National Institute of Statistics of Rwanda (NISR)

Slides:



Advertisements
Similar presentations
Approaches to using MICS for Equity/Poverty Analysis
Advertisements

11 The Multidimensional Poverty Index: Achievements, Conceptual, and Empirical Issues Caroline Dotter Stephan Klasen Universität Göttingen Milorad Kovacevic.
OPHI Oxford Poverty & Human Development Initiative Department of International Development Queen Elizabeth House, University of Oxford
2010 UNDP Report.  The Oxford Poverty and Human Development Initiative (OPHI) of Oxford University and the Human Development Report Office of the United.
ICES 3° International Conference on Educational Sciences 2014
5 th Meeting of the Poverty Alleviation Working Group February 26 th, 2015 Ankara, Turkey Making Cooperation Work For Building an Interdependent Islamic.
Analysis of Inequality across Multi- dimensionally Poor and Population Subgroups for Counting Approaches Suman Seth and Sabina Alkire Development Studies.
Multidimensional Poverty Index Human Development Report Office
HDI and its neglect in Pakistan
UNICEF Report Card 10: Measuring Child Poverty CANADIAN COMPANION (excerpts)
UNICEF Report Card 10: Measuring Child Poverty CANADIAN COMPANION (excerpts)
Monitoring Poverty in Armenia using Multidimensional Poverty Indicators Diana Martirosova National Statistical Service of the Republic of Armenia Moritz.
The Global Study on Child Poverty and Disparity Influencing Policy First National Symposium on Child Poverty in Yemen November 2008 Alberto Minujin.
Poverty Ms. C. Rughoobur Africa Statistics Day 18 November 2013.
MULTI-DIMENSIONAL POVERTY (MPI) METHODS APPLIED TO THE SAINT LUCIA LABOUR FORCE SURVEY SOME IDEAS FOR THE DEVELOPMENT OF AN OECS MULTI-DIMENSIONAL POVERTY.
Chris de Neubourg Professor of Public Policy Analysis and Management.
Summer School on Multidimensional Poverty 8–19 July 2013 Institute for International Economic Policy (IIEP) George Washington University Washington, DC.
Session 1: Child poverty outcomes and main factors behind International benchmarking and key challenges for Member States András Gábos TARKI Social Research.
Europe 2020 Joint Assessment Framework draft proposal.
Well-being and multidimensional deprivation: some results from the OECD Better Life Initiative Nicolas Ruiz.
1. 2 Introduction Purpose of the ICP UN System of National Accounts calls for comparisons of GDP across countries be using PPPs The Approach Collection.
February 07, 2012 National Institute of Statistics of Rwanda 1 NATIONAL INSTITUTE OF STATISTICS OF RWANDA EDPRS2, EICV3 & DHS4 Joint Launch Key Statistics.
OPHI Oxford Poverty & Human Development Initiative Department of International Development Queen Elizabeth House, University of Oxford
NIGERIA OPHI Summer School 2013 Group: Francesca, Geofrey, Gibson, Ismael and Maria.
NEW FRONTIERS IN POVERTY MEASUREMENT James E. Foster George Washington University and OPHI, Oxford.
RESULT-BASED M&E FRAMEWORK FOR THE VIETNAM SEDP rd Round Table – Management for Development Results Hanoi 7 February 2007 Department of National.
What is Quality of Life. How can we measure it
Advances in Mixed Method Poverty Research: Lessons Learned in a Colombian Case Study EDNA BAUTISTA HERNÁNDEZ MARÍA FERNANDA TORRES 1st of July, 2013.
Understanding child deprivation in the European Union: the multiple overlapping deprivation analysis (EU-MODA) approach SPA Conference 2014 Yekaterina.
MDGs in the OECS and the Caribbean Region OECS Secretariat Regional Meeting Grenada, November 2013 Frederic UNTERREINER Monitoring and Evaluation.
Summer School on Multidimensional Poverty Analysis 3–15 August 2015 Georgetown University, Washington, DC, USA.
Disability Inequality Index
ECOSOC Thematic Discussion on Multidimensional Poverty
Sustainable Development Goals and what Youth Can Do
Mapping MPI and Monetary Poverty: The Case of Uganda
Interstate statistical committee
Gender and Social Inequality Challenges/Evidence
Rapid assessment Toolkit
Multidimensional Poverty Index (MPI) for the Northeastern Afghanistan
The Context of Child Poverty Policies in Indonesia
Harmonization of national statistics for SDGs: methodological problems
INTEGRATING DATA FROM OTHER SOURCES
Emerging and developing economies: measures of development
Multidimensional Child Poverty: from Measurement to Policy Action
GTT Social Protection March 2017
RWANDA CHILD LABOUR SURVEY -2008
OPHI Oxford Poverty & Human Development Initiative Department of International Development Queen Elizabeth House, University of Oxford.
CAPACITY DEVELOPMENT THROUGH SYSTEMS USE, RESULTS AND sustainable development goals Workshop on New Approaches to Statistical Capacity Development,
Talking points / Roberto Bissio – Social Watch
National Transfer Accounts: Singapore 2013
AFRICA SDG index and dashboards 2018
Household Budget Survey
REGIONAL POVERTY ANALYSIS TECHNICAL WORKSHOP
Presentation on issues and data requirements
Recent activities in the measurement of multidimensional poverty
National MPI and Child MPI in Panama
Multidimensional Poverty in Arab Countries
Expert Group Meeting on SDG Economic Indicators in Africa November 2017, Addis Ababa, Ethiopia Analysis of the deprivation level of the Mozambican.
APPLICATION OF MULTIDIMENSIONAL POVERTY APPROACH IN VIET NAM
Europe 2020 Joint Assessment Framework
Expert Group on Quality of Life Indicators
Outline Millions of people live in extreme poverty: Who are they?
MPI PROGRESS UPDATE SEYCHELLES
Main recommendations and Impact on Social Statistics
National Multidimensional Poverty Index (NMPI)
Bangladesh Child-Focused
STEPS Site Report.
Poverty measurement in Mauritius
POVERTY MESUREMENT IN UGANDA
Marco Espinal Martinez
Presentation transcript:

National Institute of Statistics of Rwanda (NISR) Multidimensional Poverty Index (MPI) and Multiple Overlapping Deprivation Analysis (MODA)Reports National Institute of Statistics of Rwanda (NISR)

Outline Why measure multidimensional poverty MPI and MODA: What do they measure? How are they constructed? Key findings MPI Overall By region Robustness MODA Overlaps Conclusions and Policy Issues

Why Multidimensional Poverty Index? Monetary poverty measures just one dimension of being poor. In Rwanda, based on whether expenditure per adult equivalent is below a poverty line. Other dimensions of poverty – e.g. education, health, housing – should be taken into account for a full understanding of the phenomenon. Can be quantified with a Multidimensional Poverty Index (MPI) United Nations Development Program Human Development Report Office (UNDP HDRO); Oxford Poverty and Human Development Initiative (OPHI).

What is MODA? Multiple Overlapping Deprivation Analysis (MODA) A measure of multidimensional child poverty: Tool to evaluate impact of poverty agenda; Analyzes dimensions of poverty across “lifecycle” and acknowledges different unique needs of children in their development; Developed by UNICEF; MODA differentiates 4 age-groups for children 0-17 years. For the EICV5 MODA report, 2 age-groups were analyzed: children ages 5-14 and youth ages 15 to 17 years.

Goals of MPI/MODA measures Monitor progress in social and standard of living indicators Help design programs that accurately target the poor Help evaluate projects designed to reduce poverty Compare areas in terms of poverty and progress.

See www.statistics.gov.rw for more information History of MPI and MODA 2010: First global MPI published 2012: Rwanda’s first MPI using Population Census data. 2018: Rwanda’s first MODA released using DHS -IV 2010 and DHS -V 2014/15 data. 2019: MPI/MODA now use EICV3,4,5 data. See www.statistics.gov.rw for more information

How to construct the MPI Pick dimensions of deprivation: 4 Education, housing, public services, social services, economic activities Identify indicators for each dimension: 14 in total E.g. Number of children aged 7-15 at school Set cutoffs (“poverty lines”) to measure deprivation E.g. At least once child aged 7-15 not at school Select weights for each dimension (and indicator) Count (weighted) deprivations per person Define the poor E.g. Deprived on at least 40% of the dimensions Measure poverty: MPI = H × A H is % who are poor (“headcount”); A is average proportion of deprivations per poor person.

Note on methodology MPI uses Alkire & Foster method Unit of identification is the household; all members get same score 4 dimensions, 14 indicators MODA tailors indicators to age Unit of identification is the child 5 dimensions, 11 indicators

Rwanda’s National MPI results 1/2

Rwanda’s National MPI results 2/2

MODA dimensions and indicators

Uncensored headcount ratio Proportion of people deprived in each indicator between 2010/11 and 2016/17 Source: EICV3, EICV4 and EICV5

MPI: Key Findings Multidimensionally poor if poor on at least 40% of dimensions. Poverty intensity measures average proportion of deprivations of the poor.

Incidence of multidimensional poverty by province

Intensity (A) of multidimensional poverty by province

Multidimensional Poverty Index: national, urban/rural and provincial

Percentage contribution of each indicator to the MPI by province

Headcount poverty rate for multidimensionally poor (%) with different “k-values” (i.e. proportions of deprivations needed to qualify as “poor”)

Intensity of multidimensional poverty (A) using different “k-values”

Multidimensional poverty rate (MPI = H × A) with different “k-values”

Overlaps of Multidimensional Poverty with Monetary Poverty in Rwanda

MODA: Key Findings

Overlaps for Children 5-14 years between monetary poverty, and the incidence of multidimensional poverty (EICV5: 2016/17) Children facing at least 3 deprivations are considered multi-dimensionally poor

Overlaps for Children 5-14 years using 3 of the 5 dimensions (EICV5: 2016/17) Data further shows that 5.0% of children 5-14 are deprived in at least 4 dimensions There are no significant differences by sex of the child, but level of education of head of household has significant influence on deprivation levels of the child NB: Missing education and sanitation dimensions, but similar diagrams could be constructed.

Trends in poverty indices for children Children 5 to 14 years Children 15 to 17 years Period Multi-dimensional deprivation Headcount (H), % Average intensity among deprived (A), % Adjusted multi-dimensional deprivation headcount (MO) EICV3 (2010/11) 39.3 66.0 0.26 EICV4 (2013/14) 29.2 64.8 0.19 EICV5 (2016/17) 25.3 64.3 0.16 Period Multi-dimensional deprivation Headcount (H), % Average intensity among deprived (A), % Adjusted multi-dimensional deprivation headcount (MO) EICV3 (2010/11) 60.2 71.5 0.431 EICV4 (2013/14) 44.2 69.2 0.306 EICV5 (2016/17) 40.1 68.2 0.274 Data further shows there are no significant sex differences in ages 4-15, but level of education of head of household has significant influence on deprivation level

Conclusions MPI complements the classic monetary poverty rate Similar trends Helps identify areas needing special attention E.g. Overcrowding, health insurance, in Kigali Regionally, between 2013/4 and 2016/17: Kigali: Low poverty; but monetary policy fell, MPI did not Eastern and Southern provinces: High MPI, not falling quickly EICV and other surveys provide viable data for measuring poverty, MPI, and MODA. Use these as benchmarks for monitoring the Sustainable Development Goals (SDG) through 2030.

Policy Issues Rwanda has: Young population Rapid GDP growth Opens possibility of prioritizing children: basic needs, protected rights Goal: break cycle of intergenerational poverty

Thank you