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.