T.C.
MARMARA ÜNİVERSİTESİ
SOSYAL BİLİMLER ENSTİTÜSÜ
İKTİSAT (İNG) ANABİLİM DALI
İKTİSAT (İNG) BİLİM DALI
Growth by Poverty Alleviation or Poverty Creation: For the Case of Least
Developed Countries
Independent Research Paper
ADEM GÖK
Danışman: PROF. A. SUUT DOĞRUEL
İstanbul, 2013
Name Surname
: Adem Gök
Field
: Economics
Program
: Economics (English Medium)
Supervisor
: Prof. Dr. A. Suut Doğruel
Date
: January 2013
Keywords
: Income inequality, Growth, G2SLS
ABSTRACT
The aim of the paper is to analyze the effect of the growth on income distribution in
Least Developed Countries (LDCs) and to conclude whether growth has alleviated or created
poverty in these low-income countries.
Since the relationship between growth and income inequality does not fit Kuznets’
hypothesis, only investing in human capital is an effective strategy for alleviating poverty in
LDCs according to the estimation results.
1.INTRODUCTION
The primary objective of the Programme of Actions (PoAs), Official Development
Assistance (ODA) or Official Aid from donor countries and multinational organization is to
eradicate or at least to reduce poverty in Least Developed Countries (LDCs) as stated in the
Millennium Development Goals (MDGs). All the actions taken by World Bank, United
Nations or donor countries aim to increase the growth in LDCs by ODAs, official aid,
increasing their governance performance should be for one ultimate goal; reducing poverty in
these low-income countries.
In an earlier study; Gök (2012), it is argued that better governance leads to growth in
LDCs, and “official development assistance and official aid” received by LDCs either leads to
growth or has no significant effect on growth in LDCs. But the question whether growth
reduces or increases poverty was not asked. Thus the aim of the paper is to analyze the effect
of growth on income distribution in LDCs and to conclude whether growth has alleviated or
created poverty in these low-income countries.
Although the category of LDC include 48 countries, the lack of data especially for gini
coefficient has reduced the number of LDCs to 19 countries. Since the distribution of these 19
countries reflect all different dimensions and characteristics of 48 LDCs, the study does not
constitute any country bias.
The remainder of the paper is organized as follows. Section 2 presents the literature
review about the relationship between growth and income inequality. Section 3 starts with the
explanation of the Kuznets inverted U curve and evaluates LDCs according to Kuznets curve
and also assesses the evolution of income inequality in LDCs. Section 4 presents the empirical
analysis to evaluate the effect of growth on income inequality in LDCs. Section 5 presents the
concluding remarks.
2. LITERATURE REVIEW
The articles in the literature mostly concentrated on the effect of income distribution
on growth. Although there are articles about the effect of growth on income distribution,
inequality or poverty, there is no article inquiring the effect of growth on inequality according
to the gini coefficients (in the broader sense) in LDCs. Instead there are articles including no
econometric analysis but surveys on inequality in labor markets (in the narrower sense) as
Kimhi (2004) analyzing the effect of growth on poverty in LDCs.
Kuznets (1955) was among the first speculating about the existence of a systematic
relationship between the income distribution and development of an economy. He argues that
inequality in per capita income increases in the early stages of development, in the transition
from a rural to an industrial society, and decreases when the modern structure has penetrated
the entire socio-economic texture. The result is the well-known inverted U-shaped
relationship between inequality and per capita income, better known as “Kuznets Curve”.
After Kuznets, several theoretical models have been proposed. Some of these models
consider inequality is detrimental to growth, whereas in others it is argued that inequality is
essential to growth and it is a growth-enhancing factor.
Early studies have produced robust results supporting that the inequality reduces
growth using Barro-type cross-country growth regressions. (Alesina and Rodrik,1994,
Persson and Tabellini, 1994, Clarke, 1995, and Deinger and Squire, 1998; e.g., Dominicis et
al., 2006)
Using a panel data over 10-year intervals, Barro (2000) finds no evidence of an overall
effect of inequality on growth.
Li and Zou (1998), Forbes (2000) and Castello (2004) using a fixed-effects estimator
that controls for country-specific characteristics or a dynamic GMM estimator that corrects
for endogeneity, found support for the existence of a positive impact of inequality on growth.
According to Rodrik (2000), growth benefits the poor; the absolute number of the
people living in poverty has declined in all of the developing countries that have sustained
rapid growth over the past few decades. The LDCs are exception since they have not
sustained rapid growth as developing countries. Rodrik (2000) admits that a country could
enjoy a high growth rate without having any benefit to poorest households if income
disparities grew significantly turning rich into richer and poor into poorer. He argues that the
relationship between growth and poverty varies from country to country according to the
change of income distribution within a country. He also argues that the relationship between
growth and poverty is affected by government policies and priorities. Redistributive policies
may reduce the poverty even total income does not grow or policies increasing incomes of the
poor (investment in primary education, rural infrastructure, health, and nutrition) tend to
enhance the total productive capacity of the whole economy boosts the income of all groups
within the country. He concludes that the debate growth versus poverty is a meaningless
debate that diverts attention from the questions that should be our focus; what works, how,
and under what circumstances?
The debate still is open and no general consensus has emerged so far. As Dominics et
al., 2006 states that;
“It seems that the different conclusions arrived at in the various studies largely
depend on the econometric method employed, the countries considered in the analyses and
the employed measures for income inequality.”
3. STATISTICAL ANALYSIS
3.1. Theory: Kuznets Curve
A (inverted U shape) Kuznets curve is the graphical representation of the hypothesis
that as a country develops, there is a natural cycle of economic inequality driven by market
forces which at first increases inequality, and then decreases it after a certain average income
is attained. It suggests that the distribution of income detoriorates over the initial stages of
development as an economy transforms from rural to urban and from aggricultural to
industrial. Subsequently, inequality decreases as the labor force in the industrial sector
expands and that of the aggricultural sector declines.
Figure 1: Kuznets Curve
Source: Wikipedia
3.2. Evaluating LDCs According to the Kuznets Curve
A critical point is whether the relationship between income inequality and growth fits
the Kuznets curve or not in LDCs. If it fits the Kuznets curve, then it will not be a misleading
conclusion that the poverty in these countries will be alleviated by economic growth after a
certain point of income per capita through time. Then all the actions taken by World Bank,
United Nations or donor countries to increase the growth in LDCs is a suitable policy for
alleviating the poverty. If it does not fit the Kuznets curve, then boosting growth in LDCs
should not be primary objective to eradicate poverty, instead eradicating the poverty, as also
declared in the MDGs, should be the primary objective.
Figure 2: Kuznets Curves for LDCs
1995
2000
0.65
0.65
0.55
ANG
0.5
0.45 MOZ
0.4 ETH
SEN
UGA
0.35
0
ANG
0.6
HAI
SIE
Gini Index
Gini Index
0.6
2000
SIE
ZAM
GUB
0
1000
Poly. (Kuznet Curve)
Kuznet Curve
3000
Poly. (Kuznet Curve)
2010
0.65
ANG
HAI
Gini Index
Gini Index
2000
GDP per capita
2005
0.65
0.6
0.55
0.5
0.45
0.4
0.35
SEN
BAN
0.35
3000
GAM
0.45
GDP per capita
Kuznet Curve
HAI
0.5
0.4
YEM
1000
0.55
ZAM
SEN
0
GUB
YEM
2000
HAI
0.55
ANG
ZAM
0.45
YEM
0.35
BAN
4000
GDP per capita
ETH
0.25
0
2000
4000
6000
GDP per capita
Kuznet Curve
Poly. (Kuznet Curve)
Source: Author’s Own Calculation
Kuznet Curve
Poly. (Kuznet Curve)
Inspecting the Kuznets curves for LDCs, we see that the relationship between income
inequality and growth in LDCs does not fit the Kuznets’ hypothesis. Hence, it will not be
misleading conclusion that the boosting growth is not an appropriate strategy to eradicate
poverty in LDCs.
3.3. Evolution of Income Distribution in LDCs
Figure 3: The Evolution of Income Inequality in LDCs
Averages of GINI
48.00
47.00
46.00
45.00
44.00
43.00
42.00
41.00
Averages
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
40.00
Linear (Averages)
Source: Author’s Own Calculation
Decreasing trend in the averages of GINI-coefficient indicates the improvement in income
inequality, hence poverty alleviation in LDCs between the years 1991 and 2010.
Figure 4: The Divergence in Income Inequality of LDCs
Standard Deviation of GINI
10.00
8.00
6.00
4.00
2.00
Std. Dev.
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0.00
Linear (Std. Dev.)
Source: Author’s Own Calculation
Increasing trend in the standard deviation of GINI-coefficient indicates the divergence of
LDCs with respect to this indicator.
Figure 5: Evaluation of LDCs with respect to Income Inequality
LDCs Improved or Detoriated w.r.t. GINI
70.00
60.00
50.00
40.00
30.00
20.00
10.00
Zambia
Haiti
Angola
Source: Author’s Own Calculation
Averages
Ethiopia
Bangladesh
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
0.00
While the overall trend in LDCs is decreasing poverty, it is more informative to look for
the LDCs that are improved or deteriorated with respect to GINI-coefficient for the years
between 1991 and 2010. It can be seen that while the poverty in Ethiopia and Bangladesh has
been declined, the poverty in Zambia, Haiti and Angola has been increased.
4. EMPIRICAL ANALYSIS
4.1. Data
Dependent Variable:
lngini: Natural logarithm of (GINI-coefficient*100)
Independent Variables:
lngpc: Natural logarithm of GDP per capita
lnhumc: Natural logarithm of human capital index
lngovi: Natural logarithm of aggregate governance index
lnaa: Natural logarithm of official development assistance and official aid received
Table 1: Statistical Summary of the Variables
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------lngini |
380
3.757043
.1848221 2.976741 4.127134
lngpc |
380
6.872518
.4604329
lnaa |
380
20.23621
.8900704 17.67244 22.13445
lngovi |
380
3.91643
.2483518 2.632556 4.310601
lnhumc |
380
4.081694
.1019867 3.817806 4.306594
5.93142 8.621448
-------------+-------------------------------------------------------Source: Author’s Own Calculations
The effect of growth on income inequality is evaluated in 19 LCDs for the period
between 1991 and 2010.
4.2. Stationary Analysis
Table 2: First Generation Unit Root Tests
Variable Case
lngini
IPS (Ind. Unit Root) LLC Common Unit Root)
Constant
Constant and Trend
lngpc
Constant
Constant and Trend
lnhumc
Constant
Constant and Trend
lngovi
Constant
Constant and Trend
lnaa
Constant
Constant and Trend
-1.82728**
-4.29622***
(0.0338)
(0.0000)
-0.22871
-2.5416***
(0.4095)
(0.0055)
5.38172
7.94242
(1.0000)
(1.0000)
-3.37851***
-6.77682***
(0.0004)
(0.0000)
3.93083
-0.37428
(1.0000)
(0.3541)
-2.15739**
-6.25065***
(0.0155)
(0.0000)
-5.35906***
-6.77958***
(0.0000)
(0.0000)
-3.52186***
-5.68705***
(0.0002)
(0.0000)
-1.499*
-1.39413*
(0.0669)
(0.0816)
-2.43608***
-4.51933***
(0.0074)
(0.0000)
Note: The null hypothesis for LLC and IPS are unit root. The numbers in brackets are
the p-values for all tests. ***, **, * denote significance at 1%, 5 %, 10 % level respectively,
meaning the rejection of the null of unit root.
According to the first generation unit root tests, all the variables are stationary at least
for the two cases out of four cases.
4.3. Empirical Model and Estimation Results
lngini = α + βlngpc + γlnhumc + δ lngovi + λ lnaa
lngini and lngpc have 2-way relationship. Hence lngpc is assumed to be endogenous while
other variables are assumed to be exogenous.
Table 3: Estimation Results
Estimation
Technique
Dependent Variable
G2SLS Random-Effects IV
Regression
Lngini
Independent Variables
0.1168***
(-0.0401)
-0.0196
(0.0132)
-0.0179
(0.0362)
-0.9345***
(0.1393)
7.2355***
(0.4387)
361
lngpc
lnaa
lngovi
lnhumc
constant
Number of observations
Hausman Test
Prob>chi2 =
0.0082
Notes: Standard errors are in parenthesis. ***, ** and * denote significance levels at %
1, % 5 and % 10 respectively. lngpc is assumed to be endogenous, others are assumed
exogenous.
There is a significant positive relationship between GDP per capita and Gini-coefficient.
Hence growth creates poverty in LDCs. Since there is a significant negative relationship
between human capital index and Gini-coefficient, we conclude that investing in human
capital alleviates poverty in LDCs. There are insignificant negative relationships between
governance quality and Gini-coefficient, and “net official development assistance and official
aid received” and Gini-coefficient. Hence, even increasing assistance and aid for LDCs and
improvement in governance quality in LDCs have potential to alleviate poverty, both are not
effective strategies to combat with poverty in LDCs.
5. CONCLUSION
The relationship between growth and income inequality in LDCs does not fit the
Kuznets hypothesis. Since there is a significant positive relationship between growth and
income inequality, growth creates poverty in LDCs according to the estimation results. Even
though this result is against the results of Rodrik (2000), it fits the argument of Rodrik (2000)
that a country could enjoy a high growth rate without having any benefit to poorest
households if income disparities grew significantly turning rich into richer and poor into
poorer.
According to Galor and Moav (2004), the relationship between growth and income
disitribution is not stable over time, but depends on the stage of the development in a country.
Also according to Rodrik (2000), redistributive policies may reduce the poverty even
total income does not grow or policies increasing incomes of the poor by investment in
primary education, rural infrastructure, health, and nutrition which tend to enhance the total
productive capacity of the whole economy boosts the income of all groups within the country
as also seen the significant negative coefficient of human capital in estimation results.
REFERENCES
Barro, R.J., 2000. “Inequality and Growth in a Panel of Countries”. Journal of Economic
Growth, 5, pp.5-32.
Castello, A., 2004. “A Reassessment of the Relationship between Inequality and
Growth”:What Human Capital Inequality data Say?. IVIE Working Paper, No:15.
Dominicis, L., Groot, H.L.F. and Florax, R.J.G.M., 2006. “Growth and Inequality: A MetaAnalysis”. Tinbergen Institute Discussion Paper, TI 2006-064/3.
Forbes, K.J., 2000. “A Reassessment of the Relationship between Inequality and Growth”.
American Economic Review, 90(4), pp.869-887.
ICRG Database, 2010. “International Country Risk Guide” The PRS Group, New York.
Kuznets, S., 1955. “Economic Growth and Income Inequality”. American Economic Review,
45, pp.1-28.
Li, H. and Zou, H., 1998. “Income Inequality is not Harmful for Growth: Theory and
Evidence”. Review of Development Economics, 2(3), pp.318-334.
Wikipedia, Kuznets Curve.
<http://en.wikipedia.org/wiki/Kuznets_curve> (accessed 8 January 2013)
World Development Indicators (WDI), 2012. “Data” The World Bank, Washington, D.C.
<http://data.worldbank.org/indicator> (accessed 3 November 2012)
APPENDIX 1: LDCS INCLUDED IN THE STUDY
Angola
Haiti
Sierra Leone
Bangladesh
Madagascar
Tanzania
Burkina Faso
Malawi
Uganda
Ethiopia
Mali
Yemen Republic
Gambia
Mozambique
Zambia
Guinea
Niger
Guinea-Bissau
Senegal
APPENDIX 2: STATA OUTPUT
tsset code year
panel variable: code (strongly balanced)
time variable: year, 1991 to 2010
delta: 1 unit
. reg lngini lngpc lnaa lngovi lnhumc
Source |
SS
df
MS
Number of obs =
-------------+-----------------------------Model | 4.22251761
F( 4, 375) = 45.38
4 1.0556294
Prob > F
Residual | 8.7238287 375 .023263543
-------------+------------------------------
380
= 0.0000
R-squared
= 0.3262
Adj R-squared = 0.3190
Total | 12.9463463 379 .034159225
Root MSE
= .15252
-----------------------------------------------------------------------------lngini |
Coef. Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------lngpc | .1018426 .0182368
lnaa | -.048126 .0091102
lngovi | .0848914 .0328665
5.58 0.000
-5.28 0.000
.0659834
-.0660395 -.0302124
2.58 0.010
lnhumc | -.9108593 .0832394 -10.94 0.000
_cons | 7.416392 .3674246
20.18 0.000
.1377019
.0202656
.1495172
-1.074534 -.7471848
6.693921
8.138862
------------------------------------------------------------------------------
. estimates store ols_efficient
. xtivreg lngini lnaa lngovi lnhumc ( lngpc = l.lngpc )
G2SLS random-effects IV regression
Number of obs
=
Group variable: code
Number of groups =
19
R-sq: within = 0.1617
Obs per group: min =
19
between = 0.3311
avg =
overall = 0.2861
max =
Wald chi2(4)
corr(u_i, X)
= 0 (assumed)
=
361
19.0
19
72.59
Prob > chi2
= 0.0000
-----------------------------------------------------------------------------lngini |
Coef. Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------lngpc | .1167561 .0400807
lnaa | -.019634
.013211
2.91 0.004
-1.49 0.137
lngovi | -.0179244 .0362212
lnhumc | -.9345077 .1392593
_cons | 7.235486 .4386923
.0381994
-.0455269
-0.49 0.621
-6.71 0.000
16.49 0.000
.1953128
.006259
-.0889167
.053068
-1.207451 -.6615645
6.375665
8.095307
-------------+---------------------------------------------------------------sigma_u | .14032357
sigma_e | .08915516
rho | .71241569 (fraction of variance due to u_i)
-----------------------------------------------------------------------------Instrumented: lngpc
Instruments:
lnaa lngovi lnhumc L.lngpc
------------------------------------------------------------------------------
. estimates store iv_consistent
. hausman iv_consistent ols_efficient, sigmamore
---- Coefficients ---|
(b)
(B)
(b-B)
| iv_consist~t ols_effici~t
sqrt(diag(V_b-V_B))
Difference
S.E.
-------------+---------------------------------------------------------------lngpc | .1167561
lnaa | -.019634
.1018426
-.048126
lngovi | -.0179244
.0848914
lnhumc | -.9345077
-.9108593
.0149135
.028492
.0319303
.0079939
-.1028158
-.0236484
.004905
.096923
-----------------------------------------------------------------------------b = consistent under Ho and Ha; obtained from xtivreg
B = inconsistent under Ha, efficient under Ho; obtained from regress
Test: Ho: difference in coefficients not systematic
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
13.74
Prob>chi2 =
0.0082
(V_b-V_B is not positive definite)