Joint Research Project
Working Paper Series
Work Package 5, Working Paper 2/8, September 2001
Variations in Farm Performance in Transition
Economies: a case study of the Czech Republic
Sophia Davidova, Matthew Gorton, Belen Iraizoz1 and Tomas Ratinger
Research Group of Agricultural Economics
and Business Management, Imperial College at Wye, University of London
This Research Project is Financed by the EU Commission's 5th
Framework Programme (QLRT-1526)
1
Belen Iraizoz is grateful to Universidad Publica of Navarra, Spain, and Gobernio of Navarra for their
financial support during her research at Imperial College at Wye.
1. Introduction
In the initial literature on the transition of the CEECs, debates concerning the
restructuring of farming systems and whether certain structures have inherent
productivity advantages over others featured prominently (Mathijs and Swinnen,
Nelson 1993; Schmitt, 1993). This literature focused on the debate over whether
collective farms and their successor forms would survive, and on the economics of
farm size, which was linked to arguments about land reform and desirability of land
restitution.
On the basis of theoretical arguments concerning the superior efficiency of family
farming (Schmitt, 1991), many predicted the disappearance of co-operatives and that
variations in productivity would lead to a wholesale transfer to individual farming.
Empirical evidence on changing farm structures in the region indicates that the cooperative sector's share of total agricultural area (TAA) has shrank, but the complete
collapse of co-operative farming, predicted by some, has not occurred. Moreover,
others have argued that the superiority of individual family farming has been
overstated and that the capacity of other structures of production to be efficient has
been underestimated (Gorton and Davidova, 2001).
This paper revisits this literature by looking at the nature of variations in productivity
and profitability between farms in the Czech Republic using data from 1999 and
1998. This allows the examination of variations in total factor productivity (TFP) and
farm profitability for different structures, sizes and regions. The managerial and
operational characteristics of clusters of farms identified for the Czech Republic are
investigated in greater depth to draw out a clearer categorisation of farms, rather than
just relying on size and structural type. These clusters form the basis of a discussion
of the overall survivability of different groups of farms and, thus, the likelihood of
future restructuring.
This paper is divided into seven sections. The next section discusses the literature on
farm performance in the Czech Republic in the context of wider debates on decollectivisation. Section 3 briefly discusses the results from previous studies on farm
efficiency in the Czech Republic. The data used for the empirical analysis is discussed
in Section 4. Section 5 details the TFP and profitability indexes produced for 1998
and 1999, compared with previous studies, considering variations by farm type, size
and region. Section 6 looks at the variations between farms in greater depth by
identifying clusters of farms, considering their managerial and operational
characteristics. Relevant conclusions about the overall survivability of different
groups of farms and the likelihood of future restructuring are presented in Section 7.
2. Debates on Farm Performance and De-collectivisation in the Czech Republic
At the outset of transition, Czech agriculture was widely perceived to be inefficient
(Csaki and Lerman, 1996). In 1989, collective farms accounted for more than 60 per
cent of Total Land Area (TAL) in the Czech Republic and state farms one quarter of
the TAL. This preponderance of large farm collective structures sparked in the early
1990s a debate on two issues. The first was whether there was a clear superiority of
one organisational type, namely family farms, over corporate structures (production
co-operatives and various types of farming companies) and if so, whether the
structures will align to those prevailing in the EU, namely family farms. The second
aspect concerned the relationship between size and productivity in agriculture.
The question about “optimal farm structure” and “optimal farm size” has a long
history in agricultural economics, in general, and in transition economies, in
particular. When land reform strategies were being formulated at the outset of
transition some argued that it was desirable to preserve large farm structures and
pursue attempts to administratively impede farm fragmentation on the basis that
smaller farms are less efficient. These authors tended to see restitution strategies,
where they would lead to farm structures returning to the pre-war pattern of smallscale peasant units, as highly undesirable (Kanchev, 2000). Large farms have been
seen to have advantages stemming from economies in using lumpy inputs, better
administrative organisation, better marketing, access to credit and research and
development (Hill and Brookes, 1993). In contrast, others argued that the large farms
in Eastern Europe suffered from diseconomies of scale, so that land reform strategies
must include proposals to reduce the mean size of farms (Koester and Striewe, 1999).
The superiority of family farms over other organisational types in agricultural
production has been justified on the basis of the need to minimise both production and
transaction costs. As the costs of supervision and monitoring of hired labour in
agriculture can be high, it has been claimed that family farms appear to be the best
suited organisational form as they minimise transaction costs (Schmitt, 1993).
Focusing on collectivised agriculture in the centrally planned economies, Schmitt
(1993) adds to this argument the principal-agent problem in producer co-operatives
where the members can vote out the manager and, therefore, there can be
disincentives for the manager to monitor workers. The formulated hypothesis
(Schmitt, 1990; Hagedorn, 1994) is that “if the freedom of self-organisation is
guaranteed, mainly family farms develop and survive, except for explicable
exceptions, because they have low transaction costs” (Hagedorn, 1994: 5).
Since 1989, the Czech Republic has witnessed a growth in individual farming but not
a rapid transformation to a predominance of family farms envisaged by some
(Beckmann, 1996). The most dramatic changes were observed in 1992 and 1993, after
farming entities were forced to adopt new legal forms. By 1999, individual farms,
involving part-time farming, managed just over 20 per cent of TAL; the rest is
managed in a corporate way. Corporate farming is organised in three different legal
and management forms. Producer co-operatives are mainly successors of the previous
collective farms, however, some of them are transformed state farms. Most of them
still have outstanding liabilities to owners of co-operative assets. Limited liability
companies have their origin in the privatisation of the state farms. At the beginning of
the process, state farms’ assets were leased to small groups of people, normally
involving the former farm managers (Ratinger and Rabinowicz, 1997). Gradually the
non-land assets were sold to the lessees at favourable conditions with rescheduled
payments. The joint stock companies have a large number of shareholders (a few
hundreds). A portion of them has their roots either as state farms or as inter cooperative enterprises. However, some of the companies were created post-reform. In
1990’s several producer co-operatives were transformed in joint stock companies, as
this allowed easier transactions with company’s shares. Thus, the Czech Republic has
a set of legal and management forms that is still significantly different from the West
European 'family farm' model.
3. Previous Studies on Farm Efficiency in the Czech Republic
Three previous studies on variations in farm efficiency in the Czech Republic have
found mixed support for initial propositions drawn from the literature on the
economies of size and structure debate (Table 1).
The expected pattern of sharply rising average productivity, reflecting scale
efficiencies followed by diseconomies of scale at higher farm sizes, is present with
economies of scale for arable farming up to 750 ha. Arable farms under 150 ha are
significantly less efficient in all three studies. These studies on data for the mid-1990s
point to small farms in the Czech Republic being relatively less efficient than in
several other CEECs. Hughes (1998) argues that Czechoslovakia had a much less
conducive environment for small-scale private farming than, for example Poland and
Hungary, and this accounts for why small-scale farming appeared relatively less
efficient in the early and mid-1990s in the Czech and Slovak Republics. The
availability of external services for crop production, such as harvesting services and
inputs for small farms, have been historically more developed in Hungary and Poland
and the availability of such services are an important means of overcoming some of
the sources of diseconomies of size.
For the Czech Republic, both Hughes (1998) and Mathijs and Swinnen (2000) found
that individual private farms were significantly more productive than corporate farms
for livestock farming but not crop production. Curtiss (2000) analysed crop
production in the Czech Republic. She found that co-operatives performed better for
wheat and rapeseed cultivation compared to individual farms but the latter were
superior with regard to sugar beet production. It, therefore, appears that arguments
that co-operatives or other forms of corporate farming are inherently less efficient, for
all types of farming, compared to family farms is misplaced. Even for produce or
types of farming where the average corporate farm is less productive than the average
family farm, one still sees some co-operatives and companies which are on the
frontier or registering high TFP scores (Hughes, 2000; Mathijs and Vranken, 2000). It
appears that at least some corporate farms can solve the governance problems alluded
to in the literature or that there are some types of farming for which such problems are
less severe.
These productivity studies present some interesting findings but it is argued that
further work is required on three counts. First, it is important to see if the trends
identified for the early and mid-1990s reflect short-term characteristics of
restructuring or are more long-lasting phenomena. For example, is the comparative
inefficiency of small farms in the Czech Republic still present or has the switch to a
more market based economy created a more conducive environment for small-farms
allowing them to overcome their initial size disadvantages? Second, from the
efficiency studies it is possible to identify farms which are relatively more efficient
(e.g. on the production frontier or with a higher TFP index score) in a particular
sample. However, this says nothing about profitability and return on assets in
agriculture which will guide further restructuring in the sector. Finally, previous
studies have focused principally on the farm size, structure and efficiency debate. The
performance of farms will be shaped by many other factors than just size and
ownership type, such as agri-environmental region, inherited debts and managerial
characteristics. There is a need to consider a fuller range of factors in guiding our
assessment of farm performance in the CEECs, identifying groups of farms with
common characteristics. These points guide the profitability, productivity and cluster
analysis presented in this paper.
4. Data employed in the productivity, profitability and cluster analysis
The paper utilises data from the Czech Republic's Farm Accountancy Data Network
(FADN) which is administered by the Institute of Agricultural Economics (VÚZE).
FADN, which is implemented in all existing EU states, aims to provide a detailed
breakdown of revenues and costs incurred in agricultural enterprises based on a
sample of bookkeeping farms. The Czech sample is surveyed annually in March and
includes about 1,000 agricultural enterprises of physical and legal persons (Table 2).
As in this paper the results for 1999 are mainly presented, data used for 1999 are
discussed below.
The initial sample included 1,087 farms. After checking the individual data, 264
farms were excluded due to data inconsistency. Thus, the sample analysed included
823 farms. Fig 1 details the characteristics of the sample according to management
form and average utilised area per management form. Considering management form,
the largest group in the sample are the individual farms, 513 (62 per cent). Producer
co-operatives are the second largest group, 154 (19 per cent). The rest of the sample is
made up of 95 joint stock companies (12 per cent) and 61 limited liability companies
(7 per cent). Due to their different history, different management forms have different
average sizes. The most important difference is between the individual farms and the
other management forms, with the former being much smaller than the successors of
the previous state and collective farms.
Table 2 compares the characteristics of the individual and corporate farms included in
the FADN sample (the 823 useable records for 1999) against returns from the Czech
Republic's agricultural census. In the Czech Republic there are two main legal types
of individual undertaking in agriculture: (a) trade law farmers, subjected to business
regulations (Trade Law) like other full liability businesses and (b) solely operating
farmers, with less strict regulations. Comparing the FADN sample with returns from
the agricultural census, the former is biased to larger individual farms (in both the
trade law and solely operating categories). For example, the average size of individual
farms in the FADN sample is 134 ha compared against 18 ha in the agricultural
census. In part this difference is derived from the fact that FADN is based on bookkeeping records and so effectively includes only farms with commercial activities
(although some farms also produce for own consumption). The FADN sample
therefore excludes subsistence producers and this sector is not discussed in the scope
of this paper. Comparing the average size of co-operatives and joint stock companies
in the FADN and census samples, there are not significant differences although the
mean size of limited liability companies in the FADN sample is larger than that
recorded in the census.
The data for each farm in the sample contained total revenue and five cost items. The
five cost items were: total labour costs for hired labour including wages and social
insurance contributions, intermediate consumption (working capital), land rent,
interest, and depreciation. In addition, land and labour were also given in physical
units, annual work units (AWU) and hectares respectively. Labour was sub-divided
into hired and family. Labour costs referred to hired labour only. Land area was
given as a total and rented.
For the purposes of this analysis family labour was valued at regional farm unit labour
costs (farms in FADN are classified in 76 administrative regions). The variation in
the regional labour costs is between 62,000 CZK and 200,000 CZK per year (£ 1,2503,900). As far as land is concerned, the regional rent was applied to the own land. In
this case, agri-environmental regions were used as they better reflect the differences
in land quality. The variation in rent is from 210 CZK/ha to 1,100 CZK/ha (from £ 4
to 22).
According to the regional conditions for farming, the Czech Republic is divided into
five agri-environmental zones. They are notionally called maize region, sugar beet
region, cereal-potato region, potato region, and mountainous-forage region (Hughes
2000). The best for agriculture is the first zone (maize region) and they are listed in a
descending order.
5. Productivity and Profitability Indices
Productivity
Productivity differences are analysed by the construction of a Tornquist-Theil TFP
index for all farms relative to a base case ‘average farm’ and a comparison of the
mean indexes across sub-groups of farms. The Tornquist-Theil TFP measurement
used is based on index number theory and is recognised, as a measure of technical
efficiency, to be an acceptable alternative to econometric estimation in cases where
the data does not permit an underlying production function to be estimated (Capalbo
and Antle, 1988; Hughes, 1998). The Tornquist TFP index applied here is a relative
measure of productivity that comprises the difference between an aggregated output
index and an aggregated input index. Supposing there are two firms i and b which
produce n outputs Qj (j=1,…n) using m inputs Xk (k=1,…m), then the index t can be
defined as in equation 1:
t1 
1 n

2 j 1
R  R  ln Q  ln Q   12  S
i
b
j
j
i
j
b
j
m
k 1
i
k

 S k ln
b
X
i
k
 ln
X
b
k

[1]
Where for firm i, Rij represents the share of the value of the j’th output in the total
value of all n outputs, and Sik represents the share of the costs of the k’th input in the
total input costs of all m inputs.
The mean TFP measures for the different sub-groups in the sample are presented in
Table 3. The indexes are constructed such that the sample mean would be unity in the
case of common products throughout, with results interpreted relative to these sample
means, showing cohorts as having above or below average TFP. The indexes reveal
that producer co-operatives have the highest mean TFP, followed by joint-stock
companies and then individual farms and limited liability companies. This pattern for
1999 is similar to the order identified by Hughes (1998) for 1996 data. However,
these variations between farm structures are outweighed by differences between agrienvironmental regions. The lowest TFPs for all farm types are found in the
mountainous forage area and the most productive farms are located in the maize
region for all farm types (except production co-operatives).
An analysis of covariance (ANCOVA) for the 1999 TFP results indicates that size
(measured in terms of total assets), agri-environmental region and degree of
specialisation are significant determinants of farm productivity (Table 4). When these
variables are controlled for, the farm type (legal form) is not significant. Individual
farms have the highest standard deviation in TFP scores. Small farms specialised in
crop production (with assets up to 3 million CZK) had above average TFP scores in
1999, while small, mixed farms (weighted to livestock) were characterised by
relatively low productivity. It appears that individual farms are better in crop
production than in livestock, which is in contrast to some previous findings (Mathijs
and Swinnen, 2000).
The TFP analysis was repeated for 1998 FADN returns. It was possible to directly
compare the scores of 486 farms for 1998 and 1999, which were included in both
FADN samples. This analysis was undertaken to see if amongst these farms the
efficient and inefficient ones were more or less the same in both years. The results
indicate a high similarity in the ranking of farms (Table 5). The Spearman Correlation
coefficient indicates a significant correlation at the one percent level. From this it is
possible to conclude that there was a high degree of stability in the results between the
two years, i.e. farms that were highly productive in relative terms in one year were
highly productive in the other year and vice versa. However, while the productivity
analysis reveals differences in the relative efficiency of farms it does not say anything
about absolute profitability.
Profitability
Farm profitability is mainly analysed with reference to a private cost benefit ratio
(P_CB). For the i'th farm, the P_CB is taken to be:
P _ CB
i
  
 C C
t
f
i
i
R
[2]
i
Where Cti is the cost of tradable inputs, Cfi is the cost of non-tradable factors of
production (based on private prices or estimates for non-paid land and labour input)
and Ri is total revenue. Two other ratios are also calculated. The first, cost-revenue
plus subsidies (C_Rs), is exactly matching the entries in FADN and, therefore, CfI
does not include estimates for non-paid labour and land and Ri includes the budgetary
transfers. The second one, cost-revenue without subsidies (C_R), does not include
estimates for non-paid labour and land, but also excludes the budgetary transfers. The
rationale for calculating three different ratios is to give an insight into the effect of the
direct budgetary transfers and the valuation of all factors at opportunity costs on
different farm types and farms located in different agri-environmental regions.
The most striking feature of the profitability results is the low level of farm returns
(Table 6). The majority of farms are unprofitable under the three ratios. Out of 823
farms, 662 were loss making (80.4 per cent) applying the P_CB measure. In general,
less than a quarter of UAA and labour input are within profitable farms, and they
produced between 21 and 28 per cent of the total agricultural output in 1999 (the
percentage varies with the measure). Looking at returns by legal form, only 22, 10, 17
and 18 per cent of individual farms, limited companies, joint-stock companies and
production co-operatives were profitable respectively, when P_CB ratio is applied
(Table 7).
At first glance, individual farms are performing well. When C_Rs is used, 65 per cent
of them seem profitable, compared to 16 per cent of the limited liability companies,
22 per cent of joint stock companies and 24 per cent of co-operatives. This could
misleadingly lead to an easy conclusion about the superiority of individual farms.
However, once the budgetary transfers are subtracted, and particularly when the nonpaid labour and land are valued, it can be seen that the percentage of profitable
individual farms is not substantially different to those of joint stock companies or cooperatives. Considering agri-environmental region, the worst results were recorded
not surprisingly in the mountainous forage region, where on the basis of the P_CB
measure no farms were profitable (Table 8). However, even in the best agrienvironmental regions (maize and sugar beet) the majority of farms were loss making.
In the cereal and potato regions, 22 and 13 per cent of farms were profitable
respectively according to P_CB ratio.
When the results by region and legal type are considered together, individual farmers
register the best results in the maize region but they have the worst returns in the
cereal-potato, potato and mountainous forage regions (Table 9). The poor
performance of individual farms is principally due to the high labour inputs per unit of
land, which is valued at equivalent market rates (opportunity costs) in the P_CB
calculations. If only paid factors were to be considered in the profitability
calculations, as individual farms rely much more heavily on own labour and land,
they would then record the highest incomes per unit of land. These themes are
explored in more detail through the application of cluster analysis.
6. Cluster Analysis
Cluster analysis was conducted on the 1998 and 1999 FADN sample of Czech farms.
As the results did not show large differences between the two years, here only 1999
results are presented and discussed.
Cluster analysis is adequate for defining groups of objects, or individual farmers or
corporate farms in our case, with the maximum homogeneity within the groups while
having maximum heterogeneity between the groups (Hair et al., 1998). In identifying
the variables for the cluster formation, the analysis was hampered by multicollinearity
among the variables, and variables that are multicollinear are implicitly weighted
more heavily. Ketchen and Shook (1996) suggest two solutions to address this
problem. One is to use the Mahalanobis distance measure, which adjusts for high
correlation, the other is to apply factor analysis (with orthogonal rotation) and use the
resultant uncorrelated factor scores for each observation as the basis for clustering.
The latter procedure was followed.
Table 10 records the variables included in the analysis. From the FADN sample it is
possible to identify the structural characteristics of farms and also their financial
performance. Important variables considered included measures of size: total labour
(TOTALAWU), total output including the net current subsidies (OUTTOT3), total
assets (TOTASSET), and the utilised agricultural area (SAUTOT). A variable to
account for specialisation of the farm in arable farming (PROCRO) was included
alongside two measures of the degree of intensification. The first one is the amount of
land per annual work unit (LANAWU): with larger scores indicating lower levels of
intensification. The second one is the quantity of depreciation per annual work unit
(DEPAWU), in which case higher values are used as proxies that there is more capital
per worker employed. Two variables to account for the degree of dependence on
direct subsidies were also considered: total net current subsidies (SUBNETCR) and
the percentage of revenue derived from direct subsidies (SUBOUTP). Two variables
to account for the use of paid primary factors, the percentage of rented land
(PORRESAU) and the percentage of paid labour (PORPALAB), were included
alongside standard financial ratios (DEBTOAS, LEVERAGE, RENGO and
RENGM). To these continuous variables, two sets of dummy variables were added.
First, legal and management structure was split into four categories (individual farms,
limited liability companies, joint-stock companies and production co-operatives).
Individual farms were taken to be the reference group with dummy variables
DLFORM2, DLFORM3 and DLFORM4 for limited companies, joint stock
companies and production co-operatives respectively. A similar approach was taken
for agri-environmental region. In this case five categories (maize, sugar beet, cereal,
potato and mountain areas) are delineated with four dummy variables (DREG2,
DREG3, DREG4 and DREG5) with region 1 (maize) used as a reference.
The Kaiser-Meyer-Olkim measure of sampling adequacy is 0.67 (values below 0.50
are unacceptable) indicating that the data matrix has sufficient correlation to justify
the application of factor analysis. Bartlett’s test of sphericity accounts for the
significance of the correlation matrix. In this case it is large and statistically
significant at the 1 per cent level, so that the hypothesis that the correlation matrix is
the identity matrix can be rejected. Thus, the factorial analysis is meaningful (Table
11). The method of principal component analysis with varimax rotation is adopted.
This method assures that the obtained factors are orthogonal and so avoiding the
problem of multicollinearity between the variables used in the cluster analysis.
An eight-factor solution is adopted, choosing the factors that present an eigenvalue
greater than one (Table 12). This solution explains nearly 74 per cent of the total
variance in the data set, which is satisfactory2. The cut-off for interpretation purposes
is factor loadings greater or equal to 0.5 on at least one factor. With this criteria it is
possible to see that the first factor is related with the size of the farm measured by the
quantity of land (total land, the percentage of paid labour and the percentage of rented
land). The second factor is specialisation in crop production, which is related to the
quantity of land per annual work unit. Factor 3 can be labelled subsides (the
percentage of revenue derived from subsidies) and this factor is correlated with the
dummy for region five, where the farms receive more subsidies. Factor 4 is related to
type of region (positively with region 2 and negatively with region 3, indicating that
the characteristics of these regions must result in different patterns of farm behaviour.
Factor 5 is related to the level of debt held by the farm and with the legal form of
limited companies. Factor 6 is related to the level of financial stress (amount of rent
and interest paid with respect to gross output or the gross margin, which is also related
2
Hair et al. (1998) point out that in the social sciences it is not uncommon to consider a solution that
accounts for 60 percent of the total variance as satisfactory.
with depreciation per unit of labour). Factor 7 is positively related with one legal form
(production co-operatives) and negatively with another (joint-stock companies),
indicating that farms have a different behaviour depending on the legal form. Finally,
Factor 8 includes only the regional dummy for mountainous areas.
The factors formed the basis of the cluster analysis, following a two stage hierarchical
approach. First, a hierarchical technique was used to establish the number of clusters
and profile the cluster centres. Then, the observations were clustered by a nonhierarchical method with the cluster centres from the hierarchical results as the initial
seed points. This combined procedure allows one to take maximum benefit of the
advantages associated with hierarchical and non-hierarchical methods, while at the
same time minimising the drawbacks (Punj and Stewart, 1983; Flavian and Polo,
2000). The algorithm used in the hierarchical technique is the Ward’s method based
on squared Euclidean distances, one of the most frequently used in the literature. To
decide how many clusters exist, in many applications the method used is the analysis
of the dendogram, but with large samples this is difficult to interpret. Instead, the
criteria suggested by Fiegenbaum and Thomas (1990, 1993) is applied. It focuses on
the simultaneous analysis of the overall fit obtained within each grouping and the
improvement that is obtained in this fit with the inclusion of an additional group.
Thus, the number of groups that exist will be determined when the two conditions are
satisfied simultaneously.3 With this criteria a seven-cluster solution is obtained (Table
13). The average values of the eight factor scores for every cluster are used as seed
points for the non-hierarchical technique. The main characteristics of the clusters can
be identified as:
Cluster 1: Individual farms located in the region of sugar beet production (for
classification by legal form and region see Tables 14 and 15 respectively). These are
small farms specialised in crop production. They do not have a bad financial situation
and have large values for the quantity of land and capital per unit of labour. The
percentage of rented land and paid labour is relatively small. They receive relatively
low levels of subsidies, in absolute terms and as a percentage of total revenue. This
group is one of the best in terms of the performance results achieved. This cluster
contains farms with relatively high productivity index scores and smaller cost revenue
ratios.
Cluster 2: Production co-operatives located in the regions of sugar beet and cereal
production. These are large farms with the majority of the land rented and a
dependence on paid labour. They are not specialised. Their financial situation is one
of the worst in the sample. They receive on average a high level of subsidies but in
relative terms this is low (5 per cent of gross output). This is a group with good results
in terms of productivity and in terms of the cost-revenue ratio but suffer from
inherited debts.
Cluster 3: Individual farms, located in the cereal production region. These are the
smallest farms and present some degree of specialisation in crop production. They
3
The criteria are: (a) the percentage of intra-group variance explained with the obtained grouping being
higher than a minimum percentage which we place at 50% and (b) that the percentage increase in the
explanation of the intra-group variance, obtained with the inclusion of an additional group, does not
exceed 5%.
have a good financial situation and they use the lowest percentage of rented land and
paid labour in the sample. The difference between this group and cluster one, in
addition to the region and the level of specialisation, is that in these farms have a high
labour to land ratio and use much less capital. The results that they obtain are one of
the worst, low productivity of labour and low total factor productivity, with high costrevenue ratios.
Cluster 4: This cluster is comprised of limited liability companies situated mostly in
the sugar beet, cereal and potato production regions. These are large farms utilising
rented land and paid labour. They are not specialised in any one type of production
and they are characterised by a poor financial situation. However, they have limited
financial stress (as they pay little in rent and interest in relation to their total output).
They receive comparatively low levels of direct subsidies. The productivity and
profitability results of these farms are roughly equal to the average for the total
sample.
Cluster 5: In this group are all the farms from region 5 and only farms situated in this
(mountain) region. In general they are individual farms (65 per cent per cent of all
farms in the cluster) which are more extensive and broadly specialised in livestock
production. They have large values of land per unit of labour but also have high
depreciation per unit of labour. Their financial situation is not good and the levels of
financial stress are one of the highest. These farms are the ones that receive more
subsidies in relative terms: almost 25 per cent of their revenue is derived from
subsidies. The level of rented land is above the sample average and the percentage of
paid labour is lower than the average. The farms in this group are the worst in terms
of productivity and profitability.
Cluster 6: Is comprised of individual farms (55 per cent) and production cooperatives (34 per cent) which are all situated in region 4 (potato agri-environmental
region). They are medium size farms with an average financial situation for the
sample. These farms have less capital per unit of labour but they receive a relatively
high level of subsidies as a percentage of revenue. In terms of productivity and
profitability they present poor results, but as in the previous case, when revenue
including subsidies is accounted for, their relative situation improves because they
receive relatively more subsidies than the majority of the farms in the sample.
Cluster 7: It consists of joint-stock companies located in the sugar beet and cereal
regions. The biggest farms are in this group, with more livestock than crop
production. They have the majority of the land rented and all the labour is paid labour.
The financial situation is in the average (but with a high financial stress). These farms
present the lowest ratios of land and capital per unit of labour. These are farms with a
low percentage of output coming from subsidies, although they receive the biggest
quantity of subsidies per farm in absolute terms. Their results are not bad compared
with the sample average but without direct subsidies, their situation would be
significantly worse.
7. Conclusions
The analysis of farm level productivity and profitability in the Czech Republic
provides results supporting the view that by the end of 1990s, there is no strong
evidence that family farms perform better than the corporate type of farming. Nor is
there strong support for the view that the decrease in farm size, resulting from land
restitution, created a more inefficient structure of agricultural production where the
result was the creation of individual, well capitalised farms above 150 hectares. The
best performing farms are the individual farms employing mainly own land and
labour, with an average land area that is far smaller than the mean for the FADN
sample (164 and 654 ha respectively). At the same time, the largest farms, the joint
stock companies in cluster 7, with an average land area of 2008 ha, present a worse
performance. Their TFP is lower than the average for the sample and their total costs
are larger than revenues. It is true that the smallest individual farms in the sample
(average UAA 95 ha) have one of the worst performance, but they also differ by
employing far less capital per unit of labour. Farm location, the degree of
specialisation and capital employed are stronger determinants of farm performance
than the size of the farm. The previous literature on decollectivisation has focused
mainly on land shares (size of utilised agricultural areas of successor farms etc.).
However, evidence from the Czech Republic points to the importance of capital
employed and thus the distribution of capital (rather than just land) as an important
issue in land reform programmes. The differences in performance of the above
clusters 1 and 3, incorporating individual farms only, broadly support the results from
previous studies, stating that in the Czech Republic arable farms under 150 ha are
significantly less efficient.
Some of the production co-operatives (cluster 2), registered a very good performance
in respect to total factor productivity and profitability. They, in fact, have the highest
TFP relative to the average. This once again points out the need for more careful
approach to farm structures in the region. Due to the initial conditions, which were
completely different from Western Europe and the experience of corporate farming,
some of the farms (mainly co-operatives) managed to restructure and to overcome any
governance problems. Still there is no clear evidence about the superiority of
individual (family) farming. At the same time, producer co-operatives typically
register a bad financial situation, but this, to a great extent relates to the initial
conditions and the political design of the reform process. Producer co-operatives in
the Czech Republic have mainly non-bank long-term liabilities that can be referred to
as ‘reform debts’. These are liabilities to owners of co-operative assets who received
shares during the land reform and farm transformation, but decided not to farm.
According to the Law, they could not withdraw these assets from the co-operatives for
seven years unless they wanted the assets in order to farm individually. For cooperatives these liabilities account for 58 per cent of the total long- and short-term
liabilities.
Limited liability companies (cluster 4) do not have an outstanding performance but
their results are not worse than the average for the sample. However, they have a very
bad financial situation, which is again related to the ‘reform debts’. For the limited
liability companies these are outstanding liabilities to the State for acquiring assets
from the former state farms. For them the percentage of non-bank liabilities is even
higher than for co-operatives (62 per cent). Even ten years after the start of transition,
the overall performance of farms (especially corporate farms), is thus not only shaped
by current markets but also by inherited debts.
One of the main determinants of farm performance is the agri-environmental region to
which a farm belongs. Cluster 5 incorporates only farms located in the worst region
for agriculture. Government policy, through budgetary transfers, tries to offset this
disadvantage. Farms continue to be unproductive, but when the subsidies are taken
into account, they become profitable as a group. These farms are thus heavily
dependent on the existence of direct subsidies for less favoured areas. There are not
strong economic grounds to predict the survival of these farms under a liberal CAP.
Most probable, they may survive if there are environmental or broader rural
objectives to justify government support to keep these farms active. By and large, the
analysis shows that subsidies in the Czech Republic shelter unprofitable and
unproductive farms. The best performing farms (cluster 1) receive little subsidies
expressed as a percentage of output (3 per cent), compared to 25 per cent for the
farms in the mountainous-forage area.
Speculating about the overall survival of the different farm structures, it is likely that
corporate farming will continue to exist in parallel with family farms. The farm
restructuring process is likely to continue for all farm types and not only for corporate
farms. There are large numbers of individual farms that are loss making, with low
factor productivity scores. The main problem for almost all farms in the Czech
Republic appears to be that their revenues cannot cover costs incurred.
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Eastern European Agriculture in an Expanding European Union. Walingford:
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out of the dilemma of Ukrainian agriculture, 257-270 in Siedenberg, A. and
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perspective, New York: Physica – Verl.
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and Farm Activities in Transition Countries, Kiel: Wissenschaftsverlag Vauk,
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Ratinger, T. and Rabinowicz, E. (1997). Changes in Farming Structures in the Czech
Republic as a Result of Land Reform and Privatisation, 80-99 in Swinnen, JFM.,
Buckwell, A. and Mathijs, E. (eds.) Agricultural Privatization, Land Reform and
Farm Restructuring in Central Europe, Aldershot: Ashgate.
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organised by family farms? Will this continue to be so in the future? European
Review of Agricultural Economics, 18: 443-458.
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failed: A transaction cost approach, 143-159 in Csaki, C. and Kislev, Y (eds)
Agricultural Cooperatives in Transition, Boulder: Westview Press.
TABLES
Table 1: Previous studies on farm efficiency in the Czech Republic
Hughes (1998)
Data set(s)
VUZE panel. 1996
Methodology
Tornquvist - Theil TFP
Index
Economies of scale up
to 750 ha for arable
farming and up to 1
mil. CZK in 1996 for
livestock farms.
Individual private
farms significantly
more productive for
livestock, but not crop,
farming. Co-ops.
Perform better than
farming companies.
Size effects
Structural effects
Other factors
Mathijs and Swinnen
(2000)
Agrocensus and VUZE
panel data for 1996
Data envelopment
analysis (DEA).
Economies of scale up
to 750 ha. for crops.
For livestock family
farms more efficient
than co-operatives and
companies. No
differences between cooperatives and
companies. Co-ops can
be found on technology
frontier of all specialisations.
Curtiss (2000)
VUZE FADN 19961998
Stochastic Frontier
Analysis (SFA).
Farms above 150 ha
perform on average
better than smaller
farms for wheat and
rapeseed production.
Co-operatives perform
better than individual
farms and companies
except for sugar beet
where individual
private farms perform
best.
Regional variations
(mountainous areas
poor performance).
Table 2 - Structure and Representativeness of the FADN farm sample for the Czech Republic
Agrocensus (2000)
FADN Sample (1999)
Number Agricultural Average area Number
Agricultural Average area
Land
Land
53 460
962 325
18
513
68741
134
Individual Farms
Total
Not identified legal
18 098
26 908
1
form
Trade Law Farmers
3 384
68 772
20
Solely Operating
31 721
863 870
27
Farmer
Other
257
2 775
11
Corporate Farms
3 027
2 680 843
886
Total
Ltd
1 479
795 359
538
Joint Stock
621
779 732
1 256
Co-ops
746
1 059 453
1 420
State organisations
105
38 997
371
Other
75
7 007
93
Czech Republic Total
56 487
3 643 168
64
Source: Czech statistical office, Agrocensus, 2000, Own calculations
11
502
4248
64493
386
128
310
473120
1526
61
95
154
65499
166165
241456
1074
1749
1568
823
541 861
658
Table 3: Class means of TFP index for 1999 Czech FADN Sample
Class means
Individual farmers
Ltd Companies
Joint Stock Comp.
Production Coops
Regional averages
Maize
Sugar beet Cereal-Potato Potato reg.
Mountainous Legal
type
region
region
reg.
forage reg.
averages
1.120
1.020
0.927
0.864
0.714
0.9526
1.122
1.000
0.965
0.867
0.896
0.9511
1.049
0.967
1.060
0.965
0.799
0.9911
0.891
1.111
0.999
0.946
0.825
1.0118
1.087
1.025
0.955
0.904
0.764
Table 4: Analysis of covariance for 1999 Czech FADN sampledependent variable: TFP index
Source Type III
df
Mean Square
F
Sig.
Sum of
Squares
Corrected Model
4.626
6
.771
14.833
.000
Intercept 29.966
1
29.966
576.443
.000
Size (Total assets)
.715
1
.715
13.748
.000
Specialisation index
.687
1
.687
13.208
.000
Agri-Env. Regions
2.392
4
.598
11.502
.000
Error 42.419
816
5.198E-02
Total 818.189
823
Corrected Total 47.045
822
R Squared = .098 (Adjusted R Squared = .092)
Table 5: Rank correlation of relative efficiencies in 1999 and 1998
TFP Index
1999x98
Spearman correlation
Significance
***
*** significant at =0.01
0.55
Table 6. Profitability ratios, 1999
Max
Min
Average
StandDev
No of profitable farms
No of loss making farms
% of sample UAA in profitable farms
% of sample output in profitable farms
% of sample labour input in profitable
farms
C_R s
(+subsidies)
3.614
0.423
1.006
0.250
399
424
26
28
22
C_R (without
subsidies)
3.593
0.423
1.089
0.307
309
514
19
23
17
PC_B
4.938
0.442
1.227
0.376
161
662
17
21
16
Table 7: Number of profitable and loss making farms according to farm type (1999)
Profitable
Loss making
Profitable
Loss making
Profitable
Loss making
Individual Ltd Companies Joint Stock Comp.
farmers
C_Rs
332
10
181
51
C_R
256
6
257
55
PC_B
111
6
402
55
Production Coops
21
74
36
118
17
78
30
124
16
79
28
126
Table 8: Number of profitable and loss making farms by agri-environmental region (1999)
Maize region Sugar
beet
region
Cereal-Potato Potato reg.
reg.
Mountainous forage
reg.
C_Rs
Profitable
Loss making
13
7
160
163
Profitable
Loss making
13
7
137
186
Profitable
Loss making
7
13
70
253
153
151
54
82
19
21
124
180
32
104
3
37
66
238
18
118
0
40
C_R
PC_B
Table 9: Class means for P_CB ratios by region and legal type.
Class means
Maize
region
Sugar
region
beet Cereal-Potato Potato reg.
Mountainous Legal
type
reg.
forage reg.
averages
Individual farmers
1.046
1.166
1.293
1.366
1.694
1.2623
Ltd Companies
1.064
1.129
1.217
1.274
1.270
1.2035
Joint Stock Comp.
1.178
1.214
1.104
1.244
1.429
1.1944
Production Coops
1.256
1.068
1.143
1.178
1.341
1.1372
Regional averages
1.089
1.156
1.242
1.282
1.563
Regions and legal forms significant at =0.01. (ANOVA)
Table 10: Variables included in the factor / cluster analysis
VARIABLE
Definition
SAUTOT
OUTTOT3
TOTALAWU
TOTASSET
SUBNETCR
SUBOUTP
PORPALAB
PORRESAU
PROCRO
HERFINDA
LANDAWU
DEPAWU
DEBTOAS
LEVERAGE
RENGO
RENGM
Total land (UTIL_UAA)
Output including the net current subsidies (GrossOut)
Total labour (AWU)
Total asset (TotAS)
Net current subsidies (BAL_CURR)
Percentage of the gross output coming from net current subsidies
Percentage of paid labour
Percentage of rented land
Percentage of crop production (OutSTR)
Herfindal Index
Land per unit of labour
Capital (depreciation) per unit of labour
Debt to asset ratio
Leverage
Rental (rents and interests paid)/gross output
Rental (rents and interests paid)/gross margin
Gross margin: gross output – intermediate consumptions
Labour costs/paid awu
Rents/rented land
WAGE
LANDRENT
Table 11: Diagnostics for the Factor Analysis
MSA Total
Bartlett's Test
With dummy
0.679
12828***
Table 12: Factor Analysis for 1999 Czech FADN Sample
SAUTOT
OUTTOT3
TOTALAWU
TOTASSET
SUBNETCR
PORPALAB
PORRESAU
PROCRO
HERFINDA
LANDAWU
DREG5
SUBOUTP
DREG3
DREG2
DLFORM2
DEBTOAS
LEVERAGE
DEPAWU
RENGO
RENGM
DLFORM3
DLFORM4
DREG4
Eigenvalue
% variance
(73.89)
1
0.950
0.940
0.920
0.919
0.759
0.738
0.519
-0.188
-0.294
-0.167
-0.031
-0.036
-0.053
0.033
0.050
0.366
-0.065
-0.065
0.062
0.038
0.525
0.490
0.042
6.240
27.13
2
-0.099
-0.128
-0.160
-0.148
-0.081
-0.153
0.087
0.805
0.718
0.604
-0.148
-0.031
-0.054
0.192
-0.001
-0.069
-0.131
0.182
0.151
-0.315
-0.125
-0.179
-0.123
2.357
10.25
3
-0.015
-0.093
-0.086
-0.091
0.290
-0.051
0.116
-0.179
-0.118
0.137
0.833
0.803
-0.154
-0.216
0.127
0.087
-0.206
-0.047
0.155
-0.073
-0.061
-0.103
0.013
1.957
8.51
4
-0.016
0.057
0.050
0.052
-0.081
0.068
-0.026
0.132
0.201
-0.129
0.104
-0.141
-0.909
0.818
0.023
-0.007
-0.012
0.005
0.078
0.138
0.026
-0.047
0.041
1.545
6.72
5
0.049
-0.019
-0.031
-0.056
0.011
0.380
0.281
-0.112
-0.059
-0.048
0.016
-0.025
-0.037
-0.020
0.861
0.598
0.514
0.069
0.004
-0.049
-0.020
-0.134
0.042
1.432
6.22
6
-0.001
-0.033
-0.067
-0.012
0.031
-0.042
0.236
0.082
0.030
0.505
-0.012
0.102
-0.081
0.139
-0.120
0.111
0.115
0.670
0.639
0.580
-0.013
-0.033
-0.084
1.199
5.21
7
0.034
-0.004
0.011
-0.018
-0.035
0.206
0.229
0.022
-0.062
-0.046
0.020
-0.026
0.018
-0.037
0.008
0.472
-0.163
0.032
0.014
-0.038
-0.679
0.758
0.018
1.140
4.80
8
0.014
-0.032
-0.004
-0.044
0.063
0.047
0.173
-0.056
-0.182
0.095
-0.190
0.249
-0.330
-0.323
-0.031
0.138
0.027
-0.020
-0.085
0.012
0.088
0.072
0.961
1.046
4.55
Table 13: Cluster analysis for the 1999 Czech FADN Sample
Cluster
N FARMS
SAUTOT
TOTALAWU
OUTTOT3
TOTASSET
PROCRO
HERFINDA
DEBTOAS
LEVERAGE
RENGO
RENGM
LANDAWU
DEPAWU
PORRESAU
PORPALAB
SUBOUTP
SUBNETCR
1
2
229
111
163.97 1,510.52
4.74
78.46
4,271 48,402
7,892 76,709
0.762
0.502
0.773
0.518
0.192
0.632
0.500 -0.118
0.05
0.04
0.189
0.222
51.36
23.27
129.08 72.51
67.79
97.23
28.20
99.88
0.032
0.051
153
2023
3
194
94.91
2.57
1,909
3,897
0.627
0.647
0.121
0.326
0.03
0.027
41.97
76.43
60.05
13.11
0.056
118
4
53
1,089.44
42.95
27,911
37,893
0.515
0.551
0.928
17.683
0.03
0.052
27.44
77.78
93.68
100
0.054
1284
5
40
552.92
22.19
11,714
23,641
0.357
0.572
0.386
-3.242
0.05
0.241
42.64
84.82
82.90
46.59
0.248
1853
6
108
674.48
34.27
18,400
30,699
0.504
0.537
0.387
0.492
0.03
0.183
34.87
70.09
79.33
50.74
0.112
1099
7
77
2007.59
112.72
68,801
113,332
0.431
0.505
0.307
0.558
0.04
0.256
19.62
64.82
93.00
100
0.042
2735
Total
812
653.83
31.82
19,648
32,111
0.592
0.627
0.330
1.315
0.04
0.154
38.08
89.30
76.32
49.79
0.064
928
157.319***
136.24***
150.414***
147.571***
44.339***
55.14***
103.389***
10.789***
4.813***
3.101***
13.276***
7.641***
33.463***
178.272***
49.027***
55.015***
Cluster
N
INCFWU
VABAWU
WAGE
LANDRENT
RETASSAV
TFP11
TFP31
P_CB
C_R
C_Rs
1
229
149
307
61.24
0.826
-0.020
1.026
1.019
1.157
0.992
0.958
3
194
40
167
40.74
0.666
-0.046
0.931
0.937
1.287
1.043
0.972
4
53
0
185
126.05
0.277
-0.039
0.968
0.970
1.182
1.172
1.101
5
40
-294
206
74.18
0.522
-0.022
0.764
0.769
1.563
1.363
0.966
6
108
153
178
70.27
0.402
-0.032
0.899
0.904
1.302
1.147
1.004
7
77
0
183
132.41
0.404
-0.034
1.012
1.012
1.153
1.145
1.096
Total
812
79
219
77.94
0.574
-0.028
0.970
0.971
1.224
1.086
1.003
f
3.208***
6.34***
44.64***
11.36***
1.462
12.28***
11.498***
10.973***
12.621***
5.836***
2
111
-1,418
216
125.45
0.339
-0.002
1.039
1.039
1.119
1.110
1.048
F
Table 14: Classification of Legal Forms by Cluster Groups
1
2
3
4
T
MILFORM
Count
% row
% column
% of Total
Count
% row
% column
% of Total
Count
% row
% column
% of Total
Count
% row
% column
% of Total
Count
% row
% column
% of Total
1
225
44.50
98.30
27.70
0
0
0
0
3
3.30
1.30
0.40
1
0.60
0.40
0.10
229
28.20
100
28.20
2
2
0.40
1.80
0.20
1
1.70
0.90
0.10
0
0
0
0
108
70.10
97.30
13.30
111
13.70
100
13.70
3
194
38.30
100
23.90
0
0
0
0
0
0
0
0
0
0
0
0
194
23.90
100
23.90
4
0
0
0
0
52
86.70
98.10
6.40
0
0
0
0
1
0.60
1.90
0.10
53
6.50
100
6.50
5
26
5.10
65.00
3.20
7
11.70
17.50
0.90
2
2.20
5.00
0.20
5
3.20
12.50
0.60
40
4.90
100
4.90
6
59
11.70
54.60
7.30
0
0
0
0
12
13.00
11.10
1.50
37
24.00
34.30
4.60
108
13.30
100
13.30
df
18
18
1
Asymp. Sig. (2-sided)
0
0
0
Chi-Square Tests
Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear Association
Value
1922.490
1316.414
77.112
7
0
0
0
0
0
0
0
0
75
81.50
97.40
9.20
2
1.30
2.60
0.20
77
9.50
100
9.50
Total
506
100
62.30
62.30
60
100
7.40
7.40
92
100
11.30
11.30
154
100
19.00
19.00
812
100
100
100
Table 15: Classification of Farms by Region and Cluster
PREG
1 Count
% row
% column
% of Total
2 Count
% row
% column
% of Total
3 Count
% row
% column
% of Total
4 Count
% row
% column
% of Total
5 Count
% row
% column
% of Total
T Count
% row
% column
% of Total
1
2
3
4
5
6
7
TOTAL
11
55.00
4.80
1.40
218
68.80
95.20
26.80
0
0
0
0
0
0
0
0
0
0
0
0
229
28.20
100
28.20
2
10.00
1.80
0.20
43
13.60
38.70
5.30
65
21.70
58.60
8.00
1
0.70
0.90
0.10
0
0
0
0
111
13.70
100
13.70
1
5.00
0.50
0.10
0
0
0
0
193
64.30
99.50
23.80
0
0
0
0
0
0
0
0
194
23.90
100
23.90
3
15.00
5.70
0.40
18
5.70
34.00
2.20
16
5.30
30.20
2.00
16
11.90
30.20
2.00
0
0
0
0
53
6.50
100
6.50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
40
100
100
4.90
40
4.90
100
4.90
0
0
0
0
0
0
0
0
0
0
0
0
108
80.00
100
13.30
0
0
0
0
108
13.30
100
13.30
3
15.00
3.90
0.40
38
12.00
49.40
4.70
26
8.70
33.80
3.20
10
7.40
13.00
1.20
0
0
0
0
77
9.50
100
9.50
20
100
2.50
2.50
317
100
39.00
39.00
300
100
36.90
36.90
135
100
16.60
16.60
40
100
4.90
4.90
812
100
100
100
Chi-Square Tests
Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear Association
Value
2018.788
1486.635
290.056
df
24
24
1
Asymp. Sig. (2-sided)
0
0
0
Figure 1: Characteristics of the FADN Sample, 1999
Number of farms
Average Utilised Agricultural Area
600
500
2000
400
300
Ha
1500
200
1000
100
500
0
Individual
farmers
Ltd.
Joint
Stock
Coops
0
Individual
farms
Ltd.
Joint Stock
Coops