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Transcript of Name des Wissenschaftlers Sebastian Neuenfeldt, Alexander Gocht Thünen Institute of Rural Studies,...
Name des Wissenschaftlers
Sebastian Neuenfeldt, Alexander GochtThünen Institute of Rural Studies, Braunschweig, Germany
The analysis of farm structural change in the EU-27
Sebastian Neuenfeldt, Alexander Gocht
1. Introduction
Motivation
•Farms have a certain farm specialization and size (= farm population) farmers decide upon their productive orientation (specialization) number of farms of a certain farm population in a region
•Our point of analysis: development of farm population shares in a NUTS2 region
•Key approach: market share model analysis
• attraction of brands determine market shares of brands prices or advertising expenditures affect attraction
• utility of farmer towards each alternative farm population determine farm population share in a region (NUTS2) prices, socio-economic or other variables affect utility
22.10.2015 The analysis of farm structural change in the EU-27Page 2
Sebastian Neuenfeldt, Alexander Gocht
2. Theoretical model
Utility approach
22.10.2015 The analysis of farm structural change in the EU-27Page 3
k ,ii i
ii m
jj 1
K( )
i k k,ik 1
Us =
U
U e f (
(1)
(2) X )
i
i
U - utility of farm population i, j - another index for farm population, s - share of farm population im - number of farm population shares of the NUTS2 region
k - k-th explanatory variable wi
k ,i
i
k k ,i
th k=1,…,K explaining the utility of farm population i,
- coefficient of k-th explanatory variable, - intercept for farm population i,
f - the positive, monotone transformation of X
i the specification-error term-
Sebastian Neuenfeldt, Alexander Gocht
3. Data preparation
Data set
•1989-2011
•EU-27
•Sources: FADN, EUROSTAT, Worldbank, COCO CAPRI data base, EUGIS
Dependent variable (from FADN)
•(Up to) Sixteen farm population shares in a NUTS2 region
•Is defined by the following stratification
• eight farm specializations (cereals, dairy, grazing livestock…)
• two size classes (<> 250,000 standard output)
22.10.2015 The analysis of farm structural change in the EU-27Page 4
Sebastian Neuenfeldt, Alexander Gocht
3. Data preparation
Explanatory variables
NUTS0 level:•Economic variables• Interest rate (%), GDP growth rate(%), unemployment rate (%), CAPRI prices
(€ per tonne)•Other: dummy for decouplingNUTS2 level (aggregated from farm level):•Economic variables• FADN prices (€ per tonne) • Farm net value added and total subsidies (per Farm, per UAA, per AWU)
•Other: Age of the holder (years)
22.10.2015 The analysis of farm structural change in the EU-27Page 5
Sebastian Neuenfeldt, Alexander Gocht
3. Data preparation
NUTS3 level:
•Non-economic variables and time invariant:
• Corine land use characteristics (% on total land) (Arable land, heterogeneous agr. land, etc)
• Natural and climate condition • Aridity index• Growing degree days (mean and sd) for a threshold of 5°C and 10°C• Vegetation period (mean and sd) for a threshold of 5°C and 10°C• Slope (93m raster - %)• Elevation (93m raster – meter)
•Other: Population density (inhabitants per square km) – missing values calculated by a trend estimation
22.10.2015 The analysis of farm structural change in the EU-27Page 6
Sebastian Neuenfeldt, Alexander Gocht
3. Data preparation
How spatial information is used to define explanatory variables to account for spatial and farm population heterogeneity?
•Remind: we analyse at NUTS2 level
•We have information at NUTS3 level about the farms and some explanatory variables
•We know the distribution of farms in the NUTS3 regions of each NUTS2 region
•We built a weighted average of NUTS3 explanatory variables (like altitude) aggregated at NUTS2 level with respect to each farm population share
•With that we get farm population specific natural and climate conditions variables (which are indirectly regional specific)
22.10.2015 The analysis of farm structural change in the EU-27Page 7
Sebastian Neuenfeldt, Alexander Gocht
4. Empirical implementation
Empirical model
22.10.2015 The analysis of farm structural change in the EU-27Page 8
i
m K m R'
i,t 1 j j k,i,r j k,i,t-r k,i,i,t
i,ti
k,i,t-
t m
i,tj 1
t 4 rr 1j 2 k 1 j 1
) , sln(s )=α α d β d l X
ˆexp(y )s
n(X ε(3)
(ˆxp
4)e (y )
• Taking the log of the utility function and setting dummy variables for each of the farm population defines our empirical estimation model common OLS regression techniques• Taking car
2)
f
(
e o
i,t
the lagged structure
• Let y be the estimate of the dependent variable of • „Inverse log-centering“ transformation • non-negativity and shares sum up t
3
o one
i,t 4k,
Each farm population i estimated seperately via forward selection BIC
farm structure captured by the own lagged shares in each farm
. Histor
group mo .
ic
s del
Sebastian Neuenfeldt, Alexander Gocht
4. Empirical implementation
Lag structure of explanatories
•Generally, lags of explanatories are important
• Adaptation to changes of explanatories takes time
•One year lagged:
• CAPRI prices, FADN prices
•Up to four years lagged:
• Own lagged shares, Total subsidies, Age structure, Farm net value added, Unemployment rate, Interest rate, GDP growth rate, Population density
•Time invariant explanatories are not lagged (Corine land use characteristics, natural and climate condition)
22.10.2015 The analysis of farm structural change in the EU-27Page 9
Sebastian Neuenfeldt, Alexander Gocht
5. Results
22.10.2015 The analysis of farm structural change in the EU-27Page 10
MS/EU15Residual Sum of Squares
In Sample R2
Number of. variables
significant level <=10%
Number of. variables
significant level <=5%
Number of. variables
significant level <=0.1%
Number of. variables
significant level >10%
BL 0.866 0.963 7 66 DK 0.009 0.997 8 26 51 12DE 5.115 0.958 1 2 82EL 2.685 0.955 1 5 47 4ES 6.594 0.946 1 7 86 3FR 1.526 0.988 2 74 3IR 0.003 1.000 3 10 27 5IT 2.798 0.969 2 5 78 1NL 1.771 0.960 1 4 86 1AT 0.404 0.992 2 8 52 5PT 3.212 0.920 2 7 49 7SE 1.340 0.963 2 10 56 3FI 0.120 0.994 3 9 51 6UK 0.915 0.986 2 5 74 2
Sebastian Neuenfeldt, Alexander Gocht
5. Results – UK 98.6% R^2
22.10.2015 The analysis of farm structural change in the EU-27Page 11
Sebastian Neuenfeldt, Alexander Gocht
5. Results – PT 92% R^2
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Sebastian Neuenfeldt, Alexander Gocht
5. Results – contribution to R^2
Contribution of variable categories to the R^2
•EU-15: Most of the variance explained by farm structure, followed by agronomic characteristics (prices, income, subsidies)
•EU-12: Historic farm structure less important
•Very diverging between the countries…
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Sebastian Neuenfeldt, Alexander Gocht
5. Results – contribution to R^2 – EU-15
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
IT FR DE UK NL ES PT BL EL AT IR FI SE DK EU15
Prices at MS
Prices FADN
Population
Economic indicators at MS
Dummy decoupling
Subsidies Income FADN
Natural condition
Farm structure
Sebastian Neuenfeldt, Alexander Gocht
5. Results – contribution to R^2 – EU-12
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PL RO CZ BG HU SK EE SI LV MT EU12
Prices at MS
Prices FADN
Population
Economic indicators at MS
Dummy decoupling
Subsidies Income FADN
Natural condition
Farm structure
Sebastian Neuenfeldt, Alexander Gocht
6. Forecasting – Example Germany – autoregressive development
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Sebastian Neuenfeldt, Alexander Gocht
6. Forecasting – Example Germany – short term shock (20% increase of milk price in 2013, 2018)
22.10.2015 The analysis of farm structural change in the EU-27Page 20
Sebastian Neuenfeldt, Alexander Gocht
6. Forecasting – Example Germany – long term shock (cont. increasing milk price up to 100% in 2020)
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Sebastian Neuenfeldt, Alexander Gocht
7. Conclusion / Outlook
• First approach EU-wide for all farm groups country specific
• Depends on stratification stratification adaptable!
• The importance of the different explanatory variables as well as the influence are very heterogeneous across the European countries
• Yet to be done:
• Forecasting with „future values“ of explanatories
• Forecasting number of farms in a region per farm population
• Match results with and feed farm programming models (stratification comparable to CAPRI farm types)
• Endogenize structural change
• Baseline and scenario analysis
22.10.2015 The analysis of farm structural change in the EU-27Page 22
Sebastian Neuenfeldt, Alexander Gocht
References
Cooper, L G and Nakanishi M (1988) Market-Share Analysis: Evaluating Competitive Marketing Effectiveness, Kluwer Academic Publishers, Boston. (pdf-edition 2010)
Gocht A, Röder N, Neuenfeldt S, Storm H, Heckelei T (2012) Modelling farm structural change : a feasibility study for ex-post modelling utilizing FADN and FSS data in Germany and developing an ex-ante forecast module for the CAPRI farm type layer baseline. Luxembourg: Publications Office of the European Union, 166 p, JRC Sci Techn Rep
Neuenfeldt S, Gocht, Heckelei T (2014) Projection Results of Farm Structural Change using FADN Database – D.4.3 FADNTOOL
Gocht A, Neuenfeldt S, Röder N (2015) The Analysis of farm structural change in the EU-28 (upcoming)
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