Paper for presentation at the Transition in Agriculture - Agricultural Economics in Transition Conference, Institute of Economics, Hungarian

Academy of Sciences, Budapest, 7th November 2004

THE INTERNATIONAL COMPETITIVENESS OF HUNGARIAN AGRCULTURE: PAST PERFORMANCE

AND FUTURE PROJECTIONS1

Matthew Gortona, Sophia Davidovab, Martin Bansec and Alistair Baileyd

a) School of Agriculture, Food and Rural Development, University of Newcastle, Newcastle upon Tyne, NE1 7RU. UK. Matthew.gorton@ncl.ac.uk

b) Imperial College, Wye Campus, Wye, Ashford, Kent. TN25 5AH. UK. s.davidova@imperial.ac.uk

c) Institute of Agricultural Economics, University of Göttingen, Germany. mbanse@gwdg.de

d) Imperial College, Wye Campus, Wye, Ashford, Kent. TN25 5AH. UK alastair.bailey@imperial.ac.uk

ABSTRACT

The international competitiveness of Hungarian agriculture is assessed by estimating domestic resource cost (DRC) ratios for the pre-accession period using data for the years 2000 to 2002. The estimations indicate that Hungary was less internationally competitive in arable production, compared to the mid-1990s, due to the appreciation of the Hungarian forint, static domestic demand and improved harvests in neighbouring countries. While dairy production remained uncompetitive, pork and poultry production by corporate farms became internationally competitive due to lower feed costs and better access to EU markets. The future international competitiveness of Hungarian agriculture is assessed for the years 2007 and 2013 under three scenarios: baseline (no accession), accession with historical rates of productivity growth and accession with dynamic improvements in productivity. Predicted price changes were generated by a computable general equilibrium (CGE) model, based on the most recent version of the GTAP database, which was applied to quantify the implications of the 2004 EU accession. The analysis indicates that accession will negatively impact on the international competitiveness of Hungarian agriculture by increasing land and labour prices. To maintain competitiveness in the arable sector, Hungary will need to achieve dynamic improvements in productivity to offset the effect of higher factor costs. The dairy sector, under all scenarios is likely to remain uncompetitive.

1 The authors would like to thank Irma Gyuricza for her assistance in data collection and interpretation. The research presented in this paper was conducted as part of a project funded by the UK Department for Environment, Food and Rural Affairs (DEFRA) on the Impact on the UK of Increased Agricultural Productivity in the New Member States (EPES 0203/4).

1. Introduction

For most of the twentieth century, Hungary was a significant net exporter of agri-food products and it has been widely regarded as possessing a more internationally efficient agricultural sector than most Central and Eastern Europe Countries (CEECs) (Eiteljörge and Hartmann, 1999; Csaki, 2000). For example, a Word Bank report, published at the end of the 1990s, was entitled “Hungary: a successful agriculture and food economy in constant search for higher competitiveness” (Debatisse, 1999). Hungary’s net trade surplus in this sector is significant: in 2000, it exported agricultural products worth 2.4 billion euros, equivalent to 8 per cent of the country’s total exports (KSH, 2001). In contrast, imports of agricultural products in the same year were worth just over 1.05 billion euros (3.4 per cent of total imports). However, while remaining a net exporter, imports from the other CEECs and, in particular, the EU-15 grew steadily in the years leading up to accession. This has led some to question whether Hungary’s historically strong position is being eroded (Fertő and Hubbard, 2003), and predict diminishing fortunes for the sector (Varga, 2002). Given the strategic importance of agriculture in Hungary, two key questions emerge. First, how internationally competitive was Hungarian agriculture in the pre-accession period? Secondly, how may international competitiveness evolve in the post-accession period? This paper addresses these questions by calculating Domestic Resource Cost (DRC) ratios for the pre-accession period and estimates are presented for the years 2007 and 2013 under different policy scenarios.

The paper is structured as follows. The next section describes the DRC ratio. Section 3 outlines the approach taken for calculating DRCs for the years 2000 to 2002 and how these estimates were used in conjunction with output from a CGE model to produce projections for 2007 and 2013 under different policy scenarios. Particular attention is paid is to the treatment of productivity growth under different scenarios. Section 4 evaluates the DRC estimates for the years 2000 to 2002 in comparison to similar analysis for the mid-1990s. Projections for 2007 and 2013 are discussed in Section 5 with conclusions drawn in the final Section.

2. Methodology: Domestic Resource Cost (DRCs) Ratios

While there is a lack of consensus on how, or if at all, international competitiveness should be measured, in practice the DRC ratio has been widely applied (Tsakok, 1990). The estimation of DRCs has been perceived as useful in comparing the competitiveness of unlike production systems and assessing the comparative advantage of alternative activities (Monke and Pearson, 1989). The DRC approach has previously been employed in assessing the international competitiveness of Hungarian agriculture, for the mid-1990s, by Banse et al. (1999) and by employing the same measure as the latter authors it is possible to analyse changes that occurred during the pre-accession period.

The DRC ratio compares the opportunity costs of domestic production to the value added it generates (Tsakok, 1990). It makes a distinction between tradable inputs and non-tradable inputs, i.e. those that are not subject to international trade. The numerator is the sum of the costs of using domestic primary resources - land, labour and capital (non-internationally traded inputs) valued in terms of shadow prices. The denominator is the

value-added in border prices. The DRC for the production of commodity i can therefore be defined as:

[1]

where aij, j=k+1 to n are the technical coefficients for domestic resources and non-tradable inputs, and Vj are the shadow prices of domestic resources and non-tradable inputs. Pr

i are the border / reference prices of traded output, aij j=1 to k are the technical coefficients for traded inputs and Pr

j are the border / reference prices for traded inputs. When the DRC is smaller than 1, domestic production is efficient and internationally competitive, because the opportunity cost of domestic resources is smaller than the net foreign exchange it gains in export or saves by substituting for imports. The opposite is true when the DRC is larger than 1. However, it should be noted that DRCs are sensitive to the choice of shadow prices for non-tradable inputs, and to the choice and changes in exchange rate and international prices.

The main weakness of the DRC measure has been perceived to be its static nature which fails to capture market dynamics (Tsakok, 1990). This problem is not unique to the DRC method and is shared by most cost-benefit analysis. One approach to dealing with this weakness is to conduct sensitivity analysis by assessing the sensitivity of the estimates at varying prices and quantities of inputs and outputs (Kannapiran and Fleming, 1999).

3. Application of the DRC approach to Hungary

a) DRC calculations for the years 2000 to 2002

DRCs were estimated for eight commodities (wheat, maize, sunflowers, potatoes, sugar beet, pork chicken and milk), which were chosen because of their importance to the total agricultural output of Hungary. In estimating DRCs for each commodity a number of assumptions were made relating to the social prices for output and tradable inputs; the social costs of non-tradable domestic resources and the choice of production structures.

Social prices for outputs and tradable inputs are measured as border prices (export / import parity prices) and adjusted to the farm level. For products for which Hungary was a net exporter during the analysed year, an average f.o.b. export parity price was taken as the unadjusted reference price. In cases where Hungary was a net importer, average c.i.f. import parity prices were applied.2 As trade in sugar beet is thin, the border price and quantities for refined sugar were taken as a benchmark and then adjusted for processing. This procedure is recommended elsewhere (Zimmermann and Zeddies, 2002). Similarly as international trade in liquid milk is thin, the border price

2 For the years 2000 to 2002, Hungary was a net exporter of most of the analysed commodities (wheat, maize, sunflower, chicken, pork and milk). In these cases the f.o.b. price was taken as the border price, which was then adjusted back to the farm level. The import c.i.f price was always used for potatoes as Hungary was a net importer during the whole period.

of skimmed milk powder (SMP) was employed and adjusted back into the liquid form, accounting for the prevailing processing margin and conversion coefficient between raw milk and SMP.

The social prices for tradable inputs are also based on border prices and the data for Hungary were taken from national statistical office records (KSH, 2001; KSH, 2002, KSH, 2003). The adjustment of prices from border to farm accounted for, where appropriate, handling charges, transport, storage and maintenance costs.

Private input prices and quantities, together with information on yields were taken from Hungarian Farm Accountancy Data Network (FADN) data. This dataset provides useable information on over 1,100 agricultural enterprises and is collated by the Agrárgazdasági Kutató és Informatikai Intézet (AKII). AKII also produces a set of input-output tables for major commodities on an annual basis, which were utilised in the calculations. The input-output tables provide information on both direct and indirect input costs which were re-classified as tradable and non-tradable. For all tradable inputs the border price was adjusted back to the farm level with the exception of the miscellaneous category of ‘other materials’ for which there is no clear border price. In this case, it was assumed that the social price equalled the private price. Labour, social security, land and capital, both working and fixed (depreciation), have been treated as non-tradables.

The shadow prices of domestic resources were measured in terms of the opportunity costs of land, labour and capital employed in the production of a particular commodity. In the case of land, the opportunity costs can be indicated by the social rental value in the second best alternative. However, regarding land use there is often a problem in identifying a single clear, second best alternative as commodity systems vary in terms of the degree of risk and the desirability of crop rotation. For example, vegetable crops often provide higher returns on average than staple food crops, yet many producers continue to grow food crops because of their greater price stability over time and the limited demand for specialist crops (Monke and Pearson, 1989). In this situation, land of identical quality produces a variety of crops. To account for this, an average of suitable commodity alternatives for deriving shadow land prices is taken, following the approach used by Banse et al. (1999) and Gorton et al. (2001). The assumption behind this approach is that social land values would fall somewhere within the range of these alternatives.

In defining the shadow price of capital, there is an attempt to identify its marginal productivity. Within perfectly competitive markets marginal products are equal between alternatives but if markets are not perfectly competitive, as is assumed here, then a supply approach is preferable for valuing the shadow price of capital. Taking this approach, the economic cost of owing a fixed asset is the sum of alternative returns foregone due to investing financial capital in the asset in question. These can be approximated by the interest rate that will be earned if the amount would have been invested in the financial market plus depreciation. In the Hungarian case working capital for farmers was subsidised in 2001/02 at the level of 3 percent off the market base lending rate. The social costs of working capital were calculated as the private costs adjusted for this subsidy.

The opportunity cost for labour was taken to be the wage rate in the second best alternative, normally in non-agricultural occupations. In the Hungarian calculations, labour costs were based on Hungarian forint values for each year. Labour was

classified as skilled and unskilled. According to AKII’s input-output calculations, about one sixth of agricultural labour is skilled and the rest unskilled. The social value of labour was therefore made by applying these weights to the average wage rates of skilled and unskilled workers in non-agricultural occupations in Hungary excluding Budapest. Wages in Budapest are much higher than elsewhere in the country and were not seen as a useful guide for alternative returns to agricultural labour.

The analysis for Hungary distinguishes between individual farms and corporate farms (producer co-operatives and different forms of farm companies). This is because, in Hungary, different farm management structures employ dissimilar technologies and have unequal amounts of human capital. Therefore, there are significant variations in input use, yields and production costs between management types.

b) DRC projections for the years 2007 and 2013

DRCs were estimated for the years 2007 and 2013 under three policy scenarios: (a) baseline (non-accession), (b) accession with historic levels of productivity growth and (c) accession with productivity convergence. The three scenarios are summarised in Box 1. The aim of the analysis was to understand the sensitivity of the international competitiveness of Hungarian agriculture to a set of projected changes in prices and improvements in productivity. The methodology employed is described below.

Box 1: Policy Scenarios1. Baseline Scenario is non-accession of countries from CEE. The EU is comprised of 15 members

without any enlargement, but with full implementation of the CAP reforms agreed in June 2003. Technical progress continues for all countries and country blocks at the rates observed over the past decade.

2. Accession Scenario differs from the Baseline Scenario in that Hungary and the other nine 2004 acceding countries joined the EU, following the accession arrangements agreed in Copenhagen in December 2002, with the short-run impact of changes in support acting upon production and consumption.

3. Accession with Productivity Convergence differs from the Accession Scenario in that dynamic effects (on productivity) in Hungary are included.

Predicted changes in prices between 2002 and 2007, and 2002 and 2013 in the baseline and accession scenarios are based on results from a Computable General Equilibrium (CGE) model which is described in more detail elsewhere (Banse, 2004). Briefly the CGE model was developed to quantify the implications of the 2004 accession and is founded on a model structure original developed by Adelman and Robinson (1978) and extended by Banse (2000). It is based on the most recent version of the GTAP database and incorporates seven geographical units (Hungary, Poland, Czech Republic, other New Member States [NMS] from Central Europe which joined the EU in 2004, the UK, established EU Member States excluding the UK [i.e. EU-14] and the Rest of the World). Production is broken down into 19 commodities, including 6 crops and 3 categories of livestock production.

In estimating price effects, the DRCs calculated for 2002 were taken as the starting point and for each commodity the predicted price changes taken from the CGE model were used to forecast domestic output prices, domestic input prices and factor costs in 2007 and 2013. Border prices were assumed to adjust according to the predicted

changes in rest of the world prices, in other words, changes in rest of the world prices were viewed as the best guide to future adjustments in the social prices of outputs and tradable inputs. Predicted changes in the exchange rate under both the baseline and accession scenarios were also utilised. The strongest predicted price effects in Hungary concerned factor costs, especially land and labour, with the greatest growth estimated under the accession scenarios.

Two accession scenarios have been formulated which differ in their treatment of productivity. In the first scenario it is assumed productivity growth remains at the level achieved prior to accession. The CGE model incorporates data on these historic rates of annual productivity growth for each of the specified commodities. For Hungary these historic annual productivity growth rates have varied between 0.74 per cent for milk and 2.10 per cent for oilseeds. For the estimations for 2007 and 2013 under the accession with historic levels of productivity growth scenario, these rates remain unchanged. They were incorporated into the DRC estimations for arable crops by taking the actual yields recorded in 2002 as a base, with yields then predicted to rise by the estimated compound productivity growth rates up to 2007 and 2013. For livestock, the main technical coefficient was seen as the feed to meat conversion rate (i.e. the amount of feed used per kg of live weight gain). Taking the actual feed conversion rates contained in the 2002 DRCs as a base, the coefficients were predicted to improve in line with the estimated productivity growth rates. A similar procedure was followed for other inputs. The efficiency with which existing machinery and services are used was assumed to improve in line with the predicted annual changes in productivity growth for these commodities as specified in the CGE analysis. Predictions were made based on the assumption that existing machinery will be used more efficiently, rather than complete, qualitative changes in the capital stock. Changes in future labour use were estimated by the quantitative change in the amount of labour used in the underlying CGE input-output matrix for the production of a particular agricultural commodity. In most cases, these estimates were in the order of a 1 per cent decline in total labour input per annum.3

The second accession scenario is based on the assumption that rather than historic levels of growth being maintained, accession to the EU will substantially alter the trajectory of productivity growth in NMS and that, in the long run, convergence in levels of productivity between new and established Member States will be witnessed. The idea of cross-national convergence has its genesis in the work of Solow (1956), who hypothesised that the primary mechanism for growth was the accumulation of capital equipment. Convergence occurs because, it was argued, that low productivity (usually defined in terms of the partial productivity of labour) economies are likely to suffer smaller rates of diminishing returns to increased capital than economies with already large capital endowments and thus the output of poorer countries will grow at a greater rate than that of richer states. Furthermore, the growth rates of countries slow down as they approach their ‘steady state’ or as they catch-up with the productivity of the richest nations simply because they increasingly suffer more punitive diminishing returns to increased capital employment. This notion forms the basis of the, so called, ‘classical theory’ of convergence described by Sala-i-Martin (1996). In addition to classical convergence, more recent work (Dowrick and Rogers,

3 In the case of miscellaneous inputs, which could not be attributed to a particular commodity in the CGE model, it was assumed that the efficiency with which they are used will improve in line with the predicted changes for the category ‘other commodities’.

2002) acknowledges how productivity growth also tends to rise with the degree of trade openness and innovative capacity of an economy.

There is some evidence that EU accession can have a positive effect on the growth rates of agricultural productivity (Kelch, 2003). Kelch (2003) assessed growth in Total Factor Productivity (TFP) in 14 EU Member States between 1973 and 1997 and found evidence of a positive influence of EU accession for Spain, Portugal and Denmark. In the cases of Spain and Portugal the observed accelerated growth came principally from structural rather than technical change, which is in contrast to France and Germany where the latter factor dominated during the same timeframe. From this Kelch (2003) hypothesizes that those countries with the relatively poorest levels of productivity will have the most to gain, and will “catch up”, as a result of accession.

In the absence of reliable estimates for structural change and innovation in Hungary, the third policy scenario is based on the principles of classical convergence, current relative productivity and inter- and intra-industrial structure. Following the estimates reported in the literature (Dowrick and Rogers, 2002) a most likely constant convergence rate of 3 per cent, representing a 20 year productivity gap half life was assumed. This convergence rate was used to generate time and country specific productivity levels dependent on the single period lag of the productivity gap between country i and the leader economy using the following process:

[2]

where: Pr indicates productivity (TFP or partial land productivity), subscripts, t, index time, superscripts index the leader (EU=EU14) or follower economy (Fh = Hungary as follower) and C is the convergence rate (0.03). To interpret, Equation 2 creates productivity growth, in absolute terms, as a function of the productivity gap (and therefore of the relative starting points) and imposes a closure of the productivity gap while retaining productivity growth in the leader economy. The follower economy (Hungary) therefore realises the growth of the leader plus catch-up and convergence in the productivity gap. The gap will close at a diminishing rate so that convergence will be greatest when the gap is greatest. Commodity specific estimates were derived using data from the CGE input-output matrices for the EU14 and Hungary.4

4. DRCs Estimates for 2000 to 2002

4 As is apparent from Equation 2, the productivity level of the follower economy cannot overshoot that of the original leader. In one case, milk production, the CGE input - output matrices indicated that Hungary is currently more productive than the EU14. The implication of the application of Equation 2 in this instance would be to impose convergence from above, down to the productivity level of the EU14 which is counterintuitive. Convergence of a ‘backward’ EU14 sector toward the productivity level of Hungary was also assumed to be unrealistic. Therefore, in this case, historic rates of productivity growth are utilised.

The DRC calculations for the period 2000 to 2002 are detailed in Tables 1 and 2. Table 1 presents weighted average DRCs, based on the relative share of corporate and individual farms to total marketed output for each commodity. These weighted averages for the years 2000 to 2002 are presented alongside comparable figures for the mid-1990s taken from Banse et al. (1999). Table 2 distinguishes between the DRC ratios of individual and corporate farms.

Considering arable crops, Hungary was internationally competitive in the production of wheat, maize and sunflower seeds throughout the mid-1990s, as indicated by DRC ratios of less than 1, with only one exception.5 The DRCs for wheat and maize in 1996, the last year covered by Banse et al.’s (1999) analysis, are very similar to the figures for 2000, and sunflower production was also internationally competitive in the latter year, albeit only marginally. However during the years 2000 to 2002 the competitiveness of all three crops diminished noticeably, so that by 2002 the weighted average DRCs for each exceeded 1. Three main factors were responsible for this trend:

a) the significant appreciation of the Hungarian Forint (HUF), which became steady stronger in 2001 and 20026;

b) good harvests in Hungary in 2001 and 2002 in the face of stagnant domestic demand, placed further pressure on finding international buyers;

c) rising output in the rest of the CEEC region. Most of Hungary's arable exports in the 1990s and 2000 were to Balkan countries and the Commonwealth of Independent States (CIS). In 2001 and 2002 these states achieved far better domestic harvests, weakening the demand for imports. Ukraine, in particular, returned to being a significant arable exporter in 2001 and 2002, undermining Hungary's position.7 Due to these pressures, the average f.o.b. parity price for wheat fell from €125.86 per tonne in 2000 to €98.89 in 2002 and Hungarian wheat stocks in the marketing year 2002/3 were 500,000 tonnes higher than usual.

For each of the arable crops, corporate enterprises are more internationally competitive than individual farms (as indicated by lower DRC ratios). One substantial difference between individual and corporate farms is the amount of non-tradable costs per tonne. For example, for wheat produced by individual farms in 2001, the average cost of non-tradables per tonne was 14,705 HUF (€57.3). The corresponding figure for corporate farms was 12,377 HUF (€48.73). Differences in labour input, measured in terms of its opportunity cost, account principally for the higher proportion of non-tradable costs on individual farms.

Sunflower production by corporate farms was internationally competitive in 2000 and 2001 and about one-third of Hungary's total sunflower crop is exported. One company (Cereol Rt.) dominates commercial oilseed processing and Nemes (2003) reports that in 2002, Cereol offered domestic producers €277.7 per tonne, while the average f.o.b.

5 Maize in 1994.6 The real appreciation of the forint in the period between the fourth quarter (Q4) of 2000 and Q4 2002 was 21.3 percent with respect to Hungary's 24 main foreign trading partners, using consumer prices to calculate the real exchange rate, out of which 12.3 percent was the result of appreciation of the nominal-effective exchange rate (see Darvas, 2003).7 In 2001 and 2002, Ukraine produced 21.3 and 20.5 million tonnes of wheat compared to an average of 14.1 million tonnes in the years 1996-2000 (Prikhodko, 2003).

export parity price was €206.5 (in 2001, €241.2 per tonne f.o.b. price). With the drop in international prices, exports fell significantly, particularly as for period 2000-2002 exports of oilseeds and vegetable oil were not subsidised by the state. Hungary's main markets are Russia, Belarus, Poland, Bosnia and Slovenia.

Hungarian potato production was not competitive in any of the years studied. The area under potato cultivation has declined significantly since the early 1990s (in 1995 nearly 70,000 ha were planted compared with 34,500 ha in 2002). Production is now concentrated in the Great Plain but even there the area assigned to potatoes is small compared to other crops. Post-accession, it is expected that domestic production will further decline due to substitution by imports from Poland.

Hungarian trade in sugar and sugar beet is quite thin with the domestic market highly regulated. Two to three companies dominate the sugar industry with the vast majority of growers operating on contracts with these large players. Average sugar beet yields are around 40 t per hectare, which is on a par with the Czech Republic but low by UK and German standards (Zimmermann and Zeddies, 2002). The DRC calculations indicate that corporate farm production was internationally competitive in 2001 and 2002 but given the nature of the market, substantial changes in the area devoted to sugar beet are not expected in the short to medium term.

In contrast to arable production, Hungarian livestock farming was not internationally competitive during the early to mid-1990s and Banse et al. (1999) reported DRC ratios for milk and pork which were considerably above 1. During the 2000-2002 period, milk production on both corporate and individual farms continued to be internationally uncompetitive. While the productivity of milk production is not particularly low, as evidenced by the input-output coefficients in the CGE model, Hungary depends on high trade support (domestic quotas, high import tariffs and prior to 2001 export subsidies) to maintain its status as a net exporter. The unit values of exports are low: to give an indication of scale, the adjusted f.o.b. border value of the milk produced by individual farms in 2001 was 64 HUF (€0.26) per litre, compared to tradable and non-tradable costs of 35 and 50 HUF (€0.13 and €0.19) per litre respectively. The main non-tradable cost is the opportunity cost of labour input. This labour input on individual farms is non-salaried (own or family) and thus there is a large difference between paid costs and the DRC calculations which account for the opportunity costs of land and labour. Hungary’s unfavourable international position, particularly compared with Slovakia, in milk production has been noted elsewhere (Udovecz, 2001).

However, while milk continued to be uncompetitive, chicken producers witnessed an improvement in fortunes during the 2000 to 2002 period as the industry recovered from the Russian crisis and domestic demand for poultry expanded. Germany and Austria are currently Hungary's most important export markets with sales to these two countries rising significantly after the July 2001 agreement with the EU to open up markets.8 The industry benefited from the bumper harvests in 2001 and 2002: in the 2002 marketing year, feed costs were 10 percent lower than in 2001. During the same period, output also rose.9 However, the adoption of EU regulations governing hygiene

8 Exports of chicken meat grew approximately 10 percent in 2001. Hungary received a 102,000 tonne duty free quota from the EU and opened a 15,000 tonne import quota for poultry coming from the EU.9 Opening stocks of hens and layers in 2002 were 11 percent above the 2001 level.

and animal welfare will increase costs of production (Keszi et al. 2003) and thus decrease cost competitiveness.

In pork production, there remains a large divide between the international competitiveness of individual and corporate farms. The competitiveness of individual farm production deteriorated between 2000 and 2002 as a result of the appreciation of the HUF. Exporters have depended on state subsidies (about 20-25 per cent of total Hungarian production is exported). Export subsidies in the period 2000 to 2002 for pork ranged from 27 to 90 HUF (€0.09 to €0.39) per kg dependent on the type of meat. Future production is expected to shrink dramatically, as domestic supports were withdrawn after accession to the EU.

5. DRCs Projections for 2007 and 2013

Table 3 details the weighted average DRCs for the years 2007 and 2013 under the three policy scenarios. In each case a distinction is made between the effect of only price adjustments and changes in prices combined with productivity change. Table 4 distinguishes between individual and corporate farms and in this case, for reasons of space, only the estimated DRCs with combined price and productivity effects are reported.

In 2002, only the weighted average DRCs for sugar beet and chicken were below 1 and under the baseline scenario (non-accession) when only price effects are considered this does not change in either 2007 or 2013. While the price effects, principally growth in factor costs (land and labour), have a negative impact on competitiveness, historic rates of productivity, if maintained, are sufficient to offset the adverse effect of the price changes. For example, the DRC ratio for wheat in 2002 was 1.23 but this falls to 1.0 and 0.87 in years 2007 and 2013 respectively when both price and productivity effects are taken into consideration in the baseline scenario. A similar picture is evident for all other commodities apart from milk.

Price effects are larger under the accession scenario than in the baseline case, particularly the rises in land and labour costs. These price effects have a significantly negative impact on international competitiveness: for example, the DRC ratios for wheat and pork rise from 1.23 and 1.38 in 2002 to 1.69 and 1.8 in 2007 and 1.94 and 2.26 in 2013 respectively when only the price effects are considered. Moreover, while for the baseline case, historic levels of productivity change would be sufficient to offset the price effects, this is not so under the accession scenario for arable crops. For example, the DRC ratios for wheat and maize in 2007 are estimated to be 1.32 and 1.2 respectively when both price and historic levels of productivity change are considered, compared against ratios for 2002 of 1.23 and 1.15 respectively. In other words, to improve international competitiveness in arable production Hungary will have to achieve productivity improvements that are above historic levels to offset the adverse price effects of accession. For pork and chicken, historic levels of productivity change are sufficient to offset the adverse price effects of accession because the main increased costs associated with accession is land, which accounts for a lower proportion of total costs for these products. However, these calculations do not account for the costs of any additional investments that may be required to meet the requirements of EU animal welfare and other legislation.

If Hungary follows Spain and Portugal and benefits from a substantial rise in productivity post-accession, as hypothesised in the third policy scenario, international competitiveness would improve considerably. Under this scenario, the DRC ratios for all commodities are predicted to below 1 in 2007 and 2013 except milk, and potatoes in 2007. By 2013, the ratios for wheat, maize and sunflower seeds are all predicted to be substantially below 1.10 If enhanced productivity growth is achieved, Hungary will recapture or improve on its competitive position of the mid-1990s.

Table 4 distinguishes between individual and corporate farms. Without repeating the trends highlighted in the discussion of Table 3, two additional points can be drawn. First, accession has a more adverse effect on the international competitiveness of crops produced by individual farms than corporate enterprises. This is because corporate farms use fewer units of non-traded factors of production per unit of tradable costs than individual farms. The main price effect of accession is on land, and to a lesser extent, labour, which are far larger than changes in the social prices of outputs and tradable inputs. For example, the DRC ratios for wheat in 2007 under the accession scenario with historic rates of productivity change are forecast to be 1.72 and 1.22 for individual and corporate farms respectively, in contrast to comparable ratios for 2002 of 1.51 and 1.16 respectively. Second, without enhanced productivity growth, most of the output of individual farms will remain uncompetitive. If under accession only historic growth rates in productivity are achieved, then in both 2007 and 2013 only chicken production would be internationally competitive. Pork, in particular, would remain deeply uncompetitive and the ratios for all the crops are above 1. In contrast, even if only historic rates of productivity growth were achieved under accession by corporate farms, then in addition to chicken, sunflower, sugar beet and maize production would be internationally competitive in 2013. The ratio for wheat (1.09) suggests, given its closeness to 1, that the most efficient corporate farm producing this commodity would also be internationally competitive. For both individual and corporate farms, historic rates of productivity growth are insufficient to improve the competitiveness of Hungarian milk production.

6. Conclusions

This paper analyses the social profitability and international competitiveness of Hungarian agriculture in the pre-accession period and presents a series of projections for the years 2007 and 2013. In combining analysis for the years 2000 to 2002, with the results of the CGE model and different assumptions on productivity change, an attempt has been made to deal with the most common criticism of the DRC approach, that it is a static concept, and assess the impact of dynamic changes in prices and productivity. The analysis indicates that during the period 2000 to 2002 Hungarian international competitiveness in crops declined as a result of appreciation of the forint, static domestic demand and falling export prices as output in other CEECs rose. While livestock producers benefited from lower feed costs, milk and, to a lesser extent, pork production remained uncompetitive. The gap in international competitiveness between individual and corporate farms, apparent in the mid-1990s, has persisted.10 It should be remembered that as CGE input-output matrices indicated that Hungary is currently more productive in milk production than the EU-14 the historic rates of productivity growth were retained in the convergence scenario in this case (see footnote 4). There is a small improvement in the DRC ratio for milk under the productivity convergence scenario compared with the historic rate of productivity growth scenario because dairy farmers benefit from boosted productivity in other sectors.

Using the results of the CGE model, projected DRCs are estimated for the years 2007 and 2013 under three policy scenarios. This analysis indicates that accession negatively impacts on international competitiveness by increasing factor costs, principally land and, to a lesser extent, labour. While historic levels of productivity growth would be sufficient to offset the adverse price effects associated with accession for pork and chicken production, they would be insufficient for the analysed arable commodities. The adverse price effect of accession has a greater impact on individual farms than corporate enterprises engaged in arable production because corporate farms use fewer units of non-traded factors of production per unit of tradable costs than individual farms.

In both the corporate and individual farming sectors, to improve and even maintain their international competitiveness in crops, ‘dynamic’ improvements in productivity to offset the effect of high factor costs are necessary. If, however, Hungary does witness a substantial rise in productivity growth post-accession and convergence toward the EU-14, it will recapture or improve on its competitive position of the mid-1990s. Under this scenario, by 2013, the weighted average DRC ratios for all crops would be substantially below 1 except potatoes.

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Table 1: Comparison of DRCs for mid-1990s with weighted averages for years 2000 to 2002

Commodity 1994 1995 1996 2000 2001 2002 2000-2002

averageWheat 0.64 0.71 0.89 0.89 1.00 1.23 1.04Maize 1.28 0.90 0.82 0.81 1.24 1.15 1.06Sunflower 0.76 0.72 0.80 0.99 0.87 1.17 1.01Potatoes 2.16 1.21 1.17 1.51Sugar beet 1.51 0.85 0.75 1.04Pork 5.41 -3.84 2.88 0.94 1.26 1.38 1.19Chicken 0.38 0.38 0.62 0.46Milk 6.90 6.13 13.98 1.44 1.39 1.53 1.45

Source: own calculations, Banse et al. (1999)

Table 2: DRCs for Hungarian agriculture according to farm management type (2000-2002)

2000 2000 2001 2001 2002 20022000-2002

average2000-2002

averageType of farm individual corporate individual corporate individual corporate individual corporateWheat 1.07 0.84 1.21 0.95 1.51 1.16 1.26 0.98Maize 0.76 0.83 1.51 1.13 1.39 1.05 1.22 1.00Sunflower 1.12 0.96 1.15 0.82 1.62 1.09 1.30 0.96Potatoes 2.34 2.14 1.20 1.21 1.16 1.17 1.57 1.51Sugar beet 1.81 1.46 1.15 0.80 1.00 0.71 1.32 0.99Pork 2.27 0.82 3.84 1.02 4.55 1.08 3.55 0.97Chicken 0.54 0.34 0.53 0.34 1.02 0.53 0.70 0.40Milk 1.76 1.40 1.71 1.36 1.90 1.49 1.79 1.42

Source: own calculations

Table 3: Projected DRCs for Hungarian Agriculture under different scenarios, 2007 and 2013

Baseline Scenario Accession ScenarioAccession and Productivity

Convergence Scenario

2002 actual

2007 price effects only

2007 price and productivity

effects2013 price effects only

2013 price and productivity

effects2007 price effects only

2007 price and productivity

effects2013 price effects only

2013 price and productivity

effects

2007 price and productivity

effects

2013 price and

productivity effects

wheat 1.23 1.38 1.10 1.52 0.95 1.69 1.32 1.94 1.17 0.85 0.65maize 1.15 1.24 1.00 1.37 0.87 1.51 1.20 1.74 1.07 0.78 0.60sunflower 1.17 1.20 0.94 1.24 0.75 1.46 1.12 1.67 0.96 0.92 0.75potatoes 1.17 1.29 1.06 1.46 0.96 1.55 1.25 1.75 1.12 1.14 0.98sugar beet 0.75 0.82 0.71 0.92 0.67 1.19 0.91 1.38 0.88 0.71 0.56pork 1.38 1.49 1.17 1.71 1.07 1.80 1.38 2.26 1.31 0.82 0.58chicken 0.62 0.63 0.34 0.65 0.32 0.68 0.36 0.78 0.36 0.28 0.23milk 1.53 1.90 1.74 2.60 2.06 2.19 2.00 2.33 1.91 1.91 1.75

Source: own calculations

Table 4: Estimated DRCs in the years 2007 and 2013 for Individual and Corporate Farms under different Policy Scenarios

Baseline scenario (price and historic productivity effects)

Accession Scenario (price and historic productivity effects)

Accession & productivity convergence (price and productivity effects)

Year2002 actual

2002 actual 2007 2007 2013 2013 2007 2007 2013 2013 2007 2007 2013 2013

Farm type Ind. Corp. Ind. Corp. Ind. Corp. Ind. Corp. Ind. Corp. Ind. Corp. Ind. Corp.wheat 1.51 1.16 1.41 1.02 1.19 0.89 1.72 1.22 1.47 1.09 1.06 0.79 0.80 0.61maize 1.39 1.05 1.18 0.93 1.02 0.82 1.44 1.11 1.26 1.00 0.92 0.73 0.70 0.57sunflower 1.62 1.09 1.23 0.88 0.95 0.72 1.49 1.05 1.24 0.91 1.21 0.86 0.97 0.71potatoes 1.16 1.17 1.06 1.05 0.98 0.95 1.26 1.25 1.14 1.12 1.19 1.14 1.06 0.97sugar beet 1.00 0.71 0.92 0.67 0.85 0.64 1.29 0.84 1.22 0.82 0.95 0.67 0.75 0.53pork 4.55 1.08 2.67 1.03 2.30 0.95 3.03 1.23 2.73 1.18 1.52 0.75 1.09 0.53chicken 1.02 0.53 0.49 0.31 0.46 0.28 0.52 0.33 0.53 0.32 0.40 0.25 0.35 0.20milk 1.90 1.49 2.26 1.68 2.71 1.98 2.57 1.87 2.40 1.80 2.54 1.76 2.38 1.61

Source: own calculations