Review of Economics and Development Studies Vol. 2, No 2, December 2016
117
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Review of Economics and Development Studies
ISSN:2519-9692 ISSN (E): 2519-9706
Volume 2: Issue 2 December 2016
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Foreign influence on Pakistan's exports and imports Evidence from Pakistan
1
Irfan Hussain Khan,
2
Shumaila Hashim,
3
Muhammad Rizwan Yaseen
1
Ph.D. Scholar, Govt Collage University Faisalabad Pakistan, Irfansial007@hotmail.com
2
M Phil Scholar, Applied Economics Research Centre, University of Karachi Pakistan, kshumaila07@gamil.com
3
Assistant Professor, Govt Collage University Faisalabad Pakistan, rizwany2001@yahoo.coom
ARTICLEDETAILS ABSTRACT
History
Revised format: Nov 2016
Available online: Dec 2016
The purpose of this study is to investigate the impact of the Pakistani
currency phase action on exports and imports. Two time series data base
year and quarterly basic research use. Starting from the 1970 annual data
for about 40 years, beginning with the beginning of 2000 to 2012
quarterly data. Johnson estimates quarterly observations using common
integration techniques. In the current study results show that Pakistan first
began trading volume for the US and developed countries, the UK and
Europe. As a combination of export and import time Pakistan has
improved. Production and manufacture of semi-finished goods and
primary product alternatives, while the import of consumer goods, capital
goods and petroleum products expanded. Due to low-cost elasticity of the
export and import activity of the exchange of theoretical background
reaction support. On the other hand, if the value of the rupees fell against
the dollar, the import costs rose more than the export bills. In support of
this study, Pakistan should focus on a small number of countries to reduce
trade and expand trade. Similarly, on the basis of the goods may add some
other goods.
© 2016 The authors, under a Creative Commons Attribution-
NonCommercial 4.0
Keywords
Currency,
Import and export,
trade,
Pakistan
JEL Classification
I12, I15, I18
Corresponding author’s email address: Irfansial007@hotmail.com
Recommended citation: Khan, I. H., Shumaila, H., and Yaseen, M. R (2016). Foreign influence on Pakistan's
exports and imports Evidence from Pakistan. Review of Economics and Development Studies, 2 (2) 117-128
DOI: https://doi.org/10.26710/reads.v2i2.129
1. Introduction
Exchange rate as a measure of the level of use of the global competitiveness of the tool. National
currency in the national competitiveness index has played an important role in economic stability.
Determine the foreign exchange demand and market forces of money supply, market exchange rate.
State and monetary authorities determine that the exchange rate plays a major role in the government.
Many economists believe that currency devaluation promotes exports and hinders the importation of a
country.
The exchange rate of any country is conducive to business, investment motivation, policy and planning
decisions. As the exchange rate for two decades has become an important research area. March 1, 1982.
http://www.publishing.globalcsrc.org/reads
mailto:Irfansial007@hotmail.com
mailto:kshumaila07@gamil.com
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mailto:Irfansial007@hotmail.com
Review of Economics and Development Studies Vol.2, No 2, December 2016
118
The introduction of floating exchange rates has been considerable development, especially in the post-
Bretton Woods era, after which the exchange rate has fluctuated.
In the economic story of Pakistan, they found two important variables, namely, there was no or weak
relationship between exchange rate and trade balance, as described by some researchers such as
Greenwood (1984), Rose and Yellon), Rahman Mustafa (Case 1996), Rahman et al. (1997), Saadia
Badal (2006). From the appreciation of the last century to the Pakistani rupee devaluation against the
dollar, Pakistan's large current account surplus, according to the dollar value as high as the rupee. Then,
the Central Bank of Pakistan decided to cut interest rates steadily, and is expected to maintain export
competitiveness, from the national economy to buy the dollar level. In 1971, the currency of Pakistan
was linked to the disconnection in 1982 from the $ 100 rupee in July 1994.
2. Objectives of the Study
The objection of the study is as follows:
1. Observe the historical trends in the exchange rate of Pakistan and the import and export.
2. Observe the relationship between Pakistan's imports, exports and exchange rates.
3. Literature Review
It contains relevant materials on developed countries, underdeveloped countries and regional countries.
Literature is organized in chronological order, so that the development of major theories, concepts and
methods can be emphasized.
Hasanov and Samadora (2009) examined the impact of the real exchange rate on non-oil exports to
Azerbaijan. Quarterly data for the third quarter of 2002 to the third quarter of 2009 were used. They
estimate that the Johnson assessment is based on a long-term relationship cointegration model, however,
the short-term momentum has been estimated using the error correction model. They believe that the
Azerbaijani economy's gross domestic product (GDP) real exchange rate and no significant impact - oil
wool; both in the short and long term. The empirical results show that short-term volatility does not last
forever, but it is moving towards long-term equilibrium
Ahmed nearby (2009) examines the impact of exchange rate fluctuations on Bangladesh's international
trade. The cointegration and error correction models have been developed for empirical analysis.
Importers must use real (in local currency terms), real bilateral exchange rates (between Bangladesh and
the US economy) and industrial production indices as quarterly gross domestic product (GDP) data.
From May 2003 to December 2008, empirical results show that the real exchange rate will not only lead
to short-term, but also have a significant impact on long-term real exports and the Bangladeshi
economy. Although the exchange rate fluctuated significantly, exports were gradually increasing. The
overall conclusion is that the devaluation of the local currency against major international currencies
through Bangladesh's weak import and export economy does not improve trade balance.
He (2010) developed a theoretical model of the dynamic relationship between exchange rate and
productivity growth. He merged into a monopolistic competition, non-tradable goods industry. His
conclusion is that the currency appreciation caused by the circulation, rather than the endogenous
growth of currency depreciation. This may be due to monopolistic competition to provide non-tradable
commodity prices to drop its broad truth facts. Currency devaluation due to non-growth, due to the
distribution sector and currency depreciation, so the overall turnover exceeds the currency appreciation
Review of Economics and Development Studies Vol. 2, No 2, December 2016
119
Ijaz (2012) studied the effectiveness of the exchange rate of Pakistan's macroeconomic variables. Data
were collected from 1980 to 2009. In the case of annual time series data, fixed unit root test, Johnson
applied the long-term equilibrium relationship variable, cointegration test, and causality between
variables in each model Granger causality test (GCT). The main finding is that there is no common long-
term integration between exchange rate and inflation, trade and foreign direct investment, exchange rate
and inflation. Exchange rate and trade consolidation, exchange rate and foreign direct investment, but
did not find casualties in each case.
Khan et al. (2014) analyzed the impact of exchange rate fluctuations on Pakistan's business partners.
Monthly data from January 1970 to December 2009 collected samples of 29 trading partners in Pakistan.
Volatility measurement obtained from the GARCH model family. Panel unit root test data has been used
to identify nonstationary problems. Classify volatility as long and short term effects. The main results
are summarized as follows: First, when Pakistan uses the dollar as a vehicle currency transaction, the
impact of trade fluctuations is significant and extremely negative. Compared with these negative effects,
export imports are longer. They also found that emerging markets and low-income countries in their
own currencies rather than the use of vehicles can reduce the impact of foreign exchange fluctuations.
The domestic currency trade avoids the uncertainty and instability of the exchange rate.
Import, Export and Exchange Rate a Profile of Pakistan
It also contains information about export to and imports from various international markets.
Profile of Pakistan Trade
This section contains the history of Pakistan's trade status. Foreign trade trends were extensively
analyzed. Table 3.1 shows the balance of the payment (BOP) component, from 1981 to 2012, the
average of 10 years has been reported to make the analysis as simple as possible to the percentage of
GDP.
Table 3.1 Components of Trade Deficit (As Percent of GDP)
Source: Pakistan’s Economic Survey (various issues)
Year Export Import Trade deficit
1981-90 10.2 17.91 7.76
1991-00 13.37 17.33 3.95
2000-10 12.04 18.34 6.3
2010-11 11.6 18.9 7.3
2011-12 10.5 20.0 9.5
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4. Research Methodology
This study examines the short-term and long-term relationships between real effective exchange rates,
(million rupees), and Pakistan's real value for import and export. There are many ways to analyze, but
present research, Kemal & Kadir (2005) program. This process has several advantages. First, it isolates
short-term and long-term relationships, ie it highlights the strength of short- and long-term relationships.
Secondly, it emphasizes the behavior of the corrective mechanism (if any), that is, in its case, providing
information about the trend of balancing the impact on the system. Third, this process is directly related
to the theory. Because of the well-known macroeconomic principles, that is, exports, imports and
currency changes are interrelated.
I found that the two methods are the covariance relationships between interest variables Engle - Granger
and Johansen techniques. Engel-Granger single technology has some drawbacks. However, Johnson's
cointegration technology is based on equations, and more powerful Bean Siegel-Granger technology.
Thus, Johansen technology was used in this study. The analysis involves many steps. First, the
researchers need to determine whether the data is at rest. If the data is still, you cannot use the
cointegration of Johansen technology. However, if the data is not fixed and is an integrated order, it can
also be used. Secondly, the optimal lag of the simple vector autoregressive model is the second step. The
third step is to use the cointegration test to find long-term cooperative efficiency variables.
Finally, a representative vector error correction mechanism is configured to estimate the short-term
relationship. More details of these steps are listed. The first step is to find whether the data is at rest or not.
There are several variables that can be used to determine the static test. Enhanced - Dickey Fuller (1981),
referred to as ADF, literature is more popular test, due to its simplicity and Dickey and Fuller 1981.
Mathematically, ADF can be expressed in equation (1) for a time series variable ‘Y’ below:
eyayay ti
p
i
iitt
t
1
1
10 (1)
There are three options in equation (1)
“ a0 ” is the intercept or drift parameter of the time series.
“t” is the time trend in time series. There may be downward or upward linear trend in the
data.
It is also possible that both drifts and time trend exit in the data.
The Augmented Dickey Fuller (ADF) may be carried out for above three options, namely,
constant or intercept or drift, time trend or trend and finally both. Selection of appropriate lag length
may be decided using Akiak Information Criterion (AIC).
First, direction of causality among variables would be tested using Granger Causality test
(Granger, 1969) which is outlined by Karlsson et al., (2002) as given in equation (2) to equation (4)
tit
n
i
iit
n
i
i
n
i
itt eraaimpaaimp i 1
1
3
1
2
1
10 lnexplnlnln 1
(2)
tit
n
i
iit
n
i
i
n
i
itt eraimpaaa i 2
1
3
1
2
1
10 lnlnexplnexpln 1
(3)
Review of Economics and Development Studies Vol. 2, No 2, December 2016
121
tit
n
i
iit
n
i
i
n
i
itt eraaimpaaer i 3
1
3
1
2
1
10 lnexplnlnln 1
(4 )
Lnimp Log use as imports of Pakistan (Rs in millions). Lnexp Log of exports of Pakistan (Rs in
millions) and Lner Log of exchange rate of rupees verses dollars
Equation (2) into equation (4) is a system of equations. There are two channels it is very important: the
exchange rate and export, import rates and import causality. Based on the theory, the exchange rate
decisions the goods Different country relative prices. Exchange of a country is likely to import and
export through in some assumptions. For example, if a country's currency devaluation, which means that
the lower value of exports abroad, and the higher the value of foreign imports. It just Leads to a
conclusion that the other conditions remained unchanged, exports increased external demand and a
decline in domestic demand for imports. The degree of exchange rate on export and import depends on
many factors such as the nature of the goods, the state of global competition.
5. Result and Discussion
Exchange rate is the most important macro variables, a country's most important economic policy in
promoting China's foreign trade has played a very important role. This study can extend several
variables. The VAR model can be extended to the VAR type - the simaltanuess equation model, which
requires a larger data set for the purpose of estimation.
Summary
The statistics are shown in December 2000 for three selected variables (Rs 1 billion) in Table 5.1, ie the
export and import ratios (as opposed to rupees), logarithm. The average exchange rate was 66.69 rupees
per dollar for $ 48 after the fourth quarter from 2000-12
Table 5.1: Summary Statistics of Selected Variables
Exchange Rate (ER )
(Rs versus US $)
Log of Exports -
LNEXP
(Rs million)
Log of Imports --
LNIMP
(Rs million)
Mean 66.69654 12.24233 12.18058
Median 60.435 12.30632 12.23563
Maximum 92 12.56603 12.49833
Minimum 51.65 11.6099 11.76492
Std. Dev. 12.3551 0.234959 0.234366
Skewness 0.781148 -0.853791 -0.338577
Kurtosis 2.010606 2.888114 1.583387
Jarque-Bera 7.409277 6.344769 5.341553
Probability 0.024609 0.041904 0.069198
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122
Sum 3468.22 636.6014 633.39
Sum Sq. Dev. 7785.076 2.815498 2.801299
Observations 52 52 52
Source: Author’s calculation, data from State Bank of Pakistan
The maximum exchange rate of 92%, the lowest exchange rate of 51.65% of the dollar. The standard
deviation is a positive deviation of 12.3551,0.78. The average log exports and imports are given by
12.24 and 12.18. The largest export of logs is Rs. 125.6 crore, with a minimum of Rs 11,609,900. The
standard deviation of export and import of logs is 0,234,959 and 0,234,366 rupees respectively.
Pakistan's skewness coefficient is negative
Export and import. Pakistan's import quarterly standard deviation is lower than the lowest value for the
quarter, and exports increase the theoretical parameter, ie, the relative price elasticity of imports. Least
developed countries export to primary products to other parts of the world, which is low prices and
income elasticity
5.2 Estimation of Results
The first step quantitative analysis examines the existence of each variable unit root time series variable.
If the time series data unit root evidence then the ordinary least squares (OLS) application is inconsistent
and inefficient. If the OLS estimates the fundamental proof of the unit time series data is a pseudo-
regression model. In this context, this chapter specifically tests the root of most data units.
There are various unit root tests in the data technology, but it is important to look at the drawings and
the enhanced Dickey Fuller (ADF) test. You can build related graphs and ADF tests and the first level
differences in the series. There are three options for the ADF test application, ie whether the intercept in
the regression equation is in the regression model with or without the trend and intercept intercept and
trend. In this case, all possible options for the ADF are analyzed and the results are as follows.
Table 5.3: Test of Co-Integration
Date: 11/18/14 Time: 18:48
Sample (adjusted): 2001Q1 2012Q4
Included observations: 48 after adjustments
Trend assumption: Linear deterministic trend (restricted)
Series: ER LNEXP LNIMP
Exogenous series: @SEAS(1) @SEAS(2) @SEAS(3)
Warning: Critical values assume no exogenous series
Lags interval (in first differences): 1 to 3
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.470151 43.73070 42.91525 0.0413
At most 1 0.167848 13.24286 25.87211 0.7191
At most 2 0.088034 4.423314 12.51798 0.6803
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123
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.470151 30.48785 25.82321 0.0112
At most 1 0.167848 8.819544 19.38704 0.7428
At most 2 0.088034 4.423314 12.51798 0.6803
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
ER LNEXP LNIMP @TREND(00Q2)
-0.448272 -16.47367 -10.63826 0.710122
-0.005935 8.879199 -4.166453 -0.005617
0.041039 -6.118045 11.20179 -0.046603
Unrestricted Adjustment Coefficients (alpha):
D(ER) 1.475326 -0.428161 0.394958
D(LNEXP) -0.006340 -0.023738 -0.012162
D(LNIMP) 0.017829 0.010240 -0.023136
1 Cointegrating Equation(s): Log likelihood 10.95724
Normalized cointegrating coefficients (standard error in parentheses)
ER LNEXP LNIMP @TREND(00Q2)
1.000000 36.74927 23.73171 -1.584133
(6.11086) (4.48276) (0.07769)
Adjustment coefficients (standard error in parentheses)
D(ER) -0.661347
(0.17727)
D(LNEXP) 0.002842
(0.00548)
D(LNIMP) -0.007992
(0.00646)
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Source: Author’s calculation using Eviews, data taken from State Bank of Pakistan
The statistical differences between the two structures are zero and the alternative hypothesis. For
example, in the tracking statistics, the null hypothesis does not contain a cointegration vector with a
cointegration vector and an alternative hypothesis mark greater than zero. If the alternative hypothesis is
accepted in the second round, the replacement of the covariate vector is less tested than the null
hypothesis. If the air is accepted, the test ends and if the substitution is accepted, the other test of the
third round is performed. In the current case, the covariate vector is not rejected and accepts more than
zero. In the second round, for the one end and the alternative test, accept the cointegration vector zero.
In most cases – Eigen value statistics, no cooperation, the integrated vector zero is only for a
cointegration vector substitution test. If it is accepted, the null test is over and if the test is accepted,
proceed to step 2. In the second step, there is only one public integrated carrier zero replacement test
with only two. If the air is accepted, the test concludes that if the alternative is to be tested, it will
continue in a similar manner until the null hypothesis is unacceptable. In this case, it can be seen that the
maximum - calculated statistic is inherently 8.81, resulting in more than 19.38 we accept only a
cointegration vector space is low.
The estimated VAR model, based on the covariance test markings in Table 5.4, includes the acquisition
of Pakistani import and export dummy variables affected by seasonal factors.
C (2) is the impact of Pakistan's export lag on current exports. This is negative, which means significant
if a year lagged exports have a negative impact on the current exports. In addition, if the current annual
export growth, it will reduce exports in the next few years.
One, two, four quarterly exchange rate fluctuations have C (8), C (9) and C (10). Although the
important factor is personal, but to a lesser extent. A unit of exchange rate changes is lagging behind,
bringing logs to Pakistan exports about 0.001095 units change. Other similar explanations are common
and effective and can be done.
Table 5.4: Vector Error Correction Model for Log of Pakistan’s Imports as Dependent Variable
Using General to Specific Approach
Dependent Variable: D(LNIMP)
2 Cointegrating Equation(s): Log likelihood 15.36701
Normalized cointegrating coefficients (standard error in parentheses)
ER LNEXP LNIMP @TREND(00Q2)
1.000000 0.000000 39.99345 -1.523464
(13.9743) (0.21522)
0.000000 1.000000 -0.442505 -0.001651
(0.36830) (0.00567)
Adjustment coefficients (standard error in parentheses)
D(ER) -0.658806 -28.10576
(0.17420) (7.27195)
D(LNEXP) 0.002983 -0.106327
(0.00517) (0.21575)
D(LNIMP) -0.008053 -0.202782
(0.00641) (0.26773)
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125
Method: Least Squares
Date: 11/24/14 Time: 18:20
Sample (adjusted): 2001Q1 2012Q4
Included observations: 48 after adjustments
D(LNEXP) = C(1)*( LNIMP(-1) - 0.484236805425*LNEXP(-1) -
0.0010834444502*ER(-1) - 6.29387705268 ) + C(2)*D(LNEXP(-1)) +
C(3)*D(LNEXP(-2)) + C(4)*D(LNEXP(-3)) + C(5)*D(LNIMP(-1)) + C(6)
*D(LNIMP(-2)) + C(7)*D(LNIMP(-3)) + C(8)*D(ER(-1)) + C(9)*D(ER(-2))
+ C(10)*D(ER(-3)) + C(13)*@SEAS(1)
Coefficient Std. Error t-Statistic Prob.
C(1) -0.299486 0.124642 -2.402762 0.0035
C(2) 0.567935 0.204820 2.772846 0.0052
C(3) 0.298374 0.100318 2.974395 0.0096
C(4) 0.187309 0.084478 2.217244 0.0312
C(5) -0.082536 0.030606 -2.696712 0.0903
C(6) -0.227149 0.119689 -2.897823 0.0655
C(7) -0.211526 0.078382 -2.760782 0.0065
C(8) 0.021095 0.009208 2.290817 0.0728
C(9) 0.033555 0.015736 2.132355 0.0048
C(10) 0.073429 0.024928 2.945539 0.0005
C(13) 0.127362 0.031745 4.012007 0.0003
R-squared 0.690836 Mean dependent var 0.010217
Adjusted R-squared 0.543765 S.D. dependent var 0.116096
S.E. of regression 0.078417 Akaike info criterion -2.055492
Sum squared resid 0.227524 Schwarz criterion -1.626675
Log likelihood 60.33180 Hannan-Quinn criter. -1.893441
Durbin-Watson stat 1.878474
Source: Author’s calculation using Eviews, data taken from State Bank of Pakistan
Pakistani imports affect exports in one, two, three aspects of backwardness. This means that if the log
exports grow in wood imports surge. This may be due to the increase in exports of the country means
that due to the logical connection of the popularity of Pakistani foreign exchange imports. The second
reason may be the impact of channel imports on export earnings. If the re-exports also increase domestic
income, this could lead to more imports.
One, two, affecting the level of imports currently lagging behind imports in the third quarter was
negative. This means that if the import growth this quarter, imports will decline in the next quarter.
These results are significant. The impact of the exchange rate lag, the secondary and tertiary import
levels is positive and significant. Import exchange rate impact is greater than export, summary appendix
high - twenty three. This means that if the foreign exchange depreciation rupee, each dollar exchange
rate rises, the level of imports also rises, and vice versa.
6. Conclusions and Recommendations
Review of Economics and Development Studies Vol.2, No 2, December 2016
126
Quarterly data for the period from 2000 to 2012 to measure the impact of exchange rate fluctuations on
Pakistan's imports and exports. I'm measuring millions of dollars, which is the dollar exchange rate and
the use of data to measure the 1970 and 2013 data PKR than Xiaotang trend analysis. In its main
research, manufacturing and data alertness is estimated to be half of the manufacturing export and
import use. Analyze direct exports and imports. Discussed the concentration of import and export.
• Exports of primary commodities have declined over a period of time. This is a positive point.
• Pakistan's exports are mainly concentrated in developed countries. If the developed countries economy
suffered any crisis, Pakistan is a direct blow to this crisis.
The econometric model estimates can be summarized as follows:
All time series variables have unit root problems, so using OLS is invalid.
• All time series variables are first-order integrals and the sample size is quite large, so it is easy to apply
Johanson co-integration integration techniques.
• The best lag length standard test shows that the lag length can reach three thirds.
• Co-integration tests show that there is no co-integration vector, so there is a long-term relationship
between variables.
7. Recommendations
Pakistan's exports of primary products or raw materials can be higher in the country to reach the final
product ratio. If these final products are exported, this will lead to more foreign exchange earnings than
it is now.
• Pakistan's exports are concentrated in developed countries, not only from the perspective of a viable
market for the economy. It usually leads to the instability of the national economy in the international
economic recession or through political interference in Pakistan. Pakistan may be seeking diversification
of exports, which in many markets, not a few, so if the interference of bilateral relations between
countries, then the level of economic activity may not be very strong.
• Pakistan's share of imports from large countries and finished products, including capital goods,
petroleum products and consumer goods. A country's economic development must be completed capital
goods, but it may also be domestic consumer goods. Import substitution and export-oriented economy
are the right policy for Pakistan to get out of the crisis.
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