Multidimensional poverty measurementwith individual preferences

Koen Decancq – Marc Fleurbaey – François Maniquet

UNDP – March 2014

1. Motivation

• Poverty is multidimensional• Who is poor?

1. Motivation

• Poverty is multidimensional• Who is poor?

health

income

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line Income

Poverty Line

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line Income

Poverty Line

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line Income

Poverty Line

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line Income

Poverty Line

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line Income

Poverty Line

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line Income

Poverty Line

1. Motivation

• Poverty is multidimensional• Who is poor?

Health

Income

Poverty Line Income

Poverty Line

1. Motivation

• Multidimensional poverty measurement without paternalism?

• Let agents aggregate the dimensions themselves

“... those with a stake in the outcomes will almost certainly be in a better position to determine what weights to apply than the analyst calibrating a measure of poverty.” (Ravallion, 2011)

•Acknowledge the heterogeneity in the “opinions on the good life”

2. Multidimensional poverty measure

We axiomatically derive the following procedure

Health

Income

Poverty Line Income

Poverty Line

2. Multidimensional poverty measure

We axiomatically derive the following procedure

Health

Income

Poverty Line Income

Poverty Line

2. Multidimensional poverty measure

We axiomatically derive the following procedure

… and apply it to real-world data (from Russia)

Health

Income

Poverty Line Income

Poverty Line λ

3. Estimating preferences

• We use RLMS-HSE (1995-2005)• We consider four dimensions of life

– Equivalized expenditures – Objective (constructed) health index– Constructed house quality index– Unemploment (binary)

• Deprivation thresholds: 60% of median value in each continuous dimension

3. Estimating preferences

• Problem: we don’t observe “opinions on the good life”

• We estimate them based on life satisfaction data• We run a simple life satisfaction regression,

• with some econometric sophistications,– Heterogeneity in β coefficients– Decreasing marginal returns– Control for personality traits (in α)

• And then plot indifference maps based on β’s

3. Estimating preferences

4. Results: headcounts

4. Results: overlap of bottom 16,1 %

2,9% 3,5%

1,6% 3,6%4,1%

2,4%

5. Conclusion

• Multidimensional poverty analysis with respect for preferences …

• … is ethically attractive

• … is theoretically possible

• … is empirically implementable

Life satisfaction regression