technical efficiency and productivity analysis of spanish citrus farms
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TECHNICAL EFFICIENCY AND PRODUCTIVITY ANALYSIS OF SPANISH CITRUS FARMS. Fatima Lambarraa, Teresa Serra and José M. Gil Centre de Recerca en Economia i Desenvolupament Agroalimentaris, CREDA-UPC-IRTA. PRESENTATION OUTLINE. I. THE RELEVANCE OF THE CITRUS SECTOR II. OBJECTIVE - PowerPoint PPT PresentationTRANSCRIPT
De la Economía Agraria a la Economía Rural y Agroalimentaria
TECHNICAL EFFICIENCY AND PRODUCTIVITY ANALYSIS OF SPANISH CITRUS FARMS
Fatima Lambarraa, Teresa Serra and José M. GilCentre de Recerca en Economia i Desenvolupament Agroalimentaris, CREDA-UPC-IRTA
PRESENTATION OUTLINE
I. THE RELEVANCE OF THE CITRUS SECTOR
II. OBJECTIVE
III. METHODOLOGY OF ANALYSIS
IV. EMPIRICAL APPLICATION
V. EMPIRICAL RESULTS
VI. CONCLUDING REMARKS
I. RELEVANCE OF CITRUS SECTOR AND MOTIVATION
The leading citrus fruit producing countries are Brazil, the Mediterranean countries, the United States and China. Within the Mediterranean area, Spain is the first producer with more than 5.9 million tons (57% of EU production and 6% of the worldwide production).
Oranges are the most relevant citrus fruit produced in Spain (48% of EU production and 5% of worldwide production).
I. RELEVANCE OF CITRUS SECTOR AND MOTIVATION
Citrus fruits are the first fruit crop in international trade in terms of value with more than 10 millions tons in 2004.
The EU is the main destination as well as the main supply region, with almost half of the world imports and more than 40% of world exports.
The Mediterranean region plays a prominent role as a world fresh citrus exporter (60% of global fresh citrus fruits exports). Spain is the first exporting country with almost 25% of total exports in the world (FAO 2005).
II. OBJECTIVE
The objective of this study is to analyze the Technical Efficiency (TE) for a sample of Spanish farms specialized in citrus production and …
To decompose Total Factor Productivity (TFP) growth into its various components (using a primal approach).
III. METHODOLOGY OF ANALYSIS
To measure the TE with which farms are operating, we use the stochastic frontier methodology (SFM) (Aigner, Lovell and Schmidt 1997, Meusen and Van den Broeck 1977 ) :
We consider the general stochastic production frontier function in the presence of panel data:
Stochastic frontier model
itv ),0( 2uN
where ~ & ~ ),0( 2vN
itu
it itv -uit ity = f(x β,t)e
III. METHODOLOGY OF ANALYSIS
captures the effects of statistical noise outside the firm's control
is a one-sided, non-negative component associated with output-oriented technical inefficiencies.
We adopt the temporal pattern of technical inefficiency by Battese and Coelli (1992):
itu
itu
Stochastic frontier model
exp it iu t T u
Where ξ captures the temporal variation of individual output-oriented technical efficiency ratings, and 1, 2, ..., Tt
.
III. METHODOLOGY OF ANALYSIS
Decomposition of TFP growth
We measure productivity change and determine its various sources following the primal approach by Kumbhakar and Lovell (2000):
Technical change:t
txfT
);,(
° ° °
k kk k k
k k
ε εTFP = TΔ+(ε -1) ( )x + ( )- S x +TEΔ
ε ε
Where:
III. METHODOLOGY OF ANALYSIS
Technical efficiency change:
t
uTE
k k
k
E = w x k kk
w xS =
E
Allocative inefficiency:
°
kk k
k
εAE = ( )- S x
ε
Where: &
Scale component: ( 1) ( )kk
k
SC X
k kk k
x f(x,t; β) xε = ε (x,t; β)=
f(x,t; β)Where: & k
k
ε = ε(x,t; β)= ε (x,t; β)
We use farm-level data taken from the FADN (Farm Accounting Data Network) for the period 1995-2002.
We also use regionally aggregated derived from the Spanish Ministry of Agriculture (MAPA) & Eurostat (MAPA provided land prices & Eurostat provided other input and output price indices).
We chose our sample of farms from the specialist citrus fruits group. Specifically, we concentrate on those farms whose orange sales represent more than 70% of citrus sales.
Our sample is composed by 859 observations.
We use Frontier 4.1 to estimate SFM & SAS 9.1 to decompose TFP Change.
IV. EMPIRICAL APPLICATION
The production frontier function is approximated by the quasi-Translog functional form (Fan, 1991):
k ktt it it
Kβ +β tβ t v -u
it 0 kitk=1
y = β e x e
Xit : (1x4) vector of inputs
X1: Fertilizers and pesticides,
X2: Other variable crop-specific inputs,
X3: Hectares occupied by olive groves,
X4: Labor input measured in labor hours per year.
IV. EMPIRICAL APPLICATION
Parameter Estimate Standard Error
α0 0.6038 (0.0395)*
αK 0.3242 (0.0738)*
αL 0.1841 (0.0370)*
αF 0.2201 (0.0362)*
αO 0.1040 (0.0279)*
αKT -0.0586 (0.0610)
αLT 0.1304 (0.0386)*
αFT 0.0609 (0.0348)*
αOT -0.0060 (0.0312)
αT -0.2628 (0.0460)*
αTT -0.2330 (0.0330)*
3.2963 (0.5244)*
0.9636 (0.0066)*
ξ 0.0270 (0.0080)*
2u
Technical inefficiency model
Production function
Note: L refers to labour, K to Land, F to Fertilizers and O to other costs. * indicate that the parameter is significant at 5%.
Table 1. Maximum Likelihood Estimates of the Production Frontier Function
V. EMPIRICAL RESULTS
V. EMPIRICAL RESULTS
1995 1996 1997 1998 1999 2000 2001 2002 2003
Output Elasticities
Land 0.416 0.376 0.352 0.335 0.322 0.311 0.302 0.294 0.287
Labour -0.02 0.068 0.121 0.159 0.188 0.212 0.232 0.249 0.265
Fertilizers & pesticides
0.124 0.166 0.191 0.208 0.222 0.233 0.242 0.250 0.257
Other costs 0.113 0.109 0.106 0.105 0.103 0.102 0.101 0.101 0.100
Table 2. Output Elasticities for Spain citrus-Growing Farms, 1995-2003
V. EMPIRICAL RESULTS
Hypothesis LR test-statistic
Critical Value (a=0.05)
Average Production Function, i.e., γ=μ=ξ=0 950.54
Aigner et al., (1977) SPF model with time-invariant output-oriented technical efficiency, i.e., μ=ξ=0 32.53
Aigner et al., (1977) SPF model with time-varying output-oriented technical efficiency, i.e., μ=0 22.61
Time-invariant output-oriented technical efficiency, i.e., ξ=0
10.18
12.84
12.55
64.1726 12.6
24 9.49
25 11.1
21 3.84
0jTj
0T TT jT j
1jj
0jT j
22 5.99
23 7.81
Constant returns-to-scale, i.e.,
Hicks-neutral technical change, i.e.,
Zero-technical change, i.e.,
21 3.84
Table 3. Model Specification Tests for citrus farms
and
TE 95 96 97 98 99 00 01 02 03
<20 4 8 10 7 7 7 5 8 5
20-30 2 7 7 3 5 7 4 2 1
30-40 10 9 8 5 6 1 3 4 4
40-50 3 13 10 9 8 7 8 6 6
50-60 15 12 13 9 7 8 9 9 8
60-70 10 12 10 11 12 10 7 6 6
70-80 16 16 17 16 15 15 14 15 13
80-90 23 28 29 29 32 32 32 32 32
90> 4 6 6 5 7 7 8 7 6
Mean 63% 60% 60% 64% 64% 64% 67% 66% 69%
Table 4. Measures of Technical Efficiency for Spanish citrus farms, 1995-2003
V. EMPIRICAL RESULTS
V. EMPIRICAL RESULTS
year TFP TEC SC AE TC
1996 0.024222 0.005057 -0.00792 0.027073 0.000012
1997 0.145540 0.002790 0.021737 0.121088 -0.000074
1998 0.092292 0.004003 -0.03599 0.12438 -0.0001005
1999 0.025411 0.001828 0.030043 -0.00632 -0.000139
2000 0.0754524 0.005234 -0.015332 0.085737 -0.000185
2001 -0.031069 0.001128 0.018885 -0.05086 -0.000222
2002 -0.146991 0.002837 0.008711 -0.15828 -0.000259
2003 0.0369498 0.002598 -0.011398 0.046036 -0.000285
1999-2003average
0.0277259 0.0031843 0.0010918 0.0236067 -0.000157
Table 5. Decomposition of TFP Growth for Spanish citrus Farms
VI. Concluding Remarks
Our analysis assesses the efficiency and productivity growth of Spanish farms specialized in citrus produce.
The Spanish citrus sector production function is characterized by decreasing returns to scale, making the increase of farm size unattractive.
Results of the Stochastic frontier model suggest an improvement in efficiency levels during the period studied, from 53% in 1995 to 69% in 2003. The estimated average efficiency level is about 64.11% with 38% of farms in the sample reaching a score greater than 80%.
VI. Concluding Remarks
The TFP growth indicates an increase in average productivity of about 2.7 % per year during the period of study.
Technical efficiency change, allocative inefficiencies and scale effects constitute the most relevant components of this growth.