ISSN 1022-4734
JUJSS
Vol. 33, 2016, pp. 79-94
An Empirical Study on the Relationship between Industrial
Production and Consumer Price Index in Bangladesh
Md. Tasnimul Hasan
Department of Statistics, Comilla University, Kotbari, Comilla Bangladesh
Humayun Kiser
Department of Statistics, Comilla University, Kotbari, Comilla Bangladesh
Md. Tareq Ferdous Khan
Department of Statistics, Jahangirnagar University, Savar, Dhaka Bangladesh
Abstract
Recently Bangladesh has been promoted to lower-middle income country according to the World
Bank’s estimates of Gross National Income per capita (GNI). To look into the next stair of the
economic status, Bangladesh needs to work for waxing the economic growth. However, tackling
the fluctuations on production, employment, consumer price index (CPI) used to measure
inflation and international trade which are considered as the key components of economy, would
be onerous. This paper studies the relationship between CPI and industrial production and hence
delineates the effect of inflation on industry level production by employing Error Correction
Mechanism, Granger Causality analysis. The study also provides the forecasted industrial
production using seasonal dummy variable regression model. The data from January 2002
through June 2013 is used on the mentioned series to meet the objective of the paper. The
empirical analysis reveals a positive long-run relationship between consumer price index and
production and existence of bilateral causality between CPI and production.
Keywords: Cointregation, Error Correction Model, Causality Analysis, Forecasting.
1. Introduction
Inflation and productivity both has significant impact on economic growth like higher
rate of inflation has adverse effects on the macroeconomic variable production but mild
inflation is auspicious to production. Rising price, increase the profit expectation of the
producers. But, hyperinflation reduces the production by slowing down capital
accumulation and discoursing producers to invest in production. Indeed, hyperinflation
and fluctuation in production resulting from hyperinflation would be lead to increase
the production cost. To maintain the production cost producers may propose extensive
cuts in the budget can lead to create unemployment problem, reduce working hours and
wages of workers, compromise in product quality. Because of declined income, people
cannot buy sufficient goods like before from the market which affect the production
Hasan, Kiser and Khan
JUJSS
directly. Inflation impose higher tax rate on corporate profit which disrupts investment
plans and affect productivity.
In other way, higher productivity allowing cost reduction that flow through to product
process and thereby reduce inflation. But, high level of production can lead to
uncontrolled levels of consumption and rapid inflation.
Since 1990s, radical changes have been taken place in the industrial sector of
Bangladesh because of economic development and reforms which provides
opportunities for both domestic and foreign investors. Investment not only increases
the production but also open the windows of employment by generating new job
sectors.
2. Literature Review on Inflation and Economic Growth
Several researchers from both developing and developed countries have studied the
nexus between inflation and productivity. Their empirical analyses reveal opposite
scenario, while some studies provide suggestion in favor of negative relation between
inflation and productivity, on the other hand, some studies do not support the negative
relationship.
Barro (1995) investigated the relationship between inflation and economic growth for
more than 100 countries from 1960 to 1990. His empirical analysis suggested that the
estimated relationship between inflation and economic growth is negative. On the other
hand, Smyth (1995a, 1995b) concluded that there is no causal relationship between
productivity and inflation on the basis of annual data for the period 1951-1991 for
Germany and 1955-1990 for USA respectively,
Malla (1997) conducted an analysis for Asian countries and countries belonging to the
Organization for Economic Cooperation and Development (OECD) separately. After
controlling for labor and capital inputs, the estimated results unveiled for the OECD
countries that there exists a statistically significant negative relationship between
economic growth and inflation. However, the relationship was not statistically
significant for the developing countries of Asia.
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An Empirical Study on the
Faria and Carneiro (2001) investigated the relationship between inflation and economic
growth of Brazil. Analyzing a bivariate time series model with annual data for the
period between 1980 and 1995, they found that there exists a negative relationship
between inflation and economic growth in the short-run, but inflation does not have
any effecton economic growth in the long run.
Ahmed and Mortaza (2005) used annual data of real GDP and CPI for the period of
1980 to 2005 for Bangladesh and referred that there exists a statistically significant
long-run negative relationship between inflation and economic growth for Bangladesh.
In neoclassical views, inflation increases economic growth by shifting the income
distribution in favor of higher saving capitalists. This increases saving and thus
economic growth. Moreover, Keynesians also said that inflation may increase growth
by raising the rate of profit, thus increasing private investment.
Mallik and Chowdhury (2001) examined the short-run and long-run relationship
between inflation and economic growth for four South Asian economies including
Bangladesh, India, Pakistan, and Sri Lanka. Applying co-integration and error
correction models to the annual data they found the relationship between inflation and
economic growth was positive and statistically significant for all four countries.
Some other empirical studies found no relationship between inflation and economic
growth. One study by Sidrauski (1967) indicates that inflation has no relationship with
growth in the long run. In addition to Sidrauski, Bruno and Easterly (1995) have shown
insignificant relationship between inflation and economic growth.The other critics
argued in the context of the statistical point of view that productivity growth and
inflation have different order of integration (Sbordone and Kuttner 1994,
Cameron,Hum and Simpson 1996, Tsionas 2001, 2003). These studies claim inflation
is non stationary and productivity growth is stationary and therefore there cannot be
long run relationship.
3. Objective of the Study
The main objective of this study is to explore a statistical relationship between
industrial production and CPI in Bangladesh. The nature of the relationships, whether
positive or negative in the short run as well as in long run and examines the effect of
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CPI on industrial production. This paper also analyzes the causality issue, direction of
causality, short-run and long-run equilibrium between industrial production and CPI.
Finally, it provides the forecasted values of the industrial production.
4. Limitation of the Study
Prior to 2002, the CPI is measured on the basis of the base year 1985-86 and later on
for the years 2002 to 2013, 1995-96 base year is used and finally from 2013-14 fiscal
year, base year of all economic indicators of Bangladesh have been shifted to 2005-06.
As a consequence, this study uses the data of the series under study from 2002 to 2013
to keep consistency in the base year. Moreover, the GDP splits into various sectors
such as agriculture and forestry, industrial production (mining and quarrying, industry,
electricity, gas and water supply), service sector. This paper has taken only industrial
production into consideration among the segments of GDP and thus every result is
concentrated on it rather than the whole GDP.
5. Data and Methodology
5.1 Data
The empirical analysis has been carried out by using monthly data for consumer price
index (CPI, base: 1995-96=100) and quantum production index (QPI, base: 198889=100) from January, 2002 to June, 2013. The CPI measures changes in the price
level of consumer goods and services purchased by households. On the other hand, QPI
is the output of all industries; manufacturing, mining and quarrying, electricity. The
data were collected from monthly statistical bulletin, published by Bangladesh Bureau
of Statistics (BBS, 2002-2013). All variables are then transformed to their logarithm,
often used to stabilize the variance of a series. In recent Fiscal years, industrial
production contributes about 20 percent in the total GDP (BBS, 2015.)
The statistical packages such as Eviews, Stata, gretl and Microsoft Excel provide an
enormous support to complete the analysis of the study.
5.2 Methodology
The steps and affiliated methods to examine the relationship between industrial
production and inflation are summarized below:
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An Empirical Study on the
To test the stationarity of the series under study, unit root test is used and performed by
graphical
method,
Augmented
Dickey-Fuller
(ADF)
test(1979,
1981)
and
Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test (1992), including constant term and
trend term as exogenous variables. In checking cointegration between inflation and
production, Engel-Granger (EG) or Augmented Engel-Granger method (AEG, 1987)
and Johansen method (1990) were employed.
Primarily, to know the effect of CPI and QPI on each other, two separate simple linear
regression models of logarithm of CPI on logarithm of QPI and vise-versa are
estimated by ordinary least square method without having any consideration of
causality.
Johansen (1990) cointegration test of several I(1) time series permits more than one
cointegrating relationship while the Engle–Granger test which is based on the Dickey–
Fuller or the ADF test for unit roots in the residuals from a single estimated
cointegrating relationship. There are two types of Johansen test statistics namely trace
statistic and maximum eigen value statistic. Toda (1994) compares the small sample
properties of two statistics of Johansen test and reports that neither of the tests is
uniformly better, but, the performance of trace test is better in some situation.
Then Error Correction Mechanism (ECM) first used by Sargan (1984) and later
popularized by Engel-Granger (1987) is also performed for testing the causality
between the variables. A theorem known as Granger representation theorem, states that
if two variables are cointegrated, then the relationship between the two can be
expressed as the Error Correction Mechanism, which implies that changes in the
dependent variable are a function of the level of disequilibrium in the cointegrating
relationship, captured by the Error Correction Term (ECT). Thus, through Error
Correction Term, Error Correction Mechanism establishes an additional way to
examine the Granger causality. The ECT is expected to be negative and ranges between
0~1. Otherwise, ECT will be insignificant and meaningless. The significance of ECT
refers to long run causality. Short run causality is established by the significance of
each explanatory variable. Finally the significance of all explanatory variables
including Error Correction Term in the Error Correction Mechanism indicates the
presence of Granger causality. The mathematical specifications of the Error Correction
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Mechanism Model of industrial production on consumer price and vise-versa are as
follows:
Δ ln qpit = α0 + α1Δ ln cpit + α2 u1t -1 + e1t
(5.1)
Δ ln cpit = β0 + β1Δ ln qpit + β2u2 t -1 + e2 t
(5.2)
where, u1t -1  qpit 1  a0  a1cpit 1 and u2 t -1  cpit 1  b0  b1qpit 1 are Error Correction
Terms,  1 and 1 are short run coefficients and  2 and  2 are long run coefficients.
The ECM determines the causality between two variables, whereas the Granger
causality test determines the direction of causality.
Koop (2000) in his study states that time does not run backward. In others words,
events in the past can cause events to happen today but not the future event. This is the
idea behind the Granger causality test. But, there are controversies about the causality.
Some people believe that “everything causes everything”, whereas other people deny
the existences of causation whatsoever.
The Granger causality models for industrial production (QPI) and consumer price (CPI)
are as follows:
n
ln cpi t 
n
 α ln qpi
ι
t i
i 1
n
ln qpit =
+  β j ln cpit  j + u1t
(5.3)
j 1
n
 Ci ln qpit i +  Di ln cpit  j+ u2t
i 1
(5.4)
j 1
To determine the causality between CPI and QPI consider four cases:
n
1.
n
If  α ι  0 and  D j = 0 ; there exist a unidirectional causality from qpi to cpi
i 1
j 1
n
2.
n
If  α i = 0 and  D j  0 ; there exist a unidirectional causality from cpi to
i 1
j 1
qpi
n
3.
n
If  α i  0 and  D j  0 ;there exist a bilateral causality between cpi and qpi
i 1
j 1
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An Empirical Study on the
n
4.
n
If  α i  0 and  D j  0 ;does not exist causality between cpi and qpi
i 1
j 1
The final part of the empirical analysis consists of forecasting time series variable
“industrial production”. The industrial production series exhibits time trend and
seasonality. Thus, forecasting is performed by estimating seasonal regression model
with dummy variables to control both the time trend and seasonality of the series. The
mathematical specification of the model is as follows:
ln qpit  β0  β1time  β2 M 1  β3 M 2  β4 M 3  β5 M 4  β6 M 5  β7 M 6
 β8 M 7  β9 M 8  β10 M 9  β11M 10  β12 M 11  et
(5.5)
where,  0 is the intercept, 1 is the coefficients of time trend,  2 ,  3 ,  ,  12 are the
coefficients of dummy variables and M 1 , M 2 ,, M 11 are the dummy variables from
January to November respectively and December is the reference month.
The whole set of observation divided into two sub-samples. The first sub-samples
considered as estimation sample (2002:1, 2012:6) and the second sample considered as
forecast sample (2012:7, 2013:6).
6. Empirical Analysis
To get the preliminary idea about the nature of the data, the time series plot of CPI and
QPI are constructed and presented in Figure 6.1 and Figure6.2. These figures reveal an
upward trend for both the industrial production and consumer price which indicates
about non-stationarity of the series. To make them stationary, differencing approach is
used. The first difference of both the series (d.lncpi and d.lnqpi) are also presented in
the Figures 6.1 and 6.2. The figures implicitly indicate that the differenced series
become stationary.
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The Augmented Dickey-Fuller Test and Wiatkowski–Phillips–Schmidt–Shin (KPSS)
test are performed to test the unit root. Both the tests suggest that though the original
series are not stationary but immediately turn to stationary after first difference which
is parallel to the conclusion delineated graphically.
To show the relationship between consumer price (CPI) and industrial production
(QPI), we estimate the following two models:
ln cpiˆ  .386908  .819605 ln qpi
(6.1)
ln qpiˆ  0.217267  1.17155 ln cpi
(6.2)
The estimated models presented in the equations 6.1 and 6.2 describe the relationship
between CPI and QPI. The estimated results indicate that the coefficients of both the
regression are positive and statistically significant at 1% level of significance. More
preciously, on average a one unit increase in lnqpi leads to increase in lncpi 0.819605%
and on average a one unit increase in lncpi leads to increase in lnqpi is 1.17155 %. It
reflects from the analysis that there is a positive effect of inflation on industrial
production under study period, though, in some theoretical point of view inflation has
negative effect on production. The result of the study is analogous to that of the result
derived by Mallik and Chowdhury (2001) and supports Keynesians and neo classical
theories.
Since our study variables are non-stationary at level and stationary after first difference,
we check about cointegrating relation between variables. A number of methods for
86
An Empirical Study on the
testing cointegration are available. In this analysis, Johansen cointegration test has been
employed for testing cointegration.
Johansen (1990) approach is much more developed than Engel-Granger test. Johansen
test permits more than one cointegrating relationship, whereas Engel-Granger test is
based on residuals from single cointegrating relationship. Table 6.1 reports the result of
Johansen test of cointegrating. Trace statistic and maximum eigenvalue statistic reports
that null hypothesis of no cointegration is rejected in both cases and null hypothesis of
one cointegrating relationship less than 1 is accepted for both cases compared at 5%
significance level from Johansen and Juselius (1990). Therefore, at least one
cointegrating relationship exists between consumer price index and production and
they are moving together in the long run. Based on the result of Johansen cointegration
test, we use Error Correction Mechanism (ECM) to determine the long-run and shortrun relationship. The estimated models of Error Correction Mechanism presented in
equations 5.2 and 5.3 respectively in methodology section are as follows:
 ln qpiˆt  0.006  0.087 ln cpit  0.568u1t 1
(6.3)
 ln cpiˆt  0.006  0.0006  ln qpit  0.055 u2t-1
(6.4)
The coefficient of Error Correction Terms  2 and  2 is negative and significant at 1%
level of significance suggest that QPI moves to restore the equilibrium when system is
out of control, 56.8% of the disequilibrium are corrected per month and CPI also
moves to restore the equilibrium when system is out of control, 5.49% of the
disequilibrium are corrected per month. However, consumer price (CPI) in model (6.3)
does not appear to have significant short-run effect on industrial production (QPI) and
vise-versa in model (6.4).
Table 6.2 reports the model specification tests for Error Correction Mechanism. The
tests do not find any misspecification of the model except the rejection of null
hypothesis of normality for consumer price and production. Based on the above results,
it can be conclude that there is a long-run positive relationship between consumer price
and production.
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Table 6.3 reports the results of causality relationship formulated in equation 5.3 and 5.4
between our study variables consumer price level and industrial production and shows
that how much the result of causality depends on the number of lags. There is a bidirectional causality between consumer price level and industrial production from four
lags up to ten lags. At twelve lags, there is a unidirectional causality between
production and consumer price level with the direction from production to consumer
price and at fourteenth lags, there is no statistical causality relationship between them.
This discussion makes the point clear that the result of Granger causality depends on
the number of lags introduced in the model.
The estimated result of seasonal dummy variable regression model is presented in
Table 6.4. This regression model is developed to examine time trend and seasonal
dummies. Among eleven dummy variables, six dummy variables and time trend are
statistically significant. Table 6.5 represents the forecasted values of industrial
production for July 2012 to June 2013 along with their standard errors and 95%
confidence interval. The forecasting has been done on existing time period because of
change of base year for measuring economic indicator as mentioned in the limitations
of the study. The Figure 6.3 also exhibits the forecasted values where the shaded area
indicates 95% band width.
7. Conclusion
Literatures suggest no unique pattern of the effect of inflation on production. Some
conclude positive, while some inferred negative relationship between them. Even, there
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An Empirical Study on the
are some evidences of no relationship are found. In this regard, determining the
relationship in the country context may help the policy makers and stakeholders to take
appropriate actions when necessary. To meet this objective, this study establishes the
statistical relationship between consumer price index (CPI) and quantum production
index (QPI) for Bangladesh by using the data from January, 2002 – June, 2013.
Various time series and econometrics tools have been used such as unit root tests by
graphical method, ADF test, KPSS test, cointegration analysis, Error Correction Model,
Granger causality and finally seasonal adjustment model for forecasting. The
Cointegration test based on the work of Engel-Granger and Johansen is also used.
The empirical analysis suggests that relationship between consumer price and
production is not spurious. There is a positive long run relationship between
cointegrated variable consumer price and production in both cointegration methods.
Moreover, Error correction model has been applied to estimate short run and long run
relationship. ECT or coefficient of long run relation appears to be significant but short
run coefficient appears to be insignificant. This result is analogous with the work of
Mallik and Chowdhury (2001) and Ahmed and Mortaza (2005). Also, it unveils that
bilateral Granger causality exists between consumer price and production up to a
certain lags, after that there is no causality between two variables.
References
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Bangladesh Bureau of Statistics (2002-2013, 2015): “National Accounts wings Monthly
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R. J. Barro (1995): “Inflation and Economic Growth”, National Bureau of Economic
Research (NBER) Working Paper, No. 5326.
M. Bruno and W. Easterly (1995): “Inflation Crises and Long-Run Growth”, World
Bank Policy Research Working Paper, No. 1517.
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N. Cameron, D. Hum and W. Simpson (1996), “Stylized Facts and Stylized Illusions:
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D. A. Dickey and W. A. Fuller (1981): “Likelihood Ratio Statistics for Autoregressive
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J. R. Faria and F. G. Carneiro (2001): “Does High Inflation Affect Growth in the Long
and Short-run?”,Journal of Applied Economics, Vol. 4, No. 1 ,pp. 89-105.
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Cointegration Analysis of the Purchasing Power Parity and the Uncovered Interest
Parity for UK”, Journal of Econometrics, Vol.53, 211 - 244.
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D. Kwiatkowski, P. Phillips, P. Schmidt, and Y. Shin, (1992): “Testing the Null
Hypothesis of Stationarity against the Alternative of a Unit Root”, Journal of
Econometrics, Vol. 54, pp. 159-178.
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mimeo, Department of Economics, University of Hawai’ at Monoa, Honolulu
(1997
G. Mallik and A. Chowdhury (2001): “Inflation and Economic Growth: Evidence from
South Asian Countries”, Asian Pacific Development Journal, Vol. 8, No.1, pp.
123-135.
A. Sbordone, and K. Kuttner (1994): “Does Inflation Reduce Productivity?”, Federal
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Appendix
Table 6.1: Johansen Tests for Cointegration
Null
Alternative
r0
r 1
r 1
r2
Null
r0
r 1
Trace test
Trace statistics
56.39
5 % Critical Value
15.42
Alternative
0.23
Maximum Eigen Value
Maximum Eigen Value statistic
3.76
5% Critical Values
r 1
r2
56.16
0.23
14.07
3.76
Note: r indicates the maximum rank of cointegrating relationship. Maximum eigenvalue and trace test statistics
compared with 5% critical values from Johansen and Juselius.
Table 6.2: Model Specification Test
Tests
p-value
0.33
0.99
0.55
Autocorrelation
Heteroskedasticity (Breusch-Pagan)
Normality of residuals (Jarque-Bera test)
Normality of variables
Variable
Lncpi
Lnqpi
p-value
0.01
0.03
Table 6.3: Causality between Consumer Price Index (CPI) and Industrial Production
(QPI)
Direction of causality
Lags
F – Value
Decision
qpi → cpi
cpi → qpi
4
5.46
9.48
Reject H0
Reject H0
qpi → cpi
cpi → qpi
6
3.65
1.97
Reject H0
Reject H0
qpi → cpi
cpi → qpi
8
2.48
3.39
Reject H0
Reject H0
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An Empirical Study on the
qpi → cpi
cpi → qpi
10
1.84
2.17
Reject H0
Reject H0
qpi → cpi
cpi → qpi
12
1.60
0.80
Reject H0
Do not reject H0
1.19
0.70
Do not Reject H0
Do not Reject H0
qpi → cpi
cpi → qpi
14
Note: Null hypothesis (H0)is no causality between CPI and QPI.
Table 6.4: Estimation of Seasonal Dummy Variable Regression Model using
Observations 2002:01-2012:06
Variables
Coefficient
5.45362***
Constant
0.007***
Time
−0.008
dm1
−0.048**
dm2
−0.024
dm3
−0.077***
dm4
−0.0172
dm5
0.039**
dm6
0.023
dm7
0.016
dm8
−0.063***
dm9
−0.085***
dm10
−0.073***
dm11
Dependent Variable ln qpi
0.98
0.00
R-squared
p-value
Table 6.5: Forecasted Valuesof Industrial Production for July 2012 to June 2013
Obs.
ln qpi
Prediction
std. error
95% interval
2012:07
6.39635
6.39189
0.0452346
(6.30228, 6.48151)
2012:08
6.36232
6.39227
0.0452346
(6.30266, 6.48189)
2012:09
6.34309
6.32028
0.0452346
(6.23066, 6.40989)
2012:10
6.35475
6.30581
0.0452346
(6.21620, 6.39543)
2012:11
6.31938
6.32472
0.0452346
(6.23510, 6.41434)
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JUJSS
2012:12
6.48340
6.40487
0.0452346
(6.31525, 6.49448)
2013:01
6.49677
6.40412
0.0451520
(6.31466, 6.49357)
2013:02
6.41126
6.37128
0.0451520
(6.28183, 6.46074)
2013:03
6.42733
6.40269
0.0451520
(6.31324, 6.49215)
2013:04
6.34562
6.35666
0.0451520
(6.26721, 6.44612)
2013:05
6.45389
6.42367
0.0451520
(6.33422, 6.51313)
2013:06
6.52349
6.48724
0.0451520
(6.39778, 6.57669)
94