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

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 TABLE OF CONTENTS INTRODUCTION LITERATURE REVIEW Definition Theoretical Framework ANALYSIS SECTION 1: METHODOLOGY, DESCRIBE THE VARIABLE, DATA, STATISTIC AND CORRELATION I METHODOLOGY OF RESEARCH II DESCRIBE THE DATA Data overview Data description SECTION 2: ESTIMATED MODEL AND STATISTICAL INFERENCES 11 I LINEAR-LINEAR MODEL 11 Estimation 11 2.Testing 12 2.1 Testing hypothesis 12 2.2 Testing the model’s problems 14 II LOG-LINEAR MODEL 18 Estimation 18 Testing 19 2.1 Testing hypothesis 19 2.2 Testing the model’s problems 20 2.3 Testing Heteroskedasticity 23 CONCLUSION 25 REFERENCES 26 APPENDIX 27 INTRODUCTION Despite representing just over half of the adult population worldwide, women were underrepresented in the workforce—women were working at a lower rate than men in nearly every country, their contribution to measured economic activity, economic growth, and well-being is way below its potential According to the World Bank (2013) women now represent around 40 percent of the global labor force, 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 the labor 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 The clear understanding of such factors and their effect on women’s propensity to participate plays a very important role in determining prospective growth and development of countries It might help us come up with new ways to encourage female participation or address those problems that discourage females from participating in the labor market Although countless factors affect FLFP (Female Labor Force Participation) when analyzing, whether they really affect or not and how much they affect is still a big question Nonetheless, currently there are not so many apparent and in-depth studies on this issue Therefore, our team decided to choose the topic:” The determinants impacting on the Female Labor Force Participation” We use the Gretl prepackaged set data 4.5 Ramanathan, Women’s labor force participation to analyze some factors like: unemployment, white females, education, median earning,… And With the help of the linear regression model and log-linear model in combination with the OLS estimation method, we attempt to consider the relationship between these above factors and further ask which factors drive FLFP changes over time within countries, and which factors account for differences in FLFP rates between countries LITERATURE REVIEW Definition Female Labor Force Participation 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 not working nor seeking work FLFP is an important indicator of women’s status and benchmark of female empowerment in society (Kapsos, Silberman and Bourmpoula 2014; ILO) Theoretical Framework The theoretical framework on FLFP 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 and demographic factors, which they believed would affect female’s tendency to participate or opt-out of the labor market The main theories that have been used to analyze the labor supply of women included: “Human Capital Theory” by Becker, “The Work-Leisure Choice theory” 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 Choice model, which assumed that households; suppliers of labor in an economy are rational and seek to maximize their utility; deciding on how much time to devote to work and how much time to devote for leisure The theory was explained by Psacharopoulos and Tzannatos (1989) who further added that since the choice is based on the remuneration from work (wage rate) then the higher the wage rate, the less attractive leisure becomes and the more attractive work becomes Such relation has two effects; substitution effect and income effect Firstly, for whoever is not working, a higher wage may encourage them to join the labor market for that the opportunity cost of not working will be high; thus higher wages are said to stimulate higher participation Secondly, for those already working, a higher wage makes work 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 is known as the substitution effect as leisure time becomes more costly Individuals then tend to devote more time to work rather than leisure On the other hand, as wage rate increases, an individual’s real income rises this leads to an increase in the consumption of normal goods and if as previously assumed leisure is a normal good, 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 resulting from 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 than men” Statistical findings suggest that women are underpaid for similar jobs men complete despite having the same qualifications The statistical data collected by the U.S Department of Labor suggests that women are discriminated against in the workforce based on gender The textbook reads, “Women’s wages are also more volatile than men’s wages, and women face a much higher risk of seeing large drops in income than men” (Kennedy 2008) 2.2 Human Capital Theory After the Work-Leisure Choice theory, Human Capital Theory was developed According to Becker (1975), human capital can be defined as the productive investments embodied in individuals, including skills, abilities, knowledge, habits, and social attributes often resulting from expenditures on education, on-the-job training programs, and medical care The human capital theory was then used to analyze the relationship between labor force participation and education specifically for married women Economists argue that the relationship may be U-shaped across educational attainment categories Accordingly, participation rate was found to be high for illiterate women, lower for women at the primary and secondary education level and higher for university graduates The positive relation between education and wage rate can explain such U-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 earn some income for survival – subsistence wage Furthermore, the low level of participation for married women with a primary and secondary level of education might be explained by that women with such low levels of education mostly seek job opportunities only in specific occupations such as secretarial work Thus when there is a shortage in such jobs, women with such low educational attainment tend to stay home Besides that, it is common in most developing countries that women 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 thus not 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 most important personal variable influencing FLFPR is education The hypothesis that education 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 the sources of economic development and the distribution of income all over the world Education is mostly regarded as a specialized form of human capital, contribution to which economic growth is noteworthy The human capital theory proposes that just as physical capital – machines enhance people's economic efficiency, so human capital acquired through education improves the productivity and efficiency of individuals Studies of the sources of economic growth credibly confirm that education plays a major role in increasing output per worker In accordance, the new development theories in economics shed light on the importance of education and human resource development for long term economic growth It is usually regarded as the catalyst or engine of growth and development in the new world economy (Becker 1975; Psacharopoulos and Tzannatos 1989; Taubman and Wales 1975; 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 the labor market as compared to their counterparts in other age groups On one hand, it was empirically proven through a study undertaken in Kuwait and Jordan that age negatively affects FLFP On the other hand, a study undertaken in Pakistan has showed that the effect of age on FLFP is positive only up till the age of 49, which after then negatively affects women’s tendency to participate in the labor market It was then concluded that age could positively or negatively affect FLFP, all based on the age group considered 2.3.2 Urbanization factor In urban areas there may be more paid employment opportunities than in rural areas 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 rural areas participate in the labor force in large numbers in agriculture as unpaid family workers Thus, if a province has a large rural population the female labor force participation may be high This implies a negative sign of the impact of the urban share of a province on the female labor force participation The net effect of urban share can be empirically determined 2.3 Unemployment factor The effect of the unemployment rate on female labor force participation is ambiguous depending on the relative strengths of “discouraged-worker effect” and the “added-worker effect” Unemployment affects the probability that women entering the labor market will find a job The higher the provincial unemployment rate, the less likely will it be for women to find a job Economic and psychological costs associated with job search will be higher when the local unemployment rate is high The unemployment rate of women compared to men suggests that single women are discriminated against based on gender Anderson writes, “All women are 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 women and men increases in higher income brackets)” (Anderson 265) The statistical information 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 to struggle financially because of discriminatory employers For these reasons, women may 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 local unemployment on female labor force participation ANALYSIS SECTION 1: METHODOLOGY, DESCRIBE THE VARIABLE, DATA, STATISTIC AND CORRELATION I METHODOLOGY OF RESEARCH Using the Quantitative method to determine the relationship between women labor force participation and these influential factors’ relevant We are implementing models: Linear -linear regression and Log-linear regression by OLS- normal least square method to determine the direction of the impact independent variables on the dependent variable and regression coefficient value II DESCRIBE THE DATA Data overview - Data’s source: We use the data from Gretl source - The structure of Economic Data: cross-sectional data Data description 2.1 A brief description of each variables is given in Exhibit Variables Abbreviation wlfp Y Meaning Unit person ≥ 16 years:% in labor force % who are female yf X1 Median earning ($000s) by female ≥ $ 15 years educ X2 female ≥ 25 years: % high school years graduation or above ue X3 civilian labor force, % unemployed % urb X4 percent of population living in urban area % wh X5 females ≥ 16 years : percent white % (Exhibit 1.Description of each variables/ Source: Gretl self-aggregated ) 2.2 Describe the statistics between variables wlfp yf educ ue urb wh l_wlfp Mean 57,47 18,42 76,11 6,160 68,18 65,91 4,049 Median 57,75 18,08 77,10 6,150 68,80 69,13 4,056 Minimum 42,60 14,27 64,50 3,500 32,20 24,69 3,752 Maximum 66,40 25,62 86,10 9,600 92,60 77,73 4,196 Std Dev 4,249 2,703 5,736 1,364 14,67 9,379 0,07670 ( Exhibit Describe the statistics between variables via self-synthesis based on Gretl) 2.3 Describe the correlation between variables ● Correlation matrix for Linear- linear Model Before running the regression model, we consider the degree of correlation between variables using the command correlation Correlation Coefficients, using the observations - 50 5% critical value (two-tailed) = 0.2787 for n= 50 observations wlfp yf educ ue urb wh 1,000 0,5476 0,6582 -0,5887 0,2705 -0,1039 wlfp 1,000 0,3883 -0,0488 0,6178 -0,1264 yf 1,000 -0,3986 0,2340 0,2262 educ 1,0000 -0,1607 -0,0651 ue 1,000 -0,2293 urb 1,000 wh (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 labor force + r (ue,Y) = -0,5887 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 0.05 16 ● We have enough statistical evidence to accept the hypothesis H0 (the error is normally distributed) In the other words, our model has normal distribution \ 2.2.3 Testing Heteroskedasticity The homoskedasticity assumption states that the variance of the unobservable error (u) is constant Homoskedasticity fails whenever the variance of the unobservable changes across different segments of the population where the segments are determined by the different values of the explanatory variable In that phenomenon is called the heteroskedasticity We can use the following command “white test” to examine heteroskedasticity If the p-value is smaller than 0.05, the model has the heteroskedasticity White test 17 White's test for heteroskedasticity Null hypothesis: H0: heteroskedasticity not present Two-sided alternative hypothesis: H1: heteroskedasticity presents Test statistic: LM = 24.6526 with p-value = P(Chi-square(20) > 24.6526) = 0.215047 > 0.05 We have enough statistical evidence to accept the hypothesis H In conclusion, our model does not have heteroskedasticity CONCLUSION: Through tests of Normality, Multicollinearity and Heteroskedasticity, our linearlinear model has met the requirements of assumptions Hence, this model does not have any problems and has meaning in statistics II LOG-LINEAR MODEL 18 Estimation Describe the basic content of the value when estimating the function: _ The PRE is set up: lnwlfp= β0 + β1yf + β2educ + β3ue+ β4urb +β5wh + ui _ The SRF is set up: lnwlfp=β0 +β1yf + β2educ + β3ue+ β4urb +β5wh+ ei _ Equation of regression lnwlfp= 3,77140 +0,0134091yf+ 0,00508077educ - 0,0269259ue-0,00114536urb0,00170770wh+ e ● Meaning of coefficient β1= 0,013409 when median earning by female (yf) increase by unit ,keeping the value of other coefficients constant , the expected value of women in labor force participation increased by 1,3409% β2= 0,00508077 when educ increase by (year ), keeping the value of other coefficients constant , the expected value of wlfp increase by 0,508077% β3= -0,269259 when ue increase by ( % unemployed ),keeping the value of other coefficients constant , the expected value of wlfp decrease by 26,3259% β4= -0,00114536 when increase the percent of the population living in the urban area by unit ,keeping the value of other coefficients constant , the expected value of women in labor force participation decreased by 0,114536% β5= -0,00170770 when wh increasing by ( white female), keeping the value of other coefficients constant, the expected value of wlfp decrease by 0,14077% ● The coefficient of determination R2 19 In the results, we can see R2 which indicates that the model explain all the variability of the response data around its mean R2 = 0,749517 is quite high, which suggests that the model is a good fit Because this means 74,9517% 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 25,04833% of the variation in the wlfp 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 on dependent one We have: α = 0.05 ● Testing the variable of Median earning of female (Yf) ● Given that the hypothesis is: ��: �1 = � ● ��: �1 ≠ ● We see: P-value of yf is < 2.03e-06 < 0.05 → Reject H0 → The coefficient �1 is statistically significant ● Testing the variable of Educ: ● Given that the hypothesis is: ��: �2 = � ● ��: �2 ≠ � ● We see: P-value of educ is < 0.0001 < 0.05 → Reject H0 → The coefficient �2 is statistically significant ● Testing the variable of Ue: ● Given that the hypothesis is: ��: �3 = � ● ��: �3 ≠ � ● We see: P-value of ue is < 7.23e-06 < 0.05 → Reject H0 → The coefficient �3 is statistically significant ● Testing the variable of urb: ● Given that the hypothesis is: ��: �4= � 20 ��: �� ≠ � ● ● We see: P-value of urb is < 0.0740 < 0.05 → Reject H0 → The coefficient �4 is statistically significant ● Testing the variable of wh: ● Given that the hypothesis is: ��: �5 = � ● ��: �5 ≠ � ● We see: P-value of wh is < 0.0536 < 0.05 → Reject H0 → The coefficient �5 is statistically significant 2.1.2 Testing the overall significance Purpose: Test the null hypothesis stating that none of the explanatory variables has an effect on the dependent variable We have: � = 0.05 Given that the hypothesis is: ��: �� = � ��: ∃�� ≠ � (i = 1, 2, 3, 4,5) We have: P-value(F) = 2.38e-11 < � = 0.05 → Reject H0 → All parameters are not simultaneously equal to zero→ At least one variable has an effect on dependent one The model is statistically fitted 2.2 Testing the model’s problems 2.2.1 Testing multicollinearity In order to check whether the model has a multicollinearity problem or not, we have ways to check It includes methods: using the VIF (Variance Inflation Factors) and using the correlation between the variables each other 21 ● VIF (Variance Inflation Factors) ● 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 = 1.4326 < 10 =>Conclusion: No multicollinearity are found 22 ●CORRELATION ●Following to the above table, the correlation between each couple of variables is lower than 0.8 ●Again, we have enough statistical evidence to conclude that no multicollinearity are found in our model 2.2.2 Testing Normality Using the “normality of residual” in Gretl: Test for normality of residual : Null hypothesis: error is normally distributed Test statistic: Chi-square(2) = 9.04483 with p-value = 0.0108628 < 0.05 We have enough statistical evidence to reject the null hypothesis H It means error is not normally distributed 23 To fix this problem, we should add more observation to make the big enough to reach normal distribution Because our model now just got 50 observations for each variables Method: Increasing the number of observations until n ≥ 384 to obtain the normal distribution 2.3 Testing Heteroskedasticity We can use the following command “white test” to examine heteroskedasticity If the p-value is smaller than 0.05, the model has the heteroskedasticity 24 White's test for heteroskedasticity Null hypothesis: heteroskedasticity not present Test statistic: LM = 32.7672 with p-value = P(Chi-square(20) > 32.7672) = 0.0357784 < 0.05 We have enough statistical evidence to reject the null hypothesis H Conclusively, our model is heteroskedasticity Fix the problem The log-linear model has the heteroskedasticity problem so we try using Robust to fix the problem by using the command “Heteroskedasticity-robust standard errors, variant HC1” 25 White's test for heteroskedasticity Null hypothesis: heteroskedasticity not present Test statistic: LM = 32.7672 with p-value = P(Chi-square(20) > 32.7672) = 0.0357784 < 0.05 We have enough statistical evidence to reject the null hypothesis H Hence, the model still has the heteroskedasticity However, when using the “Robust Standard Error”, we still can’t fix the problem with heteroskedasticity ● CONCLUSION: The log-linear model has violated two assumptions of Gauss Markov, which are “Normality” and “Homoscedasticity” Thus, the log-linear model is not best model to choose CONCLUSION Our study provides novels finding on the association between the percentage of women labor force participation and the influence of other determinants These include median earnings, education, the unemployment rate, the percentage of the population living in urban areas and the percentage of white women The yf and 26 educ variables are positively related to the wlfp variable It means when the median earnings and the education of women increase, the women labor force participation will increase as well Otherwise, the ue, urb and wh variables have a negative relation with the wlfp variable It means when the unemployment rate increases, or the percentage person living in urban increases, or the percentage of white women increases, the women labor force participation will decrease The unemployment rate is the most important variable that can affect the women labor force participation, it is a negative manner We started with a descriptive summary of all the above variables then all these relations are born clearly in the regression model The results from hypothesis testing indicate that, with the individual regression coefficient testing, coefficients of variables are statistically significant Besides, the overall significance testing also shows that the model is statistically fitted To continue with the models’ problems testing, all of the models which are a lin-lin, log-lin model, not contain perfect multicollinearity The first model, lin-lin model, is the best model when it does not include any common problems The second model, the log-lin model, has problems with heteroskedasticity and non-normality of residual Despite trying to use Robust to overcome the Heteroskedasticity, but it can not be cured Therefore, the best model to choose in this circumstance is the lin-lin model No issue in recent years has drawn as much attention as the debate surrounding the women labour force participation, especially the Feminism problem has risen Hence, this report is very helpful in real situations and nowaday by providing the reader with the affection of some factors on the percentage of women in the present labour force In the report, we only study some aspects that influence women labour force participation Although there are some faults when conducting the research, we hope that this report partially provides you with more understanding about these problems to find out ways to resolve these In the future, to our perspective, this matter will be improved to meet people’s needs In conclusion, I once reaffirm my position that Women has a vital role in the global labour force As your knowledge, a lot of famous people are women, who contribute much merit in our society REFERENCES Gretl Data https://www.econstor.eu/bitstream/10419/130586/1/856920835.pdf 27 https://www.whitehouse.gov/articles/relationship-female-labor-forceparticipation-rates-gdp/ https://www.nber.org/chapters/c0603.pdf Todaro, M P., & Smith, S C (2011) Economic Development (11th ed.) World Bank (2012) World Development Report: Gender Equality and Development Sackey, H A (2005, September) Female labor force participation in Ghana: The effects of education OECD Employment Outlook (1989) - Educational Attainment of the Labor Force https://www.investopedia.com/terms/c/civilian-labor-force.asp 10 Smith J.P., Ward M.P (1985) Time-series growth in the female labor force Journal of Labor Economics 3(1): 59–90 11 International Labour Organization (2007) ILO database on labor statistics International Labour Organization Bureau of Statistics 12 International Labour Organization (ILO) Global Employment Trends for Women 2012 Geneva: ILO,2012 13 https://www.demographic-research.org/volumes/vol38/31/38-31.pdf 14 http://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.575.3838&rep=rep1&type=pdf 15 https://editorialexpress.com/cgi-bin/conference/download.cgi? db_name=CSAE2018&paper_id=441 16 Bureau of Labor Statistics 2008 “Highlight of Women's Earnings in 2007.” Report No 1008 Washington, DC: Department of Labor 17 Butrica, Barbara A., and Karen E Smith 2012a “The Impact of Changes in Couples' Earnings on Married Women's Social Security Benefits.” Social Security Bulletin 72(1): 1–10 Further reading International Labour Organization (ILO) Women in Labour Markets: Measuring Progress and Identifying Challenges Geneva: ILO, 2010 Online at: http://www.ilo.org/wcmsp5/groups/public/ -ed_emp/ -emp_elm/ trends/documents/publication/wcms_123835.pdf APPENDIX 28 obs 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 ue 6.9 8.8 7.2 6.8 6.6 5.7 5.4 4.0 5.8 5.7 3.5 6.1 6.6 5.7 4.5 4.7 7.4 9.6 6.6 4.3 6.7 8.2 5.1 8.4 6.2 7.0 3.7 6.2 6.2 5.7 8.0 6.9 4.8 5.3 6.6 6.9 6.2 6.0 6.6 5.6 4.2 6.4 7.1 5.3 obs 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 wh 58.42 54.18 64.35 65.49 55.24 69.12 71.28 64.52 69.14 55.84 24.69 70.06 62.48 70.97 75.88 70.41 72.41 51.80 77.73 56.80 74.02 65.28 73.57 49.64 69.14 71.37 72.63 66.66 77.00 64.92 58.09 60.66 60.96 72.67 69.18 65.23 73.36 72.76 75.45 54.60 70.32 66.35 58.26 64.20 obs 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 edu 66.0 86.1 78.2 66.1 75.5 83.8 78.8 77.4 74.2 70.1 78.4 80.2 75.7 74.9 80.6 80.9 65.0 68.4 79.4 78.2 79.6 76.8 82.8 64.5 73.1 81.7 82.2 77.9 82.3 75.9 74.3 74.2 70.2 78.3 75.3 73.7 81.7 73.9 71.1 67.8 78.6 66.7 71.5 84.7 obs 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 29 urb 60.4 67.5 87.5 53.5 92.6 82.4 79.1 73.0 84.8 63.2 89.0 57.4 84.6 64.9 60.6 69.1 51.8 68.1 44.6 81.3 84.3 70.5 69.9 47.1 68.7 52.5 66.1 88.3 51.0 89.4 73.0 84.3 50.4 53.3 74.1 67.7 70.5 68.9 86.0 54.6 50.0 60.9 80.3 87.0 obs 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 wlfp 52.3 66.4 54.8 51.9 57.7 62.5 60.9 61.1 52.8 59.9 63.3 56.1 57.7 57.4 57.8 58.0 51.2 50.2 57.5 63.4 60.3 55.7 62.5 52.0 56.4 55.8 60.3 62.9 64.4 58.8 53.9 55.5 59.8 57.3 54.7 53.5 56.1 52.8 58.3 58.3 58.5 55.7 56.4 58.6 obs 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 yf 15.735 25.620 18.976 14.736 23.123 20.001 24.057 20.256 17.867 18.291 20.073 16.122 20.325 17.101 16.465 17.336 16.058 15.993 17.406 22.360 23.090 20.263 19.756 14.472 17.421 15.268 16.009 19.291 20.468 23.243 16.783 22.437 16.475 14.731 18.666 16.820 18.420 18.845 19.631 16.140 14.271 16.367 18.629 17.208 45 46 47 48 49 50 4.5 5.9 5.7 9.6 5.2 5.9 45 46 47 48 49 50 77.34 61.94 69.63 76.81 72.37 70.03 45 46 47 48 49 50 75.3 82.0 83.5 66.0 78.8 83.1 45 46 47 48 49 50 30 69.4 32.2 76.4 36.1 65.7 65.0 45 46 47 48 49 50 60.7 62.3 57.9 42.6 60.1 58.7 45 46 47 48 49 50 19.951 18.613 20.607 15.299 17.465 16.260 ... rural population the female labor force participation may be high This implies a negative sign of the impact of the urban share of a province on the female labor force participation The net effect... decided to choose the topic:” The determinants impacting on the Female Labor Force Participation We use the Gretl prepackaged set data 4.5 Ramanathan, Women’s labor force participation to analyze... finding on the association between the percentage of women labor force participation and the influence of other determinants These include median earnings, education, the unemployment rate, the percentage