1. Applying expert knowledge to measure multidimensional rural poverty in Chittagong (Bangladesh) Melania Salazar- Ordóñez; Lorenzo Estepa- Mohedano; Rosa Cordón-Pedregosa 14 TH EADI General Conference 23-26 June 2014 Responsible Development in a Polycentric World

2. Content 1.Introduction and objective 2.Case study 3. Methodology 4.Results 5.Conclusions

3. 1. Introduction and objective Growing debate about measuring poverty Traditional measures vs. Multidimensional measures Technical & methodological problems: data, theoretical support, accurate indicators, critical thresholds, weight of indicators, etc. Given homogeneous weights vs. Local expert opinion – Specific country and policy context Objective: Contribute to the debate on how the indicators should be weighted to form a composite index Estimating MPI – data of households from South Kosbash (Bangladesh) – using both homogeneous weights for dimensions and indicators, and weights according to experts’ opinion –data of 29 experts in rural development in Bangladesh. Estimating statistical differences between MPI with homoge- neous weights and with weights according to experts’ opinion

4. 2. Case study Bangladesh is placed in the Gulf of Bengal, border with India and Myanmar -147,570 sq Km and 167 million people - 7 Divisions / 64 Districts / 490 Thanas / 15-20 Villages by Thana Union Parisads is the local government in rural areas Agriculture: 1/3 of the GDP, over 60% of employment, Chittagong is one of the 7 Divisions in Bangladesh (28 million of people) The 3rd contributor to national GDP 31% of rural population is living below poverty line South Khosbash is a Union conforming by 14 villages in Comilla Districts

5. Data of households from South Khosbash (Bangladesh) From Participatory Rural Appraisal process (mixing participatory appraisal methods and focus group discussions) Questionnaire to get data for estimation MPI 1 st Questionnaire Two local Workshops To introduce / eliminate variables Understanding bias 2 nd Questionnaire Reviewed by BARD members 26 questions: 1) Household member’s data; 2) Household member’s health; 3) House conditions; 4) Agriculture and livestock; 5) Household support (economic and services) and crisis; 6) Household incomes and expenses; and 7) Household member’s networking August 2011 21 survey takers 4,999 face- to-face surveys 4,641 (7,2%) 2. Case study

6. Place: South Khosbash Union VillageNumber of households% Sialdhair1653.6 Rajamara1974.2 Haripur55111.9 Sreerampur1533.3 Muguji90919.6 Khugua320.7 Chalia1513.3 Joynagar1844.0 Bhadrarpar2024.4 Kalamuri1182,5 Jangalia2996.4 Hossainpur3878.3 Moheshpur115424.9 Respondent’s profile: 99.9% household head / 92.2% men / 92.3% married / 31.3% working in agriculture, 13.3% remittances receiver / 35.1% illiterate and 13.7% primary school Household’s profile on average : 5.16 members / 2.72 men 2. Case study

7. 3. Methodology Deprivation scores are calculated by adding weights of failed indicators; if result > 33%, then household is classified as poor. Head count ratio (H): number of multidimensio- nally poor people divided by the total population Intensity of poverty (A): average indicators in which the multidimensionally poor people are deprived. MPI = H * A Multidimensional Poverty Index – MPI

8. 3. Methodology Multidimensional Poverty Index – MPI DimensionIndicatorWeight (%) Education At least one household member has less than five years of schooling 16.6667 At least one school-age child (up to grade 8) is not attending to school 16.6667 HealthAt least one household member is malnourished16.6667 At least one child has died16.6667 Standard of LivingNot having electricity5.5556 Not having access to clean drinking water5.5556 Not having access to adequate sanitation5.5556 Using polluted cooking fuel (dung, wood or charcoal)5.5556 Having a home with a dirt floor5.5556 Owning no car, truck or similar motorized vehicle while owning at most one of these assets: bicycle, motorcycle, radio, refrigerator, telephone or television 5.5556 Source: Alkire et al. (2013) Estimating MPI using homogeneous weights

9. 3. Methodology Different methods to assign weights – Statistical models – e. g. Factor analysis – Participatory methods – e.g. Analytic Hierarchy Process (AHP) Second ones incorporate Stakeholders´ opinions and concretely AHP allows judging the importance of concrete elements giving them priorities (AHP was chosen for this research) AHP is a technique that approaches complex decision problems by means of hierarchical structures and ratio-scale measures In this study, AHP is used as a weighting method to determine the weights of each MPI indicators, through expert knowledge Estimating MPI using weights for dimensions and indicators according to experts’ opinion

10. 3. Methodology Analytic Hierarchy Process (AHP)

11. 3. Methodology Analytic Hierarchy Process (AHP) Data of 29 experts in rural development in Bangladesh: four Directors, five Joint Directors and twenty Senior Scientific Officers of Development Research Centres in Bangladesh Pair wise comparisons between each dimension yielded the global weights (w gi ) Pair wise comparisons between each indicator, inside the dimensions, produced the local weights (w lj ) Pair wise comparisons were measured with Saaty’s increasing scale from 1 to 9 Estimation of the weights: row geometric mean Normalized weights (w nj )= w gi * w lj Statistical significant differences between MPI with homoge- nous weights and according to experts’ opinions weights: t Student´s test

12. 4. Results DIMENSIONS Indicators w gi w lj w nj EDUCATION0.263 At least one household member has less than five years of schooling 0.6520.172 At least one school-age child (up to grade 8) is not attending to school 0.3480.092 HEALTH0.277 At least one household member is malnourished 0.6090.169 At least one child has died 0.3910.108 BASIC SERVICES0.245 Not having electricity 0.1110.027 Not having access to clean drinking water 0.5390.132 Not having access to adequate sanitation 0.3500.086 STANDARD OF LIVING0.215 Having a home made with durable materials 0.5190.111 Owning no car, truck or similar motorized vehicle, owning at most one: bicycle, motorcycle, radio, refrigerator, telephone or television 0.4800.103 Table 2. Weight assigned by experts Source: Authors’ elaboration

13. Coherence of results with actual situation of the households at local level: 88% of HH have at least one member with less of 5 years of schooling 30% households in food deficit Only 24% have access to clean water However: 59% of HH can access electricity 62% of HH have access to adequate sanitation Only 12,7% of HH have some children not attending school 4. Results

14. Homogeneous weights Bangladesh experts opinion Difference % poor household47%55,3%+ 8,3% H (head count ratio)48%50,6%+ 2,6% A (intensity of poverty)48,7%53,7%+ 5% MP index0,2330,271+ 16,3% The deprivation scores for each household estimating with the weights given in a homogeneous way or by the experts showed statistically significant differences (t-Student = 97.56, p= 0.000).

15. 5. Conclusions Well informed local experts paint a picture of multidimensional poverty in South Khosbash substantially different from that stated by the MPI methodology Local experts take into account the critical issues not yet solved and give them a higher weight Emphasis is focussed on those critical elements that need special attention at local level An assignment of weights to the dimensions and indicators of poverty in specific contexts can put a special emphasis on specific policies to fight poverty locally. The better use of resources will improve efficiency of these policies The experts weights allow to account for context specificity, but it does not allow comparison with other countries

16. Applying expert knowledge to measure multidimensional rural poverty in Chittagong (Bangladesh) Melania Salazar- Ordóñez; Lorenzo Estepa- Mohedano; Rosa Cordón-Pedregosa 14 TH EADI General Conference 23-26 June 2014 Responsible Development in a Polycentric World