What Drives Women at Work? @workingwomenstats - Tumblr Blog

This blog was solely for the Coursera/Wesleyan course, "Passion Drive Statistics"

As the course has ended - I happily passed - I won't be updating this particular blog anymore.

If you'd like to follow me, go to: The Superb Nerd

Final: What Drives Women in the Workforce?

The association between female employment rate and the urban rate, democratization

Emily McCormick, Nashville, Tennessee, U.S.A.

INTRODUCTION

The rights of women, including strides made in the workforce, have rapidly changed within the past century, yet the rights of women to work differ around the world. In what types of countries does female employment thrive?

Does a country with a more urban population also have more women in the workforce?

Does a country with a higher polity score, that is, a higher level of democratization, also have more women in the workforce?

METHODOLOGY

Data grouped as follows:

Female employment rate was grouped as FemGroup1 (less than 30%), FemGroup2 (30%-45%), FemGroup3 (45%-60%) and FemGroup4 (greater than 60%). There were 111 null values, representing 38 percent of the data set.

Urban rate was grouped as Urban1 (less than 30%), Urban2 (30%-45%), Urban3 (45%-60%), Urban 4 (60%-75%) and Urban5 (greater than 75%). There were 81 null values, representing 28 percent of the data set.

As the polity score is a score that could be considered a categorical variable, this data was not grouped in any way.

The univariate procedure was run for female employment rate, urban rate and polity score:

The mean for the female employment rate is 47.55%, with a standard deviation of 14.63.

The mean for the urban rate is 57.14%, with a standard deviation of 24.02.

The mean for the polity score is 3.72, with a standard deviation of 6.3.

RESULTS

First Hypothesis:

H0: The female employment rate has no association with the urban rate.

H1: There is an association between the female employment rate and the urban rate.

As this hypothesis requires the analysis of two quantitative variables, the data was examined using the Pearson Correlation Co-Efficient. Is there be a strong correlation or a weak correlation between the two variables, female employment and the urban rate?

This was the Pearson Correlation procedure for my two data-managed variables, Urban and FemGroup:

The correlation co-efficient is just over 0.41, with a p-value of less than .0001, making the relationship statistically significant. With a value of 0.41, which is closer to 0 than it is to 1, this does not indicate a strong linear relationship.

To ensure that I did not overly-manage my data, I ran the Pearson Correlation for the original, ungrouped data: the urban rate and female employment rate.

Again, the p-value is .0001, and the correlation co-efficient is even closer to zero, at -0.30298; again, this does not indicate a strong linear relationship.

The scatter plot supports the conclusion that the female employment rate is not associated with the urban rate:

Second Hypothesis:

H0: The female employment rate has no association with the polity score.

H1: There is an association between the female employment rate and the polity score.

As this hypothesis requires the analysis of a quantitative and categorical variable, the data was examined using the Analysis of Variance (ANOVA). Will there be a strong correlation or a weak correlation between the two variables, female employment and the polity score?

With a p-value of 0.068, along with an examination of the means of female employment at each level of polity score, there does not seem to be a relationship - negative or positive - between female employment rate and the polity score.

A look at a box plot graph confirms that there is no association between the female employment rate and the polity score:

The ANOVA procedure requires a post-hoc test. The DUNCAN post-hoc test further confirms the lack of an association between the two variables, female employment rate and polity score.

DISCUSSION

Despite my expectations, there does not seem to be an association between urban rate and female employment nor between the polity score and female employment.

The Gapminder set suffers from many gaps in the data, making a good analysis of these factors difficult.

Female employment is a complex puzzle. Many women make the choice to stay at home, which could have little to do with where they live or how democratic the country may be. Perhaps a better indicator would be income per gender, to measure how much women are earning, or perhaps determining a level of employment, measuring blue collar versus white collar among men and women. Education levels could also contribute to a study of the progress women make in the workforce.

SAS Code:

libname mydata "/courses/u_coursera.org1/i_1006328/c_5333" access=readonly; DATA new; set mydata.gapminder_pds; IF FEMALEEMPLOYRATE= . THEN FemGroup=0; /*Category for missing data*/ ELSE IF FEMALEEMPLOYRATE LE 30.00 THEN FemGroup=1;/*LowFemEmployment*/ ELSE IF FEMALEEMPLOYRATE LE 45.00 THEN FemGroup=2;/*MidFemEmployment*/ ELSE IF FEMALEEMPLOYRATE LE 60.00 THEN FemGroup=3; /*MidHighFemEmployment*/ ELSE IF FEMALEEMPLOYRATE GT 60.00 THEN FemGroup=4; /*HighFemEmployment*/ IF urbanrate= . THEN Urban=0; /*Category for missing data*/ ELSE IF urbanrate LE 30.00 THEN Urban=1;/*LowUrbanEmployment*/ ELSE IF urbanrate LE 45.00 THEN Urban=2;/*MidLowUrbanEmployment*/ ELSE IF urbanrate LE 60.00 THEN Urban=3; /*MidUrbanEmployment*/ ELSE IF urbanrate LE 75.00 THEN Urban=4; /*MidHighUrbanEmployment*/ ELSE IF urbanrate GT 75.00 THEN Urban=5; /*HighUrbanEmployment*/ PROC SORT; by Country; /*PROC CORR; var urbanrate femaleemployrate;*/ PROC ANOVA; CLASS polityscore; MODEL FEMALEEMPLOYRATE=polityscore; MEANS polityscore/DUNCAN; PROC FREQ; TABLES FemGroup Urban polityscore; PROC Univariate; var FEMALEEMPLOYRATE; PROC Univariate; var urbanrate; Proc MEANS; var FemGroup urban; run;

#coursera #passion driven statistics #statistics #stats

Assignment 9: Moderation

After several tries, I got this one right - with a little help from good 'ol data management!

Not only do I think it would be interesting to look at urban rate and polity score for my final project, I think I'd like to look at if there is any relationship between income per person and the female employment rate. I also may examine the relationship between employment rates in general with these variables.

Due to the low p-value in ANOVA, it appears that there is a relationship between these three values that I will attempt to explore in my final project.

Assignment 7: Chi Square & Assignment 8: Correlation

Not really a part of my focus, but let's examine if the polity score (explanatory) affects the urban rate.

H0 = There is no relationship between the urban rate and polity score. (Independent)

Ha = There is a relationship between the urban rate and polity score. (Not independent)

With a p-value of 0.3627, there seems to be no relationship. However, there were a lot of missing values.

I also ran the Pearson Correlation for Assignment 8, looking at urban rate and the female employment rate. At 0.3, a correlation doesn't seem very conclusive, meaning there doesn't seem to be much correlation between the two.

Assignment 6: ANOVA

Does the polity score (explanatory) affect the female employment rate (response)?

H0 = all means are equal; polity score has no affect on female employment rate.

Ha = not all means are equal. There is a correlation between female employment and the polity score.

F value= 1.57; p value = 0.0680

Due to the p-value exceeding 5 percent, there is no association between the polity score and female employment.

Assignment 2

I've completed assignment 2, creating my log and frequency tables to examine the following:

Income per person (2010 per capita GDP)

Female employment rate

Employment rate (this might be useful for comparison)

2009 Democracy (Polity) score

2008 urban population

I'm specifically interested in whether the female employment rate rises in more democratic states, and whether urbanization as an affect on female employment.

I am a bit stumped on how to manage my data going forward, as many of the examples in the video showed variables that are more easily quantified (i.e. yes/no). I think I will group the polity scores and urban population to provide ranges. I may do the same with the female employment rate.

Getting started.

I'll be using this blog to present data on the drivers -- or non-drivers -- of female employment around the globe. Is democratization associated with female employment? What about urbanization?

Trending Blogs

Recently Viewed Blogs

What Drives Women at Work?