PHDR: Geographic Diversity of Poverty Professor Amani, ESRF Poverty and Human Development Report Geographic Diversity of Poverty

This presentation Introduction Methodologies Single indicator approach Human Development Index (HDI) Human Poverty Index (HPI) Concluding remarks General Findings

Introduction Why analysis of poverty status at sub-national level? Increased awareness among stakeholders on sub-national differences Contribution to better focused more effective policies and strategies Guidance to resource allocation of resources to local authorities, contributing to improved planning at that level

Methodology Choice of methodology to assess regional differences in status of poverty depends on purpose of the assessment To inform planning, policy or strategy development within a sector To raise awareness and advocate on the overall regional status of human development in a country Single Indicator Approach Composite Index Approach

Methodology Single Indicator Approach Based on PRSP indicators Total of 28 indicators from 4 clusters: Performance by region and ranking included Income poverty Human capabilities Survival Nutrition

Methodology Human Development Index (HDI) Summary measure of human development It measures average (regional) achievements in three basic dimensions of human development A long and healthy life (life expectancy at birth) Knowledge (adult literacy rate, gross enrolment rate) A decent standard of living (GDP per capita PPP)

Methodology Human Development Index (HDI) PHDR: consumption expenditure (CE)per capita used in stead of GDP per capita PPP. Data more reliable and more recent CE direct measure of standard of living and reflects the situation at household level better than GDP

Methodology Human Poverty Index (HPI) Summary measure of deprivation in three basic dimensions of human development Lack of a long and healthy life. Vulnerability to death at early age (probability of not surviving beyond 40 yrs) Lack of knowledge. Exclusion from learning(adult illiteracy ) Lack of a decent standard of living (population not using safe water, percentage of children <5 who are underweight)

General Findings Single Indicator Approach Analysis Interregional disparities Performance of a region on a range of indicators Identification of trends and patterns Arusha DSM Dodoma Iringa Kagera Kigoma Kilimanjaro Lindi Mara Mbeya Morogoro Mtwara Mwanza Pwani Rukwa Basic needs poverty headcount ratio (%) Rural Basic needs poverty headcount ratio (%) 43 n.a n.a Food poverty headcount (%) Rural food poverty headcount (%) 28n.a n.a TABLE 2: Income poverty indicators

General Findings Analysis Interregio nal disparities Performan ce of a region on a range of indicators Identificati on of trends and patterns Single Indicator Approach PNER Tanzania 57% Kilimanjaro 80.5% Lindi 43% Iringa Among best 5 on 12 indicators Among worst 5 on 9 indicators Dar es Salaam and Kilimanjaro region consistently among best 5 for PRSP indicators Pwani, Lindi, Rukwa consistently among worst 5 for PRSP indicators

General Findings Single Indicator Approach

General Findings Marked gap bet wee n 1-2, 2- rest

General Findings Human Development Index

General Findings Human Poverty Index Marked gap between Kilimanjaro and Mbeya Regardless of Methodology Dar es Salaam, Kilimanjaro, Mbeya and Ruvuma consistently at top end of the ranking Lindi and Shinyanga consistently at bottom end of ranking

General Findings Human Poverty Index

General Findings Inconsistencies when comparing HDI and HPI HDI rank HPI rank Pwani (11) Rukwa (20) Caused by different indicators used in HDI and HPI Absence of expenditure component in HPI improves Rukwa’s ranking, but has a negative effect on Pwani’s Ranking Introducing access to safe water in the equasion for HPI has a negative effect on the ranking of Pwani.

Concluding remarks This analysis provides further evidence on diversity of poverty in Tanzania A national perspective alone obscures details important for informed decision making on poverty reduction The methodologies used reveal both similarities in regional performance as well as differences No single methodology will provide all answers More in depth analysis required focusing on WHY some regions perform better than others Future work may also include sub-regional analysis, using census data and poverty mapping