Female Labour Participation in United States
CHAN KAI LI, LOH YUNN CINN, SHARMILA DEVI A/P VASU, SHEE HUEY CHR, TAN HUEY JANE.Faculty of Accountancy and Management
Universiti Tunku Abdul Rahman (UTAR)
Jalan Sungai Long, Bandar Sungai Long
[email protected], [email protected], [email protected], HYPERLINK “mailto:[email protected]” [email protected], [email protected]
This paper discusses about female labour participation in United States. The model analyses the relationship between female employment (EMP), fertility rate (F) and GDP per capita (G). The study used the annual data from 1990-2016. Multiple linear regression and Ordinary Least Squares Method (OLS) are applied for model testing. Result has shown that the independent variables, F and G, are positively correlated to dependent variable, EMP. It is suggested that the US government needs to take initiatives to promote more childbirth in the country as well as encouraging females to participate in the labour force.
Key Words: Female employment, fertility rate, GDP per capita
The objective of this study is to test the relationship between female employment with fertility rate and GDP per capita. The facts in the United States stated that the growth of the U.S. labor force is contributed by a major factor in the twentieth century brilliant increase in the women labor force participation rate. During this period, the U.S. economy undergone economy growth that increased in demand for labour (Toossi & Morisi, 2017). In O.E.C.D. countries, the role of labor market attrition in generating recent observations of the female employment ratio and total fertility rate. If female employment rate is to match male rate in the United States, then the overall GDP would rise by 5% (Hewlett, 2012). Female earn less than male in United State, it make up a disproportionate share of people in poverty. Even there have been significant move up in female’s status but rates of progress are slow (Caiazza, Werschkul, Williams ; Shaw, 2004). Equality has improved in the US when female earned about 62% as much as male since 1979. In 2010, on average, female earned 81% of what male earned. Female participation in labor force climbed in 1970s and 1980s, and reached 60% in 2000. However, this figure has decreased to 46.7% in 2010 and is not expected to increase in 2018.(Rocha ; Fuster, 2005).
2.1 Conceptual Model
The figure of conceptual framework is showed below for the relationship between both independent variables and dependent variable. The reliant variable (dependent) for this experiment is female employment. In the interim, the variable is affected by autonomous factors. In this manner, there are two independent variable that identified with the dependent variable which is the Gross Domestic Product (GDP) per capita and fertility rate.
30226009271003104515147159Gross Domestic Product (GDP) per Capita
00Gross Domestic Product (GDP) per Capita
2.2 Model theories
According to Fox, Klüsener ; Myrskylä (2015) stated that from the past theory, the negative relationship between GDP and fertility is decreasing overtime and moving towards positive relationship. In European countries, large number of countries are displaying constant positive relationship between GDP and fertility. GDP and fertility rate has significant positive relationship in the newly industrialized countries. Countries before industrialization had high fertility rate and low GDP, while after industrialization the GDP increased and fertility rate decreased. It had led to increase in aged population in that country (Gori ; Sordini, 2017).
The women acquire working skill to secure their jobs which causes them to ignore their childbearing. In Southern European countries, the women acquire working skill to secure their jobs which causes them to ignore their childbearing due to high unemployment rate in their countries. However, certain developed countries like Scandinavia, the government provides maternity benefits to encourage childbearing as an effort to ensure availability of future labour market (Adsera, 2003).
Broeck ; Maertens (2015) has stated that female fertility rate among the illiterate and/or poor women in developing rural areas are increasing very quickly compared to the literates and/or non-poor women. This is because the illiterate poor women is very actively participating in the labor force to improve their living quality. Based on Boldrin, Nardi ; Jones (2015) claimed that the government increases the old-age-pensions will eventually reduce fertility rate. There are strongly correlated with each other as people will depend on giving birth to themselves so that they can rely on their children when they are old if the government decreases the pensions. According to Rahman (2011) stated that GDP has no significant related with unemployment rate while it exits significant negative relationship between per capita GDP (PGDP) and unemployment rate. In the result, there are significant negative relationship between PGDP and unemployment rate while GDP is not significantly related with unemployment rate and PGDP.
In OECD countries, the cross-country correlation between the female labour force participation rate and the fertility rate had negative relationship before 1980s and changing to a positive relationship thereafter. They suggested that female employment rate increases with the increase in fertility rate (Engelhardt ; Prskawetz, 2004). Female labour participation is affected by their living area, as married women living in urban area has lower fertility rate compared to non-working women living in rural area. Besides, the occupational flexibility and working hour also affect the fertility rate of women in urban area (Tsegaye, 2011).
Sobotka, Skirbekk ; Philipov (2010) has mentioned that the fertility rate if often cyclical and being affected by the cycle of businesses. He stated that GDP has clear effect of fertility rate,while recession or financial crises shows short term decreases in the fertility rates. This is often related with the career instability and sentiments during financial crises.
Meanwhile, Cazzola, Pasquini ; Angeli, (2016) stated that employment rate may have both direct and indirect effect from the fertility rate. Because when the employment rate is declining most of the couples will postpone their idea of getting a child to secure their financial well-being and prepare themselves to cooperate with the reduced family income.
3.1 Model Variables
This research has used Ordinary Least Square (OLS) and multiple linear regressions model to measure the time series data. It showed the relationship between the dependent variable (Female Employment) and the independent variables (Fertility rate, total and GDP per capita). The constant, C, is a proper apparatus. The female employment method stated that Female Employment (Y) consists of Fertility rate, total (F) and GDP per capita (G), represented by:
Yt = f (F,G) (1)
Y = EMP = Female Employment (Persons, Thousands)
F = Fertility rate, total (Births per woman)
G = GDP per capita (Current US$)
The equation (1) substituted in equation (2) and as follows:
Yt=?0+?1 Ft-1+?2Gt-1+et (2)
t = Time trend, data range from 1990 to 2016 Yearly
e = error term
The equation (2) rephrased with lag different of 1 for Female Employment (Y) to avoid multicollinearity error of Fertility rate, total (F) and GDP per capita (G) into equation (3):
In Yt=?0+?1 (In Ft-1)+?2(In Gt-1)+et (3)
3.2 Data Sources
The yearly time series secondary data for female employment (Y) consists of fertility rate, total (F) are collected from the World Bank Open Data and GDP per capita (G) from OECD Statistics.
3.3 Sample Scope and Time Frame
There are 27 observations in total from 1990 to 2016 yearly for the data estimation period. The econometric analysis tool, Eviews used to analyse the time series data.
3.4 Introductory Analysis
Descriptive statistics summarized features of the collected data. The two types of measures are i) central tendency which includes mean and median ii) dispersion or variability which includes minimum, maximum value, standard deviation, kurtosis and skewnes (Vetter, 2017). Refer TABLE 1.
Correlation showed whether the relationship between two variables is linear or non-linear. The coefficient correlation stated the pairs of variables that connection is strong or weak (Bewick, Cheek ; Ball, 2003).
The four tests of diagnostic checking include i.) Heteroscedasticty test (White), ii.) Normality test (Jarque-Bera), iii.) Multicollinearity test (Variance Inflation Factor) and iv.) Serial Correlation test (LM) (Gujarati and Porter, 2009).
4.1 Model estimation and presentation
The econometrics model related to female labour that we referred to is summarised as follows (Tsani et al., 2013):
FLPRi, t=b0+b1LGDPi,t+b2LGDP2i,t+n=1k-1bnXn,i,t+bkMED11i,t+ ei,t(1)
Where FLPRi,t is female labour force participation rate, LGDPi,t is the log of the real GDP per capita, LGDP2i,t is its square. Xn,i,t is a set of n variables controlling for fertility, urbanization, education, customs and unemployment rates. MED11i, t is a dummy variable controlling for the countries selected. ei,t is the residual term.
The following represents the multiple linear regression which TABLE 2 has provided the result.
In Yt=?0+?1 (In Ft-1)+?2(In Gt-1)+et(2)
EMPt=-0.004447+0.096033 Ft-1+0.464189 Gt-1+0.001370(3)
R2 = 0.612255; Adjusted R2 = 0.578538; d= 1.054309
Where Y and EMP represent female employment, X1 and F represent fertility rate, X2 and G indicate GDP per capita. Time period, t = 1990 – 2016.
4.2 Discussion on parameter estimates
As we refer to our correlation test in EViews, TABLE 3 shows that fertility rate and GDP per capita have a positive relationship with female employment.
4.3 Consistency with theory and expectations
It is proven that our result is consistent with Engelhardt and Prskawetz’s study (2004). Their result shows that the female labour force participation is positively correlated with fertility rate after 1980s in OECD countries. Furthermore, GDP per capita is expected to be positively correlated with female employment (Tsani et al., 2013).
However, there is a majority of studies like Shastri (2015), Hilgeman & Butts (2009) and Zhang (2017) showed that GDP per capita has positive relationship with female employment but fertility rate has a negative relationship with female employment. Thus, fertility rate can be considered as ambiguous.
Variation of result may be due to urbanisation or ruralisation as the nature of flexibility of working hours in rural and urban areas are different (Tsegaye, 2011). With more working hours, females have insufficient time to take care of their child. The relationship of female employment can be potentially influenced by occupational variations. For instance, healthcare industries support female concentrations while manufacturing industries contribute to Black employment (Antipova, 2015). Some companies may prefer to employ males as female labour might require maternity leave and presumed to be less productive than males.
Education might have significance in female employment but due to the fact that the data for literacy rate in US is insufficient to consider it as a variable. Fertility rate tends to be higher in rural areas where females are not actively participating in higher level of studies (Broeck & Maertens, 2015). In developed countries, females are highly literate, so the opportunity cost of exiting the workforce for childcare is high; female labour force participation rates reacts conversely with fertility rate (Bloom et al., 2009).
Individual background and cultural variables are not explained in the model and this may cause the result to be different as feminism is highly discussed by the society. When a person grows up in a feminist country, females are encouraged to be engaged in labour force as much as males.
Other than that, the overall female employment in our data is identified as not directly influenced by financial crisis in 1997 and 2010 while the economic recession indirectly affects female employment through GDP per capita and fertility rate.
4.4Hypothesis testing for parameters
This test is using significant level: ? = 0.05; degree of freedom: k = 2, n?k?1 = 26-2-1 = 23
4.4.1Interpretation of ?0 Estimated Coefficient
If the GDP growth rate and fertility rate is zero, the estimated female employment rate is negative 0.004447 thousand person.
4.4.2Interpretation of ?1 Estimated Coefficient
For each 1% increase in fertility rate, on average, has the positive relationship effect of increasing in the female employment rate, by 0.096033% at the 0.05 significant level, holding constant with other variables.
H0: ?1?0H1: ?1>0Critical value: t0.05, 23=1.81246Test statistics: ?1-0 SE(?1) = 0.096033-00.139284(0.096033) = 7.17958
Conclusion: Reject H0. Since the test statistic is 7.17958 higher than the critical value, 1.81246. We have sufficient evidence to conclude that there is a significant positive relationship between the female employment rate and fertility rate.
4.4.3Interpretation of ?2 Estimated Coefficient
For each 1% increase in GDP growth rate, on average, has the positive relationship effect of increasing in female employment rate, by 0.464189% at the 0.05 significant level, holding constant with other variables.
Critical value: t0.05, 23=1.81246Test statistics: ?2-0 SE(?2) = 0.464189-00.119279(0.464189) = 8.38371
Conclusion: Reject H0. Since the test statistic is 8.38371 higher than the critical value, 1.81246. We have sufficient evidence to conclude that there is significant positive relationship between inflation rate and foreign exchange rate.
4.4.4Interpretation of R2, Coefficient of Determination
R2 = 0.612255 indicates that there are approximately 61.23% of the female employment rate in United States is explained by the fertility rate and GDP growth rate in United States.
4.4.5Interpretation of Adjusted R2
R?2 = 0.578538 indicates that approximately 57.85% of the female employment rate in United States is explained by the fertility rate and GDP growth rate in United States, after taking the degree of freedom into account.
4.5Hypothesis testing for model fit
H0: ?1= ?2=0H1: At least one ?k ? 0
Significant level: ? = 0.05; degree of freedom: k = 2, n?k?1 = 26-2-1 = 23
Critical value: F0.05,2,23 = 3.42213
Test statistics: F-test = R2k1-R2n-k-1 = 0.61225521-0.61225523 = 18.15869
Conclusion: Reject H0. Since F statistic value, 18.15869 is greater than the critical value, 3.42213. We have sufficient evidence to conclude that the female employment rate equation is significant at 0.05 significant level.
The residual diagnosis consists of Heteroscedasticty (White test), Normality test (Jarque-Bera), Multicollinearity test (VIF) and Serial Correlation test (LM) are showed in table below. Refer TABLE 4, 5, 6 and 7.
Diagnostic Tests Results Hypothesis Decision
Heteroscedasticity test (White) Prob. F (5,20): 0.1032
Prob. Chi-Square (5): 0.1073 H0: The variance is homoscedasticity
HA: The variance is heteroscedasticityP-value >0.01
H0 is accepted.
HA is rejected.
Normality test (Jarque-Bera) JB statistics: 0.1685
Prob. value:0.9192 Normality test
H0: error term is normally distributed
HA: error term is not normally distributed P-value >0.05
H0 is accepted.
HA is rejected.
Multicollinearity test (Variance Inflation Factor) R squared = 0.612255
VIF = 2.579 H0: No multicollinearity among the variables
HA: There is multicollinearity among the variables. 1<VIF<5
H0 is accepted.
HA is rejected.
Serial Correlation test (LM) Prob. F (2,21): 0.0821
Prob. Chi-Square (2): 0.0637 H0: There is no autocorrelation among the residuals
HA: There is autocorrelation among the residuals P-value >0.05
H0 is accepted.
HA is rejected.
Based on our research, we have concluded that the female employment is significantly affected by fertility rate, total and GDP per capita. When the GDP per capita increases, the country needs more labour force. As women progress for economically better living, they often have to continuously participate in the labour force, which in return further contributes to the rise in GDP per capita. The increase in fertility rate, total and GDP per capita, has positive correlation with the female participation in labour force. Which eventually leads to higher female employment. Thus, female employment depends on fertility rate, total and GDP per capita.
Since increase in fertility rate, total, increases female employment, which eventually further contributes in GDP per capita, governments should implement few policies, to encourage more childbirth. Government should introduce childcare allowance for home staying parents. This will remove the insecurity of losing job to the parents. Instead they will choose to stay at home to take care of their babies. Similar policy being implemented in Finland has shown rise in fertility rate. Other than that, governments should give at least 6 months of maternity and paternity paid leave. This will give the opportunity for the parents of the newborn to take care of their child until they can find a proper caretaker for their babies, so that they can go back to work after the leave. Otherwise, they’ll be afraid of losing the job and keep delaying childbirth. Lastly, government can also implement a policy whereby the parents gets allowance and benefits for every babies that they give birth to (Sobotka, Skirbekk ; Philipov, 2010). For instance countries like Singapore gives allowance up to 20,000 SGD for the first 3 babies that the couples have and also givens priority in buying government houses (Roberts, 2015).
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