Authority and communication in firms: Estimating archetypal
decision-making structures∗
By Hajime Katayama†, Kieron J. Meagher‡and Andrew Wait§
January 18, 2017
Abstract
We show that decision making in organizations is typically more complicated than simply choosing either to delegate or to centralize. More often than not, firms also have to consider how
much communication or consultation they would like, and even whether to have more than
one individual involved. Utilizing a unique data set, we estimate a latent-class model to identify frequently adopted combinations of decision-making rights and consultation across different
hierarchical levels relating to the implementation of a significant change – we call these authority/communication patterns archetypal decision-making structures. Our estimates identify four
archetypal structures: centralized (with limited consultation); centralized with communication;
team decision making; and delegation. The relationships between these four archetypes and the
size of the organization, worker skills, long-term employer-employee relationships, individual
and group incentives and being close to the productivity frontier are broadly consistent with
theory but no one model is rich enough to fully describe all our findings.
Key words: Centralization; delegation; communication; consultation; relational contracting;
organizational structure; latent-class model.
JEL classifications: D23, L22, L23, L29.
1
Introduction
The tension between the centralization and decentralization of resource allocation has been a core
issue since Adam Smith’s The Wealth of Nations and has played an equally important role in framing thinking in organizational economics. In analysing organizational structures Chandler (1969),
Williamson (1971) and Simon (1965) all emphasized the benefits of decentralization/delegation inherent in the M-form as compared with centralization with a U-form structure.
While U- and M-forms are easily observable in a firm’s formal organizational chart, the critical
issue for resource allocation is how actual decisions are made – a distinction that Aghion and Tirole
(1997) so sharply delineate as formal and real authority. This more specific issue of how firms make
(major) decisions has been the focus of a recent empirical literature on the economics of decentralization. Acemoglu et al. (2007) and Christie et al. (2003) analyze the choice in organizational
structure between a profit centre (decentralized) and a cost centre (centralized). Similarly, Colombo
∗ We would like to thank Bob Gibbons, Suraj Prasad, Stefanie Schurer, seminar participants at the Sloan School
of Management and the 2015 Organizational Economics Workshop. The authors are responsible for any errors.
† Faculty of Commerce, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo, 169-8050, Japan. email: [email protected]
‡ Research School of Economics, H.W. Arndt Building, Australian National University, Canberra, ACT 0200,
Australia. email: [email protected]
§ School of Economics, University of Sydney, NSW 2006, Australia. email: [email protected].
1
and Delmastro (2004), McElheran (2014) and Meagher and Wait (2014) analyze the choice between
centralizing decision making to higher-levels of management or delegating authority to a lower-level
establishment. Both Bloom et al. (2012) and Aghion et al. (2016) construct a continuous measure of
authority across a range of decisions, allowing them to focus on a single-dimensional choice between
centralization and delegation.
A richer view is taken in much of contemporary theory: a firm jointly considers the allocation of decision authority and its communication protocols. This implies that a firm’s choice of its
authority/communication structure is multidimensional, which makes it difficult to capture empirically. In this paper, we do exactly that. Utilizing uniquely-detailed establishment-level data, we
use a novel methodology to identify combinations of decision-making rights allocations and their
associated patterns of consultation. Moreover, our methodology imposes no arbitrary taxonomy on
decision-making structures. We estimate a latent-class model to endogeneously classify the authority/consultation combinations typically observed; we call these structures archetypal decision-making
structures.
As the mid-twentieth century authors like Simon and March realized, real firms face more complications than captured by the (relatively) simple choice between centralization and delegation.
Firstly, a firm needs to think about how much consultation (communication) it would like surrounding a particular decision. If a principal is going to make the decision, should she consult anyone
first? And if so, who? Should the consultation be with a senior manager, or possibly someone further
down in the hierarchy? Or maybe both? Delegating the decision does not eliminate the potential
for consultation either – the subordinate could elicit other opinions, including from the principal,
before making a decision. Moreover, there is also a question of how much consultation is required,
as well as from whom this input should come. Secondly, authority need not always be allocated to
just one individual. Decisions could be made jointly by two or more people; committees and teams
can be be assigned decision-making rights.1
Economic theory models, while focusing on the decentralization choice, have long encompassed
other issues in their description of decision making inside organizations. One group of models
considers the interaction between communication and decision authority; centralization, for example,
could involve communication from an informed subordinate, as in both Aghion and Tirole (1997)
and Dessein (2002).2 Others models consider the costs and benefits of using a team compared with
a single decision maker. Potential reasons for using many agents to make a decision include: to
make better use of dispersed information (Marschak and Radner, 1972); dispersed skills (Garicano,
2000); and to reduce delay (Radner, 1992; Meagher et al., 2003, 2004).3
Alonso et al. (2008), Rantakari (2008) and Friebel and Raith (2010) consider decision-authority
allocation when informed agents with related information strategically communicate. In these models
the quality of information provided by the parties is endogenous to the structure of decision-making
rights. Relational contracting analyzes how long-run relationships can help overcome agency issues
in delegation (Baker et al., 2002; Levin, 2003). Fama and Jensen (1983) argue for a form of partial
1 Some of the richness of the decision-making process is described in management case studies, such as Stinchcombe
(1990) on department stores and oil drilling and Bourgeois and Eisenhardt (1988) on firms in the computer industry.
2 Also see Kartik (2009) for a comparison of delegation and communication with costly lying and Rush et al. (2010)
for an analysis of advice from an expert of unknown bias.
3 Decentralized decision making can help reduce delays and/or increase throughput when agents are boundedly
rational (Radner, 1992; Van Zandt, 1998; Orbay, 2002). Also see Garicano and Van Zandt (2012) for a literature
survey.
2
decentralization in which authority is split so as to mitigate opportunistic behavior.4 Other papers
have also shown that additional consultation and input can come with a cost; Milgrom and Roberts
(1988) note that individual actors in an organization have an incentive to distort and manipulate
information and its transmission to further their own interests.5
The latent-class approach we take to endogenously classifying types of organizational structures
(archetypes) has at least two advantages.6 Firstly, it considers decision authority jointly with its
associated consultation process. Secondly, it allows the data itself to determine the number (and
nature) of the archetypal structures that emerge. That is, rather than taking a taxonomy of theoretical structures to the data, this approach identifies the number and structure of archetypal patterns
directly from the data. As it turns out, there are not two archetypes (mirroring centralization-versusdelegation) observed in the data, but four. These archetypes have the characteristics of protocols
that are: (i) centralized (with limited consultation); (ii) team decision making; (iii) centralized
decision-making with consultation of others; and (iv) delegation. Notably, in this study the least
frequently observed archetype of all of these is delegation.
While very different in approach and scope, our analysis is very much in the spirit of Bloom
et al. (2014) and Bandiera et al. (2013) in that communication and decision-making structures are
inter-related. Bloom et al. (2014) examine how different types of information technology affect communication and the allocation of decision-making authority. They find that information gathering
technologies aid greater delegation whereas communication technologies that can be used to transmit
information facilitate greater centralization.7 Using a interesting data set, Bandiera et al. (2013)
relate the style with which a CEO interacts and gathers information (structured meetings with
large numbers of participants inside the organization versus unstructured interactions often with
outsiders to the firm) to both firm performance and pay. Again, there is a strong nexus between
communication style and decision making.
Having identified four decision-making archetypes, the question remains as to when a firm is
most likely to adopt one archetype or another. Using a fractional multinomial logit, we examine the
co-factors associated with our identified archetypal decision-making structures. Our novel empirical
findings help highlight some of the aforementioned theoretical mechanisms at play. Firstly, we find
that an organization is more likely to adopt a team archetype than a centralized structure (without
consultation) in larger and more productive establishments, and when there is a greater proportion
of educated workers. In each case, it is likely that the requisite information to make an informed
decision resides not with a high-level manager, but is dispersed amongst informed workers with
valuable human capital. Similarly, team decision making is also more likely when more specific
investment is required by employees.
Secondly, communication could be beneficial when others have valuable expertise related to
the decision at hand. But to be useful, this information needs to be communicated effectively.
Delegation is increasing in the proportion of educated workers at an establishment. There is also
4 Specifically, they argue that decision management rights (proposing and implementing a project) need to be
separate from rights of control (ratifying a project can proceed and evaluating its performance).
5 Business leaders have also suggested there are downsides to communication inside the firm. For example founder
of Amazon.com, Jeff Bezos, noted ‘[w]e should be trying to figure out a way for teams to communicate less with each
other, not more’ (Slone, 2013).
6 See Greene (2003, Section 16.2.3) for a detailed description of the latent-class model, or Cameron and Trivedi
(2010, Section 17.3.6) for a detailed application.
7 Also see Rasel (2016) who examines the different effects on decentralization of IT in small and large enterprises.
3
an increase in the likelihood of the centralized with communication (relative to centralization with
limited consultation) archetype when more than 50 percent of an establishment’s workforce have
a tenure of more than 10 years. Tenure, reflecting a long-term relationship, could be facilitating
informative, rather than strategic, communication.
Finally, incentives are important. An organization is more likely to adopt either a team, centralized communication or delegation archetype than centralization (with limited communication) if
employees receive incentive pay based on profit sharing. On the other hand, the centralized (with
limited communication) archetype is more likely when workers receive incentive payments based
directly on their individual performance.
2
The data
We use the Australian Workplace Industrial Relations Survey 1995 (AWIRS 95), a cross-industry
survey (excluding agriculture, forestry and fishing and defence) of 2001 plants/establishments with 20
or more employees.8 This paper uses the General Management Questionnaire, conducted by personal
interview and completed by the most senior manager at the plant, and the Employee Relations
Management survey, also conducted by personal interview. We also make use of the Employee
Survey Questionnaire, a randomly assigned survey of employees from the surveyed establishments.
The survey identifies the establishments that introduced a significant, non-routine change in the
previous two years, where the possible changes were: new technology; new plant or equipment; a
major reorganization of the establishment’s structure; or a change to the work of non-management
employees. For the estimation sample, 52 percent of workplaces introduced new technology; 33 percent introduced major new plant, machinery or equipment; 72 percent undertook a major reorganization of the workplace structure; and 60 percent of workplaces made major changes to the
work of non-management employees. Approximately 16 percent of the workplaces surveyed did not
implement any of the possible changes.
From these four possibilities, we focus on the change that had the most significant effect on employees at the establishment.9 In establishments that implemented at least one of the four possible
reforms, the most significant change was the introduction of new technology for 16 percent of establishments. Similarly, the most significant change was the introduction of new plant or equipment in
12 percent of establishments, a major reorganization of the establishment structure in 44 percent of
establishments, and a major change to the work of non-management employees had the most impact
on employees in approximately 28 percent of establishments that implemented at least one of the
four changes.
In relation to the most significant change adopted, the general manager was asked how involved
were: higher levels of management beyond this workplace (labeled H); senior workplace managers
(S); other workplace managers here (O); and employees (E) likely to be affected at this workplace.
The possible answer options were: made the decision; had significant input; were consulted; were
informed; and were not informed.
To utilize this information in a consistent manner, we only consider establishments that specified
8 The
survey and the data are described in detail in Morehead et al. (1997).
more than one change was implemented, the general manager was asked to identify the change that ‘has had
the most significant effect on employees here’.
9 If
4
Senior manager
ot
N
rm
In
C
Employees
Percentage
10 20 30
N
ot
rm
fo
su
on
C
In
lt
t
pu
ad
M
In
e
0
N
ot
rm
fo
su
on
C
In
lt
t
pu
In
M
ad
e
0
Percentage
10
20
40
30
Other managers
fo
su
lt
t
pu
on
M
In
ad
e
ot
N
rm
0
C
In
on
fo
su
lt
t
pu
In
M
ad
e
0
10
Percentage
20 30 40
Percentage
10 20 30 40 50
Higher level management
Figure 1: Involvement in decision making at different hierarchical levels.
a level of involvement for each of the four hierarchical levels j = H, S, O, E. Figure 1 illustrates
the degree of involvement in the decision-making process for each of these hierarchical level. The
displayed patterns of involvement in the decision-making process have some intuitive appeal. Higherlevels of management (beyond the establishment surveyed) most frequently made the decision. The
next most frequent roles for higher-level management were, in turn, to have made a significant input,
be consulted, be informed and, finally, not informed, respectively. The involvement of higher levels
of management in this decision reflects, in part, that this is a significant non-routine change. The
most frequent role of the senior establishment manager is to have significant input into the decision.
Other managers at the establishment did not often make the decision, but had significant input,
were consulted or were informed of the decision with very similar frequency. Finally, employees also
rarely made the decision; they were, instead, most frequently informed of the decision.
Very small numbers of both higher levels of management and the senior manager were not
informed of the change. Also, as noted, other managers and employees were very infrequently
identified as having made the decision. To avoid issues with these small numbers of observations for
these involvement levels, we merge the informed and not informed categories for both higher levels
of management and for the senior manager – that is, the number of involvement levels have been
reduced from 5 to 4 by merging the two adjacent categories informed and not informed. Similarly,
for other managers and employees we merge the two top categories, made the decision and had
significant input.
To be specific, for each level j we assign a number yj as an ordinal description of their involvement
in the decision-making process. For higher levels of management and for the senior workplace
5
manager, the following values are assigned: made the decision (Yj = 1); had significant input
(Yj = 2); were consulted (Yj = 3); were informed or were not informed (Yj = 4). For other
managers and for employees, the following decision-making values are assigned: made the decision
or had significant input (Yj = 1); were consulted (Yj = 2); were informed (Yj = 3); were not
informed (Yj = 4). Thus for each hierarchical level in the organization there are four ordered
categories of decision-making involvement, as detailed in Table 1. The distribution involvement for
each hierarchical level using these definitions are presented in Figure 2.
Table 1: Definitions of involvement in decision making at different hierarchical levels
Level
j
Types of involvement
Yj
Higher management
H
Senior working place manager
S
Made decision
Had significant input
Were consulted
Informed/not informed
1
2
3
4
Other working place manager
O
Employees
E
Made decision/had significant input
Were consulted
Informed
Not informed
1
2
3
4
These patterns, however, mask the complexity of the involvement of the different hierarchical
layers in the decision-making process. This is potentially a bewildering array, as any combination of
differing types of involvement by the hierarchical levels is possible. For example, in some establishments the higher levels of management made the decision with little involvement in the process from
lower hierarchical levels. However, in other plants higher-level management made the decision with
significant input from either the senior workplace manager, other managers, or even the employees
(or some other combination). Some establishments report that two or more levels in the hierarchy
made the decision; in other establishments no single layer made the decision, but several different
levels might had significant input. In fact, out of the 256 possible combinations of authority and
involvement, we observe 137 unique patterns chosen by the sampled establishments. The most frequently adopted pattern involved higher levels of management making the decision while the other
three levels were informed; this pattern was chosen by 114 of the 876 establishments. The next
two most frequently observed patterns involved: (i) higher levels of management and the senior
manager having significant input while the other managers and employees were consulted; and (ii)
all hierarchical levels having significant input. The diversity of options, however, is indicated by the
fact that 68 unique structures are required to explain the authority/consultation patterns observed
in 90 per cent of establishments.
As noted above, most previous empirical research on decision-making in firms examined a more
simplified choice; for example, the analysis of Meagher and Wait (2014) restricts the allocation
of decision making to be either centralized or decentralized, and requires there to be a uniquely
identified decision maker. Unfortunately, such a simplification misses the potential nuances of input
6
Senior manager
In
fo
rm
/N
su
lt
on
C
In
pu
t
e
ad
M
ot
0
ot
In
fo
C
rm
on
/N
su
lt
t
pu
In
M
ad
e
0
Percentage
10 20 30 40
Percentage
10 20 30 40 50
Higher level management
Employees
ot
N
rm
fo
In
on
C
In
e/
M
ad
su
pu
lt
t
0
ot
N
rm
fo
In
su
on
C
M
ad
e/
In
pu
lt
t
0
Percentage
10
20
30
Percentage
10 20 30 40
Other managers
Figure 2: Involvement in decision making with combined categories.
and consultation. In this paper we attempt to synthesize this authority/consultation information
into a tractable form so as to identify commonly adopted archetypes of decision making. From there,
we will be able to estimate relationships between archetypal decision-making structure and other
firm characteristics.
3
Determining archetypes using the latent-class model
In this section we outline how the latent-class modeling approach endogenously identifies the different decision-making archetypes in our data. To provide some background, Table 2 contains the
correlation matrix of Yj , where j = H, S, O, E. As noted above, the higher level of management often
made the decision, and it is more likely that lower levels of the establishment had significant input
(particularly the senior manager) or were consulted or just informed (other managers and employees). Furthermore, greater involvement in the decision by higher levels of management is negatively
correlated with that by any other level in the establishment. However, as noted above, these summary statistics potentially hide more subtle combinations of authority, input and consultation. We
attempt to unlock these patterns now.
3.1
Methodology
To describe the data using a tractable number of economically relevant, mutually exclusive and relatively homogeneous subgroups, we conduct a cluster analysis. This process classifies similar objects
7
Table 2: Decision-making involvement correlations (N = 876)
Higher M
Senior M
Other M
Higher M
1
Senior M
-0.423
1
Other M
-0.351
0.683
1
Employees
-0.339
0.463
0.646
Employees
1
Notes: a. Source AWIRS 95. For Higher M and Senior M : y1 represents made
the decision; y2 had significant input; y3 was consulted; y4 was informed/not
informed. For Other M and Employees: y1 represents made the decision/had
significant input; y2 was consulted; y3 was informed; and y4 was not informed.
(establishments) into meaningful classes when the number of classes as well as the composition of
the classes are unknown a priori (Kaufman and Rousseeuw, 1990; Everitt, 1993). In particular, we
choose to use an approach called latent-class modelling – see, for instance, Gibson (1959), Goodman
(1974) and Lazarsfeld and Henry (1968).10
In this approach, the sample is assumed to be drawn from a population that consists of a finite
number of latent (or unobserved) classes. Subjects belonging to the same class are similar in the sense
that they come from the same probability distributions, whose parameters are unknown quantities to
be estimated (Vermunt and Magidson, 2002). Estimation of the model provides the prior probability
for each class (that is, the probability that a randomly chosen subject belongs to a particular class)
as well as the posterior probability for each subject (the conditional probability that the subject
belongs to a particular class).
We assume that the population of establishments consists of Q mutually exclusively latent classes
denoted by q ∈ {1, 2, ..., Q}. The establishments within a class are assumed to be homogeneous
in the sense that the probability of observing a particular response by level j in the hierarchy
depends only on the establishment’s latent class. We further assume that a set of the variables Y
= (YH , YS , YO , YE ) are associated only through their latent class; essentially, this is saying that the
latent class is the reason that they are correlated. This assumption, called local independence, is
standard in the literature.
Under these assumptions, the joint probability of Y conditional on q, Pr(YH = yH , YS = yS , YO =
yO , YE = yE |q = h), can be expressed as
Y
Pr(Yj = yj |q = h).
j
It follows that the joint probability of Y is
Pr(Y ) =
Q
X
h=1
10 Latent-class
πh ×
Y
Pr(Yj = yj |q = h),
j
modeling is also known as finite-mixture modelling.
8
(1)
where πh = Pr(q = h) is the mixing proportion (also called the prior probability) for class h, with
PQ
the restriction that h=1 πh = 1 (probabilities must sum up to one).
Given this, we model the probability of the level of involvement by hierarchical level j in an
establishment, conditional on the latent class, that is Pr(Yj = yj |q = h). Assume that the ‘true’
degree of involvement by hierarchical level j is represented by a continuous unobserved latent variable
Yj∗ ; note, a smaller value of Yj∗ indicates a greater level of involvement in the decision process by
hierarchical level j. The observable Yj and the unobservable Yj∗ are related in the following manner:
Yj
=
Yj∗ < µj,1,q
1
iff
2
iff µj,1,q ≤ Yj∗ < µj,2,q
3
iff µj,2,q ≤ Yj∗ < µj,3,q
4
iff µj,3,q ≤ Yj∗ ,
(2)
where µj,k,q (k = 1, 2, 3) are unknown threshold parameters.
Further, we assume that conditional on q, Yj∗ is a normal random variable with mean zero. The
variance of Yj∗ is set to one for identification. It follows that
Pr(Yj = 1|q = h) = Φ(µj,1,h )
(3)
Pr(Yj = 2|q = h) = Φ(µj,2,h ) − Φ(µj,1,h )
(4)
Pr(Yj = 3|q = h) = Φ(µj,3,h ) − Φ(µj,2,h )
(5)
Pr(Yj = 4|q = h) = 1 − Φ(µj,3,h ).
(6)
where Φ(·) is the distribution function of a standard normal random variable. Using equations (1)
and (3)–(6), we construct the likelihood function. Utilizing the standard maximum-likelihood technique, we estimate the parameters µj,k,q and πq for j = H, S, O, E, k = 1, 2, 3, and q = 1, ..., Q,
providing the conditional response probabilities for each level of hierarchy. Using Bayes theorem,
for each establishment and each class we compute the posterior probability
πh ×
Y
Pr(Yj = yj |q = h)
Y
,
Pr(Yj = yj |q = k)
k=1 πk ×
j
Pr(q = h|Y ) = PQ
(7)
j
which is the probability that the establishment belongs to a particular class, conditional on observing
its response pattern. Once the posterior probability for each class has been estimated, the establishment can be classified into its most likely class – the class for which it has the highest posterior
probability.
The final number of latent classes is determined by an iterative process. We first estimate the
model assuming that there are two classes, then re-estimate the model with three classes, then
increase the number of classes four and re-estimate, and so on. The optimal number of classes
is chosen based on several penalized likelihood criteria, namely, the Akaike Information Criterion
(AIC), the consistent AIC (CAIC), the Bayesian Information Criterion (BIC) and the adjusted BIC
(adj BIC). Based on the log-likelihood of the model (ln L), the number of parameters (m), and the
sample size (N ), these criteria deal with the tradeoff between the goodness of fit of the model and
the complexity of the model. The degree to which model complexity is penalized differs across the
9
information criteria. The AIC (Akaike, 1973) is defined as
AIC = −2 ln L + 2m,
while the CAIC (Bozdogan, 1987) differs from the AIC in that it penalizes model complexity more
severely using the sample size;
CAIC = −2 ln L + m(ln(N ) + 1).
The BIC (Schwarz, 1978) is defined as
BIC = −2 ln L + m ln(N ),
while the adjusted BIC (Sclove, 1987) modifies the BIC by replacing N with N ∗ = (N + 2)/24. For
each of these criteria, the model with the smallest value is preferred.11
Latent-class analysis is not new to empirical economics; though rarely used for classification
purposes, the idea has been applied to introduce unobserved heterogeneity into estimation models,
for example Heckman and Singer (1984). The current study is in the same spirit of a study by
Owen and Videras (2007) who examined individual religious beliefs based on a series of dichotomous
survey questions so as to find subgroups of similar individuals.
Furthermore, it is worth noting the differences between standard clustering techniques, such as
hierarchical clustering and non-hierarchical clustering, and latent-class analysis. The main difference
is that latent-class analysis adopts a probabilistic approach, while clustering relies on somewhat
subjectively or arbitrarily chosen distance measures. As a result, the decision to choose the number
of classes is less subjective with latent-class modeling because model fit can be assessed in a statistical
manner. Magidson and Vermunt (2002) demonstrate the superiority of latent-class analysis over
standard clustering; their simulations show that, where class membership was known in advance,
the latent-class approach has a lower rate of mis-classification than standard cluster analysis.
3.2
Latent-class estimation
The optimal number of latent classes is the one that gives the best fit amongst the feasible alternatives. With our data, estimation for two to four classes is feasible. Five or more classes are not
supported by the data in the sense that the estimation does not converge. Non-convergence is one
of the standard indicators of a model with too many classes (overparameterized), see Cameron and
Trivedi (2010, p. 599). The goodness of fit results, based on the four standard Information Criteria,
are reported in Table 3. A lower score means better fit, so unambiguously four is the preferred
number of classes and the estimation with four classes fits significantly better than two or three
classes. Given this, we focus on the results for four classes in the rest of the paper (results for two
and three classes are given in the Appendix).
The prior probabilities for the latent classes (πq ) describe the expected frequency of each class
of decision-making structure in the population. The results are presented in Table 4. Classes 1 and
2 each occur approximately one third of the time, Class 3 occurs 23% while Class 4 is only 9% of
11 The
justification and derivation of the four criteria are given in the original papers cited above.
10
Table 3: Goodness of Fit (Information Criteria) by Number of Classes
Information Criteria
Number of classes
AIC
CAIC
BIC
Adj BIC
2 classes
3 classes
4 classes
1324
535.4
260.8
1468.4
754.9
555.4
1443.4
716.9
504.4
1364
596.2
342.4
Notes: Smaller values indicate better fit.
the population.
While it is difficult to interpret the frequency of the classes without knowing the decision-making
structure within each class, the expected pattern of decision-making within a class is given by the
posterior/conditional probabilities. For each of the four latent classes, based on the estimated threshold parameters, we compute the posterior probabilities for the type of involvement across hierarchical
levels. The posterior probabilities, outlined in Table 4, allow us to describe the characteristics of
each class as archetypal structures.
Table 4: Estimated Involvement in Decision Making by Level with Four Classes
Higher (H) & Senior Management (S)
Level
Made
Decision
Input
Consulted
Informed or
Not Informed
Other Managers (O) & Employees (E)
Level
Made Decision
or Input
Consulted
Informed
Not
Informed
0.014
0.024
0.791
0.751
0.195
0.205
0.106
0.332
0.000
0.158
0.004
0.021
0.907
0.560
0.048
0.349
0.003
0.051
0.643
0.507
0.298
0.376
0.057
0.103
Class 1 Centralized (Prior probabilitya = 0.338∗∗∗ )
H
S
0.743b
0.011
0.163
0.112
0.030
0.195
0.064
0.682
O
E
0.000
0.019
Class 2 Team (Prior probability = 0.346∗∗∗ )
H
S
0.186
0.338
0.313
0.640
0.237
0.022
0.263
0.000
O
E
0.890
0.490
Class 3 Centralized Communication (Prior probability = 0.227∗∗∗ )
H
S
0.583
0.000
0.380
0.648
0.035
0.346
0.002
0.007
O
E
0.042
0.039
Class 4 Delegation (Prior probability = 0.089∗ )
H
S
0.001
0.384
0.349
0.389
0.306
0.227
0.344
0.000
O
E
0.002
0.015
Notes: a. For prior probability tests, H0 : p = 0, Ha : p > 0. *** Significant at 1% level, ** significant at 5% level, *
significant at 10 % level. b. Modal probability for each level in bold. H denotes higher levels of management beyond
the establishment, S is the senior manager at the establishment, O represents other managers at the establishment
and E denotes the employee group at the establishment most affected by the change.
Class 1, shows a very distinct pattern of involvement in which (typically) higher levels of management (H) made the decision, while other levels in the establishment did not. For higher levels
of management, the estimated probability of having made the decision is 74.3%. In contrast, 68.2%
of senior managers (S) were either informed or not informed. Similarly, the probability that other
managers (O) or employees (E) had a significant input is less than 1% and close to 2%, respectively.
Instead, other managers and employees are most likely to be informed (with a probability of approximately 75% for both groups). In all, Class 1 describes an archetype in which higher levels of
management made the decision with very little input from any subordinates; we call this archetypal
11
structure Centralized.
Class 2 differs considerably from Class 1. Firstly, the probabilities of the types of involvement
for higher management are relatively equally distributed; 18.6% for ‘made the decision’, 31.3% for
‘had insignificant input’, 23.7% for ‘consulted’, and 26.3% for ‘informed’. Secondly, in sharp contrast
to the Centralized archetype described in Class 1, the senior manager seems to have either made
the decision (33.8%) or had significant input (64%). Thirdly, other managers played an important
role in the decision; Pr(YO = 1|q = 2) is estimated to be 89%, suggesting that other managers
had significant input (or even made the decision). In a similar vein, the probability of employees
providing significant input is found to be larger than that of any other type of involvement. These
results overall suggest that each level in the hierarchy played a crucial and relatively similar role
in the decision making. For this reason, we name the Class 2 archetypal structure Team decision
making.
As with Class 1, higher management in Class 3 are the most important decision makers; the estimated probability that higher management made the decision is 58.3%, whereas both Pr(Yj = 1|q =
3) for j = S, O, E are smaller than 5%. But there is also an important difference between Classes
1 and 3. In Class 3, the senior manager often had a significant input (probability approximately
65%) and other managers and employees were frequently consulted as part of the decision-making
process (probabilities 90% and 56%, respectively). This archetype is characterized by higher management making the decision following consultation and significant input from lower levels, hence
our label Centralization with communication. It clearly differs from Class 1, in which there is very
little consultation; it also differs from Class 2, in which involvement and authority are shared fairly
evenly.
Finally, Class 4 characterizes the Delegation archetypal. The estimated probability that higher
management makes the decision is almost zero (0.07%); consequently, a fundamental aspect of this
archetype is that higher levels of management are not involved in making this decision. Furthermore,
unlike in the Team archetype, employees and other managers are not highly involved (with the
estimated probability that they made or had significant input being approximately 2% and 0.2% for
each group, respectively). Rather, the onus is on the senior manager to take the lead; there is an
estimated probability of almost 75% that he or she either made the decision or had significant input.
3.3
Modal involvement in decision making
We use equation (7), along with the estimated parameters, to compute the posterior probability
that a particular establishment belongs to a given archetype. We then assign each establishment to
an archetype, based on the class for which it has the highest posterior probability. For example, if
a particular establishment has the highest probability that it belongs to Class 2, we categorize it as
having the Team archetype. Table 5 presents the modal decision-making involvement for each level
in the hierarchy by archetype; these results provide some external validity to the labels chosen for
each class.
Using this same assignment rule to match establishments to the different archetypes, Figure 3
shows the distribution of involvement by hierarchical level for each archetypal structure. In the Figure, the first row is the Centralized archetype, the second row Team, the third and fourth Centralization with communication and Delegation, respectively. The columns indicate hierarchical levels,
12
Table 5: Modal involvement in decision making by latent-class group
Class
Higher
Manager
Senior
Manager
Other
Manager
Employees
1 Centralized
Made
decision
Informed or
not informed
Informed
Informed
Input
Input
Input or
made decision
Input or
made decision
Made
decision
Input
Consulted
Consulted
Consulted
Made
decision
Consulted
Consulted
2 Team
3 Centralized
communication
4 Delegation
H, S, O and E, moving from left to right. This Figure allows one to track how the responsibilities
of the different levels shift between archetype.
The results for these empirical distributions of decision-making involvement in Figure 3 are qualitatively the same as the estimated conditional/posterior probabilities in Table 4. It is interesting
to see that real authority, as opposed to formal authority, does not follow the classical dichotomy.
Specifically, Delegation accounts for only 13.4% of all remaining establishments that are not Centralized ; rather, 86.6% of these exceptions to pure centralization utilize significant communication
and sharing of (real) decision-making authority.
As noted previously, the observed archetype patterns are no doubt driven to a large degree by
the importance of the decisions in our data. Delegation may well be much more prevalent for small
decisions like reordering inputs and other day-to-day operations. Nonetheless, understanding the
way the most significant decisions are made is of primary importance and our results suggest that
the issues raised by recent theoretical contributions around strategic communication and shared
decision-making are empirical relevant.
3.4
Robustness of latent-class estimates
Now consider the robustness of the decision-making patterns estimated in the 4-class model to
varying the number of classes. There is no theoretical reason why the results for 2 and 3 classes
should resemble the estimates with the optimal number of classes (4 classes).
The traditional decision-making taxonomy has two archetypes: centralization and delegation.
Like this classical approach, the first iteration of our latent-class estimation imposes two classes,
but it allows the data to determine the nature of the resulting two archetypes/classes. As shown
in Table A1 in the Appendix, the distribution of decision-making involvement in Class 2(2) is
almost identical to the Centralized class. In both cases this archetype occurs with similar frequency:
33.8% − 37.5% of the sampled establishments. Class 2(2), however, does not resemble the classical
notion of delegation; unlike delegation this class has authority distributed between a number of
hierarchical levels and significant involvement in decision making across all levels.
13
IP
C
IF/N
M
IP
C
IF/N
IP
C
IF/N
M
IP
C
IF/N
M
IP
C
IF/N
IF
N
M/IP
C
IF
N
M/IP
C
IF
N
M/IP
C
IF
N
0
0
%
20 40 60 80 100
0
%
20 40 60 80 100
0
0
%
20 40 60 80 100
0
%
20 40 60 80 100
0
%
20 40 60 80 100
0
%
20 40 60 80 100
0
C
M/IP
C
IF
N
M/IP
C
IF
N
M/IP
C
IF
N
M/IP
C
IF
N
%
20 40 60 80 100
M
M
M/IP
0
IF/N
IF/N
%
20 40 60 80 100
C
C
0
IP
IP
%
20 40 60 80 100
M
M
0
IF/N
%
20 40 60 80 100
C
%
20 40 60 80 100
IP
%
20 40 60 80 100
M
Employees
%
0 20 40 60 80 100
Other managers
%
0 20 40 60 80 100
Senior manager
%
0 20 40 60 80 100
%
0 20 40 60 80 100
Higher level of management
Figure 3: Decision-making involvement by archetype (row) and hierarchical level (column). The
rows represent, from top to bottom, Centralized, Team, Centralized communication and Delegation.
Moving left to right, the columns indicate hierarchical levels Higher levels of management, Senior
Workplace Manager, Other Managers, Employees.
14
The results for 3 classes, reported in Table A2 in the Appendix, also echo the results for 4 classes.
Classes 3(1)-3(3) look respectively like Centralized, Team and Centralized with communication. Thus
the insight from our 4 archetypes, that communication and shared decision-making are at least as
important as pure delegation, seems robust to the number of classes employed.
4
When do firms adopt different archetypes?
The four archetypes identified in our data capture not only the centralization/delegation dimension of
a firm’s decision-allocation problem but also the connection between authority and communication.
In this section we examine how other organizational characteristics are associated with the adoption
of different archetypes. While not implying causality, we do identify some interesting economic
relationships.
4.1
Explanatory variables
Table 6 provides summary statistics for the main explanatory variables. We discuss each variable in
turn.
Firm variables.
A large firm might choose a different structure than a smaller counterpart. Indeed, both Colombo
and Delmastro (2004) and Meagher and Wait (2014) find that larger workplaces are more likely
to delegate decision-making rights.12 To explore this we include Establishment Size (number of
employees at the establishment) and seven Organizational Size dummy variables (which capture
overall firm size).
Similarly, highly-productive establishments might adopt different decision-making structures
(Bloom et al., 2012). For example, Acemoglu et al. (2007) find that decentralization is associated
with closer proximity to the industry-productivity frontier. We include a variable for establishments
that self-identify as a High productivity establishment relative to their industry rivals, coded as 1
and 0 otherwise.
Worker characteristics.
We include controls for how long it takes (on average) for a new employee in the establishment’s
largest occupational group to work to the standard expected of other employees. Specifically, two
dummy variables are included, indicating that it takes: (i) between one week and six months or; (ii)
more than six months to perform to the expected level (with the omitted category being less than
a week). It is conceivable that a firm will adopt a different archetypal structure depending on the
relative importance of worker specific knowledge (Stinchcombe, 1990; Jensen and Meckling, 1998).
More able employees, other things equal, will be better placed to contribute to the decisionmaking process. Caroli and Van Reenen (2001) and Bresnahan et al. (2002) find a positive relationship between human capital and decentralization. We include a variable – Higher qualification
12 Larger organisations might have more coordination issues (Alonso et al., 2008) or have greater economics of scale
in decision-making (Meagher and Wait, 2008).
15
Table 6: Summary statistics of the sample (N = 742)
Variable
Mean
Std Dev.
2.061
.972
186.145
249.001
.032
.119
.094
.278
.084
.101
.292
.461
.177
.324
.292
.448
.277
.302
.455
.499
.009
.275
.160
.168
.181
.206
.097
.447
.367
.375
.385
.405
.454
.135
.271
.342
.053
.698
.249
.247
.050
.223
.459
.433
.432
.218
.156
.283
.434
.127
.363
.451
.496
.333
Dependent variable
Latent-class decision group
Centralization DV
Firm characteristics
Establishment size
Organization size
< 100
100 – 499
500 – 999
1000 – 4999
5000 – 9999
10000 – 19999
> 20000
High productivity establishment
Operating hours a week
< 35 hours
35 – 40
41 – 50
51 – 84
85 – 167
24 hours a day
Worker-related characteristics
Higher qualification
> 50% with 10 yrs tenure
Time to learn job
1 week of less
1 week – 6 months
6 months of more
Individual incentives
Profit sharing
Other variables
Workplace
Workplace
Workplace
Workplace
implemented
implemented
implemented
implemented
technical change
new plant or machinery
reorganization
change work of employees
Notes: a. Source AWIRS 95.
16
– indicating the proportion of the employees at the establishment who have an advanced certificate
(which requires 1-2 years full-time study), a diploma or a university degree.
As emphasized by Gibbons and Henderson (2012), long-term relational contracts between workers
and management facilitate delegation that would otherwise not been feasible; similarly, such implicit
agreements could also encourage informative (more truthful) communication.13 To explore this we
include a dummy variable 10 years indicating if more than 50 percent of workers have been employed
at that establishment for more than 10 years.
Two dummy variables are included to capture whether an establishment’s non-managerial employees received performance-based payments based on: (i) Individual incentives; (ii) Profit sharing.
These two variables reflect contrasting types of incentives – the first is based directly on a worker’s
own performance (Prendergast, 2002), whereas the second depends on the performance of the group
(Holmstrom, 1982).
Other variables.
Dummy variables indicate the most significant change made for the establishment’s employees, where
the possible changes are: (i) technical change (the omitted category); (ii) new plant, machinery or
equipment (that are not routine replacements); (iii) a reorganization of the workplace or (iv) a
change in the work of non-management employees. Also, we include ANZSIC one-digit industry
dummy variables.
4.2
Fractional multinomial logit estimation results
The latent-class estimates provides a posterior probability distribution for each establishment over
the likelihood that it belongs to each of the four archetypes. With a fractional multinomial logit,
these probability distributions become our ‘dependent variable’. More specifically, given the set of
co-variates xi (including one) mentioned above, we are interested in the conditional means of Pi =
(Pi1 , Pi2 , Pi3 , Pi4 ), where Piq is the (estimated) posterior probability of establishment i belonging
to class q. The specification of the conditional means, E(Pi |xi ), must comply with: (i) Pi1 + Pi2 +
Pi3 + Pi4 = 1; and (ii) 0 < Piq < 1 for q = 1, 2, 3, 4. For this purpose, the multinomial specification
is convenient; specifically, for q = 1, 2, 3, 4,
exp(xi βq )
E(Piq |xi ) = P4
,
h exp(xi βh )
where β1 is normalized to 0 (in our context, parameters for the the Centralized archetype are normalized to zeros). The fractional multinomial logit model can be estimated by the quasi-maximum
likelihood method.14
The results from the fractional multinomial logit estimation are given in Table 7, where Centralized is the omitted category. The corresponding marginal effects are given in Table A3 of the
Appendix.
Firm variables.
Consistent with previous empirical studies, see Meagher and Wait (2014) for example, an increase
13 The
14 See
theory of relational contracts is developed in Baker et al. (1999, 2002); Levin (2003); Li et al. (2014).
Mullahy (2015) for details of this estimation technique.
17
in establishment size is associated with less centralization whereas a larger organizational (firm) size
is associated with greater centralization. However, our estimated four archetypes provide a more
nuanced description of decision making. Specifically, the observed decrease in the probability of
a Centralized archetype in larger establishments largely corresponds with an increase in the use
of Team decision making, with much smaller estimated marginal effects for Centralization with
communication and Delegation. With respect to changes in Organizational Size, the estimated
coefficients for both Team and Delegation are negative and significant for the dummy variables
for larger organizational sizes (for organization between 5000 and 9999, between 10000 and 19999
and greater than 20000). An organization that is larger than 20000 employees, other things equal,
experiences a 3.5 percentage point increase in the probability Centralized, and a corresponding
decrease in the probability of a Team structure (2.5 percentage points decrease) and Delegation (a
1 percentage decrease).
Acemoglu et al. (2007) find that an increase in a firm’s productivity is associated with greater
decentralization. Our estimates show that self-reported higher productivity is associated with an
increase in the use of the Team and Centralization with communication archetypes, statistically
significant at the 10% and 1% levels, respectively.
Worker characteristics.
An increase in the proportion of employees with higher levels of education is associated with an
increase in the Team, Centralization with communication and Delegation archetypes, relative to
Centralized (statistically significant at the 10%, 1% and 1% levels, respectively). Information is key
to effective decision making, and more skilled workers are more likely to be able to effectively (and
informatively) communicate with their superior. Higher-skilled workplaces are also more likely to
be dealing with complicated (non-routine) issues (Garicano, 2000); this makes it less likely that a
principal will have the requisite knowledge to make an informed decision or to understand what
is communicated to them in a timely manner, increasing the relative benefits of delegating. This
intuition is consistent with our estimations.
Turning to worker tenure, adopting Team, Centralization with communication and Delegation
are all related to having more than 50% of an establishment’s employees with a tenure of more than
10 years (all significant at the 5% level). These results are also captured in the marginal effects; if
an establishment has more than 50% of workforce who have a tenure of 10 years or more, there is a
13 percentage point decrease in the probability of Centralized, and a 4 percentage point increase in
the probability of Delegation; similarly, Team and Centralization with communication also increase
by 5 and 4 percentage points, respectively.
Tenure effects in empirical organizational economics are typically difficult to interpret because
they can confound long-term relationship with on-the-job learning. However, our estimates also
include controls for how long it takes to learn ‘the job’, discussed in the next paragraph. Our
results are consistent with relational-contracting theories that cooperative equilibria are sustained
by long-term relationships. Here we see long-term employer-employee relationship associated with
more establishments using Delegation rather the Centralized archetype. But, interestingly, our
results also suggest that long-term relationships are associated with Team and Centralization with
communication. Both of the archetypes are similar to Delegation in that they might benefit from a
mechanism to mitigate opportunism.
18
As noted, we have included two variables that captures how long it takes to learn the job, be it
either between 1 week and 6 months, or more than 6 months. The estimated coefficients for Team
are significant at the 5% level for both dummy variables. Moreover, if it takes workers more than
six months to learn the job there is a 13 percentage point decrease in the probability of observing a
Centralized and a 23 percentage point increase in the probability that the establishment adopts the
Team archetype.
The type of incentive scheme adopted also seems to matter. The use of individual incentives
is associated with a 9 percentage point increase in the Centralized archetype, whereas use of a
group-based incentive scheme, in the form of profit sharing, is associated with a 20 percentage point
decrease in Centralized and a 20 percentage point increase in Team decision making. These results
are largely consistent with the intuition that an informed decision maker can assign tasks to her
subordinates– and institute an individually-based incentive scheme. On the other hand, a less well
informed principal, who has to rely more on the input and consultation from her team, can best
elicit informative communication and cooperation using group-based incentives.
Table 7: Fractional multinomial logit coefficient estimates: dependent variable archetypal decision
structure (N = 742)
Explanatory Variable
Team
Centralized
Communication
Delegation
Establishment size*1000
1.983***
(.517)
.82*
(.547)
1.445**
(.580)
-.835
(.675)
-1.078
(.692)
-.975
(.647)
-1.508**
(.690)
-1.942***
(.698)
-1.859***
(.674)
.754*
(.442)
.754*
(.442)
.709**
(.331)
-.425
(.710)
-.349
(.729)
-.470
(.683)
-1.122***
(.737)
-.759
(.724)
-.969
(.720)
.605***
(.203)
1.396***
(.459)
.735**
(.327)
-1.039
(.708)
-1.542**
(.734)
-1.342**
(.657)
-1.982***
(.728)
-2.003***
(.737)
-2.369***
(.715)
.365
(.252)
1.702***
(.567)
.912**
(.372)
1.094**
(.477)
1.093**
(.495)
-.375
(.253)
1.531***
(.438)
.189
(.503)
.282
(.518)
-.467*
(.260)
.961*
(.494)
.360
(.562)
.180
(.620)
-.575*
(.297)
1.382***
(.521)
Organization size
100 – 499
500 – 999
1000 – 4999
5000 – 9999
10000 – 19999
> 20000
High productivity establishment
Higher qualification
> 50% with 10 yrs tenure
Time to learn job
1 week – 6 months
6 months of more
Individual incentives
Profit sharing
Log pseudolikelihood
-833.797
Wald χ2
238.33
Notes: Centralization the omitted decision-making structure. *** Significant
at 1% level, ** significant at 5% level, * significant at 10 % level. Estimates
include type of change and ANZSIC 1-digit industry categories.
19
4.3
Alternative specifications
The fractional multinomial logit model estimates the probability a particular workplace belongs to
each of the four archetypes; this is our leading specification, because it takes into account as much
information as possible about the predicted probabilities of each archetype. An alternative specification could be the standard multinomial logit model, in which each establishment is allocated
to the archetype for which they have the highest possible probability. These results are discussed
in Section 4.3.1. In Section 4.3.2 we re-characterize the decision-making in terms of the standard
centralize/delegate choice. This allows us to compare the results with the simpler traditional categorization of decision making.
4.3.1
Multinomial logit results
As a point of comparison to the fractional multunomial logit results discussed above, we estimate
how the probability of belonging to a particular decision-making class varies with organizational
characteristics using a standard multinomial logit. To do this, each workplace is allocated to the
archetype for which they have the highest probability. The estimated coefficients are shown in
Table 8, while the corrsponding marginal effects as given in Table A4 in the Appendix. Again,
Centralized is the omitted group.
Classifying each establishment by archetype with the maximal probability collapses the dispersed
probability distributions to their mode. The results from this approach (Table 8) are qualitatively
similar to fractional multinomial logit, indicating that the regression results are robust to the exact
representation of the information about the four classes.
20
Table 8: Mutlinomial logit estimates: dependent variable archetypal decision structure (N = 742)
Explanatory Variable
Team
Centralized
Communication
Delegation
Establishment size*1000
2.042***
(.539)
.768
(.609)
1.621**
(.738)
-.875
(.751)
-1.195
(.762)
-1.067
(.719)
-1.567**
(.767)
-2.062***
(.777)
-1.899**
(.753)
.725***
(.211)
.596
(.467)
.656**
(.326)
-.749
(.805)
-.635
(.807)
-.758
(.765)
-1.462**
(.835)
-.920
(.810)
-1.204
(.800)
.651***
(.227)
1.390***
(.495)
.704**
(.345)
-1.402
(.876)
-2.137**
(.939)
-1.579*
(.834)
-2.002**
(.918)
-2.213**
(.922)
-2.624**
(.906)
.448
(.303)
1.632**
(.659)
.909**
(.445)
1.167**
(.489)
1.205**
(.518)
-.317
(.273)
1.653***
(.532)
.070
(.477)
.183
(.512)
-.404**
(.290)
1.176**
(.572)
.515
(.729)
.391
(.790)
-.665*
(.387)
1.646**
(.670)
Organization size
100 – 499
500 – 999
1000 – 4999
5000 – 9999
10000 – 19999
> 20000
High productivity establishment
Higher qualification
> 50% with 10 yrs tenure
Time to learn job
1 week – 6 months
6 months of more
Individual incentives
Profit sharing
Log likelihood
-826.916
LR χ2
261.66
Pseudo R2
.137
Notes: Centralization the omitted decision-making structure. *** Significant at 1% level,
** significant at 5% level, * significant at 10 % level. Estimates include type of change and
ANZSIC 1-digit industry dummy variables.
21
4.3.2
Comparison with traditional decision making characterizations
While our rich data allows us to identify four archetypes, it is possible simplify this information
and characterize decision making in the traditional manner as being either centralized or decentralized. Here we explore how our results using four archetypes differ to the canonical dichotomous
centralization-decentralization characterization. To do this, we construct a variable Centralization
DV, coded as 1 if the higher level manager made the decision (Centralization), and zero otherwise
(Decentralization). Table 9 compares the breakdown of the two-level decision-making groups of
Centralization DV with our four archetypes estimated with the latent-class model. This comparison
gives us some confidence of the validity of latent-class model groupings, and our economic interpretation of these classes. But it also shows that the two-level centralization/decentralization hides some
important nuances, especially with regards to the Team and the Centralization with communication
archetypes.
Table 9: Tabulation of Centralization DV and the four decision-making structures (N = 742)
Centralized
Team
Centralized
communication
Delegation
Centralized
(Centralization DV = 1 )
186
46
91
0
Decentralized
(Centralization DV = 0 )
72
205
71
71
Notes: a. Source AWIRS 95.
These differences are also evident in our logit results, in Table 10, using Centralization DV as
the dependent variable; some of the significant coefficients in the fractional multinomial logit model
are no longer significant with the simpler specification. Due to its inherent obfuscation, this suggests
that empirical researchers where possible should jointly account for the authority and communication
when estimating decision-making structures.
22
Table 10: Logit estimation: dependent variable Centralization DV (N = 742)
Explanatory Variable
Coefficient
Marginal Effect
Establishment size*1000
-.985***
(.382)
.212***
(.081)
.608
(.545)
.901
(.555)
.765
(.519)
1.137**
(.566)
1.203**
(.562)
1.291**
(.547)
-.196
(.164)
-.460
(.365)
-0.440*
(.250)
.131
(.117)
.194
(.119)
.165
(.111)
.245**
(.121)
.260**
(.120)
.279**
(.116)
-.042
(.035)
-.099
(.078)
-0.094*
(.054)
-.125
(.371)
-.149
(.397)
.184
(.211)
-.508
(.381)
-.027
(.080)
-.032
(.086)
.040
(.045)
-.110
(.082)
Organization size
100 – 499
500 – 999
1000 – 4999
5000 – 9999
10000 – 19999
> 20000
High productivity establishment
Higher qualification
> 50% with 10 yrs tenure
Time to learn job
1 week – 6 months
6 months of more
Individual incentives
Profit sharing
Log likelihood
-460.733
LR χ2
94.71
Pseudo R2
.093
Notes: *** Significant at 1% level, ** significant at 5% level, * significant at 10 % level. Estimates include type of change and ANZSIC
1-digit industry categories dummy variables.
23
5
Concluding comments
Contemporary theory suggests a range of issues that can impact on the design of decision making
in a firm, including authority, communication, relationships, size, incentive contracts and specific
knowledge. This paper is one of the first attempts to empirically capture the complexity of authority/communication structures in a composite dependent variable we call an archetype. We propose
a new data-driven methodology for classifying organizations by archetype.
Using a latent-class model we identify four archetypes: centralized ; team; centralization with
communication; and delegation. It is instructive that these archetypal decision-making structures
extend beyond the simple one-dimensional choice of centralization-versus-decentralization. Rather,
our results suggest that over 50% of organizations utilize either team decision making or communication (with a centralized allocation of authority).
We find a number of co-factors that have significant relationships with the choice of particular
archetypes. Larger establishments are less centralized, whereas larger firms are more centralized. Establishments with more qualified workers involve those workers more through the use of (centralized)
communication and delegation. Long-run relationships are associated with less use of centralization
and more use of teams, communication and delegation. Specific investments are mainly related to
greater use of the team structure and less use of the other archetypes. Group incentives, in the form
of profit sharing, are related to greater use of teams and delegation.
While these results are broadly consistent with ideas from theory, no single existing model is rich
enough to fully describe our findings. We hope these results provide additional motivation to both
theoretical and empirical researchers alike to further study and understand structures in the firm
for authority and communication.
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A
Appendix: Additional Results
Table A1: Prior Probabilities of Classes and Posterior Probabilities of Involvement in Decision
Making by Level for 2 Classes
Level
H
S
Higher & Senior Management
Made
Decision
Input
0.723b
0.024
0.169
0.125
Consulted
Level
Other Managers & Employees
Informed or
Not Informed
Made Decision
or Input
Class 2(1) (Prior probability
0.038
0.069
O
0.234
0.617
E
a
= 0.375∗∗∗ )
0.000
0.013
Consulted
Informed
Not
Informed
0.073
0.036
0.745
0.746
0.183
0.205
0.444
0.452
0.040
0.226
0.007
0.032
Class 2(2) (Prior probability = 0.625∗∗∗ )
H
S
0.282
0.234
0.348
0.630
0.182
0.135
0.189
0.000
O
E
0.508
0.290
Notes: a. For prior probability tests, H0 : p = 0, Ha : p > 0. *** Significant at 1% level, ** significant at
5% level, * significant at 10 % level. b Modal probability for each level in bold.
Table A2: Prior Probabilities of Classes and Posterior Probabilities of Involvement in Decision
Making by Level for 3 Classes
Higher & Senior Management
Level
Made
Decision
Input
Consulted
Informed or
Not Informed
Other Managers & Employees
Level
Made Decision
or Input
Consulted
Informed
Not Informed
0.015
0.023
0.790
0.749
0.195
0.211
0.158
0.346
0.022
0.174
0.004
0.025
0.865
0.563
0.092
0.354
0.016
0.055
Class 3(1) (Prior probabilitya = 0.343∗∗∗ )
H
S
0.719b
0.030
0.166
0.106
0.039
0.193
0.076
0.671
O
E
0.000
0.016
Class 3(2) (Prior probability = 0.380∗∗∗ )
H
S
0.179
0.355
0.315
0.622
0.235
0.023
0.271
0.000
O
E
0.816
0.455
Class 3(3) (Prior probability = 0.277∗∗∗ )
H
S
0.479
0.036
0.376
0.607
0.091
0.351
0.055
0.005
O
E
0.026
0.028
Notes: a. For prior probability tests, H0 : p = 0, Ha : p > 0. *** Significant at 1% level, ** significant at
5% level, * significant at 10 % level. b. Modal probability for each level in bold.
29
Table A3: Fractional mutlinominal logit marginal effects (N = 742)
Explanatory Variable
Centralized
Team
Centralized
Communication
Delegation
Establishment size*1000
-0.3
(.093)
0.33
(.084)
-.067
(.071)
.036
(.040)
.154
(.153)
.185
(.147)
.180
(.157)
.330
(.137)
.328
(.142)
.350
(.165)
-.125
(.160)
-0.216
(.296)
-.129
(.054)
-.119
(.114)
.164
(.110)
-.138
(.113)
-.192
(.115)
-.270
(.109)
-.252
(.122)
.102
(.104)
-.017
(.081)
.050
(.061)
.014
(.111)
.050
(.115)
.023
(.117)
-.063
(.102)
.018
(.116)
-.001
(.132)
.034
(.093)
.145
(.066)
.040
(.050)
-.049
(.055)
.072
(.044)
-.066
(.067)
-.075
(.047)
-.076
(.047)
-.097
(.082)
-.010
(.050)
.093
(.044)
.039
(.038)
-.135
(.306)
-.133
(.113)
.090
(.075)
-.195
(.074)
.211
(.260)
.230
(.102)
-.026
(.065)
.195
(.093)
-.066
(.276)
-.062
(.092)
-.038
(.047)
-.032
(.071)
-.010
(.109)
-.035
(.058)
-.026
(.036)
.032
(.058)
Organization size
100 – 499
500 – 999
1000 – 4999
5000 – 9999
10000 – 19999
> 20000
High productivity establishment
Higher qualification
> 50% with 10 yrs tenure
Time to learn job
1 week – 6 months
6 months of more
Individual incentives
Profit sharing
Notes: Estimates include type of change and ANZSIC 1-digit industry categories.
30
Table A4: Mutlinomial logit marginal effects for decision structure (N = 742)
Explanatory Variable
Centralized
Team
Centralized
Communication
Delegation
Establishment size*1000
-.265***
(.088)
.281***
(.072)
-.060
(.074)
.044
(.048)
.159
(.123)
.197
(.123)
.180
(.117)
.279**
(.124)
.291**
(.123)
.306**
(.120)
-.116***
(.032)
-.183***
(.070)
-.124**
(.052)
-.065
(.100)
-.110
(.102)
-.096
(.095)
-.126
(.105)
.253**
(.106)
-.187*
(.102)
.077**
(.033)
-.043
(.073)
.043**
(.047)
-.026
(.094)
.031
(.094)
-.010
(.088)
-.078
(.010)
.050
(.095)
.001
(.094)
.042
(.030)
.145**
(.066)
.046
(.043)
-.066
(.052)
-.117**
(.059)
-.074
(.049)
-.076
(.057)
-.088
(.057)
-.120**
(.056)
-.003
(.022)
.081*
(.047)
.036
(.030)
-.117*
(.070)
-.124*
(.074)
.070*
(.041)
-.259***
(.083)
.201**
(.082)
.205**
(.087)
-.009
(.043)
.175**
(.076)
-.085
(.067)
-.067
(.072)
-.028
(.039)
.033
(.071)
-.000
(.054)
-.014
(.059)
-.034
(.028)
.051
(.044)
Organization size
100 – 499
500 – 999
1000 – 4999
5000 – 9999
10000 – 19999
> 20000
High productivity establishment
Higher qualification
> 50% with 10 yrs tenure
Time to learn job
1 week – 6 months
6 months of more
Individual incentives
Profit sharing
Notes: *** Significant at 1% level, ** significant at 5% level, * significant at 10 % level.
Estimates include dummies for type of change and ANZSIC 1-digit industry dummy variables.
31