Empirical Evidence of the Distributional Effects of the CAP in the New EU Member States

Transcription

1 No. 58, August 2013 Pavel Caan, d'arts Kancs and Ján Pokrvčák Emprcal Evdence of the Dstrbutonal Effects of the CAP n the New EU Member States ABSTRACT Ths study nvestgates the mpact of the SAPS (Sngle Area Payment Scheme) on farmland rental rates n the new EU member states. Usng a unque set of farm level panel data wth 20,930 observatons for 2004 and 2005 we are able to control for mportant sources of endogenety. Accordng to our results, the SAPS has a postve and statstcally sgnfcant mpact on land rents n the EU. However, the estmated ncdence s smaller than predcted theoretcally. Land rents capture only 19 cents of the margnal SAPS euro, and around 10% of the SAPS beneft non-farmng landowners through hgher farmland rental prces. As the share of rented land s hgher n corporate farms than ndvdual ones, famly farms beneft more from the SAPS than corporate farms do. Key Words: Agrcultural polcy, decoupled subsdes, captalsaton, land value JEL classfcatons: L11, Q11, Q12, Q15, Q18, P32, R12 FACTOR MARKETS Workng Papers present work beng conducted wthn the FACTOR MARKETS research project, whch analyses and compares the functonng of factor markets for agrculture n the member states, canddate countres and the EU as a whole, wth a vew to stmulatng reactons from other experts n the feld. See the back cover for more nformaton on the project. Unless otherwse ndcated, the vews expressed are attrbutable only to the authors n a personal capacty and not to any nsttuton wth whch they are assocated. Avalable for free downloadng from the Factor Markets ( and CEPS ( webstes ISBN Copyrght 2013 Pavel Caan, d'arts Kancs and Ján Pokrvčák FACTOR MARKETS Coordnaton: Centre for European Polcy Studes (CEPS), 1 Place du Congrès, 1000 Brussels, Belgum Tel: +32 (0) Fax: +32 (0) E-mal: nfo@factormarkets.eu web:

2 Contents 1. Introducton Agrculture n the NMS Rental market for agrcultural land Sngle Area Payment Scheme (SAPS) Theoretcal framework The model Other determnants of the SAPS captalsaton Econometrc specfcaton Data and varable constructon Results Conclusons References Appendx Lst of Tables and Fgure Table 1. Defnton and summary statstcs of key varables Table 2. Frst-dfference estmates wth farm fxed effects Table 3. Robustness checks Table 4. Farm sze, land rentng and non-farmng land owner gans from the SAPS Fgure 1. Effect of SAPS n the land market... 21

3 Emprcal Evdence of the Dstrbutonal Effects of the CAP n the New EU Member States Pavel Caan, d'arts Kancs and Ján Pokrvčák * Factor Markets Workng Paper No. 58/August Introducton Snce ther accesson to the EU n 2004, the new EU member states (NMS) 1 have receved consderable amounts of farm ncome support through the Sngle Area Payment Scheme (SAPS). The SAPS brought two major changes to farmers n the NMS. Frstly, the swtch to the SAPS' decoupled polcy support from farm producton. Secondly, t ncreased the average payments per farm (Caan et al., 2010). In addton to these drect effects, the SAPS also nduces ndrect changes n agrcultural nput and output markets. The dstrbutonal effects of agrcultural polcy, whch Alston and James (2002) refer to as the ncdence of agrcultural polcy, have been studed extensvely n the lterature. Theoretcal models have shown that, due to nelastc land supply, area payments may be captalsed nto land values and beneft landowners nstead of farms (Floyd, 1965; Alston and James, 2002; Guyomard et al., 2004; Caan et al., 2008). Whle the theory s able to rank clearly the varous types of agrcultural subsdes by ther ncdence level, the emprcal evdence s less conclusve on the exact captalsaton rate. Studes on area-based subsdes fnd a captalsaton rate of between 0.2 and 1.0 (e.g. Kuchler and Tegene, 1993; Barnard et al., 1997; Patton et al., 2008; Breustedt and Habermann, 2011). For example, Kuchler and Tegene (1993) estmate the mpact of land tax on land rents usng data from seven states n the US, 2 and fnd that all tax costs are ncurred by landowners, whch mples ther full (negatve) captalsaton nto land rents. Patton et al. (2008) nvestgate the mpact of varous types of CAP drect payments on land rental values n Northern Ireland for the perod , and fnd that less favoured area payments are fully captalsed nto land rents. 3 Patton et al. also fnd that area-based subsdes lead to a hgher captalsaton rate nto land values than other types of subsdes. On the other hand, Breustedt and Habermann (2011) estmate the margnal captalsaton rate for EU coupled area payments at 0.38 n the German federal state of Lower Saxony. Studes on non-area-based subsdes also fnd a captalsaton rate of between 0.2 and 1.0 whch, accordng to theoretcal predctons, should be less captalsed * Pavel Caan, European Commsson (DG JRC) and Slovak Agrcultural Unversty n Ntra (pavel.caan@ec.europa.eu); d'arts Kancs, European Commsson (DG JRC), Catholc Unversty of Leuven (LICOS) and Economcs and Econometrcs Research Insttute (EERI) (d'arts.kancs@ec.europa.eu); Ján Pokrvčák, Slovak Agrcultural Unversty n Ntra (jan.pokrvcak@unag.sk).the authors thank partcpants at conference n Berln (EAAE) for very useful comments on the artcle. The authors acknowledge fnancal support from the European Commsson FP7 project Comparatve Analyss of Factor Markets for Agrculture across the Member States. The vews expressed are purely those of the authors and may not n any crcumstances be regarded as statng an offcal poston of the European Commsson. 1 In the present study the Czech Republc, Estona Latva, Lthuana, Hungary, Poland and Slovaka are referred to as NMS. In contrast, all those countres that were already EU members before the 2004 enlargement are referred to as the old EU member states (OMS). 2 A land tax s smlar to an area payment except that t represents a cost. 3 The less favoured area payment s an area-based payment and t s pad to farmers located n less productve regons. 1

4 2 CIAIAN, KANCS & POKRIVČÁK than area-based subsdes (Goodwn et al., 2003, 2005; Lence and Mshra, 2003; Roberts et al., 2003; Taylor and Brester, 2005). Most of the exstng studes focus on North Amerca (the US and Canada), and only a few authors consder subsdy captalsaton n the EU (Breustedt and Habermann 2011; Goodwn and Ortalo-Magné, 1992; Duvver et al., 2005; Patton et al., 2008). The present study attempts to fll ths gap and analyses the dstrbutonal effects of the SAPS n the NMS. More precsely, we estmate the captalsaton rate of the SAPS nto farmland rental prces n the NMS. There are three reasons for focusng on the NMS. Frst, there s no emprcal evdence n the lterature on subsdy captalsaton n the NMS. Secondly, the SAPS (whch are areabased payments) ntroduced n the NMS are fundamentally dfferent from the subsdy system n the old EU member states (OMS) (whch are farm-based payments). 4 In addton, the structural dfferences between farms n the post-plannng NMS economes and the Western European OMS requre that they are analysed separately. We focus on land rental prces, because these are the only data avalable for the NMS at farm level. Analysng rental prces (compared to land sales prces) has two advantages. Frst, they capture to a much lower extent non-agrcultural land use pressures, such as urban housng (Caan et al., 2010). Second, gven that rental prces are avalable at farm level (whch s not the case wth land sales prces), they allow us to address mportant sources of endogenety. A potental downsde of analysng rental prces s that due to long-term rental contracts they may adjust to changes n market condtons/polces more slowly. Our man contrbuton to the lterature s to provde the frst estmates on the SAPS captalsaton rate nto farmland rental prces n the NMS. Addtonally, the fndngs of our paper may provde addtonal nsghts nto the captalsaton rate of area-based subsdes, as there are not many examples of pure area-based payments mplemented n other regons of the world. 5 Usng a large panel of farm-level data for the NMS, we are able to address mportant econometrc ssues, such as unobservable heterogenety, whch cannot be dealt wth n regonal or cross-secton studes. The dstrbutonal effects of the SAPS have mportant polcy mplcatons for the NMS, where farmland rentng s consderably more mportant than n developed countres. On average, 52% of agrcultural land s rented (the share of rented farmland vared between 28% n Poland and 96% n Slovaka n 2010). At the same tme, the dual farm structure a characterstc feature of NMS' agrculture and the fact that large corporate farms (CFs) tend to rent most of the agrcultural land whle small ndvdual farms (IFs) mostly own agrcultural land (Caan et al., 2011), may lead to a dfferentated effect of the SAPS between IFs and CFs. Ths mples that the ncome effect of the SAPS wll lkely dffer between CFs and IFs and hence across countres n the NMS. 2. Agrculture n the NMS 2.1 Rental market for agrcultural land Land s one of the man nputs n agrcultural producton. Compared to the OMS, farmland rentng s consderably more mportant n the NMS. A partcularly large share of farmland s rented n Slovaka and the Czech Republc, where rented farmland represents more than 90% of the total utlsed agrcultural area (UAA). In Hungary, Estona and Lthuana land rentng 4 OMS mplement the Sngle Farm Payments (SFP) and for ths reason t was not consdered n the paper. 5 Kuchler and Tegene estmate the mpact of land tax whch may have dfferent mechansms for mpactng land values than area-based payments, whereas Patton et al. (2008) nvestgate the mpact of less favoured area payments appled only n selected EU regons, whereas Breustedt and Habermann (2011) consder area payments coupled to land cultvated wth cereals, olseeds, proten crops and set asde land.

5 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 3 also domnates (more than 60% n 2005). In Poland and Latva farms rent less than 50% of the total land they use (Caan et al., 2011). There are also szeable dfferences n rentng behavour between dfferent types of farms. On average, CFs tend to rent most of the land they use, whle IFs tend to use both owned and rented land (CFs rent 92% whereas IFs rent 36% of the UAA n the NMS). The dual farm structure n the NMS and the szeable dfferences n the rentng behavour between IFs and CFs suggest that the gans from the SAPS may vary substantally n the NMS, f landowners absorb part of the subsdes, e.g. through hgher rental rates. Usually, n the NMS land rents are pad at the end of the season (after harvest) n cash. Often land rents are calculated based on market condtons at the end of the season partcularly for n knd rents, where they depend on the observable land productvty. 6 When producton s low, e.g. due to unfavourable weather or market condtons, the rental arrangements stpulate that rent s reduced or not pad at all (Caan et al., 2010). 2.2 Sngle Area Payment Scheme (SAPS) Snce the 1992 MacSharry Reform and the Agenda 2000, the vast majorty of the CAP subsdes n the EU are the so-called drect payments. In 2010, 39.3 bllon were spent on drect payments n the EU (EUR-Lex, 2010). They made up to two-thrds of the CAP budget and hence were a central ssue n the NMS' accesson negotatons. Durng the negotatons, the NMS and OMS agreed that the drect payments would start at 25% of the OMS' level n the frst year after the accesson and contnuously ncrease over 10 years, reachng the full level n Gven the admnstratve complexty of the CAP, whch made full mplementaton of the Agenda 2000 unfeasble n a short tme perod, t was agreed that a smplfed verson of the CAP would be mplemented n the NMS under whch farmers receve a per hectare payment (.e. SAPS). Wth the excepton of Slovena, all other NMS from Eastern Europe adopted the SAPS from the frst year of the EU accesson. 8 Accordng to EU regulatons, the total SAPS payments wll ncrease from around 1.6 bllon n 2004 to around 6.2 bllon n 2013 n the NMS, whle per hectare payments wll ncrease from 53 to 212/ha n the same perod. There s a sgnfcant varaton n the SAPS payments between countres. On average, n the perod the lowest rate s n Latva (around 45% of the NMS average) followed by Estona (55%) and Lthuana (68%). The hghest SAPS rate s n Hungary and the Czech Republc (about 125% of the NMS average) (Agra Europe, 2007; European Commsson, 2010; EUR-Lex, 2010). These dfferences n the value of the SAPS per hectare between the NMS are because the total value of the SAPS payments allocated to each country were calculated based on hstorcal agrcultural producton levels. More precsely, the total value of drect payments per country was calculated as a sum of the crop and anmal drect payments whch farmers would receve under the Agenda Dfferent years between 1995 and 2001 were used as a reference 6 In several NMS land rents are also pad n knd or through sharecroppng. For example, n Poland more than 20% of land rents n 2005 were pad n knd. In Slovaka, only half of the farms pad rents exclusvely n cash, whle the other half of the farms pad part of the rent n cash and part of the rent n knd n Usually, the n-knd payments depend on the yelds, whch mples that the rent s drectly lnked to the contemporaneous land productvty (Caan et al., 2011). 7 In addton to the SAPS, the natonal budgetary resources (the so-called top-ups) are used to ncrease the drect payments by some addtonal 30%. In general, these payments are coupled to crop and anmal producton. 8 Slovena ntroduced the Sngle Farm Payments (SFP) and for ths reason t was not consdered n the paper. The SFP dffers from the SAPS wth respect to ts mplementaton (see further) and wth respect to ts mpact on land markets (see for example Courleux et al., 2008; Klan and Salhofer, 2008; Caan et al., 2008).

6 4 CIAIAN, KANCS & POKRIVČÁK perod for dfferent products n dfferent NMS. 9 The total value of drect payments represented the country's total celng for the SAPS, whch was dvded by the utlsed agrcultural area to obtan the per hectare SAPS payment. Ths mples that the current levels of the SAPS payments are determned by past producton the hgher the agrcultural producton n the reference perod the more support a country could obtan from the CAP. After a transton perod of ten years, all NMS whch adopted the SAPS wll have to swtch to the SFP ntroduced by the 2003 CAP reform n the OMS. 10 Under the SFP, the subsdes wll be gven as a fxed set of payments per farm. In other words, the SFP s an enttlement, the sze of whch depends on the elgble area. For an ndvdual farm the sze of the SFP depends on the cultvated area n the reference perod. Under the current regulaton f the NMS were to decde to mplement the SFP ther reference perod would be Theoretcal framework 3.1 The model The conceptual framework of the present study bulds on extensve theoretcal work on the dstrbutonal effects of agrcultural subsdes (Floyd, 1965; Alston and James, 2002; de Gorter and Melke, 1989; Gardner, 1983; Guyomard et al., 2004; Salhofer, 1996; Caan and Swnnen, 2006; Caan et al., 2008). Most studes adopt partal equlbrum models ether by assumng representatve farm models, or by consderng supply-demand market representaton of the analysed sectors. Followng Caan and Swnnen (2006), who analyse the ncome dstrbutonal effects of the SAPS n a partal equlbrum model, the representatve farm's output, f ( AT ; ), depends on the amount of land, A, and the non-ncreasng to scale producton technology, T, wth f > 0, f < 0 and f > The entre land s assumed to be owned by landowners, who rent A AA T t to the farm. 12 The maxmsaton of proft functon, = pf ( A; T ) ( r s) A, together wth market clearng condtons yelds: pf = r - s (1) A A = Sr ( ) (2) f = Dp ( ) (3) where p s the prce of farm output, r s the rental rate of land, s s the SAPS, Sr ( ) s the land supply, and Dp ( ) s the output demand. Equaton (1) s the farm's margnal equlbrum condton for land derved from proft maxmsaton. Equatons (2) and (3) are market clearng condtons for land and output, respectvely. The market clearng condtons mply 9 Intally, the NMS wanted to use the reference perod from the end of 1980s, when producton was hgh because of the hgh subsdsaton of the agrcultural sector durng the Communst perod. Ths perod was not accepted by the European Commsson because t would sgnfcantly ncrease the CAP expendtures and create huge market dstortons n the NMS. 10 Accordng to EU regulatons, the NMS can keep the SAPS untl 2013, after whch tme they wll have to swtch to the SFP (EUR-Lex, 2003a 2003b; European Commsson, 2008; EUR-Lex, 2009). 11 f and f are frst and second dervatves of the producton functon wth regards to ts arguments, respectvely. 12 Ths dstncton between landowners and farmers s convenent for our explanaton but s not essental for the analyss and the derved results (Caan et al., 2008).

7 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 5 that n equlbrum land demand, A, and producton supply, f ( AT ; ), of the representatve farm s equalsed to land supply and output demand, respectvely. 13 Totally dfferentatng equlbrum condtons (1) (3) yelds the followng captalsaton effect of the SAPS (see Appendx for dervatons): dr ds = 1 pf AA 1 2 f A A p ε fς + r where ε s land supply elastcty, and ς s output demand elastcty. Wth f < 0, ε > 0 and AA ς < 0, the second and thrd terms n the denomnator on the rght hand sde of equaton (4) are negatve, mplyng that the value of the whole denomnator s postve. Overall the captalsaton rate s between zero and one, 0 dr / ds 1. Equaton (4) suggests that the captalsaton of the SAPS nto land rent, r, s ncreasng n the SAPS. 14 In addton, the captalsaton of the SAPS also depends on output prce, output demand, and land supply. The output demand elastcty, ς, measures the rate of output prce adjustment to farm producton change. The SAPS stmulates land use and hence output supply, whch exerts downward pressure on output prce resultng n ncome losses to the agrcultural sector. The output prce declne decreases n the demand elastcty, mplyng an nverse relatonshp between the SAPS and land rate: the captalsaton rate of the SAPS s ncreasng n demand elastcty. The land supply elastcty, ε, reduces landowners' gans, whereas wth fxed land supply, ε = 0, landowners beneft the full value of the SAPS ( dr / ds = 1 ). Ths s llustrated n Fgure 1. The horzontal axs shows the quantty of land and the vertcal axs measures the rental prce and subsdes. The ntal land demand of representatve farm, L, s gven by a downward slopng curve derved from the margnal condton (1), where A = L(r). Land supply s shown by an upward slopng curve: S 0 for zero elastc supply ( ε = 0 ) and S 1 for supply wth postve elastcty ( ε > 0 ). Wthout the SAPS, the equlbrum set of land allocaton and land rent s A *, r *. The SAPS shfts land demand upward to L s. The equlbrum depends on land supply elastcty. Wth nelastc land supply the equlbrum set of land allocaton and land rent s A *, r s0*, mplyng that land use s not affected (A * s0 = A * ), and the SAPS s fully captalsed nto land rent, r * s0 - r * = s. Wth hgher land supply elastcty the SAP captalsaton declnes. As shown for land supply S 1, the equlbrum shfts to A s1*, r s1*. The rent adjustment s lower than the value of the SAPS, r * s1 - r * < s, because an upward adjustment n land use, A * s1 > A *, reduces the pressure on land rents. 3.2 Other determnants of the SAPS captalsaton In addton to the relatve elastctes of land supply and demand, the captalsaton rate of the SAPS also depends on other factors (Caan et al., 2011). Accordng to Alston (2007), Krwan (2009), Plantnga et al. (2002) and Caan et al. (2011), the share of subsdes whch gets captalsed nto land values depends, among others, on polcy mplementaton detals, expectatons about changes n future polces, market mperfectons, and formal and nformal land nsttutons. (4) 13 Note that for notatonal smplcty we assume one representatve farm operatng n the agrcultural sector and behavng compettvely. Ths mples that the entre land cultvated by the representatve farm also represents the aggregate market demand for land. Ths representaton of farmng sector s equvalent to assumng N number of homogenous farms. 14 Caan and Swnnen (2006) have shown that these results also hold n mperfectly compettve land markets. In several NMS the farm sector s domnated by large corporate farms. For example, n countres such as the Czech Republc and Slovaka large corporate farms cultvate more than 70% of the agrcultural land. The domnance of corporate farms may allow them to exercse market power on the land market by beng able to alter the market rental prce.

8 6 CIAIAN, KANCS & POKRIVČÁK The captalsaton of the SAPS may be affected by the condtonalty on fulfllng certan farm management crtera. Generally, the SAPS provdes for a flat-rate, per-hectare payment to farmers pad once a year, rrespectve of the crops produced, or whether any crops are produced. However, n order to be elgble for the SAPS, farmers are requred to mantan land n good agrcultural condton. 15 If mantanng land n good agrcultural condton results n addtonal costs for farms, then the effect of the SAPS on land rents wll be smaller than theoretcally predcted. Accordng to Caan et al. (2011), the cross-complance costs are hgher n less productve areas, where land would be abandoned n the absence of the SAPS. In productve agrcultural regons t s proftable to keep land n good agrcultural condton even wthout the SAPS. The effect of the SAPS on land rents may also be affected by land rental contracts and rural nsttutons. Both formal and nformal land rental contracts mply that the transmsson of changes n polcy nto land rental prces s not nstantaneous. Rental arrangements are typcally mult-year n ther nature and cover ether wrtten contractual arrangements between farmers and landowner or reflect long-term personal relatonshps n rural communtes, sometmes among members of the same famly (Robson et al., 2002; Raney et al., 2005). Compettve pressures mght not take full and mmedate effect n such a settng (Gardner, 2002). Sluggsh adjustment of rental rates mples that the short- and ntermedate-run ncdence of polces wll be dfferent from the long-run outcome wth complete adjustment. Moreover, even wthout contractng, land markets nvolve lags and dynamcs, uncertanty and expectatons. The presence of long-term formal and nformal rental arrangements mght yeld lower captalsaton rates than predcted by the theoretcal lterature. Moreover, the sze of the bas may be country-specfc, because the types of rental contracts vary consderably between dfferent types of farms. Bg commercal type farms tend to apply wrtten mult-year types of rental contracts. Famly-type farms tend to use both wrtten and nformally arranged contracts (e.g. oral) but wth the latter beng more common. The evdence from Slovaka and Poland shows that, wth the EU accesson, the duraton of land rental contracts has ncreased (Caan et al., 2011). In Slovaka before the EU accesson contracts tended to be shorter (up to fve years). After the accesson, contracts became longer to allow farms to use European funds such as the CAP rural development payments. A rse n the number of long-term rental contracts was also observed n Poland. Long-term contracts (of a duraton of more than 10 years) ncreased from 46% to 66% n 2005 relatve to 2000 (Caan et al., 2011). The current farm behavour may be subject to uncertantes about polcy changes (OECD, 2001; Shak et al., 2005). When farmers, landowners and other market partcpants make ther decsons, they take nto consderaton the polcy rsk related to uncertanty about future subsdes. The SAPS s mplemented n the NMS for ten years - from 2004 to By the end of 2013 the SAPS wll be replaced by the SFP, whch s currently mplemented n the OMS. At the tme of the SAPS' ntroducton, the exact mplementaton date and reference perod for future payment calculatons were not known by farmers, because of frequent changes n future polcy desgn. For example, ntally the NMS had to swtch to the SFP by 2008 at the latest. Later ths date was shfted to Fnally, t was agreed to postpone the SFP mplementaton to 2013 (EUR-Lex, 2003a and 2003b; European Commsson, 2008; EUR-Lex, 2009; Bureau and Wtzke, 2010). 4. Econometrc specfcaton Accordng to the theoretcal model (4), changes n land rental rates are determned by the SAPS, output prce, output demand, and land supply. In addton, farm-specfc 15 The man objectve of ths requrement s to ensure that f land s kept fallow but used to clam the SAPS area payment then ths land s mantaned such that t can be brought nto agrcultural producton at mnmum cost.

9 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 7 characterstcs, such as market return, yeld, sectoral specalsaton, farm sze, revenue and expendtures and farm access to credt also affect the rental prce for land: Δr = β Δs + β ΔX + Δη 1 2 (5) where, Δ r s change n land rent for farm, Δ s s change n the SAPS payment per hectare, Δ X s a vector of observable covarates, whch contans change n market return, other subsdes, economc sze of farms, rato of famly labour to total labour, and assets to labltes rato. The selecton of covarates s based on the fndngs of Caan et al. (2010) and Caan et al. (2011), who study the determnants of agrcultural land values n the EU. Smlar covarates were also used n other studes on the captalsaton of subsdes nto farm land rents (e.g. Lence and Mshra, 2003; Patton et al., 2008; Krwan, 2009). As usual, η s the resdual, whch s assumed to have fnte moments. The estmaton of the SAPS captalsaton s subject to several econometrc ssues. Frstly, omtted varables, whch determne both the land rental rate and the level of subsdes, may bas the subsdy-rental rate relatonshp. Secondly, a sgnfcant share of farms do not rent any land. Excludng them from the estmatons may result n sample selecton bas. Omtted varable bas. Many farm characterstcs that nfluence both subsdes and farmland rental rates cannot be observed. Even usng farm-level data not all farm characterstcs, such as farm-level sol propertes, farmer human captal and manageral sklls, can be observed by the econometrcan. Gven that unobserved productvty factors may confound the rental rate and subsdes, ths postve correlaton between subsdes and the unobserved factors that nfluence productvty mght result n an upward bas of captalsaton estmates and confound β as a measure of the effect of subsdes on rental rates. 16 Wthout addressng the 1 unobserved farm heterogenety, s would be correlated wth η t, and the resultng OLS estmate of β would be based. We control for permanent farm-level characterstcs that 1 cause β to be nconsstent, by employng panel propertes of the FADN data, and, lke 1 Krwan (2009), nclude tme-unvaryng farm fxed effects as an explanatory varable. In our model the farm effect s absorbed by frst-dfferencng the data. 17 A further potental source of omtted varable bas s farmers' expectatons about changes n future polces. As detaled n the prevous secton, n 2013 the current SAPS payments n the NMS wll swtch to the SFP, whch s mplemented n the OMS. These polcy changes n future payments have two characterstcs, whch are mportant for the emprcal analyss: ) accordng to the legslaton n force the elgble area per farm wll be calculated based on the current land use; and ) nformaton about the most lkely changes to future polces s already avalable to farmers now. In order to ncrease ther subsdes n the post-2013 perod, proft maxmsng farmers (and landowners) mght adjust ther current rentng behavour. Ths mples that change n the rented area and change n the rental rate may be affected by future subsdes. Ths source of bas can be addressed by ncludng future subsdes as an explanatory varable on the rght-hand sde. At country level, the post-2013 SFP subsdes are approxmately known - they wll be at the SAPS level n Therefore, n order to account for the expected 16 Prevous studes (e.g. Lvans et al., 2006) have attempted to overcome the omtted varable bas by ncludng observable sol characterstcs as controls. However, because of a hghly non-lnear relatonshp between sol characterstcs and productvty, usng sol characterstcs as controls cannot fully overcome the omtted varable bas. 17 Note that n a panel wth t = 2, the coeffcents estmated from frst dfference data wll be dentcal to those obtaned by ncludng ndvdual fxed effects.

10 8 CIAIAN, KANCS & POKRIVČÁK polcy changes n future subsdes, we nclude varable future polcy changes, varable n equaton (5) defned as SAPS change n 2013 relatve to g, as a control Due to farm heterogenety and market mperfectons, such as credt constrants, not all farms are equally able to adjust ther rented area subject to long-run (post-2013) proft maxmsaton. Assumng that land markets are n equlbrum, only those farms makng postve profts would be able to ncrease the rented area as, margnally, any ncrease n farm sze would yeld negatve profts, whch can be compensated only by the most productve farms. Ths mples that g s farm-specfc. In addton to farm productvty, the regonal productvty also matters, as usually farms compete for land wthn the same regon. The hgher the average productvty n a regon the more farms wll compete for addtonal land, the greater the upward pressure on rents and, n relatve terms, the less compettve a partcular farm wll be on the land market wthn that regon. Ths mples that farm-level productvty s postvely correlated wth the post-2013 subsdes whereas regonal productvty s negatvely correlated wth them, g. In order to account for farm-level and regonal productvty, whch determne farms' ablty to adjust ther current rentng behavour wth respect to future subsdes, we condton the country-level future subsdes on the relatve farm productvty. Selecton bas. A sgnfcant number of farms n the FADN data set do not rent any land. Because of mssng left hand sde varables, these farms are excluded from our sample. If the farm rental decson s non-random, then the standard estmaton approach would result n based estmates. Indeed, one may expect that more dynamc farms and/or those wth lmted own land resources may be more nclned to partcpate on the land rental market compared to less dynamc, part-tme and/or subsstence farms. Smlarly, more productve and dynamc farms receved a hgher per hectare subsdy as, on average, they cultvate more productve, non-margnal land and have a hgher share of 'subsdy hectares' n the total cultvated area. Gven that farms wth zero rentals drop out of equaton (5) (as ther land rental prces do not exst), the farm rental selecton mght potentally bas the subsdy coeffcent. To control for the selecton bas related to farms' rental market partcpaton decsons, we employ the Heckman's sample selecton model (Heckman, 1979), and adopt a two stage estmaton approach. In order to calculate the Inverse Mlls Rato (IMR), we examne the determnants of farms' decsons to rent agrcultural land usng a Probt model by assumng that the error term follows a standard normal dstrbuton. 19 The frst stage ncludes both the land characterstcs and the characterstcs of the farm, whch are lkely to nfluence the propensty to rent land. The farm covarates are as follows: whether the farm s a famly farm, whether farmng s the prncpal occupaton, whether the farm sze has ncreased compared to the prevous year, whether the number of hours worked by own labour s larger than the number of hours worked by hred labour. The coeffcents from the frst step Probt regresson have no drect nterpretaton (beng smply the values that maxmse the lkelhood functon), but they allow us to calculate the IMR. The IMR s calculated as the rato of the probablty densty functon to the cumulatve dstrbuton functon. In the second stage, we estmate the rental equaton, where the selecton bas s controlled by ncludng the IMR computed n the frst stage. 20 If the IMR s sgnfcant and negatvely sgned n the 18 As a robustness test, we also experment wth the year 2004 and the average of 2004 and The obtaned results are smlar both n terms of sgn and magntude and therefore not reported. Note that n the presence of rgd rental markets, the varable future polcy changes may capture not only the mpact of the expected future shft to SFP on land rents but also the affect of hgher future SAPS payments whch gradually ncrease from 25% of the OMS' level n 2004 to 100% n The dependent varable h s a dchotomous ( 1, 0 ) varable ndcatng whether the -th farm rented land or not. 20 One potental lmtaton of the Heckman method s that f the Heckman's selecton model s not well-specfed, the IMR may be weaker than expected and the Heckman method may have lmted power to detect bas. As a result, a second factor to examne followng the addton of the IMR varable

11 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 9 second stage, t suggests that the error terms n the selecton and prmary equatons are negatvely correlated, mplyng that the (unobserved) factors that ncrease the probablty of land rentng tend to be assocated wth lower land rents (hgher land rents for sgnfcant and postvely sgned IMR). In both cases, the sample selecton bas needs to be controlled for n the land rental model by ncludng the IMR as an addtonal covarate. Although the FADN samplng strategy s representatve and theoretcally consstent, the estmaton of equaton (5) could suffer also from attrton bas. The FADN s an unbalanced panel, where every year 5 to 20% of farms are dropped from the sample. Farms are excluded ether because of the FADN samplng strategy of regular annual replacement of observatons and/or because of other reasons (voluntary drop-out, ext from farmng). If some groups of farms drop out from the sample more frequently than others, then the standard estmators would yeld based results. Therefore, we also test for the attrton bas usng the Heckman's sample selecton model. We fnd no sgnfcant mpact on the estmated coeffcents. 21 In order to control for unobserved productvty dfferences between regons, we nclude tmevaryng regon fxed effects, R n the vector of observable covarates X, where j stands for j FADN regons (see further). The tme-varyng regon-specfc effect, R j, captures localsed effects that mght affect farmland rents, and allows for transent shocks, such as weather or pests that affect all farms wthn a localsed regon. As a result, the tme-varyng fxed-effects model (6) s resstant to bas caused by weather and pest dfferences between regons, whch vary over tme, and, at least theoretcally, mght have affected the calculated per-hectare subsdy n the reference perod. Furthermore, n order to capture rental prce dfferences due to farm specalsaton and farm type, n the estmaton of equaton (6) we also nclude sector and farm-type dummes As a result, we obtan the followng estmable tme-varyng fxed-effects model: where g = g( φ / φ ) Δr = R + β Δs + β ΔX + β Δg + β ΔIMR + Δη j (6) Δ captures the mpact of future subsdes, where φ r and φ r are farm-level and regonal total factor productvty (TFP) measures, respectvely, and IMR s the Inverse Mlls Rato. Equaton (6) s the fnal emprcal specfcaton whch we estmate. The coeffcent of nterest s β, whch measures the share of each margnal SAPS euro that s captalsed nto land 1 rents. 5. Data and varable constructon The man source of data s the Farm Accountancy Data Network (FADN), whch s compled and mantaned by the European Commsson. The FADN s a European system of sample nto the ntal specfcaton models s whether or not there have been sgnfcant changes n any of the parameter estmates. 21 Smlarly to n the case of selecton bas related to farms' rental market partcpaton, n the frst stage we examne the determnants of farms' decsons to ext the sample usng the Probt model. In the second stage, we estmate the rental equaton wth correctng for the selecton bas by the ncluson of the Inverse Mlls Rato computed n the frst stage. Because the estmated results are not affected sgnfcantly, we do not follow ths approach. 22 The sectoral dummes capture 14 dfferent farm specalsaton types, whch change over tme, among other reasons, due to crop rotaton and other agronomc requrements. Farm-type dummes capture the restructurng of farms n the transton economes. Both farm sectoral specalsaton and farm type may affect productvty and hence rental prce. 23 We also experment wth country level varables, but ther magntude turns out to be nsgnfcant.

12 10 CIAIAN, KANCS & POKRIVČÁK surveys that take place each year and collect structural and accountancy data on EU farms. 24 In total there s nformaton about 150 varables on farm structure and yeld, output, nputs, costs, subsdes and taxes, ncome, balance sheet, and fnancal ndcators. The yearly FADN sample covers approxmately 18,000 agrcultural farms n the seven NMS whch mplemented the SAPS. In 2004, they represented a populaton of almost 1,000,000 farms, coverng approxmately 90% of the total utlsed agrcultural area and accountng for more than 90% of the total agrcultural producton n the NMS. Farm-level data are confdental and, for the purposes of ths study, accessed under a specal agreement. The FADN s unque n the sense that t s the only source of harmonsed data (the bookkeepng prncples are the same across all EU member states) and s representatve mcro-economc data n the EU. Farms are selected to take part n the survey on the bass of samplng plans establshed at the level of each regon n the EU. The FADN survey does not, however, cover all farms n the EU, but only those whch are of a sze allowng them to rank as commercal holdngs (FADN, 2010). In the present study, we use a sub-sample of the FADN data, whch covers seven NMS mplementng the SAPS. From the FADN data for two years (2004 and 2005) we create a balanced panel of farmng operatons. Although the total number of farms s roughly equal over the two years, ths masks a great deal of turnover. The populatons of nterest are those farms surveyed n both years. Ths leaves us wth 10,465 farms observed over two years, whch yelds 20,930 observatons n total. A summary of statstcs s provded n Table 1. The dependent varable farmland rental rate s constructed from the FADN data. The FADN does not report the rental rate. However, t reports the total amount of rent pad for farm land and rental charges (SE375), 25 and the utlsed agrcultural areas rented by the holder under a tenancy agreement for a perod of at least one year (SE030). From these two varables we construct the per-hectare rental rate by dvdng the total rent pad by the hectares rented. The explanatory varable the SAPS payments s also constructed from the FADN data. Every agrcultural producer n the FADN sample s asked to report both the total subsdes receved as well as to specfy the amount by subsdy type receved. Among the dfferent types of receved subsdes, farmers also report the Sngle Area Payments (SE632). We obtan the per-hectare SAPS payments by dvdng the total SAPS payments by total hectares (SE025). Accordng to the FADN data, n 2004 the SAPS accounted for 23% of all the subsdes n the NMS. 26 The dentfcaton of the rental prce change due to the SAPS s gven by three sources of SAPS varaton. The frst source s the nterregonal varaton of SAPS. Dfferent regons have 24 The surveys are conducted by natonal authortes. 25 The rental costs n the FADN data nclude not only farm land rents, but also rents for buldngs and other rental charges. We made an attempt to correct ths data ssue (e.g. by excludng hgh value rents whch may represent rental for buldngs), but one must take ths nto consderaton when nterpretng the results because the data may stll contan a measurement error bas. 26 The remanng 77% of subsdes are coupled payments and nclude subsdes such as crop and lvestock coupled payments, nvestment payments, envronmental payments, etc. These payments are allocated to farmers ndependently of the SAPS. In general, they depend on the current actvty level of the farm at whch they are targeted on (e.g. cultvated area of supported crop, stock of supported lvestock, supported farm envronmental practces). Ther exact value s set by the EU CAP regulaton. However, there s a tendency to support the lvestock sector through these payments n some NMS. The man objectve s to offset the dsadvantage of a lower farmland area avalable on lvestock farms whch may result n lower SAPS payments allocated to ths type of farm. Ths mples that the total value of coupled payments mght be correlated wth the total value of the SAPS but ther hectare values are ndependent of each other because the SAPS per hectare value does not depend on the farm actvty level. To control for ther potental mpact on land rents, we ntroduce the varable other subsdes n the estmated equaton.

13 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 11 dfferent values of subsdes, hence leadng to year-to-year regonal varaton n the hectare value of SAPS. Secondly, farms whch use non-elgble land wll have lower SAPS per hectare than farms wth all land qualfyng for the SAPS. Thrdly, although farms are elgble for the payments, n some cases the payment s clamed by landowners. These farms experence lower levels of SAPS and hence are less lkely to bd rental prce up. However, ths effect may lead to underestmaton of the mpact of the SAPS on land rent, because actually landowners beneft from the SAPS as they are drect payment recpents. There are no data avalable to account for ths effect. Accordng to the data, the coeffcent of varaton (standard devaton dvded by mean) of the frst dfferenced hectare values of SAPS (2005 relatve to 2004) vares between 0.4 and All three sources of varaton pose a lmted endogenety problem as both are ndependent of farm behavour and tend not to be lnked to farm characterstcs. The frst source of varaton s determned by past regonal productvtes 27 and the second and thrd sources are determned by polcy and market condtons, respectvely. The land elgblty s mposed by the polcy whereas whether farms or landowners apply for the SAPS s determned by the nsttutonal settng of land rental contracts. Smlarly, the covarates are constructed from the FADN data. Ther selecton s based on the fndngs of Caan et al. (2010) and Caan et al. (2011), whch study the determnants of agrcultural land values n the EU. Smlar covarates were used by other studes on captalsaton of subsdes nto farm land rents (e.g. Lence and Mshra, 2003; Patton et al., 2008; Krwan, 2009). All covarates except ratos are measured on a per-hectare bass. 28 Gven that agrcultural productvty s one of the key determnants of farmland rental rates, we nclude a varable capturng market return. The FADN reports the total output (SE131), whch s constructed by addng to total sales and own use of crop lvestock and lvestock products, the change n stocks of products, the change n valuaton of lvestock and subtractng the total purchases of lvestock. We construct the market return varable by dvdng the total output (SE131) by the total utlsed agrcultural area (SE025). A varable capturng all other subsdes s drectly avalable n the FADN data set by subtractng SAPS (SE632) from the total subsdes (SE605) and dvdng by the total area. In order to capture the hgher market power of large farms on land rental markets, we nclude a varable capturng the economc sze of farms, whch s also avalable n the FADN data set (SE005). Economc sze of holdng s expressed n European sze unts on the bass of the EU typology. 29 Gven that land rent may ncrease wth the share of famly labour due to productvty dfferences between famly and hred labour, we nclude the relatve share of famly n total labour. The rato of famly labour/total labour s constructed by dvdng unpad labour nput (SE015) by total labour (SE010) (unpad plus pad labour nput). Both are drectly avalable n the FADN data. Fnally, n order to account for farm access to credt, n the regressons we also nclude the assets to labltes rato by dvdng total fxed assets (SE441) wth long and medum-term loans (SE490) plus total fxed assets (.e. SE441/(SE441 + SE490)). The total fxed assets capture agrcultural land and farm buldngs and forest captal, buldngs, machnery and equpment, and breedng lvestock. The varable long and medum term loans (SE490) captures all loans contracted for a perod of more than one year. In addton, n order to account for farmer adjustments wth respect to the future SFP payments, we construct a varable called 'Future polcy changes', g. From Eurostat and Agra Europe (2007) we extract approxmate future SFP payments by country. Then we subtract the SFP payments n 2013 from the SAPS n 2005 to obtan future subsdy change. As 27 As a robustness test, we also perform estmatons by MS, but we do not fnd sgnfcant dfferences compared to the full sample results. 28 The per-hectare form s chosen as t allows for a more natural nterpretaton of the coeffcents. As a robustness test, we also perform the analyss usng log-levels. The obtaned results are smlar to those presented n the paper. 29 The economc sze of holdng s expressed n European sze unts on the bass of the Communty typology the total standard gross margn n euro/1200.

14 12 CIAIAN, KANCS & POKRIVČÁK explaned above, at farm level the adjustment of current behavour wth respect to the future SFP payments depends on the relatve farm productvty. Both farm level and regonal productvty measures are obtaned by estmatng TFP usng the FADN data (see Kancs and Caan, 2010). Multplyng the relatve farm productvty by the future subsdy change yelds varable Future polcy changes. A summary of key varables s provded n Table 1. In addton, we construct sx regonal varables: wheat yeld, total factor productvty, share of CFs n total land use, total utlsed agrcultural area, populaton densty and GDP growth. The last three varables are extracted from the Eurostat database. The rest of the regonal varables are constructed from the FADN data. These varables are not ncluded n the man regressons, where regon-specfc effects are captured through regonal dummes. Instead, we use these varables for robustness tests by estmatng alternatve specfcatons wth tmevaryng regonal varables. To control for regon-specfc effects we ntroduce regonal dummes. Accordng to the FADN classfcaton of the European Unon, the seven NMS (Czech Republc, Estona, Hungary, Latva, Lthuana, Poland and Slovaka) are dvded nto 66 FADN regons. Regonal dummes capture regonal unobserved heterogenety whch represent common characterstcs for all farms n the regon but may dffer between regons such as nformal and formal land nsttutons, dfferences n clmatc condtons, and market mperfectons. We use two varables to measure productvty: wheat yeld and total factor productvty. The varable wheat yeld s drectly avalable n the FADN data (SE110) and s calculated as a weghted average wheat yeld for each regon. We use ths varable to capture productvty dfferences arsng from dfferences n weather, clmatc and other condtons between regons over tme. The total factor productvty s estmated usng the Olley and Pakes estmator and s averaged by regon (Kancs and Caan, 2010). In order to account for non-agrcultural pressures on land rental markets, n robustness tests we nclude populaton densty at regonal level, regonal GDP growth and country level percentage change (2005 relatve to 2004) n the total utlsed area. These varables are extracted from the Eurostat database. As dscussed n secton 2, CFs market power has mportant mplcatons for land rents. In order to proxy for the CFs market power, we calculate weghted average shares of CFs n the total land use for each regon. 6. Results We estmate equaton (6) usng a frst dfference estmator wth farm fxed effects. The results are reported n Table 2. Model (1) s the basc model. Models (2) and (4) control for (future polcy changes), whereas models (3) and (4) control for a potental sample selecton bas. All estmatons also contan regon, sector and farm type dummes (suppressed n Table 2). All estmated models suggest that the contemporaneous subsdes (SAPS payments and other subsdes) drve up land rents n the New EU Member States. The estmates are relatvely stable across the four models. When accountng for expectatons about future changes n subsdy payments (future polcy changes), the estmated ncdence of subsdes slghtly changes, whereas the change s stronger for other subsdes than for the SAPS. These estmates mply that, when accountng for the expected future changes n subsdy payments, the rental prce of farmland ncreases between 0.18 and 0.20 for each euro of SAPS payment. The captalsaton of the SAPS s hgher than the captalsaton of other subsdes. Ths result s n lne wth our expectatons, as land-based subsdes drectly ncrease the proftablty of land, whch ncreases the competton for land resources, leadng to hgher land rents. These results are n lne wth the underlyng theoretcal model, accordng to whch the SAPS may get captalsed nto farmland rents f land supply s nelastc. Gven that emprcal studes fnd rather low (between 0.1 and 0.6) land supply elastcty (Salhofer, 2001), landowners may beneft from a substantal share of the total value of the SAPS area payments through hgher rental prces.

15 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 13 The estmated ncdence of the SAPS payments s comparable to that of Krwan (2009), who fnds that farmers who rent the land they cultvate capture around 75 percent of the subsdy, leavng around 25% for landowners. Our results are slghtly hgher but of the same order of magntude as the estmates for the OMS. Caan et al. (2011) estmate the ncdence of the SFP (whch s not an area payment but a fxed payment per farm) at 6%. The results for the OMS are lkely lower, because the SFP s only partally coupled to farm land (Caan and Swnnen, 2006; Courleux et al., 2008; Klan and Salhofer, 2008). Thus, the emprcal fndngs of the present study, of Krwan (2009) and of Caan et al. (2011) contradct the predctons of neoclasscal models. Accordng to the theoretcal lterature (Floyd, 1965; Alston and James, 2002; de Gorter and Melke, 1989; Gardner, 1983; Guyomard et al., 2004; Salhofer, 1996; Caan and Swnnen, 2006), land-based subsdes get captalsed to a large extent nto land rents. The standard predcton may not hold due to other factors constranng the adjustment of land rents, such as, mperfectons n the farmland rental market, the presence of long-term rental contracts and nformal nsttutons n the land rental markets n the NMS. Due to the fact that our estmates are based only on two years' observatons, we may not fully capture the long-term adjustments n land rents, whch may result from full renewal of rental contracts and thus further adjustment to SAPS. Secondly, the nvestgated years (2004 and 2005) were the frst two years of the SAPS' mplementaton n the NMS and land market agents were not famlar wth ths type of subsdy. Moreover, there were delays n the dstrbuton of the SAPS payments to farmers, whch mght have ncreased farm uncertanty and/or delayed subsdy captalsaton nto land values. These ssues should be addressed n future research, when more recent data become avalable. Smlarly to contemporaneous subsdes, the expected change n future subsdes (future polcy changes) nduces a postve and sgnfcant mpact on land rents. Ths mples that future subsdes affect contemporaneous farmland allocaton decsons n the NMS. These results are n lne wth studes argung that future subsdes may affect current farm decsons, therefore farmers may react dfferently to polces than expected (OECD, 2001; Shak et al., 2005). As expected, market return has a postve and sgnfcant mpact on land rents (Table 2). These results confrm prevous fndngs that agrcultural proftablty s an mportant determnant of farmland rental rates (Goodwn et al., 2003, 2005; Lence and Mshra, 2003; Roberts et al., 2003). The estmated coeffcent s statstcally sgnfcant at 1% n all estmated models. Smlarly, the relatve mportance of famly labour n total labour (famly/total labour) has a postve and sgnfcant mpact on land rents, though ts sgnfcance s lower. These results are n lne wth the lterature on farm structure (Pollak, 1985; Allen and Lueck, 1998), accordng to whch land rent may ncrease wth the share of famly labour due to productvty dfferences between famly and hred labour. Famly labour enhances farm productvty and leads to hgher land rents. However, the mportance of famly labour s less sgnfcant than the market return and subsdy varables. Farm sze has a negatve mpact on land rents; the coeffcent s sgnfcant n all models reported n Table 2. It may reflect the effect of the market power of large farms on land rental markets. At the same tme, ths varable may account for productvty dfferences between dfferent farm szes. The prevous lterature s not conclusve on the relatonshp between farm sze and productvty. The early lterature fnds an nverse relatonshp between farm sze and output per hectare suggestng that small farms are more productve than large ones (Feder, 1985). Ths result s explaned by credt market mperfectons, asymmetrc access to land and dfferences n famly labour endowment. Famly labour has a greater ncentve to work than hred labour, because t s a resdual clamant hence leadng to more effcent use of farm recourses. The more recent lterature fnds evdence aganst ths hypothess. Several recent studes have shown that when takng nto account, among other thngs, the adopton of new technology, sol qualty, regonal characterstcs, and development level, the nverse

16 14 CIAIAN, KANCS & POKRIVČÁK relatonshp between farm sze and productvty s reduced or dsappears (e.g. Bhalla and Roy, 1988; Deolalkar, 1981). Studes focusng on Eastern Europe manly analyse the productvty dfference between IFs and CFs. These studes typcally fnd that the relatve effcency depends on varous factors, ncludng the types of actvtes (e.g. crop, lvestock, vegetables), nsttutons, nfrastructure and economc condtons, whch n turn depend on the type of farm (e.g. Gorton and Davdova, 2004). The coeffcent of the CFs n land use at regonal level (not reported) s negatve and sgnfcant. Two effects may have been captured here: land transacton costs related to land wthdrawal from CFs and CFs market power. Caan and Swnnen (2006) show that both effects reduce land rents. 30 Hence, the hgher the share of CFs n land use n a partcular regon, the hgher the land transacton costs and the CFs market power are expected to be. The assets to labltes rato tends to reduce land rents, whch mples that farm access to credt may affect land rents negatvely. More credt per farm (the smaller the rato fxed assets/total labltes) mples hgher land rents and vce versa. The selecton bas was examned usng the Heckman's model. The coeffcent of the Inverse Mlls Rato, whch tests the mpact of rental market partcpaton bas, fals to be sgnfcant and parameter estmates of subsdes are to a large extent consstent wth the fxed effects estmates reported n Table 3. The fxed effect estmates of SAPS captalsaton nto land rents are found to be of smlar order of magntude as the frst dfference results reported n Table 2: farmland rents ncrease between 0.18 and 0.20 for each SAPS euro. The sgn and the magntude of the remanng varables are comparable to the results reported n table Conclusons In ths paper we analyse the ncdence of CAP subsdes n the new EU member states. Accordng to the underlyng theoretcal framework, n well-functonng land markets, area payments would be ncorporated nto land values and would thereby beneft manly landowners. On the other hand, they would ncrease the nput costs for farms. Certan factors, however, such as farm heterogenety, unequal access to the SAPS payments, formal and nformal land market nsttutons, accompanyng polcy measures and postve land supply elastcty may reduce the SAPS captalsaton nto land values. Ths s the frst paper to emprcally estmate the extent of captalsaton or the SAPS nto land values n the NMS and the frst to consder a standard area payment. Usng a unque farm-level panel data set wth 20,930 observatons for 2004 and 2005, we are able to control for mportant sources of endogenety. The estmaton results suggest a postve and statstcally sgnfcant mpact of the SAPS on land rents n the NMS. However, the effect s smaller than theoretcally predcted. Our estmates mply that 1 of the SAPS per hectare ncreases land rents by 0.19 per hectare. Ths mples that there are mportant constrants whch prevent full adjustment of land rents n the NMS. These could be, for example, due to nformaton asymmetres, long-term rental contracts, nformal rural nsttutons, and the short tme perod snce the SAPS' mplementaton. However, because n some NMS the payment may be clamed drectly by landowners and not by farmers, our estmate mght slghtly understate the total landowners' beneft from the SAPS. Based on the estmated SAPS ncdence and on the FADN data on land rentng, we can calculate the aggregate non-farmng landowner gans from the SAPS. The results are reported 30 Accordng to Caan and Swnnen (2006), land wthdrawal transacton costs ncrease the costs of rental transactons hence they reduce actvty on the rental market and push rental prces down. Corporate farms may not be prce takers n the land rental market. For example, n countres such as Slovaka, where they occupy around 90% of the land, CFs may have mportant market power and may affect rental prces. Caan and Swnnen (2006) use a domnant buyer model to show that rentng land n large quanttes allows CFs to affect market rents. Through ther rental behavour they may set the rental prce below the compettve market rent yeldng market power gan to CFs.

17 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 15 n Table On average, 10% of the total SAPS payments are channelled to non-farmng landowners through hgher rental prces n the NMS. The hghest leakages of the SAPS payments are n the Czech Republc and Slovaka, where non-farmng landowners gan approxmately 18% of the total SAPS value. They are followed by Estona, Hungary and Lthuana, where around 12% of the SAPS flow to non-farmng landowners. In Poland and Latva the leakages are the smallest, 5% and 8% of the SAPS payments, respectvely. These results suggest that the SAPS leakages are stronger for CFs than for IFs, because on average the share of rented land s larger for CFs than IFs. On average, CFs transfer to non-farmng landowners 17% of the total SAPS payments they receve, whereas IFs transfer on average only 7% of the total SAPS payments. Ths suggests that n relatve terms the dfference n land ownershp mples a hgher IF gan from the SAPS than s the case of CFs. However, the sze of CFs and IFs gans from the SAPS depends on the mpact of the SAPS on output and nput prces. We dd not nvestgate these effects n ths paper, although they are a promsng avenue for future research. Our results ndcate that the SAPS to a large extent s n lne wth the polcy objectve of mprovng the standards of lvng of the agrcultural communty, because the SAPS captalsaton rate nto land rents and ts leakage rate to non-farmng landowners are relatvely low. In 2010 the total value of the SAPS represented around 4.4 bllon (EUR-Lex, 2010). If we consder the 10% leakage rate of the SAPS to non-farmng landowners (Table 4), only around 0.44 bllon s channelled outsde the farmng sector. Secondly, the leakage rate s bgger for large CFs than for small IFs due to dfferences n land rentng patterns, whch also tends to be n lne wth the polcy objectve to promote smaller holdngs rather than large farm enterprses. The results n ths paper are subject, however, to some lmtatons. The presence of long-term rental contracts and basng our estmate on two years' data mean that we may not have been able to fully capture long-term adjustments n land rents. Addtonally, the short tme perod covered by the data and the delays n the dstrbuton of the SAPS payments to farmers mght have affected the actual captalsaton rate of the SAPS. These ssues should be addressed when more recent data become avalable and are a promsng avenue for future research. 31 We assume a 19% ncdence. To obtan the non-farmng landowner gans from the SAPS, we adjust the ncdence rate for the rental share. Implctly, we assume that the rental ncome from own land represents the return to farmers (.e. to farmng landowners).

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21 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 19 Table 1. Defnton and summary statstcs of key varables Varable Defnton Source Year Mean Stdev Dependent varable r Farmland rent (EUR/ha) FADN Explanatory varable s SAPS (EUR/ha) FADN Covarates m Market return (EUR/ha) FADN o Other subsdes (EUR/ha) FADN g Future polcy changes* EC, FADN z Farm sze (ESU) FADN h Famly labour-to-total labour rato FADN c Assets-to-labltes rato FADN Notes: The data are from the 2004 and 2005 confdental FADN mcrofles. *Own estmatons based on the Eurostat and FADN data. All monetary values are adjusted to 2004 EUR. Table 2. Frst-dfference estmates wth farm fxed effects Model (1) Model (2) Model (3) Model (4) SAPS *** *** *** *** (0.0259) (0.0358) (0.0243) (0.0349) Market return *** *** *** *** (0.0099) (0.0319) (0.0105) (0.0343) Other subsdes *** *** *** *** (0.0060) (0.0120) (0.0065) (0.0124) Future polcy changes *** *** - ( (0.0098) Farm sze ** *** ** *** (0.0192) (0.0112) (0.0184) (0.0111) Famly-to-total labour rato * ** * * (0.1458) (0.0959) (0.1513) (0.1053) Assets-to-labltes rato ** *** ** *** (0.0345) (0.0117) (0.0364) (0.0123) Inverse Mlls Rato (IMR) (1.9021) (1.8851) N R Notes: OLS estmates, robust standard errors corrected for heteroscedastcty n parenthess. All estmatons contan also regon, sector dummes and farm type dummes (suppressed). * sgnfcant at 10% level, ** sgnfcant at 5% level, and *** sgnfcant at 1% level.

22 20 CIAIAN, KANCS & POKRIVČÁK Table 3. Robustness checks Model (1) Model (2) Model (3) SAPS *** *** *** (0.0402) (0.0596) (0.0944) Market return *** *** *** (0.0131) (0.0117) (0.0239) Other subsdes *** *** *** (0.0093) (0.0073) (0.0161) Future polcy changes *** *** ** (0.0133) (0.0121) (0.0304) Farm sze ** * (0.0289) (0.0718) (0.4537) Famly-to-total labour rato * * (0.2186) (0.1684) (0.4355) Assets-to-labltes rato ** * (0.0312) (0.0516) (0.0928) N R Notes: Model (1): country-level fxed effects nstead of regonal-level fxed effects; model (2): ndvdual farms (IFs); model (3): corporate farms (CFs). OLS estmates, robust standard errors corrected for heteroscedastcty n parenthess. All estmatons contan also regon (country) and sector dummes (suppressed). * sgnfcant at 10% level, ** sgnfcant at 5% level, and *** sgnfcant at 1% level. Table 4. Farm sze, land rentng and non-farmng land owner gans from the SAPS Share of rented farmland, % Non-farmng land owner gans, % Total IFs CFs Total IFs CFs Czech Republc Estona Hungary Lthuana Latva Poland Slovaka NMS Notes: land owner gans, % - non-farmng land owner gans n percent of the SAPS; IFs- ndvdual farms, CFs - corporate farms. Source: own calculatons based on Table 2 and the FADN data for In all calculatons we assume a 19 percent ncdence.

23 EMPIRICAL EVIDENCE OF THE DISTRIBUTIONAL EFFECTS OF THE CAP IN THE NEW EU MEMBER STATES 21 Fgure 1. Effect of SAPS n the land market r S 0 S 1 r s0 * r s1 * L s s r * L 0 A * A s1 * A

24 22 CIAIAN, KANCS & POKRIVČÁK Appendx Dervaton of the SAPS captalsaton effect Totally dfferentatng equatons (1) - (3) yelds: pf AAdA + f Adp = dr ds (7) da = Srdr (8) f A da = D pdp (9) S( r) D( p) where S r = and D p = r p Rearrangng equatons (8) and (9) yelds: A da = ε dr (10) r S( r) r where ε = and ς = r A D p p f da f A Insertng equatons (10) and (11) n equaton (7) yelds: f = ς dp (11) p pf AA 2 f A A + p ε dr = dr ds fς r (12) dr Dvdng equaton (12) wth ds and solvng for yelds: ds dr ds = 1 pf AA 1 + p 2 f A A ε fς r (13)

25 Comparatve Analyss of Factor Markets for Agrculture across the Member States FP7-KBBE The Factor Markets project n a nutshell Ttle Fundng scheme Comparatve Analyss of Factor Markets for Agrculture across the Member States Collaboratve Project (CP) / Small or medum scale focused research project Coordnator Duraton Short descrpton CEPS, Prof. Johan F.M. Swnnen 01/09/ /08/2013 (36 months) Well functonng factor markets are a crucal condton for the compettveness and growth of agrculture and for rural development. At the same tme, the functonng of the factor markets themselves are nfluenced by changes n agrculture and the rural economy, and n EU polces. Member state regulatons and nsttutons affectng land, labour, and captal markets may cause mportant heterogenety n the factor markets, whch may have mportant effects on the functonng of the factor markets and on the nteractons between factor markets and EU polces. The general objectve of the FACTOR MARKETS project s to analyse the functonng of factor markets for agrculture n the EU-27, ncludng the Canddate Countres. The FACTOR MARKETS project wll compare the dfferent markets, ther nsttutonal framework and ther mpact on agrcultural development and structural change, as well as ther mpact on rural economes, for the Member States, Canddate Countres and the EU as a whole. The FACTOR MARKETS project wll focus on captal, labour and land markets. The results of ths study wll contrbute to a better understandng of the fundamental economc factors affectng EU agrculture, thus allowng better targetng of polces to mprove the compettveness of the sector. Contact e-mal Webste nfo@factormarkets.eu Partners 17 (13 countres) EU fundng 1,979,023 EC Scentfc offcer Dr. Hans-Jörg Lutzeyer