Working Paper Series: WP 1504

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AA CCooiinntteeggrraattiioonn AAnnaallyyssiiss aanndd IInnnnoovvaattiioonn AAccccoouunnttiinngg

Md. Abdul Wahab

Nurun Nahar Sultana

Md. Rezwanul Hoque

July, 2015

Research Department

Bangladesh Bank Head Office, Dhaka, Bangladesh

RD

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Capital Financing and Industrial Output Growth in Bangladesh:

A Co-integration Analysis and Innovation Accounting

Md. Abdul Wahab*

Nurun Nahar Sultana*

Md. Rezwanul Hoque**

Abstract

Economic growth in a country is entirely driven by the accumulation of technologically progressed

capital machinery and input factors along with the potential role of finance (private sector credit) to

assist the accumulation of capital. Following the hypothesis, the Bangladesh economy registered

annual growth rate more than 6 percent during the last decade. Industrial output mostly contributed

to GDP growth through the process of exports of manufacturing products. In Bangladesh, the

industry sector contributed around 30 percent in GDP and recorded over 8 percent growth in

FY2014. However, the country has a comparative disadvantage in producing capital machinery and

industrial raw materials; the import of capital goods and industrial raw materials fill this gap. The

economy covered around 23 percent by import, while imported capital machinery and industrial raw

materials occupied almost half of the total import. In this paper, we focus on the question whether

capital financing promotes industrial output growth as well as in economic growth. Based on the

available empirical evidences using the cointegration and vector error correction model we find

significant result that in the long-run there remains a positive relationship between capital financing

and industrial output growth in Bangladesh. The evidence suggests further export promotion and

imports of capital goods not only for Bangladesh, but also for other emerging economies that seeking

foster growth through economic openness.

Keywords: Economic growth, capital goods, imports, exports, cointegration, vector error

correction.

JEL Codes: C32, F31, F41, O14.

*

The Authors are Joint Director and **

Assistant Director respectively of Research Department, Bangladesh

Bank (the Central Bank of Bangladesh), Head Office, Dhaka.

The views expressed in this paper are those of the authors’ own and do not necessarily reflect those of

Bangladesh Bank and Bangladesh Government.

To reach authors please mail to mabdul.wahab@bb.org.bd; nurun.sultana@bb.org.bd; rezwanul.hoque@bb.org.bd

This paper was presented at the 2nd

International Conference on Economics and Finance, 26-28,

February 2015, Kathmandu, Nepal organized by Nepal Rastra Bank. Noted that the article was

reviewed through blind review process followed by the organizer.

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Contents

I. Introduction 3

II. Literature Review 4

III. Model, Methodology and Data 6

A. The Model Specification 6

B. Methodology 7

C. The Data Set 8

IV. Estimation Results 9

A. Unit Roots Test 9

B. Cointegration and Vector Error Correction Estimation 10

C. Impulse Responses and Variance Decompositions 13

V. Conclusion 14

Figures

1. Trends of Annual Economic growth in Bangladesh 22

2. Trends of the variables in the Model 22

3. Scatter Plots (Industrial Output, imported Capital Goods,

Private Sector Credit and Interest rate)

23

4. Generalized Impulse Response of Industrial Output in

Bangladesh

23

Tables

1. Unit Root Test Result 10

2. Speed of Adjustment in VEC 12

Appendix I 18

References 15

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1. Introduction

While, the global financial crisis of 2008 adversely affected almost all the economies of both

developed and developing countries; Bangladesh, one of the South Asian developing

economy recorded more than 6 percent GDP growth per annum since 2004 along with strong

macroeconomic fundamental. The accomplishment has drawn attention in the global

economy and has become a model in developing countries. Since early nineteen-eighties, the

gradual shifting of its polices from anti-export bias to export promotion by providing various

export incentives, by deregulating the import process through reducing tariff and non-tariff

barriers, and by implementing investment friendly fiscal and monetary policies have

significantly contributed in the remarkable economic growth of the country. Subsequently,

the industry sector has been expanded; share of industrial output in GDP became almost

double in FY20141 from 15 percent in early eighties. The annual growth rate of industrial

output also increased to 8.4 percent in FY2014 from the around 2 percent in early eighties.

Since the independence in 1971, Bangladesh has a comparative disadvantage in producing

capital machinery and industrial raw materials. The import of capital machinery and

industrial raw materials from technologically advanced countries maintained this gap. The

under policy measures during early eighties promoted the import of capital machinery and

industrial raw materials for domestic manufacturing industries that played a key role in the

process of industrialization of the country. The economy accounted around 23 percent by

imports and total import payments stood at USD 40.69 billion in FY2014; while, the imports

of capital machinery and industrial raw materials occupied almost half of total import. In

early eighties, the share of total import in GDP was around 14 percent and the imports of

capital machinery and industrial raw materials contributed around 13 percent in aggregate

import. Thus the import of capital goods stimulated the productivity of domestic industries

that increased industrial output that lead the economic growthHowever, most of economic

literatures in developing economies in Asia- like South Korea, Malaysia, Thailand,

Singapore, Taiwan find that the import of capital goods and private investment escalated

countries’ industrialization process that lead to attain a higher and sustainable growth of

industrial output (Awokuse, 2007; H. Rana, 2000). In Bangladesh economic literature,

although research on exports, remittance inflows, economic growth and poverty is numerous;

1 FY refers Fiscal Year that starts July and ends June.

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to the best of our knowledge there are little paralleled studies particularly on the area of

capital financing and economic growth in Bangladesh..

On this context, the main objective of our study is to search whether there have any long-run

relationship between industrial output growth and capital financing; analyzing the

contribution of capital financing to the growth of industrial output of Bangladesh by applying

quantitative econometric techniques. We define capital financing in terms of imports of

capital machinery and industrial raw materials, and private sector credit. These factors are

commonly used in the industry sector aiming to foster countries’ economic growth through

industrialization.

In this paper, we develop a model by taking variables- industrial output, imports of capital

goods, private sector credit and real interest rate for the period FY1980 - FY2014. The

Johansen cointegration technique and vector error correction (VEC) model along with

supportive econometric test criterions are applied in this study. We also present a synthesis of

the cointegration and innovation accounting to unveil the relationship among the variables

that used in the model for short-run and long-run dynamics. We find evidence that there

exists a positive long-run cointegration relationship between the variables of capital financing

and industrial output growth. The finding suggests that, like many other developing and

emerging economies in Bangladesh, imports of capital goods promote industrial output

growth that support the economy to achieve a higher and sustainable GDP growth in the

long-run.

The rest of the paper comprises as, Section 2 contains the literature review; Section 3

provides the description of the variables considered and the methodology. Estimating results

of the cointegration and error correction model are discussed in Section 4 and conclusion is

drawn in Section 5.

2. Literature Review

Economists have been centered in the growth hypothesis across countries and most of the

studies find that factors such as exports, imports, remittances, investment and economic

openness lead economic growth for instance. They have analyzed these factors based on

different theoretical frameworks, from the neo-classical growth model to recent endogenous

growth model. Since, the main objective of our study is to examine the existence of long-run

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positive relationship between capital financing and industrial output growth in Bangladesh,

we seek literatures those are mostly relevant in this regard as follows.

Both approaches as mentioned above, import-led growth consider technological progress

through availability of capital machinery and input materials, human capital as well as

private investment in both developed and developing countries. As, developing countries

have comparative disadvantage in producing capital goods, literatures support that imports of

capital goods promote industrialization in these countries. Evidences supporting import-led

growth can be found in the cross country study of Thangavelu and Rajaguru (2004) for India,

Indonesia, Malaysia, Philippines, Singapore and Taiwan. Similar findings are also found in

the literature of Awokuse (2007) for Poland and in Awokuse (2008) for some South

American countries.

On Indian economy, H. Rana (2000) finds that imported technology and capital goods that

use in domestic manufacturing industry has a positive impact on country’s economic growth.

The study also examines that newly established domestic industries those produce capital

goods contribute to the increase of productivity of manufacturing industries; where the

productivity of domestically produced capital goods itself seems to stem from imported

technology. For Turkish economy Uğur, A. (2008) finds a casual relationship between output

growth and total import. The author also discovers mixed causality relationship between

various categories of imports (imports of investment goods, raw materials, consumption

goods and others) and GDP growth. Accumulative capital and the relative proportion of

imported capital goods over domestically produced capital goods that use in domestic

industries directly promote the Chinese per capita income also GDP growth (Herrerias and

Orts, 2011). They argue that imported capital goods encourage Chinese GDP for long-run in

the process of trade openness.

In case of Bangladesh, Dawson (2006) finds imports negatively cause GDP and exports cause

GDP to grow fast. He uses annual data for the period 1973-2003. Pre and post reforms period

were considered that might be provide such result. Hossain et al. (2009) using annual data for

the period 1973-2009 also could not find any relationship between imports and economic

growth. They find that in long-run exports impact economic growth positively. They also find

that exports significantly promote imports in the long-run as well in the short-run but not vice

versa. Using Engel-Granger casualty test Mamun and Nath (2005) find unidirectional

casualty from exports to GDP growth over the period 1976Q1 – 2003Q4. In this study

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industrial production index is used but could not find any impact of that on GDP. On the

other hand, Sultan P. (2008) evidences that there exists a cointegration long-run relationship

between GDP and industrial value added; the Granger casualty test supports that GDP

growth rate causes industrial value added but not vice-versa. , Hossain (1995) estimates a

manufacturing production function by applying OLS for the period 1974:Q1-1985:Q4 where

and find a positive impact on manufacturing output of real expenditure which is proxy of

imported capital goods and private sector credit. .

However we find that there is no such a study that focuses particularly the impact of capital

financing on industrial output growth in Bangladesh. Our study attempts to overcome this

shortcomings clearly focusing on capital financing (imports of capital goods and private

sector credit) and industrial output growth in Bangladesh.

3. Model, Methodology and Data

3.1 The Model Specification

Economic theory asserts that acceleration of industrial production depends on the availability

of capital financing for the imports of capital machinery and industrial raw materials in both

developed and developing economies. Like many other developing countries, Bangladesh

economy requires capital machinery and input materials as the country has a comparative

disadvantage in producing such goods. The factors strongly contribute to the growth of GDP.

Based on this hypothesis, the main objective of our study is whether the capital financing and

industrial output are positively associated or not and to analyse the impact of the import of

capital goods and private sector credit as exogenous variables on industrial output growth in

Bangladesh. In this case, for simplicity we develop a four-variable model that stated as:

Y_INDt = f (IMP_CGt, PRCt, IRt) (1)

In difference form

∆Y_INDt = f (∆ IMP_CGt, ∆ PRCt, ∆ IRt) (2)

Where, Y_IND is industrial output, IMP_CG denotes imports of capital goods (including

imports of capital machinery and industrial raw materials), PRC is private sector credit from

banks, IR denotes interest rates, t is year and ∆ represents first difference of the variable.

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Assuming that imports of capital goods and private sector credit are positively associated

with industrial output; on the other hand, interest rate impacts industrial output negatively.

Following the equations 1 and 2, we develop the error correction equation that to be

estimated as follows,

∆ (LnY_INDt) = α + Σ β1∆ (LnIMP_CGt) + Σ β2∆ (LnPRCt) + Σ β3∆ IRt + εt (3)

Where, t represents year, ∆ is first difference calculation, Ln is natural logarithm, and ε is

residuals of the sample. α is constant term of the equation and βi (i = 1, 2 and 3) denotes

estimated parameters where β1, β2 > 0 and β3 < 0 (i, e, the signs of the coefficients of β1, β2

are positive and negative for β3.

3.2 Methodology

Cointegration technique is adopted in this paper to examine whether there remains the long-

run relationship among the variables of industrial output, imports of capital goods, private

sector credit and real interest rate in the model of equation 3. We also estimate the equation

by using vector error correction (VEC) model and test the adjustment dynamics for any

disequilibrium position of any shock in the economy. The whole process is developed by

three steps.

The first, testing unit roots process of the series to identify the order of integration of the

variables considering in this study. In econometric analysis most of the macroeconomic

variables are characterized by unit-root processes i.e., non-stationary (Nelson and Plosser

1982) for time series data. In general, unit roots precede cointegration test; otherwise,

regression of non stationary variables may leads to spurious results. The Phillips-Perron (PP)

test and the Augmented Dickey-Fuller (ADF) test are the most commonly used to check the

stationary of the series. However, Phillips and Perron (1988) propose a modification of the

ADF test and have developed a comprehensive theory of unit roots. The PP test has

established a t-statistics on the unit root coefficient in the ADF regression which corrected for

autocorrelation and heteroskedasticity. Maddala and Kim (1998) argue that the DF test does

not have serious size distortions, but less influential than the PP test. Choi and Chung (1995)

also argue that for low frequency data, the PP test emerges to be more persuasive than the

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ADF test. Hence, we apply the PP methodologies to test the unit roots of the variables and

whether the series are I(0) or I(1).

The second, checking the long-run relationship among the variables of the model as stated in

equation 3. If the variables are found to be I(1), i.e., series are stationary at first difference,

cointegration is to be pursued as per the Johansen approach following the literatures of

Johansen (1988), Johansen and Juselius (1990), and Juselius (2007). In this study, the number

of the cointegration relation must be less than four as there are four variables in the model.

We apply both the trace and maximum eigenvalue tests to check the existence of long-term

cointegration relationship of the variables in the model.

Finally, we analyse the long-run relationship and error correction mechanism using VEC

techniques. Moreover, in innovation accounting process we analyze the impulse response

functions and variance decompositions of the variables in the system to unveil the dynamic

relationship among the variables. The model is also being tested with the sequence of miss-

specification tests without showing any autocorrelation or normality problems.

3.3 The Data Set

In this study, we employ annual data for the period FY1980-FY2014. The period is taken that

started in FY1980 for that first, the period FY71-76 was under socialist planning, post-war

anomalies, militant coups and external shock. The first decade almost passed by

reconstructing the demolished infrastructures such as roads, highways, bridges, buildings,

houses and power stations those were badly damaged or demolished during the Liberation

War in 1971. In addition, the famine of 1974 almost crippled the economy, and causing

starvation, diseases and deaths. The global oil shock of 1974 reinforced the supply shock of

the famine; the country’s socio-economic condition became worse. In 1975, the economy was

also affected by the bloody military coup. Thus, the period FY1972-FY1975, could not be

entirely attributed in economic activities by the policy measures those were associated with

high level of nationalization process under socialist planning.

Secondly, major policy changes related to the transition of the economy from nationalization

to privatization process were carried out after 1975. Since 1976, economic policies shifted to

private sector-led export promotion industrialization strategy from public sector-led and

import-substituting strategies through the declaration of the Industrial Investment Schedule in

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1976, the withdrawal of private investment ceiling in 1978, and the promulgation of the

Foreign Investment Act in 1980. Aiming to improve competitiveness, enhancing economic

efficiency and promoting export-led growth major significant policy reforms such as

dismantling state enterprises’ interventions, increasing participation of private sector,

launching peg exchange rate with a basket of currencies by dropping single currency fixed

exchange rate (against Pound Starling), reducing tariff and non-tariff barriers, etc. were

undertaken during the period FY1976-FY1980. Due to these widespread turbulences and

transition of the economy as discussed above, the Bangladesh’s economic growth as well as

other macroeconomic indicators was highly volatile during the period FY1972-FY1979

(Appendix Figure 1). Hence, by excluding the period FY1972-FY1979, we consider sample

period that begins in FY1980 to avoid any spurious results.

Taking into account the previous discussion, our dataset consists of industrial output

(Y_IND), imports of capital goods (IMP_CG)2, private sector credit (PRC) and interest rate

(IR). All variables are in real terms3. Natural logarithm form is used here for all variables

except interest rate for convenience to get stationary more easily, and is helpful to eliminate

the heteroskedasticity problem of time series data that is very common practice in

econometrics. However, the characteristics of time series and the relationship among the

variables would not be changed. The series of imports of capital goods and private sector

credit in value (million BDT) and nominal interest rate (in percentage) have been collected

from Bangladesh Bank (BB), and industrial output (value in million BDT) and consumer

price index (CPI) have been collected from Bangladesh Bureau of Statistics (BBS). The

sources are the authorities of the respective series; there is no doubt about the originality of

the data.

4. Estimation Results

4.1. Unit roots tests

In this study, the variables that we have used as we see in Panel A of Figure 2 in Appendix I,

are most likely to have unit roots in levels. To test unit roots in the variables of the model we

apply the PP methodologies. Accordingly, the results present in Table 1, show that series of

2 Imports of capital goods refer here the imports of capital machinery and industrial raw materials.

3 We deflated nominal values of industrial output, imports of capital goods and private sector credit with the

GDP deflator (Base: FY1996=100). And real interest rate is calculated using Fishers’ equation: RIR= (Nominal

interest rate - CPI inflation rate).

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industrial output (Y_IND), imports of capital goods (IMP_CG), private sector credit (PRC)

and real interest rate (RIR) of Bangladesh for the period FY1980 to FY2014 have unit roots

in level; only private sector credit (at 5% for Model B) and interest rate (at 10% for Model A)

reject null hypothesis. However, in first differences of all the variables, the PP tests statistics

reject unit roots at 1% level of significance and thus the variables are integrated of order one,

i.e., I(1) and the series become stationary in first differences. The test is robust and consistent

in all the series irrespective of their specifications. Thus the variables now qualify to examine

cointegration.

Table 1. Unit root tests

Variable Phillips-Perron test Integration

In level In first difference remarks

Model A Model B Model C Model A Model B Model C

LnY_IND 4.58 -2.33 6.79 -4.14 -6.72 -1.76 I(1)

(1.00) (0.41) (1.00) (0.00)***

(0.00) ***

(0.07)*

LnIMP_CG -0.99 -3.48 6.52 -10.52 -10.58 -4.59 I(1)

(0.74) (0.06) (1.00) (0.00) ***

(0.00) ***

(0.00)***

LnPRC -2.21 -409.00 6.27 -4.25 -4.50 -2.33 I(1)

(0.21) (0.02)**

(1.00) (0.00) ***

(0.00) ***

(0.02)**

RIR -2.80 -2.93 -1.31 -17.28 -22.03 -14.54 I(1)

(0.07)* (0.16) (0.17) (0.00)

*** (0.00)

*** (0.00)

***

Note: Model A includes intercept, Model B includes both intercept and trend, and Model C none.

The null hypothesis reviles that the variable has a unit root.

p- values shown in the parenthesises following each adjusted t-statistics.

*, **, and *** denote the significance of the statistics at 10%, 5% and 1% levels respectively.

4.2 Cointegration and Vector Error Correction Estimation

In our four-variable case, the number of cointegration relation must be less than four. To

qualify this property we employ the Johansen cointegration approach under both Option 3

(linear deterministic trend in data assuming intercept without trend in cointegration equation

and in VAR) and Option 4 (linear deterministic trend in data with intercept and trend in

cointegration equation and no intercept in VAR). The selection of optimal lag length criterion

supports lag 1 based on the Akaike information criterion (AIC) and Schwartz Bayesian

criterion (SBC) through the estimation of unrestricted VAR with the for the analysis of

cointegration and VEC of the model (in Appendix Table A1).

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In Appendix, Table A2 demonstrates the results of Johansen cointegration. The cointegration

rank of both the trace and maximum eigenvalue tests reveals an evidence of one cointegration

relationship among the variables under the Option 3; while under Option 4, trace test shows

two cointegration relationships in the model but the maximum eigenvalue test accords no

cointegration. Both options certainly indicate that there is an evidence of cointegrating

relationship among the variables of the model. However, option 3 produce robust results in

both the trace and maximum eigenvalue tests. The tests criterion recommends the existence

of the long-run relationship among the variables- industrial output, imports of capital goods,

private sector credit and real interest rate in the model.

We apply VEC model to estimate the cointegrating equation and to synthesize the long term

relationship between endogenous and exogenous variables in the model along with short-run

adjustment dynamics. Following the Equation 3, we find the estimated cointegrating equation

that stated in Equation 4. The equation shows the long-run cointegrating relationship between

industrial output which is considered as an endogenous one and imports of capital goods,

private sector credit and real interest rate which are exogenous variables in the model. At

equilibrium level, the estimated equation is normalized on industrial output and provides a

positive sign that is expected. The estimated cointegrating equation also exhibits that the

coefficients of explanatory variables such as imports of capital goods, private sector credit

and real interest rate are significant at 5 percent level and hence possesses expected signs.

The signs of the coefficients of the variables indicate that in the long-run, industrial output is

positively associated with the import of capital goods and private sector credit; while the

impact of real interest rate on industrial output performs an opposite one. This empirical

evidence supports the hypothesis that the import of capital goods and private sector credit

lead industrial output growth in the long-run in Bangladesh.

In the cointegrating equation 4, the value of the coefficient of imports of capital goods

indicates that an increase of 1% growth of imports of capital goods causes to an increase of

0.42% in industrial output growth. This supports the fundamental role of the import of capital

goods that fulfilling the shortage of capital machinery and industrial raw materials that finally

contribute to the growth of domestic industries of Bangladesh. Related to private sector

credit, the value of the coefficient shows that 1% increase in private sector credit causes

0.31% increase in industrial output growth. On the other hand, the value of the coefficient of

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real interest rate indicates that 0.02% decrease of industrial output growth by 1% percentage

point change of real interest rate.

Cointegrating equation:

ect(t) = LnY_IND(t-1) - 0.42 LnIMP_CG(t-1) - 0.31 LnPRC(t-1) + 0.02 RIR(t-1) - 7.20 (4)

(0.1581) (0.0223) (0.0102)

Table 2: Speed of adjustment for any disequilibrium level

Error Correction: ∆LnY_IND ∆ LnIMP_CG ∆ LnPRC ∆ RIR

CointEq1: ecm (t-1)

(the speed of adjustment)

-0.1075***

0.1370 0.1852**

-6.6118**

(0.0239) (0.1557) (0.0654) (2.9170)

** and

*** indicate the significance of the statistics at the 5% level.

Numbers in parenthesis are standard errors.

The error correction term (Table 2) on the regression with first differenced industrial output

addresses the short-run adjustment dynamics of the variables, if by any means the equilibrium

relationship is stunned. The estimated result exhibits that corresponding error correction term

on first differenced industrial output provides a negative sign as expected and significant at

the 1 percent level. The coefficients of first differenced private sector credit and real interest

rate are also significant at 5 percent level but the coefficient of imports of capital goods is

insignificant and posses a positive sign. Hence, in the process of error correction term of the

estimated VEC model, the significant coefficients of the variable industrial output along with

expected sign recommends that the endogenous variable perform well to adjust relationships

if by any means any disequilibrium once the system is shocked while imports of capital

goods do not so. The value of coefficient of the first-differentiated industrial output in the

estimated model -0.1075 indicates that only 10.75 percent of the last year’s disequilibrium is

corrected current year and that requiring 9.30 years to bringing the system into the steady

state if once it is distorted.

Moreover, in Appendix, Figure 3, the scatter diagrams and regression lines with the series of

LnY_IND, LnIMP_CG, LnPRC and interest rate also exhibit the long run relationships

among the variables. The relationship of industrial output with the imports of capital goods

and private sector credit looks highly positive association that indicates a strong

interdependence among these variables particularly for a country like Bangladesh. On the

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other hand, industrial output and interest rate are negatively associated. From the diagram, we

also see that there is a positive association of imports of capital goods with private sector

credit.

4.3 Impulse Responses and Variance Decompositions

The realization of generalized impulse responses and forecast error variance decomposition

of industrial output derived from VECM, are presented in Appendix Figure 4 and Table A4.

Figure 4 presents that there exists a positive and very strong response on industrial output for

that of one standard deviation innovation in imports of capital goods and private sector credit.

On the other hand, one standard deviation innovation in real interest rate has a strong

negative and divergent impact on industrial output.

While impulse responses are very effective to measure the sign and magnitude of responses

to specific shocks, the variance decomposition analysis delivers a critical inside into the

relative importance of each variable in the system. According to the analysis of variance

decomposition we finds that share of the forecast error variance of industrial output decline

gradually for its own shock with increasing forecast horizon (Table A4 in Appendix), while

share of imports of capital goods and private sector credit increase slowly. Around 67 percent

of the forecast error variance of industrial output accounted for its own shock at 9 years. At

this time horizon remaining variability of industrial output accounted by the shock in imports

of capital goods (14%), private sector credit (11%) and real interest rate (8%). The outcomes

strongly support the results in VECM that estimated previously.

5. Conclusion

The evidence of time series analysis of our study supports that there exists a long-run

relationship among the variables of industrial output, imports of capital goods, private sector

credit and real interest rate in Bangladesh economy. The estimated result of the cointegrating

equation shows that imports of capital goods and private sector credit greatly promote

industrial output growth; while that of real interest rate performs an opposite one. Hence, the

finding establishes a hypothesis that in Bangladesh, in the long-run, imports of capital goods

lead the country’s economic growth through acceleration of the industrialization process that

is supported by related literatures of developing economies in Asia such as India, Indonesia,

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Malaysia, Philippines, Singapore, Thailand and China where imports led growth through the

process of industrialization.

The justification of this growth hypothesis can broadly be categorized into four. First, imports

of capital machinery and industrial raw materials that use in domestic manufacturing

industries increase economic scale of industries and accelerate industrial output; finally

promotes economic growth. Second, as imported capital machinery and industrial raw

material raise domestic manufacturing output that accelerate exports; the multiplier effects of

foreign exchange receipts from the export of manufacturing products further contribute to

produce more for both domestic and foreign markets as of income effect. In fact, in

Bangladesh, exports of manufacturing products are considerably benefited from its industrial

output that depends on the import of capital machinery and industrial raw materials. Third,

capital goods and industrial raw materials are imported from technologically advanced

countries reasonably at low prices; it reduces the cost of production as well as increases

productivity of domestic manufacturing industries. Finally, imports of capital goods create

many positive externalities through the process of industrialization. On the other hand,

private sector credit also encourages private investment that evolves industrialization.

Through these channels, country’s industrialization process moves ahead and industrial

output grows faster that finally contribute to the growth of GDP.

From the evidence of our study, we strongly believe that like many other developing

countries, Bangladesh economy requires the import of capital goods to sustain the current

level economic growth through industrialization process as the country has a comparative

disadvantage in producing capital machinery and qualitative input materials. Moreover, to

attain a middle income status country by 2021 with aiming to increase employment and

reducing poverty, we have to achieve higher level of growth that should be sustainable.

Obviously, there is nothing any alternative except the acceleration of its industrialization

process. Thus, more liberalized trade, and export diversification and promotion policies along

with development of infrastructure and improvement of energy and power supply to attract

FDI and to set up international multination manufacturing industry would be the foremost

concern to the authority and policy makers in Bangladesh. These finding have policy

implications not only for Bangladesh, but also for other developing economies those desire to

grow fast. Finally, this paper raises an additional question that how we estimate the import

demand function for Bangladesh that left for future research.

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-:18:-

Appendix

Table A1: VAR Lag Order Selection Criteria

Lag LogL LR FPE AIC SC HQ

0 -101.59 NA 0.00 6.03 6.21 6.10

1 76.58 305.43* 4.67

* -3.23

* -2.34

* -2.93

*

2 86.35 14.51 6.94 -2.88 -1.28 -2.32

* indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion,

HQ: Hannan-Quinn information criterion

Table A2: Johansen Cointegration Tests

Test indicator Option 3 Option 4

λ Stat CV CE λ Stat CV CE

λtrace tests HO: r = 0 HA: r > 0 52.10 47.86

(0.02)**

1 80.40 63.88

(0.00)***

1

HO: r ≤1 HA: r >1 22.53 29.80

(0.26)

0 48.51 42.92

(0.01)**

1

λmax tests HO: r = 0 HA: r = 1 29.57 27.58

(0.03)**

1 31.89 32.12

(0.05)

0

HO: r =1 HA: r =2 11.73 21.13

(0.57)

0 28.91 25.82

(0.02)

0

Note: The λtrace and λmax are estimated as per Johansen (1988) and Johansen and Juselius (1990).

CV indicates critical values calculated for the 5 percent significance level and CE stands for number of

cointegration equation.

P-values presented in the parenthesis.

r denotes for the rank of the matrix, which indicates the number of the CE between the variables.

HO and HA indicate the null and alternative hypotheses respectively.

Option 3 includes an intercept without trend in the CE and the test VAR, whereas Option 4 includes an intercept

and a trend in the CE without any trend in the VAR.

The λtrace and λmax test statistics under both models are calculated by allowing for linear deterministic trends in

data.

** and

*** denote the significance of the statistics at 5% and 1% level respectively and rejects null hypothesis.

-:19:-

Table A3: Vector Error Correction Estimates

Cointegrating equation:

ect(t) = LnY_IND(t-1) - 0.42***

LnIMP_CG(t-1) - 0.31**

LnPRC(t-1) +0.02**

RIR(t-1) - 7.20

Error Correction: ∆LnY_IND ∆ LnIMP_CG ∆ LnPRC ∆ RIR

CointEq1: ecm (t-1)

(Speed of

Adjustment)

-0.1075***

(0.0239)

0.1370

(0.1557)

0.1852**

(0.0654)

-6.6118**

(2.9170)

∆LnY_IND(t-1) 0.2127 0.7116 0.5036 -41.6039**

(0.1265) (0.8224) (0.3455) (15.4096)

∆ LnIMP_CG(t-1) -0.0532 -0.1164 -0.1025 -4.4905

(0.0307) (0.1998) (0.0839) (3.7430)

∆ LnPRC(t-1) -0.0406 0.3109 0.4148 8.8691

(0.0655) (0.4260) (0.1790) (7.9829)

∆ RIR(t-1) 0.0031**

-0.0014 0.0055 -0.2278

(0.0014) (0.0091) (0.0038) (0.1698)

C 0.0534***

0.0345 0.0462 1.9086

(0.0118) (0.0769) (0.0323) (1.4407)

R-squared 0.5864 0.0808 0.4536 0.4094

Adj. R2 0.5151 -0.0777 0.3594 0.3075

Note: *, **

and ***

refer that coefficients are significant at the 10%, 5% and 1% level respectively.

Values in parentheses against each coefficient indicate standard errors.

“∆” stands for first-order difference operator.

“ect” stands for error correction term and “ecm” is error correction model.

Values in parenthesis are the standard errors of the coefficients.

-:20:-

Table A4: Generalized Forecast Variance Decomposition of Industrial Output

eriod S.E. LnY_IND LnIMP_CG LnPRC RIR

1 0.0240 100.0000 0.0000 0.0000 0.0000

2 0.0354 99.5530 0.0845 0.1354 0.2271

3 0.0446 96.1755 1.2798 1.0647 1.4800

4 0.0563 88.7362 4.7572 3.0331 3.4736

5 0.0686 82.5109 7.6024 4.9914 4.8953

6 0.0811 77.3327 9.8481 6.8008 6.0183

7 0.0935 73.0029 11.6831 8.3882 6.9257

8 0.1059 69.4992 13.1507 9.7208 7.6294

9 0.1179 66.6735 14.3184 10.8299 8.1782

10 0.1296 64.3696 15.2599 11.7552 8.6154

11 0.1410 62.4727 16.0288 12.5300 8.9685

12 0.1520 60.8970 16.6634 13.1825 9.2571

13 0.1626 59.5757 17.1926 13.7357 9.4961

14 0.1728 58.4577 17.6384 14.2078 9.6961

-:21:-

Table A5: Autocorrelation LM Tests

Lags LM-Stat Prob

1 15.95939 0.4558

2 17.40763 0.3597

3 16.14343 0.4430

4 23.10214 0.1110

5 14.07222 0.5933

6 20.60337 0.1943

7 14.26986 0.5786

8 13.16987 0.6603

9 12.59700 0.7020

10 10.49414 0.8396

Probabilities from chi-square with 16 df.

Table A6: Normality Tests

Component Jarque-Bera df Prob.

1 0.666717 2 0.7165

2 1.395751 2 0.4976

3 3.584666 2 0.1666

4 0.096641 2 0.9528

Joint 5.743775 8 0.6759

-:22:-

Figure 1: Trends of Annual Economic Growth in Bangladesh: FY1972-FY2014 (Base 2005-06=100)

Figure 2: Trends of the variables of the model

(Industrial output, imports of capital goods, private sector credit and real interest rate of Bangladesh)

Panel A ( in levels)

Panel B ( in first difference)

4

6

8

10

12

14

-10

-5

0

5

10

15

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 14

LNY_IND (LHS) LNIM_CG (LHS)

LNPRC (LHS) RIR (RHS)

-.4

-.2

.0

.2

.4

.6

-1.0

-0.5

0.0

0.5

1.0

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 14

DY_IND (LHS) DIMP_CG (LHS)

DPRC (LHS) DRIR (RHS)

Source: Bangladesh Bank and Bangladesh Bureau of Statistics

-8

-6

-4

-2

0

2

4

6

8

10

12 FY

72

FY7

4

FY7

6

FY7

8

FY8

0

FY8

2

FY8

4

FY8

6

FY8

8

FY9

0

FY9

2

FY9

4

FY9

6

FY9

8

FY0

0

FY0

2

FY0

4

FY0

6

FY0

8

FY1

0

FY1

2

FY1

4_P

Gro

wth

Rat

e (i

n %

)

Source: Bangladesh Bureau of Statistics (BBS)

Competitive Market Economy Post war anomalies

& Policy changes

regims

-:23:-

Figure 3: Scatter Plots with industrial output, imported capital goods, private sector credit and

interest rate of Bangladesh

-.8

-.4

.0

.4

.8

GR

_IC

G

-.2

-.1

.0

.1

.2

.3

GR

_P

RC

10

11

12

13

14

15

16

-.12 -.08 -.04 .00 .04 .08

GR_YIND

IR

-.8 -.4 .0 .4 .8

GR_ICG

-.2 -.1 .0 .1 .2 .3

GR_PRC

Figure 4: Generalized Impulse Response of Industrial Output in Bangladesh

=============================

-.02

-.01

.00

.01

.02

.03

.04

.05

1 2 3 4 5 6 7 8 9 10 11 12

LnY_IND LnIMP_CG

LnPRC RIR

Response of LnY_IND to Cholesky

One S.D. Innovations