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Using administrative data to model CAP reform Sinéad McPhillips Economics & Planning Division Department of Agriculture, Food & the Marine Kevin Hanrahan.

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Presentation on theme: "Using administrative data to model CAP reform Sinéad McPhillips Economics & Planning Division Department of Agriculture, Food & the Marine Kevin Hanrahan."— Presentation transcript:

1 Using administrative data to model CAP reform Sinéad McPhillips Economics & Planning Division Department of Agriculture, Food & the Marine Kevin Hanrahan Agricultural Economics and Farm Surveys Department Teagasc

2 Overview Commission proposals on SFP DAFM analysis Irish “internal convergence” proposal Comparisons with other proposals Modelling by Teagasc on farm types Conclusions

3 COMMISSION PROPOSALS ON SFP Distribution of Direct Payments within Member States (‘internal convergence’): Progressive movement to uniform national or regional payment rates per hectare by 2019 Entitlements based on eligible hectares declared in 2014 by active farmers with at least one entitlement in 2011

4 DAFM analysis Modelling based on DAFM administrative data Objective: To quantify effects of Commission proposals, & to develop and propose alternatives Administrative data collected by DAFM (such as contained in the SPS application form) provides a wealth of useful data eligible area payment amount stocking density

5 2010 SPS database Average payment per hectare category, 2010No of farmersTotal Area2010 SPS Payment Average Area (ha) Average payment per ha (2010) 0 payment, some area7,955144,159018.10.00 0 to 201,96367,579771,20034.411.41 20 to 504,176179,2176,512,19442.936.34 50 to 10010,482397,13129,951,26337.975.42 100 to 15013,135423,44653,110,20132.2125.42 150 to 20015,462493,91986,753,34231.9175.64 200 to 25016,953571,978128,911,36333.7225.38 250 to 30016,709603,410165,984,64336.1275.08 300 to 40025,9361,025,283354,750,28539.5346.00 400 to 50011,084473,984209,656,00742.8442.33 500 to 6004,446197,559107,207,63344.4542.66 600 to 7001,81580,23951,594,06944.2643.01 700 to 80080333,00624,678,91441.1747.71 800 to 90037816,38813,801,28743.4842.13 900 to 1,0001675,9475,648,67735.6949.88 1,000+3387,7269,182,25122.91,188.44 All131,8024,720,9711,248,513,32935.8264.46

6 2010 SPS payment distribution

7 Models analysed Flat rate national Flat rate at NUTS 2 & NUTS 3 level Regions based on stocking density All resulted in large transfers within regions/local area as well as between regions

8 Example: Average payment per ha by NUTS III region, 2010

9 IRISH PROPOSAL ON INTERNAL CONVERGENCE “Approximation” - move towards the average Applies to the whole payment (green and basic) Based on commission’s proposals for external convergence Results; average gains of 29% for 65,000 farmers, average losses of 9% for 56,000. Those with highest payments lose most. 5 Member States supportive (Spain, Portugal, Italy, Denmark and Luxembourg)

10 Note: All figures are estimates only, based on modelling exercises carried out by DAFM, using eligible area and actual payments to farmers in 2010, in order to analyse the overall impact of alternative proposals on Irish farmers. Payment category (SPS euro per ha 2010)No of farmers% change compared to 2010 0 to 201,939+662% 20 to 504,129+185% 50 to 10010,350+72% 100 to 15012,998+30% 150 to 200*15,300+12% 200 to 238.0112,712+3% GAIN65,052+29% NO CHANGE: 238.02 TO 264.46 (90% to 100%)8,943- 264.47 to 30011,717-2% 300 to 40025,658-6% 400 to 50010,919-11% 500 to 6004,368-14% 600 to 7001,763-16% 700 to 800769-17% 800 to 900348-18% 900 to 1,000153-19% 1,000+221-21% LOSS55,916-9% TOTAL129,911+0% Irish Proposal – Internal Convergence Breakdown

11 OTHER PROPOSALS EMERGING However, other Member States have other ideas In addition, other proposals are coming from the European Parliament all the time – this is a moveable feast CAP reform now s.t. “ordinary legislative procedure”, i.e. co-decision of Council and Parliament

12 Note: All figures are estimates only, based on modelling exercises carried out by DAFM, using eligible area and actual payments to farmers in 2010, in order to analyse the overall impact of alternative proposals on Irish farmers. Commission proposals - national flat rate Capoulas Santos proposals on internal convergence Ireland's proposal - External convergence approach No. of farmers gaining 73,995 65,052 Average % loss +85%+56%+29% --8,943 No. of farmers losing 55,916 Average % loss -33%-23%-9% Total transfers €m €297m€197m€79m Comparative Analysis: Commission, Capoulas Santos (EP) and Irish Minister’s Proposals

13 MODELLING BY TEAGASC Adding data from the AIM and other DAFM databases (animal numbers and type) So as to allow farms to be categorised according to the FADN farm typology Similar approach to that used in Census of Agriculture typing of farms Useful for CAP negotiations Database could be adapted for a variety of analytical purposes

14 SPS Payment Share of FFI by Farm System (NFS 2010) impact on income of a euro change in subsidy depends on the farming system’s subsidy dependence Teagasc 2010 NFS (Hennessy et al. 2011)

15 Farms by Farm System and Economic Size 1 ESU = €1,200 SO S ≤ 8 ESU; 8 40 ESU

16 Flat Rate Payment Model (EC proposal) Winners and Losers by system W= 75,011 & L = 56,764

17 Results from Teagasc analysis Reform is a zero-sum game If there are losers there are winners/If there are winners there are losers Specialist dairying and tillage, which are more intensive systems, have more losers than winners, but still a substantial number of winners. Drystock farms, by contrast, have more winners than losers, but still have a surprising number of losers. Largest absolute gains/losses on those farms that are larger recipients of DP Larger relative gains on farms with smaller DP receipts Doesn’t make sense to talk about “cattle men winning” and “dairy men losing” – there are winners and losers in all farm types

18 Cattle Rearing: SPS subsidy/ha EC proposals

19 Cattle Rearing Farm System: SO/ha EC proposals

20 Dairy: SPS subsidy/ha EC proposals

21 Dairy Farm System: SO/ha EC proposals

22 CONCLUSIONS Detailed administrative data allows more precise modelling of the effects of policy change Can provide insights not provided by other data Particularly useful when comparing one proposal against another Still have to bear in mind that they are just models Not predictive of what will happen in the real world Cannot provide information on income or production effects


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