There are 4 repositories under poverty topic.
The PolicyEngine US Python package contains a rules engine of the US tax-benefit system, and microdata generation for microsimulation analysis.
A comparative assessment of machine learning classification algorithms applied to poverty prediction
The UK's only open-source static tax-benefit microsimulation model.
Useful Stata routines
The aim of the analysis is to create a high resolution map of poverty, income and literacy for El Salvador. The data on these three development indicators come from the 2017 household survey Encuesta de Hogares de Propositos Multiples (EHPM). While the survey data are only available for 1,664 segmentos, the lowest administrative units, the goal is to provide a map of the three development indicators for all the 12,435 segmentos of the country.
A tax-benefit model for Scotland
This repository contains a literature directory of papers on Earth Observation (EO), Machine Learning (ML), Causal Inference (CI), and Poverty Research.
:pencil: Projects & News
Tools for computing inequality estimates in the LIS Data Center LISSY environment.
Stata module to download National Household Survey on Living Conditions and Poverty (ENAHO) data from INEI - Perú.
Search and download FAOSTAT bulk download files
Poverty Probability Index (PPI) Lookup Tables
Data on elevated blood lead level test results for St. Louis, MO
A Morpheus adapter that loads datasets via the World Bank Open Data API
Repository of data, code and supporting materials to repeat analysis performed by ODOT Research Unit documenting pedestrian injury disparities in Oregon. The standardized scoring uses an objective approach to categorizing Census tracts based on race, ethnicity, and poverty. These categories are then used to calculate population-based pedestrian injury rates for the state.
Code to create tax filing units from the American Community Survey. Used to do simulation of federal tax policy change to evaluate changes in poverty rates.
An R client for Census Bureau's Small Area Income and Poverty Estimates (SAIPE) API
Identifying which households in Costa Rica have the highest need for social welfare assistance
Determining if Machine Learning algorithms can help classify individuals based on demographics and socio-economics drivers.
Analysis of global poverty using PCA to identify important parameters and then clustering via both K-means and Hierarchical clustering techniques.
Scripts to generate GeoJSON containing NYC zip codes, community districts, and public use microdata areas (PUMAs) using zip code tabulation areas provided by US Census Bureau. Maintained by @NYCOpportunity
Democratising Action for Attainment | Understanding attainment through the lens of poverty across the Northern Alliance region of Scotland.
The goSolve web platform
ADECOMP: Stata module to estimate Shapley Decomposition by Components of a Welfare Measure
Functions to compute national accounts coverage ratios with the LIS Data Center methodology
The Data set is picked from Kaggle which describes the Situation of the Multidimensional Measures around the globe. In this Analysis, I have tried to used Pandas, seaborn, and Ipywidgets for the End to End Analysis of the Subject.
Attempting to analyse and estimate poverty indicators at the Indian district level. First ever district level dataset with a poverty indicator.
Economic News from various financial portals
🥈🏆 SEPAKAT - Modul Integrasi is a winning project in Regsosek Hackathon 2022 organized by The Ministry of National Development Planning/Bappenas Indonesia. This module provides a single individual identification model by integrating Regsosek data as basic information which is then linked with related data using the idea of entity resolution.
#This project was a part of ISB@Insights Hackathon 2021 whose problem statement was to create a #multi-dimensional poverty and deprivation Index based on the Mission Antayodya Survey 2019 (a household survey done to examine the state of infrastructure #facilities in terms of access to healthcare, education, banking and infrastructure facilities)
A platform to aid struggling families to escape poverty. Built in 48 hours for a hackathon.
Comparativa entre algoritmos para la clasificación de personas en situación de pobreza con datos de la ENAHO del 2019.