BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRODUCTIVITY AND TECHNOLOGY 

 

The Distribution of Decision Rights within the Workplace: Evidence from Canadian, 

Australian and UK Establishments 

 

Cindy Zoghi, U.S. Bureau of Labor Statistics 

 Working Paper 363

 December 2002 

 

Results and conclusions expressed here are those of the author and have not been endorsed by the Bureau of Labor 

Statistics or the US Department of Labor. 
The Distribution of Decision Rights Within the Workplace: 

Evidence from Canadian, Australian and UK Establishments 

Cindy Zoghi* 

November 7, 2002 

Abstract: 

The optimal allocation of decision rights in an organization reflects a trade-off between 

the costs of transferring relevant information within the hierarchy and the costs that occur 

when decision-making agents have different objectives than the principal. This paper is a 

first attempt to quantitatively analyze the allocation of decision rights within the 

workplace. I use detailed questions on decision-making from three separate data sets to 

measure the decentralization of decision rights within the hierarchy, to determine what 

establishment characteristics are related to the location of decision rights, and to examine 

what human resource practices are correlated with centralization. 

JEL Classifications: D21, D23, L23 

Keywords: decision rights, hierarchies, decentralization 

* Cindy Zoghi, Bureau of Labor Statistics, Division of Productivity Research, 2 

Massachusetts Ave NE, Rm. 2140, Washington DC 20212. Phone: (202) 691-5680 

e-mail: zoghi_c@bls.gov Results and conclusions expressed here are those of the author 

and have not been endorsed by the Bureau of Labor Statistics or the US Department of 

Labor.
 2

Introduction

In a small sole proprietorship, the entrepreneur could know all the details of the 

business and could make each decision alone without requiring additional sources of 

information. As the size and scope of the business grows, however, it becomes 

increasingly difficult for any individual to possess and process all the relevant 

information and to make all the decisions alone—this is an example of “bounded 

rationality.” One solution to this problem is to transport the information to the individual 

who possesses the decision rights. This can be quite costly, both in terms of the delays in 

decision-making and in the actual information transmission costs. Another solution is to 

grant decision rights to those in possession of the relevant knowledge. This brings up 

agency costs since the objectives of those with the knowledge are not necessarily aligned 

with the objectives of the individual with the decision rights. 

As the size of modern companies has grown, there is some evidence that both of 

these solutions have been used. Falling prices in information technology have reduced 

the costs of information transmission, and the heavy use of IT in many large companies 

suggests that firms will often transport the knowledge to those with decision rights. At 

the same time, the increasing use of teams and quality circles suggests that many firms 

have begun to decentralize decision rights. Researchers have also documented the 

 

 The author wishes to thank Mike Gibbs for invaluable support and suggestions on this paper, as well as 

Dan Hamermesh, Mike Harper, Alec Levenson, Peter Meyer, Sabrina Pabilonia, Larry Rosenblum, Leo 

Sveikauskas, participants at the Bureau of Labor Statistics Brown Bag workshop and participants at the 

George Washington University Labor Studies Workshop for their comments. Tony Fang at Statistics 

Canada provided exceptional help with accessing the WES data. 
 3

accompanying adoption of personnel policies such as training and incentive pay, which 

are tools to reduce the agency problems of decentralized decision rights. 

The choice of solution to this problem is unique to the organization. Various 

features may facilitate or hinder the transmission of knowledge as well as the transfer of 

decision rights. For example, firms in a high-tech industry may have a greater amount of 

specialized or perishable knowledge that is difficult to transport—that is, difficult for a 

single decision-maker to process or comprehend. Thus, high-tech firms may be more 

likely to decentralize decision rights. 

In this paper, I develop a simple model for the optimal location of decision rights 

in a profit-maximizing firm. I then use three detailed international workplace data sets to 

analyze the distribution of decision rights within a workplace, using both objective 

information on specific workplace practices as well as subjective questions dealing with 

the balance of power in the organization. I identify features of the firm such as industry, 

size and age of organization, unionization and market structure that affect the distribution 

of decision rights. In addition, I show the relationship between the distribution of 

decision rights and the use of complementary human resource practices such as incentive 

pay, training and monitoring. 

Previous Literature 

 Much of the empirical research on workplace organization has focused on the use 

of particular organizational tools, such as teams, quality circles, job rotation and incentive 

pay, frequently within one particular industry. Womack, Jones and Roos (1990), 

MacDuffie (1995) and Pil and MacDuffie (1996) examined the “innovative human 
 4

resource practices” in the US and foreign automobile industries. Ichniowski, Shaw and 

Prennushi (1997) and Ichniowski and Shaw (1995) examine the effects of “human 

resource management practices” on productivity among steel finishing lines. Batt (1999) 

analyzes the productivity effects of Total Quality Management and self-managed teams 

compared to a more centralized work organization using data from a large 

telecommunications company. 

Recently, there has been an effort to collect cross-industry nationally 

representative data on workplace practices. Osterman (1994) used a 1992 survey of 

establishments to describe the incidence of teams, job rotation, Total Quality 

Management and quality circles among establishments with fifty or more employees. He 

found a widespread use of these practices and determined that certain establishment 

characteristics were correlated with that use. Similar data were collected in 1993 and 

1995 by the Bureau of Labor Statistics in its Survey of Employer-Provided Training. 

Gittleman, Horrigan and Joyce (1998) analyzed these data and found similar results to 

those obtained by Osterman. 

Finally, the National Employers Survey, conducted by the National Center on the 

Educational Quality of the Workforce, also surveyed US establishments in 1994 and 

1997 and asked about the use of these and other workplace and human resource practices, 

as well as the incidence of employee training. One advantage of this survey over 

previous data collections is that the data can be linked to data on establishment outcomes 

from the Census Bureau’s Longitudinal Research Database. Black and Lynch (1996), 

and Black and Lynch (2001) have taken advantage of the matched datasets and have 

found (sometimes small) significant effects on productivity and wages of these workplace 
 5

practices, mainly when multiple workplace practices are adopted in combination. 

Cappelli and Neumark (2001) and Cappelli and Carter (2000) do not find such 

productivity results, but only find that workers receive a wage premium when in a 

workplace that adopts such practices. 

One reason for the lack of consensus on the effects of workplace practices is that 

the terms are arbitrarily defined for any given establishment. This is noted in the 

conclusion of Black and Lynch (1996): “Finally, our results suggest that it is important to 

move beyond simple measures of the incidence of workplace practices such as training or 

TQM in order to understand how these types of workplace strategies/investments actually 

pay off for employers.” This study treats these practices as tools that establishments use 

in the production function—means to an end rather than the end itself. I focus instead on 

the location of decision rights and view these practices as tools that can be used to reduce 

the costs associated with that choice of location. 

 The motivation for this interpretation comes from the growing literature on 

organization theory. Hayek (1945) emphasized the importance of knowledge and its 

distribution in society: 

If we can agree that the economic problem of society is mainly one of rapid 

adaptation to changes in the particular circumstances of time and place, it would 

seem to follow that the ultimate decisions must be left to the people who are 

familiar with these circumstances, who know directly of the relevant changes and 

of the resources immediately available to meet them. We cannot expect that this 

problem will be solved by first communicating all this knowledge to a central 

board which, after integrating all knowledge, issues its orders. We must solve it 

by some form of decentralization. 

March and Simon (1958) apply the term “bounded rationality” to this notion that 

individuals are limited in the amount of knowledge that they can process and store. This 
 6

point is also made in Chandler (1962), who finds that companies organized in “unitaryform” lose efficiency as their size grows, relative to those organized in “multidivisional 

form.” More recent literature develops specific models of decentralized information 

processing and analyzes the efficiency and delay in different hierarchical structures. 

[Aoki(1986), Radner (1992), Radner and Van Zandt (1992), Athey et.al. (1994), Aghion 

and Tirole (1997)] 

 The objective of this paper is to present new empirical evidence on decision 

rights, and also to interpret the findings in the context of the organization theory 

emphasized here. The analysis is derived largely from Jensen and Meckling (1990), who 

discuss how organizations solve the problems of assigning decision rights. In the next 

section, I will show how their theory of organizational structure can be used to predict 

how firm characteristics affect the degree of decentralized decision-making. 

The Optimal Location of Decision Rights 

 The knowledge of any single decision-maker is limited by both current scientific 

progress and also by his own capacity to absorb, comprehend and process available 

knowledge. At the same time, Jensen and Meckling (1990) point out that “when 

knowledge is valuable in decision-making, there are benefits to collocating decision 

authority with the knowledge that is valuable to those decisions.” Two methods, used 

alone or in conjunction with each other, will achieve this solution: to move the 

knowledge to the individual who possesses the decision rights; and/or to shift the 

decision rights to the individual who possesses the knowledge. 
 7

 Under the first solution, the organization is faced with the costs of transporting 

knowledge. Radner (1992) defines four aspects of the processing of information that are 

costly: observation of the environment, capabilities of the processors, the communication 

network, and the delay between the observation of the environment and the 

implementation of the decision. The size of these costs will vary depending upon the 

type of knowledge being transferred, current technology, and the organizational structure. 

As a result, the nature of these costs will be uniquely determined for any given 

organization. 

 Under the second solution, the organization faces agency costs that result from 

transferring decision rights. “Agency costs include the costs of structuring, monitoring, 

and bonding a set of contracts among agents with conflicting interests. Agency costs also 

include the value of output lost because the costs of full enforcement of contracts exceed 

the benefits.” [Fama and Jensen (1983)] Again, various features of the organization 

determine the size of these costs relative to other organizations. 

 In making the decision on the optimal location of decision rights, an organization 

is faced with a trade-off between these agency costs and knowledge transfer costs. 

Figure 1 demonstrates the intuition behind this argument. On the far left-hand side of the 

figure, the organization is completely centralized—one single planner makes all decisions 

and all relevant knowledge is transferred to her. This results in zero agency costs, since 

the principal and the agent are one and the same. A movement toward the right side of 

the figure indicates decentralization, which increases agency costs and decreases the cost 

of transmitting information. When all decision rights have been transferred to the 

individuals in possession of the knowledge, the organization is completely decentralized, 
 8

as indicated by the vertical line on the far right of Figure 1. At this point, agency costs 

are at their highest, and information costs at their lowest. The organization would 

optimally choose a location of decision rights that minimizes the sum of these two 

costs—the minimum point on the total organizational cost curve. Again, these curves 

will vary from one organization to the next, depending upon organizational structure, 

current technology and types of knowledge relevant to the decision made in the 

organization. 

A simple model helps to clarify this optimization. Consider a single decision 

entailed by the production of output1

. This might be a decision on the quality or amount 

of a particular input to use, or whether to try a new production process, for example. Let 

d represent the (hierarchical) distance between the most senior manager in the firm and 

the level at which the decision is made. When d = 0, the decision is made by an agent at 

the bottom of the hierarchy, while d = D when the top manager makes the decision. 

This distance affects costs in two ways. First, a lengthy distance increases the 

amount of time it takes to make the decision. This may be costly to the firm in terms of 

lost business, reduced customer satisfaction (and thus future sales), or perishable inputs 

being lost. The extent to which delays are costly will not be the same for every firm--for 

example, costs will be lower in firms that do not employ perishable inputs, while costs 

may be higher in markets that are more volatile or competitive. These delay costs can be 

represented by φT(d,α) where φ measures the cost per unit of time, T(.) measures the 

units of time required to make the decision, and α represents other firm characteristics 

that increase how long it may take to make the decision. Following the results of Radner 

 

1

 More complex sets of decisions and decision-making properties are clearly more realistic. To preserve 

the simplicity of the model, I focus on a single decision. 
 9

(1992), Carter (1995) and others, I assume that Td > 0: as d increases, the decision is 

made farther up the hierarchy, causing greater delay due to the need to transfer 

information to and from the decision-maker and the increased queuing for the attention of 

the decision-maker. By definition, Tα > 0. 

Another cost of any given hierarchical level of decision-making arises if the 

objectives of decision-making agents do not perfectly match the objectives of the 

principal. Thus, for example, if production-line workers were to decide the output level, 

they might choose a level that minimized effort, rather than one that minimized costs. 

Again, agency costs will be higher in some types of firms, such as those where it is more 

difficult or costly to monitor workers. These agency costs are measured by A(d,γ) where 

γ represents firm characteristics that increase agency costs. Thus, Ad < 0: as the decision 

is made higher in the hierarchy, the agency costs decrease. By definition, Aγ > 0. 

Suppose, for example, the relevant decision is the optimal amount of output, q. If 

either the principal had perfect (or costless) information or the information-holding agent 

had the same objectives as the principal, the optimal output q

 would be determined by 

the standard profit maximizing model: 

Max π = Pq −WL − RK (1) 

for inputs L and K and output and input prices P, W and R, respectively. Here, however, 

regardless of which party decides q, there will be decision-making costs2 that could result 

in an optimal output level different than q



, determined by: 

Max Pq T d A d WL RK

q d

= [ ( , ) + ( , )] − − ,π φ α γ (2) 

 

2

 I am implicitly assuming that the wages of a worker are not affected by her proximity to the optimal d--in 

particular, by whether or not she makes the decision. 
 10

The size of the deviation from the optimal q will depend on the parameters α,γ and φ. 

There are still cases in which the resulting output level is the same as in the standard 

profit maximizing model such as when delay is costless (φ = 0). 

For computational ease, let us restrict our attention to the specialized case when 

the location of decision rights is separable from the output decision. Thus, rather than 

maximizing the full profit function, we can consider the problem of choosing the optimal 

location of decision rights that minimizes the size of the deviation from q



: 

Min φT(d,α) A(d,γ ) d+ (3) 

Note that this essentially describes the Jensen and Meckling diagram. When the decision 

is made by the most senior manager, d = D so that agency costs approach zero and the 

delay costs approach their highest value. When the decision is made by the productionline worker(s), d = 0, delay costs approach zero and agency costs approach their highest 

value. If the decision is made by some middle manager or supervisor, 0 < d < D, and 

both t and A have nonzero values. 

 The first order condition characterizing this minimum is: 

φTd (d,α) + Ad (d,γ ) = 0 (4) 

which shows that the decision can be made by an increasingly higher authority as long as 

the marginal benefit of doing so in terms of lower agency costs exceeds the marginal cost 

that arises from higher delay costs. An interior solution to this problem, where 0 < d < D, 

exists when this second order condition is met: 

φTdd (d,α) + Add (d,γ ) > 0 (5)
 11

Anecdotal evidence about the decision-making patterns of firms indicates that this second 

order condition may not be met for all possible decisions in every company, but the large 

number of foremen, supervisors and middle managers suggests that an interior solution 

exists quite often. 

 There are three comparative static results of interest here: 

φ ∂α

∂

∂

∂d d , , and ∂γ∂d . 

These give the changes in optimal decision-making location with respect to, respectively, 

the unit cost of delay, the organizational characteristics affecting the length of delay, and 

the organizational characteristics affecting agency costs. They are derived as: 

( , ) ( , )

( , )

φ α γ

α

φ T d A d

d T d

dd dd

d

+

− = ∂

∂

 (6a) 

( , ) ( , )

( , )

φ α γ

φ α

α

α

T d A d

d T d

dd dd

d

+

− = ∂

∂

 (6b) 

( , ) ( , )

( , )

φ α γ

γ

γ

γ

T d A d

d A d

dd

d

dd

+

− = ∂

∂

 (6c) 

In each case, the denominator is the second order condition, which must be positive for 

an interior solution. There is no a priori assumption about the cross partials of T(d,α) and 

A(d,γ), so the effects of firm characteristics on the decision-making location are 

ambiguous. In the case of the price of delay, however, the effect is unambiguously 

negative: as the price of delay increases, d becomes smaller--it becomes more likely that 

the decision will be made by someone closer to the shop floor. This is an intuitively 

appealing result. In the extreme case of a monopolist, the cost of delay is quite small, 

since the customers have no choice but to wait. Why would the head manager of a 
 12

monopoly incur agency costs when she can costlessly make the customers wait while she 

obtains the information she needs to make the decision? In the empirical sections that 

follow, I will analyze the signs of these three comparative statics, and test the hypothesis 

that the price of delay increases decentralization. 

Sources of Data 

 American data on workplace organization are largely limited to case studies of a 

single establishment or industry. A cross-industry survey of American workplace 

practices was conducted by Paul Osterman in 1992. The final data contained results on 

the use of specific workplace practices at 875 manufacturing establishments. Two 

subsequent surveys are the National Employer Survey, conducted by the National Center 

on the Educational Quality of the Workforce in 1994 and 1997 and the Survey of 

Employer-Provided Training conducted by the Bureau of Labor Statistics in 1993 and 

1995. These nationally-representative establishment surveys focused on training, but also 

had detailed questions on the use and intensity of specific workplace practices. 

 Much richer data exist abroad for addressing questions related to work 

organization and decision rights. The first such data set is the Workplace Employee 

Relations Survey (and its predecessor the Workplace Industrial Relations Survey)3

, which 

the United Kingdom has conducted in 1980, 1984, 1990 and 1998. It consists of 

responses to a face-to-face interview at the establishment with the senior person 

responsible for industrial relations or employee relations issues. The 1998 sample of 

2,191 establishments is drawn from the underlying population of all U.K. establishments 
 13

with at least ten employees4. The survey asks detailed questions about the structure of 

the organization, the amount of participation in decision-making individual workers have, 

detailed questions about unions and collective bargaining, as well as information on the 

use of specific workplace practices. 

Similar questions were asked in the Australian Workplace Industrial Relations 

Survey. The survey was conducted once in 1990 and again in 1995. Again, face-to-face 

interviews were conducted with the senior human resources manager. The 1995 sample 

consists of 2,001 establishments with twenty or more employees. This is a somewhat 

more restrictive sampling framework, since a large fraction of all establishments are very 

small. 

 A third source of information on decision-making is the Workplace and Employee 

Survey, which was conducted in 1999 by Statistics Canada5

. As in the U.K. and 

Australian data sets, face-to-face interviews were conducted with the senior person 

responsible for human resources. The sample of around 6,300 establishments was drawn 

from a stratified sample of all Canadian establishments--unlike the other two data sets, 

there is no lower limit on the number of employees. Respondents were asked detailed 

questions about training and technology use, as well as who makes decisions on a number 

of issues related to the business. Although this is the first wave of the survey, Statistics 

Canada intends to follow the workplaces for at least four years to create a longitudinal 

data set. 

 3

 Details about the WERS can be found at http://www.data-archive.ac.uk. The data are confidential, but 

can be obtained through the University of Essex for a nominal fee and by meeting confidentiality 

requirements. 

4

 A subsample of the 1980 (1990) establishments were given a special panel survey and were re-sampled in 

1984 (1998) to create two panels of data. I do not use them in this analysis. 
 14

The objectives of this empirical work are to describe the distribution of decision 

rights within organizations, to find establishment characteristics that correspond to the 

theoretical variables φ, α and γ and to determine how they affect that distribution. The 

most difficult of these tasks is to define the distribution of decision rights. In each data 

set, there are many questions that indicate the decentralization of decision rights. Some 

are quite specific, such as "who negotiated the most recent pay raise?"; others require 

more subjective estimation, such as "how much influence do workers have over the pace 

at which their work is done?" A full list of the decision-making questions appears in the 

Appendix. 

In order to create a composite variable, I attribute "points" to an establishment for 

responses that indicate decentralization, and then normalize based on the number of 

"points" possible6

. More weight is given to responses that indicate decisions made at the 

employee level than to those that indicate decentralization to a middle manager or 

supervisor. Thus, the composite index of decentralization will range between zero and 

one, with a value of zero indicating that none of the responses provided evidence of 

decision-making occurring anywhere but at the top management level. A middle score 

indicates some degree of decentralized decision rights--either to the middle management 

or to the employees. A score of one indicates that all responses pointed to decisionmaking occurring below the senior management level7

. 

 5

 Details about the WES can be found at http://www.statcan.ca/english/survey/business/wes.htm. Again, 

these data are confidential. Statistics Canada does, however, allow limited access. E-mail Ted Wannell at 

Ted.Wannell@statcan.ca for more details. 6

 The number of possible points varies across countries due to differences in survey questions available, but 

also across establishments within a country, since some questions that might indicate decentralization are 

not relevant or answerable by some establishments in the sample. 

7

 Note that the default assumption is complete centralization, and points are added to indicate 

decentralization. The index could have been structured in the reverse, by assuming decentralization and 
 15

Table 1 shows the mean value of the decision rights variable, as well as the means 

of other variables that may affect the distribution of decision rights within an 

organization. A large percent of these establishments are quite small, with over 50% 

having employment between the minimum size for the sample and fifty employees. 

Establishment size may be one component of α, since larger organizations may have 

more hierarchical levels, which may require more complicated information transfer 

networks, thus adding to the delay required to make decisions. The same might be true 

for whether the establishment is part of a multi-plant organization. 

If the establishment is in a high-tech industry, the specialized nature of knowledge 

possessed by workers is a factor that contributes to α, since it takes longer to transfer 

specialized knowledge up one hierarchical level than more general knowledge. It is 

somewhat difficult to measure the degree of technology use among establishments, 

particularly in the UK and Australian data sets. I use a question on the length of time it 

takes a new employee to learn his job to proxy for technology, although it would be 

better described as a measure of job complexity. In the Canadian survey, more detailed 

questions were asked about the introduction and use of software, computer-controlled or 

computer-assisted technologies and other technologies, which I use to identify high-tech 

establishments. The former measure results in labeling around twenty percent of 

establishments as high tech, whereas the latter only labels seven percent as high tech, 

although in both cases it would be possible to set different threshold values and label a 

larger or smaller number as high-tech. 

 

taking away points for responses that indicate centralization--this would result in a higher mean value of 

decentralization but the same distribution. 
 16

A high use of information technologies reduces the decision-making delay by 

speeding the collection and transfer of information. At the same time, IT capital is 

sometimes used to monitor workers, making agency costs lower. Thus, IT may be a 

component of both α and γ. The Australian survey contains a question on whether the 

establishment has recently adopted any new information technology, and a similar 

question regarding new software exists in the Canadian data. 

 The age of the establishment may affect the distribution of decision rights. The 

delay costs should be lower in an older organization, because the decision-makers have 

more experience and have more well-established channels for transmitting information. 

At the same time, however, agency costs should also be lower since workers have had 

more time to become familiar with the organization’s objectives, priorities and manner of 

doing business. Table 1 shows the age distribution of establishments in the three 

samples. The significantly lower age for the Canadian group results from the inclusion of 

very small establishments, which are not included in either the UK or Australian data. 

Unions are likely to increase the decision-making delay, since issues must often 

be negotiated with the union prior to the decision being made. At the same time, unions 

may increase agency costs, since the union often has quite different objectives from the 

management. The degree of unionization varies considerably across these three data sets, 

with a rate of only five percent of employees in Canadian establishments, twenty-one 

percent in the UK and over forty percent in Australia. Part of these differences are again 

due to the size restrictions imposed in the sampling framework, although not all. 

The competitiveness of the establishment’s output and input markets affects the 

price of delay, φ. In a more competitive market, the organization must respond to 
 17

demand and supply changes more quickly. The time it takes to transfer information to a 

more senior decision-maker will be very costly in terms of lost opportunities. A large 

share of the establishments--over 70% in the UK, 50% in the Australian data and 24% in 

the Canadian survey--face "intense" or "strong" market competition. According to the 

theory, those firms facing greater competition should have greater decision rights at the 

worker level. In the next section, I present the empirical strategy and results from these 

three data sources. 

Empirical Methodology and Results 

 To determine how these establishment characteristics affect the distribution of 

decision rights, I estimate the following empirical model: 

( , , , , , ) i i i i i i i i DR = f union Mkt Tech Size Age Multi Ind (7) 

where DRi is the decision rights index in establishment i; unioni is the percent of 

workers who are members of a union; Mkti is a vector that includes indicators for whether 

the establishment faces "intense", " strong" or "some or limited" competition; Techi

includes an indicator for whether i is a high-tech establishment, as well as indicators for 

whether the establishment has recently adopted new technologies and/or new products or 

new information technologies; Sizei is a vector of establishment size indicators, as 

measured by total employment; Agei is a vector of establishment age indicators, in years; 

Multii is an indicator for whether the establishment is part of a larger organization; and 

Indi is a vector of available industry variables--the number of such variables varies 

between data sets. 
 18

 The results of ordinary least squares estimation of this model for the three data 

sets are given in Table 2. In general, positively signed coefficients indicate that the 

independent variable is associated with increased decentralization of decision rights. 

Among the variables that test the predicted relationship between φ and d, the results are 

somewhat supportive of the hypothesis that the price of delay increases the 

decentralization of decision rights. In all three countries, coefficients are larger (and 

positive) for indicators of greater competition, relative to the coefficient for the 

"some/limited" competition variable, although none are statistically significant at a 95 

percent confidence interval. 

 Among the variables that potentially increase α, results are somewhat mixed. 

Size is inversely related to d--confirming that in larger companies, where information 

transmission can be much slower, decisions are more likely to be made by senior 

management. Results on the technology variables strongly indicate that establishments 

with new or intensive technology investments are likely to decentralize decision-making. 

Unionization presents a less clear pattern--with some evidence that decision rights are 

more decentralized in highly unionized establishments in Canada, but no such result in 

the U.K. or Australia. It is important to keep in mind, however, that the survey questions 

are asked of senior human resources management, whose opinions about the distribution 

of decision rights may be affected by whether or not the company is unionized. Finally, 

results on age are fairly weak and do not seem to affect the location of decision rights 

much. This was not wholly unpredicted, however, since although information 

transmission might be faster in older establishments, agency costs might also be lower, 

suggesting offsetting effects on the location of decision rights. Taken on the whole, these 
 19

results suggest that α -- establishment characteristics that increase decision-making 

delay-- are positively related to decentralized decision-making. 

 With these data it is rather difficult to identify independent proxies for γ. Age and 

information technologies are both also features of α, and it is not possible to divide the 

result into that attributable to agency costs. Thus it is not possible to measure the effect 

of establishment characteristics that affect agency costs on the optimal location of 

decision rights. 

 One concern with using a dependent variable that indexes a large number of 

diverse questions on decision-making is that an organization makes many different types 

of decisions, and the process of choosing an optimal decision-making location within the 

hierarchy may not be the same for all types of decisions. To address this issue, I break 

down the decision-making questions into four groups: those that pertain to (1) paysetting, (2) other personnel issues, (3) policies and long-term planning, and (4) 

production decisions. Complete details of the questions used for each type of decision 

are included in the appendix. This creates four separate dependent variables in each data 

set, with the exception of the Canadian data set, which did not contain information on pay 

decisions. 

I estimate the same model as previously. Results of these estimations are in 

Tables 3-6. Pay decisions are likely to be decentralized among establishments in more 

competitive markets, those with high tech or new tech, those in multi-plant organizations, 

and those that have less union participation. Size is a much less important determinant of 

decentralized pay decision-making. For other personnel decisions, however, unionization 

and the competitiveness of the market are less important, with technology and 
 20

establishment size the important determinants. Factors affecting the location of policy 

and long-term planning decisions are very similar to those affecting overall decisions--

competition (weakly), technology and establishment size. Results for production 

decisions are somewhat mixed. In the U.K., the variables that have affected other 

decisions are not significant in the case of production decisions. In the other two data 

sets, however, we see similar patterns of decentralization among larger establishments, 

those using high tech or new tech, and those in more competitive settings. Unionization 

is positively correlated with decentralization in the Canadian data set. Thus, while there 

are some differences across types of decisions, the patterns are largely similar. 

 These characteristics of the organization such as size, age, industry and 

unionization do seem to explain some of the choice of distribution of decision rights. At 

the same time, researchers have noted that the adoption of other human resource practices 

can affect the organizational structure of the workplace--in particular, many might be 

considered components of γ. These choices are, however, endogenous to the choice of 

decision location. As an example, if we observe a correlation between high employee 

decision rights and adoption of profit sharing, we cannot know whether profit-sharing 

plans are adopted in order to provide the right incentives to those decision-making 

employees or whether those employees used their decision rights to obtain a profit 

sharing plan. Thus we cannot infer causality from an observed correlation between 

decentralization and any complementary human resource practice. This does not prevent 

us from exploring which practices are in fact correlated with a particular distribution of 

decision rights. 
 21

 Recruiting and training practices may be important because they reduce agency 

costs. In these data, measurable practices include whether hiring is primarily done from 

within, whether extensive pre-employment screening is used, and whether the company 

provides a high amount of formal and/or on-the-job training. Similarly, incentive pay 

programs tie worker outcomes to desired establishment or corporate outcomes, and thus 

can be used to reduce agency costs. All three data sets contain information on the 

presence of individual and group incentive pay programs, profit-sharing, ESOPs and 

other bonus systems. Another way to reduce agency problems is by monitoring workers 

through formal appraisals, benchmarking and skill auditing. Communications practices 

can be adopted to speed the transfer of information to a senior decision-maker. This 

includes the presence of quality circles, total quality management programs and even 

suggestion boxes. Family-friendly practices, such as extended leave, maternal or paternal 

leave, on-site or subsidized child-care could reduce agency costs, and other researchers 

such as Osterman (1994) have linked family-oriented establishments to workplace 

transformations. The use of contract and temporary workers may be related to the 

optimal location of decision rights, especially if those types of workers are employed by 

an organization that faces greater uncertainty necessitating faster decision-making. 

Finally, affirmative action policies may affect the distribution of decision rights, since by 

design they restrict hiring and firing decisions. 

 To examine these correlations, I estimate the following model: 

Pr( ) ( , , , , , , ) i i i i i i i i i hrp = f DR union Mkt Tech Size Age Multi Ind (8) 
 22

where hrpi is a particular human resource practice, such as individual pay-forperformance or formal appraisals, and the remaining variables are defined as before8

. 

Results of this estimation are in Table 7. Each cell in the table represents the 

coefficient on the decision rights variable9 in the above model. Training practices are 

strongly correlated with decentralization of decision-making, but recruitment practices 

are not. Various types of incentive pay plans are correlated with higher decision rights of 

employees. The same is true for the use of monitoring tools, which may support the 

notion that steps an organization takes to reduce agency costs are complementary with 

increasing the autonomy and decision rights of individuals who are in the lower rungs of 

the hierarchical ladder. The results on practices that increase communication do not 

match the results from other components of α--in this case, a factor that can increase the 

speed or quality of information transmission is associated with increased decentralization. 

Affirmative action policies are weakly associated with increased decision rights for 

employees and supervisors. The use of contract and temp workers is strongly correlated 

with decentralized decision-making, which might further support the importance of 

decentralizing when decisions must be made quickly under uncertainty and competition. 

Family policies are not correlated with the location of decision rights. 

While we cannot infer causality from these estimations, for the most part the 

results do not contradict those obtained from estimation of model (7). Uncertainty and 

competition, and tools used to deal with that uncertainty are associated with an increase 

 

8

 Given that this only measures a correlation, I could just as easily keep the decision rights index as the 

dependent variable, and add the human resource practice to the list of right-hand side variables. 

9

 This table reflects only the correlation between the overall decision rights decentralization variable and 

the human resources practices. Results by type of decision are available from the author, but they do not 

differ greatly across types of decisions with the exception that pay decision decentralization is not strongly 

correlated with most of the practices. 
 23

in decentralized decision-making. Factors that exogenously or endogenously result in 

lower agency costs are also correlated with decisions increasingly being made below the 

top of the hierarchy. It is difficult to measure the speed of information flow in an 

organization or to identify features that increase that speed; this is not something that can 

be well addressed with these data. 

 

Conclusion 

 In this paper, I provide a simple model of the optimal location of decision rights, 

loosely based on Jensen and Meckling (1990). The model suggests that there is a tradeoff 

between the costs of delaying a decision that result if a senior manager must accumulate 

relevant information and the agency costs that result if someone lower in the hierarchy 

makes the decision. The extent to which this results in a different optimal choice depends 

upon the cost of delays, as well as organizational characteristics that affect the length of 

the delay and the size of agency costs. Using data sets from the UK, Australia and 

Canada, I created indices to measure the decentralization of decision rights from 

extensive questions on the decision-making practices of establishments. These questions 

also allow us to learn about what types of decisions are decentralized in an organization, 

and what characteristics of the establishment affect the balance of power among agents. 

 I find that factors such as market competitiveness, which raise the price of delay, 

do move the optimal location of decision rights down toward the bottom of the hierarchy. 

This confirms the main prediction of the model. Additionally, I find evidence that 
 24

establishment characteristics that increase the length of decision-making delays also 

increase the decision rights of those at the bottom of the hierarchy. Finally, I find a clear 

relationship between endogenous firm choices that reduce agency costs and higher 

decision rights of those in the bottom tiers. 

 I argue in this paper that understanding the effect of organizational capital on an 

establishment’s production must be more than counting which workplace practices the 

establishment uses. Instead these practices may be tools the organization employs to 

achieve its optimal distribution of decision rights--whether it will retain greater control at 

the top of the hierarchical structure or distribute that control to other agents. This is a 

first attempt to consider what identifiable establishment characteristics affect that optimal 

distribution and what practices are complements to such a distribution. In future work, I 

hope to examine whether there is a systematic difference in productivity that results from 

this choice of decision rights. 
 25

References 

Aghion, Philippe and Jean Tirole (1997). "Formal and Real Authority in Organizations," 

The Journal of Political Economy, 105, 1-29. 

Aoki, Masahiko (1986). "Horizontal vs. Vertical Information Structure of the Firm," 

American Economic Review, 76, 971-983. 

Athey, S. Gans, J., Schaefer, S. and Stern, S. (1994). “The Allocation of Decisions in 

Organizations.” Mimeo, Stanford University. 

Batt, Rosemary (1999). "Work Organization, Technology, and Performance in Customer 

Service and Sales," Industrial and Labor Relations Review, 52, 539-64. 

Black, Sandra and Lisa Lynch (1996). "Human Capital Investments and Productivity," 

American Economic Review, 86, 263-67. 

Black, Sandra and Lisa Lynch (2001). "How to Compete: The Impact of Workplace 

Practices and Information Technology on Productivity," Review of Economics and 

Statistics, 83, 434-45. 

Cappelli, Peter and William Carter (2000). "Computers, Work Organization and Wage 

Outcomes," NBER Working Paper 7987. 

Cappelli, Peter and David Neumark (2001). "Do 'High Performance' Work Practices 

Improve Establishment-Level Outcomes?" Industrial and Labor Relations Review, 54, 

737-75. 

Carter, Martin J. (1995). "Information and the Division of Labour: Implications for the 

Firm's Choice of Organization," The Economic Journal, 105, 385-97. 

Chandler, Alfred Dupont (1962). Strategy and Structure: Chapters in the History of the 

Industrial Enterprise. Cambridge, Mass.: MIT Press. 

Fama, Eugene and Michael C. Jensen (1983). "Agency Problems and Residual Claims," 

Journal of Law and Economics, 26, 327-49. 

Gittleman, Maury, Michael Horrigan and Mary Joyce (1998). "'Flexible' Workplace 

Practices: Evidence from a Nationally Representative Survey," Industrial and Labor 

Relations Review, 52, 99-115. 

Hayek, F.A. (1945). "The Use of Knowledge in Society," American Economic Review, 

35, 519-30. 
 26

Ichniowski, Casey and Kathryn Shaw (1995). "Old Dogs and New Tricks: Determinants 

of the Adoption of Productivity-Enhancing Work Practices," Brooking Papers on 

Economic Activity: Microeconomics, 1-65. 

Ichniowski, Casey, Kathryn Shaw and Giovanna Prennushi (1997). "The Effects of 

Human Resource Management Practices on Productivity: A Study of Steel-Finishing 

Lines," American Economic Review, 87, 291-313. 

Jensen, Michael C. and William H. Meckling (1990). "Specific and General Knowledge 

and Organizational Structure," in Contract Economics, Lars Werin and Hans Wijkander, 

eds. Oxford: Blackwell (1992), pp. 251-74. 

MacDuffie, John Paul (1995). "Human Resource Bundles and Manufacturing 

Performance: Organizational Logic and Flexible Production Systems in the World Auto 

Industry," Industrial and Labor Relations Review, 48, 197-221. 

March, James and Herbert Simon (1958). Organizations. New York: John Wiley and 

Sons. 

Osterman, Paul (1994). "How Common is Workplace Transformation and Who Adopts 

It?" Industrial and Labor Relations Review, 47, 173-88. 

Pil, Frits K. and John Paul MacDuffie (1996). "The Adoption of High-Involvement 

Work Practices," Industrial Relations, 35, 423-55. 

Radner, Roy (1992). "Hierarchy: The Economics of Managing," Journal of Economic 

Literature, 30, 1382-1415. 

Radner, Roy and Timothy Van Zandt (1992). "Information Processing in Firms and 

Returns to Scale," Annales d’Economie et de Statistique, 25/26, 265-98. 

Womack, James P., David T. Jones and Daniel Roos (1990). The Machine That Changed 

the World. New York: Rawson Associates. 
 27

Figure 1. 

The trade-off between costs owing to inconsistent objectives and costs owing to poor 

information as a decision right is moved further from the CEO’s office in the 

hierarchy. 

Costs owing to 

inconsistent 

objectives 

Completely 

centralized 

Completely 

decentralized

Total organizational costs 

Costs owing to 

poor information 

Optimal location 

of decision 

rights 

Point at which 

decision right 

and specific 

knowledge are 

collocated 

Distance of 

decision rights 

from CEOs 

office

CEOs 

office 

This figure was reproduced with permission of Michael Jensen from Jensen and Meckling (1990). 
 28

Table 1. 

Summary statistics 

 WERS AWIRS WES 

Decision Rights .2950 .1879 .1563 

Size < 25 employees .4937 .1002 .9011 

Size 25-49 employees .2585 .4027 .0557 

Size 50-99 employees .1309 .2734 .0243 

Size 100-249 employees .0742 .1600 .0132 

Size 500+ employees .0142 .0230 .0025 

Age <5 years .0885 .1244 .2889 

Age 5-9 years .1828 .1708 .2014 

Age 20+ years .4772 .4861 .2408 

Unionization .2102 .3775 .0518 

High Tech .2073 .2079 .0703 

New Tech .6764 .2886 .2383 

New IT -- .4611 .2358 

New Product .4642 -- .3269 

Intense Competition .4087 .2770 

Strong Competition .3257 .2813 .2399 

Some/Limited Competition .1239 .0552 .3046 

Part of Multi-Plant Org. .3136 .7723 -- 

Number of Observations 1759 1828 6351 
 29

Table 2. 

Effect of establishment characteristics on distribution of 

decision rights in three data sets 

 WERS AWIRS WES 

Unionization .0003 

(.0002) 

-.0220 

(.0117) 

.0729**

(.0310) 

Intense Competition .0327 

(.0184) 

.0158 

(.0148) 

Strong Competition .0166 

(.0171) 

-.0025 

(.0143) 

.0231 

(.0154) 

Some/Limited Competition .0085 

(.0217) 

-.0093 

(.0210) 

-.0102 

(.0131) 

High Tech .0208 

(.0173) 

.0055 

(.0081) 

.2667*

(.0764) 

New Tech .0414*

(.0127) 

.0154**

(.0080) 

.0132 

(.0126) 

New Product .0318*

(.0116) 

-- .0104 

(.0125) 

New IT -- .0162**

(.0070) 

-.0170 

(.0156) 

Size < 25 employees -.1050*

(.0168) 

-.0504*

(.0156) 

-.1671*

(.0266) 

Size 25-49 employees -.0877*

(.0186) 

-.0627*

(.0122) 

-.0780*

(.0294) 

Size 50-99 employees -.0747*

(.0156) 

-.0591*

(.0124) 

-.0532**

(.0268) 

Size 100-249 employees -.0354**

(.0152) 

-.0259**

(.0121) 

-.0240 

(.0269) 

Size 500+ employees .0223 

(.0172) 

.0146 

(.0158) 

-.0046 

(.0287) 

Age <5 years .0892**

(.0374) 

.0091 

(.0121) 

.0170 

(.0160) 

Age 5-9 years .0046 

(.0169) 

.0145 

(.0111) 

.0116 

(.0141) 

Age 20+ years -.0053 

(.0131) 

.0107 

(.0092) 

.0237 

(.0149) 

Multi-plant -.0061 

(.0149) 

.0243**

(.0102) 

-- 

 

R2 .1721 .1499 .1272 

Number of observations 1396 1814 5422 

Standard errors in parentheses. * and ** indicate significance at 1% and 5% 

respectively. Regressions include industry dummies. 
 30

Table 3. 

Effect of establishment characteristics on distribution of pay 

decision rights in two data sets 

 WERS AWIRS 

Unionization -.0001 

(.0002) 

-.0639*

(.0205) 

Intense Competition .0509*

(.0144) 

.0103 

(.0231) 

Strong Competition .0368*

(.0144) 

-.0132 

(.0221) 

Some/Limited Competition .0178 

(.0150) 

-.0435 

(.0269) 

High Tech -.0034 

(.0132) 

.0066 

(.0153) 

New Tech .0402*

(.0132) 

.0248 

(.0141) 

New Product -.0007 

(.0115) 

-- 

New IT -- .0205 

(.0121) 

Size < 25 employees -.0168 

(.0162) 

-.0484 

(.0299) 

Size 25-49 employees -.0138 

(.0153) 

-.0727*

(.0208) 

Size 50-99 employees -.0125 

(.0142) 

-.0536*

(.0218) 

Size 100-249 employees .0011 

(.0135) 

-.0084 

(.0214) 

Size 500+ employees -.0076 

(.0164) 

.0173 

(.0278) 

Age <5 years .0802*

(.0309) 

-.0028 

(.0192) 

Age 5-9 years .0174 

(.0193) 

.0034 

(.0200) 

Age 20+ years .0126 

(.0153) 

.0106 

(.0154) 

Multi-plant .0486*

(.0161) 

.0270 

(.0165) 

 

R2 .0912 .1578 

Number of observations 1399 1828 

See note at bottom of Table 2. 
 31

Table 4. 

Effect of establishment characteristics on distribution of 

personnel decision rights in three data sets

 WERS AWIRS WES 

Unionization .0002 

(.0004) 

-.0196 

(.0175) 

.0544 

(.0322) 

Intense Competition .0235 

(.0279) 

.0034 

(.0227) 

Strong Competition .0116 

(.0277) 

-.0141 

(.0217) 

.0209 

(.0171) 

Some/Limited Competition -.0133 

(.0333) 

.0027 

(.0347) 

.0065 

(.0151) 

High Tech .0191 

(.0207) 

.0059 

(.0125) 

.2169*

(.0783) 

New Tech .0251 

(.0222) 

.0067 

(.0121) 

.0001 

(.0154) 

New Product .0315 

(.0196) 

-- .0162 

(.0141) 

New IT -- .0197 

(.0108) 

-.0312**

(.0153) 

Size < 25 employees -.1921*

(.0233) 

-.0513**

(.0247) 

-.1743*

(.0339) 

Size 25-49 employees -.1553*

(.0249) 

-.0688*

(.0181) 

-.0859**

(.0378) 

Size 50-99 employees -.0877*

(.0208) 

-.0671*

(.0184) 

-.0504 

(.0344) 

Size 100-249 employees -.0410**

(.0197) 

-.0317 

(.0178) 

-.0454 

(.0357) 

Size 500+ employees .0540*

(.0219) 

.0178 

(.0250) 

-.0232 

(.0372) 

Age <5 years .0558 

(.0448) 

.0205 

(.0201) 

.0205 

(.0185) 

Age 5-9 years -.0024 

(.0278) 

.0163 

(.0164) 

.0032 

(.0162) 

Age 20+ years -.0079 

(.0207) 

.0155 

(.0137) 

.0066 

(.0170) 

Multi-plant -.0419 

(.0222) 

.0281 

(.0154) 

-- 

 

R2 .1572 .0795 .0697 

Number of observations 1501 1826 5627 

See note at bottom of Table 2. 
 32

Table 5. 

Effect of establishment characteristics on distribution of 

policy/planning decision rights in three data sets

 WERS AWIRS WES 

Unionization .0011*

(.0004) 

.0172 

(.0104) 

.0673 

(.0380) 

Intense Competition .0455 

(.0398) 

.0237**

(.0106) 

Strong Competition .0025 

(.0379) 

.0088 

(.0105) 

.0234 

(.0163) 

Some/Limited Competition .0150 

(.0436) 

-.0023 

(.0149) 

-.0128 

(.0137) 

High Tech .0398 

(.0317) 

.0127 

(.0092) 

.1868**

(.0840) 

New Tech .0624*

(.0253) 

.0116 

(.0077) 

.0036 

(.0141) 

New Product .0645*

(.0219) 

-- -.0091 

(.0136) 

New IT -- .0045 

(.0069) 

-.0028 

(.0174) 

Size < 25 employees -.2393*

(.0298) 

-.0566*

(.0167) 

-.0765**

(.0389) 

Size 25-49 employees -.1520*

(.0341) 

-.0447*

(.0135) 

-.0328 

(.0413) 

Size 50-99 employees -.1471*

(.0288) 

-.0492*

(.0136) 

-.0596 

(.0433) 

Size 100-249 employees -.0730*

(.0275) 

-.0242 

(.0137) 

-.0051 

(.0409) 

Size 500+ employees .0532 

(.0329) 

-.0113 

(.0192) 

-.0210 

(.0424) 

Age <5 years .1518*

(.0625) 

.0059 

(.0117) 

.0110 

(.0158) 

Age 5-9 years -.0114 

(.0340) 

.0200 

(.0106) 

.0168 

(.0160) 

Age 20+ years -.0054 

(.0260) 

.0050 

(.0090) 

.0376**

(.0173) 

Multi-plant -.0557**

(.0259) 

.0262*

(.0093) 

-- 

 

R2 .2216 .2269 .0735 

Number of observations 1552 1828 5446 

See note at bottom of Table 2. 
 33

Table 6. 

Effect of establishment characteristics on distribution of 

production decision rights in three data sets

 WERS AWIRS WES 

Unionization -.0001 

(.0003) 

-.0177 

(.0159) 

.0901*

(.0301) 

Intense Competition .0168 

(.0243) 

.0210 

(.0221) 

Strong Competition .0393 

(.0240) 

-.0014 

(.0212) 

.0319 

(.0182) 

Some/Limited Competition .0441 

(.0341) 

-.0087 

(.0300) 

.0149 

(.0175) 

High Tech .0231 

(.0213) 

-.0006 

(.0108) 

.4161*

(.0833) 

New Tech -.0016 

(.0207) 

.0178 

(.0109) 

.0201 

(.0178) 

New Product .0242**

(.0182) 

-- .0228 

(.0167) 

New IT -- .0142 

(.0098) 

-.0418**

(.0188) 

Size < 25 employees .0310 

(.0214) 

-.0516**

(.0222) 

-.2442*

(.0240) 

Size 25-49 employees -.0112 

(.0233) 

-.0670*

(.0176) 

-.1213*

(.0285) 

Size 50-99 employees -.0246 

(.0212) 

-.0717*

(.0175) 

-.0606**

(.0270) 

Size 100-249 employees -.0095 

(.0195) 

-.0424**

(.0178) 

-.0322 

(.0243) 

Size 500+ employees .0105 

(.0210) 

.0371 

(.0213) 

.0212 

(.0283) 

Age <5 years .0357 

(.0430) 

.0175 

(.0172) 

-.0052 

(.0199) 

Age 5-9 years .0102 

(.0241) 

.0266 

(.0150) 

-.0083 

(.0194) 

Age 20+ years -.0088 

(.0205) 

.0176 

(.0124) 

-.0075 

(.0195) 

Multi-plant .0088 

(.0215) 

.0127 

(.0139) 

-- 

 

R2 .0903 .1329 .1433 

Number of observations 1547 1816 5763 

See note at bottom of Table 2. 
 34

Table 7. 

Correlation between decision rights and complementary human resource

practices in three data sets 

 WERS AWIRS WES 

Recruitment and training 

 Internal labor market .0151 

(.0848) 

.0001 

(.0392) 

.0174 

(.0917) 

 Pre-employment screening .2514 

(.2122) 

-- -- 

 Significant formal training .3362*

(.1286) 

.3151**

(.1502) 

.2229*

(.0690) 

 Significant OTJ training -- -- .2516*

(.0826) 

Incentive pay 

 Individual incentive pay .1566*

(.0582) 

.0816 

(.1181) 

.1577**

(.0799) 

 Group incentive pay .1038 

(.0567) 

.1650**

(.0785) 

.0669 

(.0390) 

 Workplace incentive pay .1435 

(.0720) 

-- -- 

 Organization incentive pay .0474 

(.0428) 

.0457 

(.0237) 

-- 

 Profit sharing .3583**

(.1496) 

.1012*

(.0332) 

.0485 

(.0328) 

 Merit pay -- .1649*

(.0535) 

 ESOP .2633*

(.1017) 

-.0120 

(.1275) 

.0043 

(.0025) 

 Bonus pay -- .4126*

(.1399) 

-- 

Monitoring methods 

 Formal appraisals .4872**

(.2041) 

.7107*

(.1498) 

-- 

 Benchmarking .8744*

(.1769) 

.4597*

(.1395) 

-- 

 Skill auditing -- .4723*

(.1148) 

-- 
 35

Table 7 (cont’d) 

 WERS AWIRS WES 

Communication 

 Quality circles .3933*

(.0943) 

.1759**

(.0726) 

.2536*

(.0574) 

 Total quality management -- .6122*

(.1375) 

-- 

 Suggestion box -- -- .1573*

(.0477) 

Other policies 

 Affirmative action .2445 

(.1864) 

.2647 

(.1532) 

-- 

 Family policies .0366 

(.0592) 

-.0080 

(.0711) 

-- 

 Contracting out .7182*

(.1601) 

.6065*

(.1430) 

-- 

 Temp workers .2453*

(.0963) 

-- -- 

 Just-in-time inventorying -- .1438**

(.0649) 

-- 

See note at bottom of Table 2. 
 36

Appendix A. 

Questions used to create decision rights variables in WERS: 

Pay: 

Which of these employee relations matters forms part of the job of supervisors at this 

workplace...pay or conditions of employment?...systems of payments? 

Do supervisors have the authority to make final decisions on deciding on pay rises for 

people who work for them? 

Are there any committees of managers and employees at this workplace primarily 

concerned with consultation? Which issues are discussed...pay issues? 

Which of the following parties were directly involved in determining or negotiating this 

pay rise (if any) for employees in the largest occupational group...employee association 

officials?...non-union employee representatives? 

Which of the following statements most closely characterizes the way that pay is set for 

[each occupation]...negotiation with individual employees? 

Personnel Issues: 

Which of these employee relations matters forms part of the job of supervisors at this 

workplace...recruitment or selection of employees?...training of employees?...systems 

of payments?...handling grievances?...staffing or manpower planning?...performance 

appraisals? 

Do supervisors have the authority to make final decisions on taking on people who 

work for them? 

Do supervisors have the authority to make final decisions on dismissing workers for 

unsatisfactory performance? 

 If any non-managerial appraisals, who carries out these appraisals...an individual's 

immediate supervisor or foreman?...employees at the same level/grade?...subordinates 

or employees at a lower level/grade? 

Are there any committees of managers and employees at this workplace primarily 

concerned with consultation? Which issues are discussed...employment 

issues?...welfare services and facilities?...training? 

Policy/Planning: 

Which of these employee relations matters forms part of the job of supervisors at this 

workplace?...equal opportunities?...health and safety? 

Are there any committees of managers and employees at this workplace primarily 

concerned with consultation? Which issues are discussed...financial issues?...future 

plans or trends?...government regulations?...health and safety?...equal opportunities? 

Production Issues: 

Would you say that individual employees in the largest occupation group have (a lot, 

some, a little, none)discretion over how they do their work? 

Would you say that individual employees in the largest occupation group have (a lot, 

some, a little, none) control over the pace at which they work? 

Which of the following statements apply to the way teamworking (if any) operates at 

this workplace?...Team members are able to appoint their own team leaders...Team 

members jointly decide how the work is to be done. 
 37

Are there any committees of managers and employees at this workplace primarily 

concerned with consultation? Which issues are discussed?...production 

issues?...working practices? 

 If over the past five years management here introduced any of the changes listed (new 

technology, working time arrangements, organization of work, work techniques or 

procedures), what type of involvement did the employees likely to be affected have in 

introducing and implementing this change? (they decided, they negotiated, they were 

consulted, they were informed, there was no involvement) 

Question used to create decision rights variables in AWIRS 

Pay: 

Who usually makes decisions about changes to pay of non-managerial employees?...a 

first line supervisor or line manager? 

 Is there a joint consultative committee currently in place at this workplace? Which of 

these matters does the jcc have the authority to deal with?...pay and conditions? 

 In the last year has management at this workplace negotiated with employees as a 

group over pay rates? 

 In the last year has management at this workplace negotiated with employees 

individually over pay rates? 

Personnel Issues: 

Are Key Performance Indicators in use at this workplace? Who was involved in the 

design of these Key Performance Indicators?...first line supervisors?...employees at this 

workplace? 

Who usually makes decisions about recruitment of non-managerial employees?...a first 

line supervisor or line manager? 

Who usually makes decisions about dismissals?...a first line supervisor or line manager? 

Who usually makes decisions about allocation of resources for in-house training?...a 

first line supervisor or line manager? 

 Is there a joint consultative committee currently in place at this workplace? Which of 

these matters does the jcc have the authority to deal with?...discipline of 

employees?...individual grievances? 

 In the last year has management at this workplace negotiated with employees as a group 

over performance appraisals?...work hours?...penalty rates?...discipline and dismissals? 

...training?...leave arrangements?...child care or family leave arrangements? 

...consultation or negotiation arrangements?...grievance handling?...superannuation? 

 In the last year has management at this workplace negotiated with employees 

individually over performance appraisals?...work hours?...penalty rates?...discipline and 

dismissals?...training?...leave arrangements?...child care or family leave arrangements? 

...consultation or negotiation arrangements?...grievance handling?...superannuation? 

Policy/Planning: 

Does management at this workplace have a plan which outlines the workplace’s 

corporate goals and the ways of achieving them? Who had an input into the 

development of the current plan?...supervisors/middle management?...employees from 

this workplace? 

 Is there a joint consultative committee currently in place at this workplace? Which of 

these matters does the jcc have the authority to deal with?...financial/investment 
 38

decisions?...equal employment opportunity or affirmative action matters?...occupational 

health and safety? 

 In the last year has management at this workplace negotiated with employees as a group 

over occupational health and safety? 

 In the last year has management at this workplace negotiated with employees 

individually over occupational health and safety? 

Production Issues: 

 If over the past year management at this workplace introduced any of the changes listed 

(major new office technology, major new plant, machinery or equipment, major 

changes to how non-managerial employees do their work), how involved in the decision 

to introduce the change were other workplace managers here?...employees likely to be 

affected? (made the decision, had significant input, were consulted, were informed, 

were not informed) 

Who usually makes decisions about levels of overtime?...employment levels for the 

whole workplace?...changes in work practices? 

 Is there a joint consultative committee currently in place at this workplace? Which of 

these matters does the jcc have the authority to deal with?...introduction of new 

technology?...new product or service lines?...work organization? 

How much influence would you say workers in the largest occupation group have over 

how work is allocated to them? (none, a little, some, a lot) 

How much influence would you say workers in the largest occupation group have over 

how they do their job? (none, a little, some, a lot) 

How much influence would you say workers in the largest occupation group have over 

the pace at which their work is done? (none, a little, some, a lot) 

How much influence would you say workers in the largest occupation group have over 

the time they can start and stop work each day? (none, a little, some, a lot) 

 In the last year has management at this workplace negotiated with employees as a group 

over work practices or work organization?...retrenchments or redeployment? 

 In the last year has management at this workplace negotiated with employees 

individually over work practices or work organization?...retrenchments or 

redeployment? 

Questions used to create decision rights variables in the WES 

Personnel Issues: 

Who normally makes decisions with respect to setting staffing levels?...work 

supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to filling vacancies?...work 

supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to training?...work supervisor?...work 

group?...non-managerial employee? 

Policy/Planning: 

Who normally makes decisions with respect to choice of production technology?...work 

supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to product/service development?...work 

supervisor?...work group?...non-managerial employee? 

Production Issues: 
 39

Who normally makes decisions with respect to daily planning of individual 

work?...work supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to weekly planning of individual 

work?...work supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to follow-up of results?...work 

supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to customer relations?...work 

supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to quality control?...work 

supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to purchase of necessary supplies?...work 

supervisor?...work group?...non-managerial employee? 

Who normally makes decisions with respect to maintenance of machinery and 

equipment?...work supervisor?...work group?...non-managerial employee?