How Payment Systems Affect Physicians´ Provision Behaviour – An Experimental Investigation

Bonn Econ Discussion Papers

Bonn Graduate School of Economics

Department of Economics

University of Bonn

Discussion Paper 29/2009

How Payment Systems Affect Physicians´ Provision

Behaviour – An Experimental Investigation

by

Heike Henning-Schmidt, Reinhard Selten

and Daniel Wiesen

Financial support by the

Deutsche

Forschungsgemeinschaft

(DFG)

through the

Bonn

Graduate

School

of

Economics

(BGSE)

is gratefully acknowledged.

Behaviour An Experimental Investigation

∗

Heike Hennig-Shmidt

†

, Reinhard Selten

†

& Daniel Wiesen

†‡

July 23, 2009

Abstrat

A entral onern in health eonomis is to understand the inuene of

om-monly used physiian payment systems. We introdue a ontrolled

labora-toryexperimenttoanalyzetheinueneoffee-for-servie(FFS)andapitation

(CAP)payments on physiians' behaviour. Medial students deide as

exper-imental physiians on the quantity of medial servies. Real patients gain a

monetary benet from their hoies. Our main ndings are that patients are

overserved in FFS and underserved in CAP. Finanial inentives are not the

onlymotivationforphysiians' quantitydeisions,though. Thepatientbenet

is of onsiderable importane as well. Patients are aeted dierently by the

twopayment systems. Those patientsin needof alowlevelofmedial servies

arebetterounderCAP,whereaspatientswith ahighneedofmedialservies

gainmorehealth benetwhenphysiiansarepaid byFFS.

Keywords: Physiian paymentsystem;laboratoryexperiment;inentives;

fee-for-servie;apitation

JEL-Classiation: C91, I11

∗

ThepresentpaperisaompletelyrevisedversionofHennig-Shmidt,ReinhardSelten&DanielWiesen

(2007) Physiians' ProvisionBehavior underDierent PaymentSystemsAnExperimental

Investi-gation. Wearegratefulforhelpfulommentsandsuggestionsbypartiipantsofthefollowing

onfer-enes andseminars: IZAWorkshoponBehavioralandOrganizationalEonomis,Sozialökonomisher

AusshussdesVereinsfürSoialpolitik,IMEBE,EEA,IAREP,ESAEuropeanMeeting, iHEAWorld

Congress,UniversityofZurih,HEROattheUniversityofOslo,egeattheUniversityofGöttingenand

ErasmusUniversityRotterdam. FinanialsupportforDanielWiesenbytheKonrad-Adenauer-Stiftung

e.V.isgratefullyaknowledged.

†

BonnEonLab,LaboratoryforExperimentalEonomis,DepartmentofEonomis,UniversityofBonn,

Adenauerallee24-42,D-53113Bonn,Germany

‡

Correspondingauthor: Phone: +49(0)228739194, Fax: +49(0)228739193,e-mail:

Aentralonernin healtheonomis istounderstand theinuene ofinstitutions onthe

behaviourofators on healtharemarkets. Inpratie, eetsfrom hanging institutions

like the payment system during a health are reform are ex ante not neessarily known

to poliy makers and may inuene behaviour in an undesired way. Main addressees of

reforms are health are providers (physiians) whose behaviour is likely to be inuened

bythe paymentsystem. Theoretial health-eonomi literaturehashighlighted the

dier-ent inentivesof ommonly usedpayment systemslike fee-for-servie (FFS) or apitation

(CAP).UnderFFSphysiiansarepaidforeah medialproedureor serviedispensedto

apatientwhereasunderCAP,physiiansreeiveaxedpayment foreahpatient

irrespe-tive ofthe quantityof medial servies provided. FFS inherits an inentive to `overserve'

patients whereasCAP mayleadto underprovision of medial servies(Ellis andMGuire

1986, Newhouse 2002).

Fieldstudiesshowthatdierentpaymentsystemsdoaetphysiians'behaviour. Yet,

the results areoftennot omparable beauseof ountry-spei institutionaldierenes.

1

In some studies, more than one omponent of the payment system are varied

simultane-ously making ausal inferenes diult or even impossible. Aording to Gosdenet al.

(2001) the results are too ontraditor y to draw a denite onlusion on the diretion of

aneet.

Another empirial method is alled for that omplement s eld studies and overomes

(someof)theproblemsmentionedabove. Fuhs(2000)inhisartileonthefutureofhealth

eonomis argues that inorporating methods of experimental eonomis into health

eo-nomi researh may lead to great benets for the latter. In a similar vein, Frank (2007)

arguesin favor of applyingbehavioural eonomis methods in healtheonomis.

OurstudyontributestotheresearhagendassuggestedbyFuhsandFrank. Weusea

ontrolledlaboratoryexperimenttoimprovetheunderstandingoftheinstitutional

param-eter `payment system' by implementing the spei featuresof FFS and CAP. The main

fousof ourstudy ison how the two payment systemsinuene aphysiian's provisionof

medialservies, andweabstratfrom fatorsotherthan the payment system. Ourstudy

isone of the very rst onestakling a health eonomi topi by methods of experimental

eonomis. 2

Inour experiment, experimental physiians all of themmedial studentsdeide on

the quantity of medial servies under the two payment systems. Patients gain a

bene-t from these servies, the patient benet measured in monetary terms. Only abstrat

patients `partiipate ' in our experiment. To provide the experimental physiians with an

inentiveforfavourablebehaviourtowardsthepatients,however,themoneyorresponding

tothe benets ofall abstratpatientsis transferredto a harity aringfor real patients.

Ourmain ndingisthatphysiiansare inuenedbythe payment system. Inlinewith

1

See for example the studies by Stearnsetal. (1992) and Davidsonetal. (1992) in the US,

Krasniketal. (1990)inDenmark,IversenandLurås(2000)andGryttenandSørensen(2001)in

Nor-way,Huthinsonetal.(1996),DevlinaandSarma2008andDumontetal.,2008inCanada.

2

Finanialinentivesarenottheonlymotivationforphysiians'quantitydeisions,though;

the patient benet is of onsiderable importane aswell. Patientsare aeted dierently

bythetwopaymentsystems. Those inneedofalowlevelofmedialserviesarebettero

under CAP, whereaspatientsneeding ahighlevelofmedial serviesgaina higherhealth

benetwhen physiians arepaidbyFFS.

Our paper is organized as follows. Setion 2 skethes the theoretial and empirial

literature on physiian payment and inentives most relevant to our researh topi.

Se-tion3 states our researh questions. Experimental design and proedure are desribed in

Setion4. Setion 5providesastatistialanalysisofsubjets'behaviourwithinandaross

payment systems. Setion 6onludes.

2 Related literature

In the health eonomis literature, several authors have highlighted the dierent

inen-tivesin ommonly used payment systems like fee-for-servie (FFS) or apitation (CAP).

Conventional modeling of the physiian-patient interation relies on prot maximization,

however (for a summarysee MGuire, 2000). Reently, a number of authors departfrom

modeling physiiansas pure prot maximizers by allowing for patient benevolene in the

physiian'sobjetivefuntion;seeforinstaneEllis andMGuire(1986),Ma(1994,2007),

Jak(2005) andChoné andMa (2007).

Intheirseminalartile,Ellis andMGuire(1986)letthephysiian

3

deideonthe

quan-tityofmedialserviesasanagentof thepatientand thehospital. Thephysiian'sutility

derives from two elements the hospital's prot and the patient's benet. Aording to

Newhouse (2002), Ellisand MGuire's model is also appliable to a primary are setting

rather resembling the setup we are interested in. This implies that the physiian is

as-sumedto be onerned about her ownprot

π

andthe patient benet

B

,both depending

on the quantity of medial servies

q

. A major argument for inluding

B

into the

physi-ian'sutility funtionis theprofessional ode of medialethisthe physiian isobliged to

(HipporatiOath).

4

Ellis andMGuire nd thatFFS providesan inentive to overserve

patients whereas CAP maylead to underprovision of medial servies. Moreover,

apita-tionpaymentsan ause underprovision of neessary servies(Blomqvist, 1991) and may

leadto ream-skimming ofpatients(Newhouse, 1996 and Barros, 2003).

A rih empirial literaturehas studied various aspets of the relationship between the

methodofphysiianremuneratio nandphysiian behaviour. Someempirialevidene

sug-geststhatphysiians dorespond tonanial inentives. Krasniket al.(1990) in a

before-and-after study, analyse behaviour of general pratitioner s in Denmark when the system

is varied from a (pure) lump-sum payment to CAP supplemented by a FFS omponent.

Theynd diagnosti and urative servies to inrease and the number of referrals to

se-ondaryareandhospitals toderease. Conerningreferralrates,Iversen andLurås(2000)

3

Inthefollowing , wedenotethephysiianasfemaleandthepatientasmale.

4

See also Arrow (1963) who emphasized the importane of professional ethis; treatment should be

byNorwegiangeneralpratitioner s whenthe paymentsystemwashangedfromapratie

allowane omponent

5

omplement ed by a FFS-payment to a CAP-system with a lower

FFS-omponent . TheauthorsndreferralstobelargerunderCAP(withFFS-omponent )

omparedtoFFS(withpratie-all owaneomponent). Theinreaseinreferralsmay,

how-ever,not only beattributable to CAPbut rather to the lower FFS-omponent.

Inarandomizedontrolledstudy,Davidsonet al.(1992)investigatebehaviourof

oe-basedprimary arephysiiansunderaFFS systemwithhighandlowfeesandaCAP

sys-tem. Patients werehildren enrolled in the US- Mediaid program. Here, the frequeny of

primaryarevisitsin thehighFFSgroup washigherthanin theCAPgroup. Apparently,

CAP physiians onstrain the quantityof medial servies in order to redue their osts.

Thefundholding regulation

6

in CAP mayexplain the lower referrals to seondaryare as

the responsibilityfor hildren's medial ostseems to outweigh the inentive to minimize

ostin CAP.

In a more reent study, Dumont et al. (2008) analyse data on physiian servies from

the Canadian provine Quebe before and after a variation from FFS to a mixed system

with a base wage, independent of servies provided, and a redued FFS payment. Their

results suggest that physiians did reat to payment inentives by reduing the volume

of (billable) servies under the mixed remuneratio n system. Moreover, these physiians

inreasedthetime spentper servieandper non-linialservie. Thelatterareimportant

to insure the quality of health are but are not remunerated under FFS. The results of

Dumont et al. suggestaquantity-qualitysubstitution in healthare provision.

Oneofthemostimportantifnottheonlyontrolledeldexperimentinhealtheonomis

istheRANDhealthinsuranestudy(Newhouse and the Insurane Experiment Group1993).

Themain goalofthis experimentwastoinvestigatethe inuene of the insuranesystem

(patients' o-payment vs. free are) on patients' health are servieuse and their health

status. It wasfoundthat all typesof serviesanalysed in the study fell with ost sharing

butthe redued servieusehadnearly noadverse eet on healthfor the averageperson.

Health amongthe sik poor wasadversely aeted, however. A smallerpart of the study

was devoted to analyzing the inuene of the payment system. To this end, the authors

ompared the use of servies under fee-for-servie remuneration with that in a apitated

sta model HMO (Health Maintenane Organisation).

7

Cost savings were found to be

notieable,inpartiular dueto lower hospitaladmissionandlower estimatedexpenditure.

Notallstudies supportthe strong linkbetween physiians' payment systemsand their

behaviour, however. For example, Huthinson et al. (1996) do not nd dierenes when

omparing hospital utilization rates in Ontario (Canada) under FFS and CAP. For data

from Norwegian physiians, Gryttenand Sørensen (2001) nd that after ontrolling for

harateristis ofpatientsandgeneral pratitioner sthe eetsofphysiians'payment

sys-5

ApratieallowaneisaxedsumofmoneyNorwegianphysiiansarepaidwhenontratingwiththe

regional government.

6

Suhafundholdingsystemhas thefollowingharateristis: i) thenanialresouresforeahpatient

areheldinafundandii)thegeneralpratitionerisusuallythedeision-makerforalloatingthefunds. 7

What an be onluded from the empirial literature ited above? Based on their

meta-study, Gosdenet al. (2001) aknowledge some empirial evidene that the payment

system aets physiian behaviour. They stress, however, that eld studies fae

vari-ousdiulties like multiple and unobservableinuenes on physiians' deisions, ontext

and ountry-spei payment system variations that make the generalization of results

diult. In addition, several eld studies suer from methodolog ial problems when for

instane more than one omponent of the payment system is varied simultaneously. We

willreturn to these issuesin the next setion.

3 Researh questions

Ourmainresearhgoalistoimprovetheunderstandingonhowtheinstitutionalparameter

`payment system' inuenes physiians' behaviour. To this end, we make use of

experi-mentaleonomis methods byrunninga ontrolledlaboratory experiment.

Experimentaleonomisisavalidresearhtehniquethatansuessfullyomplement

eld and survey studies. It has a variety of advantages ompared to the latter researh

approahes (see Davisand Holt 1993, Falk andFehr 2003). Experimental data is

re-atedunder ontrolled onditions. It isgathered in experimental sessions in whih human

subjets supplied with monetary inentives

8

make real deisions in eonomiallyrelevant

deision situations. Experimental onditions and variables of interest an be varied in a

ontrolled manner. Exogenous eteris paribus variations (e.g. of the payment system)

an be easily implemente d. Therefore, hanges in behaviour an be attributed to these

modiations. Dierentexperimenters anrepeatthe same experiment under omparable

onditionsto test for the robustness ofthe results.

Contrarytolaboratorydata, elddata areolletedfromanatural environment where

many fators inuene the variable(s) of interest in a way the researher usually annot

ontrol. 9

These are for instane institutional parameters, physiians' harateristis,

un-ertaintyabout the impat of medial servies provided aswell aspatient harateristis

like health status or type of insurane. Constant patient populations during a transition

ofpaymentsystemsisimportant forthevalidityofresultsbutanmostoftennotbe

guar-anteed. Also, the methodolog ial deienies mentioned in the setion above should not

be negleted (see Gosden etal. 2001). This said, laboratory experimentatio n apparently

is a suitable researh method to suessfully omplement theoretial analyses and other

methods of empirialinvestigation.

Despite the advantages of experimental eonomis, objetions like non-representative

student subjetpools,lowinentives, asmallnumber ofobservations andthe simple

envi-ronment should be taken seriously. Yet,areful experimentation an avoid manyof these

problems(see Falk andFehr 2003).

8

Partiipantsare paidbeause theyarelikelytobehavedierentlywhen monetaryonsequenesare

in-volvedasomparedtohypothetialhoies(seeCamererandHogarth1999andHertwigandOrtmann

2001). 9

See,however,theRANDhealthinsuraneexperiment(NewhouseandtheInsuraneExperimentGroup

situationismuhmoreomplex. Yet,asthegoalofthepresentstudyistohighlight

funda-mentalonsequenes ofthe payment systemfor physiians' behaviour we think simpliity

tobe anadvantagerather than adeieny.

The fous of our study is on how the pure payment systems FFS and CAP inuene

an experimental physiian's provision of medial servies. We inorporate the two major

determinant s thataording to the theoretialliterature referredto in Setion2 inuene

aphysiian's behaviour, theownprotandthe patient'sbenet. We alsoinlude patients

with dierent health status, so-alled patient types, to aount for heterogenei ty in the

patient population.

OurrstresearhquestionisonernedwithbehaviourinFFS.Givenourexperimental

parameters,do experimental physiians tend to behave aording to whattheorypredits

in thatthey hoosea quantityof medial servies

q

F F S

larger than the patient's optimal

quantity

q

∗

ifthe prot-maximal quantity

q

ˆ

exeeds

q

∗

? Taking

q

∗

as the benhmark for

the right (best) medialtreatment,we onjeturepatientsto beoverserved under FFS.

Seond, we are interested in behaviour under CAP. Aording to preditions from

theoretial models we expet patients to be underserved in that physiians hoose

q

CAP

lower than

q

∗

.

Third, we are onerned with researh questions related to the onsequenes of both

payment systems. Howdoesprovisionbehaviourunder CAPompare tobehaviourunder

FFS? Based on our previous onjetures, we expet experimental physiians in FFS to

hoose more medial servies than in CAP. Moreover, does the mode of payment have

an impaton whether and how experimental physiians besides their own prottake the

patient benetinto aount? Given the professional ode of medialethis physiians are

obliged to,we expet themnot to behave in a ompletelyself-interested manner.

Wealsoanalysethepreviousquestionswithregardtopatienttypes. Doesthepayment

systemaetpatientswithdierenthealthstatusdierentlyastophysiians'treatment? If

so,aretheredierenesbetweenFFSandCAP?Weexpetthistobethease. TheRAND

health insurane experiment (Newhouseand the Insurane Experiment Group 1993), for

instane, showed ertain albeit small adverse health onsequenes onentrated among

sikpeoplefrom the lowest inome group.

Thelast researhquestiononerns thetradeobetween ownprotandpatientbenet

the experimental physiians are faed with. In our experiment, several Pareto-ei ent

quantity deisions exist for eah patient. Here, physiians an neither make the patient

better owithoutforegoing ownprotnor makethemselvesbetter owithoutinduing a

benet lossto the patient. We arespeially interested in the following questions: Does

behaviourwith regardtoParetoeienyandtradeosvaryin thetwopaymentsystems?

Can a lassiation of behaviour help us to get deeper insights into deision making like

ithashelped to explain behaviourin other game settings (e.g. Selten andOkenfels 1998

4.1 Design and parameters

Weanalysephysiians'provisionbehaviourunderthetwopaymentsystemsFFSandCAP.

Nootherexperimentalparameterisvaried. Theexperimentaldesignallowsforaontrolled

eteris paribus variationand abetween-subjet omparison.

Eahsubjettakingpartinourexperimentisalloatedtoaphysiian'sroledeidingon

the quantityof medial servies to be provided for given patients. Partiipants are

medi-alstudents expeted to beome physiians in the future. We deliberately hose medial

students as they most likely will identify with the deision taskin our experiment. And

we used a ontext-spei framing (see the instrutions in the appendix). Both features

areimportant aswe areinterested in how subjets deide in amedial ontext,and

iden-tiationaswellasframingseems to matterfor behaviour.

10

Weruntwotreatments. InFFS,physiiansreeiveafeeforeahunitofmedialservie

provided. In CAP, they are paida lump-sum payment (apitation) per patient

indepen-dentofthe numberofmedialserviestheydispense. Allmonetaryamountsaremeasured

in Taler, our experimental urreny, the exhange rate being1 Taler =0.05 EUR (about

$0.07).

Our experimental physiians' taskisto treat patientsbyprovidingthem with medial

servies. Patients gain a benet from these servies. The patient benet is measured in

monetaryterms. Threetypesofpatientsexist. Thesetypesdierinthe `benetfuntions'

that relate the benet a patient reeives to the number of servies a physiian provides.

Inpartiular, patients ofdierent typesneed dierent amounts of serviesin order to get

their optimal treatment (maximum benet); for speiations of all funtions see below.

Patientsin ourexperimentareabstratinthatnorealpersonspartiipate. Yet,toprovide

experimental physiians with an inentive for favourable behaviourtowards the patients,

themoney orrespondingto the benets ofallabstrat patientsis transferredto aharity

aringfor real patients.

Patients are further haraterized by illnesses. An illness has no impat on patients'

benets. InFFS,ithasanimpatonphysiians'remuneratio n, however,asthe

`remunera-tionfuntion'thatrelatesaphysiian'sremuneratio ntothenumberofserviesaphysiian

provides is determined by the respetive illness. In partiular, maximum remuneratio ns

dieraross the ve existing illnesses. The same holds for maximum prots beause the

osts a physiian has to bear are kept onstant for all deisions and aross treatments.

Reall that in CAP, physiians are paid a lump-sum apitation per patient. Therefore,

neitherillnesses northe number ofmedial serviestheydispensehave animpatontheir

remuneratio n.

In the remainder of this subsetion we desribe the experimental design in more

de-tail. Physiians deide on the quantity

q

∈ {

0

,

1

, . . . ,

10

}

of medial servies to be

10

Ahlertetal.(2008)ndlessselshness(higheridentiation)ofphysiiansinamedial(familiar)

fram-ing than inaneutral (unfamiliar)environment. A reent own studyshows non-medial studentsto

provided to their patients. 11

They deide for ve abstrat illnesses A

,

B

,

C

,

D

,

E

12 of

three patient types

1

,

2

,

3

. Patient types dier in their benet from medial servies

(

B1

(

q

)

, B2

(

q

)

, B3

(

q

)

). Eah ombination of patient type and illness represents a spei

patient 1A

,

1B

,

1C

, . . . ,

3D

,

3E (Table 1). By eah deision (

j

= 1

, . . . ,

15

), physiians

si-multaneously determine their own prot and the benet of a given patient. The patient

is assumed to be passive and fully insured aepting eah medial servie hosen by a

physiian.

Table 1:Order ofdeisions

Deision (

j

) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Patienttype 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

Illness A B C D E A B C D E A B C D E

Patient 1A 1B 1C 1D 1E 2A 2B 2C 2D 2E 3A 3B 3C 3D 3E

Physiians'remuneration. InFFS,physiiansreeiveafeeforeahunitofmedialservie

provided. Fees dier aross servies and illnesses. As points of referene for our

experi-mentalfeeswe usedtarisfor ophthalmologist servies(like thetreatment ofglauomaor

atarat)takenfromthe Germansaleofhargesandfeesforphysiianservies(EBM)

13 .

Remuneration

R

(

q

)

inreases in the quantityofmedial servieshosen(see Table 2).

In CAP, physiiansare paidalump-sum payment

R

per patient independent of their

Table2:Physiians' remuneratio n

R

(

q

)

Quantity(

q

) Illness 0 1 2 3 4 5 6 7 8 9 10 FF S A 0.00 1.70 3.40 5.10 5.80

‡

10.50 11.00 12.10 13.50 14.90 16.60 B 0.00 1.00 2.40 3.50 8.00 8.40 9.40 16.00 18.00 20.00 22.50 C 0.00 1.80 3.60 5.40 7.20 9.00 10.80 12.60 14.40 16.20 18.30 D 0.00 2.00 4.00 6.00 8.00 8.20 15.00 16.90 18.90 21.30 23.60 E 0.00 1.00 2.00 6.00 6.70 7.60 11.00 12.30 18.00 20.50 23.00 C A P 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00

‡

Duetoadisplayerroronsubjets'sreens,physiians'remunerationforillnessAat

q

j

= 4

was

speiedat8.40 instead of5.80. Physiian's protsweredisplayed orretly, however. See the

paragraph onphysiian'sprotbelow.

quantity deision. To make treatments omparable,

R

was speied at 12 Taler in CAP

whihis slightly above the average maximum protper patient a physiian ouldahieve

in FFS(11.08 Taler).

Patient benet. Patients gain a benet from medial servies, the patient benet

B

(

q

)

measuredin monetary terms. Patient benets varyarosspatient types. This reetsthe

heterogenei tyof the patient population treated bya physiian in reality, e.g. with regard

toa patient's health statusordierentseverities ofillness. Table3 showspatient benets

B

(

q

)

giventhe quantity ofmedial serviesprovided. Aommon harateristi of

B

(

q

)

is

11

The range of servies physiians an hoose from may be interpreted as those eligible for a patient

ontratingwithaertainhealthplan.

12

Wedidnotspeifyrealillnessesbeausethisturnedoutnottobefeasibleintheexperimentalsetup.

13

aglobaloptimum

q

∗

∈

[0

,

10]

. Thepatientoptimalquantity(

q

∗

) yieldsthehighestbenet

B

(

q

∗

j

)

from medial servies to the patient. The patient's optimal quantity is

q

∗

j

= 5

for patient type 1 (

j

= 1

, . . . ,

5

),

q

∗

j

= 3

for patient type 2 (

j

= 6

, . . . ,

10

) and

q

∗

j

= 7

for

patient type 3(

j

= 11

, . . . ,

15

). Afterhavingreahed theoptimum,

B

(

q

)

delines beause

providing more medial servies than

q

∗

ontributes negatively to a patient's benet at

the margin. Taking

q

∗

as the benhmark for the right (best) medial treatment, we an

identify overprovision and underprovision,respetively.

Table3:Patient benet

B

(

q

)

Quantity(

q

) Patienttype 0 1 2 3 4 5 6 7 8 9 10 1 0.00 0.75 1.50 2.00 7.00 10.00

‡

9.50 9.00 8.50 8.00 7.50 2 0.00 1.00 1.50 10.00

‡

9.50 9.00 8.50 8.00 7.50 7.00 6.50 3 0.00 0.75 2.20 4.05 6.00 7.75 9.00 9.45

‡

8.80 6.75 3.00

‡

Patientoptimalquantity

q

∗

j

yieldsthehighestbenet

B(q

∗

j

)

frommedialserviestothepatient.

It is ruial that the experimental physiians have an inentive to take the patient

benet into aount. Therefore, the money orresponding to the benets of all abstrat

patients aggregated over all deisions ofall physiianswastransferred to a harityaring

for real patients the ChristoelBlindenmission. To verify thatthe money was atually

transferredwe applied aproeduresimilarto the oneusedin Ekeland Grossman(1996).

Ineah session, a monitor randomly seleted from the partiipatin g subjets mustverify,

byasigned statement,thatahekforthetotal patientbenetiswritten andsealedinan

envelope addressed to the harity. The monitor and experimenter then walk together to

thenearestmailboxanddepositthe envelope. Themonitorwaspaidanadditional4EUR.

Physiians' prot. Further parameters relevant for physiians' deisions are osts and

prot. Likereal dotors, the experimental physiianshave tobearostsdependingon the

quantity of medial servies they hoose. We use a onvex ost funtion as assumed in

several theoretial models (e.g. Ma 1994, 2007 and Choné andMa 2007).

c

(

q

j

) = 0

.

1

q

2

j

∀

q

∈

[0

,

10]

, j

= 1

,

2

, . . . ,

15

isapplied in both treatments.

Prot (remuneration minus osts) varies arossillnesses in FFS beause feesdier for

Table4:Physiians' prot

π

(

q

)

Quantity(

q

) Illness 0 1 2 3 4 5 6 7 8 9 10 FF S A 0.00 1.60 3.00 4.20 4.20 8.00

‡

7.40 7.20 7.10 6.80 6.60 B 0.00 0.90 2.00 2.60 6.40 5.90 5.80 11.10 11.60 11.90 12.50

‡

C 0.00 1.70 3.20 4.50 5.60 6.50 7.20 7.70 8.00 8.10 8.30

‡

D 0.00 1.90 3.60 5.10 6.40 5.50 11.40 12.00 12.50 13.20 13.60

‡

E 0.00 0.90 1.60 5.10 5.10 5.10 7.40 7.40 11.60 12.40 13.00

‡

C AP 12.00

‡

11.90 11.60 11.10 10.40 9.50 8.40 7.10 5.60 3.90 2.00

‡

Physiians'maximumprot

π(ˆ

q

j

)

aording totheprot-max imizing quantity ofmedial

ser-vies

q

ˆ

j

.

illnesses, and ostsare the same for all patients. In CAP, on the other hand, prot does

Figure1:Patient benetand physiian's protfor patient 1E(deision

j

= 5

)

0

2

4

6

8

10

12

14

0

1

2

3

4

5

6

7

8

9

10

Quantity

Patient benefit

Profit

Treatment FFS

0

2

4

6

8

10

12

14

0

1

2

3

4

5

6

7

8

9

10

Quantity

Patient benefit

Profit

Treatment CAP

does, however.

For all patients in FFS, exept for patient 1A (

j

= 1

), the physiian enounters a

tradeobetween patient optimum andownprotmaximizationin that

q

∗

j

diersfromthe

prot maximizing quantity (

q

ˆ

j

). At

j

= 1

(patient 1A),

q

ˆ

j

=

q

∗

j

= 5

. For patient 3A

(

j

= 11

),

5 = ˆ

q

j

< q

∗

j

= 7

. Exept for illness A (

j

= 1

,

6

,

11

) where

q

ˆ

j

= 5

, the maximal

protisahieved at

q

j

= 10

(see leftpanel ofFigure 1for

j

= 5

).

In CAP,

q

ˆ

j

= 0

for eah deision

j

= 1

, . . . ,

15

. A higher patient benet an only

be ahieved by a physiian's deviating from her own maximal prot (see right panel of

Figure1for

j

= 5

).

4.2 Proedure

The omputerized experiment was onduted in Bonn EonLab, the Laboratory for

Ex-perimental Eonomi s at the Universityof Bonn. 42 medial students partiipated, 20 in

FFS (one session) and 22 in CAP (two sessions). We thus base our analysis on 42

in-dependent observations. Subjets werereruited by the online reruiting system ORSEE

Greiner (2004) promising a monetary reward for partiipatio n in a deision-making task.

Theexperiment wasprogrammed usingthe softwarez-Tree(Fishbaher 2007).

Upon arrival, partiipants were randomly alloated to ubiles where they took their

deisionsin omplete anonymity. Then, subjetswereprovided with the instrutions that

werereadout aloud bythe experimenter. Subjets wasgivenplentyoftime for larifying

questionswhihwereaskedand answeredin private. Tohekforsubjets'understanding

ofthe experiment weasked themto answer threetest questionsstruturedlikethe atual

experiment but with dierent parameter values. The experiment was not started unless

allpartiipantshad answeredall testquestions orretly.

The experimental physiians then made their 15 quantity deisions the sequene of

whih was predetermin ed and kept aross treatments (see Table 1). Having made their

hoies, subjetswereasked to llin aomputerized questionnaire explainingtheir

moti-vationsandthefatorshavinginuenedtheirdeisions. Finally, themonitorwasassigned

randomly. After the experiment, subjets were paidin private aording to their hoies.

perimenter then walked together to the nearestmailboxand depositedthe envelope.

Sessions lasted for about 40 minutes. The exhange rate per Taler was 0.05EUR. On

average subjets earned 6.88EUR in FFS and 7.42EUR in CAP.

14

In total,273.68 EUR

weretransferred to the Christoel Blindenmission, 6.62EUR per partiipant in FFS and

6.42 EUR in CAP. The money supported surgial treatments of atarat patients in a

hospital in Masvingo (Zimbabwe) staed by ophthalmologists of the Christoel

Blinden-mission. Averageostsforsuhanoperation amounted to30EUR.Thus,the moneyfrom

ourexperimentallowedtotreatninepatients. Notethatsubjetswerenotinformedabout

themoneybeingassignedtoadeveloping ountry(seetheinstrutionsin theAppendix).

5 Results

In the present setion, we investigate physiians' behaviour, both from the physiian's

and from the patient's perspetive for FFS as well as for CAP. Moreover, we analyse

the inuene of physiians' prots and the patient benet, and we study the impat of

the payment systemon patients' health status. We ompare behaviour arosstreatments

and,nally, we analysehow physiians' behaviouris aeted by Pareto eieny, i.e. by

tradeosbetween physiians'prot andpatient benet.

5.1 Physiians' behaviour in FFS

Ourrstresearhquestion isrelatedto behaviourunder FFS.Remember that

q

ˆ

j

=

q

∗

j

for

j

= 1

(patient1A),and

q

ˆ

j

< q

∗

j

for

j

= 11

(patient3A).Figure3showsabsolutefrequenies

ofallphysiians' deisions forall patients. Onaverage, 6.60medial serviesareprovided

(median 7.00, SD 1.85). To study how patients are treated we analyse the quantity of

Figure2:Absolutefrequenies ofquantitydeisions per patient in FFS

0

1

2

3

4

5

6

7

8

9

10

Quantity

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Decision

1 observation

1 petal = 1 obs.

1 petal = 2 obs.

Treatment FFS

medialserviesprovidedforeahpatientaveragedoverall physiians(

q

j

=

P

20

i

=1

q

ij

/

20

).

Result FFS1. In FFS, patients are overserved.

14

AveragepayosorrespondtothehourlywageofastudenthelperattheUniversityofBonn(8.32EUR).

Support:

q

j

> q

j

∗

for the 13 patients where

q

ˆ

j

>

q

∗

j

. Patient 1A (

j

= 1

) is treated

optimally by all physiians

i

, whereas patient 3A (

j

= 11

) is underserved. Testing over

all patients,

q

j

is highly signiantly larger than

q

∗

j

(

p

= 0

.

002

, Wiloxon signed ranks

test, two-sided). Individual physiians largely deviate from hoosing the patient optimal

quantities. Themeandeviationfrom

q

∗

j

,

µ

i

=

P15

j

=1

(

q

ij

−

q

∗

j

)

/

15

,ispositivefor17ofthe20

physiians,andzerofortheremainder(seeTableA.1intheAppendix). Thus,signiantly

morephysiiansprovide medialserviesthatarelarger than

q

∗

j

(

p

= 0

.

003

,binomial test,

two-sided).

Nextweinvestigatethe impat ofpatient typesonphysiians' provisionbehaviour.

Result FFS2. Overprovisionin FFS depends on patienttypes.

Support: Supportisprovidedbyteststatistisofanordertest(seeSelten1967andKuon,

1994)omparingthegivenorderofaverageserviesperpatienttypewiththeperfetorder

(2,1,3)thataounts for

q

∗

of eahpatienttype.

15

There aresixdierent possibilitiesto

assignthree ranks. Thenullhypothesisofthe ordertest isthatfor eahsubjettheorder

of observed values is arbitrary implying the mean inversion (standard deviation) being

µ

= 1

.

5

(

σ

= 0

.

9574

). As we observe

0

.

563

average inversions only, the null hypothesis

an be rejeted at the 1% level. A more in-depth analysis shows all patients of type 1

and2 to be overserved (exept for patient 1A) in thatthe number of physiians hoosing

q

ij

> q

j

∗

is larger than the number of physiianshoosing

q

ij

≤

q

∗

j

. Thisis signiant for

fourpatientsoftype1andtype 2eah(

p

≤

0

.

041

binomialtest,two-sided; seelineI/FFS

in Table A.2 ). Patients of type 3 are treated in a less onsistent way. Patient 3A (3E)is

underprovided(overprovided)andtheremainingpatientsaretreated optimallybyatleast

halfofthe physiians.

Physiian'sprot. A physiian's quantity deision determines her ownprot. Aording

toourresearhquestionsweareinterestedinwhetherprotmaximizingisamainobjetive

in general. As only12% ofthe overall hoies oinide with

q

ˆ

j

this is rather not the ase.

Themaximumprot

π

(ˆ

q

j

)

aphysiiananahieveinFFSis8.00(12.50,8.20,13.60,13.00)

TalerforillnessA(B, C,D,E);reallparametervaluesfromTable4. Choosing

q

ˆ

j

forall

j

wouldhaveyielded anaveragepayoof11.08 Taler. Physiians' atual quantitydeisions

resulted in an average overall prot of 9.17Taler (median8.00 Taler,SD 2.69 Taler), i.e.

17% lower than

π

(ˆ

q

j

)

. Average prots for eah physiian

i

vary between 6.53 and 10.93

Taler (see TableA.4 ).

16

Testingover all patients,

π

(

q

j

)

is highly signiantly lower than

π

(ˆ

q

j

)

(

p

= 0

.

001

, Wiloxonsigned rankstest,two-sided).

15

The logi behind theorder testis the following. Whenaphysiian'squantity hoie is inuenedby

patient types(

q

∗

pertype), patients inneedof alarge (low) quantity of medialservies should on

averagereeivealarge(low)amountofmedialtreatment. Ifaphysiianbehavesaordinglytheranks

assignedtothemeanquantitiesprovidedperpatienttypeshouldfollowaperfetorder,namely2,1,3.

Ameasureforthedierenebetweentheatualorderandtheperfetorderisthenumberofinversions,

i.e. thenumberofpairwisehangesneessarytotransformthegivenorderintotheperfetorder. We

alulate theaveragequantityperpatient typefor eah ofthose 16physiians whoseobservedorder

omprisesthreedierentvaluesandrankthemaordingtotheirmagnitude(seeTableA.5). Foreah

physiian,wethenalulatethenumberofinversionsneessarytoahievetheperfetorderofranks.

16

we studythe deviation of eahindividual physiian's protfrom her protmaximum, i.e.

ˆ

π

j

−

π

ij

, forpatienttypesseparately. Forthesakeofomparabilitybetween FFSandCAP

data,weompute for eahpatient the relative deviation

∆

π

ij

= (ˆ

π

j

−

π

ij

)

/

ˆ

π

j

. Table A.3

shows

∆

π

ij

averaged over all physiians. Highest deviations of up to 29% are found for

patients2Band 2E,whereaslowestdeviationsof lessthan 10%ourfor patients 3Aand

3C. There is no deviation for patient 1A beause here all physiians hoose their prot

maximum that oinides with the patient benet optimum. Average prot deviation is

14.66% for patients of type 1

17

, 21.92% for those of type 2 and 11.98% for patients of

type3.

Patientbenet. A physiian's deision also determinesthe patient benet. InFFS like

in CAP the benet maximum for patients of type 3 (

B

3

(

q

∗

j

)

) is 9.45 Taler.

B

1

(

q

∗

j

) =

B

2

(

q

∗

j

) = 10

Taler (see Table3). Ifphysiiansalways hosethe patient optimal quantity,

patientswouldhavereeivedanaveragebenet

B

(

q

∗

j

)

of9.82Taler. Atualaveragepatient

benetis8.83Taler(median9.00Taler,SD1.10Taler),i.e. 10%lowerthan

B

(

q

∗

j

)

. Further,

average patient benets determined byphysiian

i

vary between 7.52 and 9.82 Taler (see

TableA.4 ).

Summary: Under FFSpatientsareoverserved in thatsubjets onaveragehoose

quanti-ties ofmedial servieslarger than the patient's optimal quantity. Provision isdependent

on patient types as is the deviation of prots from the prot maximum. The levels of

overprovision and of prot deviations tend to derease with inreasing needs of servies.

Physiiansdo notgo forthe maximalprot. Thisbehaviourresultedin patients reeiving

asubstantial benet,only 10%on averagelessthan the maximal amount.

5.2 Physiians' behaviour in CAP

Ourseondresearhquestiondealswith behaviourunder CAP. Reallthat

0 = ˆ

q

j

< q

∗

j

for

allpatients (deisions

j

). Figure 3 shows absolutefrequenies ofall physiians' deisions

forallpatients. Onaverage,physiianshose

4

.

40

medialservies(median

5

.

00

,SD1.64).

Figure3: Absolutefrequeniesof quantitydeisions per patient in CAP

0

1

2

3

4

5

6

7

8

9

10

Quantity

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Decision

1 observation

1 petal = 1 obs.

1 petal = 2 obs.

Treatment CAP

17

Support:

q

j

≤

q

∗

j

for 11 patients. Three patients (2A, 2B, 2C) are slightly overserved

on average. Only patient 2E reeives an optimal treatment on average. Testing over all

patients,

q

j

is signiantly smaller than

q

∗

j

(

p

=0.0105, Wiloxon signed ranks test,

two-sided). Individual physiians largely deviate from the patient optimal quantity; but in

ontrast toFFS,theyunderserveinCAP.

µ

i

isnegativefor 16ofthe22physiians;

µ

i

≥

0

fortheremainder (TableA.1 ). Thus,weaklysignianlymorephysiianshoosequantites

smallerthan

q

∗

j

(

p

= 0

.

052

, Binomialtest,two-sided).

Nextweinvestigatewhether underprovision isrelated to patient types.

Result CAP2. Underprovisionin CAP depends on patient types.

Support: We again apply the order test and inlude those 19 subjets whose observed

orderomprisesthreedierentvalues(see TableA.5 ). Also inCAP, the ordertestreveals

hoies to be heavily dependent on patient types. We observe

0

.

158

average inversions.

Thus the null hypothesis an be rejeted at the 1% level. Analyzing the data in more

detailshowsthat althoughpatientsareunderserved onaverage, the number of physiians

hoosing

q

∗

j

islarger thanthe number ofphysiiansnothoosing

q

∗

j

forall patientsoftype

1 and 2. This is signiant for four patients of type 2 (binomial test two-sided; see line

I/CAPinTableA.2 ).

18

Patientsoftype3areunderservedinthatthenumberofphysiians

hoosing

q

j

< q

∗

j

is larger than the number of physiians hoosing

q

∗

j

.

19

This is weakly

signiantforonepatientoftype3(binomialtesttwo-sided;seelineI/CAPinTableA.2 ).

Moreover,thelevelofunderprovision

ν

j

ishighestforpatienttype3andlowestforpatient

type2 (seeTableA.3 ).

Physiian's prot. The maximum prot

π

(ˆ

q

j

)

a physiian an ahieve in CAP is 12.00

Taler for all illnesses (see Table 4). Physiians' atual quantity deisions resulted in an

average prot

π

(

q

j

)

of 9.79 Taler (median9.50Taler,SD 1.52 Taler), i.e. 18%lower than

π

(ˆ

q

j

)

. Average prots for eah physiian

i

vary between 7.84 and 11.48 Taler (see Table

A.4 ). 20

Testingover allpatients,

π

(

q

j

)

ishighly signiantly lower than

π

(ˆ

q

j

)

(

p

= 0

.

000

,

Wiloxonsigned rankstest,two-sided).

Howareprotsaetedbypatient typesin CAP?TableA.3shows

∆

π

ij

averagedover

all physiians. Highest deviations of 25 to 30% are found for patients of type 3 whereas

lowest deviations of 7 to 11% our for patients of type 2. Average prot deviations are

18.75% (8.71%)for patientsof type1 (2)and 27.67% for thoseof type3.

Patient benet. The maximal average benet a patient ould gainin CAP, like in FFS,

is 9.82 Taler if physiians always provided the patient optimal quantity. Atual average

patientbenetis8.56Taler(median9.75Taler,SD2.46Taler),i.e. 13%lowerthan

B

(

q

∗

j

)

. 18

Onaverage,14.6(18.4)physiianstreatpatientsoftype1(2)optimally,6.2(1.4)underprovideand1.2

(2.2)overprovide. 19

Onaverage,14.2physiiansunderservepatientsoftype3,0.2overprovidesand7.6treattheirpatients

optimally. 20

Further, average patient benets determined by physiian

i

vary between 2.73 and 9.82

Taler (seeTableA.4 ).

Summary: UnderCAP patients areunderprovided in that physiians on average hoose

quantities of medial servies smaller than the patient's optimal quantity. Provision of

servies and the deviation of prots from the prot maximum arestrongly inuened by

patient types,i.e. withinreasing needsforserviesthelevelsof underprovisionand prot

deviationstend to inrease. Alsoin CAP, physiiansdonot strive for the maximalprot.

Patientsreeived abenetbeing onaverage 13%lower than the maximum benet.

5.3 Comparison of behaviour between FFS and CAP

Ourthird researh question is related to the onsequenes of both payment systems. We

areonernedwithdierenesintheexperimentalphysiians'behaviourarosstreatments

and how patient types are aeted. We ompare physiians' prots, the provision of

medialservies,deviationsfrom

q

∗

j

,andpatientbenetlossesarosspaymentsystemsfor

allpatientsand for patient typesseparately.

The results above have already shown that experimental physiians hoose more medial

serviesin FFS than in CAP. Thus, the next result impliitly follows from Results FFS1

andCAP1.

ResultCOMP1. Patients are provided withmoremedial servies in FFSthan in CAP.

Support: Evidene is provided by Figure 4 showing the average quantity of medial

servies per deision (patient) in both treatments. Not only do physiians in FFS on

average provide 50% more servies than in CAP (6.60 vs. 4.40; median: 7.00 vs. 5.00;

SD: 1.85 vs. 1.64) but for eah deision

j

,

q

F F S

j

> q

CAP

j

. This is highly signiant

(

p

= 0

.

0000

, Mann-Whitney U test, two-sided). The piture is similar when omparing

individual deisions aross treatments for eah patient. Exept for patients 1A and 3A,

q

F F S

ij

is signiantly larger than

q

CAP

ij

(

p

≤

0

.

0010

, Mann-Whitney U test,two-sided; see

line II in Table A.2 ). Thus, in FFS a signiantly higher number of patients is provided

with signiantly moremedial serviesompared to CAP(

p

= 0

.

007

, binomial test,

two-sided).

Physiian's prot. Physiian's own prot

π

(

q

ij

)

ertainly is an important behavioural

determinant inbothtreatments. Asalreadymentioned,hoosing

q

ˆ

j

forall

j

inFFSwould

have yielded an average payo

π

(ˆ

q

j

)

of 11.08 Taler. In CAP, the maximal protis 12.00

Taler for allillnesses.

What did physiians atually do? They provided quantities of medial servies suh

that their average prots are very similar in both treatments but about 17% lower than

π

(ˆ

q

j

)

(FFS:9.17Taler,CAP:9.79Taler). Averageprotsforeahphysiian

i

varybetween

6.53and 10.93 Taler in FFS and between 7.84 and 11.48Taler in CAP. In both payment

systems,the averagephysiian doesnot aimatthe maximalahievableproteven though

singlephysiians omeverylose to

π

(ˆ

q

j

)

(see TableA.4 ).

0

1

2

3

4

5

6

7

8

9

10

Average quantity

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Decision

FFS

CAP

Patient optimal quantity

aross treatments. Exept for patients 1C, 1E, 2C, and 3E, we nd (weakly) signiant

dierenesbetween treatments.

21

For patients of type 2,

∆

π

F F S

ij

>

∆

π

CAP

ij

, for patients

oftype 3the reverse holds.

Patientbenetand patientbenetloss. We nextompare the two payment systems with

regard to how patients' health status is aeted by physiians' hoies. To this end, we

rst fous on the optimal treatment and deviations thereof. We then onentrate on the

benetlossespatientssueronaveragewhensomeorallofthemarenottreatedoptimally.

Result COMP2. Patient optimal quantities exert a stronger inuene on physiians'

behaviour in CAP than in FFS.

Support: Support omes from analyzing physiians' hoies with regard to the patient

optimal quantity. InCAP, the perentage of physiians hoosing

q

∗

j

per patient is

signif-iantly higher than in FFS (

p

= 0

.

014

, Mann-Whitney U test, two-sided). If physiians

deviatetheytend towardsoppositediretions;asigniantly largershareprovidesservies

largerthan

q

∗

j

inFFS omparedtoCAP(

p

= 0

.

000

,Fisherexattest,two-sided). InFFS,

µ

i

>

0

exeptforphysiians

i

= 3

,

4

,

17

; inCAP,

µ

i

≤

0

exeptforphysiians

i

= 4

,

19

(see

Table A.1 ). Analyzing patient types separately, we nd all patients of type 2 in CAP to

getabettertreatmentin thatsigniantlymore physiiansperpatienthose

q

∗

j

ompared

to FFS (

p

≤

0

.

011

, Fisher exat test; see line III in Table A.2 ). The same applies to

all patients of type 1 exept for patient 1A

22

(

p

≤

0

.

009

, Fisher exat test). Evidene is

mixedforpatientsoftype3. We ndnosigniant dierenefor patients3A,3C, 3E.For

patients3Band3Dphysiianshoose

q

∗

j

signiantlymoreofteninFFSthan inCAP(see

line III in Table A.2 ). In both treatments, the benet maximum for patients of type 3

(

B

3

(

q

∗

j

)

) is 9.45 Taler.

B

1

(

q

∗

j

) =

B

2

(

q

j

∗

) = 10

Taler resulting in

B

(

q

∗

j

) = 9

.

82

in FFS and

in CAP. Ourexperimental physiians atually provide quantities of medialservies suh

21

Fortype1:

p

≤

0.059

;fortype2:

p

≤

0.018

;fortype3:

p

= 0.000

,allMann-WhitneyUtest,two-sided; seelineIVinTableA.2.

22

Here,physiiansinFFSmakesigniantlymore

q

∗

j

-hoies(

p

= 0.006

,Fisherexattest;seelineIIIin

thatthe averagepatient benet

B

(

q

j

)

wasslightly largerin FFS(8.83Taler) thaninCAP

(8.56 Taler) and around 10% smaller than

B

(

q

∗

j

)

. These numbers seem to suggest that

nearly no dierenes between payment systems exist. Yet, the piture is dierent when

having a loser look at the data. Fousing on single patients and their health statuswe,

like Newhouse andthe Insurane Experiment Group (1993), nd patients to be aeted

dierently by the mode of payment (see below). Moreover, average patient benets vary

between 7.52 and 9.82 Taler in FFS and between 2.73 and 9.82 Taler in CAP (see

Ta-bleA.4 ).

Whenever a physiian

i

deviates fromhoosing

q

∗

j

when patients areeither under-or

overprovided patientssuera benet loss(

ψ

(

q

ji

) =

|

B

(

q

ij

)

−

B

(

q

∗

j

)

|).

Figure5:Averagebenet lossper patient

0

.5

1

1.5

2

Average benefit loss

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

FFS

CAP

Result COMP3. Benet losses per patient depend on patient types and dier aross

treatments.

Support: Figure 5 ontrasts the average benet loss per patient aross treatments. For

10 of the 15 patients,

ψ

(

q

j

)

CAP

> ψ

(

q

j

)

F F S

. The benets loss in FFS is larger for the

remaining patients (see also Table A.3 ). Benet losses dier signiantly for all patients

of type 2 (

p

≤

0

.

027

, Mann-Whitney U test, two-sided, seeline IV in Table A.2 ). Losses

arelargerin FFS forpatients2B

, . . . ,

2E; the reverseholds forpatient 2A.For 9ofthe 10

patients of types 1 and 3, benet losses in CAP are larger than in FFS. Dierenes are

onlysigniant for twopatientsof type1 and 3eah.

23

ResultCOMP3 suggests that for patients in need of a small quantity of medial servies

like patients of type 2, a smaller benet loss results when physiians are paid by CAP.

Patients in need of a larger quantity of medial servies, like patients of types 1 and 3,

inura smallerlossunder FFS.

Summary: The ross-treatment omparison demonstrates that physiians' hoies are

highlyinuened bythepaymentsystem. PhysiiansinFFShoosemoremedialservies

23

1A (where no losses our in FFS):

p

= 0.009

; 1E:

p

= 0.062

; 3B:

p

= 0.002

; 3C:

p

= 0.050

, all

status. In partiular, patients of type 1 and 2 are treated more optimally under CAP

thanunder FFSand thepatient benetlossissigniantly smallerin the formerpayment

systemforall but one ofpatientsof type2.

5.4 Tradeos and Pareto eieny

Inthissetion, we areonerned with the tradeobetween ownprotand patient benet

aphysiianenounters whenmakingaquantitydeision. Inpartiular,weinvestigatehow

Pareto eieny inuenesa physiian's behaviour.

Ingeneral,Pareto eienymeans thatan alloation

X

isParetopreferredto another

alloation

Y

if at leastone person is better o and no one is worse owith

X

than with

Y

. Besidesitsimportaneingeneral eonomitheory,the oneptofParetoeienyalso

playsaprominentroleinhealtheonomis(e.g. Iversen1993, DeJaegher andJegers2000

and Pau and Vera-Hernandez 2007). In the ontext of our experiment a situation is said

to be Pareto eient, if no unanimous move to another alloation of prot and patient

benet is possible. That means, a Pareto-e ient (PE) hoie involves that hanging

q

anneithermakethephysiianbetterowithoutinduingabenetlosstothepatientnor

make the patient better o withoutforegoing own prot. Pareto-ine ient (PIE) hoies

do not involve a benet/prot tradeoashanging

q

an inrease both a physiian's own

protand the patient benet;they aredominated byPareto-eient hoies.

Pareto-eient deisionoptions existforeahpatient inbothtreatments. Thenumber

ofPEbenet/prot pairsdiersaording toillnesses (inFFS only)andpatienttypes. In

FFS,physiiansanhoosebetweenoneandeightPEdeisionsper patient. InCAP,there

areeither four(patienttype2),six(patient type1)oreight(patienttype3)PEpairs. PE

hoies arepositioned onthe upper right line in Figures A.1andA.2 , the Paretofrontier,

whereasPIE deisions arethosebelowthe Pareto frontier.

It is remarkable that 597 of the 660 hoies are Pareto-eient. Thus, 95% of all

physiians' atual hoies both in FFS and CAP involve a tradeo between physiian's

own prot and patient benet. Pareto eieny guides all the deisions by 13 of the 20

physiians (65%) in FFS and by 15 of the 22 physiians (68%) in CAP. The remaining

hoies entail up to 4(9)PIE deisions per physiian in FFS (CAP).Hene, not only has

the majority of physiians Pareto eieny as their only target but also the remaining

physiians behave aordinglywith the vastmajority oftheir quantitydeisions.

To further haraterize physiians' hoies we subdivide the set of PE deisions into

ategoriesapturingvariablesofeonomiimportaneandmedialethis: ownprot

max-imum, patient benetoptimum, soial optimum.

•

PROMAXompriseshoosing

q

ˆ

j

,theprot-maximiz ingquantityofmedialservies.

Theorrespondingbenet/prot pair is(

B

( ˆ

q

j

)

, π

( ˆ

q

j

)

).

•

PATMAX onsists of

q

∗

j

-hoies maximizing the patient's benet. (

B

(

q

∗

j

)

, π

(

q

∗

j

)

) is

the resultant benet/prot pair.

•

SOCOPTis suggested bya welfare eonomis perspetive and ontains the soially

optimalhoies,i.e. deisionswhere (

π

(

q

j

) +

B

(

q

j

))

ismaximal.

NotethatpatientsexistwhereSOCOPToinideswithPROMAXand/orPATMAX

(TableA.6 ). Onlythosedeisions areassignedto SOCOPTthatarenot yet overed

bythe two previous ategories.

24

•

PAROTHisaresidualategoryomprisingtheremainingbenet/prot pairsonthe

Pareto frontier not inluded in anyof the otherthree ategories.

InFFS, 16% of all physiian's Pareto-e ient hoies areassigned to PROMAX,34% to

PATMAX, 16% to SOCOPT and 34% to PAROTH.

25

In CAP, only 2% of physiians'

hoies are attributed to PROMAX, 66% to PATMAX, 6% to SOCOPT and 26% are

overedby PAROTH.

26

Comparing both payment systems, a muh lower perentage of deisions in CAP is

motivated by

π

( ˆ

q

j

)

probably beause hoosing

q

ˆ

j

CAP

entails no provision of servies to

thepatient. Suhbehaviourwouldbeasevereviolationoftheprofessionalodeofmedial

ethis. Notieably, two thirdsofallPareto-e ient deisionsin CAPinvolve

B

(

q

∗

j

)

versus

only one third in FFS. This may be due to the fat thathoosing

q

∗

CAP

implies a lower

own-prot redution than in FFS where the physiian on average forgoes 39.6% of her

maximally ahievable protvs. only 23.3% in CAP. The soial optimum plays no role in

CAPpossibly beause

q

soc

oinides with

q

∗

for all10 patients oftypes1 and 2.

Summary: Ouranalysisprovidedompellingresults. First,nearlyallphysiians'deisions

areinuenedbyParetoeieny. Seond,thevastmajorityofthesehoies(66%inFFS

and74%inCAP) anbeexplained bymotivesbasedon variables ofeonomiandethial

importane.

6 Conlusion

Thepaper introdues a ontrolled laboratoryexperiment to test for the inuene of

pay-ment systems on physiians' provision behaviour. By assigning the monetary equivalent

ofthe patient benet to treating atual patients we substitutedthe `abstrat'patients in

ourexperiment withrealones. Ourdesign wassuessfulin eliitingbenevolentbehaviour

towardsthepatient. Notonlyweremeanbenetlossesratherlow(10%inFFSand13%in

CAP)butalsodidnearlyallexperimentalphysiiansinapost-experimenta lquestionnaire

statethe patient benetto haveinuened their deisions.

Our results are in line with the theoretial literature (e.g. Ellis andMGuire 1986)

24

Wedeidedonthisassignmentassubjetshoiesmaynotbemotivatedbythesoialoptimuminthe

rstplaeforthefollowingreason. Finding

q

soc

seemsnotstraightforwar daspartiipantsrsthaveto

alulate

π(q

j

) +

B(q

j

)

andthentheyhavetodeterminethemaximum.Seleting

q

ˆ

or

q

∗

ismuhmore

obviousgiventheinformationonthedeisionsreens(seetheInstrutionsintheAppendix).

25

Whenalulatingtheperentages,

j

= 1

isnegletedbeausehereategoriesPROMAX,PATMAXand

SOCOPToinideandweannotevendistinguishwhether

q

= 5

wasmotivatedby

q

ˆ

j

orby

q

∗

j

. 26

are overserved in FFS in that experimental physiians on average hoose quantities of

medial servies larger than the patient's optimal quantity. Provision is dependent on

patient typesasisthe deviation ofprots fromthe protmaximum. Theross-treatment

omparisonmost learlyshowsphysiians' hoiesto behighlyinuenedbythepayment

system. Physiians in FFS provide more medial servies than those in CAP do. Like

Newhouseand the InsuraneExperiment Group (1993), we found the mode of payment

to aet patients' health status. Patients in need of a low level of medial servies are

better o under CAP, whereas patients with a high need of medial servies gain more

health benet when physiians are paid by FFS. How these gains and losses are to be

weighed againsteahother isa matterof politialdeision, however.

In both remuneratio n systems, nanial inentives are not the only motivation for

physiians' quantity deisions, though. As the patient benet is of onsiderable

impor-tane, patients reeived a substantial benet the nanial equivalent of whih allowed to

treatnine realpatientsbyophthalmisurgery.

Experiments in health eonomis might serve as a 'wind tunnel' or 'test bed' before

institutional hanges are implemente d during a health are reform. Even though an

ex-periment always simplies a physiian's deision taskwhen aring for a patient it, at the

same time, allows to separating behavioural determinants. While simpliations give rise

to aution when extrapolating the results, they also suggest the lines for further

experi-mental researh like introduing unertainty about the impatof medial treatments and

patients' health status,patients' demandeets andmonitoring mehanisms.

Referenes

Ahlert,M., S.Felder,and B.Voigt(2008): HowEonomistsandPhysiiansTrade

O Eieny and Equity in Medially and Neutrally Framed Alloation Problems?

FOR655 WorkingPaper No.09/2008, Jaobs UniversityBremen.

Arrow, K.(1963): UnertaintyandtheWelfareEonomi s ofMedialCare, Amerian

Eonomi Review , 53,941969.

Barros, P. (2003): Cream-Skimm ing, Inentives for Eieny and Payment System,

Journal of Health Eonomis, 22,419443.

Blomqvist, A. (1991): The dotor as double agent: Information asymmetry, health

insurane, andmedial are, Journal of Health Eonomis, 10,411432.

Camerer,C.andR.Hogarth(1999): TheEetsofFinanialInentivesinEonomi s

Experiments: AReviewandCapital-prodution-laborFramework, JournalofRiskand

Unertainty ,18,742.

Choné, P. and C. Ma(2007): AsymmetriInformation fromPhysiian Ageny:

ming (1992): Prepayment with Oe-based Physiiansin Publily Funded Programs:

Resultsfrom Children'sMediaid Program, Pediatris, 89, 761767.

Davis, D. and C. Holt (1993): Experimental Eonomis, Prineton University Press,

Prineton, N.J.

DeJaegher, K.andM.Jegers(2000): AModelofPhysiianBehaviourwithDemand

Induement, Journal of Health Eonomis, 19,231258.

Devlina, R. and S. Sarma(2008): DoPhysiian Remuneration ShemesMatter? The

Case ofCanadian FamilyPhysiians, Journal of Health Eonomis, 27, 11681181.

Dumont, E., B. Fortin, N. Jaquement, and B. Shearer (2008): Physiians'

Multitasking and Inentives: EmpirialEvidene froma Natural Experiment, Journal

of Health Eonomis, 27, 14361450.

Ekel,C.andP.Grossman(1996): AltruisminAnonymousDitatorGames, Games

andEonomi Behavior, 16, 181191.

Ellis, R. and T. MGuire (1986): Provider Behavior under Prospetive

Reimburse-ment: CostSharing and Supply, Journal of Health Eonomis, 5,129151.

Falk, A. and E. Fehr (2003): Why Labor Market Experiments? Labor Eonomis,

10,399406.

Fan, C., K.Chen,and K.Kan(1998): The DesignofPayment SystemsforPhysiians

under Global Budget - An Experimental Study, Journal of Eonomi Behavior and

Organization, 34,295311.

Fishbaher, U. (2007): Z-tree: Zurih Toolboox for Readymade Eonomi

Experi-ments Experimenter 's Manual, Experimental Eonomis, 10, 171178.

Fishbaher, U.,S.Gähter, and E. Fehr(2001): ArePeopleConditional ly

Coop-erative? Evidenefroma Publi Goods Experiment, Eonomis Letters ,71, 397404.

Frank, R. (2007): Behavioral Eonomis and Health Eonomis, in Behavioral

Eo-nomisand Its Appliations, ed. byP. Diamondand H.Vartianinen, Prineton

Univer-sityPress,195234.

Fuhs, V. (2000): The Future ofHealth Eonomis, Journal of Health Eonomis, 19,

141157.

Gosden , T., F. Forland, I. Kristiansen, M. Sutton, B. Leese, A. Guiffrida,

M. Sergison, and L. Pedersen (2001): Impat of Payment Methodon Behaviorof

Primary Care Physiians: A Systemati Review, Journal of Health Servies Researh

andPoliy, 6,4454.

Greiner, B. (2004): An Online Reruitment System for Eonomi Experiments, in

Göttingen,7993.

Grytten, J. and R. Sørensen(2001): Typeofontratand supplier-indueddemand

for primary physiians in Norway, Journal of Health Eonomis, 20, 379393.

Hennig-Shmidt, H. and D. Wiesen (2009): Are Medial Students more Caring in

Inentive-basedDeisionTasks? AnExperimentalStudyonPaymentSystemVariation,

unpublishedmanusript.

Hertwig, R. and A. Ortmann (2001): Experimental Praties in Eonomis: A

Methodologia l Challenge for Psyhologists? Behavioral and Brain Sienes, 24, 383

403.

Huthinson, B.,S.Birh, J.Hurley, J.Lomas, and F.Stratfo rd-Devai(1996):

Do Physiian-payment Mehanisms Aet Hospital Utilization? A Study of Health

ServieOrganizationsinOntario, CanadianMedialAssoiationJournal ,154,653661.

Iversen ,T. (1993): ATheoryof HospitalWaiting Lists, Journal of Health Eonomis,

12,5571.

Iversen ,T.and H.Lurås(2000): TheEetofCapitationonGPs'Referal Deision,

Health Eonomis, 9,199210.

Jak, W.(2005): Purhasing HealthCareServiesfromProviderswithUnknown

Altru-ism, Journal of Health Eonomis, 24,7393.

Krasnik, A., P. Groenewe gen, P. Pedersen, P. von Sholten, G. Mooney,

A. Gottshau, H. Flierman, and M. Damsgaard (1990): Changing

Remuner-ation Systems: Eets on Ativity in General Pratie, British Medial Journal , 300,

16981701.

Kuon, B.(1994): Two-Person Bargaining Experiments withInomplete Information,vol.

412 ofLeture Notes in Eonomis and Mathematial Systems ,Berlin,Heidelberg, New

York: Springer Verlag.

Ma, C.(1994): HealthCare Payment Systems: CostandQualityInentives, Journalof

Eonomis and Management Strategy ,3,93112.

(2007): Altruism and Inentives in Publi and Private Health Care, in Finane

and Inentives of the Health Care System, Proeedings of the 50th Anniversary Symp

o-siumoftheYrjöJahnssonFoundation,ed.byA.SuvantoandH.Vartiainen, Government

Institutefor Eonomi Researh, Helsinki,79104.

MGuire, T. (2000): Physiian Ageny, in Handbook of Health Eonomis, Vol. 1 A,

ed.byCuyler andNewhouse, Amsterdam, North-Holland, 461536.

Newhouse, J. (1996): Reimbursing Health Plans and Health Providers: Eieny in

Press.

Newhouse,J. andthe Insurane ExperimentGroup(1993): Free forAll: Lessons

fromthe RAND Health Insurane Experiment ,Harvard UniversityPress.

Pau, O. and M. Vera-Hernandez (2007): Competition among Dierentiated Health

Plansunder Adverse Seletion, Journalof Health Eonomis, 26,233250.

Selten, R. (1967): Ein Oligopolexperiment mit Preisvariation und Investition, in

Beiträge zur experimentelle n Wirtshaftsforshung, ed. by H. Sauermann, J.C.B. Mohr

(PaulSiebek), Tübingen, 105135.

Selten, R. and A. Okenfels (1998): AnExperimentalSolidarityGame, Journalof

Eonomi Behavior andOrganization, 34,517539.

Stearns,S.,B.Wolfe,and D.Kindig(1992): PhysiianResponsestoFee-for-servie

Instrutions[translated fromGerman℄

GeneralInformation

Inthefollowingexperiment,youwillmakeaoupleofdeisions. Followingtheinstrutions

and depending on your deisions, you an earn money. It is therefore very important to

readthe instrutions arefully.

You take your deisions anonymously in your ubile on your omputer sreen. During

theexperimentyouarenotallowedto talktoanyotherpartiipant. Wheneveryouhavea

question,please raiseyourhand. Theexperimenterwillanswer yourquestionin privatein

yourubile. Ifyoudisregardtheserulesyouanbeexludedfromtheexperimentwithout

reeivinganypayment.

Allamountsofmoneyinthe experimentarestatedinTaler. Attheendoftheexperiment,

your earningswillbeonverted into Euro atan exhange rate of1 Taler=0.05 EURand

paidto youin ash.

Your deisions in the experiment

During the entire experiment you are in the role of a physiian. You have to deide on

thetreatmentof 15patients. Allpartiipantsofthis experiment aretakingtheir deisions

in the role of a physiian. You deide on the quantity of medial servies you want to

providefor a givenillnessof a patient.

You deide on your omputer sreen where ve dierent illnesses A, B, C, D and E

of three dierent patient types 1, 2 and 3 will be shown one after another. For eah

patient you an provide

0

,

1

,

2

,

3

,

4

,

5

,

6

,

7

,

8

,

9

or

10

medial servies.

Your remuneratio nis asfollows:

•

TreatmentFFS:

Adierentpaymentisassignedtoeahquantityofmedialservies. Thepayment

inreasesin the quantityof medial servies.

•

TreatmentCAP:

Foreahpatientyoureeivealump-sumpaymentthatisindependentofthe

quan-tityof medialservies.

While deiding on the quantity of medial servies, in addition to your payment you

determine the osts you inur when providing these servies. Costs inrease with

in-reasingquantityprovided. YourprofitinTalerisalulatedbysubtratingyourosts

fromyour payment .

pa-fore, your deision on the quantity of medial servies not only determines your own

profit but also the patient benefit. An example for a deision situation is given on

the following sreen.

Sreen shotFFS

Sreen shotCAP

You deide on the quantity of medial servies on your omputer sreen by typing an

integerbetween 0 and10 into the boxnamed Your Deision.

ial for a real patient. The total amount orresponding to the sum over all 15 patient

benefitsdetermined byyour deisions willbetransferred tothe harityChristoel

Blin-denmission Deutshland e.V., 64625 Bensheim, to support an ophthalmi hospital where

patients with atarataretreated.

Earnings in the experiment

Afterhavingmade your15 deisions,youroverallearningswill be alulated bysumming

up the profits from all your deisions. This amount will be onverted from Taler into

Euro atthe endof the experiment.

Theoverallpatient benefitresulting fromyour15 quantitydeisions will be onverted

into Euro aswell andwill be transferred to the ChristoelBlindenmission.

The transferral will be made by the experimenter and a monitor. The monitor writes

a hek on the amount of money orresponding to the aggregated patient benefits of

this experiment. This hek issuedto the Christoel Blindenmission will be sealed in an

envelope addressed to this harity. The monitor and experimenter then walk together to

the nearestmailbox anddeposit the envelope.

Afterall partiipants havetaken theirdeisions, one partiipant is randomlyassigned the

roleofthemonitor. Themonitorreeivesapaymentof4EURin additionto thepayment

from the experiment. The monitor veries, by a signed statement, that the proedure

desribed above wasatually arriedout.

Next,pl