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 JUJSS Hasan, Kiser and Khan
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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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: 1988- 89=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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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 lnqpi = α + α Δ ln cpi + α u + e t0 1 t 2 1 t- 1 1 t (5.1) Δ lncpi = β + β Δ ln qpi + β u + e t0 1 t 2 2 t- 1 2 t (5.2)
where, u1t- 1 qpi t 1 a 0 a 1 cpi t 1 and u2t- 1 cpi t 1 b 0 b 1 qpi t 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 n ln cpit αι ln qpiti + β j ln cpit j + u1t (5.3) i1 j1 n n ln qpit = Ci ln qpit i + Di ln cpit j+ u2t (5.4) i1 j1 To determine the causality between CPI and QPI consider four cases: n n 1. ; there exist a unidirectional causality from qpi to cpi If αι 0 and D j = 0 i1 j1 n n 2. ; there exist a unidirectional causality from cpi to If αi = 0 and D j 0 i1 j1 qpi n n 3. ;there exist a bilateral causality between cpi and qpi If αi 0 and D j 0 i1 j1
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n n 4. ;does not exist causality between cpi and qpi If αi 0 and D j 0 i1 j1
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 β2M1 β3M 2 β4M 3 β5M 4 β6M 5 β7 M 6 β M β M β M β M β M e 8 7 9 8 10 9 11 10 12 11 t (5.5) where, is the intercept, is the coefficients of time trend, , ,, are the 0 1 2 3 12 coefficients of dummy variables and M1 ,M 2 ,, M11 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:
ˆ ln cpi .386908 .819605ln qpi (6.1) ˆ ln qpi 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
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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 short- run relationship. The estimated models of Error Correction Mechanism presented in equations 5.2 and 5.3 respectively in methodology section are as follows:
lnqpiˆ 0.006 0.087 ln cpi 0.568 u (6.3) t t1 t 1 lncpiˆ 0 . 006 0 . 0006 ln qpi 0.055 u (6.4) t t2 t- 1
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 bi- directional 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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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.
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N. Cameron, D. Hum and W. Simpson (1996), “Stylized Facts and Stylized Illusions: Inflation and Productivity Revisited”, Canadian Journal of Economics, Vol. 29, No. 1, pp. 152 - 162.
D. N. Gujarati (2004): “Basic econometrics”, 4th edition, Tata McGraw-Hill Publishing limited, New Delhi.
D. A. Dickey and W. F. Fuller (1979): “Distribution of the Estimators for Autoregressive Time Series with a Unit Root”, Journal of the American Statistical Association, Vol. 74, pp. 427-431.
D. A. Dickey and W. A. Fuller (1981): “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root”, Econometrica, Vol.49, pp. 1057 – 1072.
R. F. Engle and C. W. J. Granger (1987):“Co-integration and Error Correction: Representation, Estimation and Testing”, Econometrica, Vol. 55, pp. 1-87.
R. F. Engle and B. S. Yoo (1991): “Co-integrated Economic Time Series: An Overview with New Results in R. F. Engle and Granger, C. W. J. eds., Long-Run Economic Relationships.” Oxford: Oxford University Press,pp. 237-266.
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.
G. Koop (2000): “Analysis of Economic Data”, John Wiley & Sons, New York, p.175.
S. Johansen (1988): “Statistical Analysis of Co-integration Vectors”, Journal of Economic Dynamics and Control, Vol. 12, pp. 231-254.
S. Johansen and K. Juselius (1990): “Maximum Likelihood Estimation and Inference on Co-integration with the Application to the Demand for Money”, Oxford Bulletin of Economics and Statistics, Vol. 52, pp. 169-210.
S. Johansen, K. Juselius (1992): “Testing Structural Hypotheses in a Multivariate 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.
S. Malla (2005): “Inflation and Economic Growth: Evidence from a Growth Equation.” mimeo, Department of Economics, University of Hawai’ at Monoa, Honolulu (1997
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Appendix
Table 6.1: Johansen Tests for Cointegration
Trace test Null Alternative Trace statistics 5 % Critical Value r 0 r 1 56.39 15.42 r 1 r 2 0.23 3.76 Maximum Eigen Value Null Alternative Maximum Eigen Value statistic 5% Critical Values r 0 r 1 56.16 14.07 r 1 r 2 0.23 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 Autocorrelation 0.33 Heteroskedasticity (Breusch-Pagan) 0.99 Normality of residuals (Jarque-Bera test) 0.55
Normality of variables Variable p-value Lncpi 0.01 Lnqpi 0.03
Table 6.3: Causality between Consumer Price Index (CPI) and Industrial Production (QPI)
Direction of causality Lags F – Value Decision
qpi → cpi 4 5.46 Reject H0
cpi → qpi 9.48 Reject H0
qpi → cpi 6 3.65 Reject H0
cpi → qpi 1.97 Reject H0
qpi → cpi 8 2.48 Reject H0
cpi → qpi 3.39 Reject H0
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qpi → cpi 1.84 Reject H 10 0 cpi → qpi 2.17 Reject H0
qpi → cpi 1.60 Reject H 12 0 cpi → qpi 0.80 Do not reject H0
qpi → cpi 14 1.19 Do not Reject H0
cpi → qpi 0.70 Do not Reject H0
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 Constant 5.45362*** Time 0.007*** dm1 −0.008 dm2 −0.048** dm3 −0.024 dm4 −0.077*** dm5 −0.0172 dm6 0.039** dm7 0.023 dm8 0.016
dm9 −0.063***
dm10 −0.085***
dm11 −0.073*** Dependent Variable ln qpi R-squared 0.98
p-value 0.00 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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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)
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