Z

SALALE UNIVERSITY

COLLEGE OF BUSINESS AND ECONOMICS

DEPARTMENT OF ECONOMICS

DETERMINANT OF WOMEN LABOR FORCE PARTICEPATION

IN FITCHE TWON

A RESEARCH SUBMITTED TO THE DEPARTMENT OF ECONOMICS,

SALALE UNIVERSITY IN PARTIAL FULFILLMENTS OF THE
REQUIREMENTS FOR THE TWON OF THE DEGREE OF BACHELOR OF
ARTS(B.A) IN ECONOMICS

BY: LATA AWAKE

ADVISOR: HAYILE TASFE (MSC)

DES; 2020

Fitche, Ethiopia
Table of Contents
DECLARATION
...................................................................................................................................................iv
CERTIFICATE.........................................................................................................................................v
ACKNOWLEDGEMENT........................................................................................................................vi
ACRONOMYS.........................................................................................................................................vii
ABSTRACT..............................................................................................................................................viii
CHAPTER ONE........................................................................................................................................1
INTRODUCTION.....................................................................................................................................1
1.1 Back Ground of the Study...............................................................................................................1
1.2 Statement of the problem................................................................................................................2
1.3 Research Question...........................................................................................................................4
1.4 Objective of the study......................................................................................................................4
1.4.1. General Objectives...................................................................................................................4
1.4.2. Specific objective......................................................................................................................4
1.5 Scope of the study............................................................................................................................4
1.6 Significance of the study..................................................................................................................4
1.7. Limitation of the study...................................................................................................................5
1.8. Organization of the paper..............................................................................................................5
CHAPTER TWO.......................................................................................................................................6
2. LITERATURE REVIEW.....................................................................................................................6
2.1 Definition of labor market and labor force....................................................................................6
2.2 Theoretical literature.......................................................................................................................7
2.2.1 Women Labor Force Participation: Definition and Importance...........................................7
2.2.2 The determinant factors of women’s labor force participation.............................................8
2.2.3 Family responsibility................................................................................................................9
2.2.4 Education and Training...........................................................................................................9
2.3 Empirical of literature...................................................................................................................10
2.3.1 Fertility....................................................................................................................................12
2.3.2. Marital status.........................................................................................................................13
2.3.3 Age...........................................................................................................................................13
2.3.4. Husbands earning..................................................................................................................13
2.3.5. Family size..............................................................................................................................14
 2.3.6. Agriculture and gender.........................................................................................................14
2.3.6. Informal labor and gender....................................................................................................15
2.3.7. Unpaid labor and gender.......................................................................................................15
CHAPTER THREE.................................................................................................................................16
3. METHODOLOGY OF THE STUDY................................................................................................16
3.1 Description of the study area........................................................................................................16
3.2 Data Source and Method of Data collection................................................................................16
3 .2.1 Source of data.......................................................................................................................16
3.2.2 Methods of Data Collection....................................................................................................16
3.3 Sampling techniques......................................................................................................................17
3.4. Method of Data Analysis................................................................................................................18
3.4.1. Description Analysis..............................................................................................................18
3.4.2 Econometric Analysis.............................................................................................................18
3.5. Model specification.......................................................................................................................19
3.6 Description of variables.................................................................................................................20
3.7 The diagnostic tests of post estimation.........................................................................................21
3.7.1 Chi-square test......................................................................................................................21
3.7.2. Multicollinearity test..............................................................................................................21
3.7.3. Heteroscedasticity test...........................................................................................................22
3.7.4. Goodness of fit of the model..................................................................................................22
4. DISCUSSION AND ANALYSIS........................................................................................................23
4.1. Descriptive Analyses.....................................................................................................................23
4.1.1. Socio economic and demographic characteristics...............................................................23
4.2 Econometric Analysis....................................................................................................................27
4.2.1 Econometric result..................................................................................................................28
4.2.2. Binary logit results and discussion.......................................................................................28
4.2,3 Measure of multicollinearity among the variables...............................................................30
4.2.4. Marginal effect of logistic regression model........................................................................31
4.2.5 Hetrosecdasticity test..............................................................................................................32
4.2.6 Goodness of fit of model.........................................................................................................32
CONCLUSION AND RECOMMENDATION.....................................................................................33
5.1. Conclusions...................................................................................................................................33
 5.2 Recommendations..........................................................................................................................34
References................................................................................................................................................35
APPENDIX 1............................................................................................................................................36
APPENDIX 2............................................................................................................................................40
 DECLARATION

I would like to declare the senior essay on this BA paper entitled “The determinants of women
labour force participation Fitche Town. The paper submitted in partial fulfillment of the degree
of Bachelor of Art Degree in Economics, Salale University, through the Department of
Economics done by Lata Awake.

Name of the Student by Lata Awake

Signature ____________ Date_____________________

 CERTIFICATE

This is to certify that the research prepared by Lata Awakeentitled; The determinants of women
labor force participation in Fitche town and submitted in partial fulfillment of the requirements
for Bachelor of Arts (BA) Degree in Economics Complies with the regulations of University and
meets the accepted standards with respect to Originality and Quality.

Name; ___________________ Signature; _________ Date; ___________

(Advisor)

Name; _________________________ Signature; ________ Date; ___________

(Examiner)
 ACKNOWLEDGEMENT

First and for most I would like to thanks who helped me in conducting this study and also who
made this work reach its accomplishment. Next, I would like to express my greatest gratitude to
my advisor:HayileTasfe(MSc) .for his encouragement and unreserved guidance throughout my
task of undertaking this paper. Furthermore, I would like to express my deepest gratitude to my
family for their immeasurable moral and financial support throughout journey of life.
 ACRONOMYS

EFWAB - Ethiopian Federal Women Affair Biro

MLSA -Ministry of Labor and Social Affair

WB -World Bank

UDWR -Universal Declaration of Women Right

NSZW & CAB -North Shoa Zone Women and Child Affair Biro.

FTW&CAB-Fitche Town Women and Child Affair Biro

OUPI – OromiaUrban Planning Institution.

WLFP- Women labor force participation.

ILO – International Labor Organization

 ABSTRACT
The research conducted under the topic of ‘‘determinants of women labor force participation in
the case of fitchetown’’. The main objective of this study is to identify factors determining women
labor force participation of the study area.. Data were collected from 99 respondents from 4
kebeles of Fitche Town using the multi-stage and random sampling method. The data were
collected through primary and secondary data. Primary data was collected from the student’s
through questioners and secondary data were collected from different published and
unpublished documents. To analyze the data, the researcher employed both descriptive and
econometrics analysis (logit regression) method were used with the aim of establishing the
relationship between factors related to labor force background and family background on
performance women labor force participation in fitche town. The findings revealed the existence
of a significant relationship family size,. Maritalstatus, age, family income and educational
level.Thus the study recommends to pay due attention given by all concerned body in eliminating
the adverse effect of those variables and improvement on variables that contributes to the
participation rate of women labor force. .Results of the binary logistic regression model showed
that having received (family size, family incomeand increase in education level were associated
with reduced odds of being notparticipated. This study highlights the importance of
familyincomeopportunities, as well as apprenticeship in mitigating the participation in labor
force challenge.
Keywords: Women labor force participation, binary logistic regression, Fitche
town.
 CHAPTER ONE
INTRODUCTION

1.1 Back Ground of the Study

Women are playing an increasingly role at international, national, regional and household level
economies. But they still have less advantage in economic position relative to male and face a
serious discrimination. Women are constrained from realizing their full employment and human
resource potential that contribute to economic growth and development. Economic recession in
many developed and developing countries had disproportionally negative impact on women
employment. In developing countries women discrimination in the work area tends to increase as
unemployment level goes up. This lends to proper exploitation of women hours of work (Lin,
2008).
In developing countries women participate in every aspect of farm production. Pouching is the
only activity that women usually do not undertake, but they will undertake even on this task
when male assistance is not available. They are also involved in the farm labor such as soil
preparation, sowing, weeding and harvesting (Pankhurst, 2008).

In the data surveyed on a number of 18 African countries, women are almost twice as likely as
men to be in the informal sector and above two times less than in a public and private formal job.
This explains as gender pay gap is high, but various deals among countries shows, the ratio of
average female to male weakly labor income ranged from 23% in Burkina Faso to 79% in
Ghana. Segmentation by sector of employment shows that 70% of women work in agriculture
compared to 64% of male, 6% of in small business industries and 23% of in service sector. Over
all, women were less participated in the industries and in service sector (Fort man, 2006).

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 Women in sub- Saharan African countries play a pre dominate role in agriculture including
plough. This role has been existed for a long period of time with women and they participate in
the tasks that were mean only for males (Ibid)

In Ethiopian women are live in poverty and receive lower income relative to male.
This is resulted from the work performed by women are: at home working, child bearing, very
low experienced and low paid job activity. While male participate in activity like; livestock
trade, black smith, waving, construction of hut and etc. These variation leads to wage differential
between women and men, because of wage variation women are relatively poor than men and
their participation in the formal work is relatively low (W B, 2002).

It is known that the populations of Ethiopian are predominantly engaged in agriculture and the
Ethiopian agriculture sectors are the predominance of agricultural household and nonagricultural
household. The household heads in their economy are mostly male. According to the housing
and population census of 2000 only 27% of the houses hold head are female; this implies the
women participation to be lower than that of male. ML SA (2004).

Oromia region is one of the regional states of Ethiopia and most of its people are engaged in
agricultural activities. Like other part of Ethiopia, men are leader, decision makers means decide
the working hour and the type of work performed by women. Due to this reason, the task
performed between women and man in agricultural activates are not equal. While women are
responsible for household working such as cooking, washing, carrying children, and fetching
water etc……. while the urban area of the region peoples engaged in trade and service. In this
city women are engaged in different economic and non- economic activities and they are far
worse than man in the economy. Because of low quality of labor supply and participation in the
labor force, they do not fully engage in labor market. Women are less equipped in general,
vocational education and skill training as compared to man (Yosef 2013).

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1.2 Statement of the problem
Women are actively involves in all aspect of their society’s life. They are both producer and
consumer. They are active participants in the social, economic, political and cultural activities of
their communities Despite the position that women are actively involved in the social, economic
and political facets of life, discriminatory political, economic, social rules and regulations
prevailing in Ethiopia have affected women to the extent that these societal irregularities have
ultimately barred women from enjoying the fruits of the labor. They are considered as secondary
citizens in their own countries and urban areas. Women are not empowered or there are no
strategies designed to empower them. They have will lagged behind man in all field of self-
advancement. But in reality economic development is unthinkable without the participation of
women (Kidane, 2006).

In some economic sectors, women even constitute larger proportion of the labor force than man.
Ethiopian women low received their share of the nation’s wealth (EFWAB, 2008) and also
women engage in horticulture, livestock breeding, and milk processing as well as marketing of a
wide verity of goods. Those activities carries out by women are clear indicator of their
contribution in production and other area of rural economy (Bishaw, 2003).

In many parts of developing countries, women represent an essential and underutilize force for
sustainable development. Better use of the world female population could increase economic
growth, reduce poverty, enhance social well-being and help to ensure sustainable development in
countries. Even though women have equal right to participate in the labor force with men,
however due to some factors that under them, their participation in labor force is still low.
(Winkler, 2008)

In fact, several studies were conducted in this study area. Nevertheless, there are still filling
methodology, time, place life standard and variable gap. For instance, Abera (2001) study factor
determining the level of women labor force participation in Kuyuworedaby using descriptive

3
analysis. He analyzed his study by considering sex, education, age, employment, as the main
determinants of women labor force participation. The result show up an increment in the level
of women participation in the labor force since it is conducting. And also another researcher
“Negatu”(2003) study on causes of low productivity of women in sebeta. He analyzed by adding
additional variables that affects or determinants of productivity of women such as, social
attitude, maternal status and family size using descriptive analysis. The study provides some
contribution in reducing those factors hat under productivity of women.

Even though both researchers would need their explanatory variables that determine the level of
women labor force participation but this study will be incorporate other variables like spouse
marital status ,income level ,age, education level and Family size that can effectively determine
level of women participation in labor force. By adding such variables this study will be tries to
solve the problem of low level of women participation in labor force in Fitche Town and also
there is no proper organized studies conducted on this problem, so this study will be primarily
designed to fill this gap and conducted to examine Factors affecting women participation in labor
force and also try to give insight about the effect of those factors on women participation.

So, this study tried to assess factors that determine women labor force participation in particular
study area of Ethiopia city by filling methodology and variable gap which are not included in the
previous study. Depending up on these, the researchers have tried to answer the following basic
question that arises from the viewpoints of factor determining women labor force participation to
fill the gap between the past study and present study.

1.3 Research Question

1. What are the determinants of womenlabor force participation in Fitch Town?

2. What are the performances of women labor force participation?
1.4 Objective of the study
1.4.1. General Objectives
The overall objective of the study was to examine the determinants of women labor force
participation in the case of Fitche Town.

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 1.4.2. Specific objective
To identify factors that affect women participation in the labor force in study area.
 To assess the performance women labor force participation in study area.
1.5 Scope of the study
Since the study focus on the overall factors that affect women labor force participation at town
level it calls for a good deal of knowledge, skill, experience, finance, time and organized data.
The study attempts to cover the case of Fitche town, for which the required data is available. The
study is also concerned on age, marital status, education level, employment, spouse, family
income, family size, and social attitude as the variables that affect women labor force
participation.

1.6 Significance of the study

The result of this study helps policy makers to know the factors which affect women’s
participation in the labor force and to design appropriate policies which promote their activities
in the economy. It also serves as a stepping stone for other academicians who seek for further
investigation of the findings.
1.7. Limitation of the study
The researcher faced the following limitations while conducting the overall study:-
 Lack of experiences in the field of study.
 Absence of previous study on the topic.
 Insufficient time for collecting the sufficient data.
Even though these problems would exist in the town, the researcher uses the maximum effort to
investigate the determinants of women labor force participation in the given area in a good
manner.

1.8. Organization of the paper

The study would have been organized into chapter five. Provides bravely introduction which
includes the background of the study, statement of the problem, research questions, and the
objectives of the study, the significance of the study, scope,limitation of the study and
organization of the study. The second chapter is concerned with a review of literature related to
women labor force participation in Fitche Town. The third chapter deals with the methodology
of the study including research design such as description of study, research methodology, data
type and data source of the study, data collection technique, sample size determination, sampling

5
techniques. The fourth chapter explains results and discussions. The fifth chapter will be
describes conclusions and recommendations.

CHAPTER TWO

2. LITERATURE REVIEW

2.1 Definition of labor market and labor force

Labor is a services that households supply to business firms in order to earn an income and that
business firms demand in order to produce their product. It is factors of production that is owned
by individual and rented to business firms for a period of time to be combined with other factors
of production such as land and capital to produce a good and service just as other factors of
production.
Labor market is a place where buyers and sellers of the labor come together to accomplish
transaction. The markets for labor have two sides; those are the demand side and supply side.
The demand side made up of producers of goods and services as employer who are went to
purchasing labor service. The supply side composed of individual and households as a server or
supplier of labor service (Kaufman, 2003). In labor market, the interaction of the demand for
labor and supply of labor determine wage rate, the level of employment and the distribution of
income in the economy. But supply and demand are not the only determinant of this market
outcome (McConnell and Brue, 2005).
Institutional forces represent the influence of various organization such as; union, governments
and corporations on the pricing and distribution of labor. Institution effects labor outcome in to
two different ways. Firstly, they fragment the labor market in many groups of segments, and
then they loosely connect different sub markets. Secondly through there independent effect on
wage rate also they affect labor force. (Kaufman, 2003).

6
Labor force itself compromised of two groups which are known as employed and unemployed.
the employed groups of labor are includes any one working for pie at least one hour a week who
is not in an institution or armed force. Also employed groups are counted as the two other
groups of people who work an hours or more a week without pay in a family business and these
who have a paying job but are not currently at work because of illness, bad weather, strike, or
personal reasons (Lin, 2004).

The unemployed groups is counted in the labor force because even though those peoples are not
working, they are seeking work and are these available as labor input for the economy( Kuris,
2003). The labor force participation rate measures the percentage of eligible population that is
working or seeking
Constantly, the labor force participation rate in 1950s and 1960s marked significance
development in participation for men and women. In1950s there was area division of labor
among the sexes. The greater proportion of men (86.4%) was in the labor force while only one-
third (33.9%) of women were. this disparity has shrunk considerable in the intervening 50 years.
The male labor force participation rate had declined to74.4% by 2001 while the participation rate
of women had risen to 60.1%. The most dynamic chosen in the labor force participation in the
post-world of second war result the dramatic increase in the proportion of women working in the
labor market (Ibid).

2.2 Theoretical literature

Neoclassical theory suggests that, high levels of investment in human capital and greater
participation of women in the labor market are negatively associated with lower fertility rates
(Gary B.1964). In general, the causal impact of female labor force participation on fertility may
occur along a number of complex pathways because both female labor force participation and
lower fertility may reinforce each other. The relationships between female labor force
participation and fertility have been studied based on the maternal role incompatibility
hypothesis, which states that an inverse relationship occurs between women’s work and fertility
only when the roles of worker and mother conflict (Goldstein, 1972). According to the author,
the implication of this hypothesis is that a negative relationship between female employment and

7
fertility exists to the extent that they are competing uses of time. Otherwise, we should expect to
find no relationship, or even a positive relation between employment and fertility.

2.2.1 Women Labor Force Participation: Definition and Importance

There are two important terminologies those should be defined regarding to women labor force
participation (WLFP) are Women Labor Force Participation (WLFP) and Women Labor Force
Participation Rate (WLFPR). WLFP was defined as the women’s decision to be part of the
economically active population: employed or unemployed population as compared to being part
of the economically inactive population of the economy – those neither working nor seeking
work. The standard measure for WLFP is WLFPR. WLFPR is the proportion of the working age
population that is economically active. It precisely measures the share of a country’s female
population aged 15-64 that engages actively in the labor market, either by working or looking for
work. In measuring WLFPR, the number of females in the labor force is divided by the number
of females in the working age Population. WLFP is an important indicator of women’s status
and benchmark of female empowerment in society (Kapsos, Silberman and Bourmpoula, 2014).

Women are productive agents who possess equal productivity as men. This means that they have
the potential to contribute as much as men contribute to any economy. According to Muja hid
(2014) and Fatima and Sultana (2009) the labor force participation rate plays an essential role in
determining economic development and growth. Particularly WLFP is important for the
enhancement and socio-economic development of a nation because it promotes efficiency and
equity. Generally, high female participation in the labor market implies two things; advancement
in the economic and social position and empowerment of women and hence promoting equity
and increased utilization of human potential, which can help in building a higher capacity for
economic growth and poverty reduction.
The theoretical framework on Labor force participation basically reflects the female’s decision to
be an active participant versus being an inactive participant in the labor market. Economists have
tried to explain female’s propensity to decide on one choice over another through analyzing the
impact of certain economic activity and demographic factors, which they believed as the effect
of female’s tendency to participate or optimizing the labor market. The main theories that have
been used to analyze the labor supply of women emerged in the 1960s.

8
2.2.2 The determinant factors of women’s labor force participation

In developing countries most of women are domestic worker. Domestic worker are the most
neglected class of labor as they are rarely seen and seldom heard by legal scholars. Even though
those scholars who concern themselves with human rights and labor issues tends to overlook this
category of workers are among a group of worker which are the most exploited by the employers
at list protected by law. The measure reason women are desirable as employees is that they are
willing to accept to lower wage. In addition to this measure factors that determine women labor
force participation are described as follows;

2.2.3 Family responsibility

Families in some societies expect that women should work at all, should not work once they
have children or should work only if there is a strong economic need in such society’s awareness
rising and sensitization Should be addressed not just to women themselves but also to their
families. Women particularly those who are heads of house hold with young children are limited
in their employment opportunities for the reason that include in flexible work conditions and
inadequate sharing by men and by a societies(Lin,2004).
The study by Shatal (1976) analyzed the effect of demographic and socio –economic variables
on FLFP in Pakistan by using correlation coefficient and partial correlation coefficients .The
result indicates that work participation is inversely associated with child –women ratio and
nuclear family type.
2.2.4 Education and Training

Education and training are very important for women, both as human rights and as essential tool
for achieving equality. Women have lower level of education and training may be having less
opportunity in all aspect than those who are educated and well trained. Thus, both the
preparation for access to labor market and the equality of women labor supply to education
persist in many areas, owning to the customary attitude, early marriage, pregnancy, gender bias
teaching, educational materials, sexual harassment and lack adequate availability of schooling
facilities. (Olukemi, 2008).
The educational upgrading of the female population has been major factor of social change in
the United States. For women age 25or over median years of schooling increased from

9
12.1percent to12.6percent year beetwn1970 and 1980.And the percent of graduating from high
school grow from 52.8 to65.8 percent changes in the educational composition of female
population. Most is included in any demographic or structural explanation of rising participation
rates among the female population. This is clearly raveled that the female labor force
participation rate that tend to accelerate with increasing educational attainment (monthly labor
review, June, 1970,).Ally and Qui (2001), investigated socio economic factor that influence
Kuwaiti women labor market participation decision by using a nonlinear maximum likelihood
function method for cumulative logistic probability function. It was found that women wage rate
and education were positively correlated with female labor force participation rate. However
being married and number of children were negatively correlated to it.

Cameron (2001), in his study on the impact of education on female labor force participation in
Indonesia, Korea, the Philippines, Shrilanka and Thailand, He found that a U Shaped relationship
between education and labor supply .He also found that primary schooling has either a negative
influence or no influence on female labor force participation .While; the higher education has a
positive impact on female labor force participation. The study on an econometric analysis of
inter-state variation in women labor force participation in India (2009) was carried out by Tariq
Mastoid. The empirical result suggested that, education and wage rates significantly and
positively determined urban women labor force participation rate but not in rural area women.
Unemployment rate significantly increased the participation rate in rural areas but not the
urban .While significantly increased the participation rate in rural and urban areas .Number of
young children (0-4) in the household negatively affect the participation rate both in rural and
urban areas.

2.3 Empirical of literature

There is an immense literature available pertaining to women labor force participation in

economic activities at the national and international level. Women represent a sizeable portion of
the population and require a lot of attention. They are considered as a supporting factor in the
economic development of the country and put a significant effect on overall business and
economic activities (Faridi, Chaudhry and Malik, 2011).

10
According to Ruwanpura (2004), “Economists explanations for the existence of segregated labor
markets are not new”. A review of the literature on women or gender in general indicates that
there is now a demand for a re-orientation of research and changes in the methodological
procedures used for the compilation and computation of national statistics, so as to reflect
accurately the role of women and their labor input in the national economy. An analysis of trends
in laboreconomics throughout the world reveals that sustained increase in women’s participation
in thelabor force during the last century was far below to their participation in deferent economic
activity. This fact has stimulated considerable interest in the economic analysis of a woman’s
decision to work of (Rincon, 2007). The author noted that the pioneering studies of Mincer
(1962) and Cain (1966) United States have served as a theoretical and empirical foundation for
many studies of female labor force participation.

The increasing trend toward women’s participation in the labor market in both developed and
developing countries has drawn both social and academic interest resulting in many insightful
studies on gender aspects of labor market issues. A critical review of the large literature provides
at least two general theoretical paradigms to explain the changing patterns of female labor force
participation in low-income countries (Ackahe.t.a.l, 2009).

Equally, Nam (1991) categorizes the literature into two perspectives, the modernization and the
world system perspectives. According to the modernization theorists, economic development is
positively associated with female labor force participation through change in the country’s
occupational structure (i.e. the increasing availability of service and white-collar jobs) and
increased educational opportunities, often accompanied by reduced fertility rates and household
responsibilities The modernization process is associated with increased demand for labor, a
general social acceptance of women’s education and employment, as well as lower fertility. The
relationship between education and female labor force participation has been summarized by
Standing 2010) under three hypotheses: the opportunity cost hypothesis, the relative employment
opportunity hypothesis, and the aspiration hypothesis (Nam, 2009).

First, the opportunity cost argument conceives that to the extent that there is a positive
relationship between educational investments and earnings potential, education raises the
opportunity cost of economic activity, thereby giving people a positive incentive to seek

11
employment. The relative employment opportunity hypothesis posit, that employers usually tend
to have a positive bias towards a qualified female work force rather than older male workers
whose educational qualifications increase their employment options (Oppenheimer, 2012).

Furthermore, the aspiration hypothesis is based on the human capital hypothesis that women with
higher levels of education are more likely to participate in the labor market. From this view point
that income aspirations and expectations of people are strongly determined by levels of
education, more-educated women are expected to have higher income aspirations over their less-
educated counterparts and therefore tend to be more active in the labor market (Cain, 2013).
The world system perspective, on the other hand, explains the increasing labor force
participation in the context of traditional comparative advantage international trade theory. From
the perspective of the Stopper-Samuelson theorem, global trade liberalization would lead to a
rise in the demand for unskilled labor in developing countries (Ackahetalic, 2009).
In other words, since developing countries are more likely to have a comparative advantage in
producing unskilled labor-intensive goods, one would expect international trade in these
countries to lead to a rise in the demand for and relative returns of the abundant factor; unskilled
labor in the case of developing countries.

Since more females than males tend to be unskilled and female labor is usually cheaper than
male labor, labor-intensive industries tend to be relatively dominated by females, particularly
those who are young and single (Grossman,2007)While a positive correlation between levels of
education and female labor force participation has been postulated through theoretical and
empirical findings in developing countries are rather mixed. Studies have shown that female
labor force participation is another variable which appears to be associated with lower fertility
rates in different parts of the world. Empirical evidence from both developed and developing
countries confirm that female education is associated with a greater incentive to participate in
market activity (Kalwij, 2000).

2.3.1 Fertility

The labor force participation rate among married women with children particularly young
children have been steadily increasing since 1970. In 1985 nearly half of all women with

12
children under age 18 were in the labor force compared with less than 40% in 1970. More ever,
the declining fertility rate as well as declining of family size, increasing childlessness and
delayed child bearing have freed many women to preserve employment opportunities outside the
home. Completed family size, for example decreased from 2.4 children in 1970 to 1.7 in1984
among white women and from 3.1 to 2.2 children among blacks. Recently fertility declines are
thus potentially important demographic source of post 1970 increase in overall female labor
participation (monthly labor review, June, 1970).

2.3.2. Marital status

Substantial variation exist by marital status with married women exhibiting labor force
participation rates much lower than those of the overall female population .Changes since 1970,
the marital status composition of the female population have provided potential significant
demographic sources of growth in female labor force participation. The incidences of divorce for
example increased from about 14per 1000 married women in1970 to nearly 22per1000 in1984.
In addition, the proportion of non-married women has raised rapidly .Especially among adults
reflection delayed marriage for example the median age at first marriage among women in the
United States rose from 20.6 in1970 to 22.8 in1984 (Ibid).

2.3.3 Age
Age composition is a major structural aspect of the labor force. Market related activities are
clearly associated with age. The age profile of women labor force is curve-linear reaching its
hider during the child-bearing years and after age 40 or 50 years. Women labor force exists
began to raise one significant factor of labor force structure (Ibid).
A study on factors determining the labor force participation decision of educated marriage
women in a district of Punjab by Amtu and Eatzaz (2002, by using nonlinear maximum
likelihood function for the normal probability (profit) model and cumulative logistic probability
function. The estimation result indicates that the women age has quit a sizeable impact on labor
force participation decision. The probability of female labor force participation increase with
age. The likelihood of participation of women in the labor force is expected to increase by about
0.8 percent with increase female age. They also found that education play a vital role in
determining the female labor force participation decision. Thus, there was clear evidence they
conclude that the women with higher education are more likely to participate in the labor force.

13
2.3.4. Husbands earning

Jacob minces work labor force participation of married women in 1950 in India. According to
mince’s empirical work, husband’s earnings had a negative effect on the participation of women
in the labor force. However, women earning power was a positive determinant of labor force
participation. Mince proved that wives were more likely to participate in the labor force if
husband’s earnings were lower than permanent earning transitory- income influence were
stronger than permanent income influence in explaining women’s decision to join in the labor
force. Moreover, if the education level of family head (age>35) was high, changes in permanent
and transitory-income where affect participation rate. Mincer introduced the key determinants to
women labor force participation .It also noted that unemployment and presences of young
children in household are discouraged effect on labor force participation.

2.3.5. Family size

Amtu and Eatzaz (2002), the study suggested that household size has strong positive correlation
with the female labor force participation. This is so because, the pressure on the financial
resource in household compromised of more members is higher which induce educated women
to participate in earning activities .The result shows that women living in joint family participate
are more than those living in the nuclear family. The most plausible explanation for their result is
that the pleasure of many person in joint family reduce the pressure of household chores and the
educated women can offered to came out of the home and work for cash .Thus, the women living
in large and joint families are work likely to engage in labor force participation compared to
those living in relatively small and nuclear families.
Household monthly income is another important factor influencing the labor force participation
decision of women. Their result suggested that the increased household monthly income reduce

2.3.6. Agriculture and gender

The agriculture sector of the economy is shrinking the percentage of women who are employed
in agriculture sector is increase. According to the penguin atlas of women in the world, women
make up 40%of the agricultural force in most part of the world .While in developing countries
they make up 67% of agricultural work force in term of food production worldwide. The atlas

14
shows that women produce 80% of the food in sub-Saharan Africa, 50% in Asia, 45% in
Caribbean, 25% in N/America, and Middle East and 25% in Latin America.

15
2.3.6. Informal labor and gender

The gender is frequently associated with informal labor .women employed more often than they
are for many and informal labor is an overall larger source of employment for female than it is
for male .women frequent the in formal sector of the economy through occupation like home
based worker and street vendors.
The penguin atlas of women in the world shows that in 2015 81% of women of women in Benin
were street vendors 55%in Guatemala,44% in Mexico, 33% in Kenya and4%in India. Over all
60% of women workers in developing world are employed in the informal sector .The specific
percentage are84% and 18% for women in sub- Saharan Africa and Latin America
respectively .The percentage for men in both of the area of the world are lower accounted to 63%
and 48% respectively. Globally, a large percentage of women that are formally employed also a
work in the in formal sector behind the scène (World Bank, 2017).

2.3.7. Unpaid labor and gender

Women usually work fewer hours in income generating jobs than men do. Often it is the house
hold work that is unpaid. Women and Girls are responsible for great amount of house hold work.
Feminist economist have argued for the inclusion of unpaid work in economic growth statics one
measurement that feminist have created to give a value to unpaid house hold work is to compare
the hours spend activities within the home by men and women. The penguin Atlas women in the
world show that in Madagascar women send 20 hours per week on house work, while men spend
only two. In Mexico women spend 33 hours and men spend 5 hours.

16
 CHAPTER THREE
3. METHODOLOGY OF THE STUDY
3.1 Description of the study area
Fitcheis found in North Shoa zone of Oromia regional state, of Ethiopia. The Town is locates at
114 kilometers north of Addis Ababa. Thecoordinates of the Town 9°48′N latitude and 38°24′E
longitude. It is characterized by temperate type of climate with daily temperature ranging from
18c and 27c, and is located between 2,515 and 2,547m abovesea level. The area is well known
by its rich natural resources like hot springs, fertile soil, and plain landscape (OUPI, 2010). Its
demographic wise, according to the 2007 national census reported a total population for Fiche of
27,493, of whom 12,933 were men and 14,560 were women. The majority of the inhabitants
(94.42%) reported that they practiced Ethiopian Orthodox Christianity, and 3.61% were
Protestant
The distribution of the population by broad age groups revealed that out of the total population
40, 3 and 57 percent, of its population is below 15, old age, and intermediate/ productive years of
age respectively. The ethnic composition of the town shown that there are more than seven
ethnic groups such as, Oromo, Gurage, Amhara and other and also there is economic activities in
Fitche Town such as ; agricultural ,mechanized and traditional modern industrial(OUPI, 2010).
3.2 Data Source and Method of Data collection
3 .2.1 Source of data
In order to collect the data both primary and secondary sources are used. The primary data
source was collected from questionnaires that have prepared and distributed to a sample of
women in Fitche that account for the census of women. The secondary data was collected from
different literature and write documents, such as books, magazines, OUPI, and other material
relate to the study will employ.
3.2.2 Methods of Data Collection
The primary data that the researcher uses for this study were collected from the representatives
of the target population or sample through questionnaires. These questionnaires was prepared in
accordance with the objectives of the study and in a way it could capture relevant data and
information from the respondents.

17
3.3 Sampling techniques
The research was conducted in Fitche town, NorhShoa Zone of Oromia region. The reason why
Fitche town was purposively selected by the researcher is that first, among the towns in
NorthShoa zone, the women labor force participation rate was more in Fitche town (Table 1).
Table 1: women labor force participation rate of Some Towns in NorthShoazone.
Name of Town Women labor force participation Rate (%)
Fitche 30
Kuyyu 24
Gulale 20
Source: north shoazoneWomen and Child Affair Biro
The study utilized multistage inspecting. Stage 1: Fitche town was purposively chosen for this
study. Stage 2: The essential inspecting units are kebeles. Twokebeles in particular Kebele02 and
03 out of the 4 kebeles in the town are purposively chosen. The purpose of selecting these
twokebeles was that the women labor force participation rate was high when compared with
other two pre-urban kebeles (01 and 04) (Table 2
Table 2 women labor force participation rate of fourkebeles of Fitche Town in 2020
Kebeles Number of women labor force participation

01 2200
03 2300
04 2000
Source: FitcheTownWomen and Child Affair Biro(2020)

Stage 3: For the motivation behind this exploration, the subjects considered were women labor
force participation enrolled with office of the Women and Child Affair BiroFitche, matured
between 15-65 years. Utilizing straightforward irregular inspecting strategy, the women labor
force participation was chosen from the distinguished kebeles.
The aggregate number of women labor force participationenlisted with the workplace of the
Seville Service Office, Fitche Town, from chose twokebeles viz., kebele 02 and kebele 03 were
5500. The sample size was selected by using (Yemane, 1967) method of detecting sample size by
the formula of

18
 N=total population
E= estimated error of sample (10%)
n=sample size
N 20000
n= = = 99.5 approximately 100
1+ N (e) 2 1+ 20000(0.1)2
The total number of women population in Fitche Town is 20000. The degree of accuracy is 10%
is assuming on the study. Therefore, the require target population of the study will be 99.5. The
sample respondents are select using simple random sampling techniques. The study will used
stratified sampling techniques method because this method is more representative of the true
population and it can easily improve the research. The research will be used 100 sample size
from 2 kebele of total target women labor force participation of 5500. The research would be
selected from the first kebele3200 women labor force participation, and from the second kebeles
2300 women labor force participation would be selected
Nin 3200 x 100
02 kebele (Woinadega) ni1= = =58
N 5500
2300 x 100
03 kebele (Woinadega):¿ 2= =41.8=42
5500
Total sample size=58+42=100
3.4. Method of Data Analysis
Due to the nature of data, the study, the methodologies used in this study was both descriptive
and econometric model.

3.4.1. Description Analysis

Descriptive analysis summarizes the data in to table. For independent variable descriptive
statistics is used to analyze respondent demographic characters in frequency distribution.
3.4.2 Econometric Analysis

In addition to descriptive analysis the econometric analysis was used to show the influence each
explanatory variable and dependent variable by using logit model. The dependent variable which
is female labor force participation rate can take two values: 1 for the participation rates higher
than 50 percent and 0 for the participation rates lower than 50 percent. So that the 50th percentile
(median) is used for classifying the dependent into two groups.

19
3.5. Model specification
The model shows the relationship between the labor force participation and its factors. This
research uses the multiple linear regression models because labor force participation is
determined by more than one variable.
The terminology binary logistic regression analysis the odd of success defined as to be the ratio
of the probability of success to the probability of failure.

Where, are the model parameters and for this study the dependent and explanatory variables
were defined as
Yi=1 If labour participated
0 if not participated
Moreover, other explanatory variables may be included in the study based on the data collected
from the respondent. Making some arrangements of above model or relation can be written as:-

= (logistic regression function)

Where, and X0=1; Odd ratio, the probability of an event happening is

For the event as a success.

That is: -

= , i= 1, 2…..k
After finding the odd ratio the interpretation would be applied for each explanatory variables
with respect to the obtained odd ratio that is ebionly whether participated or not.

Li = ln P (Yi=1/1-p(Yi=1) = Zi

20
 Where: P (Yi=1) =probability of being labour force participation, Zi is a function of a vector of
explanatory variables.
Zi= Bo+B1AG + B2FS + B3MRT + B4EDC + B5FI + Ui
• Where: Yi=Women labour force participation
• B1, B2, B3, B4, B5,
• Bo= constant term
• AG= age
• FS=Family size
• MRT= marital status
• EDC= educational level
• FI=Family income

3.6 Description of variables

In this study, the dependent variable (Y) is measured as continuous variable with the individual
who participation in labor force
Independent variables: are variables expected to influence the dependent variables as follows:
Age= it means age group of respondents in a year. Compared to the adults all other age group is
positively associated with unemployment (Qayyum, 2012).According to study it is taken as a
continuous variable.
Marital status= it is dummy variables that assigned for marital status that means 1 for married
and 0 for single. In this study assumed as a negative association between getting married and
being women labor force participation. It means married women are not more participate in labor
force because sometimes the income of husband is enough for their life.
Educational level=it means education level of women who participate in labor force and
according to this study it is considered as dummy variables, assigned 1 for educated and 0 for
uneducated. It is negative relation with participation of women labor force that means
uneducated women are lower in participation of labor force because they are unskilled, even if in
our country there is no job for educated.
Family size= Family size in the context of this study refers to the total number of family in
addition to the women them self. Large numbered families whether rich or poor are difficult to
maintain their life, they are characterized with a high number of family, rowdiness and this does

21
not create convenience for life. The study expects that it will affect participation of labor force
positively and it is considered as a counties variable. It will imply that the women who have a
small number of families are lowto participate in labor force.
Family income=it explains the effect of family income on the labor force participation of
women. Family income according to Escarce (2012) has a profound influence on the working
opportunities available to adolescents and on their chances of job success. Income of the family
is a determinant factor in affecting women participation labor force in different ways as it was
cited by (Garzon, 2016;; Kirkup, 2014).The researcher assumes women from low income family
background participate better than women from high income family background. High income of
family negatively affects women participation in labor force. In this study it will considered as
continuous variable measured in birr and also it affects women labor force participation
negatively.
3.7 The diagnostic tests of post estimation
3.7.1 Chi-square test
Chi-square tests will a test use to determine whether there will a relationships or association
between the dependent variable and the dummy independent or explanatory variables
The test that mainly uses to test the independent or inter correlation of two variables is called
chi-square test for independence. The objective of chi-square test will be to determine whether
there is a relationship between two categorical variables or not.

3.7.2. Multicollinearity test

It is necessary to test multicollinearity problem among continuous variables and check

associations among discrete variables, which seriously affects the parameter estimates. As
Gujarati (2003) puts it, multicollinearity refers to a situation where it becomes difficult to
identify the separate effect of independent variables on the dependent variable because of the
existing strong relationship among them. In other words, multicollinearity is a situation where
explanatory variables are highly correlated. Variance Inflation Factor (VIF) is a measure that is
often suggested to test the existence of multicollinearity.
Thus, in the present study too, variance inflation factor (VIF) used to check multicollinearity of
explanatory variables. As R2 increase towards 1, it is a linearity of explanatory variables. The
larger the value of VIF, the more troublesome or collinear is the variable Xi. As a rule of thumb

22
if the VIF is greater than 10 (this will happen if R2 is greater than 0.80) the variable is said to be
highly collinear (Gujarati, 2003). Multicollinearity of continuous variables can also be tested
through Tolerance. Tolerance is 1 if Xi is not correlated with the other explanatory variable,
whereas it is zero if it is perfectly related to other explanatory variables. A popular measure of
Multicollinearity associated with the VIF is defined as:
1
VIF= Where: X is explanatory variable and model coefficient of determination. The
1−R2
larger the value of R2 is, the higher the value of VIF causing higher collinearity in the variable
(Xj).

3.7.3. Heteroscedasticity test

The variance of the error term is not constant for all observations, the estimates of the
coefficients becomes inefficient (i.e., larger than minimum variance), as well as the estimates of
the standard errors becomes biased which leads to incorrect statistical tests and confidence
intervals. Thus to assure the assumption of constant variance of the error term, Breusch-Pagan /
Cook-Weisberg test was conductedGujarati, (2003).But robust test used in logit method.
Because, Heteroscedasticity test couldn’t have employed in this method.

3.7.4. Goodness of fit of the model

The goodness of fit of the model is measured by the statistical index called Pseudo R-squared,
which shows the proportion of the total variation in the dependent variable which is attributable
for the variation of all the explanatory variables.

23
 CHAPTER FOUR
4. DISCUSSION AND ANALYSIS
This chapter involves the result obtained from the study in the inferring the proper enter relation.
The discussion is found from the research on analysis of the determinants of women labor force
participation in Fitche Town and the discussion was usedthrough descriptive and econometric
method of data analysis.
4.1. Descriptive Analyses

Descriptive analysis is the use of statistics to describe the result of an experiment or

investigation. It is used to describe the characteristics of the data in research and provides simple
summaries about the sample. This part provides a descriptive analysis on demographic profiles
of the respondents in term of educational bacegroud, age, marital status,employment statussource
ofhousehold income and family size.
4.1.1. Socio economic and demographic characteristics
To take a deep look on the possible determinants of women labor force participation, the
investigator collects information on determinants that are interest on such as, , educational status,
age, marital status, husband income, , employment status, family size and family income source.
The information about each determinant was collected by asked 99 women from the total of
14560 female populations with the age of above 15 years and the data was better to say described
instead of analyzed.
4.1.1.2Participant and non-participant respondents in the labor force for different age group.

determinant Participant Non- participant Total

Age Number Percentage number percentage number Percentage
15-24 10 8% 12 13% 39 39%
25-34 16 33% 5 14% 47 47%
35-44 23 6% 8 5% 11 11%
45-54 4 1% 11 1% 2 2%
>54 2 1% 15 0% 1 1%

24
Total 55 67% 45 33% 100 100%
Source: own survey 2012 E.C.
Table 4.1.1.2, from the samples data, women participation in the labor force majority are located
between, 35-44 years(23%)and also followed by 25-34 years (16%). This implies that women
labor force participation increase with age specifically up to 44years old. Then after that, women
labor force participation declines with the increasing in age of an individual.
Then labor force participation can be interpreted as the rise in age of individual women increase
their capacity of to perform a certain tasks up to they are 44 years old, then after that their
participation become decline and decline with increase in age as it was responded by the
respondents.
4.1.1.3. Participant and nonparticipant respondents in the labor force for different marital status

Determinant Participant Non- participant Total

Marital Number Percentage Number percentage number Percentage
Status
Married 34 34% 25 25% 59 59%
Unmarried 16 16% 25 25% 41 41%
Widowed 0 0% 0 0% 0 0%
Divorced 0 0% 0 0% 0 0%
Total 50 50% 50 50% 100 100%
Source; Own survey, 2012E.C
Table 4.1.1.3 shows that from the sample that participant in the labor force, 34% of labor force
participants were married and 16% of labor force participants are unmarried. These indicate as
unmarried women are lowly participated in labor force. This is why because the probability of
unmarried women to participating is influenced by family decision. While married women are
also influenced by her husband decision.
4.1.1.4 Participant and non-participant respondents in the labor force for different family size
determinant Participant Non- participant Total
Family size Number Percentage number percentag number Percentage
e
0-4 7 15% 8 8% 15 15%
5-7 16 39% 40 40% 56 56%

25
 8 and 28 41% 1 1% 29 29%
above
Total 51 51% 49 49% 100 100%
Sources, own survey 2012E.C.

Table 4.1.1.4 shows that from the total sample that participate in the labor force the majority was
having above 8 family size (28%) and minority was having between 0-4 family size (7%),
whilenon-participants in the labor force majority was having 5-7 family size (40%).this implies
that better to say women with more family size were participated more than their counter part.
As the respondents said, the higher the family size response the women to highly participate in
labor force. This is because they have to spend more of their time on work in order to sustain
their family and supporting income consumption as it is responded by respondents
4.1.1.5Participant and nonparticipant respondents in the labor force for differ education
level
determinant Participant Non- participant Total
Education Number Percentage number percentag number Percentage
level e
Primary 6 6% 9 9% 15 15%
Illiterate 0 0% 16 16% 16 16%
Secondary 10 10% 6 6% 16 16%
Tertiary 50 50% 3 3% 53 53%
and above
Total 66 66% 34 34% 100 100%
Source: own survey 2012E.C
Table 4.1.1.5shows that from the total sample that participate in the labor force, 50%of the
respondents attends tertiary education level, 10%of labor force participants completed secondary
school and, 6% of labor force participants completed primary school. On the other hand, from
the sample that is not participate in the labor force,9% of labor force nonparticipants are primary,
6% are completed secondary school, and only 3% tertiary education level and 16% of
respondents are illiterate.

26
 4.1.1.6Participant and nonparticipant respondents in the labor force for employment status
determinant Participant Non- participant Total
Employment Number Percentage number percentage Number Percentage
status
Employed 17 17% 23 23% 40 40%
unemployed 35 35% 25 25% 60 60%
Total 52 52% 48 48% 100 100%
Source: own survey 2012E.C
Table 4.1.16.Shows that from the total sample that participate in the labor force, 17%of the
respondents attend employed, 35%of labor force participants completed unemployed. On the
other hand, from the sample that is not participate in the labor force,23% of labor force
nonparticipants are employed,25% are completed unemployed This indicates that labor force
participation increase as women achieved employment status.
4.1.1.7Participant and nonparticipants respondents in the labor force
withdifferenthousehold economic status
Determinant Participant Non- participant Total
Family income Number Percentage number percentage Number Percentage
source
Trade 11 11% 8 8% 19 19%
Government wage 30 30% 19 19% 49 49%
agriculture 20 20% 7 7% 27 27%
Other 3 3% 2 2% 5 5%
Total 64 64% 36 36% 100 100%
Source: own survey 2012E.C

Table 4.1.1.7 shows that from the sample that participate in the labor force, 20% of the
respondents use agriculture as family income source followed by government wage a (30%) and
trade which is 11% as family income source. On the other hand, from the total sample not
participate in the labor force, 19% of respondents are having Government wage as family income
source followed by agriculture (7%). This indicates that those women whose family income
source is government employment are more participated in the labor force. This is because, the

27
 income receives from government employment are not sufficient to meet the food requirement of
the growing family size. This is what forces them to participate on government employment
labor work .While those women use trade as family income source are less to participate in the
labor force. This is because families have not sufficient amount of income to meet the most of
necessary requirement.
4.1.1.18participant and nonparticipant respondents in the labor force for different spouse
income
determinant Participant Non- participant Total
Spouse income number Percentage number percentage Number Percentage
per month
0-4000 22 22% 6 6% 28 28%
4001-6000 12 12% 11 11% 23 23%
>6000 18 18% 31 31% 49 49%
Total 52 52% 48 48% 100 100%
Source: own survey 2012E.C.
Table 4.1.1.8 shows that from the total 52 respondents that participate in the labor force, 22% of
women participants husbands’ earning below 4000 birr per month, while from the total 48
respondents that do not participate in the labor force,31% of the women’s husbands’ earning
was above 6000 birr per month. This indicates that women labor force participation is low with
increases in husbands’ earning and nonparticipation is high with increase in husband earning. As
respondents said, this was because husbands’ income makes them dependents and ineffective in
passing decisions concerning the activities they performed.
4.2 Econometric Analysis
In this section the final result of the econometric analysis is presented based on binary logit
model estimation technique. Such presentation helps to examine is it true or not women labor
force participation is related to the explanatory variables and the estimation results are
interpreted, compared and discussed in detail.
 Predictors; family income women (FI), family size (FS), age (AG), educational level of women
(EDL) andmarital status (MSS).
 Dependent variable, women labor force participation (WLFP)

28
4.2.1 Econometric result
4.2.2. Binary logit results and discussion
Table presents the results of the estimated logistic model of the determinants of women
labor force participation in Fitche town. We, first, analyzed whether the independent variables in
our model have a significant relationship with the dependent variable. This was necessary for
determining the ability of the model to predict the dependent variable accurately. The Likelihood
ratio test was, consequently, performed to test the overall significance of all the coefficients in
the model and our results indicate that the overall model is significant at 0.002 level according to
the model Chi-square, implying that, as a whole the independent variables have a significant
contribution to predict response variables. The results of the logistic regression model estimate
revealed out of five factors, 3 variables were found to have significant influence on the
probability of being labor force participation of women in FitcheTown.
These variables include family size, family income and educational level of the respondents.
Using 0.05 level of significance as a standard for test of statistical significance, the coefficients
of all variables, with the exception of family size, family income and educational levelwere
found to be statistically insignificant. The logistic regression coefficients, Wald test and odds
ratio for each of the predictors are presented in this table. The table indicates that the coefficients
of family income and educational level are statistically significant at 0.01 levels, whereas a
coefficient of family size is significant at 0.05 levels. This shows that the three variables play
significant role in predicting the probability of being women labor force participation.
. *(6 variables, 100 observations pasted into data editor)

. logistic wlfp age fs fi edl mss

Logistic regression Number of obs = 100

LR chi2(5) = 103.35

Prob > chi2 = 0.0000

Log likelihood = -17.45922 Pseudo R2 = 0.7475

Variables Coefficient std. error. Z P-value Odd ratio

AG -1025463 .0837593 -1.22 0.221 0.9025363

29
 FS 1.420285 0.468016 3.03 0.002 4.138298
FI -.0021611 .0005996 -3.60 0.000 .9978413
EDL 2.669775 1.119006 2.39 0.017 .0692678
MSS -.3277891 1.136077 -0.29 0.773 .7205149
CONS 10.71755 3.481623 3.08 0.002 45141.16
Result Discussion from the stata result for individual test

In the above summarized model results possible explanation for each significant independent are
given consecutively as follows. According to estimated model shows family size, family income
and educational level are the variable which are statistical significant in our model
Family size (FS)

This variable affect women labor force participation positively and significant at 5% probability
level in the study area. The sample odds of high family size being labor force participation were
4.14 times higher than those of lessfamily size. This means that women who haslow family size
was less likely to be participate in labor force, compared to higher family size.The coefficients of
family size were positive as per expectation indicating that women in higher family size are more
likely to participate in labor force, as compared to those in low family size. The positive
relationship implies that increase in family size increases the probability of becoming in labor
force participation of women. This result is conformed to Qayyum (2012) who found family size
to be significant and positively related with women labor force participation.
1. Family income of women (FI)
From the result, family income has a negative coefficient and is statistically significant at 0.01.
This result is show thatas family income is increase the willing of labor force participation of is
decrease because high level of family income is it may enough for life. The sample odds of a
high family income being participate in labor force were 0.9978413times lower than those of
alow family income. This means that the women those had some high family income was less
likely to participate in labor force, compared to those low family income. The results are also in
line with the studies done by Mahlwele (2009) who found that high family income were less
likely to participate in labor force than the low family income. Gebeyaw (2011) in a study
conducted in Addis Ababa similarly found family income to have a negative impact on women
participated in labor force, and was statistically significant at 1% level of significance.

30
 2. Educational level women

Our results also provide evidence to support the view held by many that prior educational level
of is likely to positve impact on the probability of women participate in labor force. From Table
12, provided all other variables are held constant, the odds of women with some educational
level being increaselabor force participate are about 0.0692678 times higher than those of
uneducated or loss literacy. This means that a woman who has some educational level is more
likely to participate in labor force, compared to women who have no more educational level or
illiteracies. The association between women educational level and they participate in labor force
was statistically significant (p = 0.017). The results agree with studies and findings by Altman
and Gqulube (2009), as cited in Smith (2011), who found that individuals who had never held a
education before were 35% more likely to remain not participated in labor force than those who
had prior educational level. Similarly, the ILO (2004), as cited in Gebere (2011), notes that lack
of education reduces the chances of getting employment in the modern sectors of the economy.

4.2,3 Measure of multicollinearity among the variables

It is important to be familiar with the measures that are often suggested in the discussion of
multicollinearity: Contingency variable coefficient and Variance-inflation factor (VIF).

2 Variance inflation factor

Variable Vif 1/vif

Age 1.25 0.800086
Fs 1.29 0.773244
Fi 1.6 0.865145
Edl 1.03 0.971732
Mss 1.38 0.725461
Mean of vif 1.22
Based on the table VIF shows that the variance of explanatory variables inflated by the presence
of multicollinearity. Higher VIF suggests that severe multicollearity problem exist. On the other
word, if the VIF of a variable exceeds 10, according to the thumb, there is a serious
multicollinearity problem. But in this study, as the mean value of the VIF displayed in the table
above is less than 10 which is 2.89. This indicates that there is multicollinearity problem among
the explanatory variables but the effect is not that much.
31
Again, on the above table means of VIF and 1/VIF shows there is no multicollinearity problem
among variables.If mean VIF value greater than 10 and 1/VIF value greater than 1, say we can
there is multicollinearity. However, from the study the estimation result is the VIF value less
than 10 and 1/VIF than 1, implying this study is there is no problem of multicollinearity.
4.2.4. Marginal effect of logistic regression model
Marginal effects show the change in probability when the predictor or independent variables
increase by one unit. For continuous variables this represents the instantaneous change
giventhatthe unit may be very small. For binary variables, the changes from 0 to 1, so one unit as
it is usually thought.
Table 1.Result of marginal effect of logistic regression model

dy/dx Std.err z p>ǀ z ǀ

Age -0.0056161 0.0043572 -1.29 0.197
Fs .077784 0.0162396 4.79 0.000
Fi -0.0001184 0.0000101 -11.77 0.000
Edl 0.1462143 0.0468662 3.12 0.002
Mss -0.0179519 0.0619352 -0.29 0.772
dy/dx is for discrete change of dummy variable from 0 to 1

Source STATA 12.0; from own survey 2020

Family size: The marginal effect of the family size of respondents indicates that the probability
of being women labor force participation will increases by approximately 0.56% when family
size of the women increases by one year. This result confirms with the finding of
Qayyum(2012).
Family income: The marginal effect of training implies that the probability of being labor force
participation of women decrease by approximately 0.12% as family income increase by one unit.
This result confirms with the finding of Altman and Gqulube (2009), as cited in Smith (2011).
Educational level: The marginal effect of the variable shows that for women with educational
level the probability of being in participated in labor force decrease by 14.6%. The finding of this
study was found consistent with what had been found Altman and Gqulube (2009), as cited in Smith
(2011).

32
4.2.5 Hetrosecdasticity test
HettestBreusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of wlfp
chi2 (1) = 0.53
Prob>chi2 = 0.4659
The result show that the P value of Chi-square (Chi²) obtained from this test is high, accept null
hypothesis that is the P value of Chi²: prop>Chi²=0.4659 greater than 5% level of significance.
So that reject alternative hypothesis. Therefore there is no problem of hetrosecdasticity.
4.2.6 Goodness of fit of model
As we can see from the statistical analysis in table 4.1.1.19, our model yielded a high value of
pseudo R2 of 0.745. The R- squared statistics gives the proportion of total variation in
independent variable that is explained by the model, a measure of the overall goodness of fit of
the model. Therefore from the total variation 75% explain in our model this show goodness of
our model.

33
 CHAPTER FIVE

CONCLUSION AND RECOMMENDATION

5.1. Conclusions

The aim of the study was to investigate the determinants of women labor force participation in
Fitche Town. This study analyzed a onetime visit cross-sectional data on 100 representative
samples of respondents to empirically access the determinants of women labor force
participation in Fitche Town.
The primary and secondary data were collected from the different things. Having the total
population of 20,000 the selected using n = (N/1+N (e^2) and use multi-stage method to select
sample from four kebeles.After analyzing the collected information from primary respondents
the researcher develops the following conclusion: The result obtained from the descriptive
analysis and the binary logit regression model was applied. The descriptive result shows socio
economic, institutional and demographic characteristics of respondents by using percentage
(table).The current educational system of the Fitche Town in particular was of theoretical and
practical.
For econometric analysis the binary logistic regression model used to examine the relationship
between age, family size, marital status, educational level, family income, and women labor
force participation. It was hypothesized that these factors exert a strong impact on women lobar
force participation in the economy of Fitche town.
The results showed that three out of five explanatory variables tested were significant in
explaining women lobar force participation; family size, family income and educational level
were found to have major influence on women lobar force participation. The results showed that
having low family size and having high family income are associated with reduced odds of being
women lobar force participation. With regard to the sample odds of high family size being labor
force participation were 4.14 times higher than those of less family size.This means that women
who had some family size was less likely to participate in labor force compared to who had high
family size. The high level of spouse income was also restricting some women from participating

34
in the labor. Lowlevel of family size, high family income and literacy are the key factors
forwomen lobar force participation inFitche town.Finally, women lobar force participation in
Fitche town is highly depend up on level of family size, family income and educational le

5.2 Recommendations

Based on the finding reached up on, the study came up with the following recommendations. 
As it was indicated in the study, more than half of the surveyed respondents were nonparticipant
especially those who were aged above 45. Thus, the study suggests that women employment
should get due attention in order to improve their participation rate by facilitating age based jobs.
And government is expected to do so.
 The higher percentage of women participation rate in the labor force accounts higher on
women who attained primary school and above. From this, the study suggests that the concerned
body should give more attention to women education and the ways of creating public awareness
regarding the indispensible value of education.
 Public cooperation plays an important role in bringing women to the labor force. However,
some people of the town have no positive attitude toward women activity. From this, the study
suggests that different ways of altering the societies mind was given so as to increase women‟s
participation in the labor force. In addition to these providing training and rewarding role model
women were highly recommended.
 The participation rate of women labor force in the woreda was also affected adversely by the
number of children they have. Hence, the study suggests the introduction of family planning and
priorities should be given for women in better job opportunities that delimit them from having
many children.
The sample that participant in the labor force of labor force participants are married and of labor
force participants are unmarried. These indicate as unmarried women are lowly participated in
labor force. That was why because the probability of unmarried women to participating is
influenced by family decision. While married women are also influenced by her husband
decision.
Since from finding family income had negative impact on women labour force participation the
responsible should be give attention from those who have to increase labour force participation
and improve their income problem.

35
As estimated model implies that education level had positive influence on women labor force
participation in Fitche town then the government should increase the accessebility of education
to all part of the country and create favor condition for education opportunities

36
 References

Abera (2001) factor determining the level of women labor force participation in sebeta town.
Aly, Y.H.and Quisi, I.A. (1996),’’ Determinants of female labor force Participation ‘in Kuwait.
AmtualH.and Eatzaz (2002),’’factors determining the labor force participation decision of
educated married women.’’ in districts of Punjab.
Blau, F.D., Ferber, M.A., & Winkler, A.E. 1998. The Economics of Women, Men, and works.
(3rdEd.).London: Prentice Hall Inc.
ChaudhryG.and Khan. (1998) female labor force participation rate in rural Pakistan.
Ethiopia Federal Women Affair Bureau, (2008) Ministry of labor and Social Affairs.(2004)
Evans,(1993),’’the impact of socio-economic development on women’s labor Participation’’ in
Brazil.
Fong, M,(1975), female labor force participation in a modernized society; Malaya and
Singapore.
Gebeyaw, T. (2011). Socio-Demographic Determinants of Urban Unemployment: The Case of
Addis Ababa, Ethiopian Journal of Development Research, 33(2), pp. 79-125.
Gujirati, D.N.: Basic Econometrics. 3rd ed
GuvenLisaniler, F. &Ugurar, S. 2001. Occupational Segregation: The Position of women in the
Jacob M.(1950),’’labor force participation of married women’’ in India.
Micer, J.(1980),labor force participation of marriage women in Aliech Masco.
Negatu, (2003).Causes of women’s low levelproductionatsebeta town.
North CyprusLabour Market. Woman 2000, 2(1), 117-131
Ozar,s. and Gunluk-senesen, g.1998.”Detrminants of female participation in the urban
\Smith (2003),labour economic , 2nd edition, great Britain, TJI digital printing
Sultana, N.Nazli, H., &Maliks .J, (1994). Determinants of Female Time Allocation in Selected
SWSZ and WT women and child affair biro. Sectarian Participation of Women
World Bank, (1999), world development indicator 2000, Washingto

37
 APPENDIX 1

logit wlfp age fs fi edl mss

Iteration 0: log likelihood = -69.13461

Iteration 1: log likelihood = -19.859252

Iteration 2: log likelihood = -17.596478

Iteration 3: log likelihood = -17.459726

Iteration 4: log likelihood = -17.45922

Iteration 5: log likelihood = -17.45922

Logistic regression Number of obs = 100

LR chi2(5) = 103.35

Prob > chi2 = 0.0000

Log likelihood = -17.45922 Pseudo R2 = 0.7475

------------------------------------------------------------------------------

wlfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

38
 age | -.1025463 .0837593 -1.22 0.221 -.2667115 .0616188

fs | 1.420285 .468016 3.03 0.002 .50299 2.337579

fi | -.0021611 .0005996 -3.60 0.000 -.0033362 -.0009859

edl | 2.669775 1.119006 -2.39 0.017 -4.862987 -.4765641

mss | -.3277891 1.136077 -0.29 0.773 -2.554459 1.898881

_cons | 10.71755 3.481623 3.08 0.002 3.893693 17.54141

------------------------------------------------------------------------------

Marginal effect

Marginal effects after regress

y = Fitted values (predict) = .53

------------------------------------------------------------------------------

variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X

---------+--------------------------------------------------------------------

age | -.0038303 .00704 -0.54 0.586 -.017621 .00996 26.54

fi | -.0001061 .00001 -8.26 0.000 -.000131 -.000081 5087.93

edl*| -.2361393 .0761 -3.10 0.002 -.385289 -.08699 .66

mss*| -.1450699 .0808 -1.80 0.073 -.303434 .013294 .59

------------------------------------------------------------------------------

(*) dy/dx is for discrete change of dummy variable from 0 to 1

39
multicollinearity(seife, 2020)
. vif

Variable VIF 1/VIF

age 1.24 0.804824

mss 1.23 0.813108
fi 1.05 0.956283
edl 1.01 0.988161

Mean VIF 1.13

Heteroskedasticity

. hettest

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Ho: Constant variance
Variables: fitted values of wlfp

chi2(1) = 1.14
Prob > chi2 = 0.2866

40
 APPENDIX 2
SALALE UNIVERSITY

COLLEGE OF BUSINESS AND ECONOMICS

DEPARTMENT OF ECONOMICS

Dear respondents: This questionnaire is prepared by third year Economics student in Salale
University for partial fulfillment of getting BA in Economics. And to obtain essential and
relevant information about determinants of women labor force participation in the case of Fitche
town.
Note: It is important that you answer each item as thoughtfully and frankly as possible and your
responses are highly valuable and will be used for research purpose only.
1. please answer all questions;
2. you do not need to write your name on the questionnaires;
3. all the information that you may provide will be held confidential;
4. Please circle the letter of your answer for choice part and write your answer in space
provided for others.
Part I Personal information.
1. Age _____________
2. What is your current marital status?
A. Single B. Married C. widowed D.Divorced
3. What is your family size?______________
4. Is your family/husband willing to your engagement in work?
A. Yes B. No
5. If No WHY?
A. No one else is responsible to take care of the children’s
B. My parents /husband is not willing without reasons
C. Other reasons___________________________________
6. What is your educational status?
A. primary B. Secondary C. Tertiary and above D. Illiterate
7. What is your current employment status?

41
 A. Employed B. Unemployed

8. If employed what type of work you engage in?

A. private business B. Government employment
C. Agriculture D. other
9. What is age of your husband if you have? _______________
10. What is your husband/family main income source?
A. private business B. Government employment
C. Agriculture D. other
11. What is monthly income of your family/husband? ________________
12. Do you think that your husband/family income is sufficient for your expenditure?
A. Yes B. No
13. What is the amount of saving your family have? ______________

42
43