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Name des Wissenschaftlers Sebastian Neuenfeldt, Alexander Gocht Thünen Institute of Rural Studies, Braunschweig, Germany The analysis of farm structural.

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Presentation on theme: "Name des Wissenschaftlers Sebastian Neuenfeldt, Alexander Gocht Thünen Institute of Rural Studies, Braunschweig, Germany The analysis of farm structural."— Presentation transcript:

1 Name des Wissenschaftlers Sebastian Neuenfeldt, Alexander Gocht Thünen Institute of Rural Studies, Braunschweig, Germany The analysis of farm structural change in the EU-27

2 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 22.10.2015The analysis of farm structural change in the EU-27 Page 2

3 Sebastian Neuenfeldt, Alexander Gocht 2. Theoretical model Utility approach 22.10.2015The analysis of farm structural change in the EU-27 Page 3

4 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) 22.10.2015The analysis of farm structural change in the EU-27 Page 4

5 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 decoupling NUTS2 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) 22.10.2015The analysis of farm structural change in the EU-27 Page 5

6 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 22.10.2015The analysis of farm structural change in the EU-27 Page 6

7 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) 22.10.2015The analysis of farm structural change in the EU-27 Page 7

8 Sebastian Neuenfeldt, Alexander Gocht 4. Empirical implementation Empirical model 22.10.2015The analysis of farm structural change in the EU-27 Page 8

9 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) 22.10.2015The analysis of farm structural change in the EU-27 Page 9

10 Sebastian Neuenfeldt, Alexander Gocht 5. Results 22.10.2015The analysis of farm structural change in the EU-27 Page 10 MS/EU15 Residual 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% BL0.8660.963 766 DK0.0090.9978265112 DE5.1150.9581282 EL2.6850.95515474 ES6.5940.94617863 FR1.5260.9882743 IR0.0031.000310275 IT2.7980.96925781 NL1.7710.96014861 AT0.4040.99228525 PT3.2120.92027497 SE1.3400.963210563 FI0.1200.99439516 UK0.9150.98625742

11 Sebastian Neuenfeldt, Alexander Gocht 5. Results – UK 98.6% R^2 22.10.2015The analysis of farm structural change in the EU-27 Page 11

12 Sebastian Neuenfeldt, Alexander Gocht 4. Results – UK 98,6% R^2 Classification problems (merging SGM and SO classification) Structural breaks due to re-classifications Deviations due to smaller number of farm groups in a region  Erratic development in a region possible due to low number of farms  higher percentage change 22.10.2015The analysis of farm structural change in the EU-27 Page 12

13 Sebastian Neuenfeldt, Alexander Gocht 5. Results – NL 96% R^2 22.10.2015The analysis of farm structural change in the EU-27 Page 13

14 Sebastian Neuenfeldt, Alexander Gocht 5. Results – PT 92% R^2 22.10.2015The analysis of farm structural change in the EU-27 Page 14

15 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… 22.10.2015The analysis of farm structural change in the EU-27 Page 15

16 Sebastian Neuenfeldt, Alexander Gocht 5. Results – contribution to R^2 – EU-15 22.10.2015The analysis of farm structural change in the EU-27 Page 16

17 Sebastian Neuenfeldt, Alexander Gocht 5. Results – contribution to R^2 – EU-12 22.10.2015The analysis of farm structural change in the EU-27 Page 17

18 Sebastian Neuenfeldt, Alexander Gocht 6. Forecasting Why? To analyse the impacts of explanatory variable changes How? Generally: all explanatory variables kept constant  in t+1 farm population shares calculated  for each year  only the lagged farm population shares are changing and becoming changing explanatories  “autoregressive” model Specifically: one certain variable shocked  additional impact to the autoregressive development (weaken or strengthen the autoregressive development) Example Country Germany: horizon 2012:2020 Short run shock: 20% increase of milk price in 2013, 2018 Long run shock: continuously increasing milk price up to 100% in 2020 22.10.2015The analysis of farm structural change in the EU-27 Page 18

19 Sebastian Neuenfeldt, Alexander Gocht 6. Forecasting – Example Germany – autoregressive development 22.10.2015The analysis of farm structural change in the EU-27 Page 19

20 Sebastian Neuenfeldt, Alexander Gocht 6. Forecasting – Example Germany – short term shock ( 20% increase of milk price in 2013, 2018) 22.10.2015The analysis of farm structural change in the EU-27 Page 20

21 Sebastian Neuenfeldt, Alexander Gocht 6. Forecasting – Example Germany – long term shock ( cont. increasing milk price up to 100% in 2020) 22.10.2015The analysis of farm structural change in the EU-27 Page 21

22 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 22.10.2015The analysis of farm structural change in the EU-27 Page 22

23 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) 22.10.2015The analysis of farm structural change in the EU-27 Page 23

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