Farm Level Productivity: Methods and Evidence

Transcription

1 5 th meeting of the OECD Network for Farm Level Analysis, 9 March 2010 Farm Level Productivity: Methods and Evidence Catherine Moreddu and Shingo Kimura Trade and Agriculture Directorate

2 Context OECD project on factors determining productivity and competitiveness in the agri-food sector Literature reviews by two consultants: 1. Laure Latruffe, INRA, Agrocampus Ouest, clarifies concepts of competitiveness, productivity and efficiency, provides a critical assessment of methods used to measure them, reports main results and identifies areas for future research 2. Julian Alston, UC Davis, reviews evidence on the role of agricultural research and development in fostering innovation and productivity in agriculture OECD Trade & Agriculture Directorate 2

3 Content of this presentation Based on Laure s report Covers competitiveness, productivity and efficiency at farm level And factors determining productivity Measurement/estimation methods and some results OECD Trade & Agriculture Directorate 3

4 Indicators of competitiveness Trade measures Strategic management measures (Porter, 1990): competitiveness is revealed by performance indicators such as cost superiority, profitability, productivity and efficiency, which can be measured at the firm level Domestic resource cost (DRC): compares domestic (D) and border (B) prices of inputs (I) and output (j) = sum(aij *PDi) / (PBj sum(aij*pbi)) Agricultural costs of production (survey or econometric estimation) Profitability: comparison of costs and revenues Productivity/efficiency OECD Trade & Agriculture Directorate 4

5 Domestic Resource Cost Study Countries Sector Period Indicators Results Banse et al. Hungary Various crop DRC Livestock sector less and less (1999) and livestock competitive Gorton et al. (2000) Gorton et al. (2001) Gorton and Davidova (2001) Nivievskyi and von Cramon- Taubadel Bulgaria, Czech Rep. compared to EU15 and world Poland sectors Main commodities Eight commodities Wheat competitive DRC High competitiveness of wheat and barley both vis-àvis EU15 and world. Competitive in milk and beef relative to EU but not world and 1998 CEECs 1992 and 1998 Ukraine Dairy production (2008) Liefert (2002) Russia Several output and inputs DRC at farm level DRC at farm level DRC and SCB at farm level Crops more internationally competitive than livestock. Competitiveness worsens Crops most competitive. Higher competitiveness in CEECs than in the EU 15% of farms were competitive in 2005 (19% in 2004) SCB ratios Less competitive in meat than in crops. More competitive in outputs than in inputs (except natural gas) OECD Trade & Agriculture Directorate 5

6 Costs of production Study Countries Sector Period Indicators Results Mulder et al. Brazil-EU (2004) Ahearn et al. (1990) Bureau and Butault (1992) Bureau et al. (1992) Thorne (2005) Bavorova (2003) Mercosur- EU US and Canada EU member states Several protected products 1995 Unit labour costs All input cotst wheat Costs of production Wheat, sugar 1984 Costs of beet, hog and production milk EU MS and US wheat Average Denmark, Cereal Germany, production 2000 France, Ireland, Italy, UK Czech Rep. Sugar industry 1989 and 1999 Costs of production Cost indicators, Production costs Brazil costs are 15.5% of EU costs and 5% of French costs Mercosur more competitive for all products except bananas Higher in US than Canada UK and France most competitive for wheat production, Belgium and France for sugar beet, Ireland, the Netherlands and the UK for hog and Greece for milk US has by far lowest costs, Italy highest Depends if family labour and assets are included or not Higher concentration results in economies of scale and lower costs OECD Trade & Agriculture Directorate 6

7 Profitability Study Countries Sector Period Indicators Results Van Duren Canada, EU, Agri-food et al. (1991) US sector Ratio value added to sales - Value added per worker - Value added per plant US more competitive than Canada more competitive than EU Canada most competitive for meats EU and US highly competitive for beverages Viaene and Gellynck (1998) Belgium Pigmeat processing sector Net profit relative to sales - Sales divided by business assets - Net profit to own funds - Financial leverage Poor profitability Bavorova (2003) Davidova et al. (2003) Van Berkum (2009) Bezlepkina et al. (2005) Czech Rep. Dairy industry Profit/costs Fluctuations, increase in 2000 Czech Rep. agriculture Cost/revenues Most farms not profitable, even when family inputs not considered 12 new EU MS 8 candidates Dairy sector 2006 Gross margin as a % of revenue Russia Dairy farms Profit function using panel data and instrumental econometric techniques 62% for the EU15, only Slovenia, Bosnia and Poland have a higher ratio OECD Trade & Agriculture Directorate 7

8 Productivity/Efficiency: Definitions Productivity and efficiency as indicators of competitiveness (EU, 2008) Productivity is the ability of production factors to produce the output Components of productivity improvements: Efficiency increase Economies of scale Technological progress Efficiency indicates whether firms are able to use existing technology in the best way OECD Trade & Agriculture Directorate 8

9 Measurement of efficiency Partial (e.g. yields) or total factor productivity (TFP) = output/input(s) Measurement of efficiency: distance to the efficiency frontier Non-parametric methods: Construction of the efficiency frontier using, e.g. Data Envelopment Analysis (DEA): linear programming with the best performing farms of the sample Parametric methods: Specification of a production function, estimated econometrically OECD Trade & Agriculture Directorate 9

10 Productivity and technological change Partial (e.g. yields) or total factor productivity (TFP) = output/input(s) TFP measurement: Index number approach: assumes firms are efficient so measures only technological change Production function estimation Malmquist indices: decompose productivity change into efficiency change and technological change (Coelli et al., 2005). They can be calculated using parametric and non-parametric methods OECD Trade & Agriculture Directorate 10

11 Evidence on technical efficiency Brümmer et al. (2002) Hadley (2006) Zhu et al. (2008) Carroll et al. (2009) Poland, Germany, Netherlands England and Wales Germany, Netherlands, Sweden Ireland Specialised Crop and livestock farms 1996 and 2000 Dairy farms Technical efficiency Eight farm types Study Countries Sector Period Indicators Results Nasr et al. Illinois Grain farms Technical Increasing trend (1998) efficiency Giannakas et Saskatchewan Crop farms Technical Increasing trend al. (1998) 1995 efficiency Latruffe et al. (2005) Poland Technical efficiency decrease Dairy farms Cattle, cereals, dairy, sheep Technical efficiency + change Technical efficiency change Technical efficiency change Higher in Poland, then Netherlands, than Germany High scores Change: zero or negative Change of 1%, 2.8%, -1.1% respectively Deterioration followed by progress OECD Trade & Agriculture Directorate 11

12 Evidence on TFP using Malmquist indices Study Countries Sector Period Indicators Results Fogarasi and Latruffe (2009) France and Hungary COP and dairy Malmquist TFP No change or deterioration Galonopoulos et al. (2008) Latruffe et al. (2008a) Brümmer et al. (2002) Hadley (2006) 32 EU and Mediterranean countries agriculture England and Wales Malmquist TFP Malmquist TFP Dairy farms Malmquist TFP Eight farm types Poland All farms Poland, Germany, Netherlands Malmquist TFP High productivity in EU15 and CEEC Low productivity in Southern countries Convergence from 1990 deterioration Deterioration in Poland (-5%), increase in Germany (6%) and Netherlands (3%) Positive technological change OECD Trade & Agriculture Directorate 12

13 Determinants of competitiveness Within farms control: Size Other structural characteristics such as legal status, share of hired labour, debt level, specialisation, marketing. Social capital Beyond farms control National factor endowment: labour, land, capital Demand conditions: consumers preferences Government intervention Investments in research, extension, infrastructure Location of activities OECD Trade & Agriculture Directorate 13

14 Determinants of competitiveness: Methods Regressions on competitiveness scores Correlation and ranking analysis Calculations on separate samples and comparison Cluster analysis Other methods: survey of perceptions OECD Trade & Agriculture Directorate 14

15 Determinants of competitiveness: Evidence Size: 0, + or Legal status: 0 or? Factor intensity: conflicting results External factors (share of hired labour, debt level): Ambiguous Specialisation: + or High degree of commercialisation: + in CEECs Social capital: Age (+ or -); education (mainly +); gender (0 or for women because lack of access to resources); time (ambiguous); pluriactivity (0, + or -) National factor endowment: labour (+), land (+), capital (-) Demand conditions: consumers preferences (varies by product) Government intervention: price support (mainly -), environmental regulation (0 or +); organic subsidy (+); conservation (-), credit (-). Investments in research, extension, infrastructure: (+) Location of activities: significant factor, but signs depend on the region OECD Trade & Agriculture Directorate 15

16 Conclusions Competitiveness is a fuzzy concept No consensus on measurement You need more than one indicator General limitations: static nature, exchange rate, policy distortions, non-price competitiveness ignored (quality) Macro or micro? Areas for further research OECD Trade & Agriculture Directorate 16

17 Areas for further research Need for a benchmark Comparison of various approaches in a single study Inclusion of unpaid inputs, such as family labour Measurement of distortions due to government intervention R&D More on agri-food Non-price components OECD Trade & Agriculture Directorate 17

18 OECD future work ( ) In depth reviews of R&D and innovation systems, from fundamental research to adoption by the agri-food chain Issues related to the adoption of innovation, in particular the role of extension systems versus other channels Micro-level analysis of the impact of agricultural support on farm productivity OECD Trade & Agriculture Directorate 18

19 Potential areas of work by farm-level network for OECD productivity/innovation project Main questions: How efficient/productive farms are distributed within the sector/country and how it evolved? What are the determinants of the productivity growth (efficiency increase and technological change) at the farm level? More specifically, what kind of farms adopt new technology more quickly and use it efficiently, and what are the policy impacts on the efficiency /productivity increase? (e.g., payment, extension service) OECD Trade & Agriculture Directorate 19

20 Methodologies Calculation of several farm performance indicators (partial and total factor productivity) by farm (e.g., gross margin per ha, DEA score) across certain years Comparing the performance indicators by sectors, farm size and amount of support received and so on over time (crosscountry comparison can also be envisaged) Econometric analysis of the determinants of farm performance indicators (farm characteristics, policy impacts ) OECD Trade & Agriculture Directorate 20

21 Future steps 6 th Meeting Development of standardized methodology and preparation of terms of reference 7 th Meeting Presentation of the descriptive analytical results of farm performance indicators 8 th Meeting Presentation of econometric analytical results on the determinants of efficiency/productivity with main focus on the policy impacts OECD Trade & Agriculture Directorate 21

22 Thank You Trade and Agriculture Directorate Visit the network website: (Username: distribution; Password: network) Contact us: OECD Trade & Agriculture Directorate 22