BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

Workplace Organization and Innovation 

Cindy Zoghi, U.S. Bureau of Labor Statistics 

Robert D. Mohr, University of New Hampshire 

Peter B. Meyer, U.S. Bureau of Labor Statistics 

Working Paper 405

May 2007

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Workplace organization and innovation 

Cindy Zoghi, U.S. Bureau of Labor Statistics*

Robert D. Mohr, University of New Hampshire 

Peter B. Meyer, U.S. Bureau of Labor Statistics 

Abstract: This study uses data on Canadian establishments to test whether particular 

organizational structures are correlated with the likelihood of adopting process and product 

innovations, controlling for the endogeneity of the predictors. We find that establishments with 

decentralized decision-making, information-sharing programs, or incentive pay plans are 

significantly more likely to innovate than other establishments. Larger establishments and those 

with a high vacancy rate are also more likely to innovate. These findings are consistent with a 

model in which workers hold information about production inefficiencies or consumer demands 

that can lead to productive innovations and that workplace organization attributes facilitate the 

communication and implementation of those ideas. 

* Cindy Zoghi, U.S. Bureau of Labor Statistics, 2 Massachusetts Ave., NE, Washington DC 

20212, e-mail: Zoghi.Cindy@bls.gov; Robert D. Mohr, Department of Economics, Whittemore 

School of Business and Economics, University of New Hampshire, Durham NH 03824, e-mail: 

rmohr@cisunix.unh.edu; Peter B. Meyer, U.S. Bureau of Labor Statistics, 2 Massachusetts Ave., 

NE, Washington DC 20212, e-mail: Meyer.Peter@bls.gov. 

The authors thank David Autor, Michael Gibbs, Larry Rosenblum, John W. Meyer, Leo 

Sveikauskas, Timothy Van Zandt, and seminar participants at the University of New Hampshire, 

the 2005 SOLE meetings, and the NBER for helpful comments, and Lucy Chung and Yves 

Decady at Statistics Canada for exceptional support with the data. Robert Mohr thanks the 

University of New Hampshire for financial support. All views expressed in this paper are those of 

the authors and do not necessarily reflect the views or policies of the U.S. Bureau of Labor 

Statistics. 
 

I. Introduction 

This study tests whether establishments with decentralized decision-making, informationsharing programs, or incentive pay plans are significantly more likely to innovate than other 

establishments. There is reason to think this is the case. Workers who directly interact with 

customers have information about consumer preferences and constraints that managers do not 

have. This information may be important to spawning new product ideas. Similarly, workers who 

are part of the production process will know about the weaknesses and inefficiencies of the 

process; this information can generate innovative improvements to the process. If the worker with 

this private information also has the capacity to act on the information or to share the information 

with someone who has such capacity, subsequent innovations are more likely. There are costly 

tradeoffs however -- organizations that allow workers such authority will be subject to more 

internal uncertainty and surprises, and to more principal-agent problems. 

 Freeman and Lazear (1995), in their theory on works councils, point to informationsharing as a solution to this dilemma. Management and workers have different information sets 

and can therefore increase the organization’s productive efficiency by adopting institutions that 

increase the transmission of information. The result may be that “management and labor together 

discover solutions to company problems that neither would have conceived separately” (p44). 

While information-sharing can be the right solution for some establishments, it can impose costs 

of setting up an information infrastructure and costs of delay while the information is transferred 

and interpreted. These costs can be severe if it is necessary to react quickly to the environment. 

 Under rapidly changing circumstances, transferring information from the worker to another 

decision-maker is more costly. This may encourage decentralization, giving workers more 

authority to act autonomously. For example, a firm may have to redefine its product frequently, 

change technologies in its design and production, or adapt its marketing to changing 

1
 

circumstances. Partly for these reasons, high tech firms are thought to have more inclusive 

decision-making processes. Decentralization comes at a cost, however. If workers are given the 

authority to make decisions on the basis of private information, the organization will likely incur 

agency costs. As Jensen and Meckling (1995) describe, “[b]ecause they are ultimately selfinterested, the agents to whom the CEO delegates authority have objective functions that diverge 

from his or her own” (p.17). This means that the establishment faces a trade-off between the 

agency costs it faces under decentralization, and the infrastructure and time costs of information 

sharing. Any combination of these two approaches could support product and process innovation. 

 The empirical literature on innovation presents some results consistent with these 

hypotheses. A number of studies show that various inclusive and interactive work processes 

increase productivity (see Black and Lynch, 2004; Bresnahan, Brynjolfsson and Hitt, 2002, or 

Ichniowski, Shaw, and Prennushi, 1997 for excellent examples). There are far fewer articles 

linking workplace organization to innovation. Typically, large nationally representative surveys 

lack either a direct measure of innovation or detailed information about work organization. 

Michie and Sheehan (1999) use two proxies for innovation to link human resources practices with 

innovation in a large data set of UK firms. Laursen and Foss (2003) show an association between 

a variety of human resource practices to innovation in data from 1,900 Danish firms, and Rogers 

(1999) uses a panel of 698 Australian workplaces to show that better employee-management 

communications systematically increase reported innovations. Therrien and Léonard (2003) use 

the first wave of the data set used here to show that particular forms of enrichment practices affect 

the innovative nature of the establishment. 

 The contributions of this paper differ from this prior literature on workplace and 

innovation. First, our data, which include over 19,000 observations spanning a period of four 

years, are more extensive than those used in prior research in this area. Second, much of the 

existing work follows Ichniowski, Shaw, and Prennushi (1997) in focusing on the 

2
 

complementarities in human resource management techniques. One implication, supported by 

Michie and Sheehan (1999) and Therrien and Léonard (2003), is that the more types of workplace 

practices used, the greater the rate of innovation. Our approach presupposes a certain degree of 

complementarity, but also tries to identify tradeoffs in the sense that different groups of workplace 

practices imply different avenues through which firms can foster innovation. Finally, taking 

advantage of the panel aspect of our data, we control for the potential endogeneity of the 

workplace practices. 

 The remainder of this paper is divided into four sections. In Section II, a descriptive model 

highlights how either information sharing or decentralized decision rights might increase 

innovation. Section III describes the data, measures of decentralization and information-sharing 

and an empirical strategy. Section IV presents results, and section V concludes. 

II. Theories of organization 

 We develop a descriptive model, inspired in part by the intuition of Jensen and Meckling 

(1995), to highlight how organizational structures might foster the use of workers’ knowledge to 

make useful innovations. As Mookherjee (2006) points out, existing formal models do not 

provide a complete theory of decision rights that integrates information processing or 

communication costs with incentive considerations. This descriptive framework therefore 

provides intuition and insights into the trade-off between centralization and decentralization. 

Imagine that an establishment’s founders choose its organizational structure to maximize future 

profits, taking into account a link between work organization and expected returns from 

innovations. Many factors, such as industry, uncertainty of the market, speed of market change, 

and the nature of inputs, affect whether such profitable innovations are likely in any particular 

establishment. The founder has ex ante estimates of the probabilities that workers and managers 

3
 

possess private information relevant to conceiving of and implementing innovations, and the 

probability that innovations with positive expected returns will be found. 

 The model assumes that the risk-neutral founders have three choices available for 

increasing the expected benefits of innovation: decentralized decision making, information 

sharing, and the use of incentive pay. These three variables are summarized by nonnegative 

measures d, s and p. Decentralized decision making and information sharing enable the active 

use of more private information held by the establishment’s workers. Incentive pay does not 

directly lead to the transfer of private information, but reduces the costs of decentralization or 

information sharing by aligning incentives. The expected number of future innovations is a 

nonnegative function i(d,s) which is increasing in each of its arguments. Letting the average net 

expected benefit of each adopted innovation be bi, the overall net expected benefits of innovation 

from these organizational choices are bi*i(d,s). Costs associated with innovation that are 

independent of workplace organization are subsumed in bi. 

 We assume that decentralization has costs, cd(d,p), which include the possible losses from 

decisions with mutually inconsistent objectives, the costs of monitoring workers, and the 

opportunities lost because the establishment is less able to coordinate decisions at a higher level 

where information could be pooled. There are also costs to transferring information, which we 

denote cs(d,p). These include the costs of setting up and operating information infrastructure, 

including suggestion programs and problem-solving committees, and the indirect costs that are a 

function of the delay in transferring information. Incentive pay plays a special role in the model. 

It does not directly affect innovation, but instead mitigates the costs of the other two forms of 

work organization. Incentive pay encourages workers to make decisions consistent with the 

founders’ goals, reducing cd, and motivates workers to identify and share useful information, 

reducing cs. We denote operational and other costs of incentive pay systems by cp(p). The 

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founders choose an organizational structure (d, s, p) to maximize the expected net benefits of 

future innovations: 

,, ipsd d −− s − p pcpscpdcsdib )(),(),(),(:max (1)

 Since the empirical section focuses on discrete measures of the three variables, corner 

solutions--where the founder chooses not to use some aspect of work organization--are 

particularly relevant. For example, if the benefits of both information sharing and 

decentralization are small (the first derivatives, is and id, are always close to zero) or the costs 

of these work organization practices are high, then the founder will choose a centralized 

workplace, where d = s = 0. Such a centralized workplace can nonetheless be consistent with 

innovation (if bi>0 and i(d,s) has a positive intercept), or the presence of a centralized 

workplace might indicate that the founder does not pursue innovation (if bi<0). 

The model also allows for the possibility that the founder chooses to use either d, or s, or 

both. One such case occurs when workers hold information that can lead to innovations, but the 

costs to communicating this information hierarchically exceed the agency costs of empowering 

workers. This could happen if the information were tacit (hard to communicate or identify) or if 

the economic environment were to require firms to react quickly.1

 In such situations, firms might 

optimally allocate decision making about technology and work processes to the workers. A 

second possibility is that workers have production-specific information that can quickly and 

easily be communicated. In this case the marginal impact of information sharing, is, might be 

relatively large. Here, labor-management committees and other formal programs that encourage 

information sharing, suggestions and feedback may support innovation. If complementarities 

 

1

 Some examples illustrate the scenario. E-commerce has changing technologies and markets. Changing fashions can 

affect what the market wants. Difficulty or delay in computation or processing by decision makers introduces costs 

similar to difficulty or delay in communication; see Van Zandt (2003) for a model of this. 

5
 

between d and s exist, the founder may choose to use both. The founder may offset some of 

the costs of these forms of work organization through the use of incentive pay. 

 This descriptive model allows us to classify combinations of work organization practices, 

highlights implicit assumptions, and points towards some hypotheses which provide a framework 

for prediction. For example, the model discounts reasons other than innovation that managers 

would decentralize or share information.2

 The model also assumes a direction of causality so that 

innovations depend on work organization; in the empirical section we test the alternative 

hypothesis that innovations predict structure.3

 In terms of providing a framework for prediction, 

the model suggests that decentralization and information-sharing should both be positively 

associated with innovations. While fully centralized firms may also innovate, they do so only if 

bi>0, a more restrictive criteria than those faced by other firms. Thus, we expect that fully 

centralized firms should be negatively associated with innovations relative to decentralized ones. 

 Decentralization and information sharing can be either complements or substitutes. If 

information sharing improves the decision-making capacity of managers, it will reduce the 

benefits of decentralization. On the other hand, bottom-up information sharing makes monitoring 

easier and thus more closely aligns worker’s objectives to the firm, reducing the agency costs of 

decentralization. Additionally, top-down information sharing allows decentralized workers to 

make better decisions. The empirical section will examine these paths by including interaction 

terms between the three types of workplace innovation. 

While any result on the interaction terms between decentralization and information sharing 

might be consistent with the model, the model has clear predictions about the interaction between 

 

2

 Mohr and Zoghi (2006) show that decentralized workplaces have higher worker satisfaction, so firms may use 

decentralization to motivate workers rather than specifically to foster innovation. Freeman and Lazear (1995) explore 

the hypothesis that information sharing is used to transmit bad news and thereby induce effort. Black and Lynch 

(2001) and others consider the possibility that decentralization affects productivity directly. 3

 Therrien and Léonard (2003) offer a nice counterexample of a firm that adopts a new machine that may temporarily 

slow production and cause the firm to operate at less than full capacity. Managers might introduce special human 

resource practices, such as training or problem-solving teams to minimize the delay. 

6
 

incentive pay and the other two variables. It implies that incentive pay increases the effect of both 

decentralization and information sharing on innovation. In decentralized workplaces, incentive 

pay motivates workers to seek out profitable innovations. While information-sharing alone should 

result in innovation (since the decision-maker is assumed to have profit-maximizing objectives), 

we expect the effect to be strengthened if the bottom-up information sharing is accompanied by 

individual or group incentive pay, which motivates workers to identify and share useful 

information. Finally, the model suggests that incentive pay, absent information sharing or 

decentralization, should have little impact on innovation. Incentive pay at a centralized firm does 

not change the underlying problem: the worker might have the knowledge necessary to make 

improvements but not the authority to act on it, and vice versa. 

III. Empirical strategy and data description 

 A probit model describes the hypothesized relationship between the organizational 

structure of the workplace and its innovativeness: 

Prob(innovjt = 1) = Φ(α + β1dj + β2sj + β3pj + β4(djpj) + β5(pjsj) + β6(djsj) + β7(djpjsj) + γZjt + εjt) (2) 

where innovjt is an indicator for whether establishment j introduced an innovation in year t, and 

dj, sj, and pj are indicators for whether establishment j used decentralized decision-making, 

information-sharing, or incentive pay, respectively, in the initial year. The model includes 

interaction terms for the joint use of these organizational features. The descriptive model suggests 

that the coefficients on decentralization and information sharing (β1 and β2) will be positive, the 

coefficient on incentive pay alone (β3) will be zero, the interactions between incentive pay and 

the other two forms of work organization (β4 and β5) will be positive, and that the remaining 

7
 

interaction terms (β6 and β7) may be either positive or negative. Finally, Zjt includes other 

variables that are likely to affect the innovativeness of an establishment. 

 This project uses data on 6,322 establishments drawn from the 1999-2003 Canadian 

Workplace and Employee Survey (WES). Establishments were first selected from all employers 

in Canada with paid employees, except for those in the Yukon, Nunavut, and Northwest 

Territories and those in farming, fishing and trapping, religious organizations and public 

administration. These establishments were then re-surveyed annually for five years, the first four 

of which are currently available for analysis. In 1999, 6322 workplaces were interviewed, with 

data collected through personal interviews.4 In the succeeding years, responses were acquired 

from 6068, 6223, and 5818 of these establishments, using computer-assisted telephone interviews. 

Questions about workplace practices were asked only in 1999 and 2001 and only of 

establishments with more than 10 employees. Smaller establishments are dropped from our 

sample. 

The survey asks respondents whether the workplace has introduced any of four specific 

types of innovations in the preceding year: 1) new goods or services, which “differ significantly in 

character or intended use from previously produced goods or services,” 2) improved goods or 

services, which “are those whose performance has been significantly enhanced or upgraded,” 3) 

new processes, which “include the adoption of new methods of goods production or service 

delivery,” and 4) improved processes, which “are those whose performance has been significantly 

enhanced or upgraded.” Table 1 shows the share of establishments that report introducing an 

innovation in the past year.5

 In three of the four years, the majority of establishments introduced 

some kind of innovation. Product innovations were more common than process innovations, and 

 

4

 While the primary contact is typically a human resources person, in about 20% of the surveyed establishments, other 

respondents also answer some questions. 

5

 All means reported here have been probability-weighted to adjust for the sampling framework and to protect the 

confidentiality of respondents. 

8
 

more establishments reported improvements to existing products and processes than entirely new 

ones. Innovation rates of all kinds were highest in 1999 and lowest in 2002. 

The survey also elicits detailed information about work organization, including the use of 

quality circles, teams, suggestion programs, feedback, self directed work and the use of incentive 

pay programs. It records who (workers, management or some combined team) participates in 

decisions over twelve different aspects of the production process, including planning of individual 

work, purchase of machinery, staffing levels, and new product development. The level of detail in 

the information about both innovation and work organization makes the WES data set unique. 

 Consistent with the theory explored in the previous section, we classify firms according to 

the allocation of decision authority, the use of information-sharing techniques, and incentive pay 

programs. We start with decision-making. We count the number of decisions that workers 

participate in making, and categorize those establishments that delegate two or more decisions (out 

of the twelve possible) as decentralized establishments.6

 Since these questions are asked twice of 

establishments, once in 1999 and again in 2001, an establishment could change organizational 

structure over time. In most estimations we include only an establishment’s 1999 organizational 

structure. Using the 2001 structure or a combination yields similar results. 

Apart from decision-making, flows of communication between workers and management 

can support innovation. The WES gathers information about three workplace characteristics that 

indicate such inclusiveness: 1) employee suggestion programs, including employee survey 

feedback, 2) information sharing programs, “for example with response to firm’s performance, 

colleagues’ wages, technological or organizational change, etc.”, and 3) joint labor-management 

committees, which include “non-legislated joint labour-management committees and task teams 

that generally cover a broad range of issues, yet tend to be consultative in nature.” These 

 

6

 Thirty-five percent of establishments were decentralized by this definition, because they delegated two or more types 

of decisions to workers or workgroups. An alternative measure of decentralization would be how high in a hierarchy 

decision problems go to be resolved, as suggested by Clawson (1980, pp. 84-85). 

9
 

workplace characteristics seek to transfer information and get employee input without necessarily 

ceding the decision rights. We define an establishment to employ information-sharing if it has at 

least two of these three programs existing on a formal basis in the workplace. 

Finally, the survey includes indicators for the existence of an individual incentive pay plan, 

group incentive pay, or a profit-sharing plan for non-managerial employees at the workplace. We 

estimate the effects of each. When estimating interactions with the other two work organization 

variables, we use individual incentive pay. 

 Other factors that may affect the probability of innovation are included in Zjt, and the 

means of these variables are listed in Table 2. The size of the establishment, measured by the 

natural log of the number of employees, should be positively related to innovation, since there 

may be more product lines and services that are open to efficiency and quality improvements. 

Establishment age may affect innovation in that older institutions may have already invested in 

determining their internal structure. Their core technologies and ways of organizing have survived 

a longer-term selection process, so they may therefore be less likely to innovate. A strong union 

presence can increase resistance to process changes or reduce the share of rents from innovation 

that are captured by the establishment. Both would reduce the incentive to innovate.7 Employees 

in professional, technical, or managerial positions are more likely to possess valuable information 

that can be used to innovate. The model also includes a control variable for whether the 

establishment is part of a multi-plant firm, as well as industry and year indicators. 

Two variables capture the volatility of the market: an indicator for whether the 

establishment experiences seasonal peaks in demand, and the vacancy rate (the number of 

vacancies as a fraction of total employment). Both may predict innovation. The establishment 

may innovate in response to fluctuations in either the input or output markets, and seasonal ebbs 

 

7

 Hirsch and Link (1987) find that R&D spending is lower in unionized firms and Acs and Audretsch (1988) find that 

highly unionized industries produce fewer innovations. 

10
 

may provide opportunities to focus on redesigning products or production processes. To measure 

competition, we include indicators for whether the establishment is a monopoly, whether it faces 

significant competition (more than twenty competitors), and if it has non-profit status. Aghion et 

al. (2005) and Parente and Prescott (1999) theorize that monopolies are less likely to innovate, but 

recognize Schumpeterian-type arguments going the other way. For example, some monopolies 

exist because the monopolist previously innovated, and may remain the type to innovate further. 

Monopolies may also be able to benefit uniquely from competence-enhancing innovations and 

therefore have a particular incentive to innovate (Tushman and Anderson, 1986). 

 Table 3a indicates how innovativeness varies across industry, measuring the share of 

establishments that had any of the four types of innovations in 1999. Establishments in finance 

and insurance and capital-intensive (often high tech) manufacturing reported innovations more 

often than establishments in other industries did. Also especially innovative were information and 

cultural industries, and labor-intensive tertiary manufacturing, which includes firms with many 

product lines, or firms that respond quickly to changes in consumer demand. A probit estimation 

of the probability of innovation on the industrial classification, shown in Appendix Table A2, 

confirms that these four industries are associated with the highest marginal increase in probability 

of innovation. Industries reporting the fewest innovations were forestry, mining, oil and gas 

extraction, education and health services, communications and other utilities. 

 Table 3b details innovativeness across values of the other explanatory variables in Zjt. It 

suggests that the three workplace organization variables are correlated with higher innovation. 

Establishments that decentralize decision making, share information, or offer incentive pay plans 

innovate much more than those that do not do any of these.8

 Additionally, larger establishments, 

unionized establishments, those with seasonal demand peaks, and those with high vacancy rates, 

 

8

 Appendix Table A1 shows innovation rates by the individual workplace practices that comprise these organizational 

types. No single component appears to dominate this result. 

11
 

have higher innovation rates than the average establishment. Monopolies have a strikingly low 

rate of innovation. We now turn to our regression approach to determine whether these 

correlations persist when all effects are measured jointly. 

IV. Results 

 The first three columns of Table 4 report marginal effects of the probit estimation 

described in equation (2), using three alternative measures of the dependent variable: whether the 

establishment introduced a new or improved product, whether it introduced a new or improved 

process, and whether it introduced any innovation in the past year. The fourth column uses a 

Tobit estimator to measure the effect of the independent variables on the number of these four 

innovation types an establishment introduced in the past year. We treat this count as a rough 

ranking of innovativeness. These show how the predictors affect the number of types of 

innovations—they are not comparable in magnitude, nor necessarily in sign, to the first three 

columns, which measure how the predictors affect the probability of having one or any innovation. 

All columns use the pooled 1999-2002 sample and standard errors are corrected both for sample 

design and for heteroskedasticity due to multiple observations per establishment. 

Decentralized decision making and information sharing are both strongly correlated with 

innovation. Establishments that employ one of these forms of workplace organization are 14-22% 

more likely to have an innovation than the excluded group (centralized establishments without 

information-sharing or individual incentive pay programs). The use of incentive pay programs is 

also positively related to innovation, but the effect is small and statistically insignificant in three 

estimations. All of these results are consistent with the descriptive model, which predicts that 

information sharing and decentralization will both positively affect innovation, but that incentive 

pay will not foster innovation directly. The result that the marginal effect of information sharing 

is consistently larger than the marginal effect of decentralization is a significant new finding. 

12
 

The negative coefficients on the interaction between decentralization and information 

sharing suggests that the two organizational structures are more likely substitutes than 

complements, although the effects are only statistically significant in the Tobit estimation. The 

consistently negative coefficient on the interaction between decision rights and incentive pay is 

contrary to the implications of our theory. The result may reflect a resistance by workers on some 

forms of incentive pay to implement change. Freeman and Kleiner (2005), studying production 

workers in shoe manufacturing who are paid on piece rates, argue that these workers might be 

particularly resistant to change. Innovation in technology or the production process may require 

workers to be assigned to new jobs, or learn new tasks. Such changes could cause a temporary 

decrease in the individual worker’s output and therefore pay.9

 Other establishment characteristics are also correlated with innovation. Larger 

establishments are more likely to innovate, presumably because they have more activities of any 

kind, including those which can be improved or leveraged to larger scale. Stronger union presence 

does not appear to be correlated with the probability of any particular innovation, although it is 

negatively related to the number of types of innovation an establishment reports. Nonprofit 

institutions are less likely to generate innovations than comparable for-profit institutions. A high 

vacancy rate is strongly correlated with innovation as predicted; these may be establishments in 

opportunistic, turbulent circumstances. 

The findings in table 4 provide evidence that decentralization and information-sharing 

practices predict higher innovation rates. They may not cause the higher innovation rates, 

however. First, causality might go in the opposite direction -- innovation may spur workplace 

reorganization. Second, both outcomes may be caused by omitted variables. For example, it may 

be that college-educated managers spot potential innovations and that educated managers also 

 

9

 Other plausible explanations might also account for these results, however. For example a survey instrument only 

captures the presence of formal programs. Information sharing in particular might often be done informally at 

establishments that decentralize decision rights. This would bias both the coefficient on information sharing and on 

the interaction term. 

13
 

share information or grant decision rights. In this case, since manager education is unobserved, 

our results for the other variables would be biased relative to the structural relationship. 

We investigate the first of these issues by testing for causality going the other direction. 

Table 5 reports the results of three probit estimations on the probability of an establishment 

granting decision rights, using information sharing programs or using an incentive pay scheme in 

2001. We include most of the regressors in Table 4, along with measures of the number of 

innovations of any kind in each year of the sample. If past innovations are correlated with the 

workplace organization, but later ones are not, then we have evidence of a “reverse causality” 

problem. Results of this estimation do indicate the possibility that innovations lead to workplace 

organization, in particular for the adoption of information sharing and incentive pay plans, which 

are correlated with the one year lagged innovations. This is a short term phenomenon and difficult 

to pin down accurately since the observed annual variation depends on when an innovation is 

reported. For example, an establishment may commit to making a new product before the 

innovation is recognized by the survey respondent, or it may be recognized by the respondent 

before it is finished. The fact that there aren’t correlations of the same kind at a two year lag 

suggests we do not have long term reverse causality. 

We take additional steps to control for the potential endogeneity of the regressors. One 

way to remove some of this endogeneity is to restrict the analysis to the pooled 2000-2002 sample, 

and use the lagged 1999 variables as regressors. This specification gives up some information 

about the current year that might affect innovation outcomes in order to reduce short run 

endogeneity. Consider, for example, the vacancy rate. Innovative establishments are likely to 

expand by hiring, which implies that both the vacancy rate and the innovation rate is informative 

about the firm’s type, but vacancy is not the cause of innovation. A lagged variable, like the 

previous vacancy rate is likely to tell us about the firm’s type and its environment historically, 

14
 

without being a short-run result of recent innovative outcomes. It is less endogenous than currentyear vacancy rates. 

Table 6 shows the results of these estimations. The results for the workplace organization 

variables are qualitatively similar to those in Table 4. Information-sharing programs and 

decentralized decision-making remain strongly correlated with reported innovations, and the 

magnitudes of these coefficients are largely unchanged. The effect of incentive pay is also similar 

to that found in the initial estimation, but the marginal effect is slightly larger and more 

significant. The interaction of decentralization and incentive pay remains strong and negative. 

Several other coefficients change in size and significance, suggesting that they were previously 

biased due to their relationship with the endogenous regressor. The size of the establishment is no 

longer significant at the 5% level in the three probit estimations, and even negatively affects the 

number of innovation types. Seasonal peaks become insignificant in this model. The vacancy rate 

is now significant only at the 10% level in the probit estimations.10

As a further step to eliminate potential endogeneity, we control for whether the 

establishment reported any of the four innovation types in 1999. Unobservable establishment 

characteristics that affect innovative behavior are approximately held fixed in the 1999 measures 

of innovation. Table 7 shows that year 1999 innovations strongly predict current innovations, 

especially those that are of the same type, i.e. product or process. The effects of work 

organization on innovation remain qualitatively the same. Information-sharing is the strongest 

positive predictor of innovations, although the size of the effect is somewhat smaller in this 

estimation. Decentralized decision making is also positive and statistically significant for both the 

number and types of innovations. The effects of most of the other predictors remain the same. 

 

10 Further concerns about the endogeneity of the vacancy rate led us to attempt a specification in which we replaced 

the establishment’s own vacancy rate with an industry-averaged vacancy rate. This did not change the results 

significantly. 

15
 

Finally, the longitudinal nature of the data enables us to test for endogeneity by including 

establishment fixed effects to the model in equation (2). Here, effects are identified for those 

establishments that change workplace organization between 1999 and 2001. This is a noisy source 

of variation, much of which probably comes from small differences in workplace practices, 

interpretation of the survey question, or reporting mistakes. Furthermore, changes in 

organizational practices would not usually immediately yield changes in innovative activity. 

Although this is not our preferred specification, we report the results nonetheless as one control 

for endogeneity. 

Table 8 reports the results of this estimation. In the fixed effects logit models, 

establishments that adopt information-sharing techniques are much more likely to report 

innovations. Although this might be true by definition for process innovations if the respondent 

interprets changes in work organization as a process innovation, the result also holds up, and is 

even larger, in the product innovation specification. Although size and union presence were 

statistically insignificant in the pooled estimations, the effects are large and significant in this 

model. A given establishment is more likely to report innovations in periods when it is larger, and 

less likely to report innovations in periods when it has more union coverage. Those 

establishments whose demand becomes more volatile by becoming seasonal or by having an 

increased vacancy rate are more likely to innovate. The overall explanatory power of these 

estimations is quite low, confirming that our source of variation is noisy. Our aim here, however, 

is not to fully explain innovative behavior, but rather to confirm the relationship between 

organizational structure and innovation. 

V. Conclusion 

We use the Canadian WES data to identify whether an establishment is decentralized or 

centralized, based on the extent of worker participation in decision making in the establishment. 

16
 

We further measure whether or not the establishment employs information-sharing techniques to 

transfer information to and from employees. We theorize that information-sharing and 

decentralization are two alternate methods of bridging the gap between information vital to 

innovative activity and the authority to act on such information. Our theory suggests that 

incentive pay would make either method more effective. 

We test whether there is a relationship between decentralization, information-sharing, or 

incentive pay and innovation, controlling for a number of establishment characteristics like 

industry, establishment size, degree of competition, non-profit status, and demand volatility. 

Information-sharing is strongly and positively related to innovation. Decentralized decision 

making also has a positive, though weaker, relationship to innovation. Incentive pay is only 

weakly related to innovation and, surprisingly, the interaction of incentive pay with decision rights 

has a negative coefficient. While we cannot be certain that this relationship is causal, we have 

looked for evidence of reverse causality and used several different techniques to control for 

potential endogeneity. We find a consistently strong predictive effect of workplace organizational 

structure on innovation. 

17
 

References 

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18
 

Mohr, Robert and Cindy Zoghi. 2006. “Is Job Enrichment Really Enriching?” Bureau of Labor 

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Rogers, Mark. 1999. “Innovation in Australian Workplaces: an Empirical Analysis.” Australian 

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Therrien, Pierre and André Léonard. 2003. Empowering Employees: A Route to Innovation, 

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Van Zandt, Timothy. 2003. “Real-Time Hierarchical Resource Allocation.” INSEAD working 

paper. http://faculty.insead.edu/vanzandt/research-orgs/papers/Resource1.pdf 

19
 

Table 1. Percent of establishments in the WES reporting innovations 

1999 2000 2001 2002

New product or service 44.17 31.56 40.07 29.10 

Improved product or service 53.29 43.61 46.00 33.52 

New process 37.26 31.12 32.83 20.60 

Improved process 45.55 37.76 37.39 28.14 

Any innovation 63.0 54.1 57.3 45.3 

Number of observations 4123 4021 4089 3940 

Notes: All estimates are calculated using probability weights. Sample restricted to those establishments with more 

than 10 employees. 

20
 

Table 2. Workplace characteristics in 1999 WES 

Mean

Organizational type: 

Decentralized decision rights .420 

Information sharing .320 

Individual incentive pay plan (yes = 1) .422 

Centralized, without incentive pay or 

information sharing 

.279 

Industry: 

Forestry, mining, oil and gas extraction .015 

Labor-intensive tertiary manufacturing .049 

Primary product manufacturing .025 

Secondary product manufacturing .031 

Capital-intensive tertiary manufacturing .053 

Construction .051 

Transportation, warehousing, wholesale trade .133 

Communication and other utilities .021 

Retail trade and consumer services .288 

Finance and insurance .072 

Real estate, rental and leasing operations .015 

Business services .110 

Education and health services .105 

Information and cultural industries .031 

Other vars: 

Ln (establishment size) 3.28 

Union coverage rate .131 

Nonprofit institution (yes = 1) .098 

Part of multi-plant establishment .328 

Ln (establishment age) 2.19 

Seasonal demand peaks (yes = 1) .405 

Vacancy rate .037 

Profit-sharing plan (yes = 1) .151 

Monopoly (yes = 1) .081 

More than twenty competitors (yes = 1) .246 

Notes: All estimates are calculated using probability weights. Sample 

restricted to those establishments with more than 10 employees. 

21
 

Table 3a. Share of establishments innovating, by industry 

Percent

innovating

Full sample 63.0 

Industry: 

Forestry, mining, oil and gas extraction 45.0 

Labor-intensive tertiary manufacturing 66.5 

Primary product manufacturing 62.2 

Secondary product manufacturing 71.4 

Capital-intensive tertiary manufacturing 82.8 

Construction 54.0 

Transportation, warehousing, wholesale trade 70.1 

Communication and other utilities 52.6 

Retail trade and consumer services 59.6 

Finance and insurance 73.3 

Real estate, rental and leasing operations 64.9 

Business services 62.3 

Education and health services 50.1 

Information and cultural industries 68.4 

Notes: All estimates are calculated using probability weights. Sample 

restricted to those establishments with more than 10 employees. 

22
 

Table 3b. Share of establishments innovating, by 

workplace characteristics 

Percent

innovating

Full sample 63.0 

Decentralized decision rights 69.4 

Information sharing 75.2 

Individual incentive pay plan (yes = 1) 70.3. 

Centralized, without incentive pay or 

information sharing 

49.8 

Less than 50 employees 62.0 

50 – 99 employees 67.5 

100 – 249 employees 64.7 

At least 250 employees 74.8 

Unionized 64.7 

Non-unionized 62.6 

Nonprofit institution 47.9 

For-profit institution 64.6 

Establishment part of multi-plant firm 66.4 

Stand-alone establishment 61.3 

Establishment age less than five years 62.2 

Establishment age 5 – 14 years 64.6 

Establishment age at least 15 years 62.2 

Seasonal demand peaks 63.6 

No seasonal demand peaks 62.6 

Vacancy rate = 0 60.2 

Vacancy rate less than .03 73.8 

Vacancy rate at least .03 68.6 

Profit-sharing plan 71.3 

No profit-sharing plan 61.5 

Monopoly 57.0 

Not monopoly 63.5 

More than twenty competitors 64.4 

Under twenty competitors 62.5 

Notes: All estimates are calculated using probability weights. Sample 

restricted to those establishments with more than 10 employees. 

23
 

Table 4. Effect of 1999 organizational structure and establishment characteristics on 

the probability of innovation in 1999-2002 WES 

Product 

Innovation

Process 

Innovation

Either 

Innovation

Number of 

Innovation Types

1999: Decentralized 

(yes = 1) 

.1537*** 

(.044) 

.1579*** 

(.050) 

.1354*** 

(.045) 

.6914*** 

(.087) 

1999: Info-sharing 

(yes = 1) 

.2147*** 

(.056) 

.2234*** 

(.053) 

.1812*** 

(.055) 

1.1653*** 

(.103) 

1999: Incentive Pay Plan 

(yes = 1) 

.0765* 

 (.043) 

.0362 

(.046) 

.0694 

(.042) 

.3249*** 

(.091) 

Decentralization * 

Individual Incentive Pay

-.1162* 

(.062) 

-.1343** 

(.062) 

-.1221* 

 (.064) 

-.5397*** 

(.142) 

Information-sharing * 

Individual Incentive Pay

-.0757 

(.080) 

.0292 

(.076) 

.0019 

(.078) 

-.0822 

(.174) 

Decentralization * 

Information-sharing 

-.1245 

(.081) 

-.1076 

(.076) 

-.0921 

(.084) 

-.6061*** 

(.148) 

Decentralization * 

Individual Incentive Pay 

* Information-sharing 

.0382 

(.113) 

-.0269 

(.108) 

-.0001 

(.110) 

.2442 

(.250) 

Ln (establishment size) .0295** 

(.013) 

.0382*** 

(.013) 

.0373*** 

(.013) 

.0566 

(.036) 

Union coverage rate -.0023 

(.045) 

.0130 

(.046) 

.0079 

(.043) 

-.3591*** 

(.097) 

Nonprofit institution 

(yes = 1) 

-.1650*** 

(.057) 

-.1083* 

(.058) 

-.1546** 

(.060) 

-.8157*** 

(.137) 

Profit-sharing plan -.0133 

(.034) 

-.0062 

(.039) 

.0067 

(.036) 

-.1329 

(.090) 

Group incentive pay .0083 

(.038) 

.0417 

(.034) 

.0183 

(.039) 

.2144** 

(.089) 

Part of multi-plant firm 

(yes = 1) 

.0296 

(.033) 

.0664** 

(.033) 

.0427 

(.032) 

.2566*** 

(.065) 

Ln (establishment age) -.0093 

(.014) 

-.0025 

(.013) 

-.0036 

(.013) 

-.0694*** 

(.025) 

Seasonal peaks (yes = 1) .0499* 

(.028) 

.0285 

(.030) 

.0287 

(.029) 

.2538*** 

(.060) 

Vacancy rate .2208*** 

(.073) 

.1397** 

(.063) 

.2226*** 

(.078) 

1.0027*** 

(.229) 

Monopoly (yes = 1) -.0362 

(.041) 

.0133 

(.046) 

-.0401 

(.040) 

-.1461 

(.104) 

20+ competitors 

(yes = 1) 

-.0043 

(.036) 

.0097 

(.034) 

-.0025 

(.036) 

.0045 

(.067) 

Percent professional, 

technical, managerial 

.0593 

(.049) 

.0262 

(.044) 

.0527 

(.048) 

.2942*** 

(.107) 

Pseudo-R2 .064 .083 .064 .024 

Notes: Cols. 1-3 are marginal effects of probit regressions. Col. 4 are Tobit effects. All are adjusted with 

probability weights and to control for clustering due to multiple observations in the same establishment, one for 

each year. *

 = p-value<.1, ** = p<.05, *** = p<.01. Each regression also includes a full set of 13 industry and 

year indicators. N = 14,594 

24
 

Table 5. Predictive effect of innovations on workplace organization; a test of reverse 

causality 

Dependent variable: 2001 workplace organization type: 

Decentralized 

decision rights 

Information 

sharing 

Individual 

incentive pay plan 

1999: any innovation -.0294 

(.047) 

-.0329 

(.042) 

-.0133 

(.048) 

2000: any innovation -.0268 

(.046) 

.1573*** 

(.041) 

.1349*** 

(.047) 

2001: any innovation .0819* 

(.048) 

.0289 

(.043) 

.0514 

(.052) 

2002: any innovation .0275 

(.045) 

.0913** 

(.042) 

-.0446 

(.050) 

1999: Decentralized 

(yes = 1) 

.2104*** 

(.043) 

1999: Info-sharing 

(yes = 1) 

.1809*** 

(.045) 

1999: Incentive Pay Plan 

(yes = 1) 

.2676*** 

(.046) 

Ln (establishment size) .0528*** 

(.019) 

.0531*** 

(.016) 

.0536** 

(.023) 

Union coverage rate -.0335 

(.067) 

.0712 

(.053) 

-.2971*** 

(.076) 

Nonprofit institution 

(yes = 1) 

-.0346 

(.076) 

.1514 

(.100) 

-.2448*** 

(.052) 

Profit-sharing plan .1238* 

(.071) 

-.0113 

(.048) 

.0898 

(.067) 

Group incentive pay -.0067 

(.060) 

.1539*** 

(.060) 

.2130*** 

(.073) 

Part of multi-plant firm 

(yes = 1) 

-.0885* 

(.050) 

.0653 

(.046) 

.1394*** 

(.054) 

Ln (establishment age) -.0185 

(.022) 

-.0032 

(.019) 

-.0080 

(.019) 

Seasonal peaks (yes = 1) .0776 

(.054) 

-.0020 

(.043) 

.0021 

(.052) 

Vacancy rate -.3605 

(.605) 

-.3422 

(.565) 

.6905 

(.568) 

Monopoly (yes = 1) -.2470*** 

(.046) 

.0047 

(.099) 

-.0997 

(.094) 

20+ competitors 

(yes = 1) 

.0723 

(.061) 

.075 

(.052) 

-.0263 

(.048) 

Percent professional, 

technical, managerial 

.0206 

(.074) 

-.0324 

(.064) 

.0767 

(.072) 

Number of observations 3570 3501 3570 

Pseudo R-squared .17 .19 .25 

Coefficients shown are marginal effects of probit estimation. Each regression also includes a full set of 13 

industry and year indicators . * = p-value<.1, ** = p<.05, *** = p<.01. All are adjusted with probability weights 

and to control for clustering due to multiple observations in the same establishment, one for each year 

25
 

26

Table 6. Effect of 1999 organizational structure and 1999 establishment characteristics on 

the probability of future innovation in 2000-2002 WES 

1999 value of : 

Product 

Innovation

Process 

Innovation

Either 

Innovation

Number of 

Innovation 

Types

1999: Decentralized 

(yes = 1) 

.1583*** 

(.050) 

.1606*** 

(.059) 

.1281** 

(.055) 

.6849*** 

(.104) 

1999: Info-sharing 

(yes = 1) 

.2248*** 

(.075) 

.2315*** 

(.068) 

.1870** 

 (.073) 

1.1403*** 

(.124) 

1999: Incentive Pay Plan 

(yes = 1) 

.0874* 

(.049) 

.0609 

(.051) 

.0881* 

(.049) 

.3415*** 

(.113) 

Decentralization * 

Individual Incentive Pay

-.1478** 

 (.069) 

-.1793** 

(.066) 

-.1668** 

(.074) 

-.6429*** 

(.171) 

Information-sharing * 

Individual Incentive Pay

-.0955 

(.101) 

-.0313 

(.087) 

-.0470 

(.104) 

-.1587 

(.217) 

Decentralization * 

Information-sharing 

-.1117 

(.090) 

-.1128 

(.082) 

-.0658 

(.097) 

-.5548*** 

(.179) 

Decentralization * 

Individual Incentive Pay * 

Information-sharing 

.0877 

(.129) 

.0179 

(.125) 

.0846 

(.128) 

.3672 

(.301) 

Ln (establishment size) .0216 

(.015) 

.0364* 

(.015) 

.0243 

(.015) 

-.0990** 

(.044) 

Union coverage rate .0262 

(.060) 

.0128 

(.060) 

.0198 

(.060) 

-.0934 

(.128) 

Nonprofit institution 

(yes = 1) 

-.1324* 

(.067) 

-.0953 

(.066) 

-.1131 

(.073) 

-.7183*** 

(.166) 

Profit-sharing plan .0135 

(.039) 

.0272 

(.043) 

.0460 

(.040) 

.0435 

(.110) 

Group incentive pay .0359 

(.044) 

.0679* 

(.041) 

.0446 

(.043) 

.3832*** 

(.109) 

Part of multi-plant firm 

(yes = 1) 

.0185 

(.038) 

.0533* 

(.037) 

.0434 

(.038) 

.1913** 

(.079) 

Ln (establishment age) -.0098 

(.013) 

-.0164 

(.014) 

-.0111 

(.013) 

-.1141*** 

(.030) 

Seasonal peaks (yes = 1) -.0230 

(.033) 

-.0220 

(.033) 

-.0415 

(.034) 

-.1513* 

(.071) 

Vacancy rate .2157* 

(.125) 

.4795* 

(.244) 

.4033* 

(.216) 

1.9315*** 

(.298) 

Monopoly (yes = 1) .0873 

(.065) 

.0551 

(.073) 

.0670 

(.063) 

.1562 

(.128) 

20+ competitors 

(yes = 1) 

-.0093 

(.041) 

-.0185 

(.038) 

-.0047 

(.041) 

-.0779* 

(.080) 

Percent professional, 

technical, managerial 

.0442 

(.058) 

.0189 

(.060) 

.0255 

(.063) 

.3516*** 

(.134) 

Pseudo-R2 .056 .082 .059 .021 

Notes: Cols. 1-3 are marginal effects of probit regressions. Col. 4 are Tobit effects. All are adjusted with probability 

weights and to control for clustering due to multiple observations in the same establishment, one for each year. *

 = pvalue<.1, ** = p<.05, *** = p<.01. Each regression also includes a full set of 13 industry and year indicators. N = 

10,409 
 

27

Table 7. Effect of 1999 organizational structure and 1999 establishment characteristics on the 

probability of future innovation in 2000-2002 WES, with controls for 1999 innovations 

1999 value of : 

Product 

Innovation

Process 

Innovation

Either 

Innovation

Number of 

Innov. Types

Decentralized (yes = 1) .1265** 

(.051) 

.1260** 

(.064) 

.0968* 

(.057) 

.4547*** 

(.102) 

Info-sharing (yes = 1) .1771** 

(.080) 

.1785** 

(.070) 

.1416* 

(.077) 

.7646*** 

(.123) 

Incentive Pay Plan (yes = 1) .0776* 

(.046) 

.0537 

(.050) 

.0835* 

(.048) 

.2938*** 

(.111) 

Decentralization * Individual 

Incentive Pay

-.1576** 

(.068) 

-.1770** 

(.067) 

-.1726** 

(.076) 

-.6720*** 

(.167) 

Information-sharing * 

Individual Incentive Pay

-.1401 

(.102) 

-.0668 

(.084) 

-.0920 

(.108) 

-.4497** 

(.211) 

Decentralization * 

Information-sharing 

-.1168 

(.093) 

-.1124 

(.084) 

-.0682 

(.101) 

-.5845*** 

(.175) 

Decent * Individual Incentive 

Pay * Info-sharing 

.1886 

(.125) 

.0923 

(.130) 

.1699 

(.124) 

1.0068*** 

(.294) 

New product .1309*** 

(.037) 

.0607 

(.039) 

.1277*** 

(.037) 

.6385*** 

(.083) 

New process .0545 

(.044) 

.1022** 

(.044) 

.0909** 

(.043) 

.5325*** 

(.097) 

Improved product .0984** 

(.045) 

.0148 

(.042) 

.0477 

(.047) 

.4753*** 

(.095) 

Improved process .0067 

(.050) 

.0915** 

(.046) 

.0133 

(.050) 

.2405** 

(.103) 

Ln (establishment size) .0159 

(.015) 

.0316** 

(.015) 

.0189 

(.015) 

-.1261*** 

(.042) 

Union coverage rate .0439 

(.056) 

.0331 

(.055) 

.0414 

(.056) 

.0283 

(.125) 

Nonprofit institution 

(yes = 1) 

-.1062 

(.065) 

-.0964 

(.064) 

-.0969 

(.070) 

-.5872*** 

(.162) 

Profit-sharing plan .0156 

(.040) 

.0331 

(.044) 

.0485 

(.039) 

.0579 

(.107) 

Group incentive pay .0510 

(.046) 

.0756* 

(.044) 

.0572 

(.043) 

.4620*** 

(.106) 

Part of multi-plant firm 

(yes = 1) 

.0199 

(.038) 

.0458 

(.039) 

.0381 

(.038) 

.1686** 

(.078) 

Ln (establishment age) -.0056 

(.013) 

-.0153 

(.014) 

-.0072 

(.013) 

-.0910*** 

(.029) 

Seasonal peaks (yes = 1) -.0458 

(.034) 

-.0425 

(.034) 

-.0670* 

(.035) 

-.2911*** 

(.070) 

Vacancy rate .1570 

(.121) 

.4781* 

(.247) 

.3736* 

(.206) 

1.6489*** 

(.289) 

Monopoly (yes = 1) .0900 

(.072) 

.0602 

(.083) 

.0703 

(.070) 

.1644 

(.124) 

20+ competitors 

(yes = 1) 

-.0340 

(.040) 

-.0472 

(.037) 

-.0299 

(.040) 

-.2676*** 

(.079) 

Percent professional, 

technical, managerial 

.0194 

(.059) 

-.0059 

(.062) 

-.0038 

(.064) 

.1544 

(.132) 

Pseudo-R2 .090 .115 .090 .035 

Notes: Cols. 1-3 are marginal effects of probit regressions. Col. 4 are Tobit effects. All are adjusted with probability weights 

and to control for clustering due to multiple observations in the same establishment, one for each year. *

 = p-value<.1, ** = 

p<.05, *** = p<.01. Each regression also includes a full set of 13 industry and year indicators. N = 10,409 
 

28

Table 8. Effect of organizational structure and establishment characteristics on the 

innovations in 1999-2002 WES, with establishment fixed effects included 

Product 

Innovation

Process 

Innovation

Either 

Innovation

Number of 

Innovation 

Types

Decentralized (yes = 1) .1797 

(.112) 

.1004 

(.115) 

.1154 

(.112) 

.1353*** 

(.045) 

Info-sharing (yes = 1) .4972*** 

(.120) 

.1330 

(.119) 

.3547*** 

(.118) 

.3476*** 

(.049) 

Incentive Pay Plan (yes = 1) .2247** 

(.118) 

.0380 

(.120) 

.1880 

(.116) 

.1922*** 

(.047) 

Decentralization * Individual 

Incentive Pay

-.0481 

(.166) 

-.0130 

(.167) 

-.1077 

(.164) 

-.0044 

(.069) 

Information-sharing * 

Individual Incentive Pay

-.1958 

(.180) 

.2564 

(.181) 

-.0911 

(.182) 

.0179 

(.076) 

Decentralization * 

Information-sharing 

-.0408 

(.163) 

.1413 

(.162) 

.0737 

(.163) 

.0585 

(.068) 

Decent * Individual 

Incentive Pay * Info-sharing 

.1144 

(.249) 

.0231 

(.249) 

.1995 

(.253) 

.0292 

(.105) 

Ln (establishment size) .1577* 

(.087) 

.2533*** 

(.083) 

.3031*** 

(.084) 

.1350*** 

(.013) 

Union coverage rate -.1841 

(.128) 

-.2297* 

(.126) 

-.2652** 

(.127) 

-.2060*** 

(.042) 

Profit-sharing plan 

(yes = 1) 

-.0747 

(.108) 

.0468 

(.106) 

-.0268 

(.107) 

.0118 

(.038) 

Group incentive pay 

plan (yes = 1) 

.1037 

(.092) 

.0766 

(.092) 

.1182 

(.095) 

.1153*** 

(.037) 

Seasonal peaks (yes = 1) .2028*** 

(.078) 

.2376*** 

(.077) 

.2413*** 

(.078) 

.1487*** 

(.029) 

Vacancy rate .5112* 

(.324) 

.9449** 

(.422) 

1.1475*** 

(.478) 

.0204 

(.038) 

Monopoly -.2414* 

(.136) 

.0231 

(.140) 

-.0618 

(.137) 

-.1625*** 

(.052) 

Percent professional, 

technical, managerial 

-.0612 

(.127) 

-.1708 

(.130) 

-.0874 

(.129) 

.0181 

(.049) 

Pseudo-R2 .008 .008 .010 .023 

Number of observations 8623 8642 8617 14594 

Notes: Cols. 1-3 are effects of fixed effects logit regressions. Col. 4 are linear fixed effects. * = p-value<.1, ** = 

p<.05, *** = p<.01. Each regression also includes a full set of 13 industry and indicators. N = 10,409 
 

29

Table A1. Workplace organization and components in 1999 

WES, by whether or not innovating 

Fraction 

innovating

Decentralized decision-making 

 Decide on daily planning of individual work .625 

 Decide on weekly planning of individual work .629 

 Decide on follow-up of results .697 

 Decide on customer relations .680 

 Decide on quality control .688 

 Decide on purchase of necessary supplies .680 

 Decide on machine/equipment maintenance .662 

 Decide on setting staffing levels .512 

 Decide on filling vacancies .851 

 Decide on training .667 

 Decide on choice of production technology .727 

 Decide on product/service development .708 

Information –sharing 

 Suggest .774 

 Info-sharing .763 

 Committee .755 

Notes: All estimates are calculated using probability weights. Sample 

restricted to those establishments with more than 10 employees. 
 

30

Table A2. Effect of industry on the probability of innovations 

New 

Product 

New 

Process 

Improved 

Product 

Improved 

Process 

Finance and insurance 0.44 0.31 0.38 0.26 

Capital-intensive tertiary manufacturing (printing, 

machinery manufacturing, computer and electronics, 

lighting, transportation equipment) 

0.37 0.20 0.33 0.22 

Labor-intensive tertiary manufacturing (food, 

beverage, tobacco, textile, apparel, leather, furniture, 

and miscellaneous manufacturing) 

0.37 0.15 0.28 0.15 

Information and culture 0.32 0.14 0.29 0.15 

Secondary product manufacturing (chemicals; plastic, 

rubber, and fabricated metal products) 0.34 0.14 0.23 0.15 

Primary product manufacturing (wood, paper, 

petroleum, coal, metal, and nonmetallic mineral 

products) 

0.21 0.09 0.22 0.15 

Retail trade and consumer services 0.33 0.07 0.25 0.02 

Transportation, storage and wholesale trade 0.28 0.08 0.21 0.04 

Business services 0.23 0.07 0.19 0.07 

Communication and other utilities 0.23 0.09 0.16 0.07 

Education and health services 0.20 0.06 0.14 0.01 

Real estate, rental, and leasing operations 0.14 -0.01 0.12 -0.03 

Construction 0.02 -0.07 0.01 -0.09 

 

year 2000 -0.05 -0.04 -0.06 -0.05 

year 2001 -0.02 -0.04 -0.02 -0.05 

year 2002 -0.10 -0.11 -0.13 -0.11 

Notes: estimates shown are marginal effects of probit regressions, which are adjusted with probability weights and to 

control for clustering due to multiple observations in the same establishment, one for each year. The omitted 

reference industry is extraction industries (forestry, mining, oil) and the omitted reference year is 1999. 

Figures in bold are statistically significant at p<.05.