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-27


2. Theoretical model

Utility approach


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-


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)



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)



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



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)



4. Empirical implementation

Empirical model


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


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)



5. Results


MS/EU15Residual Sum of Squares

In Sample R2

Number of. variables

significant level <=10%


significant level <=5%


significant level <=0.1%


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


5. Results – UK 98.6% R^2



5. Results – PT 92% R^2

22.10.2015 The analysis of farm structural change in the EU-27Page 14


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…



5. Results – contribution to R^2 – EU-15


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


5. Results – contribution to R^2 – EU-12


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


6. Forecasting – Example Germany – autoregressive development



6. Forecasting – Example Germany – short term shock (20% increase of milk price in 2013, 2018)



6. Forecasting – Example Germany – long term shock (cont. increasing milk price up to 100% in 2020)



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



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)


name des wissenschaftlers sebastian neuenfeldt, alexander gocht thünen institute of rural studies,...

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