International development organizations consider Ecuador among the worst-hit countries in the world by the COVID-19 pandemic. According to the World Health Organization, in terms of confirmed deaths per million habitants, "Ecuador ranks fourth in the Americas and ninth worldwide." (WHO, October 2022)
Since 2018, Ecuador was already facing several macroeconomic hardships, including a recession and an unprecedented level of public debt at 45.8% of its GDP. (OECD, 2020) The pandemic led to a “deep recession that had a major impact on growing poverty levels in Ecuador.” (World Bank)
This data visualization project seeks to explore how poverty has evolved over time in Ecuador and shed light on the disparity among the poor. Who is most affected by extreme poverty? What does this disparity look like? Why should we be concerned?
Ecuador's poverty, as a percentage of the population, was on a declining trend since 2009.
The COVID-19 pandemic increased poverty back to 2009-2012 levels, peaking in 2020.
Rural areas have been the most affected, with almost half—49%—falling under poverty.
2021 shows a decline in poverty across National, Urban, and Rural levels. However, with economic instability, crime, and inequality on the rise in 2022, returning to 2010's level development will take time.
Source: Ecuador Social Registry, Open Data Portal, 2021. Data processed by the author.
There is a reason for that. The provinces with the highest concentration of poverty per 10.000 habitants are also those where the majority of rural households per 10.000 are located.
It follows the story that rural households were hit hardest by the pandemic, and hence, by poverty.
Source: Population by area, according to province, canton and parish of registration and ethnic groups, INEC, 2010. Data processed by the author.
Source: Ecuador Social Registry, Open Data Portal, 2021. Data processed by the author.
Indigenous peoples are a minority population in Ecuador.
And yet, they are the demographic group most affected by extreme poverty.
The distribution of poverty in Ecuador between males and females is similar.
There are 1.44% more females living under poverty than males, a small difference that is on parr with the higher proportion of women in the overall population.
Source: Ecuador Social Registry, Open Data Portal, 2021. Data processed by the author.
Source: Ecuador Social Registry, Open Data Portal, 2021. Data processed by the author.
As expected with individuals living under poverty, most are unemployed. However, significantly more females are unemployed than males .
Among the employed, the majority of men are day laborers, private sector workers or self-employed.
As to employed women under poverty, most are self-employed, followed by day labor, which speaks to the precariousness of jobs that lack stability and social protection.
6% more females lack access to social security, compared to males.
Not only more females are living under poverty but also fewer of them are protected by the State.
Source: Ecuador Social Registry, Open Data Portal, 2021. Data processed by the author.
Not all poverty looks the same. There are striking differences among the poor, in particular when it comes to indigenous peoples living in rural areas and females.
While indigenous peoples are a demographic minority, they occupy the second largest share of extreme poverty. Proportionally, they are worse-off than any other ethnic group in Ecuador.
There is also disparity within gender. Even though females account for slightly more of the population living under poverty, compared to males, they face unemployment in a larger proportion. Among the employed, females have jobs with more precarious conditions and instability. This carries over to social security, where 91% of females in poverty cannot access social security.
Social policy in Ecuador has systematically failed at focalizing government services properly. Part of the issue is an incomplete Social Registry that includes people categorized as "Not Poor" who have historically gained access to services such as Conditional Cash Transfers, housing bonds, and more recently, COVID-19 relief transfers.
The bigger issue, however, is the lack of studies to determine which groups are most vulnerable among the poor. This understanding is essential to effectively prioritize the delivery of social services. Making the Social Registry dataset open to the public, among other data, allows for external parties, such as university scholars, to conduct independent studies that can contribute towards an evidence-based, focalized social policy.
This project provides evidence on two vulnerable groups that require special attention in the policy-design and implementation cycle but, more information is needed. Poverty by income does not tell us the full story.
Multidimensional poverty needs to be studied with the aim of identifying the most vulnerable groups, in terms of access to decent housing, education, nutrition, among others. (World Bank)
This analysis is more urgent than ever before. The pandemic and economic recession have set back Ecuador's development by years. For example, chronic child malnutrition has now become the biggest public health concern in Ecuador, affecting 27.2% of children under the age of 2. (UN Joint SDG Fund) This correlates to the vulnerability of women under poverty, who are the primary care takers of young children.
Additional to identifying the most vulnerable groups within poverty, prioritization mechanisms need to be analyzed and transparently communicated. This includes reaching remote populations through descentralized government services and infrastructured already in place, such as medical brigades that regularly visit far-reaching communities, to guarantee a higher take-up of social services within the target population.
Growing up in Ecuador, I have witnessed the decline in extreme poverty over the last two decades. This decline was a result of both economic growth (due to an increase in oil production in the 2010's), as well as social policy oriented to promote upward social mobility. Policy examples include conditional cash transfers, bonds for people with disabilities living in poverty, children nutrition programmes, among others.
COVID-19 hit Ecuador severely and has already reversed much of this progress. The concern is that it could take a decade to reach pre-pandemic development levels. As an Ecuadorian interested in social policy, I find these reports very alarming. The economic hardship remains now a common dinner table topic back home. I saw this project as an opportunity to explore data on poverty and understand not only how it has evolved over time, but also which groups are most affected.
The main data source I used for this project is the Social Registry: a government registry that was created to record people and households living under poverty to facilitate their access to social services. This information has historically been reserved for institutional use. However, since Ecuador joined the Open Government Partnership in July 2018, an Open Data Portal has been recently launched, which now makes the data available to the public.
I opted for the Social Registry data for the following reasons:
Other data options for this topic include the statistical reports from the National Institute of Statistics and Census (INEC), which I do use complementarily. For example, I used series data on poverty rates from INEC's tabulated datasets for the area chart, and data on rural households by province.
The main limitation from this dataset is that it only contains observations of people under poverty who at some point self-registered or were registered by a third party. Even though it is currently the most complete data available, it is not a 100% comprehensive of all people living under poverty in Ecuador. This means the reality may be much harsher, in particular for families who live in remote areas and cannot access social services.
At the beginning, I was interested in a profile of poverty in Ecuador but I was not sure which direction the story would go.
I wanted to define the story based on the patterns I could identify in the data. This meant that I had to try a lot of different charts to visualize what poverty looked like at different levels: urban vs. rural, and by categories such as gender and ethnicity. Ultimately, I opted to focus on disparity among the poor and came to realize that indigenous peoples are by far the group most affected by extreme poverty. The degree of this disparity was a shocking result and one that I had not anticipated.
My goal was to balance a varied selection of charts, while also communicating the story clearly. I used an area chart to visualize poverty rates by levels because it emphasized that the rural proportion of poverty is larger that the others. I included a vertical line and an annotation to direct the viewer to the poverty rate peak in 2020.
The maps were used to show a clear relationship between the provinces with the highest poverty concentrations and those with the most rural households. The goal was to further emphasize the point that rural poverty accounts for most poverty in the country. The fact that each map comes from a different data source and shows this visual correlation gives more credence to the story.
I used the treemap and horizontal chart side-by-side to draw attention to the main point of the story: that even though indigenous peoples are a minority, they are the demographic group most gravely affected by extreme poverty. I decided to color only the "Indigenous" category to highlight this comparison.
For the section on gender, I employed color scheme consistency through the different graphs so the viewer would associate females and males more easily. The parallel chart was handy to compare the precariousness between females and males. Since there are only two categories on the left, I thought it would be manageable for the viewer to follow the story through this graph. This was also the reason I chose pie charts to compare access to social security by gender. It's easier to make the point of no access to social security with binary categories that keep pie charts readable.
On the data processing side, one of the challenges was translating all the datasets from Spanish to English, in particular for terms that are context-specific, such as the categorization for employment types used by the National Statistics Insitute.
On the coding side, I had some difficulty with the legends, ie. finding the right buckets for the map legends and getting the Swatches function to work properly. The maps also required adjustment because the projection did not render them visible in my attempts.
Overall, this was a good learning experience on how to use D3.js and communicate a story clearly with real data.