Contents

- 1 Impact of Trade Restriction on Price Stability of Textile Industry of Pakistan
- 2 CHAPTER 1: INTRODUCTION
- 3 1.1 Chapter Summary
- 4 Chapter 2
- 5 Chapter 3:
- 6 Chapter 4
- 7 Chapter 5
- 8 CHAPTER 2: LITERATURE REVIEW
- 9 2.1 Chapter Summary
- 10 CHAPTER 3: THEORETICAL FRAME WORK AND CONSTRUCTION OF HYPOTHESIS
- 11 3.1 Problem Identification / Research Problem
- 12 3.2 Research Hypothesis
- 13 3.3 Chapter Summary
- 14 CHAPTER 4: RESEARCH METHODS
- 15 4.1 Data Collection & Procedure
- 16 4.2 Research Methods
- 17 4.3 Application of Statistical Test
- 18 4.4 Chapter Summary
- 19 CHAPTER 5: RESULTS AND HYPOTHESIS TESTING
- 20 5.1 Interpretation & Assessment of Regression Analysis
- 21 5.2 Summary of Hypothesis Assessment
- 22 5.3 Chapter Summary
- 23 Impact of Trade Restriction on Price Stability of Textile Industry of Pakistan
- 24 CHAPTER 6: SUMMARY AND CONCLUSION
- 25 References
- 26 APPENDIX
- 27 List of Websites

Price instability has been an issue of long concern to economists and recently has received increased attention, mainly because of economic events taking place in the world. At different phases of price fluctuations, different groups become more vocal and more concerned about price movements rising prices affect consumers and importing nations, whereas falling prices are of vital interest to producers and exporting countries. If price stability in the world market is desired, trade liberalization, not greater restriction, is more likely to provide it. The frame work use in research is partial equilibrium or gravity model. Partial equilibrium model is use to examine trade issues in a single market or, alternatively, in a few closely-related markets. It is adaptations of standard supply and demand analysis to the particular features of trade and trade policies. It is extensively used in the analysis of countervailing and anti-dumping duties, as well as to evaluate the possible effects of many types of trade policy changes in narrowly defined sectors.

Research has examined only barriers to Pakistan textile imports. It is well known that the trade restriction between countries acts as a natural barrier to international trade. A number of authors have examined the size of this barrier and have compared it with the barriers imposed by tariffs or quotas (Water, Finger, yeats, & Cleark, 1970). Their goals were to evaluate the potential for unfettered trade to eliminate differences among countries in prices of traded goods and to show the degree to which existing differences could be attributed to natural as opposed to artificial trade barriers. In this thesis research identify another, previously unrecognized source of natural trade barriers. Research estimates the impact of trade restriction effecting price stability in textile industry with respect to textile items imported to home country, which helps to understand the impact when each restriction modified. Textile imported items comprises of textile machinery (TM), raw cotton (RC), synthetic fiber (SF), artificial silk yarn (ASY), worn clothing (WC) and other items (OI) .The results indicate that natural trade barriers play a great role in insulating domestic producers from import competition than was previously recognized. Research focus on the trade restriction i.e tariff, exchange rate and transportation cost because these are the barriers that determine the protection afforded to various domestic industries and factors of production.

This chapter lays the foundations for the study. It introduces the research objective and a brief description and justification for this study i.e impact of trade restriction on price stability of textile industry of Pakistan is discussed and major terms used in the study are defined. Finally, the out line of the study presented. This introduction chapter introduced the subject focus of this research. The rest of the thesis will be structured in the following manner.

This is the theoretical part of the dissertation i.e the literature review on “Impact of trade restriction on price stability of textile industry of Pakistan” present ending with the identification of research problem and setting hypothesis.

Thoroughly study about trade restriction and its relevance to price stability, it can be said that trade restriction is a vast study which has and need to be more focused. The area for research needs to be focused to understand its major constructs, the relationship between the variables and trade restrictions affecting the price stability in textile industry.

The different articles illustrated assorted theories and models of trade restrictions are studied with this in mind, the next section will present a theoretical framework and hypothesis for investigating the impact of trade restriction on price stability of textile industry of Pakistan.

In this chapter, a theoretical framework is provided to present the relationship between the trade restriction and textile prices. After defining problem identification / research Problem, the research hypotheses are defined and then hypothesis are established for verification.

In this chapter, the methodological issues pertinent to the investigating the impact of trade restriction on price stability of textile industry of Pakistan are presented and discussed. This chapter covers data collection, research methods and application of statistical test.

In this chapter, the interpretations & assessments of regression analysis for research hypotheses are carried-out.

In this new era of learning and development organization’s achievement and competitiveness mainly depends upon constantly humanizing performance by reducing cost, improving and creating new products and process, improving productivity and quality, growing pace to be the first to the market and all aspects of the organization must expose their competence to positively impact trade and performance. Trade is an input part in economic development. An effective use of trade can enhance a country’s growth. On the other hand, opening up opportunities and markets to international trade may leave local producers rushed off your feet by more competitive foreign producers. Mainly trade barriers work on the same principle, the imposition of some sort of cost on trade that raises the price of the traded goods. If two or more nations repeatedly use trade barriers against each other, then a trade war outcome. Trade barriers are damaging and decrease overall economic efficiency.

Rousslang & Theodore (1993) estimated the barriers that the domestic margins enforce against U.S. imports and shows that surpass the barriers enforce by tariffs and international transport costs collective. Research estimated the size of barriers and compared them with tariffs and international transport costs. Research finding indicate that natural trade barriers play a much greater role in domestic producers from import competition than was previously recognized.

Various researchers have examined the size of barrier compared the results with the barriers imposed by tariffs or quotas (Waters, Finger, Yeats & Clark, 1980).

The difference between transportation costs within the home country for imports and the rival domestic output can hand out to either reduce or exacerbated the trade barrier imposed by international transport costs, but overlook the role of transport costs within the exporting country (Finger & Yeats 1976). Longer delivery time for imports enforce additional inventory storage costs in the United States that caused wholesalers to pay a premium of from 9 to 11 percent for domestic steel over physically identical imports during the 1970s (Chase & Gamble 1982), even though sold the steel from both sources at the sale price. Wholesale margins on imports can also reflect other cost disadvantages, such as the need for currency exchanges, arising from differences in language and laws, and necessitate for imports to be processed through customs. Research found that costs within country services in transportation, wholesaling, and domestic margins impose significant barriers to international trade. In particular, Researcher estimate that barriers imposed against U.S. imports that are on average greater than those imposed by tariffs and international transportation costs collective. Though, the domestic margins do not appear to alter much the pattern of protection by phase of fabrication from that afforded by the other barriers.

Koo (1984) revealed that price differences among U.S. export ports are dependent upon export supply of wheat, import demand for wheat, and transportation costs in shipping wheat from producing regions to importing regions under an assumption of free trade. Research also revealed that the West Coast ports have the highest price for winter and spring wheat and the Great Lakes have the lowest price. Price differences between those two ports are approximately 70 cents per bushel. Contrasting tariffs, changes in ocean freight rates influence wheat price at U.S. export ports more than in importing regions. Approximately 80 percent of ocean freight rate increases (85 percent for winter wheat and 70 percent for spring and durum wheat) are borne by producers in the U.S. resulting in substantial decreases in wheat prices at U.S. export ports. This indicates that the major factors affecting wheat prices at the U.S. export ports are trade restrictions, and volatilities in ocean freight rates.

Moneta (1959) investigated the relationship between the total cost of an international traded commodity and the portion of this cost incurred in shipping the goods from country of production to country of consumption. Research estimated that ratio of transportation cost to total cost varies widely among commodity.

Sampson & Yeats (1977) evaluated the incidence of transport costs facing Australian exports to the United States, and to compare their magnitude with the existing level of most favored nation tariffs. Comparison of the transport and tariff s reveals that for two thirds of the products (66 per cent) transport costs present a greater obstacle to trade than MFN tariffs. Actually, for all the food and live animals group, except sugar, transport charges exceed tariffs. Transport costs are generally a more formidable trade barrier for chemical products, though the spread between transport costs and tariffs is lower than in the food, beverages, or coal groups. Further pursuing results indicate that attempts to export more highly processed products in a direct processing chain (i.e., pig iron as opposed to iron ore, thread as opposed to raw wool, etc.) will have little overall influence in reducing nominal transport costs. Transport costs pose a much greater barrier to Australian exports than tariffs. Research evaluated the relative importance of tariff and transportation charges on products of export interest to Australia. On an overall basis, research found that the incidence of transport and insurance costs varies greatly across products and poses a barrier at least two to three times the current level of tariffs. It has shown that the incidence of transport costs can be blunted by changing the composition of exports; possibly equal importance attaches to the achievement of economies of scale in shipping. In the bulk trades, port modernization and the use of larger vessels should automatically result in reducing transport costs; but this does not happen in the liner trades, where costs savings may merely result in higher profits to ship-owners.

Conlon (1981) estimated the transport cost protection available to import competing Australian producers, and to compare them with estimates of transport costs barriers protecting similar Canadian manufacturing industries. Research has shown that for Canada the international transport costs of a given commodity may be less than the cost of its internal transportation and this fact will tend to provide a stimulus rather than a barrier to international trade. Protection variable significantly lower in Canada than in Australia, but the relative contribution of transport costs to the total protective barrier in Canada is also substantially lower. In Australia transport costs contribute over 40 percent of the total trade barrier; in Canada contribute just over one-quarter. In effective terms, transport costs provide over 30 percent of the total barrier in Australia; in Canada provide just over 17 percent. Clearly, Australian manufacturing has developed behind higher average natural and man-made barriers to trade than has the manufacturing sector in Canada.

Casas (1983) studied to develop a systematic approach to covenant with a variety of issues carry along from the incorporation of transport costs into the standard models of international trade under different assumptions about the nature of the technology of transportation. It also estimates the effects of transport cost on commodity and factor prices, the allocation of resources and the level of welfare in the trading countries.

Research has attempted to provide the framework within which transport costs can be integrated in the general equilibrium model of international trade without desertion from the simplicity which has enabled economists in this field to end up with the wealth of outcome. The study has shown the results that the impact of the transportation on the output of the traded goods at steady domestic prices can be determined. Results also show that technological advances in the transportation segment will not essentially reduce the domestic relative price of the importable commodity.

McFarland (1981) estimated international transportation costs for US imports and changes in these costs from 1976 to 1981. Research also evaluates how the levels and the changes in these costs vary with the level of development of the import supplying country. Transportation cost persist to be a significant barrier to US imports, but comparative transportation costs have decline sharply in recent years. The ratio of transportation costs to significance is greater for US imports form developing countries and is greatest for US imports from least developed developing countries. In addition, the transportation cost disadvantage of least developed developing countries has been increasing. To study the relationship between the level of development and international transportation costs, US trading partners are grouped by per capita gross National product into four separate categories: developed countries (DCs), advanced developing counties (ADCs), middle developing counties (MDCs), and least developed countries (LDDCs), each category is then subdivided by region.

In 1965 the freight factor for US imports declined from 10 percent to 4.5 percent till 1981. The freight factor for all US imports other than petroleum products declined form 11 percent in 1965 to 5.1 in 1981.The decline in these freight factors could have resulted from a decline in freight rates, from changes in the mode of transportation used, from changes in the relative import commodity composition of imports, or from changes in the relative importance of US trading partner. (Freight rates are the ratio of transportation costs to value of a specific product moving between two countries in a specific mode of transport). It contributes the estimates of international transportation costs for US imports and it examines the relationship between international transportation costs and the level of economic development using data for 115 countries and four levels of development. Freight factors are much larger than tariffs for agricultural, mining, and petroleum products. Non manufactured goods usually have lower value per ton and therefore higher freight factors than manufactured goods, whereas manufactured goods usually have higher tariffs than non manufactured goods. Results examines that transportation costs have continued to be a significant barrier to US imports; in 1981 estimated barriers were 4.5 percent of the value of these imports. However, relative transportation costs have declined sharply in recent years. From 1976 to 1981, these costs declined between 25.8 percent and 29.2 percent. The ratio of transportation costs to value was greater for US imports form developing countries than for US imports form developed countries and was greatest for US imports from least developed developing countries.

Furthermore, the decline in relative transportation costs was smaller for least developed developing countries that for other countries. Thus, the transportation cost disadvantages of these countries has increased.

Finger & Yeats (1976) estimated and compare the importance of transportation costs and tariffs as barriers to international trade. Transportation costs tend to protect domestic producers from foreign competition. As such, the analysis differs from most previous studies of international trade problems that either neglect or assume away the weight of transportation costs. The overall results indicate that, transportation costs create a barrier at least equal to tariffs in the United States whether considered in terms of nominal or effective rates, and like effective tariffs, effective transport costs appear to increase with stage of processing. In addition to an evaluation of current magnitudes, evidence is also presented that suggests that tariffs and transportation costs have been changing in relative importance to each other.

Examination of several transportation rate indices shows that these costs have risen significantly since 1965. It seems probable that recent petroleum price increases will have significant adverse effects on trade barriers arising from transportation costs. Research has identified that transportation charges tend to be higher on products exported by LDC’s than on products exported by developed countries. To the extent that this results from inefficient port facilities in the LDC, the prescription, which follows, is obvious. But studies have indicated that shipping rates may frequently be allocated on other than a cost basis. Thus, it may be useful to investigate the development of regional transportation groupings that would improve access of the developing countries to industrial markets and also improve their bargaining position for shipping rates.

Binkley & Harrer (1981) studied the Major Determinants of Ocean Freight Rates for Grains. Research pertained to the relationship between international shipping and the comparative position of countries in the world grain trade. Research shows that larger ships lead to lower at sea transport charges. The measurement of the interaction between ship size and port costs did not yield a clear-cut answer, but the evidence points toward a positive relationship. If this is so, inadequate port facilities will hinder further savings in shipping. This is probably why some major exporters recently have made substantial port investments. Such investments are likely to be the primary source of future cost savings in export grain handling. Research has shown a negative association between a route’s grain trade volume and ship-ping rates, while this reflects the association between traffic volume and efficient port facilities.

Small importers may not be able to generate sufficient trade to benefit from high levels of shipping activity, and their erratic demand may preclude the construction of efficient grain off-loading facilities. LDCs may find themselves at a continuing disadvantage in world trade, worsening problems brought by rising food and fuel prices. Research shows that expansion should occur in the areas with relatively heavy ship traffic. But optimal port improvement and location depend upon the trade-off between inland and export transportation costs. The U.S. export efficiency grain transport system perhaps could be enhanced by a combination of domestic transport changes (such as the introduction of long-haul unit trains to certain ports) and port improvements. But neither change may be justified without the other, and it is difficult to effect such changes without coordinating policy. Research revealed that relative transport costs between producers and importers do not necessarily, nor even primarily, reflect unalterable geographic factors, but are more a function of transport and port technology and overall patterns of trade, both of which are policy relevant. This suggests that the effects of changes in international transport costs on trade is itself worthy of study, and that international trade research which does not consider transportation factors may be based on misconceptions and may generate erroneous conclusions.

Pritchett & Geeta (1994) studied tariff rates with the relation of tariff revenues to import values for Jamaica, Kenya, and Pakistan. It identified general features of tariff code and also study features apply to all developing countries, investigate the implications of these features for tariff reform.

During periods of stabilization and fiscal austerity any tariff reforms are undertaken, the possible loss of tax revenues from lowering tariff rates is commonly apparent to be an important constraint on tariff reform (Rajaram & Mitra1 992). The data reveal different stages in the countries’ trade reforms. Jamaica had already had several rounds of tariff reform, with substantial reductions in the average rate. Pakistan’s tariff code had already undergone substantial rationalization as part of the country’s modification efforts. Kenya’s trade reform primarily focused on import licensing to date, including some tariffication that raised the unweighted average. Research finds that revenues are less than the official tariff, because the appropriate rate is the marginal tariff rate. Further study revealed that in some cases exclusion will not reduce the fortification provided; it will presently create a subsidy to the exempted activity. In additional cases an exemption will effectively lower the protection provided by the tariff.

Black (1930) studied the effect of tariff duties on the prices of a commodity, compare the differentials between the markets of the United States and the potentially importing countries before and after the duty was first imposed, or was raised or lowered to a new level. Products which have an inelastic demand and an inelastic supply in the home country will yield increasingly high returns as the duty is raised. Sugar is an example of such a product. Duties on butter, beef and most products of this nature are of the opposite type, only a small duty being needed to keep out nearly all imports. Effects of duties on price differentials can be only approximately stated and that the effects on prices in the protected country can be ascertained with only still more uncertainty.

Irwin (1998) studied the changes in import prices and in tariff rates. It also provide some insight into the political process that brought about legislated changes in tariff rates and influenced the choice of specific versus ad valorem duties. Research reveals that from century following the Civil War, about two-thirds of dutiable U.S. imports were subject to specific duties, the ad valorem equivalent of which was inversely related to the level of import prices. Specific duties were the choice method of import taxation of the Republicans, whose support for protective tariffs led them to favor such duties for their enforceability, their protective insurance against falling prices, and perhaps for their obfuscation of the actual protection given. Congress retained close control of the tariff prior to 1934 and undesirable price-induced changes in the tariff between legislative acts were not pronounced. After Congress began delegating tariff-negotiating powers to the President in 1934, import price inflation contributed much more to the decline in the tariff than trade agreements.

Eden (1983) estimated that when international trade occurs between related firms, customs authorities have two tools that can be used to achieve the goals of tariff policy: tariff rates and the customs valuation method. These methods force certain transfer pricing policies, which may or may not be indirectly tied to the volume of imports, on the MNE with predictable effects on intrafirm trade, output, and tariff revenues. When falling tariff rates are accompanied by a shift in the valuation method, the impact on import-competing MNE affiliates can be significant. Research predicts that the current shift from FMV to TV by Canadian tariff authorities will emphasize the partial equilibrium effects on intrafirm trade and output of secondary import-competing MNE affiliates of the concurrent drop in Canadian tariff rates. Assuming positive net protection from the Canadian tariff structure, the expansion in imports and the fall in domestic production will be larger than one would predict on the basis of the drop in tariff rates alone. The probable drop in tariff revenues, however, will be smaller. To the extent that the MNE can manipulate transfer under the GATT transfer value principle, Research predict that, assuming the tariff rate continues to dominate the tax differential, the effects on imports and domestic production will be stronger and the drop in tariff revenues larger. Since 40 percent or more of Canadian imports are bought in markets where scope for transfer price manipulation exists, the shift from FMV(fair Market value) to TV(transfer value) at the same time as tariff rates are falling will therefore weaken the effectiveness of the Canadian tariff structure as a trade barrier.

Most countries allow free import of raw materials. However, because manufacturing proceeds through several stages, and there are tariffs at various stages, substantial anti protection can exist for industries using imports or import competing products as inputs. Therefore, primary tariffs should not be dismissed as irrelevant in countries where raw imports are duty free but should be treated as tariffs applying to earlier stages of manufacturing (Williams 1978). Since Canada is both lowering tariff rates and shifting from FMV to TV, Research finds that the expansion in imports will be larger than that predicted on the basis of the drop in rates alone. The probable contraction in tariff revenues will, however, be smaller. Therefore the joint policy changes of lowering rates and moving from FMV to TV have conflicting effects on the goals of Canadian tariff policy. Research also find that if the transfer value principle allows the MNE more freedom to manipulate transfer prices, assuming tariff rates dominate, the multinational will reduce the acceptable lower bound , imports will expand, domestic secondary production will contract, and tariff revenues will decline. This freedom therefore further weakens Canadian tariff policy as a trade barrier

Balassa (1965) estimated the height of national tariff levels are designed to give expression to the restrictive effect of duties on trade flows. In a general equilibrium framework, the restrictive effect of a country’s tariff can be indicated by the difference between potential and actual trade, when the former refers to trade flows that would take place under ceteris paribus assumptions if the country in question eliminated all of its duties. Tariffs affect the pattern of production and consumption and generally reduce imports and exports under full employment conditions as changes in relative prices associated with the imposition of tariffs lead to resource shifts from export industries to import competing industries. Research shows that, in international comparisons of the protective effect of national tariffs, one should use effective rather than nominal rates of duties.

Reynolds (2005) estimated the impact of U.S. tariff reductions on imports from the developing world using a panel of import data from 76 countries and 2,389 Generalized System of Preferences (GSP) eligible products between 1998 and 2001.

Research find that reductions in normal U.S. tariff rates over the past 30 years have slowly eroded the tariff preferences granted to developing countries through this program. Empirical results from shown that tariff reductions have indeed had a large impact on U.S. imports from developing countries.

Specifically, Research has found that a one percent reduction in MFN tariff rates on GSP-eligible products results in a 0.6 percent decline in U.S. imports of these products from the average developing country. Furthermore, the largest beneficiary countries those in Asia experience an even larger decline in imports. Based on these estimates, developing countries would have had approximately $1.0 billion more in exports to the United States in 2001 if not for the decrease in U.S. tariff rates under the Uruguay Round trade agreement between 1997 and 2001.

These results do not suggest, however, that developing countries can not significantly benefit from future global trade negotiations that reduce tariff rates around the world. Many of the products produced by developing countries such as textiles and apparel and agriculture products are ineligible for the GSP program. Moreover, the results show that a few more developed countries are reaping the lion’s share of benefits from the GSP program and, thus, will see the largest declines due to the reduction in tariff margins. However, these countries are the very ones that are most likely to lose GSP-eligibility due to competitive need limitations and income thresholds. Research shows that the reduction in preference margins on GSP-eligible products will have very little impact on trade between the United States and the poorest developing countries. Research also suggests that the United States and other industrialized countries may want to revisit whether the GSP program is the best tool to encourage economic development. The large impact that tariff reductions have had on imports from certain GSP beneficiary countries suggests that the GSP program has resulted in a significant amount of trade diversion to a few, large beneficiary countries. However, the program does not appear to be assisting the poorest countries at all.

Gardner & Kimbrough (1989) estimated the behavior of U.S tariff rates. Research had shown that tariffs and the trade balance suggest that only temporary increases in tariffs are likely to improve the trade balance significantly. Indeed, if an increase in the current tariff rate is taken to signal additional future increases as is implied by the empirical results for the income tax period, then a tariff increase will lower the relative price of current consumption and tend to worsen the trade balance. Therefore, the proposed new trades restrictions will have their intended effect of improving the trade balance only if the private sector believes are temporary. Such a belief requires the private sector to ignore the history of U.S. tariffs. The logic behind using tariffs and other trade restrictions to reduce a trade balance deficit is that raise the price of importable relative to exportable and thereby shift demand from foreign goods to domestic goods ( Dixit and Norman 1980). Temporary tariff increase raises the cost of consumption today relative to consumption tomorrow and thus raises the domestic real interest rate. This rise in the real interest rate decreases current consumption and leads to an improvement in the trade balance. A permanent tariff increase, on the other hand, changes the price of both current and future consumption and has an ambiguous effect on the trade balance (Edwards, 1987).

Red & Woodland (1991) Strict Pareto Improving Multilateral Reforms of Tariffs and specify a many nation, many commodity model of international trade containing distort arising from trade tariffs and examine the possibilities for the gradual multilateral reform of tariffs. Attention is focused upon the attainment of strict Pareto improvements in welfare, whereby every nation gains from the tariff reform, since only these reforms are guaranteed to get unanimous approval. Proportional reductions in all tariffs and a reduction of extreme ad valorem tariff rates can be welfare improving. Demonstrations that a proportional reduction in all tariffs in welfare improving in a small open, single- household economy have been provided by Foster and Sonnenschein (1970), Bruno (1972), Lloyd (1974), Hatta (1977), and Fukushima (1979). Various results rates, which typically rely upon substitutability assumptions, have been derived by Bertrand and Vanek (1971), Hatta (1977), & Fukushima (1979).

Vnaek (1964), Hatta & Fukushima (1979) considered tariff reform in a many nation world that trades in just two commodities and show that a reduction of the world’s highest ad valorem tariff rate will yield a potential increase in welfare for all nations. Hatta & Fukushima also prove that a proportional reduction in all tariff rates is potentially welfare improving. Results show necessary and sufficient conditions for the existence of such reforms, and demonstrate that various particular tariff reform proposals satisfy these conditions. Results of tariff reforms constrained to strict Pareto improvements and showed that the outcomes of negotiated tariff reforms are Pareto superior to the tariff war even if may not be free trade situations.

Olarreaga (1998) studied Tariff Reductions under Foreign Factor Ownership that within the existence of foreign factor ownership, the traditional welfare effects of tariff reform shave to be reconsidered to include income reorganization between national and foreign-owned factors.

Bhagwati & Brecher (1980) showed that immiserizing tariff reductions may occur when the relative amount of foreign-owned factors in the host country is adequately large to persuade a change in the direction of the trade pattern. Research explains that similar results can be obtained in the mirror case when foreign owned factors tend to support the existing trade pattern (i.e., trade promoting).

Mutti (1979) analyzed the economic efficiency effects of tariff concessions. Research shows the importance of considering terms of trade changes from multilateral trade negotiations. Import and export demand elasticity have been applied without adequate attention to whether competing goods change in price at the same time; this issue becomes particularly important when trading blocs play a major role in world trade. To incorporate the role of trade with developing countries show that the factor can be significant for countries that carry on a relatively large share of their trade with non-OECD nations.

Shea (1997) investigated the impact of tariff induced capital movement on the welfare of a small country. The general condition under which tariff induced capital inflow and outflow will increase or reduce the welfare of a small tariff ridden economy is investigated. Tariff induced capital movement can either increase or reduce welfare depending on the cost of capital. Result shows that tariff-induced capital movement must reduce welfare despite the possibility of taxing the return to the migrated capital. Under other conditions, however, tariff-induced capital movement can increase welfare if the return to the migrated capital is taxed appropriately.

Irwin & Temin (2001) studied The Antebellum Tariff on Cotton Textiles Revisited that American textile manufacturers were well established by the 1830s and not dependent upon the tariff for their survival. The effect of changes in the relative price of imports on domestic production (estimated using time-series data) appears to be small, even in the late 1820s and after the mid-1840s when potentially redundant tariff protection is not an issue. Part of the explanation for the relative unimportance of the tariff during this period is that, as historical contemporaries observed, British and American products were quite different from one another. Result shows that high tariffs were not an essential component of the survival and success of the later antebellum domestic cotton-textile industry, although the early cotton industry may have been protected by the Tariff of 1816.

Edwards (1989) studied Tariffs, Capital Controls, and Equilibrium Real Exchange Rates. To examine how the equilibrium real exchange rate (RER) reacts to changes in the degree of limitations to trade a general equilibrium with optimizing consumers and producers is developed. Research investigate the effects of changes in the intensity of changes in taxes on foreign borrowing on the course of equilibrium RERS and of import tariffs. In research import tariffs, both temporary and projected changes are measured. It was described that in this equilibrium real exchange rates can demonstrate attractive and convoluted behavior. Under the more general conditions, and contrary to the traditional wisdom, tariff liberalization doesn’t necessarily result in an equilibrium real depreciation. The direction in which the equilibrium RER will move will depend on a number of key parameters, and will likely vary from country to country. Expected future tariff hikes will generate an (equilibrium) real appreciation in the current period. Research has shown that the long-run equilibrium real exchange rate can experience wide swings.

Rolph (1947) estimated the shifting and burden of import duties. An examination of import duties, like other taxes, involves the dual task of determining the payment burden and the economic effects of the levies. To the extent that import taxes yield revenue to governments imposing them, some people or groups must be giving up the money which the government receives. The people who pay money to the government in the first instance, the legal taxpayers, may or may not be the final taxpayers. If people are not the final taxpayers, people act as a conduit for the money paid by other groups on its way to the Treasury. The determination of what people or groups contribute the money which becomes the Treasury’s tax revenue is the problem of tax incidence.

Taxes may, in addition, affect production, resource allocation, and the composition of products made available in the economy. Import taxes may reduce (or increase) the volume of imports or alter the composition of imports. Domestically, the production of exports may be curtailed because of import taxes and the output of products for domestic sale may be increased, and if the economy is not capable of sufficient adjustment, import duties may lead to unemployment of resources. In addition to real effects, import taxes may alter the absolute level of the national money income and change its distribution among various members of the community.

The foreign exchange relations between countries where freedom exists to buy and sell foreign exchange and to export and import goods, services, securities, and money ranges from absolutely fixed exchanges to automatically fluctuating exchanges, If the tax acts to restrict the amount of the commodity imported, it may raise the price, and thus buyers are said to bear the tax which their government imposes.

In the taxing country, the reduction in the supply of heavily taxed imports and the increase in the supply of lightly taxed or exempt imports are accompanied by a rise in the prices of the first group and a fall in the prices of the second. This occasions some redistribution of the real gain of economic activity in favor of the users of imports which are lightly taxed. The protective action of non-uniform schedules of import taxes is particularly marked in cases where tariff schedules provide heavy duties for classes of commodities which are deemed competitive with domestic production and low or zero duties upon "raw materials." Foreign-produced items such as highly fabricated articles are, in general, subject to high rates whereas raw materials (with many exceptions) are taxed at low rates or not at all. With full adjustments to gold flows, such a pattern of rates has a double-barreled action. Imports of manufactured items are curtailed and this provides a more favorable domestic market for domestic producers of commodities which are substitutes for such imports and hence "protects" the domestic market. If domestic producers of such products use imported raw materials in some measure, the low rates on such imports and the selective effect of the tariff lowers the domestic prices of these commodities. To the extent that these prices constitute a part of costs, the profitability of the production of items using imported raw materials is increased. Thus protection is offered both by increasing the demand for products which are substitutes for imports and by reducing the costs of producing them, to the extent that their production requires the use of imported raw materials. On the other hand, those producers of products which are substitutes for low-taxed or exempt imports are exposed to more competition from a "protective" tariff and are made worse off in terms of income than it would be with no tariff at all.

Wemelsfelder (1960) estimated the short-term effect of the lowering of import duties in Germany. It present that within a short period import duties were more than halved.

Research has estimated the German tariff reduction on the German economy. Notably the cut in import duties on industrial end-products appears to have had a highly stimulating effect on imports. The import elasticity has even been estimated at 8 or 10. Research shows that the increase in imports as a result of the tariff reduction has probably been at the cost of the development of the domestic production (a large substitution effect) rather than the consequence of greater consumption. In reviewing the German tariff reduction it should be noted that it came about in the course of two years and amounted to more than 50% of the tariff applied. In spite of the forced character of this reduction the output of industrial end-products hardly showed a ripple, because the normal rate of growth, and particularly the greater export opportunities, offered sufficient compensation for the effects of the tariff reduction. That in terms of welfare the part played by the tariff in the whole of the modern economic structure of a large industrial country is negligible is shown by the tentative calculation that was made of the effect of the German tariff reduction on the prosperity of the German economy. The short-term increase in the national income as a result of the cut in import duties was minimal.

The cut in the import duties on semi-manufactures appears not to have caused any distinct large shifts. In all probability the economic effect has been small, in any case much smaller than it has been in the case of finished products.

Cheng, Qiu & Wong (2001) investigated the design of optimal enticement compatible anti-dumping (AD) measures. When the domestic firm’s profit was given weight in the government’s objective function is comparatively small and if the foreign firm reports its own costs it is exposed that no AD duty should be imposed, but a invariable AD duty should be imposed if the domestic firm reports the foreign firm’s cost. When this weight is large, the AD duty is a prohibitive tariff in either case of reporting. Research estimates the optimal incentive compatible AD measures. The foreign firm’s marginal cost is known to the home and foreign firms but not to the home government. Consistent with the actual implementation of AD measures, it assume that the optimal AD measures are chosen so as to maximize some weighted average of consumer surplus, producer surplus, and net government revenue.

The explosive use of AD actions, especially those taken by the United States and European producers, to restrain foreign competitors since the 1980s has resulted in what some policy analysts call ‘anti-dumping protectionism.’ Like other trade policy tools, it would be reasonable to expect that the imposition of AD measures reflects national welfare. Indeed, according to Veugelers and denbussche (1999), ‘European antidumping legislation requires policy makers to consider the "Community’s Interest" as a whole when taking protectionist action. This Community Interest Clause corresponds quite well with economists’ notion of national welfare, which is composed of three elements, local consumer surplus, domestic firms’ profits and any possible tariff revenue.’

To arrive at an average dumping margin when an AD duty is to be imposed, the government excludes all transactions where dumping did not occur (Morkre and Kelly 1994). Research pursue if the home government attaches a large weight to the home firm’s profits, then the optimal AD duty is a prohibitive one for all. If the weight attached to the home firm’s profit is small, then the government needs to know (through policy design, not investigation) only whether dumping has occurred. If it has occurred, the home firm will file an AD petition, and the government simply applies an optimal constant specific duty, and a corresponding constant lump-sum tax.

Even though the government is free to design measures that depend on the reported cost, it is not optimal to do so, owing to the incentive compatibility constraint. If the government has information about the foreign firm’s marginal cost and attaches a relatively small weight then, it should set a lower duty when the foreign firm’s cost is higher. Research investigated the design of optimal incentive compatible AD measures that can induce the firms involved to report their truthful information and thus save both time and costs. When the home firm is relied upon to report the foreign firm’s cost, it has found that the optimal AD duty is either a prohibitive tariff (if the relative weight attached to the domestic firm’s profits is high) or one that is independent of the actual dumping margin (if the relative weight attached to the domestic firm’s profits is low). If the foreign firm is asked to report its own cost, it has found that the optimal AD duty is zero if the weight attached to the domestic firm’s profit is not large enough. The optimal AD duty is an excessive tariff, if the weight is sufficiently large. If the weight is intermediate, then the optimal AD duty is an increasing function of the dumping margin. Under the GATT/WTO rule, the optimal incentive compatible AD duty is modified by setting the optimal AD duty equal to the dumping margin.

Nowell (1932) studied the Effects of a Duty on Philippine Sugar. All of the islands have shared in increase the growth of sugar export, but the Philippines have increased their exports to the United States the most rapidly of the group. Puerto Rico, Hawaii and the Virgin Islands have about reached their maximum limits of expansion, but in the Philippines there still remain large tracts of land suitable for cane production which has yet to be brought under cultivation. Imports from Cuba during the period 1924-28 amounted to 54.1 percent of consumption, but since have gradually decreased until in 1931 represented only 37.2 percent. In the years 1924-28 the United States took 79.2 percent of Cuba’s exports, and in 1931, 76.5 percent. Cuba, however, is still the largest single contributor to the United States’ sugar supply, and is the largest exporter of sugar in the world. If the imports from the Philippines were entirely excluded by a tariff, Cuba would have ample sugar Owing to the relative ease with which sugar can be stored or transported, prices in the various markets throughout the world are kept in fairly close alignment with due allowance of course for tariff and freight differences. The Smoot-Hawley Tariff Act of 1930 imposed a rate of $2.50 per hundred on full duty sugar. But under the Cuban Reciprocity Treaty of 1903, Cuba is granted a 20 percent preferential which makes the rate now prevailing on Cuban sugar $2.00 per hundred pounds. Since Cuba stands prepared to supply all sugar required to supplement our continental and insular production, at the Havana price plus the freight and preferential duty, the rate of $2.00 per hundred represents approximately the protection afforded the continental and insular sugar producers.

The burden of a tariff duty on a commodity is normally shifted in part backwards to the foreign producers and in part forwards to the domestic consumers. The distribution of the burden between producers and consumers depends upon the relation between the elasticity of supply on the one hand and the elasticity of demand on the other.

In the study of tariff on sugar, there is considerable evidence that it is largely shifted forwards to the consumer and that if the duty were entirely removed prices would ultimately be lower than otherwise by an amount approaching the duty.

The effects of a United States tariff on Philippine sugar would depend much upon the rate imposed. A limitation would differ from a tariff in that it would not work such an immediate hardship on the Philippines; in fact, the Philippine people would have much to gain in the short run because Philippine people would share in the price increase brought about by the Cubans. Producers in all of the insular possessions, including the Philippines to the extent permitted free entry, in the continental United States, and in Cuba, would stand to gain by approximately 50 cents per hundred, largely at the expense of the American consumer. The treasury would receive but little additional revenue from the restriction as compared with the 30 millions which it would receive if a tariff were imposed on all of the imports from the Philippines.

It thus appears that producers of sugar in the continental United States would stand to gain but little by having Congress place a tariff on imports of Philippine sugar. Perhaps, however, such action would induce Cuba to make preferential duty price-effective. It is possible that under the stress of the depression Cuba might be driven temporarily, as evidenced by the recent Presidential decrees, to use its already established export monopoly to raise prices irrespective of the influence of this on Philippine production. In that event a tariff on Philippine sugar would have a negligible effect on sugar prices in the United States but would represent welcomed protection for the Cubans.

Karp & Newbery (1991) estimated buyers exercise market power by setting optimal import tariffs, taking as given the tariffs set by other buyers and the extraction paths of the suppliers. Research reveals a time consistent import tariff for exhaustible resources which is the natural counterpart to the time consistent strategy for a set of exhaustible resource producers. The tariff is easy to characterize and compute, is a function of currently observed variables (price, demand elasticity) alone, and will continue to apply to cases where both producers and importers exercise market power. Research has shown how to derive an equilibrium for the world oil market in which OPEC behaves no more collusively than a duopoly facing a competitive fringe, and in which the United States and other large importers all impose optimal import tariffs. The resulting equilibrium begins with a lower rate of extraction than the competitive equilibrium. The import tariffs tend to lower the initial price, but the producers’ oligopolistic behavior tends to increase it. The net result is to reduce the initial price. In this sense, the model suggests that oligopsony power is more effective than oligopoly power, at least for this specification of demand, and the given parameters. The inefficient early extraction of high cost producers tends to increase the initial price, The resulting equilibrium has the property that OPEC will initially have a lower share of current production than of current reserves, it shown that the implied rate of import tariff for the United States appears to be quite large.

Meese & Rogoff (1988) investigate the relationship between real interest rate and real exchange rates differentials in the, Germany, Japan, United Kingdom and United States. converse to theories based on the combined hypothesis that domestic prices are oppressive and monetary instability are predominant, research find little evidence of a constant relationship between real exchange rates and real interest rates. Research reflects on both in sample and out of sample tests. One hypothesis that is constant with result is that real disturbances such as productivity shocks may be a foremost source of exchange rate instability.

The results research has presented are slightly more favorable than the results of earlier studies. Research does find that the real exchange rate and the real interest differential have the theoretically anticipated sign (although trade balance regressors tend not to have the anticipated sign). However, the relationship is not statistically significant, and real interest differentials do not provide significant improvement over a random-walk model in forecasting real exchange rates (except in a few isolated cases). Research has already alluded to one possible explanation of why monetary models perform so poorly, which is that the disturbances impinging on exchange markets are predominantly real. Thus, models that focus primarily on monetary disturbances should not be expected to explain very much. The real shocks hypothesis require auxiliary consideration though it is not yet assured whether it will be helpful in building better empirical exchange rate models. It has proven extremely difficult to identify which real factors (such as technology shocks or changes in preferences) affected exchange rates over what periods. Still, it seems that further study along the lines of modern real business cycle research would be worthwhile. Research has also mentioned another popular current explanation of the failure of monetary exchange rate models, which is the existence of self-fulfilling expectations or exchange market bubbles. However, Flood has demonstrated that the results of rolling regression methodology are robust to the possibility of (linear) rational bubbles. Finally, examination of the empirical implications of Partial equilibrium asset pricing models of exchange rates also merits further research.

Bailey & Chung (1995) estimate the impact of political risk on the risk premiums meditate in cross sections of character equity returns from Mexico and exchange rate fluctuations, a country that has veteran momentous monetary and political instability. Mexico’s currency and sovereign debt markets indicators are engaged as causation for political risks and exchange rate. For corporate and portfolio management results show numerous implications and for the use of budding market data by researchers and propose widespread factors in pertaining to currency, budding market equity and independent debt markets. Research finds some substantiation of equity market premiums for revelation to these risks. To the extent that exchange rate fluctuations and political risk are significant, Research expects to observe similar effects in the equity markets of other countries. Results complement the importance attached to exchange rate and political risks in the international finance literature. Research also validate the usefulness of mainstream empirical asset pricing concepts and methodologies in studying international finance issues and, in particular, highlight the usefulness of information from currency and sovereign debt markets.

Research show results for the pricing of Mexican sovereign default risk have further implications for international finance. Research would expect Mexican political risk to have a significant impact only on Mexican investors, given the ability of non Mexicans to hold globally diversified portfolios. Results suggest several further directions for future research. The search for evidence of currency risk premiums in cross-sections of U.S. stocks may be more fruitful if tests are designed to capture time varying risk premiums. Further evidence may be obtained using data from developed countries that have a long history of stock prices similar to the U.S. but have experienced substantially greater price and exchange rate volatility. Similarly, results from developing countries with a more turbulent history of inflation and political changes than Mexico may prove interesting. The researcher can now observe prices for domestic equities, domestic debt, corporate Eurobonds, Brady bonds, ADRs, and country funds for such countries as Mexico, Brazil, Argentina, and the Philippines.

Jorion (1991) investigates the U.S. stock market pricing of exchange rate risk by means of two factor and multifactor arbitrage pricing models. The relation between the significance of the dollar and stock returns differs thoroughly across industries presented through evidence. Small and inconsiderable absolute risk premium attached to foreign currency coverage. It shows further reasons must elucidate why dynamic hedging policies cannot affect the cost of capital and firms choose to hedge enforced by financial managers.

Research examines the exposure of U.S. industries to movements in the value of the dollar. U.S. industries display significant cross sectional differences in their exposure to movements in the dollar. Research evaluate whether the currency exposure of U.S. firms was priced in the sense of Ross’s APT. In spite of using relatively powerful statistical techniques, the premium attached to pure foreign exchange exposure is found to be of the order of 0.2 percent per annum, which is both economically and statistically insignificant. Exchange rate risk seems to be diversifiable.

After going thoroughly in detail about trade restriction and its relevance to price stability, it can be said that trade restriction is a vast study which has and need to be more focused.

The area for research needs to be focused to understand its major constructs, the relationship between the variables and trade restrictions affecting the price stability in textile.

The different articles illustrated various theories and models of trade restrictions are studied with this in mind, the next section will present a conceptual framework and hypothesis for investigating the impact of trade restriction on price stability of textile industry of Pakistan.

There are many trade restriction that affect price stability of textile industry, but this research focuses on some of the important and very relevant restriction out of them and hence this research provides “to study the impact of trade restriction on price stability of textile industry of Pakistan”.

H#1: There is a positive relation between Tariff and domestic market price of imported textile items.

H#2: There is a positive relation between Transportation cost and domestic market price of imported textile items.

H#3: There is a positive relation between Exchange rate and domestic market price of imported textile items.

In this chapter, a theoretical framework is provided to present the relationship between the trade restriction and textile prices. After defining problem identification / research Problem, the research hypotheses are defined and then hypothesis are established for verification.

The Secondary data was obtained from the different websites and Economic Environment of textile sector of Pakistan over a period of last six years activity on prices.

To evaluate the relationship of explanatory variables were regressed using Curve estimation (Linear and Logarithmic) and durbin-watson. In the uni-variate regression analysis the proposed variables to be studied are the following:

Dependent variable

Price Stability

Independent Variables

Trade restriction: exchange rate, tariff and transportation cost.

To investigate variables and to test the hypothesis, following statistical hypothesis model were made.

P = α + β1 (Tariff) + β 2 (Exchange Rate) + β3 (Transportation Cost) +µ

Log P = α + β1 log (Tariff) + β2 log (Exchange Rate) + β3 log (Transportation Cost) + µ

In this chapter, the methodological issues relevant to the investigating the impact of trade restriction on price stability of textile industry of Pakistan are presented and discussed. This chapter covers data collection, research methods and application of statistical test.

H # 1 states, “There is a positive relation between tariff and domestic market price of imported textile items”.

Raw Cotton (Linear regression model)

The result indicates that the coefficient of correlation R = 0.298 and R2 = -0.139 at

P > 0.05, It suggests that there is no significant relationship between dependent (Raw cotton) and independent variable (Tariff). Significance value is 0.566.

The R2 = -0.139 shows that there is -13.9% variation in dependent variable (Raw cotton) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 0.146 at P > 0.05, it explains positive variation in dependent variable (Raw cotton) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 01 is rejected.

ANOVA model is explained by (9%) and remaining (91%) is residuals. Sig value is 0.566, its means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 2.245 i.e d > 2, there is evidence of positive autocorrelation.

Raw Cotton (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.283 and R2 = -0.150 at

P > 0.05, it suggests that there is no significant relationship between dependent (Raw cotton) and independent variable (Tariff). Significance value is 0.587.

The R2 = -0.150 shows that there is -15% variation in dependent variable (Raw cotton) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 243.064 at P > 0.05, it explains positive variation in dependent variable (Raw cotton) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 01 is rejected.

ANOVA model is explained by (8%) and remaining (92%) is residuals. Sig value is 0.587, its means model is not fit.

Synthetic Fiber (Linear regression Model)

The result indicates that the coefficient of correlation R = 0.925 and R2 = 0.820 at

P > 0.05, it suggests that there is significant relationship between dependent (Synthetic Fiber) and independent variable (Tariff). Significance value is 0.008.

The R2 = 0.820 shows that there is 82% variation in dependent variable (Synthetic Fiber) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 0.143 at P > 0.05, it explains positive variation in dependent variable (Synthetic Fiber) comes from 100% positive variation in independent variable (Tariff) significantly. Hence, H # 01 is accepted.

ANOVA model is explained by (86%) and remaining (14%) is residuals. Sig value is 0.008, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 3.294 i.e d > 2, there is evidence of positive autocorrelation.

Synthetic Fiber (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.922 and R2 = 0.813 at

P > 0.05, it suggests that there is significant relationship between dependent (Synthetic Fiber) and independent variable (Tariff). Significance value is 0.009.

The R2 = 0.813 shows that there is 81.3% variation in dependent variable (Synthetic Fiber) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 250.136 at P > 0.05, it explains positive variation in dependent variable (Synthetic Fiber) comes from 100% positive variation in independent variable (Tariff) significantly. Hence, H # 01 is accepted.

ANOVA model is explained by (85%) and remaining (15%) is residuals. Sig value is 0.008, its means model is best fit.

Artificial Silk Yarn (Linear Regression Model)

The result indicates that the coefficient of correlation R = 0.939 and R2 = 0.852 at

P > 0.05, it suggests that there is no significant relationship between dependent (Artificial Silk Yarn) and independent variable (Tariff). Significance value is 0.006.

The R2 = 0.852 shows that there is 85.2% variation in dependent variable (Artificial Silk Yarn) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 0.119 at P > 0.05, it explains positive variation in dependent variable (Artificial Silk Yarn) comes from 100% positive variation in independent variable (Tariff) significantly. Hence, H # 01 is accepted.

ANOVA model is explained by (88.12%) and remaining (11.80%) is residuals. Sig value is 0.008, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 3.415 i.e d > 2, there is evidence of positive autocorrelation.

Artificial Silk Yarn (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.939 and R2 = 0.853 at P > 0.05, it suggests that there is significant relationship between dependent (Artificial Silk Yarn) and independent variable (Tariff). Significance value is 0.005.

The R2 = 0.853 shows that there is 85.3% variation in dependent variable (Artificial Silk Yarn) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 209.063 at P > 0.05, it explains positive variation in dependent variable (Artificial Silk Yarn) comes from 100% positive variation in independent variable (Tariff) significantly. Hence, H # 01 is accepted.

ANOVA model is explained by (88.25%) and remaining (11.75%) is residuals. Sig value is 0.008, its means model is best fit.

Other Items (Linear Regression Model)

The result indicates that the coefficient of correlation R = 0.904 and R2 = 0.772 at

P > 0.05, it suggests that there is significant relationship between dependent (Other Items) and independent variable (Tariff). Significance value is 0.013.

The R2 = 0.772 shows that there is 77.2% variation in dependent variable (Other Items) which is explained by the variation of the model significantly.

The coefficient of regression (β) is -0.033 at P > 0.05, it explains negative variation in dependent variable (Other Items) comes from 100% positive variation in independent variable (Tariff) significantly. Hence, H # 01 is rejected.

ANOVA model is explained by (81.75%) and remaining (18.25%) is residuals. Sig value is 0.008, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 3.467 i.e d > 2, there is evidence of positive autocorrelation.

Other Items (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.882 and R2 = 0.723 at

P > 0.05, it suggests that there is no significant relationship between dependent (Other Items) and independent variable (Tariff). Significance value is 0.020.

The R2 = 0.723 shows that there is 72.3% variation in dependent variable (Other Items) which is explained by the variation of the model significantly.

The coefficient of regression (β) is -56.481 at P > 0.05, it explains negative variation in dependent variable (Other Items) comes from 100% positive variation in independent variable (Tariff) significantly. Hence, H # 01 is rejected.

ANOVA Model is explained by (77.84%) and remaining (22.16%) is residuals. Significance value is 0.020 it means model is best fit.

Textile Machinery (Linear regression model)

The result indicates that the coefficient of correlation R = 0.496 and R2 = 0.058 at

P < 0.05, it suggests that there is no significant relationship between dependent (Textile machinery) and independent variable (Tariff). Significance value is 0.317.

The R2 = 0.058 shows that there is 5.8% variation in dependent variable (Textile machinery) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is -0.258 at P < 0.05, it explains negative variation in dependent variable (Textile machinery) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 1 is rejected.

ANOVA Model is explained by (24.61%) and remaining (75.39%) is residuals. Significance value is 0.317 it means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 1.490 i.e d < 2, there is evidence of negative autocorrelation.

Textile Machinery (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.413 and R2 = -0.037 at

P < 0.05, it suggests that there is no significant relationship between dependent (Textile machinery) and independent variable (Tariff). Significance value is 0.416.

The R2 = -0.037 shows that there is -3.7% variation in dependent variable (Textile machinery) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is -377.119 at P < 0.05, it explains negative variation in dependent variable (Textile machinery) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 1 is rejected.

ANOVA Model is explained by (17.03%) and remaining (82.97%) is residuals. Significance value is 0.416 it means model is not fit.

Worn Clothing (Linear regression model)

The result indicates that the coefficient of correlation R = 0.384 and R2 = -0.066 at

P > 0.05, it suggests that there is no significant relationship between dependent (Worn Clothing) and independent variable (Tariff). Significance value is 0.453.

The R2 = -0.066 shows that there is -6.6% variation in dependent variable (Worn Clothing) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 0.003 at P < 0.05, it explains positive variation in dependent variable (Worn Clothing) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 01 is rejected.

ANOVA Model is explained by (14.7%) and remaining (85.3%) is residuals. Significance value is 0.453 it means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 1.689 i.e. d < 2, there is evidence of negative autocorrelation.

Worn Clothing (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.322 and R2 = -0.120 at

P > 0.05, it suggests that there is no significant relationship between dependent (Worn Clothing) and independent variable (Tariff). Significance value is 0.534.

The R2 = -0.120 shows that there is -12% variation in dependent variable (Worn Clothing) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 4.952 at P < 0.05, it explains positive variation in dependent variable (Worn Clothing) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 01 rejected.

ANOVA Model is explained by (10.36%) and remaining (89.64%) is residuals. Significance value is 0.534 it means model is not fit.

H # 2 states, “There is a positive relation between transportation cost and domestic market price of imported textile items”.

Raw Cotton (Linear regression model)

The result indicates that the coefficient of correlation R = 0.593 and R2 = 0.189 at

P > 0.05, It suggests that there is no significant relationship between dependent (Raw cotton) and independent variable (transportation cost). Significance value is 0.215.

The R2 = 0.189 shows that there is 18.9% variation in dependent variable (Raw cotton) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 0.228 at P > 0.05, it explains positive variation in dependent variable (Raw cotton) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 02 is rejected.

ANOVA model is explained by (35.16%) and remaining (64.84%) is residuals. Sig value is 0.215, its means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 2.161 i.e. d > 2, there is evidence of positive autocorrelation.

Raw Cotton (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.498 and R2 = 0.060 at

P > 0.05, it suggests that there is no significant relationship between dependent (Raw cotton) and independent variable (transportation cost). Significance value is 0.314.

The R2 = 0.060 shows that there is 6% variation in dependent variable (Raw cotton) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 341.441 at P > 0.05, it explains positive variation in dependent variable (Raw cotton) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 02 is rejected.

ANOVA model is explained by (24.82%) and remaining (75.18%) is residuals. Sig value is 0.314, its means model is not fit.

Synthetic Fiber (Linear regression Model)

The result indicates that the coefficient of correlation R = 0.937 and R2 = 0.848 at

P > 0.05, it suggests that there is significant relationship between dependent (Synthetic Fiber) and independent variable (transportation cost). Significance value is 0.006.

The R2 = 0.848 shows that there is 84.8% variation in dependent variable (Synthetic Fiber) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 0.114 at P > 0.05, it explains positive variation in dependent variable (Synthetic Fiber) comes from 100% positive variation in independent variable (transportation cost) significantly. Hence, H # 02 is accepted.

ANOVA model is explained by (87.85%) and remaining (12.15%) is residuals. Sig value is 0.006, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 2.628 i.e. d > 2, there is evidence of positive autocorrelation.

Synthetic Fiber (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.942 and R2 = 0.860 at

P > 0.05, it suggests that there is significant relationship between dependent (Synthetic Fiber) and independent variable (transportation cost). Significance value is 0.005.

The R2 = 0.860 shows that there is 86% variation in dependent variable (Synthetic Fiber) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 203.910 at P > 0.05, it explains positive variation in dependent variable (Synthetic Fiber) comes from 100% positive variation in independent variable (transportation cost) significantly. Hence, H # 02 is accepted.

ANOVA model is explained by (88.78%) and remaining (11.22%) is residuals. Sig value is 0.005, its means model is best fit.

Artificial Silk Yarn (Linear Regression Model)

The result indicates that the coefficient of correlation R = 0.953 and R2 = 0.885 at

P > 0.05, it suggests that there is no significant relationship between dependent (Artificial Silk Yarn) and independent variable (transportation cost). Significance value is 0.003.

The R2 = 0.885 shows that there is 88.5% variation in dependent variable (Artificial Silk Yarn) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 0.095 at P > 0.05, it explains positive variation in dependent variable (Artificial Silk Yarn) comes from 100% positive variation in independent variable (transportation cost) significantly. Hence, H # 02 is accepted.

ANOVA model is explained by (90.77%) and remaining (9.23%) is residuals. Sig value is 0.003, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 2.638 i.e d > 2, there is evidence of positive autocorrelation.

Artificial Silk Yarn (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.959 and R2 = 0.899 at P > 0.05, it suggests that there is significant relationship between dependent (Artificial Silk Yarn) and independent variable (transportation cost). Significance value is 0.003.

The R2 = 0.899 shows that there is 89.9% variation in dependent variable (Artificial Silk Yarn) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 170.203 at P > 0.05, it explains positive variation in dependent variable (Artificial Silk Yarn) comes from 100% positive variation in independent variable (transportation cost) significantly. Hence, H # 02 is accepted.

ANOVA model is explained by (91.92%) and remaining (8.08%) is residuals. Sig value is 0.003, its means model is best fit.

Other Items (Linear Regression Model)

The result indicates that the coefficient of correlation R = 0.674 and R2 = 0.318 at

P > 0.05, it suggests that there is significant relationship between dependent (Other Items) and independent variable (transportation cost). Significance value is 0.142.

The R2 = 0.318 shows that there is 31.8% variation in dependent variable (Other Items) which is explained by the variation of the model.

The coefficient of regression (β) is -0.019 at P > 0.05, it explains negative variation in dependent variable (Other Items) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 02 is rejected.

ANOVA model is explained by (45.43%) and remaining (54.57%) is residuals. Sig value is 0.142, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 3.313 i.e d > 2, there is evidence of positive autocorrelation.

Other Items (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.718 and R2 = 0.394 at

P > 0.05, it suggests that there is no significant relationship between dependent (Other Items) and independent variable (transportation cost). Significance value is 0.108.

The R2 = 0.394 shows that there is 39.4% variation in dependent variable (Other Items) which is explained by the variation of the model.

The coefficient of regression (β) is -36.658 at P > 0.05, it explains negative variation in dependent variable (Other Items) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 02 is rejected.

ANOVA model is explained by (51.53%) and remaining (48.47%) is residuals. Sig value is 0.108, its means model is best fit.

Textile Machinery (Linear regression model)

The result indicates that the coefficient of correlation R = 0.492 and R2 = 0.053 at

P < 0.05, it suggests that there is no significant relationship between dependent (Textile machinery) and independent variable (transportation cost). Significance value is 0.321.

The R2 = 0.053 shows that there is 5.3% variation in dependent variable (Textile machinery) which is explained by the variation of the model.

The coefficient of regression (β) is -0.201 at P < 0.05, it explains negative variation in dependent variable (Textile machinery) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 2 is rejected.

ANOVA Model is explained by (24.23%) and remaining (75.77%) is residuals. Significance value is 0.321 it means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 1.727 i.e d < 2, there is evidence of nagative autocorrelation.

Textile Machinery (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.407 and R2 = -0.043 at

P < 0.05, it suggests that there is no significant relationship between dependent (Textile machinery) and independent variable (transportation cost). Significance value is 0.423.

The R2 = -0.043 shows that there is -4.3% variation in dependent variable (Textile machinery) which is explained by the variation of the model.

The coefficient of regression (β) is -296.516 at P < 0.05, it explains negative variation in dependent variable (Textile machinery) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 2 is rejected.

ANOVA Model is explained by (16.55%) and remaining (83.45%) is residuals. Significance value is 0.423 it means model is not fit.

Worn Clothing (Linear regression model)

The result indicates that the coefficient of correlation R = 0.247 and R2 = -0.174 at

P > 0.05, it suggests that there is no significant relationship between dependent (Worn Clothing) and independent variable (transportation cost). Significance value is 0.637.

The R2 = -0.174 shows that there is -17.4% variation in dependent variable (Worn Clothing) which is explained by the variation of the model.

The coefficient of regression (β) is 0.002 at P < 0.05, it explains positive variation in dependent variable (Worn Clothing) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 02 is rejected.

ANOVA Model is explained by (6.1%) and remaining (93.89%) is residuals. Significance value is 0.637 it means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 1.814 i.e d < 2, there is evidence of nagative autocorrelation.

Worn Clothing (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.227 and R2 = -0.186 at

P > 0.05, it suggests that there is no significant relationship between dependent (Worn Clothing) and independent variable (transportation cost). Significance value is 0.665.

The R2 = -186 shows that there is -18.6% variation in dependent variable (Worn Clothing) which is explained by the variation of the model.

The coefficient of regression (β) is 2.784 at P < 0.05, it explains positive variation in dependent variable (Worn Clothing) comes from 100% positive variation in independent variable (transportation cost) insignificantly. Hence, H # 02 rejected.

ANOVA Model is explained by (5.2%) and remaining (94.84%) is residuals. Significance value is 0.665 it means model is not fit.

H # 3 states, “There is a positive relation between exchange rate and domestic market price of imported textile items”.

Raw Cotton (Linear regression model)

The result indicates that the coefficient of correlation R = 0.484 and R2 = 0.043 at

P > 0.05, It suggests that there is no significant relationship between dependent (Raw cotton) and independent variable (exchange rate). Significance value is 0.330.

The R2 = 0.043 shows that there is 4.3% variation in dependent variable (Raw cotton) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 93.611 at P > 0.05, it explains positive variation in dependent variable (Raw cotton) comes from 100% positive variation in independent variable (exchange rate) insignificantly. Hence, H # 03 is rejected.

ANOVA model is explained by (23.46%) and remaining (76.54%) is residuals. Sig value is 0.330, its means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 2.270 i.e. d > 2, there is evidence of positive autocorrelation.

Raw Cotton (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.481 and R2 = 0.039 at

P > 0.05, it suggests that there is no significant relationship between dependent (Raw cotton) and independent variable (exchange rate). Significance value is 0.335.

The R2 = 0.039 shows that there is 3.9% variation in dependent variable (Raw cotton) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 5504.590 at P > 0.05, it explains positive variation in dependent variable (Raw cotton) comes from 100% positive variation in independent variable (exchange rate) insignificantly. Hence, H # 03 is rejected.

ANOVA model is explained by (230.09%) and remaining (76.91%) is residuals. Sig value is 0.335, its means model is not fit.

Synthetic Fiber (Linear regression Model)

The result indicates that the coefficient of correlation R = 0.943 and R2 = 0.861 at

P > 0.05, it suggests that there is significant relationship between dependent (Synthetic Fiber) and independent variable (exchange rate). Significance value is 0.005.

The R2 = 0.861 shows that there is 86.1% variation in dependent variable (Synthetic Fiber) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 0.57.542 at P > 0.05, it explains positive variation in dependent variable (Synthetic Fiber) comes from 100% positive variation in independent variable (exchange rate) significantly. Hence, H # 03 is accepted.

ANOVA model is explained by (88.89%) and remaining (11.11%) is residuals. Sig value is 0.005, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 2.446 i.e. d > 2, there is evidence of positive autocorrelation.

Synthetic Fiber (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.943 and R2 = 0.861 at

P > 0.05, it suggests that there is significant relationship between dependent (Synthetic Fiber) and independent variable (exchange rate). Significance value is 0.005.

The R2 = 0.861 shows that there is 86.1% variation in dependent variable (Synthetic Fiber) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 3411.016 at P > 0.05, it explains positive variation in dependent variable (Synthetic Fiber) comes from 100% positive variation in independent variable (exchange rate) significantly. Hence, H # 03 is accepted.

ANOVA model is explained by (88.91%) and remaining (11.09%) is residuals. Sig value is 0.005, its means model is best fit.

Artificial Silk Yarn (Linear Regression Model)

The result indicates that the coefficient of correlation R = 0.982 and R2 = 0.956 at

P > 0.05, it suggests that there is no significant relationship between dependent (Artificial Silk Yarn) and independent variable (exchange rate). Significance value is 0.000.

The R2 = 0.956 shows that there is 95.6% variation in dependent variable (Artificial Silk Yarn) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 49.169 at P > 0.05, it explains positive variation in dependent variable (Artificial Silk Yarn) comes from 100% positive variation in independent variable (exchange rate) significantly. Hence, H # 03 is accepted.

ANOVA model is explained by (96.45%) and remaining (3.55%) is residuals. Sig value is 0.000, its means model is best fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 3.411 i.e. d > 2, there is evidence of positive autocorrelation.

Artificial Silk Yarn (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.982 and R2 = 0.956 at P > 0.05, it suggests that there is significant relationship between dependent (Artificial Silk Yarn) and independent variable (exchange rate). Significance value is 0.000.

The R2 = 0.956 shows that there is 95.6% variation in dependent variable (Artificial Silk Yarn) which is explained by the variation of the model significantly.

The coefficient of regression (β) is 2914.757 at P > 0.05, it explains positive variation in dependent variable (Artificial Silk Yarn) comes from 100% positive variation in independent variable (exchange rate) significantly. Hence, H # 03 is accepted.

ANOVA model is explained by (96.47%) and remaining (3.53%) is residuals. Sig value is 0.000, its means model is best fit.

Other Items (Linear Regression Model)

The result indicates that the coefficient of correlation R = 0.782 and R2 = 0.515 at

P > 0.05, it suggests that there is significant relationship between dependent (Other Items) and independent variable (exchange rate). Significance value is 0.066.

The R2 = 0.515 shows that there is 51.5% variation in dependent variable (Other Items) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is -11.266 at P > 0.05, it explains negative variation in dependent variable (Other Items) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 03 is rejected.

ANOVA model is explained by (61.19%) and remaining (38.81%) is residuals. Sig value is 0.066, its means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 3.438 i.e. d > 2, there is evidence of positive autocorrelation.

Other Items (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.783 and R2 = 0.516 at

P > 0.05, it suggests that there is no significant relationship between dependent (Other Items) and independent variable (Tariff). Significance value is 0.066.

The R2 = 0.516 shows that there is 51.6% variation in dependent variable (Other Items) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is -668.209 at P > 0.05, it explains negative variation in dependent variable (Other Items) comes from 100% positive variation in independent variable (exchange rate) insignificantly. Hence, H # 03 is rejected.

ANOVA Model is explained by (61.27%) and remaining (38.72%) is residuals. Significance value is 0.066 it means model is not fit.

Textile Machinery (Linear regression model)

The result indicates that the coefficient of correlation R = 0.508 and R2 = 0.073 at

P < 0.05, it suggests that there is no significant relationship between dependent (Textile machinery) and independent variable (exchange rate). Significance value is 0.303.

The R2 = 0.073 shows that there is 7.3% variation in dependent variable (Textile machinery) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is -104.505 at P < 0.05, it explains negative variation in dependent variable (Textile machinery) comes from 100% positive variation in independent variable (exchange rate) insignificantly. Hence, H # 3 is rejected.

ANOVA Model is explained by (25.85%) and remaining (74.15%) is residuals. Significance value is 0.303 it means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 1.540 i.e. d < 2, there is evidence of negative autocorrelation.

Textile Machinery (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.504 and R2 = -0.067 at

P < 0.05, it suggests that there is no significant relationship between dependent (Textile machinery) and independent variable (exchange rate). Significance value is 0.308.

The R2 = -0.067 shows that there is -6.7% variation in dependent variable (Textile machinery) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is -6138.378 at P < 0.05, it explains negative variation in dependent variable (Textile machinery) comes from 100% positive variation in independent variable (exchange rate) insignificantly. Hence, H # 3 is rejected.

ANOVA Model is explained by (25.39%) and remaining (74.61%) is residuals. Significance value is 0.308 it means model is not fit.

Worn Clothing (Linear regression model)

The result indicates that the coefficient of correlation R = 0.264 and R2 = -0.163 at

P > 0.05, it suggests that there is no significant relationship between dependent (Worn Clothing) and independent variable (Tariff). Significance value is 0.614.

The R2 = -0.163 shows that there is -16.3% variation in dependent variable (Worn Clothing) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 0.912 at P < 0.05, it explains positive variation in dependent variable (Worn Clothing) comes from 100% positive variation in independent variable (Tariff) insignificantly. Hence, H # 03 is rejected.

ANOVA Model is explained by (6.95%) and remaining (93.05%) is residuals. Significance value is 0.614 it means model is not fit.

Durbin Watson statistics was run to find out the presence of autocorrelation in the variables of every observation from a regression analysis. Durbin Watson statistic is 1.736 i.e. d < 2, there is evidence of negative autocorrelation.

Worn Clothing (Logarithmic regression model)

The result indicates that the coefficient of correlation R = 0.262 and R2 = -0.164 at

P > 0.05, it suggests that there is no significant relationship between dependent (Worn Clothing) and independent variable (exchange rate). Significance value is 0.616.

The R2 = -0.164 shows that there is -16.4% variation in dependent variable (Worn Clothing) which is explained by the variation of the model insignificantly.

The coefficient of regression (β) is 53.759 at P < 0.05, it explains positive variation in dependent variable (Worn Clothing) comes from 100% positive variation in independent variable (exchange rate) insignificantly. Hence, H # 03 rejected.

ANOVA Model is explained by (6.87%) and remaining (93.13%) is residuals. Significance value is 0.616 it means model is not fit.

Summary of hypothesis assessment is enclosed herewith from table 1-2

In this chapter, the interpretations & assessments of regression analysis for research hypothesis are carried-out.

Above diagram shows change in tariff that cause effect on imports due to which imports decline, this decline cause increase in demand and decrease in supply. Domestic buyers seek to initiate their own manufacturing facilities to cater domestic demand therefore price of domestic textile machinery increases and imported textile machinery price decline.

Above diagram shows change in tariff that cause effect on imports due to which imports decline and price of imported worn clothing increases.

This study has discussed three independent variables tariff, exchange rate and transportation cost, these independent variables have shown significant impact on synthetic fiber and artificial silk yarn. Subsequently, the best models of the price stability in relation to theses three variables (tariff, transportation cost and exchange rate) have successfully formulated. From the analysis of textile prices for the past six years it has been investigated that there is a change of price in imported synthetic fiber and artificial silk yarn clothing in the domestic market subsequently, tariff, transportation cost and exchange rate have been diagnosed as the cause of their change.

Despite the data shortcomings, however, research calculations demonstrate that dominant variables (tariff, exchange rate and transportation cost) show significant impact on the domestic price of imported synthetic fiber and artificial silk yarn. The results further indicated that liberalized trade may result in price stability. Some evidence was found that point to differential effects of import prices in Pakistan is associated with macro economic policies and prudent approach by government.

The previous studies are likely to understate seriously the size of the natural and total levels of protection that domestic producers enjoy against competition from imports. Better data on trade restrictions for traded goods are needed to help economists explain observed differences among countries in prices of traded goods and in rewards to factors of production.

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