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. Gil Centre 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 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 where ~ & ~

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): Stochastic frontier model Where ξ captures the temporal variation of individual output-oriented technical efficiency ratings, and.

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: Where:

III. METHODOLOGY OF ANALYSIS Technical efficiency change : Allocative inefficiency: Where: & Scale component: Where: &

We use farm-level data taken from the FADN (Farm Accounting Data Network) for the period 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): X it : (1x4) vector of inputs X 1 : Fertilizers and pesticides, X 2 : Other variable crop-specific inputs, X 3 : Hectares occupied by olive groves, X 4 : Labor input measured in labor hours per year. IV. EMPIRICAL APPLICATION

ParameterEstimateStandard Error α0α (0.0395)* αKαK (0.0738)* αLαL (0.0370)* αFαF (0.0362)* αOαO (0.0279)* α KT (0.0610) α LT (0.0386)* α FT (0.0348)* α OT (0.0312) αTαT (0.0460)* α TT (0.0330)* (0.5244)* (0.0066)* ξ0.0270(0.0080)* 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

Output Elasticities Land Labour Fertilizers & pesticides Other costs Table 2. Output Elasticities for Spain citrus-Growing Farms,

V. EMPIRICAL RESULTS HypothesisLR test- statistic Critical Value (a=0.05) Average Production Function, i.e., γ=μ=ξ= Aigner et al., (1977) SPF model with time-invariant output-oriented technical efficiency, i.e., μ=ξ= Aigner et al., (1977) SPF model with time-varying output-oriented technical efficiency, i.e., μ= Time-invariant output-oriented technical efficiency, i.e., ξ= Constant returns-to-scale, i.e., Hicks-neutral technical change, i.e., Zero-technical change, i.e., Table 3. Model Specification Tests for citrus farms and

TE < > Mean63%60% 64% 67%66%69% Table 4. Measures of Technical Efficiency for Spanish citrus farms, V. EMPIRICAL RESULTS

yearTFPTECSCAETC average 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 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.