Abstract - COMSATS Institute of Information Technology

Article
Does Nominal
Devaluation Improve
Income Distribution?
Evidence from Bangladesh
South Asian Survey
19(1) 61–77
© 2012 ICSAC
SAGE Publications
Los Angeles, London,
New Delhi, Singapore,
Washington DC
DOI: 10.1177/0971523114539586
http://sas.sagepub.com
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Muhammad Shahbaz
Mohammad Mafizur Rahman
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Abstract
The article aims to investigate the impact of nominal devaluation on income distribution in Bangladesh both in short and long runs. In doing so, Auto Regressive
Distributed Lag (ARDL) bounds testing has been employed for cointegration,
and Error Correction Model (ECM) has been used for short-run dynamics.
The empirical psychology has confirmed the existence of long-run relationship
between the variables. Furthermore our estimated results reveal that nominal
devaluation tends to decrease income inequality. Though economic growth
appears to improve income distribution, non-linear link between both the variables, however, depicts Kuznets’ inverted-U curve (1955). Financial development
causes further deterioration in income distribution. Trade openness contributes
to income inequality as discussed in Leontief Paradox.
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Keywords
Devaluation, income inequality, cointegration, economic growth, financial
development, trade openness, Bangladesh
Introduction
Kuznets’ hypothesis has been repeatedly investigated by numerous researchers in
economics literature. Authors utilise not only cross-sectional data but also time
series data to prove it. Kuznets’ hypothesis is basically a relationship between
economic growth and income distribution: ‘an increase in per capita income worsens (through unequalizing effect) impact on income distribution first, then lowers
(through income equalizing channel) income inequality with passage of time
Muhammad Shahbaz is Research Fellow, Management Sciences, COMSATS Institute
of Information Technology, Lahore, Pakistan.
Mohammad Mafizur Rahman is Senior Lecturer, Economics, University of Southern
Queensland, Toowoomba, Australia. E-mail: [email protected]
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Muhammad Shahbaz and Mohammad Mafizur Rahman
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(years)’. For example, Ahluwalia (1974), Berry (1974), Fields (1980) and Papanek
and Kyn (1986) conclude that economic growth deteriorates income inequality,
but the link between the two is not robust. Exploring the importance of capacity
utilisation, Mohtadi (1988) argues that insignificant relationship between income
per capita (economic growth) and income distribution is due to the ignorance of
capacity utilisation in growth–income inequality equation. He documents that
without excess capacity utilisation, economic growth worsens income distribution
in the economy and vice versa. Several studies pertaining to the literature show
unequal effect of economic growth on income distribution and confirm the existence of Kuznets’ inverted-U shaped curve. They use cross-country evidence in the
absence of adequate longitudinal data on income distribution (Barro 2000;
Bourguignon 1994; Doyle 1996; Forbes 2000; Jha 1996; Milanovic 1995; Ram
1997; Stephen 2003; Wan 2002).
Devaluation is a major policy option for many developing countries including
Bangladesh as they have been facing a persistent balance of payment deficit. The
economic arguments of devaluation, which is related to fixed exchange rate system,
are that it makes domestic goods and services cheaper relative to foreign goods and
services. Therefore, exports will increase and imports will decrease; thus, balance of
trade and balance of payment will improve. Domestic output will move to higher
level with higher employment; there will be initially an excess demand for money
due to the rise in transactions accompanying the output increase. This will push the
home interest rate above the world interest rate if the central bank does not interfere
in the foreign exchange market. Official reserves will also increase.
Therefore, a government devalues its currency mainly because of the following three benefits: (i) devaluation improves the current account, at least in the
short run, which the government may believe to be desirable; (ii) devaluation
mitigates unemployment problem (e.g., if government spending and budget deficits become politically unpopular, the government considers devaluation as the
most convenient way of boosting aggregate demand); and (iii) devaluation causes
a rise in the central bank’s foreign reserves (Krugman and Obstfeld 2003).
However, devaluation has also harmful effects on an economy. A country like
Bangladesh imports more goods and services than it exports. Its list of imports
consists of many consumer and capital goods which are essential for the very
survival and growth of the economy, and as such it is very difficult to reduce them
substantially. Hence, devaluation increases import payments. Devaluation can
only improve the balance of payment if Marshall–Lerner condition is satisfied, but
evidence shows that in many circumstances devaluation has not been successful
(Bahmani-Oskooee 1985). The work of Bahmani-Oskooee and Alse (1994) showed
that devaluation had no long-run effects on trade balance of 14 countries out of 20
countries studied. Devaluation may also cause inflation in the economy at least in
the long run. It also raises the burden of foreign debt and debt service liability.
Although studies on various effects of devaluation exist, the impact of devaluations on income inequality has been least investigated in the economics literature.
Therefore, this study will be a significant contribution in economic literature as a
South Asian Survey, 19, 1 (2012): 61–77
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Does Nominal Devaluation Improve Income Distribution?
case study for Bangladesh. This research is considered important for three reasons.
First, the study has applied advanced Auto Regressive Distributed Lag (ARDL)
bounds testing approach for cointegration. Second, Ng-Perron unit test is used to
investigate integrating order of variables. Finally, Error Correction Model (ECM)
explores the short-run behaviour of variables over the period 1985–2008.
The rest of the article is organised as follows. Section II briefly reviews the
literature; section III describes the model; section IV explains the methodological
framework; section V clarifies data sources and presents empirical analysis; section VI details sensitivity analysis; and section VII concludes the article.
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Literature Review
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Alexander (1952) examined the relationship between devaluation and income
inequality. He found that if there is a counter-cyclical movement between wages
and prices (wages are behind prices), then nominal devaluation causes inflationary effects, and then nominal profits might be obtained at the cost of nominal
wages. This phenomenon purely leads to transfer of income from wage earners
(high marginal propensity to absorb) to profit makers (low marginal propensity to
absorb).1 Similarly, Diaz-Alejandro (1965) demonstrates that the income inequality is caused by devaluation, particularly in the short run. He further argues that
devaluation not only lowers wages but also raises unemployment in the country
that hurts poor segment of population disproportionately. The same argument is
advanced by Twomey (1983).
Furthermore, Lindert (1986) concludes that the impact of devaluation through
its inflationary effects is different on different groups of people in the short run.
He explains that devaluation hurts the group more that receives its income by selling non-traded goods and services. In this scenario, devaluation increases the cost
of living of the poor group of population without an increase of their income. On
the same route, an increase in the relative price of traded goods favours the groups
that are tied most closely to producing traded goods. The same notion has been
supported by Edwards (1989) and Bigsten and Collier (1995). Similarly, BahmaniOskooee (1997) finds unequalising effect of devaluation on income distribution
using cross-sectional data from 24 countries, but Sarel (1997) shows equalising
effect of real depreciation on income distribution in low-income countries.
Haughton and Kinh (2003) use two approaches (income per capita and expenditure) to investigate the impact of devaluation on income distribution in Vietnam
utilising household’s data. They explain that devaluation benefits the poor and the
rich while hurting the middle class. Using time series data, Bahmani-Oskooee and
Gelan (2008) also document that currency devaluation increases income inequality in the US. Shahbaz and Islam (2012) also apply ARDL-OLS to examine the
relation between devaluation and income inequality for the case of a transitional
economy like Pakistan. The empirical results reveal that nominal devaluation
tends to worsen income distribution in the country.
South Asian Survey, 19, 1 (2012): 61–77
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Muhammad Shahbaz and Mohammad Mafizur Rahman
The Model
The article aims to investigate the impact of devaluation on income distribution in
Bangladesh. For this purpose, initially the OLS approach has been employed on the
degree of devaluation for the measure of income distribution. In such a way, we can
see whether nominal devaluation affects income inequality. Like most researchers,
income per capita (proxy for economic growth) has been included in the model to test
the Kuznets’ (1955) hypothesis that an increase in per capita income worsens income
distribution first; then, it slowly improves with the passage of some time. In formulating the model, other determinants are also included as control variables to avoid
the problem of misspecification. These variables are financial development and trade
openness. Finally, an empirical equation is being moulded as given below:2
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LGINI  a1  a2 LDEV  a3 LPGDP  a4 LFD  a5 LTR  t
(1)
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where GINI = Gini coefficient which is used as a proxy of income inequality,
DEV = nominal devaluation, PGDP = per capita income, FD = financial development proxied by domestic credit to private sector as share of GDP, TR = trade
openness defined as trade–GDP ratio and L stands for log.
As argued earlier, an increase in devaluation or rate of depreciation lowers the
share of the poor while benefits the rich. This situation leads to worsening income
distribution in the country. It is expected that coefficient of a2 > 0, a3 > 0, if economic growth benefits elite class of population. This shows no trickle-down effect
of economic growth to poor population of the country. Economic growth equalises income distribution when there is trickle-down effect in the country. In such
an environment, a3 < 0.
Development of informal credit, which is often the only source of borrowing
for poor people, is made easier by the growth of the formal financial system which
offers opportunities of profitable investments to informal financial institutions or
agents. Finally, in a framework of competitive markets of goods and production
factors, credit may improve the well-being of the poor and decline income inequality (Beck et al. 2004; Shahbaz 2009a, 2009b; Shahbaz and Islam 2011). It
may be documented that a4 < 0.
The estimate of a5 > 0, because openness of trade may lead to deterioration of
income distribution in case of developing economies. The main reason is that
exporting firms demand some educated workers, because trade does not benefit
workers without education, who account for the bulk of low-income households
in poor countries (Bensidoun et al. 2005; Shahbaz et al. 2007).
To find out the non-linear relation between economic growth and income inequality, squared term of GDP has been included in basic model. The modified
version of equation (1) is as follows:
LGINI  g1  g 2 LDEV  g 3 LPGDP  g 4 LPGDP 2  g 5  X  mt
South Asian Survey, 19, 1 (2012): 61–77
(2)
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Does Nominal Devaluation Improve Income Distribution?
The inequality-widening hypothesis predicts g2 > 0 and g3 = 0, and inverted
U-shaped hypothesis predicts if g2 > 0 and g3 < 0. The inequality-narrowing hypothesis predicts g2 < 0 and g3 = 0; if g2 < 0 and g3 > 0, U-shaped hypothesis predicts.
Methodological Framework
Ng-Perron Unit Root Test
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Ng-Perron (2001) developed a test statistics wherein GLS is applied to de-trend
the series Dtd. The critical values of the tests are based on those of Phillips-Perron
(1988) Za and Zt statistics, Bhargava (1986) R1 statistics and Elliot et al. (1996).
The following annotations are used:
(3)
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t2
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k   ( Dtd1 ) 2 / T 2
MZ ad  (T 1 ( DTd ) 2  f  ) / (2k )
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
2
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 2
MPTd  ( c k  cT 1 ( DTd ) 2 / f  , and , ( ck  (1 c)T 1 ( DTd ) 2 / f 

(4)
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MSB d  (k / f  )1/ 2
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MZ td  MZ a  MSB
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The de-trended GLS tailored statistics is given by:
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ARDL Approach for Cointegration
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The estimation of the impact of devaluation on income distribution is based on the
traditional view which indicates that devaluation leads to transfer of income from
wage earners (high marginal propensity to absorb) to profit makers (low marginal
propensity to absorb). In this study, Gini coefficient (GINI) for income inequality
is measured as a function of economic growth (GDP per capita), nominal devaluation (DEV), FD and trade openness (TR/GDP). Here, we have used one of the
most advanced approaches such as the bounds testing to verify the presence of
cointegration among the relevant macroeconomic variables; where xt is time series
vector xt = {DEV, PGDP, FD, TR} with yt = GINI, this approach is being begun
with an unrestricted vector autoregression:
q
zt  m   d j zt  et
j1
(5)
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Muhammad Shahbaz and Mohammad Mafizur Rahman
where Zt = [yt, xt]'; m is showing vector of constant term, m = [my, mx]' and d are
indicating matrix of vector autoregressive (VAR) parameters for lag j. As mentioned by Pesaran, Shin and Smith (PSS) (2001), two time series yt and xt can be
integrated at either I(0) or I(1) or mutually co-integrated. In this case, time series
vector xt, devaluation, economic growth, FD and trade openness can also be integrated at different orders. The error terms vector et = [ey,t, ex,t]' ~ N (0, W), where
W is definitely positive. Equation 7 in modified form can be written as a vector
error correction model as given below:
q1
Dzt  m  gzt1    j Dzt  et
j1
(6)
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  yy, j  yx, j 
 j 
  jk
  xy, j  xx, j 
k  j1
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where D = 1–L, and
(7)
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Here, g is the multiplier matrix in long run as following:
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q
 g yy g yx 
g j 
( I   j j )
 g xy g xx 
j1
(8)
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I is indicating an identity matrix. The diagonal essentials for said matrix are left
unrestricted. This implies that each of the series can be stationary either at I(0) or
I(1). This approach enables to examine the maximum cointegrating vectors that
include both yt and xt. This would investigate that either gyx or gxy can be non-zero
but not both of them. In this article, our main objective is to examine the long-run
impact of devaluation, economic growth, FD and trade openness on income distribution. Here, the restriction that is imposed is gxy = 0, which indicates that devaluation economic growth, FD and trade openness have no long-run impact on income
distribution. Under the said assumption, that is, gxy = 0, equation 1 can be rewritten
as follows:
q1
q1
j1
j1
Dyt  b  b1 yt1  b 2 xt1   by, j Dyt j   bx, j Dyt j  jDxt  mt
(9)
where;
b  m y  ' mx ; b1   yy ; b 2   yx  '  xx ; b y , j   yy , j  '  xy , j and b x. j   yx , j '  xx , j
  yx  '  xx ; b y , j   yy , j  '  xy , j and b x. j   yx , j '  xx , j
South Asian Survey, 19, 1 (2012): 61–77
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Does Nominal Devaluation Improve Income Distribution?
This is called ARDL model which is denoted by Unrestricted Error Correction
Model (UECM) (PSS 2001). Empirical evidence on coefficients of equation 9 can
be investigated by ordinary least squares and non-existence of long-run link
between the said variables can be tested by calculating F-statistics for the null
hypothesis of b1 = b2 = 0. Under the alternative hypothesis b1 ≠ b2 ≠ 0, stable relationship in long run between said variables can be described as following:
yt  j1  j2 xt  t
(10)
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where j1 = –b° / b1, j2 = b2 / b1 and vt is a stationary process having zero mean. PSS
(2001) reveal that the distribution of F-statistics is based on the order of integration of the empirical data series. The ARDL method estimates (p + 1)k number of
regressions in order to obtain optimal lag length for each variable, where p is the
maximum number of lags to be used and k is the number of variables in the equation. To establish the stability of the ARDL model, sensitivity analysis is also
conducted, which examines the serial correlation, functional form, normality and
heteroscedasticity associated with the model. The stability test is conducted by
employing the cumulative sum of squares of recursive residuals (CUSUMsq).
Examining the prediction error of the model is another way of ascertaining the
reliability of the ARDL model.
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Data and Empirical Analysis
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Data
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Annual data span is started from 1985 up to 2008. The time series data on Giniinequality (proxy for income inequality) is unavailable from 1971, but available
from 1985 onwards. We have combed World Development Indicators (WDI 2008)
for time series data of GDP per capita, FD proxied by domestic credit to private sector as share of GDP and trade as share of GDP. Finally, exchange rate (currency or
nominal devaluation) is used from International Financial Statistics (IFS 2008).
The ARDL technique has the advantage of avoiding the classification of variable into I(0) or I(1) as there is no need for unit root pre-testing. According to
Sezgin and Yildirim (2002) and Ouattara (2004), in the presence of I(2) variables,
the computed F-statistics provided by PSS (2001) become invalid, because
bounds test is based on the assumption that the variables I(0) or I(1) are mutually
cointegrated. Therefore, the implementation of the unit root test in the ARDL
procedure might still be necessary to ensure that none of the variables is integrated of order 2, that is, I(2) or beyond. For this purpose, Augmented DickeyFuller (ADF) unit root test has been employed to find out order of integration of
the concerned actors in the study. The results in Table 1 show that Gini-inequality
(GINI) is stationary at I(0) while economic growth (GDPC), nominal or currency
devaluation (DEV), FD and trade openness (TR) are integrated of order 1, that is,
South Asian Survey, 19, 1 (2012): 61–77
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Muhammad Shahbaz and Mohammad Mafizur Rahman
Table 1. Unit-Root Estimation
Ng-Perron at Level with Intercept and Trend
MZa
Variables
LGINI
–20.9608
MZt
MSB
MPT
–3.1818
0.1518
LDEV
–9.13360
–2.1011
0.2300
10.104
LPGDP
–4.92975
–1.3566
0.2751
17.3004
LFD
–9.54033
–2.1736
0.2278
9.5920
–2.5146
0.1968
7.2000
b
LTR
–12.7720
4.6687
Ng-Perron at 1st Difference with Intercept and Trend
–21.6266b
–3.2883
0.1520
4.2135
LDEV
–23.7053
b
–3.4019
0.1435
4.0781
–120.5751a
–7.7642
0.0643
0.7567
LFD
–22.9788
b
–3.3888
0.1474
3.9702
LTR
–27.0299a
–3.6246
0.1341
3.6613
show significance level at 1% (5%).
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Note:
a (b)
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LPGDP
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LGINI
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I(1). This dissimilarity in the order of integration of the variables lends support for
the implementation of the ARDL bounds testing approach rather than one of the
alternatives of cointegration tests.
The sample size is small (data span is from 1985 up to 2008). In such small sample data set, we cannot take lag more than 1 on the basis of minimum value of Akaike
information criterion (AIC) and Schwartz Bayesian criterion (SBC). Literature
reveals that the calculation of ARDL F-statistics is very sensitive with the selection
of lag order in the model (Bahmani-Oskooee and Brooks 1999; Bahmani-Oskooee et
al. 2006; Bahmani-Oskooee and Harvey 2006; Shahbaz and Rahman 2010). The
inclusion of intercept and trend is based on the assumptions of PSS (2001).
Now, let us turn to the two-step ARDL cointegration (see PSS 2001) procedure. In the first stage, the order of lag length on the first difference estimating the
conditional error correction version of the ARDL model for equation 11 is usually
obtained from unrestricted vector autoregression (VAR) by means of AIC, which
is 1 based on the minimum value of AIC as shown in Table 2. The total number of
regression models is estimated following the ARDL method in equation 2 is (1 +
1)5 = 32. The results of the bounds testing approach for cointegration posit that the
calculated F-statistics is 20.049, which is higher than the upper level of the bounds
critical value of 9.630 at 1 per cent level of significance while value of lower
bounds is 8.740.3 This implies that the null hypothesis of no cointegration cannot
be accepted and that, there exists a cointegrated relationship among the variables.
Next step is to find a long-run relationship. Partial long-run links are shown in
Table 3 through ARDL-OLS investigation.
Empirical results suggest that there exists negative link between nominal or
currency devaluation and income inequality. The impact of currency devaluation
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Does Nominal Devaluation Improve Income Distribution?
Table 2. Lag Length Criteria and ARDL Cointegration
VAR Lag Order Selection Criteria
Lag
F-Statistics for ARDL
Cointegration
FPE
AIC
SC
0
3.09e–13
–14.61682
–14.36789
7.221
1
8.66e–17*
–22.89127*
–21.39767*
20.049*
Notes: *indicates lag order selected by the criterion. FPE: Final prediction error; AIC: Akaike
information criterion; SC: Schwarz information criterion.
Diagnostic Test-Statistics: Serial Correlation LM, F = 0.0624 (0.0.9398); ARCH Test = 0.3892
(0.5415); Normality J-B Value = 1.6854 (0.4305); Heteroscedasticity
Test, F = 0.5956 (0.7906).
Dependent Variable = LGINI
LTR
0.0000
Coefficient
–58.1975
–3.3935
0.0040
–0.2350
–2.4983
0.0246
–0.5759
–4.5150
0.0004
0.2880
6.0653
0.0000
3.1243
0.0070
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Prob. Value
5.7922
0.1718
4.8157
0.0002
0.0873
1.4357
0.1716
–0.4097
–2.2275
0.0416
20.7640
3.5730
0.0028
LPGDP
—
—
—
–1.7057
–3.6024
0.0026
R-Squared = 0.97188
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Adjusted R-Squared = 0.96438
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2
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0.3487
LPGDP
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LFD
T-Statistic
4.7422
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LDEV
Coefficient T-Statistic Prob. Value
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Constant
ER
Variable
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Table 3. Long-run Results
R-Squared = 0.983043
Adjusted R-Squared = 0.977391
S.E. of Regression = 0.014103
Akaike information Criterion = –5.1258
Akaike information Criterion = –5.4498
Schwarz Criterion = –4.8768
Schwarz Criterion = –5.15141
F-Statistic = 129.6287
F-Statistic = 173.9224
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S.E. of Regression = 0.01677
Prob(F-statistic) = 0.0000
Durbin-Watson = 1.502
Durbin-Watson = 1.592
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Prob(F-statistic) = 0.0000
is reasonable to improve income distribution in Bangladesh. It may be documented
that devaluation has beneficial effect on income distribution. This strongly
contradicts Alexander (1952) hypothesis. Findings are consistent with view
claimed by Haughton and Kinh (2003) that devaluation benefits the poor class of
population. Trade openness increases income inequality in the country. There is a
positive link between trade openness and income inequality. This demonstrates
that increased trade benefits the elite class rather than the poor.4
In the case of Bangladesh, the coefficient of FD suggests that financial sector
development (easy access to credit for the private sector) increases income
inequality, supporting the inequality-widening argument and rejecting the
South Asian Survey, 19, 1 (2012): 61–77
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Muhammad Shahbaz and Mohammad Mafizur Rahman
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inequality-narrowing hypotheses. It may be posited that income distribution is
worsened by 0.35 per cent for every 1 per cent increase in credit to the private
sector (FD). Inequality is significantly negatively associated with GDP per capita,
suggesting that improvements in economic growth redistribute income and make
the society more egalitarian. It is revealed that a 9 per cent growth rate will reduce
income inequality by almost 3.6 per cent.
Finally, linear term of real per capita GDP (PGDP) carries positive and significant estimate (significant at 1 per cent) and squared term (PGDP2) carries a negative and significant coefficient (significant at 1 per cent) supporting the Kuznets’
inverted-U hypothesis. The empirical estimation shows that income inequality
increasing impact is greater than income inequality decreasing trend as shown by
coefficients of linear and squared terms of PGDP.
Table 4 confirms the short-run association between nominal devaluation and
income inequality in the case of Bangladesh. This reports the short-run coefficient
estimate which has been obtained from the ECM version of the ARDL model. The
ecmt-1 coefficient indicates how quickly/slowly variables return to equilibrium
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Table 4. Short-run Behaviour (2, 1, 1, 1, 1)
DLGINIt–1
0.4964
0.7970
3.1171
0.0089
–3.3894
0.0054
2.2719
0.0423
0.1724
3.1765
0.0080
0.2343
0.7937
0.4428
–0.8936
–2.9069
0.0132
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DLPGDP
Prob. Value
–0.2630
0.1952
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DLTR
T-Statistic
–0.3924
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DLDEV
DLFD
ecmt–1
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–0.0033
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Coefficient
Constant
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Variable
ER
Dependent Variable = DLGINI
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R-squared = 0.79931
Adjusted R-squared = 0.69897
Mean dependent var = 0.01237
S.D. dependent var = 0.02332
S.E. of regression = 0.01279
Akaike information criterion = –5.60223
Sum squared residual = 0.00196
Schwarz criterion = –5.25427
Log likelihood = 60.2211
F-statistic = 7.9659
Prob(F-statistic) = 0.001255
Durbin-Watson stat = 1.726
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Does Nominal Devaluation Improve Income Distribution?
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and it should have a negative sign with high significance. The error correction
term, ecmt-1, shows the speed of modification required to re-establish equilibrium in the short-run model. Bannerjee et al. (1998) argue that the error correction
term is significant at the 5 per cent level of significance. The coefficient of ecmt–1
is equal to –0.8936 for the short-run model and implies that deviation from the
long-term inequality is corrected by 89.36 per cent over each year. The lag length
of the short-run model is selected on the basis of SBC.
Table 4 demonstrates the short-run impacts of explanatory variables on the
dependent variable. Income distribution is also deteriorated by its differenced lag
by more than 0.49 per cent significantly. Nominal devaluation causes to improve
income distribution in short span of time. This finding seems to reject the hypothesis by Lindert (1986) that nominal devaluation increases the cost of living of the
impoverished people without an increase in their income. Increase in domestic
credit to private sector is positively linked with income inequality. This indicates
that development of financial sector increases income inequality in Bangladesh.
The coefficient of trade openness also worsens income distribution. This empirical evidence supports the Leontief Paradox, that is, fruits of trade openness are
being reaped up by elite class of population at the cost of poor segment of population in Bangladesh in the short run. Surprisingly, GDP per capita seems to increase
income inequality insignificantly.
These types of short-run dynamic impacts are maintained in the long run. The
estimated value of correction coefficient value is –0.8936, which is significant at
5 per cent level and has the correct sign. This implies a fair speed of adjustment to
the equilibrium level after a shock. Approximately, 89.36 per cent of disequilibrium from the previous year’s shock converges back to the long-run equilibrium
in the current year.
O
T
Sensitivity Analysis
N
Diagnostic tests for serial correlation, normality, autoregressive conditional heteroscedasticity and heteroscedasticity are considered, and results are shown in
Table 2. These tests show that the short-run model passes through all diagnostic
tests except Ramsey reset test in the first stage. The results indicate that there is
no evidence of autocorrelation and the model passes the test for normality. There
is no existence of white heteroscedasticity and autoregressive conditional heteroscedasticity in the model. Finally, when analysing the stability of the long-run
coefficients together with the short-run dynamics, the cumulative sum (CUSUM)
and the cumulative sum of squares (CUSUMsq) are applied.
According to PSS (2001), the stability of the estimated coefficient of the
error correction model should also be empirically investigated. A graphical
representation of CUSUM and CUSUMsq is shown in Figures 1 and 2. Following
Bahmani-Oskooee and Nasir (2004), the null hypothesis (i.e., that the regression
equation is correctly specified) cannot be rejected, if the plot of these statistics
South Asian Survey, 19, 1 (2012): 61–77
72
Muhammad Shahbaz and Mohammad Mafizur Rahman
remains within the critical bounds of the 5 per cent significance level. As it is
clear from Figures 1 and 2, the plots of both the CUSUM and the CUSUMsq are
within the boundaries, and hence, these statistics confirm the stability of the
long-run coefficients of regressors that affect the income distribution in the
country. The stability of selected ARDL model specification can also be
evaluated using the CUSUM and the CUSUMsq of the recursive residual test
for the structural stability (Bahmani-Oskooee and Nasir 2004). The model
appears to be stable and correctly specified, given that neither the CUSUM nor
the CUSUMsq test statistics exceed the bounds at the 5 per cent level of
significance (see Figures 1 and 2).
SE
12
8
U
4
IA
L
0
C
–4
95
96
97
M
94
98
99
00
M
–12
ER
–8
02
03
04
05
04
05
5% Significance
C
O
CUSUM
01
R
Figure 1. Plot of Cumulative Sum of Recursive Residuals
FO
Note: The straight lines represent critical bounds at 5 per cent significance level.
O
T
1.6
N
1.2
0.8
0.4
0.0
–0.4
94
95
96
97
98
99
CUSUM of Squares
00
01
02
03
5% Significance
Figure 2. Plot of Cumulative Sum of Squares of Recursive Residuals
Note: The straight lines represent critical bounds at 5 per cent significance level.
South Asian Survey, 19, 1 (2012): 61–77
73
Does Nominal Devaluation Improve Income Distribution?
Conclusions
IA
L
U
SE
The investigation of the correlation between economic growth and income inequality
is one of the recent routes that have been followed to study the developments in
income distribution. This scrutiny has invigorated old issues such as the Kuznets’
inverted-U curve (1955) in Bangladesh. This has also contributed to hot debates
about the pattern of income distribution during the time of market liberalism. In this
article, the impact of devaluation on income distribution has been discussed and
investigated in the long run as well as in the short run. The empirical psychology has
confirmed the existence of cointegration among the variables in our model. The estimated results reveal that nominal devaluation tends to improve income distribution
in the country. Economic growth is negatively associated with income distribution
while a non-linear link is found between both variables of the Kuznets’ inverted-U
curve (1955). FD seems to worsen the income distribution in the country. Trade
openness deteriorates income inequality as discussed in detail in Leontief Paradox.
With regard to policy implications, the following points can be made based on
the obtained results:
N
O
T
FO
R
C
O
M
M
ER
C
1. Bangladesh government should devalue its currency to remain competitive
in international market and to reduce income inequality. However, appropriate care must be taken to keep the domestic inflation low, and devaluation should not be continued for a long time (year after year) as it may have
some adverse effects.
2. Attempts must be made to increase domestic production to meet increased
demand and to reduce import dependency.
3. Government, business communities and non-government organisations
should work hard and together to achieve higher growth as economic growth
improves income distribution and thus makes the society more egalitarian.
4. Poor people and small and medium businesses/enterprises must have access
to the institutional credit facilities to increase income equality. Microfinance
institutions such as Grameen Bank, BRAC can play an important role in
this regard. Bangladesh Agriculture Bank should provide easy term loan to
the farmers, especially small farmers, as the economy is agri-dominant.
This will improve the financial power of majority population which will, in
turn, improve the income distribution of the country.
5. The economy should be opened phase by phase with careful consideration
for its infant industries. Some sorts of protection are still needed to make
the industries more viable and competitive, and to save the jobs of workers
and poor people. This will improve income distribution of Bangladesh.
Notes
1 As supported by Krugman and Taylor (1978).
2 We use log-linear modelling specification. Bowers and Pierce (1975) suggest that
Ehrlich’s (1975) findings with a log-linear specification are sensitive to functional
South Asian Survey, 19, 1 (2012): 61–77
74
Muhammad Shahbaz and Mohammad Mafizur Rahman
form. However, Ehrlich (1977) and Layson (1983) argue on theoretical and empirical
grounds that the log-linear form is superior to the linear form. Both Cameron (1994)
and Ehrlich (1996) suggest that a log-linear form is more likely to find evidence of
a deterrent effect than a linear form. This makes our results more favourable to the
deterrence hypothesis.
3 Critical values generated by Narayan (2005) can also be compared with our calculated
F-statistics for cointegration.
4 For more details, see Shahbaz et al. (2007).
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