Research

An Analysis of Determinants
of India’s Import: Panel
Regression Approach
by Manoj Kumar Sinha

Abstract
After the crisis in 1991, the Indian government introduced some changes in its policies on trade, foreign Investment, Tariffs and
Taxes under the aegis of "New Economic Reforms". The main focus of export-import policy has shifted from “import-substitution”
to “export-promotion”. The paper is focused on the impact of development variables on India’s import. Developmental
variables include infrastructure, human resource, openness, resource, production and market. Each development variable
consists of a set of related variables. This paper has used Principal Component Analysis (PCA), Composite Index and Panel
Regression Model. These help to know impact of individual developmental variable on India’s import. The period of study is
1990 – 2013. The value of KMO is over 0.6 indicating the samples are adequate and the value of Bartlett’s test is less than 0.05
ensure suitability of PCA. The overall growth rate of Indian import is 3.6 percent during more than last two decades. Resource
is most important development variable. The elasticity of resource is almost equal to one and statistically significant. Main
determinants of Indian import are resource and openness. The government should incorporate these developmental variables
while making Indian import policy under the umbrella of WTO.
Keywords
Import, Economic Development, Trade, International Trade, India

Introduction
The Hechsher-Ohlin theory postulates that the immediate cause of international trade is the difference in relative
prices, caused by the difference in relative demand and supply of factors (factor prices) as a result of differences in factor
endowment between countries. Therefore, commodities that use large quantities of scarce factors should be imported
because their prices are high while those using abundant factors should be exported because their prices are low (Jinghan,
2002). Within the framework of present scenario of globalisation, the Indian economy has been undergoing substantial
changes since 1991. Reform efforts have been continual and strong since 1991, with significant changes occurring in 1993
(Dean, Desai & Riedel, 1994). Almost all areas of the economy have been opened to both domestic and foreign private
investment, import licensing restrictions on intermediates and capital goods have been mostly eliminated, tariffs have
been significantly reduced, and full convertibility of foreign exchange earnings has been established for current account
transactions (Dutta, 1998). In post- reform period, the major contributor to import growth has been due to change in foreign
trade policy from “import-substitution” to “export-promotion” and removal of trade barriers such as reduction of tariff and
non-tariff barriers and removal of quantitative restriction with respect to import volume. However, the import-demand
function for India suggest that import-demand is largely explained by real GDP, and is generally less sensitive to import
price changes. Import liberalisation is found to have had little impact on import demand (Dutta & Ahmed, 2006). The paper
has focussed on the impact of development variables such as infrastructure, human resource, openness, resource, market
and production on India’s import.

EXIM Policy
The Export-Import Policy (EXIM Policy), announced under the Foreign Trade (Development and Regulation
Act), 1992, would reflect the extent of regulations or liberalization of foreign trade and indicate the measures for export

FIIB Business Review, Volume 5, Issue 3, July - September 2016 51

 Research
promotion. A very important feature of the EXIM policy 2013 than during 1990-2001.
since 1992 is freedom. Licensing, quantitative restrictions
Data Analysis
and other regulatory and discretionary controls have
been substantially eliminated. The Union Commerce Many researchers have carried out studies on
Ministry, Government of India announces the integrated behavior of import demand of the developing countries
Foreign Trade Policy FTP in every five year. This policy and related import to relative price of imports and
is updated every year with some modifications and new income of the country (Dutta & Ahmad, 1997; Sinha,
schemes. New schemes come into effect on the first day 1997; Cheong, 2003; Chang & Juang, 2005; Kalyoncu,
of financial year, i.e., April 1, every year. The Foreign 2006). Most of these studies found a negative relation of
Trade Policy which was announced on August 28, 2009 import with its price and negative relation with income
is an integrated policy for the period 2009-14. Export- of the country. Dutta and Ahmad (2006) concluded that
Import (EXIM) Policy provides policy and strategy of the the income and price has positive and negative influence
government to be followed for promoting exports and respectively on import demand while liberalization has
regulating imports. This policy is periodically reviewed to not significantly affected India’s import. Nasser (2013)
incorporate necessary changes as per changing domestic concludes that there is a positive relationship between the
and international environment. In this policy, approach of demand for imports and GDP and negative relationship
government towards various types of exports and imports with the index of consumer prices. Arize et al. (2004)
is conveyed to different exporters and importers. Now found cointegration relationship between real import,
in the era of globalization, no economy in the world can relative price of imports, income and foreign exchange
remain cut-off from rest of the world. Export and import reserves. Dash (2005) concluded that import has long
play a significant role in the economic development of run relation with GDP, import price, price of domestically
all the developed and developing economies. With the produced goods and foreign exchange reserves. However,
growth of international organizations like WTO, UNCTAD, import is found to be more sensitive to the price of
ASEAN, etc., world trade is growing at a very fast rate. domestically produced goods than the other factors.
Arize and Osang (2007) found similar result in the case
Import of Goods & Services of seven Latin American Countries. Further, they found
600 that the elasticity of import is greater than one with
500
400
respect to income, close to one with respect to import
$ Billion

300 price, and small with respect to foreign reserves.Sultan

200
Import
(2011) concluded that there is a long run equilibrium
100
relationship between real imports, real income, relative
0
price of imports and real foreign exchange reserves. In
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

the long run, import is found to be elastic with respect

Year
to income, and inelastic with respect to relative price
Source: Authors’ research and foreign reserves. In the short run also, there is a
Figure 1. Trends of India’s Import of Goods significant relationship between import, income, relative
and Services price and foreign exchange reserves. However in the short
run, import is found to be inelastic with respect to all of
India has been following a consistent policy these variables. The evidence suggests that depreciation
for gradual removal of import restrictions since 1991, may not give desirable results for the economy as far as
when the economic reforms were initiated. India began containing the import bill is concerned. The promotion
removing BoP related Quantitative Restrictions (QRs) of export would be a better option to take care of the
unilaterally since 1996. With this progressive removal of problem of trade deficits. Fatukasi and Awomuse (2012)
QRs maintained on BoP considerations, restrictions, still suggest that the aggregate import demand adjust to
in force only relate to those items as permissible under correct long run disequilibrium between itself and
Articles XX and XXI of the GATT on grounds such as its external reserve, real gross domestic product, real
security, health, safety, or moral conduct. While removing exchange rate and index of openness. In the short run,
QRs, the Government has taken several safeguard real gross domestic product is the major determinant of
measuresin order to guard against any surge in imports import demand in Nigeria. Ziramba and Bbuku (2013)
on account of dumping. The removals of quantitative point out that aggregate import demand is co-integrated
restrictions lead to manifolds increase in India’s import. with its determinants. Different expenditure components
This is evident from Figure 1 showing growth rate of have different import contents both in the long and
increase in import has been much higher during 2002-

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 Research
short run periods. In long run, investment expenditure, of the procedure.
final consumption expenditure and relative prices are yy There were various methods of rotation but the
major determinants of aggregate import demand in most popular method is the Varimax with the Kaiser
Namibia. Arawomo (2014) indicated that capital import normalization. The purpose of the rotation is to
has positive significant impact on economic growth in make the interpretation of the PCA more meaningful.
both short-run and long-run, although the magnitude of Method of rotation however retains the same
coefficient is higher in the long-run. information and explanatory power.
The existing literature considered following variables The method for construction of a composite
such as exchange rate, openness, output growth (GDP), index is given by Jha and Murthy (2006). Once the
income, relative price of import etc. as the determinants of number of retained principal components is determined
import. No single study has focused on a comprehensive and the rotated component scores obtained, then there
set of variables affecting a country’s import. This paper is the choice of using the principal components as such
has taken a comprehensive set of variables as the or selecting certain sub-set of variables from the larger
determinants of India’s import. set of variables. Jolliffe proposes selecting one variable
Research Methodology to represent each of the retained principal components.
The variable that has the highest loading on a component
Data Sources
is chosen to represent that component, provided that it
The required quantitative data is sourced has not already been chosen. If it has been chosen, the
online mainly from UNCTAD and World Bank website. variable with the next largest loading is selected. The
The period of study is 1990-2013. Export data of India procedure starts with the largest principal component
is taken from United Nations Conference on Trade and and proceeds to the smallest retained component.
Development (UNCTAD). Developmental variables (i.e. 3

socio-economic variables) have been classified into Index = ∑ wjxj

j
infrastructure, human resource, resources, openness,
market and production. Each variable consists of a set Xj = retained variables
of related variables. Developmental variables data are Wj = component scores (weights).
collected online from World Bank website. The paper has Panel Regression Model
used Principal Component Analysis (PCA), Composite A common panel data regression model looks like
Index and Panel Regression Model (fixed effect).
ࣳ௜௧ ൌ ߙ ൅ ܾ‫ݔ‬௜௧ ൅ ߳௜௧
Principal Component Analysis
߳௜௧ Where y is the dependent variable, x is the
The paper considers developmental variables like
independent variable, aandb are coefficients, i and t
population, GDP, and so on, which is bound to be a high
are indices for individuals and time. The error is very
degree of correlation amongst independent variables.
important in this analysis. Assumptions aboutࣳ௜௧ ൌ the
ߙ ൅error
ܾ‫ݔ‬௜௧ ൅ ߳௜௧
There could be three strategies that can be used for
term determine whether we speak of fixed effects or
dealing with such an econometric problem:
random effects. In a fixed effects model, ߳௜௧ is assumed
yy Drop all correlated variables, there is a great loss of to vary non-stochastically over i or t making the fixed
information. effects model analogous to a dummy variable model in
yy Use Principal Component Analysis (PCA) to one dimension. The panel model has designed in such a
determine the “principal variables.” way to capture two effects. The first effect is due to scalar
effect. The second effect is due the dynamic changes in
yy Use PCA for formation of a composite index.
the determinants (developmental variables).
The following consideration should be kept in mind while
Import
applying PCA:
The general form of the fixed effects model is:
yy For determining the retained component we need a
criterion. (IMPORT) it =e{[α 0 ]+[β 1 ]*(t)}.(IHR) it β2 .(IINFRA) it β3 .
(IPROD)itβ4.(IMKT)itβ5.(IOPN)itβ6.(IRES)itβ7….................. (1)
yy The PCA methodology tells us the total variance
explained by each retained principal component as Taking log on both the side and add error term
well as the cumulative percentage of the explained L(IMPORT)it= α0 + β1*(t) + β2L(IHR)it+ β3L(IINFRA)it+
variation. This is a measure of the explanatory β4L(IPROD)it+ β5L(IMKT)it+ β6L(ITOPN)it+ β7L(IRES)it +
power of the component for the information content

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 Research
Uit ................................................................................................... (1a) correlated. Irrespective of the rotation method used, the
Where, IMPORT= Import in India primary objectives are to provide easier interpretation of
results, and produce a solution that is more parsimonious.
α0 = Intercept
Infrastructure
t = 1990, 1991, .......................................................................... 2013
Infrastructure refers to the facilities through
β1 = Growth Rate of World FDI which others resources can be efficiently and optimally
β2, β3, β4, β5, β6, and β7= Elasticities of Developmental used.
Variables
Table 1. Results of Principal Component
IINFRA = Composite Index of Infrastructure
Analysis of Infrastructure
IHR = Composite Index of Human Resource
KMO and Bartlett’s Test
IOPN = Composite Index of Openness Kaiser-Meyer-Olkin Measure of Sampling
.870
IRES = Composite Index of Resources Adequacy.
Approx.
IMKT = Composite Index of Market 527.877
Bartlett’s Test of Sphericity Chi-Square
IPROD = Composite Index of Production Df 28
Sig. .000
Objective of the study Total Variance Explained
The said methodology can be used to study following Com- Extraction Sums of Squared
Initial Eigenvalues
ponent Loadings
objectives:
% of Cumula- % of Cumulative
To study trends and patterns of import of goods and Total Total
Variance tive % Variance %
services in India
1 7.648 95.603 95.603 7.648 95.603 95.603
To study the impact of economic development on the
2 .167 2.091 97.694 .167 2.091 97.694
import to India.
3 .133 1.659 99.353 .133 1.659 99.353
Hypothesis
4 .030 .372 99.725
Economic development does not affect India’s import.
5 .012 .146 99.871
Data Analysis 6 .006 .069 99.940
Principal Component Analysis 7 .003 .037 99.977
KMO and Bartlett Test of Sphericity is a measure 8 .002 .023 100.000
of sampling adequacy that is recommended to check the
Rotated Component Matrix
case to variable ratio for the analysis being conducted.
These tests play an important role for accepting the sample Component
adequacy. While the KMO ranges from 0 to 1, the accepted 1 2 3
index is over 0.6. Also, the Bartlett’s Test of Sphericity
AT_CAR .725 .557 .388
relates to the significance of the study thereby shows
the validity and suitability of the responses collected ELC_P .704 .451 .544
to the problem being addressed through the study. EN_P .703 .497 .501
For principal component analysis to be recommended EPC .682 .528 .502
suitable, the Bartlett’s Test of Sphericity must be less
RLY_GT .682 .575 .450
than 0.05. Another important aspect is the rotated
component matrix. While deciding how many factors AT_FT .513 .757 .396
one would analyze, depend on a variable which might TEL .462 .746 .469
relate to more than one factor. Rotation maximizes high RL .455 .442 .771
item loadings and minimizes low item loadings, thereby
Source: Author’s Estimation
producing a more interpretable and simplified solution.
There are two common rotation techniques- orthogonal Index of Infrastructure
IInfra = .725*AT_CAR + .746*TEL + .771*RL
varimax rotation and oblique rotation. While orthogonal
varimax rotation that produces factor structures that are
uncorrelated, oblique rotation produces factors that are

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 Research
For measuring this variable principal component analysis Table 2. Results of Principal Component
(PCA) has selected following variables as principal Analysis of Human Resources
variables-Energy production (kt of oil equivalent) (EN_P), KMO and Bartlett’s Test
Electricity production (kWh) (ELC_P), Electric power
Kaiser-Meyer-Olkin Measure of Sampling
consumption (kWh per capita) (EPC), Air transport, .781
Adequacy.
registered carrier departures worldwide (AT_CAR), Air
Bartlett’s Test of Sphe-
transport, freight (million ton-km) (AT_FT), Railways, Approx. Chi-Square 833.589
ricity
goods transported (million ton-km) (RLY_GT), Rail
Df 55
lines (total route-km) (RL) and Telcom (TEL). These
Sig. .000
variables have been used for construction of composite
index, which summaries the information contained in Total Variance Explained
these variables. The value of KMO is 0.87, which is high. Com- Extraction Sums of
po- Initial Eigenvalues
Bartlett test is highly statistically significant. Next step is nent Squared Loadings
to select principal components which are being retained. % of Cumu-
% of Cumula-
Out of eight variables, AT_CAR, TEL and RL are retained Total Variance Total Vari- lative
tive % ance %
variables. Total variance explained by principal variable
1 9.300 84.549 84.549 9.300 84.549 84.549
is 99.35 percent. Rotated component matrix helps us in
2 1.101 10.007 94.556 1.101 10.007 94.556
generating the value weights obtained from the factor
3 .504 4.582 99.138 .504 4.582 99.138
loading for constructing the composite index (Table 1).
4 .046 .422 99.560
The score used for construction of composite index of
5 .025 .227 99.787
infrastructure.
6 .014 .126 99.912
Human Resource 7 .005 .047 99.959
Human resource has considered following 8 .003 .023 99.982
variables- Primary education-pupils (P_EDU), Secondary 9 .001 .013 99.995
education-pupils (S_EDU), Secondary education, 10 .001 .005 100.000
vocational pupils (SEDU_VOC), School enrollment, 11 .000 .000 100.000
tertiary (% gross) (SCH_EN), Compensation of employees Rotated Component Matrix
(current LCU) (COM_E), Employment to population ratio,
Component
15+ (EM_POP), Labor force participation rate (LFPR), 1 2 3
Labor force (LF), Population ages 15-64 (WPOP), GDP LF .941 .333 -.038
per person employed (GDPPPE) and Population in P_EDU .922 .341 .158
largest city (POPLC). The value of KMO is 0.781, which WPOP .852 .519 .053
is high. Bartlett test is highly statistically significant. S_EDU .846 .494 .142
Next step is to select principal components which are POPLC .800 .587 .114
GDPPPE .761 .624 .168
being retained.The retained variables are LT, COM_E and
LFPR -.400 -.888 -.217
SEDU_VOC. Total variance explained by principal variable
EM_POP -.453 -.872 -.176
is 99.13 percent. Rotated component matrix helps us in
COM_E .607 .746 .246
generating the value weights obtained from the factor SCH_EN .633 .715 .237
loading for constructing the composite index (Table 2). SEDU_VOC .062 .217 .974
The loading value used for construction of composite Source: Author’s Estimation
Composite Index of Human Resource
index of human resource.
IHR = .941*LF + .746*COM_E + .974*SEDU_VOC

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Openness 7 .013 .143 99.832
Openness is one dimension of globalization 8 .012 .129 99.961
which permits movement of factor of production in the 9 .004 .039 100.000
form of raw materials, goods, capital, labour etc. with less
Rotated Component Matrix
restrictions and tariff duty. Openness can be measured
in terms of degree. India has been opened her economy Component
by reducing import tariff and other quantitative and 1 2 3
qualitative restrictions and also by promotion of export PORT .882 .230 .343
and investment for other countries since 1991. Openness FDI .857 .270 .348
has considered following variables under the study for RESV .837 .432 .315
measuring openness variable- Customs and other import
TOPEN .786 .546 .247
duties (IMD), Official exchange rate (ER), International
Tourism Number (TOURNO.), International Tourism Tourism .769 .553 .295
(TOURISM), Net barter terms of trade index (TOT), TourNo .703 .627 .316
Total Reserves (RESV), Foreign Direct Investment (FDI), IMD .649 .577 .386
Portfolio Equity (PORT) and Trade Openness (TOPN). ER .275 .926 .155
The value of KMO is 0.853, which is high. Bartlett test TOT .382 .216 .897
is highly statistically significant. Next step is to select
principal components which are being retained.The Source: Author’s Estimation
Composite Index of Openness:
retained variables are PORT, ER and TOT.Total variance
IOPN = .882*PORT + .926*ER + .897*TOT
explained by principal variable is 96.33 percent. Rotated
component matrix helps us in generating the value Resources
weights obtained from the factor loading for constructing Resource refers to factors of production
the composite index (Table 3). The loading value used for available in a country. Abundance of availability leads to
construction of composite index of openness. higher foreign trade. Resource has introduced following
variables- Total natural resources rents (TNR), Gross
Table 3. Results of Principal Component fixed capital formation (GFCF), Gross domestic savings
Analysis of Openness
(GDS), Coal rents (Coal), Forest Rent (Forest), Mineral
KMO and Bartlett’s Test Rent (Mineral), Natural Gas Rent (Gas) and Oil Rent. KMO
Kaiser-Meyer-Olkin Measure of Sampling value is 0.697, which is adequate. Bartlett’s test is highly
.853
Adequacy. significant. Out of eight variables, retained variables are
Bartlett’s Test of Sphericity Approx. Chi-Square 408.791 TNR, GFCF and Gas. Total variance explained by these
Df 36 three variables is 95.44 percent. The highest loading
value for a component of rotated matrix is used for
Sig. .000
construction of composite index in case of resource
Total Variance Explained (Table 4).
Com-
po- Initial Eigenvalues
Extraction Sums of Table 4. Results of Principal Component
nent
Squared Loadings Analysis of Resources
Total % of Cumulative Total % of Cumula-
Variance % Variance tive %
KMO and Bartlett’s Test
1 7.598 84.425 84.425 7.598 84.425 84.425 Kaiser-Meyer-Olkin Measure of Sampling Ade-
.697
2 .650 7.227 91.652 .650 7.227 91.652 quacy.
3 .421 4.677 96.328 .421 4.677 96.328 Bartlett's Test of Sphericity Approx. Chi-Square 147.654
4 .188 2.090 98.418
Df 10
5 .082 .913 99.332
Sig. .000
6 .032 .357 99.689

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 Research
Total Variance Explained Table 5. Results of Principal Component
Com- Analysis of Market
Extraction Sums of
po- Initial Eigenvalues KMO and Bartlett’s Test
Squared Loadings
nent
Cumu- Kaiser-Meyer-Olkin Measure of Sampling
% of Cumula- % of .766
Total Total lative Adequacy.
Variance tive % Variance
%
Bartlett's Test of Sphe-
ricity Approx. Chi-Square 242.014
1 5.045 63.068 63.068 5.045 63.068 63.068
2 1.250 15.623 78.691 1.250 15.623 78.691 Df 15
3 1.194 14.924 93.616 1.194 14.924 93.616 Sig. .000
4 .295 3.691 97.307 Total Variance Explained
5 .145 1.814 99.122 Com-
Extraction Sums of
po- Initial Eigenvalues
6 .069 .861 99.983 Squared Loadings
nent
7 .001 .017 100.000 Cumu-
% of Cumula- % of
Total Total lative
8 .000 .000 100.000 Variance tive % Variance
%
Rotated Component Matrix 1 4.620 76.995 76.995 4.620 76.995 76.995
2 .983 16.382 93.377 .983 16.382 93.377
Component
3 .247 4.110 97.487 .247 4.110 97.487
1 2 3
4 .105 1.755 99.242
TNR .951 .279 .031 5 .044 .739 99.980
Oil .868 .114 .225 6 .001 .020 100.000
Coal .760 .555 .117 Rotated Component Matrix
Mineral .693 .601 .234
Component
GFCF .346 .929 .028
1 2 3
GDS .340 .928 .043 CONS .926 .352 .110
Gas .321 -.058 .928 GDPPC .911 .396 .084
Forest .102 -.661 -.719 POPDEN .777 .453 .380
Source: Author’s Estimation MKTCAP .720 .643 .004
STOCK .538 .818 .148
Composite Index of Resource:
COMP .101 .072 .989
IRES = .951*TNR + .929*GFCF + .928*Gas Source: Author’s Estimation
Market Composite Index of Market
Market refers to consumption capacity of IMKT = .926*CONS + .818*STOCK + .989*COMP
population within a country. It depends on state of
economy, per capita income and other related factors.
Production
Market has considered following variables under Production means outputs readily available for
the study for measuring market variable-Market either for resale or industrial/consumer consumption.
capitalization of listed companies (MKTCAP), Total Production has considered following variables under the
Listed Domestic Companies (COMP), Total Stock Traded study for measuring production and market variable-
(STOCK), GDP per capita (GDPPC), Population density Agriculture Value Added (AG_PW), Manufacturing Value
(POPDEN) and Final consumption expenditure (CONS). Added (MFG), Industry Value Added (IND), Service
KMO value is 0.766, which is adequate. Bartlett’s test is Valued Added (SER) and Gross Domestic Product (GDP).
highly significant. Out of six variables, retained variables KMO value is 0.772, which is adequate. Bartlett’s test is
are CONS; STOCK and COMP. Total variance explained highly significant. Out of six variables, retained variables
by these three variables is 95.44 percent. The highest are MFG; AG_PW and SER. Total variance explained
loading value for a component of rotated matrix is used by these three variables is 99.99 percent. The highest
for construction of composite index in case of resource loading value for a component of rotated matrix is used
(Table 5). for construction of composite index in case of resource
(Table 6).

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 Research
Table 6. Results of Principal Component of the Durbin-Watson statistic ranges from 0 to4. As a
Analysis of Production general rule of thumb, the residuals are uncorrelated is
the Durbin-Watson statistic is approximately 2. A value
KMO and Bartlett’s Test
close to 0 indicates strong positive correlation, while a
Kaiser-Meyer-Olkin Measure of Sampling value of 4 indicates strong negative correlation. For our
.772
Adequacy. case, the value of Durbin-Watson is 2.993 indicating no
Bartlett's Test of Sphe- Approx. Chi-Square 505.855 serial correlation.
ricity
Df 10
Sig. .000
Total Variance Explained Table 7. Regression Statistics of India’s
Import
Com-
Extraction Sums of
po- Initial Eigenvalues Unstandardized Coef- Stand- T Sig.
Squared Loadings
nent
ficients ardized
Cumu- Coeffi-
% of Cumula- % of
Total Total lative cients
Variance tive % Variance
%
B Std. Error Beta
1 4.969 99.376 99.376 4.969 99.376 99.376
(Con- -72.613 23.699 -3.064 .007
2 .026 .514 99.890 .026 .514 99.890 stant)
T .036** .014 .234 2.691 .016
3 .005 .102 99.992 .005 .102 99.992
LINFRA .194 .212 .093 .915 .374
4 .000 .006 99.998
LHR .012 .099 .009 .123 .903
5 .000 .002 100.000 LOPN .014*** .007 .035 2.002 .062

Rotated Component Matrix LRES .997* .285 .730 3.502 .003

LMKT -.024 .074 -.017 -.323 .751
Component
LPROD -.109 .385 -.069 -.283 .781
1 2 3
Source: Author’s Estimation
MFG .771 .634 .058 *at 1% level of significance, ** at 5% level of significance
IND .761 .647 .050 &*** at 10% level of significance
AG_PW .716 .684 .136 India’s import has significantly increased both
in terms of quantity and value since 1991. It has shifted
SER .631 .774 .055
from “import-substitution” to “export promotion”
GDP .693 .718 .065 strategy. India has reduced significantly tariffs and non-
Source: Author’s Estimation
tariffs barriers related to import. This increases import
Composite Index of Production:
of consumer goods/services and capital goods and
IPROD = .771*MFG + .774*SER + .136*AG_PW
technology. The annual compound growth rate of import
was 3.6 percent for the period of 24 years and also
Relationship between India’s Import and statistically significant.
Economic Development
Infrastructure: Infrastructure building shall have positive
This paper has explained the impact of economic association with import of capital and technology. The
development (i.e. socio-economic variables) on the India’s result shows that the elasticity is less than one i.e. 0.194.
import with the framework of modern international A one percent increase in infrastructure development
trade theory i.e. Heckscher – Ohlininternational trade would lead to a 0.194 percent increase in import.
theory. The empirical results show that Adjusted R However, it is not conventionally statistically significant.
Square is 0.998. It means explanatory variables have
Human Resource: Human resource reflects national skill
been sufficiently explaining the India’s import. The value
and talents in terms of human capital. They have to play

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 Research
significant role in dealing with other countries during and technology. It needs to import capital and technology
course of import. However, human resource shows from advanced countries for efficient utilization of
positive relationship with import but not statistically resources. On the other hand, degree of openness
significant. determines overall import environment. Since India,
a founding member of WTO, has been liberalized her
Openness: The degree of openness has been gradually import-policy by reducing tariff and non-tariff barriers
increased. Import turnover should be increased with and removal of quantitative restriction with respect to
openness. Result follows expectation and statistically import. Now, an importer can import as much quantity of
significant. A one percent increase in openness leads to a the product as he requires. The government mustkeep in
0.014 percent increase in import. mind these developmental variables as the determinants
of import, while formulating India’s import policy. In
Resources: Resource plays a very important role in
general, resource and openness are most important
manufacturing sector. For efficient utilization of these
determinants of India’s import.
available resources India needs to imported capital
and technology from advance countries. Thus it should
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Author Profile
Dr. Manoj Kumar Sinha is an Assistant Professor, Department of Commerce at PGDAV College, University of Delhi. He holds Ph.D and M.Phil
from Department of Commerce, Delhi School of Economics besides Masters in Commerce and LLB from University of Delhi. His areas of
specialization are Finance and International Business. He has authored and published research papers in refereed and reputed national
and international journals. He has also presented research papers in the national and international conferences in India and abroad. He is
a life member of Indian Accounting Association, Delhi Chapter. He is also associated with the M.B.A and M.Com programs of Indira Gandhi
National Open University (IGNOU), New Delhi.He can be reached at mksinhadu@gmail.com.

FIIB Business Review, Volume 5, Issue 3, July - September 2016 60