1 Management Practices Across Firms and Nations Nick Bloom 1 *, Stephen Dorgan 2 *, John Dowdy 2 , John Van Reenen 1 , and Tom Rippin 2 June 2005 We use an innovative survey tool to collect management practice data from 731 medium sized manufacturing firms in Europe and the US. We find these are strongly associated with better firm performance in terms of productivity, return on capital employed (profitability), Tobin’s Q and sales growth. We also find a surprisingly large dispersion of management practices across firms with a long ‘tail’ of poorly managed firms. This presents a dilemma - why do so many firms continue to exist while apparently deploying inferior management practices? Our analysis suggests that this is due, in part, to a combination of: (i) competition, with tougher product market competition fostering better management practices; (ii) firm age, with younger market entrants utilising better management techniques; and (iii) regulation, with stronger labour market regulation apparently inhibiting the deployment of best practice management. We would like to thank the Economic and Social Research Council and Anglo-German Foundation for financial support. 1 Centre for Economic Performance, London School of Economics 2 McKinsey & Company, Inc., 1 Jermyn Street, London SW1Y 4UH * Corresponding authors: [email protected], [email protected]
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Management Practices Across Firms and Nations
Nick Bloom1*, Stephen Dorgan2*, John Dowdy2,John Van Reenen1, and Tom Rippin2
June 2005
We use an innovative survey tool to collect management practice data from 731medium sized manufacturing firms in Europe and the US. We find these arestrongly associated with better firm performance in terms of productivity, return oncapital employed (profitability), Tobin’s Q and sales growth. We also find asurprisingly large dispersion of management practices across firms with a long‘tail’ of poorly managed firms. This presents a dilemma - why do so many firmscontinue to exist while apparently deploying inferior management practices? Ouranalysis suggests that this is due, in part, to a combination of: (i) competition, withtougher product market competition fostering better management practices; (ii)firm age, with younger market entrants utilising better management techniques;and (iii) regulation, with stronger labour market regulation apparently inhibitingthe deployment of best practice management.
We would like to thank the Economic and Social Research Council and Anglo-GermanFoundation for financial support.
1 Centre for Economic Performance, London School of Economics2 McKinsey & Company, Inc., 1 Jermyn Street, London SW1Y 4UH* Corresponding authors: [email protected], [email protected]
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I INTRODUCTION
Economists have long speculated on why such astounding differences in the productivityperformance exist between firms and plants within countries, even within tightly defined sectors.For example, labour productivity varies dramatically even with the same four or five digitindustries, with these differences often highly persistent over time (Baily et al. (1992),Bartelsman and Dhrymes (1998) and Disney, Haskel and Heden (2003)).
The focus of economists has been in “chipping away” at these productivity differences throughbetter measures of inputs (capital, materials, skills, etc.). Some parts of the literature haveattempted to see how much of the residual can be accounted for by explicit measures oftechnology such as Research and Development (R&D), patents or computerisation (Griliches,1980, Stiroh, 2002). But technology is only one part of the story and a substantial unexplainedproductivity differential still remains, which panel data econometricians often label as the fixedeffects of “managerial quality” (see, for example, Mundlak (1961) and Bailey et al. (1992)).
While the popular press and Business Schools place huge stress on the importance of goodmanagement, economists have until recently had relatively little to say about managementpractices per se. A major problem has been the absence of good quality data on managerialpractices that is measured in a consistent way across countries and firms. One of the purposes ofthis paper is to present a survey instrument for the measurement of managerial practices. Wecollect original data using this survey instrument on a sample of about 730 medium sizedmanufacturing firms in the US, UK, France and Germany. After matching this data withinformation on firm accounts we are able to directly address the association between managerialpractices and firm performance.
Our work relates to the recent contribution of Bertrand and Schoar (2003) which emphasises theimportance of managers in firm performance. They focus on the impact of changing ChiefExecutive Officers (CEOs) and Chief Financial Officers (CFOs) in large quoted U.S. firms,which will tend to reflect the impact of management styles and strategies. Our work, with itsemphasis measuring the practices of middle management, complements Bertrand and Schoar(2003) by looking at firm practices as revealed by the typical middle manager1. We see practicesas more than the attributes of the top managers: they are part of the organisational structure andbehaviour of the firm, typically evolving slowly over time as CEOs and CFOs come and go.
We start by analysing the raw survey data and observe a surprisingly large spread in managementpractices across firms. Using multiple surveys of the same firm by different interviewers wecalibrate our measurement error and show that this can only account for about a quarter of thedistribution in management practices, with the remaining three quarters due to a wide underlyingdistribution of practices. Most notably, using our measure, we see that a large number of firmsare extremely badly managed with ineffective monitoring, targets and incentives.
1 In a sub-sample of 15 companies we piloted questions on the hierarchical structure of the firm and found theaverage number (standard deviation) of levels to the shop floor was 6.3 (2.1) for the CEO versus 3.4 (2.1) for theplant managers (our target management group) placing them centrally within the organisation.
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We then present compelling evidence that better managerial practices are significantly associatedwith higher productivity and other indicators of firm performance, even after accounting for ahost of measurement and econometric issues. This is true both in the Anglo-Saxon countriesinvestigated (the UK and the US) and the Continental European countries investigated (Franceand Germany); suggesting our characterization of good management practice is not intrinsicallyAnglo-Saxon biased. Across the sample management practices account for a significantproportion of the variance in TFP between firms and between countries (about 10-20%), and wenote this may actually be substantially greater than estimated due to the downward bias in ourcoefficients from measurement error.
This raises the question of why there is such a variation in management practices across firms?We present three possible explanations:
1. Product market competition, at the national sector level, plays a key role in determiningthe level of management practice, with higher competition likely to increase the exit rateof badly managed firms so improving average management practices. We find littleevidence for any additional “effort” effect of competition in getting managers to work‘harder’
2. Older firms, controlling for selection effects, have poorer management practices. This isconsistent with the idea that new entrants find it easier to adopt the better managementpractices of the era they were founded than their older counterparts2.
3. Stronger labour-market regulation significantly impedes good management practice,particularly in firms with longer tenured employees. This suggests that regulationimpedes the adoption of new management practices
These results build on the productivity analysis of the McKinsey Global Institute (MGI), whoover many years have studied and analysed productivity across a range of countries and sectors.The MGI find a lack of competition and excessive regulation are some of the main drivers ofproductivity differences across countries and across a wide range of sectors from manufacturing,to retail and construction3. Our study suggests that one of the key routes for competition andregulation to drive productivity is via management practices themselves.
The layout of the paper is as follows. Section II discussed why management practices could vary,section III discusses measuring management practices the management data, section IV themodel and the results, section V the distribution of management practice and section VIconsiders the evidence possible causes for the variations in management. Some concludingcomments are in section VII. More details of the data, models and results can be found in theAppendices.
2 A similar result was reported by Ichniowski et al. (1997) in their work on 19 US steel finishing plants, where theyfind the youth of the plant is a significant predictor of HRM best practices.3 See, for example, MGI (1998), MGI (2003a) and MGI (2003b)
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II WHY SHOULD MANAGEMENT PRACTICES VARY?
A challenge for economics is to explain why poorly managed firms can co-exist with well-runfirms in the same product market. In a competitive marketplace one would expect to see theinefficiently run firms driven out of the market4.
There are at least three groups of theories that could explain the paradox. First and foremost,firms may operate in environments with different degrees of product market competition – thisenables poorer managed firms to survive in equilibrium. Second, even if the market iscompetitive there are important dynamics that may allow inefficient firms to survive, at least forsome period of time. Thirdly and most simply, there are differential costs of good managementdriven for example, by government imposed regulatory constraints over the “right to manage”.We consider each of these theories and examine their empirical predictions.
II.A The degree of product market competition
Moving away from perfect competition means that inefficient firms can survive in themarketplace even in the long-run. But the exact theoretical relationship between competition andmanagement is complex. Crudely, one could consider two views of management practices(formally we will model this by better management being associated with a lower firm-specificmarginal cost, but this could just as well be considered as higher quality, etc.):
1. Entrepreneurs found firms with distinctive cultures that are deeply embedded and hard tochange over time. Entrepreneurs do not know exactly how well the particular type of firmwill perform until they enter a market and compete with other firms. We call this“selection of the well managed”.
2. Firms can influence management practices through a variety of mechanisms. This couldbe, for example, committing to a particular remuneration contract in a principal-agentsetting in order to elicit greater managerial effort, or investing a greater amount inimproving management in an analogous way to a non-tournament model of cost reducingR&D. Such investment could, for example, take the form of hiring the best managers. Wecall these effects “contracting for managerial effort”.
The key question we will investigate is: what is the impact of increasing product marketcompetition on management best practice?
Selection of the well managed
There is much evidence that management practices are hard to change and are deeply embeddedin the culture of an organisation. When entrepreneurs found firms they are often poorly informedas to “what works best” in a particular market context. Tough product market competition willvigorously eliminate firms with poor management practices and surviving firms will, on average,
4 For empirical evidence on competition and productivity see inter alia Nickell (1996) or Syverson (2004b)
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be those with higher managerial quality. Less competitive product markets will be characterisedby on average worse management. So it is likely that higher competition will be associated withbetter average management practices. Additionally, the dispersion of management practices willbe lower when the degree of product market competition is higher as the ‘tail’ of poorly managedfirms will be truncated. Syverson (2004a) offers supportive evidence of these predictions in hisanalysis of the US cement industry, focusing on total factor productivity. He finds that toughercompetition is associated with higher average productivity with a lower variation.
Contracting for managerial effort
The alternative approach sees the determination of management practices as the endogenouschoice of firms in response to their competitive environment. Consider a simple Bertranddifferentiated product model to show some of the forces at play. We allow firms to choosecontracts with managers after they have entered the market, but before their marginal costs arerevealed. Marginal costs are an outcome of managers’ (unobservable) efforts and a cost shock.We assume that the distribution of cost shocks is not so large that any firms exit the market (thisis in order to reduce selection effects and focus on the effort effect). “Investing in managerialeffort” is essentially choosing a higher powered incentive contract that will elicit more effort(better managerial practices) but at the cost of giving away more of the firm’s profits to themanager.
For a given number of firms an increase in competition (again indexed by an increase in productsubstitutability) has an ambiguous effect on managerial effort. On the one hand highercompetition should increase a firm’s incentives to promote managerial effort because any unitcost reduction will have a larger effect on market share. On the other hand, super-normal profitsare lower when competition is higher, so any increase in market share will generate less profit.This will tend to reduce the incentives to supply more managerial effort. Now allow the numbersof firms to change as product market competition increases. When we allow entry to beendogenous there is fall in the number of firms who choose to enter the market because profitsare lower when competition is tougher. In a free-entry long-run equilibrium there will be fewerfirms and the average firm will be larger in size. This means they have a greater desire to cutmarginal costs through higher managerial effort as they are spreading these benefits over a largerscale. In the context of this simple model (which follows Raith, 2003), once we allow forendogenous market structure an increase in product market competition unambiguously increasesgood management practices5.
Although we have discussed this in a principal-agent context the same intuition follows if weconsider a model of non-tournament process R&D. This can be regarded as an investment incost-reducing management practices prior to competing on prices. Increases in competition willhave the same impact on the incentive to make such investments as it would have on theincentives to increase managerial effort in the principal-agent set-up (see Vives, 2004). Both are
5 Schmidt (1997) allows bankruptcy costs in a principal agent model with Cournot competition. With risk neutrality,but a wealth constrained manager, the fear of bankruptcy will increase the incentive of the manager to supply effort.Nevertheless, the rent reducing effect of competition will still exist and could be large enough to completely offsetthe fear of bankruptcy effect. Compared to Schmidt (1997) it is allowing for the endogeneity of entry which makes asubstantial difference to the comparative statics as mean firm size will be larger following an increase incompetition.
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cost-reducing investments so will increase with product market competition (at least under theassumptions of the differentiated product model).
The result that increased product market competition (indexed by higher product substitutability)should improve incentives for managerial practices (though cost reducing investments) arereasonably robust, but not completely general. Vives (2004) shows that providing the market forvarieties does not shrink, the result goes through under more general forms of demand in avariety of Bertrand differentiated product models. He also shows that product market competitionwill increase effort under Cournot competition so long as output reaction functions are downwardsloping, which is the standard case.
Summary
For both reasons of selection and investment, increases in product market competition wouldtend to be associated with better managerial practices. However, it is of empirical interest notonly whether this prediction holds, but if it does, what is the mechanism through whichcompetition matters? If the impact is primarily though selection we would expect that in morecompetitive industries to see superior management practices in older ‘surviving’ firms,emphasizing the central role of entry and exit. If the impact is primarily through greatermanagerial effort we should expect to see this indicated in other outcomes (such as managerialhours) and perhaps through other measures of corporate governance6.
II.B Dynamics and the age of the firm
Even in models of perfectly competitive product markets, dynamic effects may mean that poorlymanaged firms can survive for a period of time. For example, in the Jovanovic (1982) modelfirms take time to learn about their own quality so even poorly managed firms will continueproducing for some periods before they exit. Empirically, researchers who have followedcohorts of plants show that the least productive tend to exit earlier on average. Consequently, asthe cohort ages there is an increase in average productivity within the cohort (these dynamicselection effects are mainly played out in first five years according to Jensen et al (2001), seealso Disney et al. (2003)). This pattern is made more complex, however, by the fact that newfirms will tend to begin operating with newer and presumably better practice managementtechniques relative to incumbents. As discussed above, this may be because older firms find itdifficult to change their organisations in response to new conditions7. This will give rise to cohort(or ‘vintage’) effects where more recent cohorts of firms start off with a systematically highermean level of good management practices than earlier cohorts.
The impact of these dynamic considerations is that in industries with lower product marketcompetition, and therefore few selection pressures, we will observe in the cross section that olderfirms have systematically worse management practices than the younger firms. This is because
6 Better managerial outcomes would be expected from firms more subject to tighter corporate governance (seeShleifer and Vishny, 1997).7 See Ichinowski et al (1995, 1997) for example. In their work on 19 US steel finishing plants, where they find theyouth of the plant is a significant predictor of HRM best practices, with older firms being impeded from adopting thelatest HRM practices by internal organizational rigidity.
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older firms, founded in earlier time periods, may be using less sophisticated management practicetechniques on average. In a more competitive industry the cross sectional pattern betweenmanagement quality and competition will be more hump-shaped. For the younger firms selectioneffects are very strong (in the first 5 or 10 years) and the younger firms will initially have lowermanagement scores on average than their slightly older rivals. However, eventually the cohorteffects are likely to dominate the selection effects and there will emerge a clear negativerelationship between company age and management best practices even in these morecompetitive industries.
To evaluate these we set up a very simple simulation model combining selection effects andcohort effects8. This models a large fixed population of firms with a 2% annual rate of entry andexit. The entrants are drawn from a normal distribution with an upwardly trending mean. The exitoccurs based on a noisy signal of management ability. Thus, every period worse managed firmsare more likely to exit generating a selection effect over time, while the new entrants are drawnfrom an upwardly trending distribution generating a cohort effect. For this cohort effect to exist itis only important that the new entrants improve their management practices at a relatively fasterrate than the incumbents. So while incumbents may learn and embody some of the latestmanagerial practices, they do this at a slower rate than the new entrants.
[FIGURES 1 AND 2 ABOUT HERE]
Figure 1 plots average management practice scores against log firm-age, depicting a clear humpshaped relationship. For the first fifteen years management practices improve on average as theselection effects dominate. After this initial period, however, most of the badly managed firmshave been eliminated and the cohort effect dominates, driven by worse management practicescores in the remaining upper-tail of firms. Figure 2 plots the same management practice againstlog firm-age but with the selection effects of competition turned off by removing anymanagement signal from the exit process. This generates a clear downward sloping relationshipas the cohort effects dominates in the absence of any selection mechanism removing poorlymanaged firms.
II.C Regulatory costs on managers
If firms can influence managerial practices they will weigh up the benefits and the costs ofmaking such changes (Corrada, Hulten and Sichel, 2004, consider extending the standardproduction function to allow for variety of investments in “organisational capital”). We havediscussed how the benefits of making changes will depend on the degree of product marketcompetition and the form of the production function (which itself may differ across industries).The costs of good managerial practices may vary for a number of reasons (including firm age),but one important exogenous source of variation is government regulation. High firing costs, forexample, will make it difficult to remove underperforming middle managers who may formcoalitions to block change. Since regulations differ substantially across the countries in oursample (e.g. Botero et al (2004)), this is an important potential driver of management practices.Furthermore we would expect the costs of job regulation to be most binding on firms with longer
tenured managers who have acquired more job rights, so there will be within country variation inthe effects of these regulations.
III MEASURING MANAGEMENT PRACTICES
To investigate these issues we first have to construct a robust measure of management practicesovercoming three hurdles: scoring management practices; collecting accurate responses; andobtaining interviews with managers. We discuss these in turn:
III.A Scoring Management Practices
To measure management requires codifying the concept of good and bad management into ameasure applicable to different firms within the manufacturing sector. McKinsey & Companyhave developed an interview based management practice evaluation tool which defines andscores from 1 (worst practice) to 5 (best practice) across 18 of the key management practiceswhich appear to matter to industrials firms based upon their expertise in working with thousandsof companies across several decades.
The questions in the survey order, the scoring system and three anonymous responses perquestion are provided in Appendix A1. These questions can be grouped into four areas: shop-floor management (3 questions), monitoring (5 questions), targets (5 questions) and, incentives(5 questions). The shop-floor management section focuses on some typical lean manufacturingtechniques such as the development of a lean internal supply chain, the documentation ofprocesses improvements and the rationale behind introductions of improvements. The monitoringsection focuses on the tracking of performance of individuals, reviewing performance (e.g.through regular appraisals and job plans), and “consequence” management (e.g. making sure thatplans are kept and appropriate sanctions and rewards are in place). The targets section examinesthe type of targets (whether goals are simply financial or, better, more holistic), the realism of thetargets (stretching, unrealistic or non-binding), and the range and interconnection of targets (e.g.whether they are given consistently throughout the organisation). Finally “incentives” includespromotion criteria, pay/bonuses and fixing/firing bad performers, where best practice is deemedto be an approach that gives strong rewards for those with both ability and effort. A subset of theincentives, targets and operations questions have similarities with those used in studies on HRMpractices, such as Ichinowski, Shaw and Prenushi (1997) and Black and Lynch (2002).
Since the scaling may vary across questions in the econometric estimations we convert the scores(from the 1 to 5 scale) to z-scores by normalizing by question to mean zero and standarddeviation one. In our main econometric specifications we take the unweighted average across allz-scores as our primary measure of managerial practices, but we also experiment with otherweightings schemes based on factor analysis approaches.
There is scope for legitimate disagreement over whether all of these measures really constitute“good practice”. So an important way to examine the externality validity of the measures is toexamine whether they are correlated with data on firm performance constructed from companyaccounts and the stock market. We also examine whether the relationship between management
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practices and productivity is weaker in the Continental European nations to check for “Anglo-Saxon” bias.
III.B Collecting Accurate Responses
With this management practice evaluation tool we can, in principle, provide some quantificationof firms’ management practices. However, an important issue is the extent to which we canobtain unbiased responses to these questions from firms. In particular, will respondents provideaccurate responses? As is well known in the surveying literature (see, for example, Bertrand andMullainathan, 2001) respondents answers are typically biased by their scoring grid withresponses anchored towards those they expect the interviewer thinks is “correct”. In additioninterviewers may themselves have pre-conceptions about the performance of the firms they areinterviewing and bias their scores based on their ex-ante perceptions. More generally, a range ofbackground characteristics, potentially correlated with good and bad managers, may generatesome kinds of systematic bias in the survey data.
To try and address these issues we took a range of steps to obtain highly accurate data.
First, the interviewer process was data was ‘double-blind’. Participating managers did not havetransparency that they were being scored during the interviews. The interview was introduced asa piece of research work which would take around one hour to discuss manufacturing and othermanagement practices within their firm. Furthermore, interviewers did not know the firm’sfinancials or performance in advance. This was achieved by selecting medium sizedmanufacturing firms and by providing only firm names and contact details to the interviewers(but no financial details). The interviewers were specially trained graduate students from topEuropean and US business schools, with a median age of 29 and 5 years prior businessexperience in the manufacturing sector9.
Second, the survey was executed by telephone using a ‘funnelling’ interviewing technique,supported by as many responses as necessary to open questions (i.e. “can you tell me how youpromote your employees”), rather than closed questions (i.e. “do you promote your employees ontenure [yes/no]?”), to get an accurate assessment of the firm’s typical practices. This enabledscoring to be based on the interviewer’s evaluation of the actual firm practices, rather than thefirm’s aspirations, the manager’s perceptions or the interviewer’s impressions. If an interviewercould not score a question it was left blank, with the firm average taken over the remainingquestions10.
Third, the survey instrument was targeted at plant managers, who are typically senior enough tohave an overview of management practices but not so senior as to be detached from day-to-dayoperations.
Fourth, we also collected a detailed set of information on the interview process itself (numberand type of prior contacts before obtaining the interviews, duration, local time-of-day, date and
9 Thanks to the interview team of Johannes Banner, Michael Bevan, Mehdi Boussebaa, Dinesh Cheryan, Alberic deSolere, Manish Mahajan, Simone Martin, Himanshu Pande, Jayesh Patel and Marcus Thielking.10 The average number of un-scored questions per firm was 0.13%, with no firm included in the sample if more thanthree questions were un-scored.
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day-of-the week), on the manager (seniority, nationality, company and job tenure, internal andexternal employment experience, and location), and from the interviewer (individual, UK time-of-day and subjective reliability score). Some of these survey controls are significantlyinformative about the management score (see Appendix C and Table C1)11, and when we usethese as controls for interview noise in our econometric evaluations the coefficient on themanagement score typically increased.
III.C Obtaining Interviews with Managers
The interview process takes around one hour per interview and is targeted at relatively seniormanagers within the firm. Overall we obtained a high response rate of 54% coverage rate. Thiswas achieved through a number of steps.
1. The interview was introduced as “Research” without any discussion of the firm’sfinancial position or its company accounts, making it relatively uncontroversial formanagers to participate. Interviewers did not discuss financials in the interviews both tomaximise the participation of firms and also to ensure our interviewers were truly “blind”on the firm’s financial position.
2. Questions were ordered to lead with the least controversial (shop-floor management) andfinish with the most controversial (pay, promotions and firings).
3. Interviewers performance was monitored, as was the proportion of interviews achieved sothey were persistent in chasing firms (the median number of contacts each interviewerhad per interview was 6.4) Since the questions are about practices within the firm anyplant managers can respond, so there are potentially several managers per firm who couldbe contacted12.
4. The combination of the collaboration between the London School of Economics andMcKinsey and Company and the endorsement of the Bundesbank (in Germany) and HMTreasury (in the UK) helped demonstrate to managers this was a non-commercialexercise that would provide them with potentially valuable research insight.
III.D Sampling Frame and Additional Data
Since our aim is to compare across countries we decided to focus on the manufacturing sectorwhere productivity is easier to measure than in the non-manufacturing sector. We also focused onmedium sized firms selecting a sample where employment ranged between 50 and 10,000workers (with a median of 700). Very small firms have little publicly available data. Very large
11 In particular we found the scores were significantly higher: for senior managers, later in the week and earlier in theday. That is to say scores were highest, on average, for senior managers on a Friday morning and lowest for juniormanagers on a Monday afternoon. By including information on these characteristics in our analysis we explicitlycontrolled for these types of interview bias.12 We found no significant correlation between the number, type and time-span of contacts before an interview isconducted and the management score. This suggests while different managers may respond differently to theinterview proposition this does not appear to be directly correlated with their responses or the average managementpractices of the firm.
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firms are likely to be more heterogeneous across plants and so it would be more difficult to get apicture of managerial performance in the firm from one or two interviews. We drew a samplingframe from each country to be representative of medium sized manufacturing firms and thenrandomly chose the order of which firms to contact (see Appendix B for details). We alsoexcluded any McKinsey clients from our sampling frame13 – both to avoid any perceivedconflicts of interest and ensure there was no bias in the data collection.
In addition to the standard information on management practices, we also collected information -mainly from a separate questionnaire to the Human Resource department - on the averagecharacteristics of workers and managers in the firm such as gender, age, proportion with collegedegree, average hours, holidays, sickness, occupational breakdown and a range of questions onthe organisational structure of the firm and the work-life balance. The details of thisquestionnaire are provided in Appendix A2.
Quantitative information on firm sales, employment, capital, materials etc. came from thecompany accounts and proxy statements, while industry level data came from the OECD. Thedetails are provided in Appendix B.
Comparing the responding firms with those in the sampling frame we found no evidence that theresponders were systematically different on any of the performance measures to the non-responders. They were also statistically similar on all the other observables in our dataset. Theonly exception was on size where our firms were slightly larger than average than those in thesampling frame.
III.E Evaluating and Controlling for Measurement Error
The data potentially suffers from several types of measurement error that are likely todownwardly bias our coefficients on management. First, we could have measurement error in themanagement practice scores obtained using our survey tool. To quantify this we performedrepeat interviews on 64 firms, contacting different managers in the firm, typically at differentplants, using different interviewers. To the extent that our management measure is truly pickingup general firm level management practices these two scores should be correlated, while to theextent the measure is driven by noise the measures should be independent.
Figure 3 plots the 64 average firm level scores from the first interview against the secondinterviews, from which we can see they are highly correlated (0.734). Furthermore, there is noobvious (or statistically significant) relationship between the degree of measurement error andthe absolute score. That is to say high and low scores appear to be as well measured as averagescores, and firms that have high (or low) scores on the first interview tend to have high (or low)scores on the second interview. Thus, firms that score below 2 or above 4 appear to be genuinelybadly or well managed rather than extreme draws of sampling measurement error.
[FIGURE 3 ABOUT HERE]
Analysing the measurement error in more detail (see Appendix C) we find that the question levelmeasures are noisier, with 42% of the variation in the scores are due to measurement error,
13 This removed 33 firms out of our sampling frame of 1353 firms
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compared to the average firm’s scores with 25% of the variation due to measurement error. Thisimproved signal-noise ratio in the firm level measure – which is our primary management proxy- is due to the partial averaging out of measurement errors across questions.
The second type of measurement error concerns the fact that our management practices coveronly a subset of all management practices which drive performance. For example, our interviewsdid not contain any questions on management strategy. However, so long as firms’ capabilitiesacross all management practices are positively correlated - which they are significantly within the18 practices examined - then our measure based on a subset of practices will provide a proxy ofthe firm’s true management capabilities. Again, however, this suggests that the coefficients weestimate on management are probably biased towards zero due to attenuation bias.
IV MANAGEMENT PRACTICES AND FIRM PERFORMANCE
Before we investigate the reasons for the spread of management practices across firms it is worthevaluating whether these practices can account for any variation in performance. While it is notpossible to identify any causal relationship between our management practice measures and firmperformance, a significant relationship would indicate our management scores are measuringsomething that is important to the operation of firms.
IV.A Econometric Modelling
Consider the basic production function
cit
cit
cci
ccitm
ccitk
ccitl
ccit uxMNGmkly +++++= 'γβααα (1)
where Y = sales, L = labour, K = capital and M= materials of firm i at time t in country c (notethat we allow all firms to have country specific parameters on the inputs), and lower case lettersdenote natural logarithms y = ln(Y), etc. The x’s are a number of other controls that will affectproductivity such as workforce characteristics (e.g. human capital, age), firm characteristics (e.g.firm age) and industry characteristics (generally proxied by a complete set of 3-digit SIC industrycontrols).
The crucial variable for us is management practices denoted MNG. Our basic measure takes z-scores of each of the 18 individual management dimensions examined and then averages over thevariables to get MNG. We experimented with a number of other approaches including using theprimary factor from factor-analysis and using the raw average management scores and foundvery similar results. We also looked for complementarities between the different types ofmanagement practices and found some weak support for this.
The most straightforward approach to estimating equation (1) is to run a within-groupsestimation including a large set of possible controls in a first step, and then project the fixedeffects on the management scores in a separate second step. This is analogous to the approach ofBlack and Lynch (2001) that followed a similar two step approach in their analysis of workplacepractices and productivity. We use data from 1998-2004 to average out the variables that we doobserve over time, allowing the firm level standard errors to be clustered. Alternatively, we can
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exploit the fact that we have panel data on our firms to estimate over a longer period (1994-2004), and attempt to deal with the endogeneity of the time varying inputs (capital, labour andmaterials). To do this we experiment with two alternative estimation approaches - System GMMand the Olley Pakes estimator14. Again, using these estimates of the production functionparameters we construct firm specific efficiency/TFP measures which we then relate in a secondstage to management practices and other time invariant firm characteristics.
Before turning to our results we highlight one important issue regarding the relationship betweenmanagement practices and firm performance. Due to the cross-sectional nature of our data wecan not identify the causal relationship between management practices and firm performance,and there are a number of reasons to be concerned about the potential for feedback. Negativefeedback could occur – biasing our coefficient towards zero – if more productive firms use theirrents to avoid implementing managerial “best practices” which are generally higher effort.Positive feedback could occur if well managed firms attract higher ability workers, and this is notcontrolled for by our human capital measures. So to reiterate our objective in these estimations ispurely to confirm that our management practices measures are significantly correlated with firmperformance measures, indicating they do indeed measure something that is meaningfully relatedto firm operations.
IV.B Econometric Results
Table 1 investigates the association between management practices and firm productivity using arange of alternative econometric methods. Column (1) estimates a within-groups estimator (topsection of the column (1), and then uses the estimated fixed-effects in a second-step cross-sectional regression on our management practice scores (base of column (1)) and finds asignificant and positive correlation of TFP with management. The Olley Pakes specification isestimated in the top section of column (2), for which TFP (averaged over the sample for a givenfirm) is positively and significantly correlated with our management measure (base of column(2)). In column (3) we run a System GMM specification, and again taking the predicted fixedeffects find this is also significantly related to management15.
[TABLE 1 ABOUT HERE]
We were concerned that the definition of “good management” may be biased towards an Anglo-Saxon view of the management world. Some may regard such business practices as suitable for the ‘free markets’ of Britain and America, but less suitable to those of Continental Europe. Weempirically tested this in column (4) by re-running the two stage estimation using French andGerman companies only and again found a large a highly significant coefficient on management.
Finally, in column (5) we investigated the impact of “consistency” by including the standard-deviation of the management practices examined within each firm. Controlling for the average
14 See Arellano and Bond (1991) and Blundell and Bond (1998) on System GMM estimation, and Olley and Pakes(1996) on their estimation strategy.15 Across these specifications the precise coefficients on capital, labor and materials of course change due to thedifferent identification assumptions. These three estimators are presented here to show the robustness of therelationship between management and productivity to any particular estimation approach.
14
score we find firms where management practices are consistent across the different areas aresignificantly more productive than firms where management practices are inconsistent16. Thispossibly suggests some kind of complementarity between practices. This is maybe not surprisinggiven the underlying complementarity of the targets, incentives and monitoring practices wefocus on.
Table D1 in Appendix D provides further robustness checks on the management score usingalternative measures of firm performance. In column (1) we directly estimate managementpractices in a production function, finding a positive and significant coefficient. In column (2) weadd in a range of controls including: factor coefficients interacted with country dummies to allowfor the coefficients on the conventional inputs to vary across countries due to differences in theiraccounting measurement; country and industry dummies to controls for country and industryfixed effects; a number of extended controls for hours, education, firm age, size and listingstatus; and a set of interview controls to control for any biases across interviewers and types ofinterviewees17. Including these controls moderately reduces the size and increases the standarderror on the management coefficient, although the impact is not great and management is stillhighly significant. In column (3) we use an alternative performance measure which is return oncapital employed (ROCE), a profitability measure used by financial analysts and managers tobenchmark firm performance18. The significant and positive coefficient in the ROCE equation,which also includes the same set of controls as in column (2) confirms the basic productivityresults. In column (4) we estimate a Tobin’s Q specification, which again includes the fullproduction function controls, and find a significant positive coefficient. Finally in column (5) weestimate the relationship between growth rates of sales and management practices, again with afull set of controls, and find a positive significant coefficient. This suggests firms with better(current) management practices will have higher (current) growth rates.
V THE DISTRIBUTION OF MANAGEMENT PRACTICES
Figure 4 shows the distribution of the average management scores per firm across all 18questions, plotted by country in raw form (not in z-score form). It is clear that there is a hugeamount of heterogeneity within each country with firms spread across most of the distribution.About 3% of the overall variation in firms’ average management scores is across countries, 21%is across 3-digit SIC industries and the remaining 76% is within country and industry. Thisspread is particularly wide when considered against the fact that a score of 1 indicates industryworst practice and 5 industry best practices. So, for example, firms scoring 2 or less have onlybasic shop-floor management, very limited monitoring of processes or people, ineffective and
16 For example, for a firm with an average management score of 3 its productivity will be higher on average if itscored 3 across all 18 questions than if it scored 5 in ½ of the questions and 1 in the other ½ the questions.17 In Table C1 in the Appendix we detail these noise controls with column (1) reporting the results from regressingmanagement on the full set of noise controls and column (2) the results from regressing management on our selectedset of (informative) noise controls which we use in our main regressions.18 Since ROCE is calculated as profit/(shareholder equity + long term debt) it also provides an another measure offirm performance using different accounting line items from the productivity regression.
15
inappropriate targets, and poor incentives and firing mechanisms. Thus, one of the centralquestions we focus on in section V is why do these firms exist?
[FIGURE 4 ABOUT HERE]
Looking across countries the US has on average the highest scores (3.37), Germany is second(3.32), France third (3.13) and the UK last (3.08), with the gap between the US and UKstatistically significant at the 5% level. We were concerned that some of this may simply bedriven by differences in the sampling size distribution, but these figures are robust to controls forsize and public ownership.
The presence of the US at the top of the ranking is consistent with anecdotal evidence from othersurveys19. One might suspect this was due to an “Anglo-Saxon” bias. This is why we had toconfront the scores with data on productivity to show that they are correlated with real outcomeswithin countries. Furthermore, the position of the UK as the country with the lowest averagemanagement scores indicates that the survey instrument is not intrinsically Anglo-Saxon biased.Table A1 in Appendix A provides more details behind these cross-country comparisons, andreveal a relative US strength in human-capital management (monitoring and incentives) versus aGerman and French strength in more physical-capital and operational management (shop-floorand monitoring)20.
We also undertook factor analysis on the individual questions. Interestingly there appeared to beone dominant factor that loaded heavily on all our questions – which could be labelled “goodmanagement” – which accounted for 49% of the variation. The only other notable factor, whichaccounted for a further 7% of the variation, could be labelled as “incentives minus operations”,which had a positive loading on incentives and a negative loading on shop-floor and performancemonitoring.
VI THE CAUSES OF MANAGEMENT PRACTICE VARIATIONS
The evidence presented in section IV demonstrated that good management practices aresignificantly related to firm performance, while the evidence in section V demonstrated a widedistribution in management practices. But this raises the question of why so many firms appear tobe deploying sub-optimal practices? In this section we explore the role of three factors whichmay help to explain the tail of poorly managed firms.
VI.A Management Practices, Product Market Competition and Firm Age
19 For example, Proudfoot (2003) regularly reports that US firms were least hindered by poor management practices(36%) compared to Australia, France, Germany, Spain, South Africa and the UK. Unfortunately, these are only fromthe consulting groups’ clients so are unlikely to be representative.20 In the survey we also collected two questions on organizational structure (see Appendix A2) taken fromBresnahan et al. (2002). We found in France and German firms were significantly more hierarchical (gave managersmore power relative to workers) in pace and task allocation compared to the UK and particularly the US.
16
A common argument is that variations in management practice result from the differences inproduct market competition, either because of selection effects and/or because of variations inthe incentives to supply effort. Table 2 attempts to investigate this by examining the relationshipbetween product market competition and management. We use three broad measures ofcompetition following Nickell (1996) and Aghion et al. (2005). The first is the country by 3-digitSIC industry Lerner index of competition, which is (1 – profits/sales), calculated at the averageacross the entire firm level database (excluding each firm itself)21. This is constructed for theperiod 1995-1999 to remove any potential contemporaneous feedback. The second measure is thedegree of import penetration in the country by 3-digit SIC industry measured as the share of totalinputs over domestic production. Again, this is constructed for the period 1995-1999 to removeany potential contemporaneous feedback. The third measure of competition is the surveyquestion on the number of competitors a firm faces (see Appendix A2), valued 0 for “noncompetitors”, 1 for “less than 5 competitors”, and 2 for “5 or more competitors”22.
[TABLE 2 ABOUT HERE]
In column (1) we see the Lerner index of competition measure is positive and strongly significantafter controlling for simple country levels effects, providing evidence for a positive role forcompetition in improving management practices. In column (2) we re-estimate the samespecification but now include a full set of industry, firm and noise controls, and again find thathigher competition is significantly correlated with better current management. Thus morecompetitive country-industry pairings contain firms which are on-average significantly bettermanaged. In columns (3) and (4) we run two similar specifications on lagged import penetrationas a trade competition measure and again find a significant and positive effect. Finally, incolumns (5) and (6), we run two further similar specification, but this time using firms own selfreported measure of the number of competitors they face, and again find a positive andsignificant effect: the more rivals a firm perceives it faces, the better managed it appears to be.
One issue in interpreting this competition effect as discussed in section III is that it potentiallyworks through two mechanisms:
1. Greater competition increases the relative exit rate of badly managed firms versus wellmanaged firms, and so will increase the average managerial practices of the survivors;
2. Increasing management scores through greater managerial effort.To investigate these different mechanisms we start by running a non-parametric kernel regressionto look for evidence of a selection effect.
In Figure 5 we plot the cross-sectional relationship between management practices and firm agefor two sub-samples: (i) firms in low competition industries (below the 33rd percentile of thecountry-demeaned Lerner index); and (ii) firms in medium/high competition industries (abovethe 33rd percentile of the country-demeaned Lerner index)23. For the low competition samplethere is clear evidence of younger firms employing better management practices, while for thehigher competition sample there is a humped shape relationship with an initially improving then
21 Note that in constructing this we draw on firms in the population database, not just those who participated in thesurvey.22 This question has been used by inter alia Nickell (1996) and Stewart (1990).23 The country level means are removed from the Lerner index to control for differences in accounting definitionsacross countries. Similar results are obtained by splitting around the 50th percentile of the country-demeaned Lerner.
17
subsequently deteriorating average management practices as firms’ age. Figure 6 contains asimilar plot but with the competition split determined instead by import penetration (above/belowthe 33rd percentile of the country-demeaned Import penetration ratio).
These results appear to be consistent with a selection effect of competition and technologicalevolution in management best-practices discussed in section IIB. When firms are young incompetitive industries, the selection effect dominates with the worst managed firms rapidlyexiting, so average management practices improve as the cohort ages. But once the worstmanaged firms have exited the selection effect begins to slow down and the vintage effect beginsto dominate, with the age-management relationship flattening and then turning negative. Inuncompetitive industries we should expect to see little selection effect and a dominant vintageeffect, exactly as shown in Figures 5 and 6.
[FIGURES 5 and 6 ABOUT HERE]
Table 3 estimates a linear-regression version of these sample splits showing these age-management relationship differ significantly according to the level of competition. In column (1)a straight regression of management practices on log firm-age demonstrates a significantnegative coefficient, suggesting older firms use on average use worse management practices24.In column (2) we include the competition measure finding as before a positive competitioneffect. In column (3) we add an interaction of (country-demeaned) age and competition, finding asignificant positive interaction term, and in column (4) we add in a full set of controls yielding asimilar positive interaction term. The interpretation of these terms is that age is negatively relatedto management practices, but this is significantly less negative at high levels of competition sinceage provides a stronger signal of survival and hence good management practices. That is, oldfirms in highly competitive industries are much more likely to be well managed than inuncompetitive industries as they have survived a long period of tougher market selection.
[TABLE 3 ABOUT HERE]
Table 4 appears to suggest in converse that the effort effect of competition and managementpractices may be relatively unimportant. In columns (1) to (2) we regress managerial hoursworked – our preferred measure of managerial effort - on the Lerner index and importcompetition measures of competition, finding no significant results. In columns (3) and (4) weuse sick-leave as an alternative proxy for effort, again finding no significant relationship; and incolumns (5) and (6) we use managers self-reported “work-life balance” score as another effortproxy again finding no relationship. This suggests that competition plays little if any role inincreasingly managerial practices through higher managerial effort, not inconsistent with theambiguous theoretical relationship discussed in section IIA.
[TABLE 4 ABOUT HERE]
VI.B Management Practices and Job Regulation
24 The fact that older firms are on average more productive (i.e. see Jensen at al. 2001) suggests offsettingimprovement in other un-measured factors.
18
In a number of countries the business press has long argued that government regulations impedethe ability of managers to effectively run their firms. One potential impact of regulations, inparticular labour regulation, is that these could impede the adoption of superior managementpractices.
[TABLE 5 ABOUT HERE]
To look into this Table 5 starts in column (1) by running a very basic estimation of managementpractices on the extent of labour regulations regarding dismissals, as reported by Botero et al.(2004). We find that countries with tougher labour regulation (France and to a lesser extentGermany) are worse managed on average than countries with weak regulation (the US and to alesser extent the UK). Of course a wide range of other factors varies across countries, and so thisis only an indicative result based on raw cross-country figures. To further investigate the impactof job regulation we interacted country-specific regulation with variations across firms in thetenure of workers as a proxy for the impact of these regulations, enabling an identification ofwithin country-industry effects. In column (2) we estimate the interaction effect of job-regulationand average managerial tenure. Since we have included country and industry dummies the directimpact of labour-regulation is not identified but we can see the interaction effect with tenure isnegative and significant. In columns (3) we re-run this specification including the full set ofcontrols and find this interaction remains negative and significant. Thus, we find in firms wherethe bite of dismissal costs should be highest – those with long tenured managers – the impact ofthese types of dismissal costs is greatest. This suggests that strong labour regulation inhibit theadoption of superior management practices.
VII CONCLUSIONS
We use an innovative survey tool to collect management practice data from 731 medium sizedmanufacturing firms in Europe (UK, France and Germany) and the US. We find these arestrongly associated with better firm performance in terms of productivity, profitability, Tobin’sQ, and sales growth. We also find a surprisingly large dispersion of management practices acrossfirms with a long tail of poorly managed firms. This presents a dilemma - why do so many firmscontinue to exist with apparently inferior management practices? We find that this is due, in part,to a combination of: (i) product market competition, with greater competition stimulating thedeployment of improved management practices; (ii) firm age, with younger market entrantsutilising better management techniques; and (iii) labour market regulations.
A range of potential extensions to this work are planned, including running a second survey waveon around 2,000 firm in 2006 to follow up these 731 firms to examine the time profile ofmanagement practices; extend the survey to other countries; survey multiple plants in a fewhundred firms to examine plant vs. firms effects; and gather more detailed organisationalstructure data. In terms of the current sample we hope to follow this over time and examine therole of managerial practices in market exit (bankruptcy, take-over and going private).
19
BIBLIOGRAPHY
Aghion, P., Bloom, N., Blundell, R., Griffith, R., and Howitt, P., (2002) “Competition andInnovation: An inverted U relationship”, forthcoming Quarterly Journal of Economics.
Arellano, M. and Bond, S. (1991), “Some tests of specification for panel data: Monte Carloevidence and an application to employment functions”, Review of Economic Studies, 58, pp.277-297
Baily, M., Hulten, C. and Campbell, D. (1992), “Productivity dynamics in manufacturingplants”, Brookings Papers on Economics Activity, pp 187-267.
Bartelsman, E. and Dhrymes, P. (1998), “Productivity Dynamics: US Manufacturing plants,1972-1986”, Journal of Productivity Analysis, 9, pp5-34.
Bartlelsman, E. and Doms, P. (2000), “Understanding Productivity: Lessons from LongitudinalMicrodata”, Journal of Economic Literature, 38,3, 569-594.
Bartelsman, E., Becker, R. and Gray, W. (2000), “The NBER Manufacturing ProductivityDatabase”, NBER.
Bertrand, M. and Schoar, A. (2003), “Managing with Style: The Effect of Managers on FirmPolicies”, Quarterly Journal of Economics, pp.1169-1208.
Bertrand, M. and Mullainathan, S. (2001), “Do people mean what they say? Implications forsubjective survey data”, American Economic Review Papers and Proceedings, 91(2), May2001: 67-72.
Bertrand, M. and Mullainathan, S. (2003), “Enjoying the quiet life? Corporate Governance andManagerial Preferences”, Journal of Political Economy, 111(5), pp 1043-1075.
Black, S. and Lynch, L. (2001), “How to Compete: The Impact of Workplace Practices andInformation Technology on Productivity”, Review of Economics and Statistics, 83(3), pp.434-445.
Blundell, Richard and Bond, Stephen (1998) “Initial Conditions and Moment Restrictions indynamic panel data models” Journal of Econometrics
Blundell, Richard and Bond, Stephen (2000) “GMM estimation with persistent panel data: anapplication to production functions.” Econometric Reviews, 321-340
Bresnahan, T., Brynjolfsson, E. and Hitt, L. (2002), “Information Technology, WorkplaceOrganisation and the Demand for Skilled Labour: Firm-level Evidence”, Quarterly Journal ofEconomics, pp.339-376.
Botero, J., Djankov, R., La Porta, R., Lopez-de-Silanes, F., and Shleifer, A. (2004) “Theregulation of labor”, Quarterly Journal of Economics, November
20
Caballero, R. and Hammour, L. (1994), “The cleansing effects of recessions”, AmericanEconomic Review, 84(5), 1350-1368.
Caroli, E. and Van Reenen, J. (2001), “Skill-biased Organisational change? Evidence from apanel of British and French establishments”, the Quarterly Journal of Economics, vol. 116(4),1449-1492,
Corrada, C. Hulten, C. And Sichel, D. (2004) “Measuring Capital and technology: An expandedframework” Federal Reserve Bank Discussion Paper 2004/65
Disney, R. Haskel, J. and Heden, Y. (2003), “Restructuring and Productivity Growth”, EconomicJournal, July 2003.
Foster, L., Haltiwanger, J. and Kirzan, C. (2000), “Aggregate productivity growth: lessons frommicroeconomic evidence”, NBER WP
Hopenhayn, H. (1992) “Exit, Entry and Firm Dynamics in Long Run equilibrium” Econometrica,60: 1127-1150
Ichinowski, C., and Shaw, K. (1995), “Old dogs and new tricks: determinants of the adoption ofproductivity-enhancing work practices”, Brookings Papers: Microeconomics: 1-65
Ichinowski, C., Shaw, K. and Prenushi, G. (1997), “The Effects of Human ResourceManagement: A Study of Steel Finishing Lines”, American Economic Review, pp.291-313
Jensen, J, McGuckin, Stiroh, K. (2001), “The impact of vintage and survival on productivity:evidence from cohorts of U.S. manufacturing plants”, Review of Economics and Statistics,vol.83, pp323-332
Jovanovic, Boyan (1982) “Selection and the evolution of Industry” Econometrica, 50, 649-70
Lazear, Edward (2000) “Performance pay and productivity”, American Economic Review, 1346-1361.
Lucas, R. (1978) “On the Size distribution of business firms” Bell Journal of Economics, 9, 2,508-523
McKinsey Global Institute (1998), “Driving productivity and growth in the UK Economy”,October 1998, MGI report.
McKinsey Global Institute (2003a), “Asia: The Productivity Imperative”, March 2003, MGIreport.
McKinsey Global Institute (2003a), “Turkey: Making the Productivity Growth Breakthrough”,February 2003, MGI report.
21
Melitz, Mark (2003) “The impact of trade on Intra-industry reallocations and Aggregate IndustryProductivity” Econometrica, 71, 1695-1725
Mundlak, Y. (1961), "Empirical Production Function Free of Management Bias, "Journal ofFarm Economics 43(1): 44-56
Nickell, S, Nicolitsas, D. and Patterson, M. (1997), “Does doing badly encourage managementinnovation”, Oxford Bulletin of Economics and Statistics
Nickell, S. (1996) "Competition and Corporate Performance", Journal of Political Economy, 104,724-746.
O’Mahoney, M. and De Boer, W. (2002), “Britain’s Relative Productivity Performance: Updatesto 1999” mimeo, National Institute of Economic and Social Research
Olley, S and Pakes, A. (1996), “The dynamics of productivity in the telecommunicationsindustry”, Econometrica, 64: 1263-97.
Proudfoot Consulting (2003) “Missing Millions: How Companies mismanage their most valuableresource”
Raith, M. (2003) “Competition, Risk and Managerial Incentives” American Economic Review,93, 1424-1436
Rajan, R. and Wulf, J. (2004), “The flattening firm: evidence from panel data on the changingnature of corporate hierarchies”, GSB Chicago mimeo.
Roberts, J. (2004), “The Modern Firm: Organizational Design for Performance and Growth”,forthcoming Oxford University Press, 2003
Salop, Steve (1979) “Monopolistic Competition with Outside Goods” Bell Journal of Economics10, 141-156
Schmidt, K. (1997) “Managerial Incentives and Product Market Competition” Review ofEconomic Studies 64(2) 191-213
Stewart, M (1990), “Union wage differentials, product market influences and the division ofrents”, Economic Journal, 100, 1122-1137.
Stiroh, Kevin, 2002, “Information Technology and the U.S. Productivity Revival: What Do theIndustry Data Say?”, American Economic Review, 92(5), December, 1559-1576
Syverson, Chad (2004a) “Market Structure and Productivity: A concrete example” Journal ofPolitical Economy, 112, 6, 1181-1222
Syverson, Chad (2004b) “Product substitutability and Productivity Dispersion” Review ofEconomics and Statistics, 86, 534-50
22
Figure 1: Management practices and age, with competition - simulation
Figure 2: Management practices and age, without competition - simulation
Notes: Figure 1 is from a simulation results from modelling a large fixed population of firms with a 2% annual rate of entry andexit. The exit occurs based on a noisy signal of management ability. The entrants are drawn from a normal distribution with anupwardly trending mean. Thus, every period worse managed firms are more likely to exit generating a selection effect over time,while the new entrants are drawn from an upwardly trending distribution generating a cohort effect. In Figure 2 the competitiveselection effects are switched off.
AverageManagementscore
Firm age (logs)
AverageManagementscore
Firm age (logs)
23
Figure 3: First management score on second management score1
23
45
2nd
inte
rvie
wsc
ore
1 2 3 4 51st interview score
Note: These are the scores from two interviews on the same firm but with different managers and differentinterviewers. Figure 4: Distribution of management scores by country
Note: These are the distributions of the raw management scores (simple averages across all 18 questions).
Germany
UK
France
US
0.2
.4.6
.8D
ensi
ty
1 2 3 4 5mmanagement
0.5
1D
ensi
ty
1 2 3 4 5mmanagement
0.2
.4.6
.81
Den
sity
1 2 3 4 5mmanagement
0.2
.4.6
.8D
ensi
ty
1 2 3 4 5mmanagement
24
Figure 5: Management practices, firm age and competition (Lerner Index)
Figure 6: Management practices, firm age and competition (Import Penetration)
Note: These are results from a non-parametric kernel regression of the mean management score against firm ln(age). This isperformed separately for firms in high competition sectors vs. low competition sectors (bottom third of the Lerner Indexdistribution in Figure 5 and bottom third of the import penetration distribution in Figure 6).
25
TABLE 1: PRODUCTION FUNCTION ESTIMATIONS
(1) (2) (3) (4) (5)
Estimation MethodWITHINGROUPS
OLLEYPAKES
GMM-SYSWITHINGROUPS
WITHINGROUPS
Countries All All AllFrance andGermany
All
Dependent variableLn (Y) it
sales
Ln (Y) it
sales
Ln (Y) it
sales
Ln (Y) it
sales
Ln (Y) it
sales
ln (L) it
labour
0.493
(0.030)
0.501
(0.050)
0.519
(0.085)
0.559
(0.038)
0.493
(0.030)
Ln(K) it
capital
0.128
(0.023)
0.111
(0.030)
0.085
(0.041)
0.107
(0.026)
0.128
(0.023)
ln (Materials) it,
materials
0.304
(0.018)
0.383
(0.038)
0.320
(0.043)
0.203
(0.015)
0.304
(0.018)
Firms 718 718 718 287 718
Observations 3,797 3,703 3,466 1,373 3,797
Dependent variableTFP TFP TFP TFP TFP
Management score0.112
(0.032)0.080
(0.018)0.104
(0.018)0.178
(0.060)0.092
(0.033)Within-firm st-dev ofmanagement questions
-0.208(0.104)
Observations 718 718 718 287 718
SC(2) p-value 0.582
SARGAN p-value 0.002
COMFAC p-value 0.082
NOTES: In all columns labour, capital and materials are interacted with country dummies and consolidated status to allowflexible coefficient across countries and type of account. The baseline is UK consolidated in Columns (1), (2), (3) and (5), andGerman consolidated in Column (4). Columns (1), (4) and (5) implement an OLS estimator with fixed-effects (stage 1), and thenregresses these in cross-section against the management z-scores (stage 2). Column (2) implements a version of the Olley-Pakes(1996) technique. We use a fifth order series approximation for φ(.) in stage 1. Standard errors are bootstrapped with 100replications. After calculating the parameters of labour and materials (stage 1a) and capital (stage 1b) we calculate the efficiencyterm/TFP. This is used as a dependent variable in the lower panel and regressed on management and its noise controls (stage 2).Column (3) implements the Blundell-Bond (1998) GMM-SYS technique (stage 1). Instruments for the differenced equation arelagged levels t-2 to t-3 on sales, capital, labour and materials. Instruments for the levels equation are lagged differenced t-1 onsales, capital, labour and materials. SC(2) is an LM test of second order correlation of the differenced residuals (see Arellano andBond, 1991) and SARGAN is the Sargan-Hansen test of over-identification (distributed χ2 under the Null). We impose theCOMFAC restrictions by Minimum Distance (see Blundell and Bond, 2000) and test this by a χ2 test of the COMFACrestrictions. We use the estimated coefficients to calculate TFP which is used as the dependent variable in the lower panel andregressed on management and the noise controls (stage 2).
26
TABLE 2: MANAGEMENT AND PRODUCT MARKET COMPETITION
NOTES: Coefficients from OLS regressions with standard errors in parentheses (robust to arbitraryheteroskedasticity and clustered by country * industry pair); single cross section. “Country controls” includes 4country controls. “Full controls” includes a full set of 108 SIC 3-digit industry controls, group and firm size, adummy for being listed, being consolidated, a separate dummy variable for all the selected controls in column (2)table A2: each interviewer (17), the seniority and tenure of the manager who responded, the day of the week theinterview was conducted, the time of the day the interview was conducted and an indicator of the reliability of theinformation as coded by the interviewer; “Lerner index of competition” constructed, as in Aghion et al. (2005) , asthe mean of (1 - profit/sales) in the entire database (excluding the firm itself) for every country industry pair.“Import Penetration” = ln(Import/Production) in every country industry pair. Average over 1995-1999 used.“Number of competitors” constructed from the response to the survey question on number of competitors, and iscoded as 0 for “none” (1% of responses), 1 for “less than 5” (51% of responses), and 2 for “5 or more” (48% ofresponses).
Firm age (in logs) × Lernerindex of competition (5-yearlagged)
0.900
(0.496)
1.244
(0.638)
Joint test of age terms (p-value)
0.001 0.002
Joint test of competitionterms (p-value)
0.003 0.037
Firms 727 727 727 727
Observations 727 727 727 727
Country controls Yes Yes Yes Yes
Full controls No No No Yes
NOTES: Coefficients from OLS regressions with standard errors in parentheses (robust to arbitraryheteroskedasticity and clustered by country * industry pair); single cross section. “Country controls” includes 4country controls. Firm age and Lerner index terms have country averages removed in the levels and interactionterms. “Full controls” includes a full set of 108 SIC 3-digit industry controls, group and firm size, a dummy forbeing listed, being consolidated, a separate dummy variable for all the selected controls in column (2) table A2: eachinterviewer (17), the seniority and tenure of the manager who responded, the day of the week the interview wasconducted, the time of the day the interview was conducted and an indicator of the reliability of the information ascoded by the interviewer; “Firm age” is years since incorporation. “Lerner index of competition” constructed, asin Aghion et al. (2005) , as the mean of (1 - profit/sales) in the entire database (excluding the firm itself) for everycountry industry pair.
28
TABLE 4: MANAGEMENT, PRODUCT MARKET COMPETITION AND EFFORT
NOTES: Coefficients from OLS regressions with standard errors in parentheses (robust to arbitraryheteroskedasticity and clustered by country * industry pair); single cross section. “Country controls” includes 4country controls. “Full controls” includes a full set of 108 Sic 3-digit industry controls, group and firm size, adummy for being listed, being consolidated, a separate dummy variable for all the selected controls in column (2)table A2: each interviewer (17), the seniority and tenure of the manager who responded, the day of the week theinterview was conducted, the time of the day the interview was conducted and an indicator of the reliability of theinformation as coded by the interviewer. “Lerner index of competition” constructed, as in Aghion et al. (2005) , asthe mean of (1 - profit/sales) in the entire database (excluding the firm itself) for every country industry pair.“Import Penetration” = ln(Import/Outputs) in every country industry pair. Average over 1995-1999 used.
NOTES: Coefficients from OLS regressions with standard errors in parentheses (robust to arbitraryheteroskedasticity); single cross section. “Job Regulation” is the average legal index of “cost of firing workers” and“dismissal procedures” from Botero et al. (2004). “Tenure of manager” is the tenure of the interviewed manager(usually the plant manager). “Country and industry controls” includes 4 country and 108 SIC 3-digit industriescontrols. “Full controls” includes a full set of 108 Sic 3-digit industry controls, group and firm size, a dummy forbeing listed, being consolidated, a separate dummy variable for all the selected controls in column (2) table A2: eachinterviewer (17), the seniority and tenure of the manager who responded, the day of the week the interview wasconducted, the time of the day the interview was conducted and an indicator of the reliability of the information ascoded by the interviewer.
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APPENDIX A: MANAGEMENT PRACTICE INTERVIEW GUIDE AND ANONYMIZEDEXAMPLE RESPONSES
Note: Any score from 1 to 5 can be given, but the scoring guide and examples are only provided for scores of 1, 3 and 5.
Score 1 Score 3 Score 5
1 Leanmanufacturing,introduction
Other than JIT delivery from suppliersfew modern manufacturing techniqueshave been introduced, (or have beenintroduced in an ad-hoc manner)
Some aspects of modern (lean)manufacturing techniques have beenintroduced, through informal/isolatedchange programmes
All major aspects of modern/lean manufacturing havebeen introduced (Just-in-time, autonomation, flexiblemanpower, support systems, attitudes and behaviour) ina formal way
Example: A UK firm orders in bulk and stores thematerial on average 6 months before use.The business focuses on quality and notreduction of lead time or costs.Absolutely no lean manufacturingtechniques had been introduced.
A supplier to the army is undergoing a fulllean transformation. For 20 years thecompany was a specialty supplier to thearmy, but now they have had to identifyother competencies forcing them tocompete with lean manufacturers. Theyhave begun adopting specific leantechniques and plan to use full lean by theend of next year.
A US firm has formally introduced all major elementsof lean production. It reconfigured the factory floorbased on value stream mapping and 5-S principles,broke production into cells, eliminated stockrooms,implemented Kanban, and adopted Takt time analysesto organize work flow.
2 Leanmanufacturing,rationale
Modern (lean) manufacturing techniqueswere introduced because others wereusing them.
Modern (lean) manufacturing techniqueswere introduced to reduce costs
Modern (lean) manufacturing techniques wereintroduced to enable us to meet our business objectives(including costs)
Example: A German firm introduced lean becauseall its competitors were using thesetechniques. The business decision hadbeen taken to imitate the competition.
A French firm introduced modernmanufacturing methods primarily toreduce costs.
A US firm implemented lean techniques because theCOO had worked with them before and knew that theywould enable the business to reduce costs, whilecompeting with cheaper imports through improvedquality, flexible production, greater innovation and JITdelivery.
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3 Processdocumentation
No, process improvements are madewhen problems occur.
Improvements are made in 1 weekworkshops involving all staff, to improveperformance in their area of the plant
Exposing problems in a structured way is integral toindividuals’ responsibilities and resolution occurs as apart of normal business processes rather than byextraordinary effort/teams
Example:A US firm has no formal or informalmechanism in place for either processdocumentation or improvement. Themanager admitted that production takesplace in an environment where nothinghas been done to encourage or supportprocess innovation.
A US firm takes suggestions via ananonymous box, they then review theseeach week in their section meeting anddecide any that they would like to proceedwith.
The employees of a German firm constantly analyse theproduction process as part of their normal duty. Theyfilm critical production steps to analyse areas morethoroughly. Every problem is registered in a specialdatabase that monitors critical processes and each issuemust be reviewed and signed off by a manager.
4 Performancetracking
Measures tracked do not indicate directlyif overall business objectives are beingmet. Tracking is an ad-hoc process(certain processes aren’t tracked at all)
Most key performance indicators aretracked formally. Tracking is overseen bysenior management.
Performance is continuously tracked andcommunicated, both formally and informally, to allstaff using a range of visual management tools.
Example: A manager of a US firm tracks a range ofmeasures when he does not think thatoutput is sufficient. He last requestedthese reports about 8 months ago and hadthem printed for a week until outputincreased again.
At a US firm every product is bar-codedand performance indicators are trackedthroughout the production process;however, this information is notcommunicated to workers
A US firm has screens in view of every line. Thesescreens are used to display progress to daily target andother performance indicators. The manager meets withthe shop floor every morning to discuss the day pastand the one ahead and uses monthly company meetingsto present a larger view of the goals to date andstrategic direction of the business to employees. Heeven stamps napkins with key performanceachievements to ensure everyone is aware of a targetthat has been hit.
5 Performancereview
Performance is reviewed infrequently orin an un-meaningful way e.g. onlysuccess or failure is noted.
Performance is reviewed periodically withboth successes and failures identified.Results are communicated to seniormanagement. No clear follow-up plan isadopted.
Performance is continually reviewed, based onindicators tracked. All aspects are followed up ensurecontinuous improvement. Results are communicated toall staff
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Example: A manager of a US firm relies heavily onhis gut feel of the business. He willreview costs when he thinks they have toomuch or too little in the stores shed. Headmits that as he is very busy so such areview can be quite infrequent. He alsomentioned that staffs feel like he is goingon a hunt to find a problem, so he hasnow made a point of highlightinganything good that he finds too.
A UK firm uses daily production meetingsto compare performance to plan.However, clear action plans areinfrequently developed based on theseproduction results.
A French firm tracks all performance numbers real time(amount, quality etc). These numbers are continuouslymatched to the plan on a shift-by-shift basis. Everyemployee can access these figures on workstations onthe shop floor. If scheduled numbers are not met, actionfor improvement is taken immediately.
6 Performancedialogue
The right data or information for aconstructive discussion is often notpresent or conversations overly focus ondata that is not meaningful. Clear agendais not known and purpose is not statedexplicitly
Review conversations are held with theappropriate data and information present.Objectives of meetings are clear to allparticipating and a clear agenda is present.Conversations do not, as a matter ofcourse, drive to the root causes of theproblems.
Regular review/performance conversations focus onproblem solving and addressing root causes. Purpose,agenda and follow-up steps are clear to all. Meetingsare an opportunity for constructive feedback andcoaching.
Example: A US firm doesn’t conduct staff reviews.It was just “not the philosophy of thecompany” to do that. The company wasvery successful during the last decade andtherefore didn’t feel the need to reviewtheir performance.
A UK firm focuses on key areas to discusseach week. This ensures that key areas ofthe business receive consistentmanagement attention and everyonecomes prepared. Once the discussion takesplace of what is happening they move tothe next topic. However, meetings aremore of an opportunity for everyone tostay abreast of current issues rather thanproblem solve.
A German firm meets weekly to discuss performancewith workers and management (plus daily teammeetings run by the supervisors). Participants comefrom different departments (shop floor, sales, R&D,procurement) to discuss the previous week performanceand to identify areas to improve. They focus on the rootcause of problems and agree topics to be followed upthe next week, allocating all tasks to individualparticipants.
7 Consequencemanagement
Failure to achieve agreed objectives doesnot carry any consequences
Failure to achieve agreed results istolerated for a period before action istaken.
A failure to achieve agreed targets drives retraining inidentified areas of weakness or moving individuals towhere their skills are appropriate
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Example: At a French firm no action is taken whenobjectives aren’t achieved. The Presidentpersonally intervenes to warn employeesbut no stricter action is taken. Cuttingpayroll or making people redundantbecause of a lack of performance is veryrarely done.
Management of a US firm reviewsperformance quarterly. That is the earliestthey can react to any underperformance.They increase pressure on the employeesif targets are not met.
A German firm takes action as soon as a weakness isidentified. They have even employed a psychologist toimprove behaviour within a difficult group. Peoplereceive ongoing training to improve performance. Ifthis doesn’t help they move them in other departmentsor even fire individuals if they repeatedly fail to meetagreed targets
8 Target balance Goals are exclusively financial oroperational
Goals include non-financial targets, whichform part of the performance appraisal oftop management only (they are notreinforced throughout the rest oforganisation)
Goals are a balance of financial and non-financialtargets. Senior managers believe the non-financialtargets are often more inspiring and challenging thanfinancials alone.
Example: At a UK firm performance targets areexclusively operational. Specificallyvolume is the only meaningful objectivefor managers, with no targeting of quality,flexibility or waste.
For a French firm strategic goals are veryimportant. They focus on market share andtry to hold their position in technologyleadership. However, workers on the shopfloor are not aware of those targets.
A US firm gives everyone a mix of operational andfinancial targets. They communicate financial targets tothe shop floor in a way they found effective – forexample telling workers they pack boxes to pay theoverheads until lunch time and after lunch it is all profitfor the business. If they are having a good day theboards immediately adjust and play the profit jingle tolet the shop floor know that they are now working forprofit. Everyone cheers when the jingle is played.
9 Targetsinterconnection
Goals are based purely on accountingfigures (with no clear connection toshareholder value)
Corporate goals are based on shareholdervalue but are not clearly cascaded down toindividuals
Corporate goals focus on shareholder value. Theyincrease in specificity as they cascade through businessunits ultimately defining individual performanceexpectations.
Example: A family owned firm in France is onlyconcerned about the net income for theyear. They try to maximise income everyyear without focusing on any long termconsequences.
A US firm bases its strategic corporategoals on enhancing shareholder value, butdoes not clearly communicate this toworkers. Departments and individualshave little understanding of theirconnection to profitability or value with
For a US firm strategic planning begins with a bottomup approach which is then compared with the top downaims. Multifunctional teams meet every 6 months totrack and plan deliverables for each area. This is thenpresented to the area head that then agrees or refines itand then communicates it down to his lowest level.
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many areas labeled as “cost-centers” withan objective to cost-cut despite potentiallydisproportionately large negative impacton the other departments they serve.
Everyone has to know exactly how they contribute tothe overall goals or else they won’t understand howimportant the 10 hours they spend at work every day isto the business.
10 Targets timehorizon
Top management's main focus is on shortterm targets
There are short and long term goals for alllevels of the organisation. As they are setindependently, they are not necessarilylinked to each other
Long term goals are translated into specific short termtargets so that short term targets become a "staircase" toreach long term goals
Example: A UK firm has had several years ofongoing senior management changes –therefore senior managers are onlyfocussing on how the company is doingthis month versus the next, believing thatlong-term targets will take care ofthemselves.
A US firm has both long and short termgoals. The long term goals are known bythe senior managers and the short termgoals are the remit of the operationalmanagers. Operations managers onlyoccasionally see the longer term goals soare often unsure how they link with theshort term goals.
A UK firm translates all their goals – even their 5 yearstrategic goals - into short term goals so that they cantrack their performance to them. They believe that it isonly when you make someone accountable for deliverywithin a sensible timeframe that a long term objectivewill be met. They think it is more interesting foremployees to have a mix of immediate and longer termgoals.
11 Targets arestretching
Goals are either too easy or impossible toachieve; managers low-ball estimates toensure easy goals
In most areas, top management pushes foraggressive goals based on solid economicrationale. There are a few "sacred cows"that are not held to the same rigorousstandard
Goals are genuinely demanding for all divisions. Theyare grounded in solid, solid economic rationale
Example: A French firm uses easy targets toimprove staff morale and encouragepeople. They find it difficult to set hardergoals because people just give up andmanagers refuse to work people harder.
A chemicals firm has 2 divisions,producing special chemicals for verydifferent markets (military, civil).Different levels of difficulty are appliedfor each division based on the currentdevelopments in each market.
A manager of a UK firm insisted that he has to setaggressive and demanding goals for everyone – evensecurity. If they hit all their targets he worries he hasn’tstretched them enough. Each KPI is linked to theoverall business plan and for the business to stay in theUK everyone has to work hard to get their products outthe door quickly.
12Performanceclarity and
Performance measures are complex andnot clearly understood. Individual
Performance measures are well definedand communicated; performance is public
Performance measures are well defined, stronglycommunicated and reinforced at all reviews;
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comparability performance is not made public in all levels but comparisons arediscouraged
performance and rankings are made public to inducecompetition
Example: A German firm measures performance peremployee based on differential weightingacross 12 factors, each with its ownmeasurement formulas (e.g. individualversus average of the team, increase onprior performance, thresholds etc.).Employees complain the formula is toocomplex to understand, and even the plantmanager couldn’t remember all thedetails.
A French firm doesn’t encourage simpleindividual performance measures asunions pressure them to avoid this.However, charts display the actual overallproduction process against the plan forteams on regular basis.
At a US firm self-directed teams set and monitor theirown goals. These goals and their subsequent outcomesare posted throughout the company, encouragingcompetition in both target-setting and achievement.Individual members know where they are ranked whichis communicated personally to them bi-annually.Quarterly company meetings seek to reviewperformance and align targets.
13 Managinghuman capital
Senior management do not communicatethat attracting, retaining and developingtalent throughout the organisation is a toppriority
Senior management believe andcommunicate that having top talentthroughout the organisation is a key wayto win
Senior managers are evaluated and held accountable onthe strength of the talent pool they actively build
Example: A US firm does not actively train ordevelop its employees, and does notconduct performance appraisals oremployee reviews. People are seen as asecondary input to the production.
A US firm strives to attract and retaintalent throughout the organization, butdoes not hold managers individuallyaccountable for the talent pool they build.The company actively cross-trainsemployees for development andchallenges them through exposure to avariety of technologies.
A UK firm benchmarks human resources practices atleading firms. A cross-functional HR excellencecommittee develops policies and strategies to achievecompany goals. Bi-monthly directors’ meetings seek toidentify training and development opportunities fortalented performers.
14 Rewarding high-performance
People within our firm are rewardedequally irrespective of performance level
Our company has an evaluation system forthe awarding of performance relatedrewards
We strive to outperform the competitors by providingambitious stretch targets with clear performance relatedaccountability and rewards
Example: An East Germany firm pays its peopleequally and regardless of performance.The management said to us that “there are
A German firm has an awards systembased on three components: theindividual’s performance, shift
A US firms stretches employees by setting ambitioustargets. They reward performance through acombination of bonuses linked to performance, team
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no incentives to perform well in ourcompany”. Even the management are paidan hourly wage, with no bonus pay.
performance, and overall companyperformance.
lunches cooked by management, family picnics, moviepasses and dinner vouchers at nice local restaurants.They also motivate staff to try by giving awards forperfect attendance, best suggestion etc.
15 Removing poorperformers
Poor performers are rarely removed fromtheir positions
Suspected poor performers stay in aposition for a few years before action istaken
We move poor performers out of the company or toless critical roles as soon as a weakness is identified
Example: A French firm had a supervisor who wasregularly drinking alcohol at work but noaction was taken –to help him, move himto a less critical role or even give him awarning. His performance was poor andhis team did not respect him. In fact noemployee had ever been laid off in thefactory despite incidences like this.According to the plant manager HRkicked up a real fuss whenevermanagement wanted to get rid ofemployees, and told managers their jobwas production not personnel.
For a German firm it is very hard toremove poor performers. The managementhas to prove at least 3 times that anindividual underperformed before they cantake serious action.
At a US firm the manager fired 4 people during lastcouple of months due to underperformance. Theycontinually investigate why and who areunderperforming. At another US firm the manager said“sure we fire poor performers – I’m the third plantmanager in the last 2 years, the last two got sacked fornot fixing this place”
16 Promoting highperformers
People are promoted primarily upon thebasis of tenure
People are promoted upon the basis ofperformance
We actively identify, develop and promote our topperformers
Example: A UK firm promotes based on anindividual’s commitment to the companymeasured by experience. Hence almost allemployees move up the firm in lock-step.Management was afraid to change thisprocess because it would create bad-feeling among the older employees whowere resistant to change.
A US firm has no formal training program.People learn on the job and are promotedbased on their performance on the job.
At a UK firm each employee is given a red light (notperforming), amber light (doing well and meetingtargets) a green light (consistently meeting targets veryhigh performer) and a blue light (high performercapable of promotion of up to two levels). Eachmanager is assessed every quarter on the basis of hissuccession plans and development plans forindividuals.
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17 Attractinghuman capital(talent)
Our competitors offer stronger reasons fortalented people to join their companies
Our value proposition to those joining ourcompany is comparable to those offeredby others in the sector
We provide a unique value proposition to encouragetalented people join our company above ourcompetitors
Example: A manager of a firm in Germany couldnot give an example of a distinctiveemployee proposition and (when pushed)thinks the offer is probably worse thanmost of its competitors. He thought thatpeople working at the firm “have drawnthe short straw”.
A US firm seeks to create a valueproposition comparable to its competitorsand other local companies by offeringcompetitive pay, a family atmosphere, anda positive presence in the community.
A German firm offers a unique value propositionthrough development and training programs, familyculture in the company and very flexible workinghours. It also strives to reduce bureaucracy and seeks topush decision making down to the lowest levelspossible to make workers feel empowered and valued.
18 Retaininghuman capital(talent)
We do little to try and keep our top talent. We usually work hard to keep our toptalent.
We do whatever it takes to retain our top talent.
Example: A German firm lets people leave thecompany if they want. They do nothing tokeep those people since they think that itwould make no sense to try to keep them.Management doesn’t think they can keeppeople if they want to work somewhereelse. The company also won’t start salarynegotiations to retain top talent.
If management of a French firm feels thatpeople want to leave the company, theytalk to them about the reasons and whatthe company could change to keep them.This could be more responsibilities or abetter outlook for the future. Managers aresupposed to “take-the-pulse” of employeesto check satisfaction levels.
A US firm knows who its top performers are and if anyof them signal an interest to leave it pulls in seniormanagers and even corporate HQ to talk to them andtry and persuade them to stay. Occasionally they willincrease salary rates if necessary and if they feel theindividual is being underpaid relative to the market.Managers have a responsibility to try to keep alldesirable staff.
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TABLE A1: QUESTION LEVEL AVERAGES BY COUNTRY
UK Germany FranceUS=100 US=100 US=100
Lean manufacturing, introduction 90.0
(3.50)
86.4
(3.47)
101.3(3.63)
Lean manufacturing, rationale 92.9
(3.35)
101.5
(3.32)
101(3.47)
Process documentation 89.0
(3.51)
106.9
(3.49)
99
(3.64)
Performance tracking 98.3
(3.19)
109.5
(3.17)
111
(3.32)
Performance review 94.7
(2.99)
110.2
(2.97)
104
(3.10)
Performance dialogue 93.0(3.19)
103.3(3.11)
99(3.27)
Consequence management 96.5(3.02)
108.7(3.01)
94(3.13)
Target breadth 91.1(3.53)
93.3(3.51)
94(3.66)
Target interconnection 93.7(3.56)
97.3(3.54)
78(3.68)
Target time horizon 91.9(3.69)
98.6(3.66)
92(3.83)
Targets are stretching 87.8(3.34)
104.9(3.32)
101(3.45)
Performance clarity and comparability 93.7(3.53)
80.7(3.49)
83(3.65)
Managing human capital 89.4(3.94)
99.0(3.92)
89(4.08)
Rewarding high performance 81.6(3.42)
85.2(3.42)
85(3.55)
Removing poor performers 89.4(3.04)
92.5(3.02)
83(3.15)
Promoting high performers 90.2(2.86)
104.9(2.85)
92(2.97)
Attracting human capital 90.4(2.89)
95.1(2.88)
85(2.99)
Retaining human capital 93.6(2.74)
97.7(2.73)
97(2.84)
NOTES: Standard errors of each question’s average response are reported below in brackets. Calculated from full sample of731 firms for which management information is available. Management z-scores used in the calculations.
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APPENDIX A: HUMAN RESOURCES INTERVIEW GUIDERun in parallel as the management survey but targeted at the HR departmentWorkforce Characteristics
Data Field BreakdownTotal number of employees (all employees)% with university degree (all employees)% with MBA (all employees)Average age of employees (all employees)% of employees (managerial/non-managerial)Average training days per year (managerial/non-managerial)Average hours worked per week (inc overtime, exc. breaks) (managerial/non-managerial)Average holidays per year (all employees)Weeks maternity leave (all employees)Weeks paternity leave (all employees)Average days sick-leave (all employees)% part-time (managerial/non-managerial)% female (managerial/non-managerial)% employees abroad (all employees)% union membership (all employees)Are unions recognized for wages bargaining [yes / no] (all employees)
Organisational CharacteristicsQuestion Response choice (all employees)Who decides the pace of work? [exclusively workers / mostly workers / equally /
mostly managers / exclusively managers]Who decides how tasks should be allocated? [exclusively workers / mostly workers/ equally /
mostly managers / exclusively managers]Do you use self-managing teams? [v. heavily / heavily / moderately / slightly / none]
Work-life Balance: PerceptionsQuestion Response choice (all employees)Relative to other companies in your industry [much less / slightly less / the same / slightlyhow much does your company emphasize more / much more]work-life balance?
Is it up to an individual employee (rather than [yes/no]the company) to balance their work/life balance?
Work-Life Balance: PoliciesQuestion Response choice (managerial/non-managerial)If an employee needed to take a day off at short [Not allowed / Never Been Asked / Take as leavedue child-care problems or their child was sick how without pay / Take time off but make it up laterdo they generally do this? / Take as annual leave / Take as sick leave]What entitlements are there to the following BreakdownWorking at home in normal working hours? (managerial/non-managerial)Switching from full-time to part-time work? (managerial/non-managerial)Job sharing schemes? (managerial/non-managerial)Workplace nursery or nursery linked to workplace? (managerial/non-managerial)Financial subsidy to help pay for childcare? (managerial/non-managerial)School time contracts only? (managerial/non-managerial)
Market & firm questions: Response choice# of competitors [none / less than 5 / 5 or more]# hostile take-over bids in last three years [none / one / more than one ]Average IT spend over the last 3 years
Interviewer’s assessment of the scoring reliability1 to 5 scoring system calibrated according to:1 = Interviewee did not have enough expertise for interview to be valuable; I have significant doubts about
most of the management dimensions probed]3 = Interviewee had reasonable expertise; on some dimensions I am unsure of scoring5 = Interviewee had good expertise, I am confident that the score reflects management practices in this firm
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APPENDIX B: DATA
Sampling Frame ConstructionOur sampling frame was based on the Fame dataset for the UK, Amadeus dataset for Europeand the Compustat dataset for the USA. We chose firms whose principle industry was inmanufacturing and who employed (on average between 2000 and 2003) no less than 50employees and no more than 10,000 employees. We also removed any clients of McKinseyand Company from the sampling frame (33 out of 1353 firms).
We believe that our sampling frame is reasonably representative of medium sizedmanufacturing firms. The European firms in Amadeus include both private and public firmswhereas Compustat only includes publicly listed firms. There is no US database withprivately listed firms with information on sales, labour and capital. Fortunately, there are amuch larger number of US firms listed on the stock exchange than in Europe so we are able togo substantially down the size distribution with Compustat. Nevertheless, the US firms areslightly larger than those of the other countries, so we are always careful to control for size inour analyses. Furthermore our preferred specifications allow all coefficients to be different onlabour, capital, materials and consolidation status by country.
Another concern is that we condition on firms where we have information on sales,employment and capital. These items are not compulsory for firms below certain sizethresholds so disclosure is voluntary to some extent for the smaller firms. Luckily the firms inour sampling frame (over 50 workers) are past the threshold for voluntary disclosure (the onlyexception is for capital in Germany).
We achieved a response rate of 54% from the firms that we contacted: a very high successrate given the voluntary nature of participation. Respondents were not significantly moreproductive than non-respondents. French firms were slightly less likely to respond than firmsin the other three countries and all respondents were significantly larger than non-respondents. Apart from these two factors, respondents seemed randomly spread around oursampling frame
Firm level dataOur firm accounting data on sales, employment, capital, profits, shareholder equity, long-termdebt, market values (for quoted firms) and wages (where available) came from Fame (UK),Amadeus (France and Germany) and Compustat (US). For other data fields we did thefollowing:Materials: In France and Germany these are line items in the accounts. In the UK these wereconstructed by deducting the total wage bill from the cost of goods sold. In the US these wereconstructed following the method in Bresnahan et al. (2002). We start with costs of good sold(COGS) less depreciation (DP) less labor costs (XLR). For firms who do not report laborexpenses expenditures we use average wages and benefits at the four-digit industry level(Bartelsman, Becker and Gray, 2000, until 1996 and then Census Average Production WorkerAnnual Payroll by 4-digit NAICS code) and multiply this by the firm's reported employmentlevel. This constructed measure is highly correlated at the industry level with materials.Obviously there may be problems with this measure of materials (and therefore value added)which is why we check robustness to measures without materials.
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CEO Pay and Age: In the US the S&P 1500 largest firms (which cover all sectors) arecontained in Execucomp, which provided data for 106 largest of our US firms. For theremaining firms we manually downloaded the Def14a proxy statements from the SEC toextract the details of the CEO and CFO compensation package and age over the last threeaccounting years25. In the UK the highest paid director is a mandatory line item in theaccounts and we took this as the CEO’s salary. In France and Germany we have no data onexecutive pay.Company Shareholdings: This was manually extracted from the Bloomberg online dataservice for the 10 largest shareholders and the 10 largest insider shareholders.Dates of Incorporation: For UK, French and German companies this is provided by the Fameand Amadeus datasets. For the US this was obtained from Dunn and Bradstreet.R&D: For the US firms this is provided in Compustat. For quoted European firms weobtained this from Datastream UK and Datastream Europe. For unquoted European firms wehave no R&D data.
Industry level dataThis comes from the OECD STAN database of industrial production. This is provided at thecountry ISIC Rev. 3 level and is mapped into US SIC (1997) 3 (which is our commonindustry definition in all four countries).
25 Many thanks to Guy Clark, Jatin Gulati, Sejal Mehta and Rahul Rathi for the construction of this and theBloomberg share-ownership data.
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TABLE B1 DESCRIPTIVE STATISTICS
All UK US Germany FranceNumber of firms, # 731 152 289 154 136
Management (mean z score) 0.009 -0.144 0.108 0.075 -0.077
Employment (mean) 1,964 1,750 2,476 1,880 1,215
Employment(median) 690 418 1,251 974 311
Material share of output, % 49.2 46.5 53.9 54.7 42.8
Labour share of output,% 26.5 27.9 29.5 28.0 22.9
Return of Capital Employed (ROCE), % 9.94 10.89 5.84 12.9 15.38
Tobin’s Q 2.51 2.01 2.87 1.77 1.37
Nominal sales growth rate, % 11.1 5.1 7.9 4.9 8.1
Unconsolidated, % .257 .094 0 .341 .804
Age of firm (years) 44.3 42.9 42.6 54.9 39.6
Listed firm,% 55.7 28.3 100 40.0 15.4
Multinational subsidiary, % 8.8 9.2 0 22.3 11.0
Share workforce with degrees, % 21.0 13.5 30.6 14.2 15.4
Share workforce with an MBA, % 1.00 0.97 1.89 0.07 0.20
Sickness, days/year 6.850 6.06 5.07 8.48 8.29
Hours, hours per week 40.5 40.2 44.0 38.6 35.6
Holidays, days per year 22.8 25.8 12.2 29.7 32.3
Union density, % 19.0 8.7 22.6 40.8 10.2
Number of competitors index, 1=”none”, 2=”afew”, 3=”many” 2.28 2.37 2.26 2.25 2.16
Shareholder concentration, % held by top 10shareholders 53.3 51.5 59.1 41.3 63.6
Notes: Data descriptives calculated on the full sample of 731 firms for which management information is available.
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APPENDIX C: MEASUREMENT ERROR AND NOISECONTROLS
Decomposing Variation and Measurement Error
We decompose the variation in the question level z-scores ijq into four components
ijiijiij eupmq +++= (where subscript i denotes firm and j denotes practice): the average
firm management practice im ; the practice specific deviations from the average firm
management practice ijp where ∑ = 0ijp ; the average firm-level measure error iu ; and the
practice specific deviation in measurement error from the firm average measurement error ije
where ∑ = 0ije .
Assuming that the practice deviations and measurement error deviations are i.i.d. within firms(although not across firms) we can decompose the variance in ijq as 22221 eupm σσσσ +++=using the fact that z-scores have a variance of 1. To determine these values of thesecomponents we exploit the information in the first and second interviews and the variance ofquestion scores within and between firms.
At the question level the regression coefficient from the first on the second interview
responses will take the value2222
22
eupm
pmq σσσσ
σσβ
++++
= , from applying the standard result on
the attenuation bias due to measurement error. The average coefficient26 from the first onsecond interviews and the second on first interviews is 0.578. At the firm level the regressioncoefficient of the first interview average scores on the second interview average scores will
take the value22
2
um
mq σσ
σβ
+= . The average coefficient from the first on second and second on
first interviews is 0.752. Finally, decomposing the variance in question scores within andbetween firms provides values on 22
um σσ + and 22ep σσ + of 0.466 and 0.534.
Combining these three results together with the definition of the variances allows us tocalculate 2
mσ = 0.350, 2pσ = 0.228, 2
uσ = 0.116, and 2eσ = 0.306. Thus, we estimate the ratio of
variation from management practices to measurement error to be 58:42 at the question level.This ratio rises to 75:25 at the firm level due to the higher correlation of managementpractices than measurement error across questions within the firm. Interestingly the variationin these management practices is driven both by changes in firm average managementpractices (61%) and in firm specific practice capabilities (39%).
26 The regression of the first interview questions on the second interview questions provides an estimate of themeasurement error in the second interviews, while the regression of the second on first interview questionsprovides an estimate of the measurement error in the first interview. Taking the average coefficient from thesetwo regressions provides a sample average of the measurement error.
44
TABLE C1: NOISE CONTROLS FOR MEASUREMENT ERROR IN THEMANAGEMENT INTERVIEW
45
Dependent variable is Management z-score
ExplanatoryVariable
Definition Mean Coefficient(s.e.)
Coefficient(s.e.)
All Controls Selected Controls
Male Respondent is male 0.978 0.023
(0.200)
Seniority The position of manager in theorganisation (1 to 5)
3.058 0.062
(0.029)
0.067
(0.028)
Tenure in this post Years with current job title 4.908 -0.011
(0.007)
Tenure in thecompany
Years with the company 11.887 0.002
(0.004)
Countries Total number of countries worked inover last ten years
1.188 0.076
(0.045)
0.088
(0.042)
Organisations Total number of organisationsworked in over last ten years
Manager is foreign Manager was born outside thecountry s/he works
0.026 0.241
(0.158)
Ever worked in USA The manager has worked in the USAat some point
0.425 0.142
(0.161)
Respondent is fromUS
The manager was born in the USA 0.317 0.231
(0131)
Location of manager Manager based on site or incorporate HQ
Tuesday Day of the week that interview wasconducted, (Monday base)
0.288 0.033
(0.089)
0.063
(0.088)
Wednesday Day of the week that interview wasconducted, (Monday base)
0.199 0.051(0.082)
0.038(0.080)
Thursday Day of the week that interview wasconducted, (Monday base)
0.159 0.230(0.087)
0.211(0.086)
Friday Day of the week that interview wasconducted, (Monday base)
12.45 0.132(0.091)
0.117(0.089)
Local time formanager
The time of the day (24 hour clock)interview conducted
12.45 -0.021(0.010)
-0.023(0.010)
Local time forinterviewer
The time of the day GMT (24 hourclock) interview conducted
14.28
Days from start ofproject
Count of days since start of theproject
0.003(0.001)
0.003(0.001)
Duration of interview The length of the interview withmanager (in minutes)
46.00 0.008(0.003)
0.008(0.003)
Number of contacts Number of telephone calls to arrangethe interview
5.714 0.003(0.006)
Reliability score Interviewer’s subjective ranking ofinterview reliability (1 to 5)
4.148 0.384(0.036)
0.365(0.036)
17 InterviewerDummies
F(15,699)=2.37p-value=0.003
F(15,699)=2.37p-value=0.003
46
NOTES: Coefficients from OLS regressions with standard errors in parentheses (robust to arbitrary heteroskedasticity);single cross section; 3 country dummies and 108 sic 3-digit dummies included in the regression; 731 observations
47
APPENDIX D: ALTERNATIVE PERFORMANCE ESTIMATES
TABLE D1 ALTERNATIVE PERFORMANCE SPECIFICATIONS
NOTES: Coefficients from OLS regressions with standard errors in parentheses (robust to arbitraryheteroskedasticity and autocorrelation through clustering by firm); sample period 1998-2004. “Factorcoefficients interacted with industry dummies” allows labour, capital, materials (and a dummy forsubsidiary status) to be interacted with country dummies. UK is the baseline; “Industry dummiesinteracted with country dummies” interactions allows all 2 digit SIC dummies to vary with country;“Extended controls” include the average hours worked in the firm, the average days lost to sicknessand holidays over the year, the proportion of the workforce with a college degree or equivalent, firmage, a dummy for consolidation status and a dummy for whether or not the firm has a stock marketlisting; “Interviewer controls” includes a separate dummy variable for all the selected controls incolumn (2) table A2: each interviewer (17), the seniority and tenure of the manager who responded,the day of the week the interview was conducted, the time of the day the interview was conducted andan indicator of the reliability of the information as coded by the interviewer. Growth rates of salescalculated using the formula growth rate Xit=(Xit -Xit-1) / Xit-1
(1) (2) (3) (4) (5)
Estimation Method OLS OLS OLS OLS OLS
Dependent variable Ln (Sales) Ln (Sales) ROCE Ln (Tobin’s Q)Sales
growth rateCompanies All All All Quoted All
Management 0.062
(0.015)
0.056
(0.015)
2.938
(0.779)
0.238
(0.075)
0.024
(0.009)
ln (L) it
labour
0.470
(0.023)
0.502
(0.044)
1.679
(1.953)
0.345
(0.154)
-0.016
(0.019)
Ln(K) it
capital
0.109
(0.015)
0.162
(0.027)
-0.801
(1.522)
-0.343
(0.161)
-0.002
(0.015)
ln (Materials) it,
materials
0.399
(0.022)
0.332
(0.036)
0.081
(1.287)
0.000
(0.110)
0.009
(0.015)
Firms 717 717 717 374 717
Observations 4332 4332 4332 2217 4205
Factor coefficientsinteracted withcountry dummies
No Yes Yes Yes Yes
Industry dummiesinteracted withcountry dummies(72)
