Multidimensional Poverty Measurement: The Choice of the Aggregation Function

Transcription

1 Multidimensional Poverty Measurement: The Choice of the Aggregation Function Nicole Rippin 11 December 2014

2 Outline II. The Multidimensional II. III. IV. The Multidimensional The Correlation Sensitive Policy Implications VI. Conclusions Deutsches Institut für Entwicklungspolitik (DIE) 2

3 Introduction II. The Multidimensional Hypothesis: the multiple dimensions of poverty are so closely correlated that income can serve as a proxy for all of them. This hypothesis, however, relies on critical assumptions: Ecomic Resources Choice Goods Conversion Utility IV. Policy Implications Assumption: perfect and complete markets Igring in particular: - The role of public goods - Limited access - Asymmetric information Assumption: equal individual conversion factors Igring in particular: - Personal heterogeneities - Variations in physical environment - Differences in social climate Over time, serious concerns have been raised regarding the appropriateness of these simplifying assumptions (e.g. Rawls, 1971; Sen 1985, 1992; Drèze and Sen, 1989; UNDP, 1997). Deutsches Institut für Entwicklungspolitik (DIE) 3

4 Introduction It was Amartya Sen, who developed a new approach to measure poverty and welfare: the capability approach (1979, 1985, 1992, 1999, 2009). Ecomic Resources Choice Goods Conversion Capability Set Choice Functioning Bundle Choice Utility Assumption: perfect and complete markets Igring in particular: - The role of public goods - Limited access - Asymmetric information Assumption: equal individual conversion factors Igring in particular: - Personal heterogeneities - Variations in physical environment - Differences in social climate The basic assumption is that the various dimensions of poverty, though of course correlated, are still so different from each other that income is t sufficient to capture all of them. Thus, the capability approach implies a multidimensional approach to poverty measurement. Deutsches Institut für Entwicklungspolitik (DIE) 4

5 Introduction A multidimensional approach to poverty measurement, however, requires a lot of choices: The choice of poverty dimensions, indicators, thresholds and weights in order to identify those who are deprived; the choice of an appropriate method to identify the poor among the deprived; and, finally, the choice of an appropriate method to aggregate the characteristics of the poor either in the form of a dashboard, or a composite index. Dashboard approaches such as the MDGs are unable to capture any correlations between the different poverty dimensions, which is why they are often criticized for their socalled silo-approach. Deutsches Institut für Entwicklungspolitik (DIE) 5

6 Introduction The first key message of UNDPs report What will it take to achieve the Millennium Development Goals? An international assessment highlights the relevance of the correlations or synergies between the different poverty dimensions: This Assessment tes that there are important synergies among the MDGs - acceleration in one goal often speeds up progress in others. In households where women are illiterate, child mortality is higher, implying the links between education, the empowerment of women and the health of children. UNDP (2010) Composite indices provide an opportunity to capture the correlations (or synergies) among poverty dimensions depending on the choice of the aggregation function. Deutsches Institut für Entwicklungspolitik (DIE) 6

7 Introduction It was the introduction of the first internationally comparable Multidimensional (Alkire and Santos, 2010) that made composite multidimensional poverty indices prominent. Notwithstanding the importance of the MPI for the advancement of multidimensional poverty measurement, it has some serious weaknesses. One of these is the form of the aggregation function: Whereas the income approach assumes that the multiple dimensions of poverty are so closely correlated that they can be simply replaced by income, the MPI assumes that they are completely uncorrelated thereby also denying the existence of synergies among poverty dimensions. Though people usually suffer from a different number of simultaneous deprivations, the MPI igres considerations of distributive justice. Deutsches Institut für Entwicklungspolitik (DIE) 7

8 The Structure of the MPI Dimension Main Capability Indicator Threshold (Household Level) Health Education Living Standards Bodily Health Senses, Imagination and Thought Bodily Health Control over Environment Nutrition Child Mortality Rate Schooling Enrolment Cooking Fuel Sanitation Water Electricity Floor Assets At least one of the following: 1. At least one woman age with BMI < At least one child with weight-for-age z-score < -2.0 At least one child under the age of 18 died No member with at least five years of schooling At least one child in school age t enrolled Harmful material is used for cooking (straw, dung, coal etc.) Toilet either unhygienic ( facility, open lid, etc.) or shared Water source is unprotected or more than 30 minutes away No access to electricity Floor material is earth, sand or dung Not more than one small asset and car/truck The MPI is based on the equally weighted dimensions health, education and living standards captured by 10 indicators. Poor is, who suffers from deprivations in at least 33% of the weighted indicators. The MPI is simply the sum of weighted deprivations of the poor. Deutsches Institut für Entwicklungspolitik (DIE) 8

9 The Dual Cut-off Method of the MPI Poverty severity 1 Not poor poor 0 1/3 1 Sum of weighted indicators Deutsches Institut für Entwicklungspolitik (DIE) 9

10 An Inverted Robin Hood Effect Three Dim mensions Health Education Living Standards Ten Indicators Nutrition Child Mortality Schooling Enrolment Cooking Fuel Sanitation Water Electricity Floor Assets 1/6 1/6 1/6 1/6 The MPI s dual cut-off method treats all poor households in the same way, whether they are deprived in 33% or 100% of weighted indicators. This creates a kind of inverted Robin Hood effect taking from the poorest and giving it to the less poor reduces poverty according to the MPI: Person 1 Person 2 MPI = Deutsches Institut für Entwicklungspolitik (DIE) 10

11 A New Identification Function II. The Multidimensional Consider the following intuitive identification function: Poverty severity 1 High initial poverty severity Low initial poverty severity Low substitutability High substitutability 0 1 Sum of weighted indicators If indicators are complements, the identification function is concave: a loss in one indicator can barely be compensated, a strong focus on inequality is t required. If indicators are substitutes, the identification function is convex: a loss in one indicator can easily be compensated, a strong focus on inequality is required. Deutsches Institut für Entwicklungspolitik (DIE) 11

12 The Correlation Sensitive Poverty Index Three Dime ensions Health Education Living Standards Ten Indicators Nutrition Child Mortality Schooling Enrolment Cooking Fuel Sanitation Water Electricity Floor Assets 1/6 1/6 1/6 1/6 With the new identification method, the CSPI is able to capture the correlation among dimensions and indicators as well as inequality. This creates a real Robin Hood effect : Person 1 Person 2 CSPI = Deutsches Institut für Entwicklungspolitik (DIE) 12

13 An Example from the Indian DHS 2005 A Comparison of Five Indian Households (DHS 2005) HH Education Health Living Standard MPI CSPI Years Attendance Mortality Nutrition Electricity Water Sanitation Flooring Cooking Assets The dual cut-off method increases the MPI s sensitivity to the choice of weights: household 2 and 3 are both deprived in 5 indicators, yet because nutrition has a higher weight than safe drinking water, household 2 receives a relatively high poverty value whereas household 3 is t considered poor. The influence of the choice of weights is much less severe in the case of the CSPI. A transfer from the poorer household 1 to the less poor household 2 does t change the value of the MPI (which is still 0.222); it does, however, increase poverty according to the CSPI (from to 0.143). Deutsches Institut für Entwicklungspolitik (DIE) 13

14 Policy Implications (1/4) Consider the following two poor villages: Village 1 suffers from a lack of income and medical care. Village 2 suffers in all areas except medical care: a publicly funded physician lives close to the village. Suppose, the physician asks the administrative authorities for the approval to move his medical office from village 2 to village 1.? Medical Office Village 1: School Safe drinking water Electricity Sufficient nutrition Lack of income No medical care Village 2: Medical care No school No safe drinking water No electricity Insufficient nutrition Lack of income Deutsches Institut für Entwicklungspolitik (DIE) 14

15 Policy Implications (2/4) The requested relocation would increase inequality between the villages. It would also be inefficient, as a village without sufficient nutrition and safe drinking water has everything else equal a greater need for medical care. How do the two poverty measures MPI and CSPI respond to the physician s request?? Medical Office Village 1: School Safe drinking water Electricity Sufficient nutrition Lack of income No medical care Village 2: Medical care No school No safe drinking water No electricity Insufficient nutrition Lack of income Deutsches Institut für Entwicklungspolitik (DIE) 15

16 Policy Implications (3/4) If the authorities rely on the MPI, they will approve the request: after the relocation, the situation of village 2 does t change in the e of the MPI whereas village 1 will longer be considered poor. The MPI suggests that the relocation decreases poverty.? Medical Office Village 1: School Safe drinking water Electricity Sufficient nutrition Lack of income No medical care Village 2: Medical care No school No safe drinking water No electricity Insufficient nutrition Lack of income Deutsches Institut für Entwicklungspolitik (DIE) 16

17 Policy Implications (4/4) II. The Multidimensional CSPI III. The GCSPI Aggregation Step IV. Poverty Empirical Comparisons Application If, on the other hand, the authorities rely on the CSPI, they will t approve the request: due to the inefficiency of the relocation as well as the increase in inequality, the CSPI suggests that the relocation increases poverty.? Medical Office Village 1: School Safe drinking water Electricity Sufficient nutrition Lack of income No medical care Village 2: Medical care No school No safe drinking water No electricity Insufficient nutrition Lack of income Deutsches Institut für Entwicklungspolitik (DIE) 17

18 Indian Poverty Maps according to the MPI Poverty according to MPI [0,.1] (.1,.15] (.15,.2] (.2,.25] (.25,.3] (.3,.35] (.35,.4] (.4,.45] (.45,.5] No data Censored Headcount (MPI) Censored Deprivation Count (MPI) [0,.1] (.1,.2] (.2,.3] (.3,.4] (.4,.5] (.5,.6] (.6,.7] (.7,.8] No data Kerala [0,.1] (.1,.2] (.2,.3] (.3,.4] (.4,.5] (.5,.6] (.6,.7] No data Deutsches Institut für Entwicklungspolitik (DIE) 18

19 Indian Poverty Maps according to the CSPI Poverty according to CSPI Headcount "Middle Poverty Severity" (CSPI) Headcount "High Poverty Severity" (CSPI) [0,.05] (.05,.1] (.1,.15] (.15,.2] (.2,.25] (.25,.3] (.3,.35] No data [0,.05] (.05,.1] (.1,.2] (.2,.3] (.3,.4] (.4,.5] (.5,.6] (.6,.7] (.7,.8] No data [0,.05] (.05,.1] (.1,.2] (.2,.3] (.3,.4] (.4,.5] (.5,.6] (.6,.7] (.7,.8] No data Deprivation Count (CSPI) Inequality (CSPI) Kerala [0,.1] (.1,.15] (.15,.2] (.2,.25] (.25,.3] (.3,.35] (.35,.4] (.4,.45] (.45,.55] No data Kerala [0,.025] (.025,.05] (.05,.075] (.075,.1] (.1,.125] (.125,.15] (.15,.175] (.175,.2] (.2,.25] No data Deutsches Institut für Entwicklungspolitik (DIE) 19

20 Conclusions (1/2) Multidimensional poverty measurement questions the assumption that income alone is a reliable proxy for poverty in all its various facets: the assumption that all poverty dimensions are almost perfectly correlated is considered to be too extreme. It is the merit of the Multidimensional that the advantages of multidimensional poverty measurement became well kwn. Without wishing to detract from this achievement, the aggregation function of the MPI netheless imposes assumptions which seem to be at least as extreme as those of the income approach: The assumption that poverty dimensions such as education, health, safe drinking and the like are completely uncorrelated; and the assumption that it makes difference in just how many dimensions households are deprived once they are identified as being poor. Deutsches Institut für Entwicklungspolitik (DIE) 20

21 Conclusions (2/2) As a result, the MPI: involuntarily creates inverted Robin Hood effects, where a redistribution from the poorest to the less poor reduces poverty; does t make full use of the advantages of a composite index by neglecting any kind of synergies between the different poverty dimensions; is oversensitive to the controversial choice of weights; and produces a distorted picture of the structure of multidimensional poverty in a country. The good news, however, is that by simply modifying the way in which the poor are identified, all desirable properties of the MPI (including its decomposability) can be maintained while at the same time overcoming all of the above mentioned serious weaknesses. Deutsches Institut für Entwicklungspolitik (DIE) 21

22 Thank you for your attention! German Development Institute/ Deutsches Institut für Entwicklungspolitik (DIE) Tulpenfeld 6 D Bonn Telefon: +49 (0) DIE@die-gdi.de Deutsches Institut für Entwicklungspolitik (DIE) 22