Are my prices better than my neighbours’?
Beyond allocative efficiency, a measure of price advantages
Kassoum Ayouba
Jean-Philippe Boussemart
Henri-Bertrand Lefer
Hervé Leleu
Raluca Parvulescu
Abstract
We introduce an alternative to the traditional allocative efficiency in the form of price advantage. This measure is
definedas the difference between two efficiency indexes respectively estimated on quantity and value data. We show
that the total price advantage can be decomposed as the sum of output and input-specific price effects. We first
provide the theoretical model and then an empirical application to a French farms dataset in the context of the
successive CAP reform aiming at the liberalization of agricultural prices (1992-2013).
Keywords: price efficiency; allocative efficiency; CAP reforms
JEL: Q12; Q180; C14; C61; D24
Cordonnées de l’auteur correspondant :
Raluca Parvulescu
IÉSEG School of Management,
3 rue de la Digue, 59000 Lille, France
Tél. 03.20.54.58.92
[email protected]
1
1. Introduction
One of the major objectives of the Common Agricultural Policy (CAP) is to provide revenue
safety for farmers. This was initially done by guaranteeing target prices for major agricultural
produces. This support schemes, as it may significantly alter production practices and world
prices, has been constantly criticized by the World Trade Organization (WTO) as it introduces
serious distortions to the agricultural commodity market.
To comply with the WTO requirements,the European Union has progressively and structurally
reformed its agricultural policy since the beginning of the 90s. The first major revision of the
CAP, known as the MacSharry reform (1992), replaces the farmers’ protection through
guaranteed prices with a system of direct farm payments based on the area farmed and livestock
kept. The second significant move is the Mid-term review (MTR hereafter) (2003) with the
introduction of decoupling of aid from volumes produced, to make farms more market-oriented
and to reduce distortions in agricultural production and trade. Formally, European aid is paid in
the form of a Single Payment (SP),the level of which being based either on historical farm
individual level, either on regional premium amounts, either on a combination of both. That way,
the European Commission expects farmers to respond better to market signals, i.e. to better orient
their practices towards market prices. The 2009 “Health check” reinforcesthis move through
gradual elimination of the remaining payments coupled to production by moving them into the
SP scheme.
There is an extensive theoretical and empirical literature that examines the impact of decoupling
the aids from the production on input/output allocation, income distribution, risk attitude of
farmers and investment choices (e.g. Hennessy 1998; Sckokai and Moro (2009, 2013), Latruffe et
al. (2016), Serra et al. (2008), Femenia et al., 2010), but less attention has been devoted to the
impactof decoupledaids on the farmers’ reactions to outputs prices: does the Luxembourg reform,
as it less constrains the farming activities, has led farmers to be more reactive to outputs market
prices? Does the reform lead to more market-oriented practices? Our objective in this paper is to
fulfill this gap, providing an assessment of the impact of decoupled payments on farmers’
reaction to outputs market prices.
To analyze farmer’s reaction to output prices, we opt to use frontiers models, the estimation of
which use the Data Envelopment Analysis technique (DEA, see Charneset al. (1978))1. This
method presents the advantage of conveniently dealing with multi-outputs productionand does
not require any assumption about the functional form of the production technology.
Traditionally in a DEA setting, the farmers’ ability to combine outputs in optimal proportions in
light of market prices is estimated with the output-oriented allocative efficiency score (e.g. Coelli
et al. (2002)). However this measure isbased on a relative output prices. That is, an increase in the
same proportion in output prices in a model has clearly no impact on the allocative efficiency
score.However, this is far from the farmers’ observed behavior which suggests that they are more
likely to think in terms of absolute instead of relative values.
Recognizing this fact, we propose a new way of gauging farmer’sprice environment: we
introduce the concept of price advantage. Our proposed indicatoris based on two efficiency
1
DEA uses mathematical programming to get a production frontier (the frontier or the surface that envelops the
observations as tightly as possible).
2
scores (estimated with outputs’ volumes and then with their respective values) gives a more
accurate picture of farmers price behavior than the allocative efficiency. Moreover, this total
price effect can be further decomposed as the sum of the specific outputs effects.
This paper is organized as follows. In Section 2, we present our methodology to estimate the
price efficiency. Section 3deals with the empirical application in Meuse French department and
Section 4 gives our conclusive remarks.
2. Methodology
Let Nbe the total number of producers denoted as DMUs (decision making units) using a
common technology to transform M input quantities ( QI ) into S observed outputs quantities
(QO) .
Let PI and PO the vector prices for the M inputs and respectively, the S outputs.
Let VIa,k be the cost incurred by some DMUa for the inputk. This cost is obtained as the product
between the physical quantity and its price:
VIa,k  PIa,k *QIa,k (1)
In the same way, we can define a DMUa’s revenue for some output j as
VOa, j  POa, j *QOa, j (2)
Therefore its observed total cost and respectively total revenue are given by:
M
S
k 1
j 1
VI a   VI a ,k and VOa  VOa , j
(3).
The underlying production technology, denoted T, is defined as follows:
T  {(QI,QO) | QI  RM can produce QO  RS } (4)
If T satisfies properties such as no freelunch, free disposability, closure, convexity and
boundedness, we can define, following Banker et al. (1984), its DEA representation under
constant return to scale as:
T
DEA
 {(QI , QO) | QI k 
N
  QI
n 1
n
k ,n
, k  1,..., M , QO j 
The distance to the frontierof a DMUlying in T
efficiency based on input and output quantity data.
2.1.
DEA
N
  QO
n 1
n
j ,n
, j  1,..., S ; n  0  n} (5).
will provide information on its technical
The estimation of allocative efficiency
The output-oriented allocative efficiency score estimation provides an indication about the
farmers’ ability to combine outputs in optimal proportions in light of their market prices. There
are two steps to reach this efficiency: for each DMU, to get the output-oriented allocative
efficiency, we need to estimate the revenue and technical efficiencies.
3
2.1.1. The revenue efficiency
The revenue efficiency is provided by the ratio of maximum revenue to observed revenue. For a
DMU“a”,its observed revenue (the actual one) is available from the dataset and the maximum
revenue she could get, given market prices, is obtained through the following linear program:
S
Max VO   PO
*
a
j 1
Q*o , 
N
  QO
n 1
n
j ,n
N
  QI
n 1
n
k ,n
j ,a
*QO*j , a
 QO*j ,a
j  1,...,S
 QI k , a
n  0
k  1,..., M
(LP1)
n
This program gives the optimal output levels yielding maximum revenue, given the output prices
faced by a DMU.
The revenue efficiency ( REa ) highlights the gap between a’s observed revenue (VOa ) and its
maximum attainable revenue (VOa* ) , given the technology and the outputs market prices and is
as follows:
REa  VOa / VOa* (6).
2.1.2 The output oriented technical efficiency
The output-oriented technical efficiency scoregives indication on the ability to avoid waste by
producing as much output as the technology and the available inputs allow. Let the share of
revenue from the production j in the total revenue be defined as:
 a, j 
VOa , j
S
VO
j 1

VOa , j
VOa
(7)
a, j
Bearing these notations in mind, we move to the definition of our output-oriented technical
efficiency. For the DMU“a”, this efficiency score is obtained from the following linear program:
DQa  QOa , QI a   Max  j  a , j hq j
 , hq j
N
  QO
n
n 1
j ,n
N
  QI
n
n 1
k ,n
 1  hq j  QO j ,a
 QI k ,a
j  1,..., S
k  1,..., M
n  0
n
hq  0
j  1,...,S
j
(LP2)
Using the estimated score, we can calculate the revenue ( VOaT ) the DMUa could reach if
technically efficient:
4
VOaT   (1  hq j ) VOa, j (8)
j
*
T
a
With these estimated values ( VOa and VO ) and the observed revenue level ( VOa ), we are now
able to estimate the allocative efficiency of the DMUa(AEa). It is obtained as the difference
between the whole efficiency (whose value is 1) and the allocative inefficiency. The latter is the
ratio of i) the gap between the optimal revenue ( VOa* ) and the technically efficiently revenue (
VOaT ) to ii) the actual (observed) revenue ( VOa ):
VOa*  VOaT
(9)
VOa
Therefore, the DMUa is allocatively efficient if the optimal revenue for a given output price
system and the optimal revenue when it is technically efficient are exactly the same. In this
revenue framework, the gap between VOa* and VOaT highlights the additional revenue the DMUa
can get from an appropriate reaction to output market prices.
As explained in the introduction, the allocative efficiency measure has a limitation. It is based on
the concept of relative prices, meaning that if a set of DMUs are producing two outputs and if
there is an increase in the price of these two outputs in the same proportions, the DMUshould not
change the production schemes based on this measure. Since that this fact is not what is observed
when investigating DMUs’ behavior (they make their decisions in terms of value instead of
relative prices) we have carried out a measure which fairly replicate DMUs’ behavior: the price
efficiency.
AEa  1 
2.2.
The output-based price efficiency
Given the allocative efficiency drawback, we have carried out an alternative measure of the farms
ability to react to output prices: the price efficiency.To introduce it, we need two estimates: the
technicalefficiency score (LP2) and the valueefficiency score based on output values. The latter is
obtained by using the value of output in the following linear program
DV VOa , VI a   Max  j  a , j hv j
 , hv j
N
  VO
n 1
n
j ,n
N
  QI
n 1
n
k ,n
 1  hv j  VO j , a
j  1,..., S
 QI k ,a
k  1,..., M
n  0
n
h 0
j  1,..., S
vj
(LP3)
We then define the “value-based” maximum revenue a DMUa can get from an optimal choice of
couples “price-quantity” for each production j as:
VOVa   (1  hv j )QO j ,a PO j ,a   (1  hv j )VOa, j (10)
j
j
Thus the price efficiency of theDMUais defined as the difference between the whole efficiency
(whose value is 1) and the price inefficiency. The latter is obtained as the ratio of i) the gap
5
between the “value” efficient revenue ( VOaV ) and the technically efficient revenue ( VOaT ), to ii)
the actual (observed) revenue ( VOa ).
VOaV  VOaT
PEa  1 
(11)
VOa
When the price efficiency of the DMUais lower than 1, it means that this DMU is not choosing
the optimal “price-quantity” couple. In other words, the gap between the observed revenue and
the “value” efficient revenue obtained from choosing “price-quantity” couple of the benchmark is
higher than the gap between its revenue and technically efficient revenue, obtained from
VOV VOT
choosing the optimal quantities ( a  a ). This means that this DMU is facing an
VOa VOa
unfavorable price environment. Conversely, when the price efficiency of a DMU is higher than 1,
this farm is facing a favorable price environment: thanks to its outputs and prices, the gap
between the observed revenue and the value efficient revenue is lower than the gap between its
revenue and technically efficient revenue. Finally, when the price efficiency is 1, the DMUunder
evaluation acts in a neutral price environment.
2.3.
The price efficiencydecomposition by outputs
We have shown how to characterize the price environment of a DMU using the revenue, the
technical and value efficiencies ( VOa , DQ  QOa , QI a  and DV VOa ,VI a  2. Note that,
DQ  QOa , QI a  and DV VOa ,VI a  alone are enough to characterize the environment; the
revenue has been used only to get a score (price efficiency) that can be compared to the allocative
efficiency (same construction schemes of scores).
In this section, as we are no longer pursuing a logic of comparison with the allocative efficiency,
we have limited ourselves to the use of DQ  QOa , QI a  and DV VOa ,VI a  to characterize the
price environment of a given DMU. We therefore define the price effect of a DMUaas follows:
pea  DQ  QOa , QI a   DV VOa ,VI a  (12)
When pea  0 , the DMUaacts in a favorable price environment as its distance to technical
benchmark is higher that its distance to its value benchmark. The interpretation logic of the price
effect is the same as above for the price efficiency. The only difference is that here, the indicator
is compared to 0 and earlier (for the price efficiency) it is compared to 1.
An additional interest to adopting this definition (in addition to its simplicity) is that the price
effect can be easily decomposed among outputs. Thus, for an output “j”, we get the following
measure:
pea , j   hqa , j  hva , j   a , j (13)
2
We have used
VOaV
VOaT
and
.
VOa
VOa
6
and one can observe that:
pea   pea, j (14)
j
This decomposition allowed us to investigate a step further DMUs’price environment. We can
see for each specific output its production adaptation to market price. This methodology can be
further extended in order to take into account the input-specific price effects also.
3. Empirical Application: Data and Results
This section presents an empirical application of the “price effect” concept. To that end, we start
by describing the conditions under which France has applied the CAP reforms in 2003 and 2009
respectively. We show that in a context of introduction ofdecoupled aids and a strong incentive to
adapt to the market signals, our concept of performance makes sense. We begin with a
presentation of our dataset. Then wemove to the discussion of our main results.
3.1 Conditions of the French application of the 2003 reform and 2009 “Health
check”
In France, the2003 Mid Term Review (MTR) was applied from 2006 with a partial decoupling of
aid (possible for cereals,sheep and cattle). Regarding the calculation of this aid, the French
authorities used the individual historicalbase (the average amount of subsidies received by the
farm between 2000 and 2002).For arable crops, for which theinterventionprice remained
unchanged, the level of decoupling was 75%, the rest of the aid remaining coupled to the surface.
The 2009 Health Check reform specified that the remaining coupled aids for arable crops should
disappear by 2010.
For cattle, 100% of suckler cow and calf slaughter premiums, 40% of adult slaughter premiums
stays coupled
Regarding the milk production,between 2004 and 2007,intervention prices decreasedby 25% for
butter and 15% for milk powder. At that moment, the milk production quotas were expected to
increase by 2 % in 2008 and then by 1% every year until their end in 2015. However, France
froze this measure after the 2009 collapse of milk prices. To compensate the price decrease, a
dairy premium, entirely decoupled, per ton of quota is created in 2004.
The 2009 Health check maintained the individual historical base for the calculation of the
payments but specified that all coupled aids for arable crops should disappear by 2010 and any
remaining coupled aids by 2012 (except for suckler cows for which 75% of the aids remains
coupled).
3.2 The Meuse sample
Ecoscopie de la Meuse (2000; 2010; 2012) gives a clear picture of the agricultural environment in
this department. Thus, we learn that the main agricultural activity is a mix between crops and
animal husbandry. The main contributors to the total agricultural revenue obtained in the
department are, by order of their share: crops (except fodder); milk and dairies followed by cattle
and calves. Moreover, over the last 40 years, the number of crop-specialized farms increased
7
while the number of those specialized in the mix of crops and animal husbandry, in bovines
(milk, meat or a mix) and in sheep decreased. Finally, the size of the farms is continuously larger,
which is a general trendnoted for the entire French agriculture.
Our dataset of farms observed in the Meuse department captures these evolutions. Panel data
from 1,186 farms, with a total of 11,967 observations were available for the period 19922013.3The panel is unbalanced and farms stay in the sample for an average of 10 years. Table 1
reportsthe number of farms among our sample years and shows that ourpanel of farms is
representative for the Meuse department. Indeed, according to the general agricultural census, the
total number of farms with these specializations was 1983 in 2000 (31%) and 1 448 in 2010
(32%).
The technology includes 3 outputs (M=3 in LP1-3) related to the three main productions: crops
(wheat, barley, corn, peas, rapeseed and sunflower), bovine meat (beef, young bulls and cows)
and bovine milk. Table 2 presents some descriptive statistics regarding the values of these outputs
(in constant 2010 prices=100).
The dataset provides the necessary information regarding the physical production and the value
of the revenue obtained by the farm for each production. For the estimation of the “value”
efficiency we used the information on the farm revenues for each of these productions (aids are
excluded).
Note that for the estimation of the technical efficiency, we used the volumes of each output. They
were computed as the sum of the products between the average weighted price of each produce
and their respective physical quantities.Since the average weighted prices are identical, for all
farms within the same year, all differences between farmers’ outputs are only due to a quantity
effect. Technical efficiency measures based on volumes are therefore equivalent to the quantity
estimations (LP2) in our theoretical model.
Besides the three main outputs presented above, farms in our data set have also engaged in some
other activities (pig, sheep, poultry productions, market gardening and arboriculture). Thus, a
fourth output has been used as a control variable in our estimations. Since the nature of the
produces making up for this output is more heterogeneous, we have used the revenue obtained by
the farms. Since this output (value) constraint is identical in LP2 and in LP3, it does not affect the
price advantages estimations calculated on the three previous outputs.
In terms of inputs(in LP 1-3, S=4)we used the total cultivated area, labor (the full-time
equivalent), the intermediate inputs including operating costs (e.g., fertilizers, seeds, and
pesticides) and other intermediate inputs (water, electricity, fuel, etc.) and finally, the capital
expenditures aggregate depreciation(equipment, buildings) and agricultural contractors.Table 3
presents some descriptive statistics.
3.3 Allocative and price efficiency, two distinct and independent measures of performance
for farms
The traditional efficiency measure dealing with prices is the allocative efficiency. Figure 1 shows
that the allocative efficiency for the DMUs in our data set ranges roughly between 0.8 and0.9.
Based on this indicator, one is tempted to say that farms in our data set are particularly efficient at
adopting the prevailing relative price system. To this we can add the fact that the proportion of
3
Our observations are from the “Centre d’Economie Rurale de La Meuse”, which audits farmers’ accounts. Note that
the dataset was financed as a part of an agreement with INRA.
8
allocatively efficient farms varies from 38.9% (in 1994) to 59.3% (in 2013) and that the mean for
the proportion of allocatively efficient farms for the period 1992-2013 is 47.4%.
However, the measure of allocative efficiency is computed given a set of prices for each DMU.
In this sense, it is an absolute measure because no inter-DMU comparison is made. On the other
hand, the price efficiency concept introduced is calculated by comparing the set of prices and
outputs between the DMUs. That is why, this measure is a “relative” one. Moreover, our findings
on the allocative efficiency seem to be contrasted by the graphof the price efficiency. Figure 1
shows that its variability is much wider, with successive situations of efficiency (above the unit)
and of inefficiency (below the unit). Besides, a net deterioration of this indicator starting with
2003 is noticed.This variability may be a source of information that we will seek to understand in
what follows.
Based on a very low and not significant correlation coefficient between the allocative efficiency
and the price efficiency measures, we can infer that they are independent from one another4.
Table 4 shows that throughout the years of analysis all combinations between the two measures
of efficiency are possible. Moreover, a large share of farms (in total, roughly 45%) belong to the
opposite categories of price efficient but allocatively inefficient or to the price inefficient and
allocatively efficient. At the same time, we notice that, on average, a relatively low proportion of
farms (16.38%) were both price and allocatively efficient.
In conclusion, during ourperiod of analysis two distinct evolutions may be noted:
i) an improvement of the proportion of allocativelyefficient farms. However, two opposing
effects occurred. Thus, the proportion of the allocatively efficient but price inefficient farms
increased while the proportion of the allocatively efficient and price efficient farms has
decreased. The first evolution was stronger than the latter.
ii) a decrease in the proportion of price efficient farms, be they allocatively efficient or
inefficient5.
These evolutions signal that if we consider that farms should optimize their production decisions
with respect to their own set of prices (allocative efficiency), then the farms in our data set have
performed pretty well. However, if we make a comparison between the farms’ set of prices, it
seems that while some of them performed very well, some others performed very poorly. Or, in
the context of the CAP Reforms and the increase in the urge to adapt to the market signals,
farmers are supposed to adopt the type of optimization supposed by the price efficiency measure.
That is why, the rest of our paper will deal exclusively with it.
3.4 The total price effect
The price effect of farms obtained thanks to equation (12) can be positive, negative or null
(neutral effect)6. Figure 2 gives the proportion of farms in each category during the period of
analysis. We notice that the share of farms witha neutral price effect is relatively constant through
time. While the share of farms witha positive price effect was comparable (with some exceptions)
4
The correlation coefficient is 3% and not significant at <5%.
The proportion of farms that are neutral from their price efficiency point of view is constant in time.
6
A “neutral” price effect is obtained in two situations; either the farm obtains the same inefficiency scores in the two
measures mentioned either, when it is “efficient” according to the two measures. In our case, all instances of
“neutral” price effects correspond to situations in which the farms are efficient.
5
9
to that of farms obtaining a negative total price effect before the application of the reform (19922005), after the Reform (2006-2013), a larger share of farms obtains negative price effects. This
is confirmed by Table 5 that shows a drastic drop in the mean for the share of farms with a
positive total price effect after the introduction of the reform and, at the same time, an increase in
the mean for the share of farms obtaining a negative price effect. Moreover, we notice that the
farms obtaining negative price effects become preponderant after 2006.
The analysis of the evolution total price effect conceal perhaps some contrasted evolutions
according to the type of output and its market structure. Indeed, the intended targets for the 2003
MTR were different according to the type of production. We pursue by a decomposition of this
effect into the three output-specific price effects.
3.5 Decomposition of the total price effect into output-specific price effects
Following the algebraic decomposition (equation 13),Table 6 presents somedescriptive statistics
regarding the percentage of farms producing a specific output and the sign of their price
effects.Over the period of analysis two contrasted effects can be emphasized: the increase in the
proportion of farms obtaining positive milk-specific price effects and the decrease in the
proportion of the farms obtaining crops-specific positive price effects. Knowing that the 2003
MTR targeted the decoupling of aids received for the crops productions, Figure 3shows the effect
of the Reform on the relative distribution of farms. After the application of the Reform, our
indicator of price effect shows that farmers in our data set have performed a substitution in what
their capacity to extract the market advantages from their crops production in favor of their milk
production.
4. Conclusion
If we were to analyze the evolution of the economic efficiency of farms in the Meuse by means
of their allocative efficiency, we would be tempted to conclude that this type of efficiency
increased in time and that the 2003 MTR did not have a significant impact on it. These findings
are related to the fact that the measure of the allocative efficiency is based on the farm’s own
price system. The extension that we introduce here via the price efficiency is an analysis of the
performance of the prices obtained by a farm in relation to the prices obtained by the other farms
in the sample. From this perspective, we see a reduction in efficiency over time, mainly after the
application of the 2003 MTR. We can therefore conclude that the Reform has accentuated the
gaps among farmers’ price advantages. While some of them take benefit from new price
opportunities, most of theothers do not do so and suffer from price disadvantages.
Moreover, the possibility of an analysis of each output specific price efficiency makes it possible
to emphasize different evolutions for each type of output. Thus, it appears that the reform
initiated in 2003 resulted in a decrease of the crops-specific price effect, in a stationary state for
the bovine meat-specific price effect and finally, in an increase in the milk-specific price effect.
The next step consiststo pursue our analysis by an identification of the exogenous explanatory
factors of the different price effects. Thus, the amount of aids received by a farm, the structure of
aids depending whether they are coupled or decoupled, the degree of short/long-term
indebtedness are all possible explanatory factors to the performance of farms in terms of the
prices obtained.
10
5. References
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale
inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making
units. European journal of operational research, 2(6), 429-444.
Coelli, T., Rahman, S., & Thirtle, C. (2002). Technical, Allocative, Cost and Scale Efficiencies in
Bangladesh Rice Cultivation: A Non‐ parametric Approach. Journal of Agricultural Economics, 53(3),
607-626.
Femenia, F., Gohin, A., & Carpentier, A. (2010). The decoupling of farm programs: Revisiting the wealth
effect. American Journal of Agricultural Economics, 92(3), 836-848.
Latruffe, L., Bravo-Ureta, B. E., Carpentier, A., Desjeux, Y., & Moreira, V. H. (2016). Aids and technical
efficiency in agriculture: Evidence from European dairy farms. American Journal of Agricultural
Economics, aaw077.
Moro, D., & Sckokai, P. (2013). The impact of decoupled payments on farm choices: Conceptual and
methodological challenges. Food Policy, 41, 28-38.
Serra, T., Zilberman, D., & Gil, J. M. (2008). Farms’ technical inefficiencies in the presence of
government programs. Australian Journal of Agricultural and Resource Economics, 52(1), 57-76.
Sckokai, P., & Moro, D. (2009). Modelling the impact of the CAP Single Farm Payment on farm
investment and output. European Review of Agricultural Economics, 36(3), 395-423.
Table 1. Number of farms per year used to determine the benchmark frontier
Before
2003
MTR
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
600
632
644
660
642
644
631
639
621
614
581
588
257
551
2006
2007
2008
2009
2010
2011
2012
2013
515
521
521
481
465
433
376
351
After
2003
MTR
Table 2. Period means for the revenues of the three main aggregate outputs (in 2010 constant euros) and the period trend
CROP REVENU
BOVINE MEAT REVENU
MILK REVENU
PERIOD MEAN (trend, sig. < 5%)
121 177(1.82%)
19 399(3.43%)
Table 3. Period means for the four inputs used in our analysis
TOTAL CULTIVATED
AREA (HA)
188(1.33%)
PERIOD MEAN (trend, sig. < 1%)
FULL-TIME
EQUIVALENT
2,2(-0.4%)
74 295(1.98%)
INTERMEDIATE
INPUTS
113 573(1.57%)
CAPITAL
COST
75 519(0.61%)
Table 4. Period mean for the share of farms according to their scores on price efficiency and allocative efficiency and their
period trends (in percentage points,sig. at <5%)
ALLOCATIVE EFF.(0.66)
ALLOCATIVE INEFF. (-0.67)
PRICE EFF. (0.94)
16.38 (-1.19)
20.68 (-0.59)
NEUTRAL PRICE EFF.
6.97
4.14
PRICE INEFF. (0.78)
24.08 (0.93)
27.75 (ns)
Table 5.Sub-period mean for the structure of farms depending on the sign of their total price effect (t.p.e)
11
POSITIVE T.P.E
NEGATIVE t.p.e
NEUTRAL t.p.e
Before the Reform (1992-2005)
42.31
46.69
11
After the Reform (2006-2013)
27.87
60.82
11.3
Table 6. % of farms producing a specific output and obtaining an output –specific price effect
% OF FARMS (AND TREND) ON 1992-2013 PERIOD WITH…
... a positive p.e.
... a neutral p.e.
... a negative p.e
28.9 (-1,2)
25.7 (ns)
45.4 (1,1)
BOVINE MEAT
25.9 (ns)
49.4 (ns)
24.7 (ns)
MILK
38.1 (1,2)
23.0 (ns)
38.9 (-1,3)
CROPS
Figure 1. Evolution of the per year mean allocative and price efficiencies
Figure 2. The evolution of the structure of farms depending upon the type of total price effect (p.e.) obtained
12
100
90
80
70
60
50
40
30
20
10
p.e. >0
p.e. <0
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
0
p.e. =0
Figure 3. Sub-period means for the shares of farms obtaining either a positive or a negative output-specific price
effect
60
50
40
30
20
10
0
Crops p.e.>0 Crops p.e.<0 Bovine meat Bovine meat Milk p.e.>0
p.e.>0
p.e.<0
BR
Milk p.e.<0
AR
13