A metafrontier approach to measuring technical efficiency The case of UK dairy farms

1. A metafrontier approach to measuring technical efficiencyThe case of UK dairy farms • Andrew Barnes*, Cesar Reverado-Giha*, Johannes Sauer+ • Land Economy and Environment Group, SAC, Edinburgh • +Department of Economics, University of Manchester 122nd European Association of Agricultural Economists Seminar Evidence-Based Agricultural and Rural Policy Making Methodological and Empirical Challenges of Policy Evaluation February 17th – 18th, 2011, Ancona (Italy) Centro Studi Sulle Politiche Economiche, Rurali e Ambientali associazioneAlessandroBartolastudi e ricerche di economia e di politica agraria Università Politecnica delle Marche

2. Why is important • Accurate measurement indicates efficient resource use and hence enters debates regarding food production, climate change, wider sustainability issues

3. Whole Farm Approach

4. Measuring Technical Efficiency • Frontier is drawn using parametric, semi-parametric or non-parametric approaches • Stochastic production frontier technique assumes that the noise can be disaggregated from the technical efficiency effect. Hence the SPF technique is the most commonly used in agricultural studies to compensate for some of the noise in agricultural production • So, though reduces statistical noise it infers that like should be compared with like. However, all farms are different – structures, behaviours, access to resources, quality of resources • One key factor is region....

5. The Role of Regions in technical efficiency measurement • Previous work • Use region as means to explain some of the deviation from the frontier • We argue in agriculture, region should be a defining characteristic of the area to be studied • Comparison between regions is difficult because of a different production technology

6. How do we compare?

7. Meta frontier • An overarching function that encompasses the different technologies involved across regions • The model enables the calculation of comparable efficiencies for production under different technologies relative to the potential technology available in the economy as a whole

8. Data Requirements and Issues • We take Dairy farms for the UK as an example of the metafrontier approach • Collected by separate administrative regions • England and Wales • Scotland • Northern Ireland • Some differences in attribution of inputs in these data sets, e.g. V&M in E FBS, compiled into other livestock expenses in S FBS.

10. Data Used

11. Process...of MF 1) Run separate frontiers for each region (k) 2) Solve an optimisation problem that minimises the absolute deviations between the metafrontier and the group frontiers for all the observations constrained by the fact that the output at the metafrontier is always greater or equal than the output from the k regions

12. Process.. Finally the third stage consists of the estimation of the distance of each member (farm) of each group with respect to the metafrontier, which is given by (10)

13. Summary Stats

14. Individual TE scores

15. Testing for different production technologies • LR test on sum of individual regions compared to pooled data set (UK): • Strongly rejects the null hypothesis and indicates that regional frontiers are not the same • Less strong but also rejects using English analysis • Found by all other studies... • Means that bias in country level studies exists...?

16. Running Meta-Technology Ratio

17. Meta Frontier Results

18. Conclusions • LR test rejects the common technology hypothesis even at a country level • Highlights regional constraints • Supports a regime of regional targeting of intervention • Advisory function • Best practice farms within the same region

19. Methodological Issues • Restricted by different data collection schemas (esp. Inefficiency effects (why they divert from the MF) • Explore regional deflators..... • Attempt at using EU FADN data

20. End

21. Meta frontiers