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There are many dimensions to poverty, and research and applications of new methods for better understanding this multidimensionality have blossomed in the past 10-15 years. Malaysia established an official Multidimensional Poverty Index (MPI) in 2015 as a complement to the income-based poverty measure that has been in use since the 1970s. To date the MPI has not featured prominently in policy formulation or in public discourse, being overshadowed by other measures. Most notable among these is the "B40", an income-based measure that refers to the bottom 40 percent of the income distribution. Nevertheless, the MPI remains one of several instruments available to monitor the well-being of the population and inform policy decisions. The government of Malaysia recently revised its methodology for the income poverty line and is considering revisions to the MPI as well. The objective of this policy note is to examine possible ways to improve both the relevance of Malaysia's MPI and expand its usage in informing policies. It is one of many available indicators of progress in the people's well-being, but it is also uniquely positioned both to summarize gains across many aspects of daily life and to provide insights into specific dimensions of poverty that are progressing well or are lagging. Section 2 briefly describes some of the main concepts in multidimensional poverty and highlights recent developments in research and measurement. Section 3 offers details about the introduction and use of the MPI in Malaysia, the structure and content of the MPI, and MPI statistics. Section 4 discusses ways in which the MPI indicators could be modified to conform better to the Malaysian context, using recent household survey data to quantify the effects of those modifications. Section 5 presents several examples of the multidimensional poverty approaches in comparator countries, including regional and global peers. Section 6 illustrates some basic analytical approaches that could be used to extract more policy-relevant information from the MPI about the various dimensions of poverty. Concluding remarks are provided in section 7.

Living Standards --- Poverty Lines --- Poverty Monitoring and Analysis --- Poverty Reduction

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Afghanistan has been in protracted conflict for almost four decades, with direct implications on progress towards development objectives. This context of recurring episodes of violence and insecurity, economic and political instability, and the consequent displacement of populations within and outside the nation's borders has important implications on the landscape of data and evidence available for the design, implementation, monitoring and evaluation of interventions and programs, and their timeliness and relevance. Afghans represent the world's largest and most protracted refugee population, with an estimated 3.5 million people currently living abroad as refugees for more than four decades. Given the large disparities in poverty incidence and high levels of inequality within Afghanistan, the knowledge of living standards at more disaggregated geographical levels of districts and nahias could help inform policy design and improve decision making at a sub-province level. Therefore, poverty mapping, which aims at estimating poverty incidence at levels lower than the household survey, was applied in Kabul and Herat provinces. This technical report describes the methodology and data used to produce the Kabul and Herat poverty maps and presents the resulting collection of poverty maps, the first of its kind for Afghanistan. The structure of the report is as follows. Section 2 outlines the poverty mapping methodology, specifically the small area estimation approach, applied in Afghanistan. Section 3 discusses the data sources and the various technical challenges faced with the datasets. Section 4 discusses the modeling phase, including model selection, model parameters, and assumptions. Section 5 presents the poverty maps at a district and nahia level, and section 6 concludes. The Annexes contains supporting data and analysis.

Household Consumption --- Poverty Lines --- Poverty Monitoring and Analysis --- Poverty Reduction

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The March 2020 update to PovcalNet involves several changes to the data underlying the global poverty estimates. Some welfare aggregates have been changed for improved harmonization, and some of the CPI, national accounts, and population input data have been revised. This document explains these changes in detail and the reasoning behind them. In addition to the changes listed here, a large number of new country-years have been added, bringing the total number of surveys to more than 1,900.

Poverty Assessment --- Poverty Diagnostics --- Poverty Impact Evaluation --- Poverty Lines --- Poverty Monitoring and Analysis --- Poverty Reduction

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The March 2021 update to the Multidimensional Poverty Measure (MPM) involves changes to the data underlying the multidimensional poverty estimates based on the Global Monitoring Database (GMD). This update reports new estimates for circa 2017, revising the estimates that were first published in October 2020. Some changes reflect the availability of more recent survey data for the economies already part of the GMD. Other changes are due to the addition of 9 new economies to the dataset, the release of new population data and new monetary poverty estimates. Notably, this update accompanies the launch of an online dashboard containing the data and results presented in this document. This includes an online tool that allows users to modify the weights used when aggregating the different indicators in the MPM headcount ratio.

Inequality --- Poverty Assessment --- Poverty Lines --- Poverty Monitoring and Analysis --- Poverty Reduction

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This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth-a method that departs slightly from current World Bank practice-performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.

Inequality --- Machine Learning --- Nowcasting --- Poverty --- Poverty Lines --- Poverty Measurement --- Poverty Monitoring and Analysis --- Poverty Reduction