Does Size Really Matter? Empirical evidence

on group incentives.

Christopher P. Adams∗

Bureau of Economics, Federal Trade Commission

Email: cadams@ftc.gov

October 9, 2002

Abstract

The paper empirically analyzes the economic theory and intuition that

the “free rider” problem will overwhelm firm-wide incentives in large firms.

Kandel and Lazear (1992) claim that in a simple model of an equitable

partnership, Nash equilibrium effort levels fall with the number of partners

- the 1

N

problem. The paper shows that this result is crucially dependent on

a unstated assumption on the production function. In particular, if worker

effort levels are complementary, effort levels can increase with the number

of partners. This difference may explain the empirical finding that the 1

N

problem is substantial in medical and legal practices (where effort levels are

independent), but less important in manufacturing (where effort levels are

complementary). The empirical results suggest that the use of firm-wide

incentives increases with firms size, at least for smaller firms. The results do

not support the claim that the use of other human resource practices, like

self-managed work teams, allows the firm to mitigate the 1

N

problem.

∗The author would like to thank David Balan, George Deltas, Dan Hosken, Brian

Knight, David Prentice, Jeff Pliskin, Marc Rysman and Bill Vogt for their comments, Ted

Wannell and Tony Fang at Statistics Canada, for all of their help with the use of WES

99. All errors are my own. Note that this paper does not necessarily represent the views

of the Commission or any individual Commissioners.

1
1 Introduction

The use of profit sharing amongst production line workers in large manufacturing firms, seems to defy economic logic. According to Kandel and Lazear

(1992), ‘the idea that joint ownership can do much for incentives when the

number of workers is large seems wrong on the face of it. After all, each

worker bears the full cost of his own effort, but reaps at most 1

N

of the benefit in an N-worker firm’ (pp. 801-2). Despite the general incredulousness

of economists, these types of incentives schemes are used in large firms and

moreover such schemes have been found to improve firm performance.1 This

paper takes a closer look at why firm-wide incentives should or should not be

used in large firms, and tests the explanations on a data set based on a large

survey of production line workers and the manufacturing establishments that

they work for.

In their seminal work, Kandel and Lazear (1992) argue that without peer

pressure, Nash equilibrium effort levels will fall as the number of workers is

increased. However, this is not generally true in the model analyzed by the

authors.2 The analysis presented below shows that if effort levels are complements (in the sense that the marginal value of effort increases in other

people’s effort), then Nash equilibrium effort levels may increase as the number of workers increase. The empirical section estimates the probability that

an individual production worker receives either profit sharing or employee

share ownership. The section estimates a number of different specifications,

the results suggest that the use of profit sharing increases in firm size, at

least for smaller firms.

Despite the empirical results presented below, there exists compelling

empirical evidence in support of the 1

N

problem. It seems that at least for

legal and medical partnerships, the 1

N

problem is real (Gaynor and Gertler

1See for example, Knez and Simester (2001) and their analysis of the productivity

improvements achieved at Continental Airlines after the introduction of a firm-wide bonus

scheme for the airline’s 35,000 hourly workers, or Ichniowski et al. (1997) and Boning et

al. (2001) and their analysis of the productivity improvements attributed to profit sharing

amongst production workers in the steel industry.

2

It is true if effort levels are independent of each other, which is probably the case the

authors had in mind.

1
(1995); Prendergast (1999)). Gaynor and Gertler (1995) shows that proxies

for doctor’s effort decrease as the number of doctors in the practice increases.

However, the empirical evidence on the relationship between firm size and the

use of profit sharing in manufacturing (and other industries) is not nearly

so clear. As stated above, a number of studies have found that firm-wide

incentives schemes lead to productivity improvements, even after accounting

for the selection problems inherent in such measures (Boning et al. (2001);

Knez and Simester (2001)). Boning et al. (2001) point out that this result

suggests the free rider problem is not overwhelming, at least not in steel

mini-mills.

Studies on the adoption of profit sharing using establishment and firm

level data are inconclusive. Using Canadian firm level data, Jones and Pliskin

(1997) find that larger firms are more likely to offer employee share ownership

to all non-managerial employees and profit sharing to production workers,

contradicting the standard intuition. Drago and Heywood (1995) analyze

the adoption of incentive schemes in Australian establishments. The authors

do find that profit sharing schemes are less likely to be used by larger firms,

although the estimated coefficient is not statistically significantly different

from 0. Heywood et al. (1998) analyzes the use of profit sharing in German

establishments. The authors find that larger firms are again more likely to

use profit sharing, although the coefficient is not statistically significantly

different from 0. The analysis presented below aims to complement these

papers by analyzing individual employee level data. Such data is likely to

provide more accurate information on the adoption of such a schemes, and

the relationship between the use of profit sharing and the adoption of other

human resource management practices.

The analysis presented below may help to explain the divergent empirical

results. If effort levels are (more or less) independent in medical practices

and legal partnerships, then the model predicts effort levels will decrease

with partnership size. It seems likely that the effort level choice of one

doctor has little or no effect on the productivity of the other doctors in

the practice. Doctors generally form partnerships in order to share costs

(building, nurses, receptionists) and to reduce risk.3In manufacturing, on

3This is the subject of the Gaynor and Gertler (1995) paper.

2
the other hand, it seems likely that effort levels are complementary. For

example, on a production line, if one worker is shirking or becoming careless,

the productivity of workers further down the line will be adversely affected.

It seems likely that the size of a manufacturing firm is intimately related

to the degree with which worker’s efforts are complements. The analysis of

the bonus scheme at Continental suggests that interdependence is important.

The scheme introduced by Continental gave all workers in the firm a bonus

if a firm-wide target of on-time departures was achieved. According to Knez

and Simester (2001)

A flight cannot depart until the entire ramp and gate activities

have been performed, so that poor performance by one employee

can negate good performance by the rest of the group. For example, maintenance or fueling delays will prevent a flight from

leaving on time, even if passengers and baggage are loaded... (p.

766)

It is also the case that breakdowns at one airport will affect the ability of

other airports to contribute to achieving the company wide target (especially

in a hub-spoke system).

There are two other arguments made in the literature regarding why the

1

N

problem is not overwhelming for firms using profits sharing plans. The

first is that there are economies of scale in adopting incentive schemes such

as profit sharing or employee share ownership. This would occur if there were

substantial fixed costs or large overhead in implementing these schemes. It

seems reasonable to expect that only larger firms are going to offer employee

share ownership. Jones and Pliskin (1997) suggest that at least part of the

explanation for their results lies with the existence of scales economies, particularly given the administrative costs of employee share ownership schemes.

The second is that firms use human resource management practices, such

as self-managed work teams that allow for “mutual monitoring” (Knez and

Simester (2001); Pliskin (2000)). According to Knez and Simester (2001),

To explain why the [group incentive] scheme may have been effective we argue that the organization of employees into autonomous

3
work groups4

enabled Continental [Airlines] to induce mutual

monitoring among employees within each group. (p. 743)

Che and Yoo (2002) present a theoretical model of group incentives in a

repeated game that formalizes the argument made in Weitzman and Kruse

(1990). The authors show that the implicit incentives generated by perfect (within group) monitoring and repeated interaction allow the free rider

problem to be solved. However, the authors do not model the exact situation discussed by Knez and Simester (2001), where there is perfect monitoring

within the self-managed work team but imperfect monitoring between teams.

Kandel and Lazear (1992) argue that such mutual monitoring cannot work in

a large firm with a firm-wide incentive scheme. The authors argue that the

mutual monitoring equilibrium unravels when there is imperfect monitoring

between groups. I am not aware of any theoretical analysis of the situation.

Rather than taking a stand one way or another, this paper will simply test

the hypothesis that self-managed work teams solve the free rider problem,

allowing profit sharing to be used in large firms.5

There exists a parallel discussion of group size and free riding in the

charitable giving literature. Andreoni (1988) shows that the standard model

of charitable giving, voluntary contributions to a public good, imply a free

rider problem. The author shows that the standard model suggests that

there are “limits to altruism.” In an economy with uncertainty, as the number of people in the economy grows large, the free rider problem becomes

overwhelming. There has been some work in the experimental literature to

determine whether there is a relationship between group size and free riding. However, no clear relationship has been found. Isaac and Walker (1988)

show that the free rider problem does increase with group size, but only if

the value of the marginal contribution is allowed to decrease with group size.

If the value of the marginal contribution is held constant, then size has no

4The terms self-managed work teams and (semi) autonomous work groups are used

in the literature to refer to the human resource management practice of giving decision

making power to a small group of production workers (Levine (1995); Eaton et al. (1997)).

5Knez and Simester (2001) and Che and Yoo (2002) also point out that in firms with

complementary effort levels, incentives can be further improved as co-workers can implement harsher punishments by reducing effort levels.

4
effect on the free rider problem. This argument is similar to the argument

made below that the relationship between group size and free riding is dependent on the relationship between group size and the production function.

Other experimental evidence suggests that average giving actually increases

in group size (Isaac et al. (1994)).

The rest of the paper proceeds as follows. Section Two presents a simple

theoretical analysis of the relationship between firm size and worker effort

choice. The section presents the empirical implications of this model. The

section also presents the empirical implications of the economies of scale

hypothesis and the mutual monitoring hypothesis. Section Three presents the

empirical evidence. The section discusses the data and tests the hypotheses.

Section Four concludes.

2 Theoretical Analysis of Group Incentives

This section has three parts. The section presents a theoretical model to

illustrate how the number of employees is related to individual effort choices

under a simple firm-wide incentive scheme. The implications of this model are

illustrated using a linear latent profit model of the firm’s decision to use firmwide incentives. The implications of economies of scale are also illustrated

using this model. The section also analyzes the value of using firm-wide

incentives conditional upon the use of other human resource management

practices such as self-managed work teams, that allow for mutual monitoring.

A linear latent profits model is used to illustrate the implications of the

mutual monitoring hypothesis.

2.1 A Simple Model of Group Incentives

Consider a firm with N workers.6 Each worker chooses an effort level ei ∈ <,

where i ∈ {1, 2, ..., N}, in order to maximize their utility, u(m, ei), such that

u : <

2 → <. The utility function for each worker is separable in money, such

that u(m, ei) = m − C(ei), where m is money, and C is the cost of effort,

6The model and notation follow Kandel and Lazear (1992).

5
such that C(0) = 0, C

0 > 0 and C00 > 0.7

The production function is f : <

∞ → <. For a firm with N workers

f(e1, e2, ..., eN , ...) is such that for all i > N, eiis a constant such that

∃M ∈ < such that f < M. The partial derivative on i’s effort is positive

(fi > 0), and there is diminishing returns in i’s effort (fii < 0). Assume that

the incentive scheme is an “equitable partnership”. That is, worker i receives

f

N

. Given the assumptions on f and C, for any N, worker i’s effort choice

(e

∗

i

(N)) is the solution to the following first order condition,

fi

N

− C

0

(ei) = 0 (1)

Equation (1) is equivalent to Equation (2) in Kandel and Lazear (1992). The

authors seem to believe that this equation implies that effort choice decreases

in N.

8 This, however, is not obvious and depends on how fi changes with

N. If fiis constant in N, as would be the case if f = e1 + e2 + ... + eN , then

the authors are correct.9If however, fij > 0, for i 6= j, then the opposite

may be true.

What does this mean? If the production function is such that each

worker’s marginal productivity (fi) is increasing in the number of workers

(N), then this is sufficient for e

∗

i

to be increasing in N.

10 Does such a production function exist? Is it a reasonable representation of a manufacturing

firm? Consider a Cobb-Douglas production function, f = Π∞

i=1e

α

i

, where

ei = 1 for all i > N, and α ∈ (0, 1). The following proposition shows that if

α is large enough, then effort levels increase with N.

Proposition 1 Let C(ei) = e

2

i

2

,

1. If α < 2

N+1 , then e

∗

i

(N + 1) < e∗

i

(N).

2. If α > 2

N

, then e

∗

i

(N + 1) > e∗

i

(N).

7These are the standard assumptions of the incentive literature (Holmstrom (1979);

Kandel and Lazear (1992)).

8According to Kandel and Lazear (1992), ‘The standard argument, as illustrated by

(2), is that effort falls as firm size increases’ (p. 815).

9More precisely the production function is f = e1 + e2 + ... + eN + 0 + 0 + ....

10The necessary condition is that fi

N+1 >

fi

N

.

6
Proof. From Equation (1), e

∗

i

(N) is the solution to

αeα−1

i Πj6=ie

α

j

N

− ei = 0 (2)

By symmetry, the Nash equilibrium is such that e

∗

i = e

∗

j = e

∗

for all i, j ≤ N.

Therefore e

∗ =

3

N

α

 ́ 1

Nα−2

. QED.

If for some range of N, α is large enough, then Nash equilibrium effort

levels will increase as more workers are hired. How “large” α needs to be depends on the cost of effort function. But in general, the more complementary

effort levels are the more likely that effort levels will increase with N.

Note that the Cobb-Douglas production function presented above has the

property that for a given α, the “equilibrium” production function (f(e

∗

))

will increase unboundedly as N gets large. In order to mitigate this, α must

decrease at a “fast enough” rate as N gets large. In the example, presented

above, if α =

A

N2

, for some large A, then equilibrium effort levels will increase

for small N, but eventually they will go to 0 as N gets large.

The Cobb-Douglas function is an example of a production function that

is supermodular in worker effort levels (Milgrom and Roberts (1990)). That

is, worker effort levels are complements to each other. If someone else in the

firm works a little harder, then every other worker’s marginal productivity

increases. I would argue that complementarities in effort levels are a reasonable characterization of a modern manufacturing plant, in fact the size of

the firm itself must be closely related to the existence of such complementarities. Consider the task of authoring a paper. This paper is authored by

one person. It reasonable to believe that if another economist was willing to

coauthor the paper, then my effort level would actually increase because the

marginal returns of my effort would increase. The coauthor and my effort

levels would be complementary. In economics, these complementarities are

such that two or three economists and 1 or 2 research assistants are optimal

to author a paper. In other disciplines the number can be much larger.

Analysis of this simple model shows that the intuition of the 1

N

problem

is not straight forward. If there are complementarities in the production

process, effort levels of individual workers may not decrease in firm size.

7
It is therefore not obvious that the value of using profit sharing decreases

monotonically in firm size.

2.2 A Model of Firm Choice of Profit Sharing

In a simple linear latent profit model there exist two possibilities, the latent

value of each is presented below. Let VP be the latent value to the firm, where

P ∈ {0, 1} indicates whether the worker is provided with profit sharing. First,

the value of not using profit sharing is denoted by Aij for worker i and firm

j. The latent profits of the other contract will be compared to this one.

V0 = Aij (3)

The value of using profit sharing is V1.

V1 = Aij + g(Nj ) + XiβiP + XjβjP + 2ijP (4)

where Njis the number of workers in firm j, g is some function of Nj, Xiis a

vector of observable characteristics of the worker, Xjis a vector of observable

characteristics of the worker’s firm, and 2ijP represents unobservable characteristics that affect the relative value of using profit sharing. If g(Nj ) = βN Nj

or g(Nj ) = βN log(Nj ), then the 1

N

problem exists if βN < 0. A result that

βN > 0 would be consistent with the hypothesis that there are complementarities in the production process and effort levels increase with firm size.

This result would also be consistent with the existence of economies of scale.

2.3 A Model of Firm Choice of Profit Sharing and

Teams

Consider the linear model presented above. Now assume that the firm can

choose to use two human resource management practices, profit sharing (P ∈

{0, 1}) and teams (T ∈ {0, 1}), where are either self-managed work teams

or quality circles or both. The value of these two practices is denoted VP T .

First, the value of neither using profit sharing nor groups is denoted by Aij .

The latent profits of the other contracts will be compared to this one.

V00 = Aij (5)

8
The value of using profit sharing but not using teams is V10. The variables

are defined in the previous section.

V10 = Aij + βNP log(Nj ) + XiβiP + XjβjP + 2ijP (6)

The value of using teams but not profit sharing is V01. The value of using teams may also be a function of firm size as well as other observable

characteristics of the worker and the firm.

V01 = Aij + βNT log(Nj ) + XiβiT + XjβjT + 2ijT (7)

where 2ijT represents unobservable characteristics that affect the relative

value of the contract with teams only. The value of using profit sharing

and teams is V11.

V11 = Aij + βNP T log(Nj ) + XiβiP T + XjβjP T + 2ijP T (8)

Note that with out loss of generality Equation (8) can be rewritten in a

form that is more conducive to representing the particular hypothesis test of

interest.

V11 = Aij + βNP log(nj ) + XiβiP + XjβjP + 2ijP

+βNT log(Nj ) + XiβiT + XjβjT + 2ijT

+β

∗

NP T log(Nj ) + Xiβ

∗

iP T + Xjβ

∗

jP T + 2

∗

ijP T

(9)

where the “*” variables represent the “extra” value of using both practices

together.

The mutual monitoring hypothesis is, conditional on the use of teams, the

value of profit sharing is not affected by the size of the firm, and conditional

on not using teams, the value of profit sharing decreases with size. For the

first part the appropriate difference is

V11 − V01 = βNP log(Nj ) + XiβiP + XjβjP + 2ijP

+β

∗

NP T log(Nj ) + Xiβ

∗

iP T + Xjβ

∗

jP T + 2

∗

ijP T

(10)

For the second part of the hypothesis the appropriate difference is

V10 − V00 = βNP log(nj ) + XiβiP + XjβjP + 2ijP (11)

Using these two equations the hypothesis to be tested is.

9
Hypothesis 1 Mutual Monitoring

i) βNP + β

∗

NP T = 0

ii) βNP < 0

Part (i) of Hypothesis 1 follows the reasoning of ?) and states that if

teams are used by the firm, then the value of profit sharing will be unaffected

by the size of the firm. Part (ii) is the same as the traditional 1

N

problem,

but now with the condition that the firm does not use teams.

3 Empirical Evidence on Group Incentives

3.1 Data

The empirical analysis presented below uses data from the Canadian Workplace and Employee Survey (WES) 1999. WES 1999 surveyed management

and up to 12 employees at 6,358 establishments with a response rate of around

95 %. There are 24,938 employee surveys, which is a response rate of 83 %.

It is important to realize that this is establishment level data, and that there

may be more than one establishment per firm. There are two concerns. First,

the measure of size may be not be accurate. This issue is discussed further

below. Second, the standard errors may be larger than shown because it

is not possible to tell whether two establishments are actually members of

the same firm, and thus not independent observations. For a more detailed

discussion of this issue see Jones and Pliskin (1997).

In order to reduce the variation in the type of establishments and workers

that are analyzed, the samples are limited to full-time production workers11

in non-government manufacturing establishments. This restriction substantially reduces the sample size.12 The restriction is particular important for

understanding what is meant by the use of “teams” or “circles”. In a broader

sample that includes non-production workers or non-manufacturing firms, it

is often difficult to understand what is meant by these types of human resource management practices.

11A production worker is either a skilled tradesperson or an unskilled production worker.

12The actual sample sizes are listed with the results.

10
The dependent variable, PROFIT SHARING, is 1 if the worker receives

profit sharing or is involved in a share ownership plan. PROFIT SHARING

is 0 if the worker does not have profit sharing and is not involved in a share

ownership plan.13 The use of profit sharing is determined by a direct question

of the worker. The other dependent variable is TEAM. TEAM is 1 if the

worker if they are either in a self-managed work team or a circle, TEAM is

0 if neither is true. Again, the definition is based on a direct question to the

worker.

The measure of the size of the firm is the number of full-time employees

in the establishment. Note that this measure only tells us how many workers

there are at the establishment, not the total number of full-time employees

of the firm. This is a concern because the profit-sharing plans and the share

ownership schemes may be firm wide rather than establishment wide.

The analysis uses six measures of employee characteristics. The first, EXPERIENCE is the number of years since the worker started her particular

job (not necessarily when she started at the firm). This measure is meant

to capture the worker’s knowledge and experience with the production process. The second, UNION, is 1 if the employee is a member of a union and 0

otherwise. It has also been argued that unions tend to be opposed to profit

sharing (Gregg and Marchin (1988)). The third characteristic, MALE is 1

if the employee is male and 0 if the employee is female. The fourth characteristic of the employee, SKILLED is 1 if the employee is a skilled trades

person and 0 if the employee is an operator or assembly worker. CANADIAN

BORN IS defined as you would expect. HIGH SCHOOL is 1 if the worker

completed high school, HIGH SCHOOL is 0 if the worker did not complete

high school.

There are two measures to describe the establishment’s product market.

CUSTOM which is 1 if the relative importance of improving coordination

with customers and suppliers is “very important” or “crucial.” CUSTOM is 0

if the relative importance of improving coordination with customers and suppliers is “not applicable” or “not important.” Note, observations that state

13Profit sharing and share ownership are two different schemes that are combined in

the analysis. For a discussion and analysis of the differences between the two schemes see

Jones and Pliskin (1997).

11
Variable Mean

PROFIT SHARING .13

TEAM .54

Full-Time Workers 374 (660)

MALE .78

EXPERIENCE 7.49 (7.91)

SKILLED .81

UNION .53

CUSTOM .87

QUALITY .96

CANADIAN BORN .84

HIGH SCHOOL .68

Number of Observations 1,580

Table 1: Sample Means (standard deviation)

coordination is “important” and “slightly important” are dropped. This is

done in order to increase the accuracy of the measure. The second measure

of the establishment’s product market is QUALITY. QUALITY is 1 if the

relative importance of improving quality is “very important” or “crucial.”

QUALITY is 0 if the relative importance of improving quality is “not applicable” or “not important.” Note observations that report quality as “important” and “slightly important” are dropped. Again, this is done in order to

increase the measure’s accuracy. The incentive literature suggests that it will

be more difficult to monitor the worker’s actions when those actions include

quality margins as well as quantity margins (Drago and Heywood (1995)).

Table 1 presents the sample means (frequencies) of the variables used in

the analysis. These are the unweighted means, while the regression results

are for the weighted sample. Just over 10 % of production workers have profit

sharing or share ownership, while over half are members of teams or quality

circles.14

14Because different observations are dropped during the regression analysis, these means

may not be exactly the same as for actual sample analyzed in any particular regression.

12
3.2 Results

Table 2 presents the main empirical results of the paper. The table shows

that the traditional 1

N

problem is not supported by the data. The hypothesis

implies that the coefficient on the log of the number of employees is negative.

From Column 2, the coefficient is,

βNP = .15 > 0 (12)

and statistically significantly different from 0. This result indicate that production workers in large establishments are (if anything) more likely to receive profit sharing than workers in small establishments. This is consistent

with the claim that there are complementarities in the production function

and with the argument that there are economies of scale.

Column 4 presents the results with the assumption that g(Nj ) = βN Nj +

βN2N2

j

. The coefficient is

βN2P = −.0000002 < 0 (13)

which is negative and statistically significantly different from 0. The results

suggest that for Canadian establishments with over 4,000 full time workers,

the free rider problem becomes overwhelming and the probability of using

profit sharing decreases in establishment size. A possible explanation for this

result is that there is a free rider problem and there are also economies of

scale in the adoption of profit sharing. For smaller firms, economies of scale

dominate free riding, and for larger firms, free riding dominates economies of

scale. Alternatively, it seems reasonable that complementarities will diminish

with firm size, and eventually the addition of new workers is not enough to

induce greater effort.15

The last hypothesis to be tested in this section is the mutual monitoring

hypothesis. The estimator used to test this hypothesis accounts for the endogeniety in the decision of the firm to give a worker both profit sharing and

place that worker in a self-managed work team or quality circle. The estimator is discussed in detail in Adams (Forthcoming, 2002). The estimator

allows the firm’s choice on giving a worker profit sharing and teams to be

15It seems that for academic papers in economics, this number is around 2.

13
Model 1 Model 2 Model 3

Profit Sharing β % ∆ β β

Log(N) .15 .02 - -

(.07) (.01) - -

N - - -.00004 .0008

(.00008) (.0002)

N2- - - -.0000002

- (.00000005)

Male .18 .02 .25 .25

(.16) (.02) (.16) (.15)

Experience .02 .002 .02 .01

(.01) (.001) (.01) (.01)

Skilled .23 .03 .08 -.07

(.23) (.03) (.21) (.19)

Quality -.05 -.01 .10 -.04

(.50) (.08) (.55) (.51)

Union -.33 -.05 -.08 -.26

(.24) (.04) (.21) (.21)

Custom .09 .01 .19 .21

(.30) (.04) (.34) (.30)

Canadian Born .62 .07 .64 .63

(.21) (.02) (.20) (.21)

High School -.07 -.01 .06 .00

(.15) (.02) (.16) (.15)

Constant -2.97 -2.52 -2.38

(.48) (.50) (.48)

Log Likelihood -389.77 -401.20 -382.38

Sample Size 1,390 1,400 1,400

Table 2: Probit Regressions on Profit Sharing (Robust Standard Errors)

14
made simultaneously, and it allows the two choices to interact. The model’s

key characteristics is that it allows this interaction to vary from worker to

worker in observable and unobservable ways.

Table 3 presents the estimates from the complementary Probit. From the

data, the equations are respectively (from column 2, row 3 and 19)

βNP + β

∗

NP T = .14 + .01 = .15 > 0 (14)

which is positive rather than 0, and (from column 2, row 3)

βNP = .14 > 0 (15)

which is also positive and statistically significantly different from 0. That

is, for workers not in teams, larger establishments are more likely to receive

profit sharing the workers in smaller establishments. These results are not

consistent with the Mutual Monitoring Hypothesis. This hypothesis posits

that βNP < 0 and βNP + β

∗

NP T = 0, neither of which is supported. The

results do however give further support for both the claim that there are

complementarities in the production process as well as for the argument that

there are economies of scale, both of which posit that βNP > 0.

These results also highlight the importance of the QUALITY variable.

Not only is QUALITY an important determinant of the use of profit sharing

it is also an important indicator of whether profit sharing and teams are

complements. It should be noted that “teams” includes the use of quality

circles and problem solving groups, both of which seem to be very important

for firms that produce high quality products (Boning et al. (2001); Ichniowski

et al. (1997)).

4 Conclusion

The 1

N

problem states that as firms get large, workers will be more likely to

free ride and firm-wide incentive schemes will breakdown. This paper shows

that the intuition from the simple model is not as straight forward as we have

been led to believe. Whether effort levels decrease or increase, as the number

of workers increase, is crucially dependent on the production function. If the

15
Model 3 β Robust SE

Profit Sharing(βP )

Log(N) .14 (.05)

Male -.07 (.18)

Quality .87 (.29)

Experience .01 (.01)

Skilled .19 (.21)

Candian Born .59 (.21)

Constant -3.59 (.47)

Team(βT )

Log(N) -.06 (.05)

Male .23 (.12)

Quality -.29 (.25)

Experience .0135 (.0076)

Skilled .61 (.17)

Candian Born .22 (.12)

Constant -.29 (.30)

Both(β

∗

P T )

Log(N) .01 (.05)

Quality 2.28 (.21)

Constant -4.12 (.30)

ρ12 .12

ρ13 -.20

ρ23 -.28

Log Likelihood -646443.65

Sample Size 1,918

Table 3: Conditional Estimates from a complementary Probit (Robust Standard Errors)

16
production function is separable in the effort level of each worker, then the

intuition holds. However, if there are complementarities, more workers may

increase effort levels under equitable partnership incentive schemes. This

difference in production functions, may explain why the 1

N

problem seems

to hold in legal and medical partnerships, but does not seem to hold in

manufacturing firms. Empirical results from a recent Canadian survey of

production workers and their establishments, does not support the notion

that firm size is negatively related to the adoption of firm-wide incentive

schemes. The results suggest that (at least for smaller firms), as firm size

increases the adoption of profit sharing and employee share ownership also

increases.

Another possible explanation is that large firms use human resource management practices that allow for mutual monitoring and thus mitigate the

free rider problem (Knez and Simester (2001); Pliskin (2000)). The argument

(formalized by Che and Yoo (2002)) is that if workers are able to perfectly

observe each other’s actions, then the Nash equilibrium of the repeated game

allows profit sharing to implement the Pareto optimal effort levels. It is argued that human resource management practices such as self-managed work

teams and quality circles allow workers to mutual monitor (or perfectly observe) each other’s actions. Kandel and Lazear (1992) point out that workers

may not be able to perfectly monitor workers in other teams. The authors

suggest that the argument unravels under such circumstances. I am not

aware of any theoretical analysis of this situation. This paper tests the two

empirical implications of the mutual monitoring hypothesis. First, establishments that use teams will not suffer from the free rider problem and so size

will not affect the value of profit sharing. Second, establishments that don’t

use teams will suffer from the free rider problem and so size will decrease the

value of profit sharing. The empirical findings do not support the hypothesis.

The results suggest the workers in larger establishments are more likely to

receive profit sharing, irrespective of their participation in teams.

The paper finds that in manufacturing, the use of profit sharing increases

in firm size (at least for small firms). The results are consistent with two explanations. The first is that effort choices between workers in manufacturing

are complementary. If this is true then it may be the case that effort choices

17
would increase with the number of workers and profit sharing may be more

profitable for larger firms. The second is that there are economies of scale in

the use of profit sharing and employee share ownership plans.

References

Adams, Christopher P., “Selection of “High Performance Work Practices”

in U.S. Manufacturing,” March 2002. FTC Working Paper 247.

, “The Use of Profit Sharing When Workers Make Decisions: Evidence

from a Survey of Manufacturing Workers,” in Jeffrey Pliskin and Takao

Kato, eds., Advances in the Economic Analysis of Participatory and Labor

Managed Firms, Elsevier, Forthcoming.

Andreoni, James, “Privately provided public goods in a large economy:

the limits of altruism,” Journal of Public Economics, 1988, 35, 57–73.

Boning, Brent, Casey Icniowski, and Kathryn Shaw, “Opportunity

Counts: Teams and the Effectiveness of Production Incentives,” May 2001.

NBER Working Paper 8306.

Che, Yeon-Koo and Seung-Weon Yoo, “Optimal Incentives for Teams,”

American Economic Review, June 2002, 91 (3), 525–541.

Drago, Robert and John Heywood, “The Choice of Payment Schemes:

Australian Establishment Data,” Industrial Relations, 1995, 34, 507–531.

Eaton, Adrienne, Paula Voos, and Dong-one Kim, “Voluntary and

Involuntary Aspects of Employee Participation in Decision Making,” in

David Levine, Daniel Mitchell, and Mahmood Zaidi, eds., The Human

Resource Management Handbook (Part 1), JAI Press, 1997, pp. 63–85.

Gaynor, Martin and Paul J. Gertler, “Moral Hazard and Risk Spreading

in Partnerships,” Rand Journal of Economics, 1995, 26 (4), 591–613.

Gregg, P. A. and S. J. Marchin, “Unions and the incidence of performance linked pay schemes in Britain,” International Journal of Industrial

Organization, 1988, 6, 91–107.

18
Heywood, John S., Olaf Hubler, and Uwe Jirjahn, “Variable Payment

Schemes and Industrial Relations: Evidence from Germany,” Kyklos, 1998,

51 (2), 237–257.

Holmstrom, Bengt, “Moral Hazard and Observability,” Bell Journal of

Economics, 1979, 10 (1), 74–91.

Ichniowski, Casey, Kathryn Shaw, and Giovanna Prennushi, “The

Effects of Human Resource Management Practices on Productivity: A

Study of Steel Finishing Lines,” American Economic Review, 1997, 87,

291–313.

Isaac, R. Mark and James M. Walker, “Group Size Effects in Public

Goods Provision: The Voluntary Contributions Mechanism,” Quarterly

Journal of Economics, 1988, 103 (1), 179–199.

, , and Arlington Williams, “Group Size and the Voluntary Provision

of Public Goods: Experiments Utilizing Very Large Groups,” Journal of

Public Economics, 1994, 54, 1–36.

Jones, Derek and Jeffrey Pliskin, “Determinants of the Incidence of

Group Incentives: Evidence from Canada,” Canadian Journal of Economics, November 1997, 30 (4b), 1027–1045.

Kandel, Eugene and Edward Lazear, “Peer Pressure and Partnerships,”

Journal of Political Economy, 1992, 100, 801–817.

Knez, Marc and Duncan Simester, “Firm-Wide Incentives and Mutual

Monitoring at Continental Airlines,” Journal of Labor Economics, October

2001, 19 (4), 743–772.

Levine, David, Reinventing the Workplace: How Business and Employees

Can Both Win, The Brookings Institution, 1995.

Milgrom, Paul and John Roberts, “The Economics of Modern Manufacturing: Technology, Strategy, and Organization,” The American Economic

Review, June 1990, pp. 511–528.

19
Pliskin, Jeffrey, “The Incidence of Profit Sharing and Flexible Workplace

Practices,” July 2000. Conference Paper for the 10th Conference of the

International Association for the Economics of Participation, Trento Italy.

Prendergast, Canice, “The Provision of Incentives in Firms,” Journal of

Economic Literature, 1999, 37, 7–63.

Weitzman, Martin L. and Douglas L. Kruse, “Profit Sharing and Productivity,” in Alan S. Blinder, ed., Paying for Productivity, Washington,

D.C.: The Brookings Institution, 1990, pp. 95–142.

20