Poverty Level

Research on poverty level measurement is increasingly leveraging machine learning and satellite imagery to overcome limitations of traditional survey-based approaches, aiming for more accurate and timely poverty assessments, particularly in data-scarce regions. Current research focuses on developing and benchmarking various machine learning models, including boosting algorithms (like CatBoost and XGBoost) and deep neural networks, to predict poverty levels from multi-modal data sources such as satellite imagery and household surveys. These advancements offer the potential for improved targeting of poverty reduction initiatives and more effective monitoring of their impact, contributing significantly to both scientific understanding and practical policy applications.

Papers

November 5, 2024

Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach
Mohammad Kakooei, Klaudia Solska, Adel Daoud
Time Series Constructive Approach Discrete Wavelet Transform Multispectral Data Poverty Level Poverty Reduction Poverty Prediction Multidimensional Poverty

July 8, 2024

KidSat: satellite imagery to map childhood poverty dataset and benchmark
Makkunda Sharma, Fan Yang, Duy-Nhat Vo, Esra Suel, Swapnil Mishra, Samir Bhatt, Oliver Fiala, William Rudgard, Seth Flaxman
New Benchmark Satellite Imagery Nine Year Old Child High Resolution Satellite Poverty Level Multidimensional Poverty

May 30, 2024

A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud
Future Implication Causal Machine Learning Machine Learning Analysis Downstream Decision Making Poverty Level

May 28, 2024

Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines
Erika Lynet Salvador
Machine Learning Model Machine Learning Approach Gradient Boosting Boosting Algorithm Extreme Gradient Boosting Poverty Level Adaptive Boosting Wealth Distribution Poverty Prediction

March 3, 2024

Can Poverty Be Reduced by Acting on Discrimination? An Agent-based Model for Policy Making
Alba Aguilera, Nieves Montes, Georgina Curto, Carles Sierra, Nardine Osman
Agent Based Model Group DIscrimination Poverty Level Redistribution Mechanism Policy Making Poverty Reduction

July 21, 2023

Poverty rate prediction using multi-modal survey and earth observation data
Simone Fobi, Manuel Cardona, Elliott Collins, Caleb Robinson, Anthony Ortiz, Tina Sederholm, Rahul Dodhia, Juan Lavista Ferres
Remote Sensing Image Satellite Imagery Earth Observation Poverty Level Poverty Prediction

April 27, 2023

Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India
Atharva Kulkarni, Raya Das, Ravi S. Srivastava, Tanmoy Chakraborty
LeArning Abstract Rural Connectivity Poverty Level

March 24, 2023

An Agent-Based Model for Poverty and Discrimination Policy-Making
Nieves Montes, Georgina Curto, Nardine Osman, Carles Sierra
Agent Based Model Poverty Level Discriminatory Decision Poverty Reduction

February 28, 2023

Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia
Om Shah, Krti Tallam
Machine Learning Remote Sensing Temperature Measurement Poverty Level Poverty Prediction Poverty Map

February 17, 2023

Interpreting wealth distribution via poverty map inference using multimodal data
Lisette Espín-Noboa, János Kertész, Márton Karsai
Multimodal Data Crowd Sourced Data Poverty Level Wealth Distribution Poverty Map

June 13, 2022

Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Application Proficiency Causal Inference Causal Effect Causal Effect Estimation Observational Data Propensity Score Observational Study Poverty Level Biased Causal Effect

March 2, 2022

Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain
Ola Hall, Mattias Ohlsson, Thortseinn Rögnvaldsson
Machine Learning Inherent Interpretability High Explainability Domain Knowledge Satellite Image Knowledge Extraction Social Welfare Poverty Level Scientific Explanation

February 17, 2022

Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows
Sourabh Balgi, Jose M. Peña, Adel Daoud
Causal Inference Counterfactual Inference Counterfactual Analysis Poverty Level Graphical Normalizing Flow Central Bank

November 30, 2021

Predicting Poverty Level from Satellite Imagery using Deep Neural Networks
Varun Chitturi, Zaid Nabulsi
Deep Neural Network Satellite Imagery Overhead Imagery Poverty Level Poverty Prediction

Poverty Level

Papers

Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach

KidSat: satellite imagery to map childhood poverty dataset and benchmark

A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty

Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines

Can Poverty Be Reduced by Acting on Discrimination? An Agent-based Model for Policy Making

Poverty rate prediction using multi-modal survey and earth observation data

Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India

An Agent-Based Model for Poverty and Discrimination Policy-Making

Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia

Interpreting wealth distribution via poverty map inference using multimodal data

Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa

Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain

Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows

Predicting Poverty Level from Satellite Imagery using Deep Neural Networks