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Does Wealth Inequality Matter for
Growth? The Effect of Billionaire Wealth,
Income Distribution, and Poverty
SUTIRTHA BAGCHI† & JAN SVEJNAR‡
† Villanova University
‡ Columbia University
NOVEMBER 2015
Motivation for the paper



“. . . the absence of data on the distribution of wealth
for a sufficient number of countries forces researchers
to use proxies in empirical studies. The most common
approach is to use data on income inequality as a proxy
for wealth inequality.” Aghion, Caroli, and GarciaPenalosa (1999)
Bénabou (1996) echoes this point and notes that the
lack of almost any data on the distribution of wealth is a
general problem, given that in most theories it is this
distribution rather than that of income which is the
determinant of outcomes.
Ravallion (2012) emphasizes that “wealth inequality is
arguably more relevant though this has been rarely
used due to data limitations.”
Research questions
1.
How does wealth inequality affect economic
growth?
2.
Does the relationship between growth and
inequality depend on the nature (source) of
this inequality?
e.g. Does inequality based on political connections differ
from one that is based on success as an entrepreneur?
3.
What is the relative growth effect of wealth
inequality, income inequality, and poverty?
Theoretical literature provides arguments for why
inequality is good for growth
Marginal propensity to save of the rich is
higher than that of the poor
2) Investment indivisibilities:
1)

Low inequality  Low levels of innovation  Low
productivity growth  Low growth in real GDP
per capita
3) Trade-off between equity and efficiency
… but it also provides arguments for why
inequality is bad for growth
Credit market imperfections: You cannot
borrow against your human capital
2) Greater demand for redistribution leading to a
choice of economically inefficient policies; and
3) Greater social unrest, possibly also leading to
a higher degree of macroeconomic volatility
1)
Existing empirical evidence: Mixed
 Cross - country & cross-sectional regressions
suggest that income inequality is bad for growth:
 Alesina
& Rodrik (QJE, 1994)
 Persson & Tabellini (AER, 1994)
 Results do not always hold up under robustness
checks; do not answer the question of what
happens when inequality in a given country
changes
 Distinctly different results when examined in a
panel set-up
 Forbes
(AER, 2000)
Data source for the paper

Forbes magazine’s list of billionaires:



Published list of billionaires from around the world since
1987
Estimate wealth based on the holdings of individuals in
public companies or estimated holdings in private
companies using standard price multiples
We use the Forbes ’ billionaire data set to create two
variables:



Proxy measure of wealth inequality =
Sum of wealth of all billionaires in a country/ Country GDP
E.g. Country 1 has 3 billionaires with wealths equal to $5 billion,
$2 billion and $1 billion, and country’s GDP = $500 billion.
Measure of wealth inequality = (5 + 2 + 1)/ 500 = 1.6%
Correlations between wealth distribution data
from UNU–WIDER & Forbes’ list of billionaires
Raw correlation coefficient and Spearman rank correlation
coefficient for the share of wealth going to the top decile and our
measure of wealth inequality for a sample of 18 countries are 0.54
(p-value = 0.0199) and 0.58 (p-value = 0.0122).
Cross-country correlation between the Gini coefficients of wealth
available for 22 countries for the year 2000 from the Davies et al.
(2008) data set and our measure of wealth inequality for 2002:
0.50 (p = 0.0188).
These are relatively high positive correlations
We split wealth inequality into two components
 Wealth Inequality (or Billionaire wealth/GDP)
“Politically connected” “Politically unconnected”
billionaire wealth /GDP billionaire wealth /GDP
 Classify billionaires as politically connected or not
(A billionaire can be in only one of the two categories)
 Previous example: Suppose billionaire 2 gets
classified as politically connected
 Politically connected billionaire wealth / GDP =
$2/$500 = 0.4%
 Politically unconnected billionaire wealth / GDP =
$6/$500 = 1.2%
How do we classify someone as “politically
connected”?


Extensive search on Factiva & Lexis-Nexis
“Criteria”:




Have political connections played a material role in the
success of the billionaire?
Would they have been billionaires absent political
connections?
Careful to distinguish between explicit government support
from a generally pro-business regulatory environment
Classic examples: Oligarchs from Russia or the
cronies of Suharto (Indonesia)
Ranking of countries in terms of politically connected
matches priors
Countries that rank highest in terms of politically connected wealth inequality
1.
2.
3.
4.
5.
Malaysia
Colombia
Indonesia
Thailand
Mexico
Median rank on TI’s Corruption Perceptions
Index: 32 /41 (1995) & 94/174 (2012)
Countries that rank lowest in terms of politically connected wealth inequality
1.
2.
3.
4.
5.
6.
Hong Kong
Netherlands
Singapore
Sweden
Switzerland and
United Kingdom
Median rank on TI’s Corruption Perceptions
Index: 9 /41 (1995) & 8/174 (2012)
Other countries which just follow these include – Chile, South Korea,
Philippines, Argentina, and, India. Italy has the 11th highest level of politically
connected wealth inequality in our sample – the highest of any European
country.
What we include in our data set
 20-year period from 1988 – 2007 divided into 4 periods
of 5 years duration each
 All countries in the world subject to availability of data
on covariates. When a country does not have billionaires,
we set billionaire wealth = 0 (more on this later)
 ~ 60 countries (and 160 country-period combinations)
appear in the final estimation
 Growthi,t = β0 + β1Wealth inequalityi,(t−1) + β2Income
inequalityi,(t−1) + β3Headcount povertyi,(t−1) + β4Incomei,(t−1)+
β5Schoolingi,(t−1) + β6PPPIi,(t−1) + β7Dummyi,(t−1) + αi + ηt + νi,t
One may be concerned about reverse causality
 Relationship runs not from inequality to growth
but from growth to inequality (Kuznets’
hypothesis, 1955)
The early stages of development exacerbate inequality
while later stages of development improve equality.
 Empirically this lacks support. (See e.g. Fields, 2001)

 Empirical strategy - use lags of the explanatory
variables, which are pre-determined as
regressors
 Also we use IV & GMM estimation approaches
Number of countries & billionaires on the list
Year
Countries
Number of billionaires/
billionaire families
1987
23
201
1992
31
340
1996
38
543
2002
42
568
Impact of wealth inequality, income inequality,
and poverty on economic growth (Benchmark)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Growth rate in real GDP per capita
Wealth
-0.132*
-0.547
-50.07***
Inequality
(0.0771)
(0.351)
(13.27)
Politically unconnected
-0.0464
-0.154
-48.98
wealth inequality
(0.0714)
(0.301)
(36.52)
Politically connected
-0.331***
-1.625***
-51.01**
wealth inequality
(0.0965)
(0.536)
(22.79)
0.000530
0.000753
0.000498
Income Inequality
Headcount Poverty
0.000564
0.000763*
0.000498
(0.000422)
(0.000455)
(0.000417) (0.000426) (0.000456) (0.000418)
0.000301
0.000252
(0.000296)
0.000353
0.000298
0.000243
0.000352
(0.000307) (0.000286) (0.000298) (0.000310) (0.000297)
N
160
149
160
160
149
160
R2
0.59
0.59
0.61
0.60
0.60
0.61
Including the controls: GDP/ capita; Schooling & Price level of investment S.e. in parentheses * p < 0.10, ** p < 0.05, *** p<0.01
Comparing our results with Forbes (2000) (1/2)
(1)
(2)
(3)
(4)
Panel A: Assuming income and wealth inequality to have the same effect during the entire sample period
Income Inequality
0.000751
0.000991
0.00102
0.000947
(0.000886)
(0.000830)
(0.000858)
(0.000840)
Wealth Inequality
-0.154***
(GDP used for normalization)
(0.0484)
Wealth Inequality
-0.578***
(Physical capital used for normalization)
(0.179)
Wealth Inequality
-6.255***
(Population used for normalization)
(2.061)
Number of observations
162
162
152
162
R2
0.39
0.45
0.44
0.42
F
5.343
8.717
8.740
7.138
S.e.in parentheses * p < .10, ** p <.05, *** p <.01
Comparing our results with Forbes (2000) (2/2)
(1)
(2)
(3)
(4)
Panel B: Introducing dummy variable for first half of the sample period & corresponding interactions
Income Inequality
Wealth Inequality
(GDP used for normalization)
Wealth Inequality
(Physical capital used for normalization)
Wealth Inequality
(Population used for normalization)
Income Inequality X First half of sample period
Wealth Inequality X First half of sample period
(GDP used for normalization)
Wealth Inequality X First half of sample period
(Physical capital used for normalization)
Wealth Inequality X First half of sample period
(Population used for normalization)
Number of observations
R2
F
S.e.in parentheses * p < .10, ** p <.05, *** p <.01
0.000419
(0.000894)
0.000757
(0.000858)
-0.131**
(0.0493)
0.000698
(0.000896)
0.000630
(0.000847)
-0.525**
(0.201)
0.000750**
(0.000327)
0.000492
(0.000333)
0.000614*
(0.000327)
-7.771***
(2.690)
0.000742**
(0.000317)
0.0691
(0.0797)
-0.0110
(0.324)
162
0.41
4.720
162
0.46
9.321
152
0.46
9.280
-6.665
(5.169)
162
0.46
6.751
Robustness checks
RC1: Robustness to Forbes magazine’s choice of countries for the
billionaires in the data set
RC2: Use of alternative econometric approaches:
i.
ii.
iii.
Random effects instead of a fixed effects specification
Instrumental variables
Dynamic panel methods of estimation (Arellano & Bond
difference-GMM and Blundell & Bond system-GMM)
RC3: Robustness to inclusion of additional control variables:
i.
ii.
Adding a measure of institutional quality
Controlling for the exchange rate
RC4: Using $1.25 per day per person as the poverty line
Impact of wealth inequality, income inequality,
and poverty on economic growth (Using RE)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Growth rate in real GDP per capita
Wealth
Inequality
-0.162*
-0.652
-59.05***
(0.0962)
(0.431)
(14.67)
Politically unconnected
-0.0145
0.0261
-17.52
wealth inequality
(0.0688)
(0.284)
(48.52)
Politically connected
-0.458***
-2.332***
-90.14***
wealth inequality
(0.0600)
(0.409)
(20.85)
Income Inequality
Headcount Poverty
N
-0.000143
-0.0000126
-0.000145
-0.000171
(0.000513)
-0.000151
0.0000258
(0.000435) (0.000438) (0.000509) (0.000437)
(0.000441)
0.000386*
0.000364
0.000417**
0.000434**
(0.000214)
(0.000223)
160
149
0.000406* 0.000425**
(0.000205) (0.000209) (0.000218) (0.000205)
160
160
149
160
Including the controls: GDP/ capita; Schooling & Price level of investment S.e. in parentheses * p < 0.10, ** p < 0.05, *** p<0.01
Impact of wealth inequality, income inequality, and poverty
on GDP per capita (Using Arellano-Bond difference-GMM
estimator)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Log of GDP per capita
Wealth
-0.498*
-2.059
-167.3**
Inequality
(0.292)
(1.263)
(66.25)
Politically unconnected
-0.112
-0.437
-126.2
wealth inequality
(0.220)
(0.969)
(174.7)
Politically connected
-1.514**
-6.960*
-202.2
wealth inequality
(0.720)
(3.854)
(149.4)
Income Inequality
0.00144
0.00171
0.00146
0.00160
0.00184
0.00154
(0.00173)
(0.00182)
(0.00180)
(0.00186)
(0.00194)
(0.00176)
0.00330*
0.00300*
0.00340*
0.00348**
0.00323*
0.00329*
(0.00174)
(0.00167)
(0.00174)
(0.00176)
(0.00173)
(0.00173)
Lagged log GDP per
0.660**
0.659**
0.682**
0.715***
0.716***
0.663**
capita
(0.275)
(0.277)
(0.276)
(0.265)
(0.271)
(0.280)
89
88
89
89
88
89
Headcount Poverty
N
Including the controls: GDP/ capita; Schooling & Price level of investment S.e. in parentheses * p < 0.10, ** p < 0.05, *** p<0.01
Impact of wealth inequality, income inequality, and poverty
on GDP per capita (Using Blundell-Bond system-GMM
estimator)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Log of GDP per capita
Wealth
-0.593*
-2.135
-210.9***
Inequality
(0.355)
(1.439)
(57.86)
Politically unconnected
-0.277
-0.564
-183.6
wealth inequality
(0.475)
(1.589)
(332.7)
Politically connected
-1.405*
-6.699*
-236.7
wealth inequality
(0.782)
(3.589)
(266.0)
Income Inequality
-0.00207
-0.000981
-0.00199
-0.00215
-0.00110
-0.00200
(0.00404)
(0.00334)
(0.00399)
(0.00395)
(0.00327)
(0.00387)
0.00726***
0.00633***
0.00720***
0.00733***
0.00647***
0.00726***
(0.00271)
(0.00240)
(0.00267)
(0.00277)
(0.00238)
(0.00272)
Lagged log GDP per
0.865***
0.903***
0.868***
0.867***
0.907***
0.868***
capita
(0.105)
(0.109)
(0.103)
(0.107)
(0.111)
(0.109)
161
149
161
161
149
161
Headcount Poverty
N
Including the controls: GDP/ capita; Schooling & Price level of investment S.e. in parentheses * p < 0.10, ** p < 0.05, *** p<0.01
Impact of wealth inequality, income inequality, and poverty
on GDP per capita (Using Blundell-Bond system-GMM
estimator) (Taking wealth inequality as pre-determined)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Log of GDP per capita
Wealth
-0.833*
-3.154*
-258.2***
Inequality
(0.470)
(1.898)
(90.84)
Politically unconnected
-0.389
-0.731
-94.39
wealth inequality
(0.370)
(1.210)
(311.0)
-2.092***
-10.04***
-403.3**
(0.629)
(2.866)
(162.0)
Politically connected
wealth inequality
Income Inequality
-0.000634
-0.000545
-0.000931
-0.000348
0.000497
-0.00107
(0.00255)
(0.00267)
(0.00256)
(0.00302)
(0.00296)
(0.00306)
0.00310*
0.00333*
0.00334**
0.00320*
0.00297
0.00343*
(0.00174)
(0.00189)
(0.00166)
(0.00187)
(0.00192)
(0.00184)
Lagged log GDP per
0.991***
1.031***
1.000***
0.987***
1.003***
1.000***
capita
(0.0345)
(0.0429)
(0.0323)
(0.0232)
(0.0310)
(0.0291)
161
149
161
161
149
161
Headcount Poverty
N
Including the controls: GDP/ capita; Schooling & Price level of investment S.e. in parentheses * p < 0.10, ** p < 0.05, *** p<0.01
Why is politically connected wealth inequality
detrimental?
Example 1: Birla family of India:
“The nationalists who later became free India’s power elite
rewarded the Birla family with lucrative contracts. After
independence, the Birlas continued their lavish contributions
to the ruling Congress Party. So accomplished are they in
manipulating the bureaucracy, and so vast their network of
intelligence, that they frequently obtain preemptive licenses,
enabling them to lock up exclusive rights for businesses as yet
unborn.” (Forbes, 1987)
Why is politically connected wealth inequality
detrimental?
Example 2: Tobacco billionaires in Indonesia:
 Indonesia is the only country in Asia to have not signed the
WHO Framework Convention on Tobacco Control, a treaty
that as of September 2013 had been signed by 177 parties.
 This is in spite of the fact that in Indonesia, Muslims
constitute 86 percent of the population and “smoking is
either completely prohibited in Islam or abhorrent to such a
degree as to be prohibited.” (WHO Regional Office for the
Eastern Mediterranean).
 Indonesia’s average tobacco tax of 37 percent is the lowest in
Southeast Asia and well below the global average of 70 per
cent of the sales price (South China Morning Post, 2008).
Conclusions
1.
2.
3.
High levels of wealth inequality appear to have
negative consequences for economic growth;
income inequality and headcount poverty do not
Wealth inequality arising on account of political
connections reduces economic growth v. wealth
inequality arising otherwise
Growth-related policy debate should focus on
distribution of wealth