QUESTION

SOLUTION

Executive Summary/Abstract:

In Australia a shift has been seen in the age structure of the population of the country as a whole. One of the reasons for this is due to the aging population of Australia. There have been a lot of suggestions to fight this issue so that the economic growth of the country is maintained at the acceptable level. Of the suggestions to increase the labour force participation rate is by increasing the participation of females in the labour force in Australia.

A trend has been studied and it can be said that the female participation in labour force is much lesser than the male participation in the labour force. An increase in female participation will certainly boost the work force.

For this purpose an analysis has been made in order to find out the factors that may impact the female labour force participation. Another purpose of this analysis will be to discuss what steps might be taken to improve the female labour force participation and thus be part of the economic growth of the country.

It has been viewed that there are several factors that may impact positively or negatively the female participation but certain factors had to be excluded as these variables were seen to be insignificant statistically. However for the factor that were statistically significant recommendations have also been given.

Introduction:

To find out the factors that may impact the female labour force participation and taking steps to improve the female labour force participation.

Data, Model & Method:

For the purpose of this study Following Variables have been used:

Labour Force Participation Rate (Dependent Variable)

Independent Variables

1.     Number of families that are couples with children

This number will give the impact on labour force that has children. This basically gives the relation of labour force with the working ladies that have children. Basically the couples that have children tend to be lesser inclined towards the labour force participation. Thus this becomes an important factor.

2.     Average family weekly income

Basically if the weekly income is good then in that case the females might not be willing to spend some time on generating more income. So for families that have more expectations and rely on increased weekly income will certainly look for an alternative which in this case may be that females of such families go in for work on activities to be part of labour force.

3.     Number of females with highest education as secondary school

The Secondary education has an important contribution in building the attitude thus this parameter has been considered for doing the regression analysis. The pre secondary education may not be much impact but secondary stage certainly impact  the workforce.

4.     Number of married females

The number of married females may have impact on the participation of females in labour force as this is the stage that might influence women whether to go in for being part of the economic growth or not.

The data has been collected for different government area of Australia. The regression analysis has been done based on the above four independent variables. Thus the multiple regressions has been done on the lines of below formula;

Y(t) = α + β₁X₁(t) + β₂X₂(t) + β₃X₃(t) +ɛ

The other variables like number of female in age group of 20 to 74 has not been included as the there might be more number of females in this age group but the impact will depend on the number of males in this age group. Thus this is not the independent variable but is dependent on number of male in this age group.

The results can then be interpreted as follow:

From the above output, it can be seen that the coefficients for  couples with children, number of married couples  and average family weekly income have positive value for coefficient. It has also been observed that these coefficients are all statistically significant at the 5% significance level as the t-stats is more than 1.96 for all of them.

However the variable of females in age group of 20 to 74 and number of child care were also included in the regression equation but were dropped as the t stats value for them was less than 1.96 thus were insignificant. The R-Square for our model is 0.61, thus these variables jointly explain around 61% of the variation in the participation of females.

However the weekly family income has more impact on the female participation than the number of married couples which is depicted by the high value for the coefficient.

Limitations:

Ø  The first and foremost limitation of this model is that the variables that have been selected might not be the exact variables and the regression might be showing a relationship with statistically significant vales because of other parameters that might not have been included.

Ø  There might be the case other factors that have significant statistical values might not have been included in the results.

Ø  The cases of multi co linearity and heteroskedasticity have not been identified and thus giving the incorrect results.

Ø  The R square value is only 0.61, hence the cases which would result in higher value for R square may not have been included

Ø  The regression is done for dependent variable being labour force participation rate assuming that labour force participation rate will have dependence on female workforce only but this is not true and male workforce and conditions associated with male work force might also have effect on this equation.

Conclusion & Recommendations:

Ø  The government may go in for supporting higher education for females in technical side or tertiary education as secondary schooling is showing negative relation. Thus females that go for technical education will have more inclination to be part of labour force thus it will boost the economic development.

Ø  As seen the couple with children show more inclination towards going in for participating labour workforce the government may make arrangements for married couples and give special treatments for married couples.

Ø  The Average family weekly income also has positive impact on the labour force participation of females thus the government can have policies in place that will boost the income with special consideration to women. This will certainly be of great support for those women who look for the increase in weekly income. Special attention should be given to this as this factor boosts the labour force participation rate the most.

References:

Ø  DeMaris, A. (2004). Regression with Social Data: Modeling Continuous and Limited Response Variables. Wiley.

Ø  Draper, N. R. and Smith, H. (1998). Applied Regression Analysis, 3rd ed., Wiley. Kutner, M. H., Nachtsheim, C. J., and Neter, J. (2004). Applied Linear Regression

Ø  Models, 4th ed. McGraw-Hill/Irwin. Weisberg, S. (2005). Applied Linear Regression, 3rd ed., Wiley

Ø  R. Dennis Cook; Sanford Weisberg Criticism and Influence Analysis in Regression, Sociological Methodology, Vol. 13. (1982), pp. 313-361

Ø  Rodney Ramcharan. Regressions: Why Are Economists Obessessed with Them? March 2006. Accessed 2011-12-03.

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