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

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

Outline
Motivation
Malawian Context
Accounting for contextual and distributional e¤ects
Empirical Analysis
Results
Conclusion

Motivation 1
This paper addresses two issues which have hitherto been ignored in
the existing studies on poverty and its correlates.
ISSUE 1
The poverty literature does not take into the fact that groups of
households become di¤erentiated, and that the group and its
membership both in‡uence and are in‡uenced by the group
membership.
These contextual e¤ects re‡ect the presence of externalities.
Fact
“the propensity of an individual to behave in some way varies with the
exogenous characteristics of the group.”(Manski 1993: 532)
Fact
“individuals in the same group tend to behave similarly because they have
similar individual characteristics or face similar institutional environments.”
(Manski 1993: 533)Richard Mussa (University of Malawi) Poverty in Malawi 4 June 2015 3 / 26

Motivation 2
Example
The extent of schooling at the community level can have a positive
externality e¤ect
Such educational externalities might arise for instance as uneducated
farmers learn from the superior production choices of other educated
farmers in the community (Weir and Knight, 2007; Asadullah and
Rahman, 2009)
The education externality could also arise when educated farmers are
early innovators and are copied by those with less schooling (Knight
et al., 2003).

Motivation 3
ISSUE 2
Poverty studies ignore the fact that changes in the correlates of
poverty may not only a¤ect the average level of consumption, but
may also a¤ect the distribution of consumption.
Ignoring these contextual and distribution e¤ects leads to
mismeasurement of policy interventions on poverty.
The paper develops methods for addressing these two problems.
I re-examine the determinants of poverty in Malawi

Malawian Context
The economy grew at an average annual rate of 6.2% between 2004
and 2007, and surged further to an average growth of 7.5% between
2008 and 2011
However, poverty reduction in Malawi has been marginal 7! was
52.4% in 2004, and marginally declined to 50.7% in 2011.
Increasing poverty in rural areas: questions about e¤ectiveness of
FISP
Recent panel evidence also shows marginal declines in
poverty7!40.2% in 2010, slightly dropped to 38.7%.
Inequality has also increased over the same period: Gini coe¢ cient
was 0.390 in 2004, and rose to 0.452 in 2011
A recent re-examination by Pauw, Beck, and Mussa (2014) shows
that the decrease in poverty was much larger than o¢ cially estimated:
8.2 percentage points

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 aka
linear random e¤ects model
2 STEP 2: Augmenting the multilevel/hierchical linear regression with
contextual e¤ects
3 STEP 3: Adjusting poverty headcounts for distributional e¤ects

STEP 1: Linear Random E¤ects Model 1
Household data is hierarchical/multilevel in the sense that households
are nested in communities.
Households in the same cluster/community are likely to be dependent.
This dependency ) downward biased standard errors) many
spurious signi…cant results (Rabe-Hesketh and Skrondal, 2008);
McCulloch et al., 2008; Hox, 2010; Cameron and Miller, 2015).
Suppose that the ith household (i = 1....Mj ) resides in the jth
(j = 1....Jl ) community, then the determinants of consumption
expenditure allowing for spatial community random e¤ects can be
modeled using the following two level linear regression
ln yij = β0
xij + δ0
zj + uj + εij (1)

ln yij is the log of per capita annualized household consumption
expenditure,
β and δ are coe¢ cients, xij and zj are observed household level and
community level characteristics respectively
uj N 0, σ2
u are community-level spatial random e¤ects (random
intercepts),
εij N 0, σ2
ε is a household-speci…c idiosycratic error term
The assumptions about uj , and εij imply that ζij N 0, σ2
ζ , where
σ2
ζ = σ2
u + σ2
ε .

ln yij is the log of per capita annualized household consumption
expenditure,
β and δ are coe¢ cients, xij and zj are observed household level and
community level characteristics respectively
uj N 0, σ2
u are community-level spatial random e¤ects (random
intercepts),
εij N 0, σ2
ε is a household-speci…c idiosycratic error term
The assumptions about uj , and εij imply that ζij N 0, σ2
ζ , where
σ2
ζ = σ2
u + σ2
ε .
Most importantly, ln yij N β0
xij , σ2
ζ

STEP 2: Contextual e¤ects 1
Micro (household) vs macro (community) level e¤ects of a variable
can be di¤erent
Ignoring these di¤erences can give misleading results (Neuhaus and
Kalb‡eisch,1998; Arpino and Varriale, 2012)
Decompose the household level covariate xij :
Between-community component, ¯xj = 1
Mj
∑ xij
Within-community component, xij ¯xj
Modify equation (1) to allow for the two separate covariate e¤ects to
get
ln yij = β0
w (xij ¯xj ) + β0
b ¯xj + δ0
zj + uj + εij (2)
= β0
w xij + θ0
¯xj + δ0
zj + uj + εij
where βw represents the within-community e¤ect, and βb represents
the between-community e¤ect.

STEP 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

STEP 3: Distributional e¤ects 1
Using equation (2), and noting that ζij N 0, σ2
ζ , the probability
that a household is poor can be written as
P0ij = Prob(ζij < ln z β0
w xij + θ0
¯xj + δ0
zj ) (3)
= Φ
ln z β0
w xij + θ0
¯xj + δ0
zj
σζ
!
where Φ ( ) is a distribution function of the standard normal
distribution.
A simulation model which is based on the standard linear model has
been used before by Datt and Jollife (2004) in Egypt and Mukherjee
and Benson (2003) in Malawi

Using equation (2), and noting that ζij N 0, σ2
ζ , the probability
that a household is poor can be written as
P0ij = Prob(ζij < ln z β0
w xij + θ0
¯xj + δ0
zj ) (3)
= Φ
ln z β0
w xij + θ0
¯xj + δ0
zj
σζ
!
where Φ ( ) is a distribution function of the standard normal
distribution.
This is used to simulate changes in the aggregate levels of poverty
A simulation model which is based on the standard linear model has
been used before by Datt and Jollife (2004) in Egypt and Mukherjee
and Benson (2003) in Malawi

To accomodate consumption inequality as measured by a Gini
coe¢ cient, equation (3) can be respeci…ed to get
P0ij = Φ
0
@
ln z β0
w xij + θ0
¯xj + δ0
zj
p
2Φ 1 (G +1)
2
1
A (4)
where G is a Gini coe¢ cient.

To accomodate consumption inequality as measured by a Gini
coe¢ cient, equation (3) can be respeci…ed to get
P0ij = Φ
0
@
ln z β0
w xij + θ0
¯xj + δ0
zj
p
2Φ 1 (G +1)
2
1
A (4)
where G is a Gini coe¢ cient.
This result uses the fact that under lognormality of a welfare
indicator, a Gini coe¢ cient is a monotone increasing function of σζ,
i.e. G = 2Φ
σζ
p
2
1 ( Kleiber and Kotz, 2003); Cowell, 2009).

A linear regression based Gini coe¢ cient is given (Wagsta¤ et al.,
2003)
G = ∑
k
αwk
¯xk
¯y
Cwk + ∑
k
πk
¯xk
¯y
Cbk + ∑
k
γk
¯zk
¯y
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,
Cu0
j
¯y + Cε
¯y =
...
C , is the unobserved and unexplained
component.
If spatial e¤ects are not accounted for, the decomposition reduces to
that by Wagsta¤ et al. (2003).

The e¤ect of a simulated change in a regressor on the Gini
coe¢ cient can come from two sources:
a change in the mean of the regressor
a change in the distribution of the regressor as measured by a
concentration index.

The corresponding total change in the Gini coe¢ cient emanating
from a change in a regressor is thus given by
dG =
Mean e¤ect
z }| {
1
¯y
2
4αwk (Cwk G)
| {z }
within e¤ect
+ πk (Cbk G)
| {z }
between e¤ect
3
5 d ¯xk (6)
+
Inequality e¤ect
z }| {
¯xk
¯y
2
4 αwk dCwk
| {z }
within e¤ect
+ πk dCbk
| {z }
between e¤ect
3
5

Fact
E¤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 this
paper.
These proposed changes to the basic linear simulation model ensure a
more accurate measurement of the impact of simulated policy
interventions on poverty.

Empirical Analysis
Data description, poverty lines, and variables used
I use the Third Integrated Household Survey (IHS3)
I use an annualized consumption aggregate for each household
generated by Pauw et al. (2014) as a welfare indicator i.e. the
dependent variable
Two area-speci…c utility-consistent poverty lines generated by Pauw
et al. (2014)) MK 31573 for rural areas, and MK 46757 for urban
areas.
Four groups of independent variables are included in the regressions
namely;
household )demographic,education, agricultural, employment variables
community)health infrastructure and economic infrastructure
indices) constructed by using multiple correspondence analysis
Community level means of the following variables: education,
employment, and agriculture
…xed e¤ects variables)agro-ecological zone dummies, seasonality
dummies

Results 1
Preliminaries
Wald tests results lead to the rejection of the null hypothesis of no
community random e¤ects.
This conclusion has two implication
Even after controlling for individual characteristics, there are signi…cant
community-speci…c factors which a¤ect poverty
Estimating 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 agroecological
e¤ects

Results 2
Preliminaries
11 policy simulations are conducted:
Demographic
Education,
Employment
Agriculture.
Each simulation is compared to a base scenario
Statistical signi…cance is checked using bootstrapped standard errors
Broadly, simulated policy changes:

Results 2
Preliminaries
Demographic
Education,
Employment
Agriculture.
Are more statistically signi…cant after accounting for contextual and
distributional e¤ects

Results 2
Preliminaries
Demographic
Education,
Employment
Agriculture.
Are more statistically signi…cant after accounting for contextual and
Are quantitatively larger after accounting for contextual and

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 HH
2= Adding a child to all HHs
Rural policy simulation: Population
Headcount: No CE Headcount: CE
Headcount: CE+Distribution

Results 4
-14
-13
-0
-11
-11
-0
-20
-13
-1
-19
-13
-3
-20 -15 -10 -5 0
6
5
4
3
3 =Adding 1 female with MSCE
4= Adding 1 male with MSCE
5=Adding 1 female with MSCE if JCE female in HH
6=Adding 1 male with MSCE if JCE male in HH
Rural policy simulation: Education

Results 5
-15
-9
-6
-10
-6
-7
4
3
-2
-15 -10 -5 0 5
9
8
7
7 =Adult moves from primary industry occupation to secondary industry
8= Adult moves from primary industry occupation to tertiary
9=Adult moves from secondary industry occupation to tertiary
Rural policy simulation: Employment

Results 6
4
4
-3
2
2
-2
-4 -2 0 2 4
11
10
10 =Increase diversity of crops from 0 to 1
11= Increase diversity of crops to 2, if 0 or 1
Rural policy simulation: Employment

Conclusion
This adds to literature on determinants of poverty
A re-examination of determinants of poverty in Malawi has shown
that:
Ignoring contextual and distribution e¤ects leads to mismeasurement
both quantitatively and qualitatively of policy interventions on poverty.
This turn implies that policy conclusions based on the existing methods
might be misleading.

THANKS!

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

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