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Poverty in Malawi: Contextual E/ects, Distribution, and Policy Simulations Richard Mussa Second Annual ECAMA Research Symposium, MIM 4 June 2015 Richard Mussa (University of Malawi) Poverty in Malawi 4 June 2015 1 / 26

Poverty in Malawi: Contextual Effects, Distribution and Policy SimulationsContextual and distributional effects

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Page 1: Poverty in Malawi: Contextual Effects, Distribution and Policy SimulationsContextual and distributional effects

Poverty in Malawi: Contextual E¤ects, Distribution, andPolicy Simulations

Richard MussaSecond Annual ECAMA Research Symposium, MIM

4 June 2015

Richard Mussa (University of Malawi) Poverty in Malawi 4 June 2015 1 / 26

Page 2: Poverty in Malawi: Contextual Effects, Distribution and Policy SimulationsContextual and distributional effects

Outline

Motivation

Malawian Context

Accounting for contextual and distributional e¤ects

Empirical Analysis

Results

Conclusion

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Motivation 1

This paper addresses two issues which have hitherto been ignored inthe existing studies on poverty and its correlates.ISSUE 1

The poverty literature does not take into the fact that groups ofhouseholds become di¤erentiated, and that the group and itsmembership both in�uence and are in�uenced by the groupmembership.

These contextual e¤ects re�ect the presence of externalities.

Fact�the propensity of an individual to behave in some way varies with theexogenous characteristics of the group.�(Manski 1993: 532)

Fact�individuals in the same group tend to behave similarly because they havesimilar individual characteristics or face similar institutional environments.�(Manski 1993: 533)Richard Mussa (University of Malawi) Poverty in Malawi 4 June 2015 3 / 26

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Motivation 2

ExampleThe extent of schooling at the community level can have a positiveexternality e¤ect

Such educational externalities might arise for instance as uneducatedfarmers learn from the superior production choices of other educatedfarmers in the community (Weir and Knight, 2007; Asadullah andRahman, 2009)

The education externality could also arise when educated farmers areearly innovators and are copied by those with less schooling (Knightet al., 2003).

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Motivation 3

ISSUE 2

Poverty studies ignore the fact that changes in the correlates ofpoverty may not only a¤ect the average level of consumption, butmay also a¤ect the distribution of consumption.

Ignoring these contextual and distribution e¤ects leads tomismeasurement of policy interventions on poverty.

The paper develops methods for addressing these two problems.

I re-examine the determinants of poverty in Malawi

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Malawian Context

The economy grew at an average annual rate of 6.2% between 2004and 2007, and surged further to an average growth of 7.5% between2008 and 2011

However, poverty reduction in Malawi has been marginal 7! was52.4% in 2004, and marginally declined to 50.7% in 2011.

Increasing poverty in rural areas: questions about e¤ectiveness ofFISP

Recent panel evidence also shows marginal declines inpoverty 7!40.2% in 2010, slightly dropped to 38.7%.

Inequality has also increased over the same period: Gini coe¢ cientwas 0.390 in 2004, and rose to 0.452 in 2011

A recent re-examination by Pauw, Beck, and Mussa (2014) showsthat the decrease in poverty was much larger than o¢ cially estimated:8.2 percentage points

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Accounting for Contextual and Distributional E¤ects 1

I present the contextual and distributional e¤ects in three steps:

1 STEP 1: Speci�cation of multilevel/hierchical linear regression akalinear random e¤ects model

2 STEP 2: Augmenting the multilevel/hierchical linear regression withcontextual e¤ects

3 STEP 3: Adjusting poverty headcounts for distributional e¤ects

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Accounting for Contextual and Distributional E¤ects 2STEP 1: Linear Random E¤ects Model 1

Household data is hierarchical/multilevel in the sense that householdsare nested in communities.

Households in the same cluster/community are likely to be dependent.

This dependency ) downward biased standard errors) manyspurious signi�cant results (Rabe-Hesketh and Skrondal, 2008);McCulloch et al., 2008; Hox, 2010; Cameron and Miller, 2015).

Suppose that the i th household (i = 1....Mj ) resides in the j th

(j = 1....Jl ) community, then the determinants of consumptionexpenditure allowing for spatial community random e¤ects can bemodeled using the following two level linear regression

ln yij = β0xij + δ0zj + uj + εij (1)

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Accounting for Contextual and Distributional E¤ects 3STEP 1: Linear Random E¤ects Model 2

ln yij is the log of per capita annualized household consumptionexpenditure,

β and δ are coe¢ cients, xij and zj are observed household level andcommunity level characteristics respectively

uj � N�0, σ2u

�are community-level spatial random e¤ects (random

intercepts),

εij � N�0, σ2ε

�is a household-speci�c idiosycratic error term

The assumptions about uj , and εij imply that ζ ij � N�0, σ2ζ

�, where

σ2ζ = σ2u + σ2ε .

Most importantly, ln yij � N�

β0xij , σ2ζ�

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Accounting for Contextual and Distributional E¤ects 3STEP 1: Linear Random E¤ects Model 2

ln yij is the log of per capita annualized household consumptionexpenditure,

β and δ are coe¢ cients, xij and zj are observed household level andcommunity level characteristics respectively

uj � N�0, σ2u

�are community-level spatial random e¤ects (random

intercepts),

εij � N�0, σ2ε

�is a household-speci�c idiosycratic error term

The assumptions about uj , and εij imply that ζ ij � N�0, σ2ζ

�, where

σ2ζ = σ2u + σ2ε .

Most importantly, ln yij � N�

β0xij , σ2ζ�

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Accounting for Contextual and Distributional E¤ects 4STEP 2: Contextual e¤ects 1

Micro (household) vs macro (community) level e¤ects of a variablecan be di¤erentIgnoring these di¤erences can give misleading results (Neuhaus andKalb�eisch,1998; Arpino and Varriale, 2012)Decompose the household level covariate xij :

Between-community component, xj =1Mj

∑ xijWithin-community component, xij � xj

Modify equation (1) to allow for the two separate covariate e¤ects toget

ln yij = β0w (xij � xj ) + β0b xj + δ0zj + uj + εij (2)

= β0w xij + θ0xj + δ0zj + uj + εij

where βw represents the within-community e¤ect, and βb representsthe between-community e¤ect.

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Accounting for Contextual and Distributional E¤ects 5STEP 2: Contextual e¤ects 2

The di¤erence, θ = βb � βw , represents the contextual e¤ect

When there is no contextual e¤ect, βb = βw , and equation (2)reduces to equation (1).

The existing literature on poverty has assumed no contextual e¤ect

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Accounting for Contextual and Distributional E¤ects 6STEP 3: Distributional e¤ects 1

Using equation (2), and noting that ζ ij � N�0, σ2ζ

�, the probability

that a household is poor can be written as

P0ij = Prob(ζ ij < ln z ��

β0w xij + θ0xj + δ0zj�) (3)

= Φ

ln z �

�β0w xij + θ0xj + δ0zj

�σζ

!

where Φ (�) is a distribution function of the standard normaldistribution.

This is used to simulate changes in the aggregate levels of poverty

A simulation model which is based on the standard linear model hasbeen used before by Datt and Jollife (2004) in Egypt and Mukherjeeand Benson (2003) in Malawi

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Accounting for Contextual and Distributional E¤ects 6STEP 3: Distributional e¤ects 1

Using equation (2), and noting that ζ ij � N�0, σ2ζ

�, the probability

that a household is poor can be written as

P0ij = Prob(ζ ij < ln z ��

β0w xij + θ0xj + δ0zj�) (3)

= Φ

ln z �

�β0w xij + θ0xj + δ0zj

�σζ

!

where Φ (�) is a distribution function of the standard normaldistribution.

This is used to simulate changes in the aggregate levels of poverty

A simulation model which is based on the standard linear model hasbeen used before by Datt and Jollife (2004) in Egypt and Mukherjeeand Benson (2003) in Malawi

Richard Mussa (University of Malawi) Poverty in Malawi 4 June 2015 12 / 26

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Accounting for Contextual and Distributional E¤ects 7STEP 3: Distributional e¤ects 1

To accomodate consumption inequality as measured by a Ginicoe¢ cient, equation (3) can be respeci�ed to get

P0ij = Φ

0@ ln z � �β0w xij + θ0xj + δ0zj�

p2Φ�1

�(G+1)2

�1A (4)

where G is a Gini coe¢ cient.

This result uses the fact that under lognormality of a welfareindicator, a Gini coe¢ cient is a monotone increasing function of σζ ,

i.e. G = 2Φ�

σζp2

�� 1 ( Kleiber and Kotz, 2003); Cowell, 2009).

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Accounting for Contextual and Distributional E¤ects 7STEP 3: Distributional e¤ects 1

To accomodate consumption inequality as measured by a Ginicoe¢ cient, equation (3) can be respeci�ed to get

P0ij = Φ

0@ ln z � �β0w xij + θ0xj + δ0zj�

p2Φ�1

�(G+1)2

�1A (4)

where G is a Gini coe¢ cient.

This result uses the fact that under lognormality of a welfareindicator, a Gini coe¢ cient is a monotone increasing function of σζ ,

i.e. G = 2Φ�

σζp2

�� 1 ( Kleiber and Kotz, 2003); Cowell, 2009).

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Accounting for Contextual and Distributional E¤ects 7STEP 3: Distributional e¤ects 2

A linear regression based Gini coe¢ cient is given (Wagsta¤ et al.,2003)

G = ∑k

�αwk

xky

�Cwk +∑

k

�πkxky

�Cbk +∑

k

�γkzky

�Ck +

...C (5)

Ck is the concentration index of a regressor.

The Gini coe¢ cient is decomposed into two parts.

The observed and explained component.

The second part,Cu0jy + Cε

y =...C , is the unobserved and unexplained

component.

If spatial e¤ects are not accounted for, the decomposition reduces tothat by Wagsta¤ et al. (2003).

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Accounting for Contextual and Distributional E¤ects 8STEP 3: Distributional e¤ects 3

The e¤ect of a simulated change in a regressor on the Ginicoe¢ cient can come from two sources:

a change in the mean of the regressora change in the distribution of the regressor as measured by aconcentration index.

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Accounting for Contextual and Distributional E¤ects 9STEP 3: Distributional e¤ects 4

The corresponding total change in the Gini coe¢ cient emanatingfrom a change in a regressor is thus given by

dG =

Mean e¤ectz }| {1y

24αwk (Cwk � G )| {z }within e¤ect

+ πk (Cbk � G )| {z }between e¤ect

35 dxk (6)

+

Inequality e¤ectz }| {xky

24 αwkdCwk| {z }within e¤ect

+ πkdCbk| {z }between e¤ect

35

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Accounting for Contextual and Distributional E¤ects 10STEP 3: Distributional e¤ects 5

FactE¤ectively three possible poverty simulation exercises can be performed:

1 Ignoring community level contextual e¤ects and inequality e¤ects:Datt and Jollife (2004), Mukherjee and Benson (2003).

2 Allowing for contextual e¤ects: as proposed in this paper.3 Allowing for both mean and inequality e¤ects: as proposed in thispaper.

These proposed changes to the basic linear simulation model ensure amore accurate measurement of the impact of simulated policyinterventions on poverty.

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Empirical AnalysisData description, poverty lines, and variables used

I use the Third Integrated Household Survey (IHS3)I use an annualized consumption aggregate for each householdgenerated by Pauw et al. (2014) as a welfare indicator i.e. thedependent variableTwo area-speci�c utility-consistent poverty lines generated by Pauwet al. (2014)) MK 31573 for rural areas, and MK 46757 for urbanareas.Four groups of independent variables are included in the regressionsnamely;

household)demographic,education, agricultural, employment variablescommunity)health infrastructure and economic infrastructureindices) constructed by using multiple correspondence analysisCommunity level means of the following variables: education,employment, and agriculture�xed e¤ects variables)agro-ecological zone dummies, seasonalitydummies

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Results 1Preliminaries

Wald tests results lead to the rejection of the null hypothesis of nocommunity random e¤ects.

This conclusion has two implication

Even after controlling for individual characteristics, there are signi�cantcommunity-speci�c factors which a¤ect povertyEstimating a linear model in this context is invalid

Wald test results suggest signi�cant community level mean e¤ects

Wald test results suggest signi�cant seasonal and agroecologicale¤ects

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Results 2Preliminaries

11 policy simulations are conducted:

DemographicEducation,EmploymentAgriculture.

Each simulation is compared to a base scenario

Statistical signi�cance is checked using bootstrapped standard errors

Broadly, simulated policy changes:

Are more statistically signi�cant after accounting for contextual anddistributional e¤ectsAre quantitatively larger after accounting for contextual anddistributional e¤ects

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Results 2Preliminaries

11 policy simulations are conducted:

DemographicEducation,EmploymentAgriculture.

Each simulation is compared to a base scenario

Statistical signi�cance is checked using bootstrapped standard errors

Broadly, simulated policy changes:

Are more statistically signi�cant after accounting for contextual anddistributional e¤ects

Are quantitatively larger after accounting for contextual anddistributional e¤ects

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Results 2Preliminaries

11 policy simulations are conducted:

DemographicEducation,EmploymentAgriculture.

Each simulation is compared to a base scenario

Statistical signi�cance is checked using bootstrapped standard errors

Broadly, simulated policy changes:

Are more statistically signi�cant after accounting for contextual anddistributional e¤ectsAre quantitatively larger after accounting for contextual anddistributional e¤ects

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Results 3

18

18

19

5

4

4

0 5 10 15 20

2

1

Source: Author's computation using IHS3

1 =Adding a child if there is no child in HH2= Adding a child to all HHs

Rural policy simulation: Population

Headcount: No CE Headcount: CEHeadcount: CE+Distribution

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Results 4

­14­13

­0­11­11

­0­20

­13­1

­19­13

­3

­20 ­15 ­10 ­5 0

6

5

4

3

Source: Author's computation using IHS3

3 =Adding 1 female with MSCE4= Adding 1 male with MSCE

5=Adding 1 female with MSCE if JCE female in HH6=Adding 1 male with MSCE if JCE male in HH

Rural policy simulation: Education

Headcount: No CE Headcount: CEHeadcount: CE+Distribution

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Results 5

­15­9

­6

­10­6

­7

43

­2

­15 ­10 ­5 0 5

9

8

7

Source: Author's computation using IHS3

7 =Adult moves from primary industry occupation to secondary industry8= Adult moves from primary industry occupation to tertiary

9=Adult moves from secondary industry occupation to tertiary

Rural policy simulation: Employment

Headcount: No CE Headcount: CEHeadcount: CE+Distribution

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Results 6

4

4

­3

2

2

­2

­4 ­2 0 2 4

11

10

Source: Author's computation using IHS3

10 =Increase diversity of crops from 0 to 111= Increase diversity of crops to 2, if 0 or 1

Rural policy simulation: Employment

Headcount: No CE Headcount: CEHeadcount: CE+Distribution

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Conclusion

This adds to literature on determinants of poverty

A re-examination of determinants of poverty in Malawi has shownthat:

Ignoring contextual and distribution e¤ects leads to mismeasurementboth quantitatively and qualitatively of policy interventions on poverty.This turn implies that policy conclusions based on the existing methodsmight be misleading.

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THANKS!

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