dynamics of growth, poverty, and inequality · unchanged depends on the shape of the lorenz curve...
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Dynamics of Growth, Poverty, and Inequality -Panel Analysis of Regional Data from the Philippines and Thailand-
Kurita, Kyosuke† and Kurosaki, Takashi‡
March 2007
Abstract To empirically analyze the dynamics of, and relations among, growth, poverty, and inequality, this paper proposes a new methodological framework in which due attention is paid to the fact that the entire distribution of an individual welfare indicator, say, per capita real consumption, changes over time, and that empirical variables of growth, poverty, and inequality are often compiled from the distribution of the welfare indicator. From this framework, implications are derived regarding the dynamic relationship among growth, inequality, and poverty. The dynamic relationship is empirically investigated using unique panel data of provinces in the Philippines (1985-2003) and Thailand (1988-2002), which are compiled from micro datasets of household expenditure surveys. To control for the bias due to the dynamic structure of the empirical model, we employ a system GMM estimation method and compare its results with those obtained from other estimation methods adopted in the literature. The system GMM estimation results show that lagged values of inequality reduce economic growth in both countries. This finding supports the view that inequality harms poverty reduction and economic growth, the view previously supported by cross country evidence based on static models ignoring the dynamics of the entire distribution of per capita real consumption or dynamic models using other estimation methods. We further find that the marginal effects of the lagged inequality on growth and poverty are larger in Thailand than in the Philippines. Simulation results based on the parameter estimates show that the inequality factor, which is due to differences in the initial inequality level and in its marginal impact between the two countries, explained a substantial portion of the Philippine-Thai difference in economic growth and poverty reduction during the late 1980s and 1990s. Keywords: poverty, inequality, pro-poor growth, convergence, Thailand, the Philippines. JEL classification codes: I32, O15.
† World Institute for Development Economics Research, United Nations University, Katajanokanlaituri 6 B, Helsinki FI-00160 Finland. Phone (Direct): +358-9-6159-9214; Fax: +358-9-6159-9333. E-mail: [email protected] ‡ The Institute of Economic Research, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8603 Japan. Phone +81-42-580-8363; Fax: +81-42-580-8333. E-mail: [email protected]
1 Introduction
The relationship between growth, inequality, and poverty has been one of the central issues
in development economics (Galor and Zeira, 1993; Alesina and Rodrik, 1994; Bourguignon,
2004; Shorrocks and van der Hoeven, 2004). The debate once emphasized a tradeoff between
growth and inequality, as was shown by Kuznets’ famous “inverted Uhypothesis” that in
equality rises during the initial stage of development and then declines. The recent literature,
however, has shown that such a pattern is not observed in a number of countries over time
(Deininger and Squire, 1998). Thus the emphasis of the debate has shifted to explaining the
diversity of countries’ experiences. Among the relationship, the effect of initial inequality
on subsequent growth has been one of the most debated ones. Against the conventional
view based on an incentive argument or a savingratedifferential argument that inequality
is necessary for growth, development economists found that the initial inequality is bad for
subsequent growth (Galor and Zeira, 1993; Alesina and Rodrik, 1994). Although there are
several researches that found the opposite relation (e.g., see Li and Zou, 1998; Forbes, 2000),
on balance, the existing evidence using crosscountry growth regressions seems to offer more
support for the view that inequality is harmful to growth.1 To summarize the current status
of research, the consensus could be that “initial conditions matter, specific country struc
tures matter, and time horizons matter” (Shorrocks and van der Hoeven, 2004: p.11), and
that “there are a number of concerns about the data and methods” (Ravallion, 2004: p.71).
Against this background, this paper attempts to shed new light on the discussion from
the viewpoint of the utilization of information contained in a typical dataset used for such
analysis. When a household expenditure survey dataset is available with an indicator rep
resenting individual welfare, say, percapita real consumption, the usual procedure is to
aggregate the data and to compile empirical variables of growth, poverty, and inequality.
This process seems very odd, since the three variables in the same period are dependent
each other by construction. We thus propose a new methodological framework in which we
pay due attention to the fact that the entire distribution of the percapita real consumption
changes over time and empirical variables of growth, poverty, and inequality are compiled
from the same micro data of individual consumption.2
1See Perotti (1996), Aghion et al. (1999), and Jones (2002) for a more comprehensive review on the relationship between inequality and growth.
2See also Quah (2005) for an ongoing research on characterizing the entire distribution of the welfare indicator based on motivation similar to ours.
2
Why do we have to be careful when empirical variables of growth, poverty, and in
equality are compiled from the same micro data? For instance, in the recent literature, there
has been a heated debate on how to define propoor growth.3 One of the indicators of a
propoor growth could be the growth elasticity of poverty, i.e., how much percentage the
poverty headcount index declines when the economy grows by one percent. As shown by
Kakwani (1993), the elasticity to a counterfactual growth that holds the entire Lorenz curve
unchanged depends on the shape of the Lorenz curve and the location where the poverty
line falls on the curve. Kakwani et al. (2004) and Heltberg (2004) examined these elas
ticities empirically using recent micro datasets. These exercises are valid ways to describe
dynamic changes that occurred to the entire distribution. However, it is difficult to infer
structural relationship between growth and poverty reduction from these exercises since the
changes in poverty headcount index and in average incomes in the same period are linked
by construction. This paper attempts to delink them.
We apply the methodology of this paper to two cases of the Philippines and Thailand.
Unique panel data of provincelevel percapita consumption, poverty, and inequality are
compiled from micro datasets of household expenditure surveys, covering similar periods:
19852003 for the Philippines and 19882002 for Thailand. The exercise using these panel
data is presented as an illustration to show how our methodology works. However, the
exercise itself is of great empirical interest since these two countries represent a miracle
(Thailand) and a close loser (the Philippines) in the context of the “Asian miracle” (World
Bank, 1993). A comparative study of two economies using panel datasets is rare in the
literature. By comparing the two countries, therefore, we can deepen the understanding
of a structural difference between them that was responsible for the disparity in economic
performance. Note that in the early 1980s, percapita GDP levels in the two countries were
very similar while that in Thailand in 2000 was twice to three times as large as that in
the Philippines; the poverty headcount index in 2000 using one PPP US$ per day as the
poverty line was below 2% in Thailand while that in the Philippines was 14.6% (World
Bank, 2004). In addition, the use of regional panel datasets enables us to control for data
concerns. Comparability problems due to the heterogeneity in survey designs and processing
(Ravallion, 2004) are likely to be less serious in our datasets.
The paper is organized as follows. Section 2 provides an analytical framework for
the dynamics of growth, poverty, and inequality. It also derives specifications for empirical
See, for example, the dialogue published on One Pager by the International Poverty Centre, UNDP.
3
3
�
analysis, which are estimated by a system GMM method to control for the bias due to the
dynamic structure of the empirical model. Section 3 explains the datasets with timeseries
plots, to show the actual dynamics of growth, poverty, and inequality in these two countries
when the three are investigated separately. Section 4 presents empirical results. It first gives
the system GMM estimation results and compares them with those obtained from other
estimation methods adopted in the literature. It then presents simulation results to quantify
the determinants of economic growth and poverty reduction in these two countries. Section
5 concludes the paper.
2 Analytical Framework for the Dynamics of Growth, Poverty, and Inequality
2.1 Dynamics of the Entire Distribution of PerCapita Consumption
2.1.1 Setting
Assume an economy consisting of individuals denoted by subscript i. Let yit be the welfare
level of individual i in time t, such as percapita real consumption. The cumulative distri
bution of yit across individuals is expressed by a function Ft(yit). From this distribution, we
can compile aggregate variables that are of interests, such as growth, inequality measures,
and poverty measures.
Since yit is denoted in currency units whose small fractions may not have much eco
nomic meaning, we assume that the function Ft(yit) can be reasonably well approximated
by a step function with cells with the same width on the yit axis. In other words, we assume
that the density function associated with Ft(yit) can be expressed as a discrete probability
distribution function with a fixed and finite number of cells with the same width. We denote
this probability function by πnt ≡ πt(ynt) where n = 1, 2, ..., N is the subscript for each cell.
Then,
N
Mean consumption: yt = πntynt, n=1
Consumption growth: Δyt = yt − yt−1.
In this paper, we investigate relative inequality measures that satisfy axioms of sym
metry, replication invariance, scale invariance, and the PigouDayton principle of transfers
(Foster and Sen, 1997), such as
4
� �
�
�
� �
1 N N
Gini index: Ineq1t = 2yt n=1 n�=1
πntπn�t | ynt − yn�t ,|
α(1 − α)
� πnt
� 1−
� ynt
�α� .
1 N
Class of general entropy measures, It(α) =ytn=1
The class of general entropy measures include (in the order of lower inequality aversion):
Half the square of the coefficient of variation: Ineq2t = It(2), N � �
Theil index: Ineq3t = It(1) = �
πnt ynt ln
ynt ,yt ytn=1
N � � The mean logarithmic deviation: Ineq4t = It(0) =
� πnt ln
yt ,yntn=1
General entropy measure with parameter 1: Ineq5t = It(−1).
Similarly, we investigate poverty measures that are popular in recent empirical works
and satisfy the focus axiom and decomposability (Foster and Sen, 1997). Letting P denote
a set defined on n for ynt ≤ z with z defined as the absolute poverty line,
Poverty head count index: Pov1t = πnt, �n∈P �
Poverty gap index: Pov2t = �
πnt 1− ynt ,z
n∈P
Squared poverty gap index: Pov3t = �
πnt 1− ynt
�2
, z
n∈P � �� zWatt’s poverty measure: Pov4t = πnt ln , and
n∈P ynt � z
ClarkWatt’s measure with parameter 1: Pov5t = πnt − 1 .n∈P
ynt
We are interested in the relationship between growth, inequality, and poverty. How
ever, as shown clearly in the above definitions, all empirical variables of mean consumption,
inequality, and poverty are compiled from the same distribution of Ft(yit) or πnt. In other
words, all are partial parameters that characterize and aggregate the shape of the entire
distribution. Thus, picking up one of them and then regressing it on the others, such as
regressing the poverty head count index (Pov1t) on the average welfare level (yt) and in
equality such as Gini index (Ineq1t) does not contribute much to the understanding of the
dynamics of growth, poverty, and inequality. It is rather a description of the entire distri
5
bution of Ft(yit) or πnt. Finding a negative effect of yt and a positive effect of Ineq1t on
Pov1t from such specifications cannot be interpreted as showing the structural relationship
between poverty, growth, and inequality. It should be interpreted as showing the shape of
Ft(yit) or πnt. Similarly, by taking the difference, finding a negative effect of Δyt and a
positive effect of ΔIneq1t on ΔPov1t cannot be interpreted as showing the structural rela
tionship, either. It should be interpreted as showing the shapes of Ft(yit) and Ft−1(yi,t−1).
Thus the finding of a positive effect of consumption growth and a negative effect of inequality
change on poverty reduction from such specifications is misleading and spurious in the sense
that it does not imply any dynamic relationship between inequality (growth) and poverty.
The existing studies such as Besley and Burgess (2002), Sawada (2004) do not pay sufficient
attention to this mere fact.
2.1.2 Dynamics
In period t, we observe Ft(yit) or πnt. Due to policy interventions or unexpected shocks,
denoted by a vector of Xt, the distribution in the next period will be different from the
current one. Thus, what we observe in the next period, Ft+1(yi,t+1), should be a function
of Ft(yit) and Xt. Since this relationship lies under the dynamics of growth, poverty, and
inequality, we are interested in characterizing this function.
However, characterizing this function is not possible since it is a mapping of the entire
distribution into another, conditional on vector Xt. Instead, we attempt to estimate the
function that maps parameters characterizing the current distribution into parameters char
acterizing the next period distribution, conditional on vector Xt. In other words, the basic
idea for empirical exercise is:
Lefthand side variables = vector of ln yt, Ineqt (vector of Ineqkt, k=1,2,...), and
Povt (vector of Povkt, k=1,2,...).
Righthand side variables = vector of Xt−1, ln yt−1, Ineqt−1 (vector of Ineqk,t−1,
k=1,2,...), and Povt−1 (vector of Povk,t−1, k=1,2,...).
We use ln yt instead of yt because the logarithmic form allows us direct comparison of
our empirical results with those in the existing literature and also because the logarithmic
transformation results in less heteroscedastic error terms. By estimating this system of
equations, we can infer the structural relationship between poverty, growth, and inequality.
6
For instance, if we find a positive effect of ln yt−1, a negative effect of Ineq1,t−1, and lessthan
unity effect of Pov1,t−1 on Pov1t from such a specification, we can interpret it as showing
that higher initial consumption and lower initial Gini increases the speed of reduction of the
poverty head count index.
In the specification above, the effects of the lagged variables of higher orders are assumed
away. This implies a Markov assumption. Regardless of where the economy was located in
t − 2, the distribution Ft(yit) is completely determined by Xt−1 and the lagged distribution
Ft−1(yi,t−1). Since this is a strong assumption, the significance of higher order lags may be
tested empirically. 4 When economies are highly heterogeneous, fixed effects or differenced
version of the specification above may be preferable.
2.1.3 The povertygrowthinequality triangle
In the literature on the theoretical relationship among poverty, average consumption, and
inequality, two cases have been investigated, where due attention is paid to the fact that the
three variables are derived from the same distribution of Ft(yit) or πnt (Kakwani, 1993).
(i) Meanpreserving spread: This definitely increases any measure included in Ineqt,
while leaving yt unaffected by definition. The effect of a meanpreserving spread on Povt
depends on the exact choice of the poverty measure and the position of the poverty line
relative to the mean consumption and the location where the meanpreserving spread occurs.
Thus the sign of the effect is indeterminate in general. When the meanpreserving spread
occurs when yt > z and the poverty measure adopted is based on an individual poverty score
function that is decreasing and convex in yit/z for yit < z, it is known that the poverty
measure increases (or more precisely, does not decrease).
(ii) Lorenzcurvepreserving growth: This case is useful in decomposing the change
in poverty measures into growth and redistribution components (Datt and Ravallion, 1992).
When growth occurs by shifting the entire distribution by a certain percentage, without
changing the shape of the distribution characterizing Lorenz curve at all, yt increases while
any measure included in Ineqt remains unaffected. Since any poverty measure included in
Povt decreases in such a case, simulating for the Lorenzcurvepreserving growth can quantify
the pure effects of growth on poverty a la Datt and Ravallion (1992). Although this is a
powerful empirical tool to decompose the observed changes in poverty, it is not a useful tool
for a normative analysis of the future change of poverty since we know very little about the
4This is not attempted in the empirical part of this paper, since our datasets are not sufficiently long.
7
5
conditions in which the Lorenzcurvepreserving growth can be a good proxy to the reality
or about policy tools to achieve the Lorenzcurvepreserving growth.5
Although the motivation underlying the examination of these two cases is similar to this
paper’s, we have to be careful about the restrictive assumptions underlying the examination
on the changes in the distribution Ft(yit). In the real world, however, we rarely find a case
that is reasonably close to the meanpreserving spread or to the Lorenzcurvepreserving
growth. Nevertheless, because of the intuition these two cases provide, we tend to think that
growth usually decreases poverty and an increase in inequality usually increases poverty. But
this is not always true once we move out of the two restrictive cases of the meanpreserving
spread and the Lorenzcurvepreserving growth.
As a simple case, consider the relationship between the average consumption yt, Gini
index Ineq1t, and poverty head count index Pov1t. The entire distribution of yit is given by
πnt (πnt > 0 ∀n). Initially, 0 < Pov1t < 1 and those below the poverty line are distributed
in at least two cells. In other words, we are given a set of initial values of πnt whose sum is
equal to unity, from which we calculate the set of initial values of (yt, Ineq1t, Pov1t). Can
we find a slightly different set of πnt that preserves two elements of the initial values of
(yt, Ineq1t, Pov1t) but increases/decreases the other one? As is clear from a simple mathe
matics, when N > 3, we can find these cases (note that there are four restrictions on πnt: the
sum should be unity; the value of yt, Ineq1t, and Pov1t are given). In other words, we can
find a case with no growth and no change in Gini but an increase (decrease) in the poverty
headcount index. Similarly, when the number of poverty measures and inequality measures
included in the empirical investigation increases, we can generally find a combination of πnt
with no growth, no change in all of the inequality and poverty measures except one (either
an increase or decrease), as long as N is sufficiently large.6
Therefore, by treating yt, inequality measures Ineqt, and poverty measures Povt as
different measures to describe the entire distribution of yit, we can better analyze the poverty
growthinequality triangle (Bourguignon, 2004). Whether changes in Povt are well captured
A recent study by Kakwani and Son (2006) estimated the cost required to achieve the Millennium Development Goal of halving poverty, assuming either the Lorenzcurvepreserving growth, the Lorenzcurvespreading growth, or the Lorenzcurveshrinking growth, keeping the curvature of the Lorenz curve constant. This exercise does not seem useful for the reason mentioned in the text.
6If the restrictions imposed by the value of each inequality/poverty measure are all linear, this argument is trivially true. However, most of these restrictions are nonlinear. Therefore, mathematically, it is possible that there are multiple solutions, all of which are outside the economically reasonable range. However, this never occurs in our simulation as long as we use inequality/poverty measures listed above and setting their initial levels at those found in the empirically relevant context. Simulation results are available on request.
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by growth and initial inequality but not affected by initial poverty is an empirical question in
our approach. In contrast, in the literature influenced by the argument based on the mean
preserving spread and Lorenzcurvepreserving growth, it is assumed a priori. Our approach
attempts to characterize the povertygrowthinequality triangle by taking into account the
dynamic structure that generates the entire distribution of yit over time.
2.2 Empirical Specification and Estimation Methodology
The model discussed above was for a representative economy. We assume that data for a
collection of such economies are available for the empirical analysis. Individual economy is
denoted by subscript j. It could be a country or a region within a country. Then the model
is revised as
Lefthand side variables = vector of ln yjt, Ineqjt (vector of Ineqkjt, k=1,2,...), and
Povjt (vector of Povkjt, k=1,2,...).
Righthand side variables = vector of Xj,t−1, ln yj,t−1, Ineqj,t−1 (vector of Ineqk,j,t−1,
k=1,2,...), and Povj,t−1 (vector of Povk,j,t−1, k=1,2,...).
In the empirical application, it is likely that individual measures included in Ineqjt are
highly collinear each other and individual measures included in Povjt are highly collinear
each other. This is an empirical issue we address in the next section. If the multicollinearity
problem is severe, which turns out to be the case for the Philippines and Thailand, the
system of three equations is estimated:
ln yjt = β11 ln yj,t−1 + β12Ineq1,j,t−1 + β13Pov1,j,t−1 + Xj,t−1θ1 + α1j + η1t + �1jt, (1)
Ineq1jt = β21 ln yj,t−1 + β22Ineq1,j,t−1 + β23Pov1,j,t−1 + Xj,t−1θ2 + α2j + η2t + �2jt, (2)
Pov1jt = β31 ln yj,t−1 + β32Ineq1,j,t−1 + β33Pov1,j,t−1 + Xj,t−1θ3 + α3j + η3t + �3jt, (3)
where α is unobservable and timeinvarying characteristics of economy j, η is unobservable
macro shocks that fall on all economies in period t, and � is an idiosyncratic error term. In
the theoretical model, Xt was defined as a vector of variables that affect the distribution of
F (.). In the empirical specification, the vector is decomposed into Xjt (observable factors),
ηt (factors that are unobservable but we can control for, utilizing the panel structure of the
dataset), and �jt (factors that are unobservable and we cannot control for).
9
The choice of particular measures of Ineq1jt and Pov1jt is for convenience. We can
choose others as well. Even when the multicollinearity problem exists among individual
measures of inequality, each of them may contain information not contained in the other.
For instance, Datt and Ravallion (1992) showed that redistribution mattered in poverty
changes in India even when Gini coefficient did not change. To examine this possibility,
we run a series of robustness check changing a particular choice of inequality and poverty
measures.
To facilitate the comparison of our results with those in the literature, we also estimate
a restricted version of the above system where β13 = β23 = β33 = 0. This results in the
restricted system:
ln yjt = β11 ln yj,t−1 + β12Ineq1,j,t−1 + Xj,t−1θ1 + α1j + η1t + �1jt, (4)
Ineq1jt = β21 ln yj,t−1 + β22Ineq1,j,t−1 + Xj,t−1θ2 + α2j + η2t + �2jt, (5)
Pov1jt = β31 ln yj,t−1 + β32Ineq1,j,t−1 + Xj,t−1θ3 + α3j + η3t + �3jt, (6)
In both specifications of (1)(3) and (4)(6), parameter β11 captures the convergence of
mean income among the economies. If it is between zero and one, it implies the convergence,
i.e., the growth rate is higher for regions/countries with lower initial consumption. If it is
greater than one, it implies the divergence. Since terms other than the lagged consumption
are included such as Xj,t−1, the parameter could be interpreted as the one characterizing the
conditional convergence (Jones, 2002). The difference in steady states of ln yjt is partially
controlled for by fixed effects, α1j . Xj,t−1 not only controls for the difference in exogenous
shocks that occur to the entire distribution of per capita consumption, but also controls for a
potential difference in the convergence speed attributable to observables. Similarly, param
eter β22 captures the inequality convergence, which has been analyzed by Benabou (1996)
and Ravallion (2003). If it is between zero and one, it implies the inequality convergence,
i.e., the inequality tends to decline in regions/countries with higher initial inequality. If it is
greater than one, it implies the inequality divergence.
In our specification, parameter β12 captures the effect of lagged inequality on growth.
If it is negative, it indicates that economies or regions with higher initial inequality grow
more slowly. However, our specification does not nest the one used by Banerjee and Duflo
(2003), who showed that the growth rate is a nonlinear (inverse U shaped) function of a
lagged change in inequality. To expand the specification to nest their specifications is left
10
for further research.
It may be of interest to compare the determinants of poverty a la equation (6) with
those derived under a specification that ignores the fact that all of Pov1jt, ln yjt, and Ineq1jt
are compiled from the same distribution (Besley and Burgess, 2002; Sawada ,2004). We thus
estimate the model
Pov1jt = γ1 ln yjt + γ2Ineq1jt + α3j + η3t + �3jt, (7)
and compare the estimation results with those based on equation (6).
A final note is added on the specification. The system to be estimated has a lagged
dependent variable on the righthand side. Therefore, we need to control for possible bias due
to the structure of a dynamic panel data (DPD) model. In estimating DPD models, most of
the literature employ pooled OLS, fixedeffects estimation, or firstdifference GMM methods.
However, as demonstrated by Bond et al. (2001), the firstdifference GMM estimators may
not be relevant for the small sample estimation. To overcome this problem, Blundell and
Bond (1998) proposed an alternative method of system GMM estimation. In this paper, we
thus employ the system GMM estimation method as our main specification and compare its
results with those estimated by pooled OLS or fixedeffects methods.
3 Data
3.1 Data Sources and Definitions of Empirical Variables
We compile panel data of provinces in Thailand (19882002) and the Philippines (1985
2003) from micro datasets of household expenditure surveys. Provinces are chosen as a unit
of analysis. Currently, there are 76 provinces in Thailand and 82 provinces in the Philippines.
The data sources for Thailand are Socio Economic Survey (SES). SES is conducted by
the National Statistical Office of the Government of Thailand. Since 1998, SES has been
conducted every year. A nationally representative sample was drawn each time and surveyed
with detailed questionnaire on household demography, income, and consumption, covering
approximately 10,000 to 20,000 households. In this paper, 8 rounds of SES spanning 15
years (1988, 1990, 1992, 1994, 1996, 1998, 2000, 2002) were employed. Since the number of
provinces increased after the 1992 survey from 73 to 76, the panel dataset is unbalanced.7
7This implies that some of the geographic units are not strictly comparable between the first three and the last five surveys. Adjusting for changes in the provincial boundary is left for further exercises. However, the bias from nonadjustment is likely to be small since the regression results reported in this paper are
11
The data sources for the Philippines are Family Income and Expenditure Survey (FIES).
FIES is conducted by the National Statistics Office, Republic of the Philippines. Every three
years, a nationally representative sample was drawn and surveyed with detailed questionnaire
on items similar to those in Thailand. The sample size is approximately 17,000 to 38,000
households. In this paper, 7 rounds of FIES spanning 19 years (1985, 1988, 1991, 1994, 1997,
2000, 2003) were employed. Since the number of provinces increased after the 1994 survey
from 77 to 82, the panel dataset is unbalanced.8
From this dataset, the three groups of lefthandside variables were estimated for
province j in year t: ln yjt (log of the mean consumption per capita, denoted Expenditure
in the following figures/tables), Ineqjt (inequality measures), and Povjt (poverty measures).
Percapita real consumption was calculated by dividing the total household consumption
expenditure by the number of household members without weighting and the government
price indices. For both countries, the official poverty lines were employed in this paper,
which were estimated as an absolute concept, based on the cost of basic needs including food
and nonfood expenditures. Sample observations that did not satisfy the logical checks and
sample observations with percapita consumption above the top 1% or below the bottom
1% were deleted in calculating these provincelevel variables. Following the literature, four
variables were calculated for Xjt: Education, Urban, Agriculture, and Aged. Table 1 reports
the definitions and summary statistics of the empirical variables for Thailand and Table 2
reports those for the Philippines.
3.2 Description of Growth, Poverty, and Inequality in the Philippines and Thailand
Figure 1a plots the time series of ln yjt (denoted Expenditure) for Thailand. Since there are
73 or 76 provinces in each year, unweighted mean of ln yjt across j in year t and the national
mean are plotted, together with dots showing the maximum and the minimum of ln yjt across
j in year t. The figure shows that there was a steady growth in Thailand except during 1996
and 1998 when the financial crisis fell on the economy. Throughout the period, the growth
rates of national means were higher than those of means across provinces, suggesting that
higher growth occurred in richer or populationexpanding provinces. The range between the
maximum and the minimum remained similar during the fifteen years.
qualitatively the same with those obtained when the balanced panel of provinces that did not experience boundary changes was employed.
8See the previous footnote.
12
Figure 1b plots similar information for Ineq1jt (denoted Gini). At the national level,
the inequality in Thailand declined slightly. However, not all provinces experienced reduction
in inequality. The means across provinces remained at similar levels; the minimum of Ineq1jt
across j in period t increased rather than decreased. Because the maximum of Ineq1jt across
j decreased, the figure seems to suggest the inequality convergence, although weak.
Finally, the time series of Pov1jt (denoted Poverty) is plotted in Figure 1c. The figure
showed a substantial fall in poverty headcount ratios both at the national and the provincial
level. It seems that the rapid growth of Expenditure was the major contributor to the rapid
poverty reduction in Thailand, enhanced by a slight decline of inequality at the national
level. The rate of poverty decline at the national level was similar to that of means across
provinces, suggesting that poverty reduction was experienced throughout the country.
Figures 2a to 2c plot similar time series of Expenditure, Gini, and Poverty for the
Philippines. Figure 2a shows that there was a steady growth in the Philippines as well.
Unlike in Thailand, the Philippine economy did not experience a serious fall during the
Asian financial crisis in 1997. Unlike in Thailand, the growth rates of national means were
similar to those of means across provinces, suggesting that growth occurred in both rich and
poor provinces. The range between the maximum and the minimum remained similar during
the nineteen years.
The inequality level remained flat or slightly increased in the Philippines both at the
national and provincial levels (Figure 2b). This could be one of the reasons why the rate
of poverty reduction in the Philippines was not as impressive as in Thailand. The rate of
poverty decline was slightly larger at the national level than at the provincial level in the
Philippines (Figure 2c).
The shapes of Figures 1b, 1c, 2b, and 2c did not change much when we chose different
measures of inequality and poverty. This is because inequality measures included in Ineqjt
are highly correlated each other and poverty measures included in Povjt are highly correlated
each other. To confirm this, Tables 3 and 4 show correlation coefficients among ln yjt,
five measures included in Ineqjt, and five measures included in Povjt. In Thailand, five
inequality measures have correlation coefficients in the range of 0.775 to 0.980 and five poverty
measures have correlation coefficients in the range of 0.889 to 0.999 (Table 3). Similarly in
the Philippines, the five inequality measures have correlation coefficients in the range of 0.755
to 0.992 and the five poverty measures have correlation coefficients in the range of 0.877 to
0.994 (Table 4). Because of the high correlation, we estimate a model of equations (1)(7)
13
in the next section, in which only one each from Ineqjt and Povjt is included.
Regarding the correlation among ln yjt, poverty measures, and inequality measures, the
correlation coefficient between ln yjt and poverty measures are highly negative, while those
between poverty measures and inequality measures are moderately positive. This confirms
that higher average consumption and lower inequality implies lower poverty in these two
countries. The positive correlation between the inequality and the poverty measures is not
very high, however, especially in the Philippines. The correlation coefficients are in the range
of 0.354 to 0.559 in Thailand (Table 3) and in the range of 0.042 to 0.238 in the Philippines
(Table 4).
4 Dynamics of Growth, Poverty, and Inequality in the Philippines and Thailand
4.1 Estimation Results
4.1.1 Thailand
Table 5 reports estimation results of equations (1)(3) for Thailand. Each equation was
estimated by a system GMM method which was proposed by Blundell and Bond (1998)
and applied to growth regressions by Bond et al. (2001).9 The estimation results of two
versions are reported in the table: one with year effects (ηt) only; another with ηt and Xj,t−1
(Education, Urban, Agriculture, and Aged); in addition to provincespecific effects αj . The
signs and statistical significance of parameter β are qualitatively the same between the two
versions, as far as equations (1) and (2) are concerned. In equation (3), lagged values of
Expenditure and Poverty are statistically significant only in the model without Xj,t−1.
Using the model with more controls, β11 (the convergence parameter of mean consump
tion) is estimated at 0.84, which is significantly different from zero at the 1% level but not
significantly different from one. The regression results thus are mixed with respect to the
conditional convergence hypothesis. Note that casual observations on Figure 1a suggest the
income divergence. By controlling for other factors, the parameter estimate for β11 is smaller
than one (the conditional convergence suggested) but not significantly different from one.
The parameter of inequality convergence β22 is estimated at 0.40 (the model with less
controls) or 0.43 (the model with more controls). Both are significantly smaller than one at
9The current results are based on equationbyequation system GMM estimation. By estimating equations (1), (2), and (3) simultaneously, an efficiency gain is expected. However, because the number of periods in our panel datasets is small, a panel VAR approach is not feasible in our case. System estimation using methods other than the panel VAR is left for further task.
14
the 1% level, indicating the inequality convergence. This is consistent with the finding of
inequality convergence based on crosscountry data (Benabou, 1996; Ravallion, 2003) and
casual observations on Figure 1b.
The effect on growth of lagged inequality is the most debated issue in development eco
nomics. In our model, this effect is captured by parameter β12. In Thailand, the parameter
is estimated at 1.87 (the model with more controls), which is significantly different from
zero at the 1% level. When the lagged value of Gini is increased by its standard deviation
(0.0442), the growth decreases by 0.083, which is about 1.2% of the mean of Expenditure.
This is an economically significant number since the growth rates of Expenditure between
each survey period is in the range of 1.6 to 4.9% except between the 1996 and 1998 surveys.
The effect on inequality of lagged consumption, captured by parameter β21 is estimated at
0.10 (significant at 1%). Thus a province where the initial income level was high tends to
become more equal in the subsequent period than a province with low initial income level.
All of the initial levels of Expenditure, Gini, and Poverty affect the subsequent level of
Poverty with the expected signs. However, the parameter estimates are statistically signif
icant only in the specification with less controls. As expected, the effect of lagged income
(β31) is negative and the effect of lagged inequality (β32) is positive. Judging from the ab
solute values of these coefficients in Table 5 and the standard deviations of Expenditure and
Gini in Table 1, a change of one standard deviation has a slightly stronger effect on poverty
reduction when it occurs on Expenditure than when it occurs on Gini. One coefficient which
is not analyzed in the previous literature is β33. It is estimated at 0.193 when all controls
are included and at 0.213 when only year effects are included (Table 5). Both are signif
icantly different from one. Therefore, provinces where Poverty(t1) was high (poorer) or
ΔPoverty(t1) was less negative (slower poverty reduction), subsequent change in poverty
ΔPoverty(t) tends to be more negative (faster poverty reduction).
For comparison, Table 6 reports the results estimated by pooled OLS or fixedeffect
methods. Since coefficients on Xj,t−1 are similar, the table reports only those coefficients
on lagged values of Expenditure, Gini, and Poverty. First, the sign and the significance test
results are similar between pooled OLS and system GMM results. Second, results based
on the fixed effect specifications are quite different from the system GMM results. Most
of the coefficient estimates from the fixed effect methods are statistically insignificant. The
difference is mainly due to the difference of the size of coefficients. In general, system GMM
results show larger coefficients (in absolute values) than results under the fixed effect model.
15
The difference is particularly significant for coefficients β11 and β22. Fixed effect estimates for
these parameters are positive but statistically less significant, although the null hypotheses
that β11 = 1 and β22 = 1 are rejected at 1%. Thus, the convergence in both income and
inequality is suggested by these results more strongly than by the system GMM results.
Since pooled OLS and fixed effects estimates might contain bias due to the DPD structure,
we adopt system GMM estimates for the simulation exercise in the next subsection.
Since the choice of particular measures of Ineq1jt (Gini coefficient) and Pov1jt (head
count poverty measure) is arbitrary, other measures of inequality and poverty are tried as a
robustness check. Out of twentyfive combinations, Gini in the basic specification is replaced
by one of the other four measures of inequality first and then Poverty in the basic specification
is replaced by one of the other four measures of poverty. Results are very similar to those
reported in Tables 56. Among the parameters of concern, we found β13 (the effect of lagged
poverty on subsequent growth) to become more positive and statistically significant when
GE measures replaced Gini and FGT(2) replaced Poverty; β21 (the effect of lagged income on
subsequent change in inequality) and β23 (the effect of lagged poverty on subsequent change
in inequality) to become statistically less significant when other poverty measures were used;
and β33 to become less significant when GE or FGT(2) measures were used.10
Regarding the test on the convergence, the restricted model consisting of equations
(4)(5) may be preferable, when Pov1jt is highly collinear with the linear combination of
ln yjt (or ln yj,t−1) and Ineq1jt (or Ineq1j,t−1). To facilitate the comparison with results in
the existing literature, equations (4)(7) are estimated and the results are reported in Table
7. The comparison of Tables 5 and 7 shows that β11 and β22 are underestimated by the
constrained model — both are significantly smaller than unity in Table 7.
The effect on Poverty of Expenditure is positive and that of Gini is negative, as expected,
both in specifications (6) and (7). The coefficient on Expenditure is insignificant under
specification (6) while it is statistically significant at the 1% level under specification (7);
the coefficient on Gini is about five times larger in equation (7) than in equation (6). This is
because the coefficients in equation (7) capture the spurious correlation between poverty and
growth (inequality) by the construction of data, while those in equation (6) does not include
such dilution. Therefore, as dynamic effects from growth and inequality on poverty, the
coefficients in equation (3) (Table 5) or those in equation (6) (Table 7) are better indicators.
Full results are available on request.
16
10
4.1.2 The Philippines
Table 8 reports estimation results of equations (1)(3) for the Philippines. The signs of
parameters β are exactly the same between the two versions (with less or more controls) and
the list of statistically significant coefficients are the same. The parameter of the income
convergence β11 is estimated at 1.11 when more controls are included. The coefficient is
significantly different from zero but not significantly different from one. It becomes 1.56
when only year effects are included, which is significantly different from one. Therefore, the
GMM estimation results suggest a weak income divergence. The parameter of inequality
convergence β22 is estimated at around 0.3 and significantly smaller than one. Thus a strong
convergence in inequality is indicated from Table 8. Since casual observations on Figure 2a
and 2b show neither converging nor diverging tendencies, the system GMM results in favor
of the income divergence and the inequality convergence can be explained by the additional
explanatory variables.
The effect on growth of lagged inequality β12 is highly negative in both specifications.
The parameter is estimated at 1.53 (the model with more controls), which is significantly
different from zero at the 1% level. When the lagged value of Gini is increased by its standard
deviation (0.0516), the growth decreases by 0.079, which is about 0.86% of the mean of
Expenditure. Thus, the illeffect of the initial inequality on growth in the Philippines is
slightly smaller than that of Thailand, mainly because of the difference in the size of β12.
However, since the Philippine economy experienced a slower growth, the same amount of
the illeffect might have been more hurting in the Philippines. Thus the illeffect of the
initial inequality on growth and poverty reduction will be investigated further in simulation
analyses below.
The effect of the lagged poverty on the current poverty, captured by parameter β33,
is estimated at 0.389 when all controls are included and at 0.649 when only year effects
are included (Table 8). Since both are significantly different from one, provinces which were
poorer experienced faster poverty reduction. However, these parameter coefficients are much
larger than those for Thailand. Therefore, poverty is more persistent in the Philippines than
in Thailand and the extent of poverty convergence (i.e., poorer regions experiencing faster
poverty reduction) is higher in Thailand than in the Philippines.
The robustness check of our results for the Philippines to the estimation methods shows
a pattern similar to that for Thailand. Table 9 shows that first, the sign and the significance
17
test results are similar between pooled OLS and system GMM results, and second, results
based on the fixed effect specifications are associated with smaller coefficients than those
based on the system GMM method. Nevertheless, the contrast between the system GMM and
the fixed effects results is less clear in the Philippines. As far as the statistically significant
coefficients are concerned, all three estimation methods yield qualitatively similar results.
One difference is again the income convergence test. According to the GMM results, weak
support for the income divergence was suggested, while the fixed effect results suggest the
income convergence, with the pooled OLS results in between.
Regarding the test on the convergence, the results in Table 8 may attribute some of
the growth components to poverty (parameter β13). To check this possibility, the results of
the restricted model consisting of equations (4)(5) are reported in Table 10. The results
show that the income convergence is supported, even with the GMM estimation method.
The results in favor of the inequality convergence remain the same in Table 10. Parameter
β22 was always positive with statistical significance and its magnitude was much smaller
than unity, suggesting the inequality convergence in the Philippines, like the one found by
Benabou (1996) and Ravallion (2003).
The magnitudes of the positive effect of Expenditure and the negative effect of Gini
on Poverty are sensitive to specifications in the Philippines (Table 10), as in Thailand:
the coefficients are three to four times larger in equation (7) than in equation (6). This
again warns against the use of specification (6) when the dynamic effects from growth and
inequality on poverty are of concern. However, the difference in the magnitudes is smaller
in the Philippines than in Thailand. This is consistent with the contrast of the magnitudes
of parameter β33 in Tables 5 and 8. It is much larger in the Philippines than in Thailand,
indicating that poverty is more persistent in the Philippines than in Thailand. Because of
the persistence, the bias due to the use of specification (6) in place of specification (7) is
smaller in the Philippines.
When other measures of inequality and poverty were tried, qualitatively the same results
were obtained for the Philippines. Parameter β12 was estimated with a smaller standard error
when GE measures replaced Gini or FGT(1) poverty measure replaced Poverty, with more
statistical significance.
18
4.2 Simulating the Sources for Growth and Poverty Reduction
4.2.1 Simulation methods
Given the estimation results in the previous subsection, how much of the changes in growth
shown in Figures 1a and 2a and poverty reduction shown in Figures 1c and 2c can be
attributable to (i) initial differences in average consumption, poverty, and inequality; (ii)
differences in marginal impacts of the lagged values of average consumption, poverty, and
inequality (differences in β); and (iii) differences in the distribution of other controls Xt?
We simulate these sources for growth and poverty reduction by calculating counterfac
tual dynamic paths of the two economies under several scenarios. Since our original micro
data cover different periods, we choose 1988 and 2000 as the comparison years (i.e., the two
years when we have micro data for both countries: see Figures 1 and 2). First, based on the
parameter estimates in Tables 5 and 8, we calculated the fitted values of residuals as follows:
ˆ ˆ α1j + ˆ �1jt, (8)ln yjt = β11 ln yj,t−1 + β12Ineq1,j,t−1 + β13Pov1,j,t−1 + Xj,t−1θ1 + ˆ η1t + ˆ
ˆ ˆ α2j + ˆ �2jt, (9)Ineq1jt = β21 ln yj,t−1 + β22Ineq1,j,t−1 + β23Pov1,j,t−1 + Xj,t−1θ2 + ˆ η2t + ˆ
ˆ ˆ α3j + ˆ �3jt.Pov1jt = β31 ln yj,t−1 + β32Ineq1,j,t−1 + β33Pov1,j,t−1 + Xj,t−1θ3 + ˆ η3t + ˆ (10)
For the first type of simulations (the impact of the initial differences), we give an
additional shock to one of the lefthandside variables, say, the inequality, in 1988. Then
we sequentially solve the dynamic system until the year 2000, keeping the values of X, β,
θ, ˆ η, and ˆα, ˆ � constant. For the second type of simulations (the impact of the differences
in β), we give a counterfactual value to one of the parameters in β (say, replacing β12 for
the Philippines by β12 for Thailand) in 1988 and onwards. Then we sequentially solve the
dynamic system until the year 2000, keeping the values of X, θ, ˆ η, ˆα, ˆ �, and the other
parameters of β constant. For the third type of simulations (the impact of other controls),
we give counterfactual values to one of the variables in X (say, proportionally adjusting
Xeducation for the Philippines so that the Philippine educational achievement tracks that of
Thailand) in 1988 and onwards. Then we sequentially solve the dynamic system until the
year 2000, keeping the values of β, θ, ˆ η, ˆα, ˆ �, and the other variables of X constant.
4.2.2 Dynamic impacts of inequality
In this paper, we report the simulation results for the first and the second type, focusing on
the impacts of inequality on consequent growth and poverty reduction (Table 11). In the
19
first row, the baseline values that replicate the observed dynamic paths are reported. In the
Philippines, the annual growth rate of percapita expenditure was 2.20% during the 1988
2000 period, which was associated with the poverty reduction (in terms of headcount index)
at the annual rate of 0.27%. Both of these numbers are smaller than those of Thailand:
percapita expenditure grew at 3.68% and the headcount poverty index declined at 1.40%
per annum during the 19882000 period. The baseline numbers clearly show the contrast
between the Philippines and Thailand.
In Simulation 1, we add a shock to equation (2) in 1988 so that the inequality level
in that year is halved from the actual value both in the Philippines and Thailand. The
reduction in Ineq1j,t−1 in the righthand side of equations (1)(3) increases growth rates
and decreases inequality and poverty in the next period. When the economy reaches the
year 2000, the cumulative effect on the growth of percapita expenditures and on poverty
reduction is substantial. In the Philippines, the annual growth rate of percapita expenditure
would have been much higher at 5.50% during the 19882000 period, which would have been
associated with a higher poverty reduction speed at 0.93%. Qualitatively the same change
would have occurred in Thailand: percapita expenditure would have grown at 5.88% and
the headcount poverty index would have declined at 1.63% per annum during the 19882000
period.
An interesting finding is that the counterfactual performance under Simulation 1 is
very similar between Thailand and the Philippines. Although the estimate for β12 is larger in
Thailand than in the Philippines, implying that the marginal illeffect of the lagged inequality
on growth is larger in Thailand, the simulation results show that the total, cumulative ill
effect of the lagged inequality on growth is larger in the Philippines. This is because in
Simulation 1, we halved the initial inequality level in both countries. Since the inequality
level in year 1988 was much higher in the Philippines than in Thailand, the cumulative effect
of the counterfactual shock is much larger in the Philippines than in Thailand. Thus, we
obtain the results that the counterfactual performance under Simulation 1 is very similar
between Thailand and the Philippines. In other words, the high level of initial inequality was
one of the main contributors to slower growth and slower poverty reduction in the Philippines
than in Thailand.
In Simulation 2, we replace the value of β12 for the Philippines by that for Thailand,
and we replace the value of β12 for Thailand by that for the Philippines. As shown in Tables
5 and 8, the estimate for β12 is larger in Thailand than in the Philippines, implying that the
20
5
marginal illeffect of the lagged inequality on growth is larger in Thailand. The simulation
results in Table 11 thus show the total, cumulative illeffect of the lagged inequality on
growth due to the difference in marginal impacts of the lagged values of inequality between
the two countries.
The cumulative effect of this simulation on the growth of percapita expenditures and
on poverty reduction is substantial when the economy reaches the year 2000. In the Philip
pines, the annual growth rate of percapita expenditure would have been negative (1.49%)
during the 19882000 period, which would have been associated with an increase of poverty
at the annual rate of 0.47%. Thus the Philippine economy was very fortunate that its
value of β12 is lower than the value used in the counterfactual scenario corresponding to
Thailand’s. In sharp contrast, growth and poverty reduction would have been much acceler
ated in Thailand if the economy would have had a lower value of β12 as in the Philippines:
percapita expenditure would have grown at 9.79% and the headcount poverty index would
have declined at 2.58% per annum during the 19882000 period. With this rate of poverty
reduction, the headcount poverty index would have been zero in 2000 for the majority of
provinces in Thailand. In other words, the highly negative response of economic growth
to the lagged inequality was one of the main contributors to the slow poverty reduction in
Thailand (“slow” in relative to its phenomenal growth rate). The economic performance in
Thailand is often regarded as an case of less propoor growth associated with a substantial
economic growth, resulting in a reasonably high pace of poverty reduction (Kakwani et al.,
2004; Booth, 1997). Our analysis sheds new light on this phenomenon from the viewpoint
of dynamic relationship among growth, inequality, and poverty.
Conclusion
To empirically analyze the dynamics of, and relations among, growth, poverty, and inequal
ity, this paper proposed a framework in which due attention is paid to the fact that the entire
distribution of percapita real consumption changes over time and that empirical variables
of growth, poverty, and inequality are often compiled from the same micro dataset. Implica
tions were derived from the framework, regarding the dynamic relationship among growth,
inequality, and poverty.
As an illustration, the dynamic model was applied to unique panel data of provinces
in the Philippines (19852003) and Thailand (19882002), which were compiled from micro
21
datasets of household expenditure surveys. The system GMM estimation results showed
that lagged levels of inequality reduce the speed of economic growth and poverty reduction
in Thailand directly and reduce the speed of economic growth directly and poverty reduction
indirectly in the Philippines. The magnitudes of the marginal effects of inequality were found
to be larger in Thailand than in the Philippines. It was also suggested that the fixed effect
estimation might underestimate the marginal effect of lagged inequality on subsequent in
equality change and the marginal effect of lagged income on subsequent income. Our results
showed mixed evidence regarding the conditional income convergence among provinces but
clear evidence for the conditional inequality convergence in both countries. Regarding the
specification of the poverty determinants, our analysis suggested that the regression of cur
rent poverty on current inequality and growth might overestimate the true dynamic effects
of growth and inequality on poverty reduction.
Simulation results based on the parameter estimates showed that the inequality factor,
which is due to differences in the initial inequality level and in its marginal impact between
the two countries, explained a substantial portion of the PhilippineThai difference in eco
nomic growth and poverty reduction during the late 1980s and 1990s. The comparison of
the two economies shed a new light on the understanding of a structural difference among
Asian countries. The mechanism underlying the difference in the initial inequality level and
in its marginal impact, however, still remains in the blackbox. Investigating the mechanism
utilizing micro data in these two countries is left for the next research agenda.
22
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24
Figure 1a: Time Series of Expenditure , Thailand
8.5
8
7.5
7
6.5
6
1988 1990 1992 1994 1996 1998 2000 2002
Figure 1b: Time Series of Gini , Thailand
0.5
0.4
0.3
0.2
0.1 1988 1990 1992 1994 1996 1998 2000 2002
Figure 1c: Time Series of Poverty , Thailand
0.6
0.5
0.4
0.3
0.2
0.1
0 1988 1990 1992 1994 1996 1998 2000 2002
National mean Mean across provinces Max Min
Figure 2a: Time Series of Expenditure , the Philippines
11.5
11
10.5
10
9.5
9
8.5
8
7.5 1985 1988 1991 1994 1997 2000 2003
Figure 2b: Time Series of Gini , the Philippines
0.6
0.5
0.4
0.3
0.2
0.1 1985 1988 1991 1994 1997 2000 2003
Figure 2c: Time Series of Poverty , the Philippines
1
0.8
0.6
0.4
0.2
0 1985 1988 1991 1994 1997 2000 2003
National mean Mean across provinces Max Min
Table 1: Summary statistics of regression variables, Thailand
Variable Definition Obs Mean Std. Dev. Min Max Expenditure Log mean per-capita consumption in each province in real Bahts. 599 7.0745 0.3322 6.2074 8.0794 Gini Gini coefficient of per-capita consumption in each province. 599 0.3331 0.0442 0.2243 0.4701 Poverty Head count index in each province based on per-capita consumption. 540 0.1071 0.1140 0.0000 0.5627 Education Ratio of households whose head has tertiary education (more than 12 years of schooling). 599 0.0748 0.0488 0.0018 0.3566 Urban Ratio of households who live in urban areas. 599 0.8772 0.1427 0.0000 1.0000 Agriculture Ratio of households whose head is engaged in agriculture. 599 0.5372 0.1976 0.0074 0.9608 Aged Population share of individuals aged more than or equal to 65. 599 0.1543 0.0563 0.0000 0.3424
Table 2: Summary statistics of regression variables, the Philippines
Variable Definition Obs Mean Std. Dev. Min Max Expenditure Log mean per-capita consumption in each province in real Pesos. 556 9.1725 0.6122 7.7646 10.7446 Gini Gini coefficient of per-capita consumption in each province. 556 0.3595 0.0516 0.2005 0.5165 Poverty Head count index in each province based on per-capita consumption. 556 0.4984 0.1815 0.0469 0.9071 Education Ratio of households whose head has tertiary education (more than 10 years of schooling). 556 0.1522 0.0690 0.0138 0.4055 Urban Ratio of households who live in urban areas. 556 0.3493 0.2242 0.0387 1.0000 Agriculture Ratio of households whose head is engaged in agriculture. 556 0.3705 0.1509 0.1000 0.7892 Aged Population share of individuals aged more than or equal to 65. 556 0.1304 0.0558 0.0000 0.3538
Table 3: Bi-variate correlation coefficients of mean per-capita consumption expenditure, inequality measures, and poverty measures in Thailand
Expenditur e FGT(0) FGT(1) FGT(2) Watt's
Index
Clark-Watt's
Index (-1) GE(-1) GE(0) GE(1) GE(2) Gini
Expenditure 1.0000 FGT(0) -0.7570 1.0000 FGT(1) -0.6746 0.9550 1.0000 FGT(2) -0.5991 0.8886 0.9817 1.0000 Watt's Index -0.6626 0.9454 0.9994 0.9879 1.0000 Clark-Watt's Index (-1) -0.6482 0.9333 0.9970 0.9934 0.9992 1.0000 GE(-1) -0.0572 0.3541 0.3684 0.3606 0.3681 0.3673 1.0000 GE(0) -0.1831 0.4346 0.4264 0.4017 0.4232 0.4190 0.9796 1.0000 GE(1) -0.3149 0.5081 0.4771 0.4351 0.4707 0.4630 0.9084 0.9725 1.0000 GE(2) -0.4226 0.5586 0.5082 0.4514 0.4991 0.4883 0.7753 0.8773 0.9628 1.0000 Gini -0.1769 0.4101 0.3930 0.3630 0.3887 0.3833 0.9677 0.9921 0.9634 0.8627 1.0000
Table 4: Bi-variate correlation coefficients of mean per-capita consumption expenditure, inequality measures, and poverty measures in the Philippines
Expenditur e FGT(0) FGT(1) FGT(2) Watt's
Index
Clark-Watt's
Index (-1) GE(-1) GE(0) GE(1) GE(2) Gini
Expenditure 1.0000 FGT(0) -0.7332 1.0000 FGT(1) -0.6643 0.9539 1.0000 FGT(2) -0.6100 0.8926 0.9855 1.0000 Watt's Index -0.6360 0.9209 0.9910 0.9915 1.0000 Clark-Watt's Index (-1) -0.5983 0.8771 0.9742 0.9928 0.9935 1.0000 GE(-1) 0.1268 -0.0418 0.0384 0.0764 0.0740 0.0959 1.0000 GE(0) 0.0626 0.0564 0.1221 0.1481 0.1525 0.1662 0.9786 1.0000 GE(1) -0.0263 0.1602 0.2067 0.2188 0.2282 0.2327 0.8968 0.9648 1.0000 GE(2) -0.1070 0.2121 0.2378 0.2381 0.2476 0.2446 0.6799 0.7844 0.9098 1.0000 Gini 0.0797 0.0383 0.1046 0.1317 0.1365 0.1505 0.9669 0.9919 0.9502 0.7553 1.0000
Table 5: System-GMM estimation results, Thailand
L.H.S.variable = Expenditure(t) L.H.S.variable = Gini(t) L.H.S.variable = Poverty(t) Equation (1) Equation (2) Equation (3)
(With year effect only) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.94495 0.04833 19.55 0.000 -0.08528 0.01598 -5.34 0.000 -0.06095 0.03055 -1.99 0.050 Gini(t-1) -2.15308 0.24125 -8.92 0.000 0.40460 0.07464 5.42 0.000 0.24499 0.10154 2.41 0.018 Poverty(t-1) 0.87875 0.18378 4.78 0.000 -0.14477 0.04538 -3.19 0.002 0.21302 0.11883 1.79 0.077 Cons. 1.12485 0.33858 3.32 0.001 0.82445 0.10155 8.12 0.000 0.39707 0.20482 1.94 0.056 F test for 0 slope F (9, 75) = 124.10 Prov > F = 0.000 F (9, 75) = 9.49 Prov > F = 0.002 F (9, 75) = 38.18 Prov > F = 0.000 Sargan test Chi2 (28) = 41.22 Prov > chi2 = 0.051 Chi2 (28) = 34.80 Prov > chi2 = 0.176 Chi2 (28) = 45.48 Prov > chi2 = 0.020 AR(1) z= -5.99 Prov > z = 0.000 z= -5.80 Prov > z = 0.000 z= -4.74 Prov > z = 0.000 AR(2) z= -0.52 Prov > z = 0.603 z= -0.58 Prov > z = 0.563 z= -0.08 Prov > z = 0.934
(With year effect and controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.84056 0.11925 7.05 0.000 -0.09932 0.02227 -4.46 0.000 -0.03217 0.03699 -0.87 0.387 Gini(t-1) -1.86693 0.29137 -6.41 0.000 0.42685 0.08256 5.17 0.000 0.19579 0.11061 1.77 0.081 Poverty(t-1) 0.80694 0.22470 3.59 0.001 -0.16907 0.04554 -3.71 0.000 0.19292 0.12471 1.55 0.126 Education -0.81477 0.34413 -2.37 0.020 -0.08046 0.06159 -1.31 0.195 0.06082 0.10668 0.57 0.570 Urban -0.01926 0.12551 -0.15 0.878 -0.03910 0.02457 -1.59 0.116 -0.06022 0.05172 -1.16 0.248 Agriculture -0.25471 0.09573 -2.66 0.010 -0.00994 0.01955 -0.51 0.613 0.11710 0.02600 4.50 0.000 Aged -0.05058 0.18204 -0.28 0.782 -0.15426 0.03639 -4.24 0.000 -0.10336 0.07640 -1.35 0.180 Cons. 1.99680 0.85970 2.32 0.023 0.98702 0.16056 6.15 0.000 0.21238 0.27813 0.76 0.448 F test for 0 slope F (13, 75) = 49.09 Prov > F = 0.000 F (13, 75) = 8.61 Prov > F = 0.000 F (13, 75) = 26.71 Prov > F = 0.000 Sargan test Chi2 (26) = 30.70 Prov > chi2 = 0.240 Chi2 (26) =32.92 Prov > chi2 = 0.164 Chi2 (26) = 45.99 Prov > chi2 = 0.009 AR(1) z= -5.77 Prov > z = 0.000 z= -5.67 Prov > z = 0.000 z= -4.83 Prov > z = 0.000 AR(2) z= -0.69 Prov > z = 0.489 z= -0.27 Prov > z = 0.787 z= -0.10 Prov > z = 0.923
Note: The number of observations is 437 and the number of groups in the panel is 75.
Table 6: Results under different estimation methods, Thailand
L.H.S.variable = Expenditure(t) L.H.S.variable = Gini(t) L.H.S.variable = Poverty(t) Equation (1) Equation (2) Equation (3)
1. Pooled OLS
(With year effect only) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.86076 0.03882 22.17 0.000 -0.03698 0.00756 -4.89 0.000 -0.08383 0.01582 -5.30 0.000 Gini(t-1) -1.38502 0.20161 -6.87 0.000 0.35707 0.04384 8.14 0.000 0.25856 0.08264 3.13 0.002 Poverty(t-1) 0.30309 0.12471 2.43 0.015 -0.00360 0.02643 -0.14 0.892 0.31479 0.06573 4.79 0.000
(With year effect and controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.62897 0.08169 7.70 0.000 -0.04903 0.01530 -3.20 0.001 -0.10346 0.02863 -3.61 0.000 Gini(t-1) -1.11172 0.22511 -4.94 0.000 0.36897 0.04686 7.87 0.000 0.26252 0.09119 2.88 0.004 Poverty(t-1) 0.14230 0.14520 0.98 0.328 -0.03249 0.03015 -1.08 0.282 0.24190 0.07141 3.39 0.001
2. Fixed effects
(With year effect only) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.20567 0.05777 3.56 0.000 -0.01840 0.01470 -1.25 0.211 0.01558 0.02451 0.64 0.525 Gini(t-1) -0.53511 0.20691 -2.59 0.010 0.04126 0.05267 0.78 0.434 0.05839 0.08778 0.67 0.506 Poverty(t-1) 0.00316 0.12041 0.03 0.979 -0.00358 0.03065 -0.12 0.907 0.09471 0.05108 1.85 0.064
(With year effect and controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.19813 0.07006 2.83 0.005 -0.01719 0.01778 -0.97 0.334 -0.01798 0.02955 -0.61 0.543 Gini(t-1) -0.55029 0.21024 -2.62 0.009 0.05221 0.05336 0.98 0.328 0.10722 0.08867 1.21 0.227 Poverty(t-1) 0.02936 0.12849 0.23 0.819 -0.00913 0.03261 -0.28 0.780 0.04123 0.05419 0.76 0.447
Note: Parameter estimates for other right-hand-side variables and test results are deleted for brevity. Full results as those in Table 5 are available on request.
Table 7: Estimation results for other specifications, Thailand
System GMM estimation results L.H.S.variable = Expenditure(t) L.H.S.variable = Gini(t)
Equation (4) Equation (5)
(With year effect on Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.74231 0.04418 16.80 0.000 -0.04624 0.00710 -6.51 0.000 Gini(t-1) -1.55987 0.19409 -8.04 0.000 0.28515 0.04876 5.85 0.000 Cons. 2.45819 0.31788 7.73 0.000 0.57265 0.05196 11.02 0.000 F test for 0 slope F (8, 75) = 108.63 Prov > F = 0.000 F (8, 75) = 16.06 Prov > F = 0.000 Sargan test Chi2 (28) = 38.21 Prov > chi2 = 0.094 Chi2 (28) = 34.46 Prov > chi2 = 0.380 AR(1) z= -5.59 Prov > z = 0.000 z= -5.72 Prov > z = 0.000 AR(2) z= -0.03 Prov > z = 0.980 z= -0.16 Prov > z = 0.875
(With all controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.51830 0.08932 5.80 0.000 -0.04070 0.01133 -3.59 0.001 Gini(t-1) -1.27729 0.19169 -6.66 0.000 0.29248 0.05409 5.41 0.000 Education -0.35277 0.35138 -1.00 0.319 -0.14409 0.06478 -2.22 0.029 Urban -0.08967 0.12535 -0.72 0.477 -0.02231 0.02129 -1.05 0.298 Agriculture -0.31745 0.09333 -3.40 0.001 -0.00346 0.01817 -0.19 0.850 Aged -0.17895 0.18427 -0.97 0.335 -0.12567 0.03748 -3.35 0.001 Cons. 4.24711 0.63730 6.66 0.000 0.58144 0.08678 6.70 0.000 F test for 0 slope F (12, 75) = 52.82 Prov > F = 0.000 F (12, 75) = 12.85 Prov > F = 0.000 Sargan test Chi2 (26) = 32.84 Prov > chi2 = 0.167 Chi2 (26) = 33.29 Prov > chi2 = 0.154 AR(1) z= -5.57 Prov > z = 0.000 z= -5.73 Prov > z = 0.000 AR(2) z= -0.19 Prov > z = 0.847 z= 0.21 Prov > z = 0.836
Fixed effect estimation results L.H.S.variable = Poverty(t) L.H.S.variable = Poverty(t)
Equation (6) Equation (7) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t
Expenditure(t-1) -0.01350 0.01888 -0.72 0.475 Expenditure(t) -0.30138 0.01627 -18.52 0.000 Gini(t-1) 0.13247 0.07838 1.69 0.092 Gini(t) 0.75709 0.06681 11.33 0.000 Cons. 0.13107 0.12603 1.04 0.299 Cons. 1.97931 0.10721 18.46 0.000 F test for 0 slope F test that all u_i=0
F (8, 439)
F (75, 439)
= 24.03
= 3.67
Prov > F
Prov > F
= 0.000
= 0.000
F test for 0 slope F test that all u_
F (9, 514) i=0
F (75, 514
= 124.75
) = 3.85
Prov > F = 0.000
Prov > F = 0.000
Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) -0.03303 0.02194 -1.51 0.133 Expenditure(t) -0.35668 0.01810 -19.70 0.000 Gini(t-1) 0.13858 0.07847 1.77 0.078 Gini(t) 0.70479 0.06360 11.08 0.000 Education 0.13573 0.11105 1.22 0.222 Education 0.59290 0.08656 6.85 0.000 Urban -0.15578 0.06471 -2.41 0.016 Urban -0.02288 0.03948 -0.58 0.563 Agriculture 0.04838 0.03676 1.32 0.189 Agriculture -0.00234 0.03058 -0.08 0.939 Aged -0.10540 0.06730 -1.57 0.118 Aged -0.15524 0.05449 -2.85 0.005 Cons. 0.38321 0.16962 2.26 0.024 Cons. 2.38537 0.13418 17.78 0.000 F test for 0 slope F (12, 435) = 17.49 Prov > F = 0.000 F test for 0 slope F (13, 510) = 102.89 Prov > F = 0.000 F test that all u_i=0 F test that all u_i=0
F (75, 435) = 3.15 Prov > F = 0.000 F (75, 510) = 2.48 Prov > F = 0.000
Table 8: System-GMM estimation results, the Philippines
L.H.S.variable = Expenditure(t) L.H.S.variable = Gini(t) L.H.S.variable = Poverty(t) Equation (1) Equation (2) Equation (3)
(With year effect only) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 1.55836 0.09266 16.82 0.000 -0.06690 0.02344 -2.85 0.005 -0.06109 0.05936 -1.03 0.307 Gini(t-1) -1.85169 0.37447 -4.94 0.000 0.32009 0.09194 3.48 0.001 0.02098 0.12011 0.17 0.862 Poverty(t-1) 1.22731 0.15771 7.78 0.000 -0.08046 0.03984 -2.02 0.047 0.64946 0.11223 5.79 0.000 Cons. -4.82464 0.80716 -5.98 0.000 0.85617 0.20982 4.08 0.000 0.71298 0.55572 1.28 0.203 F test for 0 slope F (8, 81) = 143.94 Prov > F = 0.000 F (8, 81) = 7.73 Prov > F = 0.000 F (8, 81) = 75.16 Prov > F = 0.000 Sargan test Chi2 (21) = 17.79 Prov > chi2 = 0.662 Chi2 (21) = 24.92 Prov > chi2 = 0.251 Chi2 (21) = 35.27 Prov > chi2 = 0.026 AR(1) z= -4.65 Prov > z = 0.000 z= -5.10 Prov > z = 0.000 z= -5.81 Prov > z = 0.000 AR(2) z= 1.32 Prov > z = 0.188 z= -0.42 Prov > z = 0.672 z= 0.82 Prov > z = 0.411
(With year effect and controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 1.11018 0.09195 12.07 0.000 -0.05712 0.02145 -2.66 0.009 -0.04026 0.03898 -1.03 0.305 Gini(t-1) -1.53444 0.30550 -5.02 0.000 0.26171 0.09013 2.90 0.005 0.06899 0.12062 0.57 0.569 Poverty(t-1) 0.73428 0.10762 6.82 0.000 -0.01029 0.03233 -0.32 0.751 0.38904 0.09272 4.20 0.000 Education -0.18135 0.25189 -0.72 0.474 0.04823 0.07855 0.61 0.541 0.00512 0.15067 0.03 0.973 Urban 0.20316 0.06920 2.94 0.004 -0.01164 0.02340 -0.50 0.620 -0.17736 0.04540 -3.91 0.000 Agriculture 0.26320 0.12791 2.06 0.043 0.09280 0.03239 2.86 0.005 -0.18729 0.07839 -2.39 0.019 Aged 0.54262 0.20358 2.67 0.009 0.08505 0.06816 1.25 0.216 -0.21626 0.15801 -1.37 0.175 Cons. -0.74383 0.72821 -1.02 0.310 0.69240 0.18866 3.67 0.000 0.70082 0.35738 1.96 0.053 F test for 0 slope F (12, 81) = 720.34 Prov > F = 0.000 F (12, 81) = 12.13 Prov > F = 0.000 F (12, 81) = 78.00 Prov > F = 0.000 Sargan test Chi2 (19) = 21.46 Prov > chi2 = 0.312 Chi2 (9) = 29.37 Prov > chi2 = 0.060 Chi2 (19) = 25.77 Prov > chi2 = 0.137 AR(1) z= -4.92 Prov > z = 0.000 z= -5.06 Prov > z = 0.000 z= -4.96 Prov > z = 0.000 AR(2) z= 0.49 Prov > z = 0.622 z= -0.51 Prov > z = 0.610 z= 0.09 Prov > z = 0.926
Note: The number of observations is 388 and the number of groups in the panel is 82.
Table 9: Results under different estimation methods, the Philippines
L.H.S.variable = Expenditure(t) L.H.S.variable = Gini(t) L.H.S.variable = Poverty(t) Equation (1) Equation (2) Equation (3)
1. Pooled OLS
(With year effect only) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.97038 0.05180 18.73 0.000 -0.04805 0.01503 -3.20 0.001 -0.14373 0.03761 -3.82 0.000 Gini(t-1) -0.52827 0.17169 -3.08 0.002 0.60846 0.05220 11.66 0.000 0.06335 0.10625 0.60 0.551 Poverty(t-1) 0.09813 0.09990 0.98 0.326 -0.05999 0.02981 -2.01 0.045 0.61854 0.07300 8.47 0.000
(With year effect and controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.81028 0.05808 13.95 0.000 -0.06615 0.01755 -3.77 0.000 -0.06690 0.04052 -1.65 0.099 Gini(t-1) -0.64029 0.16232 -3.94 0.000 0.58424 0.05042 11.59 0.000 0.13389 0.10441 1.28 0.200 Poverty(t-1) 0.22762 0.10062 2.26 0.024 -0.04699 0.03024 -1.55 0.121 0.53135 0.07376 7.20 0.000
2. Fixed effects
(With year effect only) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.27426 0.10007 2.74 0.006 0.00752 0.02811 0.27 0.789 -0.15788 0.06670 -2.37 0.018 Gini(t-1) -0.45653 0.22927 -1.99 0.047 -0.02668 0.06441 -0.41 0.679 0.28581 0.15282 1.87 0.062 Poverty(t-1) -0.01482 0.12896 -0.11 0.909 0.02072 0.03623 0.57 0.568 0.11678 0.08596 1.36 0.175
(With year effect and controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.22017 0.10160 2.17 0.031 -0.00698 0.02868 -0.24 0.808 -0.13242 0.06755 -1.96 0.051 Gini(t-1) -0.50061 0.22802 -2.20 0.029 -0.03457 0.06437 -0.54 0.592 0.31245 0.15161 2.06 0.040 Poverty(t-1) 0.01561 0.12950 0.12 0.904 0.02332 0.03656 0.64 0.524 0.07907 0.08610 0.92 0.359
Note: Parameter estimates for other right-hand-side variables and test results are deleted for brevity. Full results as those in Table 8 are available on request.
Table 10: Estimation results for other specifications, the Philippines
System GMM estimation results L.H.S.variable = Expenditure(t) L.H.S.variable = Gini(t)
Equation (4) Equation (5)
(With year effect on Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.87800 0.03031 28.97 0.000 -0.02631 0.00603 -4.37 0.000 Gini(t-1) -1.08199 0.10481 -10.32 0.000 0.20850 0.06989 2.98 0.003 Cons. 1.38347 0.25214 5.49 0.000 0.50415 0.04912 10.26 0.000 F test for 0 slope F (7, 81) = 307.41 Prov > F = 0.000 F (7, 81) = 7.46 Prov > F = 0.000 Sargan test Chi2 (21) = 17.27 Prov > chi2 = 0.695 Chi2 (21) = 25.82 Prov > chi2 = 0.213 AR(1) z= -5.07 Prov > z = 0.000 z= -5.18 Prov > z = 0.000 AR(2) z= 0.80 Prov > z = 0.422 z= -0.32 Prov > z = 0.746
(With all controls) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) 0.56290 0.11829 4.76 0.000 -0.06177 0.00936 -6.60 0.000 Gini(t-1) -0.85300 0.12013 -7.10 0.000 0.24421 0.07055 3.46 0.001 Education 0.16329 0.19481 0.84 0.402 0.08751 0.03673 2.38 0.018 Urban 0.32752 0.05932 5.52 0.000 -0.01635 0.01726 -0.95 0.344 Agriculture -0.18413 0.10155 -1.81 0.070 -0.06706 0.01896 -3.54 0.000 Aged 0.31971 0.10043 3.18 0.002 0.04999 0.03079 1.62 0.105 Cons. 3.92947 0.99701 3.94 0.000 0.81484 0.07711 10.57 0.000 F test for 0 slope F (11, 81) = 819.34 Prov > F = 0.000 F (11, 81) = 13.14 Prov > F = 0.000 Sargan test Chi2 (19) = 21.07 Prov > chi2 = 0.333 Chi2 (19) = 29.33 Prov > chi2 = 0.061 AR(1) z= -5.38 Prov > z = 0.000 z= -5.19 Prov > z = 0.000 AR(2) z= 0.54 Prov > z = 0.591 z= -0.51 Prov > z = 0.613
Fixed effect estimation results L.H.S.variable = Poverty(t) L.H.S.variable = Poverty(t)
Equation (6) Equation (7) Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t
Expenditure(t-1) -0.23611 0.03371 -7.01 0.000 Expenditure(t) -0.65882 0.01778 -37.06 0.000 Gini(t-1) 0.40847 0.12344 3.31 0.001 Gini(t) 1.05189 0.06723 15.65 0.000 Cons. 2.42889 0.27335 8.89 0.000 Cons. 5.80165 0.14425 40.22 0.000 F test for 0 slope F (7, 382) = 41.18 Prov > F = 0.000 F test for 0 slope F (8, 463) = 268.44 Prov > F = 0.000 F test that all u_i=0 F test that all u_i=0
F (81, 382) = 5.01 Prov > F = 0.000 F (84, 463) = 10.83 Prov > F = 0.000
Coef. Std. Err. t-stat P>t Coef. Std. Err. t-stat P>t Expenditure(t-1) -0.18388 0.03771 -4.88 0.000 Expenditure(t) -0.65539 0.02241 -29.24 0.000 Gini(t-1) 0.39413 0.12276 3.21 0.001 Gini(t) 1.05020 0.07294 14.4 0.000 Education -0.03274 0.14487 -0.23 0.821 Education 0.08062 0.08597 0.94 0.349 Urban 0.12420 0.09226 1.35 0.179 Urban 0.05164 0.05473 0.94 0.346 Agriculture 0.26848 0.08237 3.26 0.001 Agriculture 0.14500 0.04883 2.97 0.003 Aged 0.21610 0.14130 1.53 0.127 Aged 0.02092 0.08376 0.25 0.803 Cons. 1.79234 0.32768 5.47 0.000 Cons. 5.58747 0.19349 28.88 0.000 F test for 0 slope F (11, 378) = 27.99 Prov > F = 0.000 F test for 0 slope F (11, 378) = 146.47 Prov > F = 0.000 F test that all u_i=0 F test that all u_i=0
F (81, 378) = 3.68 Prov > F = 0.000 F (81, 378) = 9.27 Prov > F = 0.000
Table 11: Simulation results for the dynamic impacts of inequality, 1988-2000
The Philippines Thailand Annual growth Annual rate of Annual growth Annual rate of rate of per-capita poverty rate of per-capita poverty expenditure (%) reduction (%) expenditure (%) reduction (%)
Baseline 2.20 0.27 3.68 1.40 Simulation 1: Adding a shock to equation (2) in 1988 so that the inequality level in that year is halved from the actual value
Counterfactual 5.50 0.93 5.88 1.63 Simulation 2: Replacing the value of β12 (the marginal effect of the lagged inequality on growth) by the value of the other country
Counterfactual -1.49 -0.47 9.79 2.58