IZA DP No. 3096
Interactions Between Workers and the Technology of
Production: Evidence from Professional Baseball
Eric D. Gould
Eyal Winter
DISCUSSION PAPER SERIES
Forschungsinstitut
zur Zukunft der Arbeit
Institute for the Study
of Labor
October 2007

Interactions Between Workers
and the Technology of Production:
Evidence from Professional Baseball Eric D. Gould
Hebrew University
and IZA

Eyal Winter
Hebrew University
Discussion Paper No. 3096
October 2007

available directly from the author.
IZA Discussion Paper No. 3096
October 2007 ABSTRACT

Interactions Between Workers and the Technology of
Production: Evidence from Professional Baseball
*

This paper examines how the effort choices of workers within the same firm interact with
each other. In contrast to the existing literature, we show that workers can affect the
productivity of their co-workers based on income maximization considerations, rather than
relying on behavioral considerations such as peer pressure, social norms, and shame.
Theoretically, we show that a worker’s effort has a positive effect on the effort of co-workers if
they are complements in production, and a negative effect if they are substitutes. The theory
is tested using panel data on the performance of baseball players from 1970 to 2003. The
empirical analysis shows that a player’s batting average significantly increases with the
batting performance of his peers, but decreases with the quality of the team’s pitching.
Furthermore, a pitcher’s performance increases with the pitching quality of his teammates,
but is unaffected by the batting output of the team. These results are inconsistent with
behavioral explanations which predict that shirking by any kind of worker will increase
shirking by all fellow workers. The results are consistent with the idea that the effort choices
of workers interact in ways that are dependent on the technology of production. These
findings are robust to controlling for individual fixed-effects, and to using changes in the
composition of one’s co-workers in order to produce exogenous variation in the performance

to the existing liter ature, w e focus on show ing h ow t he effort choice of one w orker can
affect the effort choices of his co-workers based purely on income-m aximizing considera-
tions, rather than relyin g on behavio ral explanations such as peer pressure, sha me, etc.
In addition, w e break from the existing literature by s ho wing t hat the effortchoiceofone
worker could have a po sitive or negative e ffect o n his c o-wo r ker s. For e xam p le, a mec h a-
nism based on behavioral considera tions like peer pressure or shame predicts that a high
level of effort by one wo rker will induce oth er workers to increase their effort level, or that
alowereffort b y one wo rker cau ses other wo rkers to follow suit. We refer to both of these
cases a s a “positive interaction ” in the sense th at a change in e ffort by one wo rker causes
others to change their effort in the same direction. Howe ver, we show that a “ neg ative
in tera ctio n” between wo rke rs is also possible, in the sense that a ch an ge in effort by one
worker causes other wo rkers to c h ange their effort in the opposite direction.
Therefore, this paper con trib utes to the existin g literature by sho w ing that the in-
teraction of effort choices could work in both directions, ev en within the same firm at
the same time. In partic ular, we sho w that a “positive in teraction” should exist bet ween
comp lem entar y wor kers, while w o rkers who are substitutes may free ride off the effort of
eac h other, and th us genera te a “negative int eraction” in the effort choices of co-worke rs.
The theory is tested using panel data on the performance of baseball pla yers from
1970 to 2003. The game of baseball p rovides a clear case where pitchers and non-pitc hers
can safely be defined as substitutes fo r eac h other in team performan ce — since p reventing
runs and sco ring runs are perfect substitutes in th e team’s goal of scoring mor e runs th an
the o pposing t eam . In addition, p laye rs who are not pitc h ers are o ften comp lem ents with
eac h other since it u su ally takes more t han one player to get a h it in order to sco re a run
for the team . The empirical analysis show s that a pla ye r’s batting averag e significant ly
increases with the batting perform ance of other players on the team, but decreases with
the quality of the team’s pitch ing . Furth erm ore, a p itcher’s performance increases with the
1
pitching qualit y of the o ther pitchers, but is unaffected by the batting output of the team.
These results are in consistent with beh avioral explanations for how one w orke r affects the
per form ance of other w orkers, since a t y pical behavioral response should c ause w o rkers to

literature on the in tera ctio n of worke rs within a firm is not extensive. Winte r (2004)
demo nstrate s the ore tically the o ptim a lity o f offerin g differen tial in centive c ontra cts in or-
der to elicit worker effort whic h genera tes externalities on other workers. Kandel and
Lazear (1992) examine the theory of team production within the firm and focus on how
teams produce social pressure to solve the free-riding problem. The most related papers
to ours are by Ichino and M aggi (2000) and Mas and Moretti (2006). Ic hino and M aggi
(2000) examine shirking behav ior within a large banking firm, and show that a worke r’s
shirking beha vior significant ly respo nd s to the beha v ior of his co-worke rs when they move
across branches within the sam e firm. Using data on workers from a large g rocery story
c hain, Mas and Moretti (2006) e xam ine ho w the productivity of a worker varies according
to the productivity of other w orkers on the same sh ift, and provid e additional evidence
that be havior consideratio ns such as peer pressure and social norm s are significan t. Som e
of our empirical specifications emplo y a similar identification stra tegy in the sense tha t we
exploit differences in the composition of one’s co-workers to explain var iation in an indi-
vidual’s performan ce level over time and across workp laces. However, o ur paper differs b y
examining the theoretical and empirical di fferences in the natur e of the interaction across
workers depending on whether they are substitutes or complements w ith eac h other. In
this m anner, o ur paper contributes to the literature by pro viding a theoretical foundation
and empirical evid en ce for both positiv e a nd negative interactions in the effort c h oices of
workers i n a real work environment.
2 The model
In this section, we show h ow the effort choices of workers within the same firm interact with
each other, and ho w this interaction depends on the tec hnology of the team production
function. To do t h is, we presen t a parsimonious p rincipal-a gent model where t h e o p tim al
contract is derived und er two d ifferen t scenarios. I n o ne scen ario, players are co mp le-
mentar y to one an other, and in th e second scenario, workers are consid ered substitutes.
In order to characterize the two different types of techn olog ies, we borro w t he co ncep t o f
strategic substitution and complemen tarit y (see Milgrom and Shannon (1994) a nd Topkis
3
(1998)). Our model is similar to Holmstrom (1982) and Holmstrom and Milgrom (1991)

However, we maintain the assumption of a uniform cost for the sake of simplicity.
4
Specifically, t he p rinc ipal offers a contra ct to each member of the team, repr esented by a
v e ctor of reward s v =(v
1
,v
2
) w ith agen t i receiving v
i
if the project succeeds a nd zero
otherwise.
For a mechanism v, we have an extensive f orm game G(v) between the two play ers.
If the overall team project is successful, the project generates a benefit B for the principal.
Given a mechanism v, let q(v) be the probab ility of success in the uniqu e
3
(subgame
per fect) equilibrium of the game G(v). The principal designs the incentiv e mechanism v
optima lly, s o as t o maximize his net rev enu e, represen ted as v =argmaxq(v)[B −
P
j
v
j
].
We assu m e that th e ove rall p roject is valuable en ou gh so tha t the o ptim al m ech an ism
a wa rd s each player with a positive reward if the project is successful. That is, B is
sufficiently high (B>B
∗
) so that v
j
> 0 for both players in the optimal mech anism . N ote

mecha n ism induces either equilibrium 1 or equ ilibrium 3. (2) If th e team’s tech nol ogy s at -
isfies su bs titutio n , t h en the op timal mech a nis m induces either equilib rium 2 o r equ ilibr ium
3.
Proposition 1 asserts that unless it is a dom in ant strategy f or agen t 2 to always exert
effort (B>B
∗
), the optim a l pattern o f behavior in equilibrium will be co nsist ent with
our em pirical resu lts. If workers are com p lem entary, a fa ilure on th e part of p layer 1 will
trigger player 2 to shirk. In contra st, if workers are substitutes in pr oduction, a failure on
the part of player 1 w ill trigger p laye r 2 to exert e ffort.
The intuition for Proposition 1 is straigh tforw ard. In general, the principal will
find it co st effectiv e to pro vide incen tives for the agent to exert effort when the ma rginal
return to the w orker’s effort is high. So, if w orkers are complementary to eac h other,
player 2’s effort will have a b igg er impact on the o verall success of the team if pla yer 1
succeeded rather tha n failed. Therefo re, i n order for player 2 to exert effort, he will need
to be com pensated fo r the lower probability of team success in the case w h ere player 1
failed versus the c ase where p layer 1 succeeded. If the project’s value is sufficien tly h igh
(B>B
∗
), the principal will find it profitable to pro vide incentives to player 2 even if
pla yer 1 failed. But, if the project’s va lue is lo wer than this threshold (B
∗
<B <B
∗
),
the princip al w ill findittoocostlytoprovideincentivestoplayer2toexerteffort if pla yer
1 failed. Althou gh it might seem in tuitive that the principal would create an incen tive
mechanism to counter the urge for pla y er 2 to shirk when pla y er 1 fails, the model shows
that this is only the case w h en the value of the project is sufficiently h igh. In intermediate
cases, it is o ptim al for the p rincipa l not to waste his money on provid ing in centive s to

he exerts effort and [αp(2) + (1 − α)p(1)]v
2
if he sh irks. T h e o ptimal rewa r d for playe r
2 should make him indifferent among these two options. Hence v
2
=
c
(β−α)[p(2)−p(1)]
,and
pla yer 2 w ill exert effort un der this contract if pla yer 1 succeed ed in his task. Furthermore,
bec au se the two worke rs are com p lem e ntar ity, player 2 will shirk if player 1 failed in his
task. This follo ws from th e fact that player 2’s effort has a lower m arginal effect when
play er 1 fails and from the fact that play er 2 is indifferent between sh irking a nd exerting
effort wh en p layer 1 succeeded. Hence v
2
is a mecha nism which indu ces equ ilibriu m 1.
Consid er now a mechanism v
0
2
under wh ich pla ye r 2 exerts effort when player 1 fails in h is
task. The incentive con straint for th is mech an ism must be [βp(1) + (1 − β)p(0)]v
0
2
− c ≥
[αp(1) + (1 − α)p(0)]v
0
2
and v
0
2

and hence v
2
≥
c
(β−α)[p(2)−p(1)]
. Since w o rker s are substitutes in production, the condition
must hold that [p(1) − p(0)] > [p(2) − p(1)]. Therefore, it must be the case that v
2
>v
0
2
,
which means that under v
2
it is a do m in ant strategy f or player 2 t o exert effort. Q .E.D.
Proposition 1 shows tha t the optim a l mecha n ism in o ur m ora l hazar d m odel yields
equilib ria which are consistent with the emp ir ical results presented in the rest of the p aper.
We h ave mana ged t o do so by s pecifying only t he rewards that p layer 2 receiv es. For the
sake of completeness, w e now presen t the entire optimal mec hanism in Proposit ion 2 b y
specifyin g the rewa rds of both players.
Proposition 2 Assume the tec hn o logy is one of co mplem e n ta rity (subs titu tio n ) and that
the optim a l mechan ism yields equ ilibriu m 1 (equilibr iu m 2). (The value of th e pro ject is B
where B
∗
<B <B
∗
.) Then th e optim a l contra ct is given b y:
v
1
=

. By equating these two expression s, we get
v
1
=
c
(β−α)[βp(2)+(1−β)p(1)−(αp(1)+(1−α)p(0α)]
. Con sider now the strategy of player 2 specified in
equilibrium 2. In this case, if pla y er 1 exerts effort he w ill trigger pla yer 2 to exert effort
with p rob ab ility α. If playe r 1 shirks instead, he will trigger player 2 to ex er t e ffort with
probab ility β. The i ncentive constraint faced by pla yer 1 is now giv en by:
[β(αp(2) + (1 − α)p(1)) + (1 − β)(βp(1) + (1 − β)p(0))]v
0
1
− c =
[α(αp(2) + (1 − α)p(1)) + (1 − α)(βp(1) + (1 − β)p(0))]v
0
1
yielding v
0
1
as specified above.
8
Overa ll, the simple framewo rk in this section shows that a "po sitive" intera ction
should exist bet we en wo rkers who are c om plem ents in production, while a "negative" in-
teraction shou ld exist between workers who are substitutes. Psychologica l factors such as
peer pressure and s hame pla y no role in creating this interaction of effort c ho ices. Our
purpose is not to cla im that w orke rs can never affect each other due to beha vior al consid-
erations. Rath er, our p u rpose is to demo nstrate that t h ese intera ctio ns could r esu lt from
fully rational (income maxim izing) consideration s without relying on beha v ioral responses.
Indeed, the remain der of the pa per presen ts evidence from profession al baseball that these

can ruin a good perfor m ance b y the starter with a bad per for m an ce, or he could “save”
the game with a good performance. Since mul tiple starting pitchers are nev er used in
the same game, starting pitchers can be considered substitutes and competitors with eac h
other, while being c omplem ent s with relief pitcher s.
Table 1 presents summary statistics for the samp le of p layer s from the 1 970 to 2003
seasons. The sample inclu des all batters who batted at least 50 time s in a season a nd
pitch ers w h o p itched in at least 10 games. Th e main performance measur e for batters is
the “batting average” (BA), w hic h is defined as the nu mber of hits divided b y the n umber
of opportunities to bat (“a t-bats” ) in a season. According t o Ta ble 1 , batters obta in a h it
in 26 percen t of their chances. Another conv entional measure of batting performance is the
“on-base-percen ta ge”, which takes into consideration o ther ways a b atter can get on base
(w alks, hit by pitc h, etc.).
4
The standard indicator of a pitcher’s performance is called
the E R A (Earned Run Avera ge). This measure take s t he nu mber of bases that a pitc her
allows the opposing team to obtain, and scales it b y the nu mber of innings played, so that
it represents the ave ra ge number of runs w h ich would have been scored o ff the pitch er in
afullgame.
5
As such, a higher ERA reflects poo rer performance. The avera ge ER A is
4.83 for starting pitch ers and 4.7 0 for relief p itch ers.
6
Another ind icator of a pitch er’s
4
The exact d efinitions of the batting m easures are as follows: batting average equals the number of
hits divided by the number of at-bats. On-base-percentage is defined a s (hits+walks+number of times hit
by pitch) divided by (at-bats+walks+sacrifice flies+number of times hit b y pitch). Slugging percen tage
is equal to (singles + 2*doubles + 3*triples + 4*home-runs)/(at-bats).
5
The ERA is calculated by: (number of earned runs/innings pitched)*9.

pitching ERA)
t
+ β
2
(teammates
0
batting ave)
t
+µ
i
+ β
3
(other controls)
t
+ ε
it
where the performance of pla ye r i in year t depends on his teammates’ pitching
performance in year t, his team m ates’ batting performance in yea r t (not in clu d in g the
batting performance of pitchers), the ability of pla yer i represen ted b y µ
i
, other observa ble
con trol vari ables, and th e u nobserved random componen t, ε
it
. The other control variables
include: the batting a verage in p layer i’s division (excluding his own team) in y ear t whic h
controls for the quality of the pitching and batting in the team’s division in the sam e year ,
the team manager ’s lifetime winning percentage which is an indicator for th e qualit y of
11
the team’s coach ing, the ballpark hitting and pitc h ing factors whic h con trol for whether
theteam’sballparkiseasyordifficult for batters in yea r t, the pla yer’s ye ars o f experience

we discuss in th e nex t section, prob lems cou ld ar ise if there is a com mon shock to a ll team
membersinagivenyear.
The basic fixed-effect regressions for pitchers and batters are presented in Table
2. Colum n (1) sho w s that after con tr olling for all the other va r iab les, a giv en batter
12
has better than average years when the other batters on the team are doing well. In
contrast, column (2 ) show s that a batter ’s performance decreases when th e pitchers on his
team are pitc h ing well. (A lowe r ERA indicates stronger pitc hin g per fo rm a nc e.) The
specification in column (3) inclu d es the pe rform a n ce measures of bo th the batters and
pitc hers as explana tory va riab les, and the r esu lts are essentially unc hang ed. Thus, th e
results are robust to estimating t h e effect of pitchers and batters separately (columns (1)
and (2 )) or when they are estimated together in column (3). Therefore, the results are
not a product of a high correlation between the t wo variables.
Colum n s (5)-(7 ) present t he basic results for pitchers, and sh ow that a p itcher per-
forms better when his fellow pitc hers are doing better, but there is no significant effect
of th e team’s batting performan ce on a pitc her’s perform ance — a finding w hich repeats
itself throughout the paper. Again, the effect of the playe r’s fellow pitchers on his o w n
performance is robust to the inclusion or exclusion of the team ’s batting performance.
Regarding the other con trol variables, they all have the expected signs and are generally
significant for the batting and pitc hing regressions, although it is w orth noting that the
results are robust to excluding them.
One possible explanation f or the pitc hing re sults is that a coach is more likely t o let
a pitcher stay in the gam e lon ger, or use him in more games, if the other p itchers on th e
team are w eaker. That is, t h e coach will l et the pitc her struggle longer in the gam e when
there are weaker repl acements on the bench, t hus i nducing a positiv e correla tion bet ween
a p itch er’s ERA and tho se of his fello w pitc h ers. We can con tro l for this by including
the number o f innings a nd games played by the pitch er into the reg ression . After adding
these va riab les in to the specification, the coefficien t on his teamm ate’s ERA goes from
0.523 (t-statistic o f 6.51) to 0.62 5 (t-statistic 8.01). Therefor e, th e in tera ction be tween
pitchers appears eve n stronger after con tr o lling for how long the pitch er is left in the game.

the team’s pitc h ing ER A wo u ld be 0.0032 (the coefficie nt 0.282 in Table 2 m u ltiplied by
100, multiplied by two times 0.579, the stan d ard deviation of Team ER A in Table 1).
The predicted change in a pitcher’s ERA due toatwostandarddeviation c hange in his
teammates’ ERA is 0.588 . This p red icted increase represen ts a little more than 23 percen t
of the sta nda rd deviation of a starting pitc her’s ERA . Although these p redicted ch an ges
are not ve ry large, adding five points (the p r edicted change of 0.005 f or a batter from the
14
other b atters on the team) or increasing a pitcher’s ERA by 0.562 wo u ld probably n ot be
considered en tirely trivial to a fan or a player.
Table 3 perfor m s a sim ilar analysis to Table 2 b u t controls for unobserve d hetero-
geneitybyusingafirst-difference specification bet ween consecutive y ears rather than using
fixed-effects. The results are very similar in the sense that a batter is show n to be affected
by the batting and p itching performan ce of his teammates, while a pitc h er is affected only
b y his fellow pitc h ers. The magnitudes of the coefficients are a little differen t from the
estimates in Table 2, w ith s o m e s m a ller a n d some bigger in size, but inferen ces regarding
significance are very similar.
Overa ll, the resu lts are con sistent with the th eo ry th at players should be positively
affected by th e performance of their fello w workers w hen th ey are complements in pro-
duction (like batters between themselves), but negatively affected b y the performance of
their fellow wo rkers when they are su bstitutes in p roduction (like batters and pitchers).
Although the finding that a pitc her is positiv ely affected by other p itchers is consistent
with the t heor y, th e theory can not explain why a pitcher d oes not seem to react in e ither
direction to the team’s batting performance. However, the fact that there is a d ifferential
reaction to both types of pla yers from both t ypes of players is strong evidence against a
behavior al explanation for the results. A typical behavioral response wou ld be for any
worker to wor k hard er w hen his co-wo r kers are wo rking ha rder, regardless of wh ether th ey
are complem ents or sub stitu tes in team production. Th is prediction is clearly rejected in
the analysis. So , the differentia l responses according to the role of ea ch type of pla yer can
be viewed as evidence for the idea that the tec hnology of production significa ntly in fluences
the i nt eractio n of effort ch oices across workers.

better batters an d worse p itch ers, wh ile p itch ers play better if they move to a place with
better pitchers.
16
5.3 No t Sw it ching Teams
One explanation for th e previous set of results is that there m ight be an un observed reason
wh y certain team s ha ve good batting but bad pitching, and when a player moves to that
team his performance changes accordingly. For exam p le, it could be that the p layer is
affected by the coaching change, or that certain ballp arks favor batters over pitc hers, or the
team could pla y in a new division w here teams are v ery strong in pitching or batting, or the
team’s city may be i n a part of the country wher e t he w eather f avor s pitching or b attin g.
The basic regressions in Tables 2 and 3, as w ell as the previous regressions using only
the sample of team-c han gers, con trol for man y of these scenarios by in clud ing measur es of
managerial qualit y, indices for whether the ballpark fa vors batting or pitching, t he batting
a verage of the division in the sam e y ear, and division dummy variables. Ho wever, these
measu res ma y be im perfect. So, to c om pletely contro l for this scenario, we restrict th e
fixed-effect analysis only to the seasons in whic h the player pla yed for the same team,
man ager, and ballpark (the combination of which t he player stayed with the longest). For
batters, the fixed-effect analysis is presen ted in colum n (2) of Table 4, while the first-
difference an alysis usin g a sample of players w ho do not c ha ng e team s in consecutive yea rs
is show n i n column (3) of Table 5. For pitc h ers, th e respective regressions a re in column
(2) of Table 6 and column (3) in Table 7. Over all, the results are very similar to those
using the whole sample and the sample of pla yers who changed teams. Ho wever, the
magnitu des of the coefficients tend to be weaker for the pla yers w ho stay on the same
team over consecutiv e y ears v ersus th ose that c hange teams. T his tendency is most likely
due to the fact that most of the var iation in one’s co-w orkers com es from players changing
teams. The f act t ha t there is enough variation in o n e’s co -wo rke rs even within the same
team in consecutive seasons to explain variation in an individual’s performance supports
the in terpretation that the main results a re no t due to endogenous m o ving.
5.4 Complem ents or Competition bet we en Pla y ers?
The positive effect of fellow batters on the ind ividu al perform an ce of a b atter, or fellow

of fixed-effe cts co ntrols fo r t he overall, unobserved a bility o f each player. Howeve r, as
18
Manski ( 1993) poin ts out , t here c ould be an unobserved factor r esponsible fo r t he high or
lo w performance of all players on the team in a given y ear, and t herefore, this p roduces
a correlation bet ween the performance s of playe rs without there actually being a ca usal
effect. If this explanation w ere true, it seems incon sistent with our results which sho w
that the s ign o f t he e ffect depends on the degree of substitutability and complemen tarit y
bet ween play ers — since it is unlikely that an unobserv ed factor is inducing all batters to
do better in the same year that t h e team’s pitcher s are having bad ye ars.
Howe ve r, to fu rth er examine this possibility, we r epeat the analysis using the lifetime
performance of player i’s teammates instead of th eir curren t perform ance in year t. Using
lifetime performance allo w s us to wash out all the idiosyncratic shocks to a specificteamin
a giv en year, since the lifetime perform ance of any given pla yer d oes not c h ange from yea r
to year. As a result, variation in the lifetime performa nce o f p laye r i’s team mates stems
only from c han ges in the composition of h is co-workers. This identification str ategy is
similar to the one employed by Ichin o and M agg i (2000) and used above wh en we restricted
thesampletoplayerswhomoveteams. However,theuseofthelifetimeperformanceof
one’s teammates allows us to exp loit c ha nges in the c om position of on e’s teamm ates within
the s a m e team as well as across teams when players mo ve.
The results for ba tters are p resented in column (4) of Table 4 an d colu m n (5) o f
Table 5 , and are virtually identical to column (1) which u ses the b atting average of one’s
teamm ates i n t h e current ye ar. The first-differ en ce results in Table 5 a r e little w eaker in
significance, but the magnitude is not much lower than the coefficien t estimates in other
specifications. For pitch ers, the fixed-effect res ults are in c olumn (5) of Table 6 w h ile
the first-differen ce results appear in colum n (6) of Ta ble 7. The pitchin g results using
the l ifetim e achievements of one’s t eam mates are very similar t o those u sing their current
ac h ievements. Furtherm ore, t he last column of Tables 5, 6, 7, and 8 repeats th e an alysis
for batters and pitc hers, but uses the lifetime perform an ce as an instrumental var iable for
the current performance of one’s co-workers in a 2SLS regression. Again, the results are
v e ry sim ila r to using the lifetim e ach ieve m ent s directly as a regre ssor an d to using th e

tec h nolog y of team production. T his int erp retation i s strengthened by the many robustness
20
c hec ks with di fferen t samples and specifications, as well as instrumenting the performance
of on e’s co-w orker s with th eir lifetim e perform an ce. All of the va riation in this in strum ent
comes fr om changes in the composition of on e’s co-w orkers, and therefore, is un affected
by transitory shocks which may affect t h e perfor m an ce of a ll players on a team in a given
year.
Although the empirical analysis is performed using dat a on baseball pla yers, the re-
sults are likely to apply to m a ny w o rk en v ironments where the r e is an elemen t of team
production. Whenev er w orkers have to w ork in teams, there is bound to be complementar-
ities and substitutability between different kinds of workers, and t h erefore, the framew o rk
analyzed in this p aper is likely to be relevan t. It is important to note t h a t a key assumption
driving the theoretical results is that an individual’s w age is a functio n of the aggregate
team performance. If this assumption did not hold, there would be no “technological” ba-
sis for a work er to alter his performance according to the output of his fellow work ers, since
his wa ge w ould be purely a function of his own individual perfor mance. However, there
are two main reasons to expect that an individual’s w age is affected b y the performance
of the whole team, e ve n in cases where there a re effective w a ys to evaluate individual per-
forma nces (see also A lch ian a n d Dem setz (1 97 2)). First, a h igher team perform a nce m ay
generate higher profits, a nd th us, increase the value of the marginal product of labor. Sec-
ondly, a hig her team performance can serve as a signal fo r aspects of a pla yer’s ability that
are hard to observe or quantify, and th us, not reflected in typica l performance mea sures.
For example, individuals on a winning team project m a y be consid ered industrious workers
who are able to w ork well together with fellow wo rkers. Therefore, the “tec hn ologica l”
interaction o f effort choic es high lighte d in th is pa per is likely to be relevant for many wo rk
en vironments where cooperation among workers i s importan t.
21
References
[1] Alchian , Armen and Harold Demsetz, ”Prod uction, Information Costs and E c onom ic
Organization,” American Econom ic Review (1972) 5, 777-795.

Journal of Economics, CXV (3), A u gust 2 000, 1057-1090.
[14] Itoh, H. (1991) “In ce ntives t o Help M u lti-Agent S itua tions,” Econ ometrica, 59, 611-
636.
[15] Jacob, B r ian A., (2004 ).“P ub lic Housing, Housing Vouchers and Student Achievement:
Evid en ce from Pub lic Housin g Dem o lition s in Chica go ”American Econom ic Review
94(1), March 2004, 233-258.
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