Dynamics of Dairy Farm Productivity Growth. Johannes Sauer

Transcription

1 Dynamics of Dairy Farm Productivity Growth Analytical Implementation Strategy and its Application to Estonia Johannes Sauer Professor and Chair Agricultural Production and Resource Economics Center of Life and Food Sciences Technical University Munich

2 content I - Analytical Considerations: Productivity Dynamics and Decomposition II - Exemplary Application: The Case of Estonia

3 I - analytics 1 st part of the project: non-parametric Fisher type productivity index as a ratio of Fisher output and input indezes TFP t F = Y t F sample weights are applied ex ante to the TFP measurement consistent with interest of 2 nd part of the project X t F 2 nd part of the project: further decomposing the TFP measures into different components various productivity decomposition methodologies: non- / parametric, sector / farm level, simple / dynamic

4 I - analytics measure / formula non-parametric decomposition parametric probit/logit parametric ts regression simple weighted index dynamic weighted index averaging weighted index OP cross-sectional index LP index parametric frontier Malmquist index decomposition Luenberger index decomposition data requirements q, prices q, factors q, factors q q, enter/exit q, enter/exit q q, inputs q, factors q q

5 I - analytics measure / formula data applied non-parametric decomposition q, prices parametric probit/logit q, factors parametric ts regression q, factors simple weighted index q x dynamic weighted index q, enter/exit (x) averaging weighted index q, enter/exit (x) OP cross-sectional index q x LP index q, inputs (x) parametric frontier q, factors x Malmquist index decomposition q (x) Luenberger index decomposition q (x)

6 I - analytics sectoral decompositions based on farm level measures - various studies found persistent patterns with respect to structure and development of TFP at micro level - key patterns: - x 1. reallocation of outputs (quotas) and inputs within the sector between farm types and individual farms 2. sector entry/exit behavior as major driver for reallocation 3. differences in efficiency, technical change, and TFP are persistent over time 4. low and further declining TFP as a significant indicator for sector exit [Bruemmer et al 02, Abdulai/Tietje 07, Peerlings et al 10, Sauer10, Huettel/Jongeneel 11, Sauer/Latacz-Lohmann 13]

7 I - analytics simple weighted index - dairy industry or sector related index of productivity DP can be denoted as DP it = fεi s ft dp ft s ft - the share of farm f in dairy sector i dp ft - index of farm-level producivity (TFP) share s might be based on milk output or herd size weights on the initial share at the base period or the average share obtained by averaging over base and end period [Foster et al 1998]

8 I - analytics dynamic weighted index - separating out within farms and between farms effects from cross farms influences - considering the effects by farms entering or exiting the sector - deviations of the farm level TFP from initial sector TFP are crucial DP it = fεc s ft 1 dp ft + fεc dp ft 1 DP it 1 s ft + s ft dp ft fεn s ft dp ft DP it 1 fεx s ft 1 dp ft 1 DP it 1 fεc + C - continuing (non-exiting) dairy farms N - entering (new) dairy farms X - exiting (non-continuing) dairy farms [Baily et al 92 or Haltiwanger 97]

9 I - analytics dynamic DP it = fεc s ft 1 dp ft + fεc dp ft 1 DP it 1 s ft + s ft dp ft fεn s ft dp ft DP it 1 fεx s ft 1 dp ft 1 DP it 1 fεc + 1 st term: within farm component based on farm level productivity changes weighted by the initial shares (s ft-1 ) in the sector 2 nd term: between farm component (i.e. changing shares between farms s ft ) weighted by deviation of initial farm tfp from initial sector tfp index 3rd term: a farm level productivity change and sector share covariance final terms: productivity contribution by farms entering/exiting the sector

10 I - analytics averaging weighted index - alternative decomposition method DP it = fεc sf dp ft + fεc dp f DP i s ft + fεn s ft dp ft DP i fεx s ft 1 dp ft 1 DP i bar over variable - average over base and end year of the time period [Griliches and Regev 95] does not explicitly reflect some parts of the covariance effects averaging shares across time less sensitive to measurement error

11 I - analytics OP index - the accurate identification and measurement of entering and exiting dairy farms might be problematic - Olley / Pakes (1996) suggest a productivity decomposition formula that relies on a cross-sectional decomposition DP it = p + s ft s p ft p f - 1 st term: unweighted (within) farm productivity effect - 2 nd term: deviation of the individual dairy farms share from average sector share - 3 rd term: similar deviation in terms of productivity - bar represents the (unw) average across all farms in same dairy sector (ie country) identification of potentially disproportionate distributions of production activity related to the estimated distribution of productivity conclusion whether the allocation of resources and production has become more or less tfp enhancing over the time

12 I - analytics efficiency frontier based decompositions - different tradition of decomposing productivity - for micro-level analyses this decomposition has been developed in the context of the Malmquist productivity index - easy to decompose including different efficiency components (technical, allocative and scale related) - production technology has to be estimated (single- or multioutput framework) - no price data required in case of distance function [Farrell 57, Caevs et al 82]

13 I - analytics - econometric techniques can be used to estimate the production frontier by choosing e.g. an input oriented distance function specification - including non-neutral technical change to estimate technical progress - distance function could be defined based on a Shephards type or directional type measurement approach D I x, y; g x, 0 M N = α i x i i=1 N M M + β l y l l=1 + γ il x i y l + δ tt tt + i=1 l=1 N N + α ij x i x j + i=1 j=1 N δ it i=1 x i t + M M l=1 m=1 M δ lt l=1 β lm y l y m y l t + ε k u k [Battese and Coelli 95, Coelli et al 05]

14 I - analytics details - with θ = (α, β, γ, δ) as a vector of parameters to be estimated - ε k as a random error for observation k assumed to be independently and identically distributed with mean zero and variance σ ε 2 - to obtain the didf specification we use the mapping rule x g x, y, i.e. g x, g y = 1,0 with = number of cows - the input vector x includes cows, labor, fodder, land, veterinary input, intermediate inputs, capital, and net investment whereas the number of cows are used as the scalar - the output vector y could e.g. include milk output, arable output, livestock related output, and other output - a time trend and other trend interaction terms are incorporated. u k as the technical inefficiency part distributed as a non-negative random variable assumed to be also independently and identically distributed by truncating the nomal distribution at zero with mean z k δ and variance σ u 2

15 I - analytics - technical inefficiency effects part is further specified as u k = Z k δ + τ k components of the vector Z k at farm and/or regional level: - structural - locational - institutional - policy related - technology related - individual characteristics - [τ k as truncated-normally distributed with zero mean and variance σ τ 2 with the point of truncation being Z k δ, i.e., τ k Z k δ; u k as a non-negative truncation of N Z k δ, σ 2 ]

16 I - analytics stata program [so far 1170 lines ]

17 II - empirics 1) Estonia Fisher TFP time-series index year tsswt tsswoit Red ex-ante weighted FTFP Blue ex-post weighted FTFP

18 II - empirics simple weighted indezes Estonia - the share term is based on milk output per farm - initial share at base period / final share at end period / average share over base & end periods / share per year DP it = - alternatively sample weighted based on ex ante weights fεi s ft tfp ft DP it = fεi s ft tfp ft s ft = s ft w ft - consideration of sector shares beside productivity allows for allocative correction of initial TFP measure - distribution of shares as indication of allocative efficiency

19 1 index II - empirics simple weighted indezes year tswdp tswdpe tswdpb tswdpa

20 II - empirics making allocative changes transparent DP it = ftfp ft s ft ftfp ft fεi , 2006, 2010, 2011 relative increase in productive resource re-allocation higher than increase in farm level productivity most significant contribution of resource re-allocation to sectoral productivity growth FTFP DP

21 II - empirics sector shares based on milk output per farm (share per year) year mean std dev min max e e e e e e e e e e

22 year II - empirics sector shares & weighted sector shares & 2009 significant increases in average sector shares smq smq wsmq 95% confidence intervals

23 II - empirics OP cross-sectional indezes Estonia - deviation of the individual dairy farm s share from the average sector share (based on milk output) and the similar deviation in terms of productivity DP it = p it + s fit sit p fit p it f - differing results due to the weighting of farms productivities by the deviation of their dairy industry share in the specific year (measured by milk output) - within farm productivity developments versus between farm resource re-allocation based productivity developments

24 1 II - empirics OP cross-sectional indezes index year OP weighted productivity index tswdpop tsdpop OP weighted (sample-weighted) productivity index

25 II - empirics making allocative changes transparent part DP it = p it + s fit s it p fit p it f , increasing productivity contribution by resource allocation 1.2 ftfp ft decreasing productivity contribution by resource allocation FTFP DP OP

26 year year II - empirics sector shares deviation and productivity deviation (per year) smqd 95% confidence intervals tfpd 95% confidence intervals

27 II - empirics allocation versus technology & knowledge within part between part (simple) between part (OP) focus on innovation and investment seems the key!

28 II - empirics parametric efficiency frontier - translog production frontier with inefficiency effects and non-neutral technical change in two specifications translog lny i = N i=1 α i lnx i N N + β ij lnx i i=1 j=1 lnx j + δ tt tt + N δ it i=1 x i t + ε k.. inputs x labor (family, hired), buildings, plant & machinery, dairy cattle, land, fodder, materials, veterinary.. inefficiency effects u k = Z k δ + τ k price of milk, stocking density, hired/family lab, cap/lab, lab/cow, vet/cow, milk yield, age, lfa, natura2, organisation, (time).. non-neutral technical change components time, time * time, time * input

29 II - empirics technical efficiencies specification Graphs by year te_tltcsi_n

30 II - empirics technical efficiencies specification Technical efficiency Graphs by year

31 II - empirics efficiency effects Factor Effect Significance price of milk + ** capital per labor + * labor per cow - *** milk yield per cow + *** veterinary exp per cow + * hired lab / family lab + *** organisation - partnerships - *** age of farmer + 0 time - *** stocking rate + ** organic production - 0 less favoured area - 0 natura

32 elasttn II - empirics technical change technical change full sample year

33 II - empirics scale efficiency change

34 I - analytics measure / formula data estimation applied countries non-parametric Fisher (A) quantity, price x parametric probit/logit (b1) q, factors x parametric ts regression (b2) q, factors x simple weighted index (c1) q x Est, (Nl) dynamic weighted index (c2) q, enter/exit (x) averaging weighted index (c3) q, enter/exit (x) OP cross-sectional index (c4) q x Est, (Nl) LP index q (x) Est, (Nl) parametric frontier (d1) q, factors x x Est, (Nl) Malmquist index decomposition (d3) q (x) (Est, Nl) Luenberger index decomposition (d4) q (x) (Est,Nl)

35 II - current current work finalizing analysis for Estonia decomposition for Netherlands decomposition for New Zealand others? clearly: much more would be possible if we would have access to more and better data (e.g. entry/exit) write report / papers including these empirical findings stata program available alternatively: let us have access to the raw data we can also do the analysis at your place

36 MANY THANKS!

37 appendix

38 appendix B. parametric decompositions b1 - cross-country panel / single country time-series - identification of structural, locational, institutional, policy, technology, individual effects on TFP - qualitative response model (probit/logit) with TFP measures as dependent b2 - cross-country time-series - identification of significant shift in level and growth of TFP - ts regression (ARIMA etc.) model with TFP measures as dependent and independents

39 appendix d2 - the input oriented Malmquist index measures the change in TFP between two data points - calculating the ratio of the distances of each point relative to a common technology M I x t 1,t, y t 1,t t 1 D I x t, y t t D I x t, y t D t 1 I x t 1, y t 1 D t I x t 1, y t using period t technology as the reference technology - D I t 1 and D I t refer to estimated directional input distance frontier based production technology in years t-1 and t [Faere et al 04, Caves et al 82]

40 appendix d3 - Malmquist index decomposes into components efficiency change and technical change M I x t 1,t, y t 1,t = D I t x t, y t D I t 1 x t 1, y t 1 t 1 D I x t, y t D t I x t, y t t 1 D I x t 1, y t 1 D t I x t 1, y t st term - efficiency change (Eff) - 2 nd term - technical change (Tech)

41 appendix d3 - efficiency change further decomposed into pure technical efficiency change and scale efficiency change M I x t 1,t, y t 1,t = t D I x t, y t D t 1 I x t 1, y t 1 t 1 D I x t, y t D t I x t, y t D t Iv x t, y t D t Ic x t, y t D t Iv x t 1, y t 1 D t Ic x t 1, y t 1 D t 1 I x t 1, y t 1 D t I x t 1, y t 1 D t 1 Iv x t, y t D t 1 Ic x t, y t D t 1 Iv x t 1, y t 1 t 1 x t 1, y t 1 D Ic st term - pure technical efficiency change (Off) - 2 nd term - scale efficiency change (Seff) - 3 rd term - technical change (Tech)

42 appendix d4 - change in total factor productivity at dairy sector level can be measured using the Luenberger index formula (directional distance function) - the Luenberger productivity indicator L can be decomposed into two parts: 1 st term captures efficiency change between periods t and t+1; last four terms measure technical change L x t, x t+1, y t, y t+1 = D I t x t, y t ; g x, g y D I t+1 x t+1, y t+1 ; g x, g y + 1 D 2 I t+1 x t, y t ; g x, g y D t I x t, y t ; g x, g y + D t+1 I x t+1, y t+1 ; g x, g y D t I x t+1, - where D I t 1 and D I t referring to the estimated directional input distance frontier based production technology in years t-1 and t - negative (positive) values of the indicator L( ) imply a decrease (increase) in productivity between the two periods [Chambers 96 & 02]

43 appendix OP cross-sectional indezes Estonia decomposing the allocative part: - the annual distribution of deviations does not always follow the same pattern in the Estonian case (see e.g and 2009) - the correlation between production shares (based on milk output) and productivity (measured by unweighted and weighted Fisher indezes) seem low (values for correlation coefficients of and respectively) - only a low positive correlation between the development of production shares deviation and the development of productivity deviations can be noted

44 appendix low correlation between production distribution and productivity distribution (?) smq tfp DPa DP DPop smq wtfp wdpa wdp wdpoph