Part 1: Human Development Index and Inequality

HDI is a measure of three factors: life expectancy, level of education, and standard of living (measured by gross national income per capita). It ranges from zero to one: closer to one is better, closer to zero is worse.

The GINI Coefficient is the most commonly used indicator of the distribution of wealth in a society, and ranges from zero to one hundred. Zero indicates absolute equality, one hundred indicates absolute inequality.

Hypothesis: We suspect that in more developed countries, a higher HDI will correspond to less inequality. We think this because HDI is a measure of quality of life and standard of living, so we would expect quality of life overall to be higher in countries with less inequality (where the wealth is more spread across the population).
#Plotting HDI versus Inequality

GINI_vs_HDI <- responses %>% na.omit() %>% select(GINI.coefficient, Human.Development.Index..HDI.) %>% filter(Human.Development.Index..HDI. > .5)

#mplot(GINI_vs_HDI)

gf_boxplot(GINI.coefficient ~ ntiles(Human.Development.Index..HDI.), data = GINI_vs_HDI) %>% gf_labs(title = "GINI Coefficient based on Level of Development", caption = "")

This graph shows the GINI Coefficient by human development index. We selected countries with HDIs above .5 because we were interested in level of inequality based on development for more developed countries. The graph splits the countries into 3 categories of HDI: the first is the least development (among countries with HDI> .5), the second is more developed, and the third is the most developed. The graph shows that average inequality is lower for more developed countries, and higher for less developed countries. This confirms our hypothesis that countries with lower inequality have higher standards of living.

Part 2: Women in the Labor Force and Rural Population

Hypothesis: We were curious about the correlation between the number of women in the labor force and the rural population of a country, because the labor forces in countries with larger rural populations look very different than in more urbanized/ industrialized nations. We suspect that countries with larger rural populations will have lower female participation in the work force.
female.labor_vs_rural.pop <- responses %>% na.omit() %>% select(Labor.force..female....of.total.labor.force., Rural.population....of.total.pop.)

#mplot(female.labor_vs_rural.pop)

gf_point(Labor.force..female....of.total.labor.force. ~ Rural.population....of.total.pop., data = female.labor_vs_rural.pop, color = ~ Rural.population....of.total.pop.) %>% gf_lm() %>%  gf_labs(title = "Women in the Labor Force based on Rural Population", caption = "Colored by Rural Population")

In this graph, we looked at the relationship between the percentage of the labor force which are women, and the percentage of the population which lives in rural areas. We were somewhat surprised to see that in countries with larger rural populations, there is a higher percentage of women in the labor force. However, this conclusion makes sense because the type of work performed in rural areas is typically work that can be done in the home/ on the family’s property. This might include housework, farmwork, crafts, and selling goods in the market. Women in rural areas are typically more able to work because they can do the work required in a rural setting while also dedicating time to taking care of the home and family. In urban areas, it is more difficult for women to participate in the labor force if they also face restraints placed on them by their family duties.

Part 3: Female participation in labor force by country.

We plotted female participation in the labor force for countries where female labor force is less than 45 percent. We found that many Middle Eastern countries such as Jordan and Pakistan have very few women in the labor force (15-20 percent). This may be due to the cultural enviroment of this area where women are not expected to work.
female.labor.force_vs_country <- responses %>% arrange(desc(Labor.force..female....of.total.labor.force.)) %>% na.omit() %>% filter(Labor.force..female....of.total.labor.force.<45)

dotchart(female.labor.force_vs_country$Labor.force..female....of.total.labor.force., female.labor.force_vs_country$Country, cex = .4, main = "Female Participation in Labor Force", color = "purple", xlab= "Female percentage of labor force")