Determinants of intra-industry trade between Pakistan and selected SAARC countries.
Akram, Adnan ; Mahmood, Zafar
This paper analyses country-specific and industry-specific
determinants of intra-industry trade (IIT) between Pakistan and other
SAARC countries using panel data techniques. This paper also
disentangles total IIT into horizontal and vertical IIT. The Vertical
IIT is further divided into high-quality and low quality IIT. This paper
finds that country-specific variables are more important in explaining
the IIT relative to industry-specific variables. The decomposition of
IIT shows that in the SAARC region Pakistan's IIT is mostly
comprised of the vertical IIT. The share of horizontal IIT is
comparatively less. The paper offers specific policy recommendations for
the promotion of IIT in the SAARC region.
JEL classification: F12, F14, F15
Keywords: IIT, Horizontal IIT, Vertical IIT
1. INTRODUCTION
The Ricardian theory of international trade envisages that the
differential in technologies across countries determines the trans
national trade pattern. On the other hand, the theory of factor
proportions of Heckscher-Ohlin predicts that trade patterns are
determined by the relative factor abundance. These theories thus
conclude that trade takes place between those countries that have either
different factor endowments or technologies. But over the past few
decades, contrary to the predictions of these theories, the world has
increasingly witnessed that countries having similar technologies and
factor endowments do trade more among themselves than those that are
dissimilar [Verdoon (1960) and Balassa (1966)].
Concomitantly, it has been noticed that when economies-of-scale are
internal to firms in an industry, both the variety of goods and the
scale of production are generally constrained by the size of the
domestic market. Trade allows countries to relax such constrictions.
With trade each country specialises in a narrower range of products than
under autarky and enables countries to produce different varieties of
goods (i.e., differentiated products). Thus, with trade a country can
buy goods (varieties) from other countries that it does not produce
itself; as a result its consumers benefit from a bigger variety and of
course lower prices as well. The production of differentiated products
and demand by domestic consumers for foreign varieties give rise to what
is known as intra-industry trade (IIT). Economies-of-scale thus becomes
an independent reason for international trade to take place even when
countries have similar production technologies and primary resources
[Krugman (1979) and Lancaster (1980)].
IIT is, thus, referred to a two-way exchange of goods within the
same industry group. Evidently, the IIT share in the total trade among
developed countries is quite significant (1) and has been secularly
rising by about 5 percent annually. There is a virtual absence of IIT in
trade relations among developed and developing countries, that rather
observe the inter-industry trade pattern. Some studies find the presence
of IIT in trade between developing countries [Willmore (1972)].
Since the 1980s, many studies examined the determinants of IIT with
industry and country characteristics. Krugman (1981) argues that
economies of scale and consumers' tastes for a diversity of
products are the main determinants of IIT. Others argue that
country-specific variables such as country size, per capita income,
distance and trade orientation are the important determinants of IIT
[Stone and Lee (1995) and Hummels and Levinsohn (1993)]. Greenaway, et
al. (1995) argue that industry-specific variables, like scale economies,
firm concentration ratio and product differentiation, are the
determinants of IIT. Clark and Stanley (1999) and Greenaway, et al.
(1999) argue for both country-specific and industry-specific variables
as the determinants of IIT.
The above eclectic approach reached its climax with the above
analysis was extended to the multi-country/multi-industry analysis using
panel estimation techniques [Menon, et al. (1999)]. The need for such
studies arose as the revolution in information, communication and
transportation technologies facilitated fragmentation of global
production that provides a sound basis for growing IIT at the regional
level.
Being fairly similar to each other, SAARC (South Asian Association
for Regional Cooperation) countries satisfy the basic requirements for
the conduct of intra-regional IIT. The share of Pakistan's exports
going to SAARC countries has been hovering around 5 percent, which is
quite low as compared to its real potential. The main reason for this
meagre performance, besides others, is lack of focus in regional
policies on IIT. The regional trade share can be enhanced manifold by
focusing more on IIT, as it prompts technological progress and takes
advantage of economies of scale.
Despite the large potential of IIT for trade expansion in the SAARC
region, only a couple of attempts have been made in Pakistan to estimate
IIT levels for Pakistan's total trade [Kemal (2004) and Shahbaz and
Leitao (2010)]. Shahbaz and Leitao (2010) also study the determinants of
IIT between Pakistan and its ten major trading partners in the world
using country-specific variables.
It is also important to disentangle total IIT into horizontal lit
and vertical IIT. (2) This is because for each type of IIT the
explanatory variables are usually different. Horizontal IIT benefits
countries more with similar factor endowments by enabling them to
utilise economies of scale in production. Specialisation in vertically
differentiated products may reflect the countries' comparative
advantage in those products, their differences in factor endowments, and
high expenditure on research and development, etc. [OECD (2002)]. None
of the available Pakistani study attempted to disentangle total IIT into
horizontal IIT and vertical IIT. Within this perspective, this paper
attempts to analyse the trends in IIT and using the panel estimation
approach works out country-specific and industry-specific effects of
IIT. Finally, the paper attempts to disentangle total IIT into
horizontal and vertical IITs.
The rest of the paper is divided into three sections. Section 2
describes the methodology used in the paper. Estimation problems and
empirical results are discussed in Section 3. Finally, Section 4
concludes and offers policy recommendations for the promotion of IIT in
the SAARC region.
2. METHODOLOGY
This paper estimates the determinants of IIT by using the gravity
model approach. The gravity model has been extensively used to analyse
the impact of regional trade agreements, currency unions, migration
flows, intra-industry trade etc. The following equation is referred as
the core gravity model. It states that bilateral trade between country i
and j is an increasing function of the size of the country h and f
measured in terms of their GDP and decreasing function of the distance
between the two countries. Thus, countries similar in their relative
economic size or population will trade more with each other. Tinbergen
(1962) proposed the following gravity model to analyse the effects of
bilateral trade:
[Y.sub.hf] =[alpha] [Y.sub.h][Y.sub.f]/[D.sub.hf]
[alpha] is a constant of proportionality, [Y.sub.hf] is total
bilateral trade between home country h and trading partner f, y is
economic size of the countries measured in terms of GDP, and [D.sub.hf]
represents trade barriers between the countries. These barriers can be
distance, common language, common currency, colonial links, etc. The
volume of trade will be lesser among countries located farther from each
other. In its logarithmic form, the gravity equation can be defined as:
[Y.sub.ij]=[alpha] + [[beta].sub.1][logy.sub.i] +
[[beta].sub.2][logy.sub.j] - [[beta].sub.3]log[D.sub.ij] (1)
Since its introduction in the international trade literature by
Tinbergen (1962) and its subsequent empirical success, at present, the
gravity model is a widely used tool to estimate bilateral trade flows
between countries. The core gravity model (Equation 1) is augmented by
the inclusion of several additional variables like cultural differences,
linguistic differences, exchange rate, border effects etc., that
possibly affect a country's bilateral trade flows. Following the
tradition of Clark and Stanley (1999), Greenaway, et al. (1999) and
Turkcan (2005), we also augment the core gravity model with two types of
variables, namely, country-specific variables and industry-specific
variables for analysing the flows of intra-industry trade of Pakistan
with SAARC countries. The augmented gravity model is expressed as:
[Y.sub.jhft] = C + log [DIST.sub.hf] + log [AGDP.sub.hft] + log
[DPCGDP.sub.hft] + log [DHCAP.sub.hft] + log [AEST.sub.jhft] + log
[DVAEST.sub.jhft] + log [DPCAP.sub.jhft] (2)
[Y.sub.jhft]: Intra-industry trade flow between home country
(Pakistan) h and trading partner fin industry j in year t.
A brief account of the variables described above and their economic
relevance in the analysis are discussed below:
[DIST.sub.hf] (distance between Pakistan and its trading
partner's port of entry in nautical miles): on a priori basis, it
can be argued that trade is negatively correlated with the distance.
That is, the farther the trading partners from each other, the higher
the transportation cost.
[AGDP.sub.hft], (average GDP of Pakistan and its trading partner to
represent market size): the gravity model measures the market size both
in terms of GDP and population. In this paper we use real GDP in 2000 US
dollar prices. Small economies without trade have limited ability to
avail themselves of the economies of scale. Trade increases the size of
the market for domestic firms and thus allows them to reap the benefits
of economies of scale due to increased productivity and reduced average
costs; while consumers enjoy increased variety of available goods at
lower prices. With free trade, firms producing intermediate goods also
make use of increasing returns to scale and thereby increase the scale
of production and varieties of intermediate goods [Ethier (1982)]. Thus,
a positive sign is expected on the share of IIT and the average market
size.
[DPCGDP.sub.hft] (absolute difference in GDP per capita between
Pakistan and its trading partner): it is used as a proxy for taste and
preferences. Linder (1961) argues that per capita GDP is a measure of
people's taste and preferences and countries with similar levels of
per capita GDP have similar tastes and preferences, thus they will
engage in more bilateral trade. Countries will trade less as bilateral
differences of per capita GDP escalate. Helpman and Krugman (1985)
consider differences in per capita GDP as differences in
capital-to-labour ratio (that means countries have dissimilar factor
endowments). If there are bilateral differences in factor endowments,
then there will be lesser IIT. Thus, a negative sign is expected between
the share of IIT in total international trade and differences in per
capita income.
[DHCAP.sub.hft] (absolute difference of the percentage of
population with higher education between Pakistan and its trading
partner): we use the ratio of skilled labour to unskilled labour as a
proxy for human capital endowment. Krugman and Helpman (1985)
demonstrate that differences in factor endowments between any two
countries lead to a decrease in the level of bilateral IIT. Ethier
(1982) argues that skilled labour, mainly R&D personnel, is the
essential ingredient for the production of intermediate goods variety.
Therefore, if countries differ in their factor endowments, then the
scope of IIT reduces. Contrary to this, Feenstra and Hanson (1997) show
that a relative increase in the supply of skilled labour in the home
country as compared with the foreign country will increase the supply of
vertically differentiated goods from home to foreign country, which
leads to an increase in IIT of intermediate goods, Thus, the expected
sign of bilateral inequality in factor endowments on IIT will be
ambiguous.
The industry specific variables are defined as follows:
[AEST.sub.jhft] (Average number of establishments at industry level
between Pakistan and its trading partner): it is used as a proxy for
product differentiation. The larger the number of establishments, the
greater will be the variety of goods produced, since every firm produces
only one differentiated product in equilibrium [Krugman (1981)].
[DVAEST.jhft] (Absolute differences of value added per
establishment at industry level between Pakistan and its trading
partner): it is used as a proxy for economies of scale. Economies of
scale internal to a firm are considered as negatively related to product
differentiation. Ethier (1982) argues that the economies of scale are a
result of greater division of labour rather than due to large plant
size. And IIT in manufactured goods arises because firms find it
profitable to split the production process at different plants due to
the economies of scale achieved through division of labour. So, small
plant size is positively related to IIT. He expects a negative sign
between economies of scale accrued to a firm due to its large plant size
and IIT. On the other hand, Feenstra and Hanson (1997) argue that
vertical specialisation allows firms to produce goods at different
plants, so the plant size should be small because the different stages
of manufacturing are conducted differently at different plants. It means
that vertical specialisation leads to increase in IIT.
[DPCAP.sub.jhft] (Absolute difference of physical capital endowment
per worker at industry level): this variable is included to take into
account the effect of the differences in factor endowments. Ethier
(1982) argues that IIT is expected to be negatively correlated with the
differences in the capital to labour ratio. He assumes the
differentiated intermediate good to be capital intensive, when the
supply of capital in the home country rises relative to labour, the
number of intermediate goods produced in the home country will rise and
the producers of final goods in the home country will begin to rely on
locally manufactured intermediate goods. Thus, the share of IIT in
intermediate goods will eventually decline. Feenstra and Hanson (1997)
show that for vertical specialisation, dissimilarities in the capital to
labour ratio between the trading partners is a necessary condition.
Therefore, there is no consensus over the expected sign of bilateral
inequality in the capital to labour ratio on the share of IIT.
2.1. Empirical Model
In the preceding subsections variables were defined and their
relationships with IIT were discussed, on a priori basis. This
subsection defines the methodology to find the empirical evidence on the
relationship between lit and the included variables. For this purpose we
investigate the following model:
[IIT.sub.jhft] = C + log [DIST.sub.hf] + log [AGDP.sub.hft] + log
[DPCGDP.sub.hft] + log [DHCAP.sub.hft] + log [AEST.sub.jhft] + log
[DVAEST.sub.jhft] + log [DPCAP.sub.jhft] (3)
Equation (3) is similar to Equation (2) except that [Y.sub.jhft] is
now replaced with [IIT.sub.jhft] in Equation (3). For this we utilise
the measure developed by Grubel and Lloyd (1975):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
Where, j = 1 ... J represents industry groups, i = 1 ... I are
products in an industry j, f =1 ... F are trading partners of Pakistan
and h is home country (Pakistan). [IIT.sub.jhft] means intra-industry
trade in the ith good of the jth industry between Pakistan and its
trading partner fin year t. Equation (4) takes its values between 0 and
1. A value of 0 indicates pure inter-industry trade (no intra-industry
trade) and 1 represents pure intra-industry trade.
2.2. Data and Data Limitations
The data on the number of establishments, value added at
establishment level, gross fixed capital formation for Bangladesh, India
and Sri Lanka are taken from United Nations Industrial Statistics
published by United Nations Statistics Division. For Pakistan, the data
on these variables are taken from the Census of Manufacturing
Industries. The data on GDP, per capita GDF and education are taken from
World Development Indicators (WDI) published by the World Bank. The data
on distance between ports of the home country and the trading partner
are taken from the web. (3) Data on exports and imports of Pakistan are
taken from Foreign Trade Statistics of Pakistan, and State Bank of
Pakistan External Trade Statistics.
The available data on industry-specific variables are in Local
Currency Unit of the respective countries. To make them comparable,
values of all the variables are converted into the US dollar. All
variables are nominal; this study makes them real by using the GDP
deflator.
The latest data on the number of establishments, value added at
establishment level and gross fixed capital formation are available only
for the period up to 2000 for Bangladesh, India, and Sri Lanka. This
study uses the data for the years: 1990-91, 199596 and 2000-01. The data
on most of the variables used here are not available for other SAARC
countries: Afghanistan, Bhutan, Maldives and Nepal, that is why these
countries are not included in the analysis. Based on the trade data
obtained from the foreign trade statistics of Pakistan, we compute
values of IIT index at the three-digit level of ISIC (International
Standard Industrial Classification) Revision 3.
2.3. Decomposition of Intra-industry Trade
To disentangle the total IIT into horizontal and vertical IIT, we
apply the method proposed by Greenaway, et al. (1995). This method is
based on the ratio of the unit value of exports to the unit value of
imports. This method can be described by the following formula:
1 - [alpha] [less than or equal to] [UV.sup.hf,x.sub.i] /
[UV.sup.hf,m.sub.i] [less than or equal to] 1 + [alpha] or (5)
[UV.sup.hf,x.sub.i]/[UV.sup.hf,m.sub.i] [less than or equal to] 1 +
[alpha] or [UV.sup.hf,x.sub.i]/ [UV.sup.hf,m.sub.i] [greater than or
equal to] 1 + [alpha] (6)
[UV.sup.hf,m.sub.i] is unit value of export in the ith industry
between home country h, and foreign country f, [alpha] is unit value of
imports in the same ith industry between home h, and foreign country f
[alpha] is the arbitrarily fixed dispersion factor; it normally takes a
fixed value of 0.15. This is because the transportation and freight
costs are normally taken as 15 percent of the value of the product.
If the ratio of the unit value of exports to imports lies within
the range defined by Equation (5), then the good is classified under the
horizontal IIT and if this ratio lies within the range defined by
Equation (6) then the good is facing vertical IIT. The above formula is
based on the assumption that prices of the goods reflect their quality.
High priced goods have high quality whereas low price goods have low
quality.
3. ESTIMATION AND RESULTS (4)
The data set used in the estimation is a panel data set having two
dimensions: country and time, three country pairs and three years:
1990-91, 1990-95, and 2000-01. The number of industries differs over the
years and across countries. The data for the number of establishments,
gross fixed capital formation, and value added are reported in SITC-3
for 1990-91 and 1995-96, while data for 2000-01 of the same set of
variables are in ISIC Revision-3 (International Standard Industrial
Classification). To make the data comparable we convert SITC-3 codes
into ISIC-3 codes using the conversion method obtained from the United
Nations Industrial Classification Registry (2012). Before going for
estimation, different diagnostic tests are performed on the data to
check for any econometric problem present in the data. The four series
exhibit the presence of the unit root that is discussed in the following
sub-section. The fixed effects and random effects estimators are based
on the assumption that the error term is idiosyncratic (i.e., it is
distributed with zero mean and constant variance). Since in the panel
data we have both time-varying and time-invariant regressors, there
always exists a possibility of the correlation between the error terms
and the presence of the heteroscedasticity. This leads to
underestimation of the error term and over prediction of the regressors
of the model. For short panels, it is possible to get error-corrected
estimates of the model by using the robust command. Therefore, the
robust command is used to adjust for the correlation and
heteroscedasticity in the STATA programme.
3.1. Evidence of IIT
The results of Grubel-Lloyd (GL) indices for total manufactured
goods trade are presented in Table 1. Estimates indicate that the share
of IIT in Pakistan's total trade with Bangladesh, India and Sri
Lanka is low by international standards. These estimates are consistent
with the findings of Kemal (2004).
The trend in IIT of Pakistan is quite the same with each SAARC
country. These shares of IIT in total trade, albeit low, show a rising
trend over time. Pakistan's IIT with Bangladesh was 3.1 percent in
1990 but it increased to 19 percent in 2000. With Sri Lanka, the IIT was
4.8 percent in 1990 that rose to 8.4 percent in 2000. This shows a
significant change in the pattern of Pakistan's trade with these
countries. With India the share of IIT was 13 percent in 1990 but
declined to 7.4 percent in 1995. However, it rose to 8.3 percent in
2000. Pakistan's share of IIT with India is expected to rise
further after the granting of MFN status to India. In sum, despite some
ups and downs at country levels, the volume of Pakistan-SAARC IIT has
increased from 6.9 percent in 1990 to 11.9 percent in 2000.
3.2. Determinants of IIT
Industry-specific and country-specific determinants of IIT levels
are tested here using the fixed effects (FE) model. Table 2 reports that
country-specific variables are statistically significant at 1 percent
significance level, whereas, industry-specific variables are not very
significant in explaining the determinants of IIT.
The results reveal that the market size (measured by AGDP) exerts a
positive and significant impact on IIT. Increase in the market size due
to trade makes it feasible for firms to increase their production and
benefit from the economies of scale. The presence of economies of scale
in the production process reduces the average cost of production, thus
making firms competitive in the international market. Consequently, with
trade-led increase in profit making opportunities for firms the IIT
increases.
As expected, the distance with trading partners is found to be
negatively affecting IIT of Pakistan with selected SAARC countries. It
means that with a fall in distance, the cost of transportation and
communication decreases that causes an increase in IIT.
Differences in per capita GDP (a proxy for consumer's tastes
and preferences) have negative and statistically significant effect on
the level of IIT. This result suggests that consumers' tastes and
preferences become dissimilar (in trading partner countries) with
increase in the differences in per capita income; they start demanding
different goods. If products demanded by consumers are not available in
the region, it leads to a fall in IIT.
The bilateral inequality in human capital endowment (DHCAP) has
statistically significant and positive effect on the IIT. This result
shows that a relative increase in the supply of skilled labour at home
relative to a foreign country will increase the vertically
differentiated goods from home to foreign country. This finding is in
line with the findings of Turkcan (2005), Flam and Helpman (1987).
Regarding the industry-specific variables, the average number of
establishments does not turn out to be statistically significant in
explaining the IIT. The sign of the coefficient is opposite to the
predictions of the theory. Turkcan (2005) finds similar results for
Turkey.
The variable differences in value added at the industry level, a
proxy for economies of scale is negative but is statistically
insignificant. This implies that plant size should be reduced to
increase the level of IIT. This finding is against the theoretical
prediction of Krugman (1979) but in line with the empirical finding of
Greenaway, et al. (1995), that favours production fragmentation to
increase the number of differentiated variety, thereby leading to an
increase in the level of IIT.
The bilateral differences in the capital-labour ratio between
trading partners measure the differences in factor endowments. This
variable has a positive correlation with IIT, but turns out to be
insignificant. The positive association between DPCC and the IIT is
consistent with Feenstra and Hanson (1997), who argue that bilateral
inequality in capital-labour ratio is a necessary condition for vertical
specialisation.
So far we have discussed the estimates obtained through the FE
model. We shall now examine the RE estimates (Table 3). The RE technique
does improve the significant level and magnitude of the coefficients of
all variables relative to the FE model. But it does not make any of the
variables significant that was found to be insignificant under the FE
model. The RE model also explains more variation in the model relative
to the FE model as indicated by the value of R-square.
While choosing between the Fixed Effects (FE) and the Random
Effects (RE) models, the Hausman test is performed. Hausman rejects the
FE model in favour of the RE model. It is, therefore, concluded that the
RE estimates are efficient and consistent relative to those of the FE
estimates. This leads us to conclude that the level of IIT between
Pakistan and its trading partners in the SAARC region is affected by
random events.
3.3. Horizontal and Vertical Intra-industry Trade
The pattern of horizontal IIT and vertical IIT for Pakistan and her
selected trading partners in the SAARC region is reported in Table 4.
The table reveals that in the SAARC region Pakistan's IIT is mostly
comprised of the vertical IIT (i.e., 82.50 percent) and to a lesser
extent the horizontal IIT (17.50 percent). The vertical IIT is high
among the countries with greater differences in the level of technology
and factor endowments.
The vertical IIT is further decomposed into low vertical IIT
(LVIIT) and high quality vertical IIT (HVIIT). The share of low quality
vertical IIT in total IIT is 69.95 percent and that of high quality
vertical intra-industry trade is 12.55 percent (Table 4).
The cross-country analysis of the IIT indicates that
Pakistan's share of low quality vertical IIT (LVIIT) in total IIT
is much higher with Bangladesh (93.20 percent) and India (85.96 percent)
and is low with Sri Lanka (30.68 percent). This implies that
Pakistan's IIT with Bangladesh and India is largely composed of low
quality, low priced products.
The share of high quality vertical IIT (HVIIT) is higher with Sri
Lanka (29.38 percent) as compared to Bangladesh (3.9 percent) and India
(4.38 percent). This trade is taking place mostly in textile products
(HS 61034200, HS 61169300, and HS 61091000). The reason for the higher
share with Sri Lanka is that Pakistan is specialised in the production
of textile products while Sri Lanka is not. Pakistan exports high
quality textile products to Sri Lanka. The same is not true for
Pakistan's IIT with Bangladesh and India. The reason for the low
share of HVIIT with Bangladesh and India is that Pakistan, Bangladesh
and India specialise in the production of textile products. Besides, all
three of these countries have very restricted trade policies in
textiles.
The share of horizontal IIT in total IIT of Pakistan is low as
compared with the vertical IIT. It comes to 17.5 percent of the total
IIT. The cross-country shares reveal that in the category of horizontal
IIT, Sri Lanka is leading with 39.94 percent followed by India with 9.66
percent and Bangladesh with 2.9 percent. The relatively lower share of
the horizontal IIT in total IIT indicates that the region is trading
very little in products that are similar in quality and price. In sum,
the SAARC region's most potential lies in HVIIT, that of course is
small right now. The regional countries therefore need to implement such
policies that should enhance the share of HVIIT in the total IIT.
4. CONCLUSION AND POLICY RECOMMENDATIONS
The focus of this paper has been on analysing the trends and
determinants of the intra-industry trade of Pakistan with her major
SAARC trading partners. Specifically, the paper examines
country-specific and industry-specific determinants of intra-industry
trade. The data set used has two dimensions: country and time, which
allowed us to use the panel data techniques. Panel data techniques can
be performed on using both the fixed-effects (FE) and random-effects
(RE) models. The result of the Hausman test supported the RE model; that
is, the RE estimates are more efficient than those of the FE model.
Based on the results of the RE model, this paper concludes that
country-specific variables are more relevant in explaining IIT than
industry-specific variables. In particular, market size is found to be
positively correlated with IIT. The differences in per capita GDP
between trading partners (i.e., tastes and preferences) are negatively
correlated with IIT. The sign of the variable distance is also as
expected, that is large distance between trading partners reduces
bilateral trade. Intra-industry trade is found to be positively related
with bilateral differences in human capital confirming the Feenstra and
Hanson (1997) hypothesis that a relative increase in the supply of
skilled labour in the home country relative to foreign country will
increase the supply of vertically differentiated goods from home to
foreign country, which leads to an increase in IIT of intermediate
goods. The paper also finds an increasing share, albeit low, of IIT in
the total trade of Pakistan with the SAARC countries. The paper thus
suggests that Pakistan and its trading partners in the region should
make concerted efforts to increase the level of lit to enhance and
sustain the overall volume of the regional trade and strengthen regional
economic interests. The SAARC countries have vast potential to expand
their economic relations within the region. The competitive nature of
the SAARC countries is considered as the major impediment in the way of
regional trade expansion. This obstacle can be overcome by engaging
extensively in the IIT at the regional level. (5)
To increase the level of IIT in the SAARC region; we put forward
the following recommendations:
* Since the distance appears to be a major constraint in the way of
increasing regional trade, therefore regional governments should pay
special attention to improve not only the conditions of their transport
and communication infrastructures but also strive to reduce the cost of
shipping goods across borders.
* Manufacturing firms need to allocate more funds for research and
development to develop new and better varieties in the existing lines of
production. This should help in expanding IIT in the SAARC region.
* Textiles and clothing have a large potential to increase the
level of IIT in the region. Regional countries are currently restricting
trade in textiles and clothing by using a negative import list and other
tariff and non-tariff measures. It is, therefore, recommended that in
the future trade negotiations at bilateral or regional levels, the
governments should make efforts to remove textile products and clothing
from the negative lists and reduce other trade barriers affecting their
textiles and clothing trade.
* Vertical IIT has turned out as the major component of the (total)
IIT in the region. Therefore, in the future the regional governments
should focus on expanding and promoting the production of high-end
products for which the demand exists in the region. This would require
special incentives to develop and design high-end products.
Finally, since the size and the share of IIT in the SAARC region is
growing sharply, therefore, it is advisable for the regional governments
to encourage economies-of-scale in production, which is the basis of
this kind of international trade. For this to happen, initially some
incentives may be offered to selected firms until they attain
sufficiently large production scale that makes them competitive
regionally as well as internationally.
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(1) For instance, in 2000, IIT was comprised of 86.20 percent,
85.01 percent and 80.42 percent of total manufacturing trade of
Germany-France, Netherlands-Belgium and Luxemburg, France-Belgium and
Luxemburg [Fontagne, et al. (2006)].
(2) Horizontal IIT is defined as IIT of goods having same qualities
(e.g., automobiles of similar class and price range), whereas vertical
IIT is defined as IIT of goods having different qualities (e.g.,
automobiles of different brands).
(3) www.e-ships.net/dist.htm
(4) STATA software programme is used for estimation.
(5) Similar proposal was also made in Kemal (2004) and Mahmood
(2012).
Adnan Akram <adnan@pide.org.pk> is Staff Economist, Pakistan
Institute of Development Economics, Islamabad. Zafar Mahmood
<zafarmah@gmail.com> is Foreign Professor, Higher Education
Commission (HEC), Pakistan Institute of Development Economics,
Islamabad.
Table 1
Grubel-Lloyd Indices
(Percent)
Country 1990 1995 2000
Bangladesh 3.1 7.7 19.0
India 13.0 7.4 8.3
Sri Lanka 4.8 5.4 8.4
SAARC 6.9 6.8 11.9
Table 2
Fixed Effects (FE) Model Results for Intra-industry Trade
Variable Coefficient t-stat
DIST -0.67 -4.83
AGDP 2.39 5.05
DPCGDP -4.38 -5.19
DHCAP 1.88 3.86
AVGE -0.09 0.91
DPCC 0.13 1.06
DYAD -0.015 -0.97
R-Square 12.38
Table 3
Random Effects Model (REM) Results for Intra-industry Trade
Variable Coefficient z-stat
DIST -0.58 -6.15
AGDP 1.94 5.08
DPCGDP -3.52 -4.93
DHCAP 1.56 3.84
AVGE -0.12 -1.38
DPCC 0.14 1.47
DYAD -0.15 -1.06
R-Square 12.59
Table 4
Percentage Shares of MIT, LVIIT, and HVIIT in Total IIT: 2005-06
Intra-industry trade Bangladesh India Sri Lanka SAARC
HIIT 2.90 9.66 39.94 17.5
LVIIT 93.20 85.96 30.68 69.95
HVIIT 3.90 4.38 29.38 12.55
