Income distribution and economic growth: evidence from Islamic Republic of Iran.
Bakhtiari, Sadegh ; Meisami, Hossein ; Soleimani, Mohamad 等
INTRODUCTION
Purpose--This paper investigates the effects of income distribution
on economic growth in Islamic Republic of Iran.
Design/methodology/approach--An endogenous growth model is
specified, that includes human capital and technological progress. The
generalized autoregressive conditional heteroskedasticity (GARCH)
technique is used to estimate regression parameters.
Findings--The results show that rising income inequality, which is
measured by the Gini index and the ratio of income of the household at
the 90th percentile to the household at the 10th percentile, would
hinder economic growth in Iran. However, there is a positive relation
between economic growth and the growth in employment, investment
spending, technological progress, and human capital.
Practical implications--The main practical implication of the paper
is that the effect of a rise in inequality on economic growth is
negative in Iran.
Originality/value--The current study is believed to be the first of
its kind that enters human capital in the model, uses two different
measures of inequality, and focuses on the case of Iran.
In the last decades, the world has witnessed the global expansion
of neo-liberal economic ideas and policies, most notably drastic
cutbacks in government regulations of economic activities. The belief
that economic performance is best enhanced by freeing markets of
government interference has become widely accepted among policy planners
and politicians on both the left and the right. (1) The most visible
examples of purportedly harmful government interference in market
processes are social welfare and other redistributive programs. These
are widely regarded as both wasteful and harmful, diverting societal
resources from more productive use and discouraging initiative and
effort. In this view, program outcomes may include a more equitable
distribution of income, but at the cost of decreased economic
performance.
Neo-liberals justify the economic inequalities associated with
markets in terms of efficiency. In their views, by providing a greater
incentive for individual hard work and initiative, freer markets are
supposed to lead to more competitive national economies and a bigger
economic pie for everybody. (2) This neo-liberal argument has impressive
academic credentials, a persuasive voice in public policy debates, and
decades of worldwide political successes. However, it lacks enough
empirical supports. Simply put, there is not persuasive evidence that
increasing inequality is associated with improved economic performance.
Considering this, there is a need to empirically answer the question
that whether the rising income inequality would facilitate or hinder
economic growth in different countries.
This study attempts to answer this question in the case of Islamic
Republic of Iran. It empirically examines the impact of income
inequality on economic growth. An econometric model is used which is
based on the endogenous growth model incorporating human capital and
technological progress. In this model, Capital growth is replaced by the
investment-output ratio to avoid a high degree of multicollinearity
among input factors. Two different measures for income inequality are
considered in order to determine whether the results are robust. The
paper applies the generalized autoregressive conditional
heteroskedasticity (GARCH) model developed by Engle and Robert (2001) to
determine whether the error variance depends on past squared errors and
past error variances.
The study is organized in the following manner. The literature
review is described in Section 2. A theoretical model is presented in
Section 3 and data sources and methodology are discussed in section 4.
Empirical results are given in Section 5. Summary and conclusions are
provided in Section 6.
LITERATURE REVIEW
The relation between inequality and growth has been under
discussion for a long time. In classical economic theory, inequality of
incomes was thought to influence economic growth rates through savings
and consumption. According to Adam Smith, (3) an increased division of
labor raises productivity, but savings govern capital accumulation,
which enables production growth. It was a common belief in the 18th
century that only rich people saved. Therefore, economic growth was
possible only when there were enough rich people in society. Adam Smith
also argued that production growth would not be possible without
sufficient demand. He stated that every man should be able to provide
for himself and his family. This would constitute the threshold of
sustainable inequality and would assure a sufficient level of demand.
According to John Maynard Keynes, (4) inequality of incomes leads
to lower economic growth. Keynes argued that marginal consumption rates
are equal among all income brackets. As a result, aggregate consumption
depends on changes in aggregate income. According to Keynes, demand is
the basis of investments, and because inequality lowers aggregate
consumption, inequality of incomes will diminish economic growth.
If one seeks to evaluate the literature more specifically, he or
she might find that no study has explicitly focused on the impact of
inequality on economic growth in Iran. However, this relation or other
related ones, has been considered for other countries or group of
countries. Greenspan (5) attributed income inequality in US to
technological progress, changing organizational structure, and increase
in international trade. Technological progress raised wages for highly
skilled workers relative to unskilled workers. Globalization or trade
tends to lower the return of low-skilled workers and raise the return
for high-skilled ones. He indicated that the distribution of consumption
and wealth should also be considered in evaluating inequality. A Gini
index constructed from the US consumption spending shows that US
households were better off in the later 1990s, whereas the Gini index
based on income exhibits rising income inequality.
In his eminent work, Tyson (6) indicated that inflation eroded real
minimum wages and may have reduced the earnings of the bottom fifth
households by 20 percent and that the declining unionization may account
for 20 percent of the rise in income inequality among men. The increase
in single-parent households also contributed to the rising income
inequality. She suggested that human capital investment and college
education should be targeted at the children who come from low-income
families. To deal with the income inequality issue, the earned income
tax credit (EITC) rose by as much as 210 percent and real minimum wages
increased by 19 percent. She estimated that during 1989-1997, the
increases in the EITC and minimum wages combined to raise earnings of a
single mother by as much as 27 percent.
Considering the US economy, Feldstein (7) argued that rising income
inequality is not a problem that needs remedy. He reasoned that the
society is better off if some people receive more income while other
people's income does not decline. These high-income people were
successful because they were more productive, exhibited
entrepreneurship, worked longer hours, and could borrow money with lower
costs. He stressed that poverty is a serious concern due to long-term
unemployment, lack of earning ability, and individual choice. Reform of
the unemployment insurance (UI) program in the 1980s helped reduce
unemployment rates. Poverty can be addressed through better on-the-job
training programs in the private sector and improved education with
emphases on decentralization and competition. Monetary policy cannot
solve the poverty problem in the long run.
In their study on monetary policy and the well-being of the poor,
Romer and Romer (8) prove that a higher unexpected inflation rate
reduces income inequality; more output and inflation variability
contributes to more income inequality; a lower unemployment rate reduces
poverty; and monetary policy can provide the poor with more jobs and
more wages in the short run. They maintained that the long-term costs of
rising inflation of expansionary monetary policy would outweigh the
short-run benefits.
Comparing the relationship between income inequality and business
cycles in the case of UK, US, Italy, and Greece, Dimelis and Livada (9)
found that higher output reduces inequality in the US and the UK, but it
increases inequality in Greece. Besides, the poor suffer from high
unemployment, but they gain from high inflation.
Considering the US data since 1960, Rodriguez (10) provided
empirical support of the institutionalist view that income inequality
would cause sociopolitical instability, which would reduce economic
growth.
Based on the cross-state data and using the generalized moment
method and fixed effects, Panizza (11) found that inequality and growth
in the US have a negative relationship. However, he also indicated that
the negative relationship is weak and would vary with the methodologies
used.
Focusing on different cases, Acemoglu and Robinson (12) found that
growth might result in an "East Asian Miracle" with high
output and low inequality or an "autocratic disaster" with low
output and high inequality. In their view, it all depends on the initial
status of the countries.
Burtless (13) compared economic growth and inequality between the
US and other G7 countries and found that the US has more economic growth
and more inequality than these countries. He attributed the US situation
to less regulation in the market place and less assistance to the needy.
Inequality of income and economic growth may have indirect
relations. In sociology, inequality of incomes has been found to cause
social disorganization, which is commonly associated with increased
crime rates and lower social capital. (14) Inequality can also increase
corruption and illegal rent-seeking. (15) Property crimes, vandalism,
theft and corruption in particular can harm economic growth by
discouraging investments and lowering productivity by inflicting
additional costs on companies. (16)
THE ECONOMETRIC MODEL
Following the works of Romer and Romer, Furman and Stiglitz,
Feldstein, Tyson, Dimelis and Livada, and Acemoglu and Robinson, (17)
and some others, we can express the real GDP (Y) as a function of main
input factors such as labor (L), capital stock (K), technology (T),
human capital (HC), and income inequality (IN).
Y = F [??], K, T, HC, IN[??] (1)
Suppose that the production is a simple function of three input
factors, which are L, K, and T. In this case, estimated regression
parameters may not have the expected signs and may be inaccurate. The
reason is the high degree of multicollinearity among time series
variables. To avoid these kinds of potential problems, we differentiate
the production function and divide it by output to obtain the equation
for output growth rate.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
Now we can enter human capital and income inequality into the
equation and come up with equation (3) for estimation.
GY = [[beta].sub.1] CLY + [[beta].sub.2]IY + [[beta].sub.3]GT +
[[beta].sub.4]HC + [[beta].sub.5]IN (3)
In this equation [??]/Y = GY is the growth rate of real GDP; [??]/Y
= CLY is the ratio of change in labor employment to real GDP; [??]/Y=IY
is the ratio of change
in capital stock to real GDP; and [??]/T = GT is the growth rate of
technological progress. The coefficients. [[beta].sub.1] and
[[beta].sub.2] are marginal product of labor and capital respectively,
and [[beta].sub.3] is the output elasticity with respect to
technological progress.
Considering the eminent paper of Furman and Stiglitz, (18) four
probable factors can be involved in determining the sign of
[[beta].sub.5], namely, savings, imperfect information, agency costs,
fiscal policy, and social or political stability. Although the rich tend
to save more, the empirical result on rising income inequality and
aggregate saving is inconclusive. Segmented markets and imperfect
information often characterize a society with low equality. Asymmetric
information leads to the principal-agent problem and high agency cost
and results in extensive economic inefficiency and slow growth. Under
the pressure of increasing income inequality, the government may
consider a progressive income tax policy to redistribute income.
However, such a policy may deter capital accumulation and economic
growth. It is also likely that the rich lobby for lowering the tax rate.
If income inequality continues to worsen, social disturbances and
political instability would occur, which strains the growth.
Because of the fact that in the case of time series data, error
variance may not be constant, the GARCH model can be used. This model
may be expressed as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
Clearly in this equation, current error variance [V.sub.t] is a
function of past squared errors [e.sup.2sub.t-i] and past error
variances [V.sub.t-j]. It is also clear that if [[theta].sub.h]=0
equation (4) reduces to an ARCH model.
DATA SOURCES AND METHODOLOGY OF THE STUDY
Making use of annual data ranging from 1965 to 2008, we estimate
equation three. In doing so, Real GDP, investment spending and total
employment are taken from Time Series Data Source of the Central Bank of
Iran. Real GDP and investment spending are expressed in billions and
employment is expressed in thousands. The number of students as a proxy
for human capital and the number of industrial patents as a proxy for
technological progress, both are taken from the Iranian center for
statistics. The data for the Gini index and the ratio of income of the
household at the 90th percentile to the household at the 10th percentile
come from the Time Series Data Source of the Central Bank of Iran. It is
clear that the value of the Gini index ranges from zero (complete income
equality) to one (complete inequality).
EMPIRICAL RESULTS
In this part, the statistical results of the regressions will be
presented. Firstly, we will consider the the Gini index as the proxy for
inequality and go on to replace that by the ratio of income of the
household at the 90th percentile to the household at the 10th
percentile.
The results for the GARCH (1,1) regression are presented in Table
I. As shown in the variance equation, the coefficient of the lagged
squared residual is insignificant, but the coefficient of lagged
residual variance is significant at the 5 percent level.
All the coefficients of the growth equation are significant at 1 or
5 percent level. The sign for the Gini index is negative and
significant, suggesting that an increase in income inequality is
destructive to economic growth. If the Gini index increases by 0.1, real
GDP will decline by 0.28 percentage points. The signs for the
coefficients of other variables are also as expected. In other words, if
the status of capital accumulation, employment, human capital, and
technical progress is improved, the economic growth will enhance.
According to the estimates, if CLY rises by one, real GDP will grow
by 0.33 percentage points. On the other hand, if IY and GT increase by 1
percentage point, real GDP will grow by 0.22 and 0.59 percentage points
respectively. A one percent increase in the number of students (as a
proxy for human capital) would lead to a very slight percent growth of
real GDP.
The results of table I also show that R-Squared and adjusted
R-Squared are 0.82 and 0.78 respectively. The Durbin-Watson statistic of
1.969 is close to two indicating that the null hypothesis of
non-autocorrelation cannot be rejected.
It is possible to regress the model using OLS technique. This will
help us compare the results of GARCH model with those of OLS. The
results from the OLS regression are given in Table II. As shown, in this
case all coefficients are insignificant even at the 10 percent level. In
this model, the values of some of the coefficients are different from
those of GARCH. It appears that the GARCH estimation is more appropriate
than the OLS. Since the OLS does not consider autoregressive conditional
heteroskedasticity, its residual variance is likely to be biased, and
hypothesis tests are invalid.
Table III shows the case where, instead of Gini index, the ratio of
income of the household at the 90th percentile to the household at the
10th percentile is used as an indicator of income inequality. The
results show that the coefficient for GARCH(1) or the lagged variance is
significant at the 1 percent level. As shown, the coefficient of the
ratio of income is negative and significant at the 5 percent level. The
sign of the coefficient of HC, however, is not significant.
The outcomes of this regression are similar to those in Table II.
In other words, the coefficients of employment, capital accumulation,
human capital and technological progress are all positive, while the
coefficient of inequality is negative.
SUMMARY AND CONCLUSIONS
This paper investigates the effects of income distribution on
economic growth in Islamic Republic of Iran. An endogenous growth model
is specified, that includes human capital and technological progress.
The generalized autoregressive conditional heteroskedasticity (GARCH)
technique is used to estimate regression parameters.
The results show that rising income inequality, which is proxied by
the Gini index and the ratio of income of the household at the 90th
percentile to the household at the 10th percentile, would hinder
economic growth in Iran. However, there is a positive relation between
economic growth and the growth in employment, investment spending,
technological progress, and human capital.
For improving income inequality, the Iranian government may need to
maintain a balance between efficiency, which refers to the production of
maximum output with minimum cost, and equity, which means fair share of
output or income among the members of the society. The efficiency and
equity criteria are both important; the reason is that a country with
relatively high income inequality may face relatively more undereducated
citizens, high crime, social unrest, political instability, less
consumption spending, and other socioeconomic problems. A country with a
less efficient economic system could result in less investment, high
production costs, high prices, low productivity, and disadvantage in
global competition.
This study opens a range of areas for future research. In this
regard, one may consider different measures for human capital and
technological progress. The low level of significance of few variables
in some of the regressions may suggest that some of the measurements
shall be modified. Moreover, because economic growth and income
inequality may affect each other, a vector autoregression (VAR) model
may be considered to investigate the simultaneous relationship among
income inequality, economic growth, human capital, and technological
progress.
NOTES
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(5.) A. Greenspan. "Opening Remarks," Income Inequality:
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Kansas City, Kansas, MO, 1998.)pp. 1-9.
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Balance Between Economic Efficiency and Income Equality?," Income
Inequality: Issues and Policy Options. (Kansas City, MO: Federal Reserve
Bank of Kansas City, Kansas, MO, 1998.) pp. 337-343.
(7.) Martin S. Feldstein. "Is Income Inequality Really a
Problem?, "In Income Inequality Issues and Policy Options, A
symposium sponsored by the Federal Reserve Bank of Kansas City, pp.
357-367. Federal Reserve Bank of Kansas City, 1998.
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of the Poor," Income Inequality: Issues and Policy Options.
(Federal Reserve Bank of Kansas City, Kansas, 1998), pp. 159-201.
(9.) S. Dimelis and A. Livada. "Inequality and Business Cycles
in the U.S. and European Union Countries," International Advances
in Economic Research, Vol. 5, No. 3, 1999, pp. 321-38.
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and the History of Ethnicity in the United States," (New York: New
York University Press, 2000.)
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(16.) Robert E. Hall and Charles I. Jones. "Why Do Some
Countries Produce so Much More Output Per Worker Than Others?"
Quarterly Journal of Economics, 1999. pp. 83-116; Kevin M. Murphy,
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the Kuznets Curve;" Martin S. Feldstein: "Is Income Inequality
Really a Problem?"; C. Romer and D. Romer. "Monetary Policy
and the Well-Being of the Poor;." L. Tyson. "Commentary: How
Can Economic Policy Strike a Balance Between Economic Efficiency and
Income Equality?;" S. Dimelis and A. Livada. "Inequality and
Business Cycles in the U.S. and European Union Countries;" J.
Furman and J.W. Stiglitz. "Economic Consequences of Rising Income
Inequality," Income Inequality: Issues and Policy Options. (Kansas
City, MO: Federal Reserve Bank of Kansas City, Kansas, MO, 1998.) pp.
221-263.
(18.) J. Furman and J.W. Stiglitz. "Economic Consequences of
Rising Income Inequality."
By Sadegh Bakhtiari, Hossein Meisami, and Mohamad Soleimani *
* Sadegh Bakhtiari, Faculty of Islamic Studies and Economics, Imam
Sadiq University (I.S.U.), Tehran, Iran: Hossein Meisami, Faculty of
Islamic Studies and Economics, Iman Sadiq University (I.S.U.), Tehran,
Iran; and Mohamad Soleimani, Faculty of Management, Tarbiat Modares
University, Tehran, Iran.
Table I
GARCH regression considering the Gini coefficient
Dependent Variable: GY
Method: ML-ARCH
Date: 01/22/09 Time: 21:20
Sample(adjusted): 1348 1384
Included observations: 37 after adjusting endpoints
Convergence achieved after 1 iterations
Std. z-
Coefficient Error Statistic Prob.
CLY 0.335263 0.092965 3.606331 0.0003
IY 0.223318 0.102208 2.184942 0.0289
GT 0.256473 0.094024 2.727750 0.0064
HC 7.63E-08 3.39E-08 2.253397 0.0242
GINI -0.289204 0.112713 -2565835 0.0103
Variance Equation
C 0.002624 0.018255 0.143733 0.8857
ARCH(1) 0.292233 0.151776 1.925422 0.0656
GARCH(I) 0.887840 0.421463 2.106567 0.0352
R-squared 0.824299 Mean dependent var 0.248297
Adjusted R-squared 0.781888 S.D. dependent var 0.153664
S.E. of regression 0.071765 Akike info criterion -2.056585
Sum squared resid 0.149355 Schwarz criterion -1.708278
Log likelihood 46.04682 F-statistic 19.43614
Durbin-Watson stat 1.969459 Piob(F-statistic) 0.000000
Table II
OLS regression considering the Gini coefficient
Dependent Variable: GY
Method: Least Squares
Date: 01/08/09 Time: 22:40
Sample(adjusted): 1348 1384
Included observations: 37 after adjusting endpoints
Std. t-
Coefficient Error Statistic Prob.
CLY 0.299532 0.442439 0.677001 0.5033
IY 0.224590 0.300048 0.748514 0.4596
GT 0.457763 0.279093 1.640179 0.1108
HC 5.97E-08 4.05E-08 1.471406 0.1509
GINI -0.002854 0.003278 -0.870578 0.3840
R-squared 0.522231 Mean dependent var 0.248297
Adjusted R-squared 0.462511 S.D. dependent var 0.153664
S.E. of regression 0.150068 Akaike criterion -0.830369
Sum squared resid 0.720652 Schwarz criterion -0.612678
Log likelihood 20.36183 F-statistic 1.436448
Durbin-Watson star 1.551495 Prob(F-statistic) 0.244626
Table III
GARCH regression considering the income ratio
Dependent Variable: GY
Method: ML-ARCH
Date: 01;16,09 Time: 22:33
Sample(adjusted): 1348 1384
Included observations: 37 after adjusting endpoints
Convergence achieved after 1 iterations
Std. z-
Coefficient Error Statistic Prob.
CLY 0.103657 0.014577 7.111127 0.0000
IY 0.459545 0.089770 5.119142 0.0000
GT 0.671099 0.251147 2.672142 0.0075
HC 6.58E-08 5.68E-08 1.158781 0.2465
FTT -0.049739 0.020648 -2.408894 0.0160
Variance Equation
C 0.804860 0.413326 1.947276 0.0515
ARCH(1) 0.150000 0.605489 0.247734 0.8043
GARCH(1) 1.098560 0.062777 3.162948 0.0016
R-squared 0.630505 Mean dependent var 0.248297
Adjusted R-squared 0.541317 S.D. dependent var 0.153664
S.E. of regression 0.104070 Akike info criterion -1.308419
Sum squared resid 0.314089 Schwarz criterion -0.960112
Log likelihood 32.20575 F-statistic 7.069368
Durbin-Watson stat 1.848741 Prob(F-statistic) 0.000060
