ea_2018_1-2
DOI: 10.28934/ea.18.51.12.pp120-130
SCIENTIFIC REVIEW
Impact of Exports on Economic Aggregates of Pakistan
Hasnain Naqvi1* | Slobodan Adžić2 | Nebojša Zakić3 | Milijanka Ratković4 |
Israr Ahmad5
1 University of Hafr Al-Batin, Hafr-Al Batin, Saudi Arabia
2 Arab Open University, Al-Ardia, Kuwait
University Union – Nikola Tesla, Faculty of management FAM, Sremski Karlovci, Serbia
3 University Union – Nikola Tesla, FMN FPB, Belgrade, Serbia
4 University Union – Nikola Tesla, Faculty for Education of the Executives, Belgrade, Serbia
5 Islamic International University, Islamabad, Pakistan
ABSTRACT
The study employs CGE model, on the data provided in SAM 2007-08 for Pakistan designed by
Dorosh et al. (2012), to investigate the impact of Pakistan’s exports on the major aggregates of the
economy. To this end, three experiments have been conducted, exports are increased by 5% in the
first simulation (SIM-I), in the second simulation (SIM-II) by 10% and in the third simulation (SIM-
III) by 15%. The findings of the study reveal that increase in exports has favourable impact on the
performance of macroeconomic variables of the economy i.e. GDP, public and private consumption,
savings and investment. Domestic output level of most of the commodities has risen except mine,
food manufacturing and other manufacturing. Incomes and expenditures of all the households have
risen that results in rise of utility level of all the households. Moreover, all the households have also
recorded an increase in the values of compensating variation which implies higher level of welfare
for households. However, the value of compensating variation for non-agriculture households has
risen more than that of agriculture households indicating pro-urban effect. Equality among the
households has improved as the inequality indices have registered declining trend. The study
suggests that export promotion measures should be incorporated in poverty alleviation, income
equality and economic growth strategies.
Key words: economics, export, aggregates, CGE model. economic growth, sustainable development,
Pakistan, poverty allevation
JEL Classification: B22, D58, O4, Q01, I30, I32
INTRODUCTION
Trade openness plays an important role in the economic growth of a country and helps a
country to achieve higher economic growth, (Anderson and Babula, 2008), through efficient
utilization of resources and transmitting economic growth from one region of the world to
another. In international trade much importance is assigned to exports because exports have a
profound impact on the output of a country. The importance of exports goes back to the
mercantilist time. In view of mercantilists international trade is a “single-pie” and they stressed
on ‘more exports and less imports’. Exports are a means of valuable foreign exchange earnings
which enables us to import our indispensable inputs (technology and machinery). Exports lead
to employment generation, optimal utilization of resources and economic development of a
country. That is why exports are considered to be an engine of economic growth. Domestic
monopolies are vanished and the availability of goods is made at lower prices on one hand and
* E-mail: naqqvi23@hotmail.com
Hasnain Naqvi, Slobodan Adžić, Nebojša Zakić, Milijanka Ratković, Israr Ahmad 121
incomes of the people are raised through employment generation on the other hand. These
lower prices and higher incomes raise the consumption level (welfare) of people. Exports have
impacted the GDP of Pakistan positively and there is a strong long-term relationship between
Exports and GDP of Pakistan, (Shirazi and Manap, 2004).
The literature on the history of export performance of Pakistan shows bleak picture of the
export performance of Pakistan. During 1950s and 1960s, export performance of Pakistan
remained poor either because it did not receive serious attention or due to limited capacity. In
general Pakistan exports showed an increasing trend both in terms of value and quantity. The
rate of growth of exports remained very slow till 1972 but after that it accelerated. On the other
hand, imports have increased more than exports due to which Pakistan has always confronted
the problem in deficit in balance of trade (BOT) with exceptions of a few years (1950-51, 1954-
55 and 1972-73).
The growth rate of the world output and trade witnessed deceleration in 2011 due to
detrimental impact cast by the deteriorating global environment. Growth rate of the world
output declined from 5.3% in 2010 to 3.9% in 2011. In the same manner growth rate of the
world trade decelerated from 13% in 2010 to 5.8% in 2011. But quite opposite to it, growth rate
of Pakistan’s exports accelerated from 9.06% in 2009-10 to 28.61% in 2010-11 and Pakistan’s
imports grew at the rate of -0.32 in 2009-10 to 16.43 in 2010-11. This indicates inflating volume
of Pakistan external trade. Pakistan’s exports are characterized by high concentration in
commodity and export markets. However, shift in the nature of Pakistan’s exports has been seen
from primary products to manufactured products. The share of primary products in Pakistan’s
total exports was 33% in 1971 which has declined to 18% in 2011. Similarly, the share of semi-
manufactured goods in Pakistan’s total export was 24% in 1971 which has now declined to 12%
of total exports in 2011. On the other hand the share of manufactured goods has increased from
44% in 1971 to 70% in 2011 (Pakistan’s Economic Survey, 2011-12).
A large number of facilities/incentives have been given to the exporters to inflate the volume
of exports from Pakistan. These incentives are targeted to make exports zero-rated (exporters
pay no tax on sales abroad). These facilities include export financing (in domestic and foreign
currency), Export Credit Guarantee, concessionary rate of income tax (under the income Tax
Ordinance 1979), Common Bonded Warehouse Scheme, Export Marketing and Product Up-
gradation Fund, Duty Drawback Scheme, and Export House Scheme (Haque and Kemal, 2007).
This shows that there have been no systematic trend in Pakistan’s exports growth over the
years that calls for its analysis as it can impact different economic aggregates in a significant
way. This study aims to unfold the nexus between export and various economic aggregates in
Pakistan.
REVIEW OF LITERATURE
Economists have employed CGE models for the analysis of economic policies and for the
appraisal of costs and benefits accrued to nations through economic integration and trade
liberalization, for example, Oslington (2005), Akerman (2005), Bouet et al. (2004) and Kurzweil
(2002).
To simulate the fiscal policy, Bhattarai and Trzeciakiewicz (2016) developed a Computable
General Equilibrium Model to analyze fiscal policy in United Kingdom. They found that
investment and public consumption causes high GDP multiplier in short run. In long run,
whereas, private investment and capital income tax have high impact on GDP. Moreover, this
study also explored effectiveness of public outlays and consumption taxes.
Naqvi, et al. (2011) developed CGE model of Pakistab to analyze the impact of Agricultural
Income Tax on Household Welfare and Inequality. The model analysed the economic
implications of Agricultural Income Tax and reduction in sales tax for production activities to
adjust the budget surplus. The objective of this experiment was to determined the possibility of
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implementation of agricultural income tax in case of Pakistan and to analyse its benefits at
macro and household level. Two variables were considered in this experiment i.e., imposition of
agricultural income tax, and decrease in sales tax rates. The article concluded that the imposition
of agricultural income tax is beneficial in terms of household and economy-wide welfare
indicators.
Bouet et al. (2010) appraised the gains and losses accrued to members and non-members of
South Asia from South Asian Free Trade Agreement (SAFTA) by using CGE model. The study
assessed the costs and benefits of both the cases: including the sensitive products envisaged in
SAFTA and excluding them from the process of trade liberalization. Full trade liberalization
(including sensitive products) had been considered to cause trade diverting effects in terms of
income that was reduced due to tariff-cut. Liberalization of sensitive products was thought to be
non-beneficial for LDCs of the region. The pattern of distribution of gains among the factors of
production promised higher incomes for unskilled labourers, hence, pro-poor. However, the
consequences of SAFTA were deemed to cause low tariff income for almost all of its members.
Ahmed and O’Donoghue (2009) analyzed the impact of variations in external balance of
Pakistan developing country on the various economic entities of the economy by employing CGE
model. The study integrated the economy into 33 sectors and gauged the impacts of changes in
import prices and external savings on these aggregates. Simulation results of the study
ascertained that increase (50%) in foreign savings led to expansion of imports and contraction
of exports. The sectors expected to face reduction in their exports were cement, leather, textile
and livestock. Under these conditions the factors expected to receive increased incomes were
non-agriculture unskilled wage labour and agriculture wage labour. A rise in import prices
especially petroleum and industrial raw material led to decline in exports. The state of poverty
and income inequality was aggravated.
Gilbert (2008) investigated the impact of integration of the South Asian economies under
SAFTA on the welfare, poverty and income distribution of the concerned countries through CGE
model. The main contribution of this study in the existing literature was that it took into account
the whole South Asian region, not a single country for CGE approach. The findings of the study
revealed that almost all of the countries except Bangladesh would gain from trade liberalization,
though the gains would be modest because these countries had similar export structure. In case
of Bangladesh, unilateral reforms had been proposed to be the best option. The trade reforms
stipulated to be brought about under the auspices of SAFTA had been perceived to cast positive
impact on overall welfare. Thus, regional integration was deemed to be pro-poor especially in
Bangladesh and India. However, income inequality was liable to rise.
Panda and Kumar (2008) explored the relationships among trade liberalization, economic
growth, food security and poverty through CGE model constructed for India. The study used the
data provided in the SAM for India for the year 2003-04. The simulation results of the study
depicted that trade liberalization had a negligible impact on the growth of GDP. It was the only
agriculture sector that benefited from both unilateral and multilateral trade liberalization in
GDP while non-agriculture GDP remained invariant in face of unilateral liberalization and
declined under multilateral liberalization. Both wages and consumer prices rose but wages rose
more than prices; hence, real incomes of all households soared. Due to rise in real income,
income poverty plummeted. Consequently, lower income groups of both rural and urban
sections witnessed a decline in food intake in terms of calories while others increased the intake
of nutrients.
Siddiqui (2007) investigated and compared the effects of liberalization of agriculture trade in
the domestic and the world economy on the economic growth of Pakistan by employing both
static as well as dynamic CGE frameworks. The study used the data provided in Pakistan Social
Accounting Matrix for the year 2002 (SAM 2002). The simulation results of the study depicted
that liberalization of agriculture trade whether at domestic level or international level had
favourable impact on the economic growth of the country. However, the effects of liberalization
Hasnain Naqvi, Slobodan Adžić, Nebojša Zakić, Milijanka Ratković, Israr Ahmad 123
of agriculture trade at international level were found to be stronger than those of liberalization
at domestic level. Complete liberalization of agriculture trade was expected to increase the
incomes of both rural and urban households. However, long run consumption of rural
households increased more than that of urban households. The distribution of income was, in
the short run, improved while it was worsened in the long run.
Cockburn et al. (2006) investigated and compared the effects of trade liberalization on
different economic aggregates of seven African and Asian countries by using CGE model. Roles of
relative factor endowment, initial tariff structure, trade pattern, production pattern and income
and consumption patterns were given much consideration in explaining the results. The findings
of the study demonstrated that trade liberalization had varying effects on different commodity
sectors and household groups. Manufacturing sector had gained while agriculture sector had
lost under trade liberalization process. The urban households benefited while rural households
lost in terms of welfare. Nonetheless, overall trade liberalization raised the level of welfare and
reduced poverty. Wages of households increased more than the increase in domestic price of
consumer goods. The pro-urban effect of trade liberalization was considered to be due to
substantial fall in the returns to land.
Adam and O’Connell (2000) employed CGE model to investigate and compare the gains
accrued from aid and trade preference to a recipient developing African country. The findings of
the study substantiated that the gains from trade preference were dominant over the gains from
aid. The study advocated for transfers (whether in the form of aid or trade) that promote capital
accumulation in the receiving country. Through capital accumulation, a developing country was
thought to shift from raw exports to manufactured exports. This shift, in turn, was deemed to
enhance welfare effects of donor assistance. Transfer of resources via aid impacted the
manufactured exports and total domestic output adversely. Contrarily, trade preferences
increased domestic output and consumption. Fiscal distortions attached more importance to aid
than trade because export subsidy given to promote exports would result in increased fiscal
burden.
METHODOLOGY
The study has employed Computable General Equilibrium (CGE) model designed on the
pattern of Lofgren et al. (2002), on the data provided in SAM 2007-08 for Pakistan designed by
Dorosh et al. (2012), to investigate the impact of Pakistan’s exports on its economic aggregates.
The framework of mathematical equations is based on the neo-classical assumptions of
optimizing behaviour of economic agents: maximization of utility and output, and minimization
of costs1. Trade elasticities for different commodities in Pakistan have been borrowed from
Ahmed et al. (2008). Three experiments have been conducted to gauge the impact of increase in
Pakistan’s exports on various economic aggregates of Pakistan. In the first simulation (SIM-I)
Pakistan’s exports have been increased by 5%, in the second simulation (SIM-II) by 10% and in
the third simulation (SIM-III) by 15%.
INTERPRETATION OF RESULTS
Macro level
The GDP (at fixed cost) of Pakistan has registered growth of 1.474%, 2.911% and 4.314% in
SIM-I, SIM-II and SIM-III respectively (table 1). This rise in GDP can be attributed to increase in
investment, higher level of activities, increase in household incomes and hence higher savings.
Investment has grown by 0.942%, 2.011% and 3.193% in the respective three experiments. This
increase in investment is in line with theory as rise in institutional incomes and consequent rise
1 Mathematical equations and economic aggregation can be provided on demand.
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in their savings have resulted in higher investment. Government consumption has inflated by
0.702%, 1.418% and 2.141% while private consumption has risen by 0.936%, 1.859% and
2.769% due to rise in the incomes of institutions coupled with fall in the prices of various
consumer goods and fall in the price of imports in terms of domestic currency due to
appreciation of domestic currency.
Table 1. National Income Accounts (% Variation)
Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
GDP at Factor Prices 3377101.000 1.474 2.911 4.314
Government Consumption 408940.000 0.704 1.418 2.141
Investment 534109.000 0.942 2.011 3.193
Exports 677841.000 3.716 7.491 11.325
Imports 1030150.000 1.181 2.517 3.993
Net Indirect Taxes 251634.000 -0.163 -0.177 -0.059
Private Consumption 3037997.000 0.936 1.859 2.769
Source: research by authors
Both exports and imports have grown but the growth rate of exports is higher than that of
imports (table 2 and 3). Exports have grown by 3.716%, 7.491% and 11.325%. This surge in
exports is in conformity with economic literature as it is mainly due to rise in activity level,
consequent increase in output and rise in GDP. Moreover, increase in imported inputs has led to
increase in domestic output which in turn has increased the quantity of commodities to be
exported. Imports have registered growth rate of 1.181%, 2.517% and 3.993%. This positive
growth of imports is mainly the result of fall in the price of imports due to appreciation of
domestic currency and increase in foreign exchange reserves due to more exports.
Table 2. Quantity of Exports for Commodities (% Variation)
Commodities Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Agriculture 26415 2.924 6.085 9.478
Mine 5292 6.755 13.305 19.635
Food Manufacturing 112975 1.890 3.868 5.928
Cotton Lint/Yarn 60824 1.595 3.232 4.908
Textiles 216278 1.734 3.452 5.157
Leather 15385 2.903 5.790 8.667
Other Manufacturing 122350 6.667 13.247 19.725
Services 118322 0.689 1.339 1.956
Source: research by authors
Table 3. Quantity of Imports for Commodities (% Variation)
Commodities Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Agriculture 36087 17.960 37.678 59.154
Mine 95779 0.720 1.487 2.296
Food Manufacturing 57923 14.577 30.111 46.564
Cotton Lint/Yarn 7297 14.472 29.952 46.421
Textiles 18918 15.603 32.514 50.730
Leather 1178 14.615 30.337 47.159
Other Manufacturing 807118 3.609 7.198 10.766
Services 53953 13.118 27.215 42.307
Source: research by authors
Hasnain Naqvi, Slobodan Adžić, Nebojša Zakić, Milijanka Ratković, Israr Ahmad 125
Domestic output
Output of most of the commodities has shown rising trend except mine (C-MINE), food
manufacturing (C-FMAN) and other manufacturing (C-MANF) as shown in table 4.
Table 4. Level of Activities (% Variation)
Activities Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Agriculture 1364731 0.000 0.000 0.000
Mine 28424 -1.665 -3.156 -4.496
Food Manufacturing 673967 -0.174 -0.342 -0.503
Cotton Lint/Yarn 224415 0.289 0.561 0.820
Textiles 545403 0.530 1.029 1.500
Leather 35937 1.220 2.415 3.588
Other Manufacturing 646118 -0.724 -1.342 -1.866
Energy 189246 0.060 0.117 0.170
Services 3067054 0.059 0.106 0.142
Source: research by authors
The highest rise in the output of leather and textile is witnessed. The output of leather (C-
LEAT) has increased by 1.220%, 2.415% and 3.58% respectively in the SIM-I, SIM-II and SIM-III.
The output of those commodities has increased whose producer price and domestic price have
risen and the commodities whose producer price has fallen their output has shrunk (table 5).
However, the fall in the output of mine (C-MINE) is at the highest rate, it has observed the fall of
1.665%, 3.256% and 4.496% in the respective three experiments. On the other hand the
quantity of domestic output sold domestically has fallen (table 6). Supply of composite
commodities in domestic market has increased (table 7).
Table 5. Producer price for Commodities (% Variation)
Commodities Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Agriculture 1.000 0.393 0.744 1.056
Mine 1.000 -1.612 -2.970 -4.107
Food Manufacturing 1.000 0.432 0.842 1.235
Cotton Lint/Yarn 1.000 0.684 1.353 2.010
Textiles 1.000 0.719 1.438 2.158
Leather 1.000 0.565 1.143 1.734
Other Manufacturing 1.000 -1.272 -2.351 -3.260
Energy 1.000 0.552 1.153 1.799
Services 1.000 0.802 1.619 2.446
Source: research by authors
Table 6. Quantity Sold Domestically of Domestic Output (% Variation)
Commodities Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Agriculture 1338316 -0.058 -0.121 -0.189
Mine 23132 -3.624 -7.051 -10.296
Food Manufacturing 560992 -0.591 -1.197 -1.815
Cotton Lint/Yarn 163591 -0.198 -0.438 -0.714
Textiles 329125 -0.263 -0.574 -0.927
Leather 20552 -0.045 -0.137 -0.270
Other Manufacturing 523768 -2.477 -4.852 -7.134
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Commodities Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Energy 189246 0.060 0.117 0.170
Services 2948732 0.034 0.057 0.069
Source: research by authors
Table 7. Quantity of Composite Goods Supplied Domestically (% Variation)
Commodities Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Agriculture 1374403 0.406 0.832 1.277
Mine 118911 -0.130 -0.193 -0.197
Food Manufacturing 618915 0.802 1.627 2.473
Cotton Lint/Yarn 170888 0.416 0.808 1.179
Textiles 348043 0.582 1.153 1.718
Leather 21730 0.735 1.455 2.163
Other Manufacturing 1330886 1.200 2.402 3.604
Energy 189246 0.060 0.117 0.170
Services 3002685 0.264 0.523 0.778
Source: research by authors
Incomes of households
Incomes of all households have shown soaring trend in all the three experiments but the rate
of increase in incomes differ from household to household (table 9). The growth rate of incomes
of households who are not concerned with agriculture sector is higher than the growth rate of
incomes of those who are concerned with the agriculture sector (table 7). The income of urban
poor households (H-URPR) has registered highest growth rate of 1.485%, 2.945% and 4.381%
in the three experiments. The average prices (rewards) of all the factors have increased but the
rate of increase in the prices of labour whether skilled or unskilled has remained higher than
that of other factors (table 8). Price of unskilled labour has registered the highest rate of growth.
Moreover, the output of leather and textile has witnessed substantial increase so the demand for
skilled and unskilled labour has increased leading to increase in their rewards, hence, income of
these households has increased. The increased outputs of domestic commodities and increased
exports have indirectly increased labour demand. Resultantly, jobless workers have got
employment and a source of income.
Table 8. Average Price of Factors (% Variation)
Factors Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Own Large Farm 1.059 0.659 1.230 1.725
Own Medium Farm 1.058 0.659 1.230 1.725
Own Small Farm 1.056 0.659 1.230 1.725
Agriculture Wage 1.081 0.659 1.230 1.725
Non-Agriculture Unskilled 1.058 1.878 3.740 5.485
Skilled 1.037 1.803 3.574 5.316
Large Farm 1.054 0.659 1.230 1.725
Irrigated Medium Farm 1.063 0.659 1.230 1.725
Irrigated Small Farm 1.059 0.659 1.230 1.725
Non-Irrigated Small Farm 0.979 0.659 1.230 1.725
Capital 1.067
Source: research by authors
Hasnain Naqvi, Slobodan Adžić, Nebojša Zakić, Milijanka Ratković, Israr Ahmad 127
Table 9. Income of Households (% Variation)
Households Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Large Farm 93954 0.800 1.568 2.306
Medium Farm 226190 0.818 1.602 2.357
Small Farm 501515 0.937 1.847 2.735
Landless Farmers 104611 0.905 1.779 2.628
Rural Agriculture Landless 98471 0.948 1.867 2.760
Rural Non-Farm Non-Poor 400770 1.303 2.589 3.861
Rural Non-Farm Poor 134399 1.242 2.471 3.688
Urban Non-Poor 1744122 0.775 1.549 2.321
Urban Poor 181413 1.485 2.945 4.381
Source: research by authors
Welfare of households
Table 13 depicts that overall welfare of households has increased. Incomes of households
have risen as a result of rise in factor rewards and higher level of activities. Higher incomes have
lifted constraints to consume more (table 10) which in turn has guaranteed higher level of utility
(table 12). Moreover, the welfare effect can be assessed by comparing prices of the factors
owned by households and consumer prices of commodities (CPI). Table 8 represents average
prices of factors while consumer price indices have been portrayed in table 11. By comparing
the results of these tables it can be concluded that average factor prices have risen at higher rate
than consumer prices of commodities. Consequently real incomes of households have risen.
The results about compensating variation (CV) of households reveal that the households
would be better-off in the consequence of increased Pakistan’s exports. All the households have
positive CV. However, the highest CV is recorded by urban non-poor, non-farm non-poor, small
farm and urban poor households (H-URNP, H-NFNP, H-SF and H-URPR) respectively. The
highest value of CV for urban non-poor and non-farm non-poor (H-URNP and H-NFNP) is due to
negative change in Consumer Price Indexes (CPIs) for these households and substantial increase
in average price of factors – unskilled and skilled Labour (LA-SKU and LA-SK). The households
who have witnessed negative change in their respective CPIs are Large farm, Medium farm, Non-
farm non-poor, Urban non-poor households (H-LF, H-MF, H-NFNP and H-URNP). The low value
of CV for Large Farm households (H-LF) is due to lower rewards of factors owned by H-LF,
though it has witnessed negative change in its respective CPI. Anyhow, the lowest values of CV
are recorded by Agriculture wage, non-farm and Large farm households (H-AGW, H-0F and H-
LF) respectively. It is evident that the households related to agriculture have received fewer
gains than households related to non-agriculture.
Nevertheless, the incomes and expenditures of all the households have soared. Consequently,
an increase in utility is witnessed by all the households (table 12). However, the highest utility is
recorded by Non-farm non-poor, Non-farm Poor and Urban poor households (H-NFNP, H-NFP
and H-URPR). Conversely, the lowest gains in terms of utility are stipulated for Non-Farm and
Agriculture wage households (H-0F and H-AGW). Such pattern of distribution of utility also
confirms that more gains are promised for households engaged in non-agriculture (urban
households). However, overall, economy-wide CV has risen (table 14).
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Table 10. Consumption Expenditures of Households (% Variation)
Households Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Large Farm 84554 0.800 1.568 2.306
Medium Farm 214882 0.818 1.602 2.357
Small Farm 476443 0.937 1.847 2.735
Landless Farmers 99374 0.905 1.779 2.628
Rural Agriculture Landless 93542 0.948 1.867 2.760
Rural Non-Farm Non-Poor 360694 1.303 2.589 3.861
Rural Non-Farm Poor 127680 1.242 2.471 3.688
Urban Non-Poor 1408485 0.775 1.549 2.321
Urban Poor 172343 1.485 2.945 4.381
Source: research by authors
Table 11. Household Consumer Price Index (% Variation)
Households Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Large Farm 1.023 -0.126 -0.244 -0.355
Medium Farm 1.023 -0.130 -0.252 -0.367
Small Farm 1.020 0.082 0.156 0.222
Landless Farmers 1.021 0.140 0.268 0.385
Rural Agriculture Landless 1.020 0.204 0.392 0.563
Rural Non-Farm Non-Poor 1.022 -0.007 -0.015 -0.023
Rural Non-Farm Poor 1.023 0.143 0.272 0.390
Urban Non-Poor 1.021 -0.052 -0.098 -0.139
Urban Poor 1.022 0.136 0.261 0.377
Source: research by authors
Table 12. Utility of Households (% Variation)
Households Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Large Farm 82670 0.927 1.816 2.670
Medium Farm 210039 0.949 1.859 2.734
Small Farm 467056 0.854 1.689 2.507
Landless Farmers 97329 0.763 1.507 2.234
Rural Agriculture Landless 91732 0.742 1.470 2.184
Rural Non-Farm Non-Poor 352910 1.310 2.604 3.885
Rural Non-Farm Poor 124810 1.098 2.192 3.285
Urban Non-Poor 1379794 0.827 1.648 2.463
Urban Poor 168712 1.347 2.677 3.990
Source: research by authors
Table 13. Compensating Variation of Households
Households
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Large Farm 783.018 1531.453 2249.628
Medium Farm 2036.844 39833.94 5852.417
Small Farm 4074.444 8060.780 11972.136
Landless Farmers 759.681 1501.683 2228.701
Rural Agriculture Landless 695.911 1380.172 2054.809
Rural Non-Farm Non-Poor 4724.538 9392.037 14010.118
Rural Non-Farm Poor 1403.589 2806.924 4210.789
Hasnain Naqvi, Slobodan Adžić, Nebojša Zakić, Milijanka Ratković, Israr Ahmad 129
Households
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Urban Non-Poor 11648.041 23194.694 34648.929
Urban Poor 2322.181 4624.842 6901.769
Source: research by authors
Table 14. Economy Wide Compensating Variation
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Total Compensating Variation 0.937 1.859 2.769
Source: research by authors
Inequality
Inequality has been a question of debate. A great deal of economic literature has been devoted
to answer whether inequality retards the process of economic growth or not? A number of
different techniques and indicators are used by economists to measure inequality but the most
popular and common in practice are Hoover index and Theil indices: Theil-L, Theil-T and Theil-S.
These indicators measure intra-group and inter group inequality. However, in this study
inequality among groups has been measured. The results of these indices, as depicted in table
15, show that inequality among the household groups has decreased with the increase in
Pakistan’s exports as these indices have shown smaller values in SIM-I than in SIM-II and so on.
Table 15. Indices of Inequality
Indices Base
Simulation-I
(5%)
Simulation-II
(10%)
Simulation-III
(15%)
Theil-T 0.318 0.317 0.316 0.315
Theil-L 0.326 0.325 0.324 0.323
Theil-S 0.322 0.321 0.320 0.319
Hoover Index 0.346 0.345 0.344 0.344
Source: research by authors
CONCLUSION
The study employs CGE model, on the data provided in SAM 2007-08 for Pakistan designed
by Dorosh et al. (2012). Trade elasticities for different commodities in Pakistan have been
borrowed from Ahmed et al. (2008). Three experiments are performed to gauge the impact of
increase in Pakistan’s exports on its various economic aggregates. Pakistan’s exports have been
increased by 5% in the first simulation (SIM-I), in the second simulation (SIM-II) by 10% and in
the third simulation (SIM-III) by 15%. The findings of the study reveal that increase in exports
has favourable impact on the performance of macroeconomic variables of Pakistan’s economy
i.e. GDP, public and private consumption, savings and investment. Domestic output level of most
of the commodities has risen except mine, food manufacturing and other manufacturing.
Incomes and expenditures of all the households have risen. Resultantly, utility level of all the
households has risen. Moreover, all the households have also recorded an increase in the values
of compensating variation which implies higher level of welfare for households. However, the
value of compensating variation for non-agriculture households has risen more than that of
agriculture households indicating pro-urban effect. Equality among the households has
improved as the inequality indices have registered declining trend. All this suggests that export
promotion measures should be incorporated in poverty alleviation, income equality and
economic growth strategies.
130
Economic Analysis (2018, Vol. 51, No. 1-2, 120-130)
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Article history: Received: March 26, 2018
Accepted: June 20, 2018