145

(.507)

PEL -.486

(.683)

.550

(.534)

18.8***

(.534)

18.3

(.741)

-20.8

()

-.550

(.534)

HEL .402

(.638)

.808*

(.461)

.405

(.627)

-.402

(.638)

-.405

(.627)

-.808*

(.461)

BEL 2.105***

(.499)

-.412

(.445)

-2.516***

(.526)

-2.105***

(.499)

2.516***

(.526)

.412

(.445)

HHS .234***

(.073)

.182***

(.070)

-.051

(.070)

-.234***

(.073)

.051

(.070)

-.182***

(.070)

Constant -3.465***

(.781)

-1.513*

(.660)

1.952*

(.794)

3.465***

(.781)

-1.952*

(.794)

1.513*

(.660)

Nagelkerke

R square 0.41

N 308

Source: Estimates by author using SPSS statistical software Note: Numbers in parentheses represent std. error; *** significant at 1% level; ** significant at 5% level; * significant at 10% level


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The values of Nagelkerke R-square define the extent and validity of the operational

function and also explain how robustly the model works to predict the effects of the independent

variables on the dependent variable. The high values of Nagelkerke R-square for salary, wage

and self-employment regression are due to the greater homogeneity of the data for these fields

in the survey. The values of R-square decrease for the household (underemployment) survey

when the data are less homogenous.

Importantly, the sample for salary, wage and self-employment all contains information for

economically-active women, so the dependent variable has only the value of 1. In order to have

an arbitrary value of 0 and 1 for logistic regression, those unemployed women (underemployed)

are considered as the control sample for each salary-employed group, wage-employed group and

self-employed group. The logistic regression follows both enter method and “backward

elimination” method. The backward elimination method is a stepwise of fitting regression model.

The choice of predictor variables is carried out automatically.

Conclusion

In this paper, we studied the determinants of female labor force participation using a special case

study of Mazar-e-Sharif city. An attempt was made to determine a clear answer to the question:

“what factors influence women’s decisions to participate in the labor market?” To address this

question, we examined empirically survey data from Mazar-e-Sharif city of Balkh province. The

following conclusions were reached for this case study.

Educational attainment is positively associated with women’s participation in the labor

market. Each additional year of education increases the probability of a woman being

economically active. A woman with a bachelor’s degree has a higher chance to be employed in

certain high-profile jobs. Years of schooling also increase the probability of a woman being self-

employed.

Traditionally, men have typically demanded women’s household labor and women do

constitute the primary household labor force in Afghanistan. Therefore, marriage can change the

behavior of women such as when a married woman focuses on household routines or her familial

responsibilities and therefore does not participate in the labor market. However, the results of

multinomial logistic regression shows that marital status has a positive impact on the permanent

basis of labor (salary employment).


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As size of the family increases, the probability that a woman participates in the labor

market decreases. This may be due to the greater familial responsibility of the woman in the

household as she might take care of the children. Conversely, there may be more males in the

family who can contribute to the family income and according to Afghan culture, if a family has

enough financial resources, the preference is not to send women into the labor market. In addition,

if a woman’s husband is employed in economic activities, the probability that she participates in

the labor market decreases. However, there is a positive association between women’s labor

force participation and family size for those women who can either be employed in a high-profile

or permanent job or be employed in a personal business. Also, an increase in the GDP per capita

of the household by one AFNs (Afghani currency) will reduce the probability of a woman working

in the labor market in wage employment by 42 percent.

As this study shows, educational attainment acts as the key factor for women’s labor force

participation. Investment in educational facilities and encouraging women to get an education will

increase the female labor force participation in Afghanistan. Furthermore, establishing vocational

institutes for women to learn professions can also increase women’s participation in the labor

market.

This case study has attempted to ascertain the determinants of female labor force

participation. In relation to family composition, marital status has a negative impact on the female

labor supply. Further research may seek to focus on the factors influencing married women’s

labor force participation.


14

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