How does a country's income (GDP per capita in USD) affect the enrollment rates of men and women, and how do they differ between each regions of the world from 1999 to 2005?
The reason why we want to use data manipulation and data visualization to solve this question and topic is because throughout history, women have encountered greater obstacles of obtaining the same level of education as men. Many people widely believe that "third-world regions" tend to struggle with educational gender equality compared to "first-world regions" who typically tend to have higher individual and family incomes.
We plan to use multiple datasets with one being about the number of secondary education enrollment by country and by gender from 1999 to 2005. The key features of this dataset includes the gender and country or area for each year. Another dataset contains the GDP per capita in USD of different countries from 1960 to 2023. The key features of this dataset includes the GDP per capita of each country for every year. Because we are only looking from 1999 to 2005, we will filter to only look at the GDP per capita during the years we are interested in. The last dataset we are using is about census data of countries for each year. The key features of this dataset includes the total population and sex ratio for each country for every year which we will also look at only 1999-2005.
With the combination of these datasets we will have the GDP per capita of each country and region along with the number of secondary enrollments by gender for each country. With this information, we can also compare with the ratio of men to women in each country and look at the potential correlation in GDP per capita and the enrollment rates of between men and women. With enrollment rates, GDP per capita, and sex ratios available by country and year, these datasets provide a sufficient foundation to analyze how economic factors relate to gender differences in education. Filtering each dataset to the same time period (1999-2005) ensures consistency in our comparisons. By visualizing enrollment rates and GDP data across regions, we can reveal trends and disparities, making it possible to answer our question with clarity.
GDP per capita: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD
Enrollment in secondary education: https://data.un.org/Data.aspx?d=GenderStat&f=inID%3a64
Population information: https://www.census.gov/data-tools/demo/idb/#/table?COUNTRY_YEAR=2024&COUNTRY_YR_ANIM=2024https:%2F%2Fwww.census.gov%2Fdata-tools%2Fdemo%2Fidb%2F%23%2Ftable%3FCOUNTRY_YEAR%3D2024&menu=tableViz&TABLE_RANGE=1999,2005&TABLE_YEARS=1999,2000,2001,2002,2003,2004,2005,2024&TABLE_USE_RANGE=Y&TABLE_USE_YEARS=Y&TABLE_STEP=1&TABLE_ADD_YEARS=2024
Continents and regions: https://www.kaggle.com/datasets/andradaolteanu/country-mapping-iso-continent-region
For our program, we came up with 3 initial viuslizaions for our data:
One pattern we were curious to find was if there was any noticeable trends between GDP and college enrollment. To find this, we first needed to get an idea for GDP per capita. In our first visualization, we plotted average GDP per capita by region from 1999 to 2005, to see if there were any major differences between regions or any trends. A simple line graph was the best possible way to display this question, due to its ability to both compare different groups and obsere trends within groups individually. With this visualization, we can see a few main things; first, North America has by far the highest GDP per capita, with Europe and Central Asia a distant second. From this, we can get a good idea if there's any relation between GDP and college enrollment if these two regions have enrollment numbers substaintially greater than other regions. Secondly, all regions have seen an uptick in average GDP per capita between 1999 and 2005.
Next, we created a visulization for college enrollment by region, to observe enrollment changes over time. Since each region has vastly different levels of population, we chose to graph a growth in percentage of enrollment instead, to get a better picture of growth. Similar to our Average GDP visualization, a line graph provides the best way to track growth over time, while allowing an easy comparison of regions. From this visualization, we found some surprising things. Despite having the lowest average GDP per capita of any region, percent growth in college enrollment in Sub-Saharan Africa was far and away the highest of any group, seeing almost a 50% increase bewteen 1999 and 2005. Additionally, Europe and Central Asia has actually seen a decrease in college enrollment during that time -- even though it has a high average GDP per capita compared to other regions -- while North America's enrollment has mostly stagnated between 2003 and 2005.
3) How does a country's income level affect the enrollment rates of men and women between 1999 to 2005?
Following the results of our second visuaization, we decided to observe a different variable: income levels between genders. To do this, we combined data from regions based on their income level, and categroized them based on if they were high-income or low-income. From there, we could compare average enrollment across genders, which also helped alleviate the previous issue of different regions having varying populations. This time, instead of a line graph, we elected to try a bar graph, since we wanted to directly compare categorical variables, and wanted to compare different groups within each variable. We did still include a line to show overall trends in enrollment, however, which could give users our of future RShiny application an option to toggle between a bar or line. What we found brought some more interesting observations; men did have higher average enrollment than women, regardless of income level, but past 2003, average enrollment for low-income males actually supasses average enrollment for high-income males. Conversely, average enrollment for high-income females still trumps that of low-income females, even past 2003.