On track or not? Projecting the global Multidimensional Poverty Index

Alkire, Sabina; Nogales, Ricardo; Quinn, Natalie Naïri; Suppa, Nicolai

doi:10.1016/j.jdeveco.2023.103150

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…where MPI i represents the value for the ith county, W j represents the weight of the jth variable, and P ij represents the standard value of the jth variable for the ith county. Numerous studies have demonstrated the effectiveness of the MPI calculated from statistical data in reflecting the level of poverty within a specific area [3,8,9]. Consequently, we used the MPI calculated from statistical data as the actual MPI to serve as a reference for the MPI that was predicted by the linear regression and machine learning models.…”

Section: Calculation Of County-level Mpi From Statistical Datamentioning

confidence: 99%

“…Unlike indicators that are solely focused on income or economic factors, the MPI serves as a comprehensive poverty measurement tool, considering multiple dimensions such as natural conditions, health, education, and living standards. It provides a more global and in-depth analysis of poverty, offering a comprehensive reflection of individual or household poverty across various aspects [8]. Consequently, the MPI has become one of the most widely used tools to analyze regional poverty characteristics.…”

Section: Introductionmentioning

confidence: 99%

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County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

Zheng,

Zhang,

Deng

et al. 2024

Remote Sensing

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The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment methods predominantly rely on NL data, and the potential of combining multi-source geospatial data for poverty identification remains underexplored. Therefore, we propose an approach that assesses poverty based on both NL and geospatial data using machine learning models. This study uses the multidimensional poverty index (MPI), derived from county-level statistical data with social, economic, and environmental dimensions, as an indicator to assess poverty levels. We extracted a total of 17 independent variables from NL and geospatial data. Machine learning models (random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and traditional linear regression (LR) were used to model the relationship between the MPI and independent variables. The results indicate that the RF model achieved significantly higher accuracy, with a coefficient of determination (R2) of 0.928, a mean absolute error (MAE) of 0.030, and a root mean square error (RMSE) of 0.037. The top five most important variables comprise two (NL_MAX and NL_MIN) from the NL data and three (POI_Ed, POI_Me, and POI_Ca) from the geographical spatial data, highlighting the significant roles of NL data and geographical data in MPI modeling. The MPI map that was generated by the RF model depicted the detailed spatial distribution of poverty in Fujian province. This study presents an approach to county-level poverty evaluation that integrates NL and geospatial data using a machine learning model, which can contribute to a more reliable and efficient estimate of poverty.

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Section: Calculation Of County-level Mpi From Statistical Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

Zheng,

Zhang,

Deng

et al. 2024

Remote Sensing

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“…Alkire, Center Director of the Oxford Centre for Poverty and Human Development, used panel data to study the dynamic evolution of multidimensional poverty and income poverty and their differences in China and assessed the robustness and stability of multidimensional indicators to changes in weights and indicators [ 24 ]. In the global area, he provides projections of multidimensional poverty for 75 countries and also explores the impact of COVID-19 on global levels of multidimensional poverty [ 25 ]. Methodological advances are presented to identify the time path of multidimensional poverty on the one hand, and empirical results useful for policy purposes are provided on the other.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal differences and convergence analysis of multidimensional relative poverty in ethnic areas

Cheng,

2024

PLoS ONE

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Reducing multidimensional relative poverty is one of the important issues in the current global poverty governance field. This article takes 12 ethnic regions in China as the research object and constructs a multidimensional relative poverty measurement system. The calculated multidimensional relative poverty index is decomposed according to provinces, cities, dimensions, and indicators. Then, the Dagum Gini coefficient and convergence analysis are used to analyze spatiotemporal heterogeneity and convergence characteristics. The results show that the multi-dimensional relative poverty situation of various provinces in ethnic minority areas has improved from 2012 to 2021, among which Tibet province is the most serious and Shaanxi is the best. According to the analysis of convergence, it was observed that there is no σ-convergence of multidimensional relative poverty in ethnic areas in general, and there is absolute β-convergence in general and in the southwest and northwest regions, and there is no absolute β-convergence in the northeast region. Based on this, policy recommendations for reducing multidimensional relative poverty are proposed at the end of the article. Compared with previous studies, this article focuses on ethnic regions that are easily overlooked. Starting from the dimensions of economy, social development, and ecological environment, the poverty measurement system has been enriched.

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Naudé

2023

Economic Growth and Societal Collapse

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On track or not? Projecting the global Multidimensional Poverty Index

Cited by 7 publications

References 21 publications

County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

Spatial and temporal differences and convergence analysis of multidimensional relative poverty in ethnic areas

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