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2012 Cambridge Business & Economics Conference ISBN : 9780974211428

The Impact of Tariff Reductions on Real Imports in

Malaysia from 1980-2010

Written by Juita Mohamad

Graduate School of Asia Pacific Studies, Waseda University,

Tokyo, JAPAN

Phone number: 00819091054007

Email: [email protected]

June 27-28, 2012Cambridge, UK 1


The Impact of Tariff Reductions on Real Imports in Malaysia from 1980-2010

Abstract

This paper investigates the long run relationship of drastic tariff reductions on the real

imports from 1980 - 2010 in Malaysia using the Johansen cointegration analysis. Time series

data for eight sectors according to the Standard International Trade Classification were

compiled. A dynamic error correction model is used to overcome the limited number of

observations for each of the sectors. A log-linear regression is run for each sector to test the

effects of income, domestic price, import price, tariff rates on real imports in each of these

sectors. With the independent variables selected, it is expected that as imports increase,

income and domestic price increase, while import price and tariff rates decrease. The results

from the regression exercise are mixed. It is observed that those sectors conforming to the

hypothesis are sectors concerning with basic necessities of the Malaysian consumers and

producers. The import demand for sectors with basic necessity goods are more sensitive with

the changes in tariff rates compared to sectors with non-basic necessity goods. For the latter

group, even when tariff rates are increasing, import demand still increases as these products

are mostly intermediary goods needed for production and processing activities. This

interpretation is appropriate for the case of Malaysia, whereby manufacturing activity is the

main driver of its domestic economy since the early 1990s. This study is beneficial to

determine the behaviour pattern of each sector in the wake of reduced import prices and

drastic tariff cuts.

Key words: tariff reductions, real imports, Stolper-Samuelson theorem, domestic price,

import price, Malaysia



Introduction

As the world economy shifts into a more globalized era, presently every developing nation is

harnessing their resources in aneffort to take part in a more free trade regime.Globalization

promotes the practice of free trade and it is believed to promote a more levelled playing field

in the world market, as tariff rates are driven down to near 0%. In other words, protectionism

policy is at the brink of extinction with the rise of globalization. Globalisation in the long run,

rewards efficient producers the competitive advantage that ensures its position in the global

market.Even with the promise of more wealth and ensuring increased welfare for all, the

issue of increased competition domestically in the midst oftrade liberalization for both

developed and developing nations, are widely discussed. In his book Making Globalization

Work(2006), Stiglitz emphasized that with globalization, "Everyone was supposed to be a

winner - those in both developed and thedeveloping world. Globalization was to bring

unprecedented prosperity to all."

Even Adam Smith, 1776 promoted the idea of free tradei. Here was how he put it at that time:

It is the maxim of every prudent master of a family, never to attempt to make at home what it

will cost him more to make than to buy.. . . If a foreign country can supply us with a

commodity cheaper than we ourselves can make it, better buy it of them with some part of the

produce of our own industry, employed in a way in which we have some advantage.

With all of the advantages of free trade, some economists are sceptical about the downside of

globalisation which includes increased competition in the domestic economy which can lead

to the absolution or demise of local businesses. With the world as one big market, local

producers are forced to compete with cheaper imported goods or higher quality products. The

survival of the fittest is being tested in today’s economic environment, with more pressure

being put not only onto business, but nations as well.



Malaysia, a small open economy, in the South East Asian region, is taking this threat and

turning it into an opportunity. The ASEAN bloc has become the hub for intratrade activity.

Intra- regional trade has been growing. According to Otsuki, 2011, intra-regional trade for

manufactured goods within the ASEAN region has increased to 150 billion USD in 2008 if

compared to 70 billion USD in year 2000ii. What makes this region a part of the world engine

of economic growth nowadays, is that as we increasingly trade intermediary goods among

ourselves in the assembly process of commodities, we also import and re-export the goods,

outside the region to China, the US and Europe as end users for our products. This activity is

how we are known as the entreportcenter.

Due to the importance of this intra trade activity as the driver of the nation’s economy, it is

important to examine the effects of the reduction of tariff rates on real imports. It is also

important to see how changes in income, domestic pricing, import pricing effect real imports.

The study will take a look at the behaviour of these variables in different sectors according to

the segregation of Standard Industrial Trade Classification, Revision 3 at one digit leveliii.The

paper examines how reduction in import duties affect the demand for real imports in selected

sectors in a small, open, developing economy of Malaysia using time series data from 1980 to

2010.

Background on Tariff Reduction through AFTA and WTO Initiatives

On the 8th of August 1967, a simple yet clear agreement was drawn up between the six

Founding Fathers of the six main Association of Southeast Asian Nations (ASEAN)

members, which were Indonesia, Malaysia, the Philippines, Singapore and Thailand.

According to the document, ASEAN is seen as representing ―the collective will of the



nations of Southeast Asia to bind themselves together in friendship and cooperation and,

through joint efforts and sacrifices, secure for their peoples and for posterity the blessings of

peace, freedom and prosperity. It is with that basic drive of cooperation and joint efforts,

ASEAN Free Trade Area (AFTA) came about in year 1992. When the AFTA agreement was

signed, the primary goals were to ―increase ASEAN's competitive edge as a production base

in the world market through the elimination, within ASEAN, and to attract more foreign

direct investment to ASEAN. Looking back since its inception, AFTA has come a long way

in changing trade patterns in Malaysia and the South East Asian region. As from year 2002, it

is now in full swing, ― aiming to promote the region‘s competitive advantage as a single

production unit. Even though specific rules and priorities are given for the elimination of

tariff mainly for manufacturing and agriculture products in member countries, the non-tariff

barrier is also expected to promote greater economic efficiency, productivity, and

competitiveness. To observe how far the region has come in promoting a leveled playing

field for its member countries, as of 1 January 2005, ― tariffs on almost 99 percent of the

products in the Inclusion List of the ASEAN-6 have been reduced to no more than 5 percent.

More than 60 percent of these products have zero tariffs.

The average tariff for ASEAN-6 has been brought down from more than 12 percent when

AFTA started to 2 percent today. The implementation of the Common Effective Preferential

Tariff –AFTA Scheme (CEPT-AFTA) was even accelerated further in January 2004, when

Malaysia decided to reduce tariffs for Completely Built Up (CBUs) and Completely Knocked

Down (CKDs) automotive units to smoothly meet its CEPT commitment one year earlier

than schedule. This proved to be a big commitment on behalf of Malaysia, as Proton, its

national car industry have been shielded under heavy protectionist policy since year 1985.

When full trade liberalization is achieved, the area could not only be beneficial for the 10

member countries but also holds attractiveness for trading partners all around the worldiv.



Malaysia is a founding Member of the WTO by virtue of its membership in the GATT since

1957. Through active participations in WTO negotiations, Malaysia continues to ensure that

trade regulations and trade measures that are negotiated are fair and provide the flexibility for

Malaysia to continue its development policyv. To date, under the commitment of

GATT/WTO, the average MFN applied tariff rates for Malaysian goods are down to 8.8% in

year 2010 compared to 11.3% in year 1995vi. This shows the continuous effort and

commitment of Malaysia as a WTO member and how tariff rate reductions are not only

induced by the ASEAN bloc agreement regionally but also the commitments of the

GATT/WTO at an international level.

Literature Review

There are many empirical studies on demand of imports. For example in the case of

Malaysia, the studies which are conducted to examine the behaviour of import demand are

Mohammad (1980), Semudram (1982), Awang (1988), and the Malaysian Institute of

Economic Research (MIER) Annual Model (1990). These studies estimated a traditional

import demand function, where the dependant variable is the volume of imports, where the

independent variables are real income and relative prices. For these studies, the assumption is

that data are stationary. These studies were done when cointegration analysis and error

correction model were not standard practice for time series analysis. Hence they used OLS

regression models or partial adjustment approaches to estimate the import demand function.

Granger and Newbold (1974) emphasized that if the stationary assumption is not satisfied,

this can lead to spurious regression. Due to this, the OLS results would be unreliable.

From previous literature review, the traditional import demand function has branched out, to

include other independent variables which can affect real import such as tariff rates. Caesar



(2011) have used tariff and exchange rate as an extension of the general import demand

function to study the determinants of import demand in Zambia. Egwaikhide (1999) have

incorporated tariff rates into its import prices, in the empirical study for Nigeria. Karacaovali

(2011) has also incorporated tariff rates in its model.

The choice between a linear and log-linear import demand equation is important because the

influence of explanatory variables on demand is affected by the functional form. Kmenta

(1971) stated that misspecification of the functional form can result in misspecification of the

error term and violation of the classical assumptions of the error term. This leads the

estimates to be biased.

Previous studies by Khan and Ross (1977), Boylanet. al (1980) and Doroodian et. al (1994)

has proven that specification of a log-linear form is preferable when estimating import

demand functions.

Data

The variables used for this study areReal Imports, Gross National Income, Domestic Price

Index, Import Price Index and Simple Average Tariff Rates. All of the annual data from year

1980-2010 were supplied by the Department of Statistics Malaysia except for the tariff rates,

which were obtained from the WITS website. The real imports variable is based on import

value and not quantity as it is more comparable among the goods in a certain sector. The

sectors are divided into 9 Standard of Industrial Trade Classification at 1- digit level. The

author constructed an index for the real imports for each sector.For a more accurate analysis,

the author chose the log form, so that the result of the coefficients of the variables can be

interpreted as degree of elasticity. Due to lack of data, the author has focused her analysis on



only 8 sectors instead of 9 sectors according to the SITC Revision 3 segregation. These

sectors are: 0. Food and Live Animals, 1. Beverages and Tobacco, 2. Crude Materials

Inedible Except Fuels, 3. Mineral fuels, Lubricants and Related Materials, 4. Animal and

Vegetable Oils, Fats and Waxes, 5. Chemicals and Related Products, 6. Manufactured Goods

Classified Chiefly by Material, Machinery and Transport Equipment, 8. Miscellaneous

Manufactured Articles. The author did not include the last sector, 9. Commodities and

Transactions Not Classified Elsewhere in the SITC in this study due to data unavailability.

Methodology and Findings

This methodology is replicated from the study by Tang T.C. and Mohammad H. A. (2000),

which looked at the demand of aggregate import as a whole for Malaysia. Instead of looking

into aggregate imports, the author uses the same analytical steps in observing the different

behavior of demand of imports for 8 different sectors.

The traditional formula for an import demand function can be specified that relates the

quantity of imports demanded to income, the price of imports and the price of the domestic

substitute. Import demand at time t is written as below:

Mit = ƒ (Ydt, Pmit, Pdit) (1)

where Mit is the quantity of imports demanded for commodity class i (to the ithat one digit

level of the Standard International Trade Classification) at period t, Ydt is domestic income,

Pmit is the price level for the import commodity class i and , Pdit is the price level for the

domestic good i at time t.

The theory of demand suggests that ordinary demand functions are homogenous of degree

zero in prices and income. This implies that the absence of money illusion and allows the



demand for imports to be expressed as a function of real income and relative prices

(Siddique, 1997). Therefore the traditional import demand function can be rewritten as:

Mit = g (Yt, Rit) (2)

Where Ytequals toYdt/Pdtand represents real domestic income and Ritequals to Pmit/Pdit, which

is the price of the ith digit import relative to the domestic price. This equation has the

assumption that the effect of 2 price variables on demand will be equal but in opposite

directions. Gafar, 1988 explained that the two most common functional forms used in the

literature are in the linear and log-linear formulations. In linear terms, the empirical import

demand function can be written as:

Mit = δ + α0Yt+β0Rit +εit (3)

Where δ is the constant term, α0 is the marginal propensity to import,β0 is the import

coefficient of relative prices andεit, is the random error term. From economic theory point of

view, it is expected that α0>0. However Goldstein and Khan, 1976 explained that if imports

represent the difference between domestic consumption and domestic production of imported

goods, production may rise faster (slower) than consumption in response to rise in real

income. Due to this, imports could fall (rise) as real income increases, resulting in negative

(positive) sign for the coefficient α0.

The author however has constructed an adjusted import demand function, to include the tariff

rate variable, T as an extension from the traditional function. Additionally in this new

equation, the import price level and domestic price level are disaggregated to relieve the

restrictions imposed on the traditional demand function.The author has chosen to

disaggregate the log import price and log domestic price, due to the assumption that these

prices do not move in the same degree, and also in opposite directions. For this kind of study



both the linear and log linear specifications have been used as highlighted in the literature

review section. The author has chosen the latter due to the fact that it is more commonly

used. At the end of the day, the selection between these 2 forms of specifications is intuitional

based on previous literatures.

With thedisaggregation of the two prices, the behaviour of the import prices and domestic

prices can be observed, for specific commodity groupings. As log-linear form is mostly

adopted for this empirical study, the newly adjusted import demand function can be written

as:

lnMit = δ + φ0lnYt-β0lnIit +ѲlnDit- σlnTit +εit (4)

whereln is the natural logarithm, Iitis the import price level of the ith commodity group, Dit is

the domestic price level of the ith commodity group andTit, is the tariff rate for the ith

commodity group according to the SITC segregation. Εit is the random error term.

For this study, Error Correction Model (ECM) is being used due to limited annual

observations for each sectors from year 1980-2010 (only 31 observations). ECM is the most

appropriate for limited observations in time series. OLS is not appropriate for this time series

study, as the outcome will be highly unreliable as mentioned in the literature review section.

Before the ECM analysis could take place, the author tested the time series data for each

sector for multicollinearity problems. For each sector, Augmented Dickey Fuller (ADF)

testing was undergone. The regression equation for ADF test (Dickey&Fuller, 1979) is stated

as follows:

∆ Yt=a+bt+cYt−1+∑i=1

k

d ∆ Yt−1+et (5)



where∆ is the first difference operator, t refers to time trend, and k is additional terms in the

first differences for the Augmented Dickey-Fuller test, et is the regression error assumed to

be stationary with zero mean and constant variance. The test were carried out to test the null

hypothesis of a unit root (c=0). The results are presented in Table 1 below. The table

highlighted that all variables real imports, GNI, Domestic Price, Import Price and Simple

Average Tariff are integrated in order one, I(1). This means that they are stationary in their

first difference.

Insert Table 1a, 1b and 1c here

After the ADF test were undergone, the author went on to the vector error correction model.

Before the modelling could be done, for the Johansen method, it is crucial to specify an

appropriate lag length for the VAR. For all of the eight subsectors, 2-year lag length was the

most appropriate. As for the trace test, for each different sectors the cointegration ranking

differs. Table 2 below, shows the Johansen test for cointegration and the appropriate lags

chosen for each of the eight sub sectors. Table 3 presents the normalised cointegrating

equation estimate for all of the subsectors.

Insert Table 2a-h, here

Insert Table 3a-h, here

In the next step, error correction model was estimated. The rank is set to 1, which is the

default number of the error correction terms. An error correction model was estimated to

examine the long run behaviour of Malaysian imports (according to their 8sectors). The

lagged residual from the Johansen Cointegration Equation was included the dynamic general

ECM. The general equation for ECM with 2 lag length is stated as below:



∆ln Mt = b0 + b1i∆ lnM t-2 + ∑i=0

n

b 2i ∆ln Yt-2+ ∑i=0

n

b 3i ∆lnIt-2 + ∑i=0

n

b4 i ∆lnDt-2 +∑i=0

n

b5 i ∆lnTt-2

+ b6 ECt-2 + error term (6)

where EC is residual error derived from the cointegrating vector. This dynamic general

equation is used separately for all the 8 sectors and presented in the next section.

Analysis of Findings

The coefficients of income, domestic prices, import prices and simple average tariff rates

shows both expected and unexpected signs and are both significant and not significant,

depending on the sector being analysed. In this section a detailed breakdown of the findings

will be presented.

Let us look at the normalised cointegrating coefficients results for each of the 8 sectors in

Table 3 below.

Insert Table 3 here – result for vec for all sectors- make table

Let us take a look at the results of the coefficients, sector by sector.

A very interesting finding from this analysis is that the coefficient for GNI as a proxy of

income, α0 is negative in relations to import demand. From economic theory point of view, it

is expected that α0>0. However Goldstein and Khan, 1976 explained that if imports

represent the difference between domestic consumption and domestic production of imported

goods, production may rise faster (slower) than consumption in response to rise in real

income. Due to this, imports could fall (rise) as real income increases, resulting in negative

(positive) sign for the coefficient α0.



For Sector 0, Food and Live Animals, all the signs exhibited the expected signs. In this case,

even when income decreases by 1 point, real imports will still increase by 0.61 point. In this

sector, tariff rates do affect real imports. When average tariff decreases by1 point, real

imports will increase by 0.17 point.

The second sector which exhibits the expected signs for its variables is Sector 6.

Manufactured Goods Classified Chiefly by Material. For this sector, in the long run, domestic

and import prices play important roles in affecting real imports. The long run elasticities of

import demand with respect to import price and domestic price are -27 and 30. It is not

surprising then that the tariff coefficient sign is positive. For this sector in the long run, even

when tariff increases by 1 point, real imports will still increase by 1.18 points. It is clear that

for manufactured goods, tariff rates do not affect the import demands for this sector.

A 1 point increase in import price, will decrease real imports by 27 points, while a 1 point

decrease in domestic price, will decrease imports by 31 point. With our re-exporting activity,

the more we export the goods, the more imports are needed as intermediary goods to support

this activity.This is not surprising as Malaysia’s imports and exports of manufactures from

overall merchandise trading account for 70% as of year 2010. This is further highlighted by

Graph 1 in the Appendix.

The last sector with expected signs for its variables is Sector 8, Miscellaneous Manufactured

Articles. In this sector, footwear, furniture and articles of apparel and clothing accessories

which are basic necessity goods are included. The implied long run elasticties of import

demand with respect to import price and average tariff are -1.97 and -1.60. Here tariff rate

does play a role in affecting real imports.

For all the other sectors, the variable signs are not as expected. However, due to the fact that

this paper focuses on the effect of trade reform on imports, it is important to see how changes



in tariff rates do or do not, influence the changes in import demands for different goods in

different sectors. Table 4 below present the results for the signs and significance of the tariff

rate variable.

Insert Table 4 here – tariff rate

As can be observed in the Table above, only 3 sectors are presented to have negative signs for

their tariff variable in relations to real import. These sectors are Food and Live Animals,

Animal and Vegetable Oils, Fat and Waxes and Miscellaneous Manufactured Articles. Their

coefficients are -0.17, -3.38 and -1.60 respectively with all being highly significant at 1%

level. What all of them have in common is that, these are basic necessity goods. The rest of

the sectors have positive signs for their tariff variables in relations to real import. These

sectors are 1. Beverages and Tobacco, 2.Crude Materials Inedible Except Fuels, 3.Mineral

fuels, Lubricants and Related Materials, 5.Chemicals and Related Products, 6. Manufactured

Goods Classified Chiefly by Material and 7. Machinery and Transport Equipment. Their

coefficients are 0.11, 0.67, 1.30, 0.49, 1.18 and 0.11 respectively, with all being highly

significant at 1% and 5% level except for Sector 7. For this sector, tariff rate is insignificant

in affecting import demand. These are all non- basic necessity goods except for beverages

and tobacco sector. Beverages and tobacco sector belongs to a special group. Higher taxes

have always been imposed on tobacco and alcoholic beverages in Malaysia, making it more

expensive and therefore, unavailable for youths and children. Due to this even when tariff

rates are higher, due to preferences or lack of choice, these goods are still high in demand for

Malaysian consumers.

Examining the results of the analysis, it can be concluded that the import demand for sectors

with basic necessity goods are more sensitive by the changes in tariff rates compared to

sectors with non-basic necessity goods. For the latter group, even when tariff rates are



increasing, import demand still increases as these products are mostly intermediary goods

needed for production and processing activities. This interpretation is appropriate for the case

of Malaysia, whereby manufacturing activity is the main driver of its domestic economy.

Conclusion

A number of conclusions can be drawn from this study. Firstly, if researchers are to obtain

robust results it is important they choose the right methodology, appropriate for its time series

data limitation. As this data set, has only 31 observations for each of its 8 sectors, the author

has chosen the dynamic error correction model to estimate the long run behaviour of

Malaysian imports according to sectors from year 1980-2010.

Secondly, the negative sign presented for the income coefficient suggests that in Malaysia,

according toGoldstein and Khan, 1976 imports represent the difference between domestic

consumption and domestic production of imported goods, production may rise faster (slower)

than consumption in response to rise in real income. Due to this, imports could fall (rise) as

real income increases, resulting in negative (positive) sign for the coefficient α0.

Thirdly, the empirical results suggest that only 3 sectors which include basic necessity goods

for end users and producers, have all the expected signs for their variables. These sectors are

Food and Live Animals, Manufactured Goods Classified Chiefly by Material and

Miscellaneous Manufactured Articles.

Fourthly, as this paper focuses on the effect of trade reform on imports, it is important to see

how changes in tariff rates do or do not, influence the changes in import demands for

different goods in different sectors. The import demand for sectors with basic necessity goods

are more sensitive by the changes in tariff rates compared to sectors with non-basic necessity



goods. For the latter group, even when tariff rates are increasing, import demand still

increases as these products are mostly intermediary goods needed for production and

processing activities. This interpretation is appropriate for the case of Malaysia, whereby

manufacturing activity is the main driver of its domestic economy since the early 1990s.


iTaken from the Concise Encyclopedia of Economics, http://www.econlib.org/library/Enc/FreeTrade.htmliiStatistics were taken from the paper by Otsuki, 2011 at http://www.osipp.osaka-u.ac.jp/archives/DP/2011/DP2011E006.pdfiiiFor a more information please visit http://unstats.un.org/unsd/cr/registry/regcst.asp?cl=14ivInformation obtained from the ASEAN Secretariat websitevTaken from the MITI website, http://www.miti.gov.my/cms/content.jsp?id=com.tms.cms.section.Section_f5694606-c0a81573-78d578d5-759be8c9viTariff rates obtained by the World Trade Indicators Report 2010 athttp://info.worldbank.org/etools/wti/docs/Malaysia_taag.pdf

References

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Dickey, D. A., & Fuller, W.A (1979) Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, 427-431

Egwaikhide, F. (1999) Determinants of Imports in Nigeria: A Dynamic Specification, AERC Research Paper 91, African Economic Research Consortium, Nairobi

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Appendix

Table 1a

Sector/Variable 0 1 2 3

ADF level

FD ADF level

FD ADF level

FD ADF level

FD

ln imports -2.83-

3.84

* -3.15-

3.84

* -2.48-

0.41

** -2.17-

2.75

*

lngni -0.81-

3.43

** -0.81-

3.43

** -0.81-

3.43

** -0.81-

3.43

**

lndomprice -2.14-

3.41

** -2.23-

3.41

* -1.56-

4.17

*** -1.43

-3.25

***

lnimpprice -4.33-

4.65

*** -2.44

-4.6

5 -2.22

-4.49

*** -1.91

-2.67

***

lnsimpleavtariff -2.26-

4.30

*** -2.58

-4.3

0

*** -2.50

-3.70

** -1.72-

3.20

**

*,**,*** denoted rejection of a unit root hypothesis based on MacKinnon’s critical value at 1 percent, 5 percent and 10 percent.Note:

1) Constant and trend were included in level, and only constant in first difference (refer to Baghestani& Mott, 1997). In common practice, an augmentation of one or two, generally appears to be sufficient to secure lack of autocorrelation of the error terms (Ghatak, Milner &Utkulu, 1997) One augmented lag was used due to limitation of annual data (refer to Doroodian, Koshal& Al- Muhanna, 1994:912)

Table 1b

Sector/Variable 4 5 6 7

ADF level

FD ADF level

FD ADF level

FD ADF level

FD

ln imports -4.174 -7.13 *** -4.174 -7.13 *** -1.456 -2.926 * -0.665 -

2.459

lngni -0.805 -3.425 ** -0.805

-3.42

5** -0.805 -

3.425 ** -0.802 -3.596

**

lndomprice -2.428 -7.214 *** -2.428

-3.36

1** -2.587 -

3.566 ** -4.052 -5.649

***

lnimpprice -3.952 -3.828 *** -3.952

-4.25

8*** -3.007 -

3.458 ** -1.887 -4.447

***

lnsimpleavtariff -2.247 -3.97 *** -2.247-

3.396

** -1.672 -3.602 ** -2.35 -

3.375**



Table 1c

Sector/Variable 8

ADF level

FD

ln imports -0.154-

2.823

*

lngni -0.802-

3.596

**

lndomprice -0.925-

3.545

**

lnimpprice -3.88-

3.649

**

lnsimpleavtariff -2.336-

3.968

***



Results for Trace Test for Cointegrating Vector

Table 2aSector 0

5 55 424.33671 0.06762 4 54 423.32156 0.37251 2.0303* 3.76 3 51 416.56421 0.43501 15.5450 15.41 2 46 408.28555 0.59316 32.1023 29.68 1 39 395.24529 0.92848 58.1829 47.21 0 30 356.99761 . 134.6782 68.52 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 1982 - 2010 Lags = 2Trend: constant Number of obs = 29 Johansen tests for cointegration

Table 2bSector 1

5 55 335.07264 0.09196 4 54 333.67385 0.23435 2.7976 3.76 3 51 329.80182 0.42424 10.5416 15.41 2 46 321.79697 0.55042 26.5513* 29.68 1 39 310.20511 0.86192 49.7351 47.21 0 30 281.49667 . 107.1519 68.52 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 1982 - 2010 Lags = 2Trend: constant Number of obs = 29 Johansen tests for cointegration

Table 2cSector 2

5 55 348.17139 0.06173 4 54 347.24741 0.18070 1.8479 3.76 3 51 344.35754 0.29036 7.6277 15.41 2 46 339.38405 0.50066 17.5747 29.68 1 39 329.31429 0.77389 37.7142* 47.21 0 30 307.75654 . 80.8297 68.52 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 1982 - 2010 Lags = 2Trend: constant Number of obs = 29 Johansen tests for cointegration

Table 2dSector 3


Table 2eSector 4

5 55 295.59271 0.22660 4 54 291.8668 0.23704 7.4518 3.76 3 51 287.94386 0.48071 15.2977* 15.41 2 46 278.44208 0.61654 34.3013 29.68 1 39 264.54345 0.68730 62.0985 47.21 0 30 247.68692 . 95.8116 68.52 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 1982 - 2010 Lags = 2Trend: constant Number of obs = 29 Johansen tests for cointegration

Table 2fSector 5

5 55 421.5472 0.01536 4 54 421.32271 0.19980 0.4490 3.76 3 51 418.09083 0.31678 6.9127 15.41 2 46 412.56721 0.48904 17.9600 29.68 1 39 402.83103 0.72946 37.4323* 47.21 0 30 383.87484 . 75.3447 68.52 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 1982 - 2010 Lags = 2Trend: constant Number of obs = 29 Johansen tests for cointegration

Table 2gSector 6


Table 2hSector 7

5 55 403.62435 0.09174 4 54 402.22917 0.20885 2.7904 3.76 3 51 398.83227 0.30705 9.5842 15.41 2 46 393.51361 0.52735 20.2215 29.68 1 39 382.64727 0.56686 41.9542 47.21 0 30 370.51508 . 66.2185* 68.52 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 1982 - 2010 Lags = 2Trend: constant Number of obs = 29 Johansen tests for cointegration

Table 2iSector 8

5 55 429.25134 0.12411 4 54 427.32992 0.15845 3.8428 3.76 3 51 424.82861 0.32258 8.8455 15.41 2 46 419.18136 0.46403 20.1400 29.68 1 39 410.13805 0.51002 38.2266 47.21 0 30 399.7938 . 58.9151* 68.52 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 1982 - 2010 Lags = 2Trend: constant Number of obs = 29 Johansen tests for cointegration

Results of Normalized Cointegrating Coefficients

Table 3aSector 0

_cons 1.810574 . . . . .simpleavta~f -.1693067 .0414817 -4.08 0.000 -.2506093 -.088004 impprice -1.993184 .1447514 -13.77 0.000 -2.276891 -1.709476 domprice .8931411 .1284432 6.95 0.000 .641397 1.144885gniconstbill -.6060141 .0592761 -10.22 0.000 -.7221931 -.4898351 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3bSector 1

_cons .261836 . . . . .simpleavta~f .1094989 .0112684 9.72 0.000 .0874133 .1315846 impprice 2.856678 .1577977 18.10 0.000 2.5474 3.165955 domprice -2.085972 .3314365 -6.29 0.000 -2.735576 -1.436369gniconstbill -1.131859 .1309922 -8.64 0.000 -1.388599 -.8751192 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3cSector 2

_cons -1.549599 . . . . .simpleavta~f .6654426 .0850619 7.82 0.000 .4987243 .8321609 impprice 2.936185 .4216616 6.96 0.000 2.109743 3.762626 domprice -1.932647 .2743563 -7.04 0.000 -2.470376 -1.394919gniconstbill -.9276013 .0876075 -10.59 0.000 -1.099309 -.7558938 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3dSector 3

_cons -8.292437 . . . . .simpleavta~f 1.296926 .3905323 3.32 0.001 .5314964 2.062355 impprice 4.937531 1.463931 3.37 0.001 2.068279 7.806782 domprice -1.237617 1.578216 -0.78 0.433 -4.330864 1.85563gniconstbill -.8279099 .4080996 -2.03 0.042 -1.627771 -.0280493 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3eSector 4

_cons 30.73145 . . . . .simpleavta~f -3.38468 1.320966 -2.56 0.010 -5.973726 -.7956346 impprice -6.746218 5.327957 -1.27 0.205 -17.18882 3.696385 domprice -7.264583 .9989268 -7.27 0.000 -9.222443 -5.306722gniconstbill -.4917603 2.016952 -0.24 0.807 -4.444913 3.461393 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3fSector 5

_cons -4.475497 . . . . .simpleavta~f .4926158 .19913 2.47 0.013 .1023282 .8829035 impprice 12.77593 2.420679 5.28 0.000 8.031487 17.52038 domprice -7.801147 1.645763 -4.74 0.000 -11.02678 -4.575511gniconstbill -3.087823 .2341412 -13.19 0.000 -3.546731 -2.628915 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3gSector 6

_cons -3.287551 . . . . .simpleavta~f 1.180727 .2482223 4.76 0.000 .6942204 1.667234 impprice -26.91914 4.642102 -5.80 0.000 -36.01749 -17.82079 domprice 30.65089 4.659498 6.58 0.000 21.51844 39.78334gniconstbill -3.101338 .5490705 -5.65 0.000 -4.177496 -2.025179 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3hSector 7

_cons -5.643254 . . . . .simpleavta~f .107072 .2681926 0.40 0.690 -.4185758 .6327198 impprice 1.654194 .897383 1.84 0.065 -.1046447 3.413032 domprice 3.712937 1.938091 1.92 0.055 -.0856527 7.511526gniconstbill -3.239978 .2745702 -11.80 0.000 -3.778126 -2.70183 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 3iSector 8

_cons 9.299292 . . . . .simpleavta~f -1.60409 .2347441 -6.83 0.000 -2.06418 -1.144 impprice -1.974583 .6130574 -3.22 0.001 -3.176154 -.7730128 domprice .6313831 1.47894 0.43 0.669 -2.267286 3.530052gniconstbill -2.854489 .5562836 -5.13 0.000 -3.944785 -1.764194 realimports 1 . . . . ._ce1 beta Coef. Std. Err. z P>|z| [95% Conf. Interval] Johansen normalization restriction imposed

Table 4 : Coefficients of Tariff Rates for Each Sector

Sector Coefficient of tariff

Significance level

0 -0.167 ***1 0.11 ***2 0.67 ***3 1.3 ***4 -3.38 ***5 0.49 **6 1.18 ***7 0.11 8 -1.6 ***

Note: *,**,*** denote significance at 10 percent, 5 percent and 1 percent respectively.

Graph 1: Malaysian Manufactures Imports and Exports from 1990-2010

Source: World Development Indicator website

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