economic growth and institutional variables
TRANSCRIPT
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The Institutional Foundationsof Inequality and Growth
LEWIS DAVIS* & MARK HOPKINS***Union College, Schenectady, New York, USA, **Gettysburg College, Pennsylvania, USA
Final version received June 2010
ABSTRACT After a decade of research, the effect of inequality on long-run economic growthremains unresolved, in part because researchers have treated omitted variable bias as anestimation problem rather than a deeper question of causality. In this article we argue that thekey omitted variable is the quality of economic institutions. Using both cross-country and paneldata specifications, we find no direct effect of inequality on growth in the long-run. Rather, the
protection of property rights simultaneously raises growth rates and reduces income inequality.We interpret these findings as evidence that insecure property rights disproportionatelydisadvantage the poor.
1. Introduction
After more than a decade of empirical work, there is little consensus regarding the
impact of income inequality on economic growth. Early investigations by Alesina
and Rodrik (1994) and Persson and Tabellini (1994) using cross-country growth
regressions concluded that initial income inequality is associated negatively with
future growth. Later researchers argued that these estimates may be subject to bias
from omitted variables. Li and Zou (1998) and Forbes (2000) use panel datatechniques to control for omitted variables, and find that income inequality is good
for growth.
While it effectively controls for omitted variable bias, the second wave of empirical
work on inequality and growth is subject to two criticisms. First, because they
typically use variables measured over five-year periods, panel techniques generate
estimates that capture the relatively high-frequency co-movements of growth and
inequality. In contrast, the theoretical literature on growth and inequality focuses on
mechanisms that may not fully manifest themselves over such short time horizons.
Correspondence Address: Lewis Davis, Department of Economics, 807 Union Street, Schenectady,
Journal of Development Studies,
Vol. 47, No. 7, 977997, July 2011
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For example, Galor and Zeira (1993), Piketty (1997) and Aghion and Bolton (1997)
argue that, in the presence of capital market imperfections, income and wealth
inequality increases the share of potentially profitable investments that are
unrealised due to credit constraints. The impact of credit constraints is likely to be
particularly strong with respect to investments in human capital, since human capital
cannot serve as collateral. But human capital investments also have a long gestation
period and may generate significant returns that are not fully captured within a given
five-year period.
A similarly long time horizon may apply to political economy mechanisms linking
growth and inequality. Alesina and Rodrik (1994) and Persson and Tabellini (1994)
argue a political economy argument in which inequality increases the demand for
redistributional policies that blunt the incentive to invest. But in the presence of
significant rigidities in the political system, the full impact of a change in inequality
on policy outcomes may not be realised in a given five-year period.1 The mismatch
between the time horizons of the theoretical and empirical literature leads to thesuspicion that panel data estimates may not be that informative about the
relationship between inequality and long run economic growth.
Second, the second wave regressions rely on panel estimators that primarily exploit
the time variation in growth and inequality within countries. However, as first
pointed out by Li et al. (1998), most of the variation in income inequality is across
countries rather than time. As a result, panel estimators that rely on the time variation
in inequality are highly inefficient. However, the use of more efficient random effects
(GLS) panel estimators is typically found to be misspecified under the strong
assumption that the country effects are uncorrelated with the included variables.This article presents a unified view of the relationship between growth and
inequality that addresses these weaknesses and resolves the apparent puzzle in the
existing empirical literature. We begin by noting that measures of growth and
inequality are derived from moments of the same, evolving distribution of individual
incomes, as pointed out by Lundberg and Squire (2003), and that as a result it makes
sense to think of growth and inequality as being generated by a common set of
processes. From this perspective, the question that has motivated much of the
research in this area Persson and Tabellinis (1994: 600) query Is inequality
harmful to growth? is itself misleading. We believe a more revealing question isthat pursued in this article: What are the determinants of inequality and how are
they related to economic growth?
Following the theoretical contribution of Davis (2007), we argue that economic
institutions related to the protection of property rights are a fundamental
determinant of income inequality. Indeed, in the most influential work on
institutions and growth, Acemoglu et al.s (2001, 2002) and Engerman and
Sokoloffs (1997, 2002), the authors clearly indicate that their understanding of
institutional quality is fundamentally about the distribution,rather than the average
level, of property rights protection. While the indices used to measure institutional
quality empirically are not well designed to make this distinction, in many cases what
has been referred to as institutional quality may to a large degree reflect institutional
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Given a large body of evidence that institutions affect economic growth, our
finding that institutions also affect inequality suggests that the omission of
institutional variables may be responsible for the negative relationship between
growth and inequality reported in cross-country regressions.2 In Section 3 we re-
investigate the cross-country relationship between inequality and growth. We find
that when both institutions and inequality are included in a growth regression,
institutions have a statistically significant impact on growth, while inequality does
not. We extend this analysis to address the panel data evidence on inequality and
growth in Section 4, showing that institutional quality explains much of the country
specific intercepts generated by a canonical fixed effects regression. Using this
information, we construct an efficient GLS estimator that allows us to identify
separately the long run, between-country and short run, within-country effects of
inequality on growth. Consistent with earlier evidence from fixed effects models, we
find that short run changes in inequality over time are positively associated with
growth within countries. The variation in long run inequality levels across countriesis not a significant determinant of growth, however, providing the most direct
refutation yet of the claims that inequality directly slows economic growth.
Our research is closely related to the third wave empirical literature that challenges
the conclusions of the second wave panel data estimates on methodological grounds.
Like us, Banerjee and Duflo (2003) motivate their work by noting the conflict
between the cross sectional and fixed effects estimates of the inequality on economic
growth. Barro (2000) uses a random effects estimator to consider the effect of
inequality on growth. However, unlike us, Barro does not conduct a Hausman
specification test, leaving open the possibility that his coefficient estimates are biased.The argument that both growth and inequality should be treated as endogenous
motivates the empirical specification employed by Lundberg and Squire (2003). Our
research is distinguished from this work in two ways. First, none of these papers
considers a fundamental role for institutions in determining income inequality, and
second, none addresses the long run relationship between growth and inequality that
is our focus here.
The debate over the relationship between inequality and growth touches on the
compatibility of two fundamental development goals, increasing economic growth
and achieving a more equitable distribution of income. While the first wave empiricalliterature finds these goals are compatible, the second wave suggests instead that
advances on one margin will inevitably require sacrifices on the other. The results
presented here suggest a third possibility, that whether or not a trade-off between
growth and equality exists may depend on the particular policy instrument in
question. In particular, our results suggest that reforms that improve the security of
property rights for the poor will tend to advance both development goals, increasing
economic growth while reducing income inequality.
2. The Institutional Foundations of Inequality
Over the last decade, a number of authors have presented empirical evidence that a
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protections. This is explicit with measures of political institutions, such as democracy,
that intentionally measure the equality of political power and participation. But
measures of the quality of economic institutions also contain a distributional
dimension, as the protection of property rights implicitly depends on the equality of
individuals before the law. Moreover, where private property rights are not well
protected through public institutions, agents will use private resources to protect their
property. In this situation, there will be unequal protection of property reflecting the
inequality of wealth and political power which agents have at their disposal.
Though largely overlooked, our emphasis on the distributional dimension of
institutional quality, and our claim that low quality institutions are associated with
the inequality of economic and political rights, finds support from some of the most
influential work on institutions and development. For example, Acemoglu et al.
(2001) argue that in colonies where European mortality rates were high, settlers
adopted extractive institutions that tended to retard development. In Acemoglu
et al. (2002: 1235), they clearly identify extractive institutions with the unequalprotection of property rights, defining extractive institutions as those that
concentrate power in the hands of a small elite and create a high risk of
expropriation for the majority of the population, [and thus] are likely to discourage
investment and economic development.
Engerman and Sokoloffs (1997, 2000, 2002) influential work on comparative
American development also highlights the distributional dimension of weak
institutions. For example, Sokoloff and Engerman (2000: 221) clearly identify Latin
American underdevelopment with institutions that protected the privileges of the
elites and restricted opportunities for the broad mass of the population to participatefully in the commercial economy. From this perspective, high levels of inequality are
not simply an unintended consequence of weak institutions and poorly protected
property rights. Rather, they reflect a deliberate attempt by colonial elites to
maintain high disparities of economic and political power.
Finally, we note that our emphasis on the institutional foundations of inequality is
consistent with existing empirical on the determinants of income inequality. Li et al.
(1998) and Lundberg and Squire (2003) find that the cross-country pattern of income
inequality is explained by levels of financial development, education, land inequality
and civil liberties, a result they interpret as support for theories that emphasise capitalmarket imperfections and political economy mechanisms. However, as noted by
Engerman and Sokoloff (1997), while these variables may be proximate determinants
of income inequality, they may also be viewed as the expression of deeper institutional
structures that manifest themselves through their influence on land tenure and
settlement, the provision of public education, the regulation of financial organisa-
tions, and restrictions on political participation. That is, they are all reflections of
institutions designed to restrict the economic and political power of the poor.
3. Data
While there are numerous empirical measures of institutional quality, none that we
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and development, choosing one variable each as measures of the quality of economic
and political institutions. Our measure of democracy is the polity 2 variable from the
Polity IV database described in Marshall and Jaggers (2006). Our measure of the quality
of economic institutions is the Freedom from Expropriation variable constructed by
the International Country Risk Guide (ICRG). This variable has been used as a
measure of property rights protection in a number of studies, including Knack and
Keefer (1995) and Acemoglu et al. (2001). As these scores are only available for the
second half of our sample period, we use a single observation for each country
representing the average expropriation risk in the country over all years.
We measure income inequality using a cross-country panel of Gini coefficients
compiled by Milanovic (2006). To facilitate transparency, we use those observations
suggested by Milanovic (2006) and then adjust Gini coefficients for remaining
differences in survey sources and methodologies using a hedonic regression on survey
type, resulting in an unbalanced panel of observations over 6 five-year periods, from
1961 through 2000.3 For consistency, our cross sectional regressions employ theaverage Gini coefficient for each country, as reported in the panel. As noted by Li
et al. (1998), inequality levels are highly stable over time, so that the use of an
average, rather than an initial or final level of inequality, is not important to our
results.
In measuring our controls we depart slightly from the data sources and definitions
used by Li et al. (1998). Following Sokoloff and Engerman (2000), we use primary
rather than secondary school enrollment rates as a measure of access to human
capital. For land inequality, we use a more recent set of estimates compiled by
Frankema (2005). Similarly, we use the ratio of private credit to GDP from Becket al. (2000) to measure financial development rather than the ratio of M2 to GDP.
The differences in variable definitions are not essential to our results.
Results
Column (1) of Table 1 shows that our baseline regression by itself explains over 40
per cent of the cross-country variation in inequality. The variables all have the
expected sign, and two of the four, land inequality and political freedom, are
statistically significant at the 5 per cent level. White heteroskedasticity-correctedstandard errors are reported and used for inference throughout. As seen in column 2,
including our proxy for the quality of economic institutions significantly increases
the fit of the regression, and the coefficient on freedom from expropriation is large
and statistically significant at the 1 per cent level. The partial correlation, shown
in Figure 1, implies that a one-standard deviation increase in freedom from
expropriation (1.8 on a 10 point scale) corresponds to a 7 point decrease in the
average level of the Gini coefficient, or roughly of a standard deviation.
Columns (3) through (6) pair freedom from expropriation risk with each variable
individually in none of these regressions are the results qualitatively different from
those reported in column (2). While economic institutions clearly have a strong
association with inequality, with the exception of land inequality, none of the
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Table1.Institutio
nsandinequality(dependen
tvariableisaverageincome
Gini,19602000)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
ment,
70.0729
(0.061)
70.0471
(0.052)
0.0289
(0.042)
redit
76.832
(4.17)
5.579
(3.95)
1.705
(3.98)
ni
17.17**
(8.33)
18.70***
(4.47)
10
.86*
(5
.63)
11.73*
(6.03)
12.22**
(6.03)
5.728(4.66)
8.442*
(4.96)
cy,
70.395**
(0.16)
70.125
(0.15)
70.0906
(0.14)
ation
73.844***
(0.75)
73.801***
(0.37)
74.378***
(0.64)
73
.456***
(0
.42)
73.674***
(0.49)
73.095***
(0.77)
72.585***
(0.76)
71.623
***
(0.53)
71.516**
(0.74)
al
2.854
(27.1)
al d
70.220
(1.60)
sector
0.134
(0.089)
5.896
***
(1.92)
from
r
70.106
**
(0.043
)
ed
2.291(2.17)
N
N
N
N
N
N
N
N
N
Y
43.69***
(6.40)
63.22***
(6.99)
69.68***
(4.26)
76.08***
(3.84)
62
.97***
(6
.24)
72.08***
(3.61)
51.45
(114)
51.31***
(9.70)
51.47*
**
(5.68)
44.46***
(7.05)
ons
59
53
70
58
70
74
7
0
56
69
70
d
0.43
0.67
0.48
0.60
0
.48
0.50
0.48
0.54
0.64
0.71
obuststandarderrorsinparenthese
s.***p5
0.01,**p5
0.05,*p5
0.1.
nclude
EastAsiaPacific,SouthAs
ia,LatinAmericaandCarib
bean,Sub-SaharanAfrica,a
ndMiddleEastandNorthAfrica.
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clearest, most direct impact on income inequality, we retain only land inequality in
further robustness checks of the role of economic institutions.
Because of model uncertainty regarding the proper specification of the inequality
regression, we include additional control variables in columns (7) through (10). To
control for the presence of a Kuznets-curve type pattern, found in cross sectional
data by a number of authors including Li et al. (1998) and Barro (2000), we include
the log of per capita income in 1970 and its square in column (7). Neither income
variable is statistically significant, while expropriation risk remains strongly so. In
column (8) we introduce a measure of the informal sectors share of output,constructed by Friedman et al. (2000), motivated by several papers that have
suggested a link between the share of the informal sector of the economy and income
inequality (for example, Rosser et al., 2000; Davis, 2007).4 The inclusion of
informality results in a lower estimate for the magnitude of the coefficient on
freedom from expropriation, ostensibly because part of the reason expropriation risk
increases inequality is because it discourages participation in the formal sector. The
coefficient on expropriation risk retains its statistical significance, however,
suggesting that it may play a fundamental role in shaping the income distribution
beyond simply creating a barrier to formality.
Omitted geographic and regional characteristics may also bias our estimates.
Geography has been linked to institutional quality by Hall and Jones (1999), and is
Figure 1. The partial relationship of expropriation risk and inequality.
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geographic and regional variables reduces the magnitude of the coefficient on
freedom from expropriation by nearly one half, but it remains statistically significant
at the 5 per cent level or better.5 These results suggest that freedom from
expropriation is not simply a proxy for omitted regional or geographic variables.
Overall, the results presented in Table 1 are consistent with the hypothesis that the
quality of economic institutions plays an important key role in determining the level
of income inequality across countries.
Addressing the Endogeneity of Institutional Quality
Conclusions based on the regression results reported above are subject to three
criticisms related to the treatment of institutional measures as exogenous regressors.
The first, articulated well by Acemoglu et al. (2001), is that the institutional variables
are derived from expert opinion and survey data, and are thus potentially subject to
systematic measurement error. This will occur, for example, if experts tend to seebetter institutions in countries that experience higher growth rates or less income
inequality. The second is reverse causation: a number of papers argue that it is
income inequality that reduces property rights protection, not vice versa (Keefer and
Knack, 2002; Glaeser et al., 2003). Finally, as stated earlier, these regressions are
intentionally parsimonious and the omission of variables that are simultaneously
correlated with institutions and inequality could bias our coefficient estimates.
We address these issues by instrumenting for our key institutional variable, freedom
from expropriation, to avoid bias introduced by the potential correlation between it and
the unobserved determinants of income inequality. In doing so, we rely on instrumentsfor contemporary institutions that have been previously used in the empirical growth
literature. This literature relies heavily on the argument that colonisation resulted in
generating an exogenous shock that influenced the path of institutional development.
The instruments we use are language and geography variables from Hall and Jones
(1999), legal heritage from Beck et al. (2000) and setter mortality rates from Acemoglu
et al. (2001). Many readers will be familiar with these instruments and the arguments
made for why they are likely to be uncorrelated with current income. Our arguments for
why these instruments for the first moment of the income distribution can also plausibly
be considered uncorrelated with higher moments are sufficiently similar that we omitthem here and refer interested readers to the Online Appendix.
Table 2 presents two stage least squares estimates that confirm our earlier finding
that the protection of property rights has a strong, statistically significant and robust
effect on income inequality. For each specification, the second stage regression of
average inequality on predicted institutional protections is reported in the first
column (A), with results from the first stage displayed in the second column (B).
Using a variety of instruments and controls in columns (1) through (5), we
consistently find that freedom from expropriation is significant at the 1 per cent level
with a coefficient of similar magnitude to that reported in Table 1.
Below each specification we report the p-value from Hansens J statistic and in
each case a test of overidentifying restrictions (OIR) fails to reject the joint null
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Tab
le2.Institutionsandinequalityusingtwo-stageleastsqu
ares
(1)
(2)
(3)
(4)
(5)
A
B
A
B
A
B
A
B
A
B
t
avgini
exprop
avgini
exprop
avgini
exprop
av
gini
exprop
avgin
i
exprop
tionrisk
73.695***
(0.41)
74.584***
(0.65)
73.616***
(0.42)
72.3
24***
(0.4
8)
73.171***
(0.75)
14.53***
(5.10)
70.269
(1.28)
15.07**
(6.20)
1.245
(0.80)
15.90***
(4.78)
70.145
(1.17)
10.4
9*
(5.3
2)
0.879
(1.23)
17.11***
(6.01)
71.507
(1.13)
language
0.862**
(0.33)
0.290
(0.29)
0.885**
(0.34)
1.346***
(0.37)
0.906**
(0.41)
rom
70.0123
(0.033)
0.0160
(0.027)
70.00186
(0.029)
70.0509
(0.031)
70.0983*
(0.051)
rom
quared
0.00129
**
(0.00054
)
0.000704
(0.00042)
0.00115**
(0.00050)
0.00155***
(0.00049)
0.00245**
(0.0012)
alorigin
70.988**
*
(0.28)
70.618**
(0.24)
70.971***
(0.28)
70.618**
(0.26)
70.711**
(0.27)
dit
5.296
(3.70)
1.843***
(0.44)
0.0242
(0.023)
0.00381
(0.0063)
anAfrica
8.9
23***
(3.3
5)
71.158***
(0.41)
erica&
n
6.2
26***
(2.1
0)
71.809***
(0.42)
morta
lity
70.608***
(0.15)
ons
64
64
55
55
64
64
64
64
44
44
0.56
0.66
0.64
0.79
0.57
0.66
0.6
8
0.75
0.35
0.64
HansensJ
0.269
0.615
0.391
0.8
91
0.550
naldF-
stat
26.34
16.03
26.08
17.5
6
12.94
luefor
ximal
ias
10.27
10.27
10.27
10.2
7
10.83
buststa
ndarderrorsinparentheses.***p5
0.01,**p5
0.05,*p5
0.1.
B-columnsreportfirst-stageregressionresults.
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the reported Cragg-Donald F-statistic and the critical values calculated by Stock and
Yogo (2005), which vary by number of instruments. In every regression we are able
to reject the null hypothesis that the bias of the IV estimator exceeds the bias of the
OLS estimator by more than 10 per cent. These diagnostics provide us some
confidence that our instruments are neither invalid nor weak.
A potential criticism of the model estimated in column (1) is that our instruments
might not properly be excluded from the second stage regression, influencing
inequality instead through alternate channels. Beck et al. (2004) have argued, for
example, that legal heritage influences inequality through financial sector regulation
and development. A similar criticism is that the share of a population that speaks a
European language might be directly related to income distribution through
channels related to international trade and capital flows. To control for these effects
we include the ratio of private credit to GDP to proxy for financial development in
column (2) and a measure of openness in column (3). In both cases the coefficient on
freedom from expropriation remains significant while neither private credit nor tradeis significant as a determinant of either inequality or expropriation risk.
To control for other potential omitted factors, dummy variables are introduced in
the specification in column (4) indicating whether a country is in either Latin
America and the Caribbean or in Sub-Saharan Africa, both of which have a
statistically significant association with both inequality and expropriation risk.
Adding these controls reduces the economic significance of freedom from
expropriation on inequality, but the coefficient remains strongly significant. These
results suggest that the correlation between inequality and expropriation risk is
somewhat stronger across than within regions, but omitted regional variables cannotfully account for the influence of institutions on inequality.
In the fifth column, we include European settler mortality rate as an additional
instrument. This lowers sample size significantly, but Acemoglu et al. (2001) present
strong arguments for the relevance and exogeneity of this variable as an instrument for
expropriation risk, so we consider it useful for assessing the validity of our other
instruments. With the expanded instrument set, the coefficient on freedom from
expropriation is slightly reduced in magnitude but it remains significant at the 1 per cent
level and the tests statistics for instrument validity and weakness both remain above
their critical values.7
Thus we believe our choice of excluded instruments is reasonableand, more importantly, that our conclusions regarding the importance of economic
institutions for determining inequality are not sensitive to a specific set of instruments.
4. Re-evaluating the Cross Country Evidence on Growth and Inequality
The fact that stronger property rights are associated with lower inequality has
important implications for the empirical literature on inequality and growth. Since
expropriation risk has been shown in previous literature to be an important
determinant of economic growth, regressions that include inequality but not
expropriation risk will generate negatively biased estimates of the influence of
inequality on growth.
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the growth and inequality literature (for example, Alesina and Rodrik, 1994; Persson
and Tabellini, 1994). The dependent variable is the average growth rate of real per
capita income (PPP) from 1970 to 1995, a period that maximised sample size. This is
regressed on average income inequality and several control variables, including
initial income, to capture conditional convergence effects, and the primary
enrollment rate in 1970, a measure of human capital investment used by Alesina
and Rodrik (1994). To avoid potential endogeneity problems between investment
and growth, we do not include the average investment rate directly as a growth
regressor but use instead two variables that influence investment decisions, the
domestic price of investment goods, averaged from 1970 to 1990, and private credit
relative to national income, a measure of financial development.
The results from our benchmark regression, reported in column (1) of Table 3,
confirm the central finding of the first wave of empirical work on growth and
inequality: the coefficient on inequality is large, negative and highly significant, in
this case at the 1 per cent level. With the exception of the price of investment goods,all of our control variables have the expected sign and are significant as well. In
column (2), we introduce our political and economic institutional variables to the
regression. A common hypothesis in the literature is that democracy has a nonlinear
effect on growth, positive at low levels and negative at high levels, so we allow for a
nonlinearity by including a quadratic term for political rights as well. Overall,
political freedoms appear to be negatively associated with growth, although this
effect is not statistically significant in most of the specifications we consider.
Freedom from expropriation has a positive effect on growth that is both
statistically and economically significant. Specifically, in column (2), an increase infreedom from expropriation of one standard deviation (1.83 out of 10 points, or
roughly the difference between Panama at 5.66 and Chile at 7.5) is associated with a
1.43 percentage point increase in the average annual growth rate. In addition,
introducing expropriation risk dramatically reduces the measured effect of inequal-
ity, which is no longer statistically significant. These results are consistent with the
argument above, that the omission of institutional variables introduced bias into
earlier cross-country regression estimates and led to mistaken inference regarding the
role of income inequality on growth.
Institutions, development and inequality all vary systematically with geographyand regional location, as countries in the tropics tend to suffer both from low levels
of property rights protection and from geographic attributes that negatively
influence economic growth through their effect on the disease burden, the fertility
of the land and average temperatures. To consider for this, column (3) adds two
geographic control variables, a dummy variable indicating landlocked countries and
the absolute distance from the equator, and country (4) includes regional dummies.
The inclusion of these geographic variables reduces the coefficient on freedom from
expropriation somewhat, but does not qualitatively alter our conclusions: good
economic institutions have a robust positive relationship with economic growth. In
contrast, we cannot reject the hypothesis that a countrys average level of inequality
does not affect its long run rate of growth.
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Table3.The
roleofinstitutionsandinequalityincross-countrygrowthregressions
nt
Averageper-capitaincomegrowth,19707
1995
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
al
71.322***
(0.38)
71.695***
(0.31)
71.859***
(0.33)
71.835***
(0.36)
71.172***
(0.33)
71.691***
(0.31)
71.908***
(0.32)
71.809***
(0.36)
redit
2.669***
(0.72)
1.259**
(0.58)
1.405**
(0.58)
1.002
(0.66)
2.606***
(0.82)
1.029*
(0.61)
1.331*
(0.66)
0.938
(0.64)
ment
70.0149
(0.0090)
70.00186
(0.0071)
70.00244
(0.0072)
70.00423
(0.0056)
70.00644
(0.0095)
0.000650
(0.0078)
70.000979
(0.0079)
70.00160
(0.0050)
ment
0.0406***
(0.0093)
0.0398***
(0.0095)
0.0406***
(0.0099)
0.0220*
(0.012)
0.0501***
(0.013)
0.0439***
(0.013)
0.0413***
(0.013)
0.0220*
(0.013)
Gini
70.0737***
(0.024)
70.0394
(0.024)
70.0326
(0.026)
0.00784
(0.031)
ni
72.508
(1.51)
71.088
(1.17)
70.252
(1.36)
70.802
(1.21)
ation
0.784***
(0.19)
0.689***
(0.22)
0.786***
(0.28)
0.902***
(0.18)
0.796***
(0.22)
0.832***
(0.28)
cy
70.0706*
(0.038)
70.0658
(0.039)
70.0290
(0.040)
70.0664*
(0.035)
70.0595
(0.037)
70.0446
(0.039)
cy d
70.0121*
(0.0069)
70.0114
(0.0069)
70.00784
(0.0064)
70.0108
(0.0071)
70.0102
(0.0071)
70.00852
(0.0064)
hy
Yes
Yes
Yes
Yes
ons
63
55
55
55
59
53
53
53
d
0.50
0.70
0.71
0.79
0.42
0.68
0.70
0.80
red
0.45
0.65
0.65
0.72
0.37
0.63
0.63
0.73
obuststandarderrorsappearinparenthesesbelowcoefficientes
timates.Significancenoteda
s***p5
0.01,**p5
0.05,*
p5
0.1.
(3)and(7)includetwogeography
controls,adummyvariableforbeinglandlockedanddistancefromtheequator.Colu
mns(4)and(8)
egionaldummyvariablesascontrols.
988 L. Davis & M. Hopkins
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T
able4.Cross-sectionalsimultaneousequationsregression
s
(1)
(2)
(3)
(4)
(5)
t:
growth
inequality
growth
inequality
growth
inequality
gro
wth
inequality
growth
inequality
71.656***
(0.34)
7
1.848***
(0.26)
72
.123***
(0
.35)
71.8
50***
(0.3
5)
72.252***
(0.42)
970
0.0443***
(0.0083)
70.0732
(0.051)
0.0410***
(0.0076)
70.0471
(0.046)
0
.0380***
(0
.0083)
70.0345
(0.055)
0.0
251**
(0.0
11)
0.0454
(0.056)
0.0411***
(0.012)
70.0438
(0.075)
ent
70.00843
(0.0074)
7
0.00412
(0.0070)
70
.00333
(0
.0081)
70.0
0494
(0.0
096)
70.00463
(0.0099)
2.490***
(0.71)
76.833*
(3.58)
1.757***
(0.64)
5.579
(3.42)
1
.063
(0
.86)
6.759
(4.34)
2.0
28**
(0.8
3)
7.812**
(3.67)
1.439
(2.05)
18.16*
(9.65)
ni
70.101**
(0.043)
7
0.0590
(0.054)
70
.0276
(0
.061)
70.1
15
(0.0
92)
70.0000875
(0.072)
17.21**
(7.20)
18.70***
(6.09)
18.31***
(6.80)
12.19*
(7.00)
24.57**
(10.2)
y
70.394***
(0.14)
7
0.0374
(0.024)
70.125
(0.13)
70
.0693*
(0
.038)
0.175
(0.20)
70.0
383
(0.0
34)
0.145
(0.18)
70.199**
(0.078)
0.327
(0.40)
tion
0.543**
(0.27)
73.844***
(0.74)
1
.040**
(0
.48)
74.912***
(1.27)
0.3
75
(0.3
7)
74.257***
(1.36)
1.713**
(0.79)
77.120**
(2.82)
71.1
91
(1.3
9)
10.85***
(3.51)
erica
0.5
90
(1.2
6)
4.666
(3.26)
15.62***
(4.01)
43.68***
(4.86)
11.68***
(4.20)
63.22***
(5.49)
9
.525*
(4
.89)
68.88***
(7.79)
16.7
8***
(6.1
6)
57.18***
(8.40)
4.662
(6.69)
76.75***
(15.7)
ns
59
59
53
53
52
52
52
52
35
35
0.55
0.43
0.66
0.67
0
.63
0.62
0.6
4
0.70
0.41
0.20
ents
None
None
Polity1960,French,
EuropeanLanguage,
distequat,dist2
Polity1960,French,
Eu
ropeanLanguage,
distequat,dist2
Polity1960,French,
mortalit
y,distequat,dist2
us
s
Growthandinequality
Growthandinequality
Growth,inequality,
polity1970,exprop
G
rowth,inequality,
polity1970,exprop
Grow
th,inequality,
polit
y1970,exprop
ndarderrorsinparentheses.***p5
0.0
1,**p5
0.05,*p5
0.1.
990 L. Davis & M. Hopkins
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additional controls, however, one key finding is not. In column four, we include
regional dummy variables for Sub-Saharan Africa and Latin America and the
Caribbean, and find that freedom from expropriation is no longer significant in the
growth regression.8 This suggests that the coefficient estimate in column (3) reflects
interregional variations in institutions more than intra-regional variations. Overall,
however, these regressions confirm our hypothesis that economic institutions matter
for both growth and inequality, but inequality itself does not have an independent
effect on economic growth.
5. Identifying the Role of Institutions in Panel Data Fixed Effects
Clearly, we are not the first to claim that the estimates from cross-country
regressions might suffer from omitted variable bias. The standard approach to this
problem has been to alter the type of estimation being done: exploiting the time
variation in panel data to net out unobservable country specific, time invariantcharacteristics. An alternative method of controlling for intrinsic country specific
heterogeneity is through estimation of random effects, or country specific residuals.
The problem for Forbes (2000) and others is that a Hausman specification test rejects
the maintained assumption that the omitted time invariant variables are
uncorrelated with the included variables, suggesting that coefficients estimated using
random effects might not be consistent. Rather than opting for a less efficient
estimator, we address this dilemma by identifying the time invariant omitted
variables that cause the random effects estimator to be rejected in a Hausman
specification test.This approach has a number of advantages. First, parameter estimation using a
random effects estimator is efficient. The estimates generated by the random effects
estimator are, by construction, a weighted average of the fixed effects estimates
derived from period-to-period variations within countries and a between estimator
using cross-country averages. Second, the use of a random effects estimator allows us
to estimate the effect of relatively time invariant variables including many
institutional measures that cannot be identified within a fixed effects specification.
Third, the use of fixed effects estimator changes the question from the effect of
inequality on long run growth in aggregate supply to a much shorter run correlationbetween inequality and growth, typically over a series of five-year periods. By
employing a random effects estimator we are able to produce separate estimates for
the short run and long run effects of inequality on growth.
In particular, we replace the standard specification for panel data regressions,
Yit ai bXit gZi eit 1
with the more flexible specification,
Yit ai bW Xit Xi b
BXi gZi eit 2
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data set. This approach has two advantages. First, the short run and long run effects
of inequality on growth can be estimated at the same time, within the same modelling
framework, using a random effects (GLS) estimator. Perhaps more importantly, it
allows us to test empirically whetherbW bB. That is, our approach allows us to testa key assumption that underlies much of the existing literature on inequality and
growth, that the short run relationship between inequality and growth provides
insight into the longer run relationship.
Random Effects Estimation
The evidence on inequality, institutions and growth from Sections 3 and 4 suggests
that institutions may be a primary source of the omitted variable bias generating
conflicting results between the first and second waves of growth and inequality
regressions. To test whether this is in fact the case we employ a series of panel
regressions reported in Table 5. Column (1) reports the Forbes specification with fixedeffects while column (2) reports the same estimates using a random effects estimator
and the specification in Equation (1). If the unobserved country specific effects are
uncorrelated with the regressors, both estimates will be consistent estimators of the
same quantities but the random effects estimator the GLS estimator will be more
efficient. A Hausman specification test strongly rejects this maintained hypothesis,
however, suggesting that the random effects estimator is inconsistent. This is the
conclusion reached by Forbes (2000) as well as other researchers, which has led to
popularity of less efficient fixed effects estimators in panel growth regressions.
In column (3), we adopt the specification in Equation (2), which allows us toidentify separately the short run and long run effects of inequality on economic
growth. The results in column (3) confirm that, as expected, the use of random effects
estimator in column (2) conflates very different short run and long run effects.
Specifically, the coefficient on inequality reported in column (2) appears not to have
been significant because it is constructed from a weighted average of a short run
positive relationship within countries and a long run negative relationship across
countries. As expected, the coefficient estimate on within country inequality in
column (3) is quite similar to that reported using fixed effects in column (1), while the
between country estimate is similar to that reported in column (1) of Table 3.In columns (4) and (5) we control for the quality of economic and political
institutions. Due to data limitations we continue to use a single time invariant
average score for expropriation risk, but we exploit the long, balanced time series in
the Polity IV database to include a time varying measure of political rights. Because
the uncertainty associated with changes in political institutions in any direction may
have a negative impact on growth, we control for this separately with a dummy
indicating periods involving a significant political transition (during which the Polity
IV database provides no scores). The results from columns (4) and (5) confirm our
earlier conclusions: the coefficient on freedom from expropriation is large, positive,
and statistically significant at the 1 per cent level. In contrast, the coefficient on the
quality of political institutions is not statistically significant, though political
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Table5.Institutions,inequalityandgrow
thusingaGLSrandom-effe
ctsestimator
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
income
73.718***
(0.50)
70.489*
(0.28)
70.751***
(0.28)
71.402***
(0.33)
71.399***
(0.34)
71.874**
*
(0.34)
72.267***
(0.36)
71.275***
(0.31)
71.508***
(0.33)
econdary
g
0.976***
(0.28)
0.274
(0.22)
0.330
(0.21)
0.264
(0.22)
0.263
(0.22)
0.408*
(0.21)
0.515**
(0.21)
0.417*
(0.22)
0.416*
(0.22)
vestment
70.00751*
(0.0040)
70.0131
***
(0.0039
)
70.0113***
(0.0038)
70.0251***
(0.0054)
70.0246***
(0.0054)
70.0251*
**
(0.0053)
70.0216***
(0.0053)
70.0103**
(0.0044)
70.00951**
(0.0038)
ni
0.0857***
(0.029)
70.0285
(0.019)
ni
0.0904***
(0.030)
0.0663**
(0.031)
0.0670**
(0.030)
0.0633*
*
(0.030)
0.0690**
(0.030)
0.0788***
(0.030)
0.0863***
(0.029)
ni
)
70.0917***
(0.022)
70.0273
(0.028)
70.0307
(0.028)
70.0253
(0.028)
0.0188
(0.035)
70.0947***
(0.024)
70.0351
(0.032)
tionrisk
0.990***
(0.19)
0.969***
(0.19)
0.938**
*
(0.19)
1.202***
(0.21)
y
70.00133
(0.024)
ansition
71.612**
(0.81)
d
71.454**
(0.62)
71.474**
(0.60)
alisation
70.0245*
**
(0.0074)
70.0216***
(0.0078)
mmies
Included
F
43.4
Included
F
33.4
ffects
Fixed
Rando
m
Random
Random
Random
Random
Random
Random
Random
ons
450
450
450
402
398
395
402
427
450
roups
88
88
88
72
72
71
72
83
88
(within)
0.183
0.029
0.095
0.176
0.184
0.196
0.201
0.130
0.150
(betwe
en)
0.076
0.043
0.071
0.209
0.209
0.327
0.428
0.107
0.191
(overa
ll)
0.011
0.042
0.089
0.199
0.209
0.270
0.356
0.149
0.206
chi-squ
are
92.12
63.1
19.48
22.4
4.02
0.85
50.09
40.53
testp-v
alue
0.000
0.000
0.002
.0004
0.55
0.973
0.000
0.000
pendentvariableisper-capitaincomegrowthoverfollowingfive-yearp
eriod.
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remaining columns, however, that expropriation risk is the key to eliminating the
omitted variable bias.
In columns (6) and (7) we add two additional sets of controls. Column (6) includes
the landlocked dummy and the ethnic fractionalisation score, while column (7)
includes six regional dummy variables. When economic institutions are used in
conjunction with either of these additional sets of controls, the consistency of the
random effects estimator cannot be rejected. As seen in columns (8) and (9), the
inclusion of expropriation risk is the key to eliminating omitted variable bias. In
these columns, we include the new controls but omit freedom from expropriation,
and in both of these cases the Hausman test rejects the random effects specification.
These results support our contention that, more than other potential excluded
controls, the omission of economic institutions led both to biased estimates in the
first wave and inefficient estimates in the second wave of the growth and inequality
literature.
The most noteworthy aspect of Table 5 is that it nests and ultimately reconciles theresults from the first and second wave of the growth and inequality regressions within
a single unified framework. In column (3), we see that the long run level of inequality
in a country has a negative and significant effect on growth rates, while short run
variations in equality from its average level are positively associated with growth. In
columns (4) through (9) we see clearly that the apparent long run association is, in
fact, spurious: it is an artifact of the joint association of growth and inequality with
omitted property rights. This leads us to conclude that the final answer to the long
pondered question of whether inequality affects long run growth is no.
Our results also suggest caution regarding how we interpret the positiverelationship reported by Li and Zou (1998) and Forbes (2000). Although we too
find evidence of a positive short run relationship within countries, the random effects
model does not support the position that the short run and long run relationships are
the same. Over five-year periods, the mechanism generating this correlation could
well be more closely associated with the business cycles than with long run economic
growth if, for instance, inequality is positively correlated with the unemployment
rate. A full exploration of the short run dynamics of inequality and growth within
countries lies beyond the scope of this paper.
6. Conclusion
This article has argued that a countrys institutions are a critical determinant of the
level of income inequality. While our results do not support the commonly held view
that democratic political institutions are an important determinant of income
inequality, we find a strong negative relationship between the protection of property
rights and income inequality. Moreover, this relationship is robust to the addition of
controls and the use of instruments to control for the endogeneity of our
institutional variables. The evidence presented suggests that the risk of state
expropriation creates an institutional climate more costly to the poor and
disenfranchised than to economic and social elites. We believe this evidence supports
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Engerman, S.L. and Sokoloff, K.L. (1997) Factor endowments, institutions, and differential paths of
growth among new world economies: a view from economic historians of the United States, in: S.
Haber (ed.) How Latin America Fell Behind: Essays on the Economic Histories of Brazil and Mexico,
18001914(Stanford: Stanford University Press), pp. 260304.
Forbes, K.J. (2000) A reassessment of the relationship between inequality and growth. American Economic
Review, 90(4), pp. 869887.
Frankema, E. (2005) The colonial origins of inequality: a global investigation of land distribution.
Manuscript. Groningen Growth and Development Centre, University of Groningen, The
Netherlands.
Friedman, E., Simon, J., Kaufmann, D. and Zoido-Lobato n, P. (2000) Dodging the grabbing hand: the
determinants of unofficial activity in 69 countries. Journal of Public Economics, 76(3), pp. 459493.
Galor, O. and Zeira, J. (1993) Income distribution and macroeconomics. Review of Economic Studies,
64(1), pp. 3542.
Glaeser, E., Scheinkman, J. and Shleifer, A. (2003) The injustice of inequality. Journal of Monetary
Economics, 50(1), pp. 199222.
Hall, R.E. and Jones, C.I. (1999) Why do some countries produce so much more output per worker than
others? Quarterly Journal of Economics, 114(1), pp. 83116.
Keefer, P. and Knack, S. (2002) Polarization, politics and property rights: links between inequality and
growth. Public Choice, 111(12), pp. 127154.
Knack, S. and Keefer, P. (1995) Institutions and economic performance: cross-country tests using
alternative institutional measures. Economics and Politics, 7(3), pp. 207227.
Li, H., Squire, L. and Zou, H. (1998) Explaining international and intertemporal variations in income
inequality.Economic Journal, 108(446), pp. 2643.
Li, H. and Zou, H. (1998) Income inequality is not harmful for growth: theory and evidence. Review of
Development Economics, 2(3), pp. 318334.
Lundberg, M. and Squire, L. (2003) The simultaneous evolution of growth and inequality. Economic
Journal, 113(487), pp. 326344.
Marshall, M.G. and Jaggers, K. (2006) Political regime characteristics and transitions, 18002006. Polity
IV Project. Center for International Development and Conflict Management (CIDCM), University ofMaryland, College Park, MD.
Mauro, P. (1995) Corruption and growth. Quarterly Journal of Economics, 110(3), pp. 681712.
Milanovic, B. (2006) Description of all the Ginis database. World Bank, accessed at: http://
go.worldbank.org/9VCQW66LA0.
Persson, T. and Tabellini, G. (1994) Is inequality harmful for growth? American Economic Review, 84(3),
pp. 600621.
Piketty, T. (1997) The dynamics of the wealth distribution and the interest rate with credit rationing.
Review of Economic Studies, 64(2), pp. 173189.
Rodrik, D. (2000) Participatory politics, social cooperation, and economic stability. American Economic
Review, 90(2), pp. 140144.
Rosser, J.B., Jr., Rosser, M.V. and Ahmed, E. (2000) Income inequality and the informal economy intransition economies. Journal of Comparative Economics, 28(1), pp. 156171.
Sokoloff, K.L. and Engerman, S.L. (2000) Institutions, factor endowments, and paths of development in
the new world. Journal of Economic Perspectives, 14(3), pp. 217232.
Stock, J.H. and Yogo, M. (2005) Testing for weak instruments in Linear IV Regression, in: D.W.K.
Andrews and J.H. Stock (eds) Identification and Inference for Econometric Models: Essays in Honor of
Thomas Rothenberg (New York: Cambridge University Press), pp. 80108.
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