Applied Economics Ph.D. Candidate
Poverty assessment
In this project I focus on showing the work I've done related to analyzing poverty in three parts of the world: US, South Asia and Latin America.
Poverty georeferentiation
Poverty georeferentiation
Poverty georeferentiation
Poverty georeferentiation
Poverty specialized analysis
Data analysis and visualization
Poverty overview
Using data household surveys from the countries in the South Asia region, I was able to create multiple dashboards with information about how the region is compared to the rest of the world in terms of poverty. Additionally, I created dashboards comparing poverty and different indicators such as GDP growth, shared prosperity, etc.
Software: Stata, R, R-shiny.
Non-monetary poverty: MPI
I produced a dashboard in PowerBI that shows the Multidimensional Poverty Index for the SAR region.
This dashboard allows us to make analys not only overtime, but also at the regional level for each country.
Software: Stata, R, PowerBI
Poverty from different perspectives
Poverty can be seen from multiple perspectives. In this data visualization project I created dashboards to show: 1) the differences between urban and rural areas; 2) the differences between the shares of food and non-food expenditures in South Asian households; and 3) the differences in water and sanitation access based on poverty status.
Software: Stata, R, R-shiny.
Geoprahic analysis systems (GIS)
Poverty georeferentiation
Using data from data collected from an experiment in the US, I was able to identify geo-localizations of participants and classify them according to their poverty status and other socio-economic characteristics. This information allows me to classify participants in groups of income. In addition, this information was used to create poverty proximity based on distance from each participant.
Software: R
Poverty classification based on geographical clusters
Using information from property owners locations in Minnesota (US), I used GIS tools to create an intersection map between their locations and the socio-economic information from the American Community Survey (2019). With these two sources, I created poverty and race clusters (using K-means) based on geographical locations.
Software: R and ArcGIS
Public Policy
Disability and poverty in LAC
Using more than 30 million of observations from Censuses from Latin America and the Caribbean, I estimate the relationship between poverty and disability status in the region.
Software: Stata, LaTex
Migration and poverty in LAC
Considering the huge importance of the link between poverty and migration, I participated in programs related to improve life conditions of migrants in Central America and Chile.
Software: Stata, LaTex, ArcGIS
Poverty and youth development in LAC
I had the opportunity to work in projects with the aim of reducing poverty in young populations from different angles: Developing of soft-skills through music, job training programs, and sports
Software: Stata, LaTex, ArcGIS