Applying expert knowledge to measure multidimensional rural

poverty in Chittagong (Bangladesh)

Melania Salazar- Ordóñez;Lorenzo Estepa- Mohedano;

Rosa Cordón-Pedregosa

14TH EADI General Conference 23-26 June 2014Responsible Development in a Polycentric World

Content

1. Introduction and objective2. Case study3. Methodology4. Results5. Conclusions

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

2. Case studyBangladesh 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 ThanaUnion 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 lineSouth Khosbash is a Union conforming by 14 villages in Comilla Districts

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

1st QuestionnaireTwo local Workshops

• To introduce / eliminate variables

• Understanding bias

2nd QuestionnaireReviewed 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

August2011

21 survey takers

4,999 face-to-face surveys

4,641 (7,2%)

2. Case study

Place: South Khosbash Union

Village Number of households %Sialdhair 165 3.6 Rajamara 197 4.2 Haripur 551 11.9 Sreerampur 153 3.3 Muguji 909 19.6 Khugua 32 0.7 Chalia 151 3.3 Joynagar 184 4.0 Bhadrarpar 202 4.4 Kalamuri 118 2,5 Jangalia 299 6.4 Hossainpur 387 8.3 Moheshpur 1154 24.9

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

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

3. MethodologyMultidimensional Poverty Index – MPI

Dimension Indicator Weight (%)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

Health At least one household member is malnourished 16.6667

At least one child has died 16.6667

Standard of Living Not having electricity 5.5556

Not having access to clean drinking water 5.5556

Not having access to adequate sanitation 5.5556

Using polluted cooking fuel (dung, wood or charcoal) 5.5556

Having a home with a dirt floor 5.5556

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

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

3. MethodologyAnalytic Hierarchy Process (AHP)

3. MethodologyAnalytic 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 (wgi)

• Pair wise comparisons between each indicator, inside the dimensions, produced the local weights (wlj)

• Pair wise comparisons were measured with Saaty’s increasing scale from 1 to 9

• Estimation of the weights: row geometric mean• Normalized weights (wnj)= wgi * wlj • Statistical significant differences between MPI with homoge-nous

weights and according to experts’ opinions weights: t Student´s test

4. ResultsDIMENSIONS Indicators

wgi wlj wnj

EDUCATION 0.263 At least one household member has less than five years of schooling

0.652 0.172

At least one school-age child (up to grade 8) is not attending to school

0.348 0.092

HEALTH 0.277 At least one household member is malnourished 0.609 0.169 At least one child has died 0.391 0.108BASIC SERVICES 0.245 Not having electricity 0.111 0.027 Not having access to clean drinking water 0.539 0.132 Not having access to adequate sanitation 0.350 0.086STANDARD OF LIVING 0.215 Having a home made with durable materials 0.519 0.111 Owning no car, truck or similar motorized vehicle, owning at most one: bicycle, motorcycle, radio, refrigerator, telephone or television

0.480 0.103

Table 2. Weight assigned by experts

Source: Authors’ elaboration

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 waterHowever: • 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

4. Results

Homogeneous weights

Bangladesh experts opinion

Difference

% poor household 47% 55,3% + 8,3%

H (head count ratio) 48% 50,6% + 2,6%

A (intensity of poverty) 48,7% 53,7% + 5%

MP index 0,233 0,271 + 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).

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

Applying expert knowledge to measure multidimensional rural

poverty in Chittagong (Bangladesh)

Melania Salazar- Ordóñez;Lorenzo Estepa- Mohedano;

Rosa Cordón-Pedregosa

14TH EADI General Conference 23-26 June 2014Responsible Development in a Polycentric World

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