-Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

This paper implements the profit change decomposition methodology developed

. Profit change over time is first decomposed into a price effect and a quantity effect; the quantity effect is then decomposed into a productivity effect and an activity effect; in turn, the productivity effect is subdivided into a technical efficiency effect and a technical change effect, while the activity effect is divided into a scale effect, resource mix effect and product mix effect. The end result is therefore a measure of six distinct components of profit change.

The methodology is used to investigate profit changes for a sample of cereal farms drawn from the Farm Business Survey in England and Wales for the period 1982 to 2000. The results of the analysis show an overall decline in profit levels for the period at the average speed of £4,400 annually, with the major part of this decline attributable to a negative price effect amounting to £7,000 annually on average. However, this was to some degree offset by a positive quantity effect largely driven by the positive contribution of technical change to profit growth, worth £4,000 annually on average. .

The literature on productivity and efficiency measurement has grown dramatically since the publication of seminal papers by [START_REF] Solow | A Contribution to the Theory of Economic Growth[END_REF], [START_REF] Charnes | Measuring Efficiency of Decision Making Units[END_REF], [START_REF] Aigner | On estimating the industry production function[END_REF] and [START_REF] Aigner | Formulation and Estimation of Stochastic Frontier Models[END_REF]. Increasingly sophisticated methodologies have been developed to tackle noisy data; reduce the restrictions imposed by functional forms and behavioural assumptions; identify the components and determinants of productivity growth; and address many other theoretical and econometric issues. The techniques have been applied to a large variety of contexts and sectors, in particular agriculture, where hundreds of published papers on productivity and efficiency analysis exist.

In the UK, productivity growth in agriculture has been the focus of a recently completed DEFRA project (Department for Environment, Food and Rural Affairs, 2003), the results of which are summarised in a special issue of the Journal of Agricultural Economics (July, 2004).

Yet, as noted recently by [START_REF] Lovell | Future Research Opportunities in Efficiency and Productivity Analysis[END_REF], while economists are interested in the efficiency and productivity with which businesses operate, business people are concerned with their profitability. It is clear that these concepts all relate to each other: all things being equal, a more productive business is also more profitable, and, in a temporal framework, fast productivity growth translates into fast profit growth. However, in the real world, 'other things' are not equal, which makes it difficult to decompose profit variation into variation in efficiency and productivity, and variation in other sources. The same set of arguments applies to agriculture, where farmers and policy makers are primarily interested in farm profit and the returns to the farm's fixed assets, and not efficiency and productivity per se. We therefore propose to investigate the relationship between profit change and productivity growth in the UK cereal sector, and to show how this decomposition brings useful insights into the current debate on agricultural policies.

The linkage between productivity change and profit has been previously analysed by the management accounting literature (see [START_REF] Miller | Profitability = Productivity + Price Recovery[END_REF], but this literature lacks a sound underlying theoretical framework and overall consistency.

However, more recently, production economics has been used on a few occasions to guide this inquiry [START_REF] Grifell-Tatjé | Profits and Productivity[END_REF][START_REF] Han | Profit Composition Analysis: A Technique for Linking Productivity Measurement and Financial Performance[END_REF] and, in this paper, we propose to implement the methodology developed by [START_REF] Grifell-Tatjé | Profits and Productivity[END_REF]. The end result is a measure of six distinct components of profit change, which can help us answer important questions for the cereal sector in England and Wales. For instance, how significant has been the adoption of new technologies, which drives technological change, for the growth of farm profit? What has been the impact of the recent decline in cereal prices on farm profits? Does output diversification represent an effective way of limiting the recently observed decrease in farm income? Do intensification and/or growth in farm size represent realistic options for farmers to maintain their income levels? Answers to these questions have obvious implications for, among others, R&D, structural and price policies.

To the best of our knowledge, ours is the first attempt to decompose farm income growth into variations in productivity and variation in other sources. It is worth emphasizing that the approach differs from the more standard investigation of profit/cost efficiency i in the sense that it does not rely on the assumption of cost minimisation or profit maximisation. What we describe is akin to an accounting relationship between profit change and productivity change, which is valid for any criterion of economic optimality. This is an attractive feature of the approach for the analysis of farm profit because farmers tend to maximise multiple criteria.

The paper is structured in the following way. The methodology is reviewed in section II, followed by a brief exposition of the way it is implemented. The data set and variable construction are described in section IV and section V presents the results of the analysis. Finally, results are discussed and conclusions made in section VI.

II. Methodology

Following [START_REF] Grifell-Tatjé | Profits and Productivity[END_REF], we consider a farm using an input vector x of dimension K to produce an output vector y of dimension M. The corresponding price vectors are denoted by w and p respectively. Note that vector w includes both market prices for those inputs that are purchased outside of the farm, and shadow prices for the fixed factors of production. In each period t, total economic profit generated on the farm is simply:

t t t t t x w y p - = π (1)
We now develop a decomposition procedure of farm profit growth (π t+1 -π t+1 ) between periods t and t+1, which is easily derived from commonly available farm-level data and relies on the microeconomic theory of the firm. The first

F o r P e e r R e v i e w
stage is straightforward and has long been acknowledged in the financial literature (e.g., Kurosawa, 1975).

Stage 1:

The profit change between periods t and t+1 decomposes as: The price effect captures the impact of price changes on profit, for a given production plan of the farm. The quantity effect measures the change in profit attributable solely to variations in the farm's production plan, because prices are held constant. Note that the quantity effect is calculated by using base-period prices and is therefore interpretable as the difference between two Laspeyrestype quantity indices; the price effect is calculated from current-period quantities, and is therefore interpretable as the difference between two Paasche-type price indices. A similar decomposition would follow from choosing base-year quantities and current-year prices instead. The quantity effect can then be decomposed into five additional effects, through two additional stages, which are based on distance functions that we now need to define. The within-period output distance function is defined as [START_REF] Coelli | An Introduction to Efficiency and Productivity Analysis[END_REF] The activity effect is not calculated from observed output vectors but from the technically efficient output vectors y b and y c given a single (period-(t+1))

technology and the two input vectors x t and x t+1 . It therefore nets out any possible change in efficiency and technology between the two periods. What is left is the change in profit resulting from changes in the scale of operation, the choice of input mix and the choice of output mix (scope), as is made clear in stage 3.2 of the decomposition below. In the one-input, one-output case, the activity effect would simply correspond to the move along the year-(t+1) production function between the technically-efficient projections of production plans (x t+1 , y t+1 ) and (x t , y t ). The next stage of the decomposition involves further disaggregation of the productivity and activity effects.

Stage 3.1: The productivity effect between periods t and t+1 decomposes as:

4 4 4 4 4 3 4 4 4 4 4 2 1 4 43 4 42 1 4 4 4 4 3 4 4 4 4 2 1 Effect Efficiency Technical 1 Effect Change Technical Effect ty Productivi 1 )] ( ) [( ) ( )] ( ) [( t a t c t a b t t c t b t y y y y p y y p y y y y p - - - - - = - - - + + (7)
where:

represents the maximum output vector achievable from input vector x t and year-t technology. The difference (y b -y a ) captures by how much that maximum changes between two adjacent periods. Given that the input vector x t and the output mix (defined by y t ) are held constant, this change is solely attributable to the evolution of the technology between the two periods. If

p - - - + - - - = - - - + + (8)
where

. Note that y c and y d are both technically efficient vectors given the input vector x t+1 and the period-(t+1) technology. However, y c is defined with the same output mix as y t+1 , while y d is defined with the same output mix as y t . The difference (y c -y d ) therefore captures a change in output vector solely attributable to a change in output mix, which is aggregated across products using base-period prices to give the product mix effect. Hence, a positive product mix effect indicates that an improvement in the outputorientated allocative efficiency of the firm makes a positive contribution to profit growth.

) , ( /

1 1 t t t o t d y x D y y + + =
The resource mix effect is the mirror image of the product mix effect in input space. It is based on the input vector . Note that from the definition of y d , input vector x t+1 belongs to the y d -isoquant defined with respect to the year-(t+1) technology. It is also evident that x e belongs to that same isoquant. The difference between the two input vectors x t+1 and x e therefore lies solely in the input mix, which is the same as that of x t in the case of x e . Hence, the resource mix effect measures the potential decrease in cost resulting from the change in the input mix between two adjacent periods. In other words, the It is important to note that the method did not impose any behavioural assumption. This is so because our purpose is not to evaluate the performance of a given farm with respect to an arbitrarily chosen concept of optimality, such as profit maximisation or cost minimisation, but to explain observed inter-annual changes in profit. Hence, in that respect, this study departs fundamentally from the more traditional analysis of farm allocative and economic efficiency, although it also shares a great deal with that type of empirical inquiry.

III. Implementation

While the previous decomposition is conceptually simple, it involves several quantities (y a , y b , y c , y d and x e ) which are not directly observable. Note, however, DEA models build a technological frontier by piece-wise linear envelopment of the observed input and/or output vectors [START_REF] Coelli | An Introduction to Efficiency and Productivity Analysis[END_REF]. Following Grifell-Tatjé and [START_REF] Grifell-Tatjé | Profits and Productivity[END_REF], we make the assumption that the technology cannot be forgotten, and therefore rule out the possibility of technological regression. This implies that the year-t technological frontier is constructed by considering all observations prior to, and including, year t. Hence, all the DEA-type problems presented below are sequential rather than contemporary (see [START_REF] Suhariyanto | Asian agricultural productivity and convergence[END_REF], for a discussion of the relative merits of the two approaches).

Suppose that we observe the input and output vectors

) of a particular farm f in any year t. We denote by I s the number of observations on producers that we use to build the technological frontier in each year s=1, 2, …, t. The sequential output matrix Y in year t is defined simply as

∑ = × t s s I 1 K [ ] t I t x ..... t x 1 I x x
,.... ,..... ,....... where λ is a column vector of dimension ∑ , and 1 is a row vector of ones of the same dimension. iii By choosing (x, y)= (x t , y t ) and p=t in problem (9), we retrieve the value of , from which vector y a is easily calculated. Similar reasoning allows for calculation of vectors y b , y c and y d . Finally, the input vector is retrieved from the solution to the following linear programming problem:

= p s s I 1 v ) , ( t t t o y x D ) , ( / 1 d t t i t e y x D x x + = [ ] 0 1 . 1 . . min ) , ( 1 1 1 1 1 1 ≥ = ≤ ≤ = + + + - + + λ λ φ λ λ φ v t t s t s d d t t i x X Y y t s y x D (10)
Altogether, the method involves solving five different linear programming problems for every farm and every pair of adjacent periods. Hence it is computer intensive as illustrated by the fact that, given the size of our data set, we had to solve 12,510 such problems. iv

IV. Data

The farm-level production data is drawn from the Farm Business Survey (FBS) for England and Wales (Department for Environment, Food and Rural Affairs • Other crops output (y2) is the sum of all quantities of all crops other than cereals grown on each farm, measured in tonnes (note that none of the farms in the sample produce any livestock outputs);

• Other crops price (p2) is calculated in a similar way to p1 (again, note that any Arable Area Payments received are included within the calculation of revenue);

• Hours of family and managerial unpaid manual labour (x1);

• Price of family and managerial unpaid manual labour (w1), calculated by dividing an imputed valuation of payments to unpaid labour by number of hours worked (this imputed valuation is internal to the FBS);

• Hours of all classes of hired labour (x2);

• Price of hired labour (w2), calculated by dividing payments to hired labour by number of hours worked;

• Area of land utilised for agricultural production (x3), measured in hectares;

• Rental price of land (w3) (rent is imputed within the FBS for owner occupied farms); Quantity of variable inputs (x4) -this is an index constructed by summing annual farm expenditures over four major categories: fertiliser, seeds, crop protection products and other general expenses (fuel, water, etc.) and dividing the result by the price index w4 described below;

• • Price of variable inputs (w4) -this is a divisia price index created from national DEFRA price indices for the various categories of inputs that make up this aggregate input (1990 = 1), note that since we do not have Summary statistics for these variables -and for operating profit (simply defined as the result of the product of output quantities and output prices minus the product of input quantities and input prices) -for the sample over the whole period (1982 to 2000) are presented in Table 1. Note that all prices have been deflated to 1990 levels using the appropriate annual price indices published by Defra. first three years of the period covered here, became negative in 1985 and remained so through the rest of the 1980s. There was some recovery close to break-even levels in the early 1990s and then two years of very positive profits in 1995 and 1996. Since 1996 there has been a dramatic decline in profit levels to a low in 2000.

V. Results

The results of implementing the profit decomposition are summarised in and Table 3. Table 2 shows average annual changes in profits over the period covered by the sample whilst Table 3 reports Table 2 Considering Table 2, total profit change follows a broadly similar pattern of change to that described in Figure 1 excepting those observations for the final few years of the data v . Total profit change is negative in 10 of the 18 observed periods, and on average (see the final row of period. The analysis therefore confirms the conventional wisdom that returns to the resources used in cereal production declined drastically in the 1980s and 1990s, resulting in significantly lower farm incomes and exit from the sector. This decrease in profitability is explained primarily by the price component of profit change that was chiefly negative (for 13 of 18 periods) and on average amounted to approximately £7,000 annually. However, negative price change effects were to some extent offset by a chiefly positive quantity change effect (for 11 of 18 periods) which averaged almost £3,000 a year over the whole period. This quantity change effect is made up of productivity and activity components that themselves can be further decomposed. The productivity effect contributes most (£2,454) to the average positive value for the quantity effect over the whole period, of this a negative value of £1,660 is attributed to the technical efficiency effect whilst the average technical change effect more than offsets this negative amount with a positive value of £4,114. As expected from the use of sequential (rather than contemporary) frontiers in all DEA problems, all the annual values for the technical change effect are positive, but the quantitative results demonstrate that technical change has made a very important and positive contribution to the profitability of this sample of cereal farms throughout the period studied. The activity component of the total quantity effect is generally smaller than the total productivity effect (both annually and on average) totalling £375 annually over the period. Of this total: scale effects are mostly negative (16 of 18 periods) and are almost £1,000 on average between 1982 and 2000; resource mix effects are mostly positive (11 of 18 periods) and are, on average, £1,192 per year for the whole period; and product mix effects are mostly positive (11 of 18 periods), but average less than £100 a year for the period sampled.

From Table 3 and Figures 2 to 5 that report changes in profit with respect to the base year, 1982, expressed on a per annum basis, it is easier to determine some broad longer term patterns of profit change occurring within this sample. Figure 2 presents graphically the first step of the decomposition and confirms the previous observation that overall profit declined over the entire period due to a large and negative price effect that was only partially compensated by a consistently positive quantity effect. Further, the figure allows us to identify three distinct sub-periods: from 1982 to 1987, the profitability of cereal farms worsened; from 1987 to 1995, profitability recovered up to its 1982 level; and finally, from 1995 to 2000, profits decreased again. Figure 2 also makes clear that the key determinant of the evolution of farm profit lies with the price effect rather than the quantity effect. Indeed, excluding the first few years, there is a remarkable correlation between the evolution of total profit change and the price effect, while the quantity effect appears relatively constant.

Figure 3 presents the second step of the decomposition. While the picture remains blurry for most of the 1980s, it then becomes evident that it is the positive productivity effect that represents the main component of the quantity effect, while the activity effect, in the end, appears quantitatively unimportant. growth, the technical efficiency effect has been more variable. Further, comparing Figures 2 and4, we note broad similarities in the way the price and technical efficiency effects evolve over the entire period, with in particular a steady increase over the 1985-1995 period followed by a sharp decline. This suggests that farmers, when facing favourable prices, tend to take more care in managing their crops than when prices are depressed. Figure 5 breaks down the activity effect and shows that the scale effect remains relatively constant throughout the period and contributes negatively to profit growth by £1,000 annually. This effect occurs in a context of concentration of the sector as indicated by a steady increase in the average farm size over time within our sample vi . Our results therefore suggest that, somewhat unexpectedly, the exploitation of (assumed) economies of scale does not represent an effective means of maintaining farm profitability in the cereal sector. Finally, we note that the resource mix and product mix effects follow the same pattern over time, and, while the two effects contribute positively to profit growth through most of the period, none of the effect dominates the other. Altogether, there is limited evidence that farmers, by switching production from cereals to other crops, managed to increase profits in a sustainable manner over the period considered.

To summarise, the analysis shows that the overall negative profit change for this sample is largely driven by a large negative price effect, averaging £7,200 per year between 1982 and 2000. This negative price effect is to some extent offset by a positive quantity effect (averaging about £2,800), which is composed of positive productivity and activity effects. The positive productivity effect is driven by the consistently positive effect of technical change on profits while

VI. Discussion and conclusions

That output price should play such an important role in determining the profit levels of cereals farms is probably an unsurprising conclusion to draw from the results presented in the previous section. The analysis clearly shows that negative price effects largely account for the overall negative profit change that cereal farms have experienced over this period. Year on year changes in profit from 1995 onwards have been particularly heavily impacted by policy change that has occurred since 1993 with the introduction of direct payments to farmers (the Arable Area Payments Scheme), the resulting fall in market prices for cereals and also by the relative strength of sterling during this period.

What is perhaps more unexpected is the relative size of the quantity effect on total profit and the magnitude and direction of the various elements of this effect.

The consistent, positive technical change effect is the most important element of the overall quantity effect on profit change. This implies that the best practice farms within the sample have consistently driven the production possibility frontier outwards with consequent positive effects on profit. Conversely, the analysis shows that technical efficiency has, on average, had a negative effect on profit change implying that those farms not on the best practice frontier are falling further behind. However, it should also be noted that the analysis used 20 The activity effect also contributes to the positive nature of the overall quantity effect, although to a lesser extent. This is mostly due to a generally positive resource mix effect. Since this effect measures the allocative efficiency of input use then increased efficiency in this sense would seem to be a rational response to deteriorating output prices. There is also a smaller, but also generally positive product mix effect. Again, substitution away from cereals to other arable crops in response to relative output prices or changes in the policy environment would represent a rational response to changes in cereal prices. Positive resource and product mix effects were partially offset by (almost) consistently negative scale effects. However, the analysis does not enable us to categorically state that this indicates the presence of decreasing returns to scale for this sample, but it does coincide with similar evidence found for a sample of UK cereal farms reported in Department for Environment, Food and Rural Affairs (2003).

This paper uses the profit decomposition method developed by Grifell-Tatjé and [START_REF] Grifell-Tatjé | Profits and Productivity[END_REF] to analyse the profit levels of a sample of UK cereal farms for the period between 1982 and 2000. Over this period the analysis shows the decline in profit levels that the sample exhibits can be mostly attributed to falling cereal prices, which in the later years of the period studied are due to CAP reform.

Cereal prices are, however, outside of the control of the farmer whilst aspects of

  there is technological progress (regression), (y b -y a )>0 (<0), and the technical change effect is clearly positive (negative). Vector y c corresponds to the proportional expansion of vector y t+1 which reaches the period t+1 technological frontier (i.e., it is the technologically efficient output vector associated with (x t+1 , y t+1 ) and the period-(t+1) technology). It follows that the difference (y c -y t+1 ) captures the degree of technical inefficiency of the farm in year t+1, while (y b -y t ) captures the degree of technical inefficiency of the farm in year t. The term in square brackets of the technical efficiency effect therefore measures the change in technical inefficiency between two adjacent periods, aggregated across outputs using base-year prices. Clearly, an improvement in technical efficiency makes a positive contribution to profit growth.

  reflects changes in the input-orientated allocative efficiency of the firm, with improved efficiency contributing positively to profit growth.Finally, note that vectors y d and y b are two technically efficient output vectors computed with respect to the same year-t+1 technology and the same output mix (defined by y t ). The only difference lies in the reference input vector, which is x t for y b and x t+1 for y d . Hence, quantity (y d -y b ) accounts for the growth in production that is solely attributable to the change in input vectors between two adjacent periods. Using base-year prices to transform these quantity changes into values, we obtain a pure scale effect, or, in other words, the change in profit solely attributable to a change in the level of input use. This concludes our presentation of the profit decomposition into two effects in the first stage, two effects in the second stage, and five effects in the third stage.

  quantities are derived from observed output and input vectors as well as the unobserved values of several distance functions. The purpose of this section is to explain how the values of those distance functions can easily be retrieved by DEA-type linear programming problems.

  value of the output distance function evaluated at input-output combination (x, y) with respect to the sequential technological frontier in period p is inferred from the solution to the following linear-programming problem:

  forWales, 2003) covering the production years from 1982 to 2000. The FBS is an annual survey of more than 2,800 farms that are selected from a random sample of census data that is stratified according to region, economic size of farm and type of farming. It should be noted that only farm businesses above eight European size units (ESU) are included in the FBS.According toDefra (2002) approximately 18% of all holdings classified as cereal farms in England were below 8 ESU in 2000. Accordingly the sample used here cannot be seen to be truly representative of the population of cereal farm holdings in England and Wales although it can be considered to represent those holdings that produce the greater part of cereal output.A sub sample of 436 cereal farms (defined here as those farms where 60% or more of total revenue is derived from cereal enterprises) observed for varying numbers of years (the mean duration being 6.74 years with the minimum number of years being 3) are extracted from this dataset to form an unbalanced panel totalling 2938 observations. The variables used in the analysis are constructed as follows:• Cereal output (y1) is constructed as the sum of production (tonnes) over all cereal enterprises for each farm;• Cereal price (p1) is generated by dividing the sum of all revenue from cereal production by quantity of cereal produced on each farm (note that Arable Area Payments are included in revenue)

  inputs at the farm-level we assume that all farms in the sample face the same set of input prices; Quantity of capital services (x5), which is constructed in an attempt to represent the flow of services emanating from capital stock items such as machinery, buildings and land improvements and which is measured by summation over these elements of maintenance and running costs, depreciation charges and interest on the capital stock and dividing the result by the capital price index (w5) described below;• • Price of capital services (w5) -another divisia price index created from national DEFRA price indices for the various categories of capital inputs that make x5 (again, 1990 = 1 and we make the same assumptions for this as were done for w4).

Figure 1

 1 Figure 1 describes the evolution of annual mean operating profit for this sample

  cumulative changes in profits, i.e. changes relative to the start of the sample in 1982, expressed on a per annum basis. Each Table reports the total profit change in the second column and then shows how this is decomposed into price and quantity elements in successive columns. The quantity element is then further decomposed into productivity and activity components (which are in turn composed of further sub-components; technical efficiency and technical change in the case of the productivity component, and scale, resource mix and product mix in the case of the activity component). The various elements of the decomposition are shaded within the tables in an attempt to increase the clarity of presentation.

Figure 4

 4 Figure 4 then presents the decomposition of the productivity effect and

  have been generally negative. The scale component of the activity effect is predominantly negative whilst this is more or less offset by chiefly positive resource and product mix effects.

  a deterministic nature and cannot account for factors outside of the control of the farm, such as climatic variation. Given the effect of climate on cereal yield, much of the variation the analysis detects in technical efficiency might be attributable to this.

  technical efficiency, technical change, scale of operation and scope are factors the farmer can influence to a greater or lesser extent. The analysis demonstrates that most of these controllable factors have contributed positively to profit change for this sample (except for technical efficiency and scale) and of these technical change has played the most important role.

Figure

  Figure 1: Annual mean operating profit English and Welsh Cereal Farms

  :

	orientated measure of technical efficiency. The concept is easily extended to
	measure the distance of any input-output combination (x, y) with respect to any Stage 2: The quantity effect between period t and period t+1 decomposes as
	particular technology f: { min ) , ( y x D f o = δ 4 : y 4 4 4 4 3 4 4 4 4 4 2 1 1 ) ( ) ( t t t t t t w x x p y y ---+ + 1 Effect Quantity (6)	/ = δ	) 4 x 4 4 ( f 4 P ∈ 2 4 4 4 4 3 1 1 )] ( ) [( t c t b t y y y y p ---+ Effect ty Productivi } (4) +	4 4 4 4 4 3 2 4 4 4 4 4 1 1 )] ( ) ( [ t t t b c t x x w y y p ---+ Effect Activity
	where the only new notation, P f (x), represents the output set associated with where is interpreted as the maximum output achievable with ) , ( / 1 t t t o t b y x D y y + =
	technology f. Because the production plan (x,y) is not necessarily observed when input vector x t and year-(t+1) technology; while is ) , ( / 1 1 1 1 + + + + = t t t o t c y x D y y
	technology f is available, however, we cannot conclude a priori how the value of
	interpreted as the maximum output achievable with input vector x t+1 and year-
	{ δ o min ) t = r : y P / t δ e P ∈ e ( t t x r ) o compares to unity. In the remainder of the paper, we use the adjacent-periods , ( t t o y x D (3) e w output distance functions D t+1 D f o (x t , y t ) and D t o (x t+1 , y t+1 ), as well as the mixed-periods distance function D t+1 o (x t+1 , y t ), which can all be greater or smaller than unity. Our decomposition also involves the input distance function, which was first where L t (y t ) denotes the input requirement set, given the technology in period t and the output vector y t . It measures the largest factor of proportionality ρ by which the input vector x t can be scaled down in order to produce a given output F o r P e e r e w (t+1) technology. Hence, the difference (y b -y t ) represents the distance of the observed input-output combination (x t , y t ) from the year (t+1) technological frontier; while (y c -y t+1 ) represents the distance of the observed input-output combination (x t+1 , y t+1 ) from the same frontier. It follows that the inside of the square bracket of the productivity effect measures the extent to which the firm got closer to a given technological frontier between periods t and t+1, which is a where P F R e v i } introduced by Shepard (1970). With respect to the within-period technology, it is defined as: way of assessing its productivity growth ii . As should be expected, a firm getting R e nearer the technological frontier generates a productivity effect that contributes { ) ( / : ) , ( t t t t t t i y L x Max y x D ∈ = ρ ρ (5) v i positively to profit growth.
	vector y t with the period-t technology. The within-period input distance function
	takes a value no smaller than unity and its reciprocal is the well-known Farrell
	(1957) input-based index of technical efficiency. As discussed previously for the
	output distance function, it is easily extended to accommodate any input-output
	combination (x, y) and any technology f. We now proceed to present the second
	stage of our decomposition.

t (.) denotes the output set derived from the period-t technology. Because the input-output combination (x t , y t ) is observed in year t, δ=1 is always technologically feasible and it follows that the within-period output distance function is necessarily smaller than or equal to unity. Its value represents the percentage of maximum achievable output that the firm produces, given its input vector and the year-t technology. It also corresponds to the Farrell-type output

  The third stage of the profit decomposition concludes with the disaggregation of the activity effect. The activity effect from period t to period t+1 decomposes as:

	Stage 3.2: 4 4 4 4 3 4 4 4 4 2 1 4 4 3 4 4 2 1 4 43 4 42 1 4 4 4 4 4 3 4 4 4 4 4 2 Effect Scale Effect Mix Resource Effect Mix Product Effect Activity 1 1 1 ) ( ) ( ) ( ) ( ) ( ) ( t e t b d t e t t d c t t t t b c t x x w y y p x x w y y p x x w y y
	a y =	y	t	/	t o D	(	x	t	,	y	t	)
	F o
					r
									P e
												e
												r
												R e
												v i
												e w

Table 2 )

 2 total profit declined annually by more than £4,000 between 1982 and 2000, or £72,000 over the entire

	Page 16 of 33

Table 1 : Summary statistics for sample 1982 -2000

 1

	Mean	Standard	Minimum	Maximum
		Deviation

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

Table 2 : Profit Change Decomposition for English and Welsh Cereal Farms 1982-2000 -Average annual changes (£)

 2

	Period

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript F o r P e e r R e v i e w

Table 3 : Profit Change Decomposition for English and Welsh Cereal Farms 1982-2000 -Cumulative changes (£) expressed on a per annum basis

 3

	Period

< D2 -Production and Organizations < D -Microeconomics, Q12 < Q1 -Agriculture < Q -Agricultural and Natural Resource Economics, Q16 -R&D|Agricultural Technology|Agricultural Extension Services < Q1 -Agriculture < Q -Agricultural and Natural Resource Economics

i For an example of this approach, see [START_REF] Coelli | Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: A non-parametric approach[END_REF].

ii The well-known Malmquist productivity index relies on the same idea.

iii Note that we estimate VRS models. The corresponding CRS models, which are more restrictive, are easily obtained by removing the constraint . iv The linear programming problems were solved using the Mathematica software, which took roughly four hours on an ordinary PC. The codes are available from the authors upon request. v The difference is explained by the fact that the average profit depicted in Figure 1 is calculated over different sub-samples of farms in each year, while the profit changes reported in Tables 2 and3


Xiaohui Liu
email: x.liu2@lboro.ac.uk

Chang Shu

Hong Kong Monetary

Peter Sinclair

Methods: Multiple/Simultaneous Equation Models < C -Mathematical and Quantitative Methods, O53 -Asia including Middle East < O5 -Economywide Country Studies <

Keywords: C32 -Time-Series Models < C3 -Econometric Trade, Foreign direct investment, Asian Economies, the VECM

Despite the increasing role of foreign direct investment (FDI) in economic development, very limited research has been carried out on the causal links between trade, FDI and economic growth in Asian economies. This study examines empirically the interplay between exports, imports, FDI and economic growth for nine Asian economies by conducting multivariate causality tests in the VECM framework. The results reveal twoway causal connections between the four variables for most of the sample economies. These findings suggest that export expansion, import liberalisation and FDI inflows are integral parts of the growth process in Asian economies.

Introduction

Empirical investigation of the causal link between trade and economic growth is an important theme in trade and development literature. A large number of empirical studies have been carried out on causality between trade and growth, particularly for East Asian economies (Hsiao, 1987;Chen and Tang, 1990;Ahmad and Harnhirun, 1995;Islam, 1998;Kwan et al, 1999;Hatemi-J, 2002 andJin, 2002;Love and Chandra, 2004;Thangavelu and Rajaguru, 2004). These studies have shed some light on the issue and provided some useful insights regarding the evaluation of trade and development strategies. However, most of the existing studies exclude foreign direct investment (FDI) and only consider trade-growth linkage, despite the fact that FDI inflows and multinationals have become the driving forces of economic growth and technology transfer (Kokko, 1994;Oulton, 1998;[START_REF] Xu | Multinational enterprises, technology diffusion, and host country productivity growth[END_REF]. Over the last two decades, East Asian economies, in particular, have received increasing amounts of inward FDI which reached a record level of US$143 billion in 2000 (UNCTAD, 2001). In contrast, comparatively few studies have been conducted on the relationship between trade, FDI and economic growth for Asian economies in a multivariate framework.

It is important to examine the linkage between trade, FDI and economic growth for Asian economies in order to provide evidence as to whether rapid economic growth in the region is driven by trade and FDI, or whether there is reciprocal impact between growth, trade and FDI. Such an investigation is able to explore to what degree the (Blomstrom and Sjoholm, 1999;Gao, 2004). Therefore, we consider exports, imports and FDI as external links, and attempt to test the causal relationship between these factors and economic development in selected Asian economies.

The paper is structured as follows: Section 2 discusses the theoretical foundations for possible bi-directional linkage between external factors and economic growth. Section 3 introduces the methodology and data used in the study, while the following section presents and analyses empirical results. Section 5 concludes with policy implications.

2 Trade, FDI and Growth: a discussion of theoretical linkage and the empirical evidence The theoretical foundations for empirical studies on trade, FDI and growth are derived from neo-classical and endogenous growth models. In neo-classical growth models, factor accumulation is considered as a driving force of growth. FDI, as a factor of production, increases investment as well as enhances efficiency, and provides growth momentum in the medium-term. Application of the Solow model (1956) implies that a trade-induced rise in production leads to transitional growth when the economy adjusts to a new steady-state equilibrium as specialisation and the realisation of external economies of scale through trade allow the economy to transform its given input into a greater value of final output. Ben-David and Loewy (2003) extend the traditional neoclassical exogenous growth model by incorporating the impact of openness on growth within a multi-country framework and the results from their model indicate that The new endogenous growth models establish the links between long-run growth and technological progress, and provide a framework in which trade and FDI can permanently increase the rate of growth in the host country through technology transfer, diffusion, and spillover effects. Romer (1993) points out that one benefit that trade brings is access to new ideas. Grossman and Helpman (1991) have constructed a theoretical model to show formally that trade in goods serves as a conduit for knowledge flows between countries. These flows in turn serve to increase the productivity of capital and labour and hence the growth rate of per capita output. Borensztein et al (1998) develop a growth model in which technological progress, represented by increasing varieties of capital goods, is determined by FDI as multinationals can reduce the costs of introducing new varieties of capital goods. Thus, FDI acts as the main vehicle of technology transfer. In addition, the human capital building model as presented by Lucas (1988) may suggest that trade and FDI could enable inter-country technology transfer.

Although trade and FDI are important for economic growth, the causal link between them is not necessarily uni-directional as the level of economic development can also influence trade and FDI. Theoretical justifications for reverse causation from growth to trade and FDI have long been discussed in development literature [START_REF] Kanamori | Economic growth and exports[END_REF]Diaz-Alejandro, 1975;Kravis, 1970;Findlay, 1984). It is argued that output growth has a positive impact on productivity growth, which in turn acts as a stimulus to exports (Kaldor, 1967). Jung and Marshall (1985) suggest that internal growth 6 mechanisms better explain export growth rather than the reverse. In new trade theory, the market structure and output expansion may trigger significant changes in exports through a process of "cumulative causation" [START_REF] Venables | Trade policy, cumulative causation, and industrial development[END_REF]. A more recent study by Berthelemy and Demurger (2000) has theoretically established that FDI is influenced by the growth rate of the economy in the long run, and thus provides a formal justification of the causal link between growth and FDI.

Given the possible bi-directional relationship between trade, FDI and economic growth postulated by the theories, the issue becomes empirical and can be verified through statistical tests. Attempts have been made to establish a causal link between trade, FDI and growth empirically. Previous studies in this area fall roughly into three groups. The first group focuses on the impact of trade or openness on economic growth.

Early works, including Balassa (1978, 1984), Feder, (1983), and Kavoussi (1984), only consider causation running from exports to economic growth. Studies of this kind assume implicitly that exports are exogenous without taking into account reverse causation.

Studies in the second group examine the impact of economic growth on FDI.

The extent to which FDI can be explained by economic growth is the focus of attention, particularly the determinants of FDI in developing countries [START_REF] Wang | Determinants of inflow of foreign direct investment in Hungary and China: time-Series approach[END_REF]Dees, 1998). These studies, which assume one-way causality from growth to FDI, appear to indicate that the growth potential in developing economies is the main determinant of inward FDI. These studies have, however, also neglected the possible simultaneity between FDI and growth. The third group of studies attempts to investigate bi-directional causal links between trade and growth or FDI and growth, by conducting causality tests. Mixed results have been obtained. Jung and Marshall (1985) have found that only four out of 37 developing countries support the export-led growth hypothesis. Studies by Chow (1987), Hsiao (1987), and Chen and Tang (1990) are only able to provide weak support for the export-led growth hypothesis even for highly export-oriented economies such as Hong Kong, Singapore and Taiwan. Those results are consistent with Young's findings (1994) which indicate that outward orientation may or may not be associated with rapid productivity growth, and the fast expansion of East Asian NIEs should not be viewed as evidence of the potential dynamic gains from outward-oriented policies. On the other hand, Ahmad and Harnhirun (1995), Biswal and Dhawan (1998), Islam (1998), Shan and Sun (1998) have found bi-directional causality for most of the East Asian NIEs and China. However, most of the cited studies have treated exports as the principal channel through which openness can affect economic growth. None of them include FDI inflows in their empirical analysis despite the fact that East Asian economies have received increasing amounts of inward FDI. Therefore, using trade as a proxy for openness may be inadequate without taking the impact of FDI into account (Goldberg and Klein, 1999). Furthermore, the existence of a FDI-growth nexus is not considered by excluding FDI variable from causality tests.

The recent study by Nair-Reichert and Weinhold (2001) acknowledges the problems mentioned above by considering the complex and heterogeneous relationship between FDI and growth. They have found a causal relationship from FDI to growth, and some evidence that the efficacy of FDI is higher in more open economies. However, 8 in their study, imports are not used as the measure of openness and this may underestimate the impact of trade induced by imports (Keller and Yeaple, 2003).

Extending previous work in this area, the present study attempts to estimate the complex interaction between trade, FDI and growth for selected Asian economies in a multivariate framework.

The methodology and data

We are interested in the interplay of four variables, GDP, FDI, Exports and Imports; to set the scene, therefore, consider a VAR involving four variables, W, X, Y, and Z. In a stationary setting, a Granger causality test in such a structure would be carried out (following Ghartey, 1993) via the following regressions: where the lag lengths, (m, n, p, q) are determined so that u 1t , u 2t , u 3t and u 4t are serially uncorrelated. The null hypothesis 'Y does not Granger cause X, given W and Z' is tested via a standard F-test, being rejected if the 2j in equation ( 2) are jointly significant.

t l t m l l k t m k k j t m j j i t m i i t u X Y Z W W 1 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (1) t n l l t l n k k t k j t n j j n i i t i t u W Z Y X X 2 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (2) t l t p l l k t p k k j t p j j i t p i i t u w Z X Y Y 3 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (3) t l t q l l k t q k k j t q j j i t q i i t u W X Y Z Z 4 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (4) t = 1,2,...,N
Similarly, if the 2j in equation ( 3) are jointly significantly different from zero, the null hypothesis that X does not Granger cause Y, given W and Z, is rejected and so on.

However, if the series are non-stationary, it becomes important to determine whether or not they are cointegrated, as this affects the sampling distributions of the causality tests. Toda and Phillips (1993) show that levels autoregressions are an unreliable basis for inference about causality in the nonstationary case, since the sampling distributions of the test statistics depend on the ranks of certain sub-matrices in the cointegrating space.

They thus favour the use of the VECM framework of Johansen (1988) in which the necessary information about the cointegrating space is available, and where causality test statistics will usually have asymptotic Chi-square distributions. We adopt this approach.

When the series in the VAR are cointegrated, and have two cointegrating vectors, ignoring the higher-order dynamics, equations (1)-(4) above can be rewritten in the VECM form: Hall and Milne (1994, P.600-601) introduce the notion of the absence of weak causality to denote the situation in which the long-run level of one or more variables is unaffected by the levels of others. In equation ( 5), this is testable via zero restrictions on ij . Following Hall and Milne, if weak non-causality is rejected, then Granger non-causality, which in addition involves the remaining higher-order short-run dynamics, is also rejected. Thus bi-directional causality can be explored by estimating the full VECM proposed in equation ( 5), and testing restrictions on the long-run adjustment coefficients.

Data

Annual data for the value of exports and imports in goods for nine economies are taken from UNCTAD (2003). The export and import price indices are used to deflate the value of exports and imports for most economies, but GDP deflators (1995=100) are used as an alternative when such an index is not available in the cases of Singapore and Thailand. There are several sources for obtaining inward FDI including the UNCTAD (2003), Reserve Bank of India (2003) andBank Indonesia (2003). Annual GDP series are taken from the International Financial Statistics (2003). Both GDP series and inward FDI are deflated using GDP deflators. The sample period spans from 1970 to 2002 for the nine economies. The series are transformed into logarithms so that the first differences correspond to growth rates. Table 1 presents the results from ADF unit root tests with lag length chosen by downward search (t-test on the longest lag). The null hypothesis of a unit root in the logarithm is not rejected for any of the four variables in levels. However, each of the logged series is stationary in first differences. Therefore all the variables are integrated of order one.

Empirical results

Unit root tests

Insert Table 1 here

Cointegration tests

When testing cointegration relationships we first need to decide whether deterministic components such as constant, time trend and dummy variables should be included in the model. Using the general-to specific approach, a specific model for each economy is chosen. The selection criterion for lag length is the combination of Akaike Information Criterion and diagnostic tests. The selected lag length should enable residuals to pass various diagnostic tests, including serial correlation, normality test, function form, ARCH and heteroskedasticity.

Second, the test for cointegration rank described by Johansen and Juselius (1990) is applied, and the results are reported in Table 2. Both trace statistics and maximal eigenvalue statistics show that the VAR for each economy has two cointegrating vectors; both are significant at the 5% and 1% levels. The existence of cointegration between the variables suggests that there are long-run relationships between the four variables, implying that there is a tendency for the four variables to move together. 2 here

Testing for weak exogeneity and causality

The results from cointegration tests indicate that the causality tests should be conducted via Equation ( 5). As stated in the methodology section, the main interest of the study is to test long-run relationships, hence we investigate the long-run causality between the four variables which is equivalent to testing long-run loading factors in equation ( 5),

such as 0 = ij a
. The results reported in 13 reflect the fact that India has been a relatively closed economy with an inward-looking policy which was relaxed relatively late, with final removal of its last features coming only in the mid-1990s. Therefore, it is highly likely that the impact of trade and FDI on GDP is relatively small at the aggregate level. However, it is very surprising that Korean GDP is only weakly affected by external factors, given that the remarkably rapid economic growth over most of the last few decades has been accompanied by persistent export expansion. The possible reason may be that South Korea has adopted an export promotion policy under import protection. It is also well known that the Korean government restricted inward FDI until the Asian financial crisis. All these factors may create distortion, and limit the impact of trade and FDI on GDP.

The null hypothesis that FDI is exogenous is universally rejected across the sample economies, implying that inward FDI is attracted by the growth prospects and the level of external trade in the region. The results are consistent with previous studies which have found that rapid GDP growth and intensive external trade act as the main determinants of inward FDI (Broadman and Sun, 1997;Sun, 1998).

The evidence rejects the null hypothesis that exports are exogenous for all the sample economies. The only weak result obtained is for Malaysia where the null hypothesis is rejected at the 10% significance level, showing a weak causal link between exports and the other three variables. The results indicate that exports and the other three variables mutually affect each other. In terms of imports, the results in seven out of nine sample economies reject the hypothesis that imports are exogenous, suggesting that imports are linked to GDP, exports and FDI. However, the null hypothesis is not rejected in the cases of India and Malaysia, confirming that only a one-way causal link 14 running from imports to GDP exists. Import-led growth has been achieved in these two countries. In particular, Indian imports grew faster than exports between 1970 and 1990.

Import liberalisation helped reduce import shortages, and hence facilitated economic growth (Nidugala, 2000).

Based on the four weak exogeneity tests (weak causality -after Hall and Milne) which massively reject the null hypothesis for each of the four variables for seven out of nine sample economies, we conclude that each is weakly caused by the other three. This implies in turn that we have bi-directional Granger causality for the majority of the sample economies. Bi-directional causalities between the four variables do not support a trade-led or a FDI-led growth hypothesis (Kwan and Kwok, 1995;Nair-Reichert and Weinhold, 2001). The findings suggest that export expansion, import liberalisation and FDI inflows are integral elements in the economic growth process and that these external links go hand in hand with growth in Asian economies.

Conclusions

This study examines empirically the causal links between FDI, trade and growth for nine Running title: Trade, FDI and Economic Growth Abstract Despite the increasing role of foreign direct investment (FDI) in economic development, very limited research has been carried out on the causal links between trade, FDI and economic growth in Asian economies. This study examines empirically the interplay between exports, imports, FDI and economic growth for nine Asian economies by conducting multivariate causality tests in the VECM framework. The results reveal twoway causal connections between trade, inward FDI, inward merger and acquisitions (M&As), and growth for most of the sample economies. There is a unidirectional causal link running from outward M&As to growth and trade. These findings suggest that export expansion, import liberalisation, FDI inflows and inward M&As are integral elements of the growth process in Asian economies. The relationship between trade and economic growth has received increasing attention from academics and policy markers. A large number of empirical studies have been carried out on causality between trade and growth, particularly for East Asian economies (Hsiao, 1987;Chen and Tang, 1990;Ahmad and Harnhirun, 1995;Islam, 1998;Kwan et al, 1999;Hatemi-J, 2002 andJin, 2002;Love and Chandra, 2004;Thangavelu and Rajaguru, 2004;[START_REF] Awokuse | Export-led growth and the Japanese economy: evidence from VAR and directed acyclic graphs[END_REF]. These studies have shed some light on the issue and provided some useful insights regarding the evaluation of trade and development strategies. However, most of the existing studies exclude FDI and only consider tradegrowth linkage with the exception of [START_REF] Yao | On economic growth, FDI and exports in China[END_REF], despite the fact that FDI inflows and multinationals have become the driving forces of economic growth and technology transfer (Kokko, 1994;Oulton, 1998;[START_REF] Xu | Multinational enterprises, technology diffusion, and host country productivity growth[END_REF]. Over the last two decades, East Asian economies, in particular, have received increasing amounts of inward FDI which reached a record level of US$155.5 billion in 2005 (FDI Confidence Index, 2005). In contrast, comparatively few studies have been conducted on the relationship between trade, FDI and economic growth for Asian economies in a multivariate framework.

It is important to examine the linkage between trade, FDI and economic growth for Asian economies in order to provide evidence as to whether rapid economic growth in the region is driven by trade and FDI, or whether there is reciprocal impact between growth, trade and FDI. Such an investigation can explore the degree to which the positive The sample used in this study consists of the first and second wave of newly industrialised economies (NIEs) in East Asia, notably Hong Kong, Singapore, South Korea, Taiwan, Indonesia, Malaysia, the Philippines and Thailand. In addition, a large developing country in the region, India, is also included. An appropriate model will be selected for individual economies in order to best capture the complicated relationship between these variables. This research differs from previous studies in several ways. First, instead of using different measures of openness and trade policy, we examine the three basic elements of development strategy, notably exports, imports and FDI. Most of the previous causality tests assume implicitly that the coefficients on exports and imports are homogeneous (Krishna, et al, 2003) and use trade shares to measure openness. However, in this study, we treat exports and imports separately to allow for the possibility that their influence is asymmetric. Second, we regard FDI, including cross-border M&As, as a key factor of economic growth, given that in addition to its direct contribution to GDP, FDI has played an increasingly important role in technology transfer and R&D spillovers in developing countries ( Blomstrom and Sjoholm, 1999;Gao, 2004). Therefore, we consider exports, imports and FDI as external links, and attempt to test the causal relationship between these factors and economic development in selected Asian economies.

The paper is structured as follows: Section 2 discusses the theoretical foundations for possible bi-directional linkage between external factors and economic growth. Section 3 introduces the methodology and data used in the study, while the following section presents and analyses empirical results. Section 5 concludes with policy implications.

2 Trade, FDI and Growth: a discussion of theoretical linkage and the empirical evidence The theoretical foundations for empirical studies on trade, FDI and growth are derived from neoclassical and endogenous growth models. In neoclassical growth models, factor accumulation is considered as a driving force of growth. FDI, as a factor of production, increases investment as well as enhances efficiency, and provides growth momentum in the medium-term. Application of the Solow model ( 1956) implies that a trade-induced rise in production leads to transitional growth when the economy adjusts to a new steadystate equilibrium as specialisation and the realisation of external economies of scale through trade allow the economy to transform its given input into a greater value of final output. Ben-David and Loewy (2003) extend the traditional neoclassical exogenous The new endogenous growth models establish the links between long-run growth and technological progress, and provide a framework in which trade and FDI can permanently increase the rate of growth in the host country through technology transfer, diffusion, and spillover effects. Romer (1993) points out that one benefit that trade brings is access to new ideas. Grossman and Helpman (1991) have constructed a theoretical model to show formally that trade in goods serves as a conduit for knowledge flows between countries. These flows in turn serve to increase the productivity of capital and labour and hence the growth rate of output per head. Borensztein et al (1998) develop a growth model in which technological progress, represented by increasing varieties of capital goods, is determined by FDI as multinationals can reduce the costs of introducing new varieties of capital goods. Thus, FDI acts as the main vehicle of technology transfer.

In addition, the human capital building model as presented by Lucas (1988) may suggest that trade and FDI could enable inter-country technology transfer, particularly if it reduced the cost or raised the productivity of training.

Although trade and FDI are important for economic growth, the causal link between them is not necessarily uni-directional. The level of economic development can also influence trade and FDI. Theoretical justifications for reverse causation from growth to trade and FDI have long been discussed in development literature [START_REF] Kanamori | Economic growth and exports[END_REF]Diaz-Alejandro, 1975;Kravis, 1970;Findlay, 1984). It is argued that output growth has a positive impact on productivity growth, which in turn acts as a stimulus to exports (Kaldor, 1967). Jung and Marshall (1985) suggest that internal growth mechanisms better explain export growth rather than the reverse. In new trade theory, the market structure and output expansion may trigger significant changes in exports through a process of "cumulative causation" [START_REF] Venables | Trade policy, cumulative causation, and industrial development[END_REF]. A more recent study by Berthelemy and Demurger (2000) has theoretically established that FDI is influenced by the growth rate of the economy in the long run, and thus provides a formal justification of the causal link between growth and FDI.

Regional integration also affects the inflow and outflow of FDI among member and non-member countries. This is because regional economic integration which results in reductions in trade barriers and investment restrictions may change location-specific advantages, and so affect FDI inflows directly. Moreover, regional economic integration may indirectly affect economic growth, which in turn should affect FDI flows.

Theoretically, a regional integration agreement (RIA) would lead to an increase in inward FDI among member countries due to factor endowments, agglomeration economies and favourable regulatory environments. Inflows of FDI from non-member countries into the region are likely to increase if the average level of protection facing outsiders increases, or if the establishment of an RIA raises fears about future protection. In addition, inflows of FDI may also rise if a RIA leads to an enlarged regional market which acts as a magnet for 'outsiders' (Blomstrom and Kokko, 1998). For example, intra-regional FDI flows in Asia have increased over the years, accounting for an estimated 46% of total flows to the region in 2002. To non-member countries, Asia is an attractive destination for FDI and cross-border M&As, amounting to 23% of total FDI worldwide in 2004 (The World Investment Report, 2005). Early works, including Balassa (1978, 1984), Feder, (1983), and [START_REF] Kavoussi | Export expansion and economic growth: further empirical evidence[END_REF], only consider causation running from exports to economic growth. Studies of this kind assume implicitly that exports are exogenous without taking reverse causation into account.

Studies in the second group examine the impact of economic growth on FDI. The extent to which FDI can be explained by economic growth is the focus of attention, particularly the determinants of FDI in developing countries [START_REF] Wang | Determinants of inflow of foreign direct investment in Hungary and China: time-Series approach[END_REF]Dees, 1998). These studies, which assume one-way causality from growth to FDI, appear to indicate that the growth potential in developing economies is the main determinant of inward FDI. These studies have, however, also neglected the possible simultaneity between FDI and growth.

The third group of studies attempts to investigate bi-directional causal links between trade and growth or FDI and growth, by conducting causality tests. Mixed results have been obtained. Jung and Marshall (1985) have found that only four out of 37 developing countries support the export-led growth hypothesis. Studies by Chow (1987), Hsiao (1987), and Chen and Tang (1990) are only able to provide weak support for the export-led growth hypothesis even for highly export-oriented economies such as Hong Kong, Singapore and Taiwan. Those results are consistent with Young's findings (1994) which indicate that outward orientation may or may not be associated with rapid Ahmad and Harnhirun (1995), Biswal and Dhawan (1998), Islam (1998), Shan and Sun (1998) have found bi-directional causality for most of the East Asian NIEs and China. However, most of the cited studies have treated exports as the principal channel through which openness can affect economic growth. None of them include FDI inflows in their empirical analysis despite the fact that East Asian economies have received increasing amounts of inward FDI. Therefore, using trade as a proxy for openness may be inadequate without taking the impact of FDI into account [START_REF] Proudman | Openness and Growth[END_REF]Goldberg and Klein, 1999). Furthermore, the existence of a FDI-growth nexus is not considered by excluding FDI variable from causality tests.

The most recent study by Nair-Reichert and Weinhold (2001) acknowledges the problems mentioned above by considering the complex and heterogeneous relationship between FDI and growth. They have found a causal relationship from FDI to growth, and some evidence that the efficacy of FDI is higher in more open economies. However, in their study, imports are not used as the measure of openness and this may underestimate the impact of trade induced by imports (Keller and Yeaple, 2003). Extending previous work in this area, the present study attempts to estimate the complex interaction between trade, FDI and growth for selected Asian economies in a multivariate framework. We are interested in the interplay of four variables, GDP, FDI, Exports and Imports; to set the scene, therefore, consider a VAR involving four variables, W, X, Y, and Z. In a stationary setting, a Granger causality test in such a structure would be carried out (following Ghartey, 1993) via the following regressions:

t l t m l l k t m k k j t m j j i t m i i t u X Y Z W W 1 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (1) t n l l t l n k k t k j t n j j n i i t i t u W Z Y X X 2 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (2) t l t p l l k t p k k j t p j j i t p i i t u w Z X Y Y 3 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (3) t l t q l l k t q k k j t q j j i t q i i t u W X Y Z Z 4 1 4 1 3 1 2 1 1 0 + + + + + = = = = = (4) t = 1,2,...,N
where the lag lengths, (m, n, p, q) are determined so that u 1t , u 2t , u 3t and u 4t are serially uncorrelated. The null hypothesis 'Y does not Granger cause X, given W and Z' is tested via a standard F-test, being rejected if the 2j in equation ( 2) are jointly significant.

Similarly, if the 2j in equation ( 3) are jointly significantly different from zero, the null hypothesis that X does not Granger cause Y, given W and Z, is rejected and so on.

However, if the series are non-stationary, it becomes important to determine whether or not they are cointegrated, as this affects the sampling distributions of the causality tests. (1993) show that levels autoregressions are an unreliable basis for inference about causality in the nonstationary case, since the sampling distributions of the test statistics depend on the ranks of certain sub-matrices in the cointegrating space. They thus favour the use of the VECM framework of Johansen (1988) in which the necessary information about the cointegrating space is available, and where causality test statistics will usually have asymptotic Chi-square distributions. We adopt this approach.

When the series in the VAR are cointegrated, and have two cointegrating vectors, ignoring the higher-order dynamics, equations ( 1)-( 4) above can be rewritten in the VECM form: (5) where the parameters of interest are in the long-run (i.e. cointegrating) vectors, ij , and the adjustment coefficients (loading factors), ij . Hall and Milne (1994, pp 600-601) introduce the notion of the absence of weak causality to denote the situation in which the long-run level of one or more variables is unaffected by the levels of others. In equation ( 5), this is testable via zero restrictions on ij . Following Hall and Milne, if weak noncausality is rejected, then Granger non-causality, which in addition involves the remaining higher-order short-run dynamics, is also rejected. Thus bi-directional causality can be explored by estimating the full VECM proposed in equation ( 5), and testing restrictions on the long-run adjustment coefficients. The data on cross-border M&As are obtained from the UNCTAD FDI dataset.

The variables of inward and outward M&As are deflated using GDP deflators. The time span for the M&A data is from 1990 to 2004 and is relatively short for a time-series analysis; hence, we conduct a panel causality test using the variables of inward and outward M&As to replace the FDI variable.

The reason for incorporating M&As into the study is that M&As represent an increasing trend in Asian economies, amounting to $25 billion in 2005 (The World Investment Report, 2005). It has been observed that the governments of the sample countries, such as South Korea, Indonesia, the Philippines and Thailand have adopted favourable policies and measures to encourage cross-border M&As. In particular, South Korea relaxed all the restrictions on acquisitions by foreign investors in 1998 as one of the structural reforms in the wake of the economic crisis. As a result, the share of M&A investment in total FDI inflows into the country rose to 53% in 1998 (The World Investment Report, 2005). In addition, many Asian countries have introduced favourable policy measures in order to enhance their economies' attractiveness for FDI. For example, China has relaxed ownership restrictions and geographical limitations previously imposed on FDI in distribution services. The Indian government has also adjusted foreign-equity ceilings in various industries in order to attract more foreign investors. Similar measures have been taken by the other sample countries. These incentives towards FDI and M&As play an important role in inducing FDI inflows. As all the sample economies have adopted similarly favourable incentives and policies towards FDI and M&As during the sample period, we mainly focus on the inter-relationship between FDI, M&As, GDP, Exports and Imports by taking favourable policies towards FDI as given.

Empirical results

Unit root tests

The series are transformed into logarithms so that the first differences correspond to growth rates. Table 1 presents the results from ADF unit root tests with lag length chosen by downward search (t-test on the longest lag). The null hypothesis of a unit root in the logarithm is not rejected for any of the four variables in levels. However, each of the logged series is stationary in first differences. Therefore all the variables are integrated of order one. We also conduct a panel unit-root test for the variables of M&As, GDP, exports and imports. The results summarised in Table 2 suggest that the variables contain a unit root in levels and are stationary in first differences.

Insert Tables 1&2, here When testing cointegration relationships we first need to decide whether deterministic components such as constant, time trend and dummy variables should be included in the model. Using the general-to specific approach, a specific model for each economy is chosen. The selection criterion for lag length is the combination of Akaike Information Criterion and diagnostic tests. The selected lag length should enable residuals to pass various diagnostic tests, including serial correlation, normality, function form, ARCH and heteroskedasticity. Second, the test for cointegration rank described by Johansen and Juselius (1990) is applied, and the results are reported in Table 2. Both trace statistics and maximal eigenvalue statistics show that the VAR for each economy has two cointegrating vectors; both are significant at the 5% and 1% levels. The existence of cointegration between the variables suggests that there are long-run relationships between the four variables, implying that there is a tendency for the four variables to move together.

We also perform a panel integration test. The results (Table 4) show that there is one panel cointegration vector between inward M&As, outward M&As, GDP, Exports and Imports. The existence of the long-run relationship between the five variables in the panel cointegration test suggests that it is appropriate to conduct a long-run panel causality test.

Insert Tables 3 and4, here

Testing for weak exogeneity and causality

The results from cointegration tests indicate that the causality tests should be conducted 5). As stated in Section 3, the main interest of the study is to test long-run relationships, hence we investigate the long-run causality between the four variables which is equivalent to testing long-run loading factors in equation ( 5), such as 0 = ij a .

The results reported in It should be noted that the null hypothesis, that GDP is exogenous, is rejected only at the 10% significance level in the cases of India and South Korea. This may reflect the fact that India has been a relatively closed economy with an inward-looking policy which was relaxed relatively late, with final removal of its last features coming only in the mid-1990s. Therefore, it is highly likely that the impact of trade and FDI on GDP is relatively small at the aggregate level. However, it is very surprising that Korean GDP is only weakly affected by external factors, given that the remarkably rapid economic growth over most of the last few decades has been accompanied by persistent export expansion. One reason might be that South Korea adopted an export promotion policy under import protection. It is also well known that the Korean government restricted The null hypothesis that FDI is exogenous is universally rejected across the sample economies, implying that inward FDI is attracted by the growth prospects and the level of external trade in the region. The results are consistent with previous studies which have found that rapid GDP growth and intensive external trade act as the main determinants of inward FDI (Broadman and Sun, 1997;Sun, 1998).

The evidence rejects the null hypothesis that exports are exogenous for all the sample economies. The only weak result obtained is for Malaysia where the null hypothesis is rejected at the 10% significance level, showing a weak causal link between exports and the other three variables. The results indicate that exports and the other three variables mutually affect each other. In terms of imports, the results in eight out of ten sample economies reject the hypothesis that imports are exogenous, suggesting that imports are linked to GDP, exports and FDI. However, the null hypothesis is not rejected in the cases of India and Malaysia, confirming that only a one-way causal link running from imports to GDP exists. Import-led growth has been achieved in these two countries.

In particular, Indian imports grew faster than exports between 1970 and 1990. Import liberalisation helped reduce import shortages, and hence facilitated economic growth (Nidugala, 2000).The result shows that imports of capital goods may have been especially important. They are found to be a more important influence on growth than openness by Sala-I-Martin et al (2004).

Based on the four weak exogeneity tests (weak causality -after Hall and Milne) which massively reject the null hypothesis for each of the four variables for 8 out of 9 sample economies, we conclude that each is caused by the other three. This implies in turn that we have bi-directional Granger causality for the majority of the sample economies. Bi-directional causalities between the four variables do not support a tradeled or a FDI-led growth hypothesis (Kwan and Kwok, 1995;Nair-Reichert and Weinhold, 2001). The findings suggest that export expansion, import liberalisation and FDI inflows are integral elements in the economic growth process and that these external links go hand in hand with growth in Asian economies.

Insert Table 6, here

The results from panel causality tests show that there are two-way relationships between GDP, Inward M&As, Exports and Imports as found in the time series analysis, suggesting that these variables all affect each other. However, the result is unable to reject the null hypothesis that the variable of outward M&As is exogenous, indicating that outward M&As from emerging Asian economies may be driven by firm level strategies and changes in the global business environment rather than countries' economic growth and trade perspectives.

Taken together, we have found evidence that inward FDI, either in greenfield investment or cross-border M&As, is significantly affected by countries' growth and external trade. In turn, FDI and inward M&As also boost economic growth as well as trade. Hence, we have obtained evidence that inward FDI and inward M&As have a similar impact on host countries. However, there is a uni-directional relationship between growth and outward M&As, suggesting that outward M&As in the sample economies have generated a detectable impact on growth -and also on trade -at the aggregate level. This study investigating the causal links between growth, FDI and trade has yielded a number of interesting insights into the complex relationship between these variables. It will be worthwhile in further research to examine the interrelationship between R&D investment, trade, FDI and growth.

F

  trade liberalisation can affect the endogenously determined growth rate in the steady state.

  the parameters of interest are in the long-run (i.e. cointegrating) vectors, ij , and (loading factors), ij .

JEL

  , FDI and growth is due to the fact that trade and FDI stimulate growth, and to what extent this positive association reflects the fact that growth leads to trade and FDI. In addition, there is a lack of research on the impact of cross-border merger and acquisitions (M&As) on growth in Asian economies although M&As have become a more important source of foreign investment and a dominant mode of FDI entry into the region. In general, the region has experienced a rapid increase in crossborder M&As since the late 1990s[START_REF] Chen | A Review of Cross-border Mergers and Acquisitions in APEC[END_REF].Moreover, by including economies at different stages of development in the study, it is possible to address the question of whether the level of development affects the pattern of the relationship between the four important economic variables. From a policy point of view, investigating the causal links between trade, FDI and growth generates important implications for the development strategies of developing countries.

  incorporating the impact of openness on growth within a multi-country framework and the results from their model indicate that the extent of trade liberalisation can affect the endogenously determined growth rate in the steady state.

  bi-directional relationship between trade, FDI and economic growth postulated by the theories, the issue becomes empirical and can be verified through statistical tests. Attempts have been made to establish a causal link between trade, FDI and growth empirically. Previous studies in this area fall roughly into three groups. The first group focuses on the impact of trade or openness on economic growth.

  the value of exports and imports in goods for ten economies are taken fromUNCTAD (2003). The export and import price indices are used to deflate the value of exports and imports for most economies, but GDP deflators (1995=100) are used instead for Singapore and Thailand, where those indices are unavailable. There are several sources for obtaining inward FDI including the UNCTAD (2003), Reserve Bank ofIndia (2003) and Bank Indonesia (2003). Annual GDP series are taken from the International FinancialStatistics (2003). Both GDP series and inward FDI are deflated using GDP deflators. The sample period spans from 1970 to 2002 for the nine economies.

  empirically the causal links between FDI, trade and growth for nine Asian economies by conducting multivariate causality tests in the VECM framework and panel data analysis by incorporating inward M&As and outward M&As in the estimation.The results reveal two-way causal connections between trade, inward FDI, inward M&As and growth for most of the sample economies, indicating that external links and growth mutually affect each other. We have found that there is a unidirectional causal link running from outward M&As to growth and trade, indicating that outward M&A activities in the sample economies may be driven by firms' global strategies and changes in the global business environment. The findings suggest that export expansion, import liberalisation, FDI inflows and inward M&As are integral elements in the growth process in the sample Asian economies. The existence of bi-directional causal links suggests that development strategies should be designed to promote inward FDI, inward M&As, trade and growth simultaneously.

Page 10 of 48 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

	F o
	r
	P
	e e r
	R e v i e w
	11

Table 1 :

 1 Asian economies by conducting multivariate causality tests in the VECM framework. Unit root tests: Null hypothesis: LGDP, LFDI, LEX and LIM contain a unit root

	The results reveal two-way causal connections between trade, FDI and growth for most
	of the sample economies, indicating that external links and growth mutually affect each
	other. The findings suggest that export expansion, import liberalisation and FDI inflows
	are integral elements in the growth process in the sample Asian economies. The
	existence of bi-directional causal links suggests that development strategies should be

Table 2 :

 2 Results from cointegration tests

	Economy	Maximal eigenvalue statistics		Trace statistics
		Under the H0: rank =r		Under the H0: rank = r
		r=0	r 1	r 2	r=0	r 1	r 2
	Hong Kong (4)	85.07**	35.36**	7.08	145.4**	60.4**	10.2
	India (4)	48.99**	25.6**	9.48	90.27**	40.28**	14.69
	Indonesia (3)	48.2**	23.45**	10.33	87.04**	38.84**	15.37
	S. Korea (4)	39.21**	31.98**	13.05	95.55**	56.34**	10.44
	Malaysia (4)	54.41**	32.31**	9.66	104.2**	50.06**	17.75
	Philippines (3)	36.91**	25.18**	7.13	69.75**	32.84**	7.66
	Singapore (2)	30.89**	23.6**	11.8	79.7**	48.81**	18.01
	Thailand (3)	31.9**	29.32**	10.4	79.89**	47.99**	18.67
	Taiwan (4)	57.09**	34.9**	11.5	105.4**	48.33**	15.75
	Notes: figures in parentheses are the number of lags used.

Table 3 :

 3 System exogeneity tests: long-run causality

	Economy	System exogeneity tests: X 2 (2)
	H0:

the variables (LGDP, LFDI. LEX & LIM) weakly exogenous to system

			Submitted Manuscript				Page 22 of 48
	Trade,
		LGDP		LFDI	LEX			LIM
		LR test	P-value	LR test	P-value	LR test	P-value LR test	P-value
	Hong Kong	7.16	<0.05	22.41	<0.01	19.21	<0.01	22.98	<0.01
	India	4.66	<0.10	34.51	<0.01	18.89	< 0.01	0.67	< 0.95
	Indonesia	62.91	< 0.01	27.59	< 0.01	40.09	<0.01	50.18	<0.01
	S. Korea	5.06	< 0.10	26.05	< 0.01	17.41	< 0.01	12.10	< 0.01
	Malaysia	15.19	<0.01	12.79	<0.01	5.42	<0.10	1.31	<0.52
	Philippines	10.83	<0.01	19.90	< 0.01	6.47	<0.05	7.19	<0.05
	Singapore Thailand Taiwan	17.75 7.43 22.76 F o	<0.01 <0.05 <0.01	8.02 29.83 16.78	<0.05 < 0.01 <0.01	10.10 5.83 11.67	< 0.01 < 0.05 <0.01	8.16 16.55 7.09	<0.05 <0.01 <0.05
		r
		P
			e e r
				R e v i e w
								1

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

Foreign Direct Investment and Economic Growth in Asian Economies

	Xiaohui Liu *
	Business School
	Loughborough University
	Leicestershire LE11 3TU
	Chang Shu
	Hong Kong Monetary Authority
	Peter Sinclair
	Department of Economics
	Birmingham University
	October 2006

Page 29 of 48 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  and the fast expansion of East Asian NIEs should not be viewed as evidence of the potential dynamic gains from outward-oriented policies. On the other hand,

	Submitted Manuscript
	productivity growth,
	F o
	r
	P
	e e r
	R e v i e w
	8

  Table3reveal that exports, imports and FDI affect GDP in most of the sample economies at the 1% or 5% significance levels. The results confirm that external links represented by exports, imports and FDI have generated beneficial effects on economic growth in most of the sample economies, indicating that the external links may act as the principal channel through which technology spillover and learning-bydoing take place as suggested by endogenous growth theories. The evidence that economies with different levels of development exhibit the same pattern in trade-growth and FDI-growth nexus suggests that trade and FDI are important factors in enhancing economic growth, regardless of the initial level of development.

	Insert Table 5, here

Page 36 of 48 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  Asian financial crisis. All these factors may create distortions, and limit the impact of trade and FDI on GDP.

	Submitted Manuscript
	inward FDI until the
	F o
	r
	P
	e e r
	R e v i e w
	15

Page 37 of 48 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o
	r
	P
	e e r
	R e v i e w
	16

Table 1 : Results from Unit Root Tests Null hypothesis: LGDP, LFDI, LEX and LIM contain a unit root.

 1 Notes: ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

	o
	r
	P
	e e r
	R e v i e w

Table 2 : Results from Panel Unit Root Tests Null hypothesis: LGDP, LEX, LIM, LIMA and LOMA contain a unit root.

 2 Notes: LIMA and LOMA represent the log value of inward M&As and outward M&As.

	Variables	Levin and Lin Test	Levin, and Lin Test
		(incl. trend)	(intercept only)
		Level	Difference
	LGDP	-1.13	-3.52***
	LEX	0.22	-4.58***
	LIM	-0.08	-4.78***
	LIMA	1.69	-7.71***
	LOMA	0.94	-5.43***

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

Table 3 : Results from Cointegration Tests Economy Maximal eigenvalue statistics Trace statistics Under the H0: rank =r Under the H0: rank

 3

	= r

Table 6 : Results from Weak Exogeneity / Panel Causality Tests

 6

	Sample period: 1990 -2004
	System exogeneity tests: X 2 (2)	LR test	P-value
	LGDP weakly exogenous to system	5.949	<0.05
	LEX weakly exogenous to system	14.613	<0.01
	LIM weakly exogenous to system	6.269	<0.01
	LIMA weakly exogenous to system	20.248	<0.01
	LOMA weakly exogenous to system	0.156	<0.70

Page 48 of 48 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

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 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 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 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 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 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 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