Farm Productivity and Profitability: A Comparative Analysis of Selected New and Existing EU Member...

Farm Productivity and Profitability: AComparative Analysis of Selected Newand Existing EU Member States1

SOPHIA DAVIDOVA1, MATTHEW GORTON2, TOMAS RATINGER3,KATARZYNA ZAWALINSKA4 & BELEN IRAIZOZ5

1Imperial College Wye Campus, AEBM, Wye, Ashford, Kent TN25 5AH, UK.E-mail: [email protected] of Newcastle, Newcastle, UK3The Czech Institute of Agricultural Economics, Czech Republic4CASE Foundation, Poland5Public University of Navarra, Spain

This paper attempts to shed light on the recent performance of farms in the Czech

Republic, Hungary and Poland and to compare the findings with the situation in two

regions of existing EU Member States. Utilising farm survey data, ratios of

agricultural profitability and productivity have been estimated. Analysis indicates

that Hungarian farms have the best prospects among the analysed CEECs according

to their profitability. The poor profitability and structural problems of Polish

agriculture are highlighted. Family farms are less productive than corporate farms in

the Czech Republic and Hungary despite the expectations at the outset of the

reform that better incentives would boost their productivity.

Comparative Economic Studies (2005) 47, 652–674. doi:10.1057/palgrave.ces.8100066

Keywords: farm performance, profitability, productivity, EU accession

INTRODUCTION

The 1990s witnessed widespread changes in farm structures, governmentpolicies and agricultural markets in the Central and East European Countries(CEECs) that are New Member States (NMS) of the EU. These changes have

1This paper is based on research conducted within the EU FP5 IDARA Project, QLRT-1999-1526.

The authors are grateful for the financial support and the usual disclaimers apply. The authors also

thank Barna Kovacs and Tamas Mizik for their collaboration.

Comparative Economic Studies, 2005, 47, (652–674)r 2005 ACES. All rights reserved. 0888-7233/05 $30.00

www.palgrave-journals.com/ces

resulted in a more differentiated set of farming systems that have to deal withthe effects of international trade liberalisation and competition in an enlargedsingle European market. In view of the enlargement of the EU, there has beena growing interest in the competitiveness, productivity and profitability offarming in the CEECs. More specifically, farm performance has been seen ascritical in several debates about the implementation of the CAP in the NMS,such as how farm structures and the agricultural labour force will evolve inthe region, whether collective farms and their successor forms will survive inmature market conditions and how adoption of the CAP will affect farmprofitability.

This paper presents an overview of the key findings of researchundertaken on the performance of CEEC farms, which sought to shed lighton these issues. The research investigates the private profitability and totalfactor productivity (TFP) of farms classified by several variables includingsize, legal type and agri-environmental region. This is used as the basis for adiscussion of the overall survivability of farms in different countries and,thus, the likelihood of future restructuring. The study focuses on three CEECs,namely the Czech Republic, Hungary and Poland and concerns bothcorporate and individual farms. In order to compare the findings from theCEECs with the situation in existing EU Member States, similar analysis wasconducted for two contrasting regions, the region of Navarra in Spain andSouth-East England. These areas were chosen to reflect the diversity ofagricultural regions that already exist within the EU: Southern andmountainous agriculture in Navarra and North European lowland agriculturein England, and small family farms in Navarra and much larger farms inEngland using a high proportion of hired labour.

AGRICULTURAL TRANSITION AND FARM PERFORMANCE IN THE CEECS

At the outset of transition, agriculture in the CEECs was widely perceived asinefficient (Brooks et al., 1991). Three main factors were believed to accountfor inefficiency: (a) inappropriate farm sizes, (b) the weakness of state- andcollective-owned farms as an organisational type and (c) central planning.The centrally planned environment insulated farms from market signals andby providing enterprises with soft budget constraints created a disincentive toimprove efficiency (Lerman et al., 2001). Dismantling the commandeconomy, liberalising prices and instituting hard budget constraints weretherefore seen as mechanisms for improving productivity in the agriculturalsector of the CEECs. However, improvements in efficiency were seen asachievable not just from macroeconomic reform but also from ‘micro-level’

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changes in farm size, organisational type and other agency factors (Lermanet al., 2001).

As the main specific features of the CEECs at the outset of transition wereperceived to be the large size of commercial farms and the dominant shareof agricultural land managed by cooperative and state farms, size andorganisational type have been the most frequent factors considered ininvestigating variations in performance (Hughes, 2000a). While other factorsare clearly also important in determining performance (such as agri-environmental characteristics, endowments of human capital and the natureof up- and downstream markets) (Mathijs and Vranken, 2001), these issueshave received comparatively less attention (for a review, see Gorton andDavidova, 2004). In this section of the paper, the CEEC-specific debate on sizeand organisational type is introduced and previous analyses of variations inagricultural performance in the Czech Republic, Hungary and Polanddiscussed. This provides a basis for the cross-national comparative analysispresented in Sections ‘Methodology’ and ‘Data Sets’.

Farm sizeFormer Czechoslovakia and Hungary were characterised by a bi-polar farmstructure. Agricultural land use in both states was dominated by large stateand cooperative farms supplemented by small-scale auxiliary plots. By theend of the 1980s in the Czech Republic, cooperative and state farmsaccounted for 61 and 25 percent of the total agricultural area, respectively. Forthe same year in Hungary, cooperative and state farms accounted for 75 and15 percent of the total area, respectively. In both countries, the mean farmsize of state and cooperative farms was in excess of 4,000 and 2,000 ha,respectively. In contrast, Poland never extensively collectivized, and duringthe 1980s, private farms accounted for approximately 75 percent ofagricultural land and a similar share of agricultural output (GFA, 1997).These private farms were small by Western standards (over 2 million unitswith approximately 1.4 million ha). Regarding the socialised farms that wereestablished in Poland, state farms were in size and employment levels similarto those in Hungary and Czechoslovakia but the cooperatives were smaller(mean of 297 ha in 1985) (GUS, 1990).

The size of state and cooperative farms was widely perceived as sub-optimal (Brooks et al., 1991). Such large farms, it was argued (Schmitt, 1993),suffered from high transaction costs due to the large number of workersemployed in absolute terms and per hectare (Lerman et al., 2001). With sucha large hired labour force, the costs of monitoring effort were seen as a causeof diseconomies of scale. In Poland, the persistence of small-scale peasantfarms was also seen as sub-optimal in that economies of scale were not


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realised (GFA, 1997). In short, the bi-polar structure, it was argued (Lermanet al., 2001), meant that agricultural land use was dominated by collectivefarms that were too large and peasant farms in Poland or auxiliary plots in theCzech Republic and Hungary that were too small.

The relationship between efficiency and farm size in the post-communistera has been extensively studied and analysis for the Czech Republic,Hungary and Poland is reviewed below. Hughes (2000a), through acomparison of total factor productivity, found evidence of economies ofscale for arable farms in the Czech Republic for up to 750 ha and littleevidence of diseconomies of scale above this threshold. For Hungary, incontrast, he found that diseconomies of scale did appear to set in above500 ha, supporting the view that the communist era collective farms wereexcessively large. In Poland, empirical work has concentrated on individualfarms. In an early study using data for 1993, van Zyl et al.’s (1996) TFPanalysis indicated that individual farms that were relatively large by Polishstandards (above 15 ha) were, on average, less efficient than farms below15 ha in size. This result was surprising for the authors in that it appeared toshow that the smallest peasant farms were not less efficient. However, giventhe absence of significant numbers of individual farms of above 25 ha, itcould not be concluded that economies of scale did not exist for sizes outsidethe sample range. In addition to estimating TFP, van Zyl et al. (1996) alsoapplied data envelopment analysis (DEA). From the DEA analysis, theydiscovered no significant differences in scale efficiency between farm sizes.Latruffe et al. (2005), using more recent data and applying DEA analysis,found, however, a significant difference in terms of scale efficiency with thesmallest farms (between 1–2 and 2–5 ha) being the least efficient for bothcrop and livestock production.

The empirical evidence on the relationship between farm size andefficiency is less clearcut than many supposed at the outset of transition.There is both mixed evidence on the view that the state and collective farmswere too large (some support for Hungary but not the Czech Republic) andthat the peasant farms in Poland were too small (only for very small farmsunder 5 ha is there evidence of clear inefficiencies of scale). These findingspoint to the importance of factors other than size in determining efficiencyand that there is no clear cross-national optimal farm size.

Organisational type

In addition to inappropriate size, state and cooperative farms were widelyseen as an inefficient organisational form (Schmitt, 1993). Institutionaleconomists, in particular, have argued that family farms are a superiororganisational type in agriculture in that they minimise transaction costs and


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are therefore more efficient than cooperative, state-owned or corporateenterprises (Schmitt, 1993; Hagedorn, 1994). Family farms, it is argued(Pollak, 1985), do not suffer from a principal-agent problem in that incentivesfor workers are internalised as the family provides both management andlabour. As the costs of supervising and monitoring hired labour in agriculturecan be high:

‘All production cooperatives based on member labor suffer from shirking andfree riding, which are induced by strong moral-hazard behavior among themembers. In socialist agriculture, these weaknesses were further aggravatedby monitoring and enforcement difficulties associated with size (also a well-known universal factor) and by the evils of the administrative commandsystem (a unique feature in the socialist countries)’.(Lerman et al., 2001, p. 33).

As with the assumptions on size, a number of empirical studies haveanalysed the relationship between organisational type and efficiency. Thesestudies have looked at the differences between individual and corporatefarms, where the latter category includes production cooperatives, joint stockcompanies and limited liability firms. Most corporate farms have their originsin the state and cooperative farms of the communist era and a review of thetransformation of collective farms in the Czech Republic and Hungary ispresented in Hughes (2000b) and Csaki and Lerman (1998), respectively.

Using data for the mid-1990s and applying TFP and DEA analysis, bothHughes (2000b) and Mathijs and Vranken (2001) found that, when otherfactors were controlled for, family farms in Hungary did appear to be moreefficient than their corporate counterparts. In the Czech Republic, a similarsignificant difference was found for livestock farming but not crops. Curtiss(2002), who applied Stochastic Frontier Analysis (SFA) for analysing arablefarms in the Czech Republic, found that cooperatives performed better thanindividual farms in wheat and rapeseed production but that the latter weresuperior for sugar beet cultivation.

As with farm size, the evidence on the relationship between efficiencyand farm type is therefore not clearcut. While there is support for the initialpropositions for Hungary, in the Czech Republic the results are morecomplex. Moreover, even where the average corporate farm is less efficientthan the average family farm, some cooperatives and companies performwell. This suggests that at least some corporate farms can solve thegovernance problems discussed by Lerman et al. (2001).

While empirical work to date has been informative, further research canbe justified on two counts. First, the research to date has been based mainlyon data for the early and mid-1990s. This was a period of immense change in


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agriculture with a widespread and significant drop in output throughout theCEECs (OECD, 2000). In some countries, reforms were delayed and changesin ownership and management recorded in official statistics masked thedevelopments at the farm level (Hughes, 2000b). It is therefore important tosee if the trends identified for the early and mid-1990s reflect merely thecharacteristics of initial transition or more durable phenomena. Second, thestudies reported above analyse variations in relative efficiency on a country-by-country basis, identifying the characteristics of farms that are relativelymore efficient in a particular sample. However, this offers little insight into thecomparative cross-country picture and the profitability of farms and returnson resources employed. An analysis of profitability is important tounderstand the probable nature of future restructuring in the sector,especially when compared against existing EU Member States. Theseobjectives guide the methodology presented below.

METHODOLOGY

ProfitabilityFarm profitability is analysed through the estimation of ratios between thecosts and revenues for each farm. A ratio smaller than one indicates aprofitable farm and vice versa. The use of ratios has been preferred as itsimplifies cross-farm and cross-country comparisons. Costs include labour,land, capital (depreciation and interest) and intermediate consumption. Therevenue side includes proceeds from the sale of agricultural products, thevalue of non-marketed agricultural output, proceeds from other activities andnet current subsidies. Revenues from other activities refer to proceeds fromgainful activities that are inseparable from the main farm accounts (Tantonand Williams, 2000). As the share of the revenue from these other activities israther small, for example, 3.9 percent in Navarra and 8.4 percent in Hungary,and as farm accounting records do not split the costs of these activities buttreats them as integrated into total farm costs, the overall revenue and costsfor each farm were taken into account in the analysis. The most importantsource of ‘other revenue’ in the CEECs is from renting out agricultural land.

Three cost-revenue ratios for each farm have been calculated. The centralratio used as a reference is the private cost benefit ratio (P_CB). For the ithfarm, the P_CB is taken to be:

P CBi ¼ðCt

i þ Cfi Þ

Rið1Þ


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where Cit is the cost of tradable inputs, Ci

f is the cost of non-tradable factors ofproduction (based on private prices or estimates for non-paid land and labourinput) and Ri is revenue excluding current subsidies net of taxes. This ratioprovides a framework for evaluating profitability when the full opportunitycosts of all factors are assessed. The initial data did not include a notional rentfor owned land and wages for non-paid labour input. For this reason, for non-paid land and labour input, a set of shadow prices were estimated usingregional averages. Family labour was valued using the average regional farmunit labour costs. Farm labour costs were used as it was assumed that most ofthe farmers in the studied CEECs had low opportunity costs and their secondbest alternative would be to become farm workers. Given significant spatialvariations in wage rates, adjustments were made at the regional level. As far asland was concerned, if a farm had a mix of rented and owned land, the rentpaid was imputed to the owned land, as it was assumed that rented and ownland were in close proximity, and thus, were of a compatible quality. If a farmdid not rent land, then the average regional rent was applied to the owned land.

Two other profitability ratios were also calculated. The first, cost–revenueplus subsidies (C_Rs), exactly matches the entries in the EU’s FarmAccountancy Data Network (FADN) that was transposed to the CEECs and,therefore, Cf

i does not include estimates for non-paid labour and land, and Ri

includes the net current subsidies. This ratio is used as an indicator of farmsurvivability. If farmers can cover their paid costs, they may continue farmingeven though the returns to their own factors might be very low or zero (Ellis,1988). The final ratio, cost–revenue without subsidies (C_R), does not includeestimates for non-paid labour and land, and also excludes direct subsidies. Therationale for calculating this last ratio is to give an insight into the effect of thedirect budgetary transfers on different farm types and between countries. It isexpected that the ranking of farms will change with the use of different ratiosdepending on their integration into factor markets, namely the role of hiredlabour and rented land in the farming process. Such effects are deliberatelysought in order to provide a complete assessment of the economic profitability,survivability and dependence on government transfers of farms in differentcountries and with varying characteristics. As the approach is static, little,however, can be said about dynamic adjustments to changes in policy.

In addition to the ratios used in the profitability analysis, some standardfinancial ratios (Debt to assets, Leverage, RENGO and RENGM) wereconstructed on a farm-by-farm basis. The Debt to assets ratio represents theproportion of assets owed to creditors. Leverage is the relationship betweenthe total loans and the net worth of the farm. It measures the degree to whichdebt is used to finance the farm business. RENGO is the ratio of rental costs(rents plus interests paid) to gross output and RENGM is the relationship


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between such rental costs and the gross margin. The last two indicators aremeasures of ‘financial stress’ – the pressure placed on a farm by therepayment of rent and interest (Franks, 1998). All value units are expressed ineuros for ease of international comparison.

Productivity

Productivity differences were estimated by the construction of a Tornqvist–Theil TFP index for all farms in the sample relative to a base case ‘averagefarm’ with results interpreted relative to the sample mean, showing groups offarms having above or below average TFP scores. The Tornqvist–Theil TFPindex is recognised as a measure of technical efficiency and is considered tobe an acceptable alternative to econometric estimation in cases where thedata do not permit an underlying production function to be estimated(Capalbo and Antle, 1988). The Tornqvist–Theil TFP index applied here is arelative measure of productivity, comprised of the difference between anaggregated output index and an aggregated input index. Supposing there aretwo firms i and b that produce n outputs Qj (j¼ 1,yn) using m inputs Xk(k¼ 1,ym), then the index t can be defined as:

t1 ¼ 1

2

Xn

j¼1

Rij þ Rb

j

� �ln Qi

j � ln Qbj

� �

� 1

2

Xm

k¼1

Sik þ Sb

k

� �ln Xi

k � ln Xbk

� � ð2Þ

where for firm i, Rji represents the share of the value of the jth output in the

total value of all n outputs, and Ski represents the share of the costs of the kth

input in the total input costs of all m inputs. Two TFP indices have beencalculated, one including estimated costs for own land and labour (TFP1) andone with paid costs only (TFP2).

DATA SETS

Data were extracted from FADN surveys, which are implemented in all EUMember-States and some New Member States of the EU. Derived fromnational surveys, FADN is an important source of micro-economic data andis widely used for farm-level analysis. It is broadly representative forcommercial agricultural holdings.2 As the survey does not cover all

2 A commercial holding is defined as a farm that is large enough to provide the main gainful

activity of a farmer and a level of income sufficient to support his or her family.


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agricultural holdings except only those that are of a size that can beconsidered as commercial, FADN is biased towards larger holdings incomparison to the total farm population. Therefore, the FADN sampleexcludes subsistence producers and this sector is not discussed in the scope ofthe paper. Although the subsistence sector is important in some CEECs,studying larger farms and excluding the purely subsistence ones is adequatefor a comparison with the EU, as the very smallest ‘farms’ are likely tocontinue to produce for self-consumption, and to be less integrated into themarket and exposed to competitive pressures even after accession (Kostovand Lingard, 2002).

FADN is still being piloted in Poland and at present the only national,annual, farm survey is conducted by the Polish Institute of Agricultural andFood Economics (IERiGZ)3, and this has been used as the main source ofdata. In the UK and Spain, FADN data collection is not organized on anational basis but through a network of regional surveys. While aggregationsof a limited range of variables are available at national level, it is difficult tocollate individual farm data at a national scale. For this reason, one regionwas selected from Spain and the UK in order to reflect some of the variationsin farm characteristics and natural conditions existing in the EU-15. Table 1details the main characteristics of the datasets used in the analysis.

In the Czech Republic, the initial sample included 1,087 agriculturalenterprises of physical and legal persons, which collectively managed887,026 ha. After checking the individual data, 264 farms were excludeddue to missing or inconsistent information, so the analysed sample included823 farms. Considering management form, the largest group in the sample areindividual farms, numbering 513 (62 percent) with an average size of 134 ha.Producer cooperatives are the second largest group, 154 (19 percent). The restof the sample is made up of 95 joint stock companies (12 percent) and 61limited liability companies (7 percent). The average size of corporate farms(cooperatives and companies) is 1,526 ha.

Hungary’s FADN provides useable information on over 1,100 agriculturalenterprises (individual and corporate farms). The 233 corporate farms thatwere included in the sample were made up of 21 partnerships, 66 limitedliability companies, 10 joint ventures, 71 cooperatives and 65 other legalforms. For both Hungary and the Czech Republic, the main differencebetween the FADN and Agricultural Census returns is the lack of ‘farms’below 1 hectare in size.

The Polish sample included 1,001 observations of only individual farms,as corporate farms have played a far more limited role than in the Czech

3 Instytut Ekonomiki Rolnictwa i Gospodarki Zywnosciowej.


660


Republic and Hungary. While it would have been beneficial to cover thecorporate farm sector, such enterprises are not included in the IERiGZdatabase and as individual farms account for approximately 80 percent ofUtilised Agricultural Area (UAA) in Poland, the survey does cover thebackbone of Polish agriculture. A close examination of the data brought aboutthe removal of 22 farms and the analysis was carried out with 979observations. Classified by UAA, the highest proportion of the sample farmswas between 10 and 25 ha (41 percent). Farms above 100 ha accounted foronly 4 percent of the sample.

Finally, regarding the data available, it should be noted that although theconclusions are affected by the sample size, the existing samples were largeenough to justify quantitative analysis. However, attention should be paid tothe differences in data collection procedures between countries (especially inthe allocation of fixed costs). While most New Member States are harmonisingtheir own surveys with FADN procedures, this is still an on-going process. Thecross-national analysis of data should therefore be seen as a way ofhighlighting broad trends and differences rather than giving pinpoint results.

As mentioned above, in the Czech Republic and Hungary, there are twomajor management types, individual farms and corporate farms, and differentorganisational types within the corporate group. In theory, there should besubstantive differences between different corporate forms, particularly intheir decision-making process (Hughes, 2000b). However, in the CEECs suchdifferences are frequently far from clearcut. For example, often cooperativesdo not apply the ‘one man one vote’ principle and their operation is similar tothose of companies. In several joint stock companies, managers are also the

Table 1: Characteristics of the data sets used in the analysis

Country Year(s)analyseda

Type Useablenumber of

farm records

Comments

Czech Republic 1998–1999 FADN 823Hungary 2000 FADN 1,121Poland 1999 IERiGZ 1,001 Only individual farmsNavarra Spain 1996–1999 FADN 369South-EastEngland

1999 FBS 183 The UK farm business survey (FBS) isorganised at the request of the Departmentof Environment, Food and Rural Affairs(DEFRA). Only a proportion of farms surveyedcontributes to EU FADN. FBS indicators areconsistent with FADN indicators.

a For comparative purposes, the presentation of Czech and Spanish results in this paper focuses on datafor 1999.


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largest shareholders. In addition, the pace of change in organisationalstructures is still strong in the CEECs. For these reasons, and having in mindthe relatively small numbers of different corporate types, the emphasis is noton explaining variations in the performance of different corporate forms butmainly on comparisons between individual and corporate farms, and cross-country comparisons.

BASIC FARM CHARACTERISTICS: A COMPARATIVE PICTURE

Table 2 compares the sample farms according to four sets of variables: size,rented factors, intensification and income variables. Size variables are theutilised agricultural area (UAA) per farm, the value of output and total assetsper farm, and labour input measured in annual work units (AWU). Rentedfactor variables are the shares of hired labour and rented land in total labourinput and land utilized, respectively. Also two measures of relative factor useare shown in Table 2. The first one is the amount of land per AWU with largerscores being an indicator of less intensive agriculture. The second measure isthe value of depreciation per annual work unit (DEPAWU), in which casehigher values are used as a proxy for greater capital per worker employed.The returns to hired labour are expressed as wages paid per hired AWU.

Table 2 shows that according to the four size measures (average UAA perfarm, average output, total assets and labour input), the countries fall intothree groups. These three groups are: the largest farms (the Czech Republic),medium size farms (Hungary and South-East England, although measured byassets, the South-East English farms are the largest)4 and small farms (Polandand Navarra).

The main differences among the CEECs in farm size stem from theexistence, or the lack of, corporate farms. Although corporate farms arewidespread in both the Czech Republic and Hungary, there is a considerabledifference between the two countries. The land area available to successorfarms in Hungary decreased substantially during transition as a result of theadopted procedures of land reform and farm restructuring, especially becausea large area of land had to be set aside for compensation purposes under theCompensation Act (OECD, 1994). Thus, although in the Hungarian FADNsample there were more than 200 corporate farms, 55 percent of them werebelow 300 ha. As mentioned, in the Czech Republic, the average size of thecorporate farms was above 1,000 ha. In addition, in comparison to Hungary,

4 Capital assets are valued at the cost of replacing them. Therefore, fixed capital items for

South-East England are re-valued each year to reflect changes in their market value (Tanton and

Williams, 2000).


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corporate farms use a much higher share of UAA in the Czech Republic. Thesedifferences are reflected in the larger average area, output and assets per farmin the Czech Republic compared with Hungary.

The Polish farms are the smallest by all size measures. However,according to size they appear closer to farms in the EU (Navarra, Spain) thantheir counterparts in the Czech Republic. When assets are measured per ha,then the Polish farmers seem to be much better capitalised than farms in theCzech Republic or Hungary. This results from their longer history ofindependent farming. Not surprisingly, South-East England has the mostcapitalised farms, having assets per ha more than 60 percent higher than inNavarra and nearly three times that of Poland.

The differences between the countries regarding the use of land rentaland labour markets are striking. The Polish farms rely almost entirely on theirown resources. Only 6 percent of labour input is accounted for by hiredlabour and only 17 percent of total land is rented. Thus, they are dependenton the initial family endowment of resources and familial human capital. Thislack of integration into factor markets is a clear indicator of the peasantcharacter of Polish agriculture. Most of the farms in Navarra are located inmarginal areas (Less Favoured Areas [LFAs] or former Objective 5b areas)5.

Table 2: Background sample characteristics, 1999a

CzechRepublic

Hungary Poland NavarraSpain

South-EastEngland

Average UAA per farm (ha) 658 202 25 50 141Average outputb (EUR) 532,665 224,073 18,000 97,000 399,753Average total assets (EUR) 870,542 204,484 86,000 292,000 1,345,154Average total assets per ha (EUR) 1,450 1,977 3,440 5,840 9,540Average AWU per farm 32 7.45 1.85 1.49 6.35Land rented (%) 76 42 17 45 34Hired labour input (%) 50 31 6 10 53Land per AWU (ha) 38 53 13 36 41DEPAWU (EUR) 2,421 2,427 1,294 6,281 7,810Average paid wage (EUR per paid AWU) 3,552 3,490 2,308 12,312 18,790

a For Hungary 2000 as the 1999 data set had many inconsistencies.b Output includes net current subsidies.

5 Objective 5 (b) areas were selected to receive special support from the EU’s Common

Agricultural Policy (CAP). According to the Council Regulation (ECC) No. 2052/1988 of June 1988

(Official Journal, L144, 27.05.1988), ‘Areas eligible under Objective 5 (b) shall be selectedy taking

into account in particular the degree to which they are rural in nature, the number of persons

occupied in agriculture, their level of economic and agricultural development, the extent to which

they are peripheral and their sensitivity to changes in the agricultural sector, especially in the context

of reform of the Common Agricultural Policy’.


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Less favoured areas include mountainous areas, which are in danger ofabandonment due to low land productivity and difficult cultivation, butwhere the conservation of the countryside is a priority, and other areas whereagriculture is affected by a specific handicap. Farmers in EU LFAs receivecompensation payments to help ensure that they continue farming, maintainthe countryside and promote a viable rural community. In addition, with anaim of achieving regional economic and social cohesion, in the 1990s the EUused Structural Funds to promote rural development in regions with a lowlevel of socio-economic development which had a high share of agriculturalemployment, poor agricultural incomes or were subject to rural depopulation(Objective 5b). Although most of the sampled farms in Navarra are located inthese marginal areas and rely on own labour, in contrast to Poland they alsorent land in order to achieve a reasonable size and generate an acceptableincome: family farms in Navarra have three times as much land per AWUthan in Poland. The Czech Republic and Hungary, due to their corporatefarms that depend almost fully on rented land and hired labour, are nearer tothe English case of large family farms in terms of the extensive use of landrental and labour markets.

Another striking difference concerns the pay of hired labour. In this case,the clear divide is between existing and new EU Member States. Althoughtheoretically accession to the EU may accelerate the equalisation of productand factor prices, the order of magnitude of the differences in agriculturalwages is such that most probably a large gap will persist for a long time post-accession. Incentives for agricultural labour from the CEECs to move to work,at least seasonally, in West European farms, are likely to persist and this is aphenomenon that currently occurs.

FARM PROFITABILITY

The differences in farm structural characteristics bring about importantconsequences for the profitability of farming. Table 3 presents the averagefarm profitability for the sample farms in each of the analysed countriesaccording to the three profitability ratios.

The private cost–benefit ratio is sensitive to the shadow prices applied tonon-paid labour and own land. This is particularly important for individualfarms that mainly rely on own resources. However, as shown in Table 3, evenin regions where farming uses mainly own resources, as in Navarra, if theresources are effectively used, farms can be near the break-even pointaccording to the private cost–benefit ratio.

The most profitable farms are in Navarra and Hungary. The fact that inthis group there is one existing and one new EU Member State tends to


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undermine any easy generalisations. The profitability of farms in Navarracannot be solely attributed to CAP headage and acreage payments andtransfers received because of their location in LFAs or objective 5b areas. It istrue that direct payments account for 13.6 percent of the gross output ofNavarra’s farms and that almost all sample farms receive direct payments(Table 4). However, the importance of direct payments in South-East Englandis not substantially different but the English farms are unprofitable on boththe private cost–benefit and cost–revenue without subsidies ratios.

The Hungarian farms have the best prospects among analysed NewMember States according to their profitability. They are near the break-evenpoint on the private cost–benefit ratio and are profitable according to theother two ratios (Table 3). They achieve this profitability with more modestdirect payments than in existing EU states. Net current subsidies account forslightly more than 5 percent of the gross output (Table 4).

For the Czech Republic and Poland, agricultural profitability is a majorproblem. The average scores for each of the three profitability ratios are above1. Even without accounting for the opportunity costs of own resources, andincluding net current subsidies, 52 percent of the sample farms in the CzechRepublic and 40 percent in Poland are unprofitable. While Polish farmers donot benefit from direct payments, the Czech farms receive more net current

Table 3: Profitability ratios

CzechRepublic


South-EastEngland

Average private cost-benefit score 1.224 1.03 3.83 1.098 1.374% of sample profitable on private cost–benefit 20 60.9 8.7 45.0 14.8

Average cost–revenue without subsidies score 1.086 0.81 1.01 0.714 1.095% of sample profitable on cost–revenuewithout subsidies

38 81.5 60.4 85.4 42.6

Average cost–revenue plus subsidies score 1.003 0.76 1.01 0.604 0.922% of sample profitable on cost–revenueplus subsidies

48 85.4 60.4 92.7 74.9

Table 4: Direct payments

CzechRepublic


South-EastEngland

Direct payments as % of gross output 6.4 5.2 0.03 13.6 14.0% of sample that receive direct payments 80 82 1.9 99.2 72.1


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subsidies in relative terms than the Hungarian farmers, but nevertheless theirprivate profitability is low.

Undoubtedly, agri-environmental conditions play an important role infarm performance. For example, the Czech farms are classified into five agri-environmental regions that reflect different conditions for farming, notionallycalled the maize, sugar beet, cereal-potato, potato and mountainous-forageregions. The best for agriculture is the first zone (maize region) and they arelisted in descending order. In terms of agri-environmental regions, the worstresults were recorded, not surprisingly, in the mountainous forage regionwhere, on the basis of the private cost–benefit measure, no farms wereprofitable (Table 5). However, even in the best agri-environmental regions(maize and sugar beet) the majority of farms were loss making. In the cerealand potato regions, only 22 and 13 percent of farms were profitable,respectively, according to the private cost–benefit ratio.

The comparison of average profitability scores by legal form for the CzechRepublic and Hungary (Table 6) reveals that Czech farms are uniformly lossmaking with the exception of individual farms on the cost–revenue plussubsidies ratio. The opposite is true for Hungary with only one ratio above 1,the private cost–benefit measure for individual farms.

When the results by region and legal type are considered together,the individual farmers in the Czech Republic register the best results in themaize region but they have one of the worst returns in the mountainousforage regions according to P_CB and C_R ratios (Table 7). Only whensubsidies are accounted for in the revenue (C_Rs), can individual farms beidentified as having the highest profitability in all agri-environmental regions.

Table 5: Profitable and loss-making farms according to agri-environmental region, Czech FADN sample,1999, number and percentage

Maizeregion

Sugar beetregion

Cereal-Potatoregion

Potatoregion

Mountainousforage region

P_CBProfitable 7 (35%) 70 (22%) 66 (22%) 18 (13%) 0 (0%)Loss making 13 (65%) 253 (78%) 238 (78%) 118 (87%) 40 (100%)

C_RProfitable 13 (65%) 137 (42%) 124 (41%) 32 (24%) 3 (7%)Loss making 7 (35%) 186 (58%) 180 (59%) 104 (76%) 37 (93%)

C_RsProfitable 13 (65%) 160 (50%) 153 (50%) 54 (40%) 19 (47%)Loss making 7 (35%) 163 (50%) 151 (50%) 82 (60%) 21 (53%)


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The poor economic performance of the Polish and Czech farms is alsoclear from Table 8, where the net value added (NVA) per ha and AWU ispresented. Their labour productivity is also low; in Poland NVA per AWU isnearly 12 times less than in South-East England and 14 times less than inSpain. This is in line with Pouliquen’s estimates that the value added perworker in Poland is equal to only 8 percent of the EU level (Pouliquen, 2001).The best performers according to NVA are the Spanish farms. Hungarianfarms again record good results.

The lack of profitability makes the long-term viability of a large numberof Czech and Polish farms questionable unless they manage to restructure.The issue is even more serious in the Czech Republic due to the high level offarm indebtedness (Table 9). Czech farms are funded by debt and averagedebts are higher than the net worth of the farms (leverage above 1). However,their financial stress is not as high as would have been expected by their levelof indebtedness, in fact it is less than in the two EU regions. This is becausemost of the debt is in the form of non-bank liabilities, held by the successorfarms either to individual owners of the assets for producer cooperatives or tothe state for limited liability companies. As a result of the adopted reformlegislation, these farms did not need to repay these debts for several yearsafter their establishment.6 For this reason, the financial stress is lower than itwould have been under similar situations in Western Europe.

Polish farmers do not rely on external financing either due to externalconstraints (access to credit) or personal choice.

TOTAL FACTOR PRODUCTIVITY

TFP scores are expressed in relation to the sample mean that has beennormalised to unity. While one is able to identify farms which are relativelymore efficient with a higher TFP index score in a particular sample for onecountry, this might bear little relationship to what may be consideredinternationally productive. Therefore, what it is possible to compareinternationally is the share of farms that have high TFP scores in eachsample and whether they produce the predominant portion of output and to

6 Limited liability companies are to a large extent successors of the former state farms. Their

assets had to be purchased and the new owners had to pay an initial instalment while the rest was

recorded as long-term liabilities to the state. Cooperatives carry liabilities to former, currently non-

farming, owners of assets (so-called eligible persons). The start of the repayment of these liabilities

was delayed for 7 years. However, since January 2000 eligible persons have been entitled to claim

their assets from cooperatives.


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what extent they depend on net current subsidies. The ranking of productivityscores between different management types can also be compared.

Table 10 presents the country results according to TFP1 (includingestimated costs for own resources).

Table 6: Profitability ratios for the Czech and Hungarian samples according to management type

Hungary Czech Republic

Individualfarmers

Ltd Coops Othercorporate

farms

Individualfarmers

Ltd Jointstock

Coops

Private Cost–BenefitAverage score 1.09 0.86 0.78 0.93 1.26 1.20 1.19 1.14

Cost–Revenue without subsidiesAverage score 0.82 0.86 0.78 0.79 1.05 1.19 1.19 1.13

Cost–Revenue plus subsidiesAverage score 0.77 0.77 0.75 0.74 0.95 1.10 1.13 1.07

Table 7: Profitability according to agri-environmental region and management form, Czech FADN sample,1999

Class means Maizeregion

Sugar beetregion

Cereal–Potatoregion

Potatoregion

Mountainousforage region

Legal typeaverages

P_CBIndividual farmers 1.046 1.166 1.293 1.366 1.694 1.2623Ltd companies 1.064 1.129 1.217 1.274 1.270 1.2035Joint stock comp. 1.178 1.214 1.104 1.244 1.429 1.1944Production coops 1.256 1.068 1.143 1.178 1.341 1.1372Regional averages 1.089 1.156 1.242 1.282 1.563

C_RIndividual farmers 0.901 0.997 1.052 1.089 1.391 1.0467Ltd companies 1.064 1.125 1.200 1.263 1.256 1.1932Joint stock comp. 1.178 1.207 1.096 1.240 1.429 1.1879Production coops 1.256 1.059 1.134 1.173 1.336 1.1297Regional averages 1.002 1.040 1.081 1.157 1.363

C_RsIndividual farmers 0.880 0.961 0.978 0.895 0.898 0.9544Ltd companies 1.029 1.086 1.121 1.134 1.022 1.0981Joint stock comp. 1.152 1.167 1.038 1.170 1.071 1.1302Production coops 1.235 1.024 1.062 1.095 1.198 1.0658Regional averages 0.979 1.004 1.008 1.023 0.966


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In all countries, farms with TFP scores above 1 are in a minority and inthe case of Navarra they constitute only 29 percent of the sample farms. Atfirst glance, it seems that the results for Navarra are contradictory: too high apercentage of profitable farms and too low a share of farms with higher thanthe average productivity. However, a more detailed analysis shows that all thefarms that have a TFP score above unity are also profitable according to allprofitability ratios. Productive farms account for 62.7 per cent of all Navarrafarms that are profitable according to P_CB ratio. Thus, productivity andprofitability are related (w2 coefficient significant at the 0.01 level).

Two important features stem from the productivity analysis. With theexception of Spain, the minority of productive farms produces a majority ofthe total output. From this point of view, once again Hungary has the bestperformance with 85 percent of the output produced in farms having

Table 8: Net value added (NVA) per farm, hectare and AWU (EUR)

Czech Republic Hungary Poland NavarraSpain

South-East England

NVA per farm 97,166 125,483 4,685 44,723 142,959NVA per ha 116 974 199 1,961 1,436NVA per AWU 3,472 16,481 2,044 29,185 23,665

Table 9: Financial ratios for the sample farms


South-East England

Debt to assets 0.33 0.16 0.03 0.08 0.15Leverage 1.53 0.39 0.04 0.11 0.25RENGO 0.04 0.03 0.02 0.05 0.09RENGM 0.09 0.04 0.04 0.12 0.36

Table 10: Farm productivity (TFP1 scores)


South-EastEngland

No of high productivity farms (TFP41) 381 488 346 106 86% of high productivity farms 46 44 35 29 47% of sample UAA in TFP41 farms 53 64 63 29 38% of sample output in TFP41 farmsa 60 85 56 37 69% of sample subsidies in productive farms 46 49 52 33 26

a Output includes net current subsidies.


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technically efficient input–output combinations. The results for South-EastEngland indicate that the productive farms tend to rely less heavily on netdirect subsidies. In South-East England, 47 percent of productive farmsabsorb only 26 percent of the total net current subsidies of the sample. This,however, is not the case in the other analysed countries.

In Hungary and the Czech Republic, according to management type,corporate farms have higher TFP scores than individual farms (Table 11).

Family farms are less productive despite the high expectations at theoutset of the reform process that better incentives involved in individualfarming would boost their efficiency. The reasons for this result are complex,including the long-standing tradition of farming in association in the NMSand a high share of hired labour in corporate farms allowing them to recruitlabour with necessary skills for technical agricultural and managementpositions. In some cases, former collective farm managers were able to siphonoff the most attractive parts of the business into new corporate farms thatyield good returns. The argument that corporate farms benefit solely fromeconomies of size does not seem to hold, at least for Hungary. When for thepresent data set the size has been controlled for, individual farms stillappeared as less productive than their corporate counterparts.

CONCLUSIONS

The 1990s witnessed extensive restructuring that created a more complexpattern of farming in Central Europe. As a result, there is no neat divide inprofitability between Western and Central Europe. The estimated profitabilityratios indicate that farms in Hungary and Navarra fared the best, with theworst problems being in parts of the Czech Republic and Poland. The maindifference between the two West European cases and Central Europe is not in

Table 11: Farm productivity by management type (TFP1 scores)

Hungary Czech Republic

Management type Average TFP score Management type Average TFP score

Family farms 0.96 Family farms 0.987Limited liability companies 1.16 Limited liability companies 0.971Cooperatives 1.19 Cooperatives 1.033Joint ventures 1.42 Joint stock companies 1.035Other 0.96 Other N/A

Total 1.00 Total 1.00


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terms of the number of farms that are profitable but rather in terms of capitalintensity and wage rates.

In explaining the relatively poor profitability of farms in the CzechRepublic, Poland and South East England, a number of factors can be cited.Comparing farms in the Czech Republic with their Hungarian counterparts,the former appear overmanned. In the Czech case, land per AWU issignificantly lower than in Hungary (38 ha compared with 53 ha) despiteaverage wage rates being higher. Agri-environmental conditions also play apart with profitability in less attractive areas (eg potato and the mountainousforage regions) being significantly worse. As a result, Net Value Added perAWU is over four times greater in Hungary compared to the Czech Republic.

Comparing the family farms of Navarra with those in Poland, it isapparent that in the former case farms rent in more land to generate areasonable return. In the Polish case, farms are relying to a far greater extenton family-owned land. The Spanish farms are also better capitalised. Polishfarms have high average values for total assets per ha by Central Europeanstandards, but they are still significantly less than in the Spanish case. Inaddition, the quality of Polish capital has been questioned (Latruffe et al.,2005). Latruffe et al. (2005) in their efficiency analysis identify that manyPolish farmers have purchased an extensive range of machinery andequipment irrespective of their farm’s size and the potential efficiency withwhich such capital could be used. The maintenance costs for old and obsoletecapital inherited from the communist era are high. While farmers in a betterfinancial state and with larger farms have invested in modern and moreexpensive equipment, the bulk of the smaller farms have invested in old,second-hand machinery. The smallest farms as a result allocate the highestpercentage of depreciation in comparison to the original costs of capital(IERiGZ, 1998, 2002). The relative superiority of farms in Navarra comparedto Poland is apparent for all three ratios and cannot just be reduced to theeffect of direct payments, although the latter play their role in supportingprivate profitability.

While the majority of Polish farms are unprofitable when the opportunitycosts of own land and labour input are accounted for, 60 percent break-even ifonly paid costs are considered. If self-exploitation (accepting low returns toowned labour and land) occurs, as in many peasant societies (Ellis, 1998), thesurvivability of small-scale farms in Poland is likely to be greater thaneconomic cost benefit analyses would predict.

In Hungary and the Czech Republic, when the opportunity costs for ownlabour and land are accounted for and farms operate without currentsubsidies (private cost–benefit ratio), corporate enterprises are the mostprofitable. Corporate farms in both countries, however, do suffer from


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relatively high debts, often due to the nature of the reform process. They alsorely almost exclusively on hired labour and rented land so that unlike mostsmall individual farms they cannot rely on self-exploitation as a strategy tocope with a downturn in agricultural fortunes. As a result, the farms that arethe most competitive during an era of good returns may not be those bestplaced to weather a period of poor agricultural profitability.

In South-East England, high factor costs are important determinants of itsrelatively poor profitability. For example, NVA per AWU in South-EastEngland was 23,665 euros compared to an average wage rate per AWU of18,790 euros. In Navarra, NVA per AWU was 29,185 euros compared againstan average paid wage rate of 12,312 euros. Thus, wages account for theequivalent of 79.4 percent of NVA per AWU in South-East England comparedagainst only 42.2 percent in Navarra.

As measured by the P_CB ratio (full cost–benefit), the greatest structuralproblems lie in Poland. The returns on own labour and land are exceptionallylow and the figures on poor private profitability mirror the findings ofresearch on the international competitiveness of Polish agriculture (Gorton etal., 2001). The majority of individual farms persist through a lack of otheremployment options and a degree of self-exploitation – too many people aretrying to earn a living out of too small farms. To deal with this problem, thestimulation of the non-farm rural economy is paramount. At present, thelatter is underdeveloped in Poland (Chaplin et al., 2004) and this hindersstructural adjustment. Chaplin et al. (2004) identify that diversification (bothenterprise and/or off-farm employment) is linked to the level of generaleducation and the availability of public transport. In Poland, the educationalattainment of farmers is low and infrastructural issues are poorly addressed incurrent EU-led initiatives for rural development. Dealing with structuralproblems in rural Poland will thus require a greater emphasis on improvingeducational attainment and mobility.

For the years analysed, direct payments in Poland were insignificant. InHungary and the Czech Republic about four-fifths of commercially orientedfarms received direct payments but these were much less in absolute termsand as a percentage of gross revenue than in existing EU Member States. InDecember 2002, the Copenhagen European Council concluded the accessionnegotiations with countries from Central Europe. It decided that directpayments for acceding countries should be ‘phased-in’ over a period of 10years from an initial level of 25 percent of the direct payments granted tofarmers in the current EU Member States. However, national governments can‘top-up’ the direct payments. Subject to authorisation by the EuropeanCommission, they can top-up by up to 30 percent or to a maximum of 10percent above the level that farmers received under pre-accession national


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schemes. The introduction of these direct payments in the NMS will have asignificant impact. However, it is important to note that there is a core offarms in Western and Central Europe that could potentially survive withoutdirect payments and that are not strongly dependent on policy protection.Moreover, Chaplin et al. (2004) found that increases in agricultural pricesupport and the introduction of direct payments lowers the propensity offarmers to diversify and vice versa. Therefore, while direct payments couldimprove the private profitability of agriculture in Central Europe, they arelikely to impede restructuring in countries like Poland where structuralchange and the movement of labour out of agriculture are critical.

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