Russo, Sil ia; Raggi, Me i; Bimba i, Ba ba a; Po ella o, And ea; Viaggi, Da ide
A icle
Fa m cha ac e is ics and exogenous ac o s in luencing
he choice o buy land in I aly
Bio-based and Applied Economics (BAE)
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Sugges ed Ci a ion: Russo, Sil ia; Raggi, Me i; Bimba i, Ba ba a; Po ella o, And ea; Viaggi, Da ide
(2025) : Fa m cha ac e is ics and exogenous ac o s in luencing he choice o buy land in I aly, Bio-
based and Applied Economics (BAE), ISSN 2280-6172, Fi enze Uni e si y P ess, Flo ence, Vol. 14, Iss.
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Bio-based and Applied Economics
BAE
Ci a ion: Russo, S., Raggi, M., Bim-
ba i, B., Po ella o, A. & Viaggi, D. (2025).
Fa m cha ac e is ics and exogenous
ac o s in luencing he choice o buy
land in I aly. Bio-based and Applied
Economics 14(1): 49-73. doi: 10.36253/
bae-15548
Recei ed: Decembe 20, 2023
Accep ed: May 9, 2024
Published: June 13, 2025
Da a A ailabili y S a emen : All el-
e an da a a e wi hin he pape and i s
Suppo ing In o ma ion iles.
Compe ing In e es s: The Au ho (s)
decla e(s) no con lic o in e es .
Associa ed Edi o : Luca Sal a i-
ci, Valen ina Raimondi, F ancesco
Pagliacci
Edi o in Chie : Fabio Ba olini
ORCID
MR: 0000-0001-6960-1099
BB: 0000-0002-4826-5003
AP: 0000-0002-6980-4862
DV: 0000-0001-9503-2977
Fa m cha ac e is ics and exogenous ac o s
in luencing he choice o buy land in I aly
Sil ia Russo1,*, Me i Raggi2, Ba ba a Bimba i3, And ea Po ella o3,
Da ide Viaggi1
1 Depa men o Ag icul u al and Food Sciences, Alma Ma e S udio um, Uni e si à di
Bologna, Viale Fanin 50, Bologna, 40127, I aly
2 Depa men o S a is ical Sciences “P. Fo una i”, Alma Ma e S udio um, Uni e si y o
Bologna, Via Delle Belle A i, 41, BO, Bologna, 40126, I aly
3 CREA – Resea ch Cen e o Ag icul u al Policies and Bioeconomy, Legna o (PD), I aly
*Co esponding au ho . E-mail: sil ia. usso7@s udio.unibo.i
Abs ac . Access o land is one o he key ac o s o a m g ow h. Howe e , ela ed
esea ch is cha ac e ised by impo an gaps, in pa icula , acing he change o e ime
in he na u e and ole o d i e s o he land ma ke . The objec i e o his pape is o
iden i y he endogenous and exogenous ac o s ha a ec he decision o pu chase
land in I aly be ween 2013 and 2020. Fi e p obi eg ession models we e implemen ed
o unde s and he ole o a se o di e en de e minan s in land in es men decision.
The esul s show ha ac o s ela ed o capi al in machine y and plan , ene gy p oduc-
ion and he p esence o a successo o young a me a e endogenous ac o s ha posi-
i ely in luence he pu chase decision. The a io o en ed land o u ilised ag icul u al
a ea and o amily wo k uni s o o al wo k uni s a e endogenous ac o s ha nega-
i ely a ec he pu chase decision. Exogenous ac o s ela ed o he cos o capi al and
in la ion a e a ec he pu chase o land in an opposi e way, nega i ely and posi i ely
espec i ely. The ole o U ilised Ag icul u al A ea and Value Added pe hec a e a ies
depending on he specialisa ion conside ed. The esea ch can suppo policymake s in
designing policies o p omo e he su i al and g ow h o a ms, as well as o acili a e
land in es men by educing ba ie s o land acquisi ion.
Keywo ds: ag icul u al land ma ke , land pu chase, p obi eg ession model, in es -
men decision, pu chase decision.
JEL Codes: Q15, Q12.
1. INTRODUCTION
Land ep esen s a du able, ixed, he e ogeneous, and non- ep oducible
esou ce and is one o he key p oduc i e ac o s o a a m. The pu chase o
land is one o he ways h ough which a a me can access his ixed p o-
duc i e ac o and ep esen s a o m o in es men in a capi al good. Com-
pa ed o o he o ms o a m size g ow h, on he one hand, he pu chase o
land may equi e a majo inancial commi men and hus limi s he in es -
men in o he p oduc i e asse s (Jeong e al., 2022; Swinnen e al., 2016).
50
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
Sil ia Russo e al.
On he o he hand, he ull ans e o igh s allows he
new owne o use he land as a colla e al asse in o de
o ha e g ea e access o c edi (Binswange e al., 1995;
B ad ield e al., 2023; Swinnen e al., 2016). In compa i-
son o in es men s in o he ypes o on- a m asse s, he
pu chase o land a ely akes place a he same ime as
i is planned because i is no ce ain ha he a me
will ind he supply on he local ma ke mee ing his/
he needs/capaci y (Elho s , 1993). Fo he a me , he
a ailabili y o land can be one o he main obs acles o
he de elopmen and g ow h o he a m (Yano e e al.,
2024). The land ma ke is cha ac e ised by igid supply
and he pu chase o land a om he a m cen e would
lead o inc eased cos s and down ime (Co elee e al.,
2008; Schimmen i e al., 2013). Fo all hese easons, he
land ma ke is gene ally de ined as hin and local.
The lack o da a a ailabili y and he absence o well-
s uc u ed da abases on land ansac ions, especially
in Eu ope, has in luenced and limi ed he esea ch on
he land ma ke (De Noni e al., 2019). O e he yea s,
esea ch mainly ocused on iden i ying he de e minan s
o land alue in speci ic local ag icul u al land ma ke s
o on how ag icul u al policy paymen s could in luence
land alue (Baldoni e al., 2023; Czyzewski e al., 2017;
La u e and Le Mouël, 2009; Michalek e al., 2014; Va -
acca e al., 2022). Howe e , when analysing he li e a u e
ela ing o he in es men decision, he e appea s o be
li le ex-pos empi ical esea ch ha akes in o consid-
e a ion he in es men in land.
The objec i e o his pape is o iden i y de e mi-
nan s ha ha e in luenced he a me ’s decision o pu -
chase land in I aly be ween 2013 and 2020. The wo k
is ca ied ou using FADN da a and ac o s a e selec ed
based on a li e a u e e iew and da a a ailabili y. The
main no el y o he pape is ha we use an o iginal ana-
ly ical amewo k and a concep ual model de eloped on
he basis o he li e a u e analysis using mul iple s eams
o esea ch, namely s uc u al change in ag icul u e and
he g ow h o a m size, he in es men decision and he
land ma ke li e a u e.
The pape con inues in Sec ion 2 wi h he design
o he amewo k. In Sec ion 3 we p oceed wi h he
desc ip i e analysis o he a ailable da a and he p esen-
a ion o he me hodology. In Sec ion 4, he esul s o he
analysis a e p esen ed and will be discussed in Sec ion
5. Sec ion 6 is dedica ed o he conclusions d awn om
his s udy.
In o de o con ex ualize his esea ch, a p emise is
needed. To he bes o ou knowledge, he e a e no s ud-
ies ha ha e iden i ied he ac o s ha may in luence he
decision o I alian a me s o in es in land. Consequen -
ly, his s udy and i s esul s should be conside ed a p e-
limina y explo a o y a emp o iden i y and unde s and
he e ec s o ce ain ac o s selec ed based on he o igi-
nal analy ical amewo k.
2. THEORETICAL FRAMEWORK
Land is a ac o o p oduc ion ha is s ongly con-
nec ed o and no di isible om h ee o he a m inpu s
such as machine y, ( amily) labou and buildings (Plog-
mann e al., 2022).
O e he yea s, mechanisa ion and echnological
inno a ion ha e played an impo an ole in imp o ing
a me s’ labou managemen and eplacing he labou
o ce lea ing u al a eas o be e paid non-ag icul u -
al wo k. The adop ion o machine y and echnological
inno a ion, especially when i is expensi e and com-
plex, ha e s imula ed a me s o alloca e hei manage-
ial skills, capi al, and a m asse s o he p oduc ion
o a ew ypes o ou pu and, hus, a m specialisa ion.
These h ee ac o s ha e con ibu ed o he de elopmen
o bo h economies o scale and size. Al hough echno-
logical inno a ion is accessible o small and la ge a ms,
he la e seem o ha e mo e inancial and manage ial
capaci ies, bo h in e nal and ex e nal, o in es in his
ac o . Thus, he g ow h in a m size induced by ech-
nological inno a ion seems o be s onge in la ge a ms
han in small ones. Acco ding o he heo e ical li e a-
u e, hese dynamics gene a e p essu es on small a ms
ha migh decide o exi he ag icul u al sec o (Plog-
mann e al., 2022). In his ega d, esea che s ha e iden-
i ied “o - a m income” as a ac o ha could play a dual
ole in he su i al o small a ms. On he one hand, he
income gene a ed by o - a m ac i i ies could ep esen
he i s s ep o he a m’s exi om he sec o . On he
o he hand, his sou ce o income could allow he a me
o emain wi hin he ag icul u al sec o because i could
con ibu e o he s abilisa ion o he a me ’s income and
acili a e access o c edi , in es men in a m asse s, and
s imula e he g ow h o a ms managed by young a m-
e s (Godda d e al., 1993; Hallam, 1991; Ha ing on and
Reinsel, 1995; Key, 2020; Neuen eld e al., 2019; Weiss,
1999; Zimme mann e al., 2009).
Human capi al is one o he main ac o s ha can
in luence a a me ’s in es men decision. When alking
abou human capi al, e e ence is made o demog aphic
cha ac e is ics o he a me and hei amily. In pa icu-
la , he age o he a me and he p esence o a po en ial
successo , and he le el o educa ion a e among he main
cha ac e is ics ha can a ec a m size and in es men in
land. As he age o he a me inc eases, he a m en e s
he so-called ma u i y and/o decline phase and he a me
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Fa m cha ac e is ics and exogenous ac o s in luencing he choice o buy land in I aly
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
may be mo e eluc an o inc ease he a m size (B emme
and Oude Lansink, 2002). The p esence o a po en ial suc-
cesso could p e en he a m om en e ing he decline
phase and hus posi i ely a ec he in es men in a ixed
inpu (Hube e al., 2015). Fu he mo e, he pu chase o
land could en ail a majo inancial commi men and he
applica ion o a bank loan. In his ega d, he p esence o
a young a me o a successo could posi i ely in luence
he ime ho izon o he in es men and a ou he pu -
chase decision (Elho s , 1993; Hube e al., 2015; Oskam e
al., 2009; Oude Lansink e al., 2001). In addi ion o hese
ac o s, human capi al also includes manage ial skills ha
i no possessed by he a me can be ound in he ex e nal
en i onmen e.g. by u ning o ad iso y and consul ancy
se ices (Boehlje, 1992). Acco ding o he li e a u e, la ge
a ms may ha e g ea e economic and inancial capaci ies
o access such se ices.
The decision o in es in land is no only in luenced
by s uc u al and socio-demog aphic cha ac e is ics o
he a m, bu also by exogenous ac o s such as he mac-
oeconomic en i onmen , land ma ke egula ions, and
ag icul u al policies.
The pu chase o land ep esen s an in es men in a
capi al good ha may equi e a majo inancial commi -
men . In his sense, he cos o capi al and he inancial
posi ion o he a me could in luence he decision and
he le el o in es men . As he in e es a e inc eases, he
p obabili y ha he a me is willing o in es and he
le el o in es men dec eases.
Land is no only an impo an p oduc ion asse o a
a m bu also a “sa e- hea en” asse (Schimmen i e al.,
2013), a ac ing he in e es o non- a me s who decide
o in es in i o p o ec he capi al alue om in la ion.
An inc ease in he in la ion a e leads o an inc ease in
he p ice o land and ice e sa (Elho s , 1993; Law-
ley, 2021; Szymańska e al., 2021; Thijssen, 1996). Poli-
cymake s can use land egula ion as an ins umen o
de end he a me s’ posi ion and mi iga e po en ial spec-
ula i e o ce in a mland ma ke . Each Eu opean Mem-
be S a e has ull decision-making powe o e i s own
land egula ion. In gene al, Wes e n Eu opean coun ies
ha e a mo e libe al land egula ion han Eas e n Eu o-
pean coun ies. Among he Wes e n coun ies, I aly is
one o he Eu opean coun ies wi h he mos libe al land
egula ion (Swinnen e al., 2016). Wi h he aim o acili-
a ing access o land o medium-sized a ms wi h inan-
cial means, many Eu opean coun ies ha e p o ided o
he igh o p e-emp ion o be exe cised ei he by local
go e nmen s, as in F ance, Ge many and he Ne he -
lands, o by a me s, as in I aly (Galle o, 2018). In pa -
icula , he I alian go e nmen in oduced his ins u-
men o educe he agmen a ion o I alian a ms, o
imp o e he consolida ion o he I alian ag icul u al sec-
o and o acili a e he de elopmen o amily a ms. In
I aly, A . 8 Law n. 590/1965 and a . 7 Law n. 817/1971
es ablish ha he I alian a me may exe cise he igh o
p e-emp ion o land i a leas one o h ee cases occu s:
a) he/she is he co-owne o he a m, b) he/she is a p o-
essional a me who di ec ly bo de s land o sale, c) i
he/she has been en ing he land o a leas wo yea s
(Legge 590/1965; Legge 817/1971).The igh o p e-emp-
ion has also been ex ended o ag icul u al pa ne ships
(as a ule, simple pa ne ships, and gene al pa ne ships)
i a leas hal o he pa ne s a e “owne -ope a o a m-
e ”. Subsequen ly, be ween 2009 and 2016, he I alian
S a e implemen ed ax concessions o imp o e he a m-
e ’s posi ion. The law s a es ha : a) he I alian a me
wi h a amily a m does no ha e o pay income ax o
land use ax; b) he I alian a me is exemp om pay-
ing income ax on he use o he land; c) in case o land
pu chase, when he buye is a “owne -ope a o a me ”
o p o essional ag icul u al en ep eneu , she/he will
pay only 1% o he pu chase p ice as ax, while any o he
buye will pay 15%. In 2017, he Eu opean Pa liamen
called on all Membe S a es o e iew hei land egu-
la ion in o de o ensu e ai access o land and o p e-
en i om being concen a ed wi hin a ew la ge a ms
(Eu opean Pa liamen , 2017).
In addi ion o p ese ing he a me ’s posi ion, land
egula ion in luences he capi aliza ion o subsidies p o-
ided by ag icul u al policies wi hin he alue o land
and en al a es. S ingen land egula ion on he land
ma ke and land en al ma ke would educe he capi-
alisa ion o subsidies wi hin he land p ice and en .
The li e a u e p esen s bo h heo e ical and empi ical
s udies on whe he and how much o he subsidies p o-
ided h ough policies a e capi alised wi hin he land
p ice alue. F om a heo e ical s udy, in a pe ec ma -
ke , decoupled di ec paymen s, coupled di ec pay-
men s, u al de elopmen p og ammes and en i onmen-
al paymen s could be capi alised wi hin he land p ice.
Howe e , empi ical s udies sugges ha capi alisa ion in
a eal land ma ke is lowe han heo ised and depends
on many ac o s such as subsidy ype, land supply elas-
ici y and a m c edi cons ain s. In addi ion o in lu-
encing land alue, subsidies can also in luence a a me ’s
in es men decision and le el o in es men . Subsidies
we e in oduced wi h he main objec i e o suppo ing
he a me ’s income and ep esen a o m o income no
a ec ed by p oduc ion isks. Consequen ly, subsidies
could posi i ely in luence he in es men decision and
le el especially in he p esence o an impe ec ma ke .
The iden i ied ac o s a e no independen bu in e -
ac and in luence each o he (Zimme mann e al., 2009).
52
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
Sil ia Russo e al.
In he li e a u e, ou empi ical s udies conce n-
ing he a m size g ow h we e iden i ied ha adop ed
a eg ession model wi h he a m size as he depend-
en a iable (Akimowicz e al., 2013; B emme and
Oude Lansink, 2002; B enes-Muñoz e al., 2016; Weiss,
1999). In he li e a u e ela ed o in es men decision,
wo empi ical esea ches we e iden i ied ha also con-
side ed land as a o m o in es men (Elho s , 1993;
Oskam e al., 2009). In addi ion, Jeong e al. (2022)
iden i ied a m economic cha ac e is ics ha could
a ec he decision o buy o lease land in Ko ea by
adop ing he machine lea ning algo i hm “ andom o -
es ”. Finally, Zieme and Whi e (1981) a emp ed o be -
e es ima e a mland demand in Geo gia be ween 1970
and 1978 by accoun ing o he p ocess unde lying he
decision o pu chase.
Based on he li e a u e e iew, ac o s endogenous
and exogenous o he a m ha may ha e an in lu-
ence ha e been iden i ied and summa ised in a concep-
ual model shown in Figu e 1. Simila o he s udies on
a m size g ow h (Zimme mann e al., 2009), we do no
assume ha he iden i ied exogenous and endogenous
ac o s a e independen o each o he , bu ha hey
in e ac and condi ion each o he .
3. MATERIALS AND METHODOLOGY
3.1. Da a and desc ip i e analysis
The analysis was conduc ed on I alian FADN da a o
I alian a ms obse ed be ween 2013 and 2020. The da a
ep esen an unbalanced panel da a consis ing o 84610
obse a ions ep esen ing 24212 a ms. On a e age, he
same a m emains in he sample o abou 3 o 4 yea s.
Fo each a m, he e is in o ma ion on he s uc u al
cha ac e is ics o he a m, da a on he a m’s balance
shee , and da a on he socio-demog aphic cha ac e is ics
o he a ms.
O he 24212 a ms in he sample, 919 made a leas
one in es men in land du ing he pe iod in ques ion, o
hese 176 a ms made mo e han one in es men (Table 1).
A ound 90% o he sample is cha ac e ised by spe-
cialised a ms in ce eals, a able c ops, ho icul u e, ui
Economic
en i onmen
Land ma ke
Policy: Eu opean
ag icul u al
policies and land
egula ion
Pu chase o
Land
Fa me ’s socio-
demog aphics
cha ac e is ics
Fa m s uc u al
cha ac e is ics
Endogenous ac o s
Exogenous ac o s
Figu e 1. Concep ual model de eloped based on he li e a u e e iew.
53
Fa m cha ac e is ics and exogenous ac o s in luencing he choice o buy land in I aly
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
c ops, oli e g owing, i icul u e, dai y ca le, he bi o es
and g ani o es. The emaining 9.45% by non-special-
ised a ms, o which 9.4% a e mixed c op and li es ock
a ms. Thi y- wo pe cen o he sample is specialised
in annual c ops, 29.9% a e pe manen c ops and 27.8%
li es ock a ms (Table 2). Thi y-nine pe cen o he land
pu chases we e conduc ed by a ms specialising in pe -
manen c ops, ollowed by a ms specialising in annual
c ops and li es ock. In pa icula , 18% o he eco ded
ansac ions we e conduc ed by a ms specialising in
ui c ops, 16.5% by ineya ds, and 12% by a ms spe-
cialising in a able c ops (Table 2).
In e ms o a e age UAA, specialised li es ock a ms
a e he la ges , ollowed by annual c ops and pe manen
c ops. Among all specialisa ions, a ms specialised in
i icul u e ha e he smalles a e age a m size ollowed
by hose specialised in ui c ops and ho icul u e.
The e is an impo an di e ence in a m size be ween
ho icul u al a ms and hose specialised in o he annu-
al c ops. Fa ms specialised in pe manen c ops ha e low-
e “RENT/UAA” a ios han a ms specialised in annual
c ops and li es ock (Appendix 1).
3.2. Empi ical Model
Since he in es men decision ep esen s a disc e e
p oblem (Elho s , 1993), o es ima e he p obabili y o pa -
icipa ion decision we adop ed a p obi eg ession model.
The empi ical model implemen ed o conduc he
quan i a i e analysis was de eloped based on he con-
cep ual model in igu e 1 and peculia i ies o FADN
da a. In pa icula , he cha ac e is ics o ou da abase
did no allow us o conduc a dynamic analysis, which
would be app op ia e since in es men s in capi al s ock
a e no annual in es men s (Le eb e e al., 2015) and
gene ally do no occu a he same ime as hey a e
planned (Elho s , 1993).
The empi ical p obi model used is desc ibed by he
ollowing equa ion:
Whe e:
y*i is he bina y dependen a iable ha assumes a alue
equal o 1 in he yea in which he pu chase occu s, 0
o he wise.
εi is he composi e e o e m.
i ep esen s he single obse a ion,
xki is he obse ed alue o explana o y a iables ha
desc ibed ac o s linked o a m cha ac e is ics, a me
Table 1 Desc ip i e analysis: Dimension o he unbalanced panel
da a.
Full Sample Buye %
Numbe o obse a ions 84610 1095 1.3
Numbe o a ms 24212 919 3.8
Table 2. Desc ip i e s a is ics o he sample based on a m specializa ion.
Specializa ion
Sample
% To al obse a ion
Buye s
% To al obse a ion
N. Obse a ions N. Obse a ions
No specialisa ion: 7997 9.45 91 8.3
Unclassi iable a ms 11 0.013 0 0
Mixed c ops and li es ock a ming 7986 9.4 91 8.3
Annual C ops 27796 32.9 312 28.5
Ce eals 8812 10.4 102 9.3
A able C ops 10292 12.2 133 12.15
Ho icul u e 8692 10.3 77 7.03
Pe manen C ops 25305 29.9 432 39.45
F ui C ops 10721 12.7 202 18.45
Oli e g owing 4034 4.8 47 4.3
Vi icul u e 10550 12.5 183 16.7
Li es ock a ms 23512 27.8 260 23.75
Dai y ca le 7339 8.7 102 9.3
He bi o es 12108 14.3 102 9.3
G ani o es 4065 4.8 56 5.1
TOT 84610 100 1095 100
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Sil ia Russo e al.
socio-demog aphic cha ac e is ics and exogenous a i-
ables.
The e ec o xi on is ep esen ed by . and a e espec-
i ely he in e cep and he e o s o i.
The equa ion is es ima ed using he ‘glm’ unc ion
in Rs udio o he ‘s a s’ package.
The explana o y a iables (Table 3) in oduced in
he p obi model a e lis ed and de ined below.
3.2.1. Desc ip ion o he explana o y a iables and
expec ed ela ion
U ilized ag icul u al a ea
I is unclea wha e ec he ini ial size o he a m
may ha e on he g ow h o a m size and on he in es -
men decision. Gi en he na u e and cha ac e is ic o
he da a o his a iable, i was decided o in oduce as
an explana o y a iable he “UAA SQ” which ep esen s
he squa ed alue o he o al ini ial UAA o he a m
ega dless o whe he i is owned, leased, o ee use. The
use o he squa e a iable is able o ca ch he non-linea
e ec o i . Assuming ha a m size can also be a meas-
u e o he a m’s abili y o gene a e income (Oude Lan-
sink e al. 2001), we expec his a iable o ha e a posi-
i e e ec on he in es men decision.
Value added pe hec a es
This a iable was in oduced as an explana o y a i-
able ep esen ing he p oduc i i y o land. Th ough his
a iable, he aim is o unde s and whe he he p oduc-
i i y pe hec a e de i ed om he a m’s ac i i y a ec s
he g ow h o he a m size h ough pu chase. Acco d-
ing o he li e a u e, he a me is encou aged o buy
land when p oduc i i y is high (Ciaian e al., 2010).
The e o e, i is assumed ha , as p oduc i i y pe hec a e
inc eases, he likelihood o he a me in es ing in land
inc eases.
Value added pe o al wo k uni
This explana o y a iable ep esen s he p oduc i i y
o a m labou . I is de ined as he a io o alue added
o o al wo k uni s. I is assumed ha as p oduc i i y pe
labou uni inc eases, he p obabili y o he a me pu -
chasing landalsoinc eases.
P oduc ion Specialisa ion
When no ocusing on a single specializa ion (e.g.,
he dai y sec o ), he esea che s in oduced a ca ego i-
cal a iable ela ed o a m specialisa ion (e.g Akimo-
wicz e al., 2013) in o de o unde s and whe he he
ype o a m could in luence he a m g ow h o in es -
men decision. This is p obably ela ed o he ac ha
he ype o asse s needed by a a m a ies acco ding o
hei specialisa ion (Le eb e e al., 2015). The da a a
ou disposal include specialised and non-specialised
a ms. Specialisa ion is de ined acco ding o he echni-
cal-economic o ien a ion o he FADN da abase (FADN,
2018). In con as o his o iginal classi ica ion, in his
model a ms classi ied as “mixed c op and li es ock”
a e included in he “non-specialised a ms”. Specialised
a ms all in o 9 ca ego ies: Ce eal c ops, a able c ops,
ho icul u e, ui c ops, oli e c ops, i icul u e, dai y
ca le, he bi o es and g ani o es. The e o e, he explana-
o y a iable was in oduced in o he model o accoun
o he nine specialisa ion ca ego ies. “No specialisa ion”
is used as he e e ence ca ego y since specialisa ion is
one o he main d i e s o he sea ch o economies o
scale and a m g ow h. In oducing his a iable allows
us o unde s and no only whe he specialised a ms
in es mo e han non-specialised ones, bu also whe he
he p obabili y o buying land in I aly changes as spe-
cialisa ion changes.
U ilised ag icul u al a ea *Specialisa ion
The necessa y asse s o a a m and he “op imal
size” a y depending on he ype o a ming (Le eb e e
al., 2015; Plogmann e al., 2022). In o de o es whe h-
e he e ec o a m size can a y acco ding o he ype
o a ming, i was decided o combine he wo p e ious
a iables “UAAsq” and “Specialisa ion”., (B emme and
Oude Lansink, 2002).
Ren /U ilised ag icul u al a ea
On he one hand, en ing allows he a m mo e lex-
ibili y and he possibili y o in es i s liquidi y in o he
p oduc i e asse s (Swinnen e al., 2016). On he o he
hand, land managed as p ope y allows he a me o use
i as colla e al capi al and hus o ha e g ea e access o
c edi (Swinnen e al., 2016). I was decided o in oduce
he a io o he land managed unde en o he o al u i-
lised ag icul u al a ea o he a m as an in e se meas-
u e o he amoun o colla e al a ailable (Benjamin and
Phimis e , 2002; Le eb e e al., 2015). Howe e , expec a-
ions on he di ec ion o he e ec s o his a iable a e
ambiguous.
Machine y Plan Value
Machine y and plan ep esen ano he o m o col-
la e al capi al o a a m. I is assumed ha high alues
o his a iable co espond o a a m’s ecen in es men
in such p oduc i e asse s ha a y p opo ionally o he
a m a ea (Plogmann e al., 2022). Fu he mo e, he e is
a co ela ion be ween he in en ion o pu chase land and
in es men in o he a m asse s (Le eb e e al., 2015).
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Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
The e o e, i is hypo hesised ha he a m is inclined
o pu chase wi h he aim o maximising he p oduc i e
capaci y o he asse in which i has p e iously in es ed.
Common Ag icul u al Policy
The Common Ag icul u al Policy has been iden i-
ied as an exogenous ac o ha can in luence he land
p ice, bu also he decision and le el o in es men . Sub-
sidies ecei ed and capi al inancing a e no he same o
e e y a m and o his eason i can be conside ed as an
endogenous a iable linked o s uc u al cha ac e is ics
o he a m. I was decided o in oduce wo con inuous
a iables. he i s , he a io o income subsidies pe hec-
a e ela ed o he i s pilla o he Common Ag icul u -
al Policy and COM. The second, he alue o he in es -
men subsidies ecei ed by he a m be ween 2013 and
2020 and connec ed o he measu es o he second pilla
o he Common Ag icul u al Policy.
P e-pu chase
In es men in land is a planned, long- e m in es -
men (Elho s , 1993; Oskam e al., 2009; Oude Lansink
e al., 2001). The land ma ke is hin and local, and i
could be di icul o a a me o ind he amoun o land
he wan s a one ime. (Co elee e al., 2008; Elho s ,
1993). The e o e, i may happen ha he a me mus
make mo e han one pu chase o each he desi ed le -
el o in es men . The dummy a iable “P e_Pu chase”
assumes a alue equal o one when he pu chasing a m
has al eady made a pu chase p e iously be ween 2011
and 2020.
Di e si ica ion ac i i ies
In he li e a u e e iewed, esea che s ha e no con-
side ed he ole ha a m- ela ed ac i i ies can ha e on
a m g ow h and he in es men decision. The ela ed
ac i i ies ha can be s imula ed by RDP measu es allow
o a di e si ica ion o he a m ac i i y and ep esen a
di e en o m o income o he ag icul u al i m. Th ee
dummy a iables we e in oduced o h ee ag icul u al
ela ed ac i i ies: ag o ou ism, ene gy p oduc ion and
con ac ing. I is expec ed ha conduc ing ag icul u al
ela ed ac i i ies inc eases he p obabili y ha he pu -
chase will occu .
Family wo k uni s
Family labou can be conside ed as a ixed inpu o
p oduc ion wi hin he a m (Elho s , 1993) and Elho s ’s
esea ch showed ha as amily labou inpu inc eases,
in es men inc eases. Weiss (1999) and Oude Lansink
e al. (2001) showed ha he numbe o amily membe s
a ec s a m g ow h and he in es men decision. The
a iable FWU/TWU was in oduced in o he model as
a measu e o how much he business depends on amily
labou . I is hypo hesised ha amily a ms ha e a g ea -
e in e es in in es ing in he a m and a m g ow h and
hus, as his a io inc eases, he p obabili y ha he a m
in es s in land inc eases.
Age o a me and successo
The age o he a me and he p esence o he suc-
cesso can a ec he g ow h o he a m and he in es -
men decision. Since he e may be se e al a me s and
po en ial successo s wi h di e en ages on he same a m,
i was decided o c ea e ou dummy a iables ela ed o
he holde and his/he age, and one ela ed o he p es-
ence o he successo . In pa icula , ou age anges we e
iden i ied o which dummy a iables co esponded. Each
dummy a iable ela ing o he holde akes he alue o
one i he e a e no successo s o ha obse a ion and i
he holde o all he holde s all wi hin he ange de ined
by he dummy a iable. I he obse a ion co esponds
o mo e han one holde alling in di e en age g oups,
all a iables ela ed o he holde s will ha e alue ze o.
The a iable ela ing o he p esence o a successo will
ake a alue o 1 i he e is a leas one po en ial successo
be ween he ages o 1 yea and 40 yea s. A successo was
he one who was classi ied wi hin he da ase as he ‘son’
o ‘g andson’ o he a me .
O - a m income
In he li e a u e, i is unclea whe he he ea ning o
an o - a m income can be a p elude o lea ing he sec-
o o ep esen s a o m o income ha allows he a m
o su i e be e and no lea e he sec o (Le eb e e
al., 2015; Plogmann e al., 2022). Based on he a ail-
able da a, a dummy a iable was c ea ed which akes he
alue o 1 i he a me o a membe o his o he am-
ily who is employed pa - ime o ull- ime on he a m
ea ns an o - a m income >2000 eu os.
3.2.2. Exogenous ac o s
As men ioned be o e, land is conside ed an asse ha
can be used as colla e al and a sa e in es men op ion.
The model in oduces wo ex e nal ac o s: in la ion a e
and in e es a e. I is assumed ha when in la ion a es
ise, he likelihood o pu chasing land also inc eases.
Howe e , buying land may equi e a signi ican in es -
men ha he a m may need o inance h ough a bank
loan. As in e es a es go up, he p obabili y o making
such an in es men dec ease. The in la ion a e alues,
Consume P ice Index-CPI, a e ob ained om he ISTAT
websi e e e y Decembe o he e e ence yea , while he
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Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
Sil ia Russo e al.
in e es a e is de e mined by he a e age annual yield o
I alian BTPs (Mul i-yea T easu y Bonds), which can be
ound on he websi e o he I alian T easu y Minis y.
3.2.3. Desc ip i e analysis o explana o y a iables
Table 4 shows he desc ip i e analysis o he a i-
ables included in he model, in pa icula each a iable
has wo alues: one o all a ms and one o “buye s”
( a ms in es ing in land du ing he e e ence pe iod).
The a e age ini ial a ea o he sample is 33.7 ha, which
inc eases by abou 5 ha i only buye s a e aken in o
accoun . The alue ela ed o alue added pe hec a e
(VA/ha) exhibi s signi ican a ia ions among he a ms.
Ne e heless, hese di e ences dec ease conside ably
when only he buye s a e conside ed. Fu he mo e, he
a e age alue o he a iable “VA/ha” is lowe o he
buye s, whe eas he median alue o buye s is highe
han he alue wi hin he en i e sample. The a e age al-
ue o machine y and plan o he a ms ha in es ed in
land is mo e han wice as high as he sample a e age.
Table 3 De ini ion o he explana o y a iables and expec ed e ec s on he decision o buy land.
Va iables Speci ica ion Type o a iable Expec ed
e ec
Fa m s uc u al cha ac e is ics
UAAsq U ilised Ag icul u al A ea squa e Con inuous +
P oduc ion specialisa ion Ag icul u al specialisa ions conside ed a e: ce eals, a able c ops,
ho icul u e, ui c ops, oli e g owing, i icul u e, dai y ca le,
he bi o es, g ani o es.
Ca ego ical;
Non-specialised a ms as e e ence
+
VA/ha Ra io be ween Value added (excluding Income subsidies and
COM subsidies) and UAA
Con inuous +
VA/ TWU Ra io be ween Value Added and o al wo k uni s Con inuous +
UAASQ *P oduc ion
Specialisa ion
Con inuous*ca ego ical; non-
specialised a ms as e e ence
+
VA/ha*Specialisa ion Con inuous*ca ego ical;
Non-Specialised a ms as e e ence
+
VA/TWU*Specialisa ion Con inuous*ca ego ical;
Non-Specialised a ms as e e ence
+
RENT/UAA The a io o he en ed UAA o he UAA Con inuous +/-
Machine y_ Plan alue Value o Machine y+ equipmen + plan ; I ep esen s a p oxy
a iable o le el o inno a ion on a ms
Con inuous +
Income subsidies/ha Aid pe hec a e p o ided by Fi s Pilla and COM Con inuous +
In es men subsidy In es men aid (Second Pilla ) Con inuous +
Ene gy P oduc ion Fa m p oduces enewable ene gy Dummy +
Subcon ac ing ac i i ies Fa m ca ies subcon ac ing ac i i ies Dummy +
Ag o ou ism Fa m ca ies ou ag o ou ism ac i i ies Dummy +
P e_pu chase Pu chases made be ween 2010-2020 Dummy +
FWU/TWU Ra io o amily wo k uni s o o al wo k uni s Con inuous +
Fa me sociodemog aphic cha ac e is ics
FARMER_18_39 The a m manage is be ween 18 and 39 yea s old Dummy +
FARMER_40_49 The a m manage is be ween 40 and 49 yea s old Dummy +
FARMER_50_59 The a m manage is be ween 50 and 59 yea s old Dummy -
FARMER_OVER60 The a m manage is aged 60 old o olde Dummy -
SUCC_1_39 The e is a po en ial successo aged be ween 1 and 39 on he a m Dummy +
OFFFARM_INCOME Fa me wi h non-ag icul u al income >2,000 eu o; Child en/
g andchild en, a he -in-law, pa en , wi e employed pa - ime o
egula ly wi h non-ag icul u al income >2000 eu o
Dummy +
Exogenous a iables
INTEREST RATE In e es a e eco ded o each yea on he Minis y o he
T easu y websi e
Con inuous +
INFLATION_ RATE In la ion a e aken o each yea om he ISTAT websi e Con inuous -
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Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
a ec he in es men decision. The esul s om he i e
models sugges ha , in gene al, being be ween 18 and
50 yea s old has a posi i e e ec on he decision o buy
land. This p obabili y is e en highe i he a me is aged
18-40. As was hypo hesised, he a iable on he pe cep-
ion o o - a m income has a posi i e in luence on he
pu chase decision and is one o he mos s a is ically sig-
ni ican a iables (p<0.001).
Consis en wi h he hypo hesis, he a iables ela -
ing o he ex e nal mac oeconomic en i onmen , i.e.,
he in la ion and in e es a es, in luence he in es -
men decision in opposi e di ec ion. As he in la ion a e
inc eases, he p obabili y o in es men decision inc eas-
es. As he in e es a e inc eases, he p obabili y ha he
a me will in es in land dec eases.
5. DISCUSSION
In compa ison o he ew empi ical s udies on he
g ow h o a m size and in es men decision includ-
ing land, his esea ch is conduc ed on he en i e FADN
da ase collec ed a he na ional le el. The analyses a e
no based on a sample o a ms specialising in one ype
o a ming and/o loca ed in a speci ic and limi ed geo-
g aphical a ea. Ou da a a e cha ac e ised by 90% obse -
a ions o a ms specialising in 9 di e en p oduc ions
and di e ing in a m and socio-demog aphic cha ac e -
is ics. This he e ogenei y o he analysed sample is due
o in insic cha ac e is ics o he I alian ag icul u al sec-
o . The a e age su ace a ea o he a ms in he sam-
ple is abou 30 ha abo e he a e age UAA eco ded in
he las ISTAT 2010-2020 census (ISTAT, 2022). This is
because he FADN sample is s a i ied and selec s com-
panies wi h a S anda d Ou pu abo e 8,000 Eu o. In any
case, he FADN da a main ain a ce ain deg ee o ep-
esen a i eness o he ag icul u al sec o and ep esen a
use ul esou ce in e ms o he amoun o da a collec ed
in Eu ope (Cen e o Eu opean Policy S udies, 2008;
Ciaian e al., 2010).
Ou o he o al obse a ions, only 1095 (1.29%)
in es ed in land be ween 2013 and 2020. The high num-
be o ze o-obse a ions can be a ibu ed o he speci ic
cha ac e is ics o he land ac o and o he land ma -
ke , as i is unlikely ha a ms in es in capi al goods
e e y yea (Elho s , 1993; Nilsen and Schian a elli, 2003;
Oskam e al., 2009). The high numbe o ze o-obse a-
ions and he complexi y o igno ing he he e ogenei y
e ec a e some o he easons why quan i a i e esea ch
using mic o-da a in he in es men decision-making
p ocess is challenging (Elho s , 1993).
In he empi ical s udies on he a m size g ow h
and he in es men decision, he ole o u ilised ag i-
cul u al a ea is unclea . The i e models do no allow o
cla i y, bu o be e unde s and he ole o his a iable.
The ini ial a m size in luences he in es men decision
nega i ely bu has a di e en e ec depending on a m
specialisa ion. This had al eady pa ly eme ged in he
s udy conduc ed by B emme and Oude Lansink (2002),
which ound ha UAA had a posi i e in luence on he
size g ow h o a able c ops a ms and a nega i e in lu-
ence on he g ow h o a ms specialized in p o ec ed ho -
icul u e. In his esea ch, he posi i e e ec o he a i-
able “UAA SQ” in he case o a ms specialising in ui
c ops, i icul u e and ho icul u e can be linked o wo
di e en conside a ions. The i s one is linked o cha ac-
e is ics o he FADN da a. The mean and median alue
o pe manen c ops and ho icul u e a ms is lowe han
o o he c ops. This could con i m he hypo hesis ha
when a a m is e y la ge i does no end o in es in he
land inpu (Le eb e e al., 2015). The second one is ela -
ed o he in insic cha ac e is ics o he ype o a ming.
Va iable Es ima e S d.e o S a is ic p. alue
FARMER_OVER60 -0.05757 0.08041 -0.71592 0.474042
SUCC_1_39 0.197505 0.083187 2.374226 0.017586 *
OFF_FARM INCOME 0.116606 0.03276 3.559429 0.000372 ***
Exogenous ac o s
In la ion a e 0.102362 0.031866 3.212269 0.001317 **
In e es a e -0.12011 0.020178 -5.95262 2.64E-09 ***
N. obse a ions 84610
N. a ms 24121
Pseudo R20.2
AIC 9440
Signi . codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.
Table 8. (Con inued).
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Sil ia Russo e al.
Table 9. P obi eg ession esul s based on he Model 5 speci ica ion.
Va iable Es ima e S d.e o S a is ic p. alue
(In e cep ) -1.99498 0.115489 -17.2742 7.36E-67 ***
Fa m s uc u al cha ac e is ics
UAASq -2E-05 1.11E-05 -1.81586 0.069391 .
VA/ha -7.9E-07 2.86E-06 -0.27699 0.781788
VA/TWU 1.32E-06 1.65E-06 0.799903 0.423767
No specialisa ion
Ce eals 0.07122 0.092882 0.766783 0.443211
A able C ops 0.09771 0.082926 1.178278 0.238686
Ho icul u e -0.13821 0.097122 -1.42309 0.15471
F ui C ops 0.023628 0.081487 0.28996 0.771847
Oli e g owing 0.051554 0.121524 0.424233 0.671396
Vi icul u e 0.057211 0.077299 0.740133 0.459219
Dai y ca le 0.082709 0.093701 0.88269 0.377404
He bi o es -0.07159 0.080316 -0.89129 0.372772
G ani o es -0.03472 0.103522 -0.33543 0.737302
UAAsq*No specialisa ion
UAAsq*Ce eals 1.78E-05 1.13E-05 1.576344 0.114946
UAA sq*A able C ops 1.49E-05 1.16E-05 1.281408 0.200051
UAA sq*Ho icul u e 1.92E-05 1.16E-05 1.644969 0.099976 .
UAA sq*F ui C ops 2.51E-05 1.15E-05 2.170636 0.029959 *
UAA sq*Oli e g owing -4.5E-05 5.18E-05 -0.86149 0.388966
UAA sq*Vi icul u e 2.2E-05 1.15E-05 1.912193 0.055851 .
UAA sq*Dai y ca le 1.7E-05 1.14E-05 1.488127 0.136717
UAA sq*He bi o es 1.94E-05 1.11E-05 1.747083 0.080623 .
UAA sq*G ani o es -1.1E-05 1.95E-05 -0.56654 0.571024
VA/ha*No specialisa ion
VA/ha*Ce eals -8.6E-05 5.8E-05 -1.48766 0.136839
VA/ha*A able C ops -8E-06 8.82E-06 -0.9121 0.361714
VA/ha*Ho icul u e -3.7E-06 3.63E-06 -1.02881 0.303568
VA/ha*F ui C ops 9.08E-06 4.61E-06 1.968187 0.049047 *
VA/ha*Oli e g owing -4.3E-05 3.07E-05 -1.41899 0.155902
VA/ha*Vi icul u e 1.76E-06 4.38E-06 0.401473 0.688072
VA/ha*Dai y ca le 6.37E-06 8.45E-06 0.753606 0.451086
VA/ha*He bi o es -1.1E-05 1.02E-05 -1.0554 0.291243
VA/ha*G ani o es -1.8E-06 3.54E-06 -0.50791 0.611519
AV/TWU*No specialisa ion
AV/TWU*Ce eals 2.36E-07 2.02E-06 0.116892 0.906946
AV/TWU*A able C ops -1.7E-06 2.09E-06 -0.81922 0.412663
AV/TWU*Ho icul u e 7.66E-07 2.04E-06 0.375434 0.707337
AV/TWU*F ui C ops -7.4E-07 2.09E-06 -0.35518 0.722453
AV/TWU*Oli e g owing 2.06E-06 3.96E-06 0.519424 0.603465
AV/TWU*Vi icul u e -5.5E-08 1.86E-06 -0.02988 0.976165
AV/TWU*Dai y ca le -2.7E-06 2.1E-06 -1.29293 0.196035
AV/TWU*He bi o es -5.1E-07 1.9E-06 -0.27174 0.785824
AV/TWU*G ani o es 5.28E-07 1.79E-06 0.295245 0.767807
RENT/UAA -0.12308 0.034091 -3.6102 0.000306 ***
FWU/TWU -0.35157 0.051736 -6.79551 1.08E-11 ***
Machina y_ Plan Value 3.23E-07 7.85E-08 4.112943 3.91E-05 ***
(Con inued)
65
Fa m cha ac e is ics and exogenous ac o s in luencing he choice o buy land in I aly
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
Unlike annual c ops, a ms p oducing pe manen c ops
may p e e o es ablish new o cha ds on hei own land.
Ob iously, pe manen c ops equi e a highe in es men
cos and e u n on in es men ime han annual c ops.
This could explain why a me s specializing in pe en-
nial c ops migh ha e an incen i e o buy land because i
g an s hem a p ope y igh ha canno be gua an eed
by he en al con ac . This aspec could be pa icula ly
ele an in a coun y like I aly whe e he law allows leas-
es o less han 15 yea s. This conside a ion could explain
he posi i e e ec exe ed by land p oduc i i y in he case
o companies specialized in ui c ops.
Wi h ega d o he e ec o specialisa ion, he esul s
showed ha specialisa ion pe se does no a ec he
p obabili y o land pu chase o he a ms in he sample,
con a y o wha was assumed on he basis on he heo-
e ical li e a u e. The in oduc ion o in e ac ions o his
ca ego ical a iable wi h he a iables UAA, VA/ha, and
VA/TWU has allowed o a be e unde s anding o he
beha iou o hese ac o s. The esul s o he in e ac ions
sugges ha he e ec o i m size, ini ia ion, and a m
p oduc i i y may a y acco ding o he specializa ion.
Consequen ly, specialisa ion plays an impo an and c u-
cial ole in unde s anding and di e en ia ing he e ec
o o he ac o s on he p obabili y o land in es men .
This would con i m wha has eme ged om he heo-
e ical li e a u e, namely ha he ac o s ha can de e -
mine a m g ow h a e no independen bu in e ac wi h
each o he . The e ec o specialisa ion on a m g ow h
and size had al eady eme ged in he esea ch conduc ed
by Akimowicz e al. (2013) acco ding o which speciali-
sa ion in luenced a m size, changes in a m size and
g ow h in ensi y in he Midi-Py enees egion be ween
2000 and 2007.
Al hough heo e ically i would be desi able o
a a m o ha e a balance be ween owned and en ed
land, as he a io o en ed o o al a ea (RENT/UAA)
inc eases, he likelihood o a ms inc easing hei sha e
o owned land dec eases. This esul could be a con i -
ma ion o he indings o he las census o he I alian
ag icul u al sec o acco ding o which he amoun o
land managed unde lease has inc eased and his o m
o managemen is also becoming es ablished in I aly
(ISTAT, 2022). The desc ip i e analysis o he da a in
Annex 1 shows ha he a ms in he sample specialised
in pe manen c ops ha e a lowe “RENT/UAA” a ios
han hose specialised in annual c ops and li es ock
a ming. Unde s anding whe he isola ing his a iable
would ha e a di e en e ec depending on he speciali-
sa ions would be in e es ing.
The esul s o he alue o capi al o machine y and
plan con i m wha he B emme e al. (2002), Le eb e
Va iable Es ima e S d.e o S a is ic p. alue
Subsidies UE/SAU` 1.88E-05 1.72E-05 1.093637 0.274114
Capi al Accoun 1.7E-06 1.11E-06 1.532366 0.125432
Ene gy p oduc ion 0.147376 0.060086 2.452755 0.014177 *
Subcon ac ing ac i i ies 0.213639 0.058772 3.635061 0.000278 ***
Ag o ou ism -0.04862 0.062315 -0.78028 0.435227
P e_PURCHASE 15.53225 38.49138 0.403525 0.686562
Fa m socio-demog aphic cha ac e is ics
FARMER_18_39 0.209433 0.081497 2.569824 0.010175 *
FARMER_40_49 0.163576 0.079486 2.057915 0.039598 **
FARMER_50_59 0.088748 0.079589 1.115078 0.264817
FARMER_OVER60 -0.05863 0.080534 -0.72797 0.46663
SUCC_1_39 0.194356 0.083337 2.332157 0.019692 *
OFF_FARM INCOME 0.113544 0.032812 3.460433 0.000539 ***
Exogenous ac o s
In la ion a e 0.102934 0.031912 3.225551 0.001257 **
In e es a e -0.12062 0.020214 -5.96728 2.41E-09 ***
N. obse a ions 84610
N. a ms 24612
Pseudo R20.2
AIC 9450
Signi . codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.
Table 9. (Con inued).
66
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Sil ia Russo e al.
e al.(2015), and Jeong e al. (2022) esea ch had al eady
ound. The o me had shown how he deg ee o mecha-
nisa ion in luenced a m g ow h o a able c ops and
ho icul u e in he Ne he lands. Le eb e e al. (2015)’s
s udy o a me s’ in es men in en ions in six Eu ope-
an coun ies had shown ha a me s a e mos likely o
in es in one asse class a e hey ha e al eady in es ed
in ano he . The co ela ion be ween he in en ion o
in es in wo ypes o asse s was also shown be ween
land and he pu chase o machine y and machine y
eplacemen . Fu he mo e, esea ch by Jeong e al.
(2022) had shown ha he alue o in en o y and ixed
asse s we e posi i ely ela ed o land acquisi ion.
To he bes o ou knowledge, no esea ch in he
li e a u e has included a iables ela ed o ag icul u al
policies and ac i i ies in models o explain a m g ow h
o land in es men . The esul s on subsidies and und-
ing ela ed o Eu opean Ag icul u al Policies do no
con i m he hypo hesis ha ag icul u al policies can
di ec ly in luence he decision o in es in land. Ra he ,
subsidies could be seen as a use ul ool o he a me
o manage pe iods o ma ke luc ua ions a he han as
a o m o income o make a long- e m in es men . The
RDP measu es do no di ec ly inance land pu chases,
bu hey encompass inancing o in es men s in an-
gible a m asse s, inno a ion, and a m di e si ica ion.
The co ela ion analysis allows us o exclude he p es-
ence o a ela ionship be ween “Capi al accoun ” and he
alue o machine y, and o hese wo a iables wi h he
dummy a iable ela ing o subcon ac ing and ene gy
p oduc ion. The esul s o he la e wo a iables and
he o ms o income de i ed om o - a m income lead
o he conclusion ha he in es men in a capi al good is
suppo ed by o ms o income de i ed om a di e si ica-
ion o he ac i i ies ca ied ou by he a me .
The esul s con i m he conclusions o p e ious
esea ch on he posi i e e ec o he p esence o a succes-
so and a young a me . Indeed, in line wi h he hypo hesis,
he p esence o a young a me o a a me unde 50 yea s
o age posi i ely in luences he p obabili y o pu chase. This
is p obably due o he ac ha he age o he holde has an
impac on he ime ho izon o he in es men .
The esul s o he in oduced exogenous a iables
con i m he hypo hesis. The mac oeconomic con ex
in luences he in es men decision. The in la ion a e was
no included in he empi ical li e a u e analysed on a m
size g ow h and in es men decision, while he esul s
ega ding he cos o capi al con i m wha has al eady
ound by Elho s (1993) and Oskam e al. (2009). I is
wo h no ing ha he e we e no signi ican changes in
in e es a es and in la ion a es du ing he conside ed
pe iod. I would be necessa y and use ul o obse e a ms
o e a longe pe iod o ully unde s and he impac o
exogenous ac o s ela ed o he mac oeconomic con ex ,
such as hose ha ha e occu ed in he las wo yea s.
The models explain 19% o he land in es men
decision, sugges ing ha he e a e o he ac o s no con-
side ed ha in luence he decision o pu chase land. The
ela i e Pseudo R2 alue is lowe han ha o o he s ud-
ies on s uc u al change bu mo e in line wi h s udies
on in es men decision. As in o he esea ch (i.e. (Aki-
mowicz e al., 2013), he a ailable da a and hei quali y
ha e in luenced he choice o explana o y a iables and
he ype o analysis. I was no possible o conduc he
analysis on balanced panel da a and include explana o y
a iables ela ed o he inancial posi ion o he a m,
i s local a ea, and na ional and municipal land egula-
ion. In es men s in capi al goods could ep esen a sig-
ni ican in es men ha may e en equi e a bank loan.
These a e a ional decisions ha he a me makes a e
analysis o he in e nal and ex e nal business con es .
The e o e, in o de o s udy and unde s and his ype o
in es men i would be app op ia e o ca y ou he anal-
ysis on a ms obse ed o e a long pe iod o ime. When
es ing and implemen ing he model, we a emp ed o
include he egional a iable as a ca ego ical a iable.
Howe e , his a iable educed he s a is ical signi icance
o o he explana o y a iables ela ed o a m s uc-
u e. The egional a iable al eady con ains in o ma ion
ela ed o o he a iables such as specialisa ion, UAA,
and RENT/UAA. This is because he I alian e i o y is
highly he e ogeneous ega ding e i o ial s uc u e, p o-
duc ion, and a m managemen . Fo his eason, i was
p e e ed no o include i . Fu he mo e, he land ma -
ke is hin and local, and he absence o p ecise geoloca-
ion da a o a ms p e en ed he conside a ion o o he
ex e nal ac o s. Fa me s end o buy land nea hei
ac i i y o educe and a oid down ime (Co elee e al.,
2008). In his ega d, he in oduc ion o a iables ela -
ed o he igh o p e-emp ion could be use ul in unde -
s anding he I alian land ma ke , gi en ha such igh is
p o ided o wi hin I alian legisla ion.
Finally, in addi ion o da a a ailabili y, he lack o
li e a u e has in luenced he design o he heo e ical
amewo k o de eloping he concep ual model and he
in e p e a ion and discussion o he esul s.
6. CONCLUSIONS
This esea ch ep esen s a i s a emp a an ex-pos
s udy using mic oda a o iden i y he ac o s ha ha e
in luenced he land in es men decision in I aly by in o-
ducing a iables ela ed o s uc u al and socio-demo-
67
Fa m cha ac e is ics and exogenous ac o s in luencing he choice o buy land in I aly
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
g aphic cha ac e is ics, economic pe o mances, ag i-
cul u e policies and he mac oeconomic en i onmen .
The esul s showed ha mo e han subsidies p o ided by
ag icul u al policies, income-gene a ing ac i i ies om
o he on- a m and o - a m ac i i ies posi i ely in luence
land in es men . In addi ion, specialisa ion appea s o be
an impo an ac o no so much in he pu chase deci-
sion, bu in unde s anding and di e en ia ing he e ec
o o he a m s uc u al ac o s on he likelihood o land
in es men . The a iables RENT/UAA and Family Wo k
uni s/To al Wo k Uni s a e he main a m cha ac e is-
ics ha nega i ely in luence he p obabili y o pu chas-
ing land in I aly. As expec ed, he p esence and age o
he successo ha e been con i med as impo an socio-
demog aphic cha ac e is ics o g ow h h ough acquisi-
ion. The esea ch shows ha he in e es a e and in la-
ion a e in luence he p obabili y o buying land. The
i e implemen ed models explain app oxima ely 20%
o he land in es men decisions o he analysed a ms.
The e o e, o he ac o s and he in e ac ion be ween ac-
o s can in luence a me s’ decisions.
The lack o a well-s uc u ed da abase condi-
ioned and limi ed his esea ch as well as he empi ical
esea ch analysed in he li e a u e on a m size g ow h
and land in es men decisions. In pa icula , p obi
analysis on a balanced panel o a ms obse ed o e
a long pe iod o ime was no possible wi h he a ail-
able da abase. In es men in land is much less equen
han o he ypes o in es men . I is made ollowing a
a me ’s conside a ion o a ailable a m asse s, his/he
own inancial esou ces, he supply o land on he local
land ma ke , and mac o-economic ac o s (i.e. in e es
a e and in la ion a e). Fo his eason, he analysis o a
balanced panel o a ms obse ed o a long ime could
allow a mo e accu a e analysis o he e ec o de e mi-
nan s on he decision o pu chase land. In addi ion, he
da abase in luenced he iden i ica ion and selec ion o
a iables ha could bes cap u e he de e minan s ha
may in luence he a me ’s decision and p e en ed he
in oduc ion o a iables ela ed o e.g. he inancial si u-
a ion o he a m and land egula ion.
In he u u e, he p oblem o he s uc u ed da a-
base could be sol ed by linking he da abases a ailable
o di e en I alian ins i u ions. The a ailabili y o a well-
s uc u ed da abase could be use ul o cap u e and con-
inuously moni o he dynamics and changes wi hin he
land ma ke and in a m managemen . The g ow h and
sp ead o en ed land and he en y in o he ag icul u al
sec o o young a me s willing o pu chase land could
equi e he upda ing and adap a ion o cu en land pol-
icies and egula ions ha di ec ly and indi ec ly in lu-
ence a m managemen choices and could p o ide ools,
including inancial ones, o e ec i ely suppo gene a-
ional u no e wi hin he sec o by acili a ing access o
land and a oiding he loss o ag icul u al land.
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Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
Sil ia Russo e al.
APPENDIX
Appendix 1. Desc ip i e analysis o sample a m size based on a m specializa ion
Specialisa ion
Min
VA/UL
Mean
VA/
TWU
Median
VA/
TWU
sd
VA/
TWU
Max
VA/
TWU
UAA
RENT/
VA/ha UAA
RENT/
VA/ha UAA
RENT/
VA/ha UAA
RENT/
VA/ha UAA
RENT/
VA/ha
UAA UAA UAA UAA UAA
No Specialised 0.01 0 -9964.8 -232589 32.33 0.38 4014.3 26437 15.77 0.21 1340.7 19547 51.39 0.41 29376.48 26155.84 920.1 1 1486980 574922
Annual c ops 0.06 0 -71723 -153306 36.17 0.41 9380 34724 18 0.28 1370 24767 60.23 0.42 35042.68 36542.29 1754 1 1395764 1069950
Ce eals 0.06 0 -13250 -153306 51.51 0.4 1039.6 38694 30 0.26 867.9 26348 67.6 0.41 1542 39848 1279 1 103921.6 509952
A able c ops 0.29 0 -20331 -111453 39.3 0.44 2719.2 32453 21.35 0.41 1083.4 23383 64.7 0.42 5877.1 38000 1754 1 120828.1 1069950
Ho icul u e 0.07 0 -71723 -107227 16.9 0.38 25721 33388 4.5 0.15 9282 25162 37.29 0.42 59108 30447 1101.75 1 1395764 617558
Pe manen c ops 0.2 0 -35024 -96116 14.73 0.27 6033 29864 7.81 0 3928 23011 25.13 0.39 7820.81 27845 526.87 1 248960 728767
F ui c ops 0.2 0 -5497 -29242 14.03 0.26 6514 29666 7.53 0 4472 23411 24.5 0.38 7182.5 24810 413 1 7182.5 416912
Oli e c ops 0.85 0 -3148 -23401 18.86 0.26 2903 23265 10.55 0 2199 19409 28.43 0.39 2953.2 18167 394 1 67758 205770
Vi icul u e 0.3 0 -35024 -96116 13.85 0.28 6741 32589 7.14 0 4185 24348 24.25 0.39 9299 32924 526.87 1 248960 728767
Li es ock sec o 0.05 0 -209342 -838045 51.74 0.46 6680 44475 28 0.44 1727 31113 73.45 0.41 44480 49666 1687.54 1 3792972 863036
Dai y ca le 0.2 0 -9222 -164891 49.13 0.48 4292 48736 27 0.49 2917 37914 65.66 0.4 5119 42163 770 1 90039 796655
He bi o es 0.1 0 -52896 -234751 61.15 0.47 2347.2 33066 36.26 0.44 853.1 24237 83.43 0.41 12450 36867 1687 1 403098 587929
G ani o es 0.05 0 -209342 -838045 28.43 0.44 23898 70768 13.19 0.37 7439 52388 43.27 0.42 102830 76804 514.55 1 3792972 863036
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Fa m cha ac e is ics and exogenous ac o s in luencing he choice o buy land in I aly
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
Appendix 2 Analysis o ela ionships among independen a iables
Table A1. Resul s o Pea son co ela ion analysis (Pa 1).
Va iable UAASq VA/ha VA/TWU Specializa ion RENT/UAA FWU/TWU Machine y_
Plan Value
Subsidies UE/
SAU`
Capi al
Accoun
Ene gy
p oduc ion
Subcon ac ing
ac i i ies Ag o ou ism P e_
PURCHASE
Fa m s uc u al cha ac e is ics
UAASq 1 -0.023 0.167 0.023 0.048 -0.140 0.206 0.000 0.020 0.043 0.027 0.027 0.007
VA/ha -0.023 1 0.123 0.028 -0.016 -0.120 0.015 0.068 0.008 0.019 -0.010 0.014 -0.001
VA/TWU 0.167 0.123 1 0.119 0.108 -0.152 0.272 0.105 0.038 0.151 0.045 0.004 0.023
Specializa ion 0.023 0.028 0.119 1 0.029 -0.028 0.037 -0.004 0.027 0.055 -0.069 0.003 -0.003
RENT/UAA 0.048 -0.016 0.108 0.029 1 0.023 0.045 0.017 0.025 0.047 0.065 -0.007 -0.005
FWU/TWU -0.140 -0.120 -0.152 -0.028 0.023 1 -0.184 -0.055 -0.064 -0.076 -0.001 -0.060 -0.024
Machina y_ Plan Value 0.206 0.015 0.272 0.037 0.045 -0.184 1 0.044 0.121 0.281 0.083 0.037 0.041
Subsidies UE/SAU` 0.000 0.068 0.105 -0.004 0.017 -0.055 0.044 1 0.029 0.015 -0.004 -0.015 0.000
Capi al Accoun 0.020 0.008 0.038 0.027 0.025 -0.064 0.121 0.029 1 0.058 0.008 0.017 0.030
Ene gy p oduc ion 0.043 0.019 0.151 0.055 0.047 -0.076 0.281 0.015 0.058 1 0.060 0.071 0.018
Subcon ac ing ac i i ies 0.027 -0.010 0.045 -0.069 0.065 -0.001 0.083 -0.004 0.008 0.060 1 0.00 0.01
Ag o ou ism 0.027 0.014 0.004 0.003 -0.007 -0.060 0.037 -0.015 0.017 0.071 -0.004 1 0.003
P e_PURCHASE 0.007 -0.001 0.023 -0.003 -0.005 -0.024 0.041 0.000 0.030 0.018 0.010 0.003 1
Fa m socio-demog aphic cha ac e is ics
FARMER_18_39 2E-05 -0.010 0.004 0.042 0.168 0.000 0.033 -0.001 0.038 0.001 0.008 0.021 0.005
FARMER_4049 4E-03 0.014 0.046 0.040 0.099 -0.042 0.020 -0.003 0.013 0.011 0.019 0.013 0.009
FARMER_5059 -6E-03 0.004 0.015 -0.006 -0.008 -0.017 -0.014 -0.004 -0.017 0.002 0.007 -0.013 -0.002
FARMER_OVER60 -2E-02 -0.019 -0.083 -0.113 -0.183 0.026 -0.085 -0.008 -0.037 -0.049 -0.053 -0.049 -0.020
SUCC_1_39 2E-02 0.012 0.023 0.070 -0.025 0.033 0.064 0.002 0.017 0.046 0.023 0.047 0.012
OFF_FARM INCOME -3E-03 -0.018 -0.064 -0.015 -0.073 -0.016 0.005 -0.018 -0.003 0.001 0.002 0.037 0.019
Exogenous ac o s
In la ion a e 5E-03 0.001151 0.008 0.001 -0.003 0.006 -0.001 -0.027 -0.008 -0.001 -0.001 -0.003 -0.003
In e es a e 6E-03 0.008741 -0.015 0.001 -0.032 -0.004 -0.001 -0.011 0.017 -0.020 -0.013 -0.016 -0.016
72
Bio-based and Applied Economics 14(1): 49-73, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15548
Sil ia Russo e al.
Table A1. Resul s o Pea son co ela ion analysis (Pa 2).
Va iable FARMER_
18_39
FARMER_
4049
FARMER_
5059
FARMER_
OVER60 SUCC_1_39 OFF_FARM
INCOME In la ion a e In e es a e
Fa m s uc u al cha ac e is ics
UAASq 0.000 0.004 -0.006 -0.015 0.022 -0.003 0.005 0.006
VA/ha -0.010 0.014 0.004 -0.019 0.012 -0.018 0.001 0.009
VA/TWU 0.004 0.046 0.015 -0.083 0.023 -0.064 0.008 -0.015
Specializa ion 0.042 0.040 -0.006 -0.113 0.070 -0.015 0.001 0.001
RENT/UAA 0.168 0.099 -0.008 -0.183 -0.025 -0.073 -0.003 -0.032
FWU/TWU 0.000 -0.042 -0.017 0.026 0.033 -0.016 0.006 -0.004
Machine y_ Plan Value 0.033 0.020 -0.014 -0.085 0.064 0.005 -0.001 -0.001
Subsidies UE/SAU` -0.001 -0.003 -0.004 -0.008 0.002 -0.018 -0.027 -0.011
Capi al Accoun 0.038 0.013 -0.017 -0.037 0.017 -0.003 -0.008 0.017
Ene gy p oduc ion 0.001 0.011 0.002 -0.049 0.046 0.001 -0.001 -0.020
Subcon ac ing ac i i ies 0.01 0.02 0.01 -0.05 0.02 0.00 0.00 -0.01
Ag o ou ism 0.021 0.013 -0.013 -0.049 0.047 0.037 -0.003 -0.016
P e_PURCHASE 0.005 0.009 -0.002 -0.020 0.012 0.019 -0.003 -0.016
Fa m socio-demog aphic cha ac e is ics
FARMER_18_39 1 -0.211 -0.222 -0.256 -0.134 -0.003 -0.010 0.005
FARMER_4049 -0.211 1 -0.283 -0.325 -0.170 0.021 0.002 0.014
FARMER_5059 -0.222 -0.283 1 -0.342 -0.179 0.019 0.002 -0.014
FARMER_OVER60 -0.256 -0.325 -0.342 1 -0.206 -0.119 0.001 -0.004
SUCC_1_39 -0.134 -0.170 -0.179 -0.206 1 0.103 0.006 -0.006
OFF_FARM INCOME -0.003 0.021 0.019 -0.119 0.103 1 -0.005 -0.014
Exogenous ac o s
In la ion a e -0.010 0.002 0.002 0.001 0.006 -0.005 1 0.326
In e es a e 0.005 0.014 -0.014 -0.004 -0.006 -0.014 0.326 1
