Changes in Health, Education, and Income Inequality in Sub-Saharan Africa
This visualization investigates the ways in which education inequality, health inequality, and income inequality changed from 2010 to 2014 in the different countries of Sub-Saharan Africa.
The numerical value for each inequality is the Atkinson Measure as reported by the UNDP in the 2015 Human Development Report . The Atkinson Measure is a measure of inequality that varies between 0 and 1 -- or 0% and 100%, equivalently -- with 0 indicating no inequality and 1 indicating complete inequality. The visualization is a synthesized re-rendering of three charts found in Chapter 11 of Income Inequality Trends in Sub-Saharan Africa , which plot the same data. The intended goal is to emphasize and clarify the connection between these three charts, providing a user-friendly way of tracking both the absolute and relative change undergone by each individual country betwen 2010 and 2014 in all three areas of inequality.
This project is an exploration of the Gini coefficient and Gender Inequality Index as outlined by the UNDP.
The text Income Inequality Trends in Sub-Saharan Africa states that as the economic status of women improves, so does the economic status of entire families. This visualization intends to show clear relationships between gender and income inequality, as well as educate the user on the calculation of male and female indexes. By understanding the impact of gender equality on income equality, and the difference in male and female indexes, we can work to raise female indexes, thus lowering gender and income inequality.
How does inequality change over time?
The Gini coefficient is a measure of inequality. The coefficient can change in response to policy decisions, national and international events, and economic factors. Line charts that visualize changes in the Gini score over time for many countries are challenging to read because many lines crossing paths are difficult to follow. This scatterplot tool displays changes in the Gini coefficient between any two years for all countries for which data are available in those two years. The two years can be consecutive or non-consecutive.
How have income categories changed in Sub-Saharan Africa?
This visualization focuses on income quintile trends in Sub-Saharan Africa. It starts with a broad visual of change within quintiles for all countries, and then navigates to Ghana, Burkina Faso and Tanzania. These three countries were chosen to represent three different types of inequality trends on the African continent: Ghana shows rising inequality, Burkina Faso falling, and Tanzania an inverted u-shaped ∩ inequality trend.
In the opening segment, income quintiles are illustrated by a single positive/negative bar chart sorted by amount of change. The user can hover over the chart to expand and read details on mouseover.
In the three country section, income quintiles are illustrated by two charts: a stacked bar chart to represent change in proportion of each country’s total income, and a positive/negative bar chart to illustrate percentage change. Text is generated from the data as the user interacts with the graphs.
What does the economic complexity index look like near other indexes?
This is a project to focus on how to develop African economies (with all its weakness and strenghs). The Economic Complexity Index is chosen as a tool to understand Sub-Saharan countries. The ECI, though, is a black box; even with a whole section trying to unpack its logic, it's complex: it tries to assess how the economy of a country is developed and its potential to breach into other areas of economic activity.
The UNDP report tries to use it with other indexes such as the log of GDP as a way to see correlations and understand its behavior, but maybe there's a chance to translate its effects in real world with variables that are closer to real-life experiences. Hence, we intersect the ECI with not only its log of GDP per capita, but also the GINI. Here we have a 3-pointed index, pointing out different traits for each Country: their techincal abilities, their abundance and its effect on society.
The visualization has two features: it uses a slopegraph to see how the GDP per capita, ECI and GINI are connected in a country. It also shows, as a secondary feature, the time series for each of of those indexes for the Country selected.
The Curious Case of South Africa’s Income Inequality
This visualization investigates South Africa’s high income inequality with three angles to help decipher its curious case of recently having recorded one of the highest income inequality in the world.
Most of the Sub-saharan African countries have a very different economic composition than South Africa’s. While most SSA countries' Gini correlates with education and agriculture, South Africa has a very high literacy rate and very low agricultural composition. In the first visualization, we see all countries with a recorded Gini coefficient of 70 or higher which happens to consist of eight African countries with South Africa being the most recent to join those ranks. This visualization charts a possible political event that may help explain why that specific country reached a Gini coefficient of 70 or higher during that year. The second and third visualizations look at two countries (Ethiopia and Tanzania) that have been case studies in the recent UNDP report, Income Inequality Trends in sub-Saharan Africa: Divergence, Determinants and Consequences. With the analysis of these two countries, I hope to paint a path of analysis that might help to explain South Africa’s high inequality.
This is an investigative visualization using qualitative data for a closer read into the UNDP report, while trying to redirect the qualitative analysis to start a conversation on recommendations for South Africa. It seems that with a drive away from agriculture and towards services (which seems inevitable with higher education rates), inequality rises exponentially, so a close read into South Africa's economy may help prevent high inequality in the development of other SSA countries.
Agricultural Productivity in Sub-Sarahan Africa
This project aims to explore the effect of agricultural investment and productivity on the welfare and equitable share of resources in Africa.
This project was motivated by the United Nations Development Programme's (UNDP) newly launched study on income inequality in Sub-Saharan Africa (SSA). In these countries, up to 60-70 percent of labour is employed in agriculture, yet it generates only 25-30 percent of GDP. Additionally, crop yields have remained basically stagnant throughout the region despite rising population growth, such that today these countries produce 30 percent less food per person than in 1960s.
Visualizing Primary Sector Shifts in Sub-Saharan Africa
These visualizations examine commodity dependency and land inequality trends in Sub-Saharan Africa's primary sector (farming and extractives) from the mid-2000s until 2015, the most current reporting period.
Since "[a]griculture is Africa’s largest economic sector, representing 15 percent of the continent’s total GDP, or more than $100 billion annually" (McKinsey, 2010) and since farming represents 60 percent of all jobs on the continent (Brookings, 2016), crop yields, land ownership patterns, and productivity gains in tillage have an outsized impact on local economies. By the same token, since Africa holds 30 percent of the world’s mineral reserves, 10 percent of the world’s oil, and 8 percent of the world’s natural gas (World Bank 2017), the specter of the resource curse is never far from policymakers’ minds.
Of particular concern to UNDP and the sustainable development community is reprimarization risk, which in simplest terms means a return to or backslide into primary commodities as the main source of export revenues, as opposed to upward growth in value-added goods and services.
We invite you to explore these complex dynamics -- including Dutch disease and smallholder dualism -- in the accompanying pages.
Urbanization in Subsaharan Africa
This project seeks to look at the relationship between demographic developments and inequality trends, with a focus on urbanization. As world population continues to grow, an increasing number of people are moving to cities aspiring better living conditions, higher quality education and greater economic opportunities.
In 2008, for the first time in human history, half of the world’s population lived in towns and cities. Africa is currently the least urbanized continent, but its urbanization rate of 3.5 percent per year is the fastest in the world.
In 1980, only 28 percent of Africans lived in urban areas. Today, the number of Africans living in cities is 40 percent, and is projected to grow to 50 percent by 2030. As this has immediate social-economic impacts, it is interesting to investigate the relation between urbanization data and inequality trends.
A Comparison of Enrollment Rates in Primary, Secondary and Tertiary Education across SSA Countries
Education is one of the major concerns that induce inequality in Sub-Saharan Africa. With the improvements in primary education, education enrollment in general is still having a lot of issues. This visualizaion aims to provide a broad perspective for investigating enrollment situations across different regions in SSA, as well as specific changes in the past few years within each country.
Gender inequality is a major concern in education. I am bringing an extra perspective to compare the differences in enrollment rates of each gender.
Gender Differences in Expected & Mean Years of Schooling in Sub-Saharan Africa
This project explores the differences between the expected years of schooling and the mean years of schooling based on gender from the 2013 United Nations Development Programme (UNDP) Education Report.
Africa has seen an increase in the number of school children gaining access to school. However, there still seems to be a gap in education outcomes based on gender differences. This data visualization project explores these differences. From the "Income Inequality Trends in Sub-Saharan Africa" report, we know that Sub-Saharan Africa has experienced advancement in human development. However, the report also highlighted gender inequality and differences in human development. Since education is a factor in both calculating the Human Development Index, this data visualization project explores those differences by looking at differences in “expected years of schooling” and “mean years of schooling” by both male and female identified individuals. Expected years of schooling is defined as “number of years of schooling that a child of school entrance age can expect to receive if prevailing patterns of age-specific enrolment rates persist throughout the child’s life” (Unesco). Mean years of schooling is calculated by averaging the number of years of school accomplished by individuals 25 years or over.
Animated Time-Series and Choropleths for Worldwide Education Enrolment and Income Inequality
Education quality, as a determining factor on social mobility/income inequality, tends to make itself known across long-term perspectives. With this as a guiding insight, this interactive visualisation tool is oriented towards macro-level, broad insights on the ways that educational access relate to and interact with income and social equality, on a country-by-country basis.
Each map tracks a single metric across a time interval from 1970-2015- the Gini Income Inequality Coefficient, and two Net Enrolment Ratios(NER) for both Primary and Secondary Education. The year of each metric can be selected via the top slider, with tooltips displaying the specific data for each country.