Women’s Labor Force Participation Analysis on Formal and

Informal Business Sectors

Muryani Muryani and Ginanti Permata Hatiku

Fakultas Ekonomi dan Bisnis, Universitas Airlangga

muryani2008@yahoo.co.id

Keywords: Women’s Labor Force Participation, Work Sector, Individual Characteristics, Multinomial Logit.

Abstract: The purpose of this research is to know the determinant affecting the women’s labor force in formal and

informal sectors in Indonesia. The data used in this study is Cross Section data from the National Labor Force

Survey 2014. Analysis of Multinomial Logit Regression shows that primary education, secondary education,

tertiary education, age, and income significantly influence the women’s labor force participation in the formal

sector. Meanwhile, primary education, secondary education, tertiary education, age, marital status, and

income significantly influence the women’s labor force participation in the informal sector.

1 INTRODUCTION

Job opportunity for men and women is not the same.

Some common issues that usually arise for women

job seekers are low human resource quality and

difficulty of finding jobs with a decent salary. Based

on the publication of International Labor

Organization of Indonesia during 2014, women’s

participation in the labor force is still low. In August

2014, the total labor force reached 121.87 million

people increased by 1.7 million people, compared

with August of 2013 the labor force as much as

120.17 million, while the open unemployment rate in

Indonesia in August 2014 amounted to 5, 94 percent

decrease compared to August 2013 amounted to 6.17

percent (BPS, 2014). However, many women are

reported to came join in the labor force during 2014.

Many working women are also reported of still

having full household-responsibility. Education level

also has a positive relationship with women’s labor

force participation, meaning as the education level

gets higher, the possibility of women getting jobs is

also higher (Simanjuntak, 1995).

The result of this research applies in all age levels

in both urban and rural areas. Further research also

shows that married women tend to have higher levels

of labor force participation rate than unmarried

women (Dogrul, 2012).

Theoretically, the relationship of working hours

and income exists, so, many people choose to have

more working hours to get more income. But, a

research from Bellante and Janson (1990) shows that

women with high-paying jobs tend to shorten their

working hours and prefer of having longer spare time.

2 METHODS AND DATA

The type of data used in this study is secondary data

in the form of cross-section data from year the 2014.

Source of data sample is from Survei Angkatan Kerja

Nasional (SAKERNAS) that contains household data

from all provinces and cities in Indonesia. Selected

sample data is composed of 229.103 data from

women in the productive age of 15-64 years old.

This research uses both the qualitative and

quantitative approaches. Qualitative approach is used

to find the probability of events (Gujarati and Poter,

2009), while the quantitative approach focusses on

value measurement and hypothesis testing. The

quantitative approach is done through Multinomial

Logit regression method. The researcher defines what

the independent and dependent variables that

correlate with the topic are to be processed in both

urban and rural areas with the Multinomial Logit

regression, with which authors analyze and interpret

statistically and economically.

Here is the equation model to be used in this

research:

LFPwomen = βo + β1educprimer +β2educsekunder

+β3eductersier + β4umur + β5statprkwn + β6lokasi +

β7pendapatan + ui

(1)

LFPwomen : Women’s Labor Force Participation

814

Muryani, M. and Hatiku, G.

Women’s Labor Force Participation Analysis on Formal and Informal Business Sectors.

In Proceedings of the 1st International Conference on Islamic Economics, Business, and Philanthropy (ICIEBP 2017) - Transforming Islamic Economy and Societies, pages 814-817

ISBN: 978-989-758-315-5

βo : Intercept

β1-β8 : Regression Parameter (coefficient)

β1educprimer : Primary Education

β2educsekunder :Secondary Education

β3eductersier :Tertiary Education

β4umur : Women’s Productive Age (15-65

years old)

β5statprkwn : Marital Status

β6lokasi : Location

β7pendapatan: Income

Multinominal Logit regression is done three times

with the sample from all areas in Indonesia, urban-

area only, and rural-area only. Statistical analysis of

all sample-testing is done through Likelihood Ratio.

Significance parameter testing of each variable is

done through Z-statistic and Goodness of Fit model

with R-square (R2), that is also called by Pseudo R-

square in the Multinominal Logit model. Coefficient

interpretation to interpret the influences of

independent variables to dependent variable is using

RRR.

3 RESULTS AND DISCUSSION

Likelihood Ratio from the Multinominal Logit

regression for all areas in Indonesia, urban-area only,

and rural-area only has been equal to 0.000. It means

that all variables (primary education, secondary

education, tertiary education, age, marital status,

location, and income) are influencing the women’s

labor force participation significantly in the formal

and informal sector.

The value of pseudo R

from regression analysis

that is 0.3131 indicates a very good model fit. The

value of pseudo R

for urban-area only is 0.3527,

meaning that 35.27% of the variation in dependent

variable is derived from the variation in independent

variables in the model. Meanwhile, the value of

pseudo R

for rural-area only is 0.2445, meaning that

24.45% of the variation in dependent variable is

derived from the variation in independent variables in

the model. But it must be noted that the main

determinant of model quality is not the value of

pseudo R

, but from the Z-score to test the

statistical significance and the direction of the

coefficient itself.

Table 1: Comparison on the Result of Multinomial Logit Regression

Independent

Variable

Economic Sector

Correlation

Indonesia

Urban

Rural

Primary

Education

Formal

(+)

Informal

(-)

Secondary

Education

Formal

(+)

Informal

(-)

Tertiary

Education

Formal

(+)

Informal

(-)

Age

Formal

(-)

Informal

(+)

Marital status

Formal

Not significant

(+)

Informal

(+)

Location

Formal

Not significant

Informal

(-)

Income

Formal

(+)

Informal

(+)

Source: Regression Output of Multinomial Logit in Stata/SAKERNAS 2014.

3.1 Education Level Variable

The level of education used in this study consisted of

dummy primary education, secondary education and

dummy dummy tertiary education. Usage category in

education supported by research and Abdusammad

Usman (2016) which states that the size of the

education being classified into four levels, namely

primary, secondary, tertiary, and no education

(classified by the classification of human needs

according to its intensity). The result shows that more

people with secondary education as the latest diploma

can increase the participation rate in the formal sector

by 5.265 times in all areas in Indonesia and by 4.009

times in urban areas, which is higher than the other 2

Women’s Labor Force Participation Analysis on Formal and Informal Business Sectors

815

variables representing education level. However, the

analysis of data shows a different result in rural areas,

where more people with tertiary education as the

latest diploma can increase the probability of

women’s labor force participation in the formal sector

by 10.679 times, higher than the 2 other variables.

This is because there is still a lack of labor force

participation in the formal sector that is caused by

limited access to education in rural areas.

In the informal business sector, the effect of those

three education level variables on three group areas

on women’s labor force participation is insignificant,

which is depicted from the negative coefficient

results. It means that higher education level lowers

the women’s labor force participation in the informal

sectors. This result is supported by a research from

Dogrul (2012), that states education seems to have a

negative effect towards labor force participation in

the informal business sector. Women also have a

higher level of participation to work in the informal

sector than the formal sector. The reason behind this

is because the main considerations in the informal

business sector are capital and skill, instead of the

level of education.

3.2 Age Variable

The result shows that age variable significantly and

negatively influencing the probability of women’s

labor force participation in the formal sector. For

example, as women get older, there is a decrease of

participation by 0.995 times in all areas of Indonesia

and by 0.994 times in urban areas, which can be

caused by certain limitations in the formal business

sector. This result is supported by Borjas (2013) that

said, people usually reduce their labor participation

when they are already retired. Hill (1983) also said

that formal business sector has a more rigid system

than informal business sector.

Furthermore, age variable seems to have a

significant positive effect on women’s labor force

participation in the informal business sector, whether

it is in all areas in Indonesia, urban-area only, or rural-

area only. Using the data from all areas in Indonesia,

age is proven to increase labor-force participation by

1.031 times, while in urban-area only, it successfully

increases the participation rate by 1.037 times, and in

rural-area only, an increase in age can increase the

participation rate by 1.027 times. Antyanto (2014)

said that there are many groups of elderly workers in

informal business sector. This phenomenon can be

caused of the public opinion that informal business

sector is more secure, while overlooking the age

variable of its workers.

3.3 Marital Status Variable

The analysis in this research shows that marital status

has a significant and positive effect on women’s labor

force participation in the formal sector in rural areas

by 2.031 times. Whereas in the informal business

sector, marital status has a significant positive effect

on the three group areas, with data from the overall

area in Indonesia shows an increase by 2.020 times,

in urban-area only shows an increase by 1.970 times,

and in rural-area only shows an increase by 2.031

times. As Chinhui research and Potter (2006) stated

that the participation of married women working for

a higher percentage of income distribution and

increase the percentage of female labor participation.

3.4 Location Variable

This research uses 3 variables that explain locations;

urban-area and rural-area. Our analysis shows that

location has a significant and negative relationship

with women’s labor force participation in the

informal sector, which means that women in urban

areas are less likely to work in the informal business

sector by 0.371 times than women in rural areas. It is

like a study conducted by Contreras (2011) which

states that a great competition in urban areas makes it

possible to be able to work in urban areas smaller than

the rural region

3.5 Income Variable

Formal business sector usually pays their workers

higher than the informal business sector. But the

result of this research shows that the relationships

between women’s labor force participation with

income in all 3 groups areas and in both formal and

informal business sectors are positive and significant.

This research’s analysis shows that any increase in

income results in the increase of participation rate by

100%. This is consistent with research Bibi and Asma

(2012) which states that income is an important

variable in determining the labor force participation

of women and act as an incentive to work. In many

previous studies that found that the formal sector tend

to have higher wages than the informal sector

4 CONCLUSIONS

Based on the results of this research and the

discussion that have been done on the determinant

affecting the women’s labor force participation in

formal and informal sectors in Indonesia using

ICIEBP 2017 - 1st International Conference on Islamic Economics, Business and Philanthropy

816

Multinominal Logit regression, the conclusions are

drawn as follows:

 People with higher education levels have a

higher probability to work in the formal sector

than in the informal sector, while in the

informal sector, people with an education

degree have lower probability than those who

do not work. This is because the main

considerations in the informal business sector

are capital and skill, instead of the level of

education. Marital status also has a significant

and positive relationship with women’s labor

force participation in both formal and informal

sectors. Women’s labor force participation in

formal sector continues to decrease with age,

while in the informal sector, age does not show

any effect on women’s labor force

participation. Finally, any increase in income

results in the increasing probability of labor

force participation of women in both informal

and formal sectors. (These conclusions apply to

all the group area, which is all areas in

Indonesia, urban-area only, and rural-area

only);

 Based on Multinomial Logit regression, the

three education variables (primary, secondary,

and tertiary education), age, marital status,

location, and income is simultaneously and

significantly affect the preferences of female

labor force in selecting job sector, whether it is

in formal or informal. Partially, the result of

testing in all areas in Indonesia on primary

education, secondary education, tertiary

education, age, and income variables show a

significant relationship in formal and informal

business sectors. Marital status and location

variables also influence the women’s labor

force participation in the informal sector.

While in urban-area only, primary education,

secondary education, tertiary education, age,

and income variables show a significant

relationship in formal and informal business

sectors, and marital status variable shows a

significant relationship in informal sectors.

Furthermore, primary education, secondary

education, tertiary education, marital status,

and income show a significant relationship in

formal and informal business sectors in rural-

area only, while age has a significant effect in

the informal sectors in rural-area only.

Here are some recommendations and suggestions

for further research:

 The data used in this research shows that

women’s labor force tends to work in the

informal sector. Government should support

and help them by giving them training that will

increase their expertise and by providing them

with increased micro and small enterprise loans

to help some additional capital assistance, so

they can increase their production and overall,

we hope they can help in maximizing our

economic growth;

 It is the author's hope that further research will

be done more dynamically, where the

observation is done on several years with an

updated data. Furthermore, we hope that

further research will include factors other than

those being examined in this research.

REFERENCES

Antyanto, I. N., 2015. Analisis Faktor-Faktor yang

Mempengaruhi Tenaga Kerja Memilih Sektor Informal

Sebagai Mata Pencaharian (Studi Kasus Pada Pasar

Penampungan Sementara Merjosari, Malang).

JurnalIlmiahMahasiswa FEB. 3(1).

Bellante, D., Janson, M., 1990. Ekonomi Ketenagakerjaan,

Lembanga Penerbit Fakultas Ekonomi Universitas

Indonesia. Jakarta.

Bibi, S., Asma, A., 2012 Impact of Internal Marketing on

Market Orientation and Business Performance

Borjas, G. J., 2013. Labour Economic, Mc-Graw-Hill

Companies, Inc.New York,

6th

edition.

BPS, 2014 Berita Resmi Statistik: Tingkat Pengangguran

Terbuka (TPT) tahun 2014, [Online] Available at:

www.bps.go.idpada 4 Juli 2017

Chinhui, J., Simon, P., 2006. Changes in Labor Force

Participation in the United States. Journal of Economic

Prespective. 20 (3): 27-46

Contreras, D. G., 2011. Tournaments Incentives for

Teachers: The Case of Chile. Working Paper.

Dogrul, H. G., 2012. Determinants of Formal and Informal

Sector Employment in The Urban Areas of Turkey.

International Journal of Social Sciences and Humanity

Studies. Vol 4, No 2.

Gujarati, D. N., Poter, D. C., 2009. Dasar-dasar

Ekonometrika, Salemba Empat. Jakarta,

2nd

edition.

Hill, M. A., 1983. Female Labor Force Participation in

Developing and Developed Countries-Consideration of

the Informal Sector. The Review of Economics and

Statistics.65 (3): 459-468.

Simanjuntak, P. J., 1985. Pengantar Ekonomi Sumber daya

Manusia, Lembaga Penerbit Fakultas Ekonomi

Universitas Indonesia. Jakarta.

Usman, O., Abdussamad, S., 2016. Education and Labor

Force Participation of Woman in North Cyprus:

Evidence feom Binomial Logit Regression Model.

MPRA Paper. No 7.

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