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1 Metafrontier Framework for the Study of Firm-Level Efficiencies and Technology Gaps D.S. Prasada Rao Centre for Efficiency and Productivity Analysis.

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Presentation on theme: "1 Metafrontier Framework for the Study of Firm-Level Efficiencies and Technology Gaps D.S. Prasada Rao Centre for Efficiency and Productivity Analysis."— Presentation transcript:

1 1 Metafrontier Framework for the Study of Firm-Level Efficiencies and Technology Gaps D.S. Prasada Rao Centre for Efficiency and Productivity Analysis School of Economics The University of Queensland. Australia Joint research with George Battese, Chris ODonnell and Alicia Rambaldi

2 2 Outline Motivation Meta-frontiers for efficiency comparisons across regions –Conceptual framework –Methodology DEA Stochastic Frontiers –Application to global agriculture Metafrontiers and productivity growth –Metatfrontier Malmquist Productivity Index (MMPI) –Decomposition of MMPI Catch-up and convergence term –Cross-country productivity growth

3 3 Motivation Hyami (1969) introduced the concept of meta- production function The metaproduction function can be regarded as the envelope of commonly conceived neoclassical production functions (Hyami and Ruttan, 1971) Work on Indonesian Garment industry by regions National and international benchmarking studies – integrating a national study with data from other countries Performance of globalised and non-globalised economies

4 4 Basic Framework: Production Technology We assume that there is a production technology that allows transformation of a vector of inputs into a vector of outputs T = {(x,q): x can produce q}. It can be equivalently represented by –Output sets – P(x); Input sets – L(y) –Output and input distance functions

5 5 Basic Framework: Production Technology Properties of P(x) –0 P(x) (inactivity); –If y P(x) then y* = y P(x) for all 0 < 1 (weak disposability); –P(x) is a closed and bounded set; and –P(x) is a convex set. Output distance function is defined as: In this paper we just focus on output distance functions

6 6 Distance Functions Output Distance Function D o (x,y) The value of the distance function is equal to the ratio =0A/0B. Input Distance Function y 1A y 2A B C A y1y1 0 y2y2 P(x) PPC-P(x) D i (x,y) The value of the distance function is equal to the ratio =0A/0B.

7 7 Group frontier vs. metafrontier We assume that there are k groups of firms or DMUs included in the analysis. The group specific technology, output sets and distance functions can be defined, for each k=1,2,…K as

8 8 Group frontier vs. metafrontier The metafrontier is related to the group frontiers as: –If – – If D(x,y) represents the output distance function for the metafrontier, then

9 9 Metafrontiers Figure 1: Technical Efficiencies and Technology gap ratios

10 10 Technology Gap Ratio The output-orientated Technology Gap Ratio (TGR): Example: Country i in region k, at time t TE(x,y) = 0.6 TE k (x,y) = 0.8 Then, TGR = 0.6/0.8=0.75 The potential output vector for country i in region k technology is 75 per cent of that represented by the metatechnology.

11 11 A C B 0 Technology Gap Ratio (cont.) y1y1 y2y2 k th group Metafrontier

12 12 Using DEA: –Run DEA for each group separately and compute technical efficiency scores, TE k ; –Run DEA for all the groups together – pooled data and compute TE scores; –Compute TGRs as the ratio of the scores from the two DEA models; and –Given that DEA uses LP technique it follows that TE k (x,y) TE(x,y) for each firm or DMU Computation of TGRs

13 13 Using SFA –Estimate stochastic group frontiers using the following specification which is a model that is linear in parameters; us represent inefficiency and vs represent statistical noise. Meta frontier is defined as: such that for all k =1,2,…K Computation of TGRs

14 14 Estimate parameters for each group frontier and obtain. Identify the metafrontier, by finding a suitable, that is closest to the estimated group frontiers – need to solve the optimisation problem (using method described in Battese, Rao and ODonnell, 2004). Identifying the meta frontier

15 15 Computation of TGRs This is same as solvin We can decompose the frontier function as below:

16 16 Thus we have: These estimates are based on the estimated coefficients from the fitted SF models Computation of TGRs TE of i-th firm in k-th group frontier TE of i-th firm from the metafrontier Estimated TGR for each firm

17 17 The SF approach described here can be applied only for single output firms. For multi-output firms currently we use DEA approach. Work on the use of multi-output distance functions for the purpose of identifying the meta-frontier is in progress. Weighted optimisation in identifying the metaftontier: firm-specific weights Possibility of a single-step estimation of group and meta-frontiers using a possible seemingly unrelated regression approach. SF Approach – further work

18 18 Inter-regional comparisons of agricultural efficiency Coelli and Rao (2005) data set 97 countries and five-year period Pool 5-year data for all the countries Four groups of countries: –Africa: 27 countries –The Americas: 21 countries –Asia: 26 countries –Europe: 23 countries agricultural output – expressed in common 1990 prices Five inputs: land; labour; tractors; fertiliser; livestock An empirical application

19 19 DEA and SF results are presented for selected countries and regional groupings. –Results are presented as an average over the 5- year period with min. and max values reported. –For each country TE levels with respect to the group-frontier as well as TGRs are reported. DEA results: –TE of South Africa is relative to its group (Africa) frontier but it is only when measured against metafrontier showing a TGR of 0.633; –Average TGR for Asian countries is –DEA-MF values with maximum equal to 1 indicate that some countries from those regions were on the metafrontier at least in one year. Results

20 20 SFA is based on translog specification Pooled translog model is also presented The Likelihood-ratio test rejects the null hypothesis of identical group frontiers – shows that metafrontier framework is appropriate Some major differences between SFA and DEA results SFA efficiency scores are typically lower than those under DEA Indonesia, for example, has an efficiency score of under SFA compared to using DEA. SFA-MF efficiency estimates appear to be more plausible than SFA-POOL efficiency estimates – suggests the use of metafrontiers. Results

21 21 Metafrontier Malmquist Productivity Index Measuring productivity growth over time for different countries. Extension of metafrontier work to panels Quantification of relative technological progress and technology gap between economies and its evolution through time. Concept of Malmquist Productivity index is used along with metafrontiers

22 22 Malmquist Productivity Index MPI. Caves, Christensen and Diewert (1982). Two technologies and two observed points, t and t+1 MPI is geometric mean

23 23 Decomposition of MPI into – Technical Change, – Technical Efficiency Change, Malmquist Productivity Index (cont.)

24 24 Graphical Representation y k 1,t+1 k 1,t (x t+1, y t+1 ) (x t, y t ) M t+1 MtMt A* A B C* C D 0 x

25 25 GMPI and MMPI

26 26 –TEC * and TEC K GMPI and MMPI Decompositions TGR_GR is a relative technological gap change of the specific region from period t to t+1 evaluated at each periods specified input-output mix

27 27 –TC* and TC k GMPI and MMPI Decompositions (cont.) TGR -1 can be interpreted as the inverse of the relative technological gap change, which is benchmark time period invariant

28 28 GMPI and MMPI Decompositions (cont.) MMPI can then be expressed as: If the second term is not equal to 1, a single frontier approach will under/over estimate productivity change.

29 29 Empirical Application 69 Countries 1982 – 2000 Four Geographical Regions –The Americas (AM) - 18 countries –Europe (EU) - 19 countries –Africa and the Middle East (AF) - 18 countries –Asia-Pacific (AP) - 14 countries

30 30 Variables: –Real GDP (a chain index in 1996 international dollars) –Capital Stock (constructed from PWT using the perpetual inventory method) –Total Labour Force (World Development Indicators) Estimated with DEA –(see ODonnell et al (2005)) –19 periods Empirical Application

31 31 Empirical Application (cont.)

32 32 MMPI is generally higher than GMPI with the exception of the Americas during ; Metafrontier technical change seems to be only marginally higher than the group-specific technical change estimates – no evidence that any particular region is falling behind; African region has shown some signs of catch- up; There are few instances of technological regression – a phenomenon that is generally seen when DEA is applied. Need to replicate these using SF models MMPI-GMPI Results

33 33 Metafrontier concept is very useful in international benchmarking studies Choice of country or firm groupings is dictated by the particular problem under consideration Analysis is sensitive to the choice of groupings The basic framework has been developed, but further work needs to be focused on: –The estimation of metrafrontiers for multi- output/multi-input firms; –Efficient estimation of metafrontiers: possibility of a single-step estimator of the metafrontiers; –Estimation of MMPI using SF approach Conclusions

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