The Market for Sculptures: an Adjacent Year Regression Index

The Market for Sculptures: an Adjacent Year Regression Index

The Market for Sculptures:
an Adjacent Year Regression Index
Marilena Locatelli-Biey
Department of Economics
University of Torino - Italy
E-mail: [email protected]
Roberto Zanola
Department of Public Policy and Public Choice - Polis
Corso Borsalino, 50 – 15100 Alessandria - Italy
E-mail: [email protected]
ABSTRACT: This paper analyses the performance of an investment in sculptures during the period 1987-1995 by applying
the hedonic price technique with time dummy variables to a sample of over 27,000 sales occured at auctions. The main
finding is that the trend of the rate of return on an investment in sculptures is more stable than those associated to alternative
forms of investment during the analysed period.
JEL Classification Numbers: C5, Z1.
Key Words: Auction; Hedonic Price; Sculpture; Return.
1
Introduction
Sculptureis theartofproducinginthreedimensions representations ofnatural
orimagined forms anditembraces avarietyofconstruction-techniques as well
as a variety ofmaterials1 . T he most importantclassi…cation ofsculpture is
basedon therawmaterialused, butitis alsoimportantthesize, thefunction,
and theperiodoftheproduction.
T he marketforsculptures has characteristics thatmake it di¤ erent from
othermarkets forcollectibles. A lthough similarto the marketforpaintings,
the marketforsculptures is smallerthan it, with about3-5 per-centoftotal
collectiblessoldatauctions. A numberofreasons explainthis, suchas technical
di¢culties and thehigh costs ofproduction as wellas thefactthatsculptures
needappropriatespacestobeplacedorbecauseofthelowernumberofsculpture
collectors than paintingcollectors [L ocatelli-B ieyand Z anola, 19 9 9 a].
T hepurposeofthis paperis toexaminetheinvestmentvalueofsculptures.
A lthough there areanumberofstudies thathave investigated the investment
valueofcollectibles, suchaspaintings[A nderson, 19 7 4;Stein, 19 7 7 ;B uelensand
G insburgh, 19 9 3; de la B arre etal., 19 9 4;Chanel, 19 9 5; Chaneletal., 19 9 6
;
M ossetto and L azzaro, 19 9 6; A gnello and P ierce, 19 9 6; Candela and Scorcu,
19 9 7 ; R enneboogand V an H outte, 19 9 8 ; A gnelloand P ierce, 19 9 8 ;L ocatelliB iey and Z anola, 19 9 9 b]
, prints [P esando, 19 9 3], violins [R oss and Z ondervan,
19 8 9 ], wine [N erlove, 19 9 5; Combis et al., 19 9 7 ], antique furniture [G raeser,
1T
herawmaterialdepends ontheconstructiontechnique. H andmodellinguses terracotta,
orrawmaterialthatallows toproduceglazed ceramics. Castingis an ancienttechniquewhich
uses bronze. Carving is performed by usinga numberofdi¤ erentmaterials, such as marble,
stone, wood, ivory, bone, and, more recently, plasterand resin.
1
19 9 3], photos [P ompe, 19 9 6], toourknowledgethis itis the…rsttimethatthe
performanceofsculptures as …nancialassets is studied.
T his studyapplies thehedonicpricetechnique2 withtimedummyvariables
which allows priceindexes forsculptures and shadowvalues forcharacteristics
to be simultaneously estimated. D ata are drawn from auctions hold during
the period 19 8 7 -19 9 5, as compiled by M ayerInternationalA uction R ecord on
CD -R om.
T heremainderofthispaperisorganisedasfollows. Section2 brie‡youtlines
the methodology of the hedonic technique. Section 3 de…nes the empirical
speci…cation of the model. Section 4 describes the data set used, which is
compiled from M ayerInternationalA uction R ecords on CD -R om. Section 5
summarises thebasic…ndings. Conclusions aredrawn in Section 6.
2 S ee G
ordon (19 9 0);G riliches (19 9 0);and T riplett(19 9 0).
2
2
M ethodology
T heapproachusedtoestimateapriceindexforsculptures is thehedonicprice
technique as developed in similar studies analysing the investment value of
collectibles. T hehedonicapproach involves theestimateofthe implicitprices
toeachcharacteristicincludedintheequationbyallowingtocontrolforpossible
non-temporaldeterminants ofpricevariations.
A setofj-characteristics, xjkt, withj = 1 ;:::n;areidenti…edforaregression
ofthepriceofthesculpturek, withk= 1 ;:::;m, soldinyeart, witht= 1 ;:::;T ;
on its j-characteristics, such that:
lnpkt = ¯ 0 +
TX
¡1
¯ tzt +
t=1
Xn
° jxjkt + "kt
(1)
j=1
wherelnpkt is thelogarithm ofthepriceofsculptures soldinyeart;zt is a
dummyequalto1 when asaleoccurs in yeart, 0 otherwise;¯ 0 ;¯ t and ° j are
coe¢cients tobe estimated, where ° j is the implicitprice; "kt is the random
P
errorterm, with "s N (0 ; k-I
.
T)
T wo observations are necessary. First, an implicit assumption in (1) is
thatthesocialvaluationofsculpturecharacteristics does notchangeovertime
given the shortness ofthe analysed period. Secondly, the modelis speci…ed
in semi-logtransformations in ordertohandle the severe skewness in auction
prices. T ransformingthe dependentvariable, enables us touse ordinary least
squares estimators withouthavingtoworryaboutthesensitivityoftheresults
toskewness.
B asedonhedonicregressionequation(1), anumberofpriceindexes canbe
computed3. In this studyweadopttheadjacentyearregressions index, which
implicitlyweights each observation equally[B erndtetal., 19 9 5]
.
3In this
respect, we are only considering sculptures as …nancialassets, withoutany refer-
3
B y subtractingfrom the logarithm ofthe auction price the implicitprices
given tospeci…c characteristics, the characteristic-free price ofsculpture kin
yeartis equalto:
Xn
zkt = lnpkt ¡
° jxjkt
(2)
j=1
G iventhefunctionalform ofequation(1), theannualreturnona‘standardised
sculpture’[B uelens and G insburg, 19 9 3]maybewritten as:
¯t =
1 X
zkt
mt
(3)
Settingthepriceindexattimetequalto1 , thepriceindexattimet+ 1 is
given byexponentiatingthecoe¢cientassociated tothetimedummyvariable
from adjacentyearregressions. D e…neI
t thepriceindexattimet, theadjacent
yearregression indexis:
I
t+ 1 = I
t (1 + ³)
b̄
b̄
where» is equaltoe( t+ 1 ¡ t)¡1 :
(4)
ence to their psychic returns [B aumol, 19 8 6; Frey and Eichnberger, 19 9 5; S antagata, 19 9 8 ;
Czujack, 19 9 7 ]
.
4
3 FunctionalForm
T hehedonicregressionframeworktakes intoaccountthee¤ ectofheterogeneity
on prices by controlling fora numberofdi¤ erences in characteristics among
sculptures. T hedependentvariableis thelogarithm ofsaleprice, de‡ated and
reduced by 15% to correctfortransaction fees charged by auction houses to
sellers. T heindependentvariables areclassi…ed as follows:
²P roduction A vailabledatadonotallowustodistinguishbetweendi¤ erent
schools. H owever, as suggested bysomeauctionexperts contacted byus,
threedi¤ erentperiods ofproductionmaybeidenti…ed: old, forsculptures
produced before 18 00; mod, forsculptures produced between 1801 and
19 30;and cont, forsculptures produced after19 31.
²N ationality U sing the frequency with which the sculptors’ nationalities
appearin the data set, the following dummy variables are introduced:
France, natf;U sa, natus;G ermany, natd;Italy, natit;andG reatB ritain,
natgb:
²Size T hereis nostandard measureofsizeduethevarietyofconstruction
techniques4. In what follows, foreach observation we use the highest
dimension value(width, length, thickness), size, as aproxyforvolume.
²M edia A setofdummy variables is used: bronze, bro, and marble, mar,
are the traditional raw materials used, expecially for old and modern
sculptures, whileresin and plaster, res, are used in contemporary sculptures. Finally, giventhehighnumberofsculpturesofthis kind, alsoivory,
ivo, and terra-cotta, ter, areconsidered.
4Forinstance,
there are sculptures in the round, which can be viewed from any direction,
as wellas incised relief, in which the lines are cutintoa ‡atsurface.
5
²Salerooms and Cities of Sale Sotheby’s, soth, and Christie’s, chr; are
knowntobetheleadingauctionhouses inthis kindoftransactions. T heir
overallperformances di¤ eronly slightly. T hemostimportantsculptures
auction’ markets are N ew Y ork, ny, L ondon, lon, and P aris, par. A
furthercity is introduced, R ome and M ilan, rom, given the importance
oftheItalian marketforarts.
²M asterpieces Inordertotakeintoaccountthemostvaluedsculptors, fol-
lowingP esando(19 9 3), dummyvariables proxythemasterpieceportfolio
ofthe top 20 percentofsculptures by prices, top20 , as wellas the inexpensiveportfolio, low20 ;theremainderis assignedtothemiddlemarket,
mid20 .
²P eriod: a setofdummies, d, is introduced foreach yearbetween 19 8 7
and19 9 5.
Formally, ourspeci…cation is given by:
lnp = ¯ 0 + ¯ 1 old+ ¯ 2 mod+ ¯ 3 natf + ¯ 4 natus+ ¯ 5 natd+ ¯ 6 natit+
¯ 7natgb+ ¯ 8 size+ ¯ 9 bro+ ¯ 1 0 mar+ ¯ 1 1 res+ ¯ 1 2 pla+ ¯ 1 3 ter+
¯ 1 4 ivo+ ¯ 1 5 soth+ ¯ 1 6 chr+ ¯ 1 7ny + ¯ 1 8 lon+ ¯ 1 9 par+ ¯ 20 rom +
30
X
¯ 21 top20 + ¯ 22 low20 +
¯ jdj + "
(5)
j=23
6
4 D ata
T he data used in this paperare drawn from auctions hold duringthe period
oftime 19 8 7 -19 9 5. T he source ofthis data is the 19 9 5 edition ofthe M ayer
InternationalA uction R ecords (M IA R ) onCD -R om, which contains records of
27 119 sculptures soldattheworld’s majorauctions5 . Foreachsculptureinthe
dataset, anumberofinformations areprovided. P rices aregross ofthebuyers’
and sellers’ transaction fees paid to auction houses and are recorded in four
di¤ erentcurrencies. N oinformations is providedontheoriginofthesculptures
andexhibitions ofthesculptures. Forthesakeofsimplicity, weassumethatall
sales occurattheend ofeach period. A llsculptures arepriced in U S. dollars,
de‡ated by using the U S. consumerprice index (19 9 0 = 100)6 toremove the
generaltrend ofin‡ation. P rices are 15% less to correctfortransaction fees
chargedbyauctionhouses tosellers, butas inmoststudies, wedonottakeinto
accountstorageand insurancecosts.
Summarystatistics ofthesamplearedisplayed in T able1.
[T A B L E 1]
5 Even ifauction records alone donotre‡ecttheentire market-
since auction houses have
little incentive to sell out of fashion sculptures and, besides, ‘bought in’ works may in‡ate
prices [G oetzmann, 19 9 3]- it is not clear whether prices are biased upwards ordownwards
[A gnelloand P ierce, 19 9 8 ].
6S ource: T he FedaralR eserve B ank.
7
5
R esults
FollowingB erndtetal. (19 9 5), sinceheteroscedasticity may be present, standarderrors andvariance-covariancematrices ofthecoe¢cients havebeencomputed byusingtheW hite(19 80) heteroscedasticity-robustprocedure. T able2
displays themainresults obtained inthehedonicregression.
[T A B L E 2]
In almostallcases the coe¢cients aresigni…cantly di¤ erentfrom zeroat5
percentoreven at1 percentprobability level. T he coe¢cients associated to
each kind ofindependentvariablecan beused toranksculptures accordingto
thepriceofa‘normalised’sculpture, thatis, theprice ofasculpturewhen all
theothervariables areassumed tobeatastandard level.
Sculptures producedbefore18 00 arethemostpro…tableduetothescarcity
ofoldestsculptures, whilethoseproducedafter19 30 performsworsethansculptures producedbetween1801 and19 30. A s tonationality, theG erman, theEnglish and the Italian sculptors secure the highestprices. P rices are increasing
in size, as in thecaseofpaintings [B uelens and G insburg, 19 9 3;Chaneletal.,
19 9 6
;A gnelloandP ierce, 19 9 6;CandelaandScorcu, 19 9 7 ;R enneboogandV an
H outte, 19 9 8 ;A gnelloand P ierce, 19 9 8 ]
.
T hemostexpensivemediais marbleduetothehigh costofproduction, as
wellas technicaldi¢culties in producing sculptures by using this media. B y
contrast, othermediadisplaynegativecoe¢cients, even ifbronzeand resin are
notstatisticallysigni…cant.
P rices recorded atSotheby’s are higherthan those atChristie’s, and N ew
Y ork is the city where prices ofsculptures are the highest. B y generalising
P esando’s analysis ofthe marketforprints, a possible reason forthis is due
to N ewY ork’s capacity toattracthigh quality sculptures because the bidder
8
audienceis trulyinternational. Finally, masterpieces perform betterthanmiddlemarketartists;bycontrast, inexpensiveportfolioworks perform worsethan
middlemarketportfolio.
T he set of dummy variables introduced for each year between 19 8 8 and
19 9 5 is used in equation (4) to build the adjacentyearregressions index for
sculptures, which is reproduced in T able3.
[T A B L E 3]
A marketshockcharacterises theperiod19 8 7 -19 9 5 coveredbythedata-set,
since after a boom period until 19 9 0, the market for collectibles registers a
non-boom period from 19 9 1 to19 9 4. T he rate ofreturn ofan investmenton
sculptures seems tofollowthe trend ofthe rate ofreturn on paintings during
thesameperiodoftime[CandelaandScorcu, 19 9 7 ;L ocatelli-B ieyandZ anola,
19 9 9 b]. R atesofreturnarepositiveuntil19 9 0, followedbyanegativetrendfrom
19 9 1 to19 9 4, with apositivesign in 19 9 5, butstilllowerthan thepriceindex
19 8 7 . H owever, the trend ofthe performance ofan investmentin sculptures
is lower than those associated to alternative forms of investment. Figure 1
compares the de‡ated index for sculptures with the de‡ated indexes of real
returns on U S stocks, U S 30 yeargovernmentbonds and gold.
[ FIG U R E 1]
D uringtheboom periodinvestments insculptures earnedlowerrealreturns
than U S stocks, U S 30 yeargovernmentbonds, and gold, forms ofinvestment
characterised byacomparabledegreeofrisk. H owever, startingfrom 19 9 1, returnsonsculpturesdecrease, butatalowerratethanotherkindsofinvestment.
T his resultmay be due tothe characteristics ofthe marketforsculptures, as
illustrated in Section 1. In fact, the demand forthis kind ofcollectibles is expectedtoberatherinelastic, sothatthereturns onsculptures arenotsomuch
a¤ ected byboom and non-boom periods.
9
6 Conclusions
T his paperanalyses therateofreturnonsculptures, akindofcollectibleabout
which very little is known. A lthough the market for sculptures only represents about3-5 percentoftotalcollectibles sold atauctions, good investment
opportunities may exist. In ordertoaddress this question, the adjacentyear
regressions index forsculptures, sold atauctions duringtheperiod 19 8 7 -19 9 5,
has been computed byusingan hedonicpricetechnique.
O urresults canbesummarised as follows. T herateofreturnonsculptures
seemstofollowthetrendofothercollectiblesduringtheanalysedperiod, suchas
paintings [Candelaand Scorcu, 19 9 7 ;L ocatelli-B ieyand Z anola, 19 9 9 b]. H owever, thetrendoftheperformanceofaninvestmentinsculpturesislowerduring
theanalysed period, duetotheinelasticity ofthedemand which characterises
this market.
A numberofissues remain forfurtherresearch. Following similarstudies
which applythehedonictechniquetoestimatereturns on paintings, itmaybe
interestingtoanalyse the production ofa restricted numberoffamous sculptures. T his allows us bothtoisolatefactors thatin‡uenceprices and tocollect
additionaldata towhatis listed in published data sets [Czujack, 19 9 7 ]
. Furthermore, inthis studyweonlyfocuseonthereturns onsculptures as …nancial
assets. H owever, anumberofauthors [B aumol, 19 86;Frey and Eichenberger,
19 9 5;Santagata, 19 9 8;Czujack, 19 9 7 ]highlightthatvisualarts arealsocharacterised by aesthetic returns, an analysis of which may represent a future
developmentofthis paper.
A cknowledgments
T heauthorsaregratefulfor…nancialsupportfrom CN R program oncultural
10
goods. T his paperhas bene…ted from comments by G .B rosio, M .Ferreroand
W .Santagata. T heusualdisclaimers apply.
11
7
R eferences
A gnelloR .J. and P.R .K.P ierce(19 9 6), FinancialR eturns, P riceD eterminants,
andG enreE¤ ects inA mericanA rtInvestment, JournalofCulturalEconomics,
20(4), 359 -383.
A gnelloR .J. andP.R .K.P ierce(19 9 8), D isaggregatedreturns andriskin art
purchases. T he case ofA merican P aintings sold atauction from 19 7 1-19 9 6
,
U niversityofD elaware, mimeo.
A nderson R .C. (19 7 4), P aintings as an Investment, Economic Inquiry, 12,
13-26
.
B aumolW .J. (19 86
), U nnaturalV alue, orA rtInvestmantas FloatingCrap
G ame, A merican EconomicR eview, 7 6
, 10-14.
B erndtE.R ., Z .G riliches andN .J.R appaport(19 9 5), Econometricestimates
ofpriceindexes forpersonalcomputers inthe19 9 0’s, JournalofEconometrics,
6
8, 243-26
8.
B uelens N . andV .G insburgh(19 9 3), R evisitingB aumol’s ”art‡oatingcrap
game”, European EconomicR eview, vol. 37 , 1351-137 1.
CandelaG . and E. Scorcu (19 9 5), Ilprezzodei dipinti, Clueb, B ologna.
CandelaG . and E. Scorcu (19 9 7 ), A P riceIndexforA rtM arketA uctions.
A n application totheItalian M arketofM odern and ContemporaryO ilP aintings, JournalofCulturalEconomics, 21, 3, 17 5-19 6.
CaseB . andJ.M .Q uigley(19 9 1), T heD ynamics ofR ealP rices, T heR eview
ofEconomics andStatistics, 7 3, 50-58.
ChanelO . (19 9 5), Is the A rt M arket B ehaviourP redictable?, European
EconomicR eview, 39 , 519 -527 .
ChanelO ., A .L .G erard-V aretandV .G insburg(19 9 6), T heR elevanceofH edonicP riceIndices, JournalofCulturalEconomics, 20, 1-24.
12
Combris P., S.L ecocq and M .U isser (19 9 7 ), Estimation of H edonic P rice
Equation forB ordeaux: does Q uality M A tter?, Economic Journal, 107 , 39 0402.
Czujack C. (19 9 7 ), P icasso P aintings atA uction, (19 6
3-19 9 4), Journalof
CulturalEconomics, 21, 3, 229 -247 .
delaB arreM ., S.D occioandV .G insburg(19 9 4), R eturns onImpressionist,
M odernandContemporaryEuropeanP aintings, 19 62-19 9 1, A nnalesd’Economie
etStatistique, 35, 143-181.
FreyB .S.andW .W .P ommerehene(19 8 9 ), M uses andM arkets;Explorations
in the Economics ofthe A rts, O xford, B asilB lackwell.
FreyB .S.andR .Eichenberger(19 9 5), O ntherateofreturnintheartmarket:
Surveyand evaluation, European EconomicR eview, 39 , 528 -537 .
G insburgh V . and A .F. P enders, (19 9 7 ), L and A rtists and A rt M arkets,
JournalofCulturalEconomics, 21, 3, 219 -228 .
G oetzmann W . N . (19 9 3), A ccounting ForT aste: A rt and the Financial
M arkets O verT hreeCenturies, A merican EconomicR eview, 83(5), 137 0-137 6
.
G ordonR .(19 9 0), T hemeasurementofdurablegoodsprices, Chicago, Chicago
U niversityP ress .
G reeneW .H .(19 9 7 ), EconometricA nalysis, T hirdEdition, N ewY ork, M cM illan.
G raeser(19 9 3), R ateofR eturn toInvestmentin A merican A ntique Furniture, Southern EconomicJournal, 59 , 817 -8 21.
G riliches Z . (19 9 0), H edonicprice indexes and the measurementofcapital
and productivity: Some historicalre‡ections, in B erndtE.R .and J.E.T riplett
(eds), Fifty years of economic measurement, Chicago, U niversity of Chicago
P ress.
L ocatelli-B ieyM . and R .Z anola(19 9 9 a), Ilmercatodellesculturein Italia:
13
gli operatori eillororuolo, A edon, 2.
L ocatelli-B ieyM .andR .Z anola(19 9 9 b), T heM arketforP aintings: A Shortrun P riceIndex, JournalofCulturalEconomics, 23(3), 209 -219 .
M ossettoG . and E.L azzaro(19 9 6), L e D eterminati dei P rezzi delle O pere
d’A rte, U niversityofV enice, mimeo.
N erlove M . (19 9 5), H edonic price functions and the measurementofpreferences: T hecaseofSwedish wineconsumers, European EconomicR eview, 39 ,
16
9 7 -17 16
.
P esandoJ. (19 9 3), A rts as an Investment: T heM arketforM odern P rints,
A merican Economics R eview, 8 3(5), 107 5-108 9 .
P ompeJ.(19 9 6
), A nInvestmentFlash: T heR ateofR eturnforP hotographs,
Southern EconomicJournal, 6
3(2), 48 8 -49 5.
R enneboogL .andT .V .H outte(19 9 8), T heM onetaryA ppreciationofP aintings: form R ealism toM agritte, T ilburgU niversity, mimeo.
R oss M .H .and S.Z ondervan (19 8 9 ), CapitalG ains and the R ate ofR eturn
on aStradivarius, EconomicInquiry, 27 , 529 -540.
SantagataW . (19 9 8 ), Simboloe merce, IlM ulino- R icerca.
Stein J.P. (19 7 7 ), T he monetary appreciation ofpaintings, JournalofP oliticalEconomy, 8 5, 1021-1035.
T riplett J.(19 9 0), H edonic M ethods in Statistical A gency Environments:
an IntellectualB iopsy, in B erndtE.R . and J.E.T riplett(eds.), Fifty Y ears of
Economic M easurement: the Jubilee Conference on R esearch in Income and
W ealth, Chicago, ChicagoU niversityP ress.
W hite (19 8 0), A heteroskedasticity-consisten covariance matrix estimator
and adirecttestforheretoskedasticity, Econometrica, 48 , 8 17 -8 38.
14
Table 1: Descriptive Statistics
Variable
poddc
old
mod
cont
natf
natus
natd
natit
natgb
size
bro
mar
res
ivo
ter
soth
chr
ny
lon
par
rom
low20
mid20
top20
d87
d88
d89
d90
d91
d92
d93
d94
d95
Number of obs. = 27119
Mean
26595.1300
0.0278
0.4879
0.4751
0.3249
0.1610
0.0857
0.0806
0.0643
288.1932
0.5535
0.0503
0.0023
0.0417
0.0256
0.2458
0.2074
0.2622
0.1743
0.1738
0.0217
0.2002
0.6001
0.1997
0.0611
0.0784
0.0918
0.0999
0.0757
0.1748
0.1238
0.1515
0.1430
Std. Dev.
Min
125903.6
0.1645
0.4999
0.4994
0.4683
0.3676
0.2799
0.2722
0.2453
375.4276
0.4971
0.2186
0.0481
0.1998
0.15780
0.4306
0.4055
0.4398
0.3794
0.3789
0.1456
0.4001
0.4899
0.3998
0.2396
0.2687
0.2888
0.2999
0.2646
0.3798
0.3293
0.3585
0.3501
15
Max
4.0716
0
0
0
0
0
0
0
0
3.9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7200002
1
1
1
1
1
1
1
1
7924
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Table 2: Hedonic Price Equation
Variables
Robust
Coefficent
old
mod
natf
natus
natd
natit
natgb
size
bro
mar
res
ivo
ter
soth
chr
ny
lon
par
rom
top20
low20
d88
d89
d90
d91
d92
d93
d94
d95
constant
Number of obs.
R2
0.1105
-0.0817
0.0499
0.0912
0.1260
0.1134
0.1151
0.0002
-0.0094
0.0405
-0.0348
-0.0543
-0.0556
0.1064
0.0703
0.2282
0.1028
0.0497
0.1221
2.3530
-1.6582
-0.0420
0.0597
0.0723
-0.0891
-0.1027
-0.1191
-0.1776
-0.1328
8.4277
27119
0.79
F
3967.58
Standard
Error
0.0296
0.0100
0.0121
0.0153
0.0162
0.0199
0.0215
0.0000
0.0107
0.0220
0.0817
0.0229
0.0268
0.0178
0.0194
0.0196
0.0187
0.0122
0.0321
0.0129
0.0091
0.0260
0.0253
0.0246
0.0247
0.0222
0.0227
0.0225
0.0225
0.0214
0,95%
Confidence Interval
0.0525
0.1684
-0.1013
-0.0622
0.0261
0.0736
0.0612
0.1212
0.0942
0.1578
0.0744
0.1524
0.0730
0.1572
0.0002
0.0002
-0.0305
0.0116
-0.0026
0.0835
-0.1937
0.1265
-0.0993
-0.0094
-0.1082
-0.0030
0.0714
0.1413
0.0323
0.1083
0.1899
0.2666
0.0662
0.1394
0.0258
0.0736
0.0593
0.1849
2.3275
2.3781
-1.6760
-1.6403
-0.0929
0.0090
0.0100
0.1093
0.0241
0.1205
-0.1376
-0.0406
-0.1462
-0.0593
-0.1635
-0.0746
-0.2218
-0.1335
-0.1769
-0.0887
8.3858
8.4696
16
Table 3. Price indexes
Gold*
Dow Jones*
100.00
100.00
Year
1987
US 30Y bonds* Sculptures
100.00
100.00
1988
87.46
90.61
101.11
95.89
1989
79.69
110.42
111.74
106.15
1990
76.94
117.86
108.03
107.50
1991
74.53
128.76
112.75
91.47
1992
69.85
144.45
116.70
90.24
1993
79.55
154.99
129.84
88.77
1994
79.25
166.88
113.89
83.73
1995
79.95
197.44
133.57
87.56
* a Source: Torino Finanza.
Figure 1. Comparison between different deflated price indexes
200,00
175,00
150,00
125,00
100,00
75,00
50,00
25,00
0,00
1987
1988
1989
Sculptures
1990
Gold
1991
1992
D.J. Stocks
17
1993
U.S. 30Y Bonds
1994
1995
Working Papers
The full text of the working papers is downloadable at http://polis.unipmn.it/
* Economics Series
**Political Theory Series
2000
n. 15*
Marilena Locatelli-Biey and Roberto Zanola, The Market for
Sculptures: An Adjacent Year Regression Index
2000
n. 14*
Daniele Bondonio, Metodi per la valutazione degli aiuti alle
imprese con specifico target territoriale
2000
n. 13*
Roberto Zanola, Public goods versus publicly provided
private goods in a two-class economy
2000
n. 12**
Gabriella Silvestrini, Il concetto di <<governo della
legge>> nella tradizione repubblicana.
2000
n. 11**
Silvano Belligni, Magistrati e politici nella crisi italiana.
Democrazia dei guardiani e neopopulismo
2000
n. 10*
Rosella Levaggi and Roberto Zanola, The Flypaper Effect:
Evidence from the Italian National Health System
1999
n. 9*
Mario Ferrero, A model of the political enterprise
1999
n. 8*
Claudia Canegallo, Funzionamento del mercato del lavoro in
presenza di informazione asimmetrica
1999
n. 7**
Silvano Belligni, Corruzione, malcostume amministrativo e
strategie etiche. Il ruolo dei codici.
1999
n. 6*
Carla Marchese and Fabio Privileggi, Taxpayers Attitudes
Towaer Risk and Amnesty Partecipation: Economic Analysis
and Evidence for the Italian Case.
1999
n. 5*
Luigi Montrucchio and Fabio Privileggi, On Fragility of
Bubbles in Equilibrium Asset Pricing Models of Lucas-Type
1999
n. 4**
Guido Ortona, A weighted-voting electoral system that
performs quite well.
1999
n. 3*
Mario Poma, Benefici economici e ambientali dei diritti di
inquinamento: il caso della riduzione dell’acido cromico dai
reflui industriali.
1999
n. 2*
Guido Ortona, Una politica di emergenza contro la
disoccupazione semplice, efficace equasi efficiente.
1998
n. 1*
Fabio Privileggi, Carla Marchese and Alberto Cassone, Risk
Attitudes and the Shift of Liability from the Principal to the
Agent
18
Department of Public Policy and Public Choice “Polis”
The Department develops and encourages research in fields such as:
• theory of individual and collective choice;
• economic approaches to political systems;
• theory of public policy;
• public policy analysis (with reference to environment, health care, work, family, culture, etc.);
• experiments in economics and the social sciences;
• quantitative methods applied to economics and the social sciences;
• game theory;
• studies on social attitudes and preferences;
• political philosophy and political theory;
• history of political thought.
The Department has regular members and off-site collaborators from other private or public organizations.
19
Instructions to Authors
Please ensure that the final version of your manuscript conforms to the requirements listed below:
The manuscript should be typewritten single-faced and double-spaced with wide margins.
Include an abstract of no more than 100 words.
Classify your article according to the Journal of Economic Literature classification system.
Keep footnotes to a minimum and number them consecutively throughout the manuscript with superscript Arabic numerals.
Acknowledgements and information on grants received can be given in a first footnote (indicated by an asterisk, not
included in the consecutive numbering).
Ensure that references to publications appearing in the text are given as follows:
COASE (1992a; 1992b, ch. 4) has also criticized this bias....
and
“...the market has an even more shadowy role than the firm” (COASE 1988, 7).
List the complete references alphabetically as follows:
20
Periodicals:
KLEIN, B. (1980), “Transaction Cost Determinants of ‘Unfair’ Contractual Arrangements,” American Economic Review,
70(2), 356-362.
KLEIN, B., R. G. CRAWFORD and A. A. ALCHIAN (1978), “Vertical Integration, Appropriable Rents, and the
Competitive Contracting Process,” Journal of Law and Economics, 21(2), 297-326.
Monographs:
NELSON, R. R. and S. G. WINTER (1982), An Evolutionary Theory of Economic Change, 2nd ed., Harvard University
Press: Cambridge, MA.
Contributions to collective works:
STIGLITZ, J. E. (1989), “Imperfect Information in the Product Market,” pp. 769-847, in R. SCHMALENSEE and R. D.
WILLIG (eds.), Handbook of Industrial Organization, Vol. I, North Holland: Amsterdam-London-New York-Tokyo.
Working papers:
WILLIAMSON, O. E. (1993), “Redistribution and Efficiency: The Remediableness Standard,” Working paper, Center for
the Study of Law and Society, University of California, Berkeley.
21