factors affecting females labor force participation rate of 44 countries from 2000 to 2010

Page 3DECLARATION The research on “Factors affecting Female Labor Force Participation Rate in 44 developed countries from 2000 to 2010” was carried out by Group 1, consisting of members

FACULTY OF INTERNATIONAL ECONOMICS

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DECLARATION The research on “Factors affecting Female Labor Force Participation Rate in 44 developed countries from 2000 to 2010” was carried out by Group 1, consisting of

members Nguyen Thi Dieu An, Nguyen The Duc, Nguyen Trung Kien, Nguyen Quang Tung, Tran Thanh Tung

We hereby declare that we have collected the data and conducted the research ourselves

We do not use any outside help or use information not mentioned in the references to this research

Hanoi, December 9th 2022

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ACKNOWLEDGEMENTS Our group truly appreciates all the lectures that MS Nguyen Thuy Quynh gave us during the course time In the period we created this essay, you provided us with a wide range

of valuable knowledge, along with support when we met difficulties For us, it is an honor to have you as our teacher

We have put all our efforts into completing this essay based on what we learned from the course Our essay may have shortcomings, so we hope to receive your suggestions and comments in order to be better in the future

We are looking forward to having you accompany us on future subjects In the end, we sincerely thank you, Mrs Nguyen Thuy Quynh, for your help in completing our report

to the fullest

Group 1

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ABSTRACT This paper estimates a multiple linear regression model of various theories on the Female Labor Force Participation (FLFP) rate in the presence of a few factors such as fertility rate, urbanization rate, female unemployment rate, GDP per capita growth rate, and female tertiary education STATA program and the Ordinary Least Square (OLS) estimation method are used to estimate the data and find the correlation between these stated above determinants and the FLFP rate in developed countries over every region around the world (OECD group included) The data is collected from the World Bank online database, and the OECD database, covering the period from 2000 to 2010 In conclusion, we have found that an increase in the fertility rate and female unemployment rate would result in a significant decrease in the FLFP rate On the contrary, a higher urbanization rate, GDP growth, and female tertiary education rate would lead to an increase in the FLFP rate

In this paper, there will be inevitable shortcomings We would appreciate all the comments and advice in order to improve the report further

Keywords: Female labor force participation, developed countries, OECD countries

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ABBREVIATION

Y: Female labor force participation rate FLFP

X2: Female Unemployment rate UNE

X4: GDP per capita Growth rate GDP

X5: Female tertiary education rate EDU

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TABLE OF CONTENT

1.1 Definition and theory of Labor Force 11

1.1.2 Female labor force participation rate 11 1.1.3 Factors affecting female labor force participation rate 11 1.2 Overview of Female labor force participation rate 12

2.2.3 Theoretical relationship between a dependent variable and independent

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3.2.1.1 Statistical significance of individual coefficients 25 3.2.1.2 Overall significance of the multiple regression 27 3.2.1.3 Joint significance of a group of variables 28 3.2.2 Consistency of the regression results with the theories 28 3.2.2.1 Consistency of the regression result with the theories 28 3.2.2.2 Mechanism of the relationships between variables 28 3.3.1 Recommendations and limitations: 29

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TABLE OF FIGURES

Table 1 Theoretical relationship between dependent and independent variables 19

Table 2 Summary statistics of the regression model’s variables 20

Table 3 Correlation coefficients among variables using command [corr] 21

Table 4 Estimated model from STATA 23

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INTRODUCTION Women make up half of any country's population In most nations, however, women contribute less than men toward the value of recorded production - both quantity and quality in labor force participation, educational achievement, and skills The underutilization of female labor has obvious implications for economic welfare and growth The reasons for this deviation could be answered by looking into the determinants affecting the labor force The Female labor force participation rate has long been a controversial topic, to see whether women should stay at home playing the homemaker role, or should they participate in the production of the modern labor force

As defined by the World Bank, the female labor force could be understood as a percentage of the total to show the extent to which women are active in the labor force Though, there were waves of feminine support for females to join in the labor force; the differences in the levels of female labor force participation were still significant in the early 2000s (Thevenon, 2013)

In this research, we aim to investigate the FLFP rate in developed countries over every region in the world in correlation to the variables affecting them, covering data from an 11-year period from 2000 to 2010 Using panel data analysis, the determinants of female labor force participation rate are taken as fertility rate, urbanization rate, female tertiary education rate, GPA per capita growth rate, and female u nemployment rate From the early 2000s to 2010s, female labor force participation has increased in the majority of developed countries around the world Over the past several decades, female labor force participation has increased in the majority of Organization for Economic Co-operation and Development (OECD) nations (Kinoshita and Guo (2015)) The research paper of our group consists of the following contents:

In addition to cross-country variance in aggregate participation rates, Jaumotte (2003) cited Burniaux et al (2003) as saying that female labor force participation is the most significant factor in explaining increases in aggregate participation rates A high female participation rate suggests that women's economic and social status will improve and that there will be more room for economic growth (Özsoy and Atlanta (2010)) The greater the female labor force participation, the greater is the labor supply, so that economic growth is boosted

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SECTION I OVERVIEW OF THE TOPIC 1.1 Definition and theory of Labor Force

1.1.1 Labor force participation rate

The labor force, or workforce, is the total number of people who are currently employed plus the number of people who are unemployed and seeking employment This number does not include people who are unemployed and not seeking employment, such as students and retirees People who would like a job but are not currently looking for one are also not considered part of the labor force In short, the workforce includes those who either have a job or are actively seeking one (US Bureau of labor)

1.1.2 Female labor force participation rate

According to The World Bank, “Female labor force as a percentage of the total show the extent to which women are active in the labor force Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.”

Female labor force participation is an important indicator of women’s status and benchmark of female empowerment in society (Kapsos, Silberman and Bourmpoula 2014; ILO)

1.1.3 Factors affecting female labor force participation rate

1.1.2.1 Fertility rate

The fertility rate is the average number of children born to women during their reproductive years.The fertility rate is calculated for women within the childbearing age range, commonly ages 15-44 Together with mortality and migration, fertility is an element of population growth, reflecting both the causes and effects of economic and social developments

1.1.2.2 Female unemployment rate

The female unemployment rate measures the share of female workers in the labor force who do not currently have a job but are actively looking for work Women who have not looked for work in the past four weeks are not included in this measure

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1.1.2.3 Urbanization rate

Urbanization rate describes the projected average rate of change of the size of the urban population over the given period of time Meanwhile, the tempo of urbanization is an indicator of the speed at which an area is moving toward an urban classification (Ritchie and Roser)

1.1.2.4 Growth rate of GDP per capita

It is a global measure for gauging the prosperity of nations and is used by economists to analyze the prosperity of a country based on its economic growth At its most basic interpretation, per capita GDP shows how much economic production value can be attributed to each individual citizen

1.1.2.5 Female tertiary education rate

Female tertiary education rate is the total enrollment of women in tertiary education, regardless of age, expressed as a percentage of the eligible five-year age group following

on from the secondary-level school leaving population (Anne McDaniel)

1.2 Overview of Female labor force participation rate

Across all over the world, women have been facing inferior income opportunities when compared to men Women are less likely to actively pursue employment or work for pay Just over 50% of women worldwide participate in the labor force, compared to 80%

of their male counterparts Women are less likely to work in formal employment and have fewer opportunities for business expansion or career progression When women do work, they earn less Emerging evidence from recent household survey data suggests that these gender gaps are heightened due to the COVID-19 pandemic

According to Mujahid (2014) and Fatima and Sultana (2009), the labor force participation rate plays a crucial role in determining economic growth and development Particularly FLFP is essential for the enhancement and socio-economic development of

a nation because it encourages efficiency and equity Generally, high female participation in the labor market implies two things: advancement in the economic and social position as well as empowerment of women and hence promoting equity and increased utilization of human potential, which can contribute to increase the capacity for economic growth and reducing poverty

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Although the trend for female labor force participation is flat in the majority of regions, there is considerable regional variance in the degree of female participation More than half of the women (aged 15 to 64) participate in the job market in five of the seven areas However, in South Asia, the Middle East, and North Africa, only a quarter or less do so 1.3 Literature review:

Since the pioneering study of Mincer (1962) with the "Work-Leisure Theory" created

in the 20th century, the analysis of FLFP has received much attention The "Household Production Theory" by Becker and Mincer and the "Human Capital Theory" by Schultz and Becker were some of the theories in the field that came after this Studies conducted

in Pakistan and the United States (Goldin 1994; Psacharopoulos and Tzannatos 1989; Sackey 2005; Schultz 1961) showed that women’s participation is dependent on a

country’s level of development Through research conducted in Kuwait, Pakistan,

Nigeria, and Egypt, Becker (1975), Psacharopoulos and Tzannatos (1989), Schultz (1961), and Khadim and Akram (2013) demonstrated that education is one of the major determinants influencing women's willingness to engage Additionally, other demographic factors like marital status, household size, religion, and geographic location (urban/rural residency) do influence women's participation decisions, according

to Psacharopoulos and Tzannatos (1989), Faridi, Chaudhry, and Anwar (2009), Khadim and Akram (2013), Schultz (1961), and Agüero and Marks (2008)

Shami et al (2019) discovered that a 1% increase in education will boost a woman's labor force participation by 0.145% in South Asian countries Rising educational attainment is consistently associated with higher rates of female labor force participation

in emerging nations, according to a study by Klasen et al (2019) Aldan and Ozturk (2019) showed that the recent increase in female labor participation in Turkey is significantly influenced by an increase in education levels

According to some earlier research, the fertility rate is also one of the main socioeconomic factors influencing women's engagement in the labor force Mishra and Smyth (2010) discovered a substantial inverse correlation between female labor force participation and fertility rates in 28 OECD nations Mahmoudian (2006), who also agreed with the findings above, said that lower labor force involvement among Iranian women was significantly correlated with higher fertility rates

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Female unemployment has also been studied in the literature in relation to the female labor force participation rate For instance, Tansel (2002) showed that female unemployment has a bad impact on women's involvement in the labor force A similar finding found a long-term association between female labor force participation and unemployment, and data suggested that declining female unemployment rates would lead to higher female participation rates (Ozerkek, 2013)

As an economic variable, GDP per capita is commonly used as a determinant of female labor force participation Mehmood et al (2015) found that the rate of female labor force participation in Muslim nations was positively correlated with GDP growth and per-capita income in the nation According to the findings of Shami et al (2019), a 1% rise

in economic growth will contribute to a 0.754% increase in female labor force participation in South Asian countries Mammen and Paxson (2000), expanding on work

by Goldin (1995), find the relationship between female participation rates and per capita income to be U-shaped

This also combine with urbanization rate, as it has positive relationship with female labor force participation rate Still another variable examined for its determining role of female labor force participation rate is urbanization Fatima and Sultana (2009) revealed

a positive relationship between urbanization and female labor force participation In Pakistan and some MENA countries, Aboohamidi and Chidmi (2013) found that urbanization rates had a positive effect on female labor force participation In contrast, Uraz et al (2010), explained that low-skilled female individuals that migrated from rural

to urban areas found it difficult to find jobs - thus leading to a decrease in the female labor force participation in Turkey

Research gap:

The main issue of the related researches stated above was the year limit of the database

of education variables This could be insufficient for social factors to provide a precise evaluation of the rate of female labor force participation Furthermore, each research investigated only a few specific regions, so the theory tested may not be correct for the general population as a whole Another problem related to the theories is that the independent variables in the model are from different papers, combining these variables could result in some errors Last but not least, since most variables are in the socio-

H1: Fertility rate has a negative impact on FLFP

H2: Unemployment rate has an inverse relationship with FLFP

H3: Urbanization impact positively on FLFP

H4: Growth rate of GDP per capita has a positive effect on FLFP

H5: Female tertiary education rate affect FLFP positively

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SECTION II MODEL SPECIFICATION AND DATA

2.1 Methodology

2.1.1 Method used to derive the model

In this research paper, we use a multiple linear regression function to

explain the relationships between a dependent variable and other independent

variables

As the Ordinary Least Squares (OLS) method is the easiest and most popular way to estimate the parameters of a linear regression model, in this paper we apply the OLS estimation method to analyze the correlation matrix between regressands and regressors STATA software is used most of the time in the process of estimating unknown parameters and building a population regression model

2.1.2 Method used to collect and analyze the data

We draw the conclusion that five causes are causing significant change After identifying the independent variables, we collect and analyze data from reputable sources (World Bank online database, OECD database, ) to examine how closely those variables are related

2.2 Theoretical model specification

2.2.1 Proxy to measure

Our research scope consists of three parts: content scope, time scopescope, and spatiascope The content scope is research on factors affecting the Women Labor Force Participation rate in European countries The time scope of the data in this research is

2000 2010 The spatial scope includes 44 European countries, including those –participating in The Organization for Economic Co-operation and Development (OECD), a forum for governments to share experiences and seek solutions to common economic and social problems

2.2.2 Specify the model

With the application of the methodology of econometrics, in this research our

team will use 5 variables to progress this model:

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X1: Fertility rate (FER)

X2: Female Unemployment rate (UNE)

X3: Urbanization rate (URB)

X4: GDP per capita Growth rate (GDP)

X5: Female tertiary education rate (EDU)

- The regression coefficient of Fertility rate is expected to be negative because the effect

of fertility on female labor supply is strongest during the fertile years (20 39 years of –age) The fertility rate equals 1000 times the ratio of the number of live births to the total female population

𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 = ( √𝑛 𝑢𝑟𝑏𝑎𝑛 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑒𝑛𝑠𝑢𝑠 (𝑡 + 𝑛)𝑢𝑟𝑏𝑎𝑛 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑐𝑒𝑛𝑠𝑢𝑠 (𝑡) − 1) ∗ 100

- There is a significant rise in GDP due to higher labor input As women earn money, the household's income increases thereby leading to more consumption of goods and services which increases GDP

𝐺𝑟𝑜𝑤𝑡ℎ 𝑟𝑎𝑡𝑒 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 =𝑜𝑓 𝑐𝑜𝑢𝑛𝑡𝑟𝑦′𝑠 𝑔𝑟𝑜𝑠𝑠 𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑝𝑟𝑜𝑑𝑢𝑐𝑡

𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛

- The level of education exerts a significant positive effect on women in society Female tertiary education rate is calculated by dividing the number of women enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100

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𝐹𝑒𝑚𝑎𝑙𝑒 𝑡𝑒𝑟𝑡𝑖𝑎𝑟𝑦 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 =𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑤𝑜𝑚𝑒𝑛 𝑒𝑛𝑟𝑜𝑙𝑙𝑒𝑑𝑡ℎ𝑒 𝑤𝑜𝑚𝑒𝑛 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑜𝑓 ∗ 100

In this regression model, Female Labor Force Participation rate is the dependent variable; Fertility rate, Female Unemployment rate, Urbanization rate, GDP per capita Growth rate, and Female tertiary education rate are independent variables It can be seen clearly that these variables have a linear relationship

In this research paper, we will build a multiple linear regression model to analyze the correlation between them

Linear regression model:

𝒀 = 𝛽0+ 𝛽1𝑋1+ 𝛽2𝑋2+ 𝛽3𝑋3+ 𝛽4𝑋4+ 𝛽5𝑋5The population regression function in Stochastic form:

Y Dependent Female Labor Forc

Participation rate

%

X1 Independent Fertility rate % -

X2 Independent Female Unemploymen

rate

%

-

X3 Independent Urbanization rate % +

X4 Independent GDP per capita Growt

rate

%

+