Assessing the Poverty
Impact of Economic
Growth:
The Case of Indonesia
B. Essama-Nssah and Peter J. Lambert
World Bank Poverty Reduction Group
and
University of Oregon
October, 2006
Introduction

Context

Poverty Reduction:
 A fundamental objective of development (MDGs)
 Hence we need




a metric for assessing effectiveness in terms of this
objective.
meaningful ways to assess poverty impact of shocks and
policies.
Economic growth:
 Potentially a powerful instrument of poverty reduction.
 Yet, same rate of growth can lead to different levels of
poverty reduction in different circumstances.
Issue

When to declare a growth pattern pro-poor?
2

General approach


Logic of social impact evaluation understood as
an assessment of changes in individual and
social welfare attributable to a shock or policy.
Three basic dimensions:



Identification of individual situations.
Aggregation of individual situations into an
indicator of a social state.
Ranking of social states
3

Specification



Identification:
 Individual situations represented by point elasticity
of income w.r.t. a change in aggregate income.
Aggregation:
 Use members of the class of additively separable
poverty measures (e.g. Watts, FGT).
Ranking:
 A pro-poor growth pattern achieves a reduction in
poverty over and above that which is feasible in a
benchmark case (either desirable or counterfactual).
 We choose as benchmark the amount of poverty
reduction attainable under distributional neutrality
(equiproportionate growth).
4

Case of Indonesia




A long reputation of high achievements in growth and
poverty reduction, even in the face of adverse
conditions (Ravallion and Huppi 1991; World Bank 1996).
Is the observed poverty reduction more or less than we
would have observed had growth been distributionally
neutral?
We answer this question using 1993-2002 data.
We also unbundle the pattern of growth, to identify the
contributions of income components to the overall
outcome.
5

Focus of the rest of the presentation:

Evaluation Framework



Empirical Results




A General Impact Indicator
Poverty Focus
Data
Profile of Poverty and Inequality
Pro-Poorness
Conclusion
6
Evaluation Framework

Identification



Let x be an individual characteristic (e.g.
income) that is responsive to growth (or policy
change).
Let f(x) be the frequency density function for
x among the population.
Let u(x) be any individual attribute (e.g.
poverty), a function of x.
7

Aggregation

Average x:

Average u(x) :
mx
y   xf ( x)dx
0
mx
W   u ( x) f ( x)dx
0

Impact


Growth rate of mean of x:
 
y
y
Growth pattern (elasticity of individual income with
d ln( x) dx / x
respect to y):
q ( x) 

d ln( y) dy / y

q(x) is a normalized growth incidence curve (Ravallion &
Chen, 2003).
8

Impact, continued

Change in average u(x) induced by change in y:

As a proportion: W
W



mx
W 0
xu  ( x)q ( x) f ( x)dx
As an elasticity:
 wy
d ln( W ) 1


d ln( y) W

mx
0
xu ( x)q( x) f ( x)dx
Component breakdown (x=x1 +x2  xq(x)=x1 q(x1)+x2 q(x2)):
1 mx
1 mx

 wy   x1u ( x)q( x1 ) f ( x)dx   x2u( x)q( x2 ) f ( x)dx
W 0
W 0

9

Poverty Focus

x is now income or expenditure and mx is the poverty
line, call it z.

The function u ( x)   ( x |
individual deprivation.

Overall poverty
z)
is an indicator of
z
W  P    ( x | z ) f ( x)dx
0
10

Poverty Impact

Aggregate poverty elasticity:
 wy
1 z
 P (q)   x ( x | z )q( x) f ( x)dx
P 0
- Decomposition by income components:
1 z
1 z
 P (q)   x1 ( x | z )q( x1 ) f ( x)dx   x 2 ( x | z )q( x 2 ) f ( x)dx
P 0
P 0
11

Indicator of pro-poorness:
 P (q)  P[ P (q0 )   P (q)]
z
  { x ( x | z )}[q( x)  1] f ( x)dx
0

where q0 is the growth pattern associated with distribution
neutrality, i.e. q0(x) = 1 for all x.
-
Indicator measures the difference between poverty reduction under
distribution neutrality and amount produced by observed growth
pattern.
-
Typically, the growth elasticity ΦP(q) is negative, since positive growth induces poverty
reduction for all incomes below the poverty line.

If the pro-poorness index πP(q) is positive, the observed growth pattern leads to more
poverty reduction than the benchmark case
12

Factoring growth into scale and
distribution contributions
Elasticity:
1 z
1 z
P (q)   x ( x | z ) f ( x)dx   x ( x | z )[ q( x)  1] f ( x)dx  P  P
P 0
P 0
Pro-poorness:
 P (q)   P  P

growth is pro-poor if distributional component of
poverty elasticity is negative.
13

Ratio Comparisons


Instead of an additive comparison, one can consider the
ratio of the actual elasticity to the benchmark
elasticity. Growth is pro-poor if this ratio is greater
than 1.
Ratio comparison for Watts index leads to Ravallion and
Chen’s (2003) “mean growth rate for the poor”
measure

Ratio comparisons also underlie the Kakwani et al (2004)
measure called “poverty equivalent growth rate”.

Decomposability not established for ratio measures
14
Empirical Results

Two types of datasets for Indonesia used
in this study



Distribution of household expenditure per decile
(in 1993 PPP dollars) from World Bank Global
Poverty Monitoring database.
SUSENAS household surveys (1999, 2002) used to
achieve decomposition of pro-poorness across
income/expenditure components
Poverty line: about 2 dollars a day.
15
Poverty and Inequality in
Indonesia

1993-1996



1996-1999



Poverty falls while inequality increases.
Likely outcome of successful adjustment to oil shock
Poverty increases while inequality falls
Likely outcome of the 1997 Asian financial crisis.
1999-2002


Poverty falls, inequality goes up.
Signs of recovery: macroeconomic stability associated with
reduced vulnerability to external shocks.
16
Profile of Poverty and Inequality
in Indonesia, continued
1993
Headcount
61.53
Poverty Gap
21.03
Squared Poverty Gap 9.16
Watts
28.12
Gini
32
1996
50.49
15.33
6.02
19.73
36
1999
55.29
16.56
6.49
21.33
31
2002
52.41
15.68
6.09
20.12
34
Source: Authors’ calculations
17
Pro-Poorness in Indonesia

Additive comparisons
Watts Poverty Gap Squared Poverty Gap
1993-1996
-0.22
-0.14
-0.09
1996-1999
-0.20
-0.14
-0.08
1999-2002
-0.039
-0.022
-0.015
1993-2002
-0.13
-0.09
-0.05
1996-1999 (Counterfactual) 0.20
0.14
0.08
Source: Authors’ calulations

Ratio Comparisons
1993-1996
1996-1999
1999-2002
1993-2002
1996-1999 (Counterfactual)
Watts Poverty Gap Squared Poverty Gap
0.64
0.65
0.64
0.60
0.61
0.58
0.74
0.74
0.73
0.79
0.77
0.80
1.40
1.39
1.42
Source: Authors’ calculations
18

Growth pattern in Indonesia for 19992002
Aggregate Pattern of Growth (1999-2002)
1.6
1.4
1.2
1.0
0.8
0.6
0
10
20
30
40
50
60
70
80
90
100
Cumulative Percentage of the Population
19

Components of growth for 1999-2002
Incidence of Growth on Expenditure Components (1999-2002)
4
3
2
1
0
-1
-2
0
10
20
30
40
50
60
70
80
90
100
Cumulative Percentage of Population
Rice
Other Food
Education
Health
Other Non-Food
20

Pro-poorness components in Indonesia
for 1999-2002
Watts Poverty Gap Squared Poverty Gap
Rice
-0.173
-0.125
-0.062
Other Food
0.044
0.037
0.015
Education
0.005
0.003
0.002
Health
0.009
0.006
0.003
Other Non-Food 0.077
0.057
0.027
Aggregate
-0.039
-0.022
-0.014
Source: Authors’ calculations
21

Distributional component of growth
Shapley Decomposition
1993-1996
1996-1999
1999-2002
1993-2002

Overall
-5.70
1.23
-0.88
-5.35
Growth Distribution
-8.83
3.13
4.97
-3.74
-2.97
2.09
-6.87
1.52
distributional effect alleviated some of the negative
impact of the 1997 economic crisis.
22

Component Analysis, 1999-2002

Outcome driven mainly by what happened to
expenditure on rice, with some help from
expenditure on other food items.


Rice represents 26 percent of total expenditure
for the poor.
Total food expenditure including rice is about 73
percent of total household expenditure for the
poor.
23

Winners and losers among the poor



Actual growth pattern for 1999-2002 crosses
benchmark pattern twice before the headcount
ratio (55 percent).
q(x) below benchmark up to 20th percentile, and
between 43rd and 55th percentiles (these are
the losers)
winners located between 20th and 43rd
percentiles.
24

Appraisal of gains and losses

Pro-poorness at percentiles:
Pro-Poorness at Percentiles (1999-2002)
0
-1
Squared Poverty Gap
-2
Poverty Gap
-3
Watts
-4
-5
0
10
20
30
40
50
60
70
80
90
100
Cumulative Percentage of the Population
25

The gains versus the losses


Percentile pro-poorness curves lie below zero: economic
growth not pro-poor at any percentile up to the
headcount.
according to our metric, benefits enjoyed by the poor
between the 20th and the 43rd percentiles not high
enough to compensate for losses experienced by those
who came before.
26
Conclusions

Pro-poor growth analysis fits nicely within the logic of social
evaluation

An assessment of changes in individual and social
welfare attributable to the process of economic growth.

Measurements depend critically on underlying value
judgments.

Our metric for pro-poorness is defined by the following
choices:



(1) individual outcomes represented by point elasticity of
income;
(2)social welfare measured by poverty measures in the
additively separable class;
(3) standard of comparison based on distributional neutrality.
27

Application of this approach to data for Indonesia reveals
that:


Overall poverty reduction achieved over period 1993-2002
far below what distributionally neutral growth would have
achieved.
Focusing on the 1999-2002 period:


Some poor people gained from the economic growth that occurred
over that period, but these gains do not measure up to the losses
suffered by the rest of the poor.
The behavior of categories of expenditures over the same period
reveals that the weak performance is due mainly to changes in
food expenditure.
28
References





Essama-Nssah, B. (2005). A unified framework for pro-poor growth analysis.
Economics Letters, vol. 89, pp. 216-221.
Essama-Nssah, B. and Lambert P.J. (2006). Measuring the Pro-Poorness of Income
Growth within an Elasticity Framework. World Bank Policy Working Paper No. 4035
(October)
Kakwani, N.C., S. Khandker and H.H. Son (2004). Pro-poor growth: concepts and
measurement with country case studies. Working Paper Number 2004-1, International
Poverty Center, Brasil.
Kraay, Art. (2006). When is growth pro-poor? Evidence from a panel of countries
Journal of Development Economics 80, 198-227.
Ravallion, Martin and Huppi Monika. 1991. Measuring Changes in Poverty: A
Methodological Case Study of Indonesia during an Adjustment Period. The World
Bank Economic Review, Vol. 5, No. 1:57-82.

Ravallion, M. and S. Chen (2003). Measuring pro-poor growth. Economics Letters, vol.
78, pp. 93-99.

World Bank (1996). Indonesia: dimensions of growth. Report No.15383-IND. Country
Department III, East Asia and Pacific Region. Washington, D.C.: The World Bank.
29
End.
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