tiểu luận kinh tế lượng the determinants impacting on female labor force participation

Generally, high female participation in the labor market implies two things; advancement in the economic and social position, and empowerment of women.. Increasing female labor force par

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS

………… o0o…………

ECONOMETRICS FINAL EXAM

TOPIC: THE DETERMINANTS IMPACTING ON FEMALE LABOR FORCE PARTICIPATION

Class : 57 JIB

Lecturer : Dr Tu Thuy Anh

Dr Chu Thi Mai Phuong Group 21 : Nguyễn Đặng Sơn- 1815520093 Nguyễn Thị Thương-1815520224 Chu Diệu Linh-1815520186

Ha Noi - 2019

SECTION 1: METHODOLOGY, DESCRIBE THE VARIABLE, DATA,

INTRODUCTION

Despite representing just over half of the adult population worldwide,women were underrepresented in the workforce—women were working at a lowerrate than men in nearly every country, their contribution to measured economicactivity, economic growth, and well-being is way below its potential According tothe World Bank (2013) women now represent around 40 percent of the global laborforce, but in most countries, women labor force participation is much less than that

of men However, these gender differences have been narrowing substantially, and

in most countries around the world now, the share of women who are part of thelabor force is higher today than half a century ago

Generally, high female participation in the labor market implies two things;

advancement in the economic and social position, and empowerment of women

Increasing female labor force participation rates creates an opportunity for countries

to increase the size of their workforce and achieve additional economic growth Theclear understanding of such factors and their effect on women’s propensity toparticipate plays a very important role in determining prospective growth anddevelopment of countries It might help us come up with new ways to encouragefemale participation or address those problems that discourage females fromparticipating in the labor market Although countless factors affect FLFP (FemaleLabor Force Participation) when analyzing, whether they really affect or not andhow much they affect is still a big question Nonetheless, currently there are not somany apparent and in-depth studies on this issue Therefore, our team decided tochoose the topic:” The determinants impacting on the Female Labor ForceParticipation”

We use the Gretl prepackaged set data 4.5 Ramanathan, Women’s labor forceparticipation to analyze some factors like: unemployment, white females, education,median earning,… And With the help of the linear regression model and log-linearmodel in combination with the OLS estimation method, we attempt to consider therelationship between these above factors and further ask which factors drive FLFPchanges over time within countries, and which factors account for differences inFLFP rates between countries

LITERATURE REVIEW

1 Definition

Female Labor Force Participation was defined as the women’s decision to be part ofthe economically active population: employed or unemployed population ascompared to being part of the economically inactive population of the economy –those not working nor seeking work FLFP is an important indicator of women’sstatus and benchmark of female empowerment in society (Kapsos, Silberman andBourmpoula 2014; ILO)

2 Theoretical Framework

The theoretical framework on FLFP reflects the female’s decision to be an activeparticipant versus being an inactive participant in the labor market Economistshave tried to explain female’s propensity to decide on one choice over anotherthrough analyzing the impact of certain economic and demographic factors, whichthey believed would affect female’s tendency to participate or opt-out of the labormarket The main theories that have been used to analyze the labor supply ofwomen included: “Human Capital Theory” by Becker, “The Work-Leisure Choicetheory” by Mincer

2.1 The Work-Leisure Choice theory

The simplest analysis of women’s choice goes back to the early 1960s to Mincer(1962) and the neoclassical microeconomic model known as; Work-Leisure Choicemodel, which assumed that households; suppliers of labor in an economy arerational and seek to maximize their utility; deciding on how much time to devote towork and how much time to devote for leisure The theory was explained byPsacharopoulos and Tzannatos (1989) who further added that since the choice isbased on the remuneration from work (wage rate) then the higher the wage rate, theless attractive leisure becomes and the more attractive work becomes Such relationhas two effects; substitution effect and income effect Firstly, for whoever is notworking, a higher wage may encourage them to join the labor market for that theopportunity cost of not working will be high; thus higher wages are said to stimulatehigher participation Secondly, for those already working, a higher wage makeswork more attractive for that it has a higher rate of return than leisure Encouraging

participation or working more time as a result of an increase in the wage rate isknown as the substitution effect as leisure time becomes more costly Individualsthen tend to devote more time to work rather than leisure On the other hand, aswage rate increases, an individual’s real income rises this leads to an increase in theconsumption of normal goods and if as previously assumed leisure is a normalgood, the higher wage would persuade individuals to consume larger quantity (time)

of leisure and reduce hours of work and that is known as the income effect resultingfrom a wage increase (FRF 1979; Heckman 2014)

According to the textbook “Race, Class, and Gender”, it can be said that

“Women are at a higher risk of financial disadvantage in modern-day society thanmen” Statistical findings suggest that women are underpaid for similar jobs mencomplete despite having the same qualifications The statistical data collected by theU.S Department of Labor suggests that women are discriminated against in theworkforce based on gender The textbook reads, “Women’s wages are also morevolatile than men’s wages, and women face a much higher risk of seeing large drops

in income than do men” (Kennedy 2008)

2.2 Human Capital Theory

After the Work-Leisure Choice theory, Human Capital Theory wasdeveloped According to Becker (1975), human capital can be defined as theproductive investments embodied in individuals, including skills, abilities,knowledge, habits, and social attributes often resulting from expenditures oneducation, on-the-job training programs, and medical care The human capitaltheory was then used to analyze the relationship between labor force participationand education specifically for married women Economists argue that therelationship may be U-shaped across educational attainment categories

Accordingly, participation rate was found to be high for illiterate women, lower forwomen at the primary and secondary education level and higher for universitygraduates The positive relation between education and wage rate can explain suchU-shaped relationship (Schultz 1961) Higher labor force participation at low levels

of education – illiterate and thus low wages can be explained by the need to earnsome income for survival – subsistence wage Furthermore, the low level ofparticipation for married women with a primary and secondary level of educationmight be explained by that women with such low levels of education mostly seekjob opportunities only in specific occupations such as secretarial work Thus whenthere is a shortage in such jobs, women with such low educational attainment tend

with lower levels of education to work in the household – household production or

in the informal sector, which is excluded from the definition of the labor force

Consequently, informal sector workers are not included in the labor force and thusnot reflected in the FLFPR, therefore, indicating a low female participation rate(Cameron et al 2001; Lincove 2005; Schultz 1961)

Particularly, studies of female labor force participation suggest that the mostimportant personal variable influencing FLFPR is education The hypothesis thateducation can be generally treated as an investment in human capital has proved to

be influential and helpful in its way and to be a key ingredient in studies of thesources of economic development and the distribution of income all over the world

Education is mostly regarded as a specialized form of human capital, contribution towhich economic growth is noteworthy The human capital theory proposes that just

as physical capital – machines enhance people's economic efficiency, so humancapital acquired through education improves the productivity and efficiency ofindividuals Studies of the sources of economic growth credibly confirm thateducation plays a major role in increasing output per worker In accordance, thenew development theories in economics shed light on the importance of educationand human resource development for long term economic growth It is usuallyregarded as the catalyst or engine of growth and development in the new worldeconomy (Becker 1975; Psacharopoulos and Tzannatos 1989; Taubman and Wales1975; OECD 1989)

2.3 Other Factors Influencing Female Labor Force Participation

2.3.1 Age Factor

Women in their twenties and thirties have higher chances to participate in thelabor market as compared to their counterparts in other age groups On one hand, itwas empirically proven through a study undertaken in Kuwait and Jordan that agenegatively affects FLFP On the other hand, a study undertaken in Pakistan hasshowed that the effect of age on FLFP is positive only up till the age of 49, whichafter then negatively affects women’s tendency to participate in the labor market Itwas then concluded that age could positively or negatively affect FLFP, all based onthe age group considered

2.3.2 Urbanization factor

In urban areas there may be more paid employment opportunities than in ruralareas Thus, the higher the proportion of the population living in urban areas, the

higher will be the female labor force participation However, most women in ruralareas participate in the labor force in large numbers in agriculture as unpaid familyworkers Thus, if a province has a large rural population the female labor forceparticipation may be high This implies a negative sign of the impact of the urbanshare of a province on the female labor force participation The net effect of urbanshare can be empirically determined

2.3 Unemployment factor

The effect of the unemployment rate on female labor force participation isambiguous depending on the relative strengths of “discouraged-worker effect” andthe “added-worker effect” Unemployment affects the probability that womenentering the labor market will find a job The higher the provincial unemploymentrate, the less likely will it be for women to find a job Economic and psychologicalcosts associated with job search will be higher when the local unemployment rate ishigh The unemployment rate of women compared to men suggests that singlewomen are discriminated against based on gender Anderson writes, “All womenare disproportionately at risk in the current foreclosure crisis, since women are 32%

more likely than men to have subprime mortgages (One-third of women, compared

to one-fourth of men, have subprime mortgages; and, the disparity between womenand men increases in higher income brackets)” (Anderson 265) The statisticalinformation illustrates the dramatic difference between men and women in regards

to finances It can be inferred that men are favored in the workforce over women

Women are discriminated against based on their gender and thus are more likely tostruggle financially because of discriminatory employers For these reasons, womenmay be discouraged from looking for a job and drop out of the labor force

Therefore, the discouraged-worker hypothesis implies a negative effect of the localunemployment on female labor force participation

We are implementing 2 models: Linear -linear regression and Log-linear regression

by OLS- normal least square method to determine the direction of the impactindependent variables on the dependent variable and regression coefficient value

II DESCRIBE THE DATA

1 Data overview

- Data’s source: We use the data from Gretl source.

- The structure of Economic Data: cross-sectional data

2 Data description

2.1 A brief description of each variables is given in Exhibit 1

Variables Abbreviation Meaning Unit

wlfp Y person ≥ 16 years:% in labor force

who are female

urb X4 percent of population living in urban area %

(Exhibit 1.Description of each variables/ Source: Gretl self-aggregated )

2.2 Describe the statistics between variables

2.3 Describe the correlation between variables

Before running the regression model, we consider the degree of correlation betweenvariables using the command correlation

Correlation Coefficients, using the observations 1 - 505% critical value (two-tailed) = 0.2787 for n= 50 observations

(Exhibit 3.The correlation between variables? Source: Gretl self-aggregated )

Look at the table of correlation, we draw some comments:

+ r( yf,Y) = 0,5476 >0 =>The variable yf is positively correlated with the variable

Y On that basis, the regression coefficient of yf is marked with (+) The correlation

between yf and Y is a strong mean correlation (= 54,76%) + r( educ,Y) = 0,6582 >0 => The variable educ is positively correlated with the variable Y On that basis, the regression coefficient of educ is marked with (+).

Besides, experience and education affect 65,82% on women’s participants in laborforce

+ r (ue,Y) = -0,5887 <0.The variable ue is negatively correlated with the variable

Y On that basis, the regression coefficient of ue is marked with (-)

+ r (urb,Y) =0,2705 >0 The variable urb is positively correlated with the variable

Y On that basis, the regression coefficient of urb is marked with (+).Living in urban

or rural areas also has a relative impact on the female labor force, but in urban areas

in terms of opportunities, employment will increase more women in the labor force.

(=27,05%)

+ r (wh,Y) = -0,1039<0.The variable wh is negatively correlated with the variable

Y On that basis, the regression coefficient of wh is marked with (-).The racial

difference affects the female labor force, the two-correlation coefficient is negative,indicating that the trend of participation in the labor force is less white women thanfor black

In general, the correlation between independent variables is not high, the

highest correlation coefficient is only 0.6178 between urb and yf Because there is

no correlation coefficient of magnitude exceeding 0.8, it is possible to predict thatthe model has no collinearity phenomenon when regressing

SECTION 2: ESTIMATED MODEL AND STATISTICAL INFERENCES.

I LINEAR-LINEAR MODEL

1 Estimation.

Describe the basic content of the value when estimating the function:

- The population regression function (PRE) is set up:

wlfp = β 0 + β 1 yf + β 2 educ + β 3 ue+ β 4 urb +β 5 wh + u i

-The Sample regression function (SRF) is set up:

wlfp = β 0 +β 1 yf + β 2 educ + β 3 ue+ β 4 urb +β 5 wh+ e i ( e i is error)

- Equation of regression:

wlfp = 41,5811+0,796960yf +0,284961educ 1,45164ue 0,0744791urb

-0,0978928wh + e

● Meaning of coefficient

β 0 : is estimator of β 0 =41,4811 , when independent variables YF, YM, EDUC, UE,

MR, DR, URB, WH are 0, the mean value of the dependent variable equal 41,5811.

e i : is the residual ( the estimator of e i)

β 1, β 2 ,β 3 ,β 4 ,β 5: ( the estimate of slope coefficient) when the value of these variables

YF, YM, EDUC, UE, MR, DR, URB, WH change in one unit ( the remaining factors

are constant), the mean value of dependent variable ( WLFP) will change follows β 1,

β 2 ,β 3 ,β 4 ,β 5

β 1 = 0,796960 when yf increase by 1 ( dollar earning by female ), holding the value

of the other coefficient constant, the estimated value of wlfp increase by 0,796960

β 2 =0,284961 when educ increase by 1 ( year) , holding the value of the other coefficient constant, the estimated value of wlfp increase by 0,284961

β 3 =-1,45164 when ue increase by 1 (% unemployed ), holding the value of the other coefficient constant, the estimated value of wlfp decrease by -1,45164

β 4 = -0,074479 when urb increase by 1 (% of population living in urban area), holding the value of the other coefficient constant, the estimated value of wlfp

decrease by -0,074479

β 5 =-0,0978928 when wh increase by 1 (white female), holding the value of the other coefficient constant, the estimated value of wlfp decrease by -0,0978928

In the results, we can see R 2 which indicates that the model explains all thevariability of the response data around its mean

R 2 = 0,758038 is quite high, which suggests that the model is a good fit Becausethis means 75,8038% of the sample variation in the percentage vote for the

dependent variable ( women in labor force participation ) is explained by the

changes in the independent variables ( Median earning, education, unemployed,urban area and white female).Other factors that are not mentioned explain the

remaining 24,1962% of the variation in the wlfp.

2.Testing

2.1 Testing hypothesis

2.1.1 Testing an individual regression coefficient Purpose:

Test for the statistical significance or the effect of independent variables ondependent one We have: α = 0.05

● Testing the variable of Median earning of female (Yf)

● Given that the hypothesis is:

● 𝑯𝟎: 𝜷1 = 𝟎

● 𝑯𝟏: 𝜷1 ≠ 0

● We see: P-value of yf is < 1.09e-05 < 0.05 → Reject H0 → The coefficient𝛽1 is statistically significant

● Testing the variable of Educ:

● Given that the hypothesis is: 𝑯𝟎: 𝜷2 = 𝟎

● We see: P-value of educ is < 0.0001 < 0.05 → Reject H0 → The coefficient

𝛽2 is statistically significant.

● Testing the variable of Ue:

● Given that the hypothesis is: 𝑯𝟎: 𝜷3 = 𝟎

● We see: P-value of ue is < 1.23e-06 < 0.05 → Reject H0 → The coefficient

𝛽3 is statistically significant.

● Testing the variable of urb:

● Given that the hypothesis is: 𝑯𝟎: 𝜷4= 𝟎

● 𝑯𝟏: 𝜷𝟒 ≠ 𝟎

● We see: P-value of urb is < 0.0118 < 0.05 → Reject H0 → The coefficient

𝛽4 is statistically significant.

● Testing the variable of wh:

● Given that the hypothesis is: 𝑯𝟎: 𝜷5 = 𝟎

Purpose: Test the null hypothesis stating that none of the explanatory variables has

an effect on the dependent variable.We have: 𝛼 = 0.05Given that the hypothesis is: 𝑯𝟎: 𝜷𝒊 = 𝟎

𝑯𝟏: ∃𝜷𝒊 ≠ 𝟎 (i = 1, 2, 3, 4,5)

We have: P-value(F) = 1.57e-12 < 𝛼 = 0.05 → Reject H0 → All parameters are notsimultaneously equal to zero→ At least one variable has an effect on dependentone

The model is statistically fitted

2.2 Testing the model’s problems 2.2.1 Testing multicollinearity

Multicollinearity is the phenomenon of the independent variables in the model thatare interdependent and are shown as a function

+ Because of less data collection, not comprehensive

+ Due to the nature of the independent variables are correlated

+ Due to several models produced multicollinearity

✔ Consequences of multicollinearity:

+ Estimates variance becomes less accurate

+ The value of the term t becomes smaller than actual when R2 is quite high test and F becomes less effective

T-+ The estimated value when volatility changes in the data model

+ The value of the estimated volatility likely to change ( draw or add ) thevariables involved in the multicollinearity phenomenon

In order to check whether the model has a multicollinearity problem or not, we have

2 ways to check It includes 2 methods: using the VIF (Variance Inflation Factors)and using the correlation between the variables each other

a VIF (Variance Inflation Factors)

b. Using the following command vif regression to examine multicollinearity

“VIF” commands specific to the variance inflation factor, if a variable’s value vif >

10, the model has the possibility of multicollinearity

All the VIF of each variables has the value < 10

● MeanVIF = 1.523 < 10

● Conclusion: No multicollinearity are found.

b Correlation