OPHI has recently published three new research papers. One Working Paper deals with how the Colombian Multidimensional Poverty Index has been used in the policymaking process in that country, another WP examines the relationship between income poverty and multidimensional poverty in China, and a Research in Progress paper looks at ways to improve the quality and quantity of poverty data.
Roberto Angulo’s Working Paper, ‘From Multidimensional Poverty Measurement to Multisector Public Policy for Poverty Reduction: Lessons from the Colombian Case,’ (pdf) focuses on the analysis of conceptual, normative and institutional issues of the Colombian Multidimensional Poverty Index. The principal questions are the following: What is the decision-making process that lies behind the Colombian experience? What are the main lessons from the Colombian case in terms of institutional arrangements for the implementation of its index? Although the medium and longer-term effects of the Colombian MPI on poverty reduction are still to be seen and thus to be evaluated, there are some important lessons. First, a multidimensional poverty index’s utility in terms of public policy depends not only on the mathematical and statistical robustness guaranteed by the Alkire Foster methodology but also on the ability of the policy maker to represent the public policy priorities through its normative choices. Second, acknowledging the inherent trade-offs involved in conceptual, statistical and public policy concerns is key to accurately defining the purpose of the measure. Finally, if the purpose of the MPI is to stimulate coordinated action to reduce poverty, an accurate design will not be enough; it is also necessary to provide a solid institutional architecture that supports the process from the design of the index to its application.
A Working Paper by Xiaolin Wang, Hexia Feng, Qingjie Xia and Sabina Alkire, entitled ‘On the Relationship between Income Poverty and Multidimensional Poverty in China,’ (pdf) attempts to examine the theoretical relationship between income poverty and multidimensional poverty. It also explores the empirical linkages and discrepancies between these two types of poverty using the Alkire-Foster multidimensional poverty measurement method with 2011 China Health and Nutrition Survey data. Regarding the relationship between income poverty and multidimensional poverty, poverty can be summarized as not the mere lack of income but the deprivation of human basic capability, covering both monetary and non-monetary poverty. The statistical analysis on income poverty and multidimensional poverty measurement shows that the coincidence of income poverty and multidimensional poverty is 31%. In other words, 69% of multidimensionally poor households are not considered poor in terms of income poverty. The econometric results indicate that an increase in income can significantly reduce the incidence of multidimensional poverty in each dimension, but the impact is limited.
Sabina Alkire’s Research in Progress paper, ‘Towards Frequent and Accurate Poverty Data,’ (pdf) is an update on previous work on this topic. It is increasingly acknowledged that data availability plays a crucial role in the fight against poverty. Poverty data has increased in both quantity and frequency over the past 30 years, but still lags behind the data available on most other economic phenomena. Yet there are vibrant experiences that are often overlooked:
- Data for monetary & multidimensional poverty dramatically increased since 1980.
- Sixty countries already produce annual updates to key statistics.
- Some have continuous household surveys with cost-cutting synergies.
- International agencies have probed short surveys for comparable data.
- Certain regions have agreed on harmonised variable definitions across countries.
- New technologies can drastically reduce lags between data collection and analysis.
The post-2015 agenda identified the need for regularly updated data to monitor the Sustainable Development Goals. This paper points out existing experiences that shed light on how to break the cycle of outdated poverty data and strengthen statistical systems. Such experiences show that it is possible to generate and analyse frequent and accurate poverty data that energizes and enables poverty eradication.