JEL Code: F10 -General < F1 -Trade < F -International Economics, O10 -General < O1 -Economic Development

Economic theory suggests that development is enhanced through income growth, which is driven through increased trade. However, the empirical evidence of such a relationship most of the times is proved to be weak. In this study we try to determine the factors influencing this relationship by measuring "trade efficiency". Using the Data Envelopment Analysis (DEA) window method for a sample of 16 OECD countries, we obtained the efficiency scores and the optimal output levels for the inefficient countries for a time period of five years under consideration. Results drawn from the broadly used ratio analysis were also compared to the results derived from the DEA model. Our empirical findings show that "trade efficient" countries have clear characteristics like low exchange rates for exports, low R&D intensity, high value intra industry trade and positive impact of net trade on their GDP.

Introduction

The origins of the theoretical literature about trade and economic development are absolute and comparative advantage, as well as the Hecksher-Ohlin model and their followers. Although some models find that growth can affect patterns of international trade, there is no clear evidence about the causal relation between these variables. Empirical literature relating trade and growth has been dedicated to assess the pattern of trade policy on growth, basically trying to find a causal relationship between openness and growth, or more specially, if trade causes growth [START_REF] Mccombie | Economic Growth and the Balance of Payments constraint London[END_REF][START_REF] Blecker | Structural Roots of U.S. trade problems: income elasticities, secular trends and hysteresis[END_REF][START_REF] Edwards | Trade orientation, Distortions, and Growth in Developng Countries[END_REF][START_REF] Edwards | Openness, Productivity, and Growth: What Do We Really Know[END_REF][START_REF] Frankel | Does Trade causes Growth?[END_REF][START_REF] Grosman | Comparative advantage and long-run growth[END_REF][START_REF] Harrison | Openness and Growth: A time-series cross-country analysis for developing countries[END_REF][START_REF] Harrison | Who gains from trade reform? Some remaining puzzles NBER Working Papers[END_REF]Rodriguez and Rodrik 1999). Some authors try to demonstrate that open economies tend to converge faster to steady state growth than the closed ones [START_REF] Edwards | Trade orientation, Distortions, and Growth in Developng Countries[END_REF][START_REF] Edwards | Openness, Productivity, and Growth: What Do We Really Know[END_REF][START_REF] Krueger | Trade Policy and Economic Development: How We Learn[END_REF][START_REF] Ben-David | Trade and the Rate of Income Convergence[END_REF]. Others have found that openness can prevent economic growth due to the harmful effects on infant industries or due to the balance-of-payments constraint in a demandled approach. Empirical evidence in several countries, mainly in developing ones, seems to support these studies [START_REF] Mccombie | Growth in an international context: a Post Keynesian view[END_REF]. Still others are sceptical about the power of openness in pushing up economic growth even using similar methodology with those who advocate the benefits of opening up to growth (Rodriguez and Rodrik, 1999;Rodrik, 1999;[START_REF] Harrison | Who gains from trade reform? Some remaining puzzles NBER Working Papers[END_REF].

Thus although the relationship between trade and growth has been made in several theoretical and empirical studies the association between them is difficult to be established.

The growth literature leads to problems such as the endogeneity of the variables whereas empirical policy literature has been proved to be weak in trying to make a clear correlation between openness and growth.

Taking these problems into consideration this study examines empirically the relationship between trade and growth by measuring trade efficiency in terms of its contribution to growth and establishing the major components derived from several development and trade governmental policies. Using data from the manufacturing sector of 16 OECD countries the proposed model compares their trade policies by analysing efficiency in terms of contribution to GDP. Moreover, it extracts the major factors influencing trade efficiency and suggests policies for their maximisation. This is obtained by using the Data Envelopment Analysis (hereafter DEA) window method in order to compare trade efficiency for the period from 1996 to 2000. For this reason the paper uses for the first time in this type of formulation a number of ratios. Namely we use and construct indicators for the Research and Development intensity of each country in terms of production, the value added shares from the manufacturing sector relative to the total economy, the intra industry trade, the net trade to GDP and the exchange rates for exports.

The proposed model, using chronically these five factors in an input-output framework, determines the trade efficiency of each country in terms of its contribution to economic development. From the analysis we obtained the efficiency scores and the optimal output (ratios) levels for inefficient countries for all the five years under consideration. The results drawn from the broadly used ratio analysis were also compared to the results derived from the DEA window model.

The paper is organized as follows. Section 2 presents a review of the existing literature. In section 3 the technique adopted both in its theoretical and mathematical formulation is presented. Section 4 discusses the ratios used in the formulation of the proposed model. In section 5 the empirical findings of our study are presented. The final section concludes the paper discussing the derived results and the implied policy implications. [START_REF] Krugman | International Economics[END_REF]. More often these factors are hard to be distinguished and separated as they are interrelated. For instance, [START_REF] Antweiler | Increasing returns and all that: a view from trade[END_REF] emphasise the fact that if there is a large scale production and market power then there is the possibility to be used for development of efficient technologies and therefore comparative advantage may lead to market power and economies of scale and via versa. [START_REF] Chui | North-South models of growth and trade[END_REF] highlight the importance of trade and growth as trade policies may be derived from their growth performances. Comparative advantage is the fundamental "ingredient" of markets' trade ability and countries' manufacturing trade structure. [START_REF] Engelbrecht | Business sector R&D and Australia's manufacturing trade structure[END_REF] investigating the impact of R&D intensity on trade performance in the Australian manufacturing sector has found that despite the increase in business R&D expenditure by Australian government in order to increase technological capability, there wasn't any direct impact on its trade performance.

Other studies emphasise the fact that theoretical growth theories are focusing more on the relationship of trade policies and growth, rather than the relationship between trade volumes and growth [START_REF] Yanikkaya | Trade openness and economic growth: a cross-country empirical investigation[END_REF]. There are considerable differences between these two especially when other factors such as geographical location, country size and income are taken into account (Rodriguez and Rodrik, 2001). [START_REF] Yanikkaya | Trade openness and economic growth: a cross-country empirical investigation[END_REF] using two types of openness measures finds that growth effects of trade with developed countries are not considerably different from trade with developing countries.

Furthermore, other studies in order to investigate the impact of trade on economic development have focused their investigation on the prices of tradable goods. Bhallam and Lau (1992) have noticed that relative prices of tradable goods relative to international prices F o r P e e r R e v i e w 5 have a positive effect on GDP growth. [START_REF] Barro | Economic Growth in a Cross Section of Countries[END_REF] found that prices of investment goods in relation to international prices influence GDP growth per capita while [START_REF] Dollar | Outward oriented developing economies really do grow more rapidly: evidence from 95 LDCs, 1976-85[END_REF] found that prices on traded goods have the same impact on GDP.

Finally, there is significant support for studies investigating China linking trade openness, Foreign Direct Investment (FDI), economic development and trade performance (among others, [START_REF] Liu | An empirical investigation of the causal relationship between openness and economic growth in China[END_REF][START_REF] Chuang | FDI, trade, and spillover efficiency: evidence from China's manufacturing sector[END_REF][START_REF] Choi | Foreign direct investement and income convergence[END_REF]). [START_REF] Yao | On economic growth, FDI and exports in China[END_REF] examines the effects of exports and FDI on economic performance of China finding that export promotion and adoption of world technology and business practices may be proved useful for other developing countries. Moreover [START_REF] Liu | Relationships between economic growth foreign direct investment and trade: evidence from China[END_REF] investigating the causal link between, inward foreign direct investment, economic growth and trade in China found that there is a reinforcement of FDI, economic growth and trade as a result of China's "open door" trade and investment policy.

Additionally [START_REF] Cuardos | Openness and growth: Re-Examining foreign direct investment, trade and output linkages in Latin America[END_REF] using exports, domestic output, inward FDI and foreign income, confirm the applicability of the export-led growth hypothesis in the economies under examination (Mexico and Argentina). The effects of trade liberalisation on the technical efficiency have been examined also by Hossain and Karunarante (2004)

using a

Cobb-Douglas production frontier to assess Bangladesh manufacturing sector's technical efficiency. They show that the increase of improvement in technical efficiency is due to the regime's external trade policies. Moreover, the majority of export-oriented industries had a high degree of technical efficiency due to a "competitive push", which leads to an economic development of the whole industry.

As has been written there are different ways that trade may or may not influence and stimulate economic development. However, trade policies play a major role on taking advantage of such effects and therefore give an initiative to investigate on more depth taking into account parameters which influence countries' economic development.

The Technique

In this study we propose the application of Data Envelopment Analysis window method, which facilitates the comparison of trade efficiency for a sample of 16 OECD countries with the simultaneous use of multiple criteria. In this way trade efficiency for each country is determined. The comparison of relative efficiency of all countries is carried out, relying on the derived efficiency ratio for every country as the solution of a mathematical model. The higher a country's efficiency ratio in relation to the corresponding ratio of another country the higher is the efficiency of that country.

We may think of DEA as measuring the technical efficiency of a given country by calculating an efficiency ratio equal to a weighted sum of outputs over a weighted sum of inputs. For each country (hereafter DMU or Decision Making Unit) these weights are derived by solving an optimization problem which involves the maximization of the efficiency ratio for that country subject to the constraint that the equivalent ratios for every country in the set is less than or equal to 1.

That is DEA seeks to determine which of the 16 DMUs determine an envelopment surface or efficient frontier. DMUs lying on the surface are deemed efficient while DMUs that do not lie on the frontier are termed inefficient and the analysis provides a measure of their relative efficiency. The solution of the model dictates the solution of 16 (the number of countries) linear programming problems, one for each DMU. It provides us with an efficiency measure for each DMU and shows by how much each of a DMU's ratios should be improved if it were to perform at the same level as the best performing countries in the sample. In this way we extract an efficiency ratio for each country, which shows us by how much the ratios of each country could be improved in order to reach the same level of efficiency with that of the most efficient country in the sample. The fundamental feature of DEA is that technical efficiency score of each DMU depends on the performance of the sample of which it forms a part. This means that DEA produces relative rather than absolute measures of technical efficiency for each DMU under consideration. DEA evaluates a DMU as technically efficient if it has the best ratio of any output to any input and this shows the significance of the outputs/inputs taken under consideration.

DEA models (CRS vs VRS)

Under the restriction of constant returns to scale (CRS), [START_REF] Charnes | Measuring the Efficiency of Decision Making Units[END_REF] specify the linear programming problem representing the fitting of an efficient production surface to the data. An extension permitting variable returns to scale (VRS) is provided by Banker et al. (1984). The latter assumption requires an additional constraint on the solution compared with the constant returns to scale case and the resulting efficiency estimate will be greater than that obtained under constant returns to scale. Thus where the methods yield different values the index obtained under variable returns takes account of scale related effects and therefore represents pure technical efficiency alone whereas the constant returns to scale measure represents overall technical efficiency in which pure technical and scale efficiency are combined. Banker et al. (1984) show that the index of overall efficiency is equal to the product of the scale and pure technical efficiency indices. Hence, an index of scale efficiency can be obtained by manipulating the DEA results obtained under the assumption of constant and variable returns.

Advantages and limitations of DEA methodology

DEA modelling can incorporate multiple inputs and outputs. To calculate technical efficiency it only requires information on output and input. This makes it particularly suitable for analysing the efficiency of trade policies. Possible sources of inefficiency can be determined as well as efficiency levels. The technique gives the ability to decompose technical inefficiency to be decomposed into scale effects. By identifying the 'peers' for the countries which are not observed efficient DEA provides a set of potential benchmarks that the policy makers of the countries can look for ways of improving the effect of their trade policies on economic development.

However some major disadvantages when using this technique must be observed.

First, having a deterministic nature DEA produces results that are particularly sensitive to measurement error. If one country's inputs are understated or its outputs overstated then that country can distort the shape of the frontier and reduce the efficiency scores of nearby countries. Second, it only measures efficiency relative to best practice within the particular sample. Thus, it is not meaningful to compare the scores between two different studies because differences in best practice between the samples are unknown. Third, DEA scores are sensitive to input and output specification and the size of the sample. There are different rules as to what the minimum number of countries in the sample should be. One such rule is that the number of DMUs in the sample should be at least three times greater than the sum of the number of outputs and inputs included in the specification [START_REF] Nunamaker | Using data envelopment analysis to measure the efficiency of nonprofit organisations: a critical evaluation[END_REF]. Despite the limitations, DEA is a useful tool evaluating the effect of trade policies on economic development.

The proposed model

Consider N DMUs (in our case 16 OECD countries), each producing m products using n inputs. Efficiency is measured as:

1 1 / m n k ik ik jk jk i j f b y c x = = = (1)
Where y ik (>0) is the amount of output i by the kth DMU, x jk (>0 ) is the amount of input j used by the kth DMUs, b ik and c jk are the weights (or multipliers) for the output and the input respectively. The efficiency ratio (1) is maximised subject to the constraints: (3)

According to the first inequality the efficiency ratios cannot exceed one while according to the second the weights are positive and are determined by DEA in such a way as each DMU maximises its own efficiency ratio.

The problem can be formulated as an ordinary linear program. That is:

Maximize 1 1 m k i k i k i i k i l i f b y c x = = (4) subject to 1 1 1 1 0 m n ik ik jk jl i j ik il j k jl i j b y c x c x c x = = (5) 1 1 1 n i k j k j j k j l j b x c x = = (6) and 1 1 0 , 0 i k j k i k i l j k j l i j b c c x c x (7)
The envelopment problem is formulated as a "non-Archimedean infinitesimal" and it can be expressed as: In the linear programming duality theory the optimal value of k (the overall technical efficiency) equals the optimal value of f k ( k lies between zero and one). In (8) represents the lower bound for the weights and ensures that the optimal solutions are at finite non-zero external points and that the optimal solutions are at finite non -zero extremal points. It also ensures that the optimal value of f k is not affected by the slack in input j.

Minimize 1 1 m n k lk jk i j s s + = = + (8) subject to 0 N k l il il lk l y y s + = 1, ... i m = (9) 0 N k jk kl jl jk l x x s = 1, ... j n = (10)
Technical efficiency is achieved only when k =1 (ensuring that DMUs is on the frontier) and S lk + = S jk -= 0 (excluding external points). An inefficient DMU can become efficient by adjusting outputs and inputs as follows:

* lk lk lk y y s + = + (12) and * jk k jk jk x x s = . ( 13
)

Figure 1 about here

The problem in ( 8) through ( 11) assumes constant returns to scale (CRS). Figure 1 illustrates the approach using one output and one input. The frontier OF is the solution of the formulated problem in ( 8)-( 11). Countries on the frontier have an efficiency score of one. Countries located inside the frontier have an efficiency score of less than one. For example country s located at point W is inefficient and the overall technical efficiency is measured by the ratio ML/MW.

The overall technical efficiency can be broken into pure technical and scale efficiency.

To do that we solve the above linear programming problem with the additional restriction that which allows for non-increasing returns to scale (NIRS). In Figure 1, this case is represented by the OBCD frontier. For country s located at point W the efficiency is given by / s ML MW = , which also equals s . By construction s s and s s if s = s and scale inefficiency exists then it is due to decreasing returns to scale. If s s then the scale inefficiency is due to increasing returns to scale [START_REF] Halkos | Efficiency measurement of the Greek commercial banks with the use of financial ratios: a data envelopment analysis approach[END_REF]).

The DEA model illustrated above has been introduced by [START_REF] Charnes | Measuring the Efficiency of Decision Making Units[END_REF]; however a variation of this model will be used based on moving averages introduced by [START_REF] Charnes | A Developmental Study of Data Envelopment Analysis in Measuring the Efficiency of Maintenance Units in the U.S. Air Forces[END_REF].The use of this variation is due to its ability to handle multiple outputs and inputs and their efficiencies over time [START_REF] Charnes | Extension to DEA Models[END_REF]). Asmid et al. (2004), highlight the fact that there are no technical changes within each of the windows because all DMUs in each window are measured (compared) against each other and suggest that in order for the results to be credible a narrow window width must be used. Then a window k w with k x w observations is denoted starting at time k, 1 k T with width w, 1 w T k . So the matrix of inputs is given as:

( )

1 2 1 2 1 2 1 1 1
, ,...., , , ,...., , , ,....,

N N N kw k k k k k k k w k w k w X x x x x x x x x x + + + + + + =
and the matrix of outputs will be:

( )

1 2 1 2 1 2 1 1 1 , ,....,

Data

Using data for 16 OECD countries (Table 1) from "Bilateral Trade Database" 1 and for a time span of five years (1996)(1997)(1998)(1999)(2000) 2 a number of ratios were constructed and are used in our empirical analysis. Where ANBERD and PROD are business enterprise Research and Development and production at current prices respectively. For each country this indicator expresses the R&D expenditures by the total manufacturing sector relative to the production. This ratio was constructed in order to approach the concern of a country to deal with technological developments and the speed with which the country adapts them.

The second ratio shows the value added shares from manufacturing sector relative to the total economy (VASH). That is 100

K i i K total VALU VASH VALU = (18)
Where VALU is the value added at current prices. For a given country, this indicator shows the value added contributed by manufacturing sector relative to total value added for all industries. The valuation of value added differs among countries and may therefore influence the interpretation of this indicator. Value added is measured at basic prices for all countries except JAPAN and the USA, which are used in producer or market prices.

The third indicator shows the intra industry trade (IIT). This aspect of the structure of international trade has not received much attention in the existing trade performance literature. In our construction it is expressed as:

( ) ( ) . . 1 100 k k i i k i tot manuf k k i i i EXPO IMPO IIT EXPO IMPO = + ( 19
)
where EXPO and IMPO are the total exports and imports of goods at current prices. Intra industry trade is the value of total trade remaining after subtraction of the absolute value of net exports and imports of manufacturing industry. For comparison between countries this measure is expressed as a percentage of manufacturing industry's combined exports and imports. This index ranges from 0 to 100. If a country exports and imports roughly equal

Empirical Results

Using a conventional ratio analysis as presented graphically in Figure 2a-f different conclusions can be derived looking at the countries from six different measurement perspectives. Looking at Figure 2a and in the case of Great Britain we observe an increase of its exchange rate for exports over the five years with its prices of the exchange rate significantly higher compared to the other countries.

Quite different is the performance of the EXCR of Ireland compared to Spain and Belgium. In our study the exchange rate for exports is a "key" determinant of trade efficiency;

higher trading performances are influenced by the exchange rate policies of the countries under investigation. A consistency between exchange rates and trade policy helps to implement successfully structural economic reforms and foster economic development of the countries [START_REF] Richaud | Real exchange rate and openness in emerging economies: Argentina in the long run[END_REF]. [START_REF] Guerrieri | Technology and international competitiveness: The interdependence between manufacturing and producer services[END_REF] emphasize the importance of manufacturing base of a country as a key determinant affecting trading performance. Country's ability to develop a dynamic economy is linked to the structure of its manufacturing sector. In that respect Ireland, Finland, Belgium and Germany have higher index prices (Figure 2b) in terms of the value [START_REF] Sharma | Horizontal and vertical intra-industry trade in Australian manufacturing: does trade liberalization have any impact?[END_REF] emphasizes the fact that structural changes of manufacturing sector and trade liberalization policies have a major impact on countries' total intra-industry trade.

Figure 2c illustrates the intra industry trade of manufacturing for each country over the years.

Australia and Japan have the lowest performance in terms of exports and imports at current prices. The highest price is observed for Belgium, France, Great Britain, the Netherlands and Spain. Moderate trade performance has been noticed for the USA, Canada, Denmark and Sweden.

The policies aiming to support R&D in order to change the countries' manufacturing trade structure have broadly analyzed by many authors providing unclear results. Among others [START_REF] Engelbrecht | Business sector R&D and Australia's manufacturing trade structure[END_REF] emphasizes the fact that trade and economic development policy makers must be skeptical about the interrelation between R&D expenditure, R&D supporting policies and trade performance. Moreover looking at figure 2d the performance of countries in terms of their R&D expenditure over the five years time period can be observed. We notice that Sweden, Japan and the USA have a significant higher performance in terms of R&D expenditure compared to the other countries. A medium performance is observed for Germany, France, Finland and Great Britain. The lowest performance has been noticed for Spain and Italy. Figure 1e indicates the net trade of commodities as a percentage of GDP.

Observing the performance of countries we realize that Finland, Norway, Sweden and the Netherlands have the highest contribution to their GDP from trade whereas Australia, Great

Britain and the USA have a negative contribution. In the case of Norway the first 4 years present a tremendous increase of trade as its economy was based mainly on exports while an Finally all the above conventional analysis must be viewed and compared along with the last graph illustrated in figure 1f in order to have a clear view of trade efficiency and its impact on economic development for the countries examined. Looking at Figure 1f we observe that the two countries with the highest GDP rates are the USA and Japan. These are the countries, which have the lowest impact of trade on their GDP (figure 1e), which in turn raises the question between economic development, trade policies and trade efficiency.

Using conventional ratio analysis shows us the performance of the countries under review but from (in our case) six different angles. However it is difficult to have a clear view of countries' trade contribution to economic development even though the observations through the ratios give us detailed insights of the factors that affect trade efficiency. In order to overcome the problem of "multiple views" we use DEA modeling to observe trade efficiency in terms of a number of inputs and outputs, which will provide us with a unified and simultaneous picture of trade efficiency among the countries considered.

Focusing our interest on changes in efficiency over time DEA window analysis is used for analysis purposes. In such a case moving average analogue is applied. DMUs in each period are treated as if they were different DMUs. A DMU's performance in a particular period is contrasted with its performance in other periods in addition to the performance of the other DMUs.

In our case the DMUs are the OECD countries (n=16) over five years period (p=5)

and we proceed our analysis by using a three -year (w=3) window. Each DMU (country) is represented as if it was a different DMU for each of the three years in the first window (Years 1, 2 and 3). An analysis of the 48 (nw = 3 x 16) DMUs is taking place. The window is then moved one period by replacing Year 1 with Year 4 and an analysis is performed on the second On the other hand if a country has an optimal pure technical efficiency score (100) and low scale efficiency score this may imply that the trade overall inefficiency is attributed to comparative disadvantages conditions. Australia may be viewed as an example of this case

where it has an optimal pure technical efficiency (year 5) and a relative scale efficiency score of 0.71. Finally our results show that Australia and Norway display increasing returns to scale while Belgium, Italy, the Netherlands, and Spain exhibit constant returns to scale and the rest of the countries decreasing returns to scale. being the lowest whether measured by window (GDW) or by year (GDY). Moreover, Belgium has the second best performance with an overall mean efficiency of 99.49 and with a variance of 1.1. Observing Italy we notice that even though is fourth in terms of its trade efficiency (with a mean of 97.28) it seems that it hasn't a stable performance with a variance of its efficiency of 24.9 and with a greatest window difference of 15.1. Table 4 indicates also a low trade performance for Sweden, Australia, Germany, Finland, Ireland and Japan.

Generally, the most consistent trade performers are Spain, Belgium and the Netherlands with very high trade efficiency means and low variances. 5 provides us with the rankings of all the countries according to highest scores obtained from conventional ratio and window analyses. Furthermore, looking at the rankings according to the value added shares from manufacturing sector relative to the total economy of the countries (VASH) we realize that Ireland and Finland have the highest performances even though when looking at the window analysis ranking they are in the 14 th (Finland) and 15 th place (Ireland). The fact that they are so high in the ranking of VASH explains the fact that they have so high scores in terms of pure technical efficiency (Table 3).

Looking at the rankings for R&D expenditures by the total manufacturing sector relative to the total economy (RDIP) we realize that Japan lies on the 3 rd place compared to the trade efficiency ranking which has the worst trade performance. Countries which are the last in the ranking of RDIP ratio are the most trade efficient according in the DEA window analysis (Spain) 3 .

In the same lines, when we observe the exchange rate for exports for each country we realize that countries with higher exchange rates are the ones which are in the lower places of our DEA ranking and therefore they are less trade efficient compared to Belgium and Spain, which have the lowest average exchange rate prices for exports.

Table 5 about here

Figure 3 provide us with essential information comparing overall efficiency and ratios.

More analytically we realize graphically that countries which are more trade efficient have, as expected, lower exchange rates for exports. Moreover countries with lower research and development expenditure are more trade efficient. This is justified by the fact that most of the 3 An economic interpretation may rely on that a country has to decide if it will be a technological leader or a technological follower. The former case requires the involvement in expensive R & D activities. This may lead to new inventions through patents or even to nowhere. On the other hand the technological follower has to search for access to the technology developed by the leader. This may be achieved either by developing a similar version but having to bear a lower R & D cost compared to the leader or by licensing the new technology from the leader [START_REF] Blake | A short course of Economics[END_REF]. services are costly to be traded due to tariffs and taxes which are imposed from the importing countries. As expected countries with higher value of IIT are trade efficient.

Figure 3 about here

These results are supported by the derived targeted values presented in table 6. These values are obtained for the trade inefficient countries in order to become efficient. It is noticeable that the targeted values for VASH and RDIP ratios require moderate changes for inefficient countries in order to become trade efficient. On the other hand looking at the targeted values for EXCR these are quite high. Taking Japan as an example we realize that in order for Japan to become trade efficient it has to reduce its exchange rates for exports (probably making its commodities more competitive), increasing significantly the intra industry trade and enhancing policies for trade to contribute to the country's growth. A similar picture is valid in the case of Australia, Canada and Great Britain while Germany, Ireland and

Italy have to reduce their exchange rates for exports, increasing their IIT ratio.

Table 6 about here

Conclusions and Policy Implications

In this study we performed an application of DEA window analysis in order to compare international trade efficiency by using conventional ratio measures in the suggested model and for the time period 1996-2000. The efficiency scores and the optimal ratio levels for inefficient countries for all the five years of the study were obtained. Results drawn from the broadly used ratio analysis were also compared to the results derived from the DEA window model. The advantage of using DEA compared to economic ratios is that DEA provides us with an overall objective numerical score, ranking, and efficiency potential improvement targets for each one of the inefficient units. The results of our study support the empirical findings of Richard et al. (2000) emphasizing the consistency of countries' exchange rate policy and their trade efficiency.

Furthermore trade efficiency is also associated with structural changes in the manufacturing sector and trade liberalization policies. This is also supported by [START_REF] Sharma | Horizontal and vertical intra-industry trade in Australian manufacturing: does trade liberalization have any impact?[END_REF]. In terms of the importance of the value added by the manufacturing sector and its affect to trade efficiency our empirical results find support in the study of Guerriere and Meliciani (2005).

Additionally our results indicate that R&D intensity have no major effect on trade efficiency supporting [START_REF] Engelbrecht | Business sector R&D and Australia's manufacturing trade structure[END_REF] view. This implies that there is no obvious link between trade performance and R&D supporting policies.

According to our findings trade efficient countries have the following clear characteristics:

-Low exchange rates. As expected looking at the exchange rate for exports for each country we realize that countries with higher exchange rates are the ones, which are in the lower places of our DEA ranking and therefore they are less trade efficient.

-Low R&D intensity. Countries with low ranking according to their RDIP ratio are the most trade efficient in the DEA window analysis.

-High value intra industry trade. This is emphasising the liberalization policies of a country and its contribution to trade efficiency.

-The combination of the above mentioned factors has positive effect on the contribution of net trade to GDP of each country.

-Countries with high ranking according to their VASH ratio have high scores in terms of pure technical efficiency.

-Scale operation does affect the trade efficiency of the country. The larger the divergence between overall and pure technical efficiency scores the lower the Although our study does not support directly trade's contribution to economic development it seems that trade contributes to the reform of manufacturing structure and market openness, which in turn causes growth. In that respect our study of trade efficiency comes along with the view of causal relationship between openness and growth as has been expressed by several of scholars [START_REF] Mccombie | Economic Growth and the Balance of Payments constraint London[END_REF][START_REF] Blecker | Structural Roots of U.S. trade problems: income elasticities, secular trends and hysteresis[END_REF][START_REF] Edwards | Trade orientation, Distortions, and Growth in Developng Countries[END_REF][START_REF] Edwards | Openness, Productivity, and Growth: What Do We Really Know[END_REF][START_REF] Frankel | Does Trade causes Growth?[END_REF][START_REF] Grosman | Comparative advantage and long-run growth[END_REF][START_REF] Harrison | Openness and Growth: A time-series cross-country analysis for developing countries[END_REF][START_REF] Harrison | Who gains from trade reform? Some remaining puzzles NBER Working Papers[END_REF]Rodriguez and Rodrik 1999).

Institutional change and infrastructure modernization agendas are complex and priorities differ across countries. Therefore trade contribution to economic development may vary from country to country according to the factors affecting the key elements distinguish trade theory as has been expressed by [START_REF] Krugman | International Economics[END_REF]. For instance the cost of moving goods across international borders is now as important as tariffs in determining the cost of landed goods. The ability of countries to deliver goods and services in time and at low costs is a key determinant of trade efficiency and its contribution to countries' economic development. Finally the historical and institutional context of each country must be taken into account along side with the micro-and macroeconomic aspects to growth and with their interrelations with trade. 9 0 0 0 0 0 0 8 0 0 0 0 0 0 7 0 0 0 0 0 0 6 0 0 0 0 0 0 5 0 0 0 0 0 0 4 0 0 0 0 0 0 3 0 0 0 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 -1 0 0 0 0 0 0

  In general, three are the main factors distinguishing trade theory: a) Comparative advantage; b) Resources availability and c) Increasing returns to scale technology and imperfect competition

  technical and allocative inefficiency. Furthermore, allows

.

  variable returns to scale (VRS). In Figure1the VRS case is represented by the straight line segment BC where the points at the line AB are weekly efficient. The pure technical efficiency of country s located at point W is given by the ratio MI/MW= s . The degree of scale efficiency is computed as / By construction s exceeds s . If the value of s is one the country is scale efficient. If scale inefficiency exists it can be due to either increasing or decreasing returns to scale (IRS or DRS). To differentiate IRS from DRS we solve again the same linear programming problem with the additional restriction of 1

  products the IIT index is high. If trade is mainly one-way (whether exporting or importing) the IIT index is low. Furthermore, the ratio NTGDP has been constructed in order to indicate the contribution of net trade to GDP of each country. That is: NTGDP = [(Exports of commodities -Imports of commodities)/ GDP] * 100 (20) Finally, an indicator of the exchange rate for exports for each country (dollars per local currency) EXCR has been used.

Figure

  Figure 2 about here

  their manufacturing sectors relative to the total value added for all their industries. Finally, Norway appears to have the lowest value added shares compared to the other countries.

  for net trade performance for the last year under consideration can be noticed.

  (Years 2, 3 and 4) of these 48 DMUs. The process continues moving the window one period and concluding with the final (third) analysis of 48 DMUs for the last three years (Years 3, 4 and 5). This procedure implies p-w+1 separate analyses, where each analysis examines n*w DMUs.

  included in the constraint sets along with similar performance data of the other OECD countries for Years 1, 2 and 3. Concluding the results of the first window analysis include all the 48 efficiency scores under the column headings for Years 1 to 3 in the first row of each OECD country.

  tradable are agricultural products. Furthermore high technology goods and

  efficiency (in trade terms) and the more adverse the impact of scale size on trade efficiency.

  Figure 1: DEA output-input frontier

Figure 2 :

 2 Figure 2: (a) Exchange rate for e xports; (b) Value added shares; (c) Intra Industry Trade; (d) R&D Intensity; (e) NTGDP; (f) GDP.

  DEN FIN FRA DEU IRL ITA JPN NLD NOR ESP SWE GBR USA

Table 1

 1

				about here
	Specifically, the first ratio is an indicator showing the R&D intensity of each country
	in terms of production (RDIP). That is:
	RDIP	k	=	k ANBERD PROD	k	100	(17)

Table 2a

 2a illustrates the results of the analysis in the form of overall efficiency and pure technical efficiency while Table2bpresents the scale efficiency scores for the performance of the 16 OECD countries considering the VASH, RDIP and EXCR as inputs and the IIT and NTGDP ratios as outputs. The underlying framework of the window analysis is illustrated on this table. For the first window Australia (AUS) is represented in the constrains of the DEA model as if it was a different DMU in the years 1, 2 and 3. Therefore, when Australia is evaluated for its Year 1 efficiency, its own performance data for Year 2 and Year 3 are

Table 2a -b about here Scale

 2a

	more adverse the impact of scale size on trade efficiency. Scale scores results are presented in
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	overall efficiency compared to pure technical efficiency its scale efficiency will be below 1
	(Thanassoulis, 2001). A lower overall efficiency score compared to pure technical efficiency
	score suggests that a country is efficient in trade terms in the former case and less efficient
	when we control for scale size (in trade terms). This means that scale operation does impact
	18

efficiency scores are calculated by dividing overall efficiency by pure efficiency as can be found in

[START_REF] Coelli | An introduction to Efficiency and Productivity Analysis[END_REF]

. If the overall efficiency and pure technical efficiency of a DMU (country) are equal then the scale efficiency is 1. If however the DMU has lower the trade efficiency of the country. Therefore, the larger the divergence between overall and pure technical efficiency scores the lower the value of scale efficiency (in trade terms) and the

Table 2b .

 2b As it can be observed for instance Canada has a low pure technical efficiency score in year 5 of 0.8594 or 85.94% and relatively high scale efficiency (0.964 or 96.4%). This means that the overall trade inefficiency of that country in the overall efficiency model (0.8292 or 82.92%) is attributed mainly to inefficient trade policies and comparative disadvantages. The same holds also for other countries such as Denmark, Japan and Norway.

Table 3

 3 All the countries forming the third group have an increase in their overall trade efficiency with the highest increase observed in Japan (12.3%) and the lowest for Sweden (0.82%). However it is worthy mentioning that Finland and Ireland although they have low overall efficiency scores they have extremely high scores of pure technical efficiency.

	decomposes overall average efficiency scores for each country in each
	window clarifying trends of trade efficiencies over the years. Similarly, pure technical
	efficiency has been decomposed. Countries can be distinguished into three different groups:
	countries with an overall efficiency over 90% (Group 1), with an overall efficiency between
	80% and 90% (Group 2) and with overall trade efficiency below 80% (Group 3). The first
	group includes Belgium, France, Italy, the Netherlands, Norway, and Spain. It is worth
	mentioning that in the cases of Belgium and France we observe a tendency of decrease over
	the three windows of 1.01% and 0.4% respectively whereas for the other countries of the
	group there is an increasing trend of overall trade efficiency. Group 2 consists of Canada,
	Denmark, Great Britain and the USA. From these countries only Canada indicates a decrease

on its efficiency (0.19%) over the three windows, whereas the USA has the highest increase of 4.35%. Finally the third group includes Australia, Finland, Germany, Ireland, Japan, and

Table 3

 3

about here Table

4

corroborates the results shown in table 3 by reporting rankings, means and variances across all windows, the greatest differences by window and by year. It illustrates the relative stability of each country's overall trade efficiency results and its further indication of the trade efficiency and stability of Spain. Given the fact that Spain reports an overall efficiency (in trade terms), no variability is a strong indication of healthy and strong trade performance. Stability in performance is further observed by the greatest difference scores

Table 4

 4

	about here

Table

Table 1 :

 1 Description and variable codes.

	Submitted Manuscript

Table 2 (

 2 a): Window Analysis; Overall Efficiency, Pure Technical Efficiency.

			Overall Efficiency		Pure Technichal Efficiency
	DMUs/Years	Year 1	Year 2	Year 3	Year 4	Year 5	Year 1	Year 2	Year 3	Year 4	Year 5
	AUS	65,579	67,711	71,333			65,624	68,04	71,419
			67,711	71,333	72,355			67,714	71,334	72,496
				69,651	70,692	71,067			71,177	72,493	100
	BEL	100	100	100			100	100	100
			98,572	99,929	100			100	99,977	100
				96,981	100	100			97,9	100	100
	CAN	86,21	81,49	85,636			87,063	84,571	90,636
			81,49	85,636	86,447			83,633	89,602	88,283
				84,913	85,017	82,917			89,602	87,018	85,937
	DEN	87,248	86,614	90,678			90,545	90,158	93,457
			86,609	90,667	89,695			89,848	93,158	91,03
				90,176	88,848	91,486			92,295	90,419	92,927
	FIN	58,583	66,817	58,373			87,056	95,285	94,007
			66,139	57,768	65,333			95,277	93,9	96,921
				57,768	65,333	70,44			93,872	96,894	99,442
	FRA	96,759	94,97	97,637			98,874	99,175	100
			94,959	97,626	95,599			99,04	100	98,576
				97,146	94,754	96,32			100	97,514	98,922
	DEU	65,296	67,39	67,666			85,109	85,946	84,83
			67,39	67,666	67,82			85,371	84,263	84,664
				67,657	67,813	68,061			83,019	83,415	84,691
	IRL	58,723	53,521	64,938			79,51	78,108	85,895
			51,242	64,144	62,625			77,677	84,975	82,89
				64,144	62,593	63,288			84,975	82,89	79,192
	ITA	84,819	100	100			90,816	100	100
			99,061	100	95,867			100	100	95,924
				100	95,855	100			100	95,865	100
	JPN	33,422	38,176	41,741			42,741	47,876	52,267
			38,176	41,741	42,038			47,556	51,918	51,948
				41,736	42,033	43,508			51,151	51,182	50,889
	NLD	96,065	96,27	98,307			100	100	100
			96,27	98,307	99,984			100	98,923	100
				95,96	97,7	100			98,829	100	100
	NOR	96,121	91,231	100			100	92,027	100
			91,231	100	100			91,97	100	100
				100	100	93,24			100	100	93,565
	ESP	100	100	100			100	100	100
			100	100	100			100	100	100
				100	100	100			100	100	100
	SWE	72,94	69,763	69,107			84,995	96,422	95,565
			68,074	69,082	77,055			95,745	94,892	99,145
				68,035	77,055	68,452			94,752	99,014	94,508
	GBR	83,228	83,688	85,226			95,442	96,624	96,865
			83,688	85,226	85,586			95,978	96,217	95,92
				83,136	83,404	87,425			94,797	94,504	94,267
	USA	85,457	86,658	90,121			88,684	89,968	93,269
			86,658	90,121	92,038			89,202	92,161	94,031
				90,11	92,026	91,499			90,142	92,026	91,5

Table 2 (

 2 b): Window Analysis; Scale Efficiency.

				Scale Efficiency
	DMUs/Years	Year 1	Year 2	Year 3	Year 4	Year 5
	AUS	0,999 (DRS)	0,995 (DRS)	0,998 (DRS)
			1 (CRS)	1 (CRS)	0,998 (DRS)
				0,978 (IRS)	0,975 (IRS)	0,710 (IRS)
	BEL	1 (CRS)	1 (CRS)	1 (CRS)
			0,985 (IRS)	1 (CRS)	1 (CRS)
				0,990 (DRS)	1 (CRS)	1 (CRS)
	CAN	0,990 (DRS)	0,963 (DRS)	0,944 (DRS)
			0,974 (DRS)	0,955 (DRS)	0,979 (DRS)
				0,947 (DRS)	0,977 (DRS)	0,964 (DRS)
	DEN	0,963 (DRS)	0,960 (DRS)	0,970 (DRS)
			0,963 (DRS)	0,973 (DRS)	0,985 (DRS)
				0,977 (DRS)	0,982 (DRS)	0,984 (DRS)
	FIN	0,672 (DRS)	0,701 (DRS)	0,620 (DRS)
			0,694 (DRS)	0,615 (DRS)	0,674 (DRS)
				0,615 (DRS)	0,674 (DRS)	0,708 (DRS)
	FRA	0,978 (DRS)	0,957 (DRS)	0,976 (DRS)
			0,958 (DRS)	0,976 (DRS)	0,969 (DRS)
				0,971 (DRS)	0,971 (DRS)	0,973 (DRS)
	DEU	0,767 (DRS)	0,784 (DRS)	0,797 (DRS)
			0,789 (DRS)	0,803 (DRS)	0,801 (DRS)
				0,814 (DRS)	0,812 (DRS)	0,803 (DRS)
	IRL	0,738 (DRS)	0,685 (DRS)	0,756 (DRS)
			0,659 (DRS)	0,754 (DRS)	0,755 (DRS)
				0,754 (DRS)	0,755 (DRS)	0,799 (DRS)
	ITA	0,933 (DRS)	1 (CRS)	1 (CRS)
			0,990 (IRS)	1 (CRS)	0,999 (IRS)
				1 (CRS)	1 (CRS)	1 (CRS)
	JPN	0,781 (DRS)	0,797 (DRS)	0,798 (DRS)
			0,802 (DRS)	0,803 (DRS)	0,809 (DRS)
				0,815 (DRS)	0,821 (DRS)	0,854 (DRS)
	NLD	0,960 (DRS)	0,962 (DRS)	0,983 (DRS)
			0,962 (DRS)	0,993 (DRS)	1 (CRS)
				0,970 (DRS)	0,977 (DRS)	1 (CRS)
	NOR	0,961 (IRS)	0,991 (DRS)	1 (CRS)
			0,991 (DRS)	1 (CRS)	1 (CRS)
				1 (CRS)	1 (CRS)	0,996 (IRS)
	ESP	1 (CRS)	1 (CRS)	1 (CRS)
			1 (CRS)	1 (CRS)	1 (CRS)
				1 (CRS)	1 (CRS)	1 (CRS)
		0,858
	SWE	(DRS)	0,723 (DRS)	0,723 (DRS)
			0,710 (DRS)	0,728 (DRS)	0,777 (DRS)
				0,718 (DRS)	0,778 (DRS)	0,724 (DRS)
	GBR	0,872 (DRS)	0,866 (DRS)	0,879 (DRS)
			0,871 (DRS)	0,885 (DRS)	0,892 (DRS)
				0,876 (DRS)	0,882 (DRS)	0,927 (DRS)
	USA	0,963 (DRS)	0,963 (DRS)	0,966 (DRS)
			0,971 (DRS)	0,977 (DRS)	0,978 (DRS)
				1 (CRS)	1 (CRS)	1 (CRS)

Table 3 :

 3 Average efficiency scores for each country in each window

			Overall efficiency		Pure Technical Efficiency
	DMUs/				%				%
	windows'				Difference			Difference
	averages	window 1	window 2	window 3	w1-w3	window 1	window 2	window 3	w1-w3
	AUS	68,21	70,47	70,47	3,32	68,36	70,51	81,22	18,82
	BEL	100,00	99,50	98,99	-1,01	100,00	99,99	99,30	-0,70
	CAN	84,45	84,52	84,28	-0,19	87,42	87,17	87,52	0,11
	DEN	88,18	88,99	90,17	2,26	91,39	91,35	91,88	0,54
	FIN	61,26	63,08	64,51	5,32	92,12	95,37	96,74	5,02
	FRA	96,46	96,06	96,07	-0,40	99,35	99,21	98,81	-0,54
	DEU	66,78	67,63	67,84	1,59	85,30	84,77	83,71	-1,86
	IRL	59,06	59,34	63,34	7,25	81,17	81,85	82,35	1,46
	ITA	94,94	98,31	98,62	3,87	96,94	98,64	98,62	1,74
	JPN	37,78	40,65	42,43	12,30	47,63	50,47	51,07	7,24
	NLD	96,88	98,19	97,89	1,04	100,00	99,64	99,61	-0,39
	NOR	95,78	97,08	97,75	2,05	97,34	97,32	97,86	0,53
	ESP	100,00	100,00	100,00	0,00	100,00	100,00	100,00	0,00
	SWE	70,60	71,40	71,18	0,82	92,33	96,59	96,09	4,08
	GBR	84,05	84,83	84,66	0,72	96,31	96,04	94,52	-1,86
	USA	87,41	89,61	91,21	4,35	90,64	91,80	91,22	0,64

Table 4 :

 4 Window analysis -Rankings, means, variances, greatest difference within window (GDW) and greatest difference in the same year but different window (GDY)

	DMUs	GDW	GDY	Mean	Variance	Ranking
	ESP	0	0	100	0	1
	BEL	3,019	2,948	99,498	1,11131875	2
	NLD	2,3	2,347	97,6514444 2,62662003	3
	ITA	15,181	0,939	97,2891111 24,9333661	4
	NOR	8,769	0	96,8692222 15,7898797	5
	FRA	2,667	0,845	96,1966667	1,353155	6
	USA	3,463	0,012	89,4097778 6,29850244	7
	DEN	4,058	0,847	89,1134444 3,50342853	8
	GBR	4,021	2,182	84,5118889 2,09828561	9
	CAN	-4,72	1,43	84,4173333	3,787293	10
	SWE	9,02	1,689	71,0625556 13,7114723	11
	AUS	3,622	1,682	69,7146667	5,016313	12
	DEU	2,094	0,009	67,4176667 0,67695725	13
	FIN	-8,444	0,678	62,9504444	23,288344	14
	IRL	12,902	2,279	60,5797778 25,0549984	15
	JPN	4,754	0,005	40,2856667 9,85725525	16

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	Submitted Manuscript
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Table 5 :

 5 Rankings and average values according to the ratios used and the DEA window analysis. Overall efficiency versus VASH; RDIP; EXCR; IIT; NTGDP and GDP

	VASH /Rank	DMUs	Average value of Years 95-00	RDIP/ Rank	DMUs	Average value of Years 95-00	NTGDP/ Rank	DMUs	Average value of Years 95-00
	1	IRL	30,2443289	1	SWE	3,76734542	1	FIN	7,616704
	2	FIN	24,4871573	2	USA	3,09521077	2	NOR	6,325158
	3	DEU	22,5550636	3	JPN	3,0364985	3	SWE	6,28069
	4	JPN	21,9856671	4	DEU	2,5006202	4	NLD	5,827938
	5	SWE	21,6603509	5	FRA	2,3152444	5	BEL	4,213006
	6	ITA	21,49262	6	FIN	2,07312906	6	ITA	3,988228
	7	GBR	20,5156603	7	GBR	1,90891696	7	DEN	3,977936
	8	BEL	19,7921173	8	DEN	1,81608936	8	IRL	2,82598
	9	ESP	18,6007695	9	BEL	1,59154751	9	CAN	2,473358
	10	FRA	18,4975284	10	NLD	1,58890337	10	FRA	2,00105
	11	CAN	18,1106355	11	NOR	1,31297721	11	JPN	1,37107
	12	NLD	17,3460502	12	CAN	1,23108328	12	DEU	0,974004
	13	USA	16,8977581	13	AUS	1,18116865	13	ESP	0,252354
	14	DEN	16,7656618	14	IRL	0,97939034	14	GBR	-0,5001392
	15	AUS	13,5770863	15	ITA	0,70466916	15	AUS	-0,881146
	16	NOR	12,7616214	16	ESP	0,55962193	16	USA	-1,36823
	EXCR /Rank	DMUs	Average value of Years 95-00	IIT/ Rank	DMUs	Average value of Years 95-00	Window Analysis Rank	DMUs	Averages scores/ window analysis
	1	GBR	1,593832	1	BEL	89,2362129	1	ESP	100
	2	IRL	1,529264	2	FRA	87,7038911	2	BEL	99,498
	3	USA	1	3	GBR	86,1009596	3	NLD	97,65144444
	4	AUS	0,725403	4	NLD	84,1171103	4	ITA	97,28911111
	5	CAN	0,7181632	5	ESP	82,3567296	5	NOR	96,86922222
	6	DEU	0,6256474	6	DEU	76,4748752	6	FRA	96,19666667
	7	ITA	0,609308	7	USA	75,919439	7	USA	89,40977778
	8	NLD	0,5563836	8	DEN	73,4269361	8	DEN	89,11344444
	9	FIN	0,2040604	9	CAN	72,8600179	9	GBR	84,51188889
	10	FRA	0,1836148	10	SWE	71,6184693	10	CAN	84,41733333
	11	DEN	0,162012	11	ITA	67,428486	11	SWE	71,06255556
	12	NOR	0,145913	12	IRL	64,9667409	12	AUS	69,71466667
	13	SWE	0,1353584	13	FIN	64,6119426	13	DEU	67,41766667
	14	JPN	0,11200894	14	NOR	61,022402	14	FIN	62,95044444
	15	BEL	0,0302406	15	AUS	46,3137573	15	IRL	60,57977778
	16	ESP	0,0073672	16	JPN	44,0572182	16	JPN	40,28566667

Table 6 :

 6 Targeted values for the trade inefficient countries to become trade efficient

	Dmus/ratios	VASH	RDIP	EXCR	IIT	NTGDP
	AUS	12,9000 1,0318	0,6282	45,2704	-1,8017
	(targeted)	12,8980 1,0313	0,3166	63,6522	3,3598
	BEL	19,6071 1,6294	0,0276	91,3616	4,2639
	(targeted)	19,6071 1,6294	0,0276	91,3616	4,2639
	CAN	18,7393 1,3475	0,6742	75,5266	2,0036
	(targeted)	18,7362 1,3478	0,3808	90,9671	4,0349
	DEN	16,6914 2,1607	0,1496	75,1113	2,0172
	(targeted)	16,6926 1,4218	0,1524	79,4656	4,0882
	FIN	25,4403 2,4296	0,1874	62,9306	8,8126
	(targeted)	25,4423 2,1561	0,1914	120,5730	6,0826
	FRA	18,5611 2,1580	0,1698	88,0536	2,6523
	(targeted)	17,1893 1,2474	0,3551	83,5652	3,7629
	DEU	22,5416 2,5200	0,5694	77,3750	1,4948
	(targeted)	24,0527 1,0068	0,1085	109,5830	1,1127
	IRL	32,4701 0,8916	1,4285	61,3953	3,0235
	(targeted)	26,1745 0,8977	0,0140	117,2800	0,0982
	ITA	21,2019 0,6300	0,5761	68,1938	3,4049
	(targeted)	18,4952 0,6343	0,0099	82,8715	0,0694
	JPN	21,1880 3,3273	7,6639	46,4932	1,8352
	(targeted)	21,1852 1,9259	0,6420	106,8580	6,8226
	NLD	16,8322 1,5316	0,5051	84,8929	5,4151
	(targeted)	16,8322 1,5316	0,5051	84,8929	5,4151
	NOR	13,0402 1,1802	0,1326	59,7283	1,8959
	(targeted)	13,0410 1,1151	0,1349	62,2946	3,2517
	ESP	18,6643 0,6356	0,0067	83,6107	0,0672
	(targeted)	18,6643 0,6356	0,0067	83,6107	0,0672
	SWE	22,1579 3,7589	0,1259	72,7133	6,2853
	(targeted)	22,1603 1,8678	0,1292	104,5160	5,1610
	GBR	19,4651 1,9974	1,6570	85,8265	-0,9904
	(targeted)	19,4626 1,7693	0,5898	98,1685	6,2678
	USA	16,3013 3,1813	1,0000	75,2260	-1,8286
	(targeted)	16,2991 1,4817	0,4939	82,2121	5,2490

Page 36 of 36 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Acknowledgements: Thanks are due to an anonymous referee for the very helpful and constructive comments. Any remaining errors are solely the authors' responsibility.


John Davis

Paul Caskie

Michael Wallace

of Farm Firms, Farm Households, and Farm Input

Keywords:

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

Financial support for EU farmers seeking early retirement from agriculture is a discretionary element of CAP rural development policy and some EU member states, most notably France, Ireland and Greece have chosen to implement the measure. The policy objectives of the EU Early Retirement measure, as well as rules governing the operation of the Scheme, are set out in Chapter IV of Council Regulation (EC) 1257/1999 and in Section 4 of a supplementary Commission Regulation (EC) 1750 1999:

• To provide an income for elderly farmers who decide to stop farming • To encourage the replacement of such elderly farmers by farmers able to improve, where necessary, the economic viability of the remaining agricultural holdings • To reassign agricultural land to non-agricultural uses where it cannot be farmed under satisfactory conditions of economic viability.

The focus of this paper is restricted to mainly economic aspects of the measure especially in relation to structural adjustment and whether the removal of older farmers from the industry would be likely to produce benefits that exceed the additional costs incurred. We explore these issues, which have not been rigorously addressed in the literature to date [START_REF] Caskie | An Economic Study of Farmer Early Retirement and New Entrant Schemes for Northern Ireland[END_REF], using data from farms in Northern Ireland. After briefly reviewing the provisions of the EU's Early Retirement Scheme we examine qualitative evidence from a survey of farmers' attitudes and opinions concerning retirement. In order to quantify the possible benefits of a Scheme we then analyse data from the Farm Business Survey to determine the likely effects of changes in farm size and operator age on performance. We conclude that the economic arguments for introducing an Early Retirement Scheme are relatively weak.

Implications of Delayed Farmer Retirement

If older farmers delay their retirement, then the opportunities for family successors or for other new entrants may be reduced. [START_REF] Errington | Getting Out of Farming. Part Two: The Farmers[END_REF] referred to a 'reluctance to retire' and the 'inheritance dilemma'. [START_REF] Gasson | Carry On Farming, A Study of How English Farmers Have Adapted To the Changing Pressures on Farming[END_REF] identified the desire to remain in some way involved in the business, leading to a high level of potential semi-retirees. For a family successor, handover may be delayed to such an extent that the business eventually transferred is depleted or the transfer occurs too late for the recipient to achieve their ambitions for the farm (McCrostie Little and Taylor 1998). [START_REF] Vidal | Twenty Years of European Agriculture: Proportion of Young People In Agriculture Remains Stable[END_REF] found that the average physical and economic sizes of holdings run by young farmers were greater than those of other holdings and were also increasing more rapidly. Aside from its impact on potential new entrants, a low rate of exit from farming may reflect some underlying distress for the individuals concerned. This may be the case particularly where there is a lack of affordable and suitable housing for retiring tenant farmers [START_REF] Lobley | Implications of Changes in the Structure of Agricultural Businesses[END_REF]. The EU Farmer Early Retirement Scheme Briefly, the regulation provides for assistance of up to €15,000 (approximately £9,500) per farmer per year. Payments can be made for a maximum of 15 years but in any case shall not continue beyond the applicants 75 th birthday or exceed €150,000 in total value. Where payments extend beyond the normal state retirement age, the recipient shall only receive the difference between the state pension and the early retirement pension, as a supplement to the former. The applicant must also meet the following conditions:

F

• be not less than 55 years of age and no more than 65 years when they enter the Scheme, • have practiced farming for the last 10 years,

• stop all paid and unpaid commercial farming indefinitely (participating farmers may retain up to 1 hectare and the use of farm buildings for noncommercial farming). The land previously farmed by the participating farmers can be released for either continued agricultural use or non-agricultural uses such as forestry of the creation of a nature reserve.

The limited evidence on Early Retirement Schemes within Europe to date suggests that they have been used to achieve both social and structural objectives. Scheme designs have varied and depended largely on national objectives and the existing pattern and structure of agriculture in each country. In most cases rigorous evaluations of the Schemes were not carried out and so it is difficult to say if they represented good value for money. Experience with the first French Early Retirement Scheme (1992-94) highlighted the difficulties inherent in achieving multiple policy objectives [START_REF] Allaire | The Farmer Early Retirement Scheme 1992-1994: First Balance and Structural Impacts[END_REF][START_REF] Brangeon | Early Retirement Scheme and Low Income in the Agricultural Sector[END_REF]. The second French scheme (1995)(1996)(1997) had only short-term effects in that some farmers brought forward their retirement decision but this was followed by a period when the number retiring was below trend (Dauce et al., 1998). Similar results were found for a scheme introduced in the Republic of Ireland, as well as the fact that larger farms, dairy farms and farms located in traditionally more prosperous farming regions, were over-represented [START_REF] Murphy | Evaluation of the Scheme of Installation Aid for Young Farmers: Including an Assessment of the Effects of the Scheme of Early Retirement from Farming, Analysis and Evaluation Unit[END_REF].

Farmer Survey

A telephone survey of 351 farmers (response rate 56 per cent.) aged between 50 and 65 was undertaken to ascertain opinions, concerns and priorities on retirement and the possible introduction of an Early Retirement Scheme to assist structural adjustment in agriculture. A stratified random sample-according to farm size, type and locationwas drawn from participants in the EU Structural Survey for Northern Ireland.

Retirement plans and financial provisions. Approximately half the respondents indicated that they expected to fully retire from farming at some stage, while a further one-third expect to semi-retire2 . Subsequent analysis revealed that farmers with 'small' or 'medium/large' farms were more likely to indicate an intention to retire than those with 'very small' farms. The likelihood of not retiring from farming was greatest for farmers with cattle and sheep farms. Respondents with a spouse were 4 more likely to report an intention to retire fully, while those without a spouse intended to semi-retire or not retire at all. When farmers had an identified successor, they were more likely to indicate an intention to semi-retire or fully retire.

Almost 60 per cent of all farmers, but mostly older respondents, indicated that retiring in the next three years was something that they had given serious consideration-in order of importance, poor health and low incomes were found to have a significant bearing on this intention. The majority (87 per cent) intended to finance their retirement from more than one source (Figure 1).

Almost 58 per cent of farmers surveyed indicated that they have made some contribution to a self-employed/private pension scheme. Despite this, 54 per cent of the 287 farmers who said they expect to semi-retire or fully retire anticipated a household income of less than £10,000 per annum. Only 18 per cent of farmers without a spouse anticipated an annual income in excess of £10,000, compared to 30 per cent for farmers with a spouse. Respondents with 'medium/large' farms anticipated higher annual household income in retirement than those with 'very small' or 'small' farms. Farmers with off-farm income also anticipated higher levels of household income those without off-farm income.

Percentage of farmers

Willingness to participate in an Early Retirement Scheme. Some 270 (77 per cent) respondents said they would participate in an Early Retirement Scheme-financial considerations were uppermost. Health factors were very important or important in the decision to participate for 92 per cent whilst relieving themselves from the responsibility of running a farm business (75 per cent) and farm work (70 per cent) were also very important or important in choosing to participate. Succession issues also seemed to be influential in deciding to participate in the Scheme. The full range of factors is summarised in Table 1. Of the 291 farmers with children, 80 per cent claimed they would participate compared to 63 per cent of those without children. Where the successor was known, 83 per cent of farmers said they would enter the Scheme, compared with 69 per cent for those with no successor. Martial status also had an important bearing on intentions-78 per cent of married respondents responded positively compared to 63 per cent of single respondents.

Structural Adjustment Issues

The main 'problems' associated with the structure of the region's agriculture are the relatively small scale of the majority of farm businesses and, more tentatively, the high average age of the farming population. Of the approximately 30,000 farm businesses, 54 per cent are officially classed as 'very small'. This poses difficulties in generating sufficient income to support farm families and although many farmers have other sources of earned income, household earnings are often low and underemployment remains a real problem [START_REF] Davis | New Perspectives on Farm Household Incomes[END_REF]. There is evidence that low profits lead to under-investment in fixed assets and over time a rundown in the capital base of smaller farms (Wallace, 1998). This further erodes the medium to long-term capacity of these farms to generate profits. Inevitably farmers are exiting the industry. However, while the total number of farmers has been decreasing (1 per cent per year), the rate of adjustment has been insufficient to reduce the proportion of farms in the 'very small' category, and the low-income problem persists.

The majority of the region's farmers are aged 50 years and over, while fewer than 10 per cent are less than 35 years. This is a common and long-standing feature of agriculture in the region and across Europe, which may have implications for the management and profitability of farms. Research suggests that a farmer's goals are likely to be influenced by his, and his household's, life cycle stage [START_REF] Gasson | The Farm Family Business[END_REF][START_REF] Potter | The Farm Family Life Cycle, Succession Paths and Environmental Change in Britain's Countryside[END_REF]. When young, a farmer may place a high priority on goals such as farm expansion and income maximisation, but later in life will favour risk reduction and security. Arguably, a farming population with a preponderance of older farmers will generate lower profits (and incomes) than would otherwise be the case.

Business size and age are linked, as nearly 30 per cent of farmers with 'very small' farm businesses in Northern Ireland are aged 65 years and over, compared with only 13 per cent in the 'medium/large' category. It may be hypothesised, therefore, that reducing the number of older farmers, especially those with smaller farm businesses, would have positive effects on structural problems and farm performance.

Operator Age, Farm Size and Performance

We analysed Farm Business Survey (FBS) data to examine two questions relevant to the potential benefits of an Early Retirement Scheme:

• Are larger farms significantly different from smaller ones in terms of financial performance? • Are younger farm operators significantly different from older ones in terms of financial performance?

Farm Size and Financial Performance. Analysis of FBS data indicates that there are significant differences in financial performance associated with size within farming (Tables 2 and3). In the case of dairy farms the average Family Farm Income per adjusted hectare farmed ranged from £271 for 'small' dairy farms compared to £613 for the 'large' category (significant at P<0.05 level). Most of the difference in performance was due to higher levels of output per hectare on larger dairy farms (P<0.01). The picture for beef and sheep farms was similar to that for dairying although the performance differentials were statistically less significant. Average Family Farm Income was £57 per hectare farmed for the 'very small' category compared to £185 for the 'medium' sized farms (P<0.1). Larger beef farms produced significantly higher output per hectare (P<0.1). Age of Operator and Financial Performance. The sample was split into 'younger' farmers (<55 years) and 'older' farmers (55 or more years). The age of the farm operator did not to have a statistically significant effect on performance. On both dairy farms (Table 4) and beef/sheep farms (Table 5) older farmers actually had significantly higher levels of output per hectare but also significantly higher total costs per hectare. The net effect was that Family Farm Income per hectare farmed was not significantly different between younger and older farmers. The high level of unexplained variation (Table 6) is not untypical of this type of estimation involving cross-sectional data characterised by high variation across observations.

The model indicated that Age was not significantly related to Family Farm Income per hectare farmed. The 'scale' variable, Output, was positive and statistically significant (P<0.01) indicating that larger farms generated higher profits per hectare; an increase in scale leading to an increase in farm output of £10,000 was associated with an increase in Family Farm Income of £19.70 per hectare farmed. The coefficient of the farm type dummy variable (Ftype) was statistically significant (P<0.01) and suggested that the profitability of beef farms was on average £208 per hectare less than that for dairy farms. Finally, the variable Conprop, reflecting the proportion of land area farmed under annual rental contracts, was statistically significant (P<0.01); indicating that expected Family Farm Income using entirely short-term rented land was £252 per hectare less than when all farmed area was owned.

w

Discussion and Conclusions

There is clear evidence of significant ongoing structural change occurring in the Northern Ireland farm sector. Despite this a large proportion of farms remain relatively small and unprofitable. The attraction of the Early Retirement Scheme is its potential to accelerate structural adjustment by facilitating the transfer of resources from older farmers to younger farmers who are expanding their businesses, through the offer of subsidies that supplement the market value received for resources released. Thus, if age or business size is correlated to financial performance, potential benefits may accrue from a reduction in the number of (older) farmers and some increase in the average size of remaining farm businesses.

Considering age first, research suggests the existence of differences in life cycle goals for different age groups. Younger people have a longer planning horizon and tend to invest more heavily than farmers in comparable older age groups. It might be anticipated that this would yield benefits in the form of productivity improvements and profitability; the available evidence, however, does not show any significant differences in farmer performance related to age. Our analysis of Farm Business Data showed no link between performance and age. We cannot, therefore, anticipate any significant improvements in profitability from a policy that simply replaces farmers aged 55 to 65 by younger people.

Turning to the benefits from farm expansion, the case here rests on the relationship between size and performance. In addition, changes in land use also offer the possibility of substituting relatively low with relatively high profit enterprises. Our analysis indicated that substantially higher levels of output and income per hectare were associated with increases in farm size. Income increases were most noticeable when moving from the smallest beef or dairy farm categories to medium size units of either type. Further increases in farm size yielded more modest performance improvements. Likewise a change from beef and sheep production to dairy farming showed significant income gains per hectare. An accurate assessment of the potential gains to be made from transfers that involve a change in land use and farm expansion is difficult to make because it depends on assumptions about relative farm sizes and enterprise types and, crucially, on the number of cases where retirement would lead to the land released being used for expansion. EU regulations on the Early Retirement measure do not require that those taking over released land expand the holding. In fact, the potential for an Early Retirement Scheme to release land for farm expansion in the case of Northern Ireland may be quite limited. Our farmer survey revealed that of whose willing to participate in an Early Retirement Scheme, most intended to transfer their holdings to a son or daughter, who would be setting-up independently in farming for the first time. No farm enlargement would occur. We estimate that only 30 per cent of participating farmers would make land available for farm expansion. These were predominately smaller farm operators, so the proportion of land released for farm expansion would be lower, at approximately 20 per cent of the total area covered by a Scheme.

Nevertheless, it is possible to illustrate the potential benefits arising from the transfer of an individual farm as a result of participating in an Early Retirement Scheme. We can take an example where the farm to be transferred is of a type and size with low profits per hectare and the transferee is of a type and size with higher profits per hectare. Substantial divergence is found between 'very small' beef and sheep farms (the commonest type and size of farm), and a weighted average of 'small', 'medium' and 'large' dairy farms (the farm type with the highest profits). Using the regression equation and data from the farmer survey, the additional Family Farm Income per hectare achievable under this scenario was approximately £150 per ha, generating a total increase in farm income of approximately £3,000 per year. This then is an indicator of the maximum annual pension that could be justified for 'very small' beef and sheep farmers under an Early Retirement Scheme based on benefits arising from size and land use effects. As this category of farmer could be considered the prime target group for such a policy, (a disproportionately high number are aged over 55) it might be argued that a flat rate pension of £3,000 per year would represent the optimal policy design.

It is difficult to predict if a pension of this size would prove attractive to farmers in the target group. When a more generous package was introduced in the Republic of Ireland only 22 per cent of those eligible participated, amongst whom individuals with larger farm businesses were over-represented. A pension of £3,000 would be well below the upper limit set by the EU Regulation of approximately £9,500 per farmer per annum and total payments per farmer over the life of the Scheme of £95,000. It would, however, be in-line with the more modest flat rate payment now operating in France (£3,500 per farmer per year).

A further issue concerns possible deadweight losses: the extent to which an Early Retirement Scheme would compensate participants for something they intended to do anyway. Survey responses on planned retirement age in the absence of a scheme, indicated that the deadweight quotient was about 23 per cent. Deadweight, in this context, is defined as pension payments made after the date at which recipients intended to retire in the absence of a Scheme. This assumed that farmers entered a scheme at the youngest eligible age and received a flat rate payment that ceased at age 65.Surveyed farmers stated that participation in a scheme -were it available at the time of the survey -would have brought forward their retirement by an average of four years. This illustrates the essentially short-term effect of the Early Retirement Scheme and is in line with findings in the Republic of Ireland and France. That is, the Overall, under the conditions outlined above, the economic case for the introduction of an Early Retirement Scheme appears weak. Under certain assumptions, the benefits of releasing land are real but relatively modest. The maximum payment to 'very small' beef and sheep farmers that might be justified on efficiency grounds is of the order of £3,000 per annum. Moreover, realising these benefits depends crucially on farm expansion and change in farm use to secure better performance. Without farm enlargement current structures are largely preserved and the main effect is the transfer of resources from one generation to the next, albeit a few years earlier than would otherwise have been the case. No age related benefits could be detected. The positive effects, therefore, are essentially short-term and the policy would be very likely to incur a significant proportion of deadweight loss.

Figure

  Figure 1. Sources of anticipated finance for retirement

  of Farm Business Survey (NI), 2001 Note: (1) Figures in parentheses are standard deviations.(2) Total costs per hectare are the summation of fixed and variable costs per hectare; imputed costs for owned land and unpaid family labour have not been included.

  would quite quickly occur in the absence of any policy intervention.

Table 1 : Factors influencing participation in an Early Retirement Scheme

 1

		Very	Important Unimportant	Very	Don't
	important			unimportant	know
	Health considerations	224	99	25	1	2
	Value of the annual pension	229	97	16	2	7
	Number of years eligible for payment	181	123	28	4	15
	Availability of non-farm employment	32	94	147	64	14
	Desire to be free of the
	responsibility of running	125	138	66	10	12
	a farm business
	Desire to be free from work	118	127	77	20	9
	Housing arrangements for self and/or successor	96	130	83	25	17
	Desire to establish successor on the farm	121	137	73	10	10
	Source: Farmer Retirement Survey

Table 3 : Beef and sheep farms performance by size of holding

 3

	EU Farm Size Category	Very Small	Small	Medium	Chi-Sq Test for sig.
		< 8 ESU	8-39.9 ESU	40-99.9 ESU	difference between
		(n = 21)	(n = 130)	(n = 19)	size groups
	Average Age (years)	64 (± ± ± ± 11.2)	53 (± ± ± ± 12.3)	55 (± ± ± ± 16.3)	NS
	Adj. Area Farmed (ha)	20.2 (± ± ± ± 7.7)	55.8 (± ± ± ± 25.3)	124.0 (± ± ± ± 35.0)	***
	Total Farm Output per	855 (± ± ± ± 470)	789 (± ± ± ± 291)	916 (± ± ± ± 324)	*
	hectare(£)
	Total Costs per hectare (£)	798 (± ± ± ± 340)	669 (± ± ± ± 288)	731 (± ± ± ± 288)	NS
	Family Farm Income per	57 (± ± ± ± 310)	120 (± ± ± ± 168)	185 (± ± ± ± 121)
	hectare (£)

*

Quartiles

of Family Farm Income per ha:

	F o r
	P
	e
		e r
		R e v
			i e
			w
	Bottom 25%	-110	108	89	-
	Top 25%	212	226	270	-

Source: Analysis of Farm Business Survey, 2001. Note: (1) Figures in parentheses are standard deviations. (2) Total costs per hectare are the summation of fixed and variable costs per hectare; imputed costs for owned land and unpaid family labour have not been included.

Table 4 : Dairy farm performance by age of farm operator

 4

	Operator Age (years)	under 55	55 or over	t-Test for sig.
		(n = 69)	(n = 80)	difference between
				groups
	Average Age (years)	45 (± ± ± ± 6.4)	63 (± ± ± ± 5.9)	-
	Farm Size (ESUs)	52.9 (± ± ± ± 31.0)	57.6 (± ± ± ± 36.6)	NS
	Adj. Area Farmed (ha)	53.2 (± ± ± ± 24.5)	56.7 (± ± ± ± 36.9)	NS
	Total Farm Output per hectare (£)	1,471 (± ± ± ± 559)	1,791 (± ± ± ± 653)	***
	Total Costs per hectare (£)	1,122 (± ± ± ± 342)	1,369 (± ± ± ± 460)	***
	Family Farm Income per hectare (£)	349 (± ± ± ± 392)	422 (± ± ± ± 305)

NS Quartiles of Family Farm Income per ha:

	Bottom 25%	163	241	-
	Top 25%	561	630	-

Source: Analysis of Farm Business Survey, 2001. Note: (1) Figures in parentheses are standard deviations. (2) Total costs per hectare are the summation of fixed and variable costs per hectare; imputed costs for owned land and unpaid family labour have not been included.

Table 5 : Beef and sheep farm performance by age of farm operator

 5

	Operator Age (years)	under 55	55 or over	t-Test for sig.
		(n = 81)	(n = 93)	difference between
				groups
	Average Age (years)	44 (± ± ± ± 8.0)	64 (± ± ± ± 8.5)	-
	Farm Size (ESUs)	28.1 (± ± ± ± 27.1)	22.3 (± ± ± ± 20.1)	NS
	Adj. Area Farmed (ha)	71.7 (± ± ± ± 56.2)	54.1 (± ± ± ± 36.2)	**
	Total Farm Output per hectare (£)	757 (± ± ± ± 279)	900 (± ± ± ± 475)	**
	Total Costs per hectare (£)	616 (± ± ± ± 230)	788 (± ± ± ± 408)	***
	Family Farm Income per hectare (£)	141 (± ± ± ± 158)	112 (± ± ± ± 222)

NS Quartiles of Family Farm Income per ha:

  Modelling of Age and Scale Effects. To understand better the relationships between farm performance, age and scale of holding a number of regression equations were estimated using Ordinary Least Squares (OLS) on FBS data for all dairy and beef/sheep farms in the sample. Successive equations explaining farm profitability featured alternative selections of dependent and independent variables but it was found that the most robust equation was:

		Profadjha = f(age, output, ftype, conprop)
	Where,	Profadjha	Family Farm Income per adjusted ha farmed (£)
		Age	Farmer age in years
		Output	Value of farm output (£) -Measure of "scale"
		Ftype F o r Conprop	Dummy variable for farm type Ftype = 0 if dairying; Ftype = 1 if beef/sheep Proportion of area farmed rented in conacre
		P
		e
		e r
			R e v
			i e
			w
	Bottom 25%		35	5	-
	Top 25%		241	226	-

Source: Analysis of Farm Business Survey, 2001. Note: (1) Figures in parentheses are standard deviations. (2) Total costs per hectare are the summation of fixed and variable costs per hectare; imputed costs for owned land and unpaid family labour have not been included.

Table 6 : Factors influencing farm performance

 6

	Dependent Variable: PROFADJHA
	Method: Least Squares
	Observations: 318
	White Heteroskedasticity-Consistent Standard Errors & Covariance
	Variable	Coefficient	Std. Error	t-Statistic	Prob.
	CONSTANT	262.8309	72.38319	3.631104	0.0003
	AGE	0.481734	1.228744	0.392054	0.6953
	OUTPUT	0.001970	0.000326	6.050194	0.0000
	FTYPE	-207.7678	29.88261	-6.952799	0.0000
	CONPROP	-251.9535	56.08133	-4.492646	0.0000
	R-squared	0.407793	Mean dependent var	260.1001
	Adjusted R-squared	0.400225	S.D. dependent var	285.3315
	S.E. of regression	220.9755	Akaike info criterion	13.64958
	Sum squared resid	15283840	Schwarz criterion	13.70873
	Log likelihood	-2165.283	F-statistic	53.88281
	Durbin-Watson stat	1.824094	Prob(F-statistic)	0.000000

Reported survey results are statistically significant at P<0.05 level.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

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