Dilemmas Concerning EU Trade Preferences

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Arguments against Trade Liberalization

The new trade theory is an alternative way of explaining international trade; ‘…the traditional constant returns, perfect competition models of international trade have been supplemented and to some extent supplanted by a new breed of models that emphasizes increasing returns and imperfect competition.’ (Krugman, 1987, p.131) Apart from comparative advantage, these set of models emphasize the importance economies of scale and network effects. The theory argues that ‘countries do not necessarily specialise and trade solely in order to take advantage of their differences; they also trade because of increasing returns, which makes specialisation advantageous per se.’ (Krugman, 2007, p.425)
As a consequence of the immense impact of increasing returns to scale, with first mover advantages, it might be in a nation’s interest after all to protect infant industries. What is more, the world all together, in the long run loses without the possibility to protect them. Even though a country would possess absolute advantage, or a lower average cost curve as a whole in an industry, it would be difficult for it as a newcomer to enter the world market. This since it would in its initial stages have a higher average cost curve than the first-movers, because of the advantages of economies of scale, and therefore initially make losses. It is not able to enter and compete at the world market, because economies of scale have created a natural monopoly with no competition and consequently the world loses. A country may dominate in an industry simply as they were fortunate to be the first ones to exploit it; history plays an important role here in who produces what. This might be of peculiar interest to developing countries as their industries, in their infant newcomer stages, might not be able to withstand competition on the world market.
Even though this theory is in general accepted by academics, trade policy is in reality little affected by it; no one wants to come out as a protectionist. What is more, even if the theory holds theoretically, it is close to impossible pragmatically. Once an industry has been supported it is hard to remove that support without committing political suicide. One can simply look at the French farmers’ political influence to observe that fact.
Due to the new trade models Paul Krugman states: ‘Free trade is not passé…but it can never again be asserted as the policy that economic theory tells is always right.’ (Krugman, 1987, p.132) It is not as simple as claiming that since free trade could potentially increase least developed countries’ export growth, it is merely good for them. What trade liberalization can cause is a surge in imports without a corresponding surge in exports. This causes trade deficits to rise, deterioration in the balance of payments and worsening of external debt. This all constraints growth prospects and often results in persistent stagnation or recession (Khor, 2000a). As previously discussed, it must be taken into account that there are increasing returns to scale and imperfect competition in the global economy, and as a consequence the LDC’s might not endure competing on the world market.
In contrast to neoclassical trade theory, Khor (2000a) also argues that LDC’s at least initially, lose significantly when it comes to trade liberalization. More developed countries gain somewhat since they have a better chance at competing on the world market. Nevertheless, developed countries are the largest net gainers of multilateral trade liberalization with lower consumption prices.
What is more, in most developing countries, small farmers are essential building stones; their income and products, especially food, form the foundation of their economies. These small businesses could be threatened by agricultural liberalization and thus cheaper imports. Developing countries would then become less self-sustained and more dependent on imports for their food supplies, decreasing national food security (Khor, 2000a).
It is therefore rather divisive that some countries within the EU demand liberalization in the countries they preferably trade with. Oxfam demands that EU leaders should agree: ‘not to demand that poor countries liberalise in return for reductions in the current high and unfair level of EU protectionism.’ (Oxfam, 2002, p.15) Likewise Khor (2000a) argues that developing countries must have the opportunity to make strategic choices in finance, trade and investment policies, where they can decide on the pace and extent of liberalization and also have the opportunity to protect local business ventures. Furthermore he argues that ‘trade liberalization should not be pursued automatically, rapidly, as an end in itself, or in a “big bang” manner.’ (Khor, 2000b, p.14)
It is important to distinguish whether the successful conditions for liberalization are present in a country yet or not. As for the 10 other developing countries not included by the EBA agreement, this could be a side-effect that they could suffer from; imports from their neighboring countries could become cheaper than their domestic production, and as a result their economy weakens.
Since competitive pressures make inefficient industries and companies vanish, this would as previously stated probably lead to a tougher establishment for the developing countries than for the developed. The start -up capital for building up new efficient and competitive industries might not be available in the low-income countries. Here aid could be invested by richer nations as special trade agreements are abandoned. This aid for trade could for instance cover start-up costs, so that there may be a chance to compete on the world market.
On the other hand, the comparative advantage which developing countries usually have over MDC’s is that their production costs tend to be on a significantly lower level. Considering the EBA countries, they now have the opportunity to strengthen their positions by trading with the EU at a lower opportunity cost than its ‘neighbor’ developing countries. According to the Heckscher-Ohlin model however, there would in the long-run be factor-price equalization and labor costs would converge.
Yet another issue with trade liberalization, leading to specialization, is that developing countries become even more dependent on agriculture, which is not always good for development; the primary sector is often very vulnerable to natural hazards and not always that effective in generating economic growth. Nevertheless, it is hard to start somewhere else, where the factors of production are not prevalent. But as a whole, diversifying the domestic economy into different sectors of production is a good long-term safety measure for national welfare.
What is more, when economies of scale drive small farmers out of business in making production efficient, this specialization drives out the diversity of crops. Diversity of crops is a natural protection against natural hazards as some crops can withstand vermin, drought or floods better than other. So is the matter with variety within crops as well. It is often the case that variety within a crop disappears with bulk production; the import of rice into Haiti is a tragic example. An old Haitian rice farmer tells in a documentary of how imports of rice from Florida out -competed local rice farmers, and nowadays merely about three rice varieties remain out of about a hundred (Kunskapskanalen, 2011). These mass-produced rice varieties are often genetically modified and contain fewer nutrients. Another local man in the documentary claimed that with the old rice sorts they would stay full for a day, with the imported but for an hour.

Data and Empirical Model

The Gravity Model of Trade

The empirical model employed in this thesis is the gravity model of trade. It is one of the most applied in measuring bilateral trade flows and James Anderson describes it: ‘probably the most successful empirical trade device of the last twenty-five years’. (Anderson, 1979, p.106) The econometric model was first applied by international economists Jan Tinbergen (1962) and Pöyhönen (1963). In its initial stages it was disregarded for lacking sufficient theoretical substantiation, but from the late 1970’s and onward, several formal foundations were laid and the model became increasingly recognized; particularly because of theories concerning imperfect substitutes, but also from an arising interest in the matter of geography and trade within economics (Frankel, 1997). Important contributors to the theoretical groundwork include James Anderson, Paul Krugman, Elhanan Helpman and Jeffrey Bergstrand.
The model has its name from as Isaac Newton’s law of gravity in physics. Just as ‘the gravitational attraction between any two objects is proportional to the product of their masses and diminishes with distance, the trade between any two countries is, other things equal, proportional to the product of their GDPs and diminishes with distance.’ (Krugman Obstfeld, 2006, p.13) In other words, the gravity model estimates trade to be positively related to the GDP of the countries measured and inversely related to distance. Factors which can also be included in an extended version of the model include: income level, colonial history, common languages, topography and trade arrangements. In analyzing the volume of trade, distance represents transport costs and e.g. communication costs are covered by the common language dummy.
The general gravitational equation would take the following form:
Tij=A * Yi * Yj/ Dij
Where Tij is the trade between country and j, Yi and Yj is the GDP of each country respectively, Dij is the distance between the countries and A is a constant (Krugman & Obstfeld, 2006).

Regression Model

Three gravity model regressions are run in this study. Firstly, two with trade as dependent variable for each of the years of the study, 2000 and 2004:
Ln Tradeij = β0 + β1 Ln Distanceij + β2 Ln Export GDPcapi + β3 Ln Import GDPcapj + β4 Ln POPi + β5 Ln POPj6 Ln Gapij + EBA_D + Comlang_off + εij
Where Ln Tradeij is the logarithmic trade between country i and country j. Country i is the developing exporting country and country j is the importing more developed. Ln Distanceij is the logarithmic distance between country i and country j. Ln Export GDPi is country i:s logarithmic real GDP per capita, and so is import GDPj for country j. Ln POP is the logarithmic population and Ln Gapij is the logarithmic absolute difference between country i:s and country j:s Real GDP per capita4.
Secondly, one with DlnTrade as dependent variable:
DlnTradeij = β0 + β1 Ln Distanceij + β2 Ln Export GDPcapi + β3 Ln Import GDPcapj + β4 Ln POPi + β5 Ln POPj6 Ln Gapij + EBA_D + Comlang_off + εij
Where DlnTrade is the logarithmic change in trade between country i and country j from 2000 to 2004; the rest of the equation remains unchanged.
The Human Development Index (HDI) composed by the UN, is a good measure to quickly obtain an indication of a country’s living standard. As the figure (UN, 2012a) shows, the HDI is composed of the three dimensions health, education and living standards, with the respective four indicators of life expectancy at birth, mean years of schooling and GDP per capita. The scale goes from zero to one, where the LDC’s rank the lowest on the scale and the MDC’ s the highest, or closest to one. There is a large gap between the 40 countries measured, ranging from 0.224 in DR Congo to 0.906 in Australia with a standard deviation of 0,214516. The EBA countries hold a mean of 0,34890, the other LDC’s of 0,58667, the MDC’s of 0,75030 and the EU countries, the highest of 0,84680.

Data Sources

The import statistics are retrieved from the UN Comtrade database. Trade figures are in current dollars and therefore not inflation–adjusted. Import data by the more developed  countries is here considered, since it is more often recorded than is export data by the developing countries. In the Comtrade database, imports reported by one country do not necessarily coincide with exports reported by its trading partner. Distances between capitals and common official language are retrieved from the CEPII database. As for GNP per capita and population, the database Penn World Table is used as source.

Results

The control variables show expected results in accordance with the theory; the GDP per capita contributes positively and significantly for the exporting and the importing country, both in 2000 and 2004. The population variable showing market size is also in line with the gravity model, positive. The beta coefficient for distance is as expected negative since trade diminishes with distance. As for the EBA coefficient, it is also significant for both years; signifying that trade from the EBA countries was stronger, or preferred to the European Union, both before and after the implication of the agreement. This could partly be explained by that many of the least developed countries also comply under the Cotonou agreement for African, Caribbean and Pacific states. The beta coefficient went from 0,548 in 2000 to 0,530 in 2004, which is not a significant difference. The fit of the model is good with an R-square of 0,567 for 2000 and slightly higher 0,582 for 2004.
Another regression was run with DlnTrade as dependent variable (Table 4.3). DlnTrade is the difference in trade between 2000 and 2004. The t-statistic for the EBA dummy is insignificant and negative, indicating that the EBA countries’ exports to the EU have not increased more than the other developing countries’ have outside the agreement. In other words, the EBA trade agreement has not accelerated trade for the least developed countries comparatively. This nullifies the hypothesis that the Everything But Arms initiative would significantly increase the import value to the EU from the least developed countries within the EBA framework, but relatively diminish import values from the other developing countries. It implies that the EBA agreement has been ineffective for the LDC’s in its initial three years of implementation. Neither has there been a significantly negative trade diversion for the other developing countries.
On the change between 2000 and 2004 (Dln for Delta logarithmic) a mean was conducted. This mean conveyed that the developing exporting countries’ GDP (DlnGDPEx_mean) had increased by more than the importing more developed during the time period studied, and likewise population. Additionally, trade between the 40 countries had grown.

Conclusion and Suggestion for Further Studies

Even though the result is in disagreement with the hypothesis, it is not completely unforeseen. As a matter of fact, the Overseas Development Institute did not expect the increase in trade by the EBA agreement to be significant: ‘When differentiation for the Least Developed was introduced, the implicit assumption was that it was harmless to offer them higher preferences because they would be unable to increase their exports very much, and therefore that any diversion would be negligible.’ (Page and Hewitt, 2002, p.95) Their capacity to increase their trade was so low that it was not expected to affect the other developing countries considerably. Markedly, it confirms the results of the previous research earlier discussed. This makes one wonder why there was such commotion over banana, sugar and rice exports in the first place; delaying liberalization for these three products, and why there was not a more generous offer in the first place.
Regardless of the implementation of the Everything But Arms initiative, the EU is largely unmoved and secure from being outcompeted by the least developed countries; they remain trapped in their vicious cycle of poverty. Although the EBA initiative is progress, one could wonder if the EU in fact commits to it to calm down critics and avoid larger reforms within agricultural policy that would cost the EU all the more. The union shows double standards continuing to subsidize its own inefficient agricultural sector, profiting at the cost of the world’s poorest nations, while at the same time frivolously donating aid, accounting for more than half of the world’s Official Development Assistance (ODA, 2004).
The fact that EU’s domestic agricultural subsidies prevent the LDC’s from entering their market also implies they are hindering them from sustainably building up their own economies. Moreover, EU’s protectionist bureaucracies consisting of rules of origin and health and safety standards furthermore entangle and constrain the LDC’s from expanding their export sectors. It is as if they are hurting the LDC’s, but sendin g them band-aids (aid) as compensation and immediate relief. Rather controversial, as the EU claims to pursue ‘sustainable development’ (Montes & Migliorisi, 2004). Conclusively, the fact remains that the EU has yet to do, to go from word to deed.
As for further studies, other trade preferences could be taken into consideration. For example, analysis could be conducted on how the EBA initiative affected the least developed countries under the Cotonou agreement, compared to the non-African Caribbean Pacific countries outside it. Additionally, colony, colonial tie after 1945, contingency between countries and landlocked countries, could be included as dummy variables in the model. Also, more specified language dummies such as common second or third language could be included. A larger time span could be accounted for, both before and after the implementation of the agreement, perhaps in a 20 year time-line. Finally, new data on the costs associated with importing procedures from the World Bank could be included in an extended version of the model.

Table of Contents
1 Introduction and Methodology
2 Theoretical Framework
2.1 Arguments for Trade Liberalization
2.2 Arguments against Trade Liberalization
2.3 Beggar-thy-neighbor Effect
2.4 Trade and Growth
2.5 Dilemmas Concerning EU Trade Preferences
3 Data and Empirical Model 
3.1 The Gravity Model of Trade
3.2 Regression Model
3.3 Data Sources
4 Results 
5 Conclusion and Suggestion for Further Studies
References
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EU Trade Preferences and Developing Countries A Gravity Model Investigation of the Everything But Arms Treaty

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