Monday, March 7th, 2022
11:00 a.m. – 12:15 p.m. ET
via Zoom
We were pleased to invite you to a joint virtual event with the Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development Program (UNDP) Human Development Report Office (HDRO) on March 7, 2022 with panelist remarks from Mauricio Gallardo, Associate Professor of Econometrics and Statistical Methods at Universidad Católica del Norte in Chile.
Bayesian network methods have recently gained great popularity in machine learning literature and applications to model uncertainty in complex phenomena that include relationships between multiple random variables. However, these models are not commonly applied in economics and development studies. Here, we introduce the Bayesian network classifier models to estimate the probability of a person to be welfare deprived in one and multiple dimensions. These probabilities are then used for measuring vulnerability to multidimensional poverty (VMP) in four alternative measurement frameworks. Currently, two of them can be found in the literature, but have been estimated with Probit and Logit models, which are unidimensional strategies. Instead of that, in this study, we follow a multidimensional strategy to solve an estimation problem that is multidimensional in nature. Two new VMP measurement procedures based on Bayesian network classifiers estimates are also introduced in this article. We illustrate the four estimation procedures using the household survey and the census data from Chile 2017. A 5-fold cross-validation exercise verifies a high predictive performance of these Bayesian network classifier models, with the highest accuracy being that of one of the new measurements that we put forward. Our findings reveal that the Bayesian network classifier models offer an adequate alternative to face the policy challenge of measuring vulnerability to multidimensional poverty.
About the Speaker
Mauricio Gallardo is Associate Professor at Universidad Católica del Norte in Chile where he teaches Econometrics and Statistical Methods. He holds a master’s degree in Philosophy from Saint Petersburg State University in Russia, a master’s degree in Economics from Pontificia Universidad Católica in Chile, and a Ph.D. in Economics from Universidad Nacional de La Plata in Argentina. Before entering the academy, he worked for the Statistical Division at the Central Bank of Chile. He has also worked providing technical assistance to international organizations on statistical issues. His research interests are related to poverty, vulnerability, and inequality of opportunities.
About the Discussant
Stefan Sperlich made his diploma in mathematics at the University of Göttingen and holds a PhD in economics from the Humboldt University of Berlin. From 1998 to 2006 he was Professor for statistics at the University Carlos III de Madrid, from 2006 to 2010 chair of econometrics at the University of Göttingen, and is since 2010 professor for statistics and econometrics at the University of Geneva. His research interests are ranging from nonparametric statistics over small area statistics to empirical economics, in particular impact evaluation methods. He has been working since about 15 years as consultant for regional, national and international institutions, participated in development programs like EUROSOCIAL, is cofounder of the research center ‘Poverty, Equity and Growth in Developing Countries’ at the University of Göttingen, and is research fellow at the Center for Evaluation and Development (Mannheim, Germany). He published in various top ranked scientific journals of different fields and was awarded with the Koopmans econometric theory prize (among others).
About the Series
The Institute for International Economic Policy (IIEP) at George Washington University and the Oxford Poverty and Human Development Initiative (OPHI), with the support of the United Nations Development Programme’s Human Development Report office (UNDP HDRO), are pleased to host a special seminar series on the global Multidimensional Poverty Index (global MPI). Goal 1 of the Sustainable Development Goals (SDGs) is to end poverty in all its forms and dimensions. The global MPI offers a tool to make progress towards this goal.
Bringing together the academic and policy spheres, this series of seminars will highlight topics such as race, ethnicity, gender, and caste, the statistical capacity of nations, social protection, the use of geospatial mapping in tracking poverty, poverty and refugees, and evaluating whether we’re on track to meet UN SDG Goal #1. The sessions will also include work that applies the global MPI methodology, the Alkire Foster method, to innovative measures.
The seminars are taking place online on Mondays at 11 a.m. EST. They will be hosted by IIEP Professor James Foster and are open to everyone focused on improving the lived experience of those who are deprived.
