Hrm and Employee Productivity
This paper has been prepared for a chapter in the Handbook of Labor Economics Volume IV edited by David Card and Orley Ashenfelter - Hrm and Employee Productivity introduction. We would like to thank the Economic and Social Research Council for their financial support through the Center for Economic Performance. This survey draws substantially on joint work with Daron Acemoglu, Philippe Aghion, Eve Caroli, Luis Garicano, Christos Genakos, Claire Lelarge, Ralf Martin, Raffaella Sadun and Fabrizio Zilibotti.
We would like to thank Orley Ashenfelter, Oriana Bandiera, Alex Bryson, David Card, Edward Lazear, Paul Oyer, John Roberts, Kathy Shaw and participants in conferences in Berkeley and the LSE for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. © 2010 by Nicholas Bloom and John Van Reenen. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.
More Essay Examples on Firm Rubric
Human Resource Management and Productivity Nicholas Bloom and John Van Reenen NBER Working Paper No. 16019 May 2010 JEL No. L2,M2,O32,O33 ABSTRACT In this handbook of labor economics chapter we examine the relationship between Human Resource Management (HRM) and productivity. HRM includes incentive pay (individual and group) as well as many non-pay aspects of the employment relationship such as matching (hiring and firing) and work organization (e. g. teams, autonomy). We place HRM more generally within the literature on management practices and productivity.
We start with some facts on levels and trends of both HRM and productivity and the main economic theories of HRM. We look at some of the determinants of HRM – risk, competition, ownership and regulation. The largest section analyses the impact of HRM on productivity emphasizing issues of methodology, data and results (from micro-econometric studies). We conclude briefly with suggestions of avenues for future frontier work. Nicholas Bloom Stanford University Department of Economics 579 Serra Mall Stanford, CA 94305-6072 and NBER [email protected]
edu John Van Reenen Department of Economics London School of Economics Centre for Economic Performance Houghton Street London WC2A 2AE United Kindom and NBER j. [email protected] ac. uk 1. Introduction Traditionally, labor economics focused on the labor market rather than looking inside the “black box” of firms. Industrial sociologists and psychologists made the running in Human Resource Management (HRM). This has changed dramatically in last two decades. Human Resource Management (HRM) is now a major field in labor economics.
The hallmark of this work is to use standard economic tools applied to the special circumstances of managing employees within companies. HRM economics has a major effect on the world through teaching in business schools, and ultimately what gets practiced in many organizations. HRM covers a wide range of activities. The main area of study we will focus on will be incentives and work organization. Incentives include remuneration systems (e. g. individuals or group incentive/contingent pay) and also the system of appraisal, promotion and career advancement.
By work organization we mean the distribution of decision rights (autonomy/decentralization) between managers and workers, job design (e. g. flexibility of working, job rotation), team-working (e. g. who works with whom) and information provision. Space limitations mean we do not cover matching (see Oyer and Schaffer, this Volume) or skill development/training. Second, we will only devote a small amount of space to employee representation such as labor unions (see Farber, this Volume). Third, we should also mention that we focus on empirical work
rather than theory (for recent surveys see Gibbons and Roberts, 2008, and in particular Lazear and Oyer, 2008) and micro-econometric work rather than macro or qualitative studies. Fourth, we focus on HRM over employees rather than CEOs, which is the subject of a vast literature (see Murphy, 1999, or Edmans, Gabaix and Landier, 2008, for surveys). Where we depart from several of the existing surveys in the field is to put HRM more broadly in the context of the economics of management. To do this we also look in detail at the literature on productivity dispersion.
The structure of the chapter is as follows. In Section 2 we detail some facts about HRM and productivity both in the cross sectional and time series dimension. In Section 3 we look at the impact of HRM on productivity with an emphasis on methodologies and the mechanisms. In Section 4 we 2 discuss some theoretical perspectives, contrasting the usual “Design” approach to our concept of HRM as one example of “management as a technology”. In Section 5 we discuss some of the factors determining HRM, focusing on risk, competition, ownership, trade and regulation. Section 6 concludes.
2. Some facts on HRM and productivity 2. 1. HRM practices In the 1970s the general assumption was that incentive pay would continue to decline in importance. This opinion was based on the fact that traditional unskilled jobs with piece-rate incentives were declining, and white collar jobs with stable salaries and promotion based incentives were increasing. Surprisingly, however, it appears (at least in the US) that over the last three decades a greater proportion of jobs have become rewarded with contingent pay, and this is in fact particularly true for salaried workers.
There are two broad methods of assessing the importance of incentive pay: Direct and Indirect methods. Direct methods use data on the incidence of HRM, often drawn from specialist surveys. Indirect methods use various forms of statistical inference, ideally from matched worker-firm data, to assess the extent to which pay is contingent on performance. We deal mainly with the direct evidence and then discuss more briefly the indirect evidence. 2. 1. 1. HRM measured using direct methods Incentive Pay Individual incentive pay information is available from a variety of sources.
Using the Panel Study of Income Dynamic (PSID) Lemieux, McCleod and Parent (2009) estimate that about 14% of US prime age men in 1998 received performance pay (see Figure 2. 1). They define a worker as receiving performance pay if any part of compensation includes bonus, commission or piece rate1 (data on stock options and shares is not included). They find a much higher incidence of performance pay jobs (37% on average between 1976-1998) defined as a job where a worker ever received some kind of 1 Overtime is removed, but the question is imperfect pre-1993 which could lead to undercounting performance pay.
3 performance pay2. They also look at the National Longitudinal Survey of Youth (NLSY) which shows coverage of performance pay jobs for men of 26% in 1988 to 1990. Other papers deliver similar estimates of around 40% to 50% of US employees being covered by some form of performance pay. For example, using the US General Social Survey Kruse, Blasi and Park (2009) estimate that 47% of American workers were covered by some group incentive scheme in 2006. Of this 38% of employees were covered by profit sharing, 27% by gain-sharing, 18% by stock ownership (9% by stock options) and 4.
6% by all three types. Lawler et al (2003) surveyed Fortune 1,000 corporations between 1987 and 2002 asking detailed questions on their HRM3. Using midpoints of their results (which are in bands) Lemieux et al (2008) calculate that 44% of workers were covered by incentive pay in 2002. It is also interesting to look at the trends in incentive pay over time. In US data, Lemieux, McCleod and Parent (2009) find that for the wider definition of performance pay (if the worker was eligible for any performance related pay) the incidence rises from 38% in the 1970s to 45% in the 1990s (see Figure 1).
Interestingly, this rise in performance pay was mostly driven by increases in performance pay for salaried workers, for whom this rose from 45% in the 1970s to 60% in the 1990s. In contrast hourly paid workers have both lower levels and growth rates in performance pay. Lawler et al. (2003) show similar rises in performance pay, increasing from 21% (1987) to 27% (1990) to 35% (1996) to 45% (2002). Lazear and Shaw (2008) also show some breakdown trends reproduced in Table 2.
1, showing again performance pay has clearly increased over time in the US. In the UK the British Workplace Employment Relations Surveys (WERS) contains a cross section of all establishments with 25 or more employees in the UK (over 2,000 in each year). There are consistent questions in 1984, 1990 and 2004 on whether the firm used any form of performance/ contingent pay for workers both individually and collectively (e. g. team bonuses, Profit-related pay or Employee Share Ownership Schemes). Figure 2.
2 shows that 41% of UK establishments had contingent pay in 1984, and this rose to 55% twenty years later. Two other points are noteworthy. First, this time series change is driven by the private sector: not only was the incidence of incentive pay very low in the public sector 10% or less, it actually fell over time (Lemieux et al 2009 exclude the The difference is somewhat surprising as it suggests that performance pay jobs only pay out infrequently, which doesn’t comply with casual observation (e. g. piece rates will almost always pay something).
3 The problem with the Lawler surveys is that the sampling frame is only larger companies compared to the more representative individual level PSID. Furthermore, the response rate to the survey has declined rapidly from over 50% in 1987 to only 15% by 1999. This poses a serious concern that the time series trends are not representative even of larger firms. 2 4 public sector in their US analysis). Second, the growth of incentive pay in the UK is primarily in the 1980s with no growth in the 1990s, similar to the US results shown in Figure 1.
So in summary, the evidence is that overall performance pay related covers about 40% to 50% of US workers by the 2000s, and pay has been increasing over the last three decades, particularly over the 1970s and 1980s. A number of reasons suggested for the increase in performance related pay which we will examine in detail in section 5 below. Other HRM Practices Turning to more general forms of HRM than pay, like self-managed teams, performance feedback, job rotation, regular meetings, and training it becomes rather harder to summarize the existing information.
In the cross section there are a number of surveys with different sampling bases, response rates and questions making them hard to compare. Perhaps the most representative example for the US is Black and Lynch (2001, 2004) who helped collected information from a survey backed by the US Department of Labor (used also by Cappelli and Neumark, 2001). In 1996, for example, about 17% of US establishments had self-managed teams, 49% in formal meetings and 25% in job rotation. Lawler et al. (2003)’s data of larger firms unsurprisingly shows a greater incidence of “innovative” HRM practices.
In their data for 1996, 78% of firms had self-managed teams and this covered at least 20% of the workforce for just under a third of all corporations. Bryson and Wood (2009) present an analysis of “high involvement” HRM using the UK WERS data (see Table 2. 2). About half of all UK establishments had “team-working” in 1998. More interestingly, the WERS data allows an analysis of changes over time. The incidence of teamwork (as indicated by “team briefings” has grown from 31% in 1984 to 70% in 2004 and “suggestion schemes” has grown from 22% in 1984 to 36% 20 years later.
Disclosure of Information regarding investment plans has risen from 32% to 46% over the same period. Most other forms of innovative HRM look remarkably stable, however, with the exception of incentive pay that has already been discussed. Wider International Comparisons To compare a wider basket of countries beyond the UK and US the best source of information is probably the Bloom-Van Reenen (2007) surveys on general management practices. These have some specific questions on HRM or “people management”, which have been collected from 17 countries.
Since we will refer to this work at several points we describe the methodology in a little detail as it is somewhat different than the standard HRM surveys described above. The essential method was to start 5 with a grid of “best practices” in HR and non-HR management and then score firms along each of the eighteen dimensions of this grid following an in-depth telephone interview with the plant manager. These eighteen dimensions covered three broad areas: monitoring, target setting and people management (see Appendix Table A1 for details).
The people section covers a range of HR practices including whether companies are promoting and rewarding employees based on worker ability and effort; whether firms have systems to hire and retain their most productive employees; and whether they deal with underperformers through retraining and effective sanctions. For example, we examine whether employees that perform well, work hard and display high ability are promoted faster than others. To obtain accurate responses from firms the survey targetted production plant managers using a ‘double-blind’ technique.
One part of this double-blind technique is that managers are not told they are being scored or shown the scoring grid. They are only told they are being “interviewed about management practices for a research project”. To run this blind scoring we used “open” questions since these do not tend to lead respondents to a particular answer. For example, the first people management question starts by asking respondents “tell me how does your promotion system work” rather than a closed question such as “do you promote on ability (yes/no)”.
Interviewers also probed for examples to support assertions, for example asking “tell me about your most recent promotion round”. The other side of the double-blind technique is interviewers are not told in advance anything about the firm’s performance to avoid prejuduice. They are only provided with the company name, telephone number and industry. Since the survey covers medium-sized firms (defined as those employing between 100 and 10,000 workers) these would not be usually known ex ante by the interviewers.
These management practices were strongly correlated with firm’s performance data from their company accounts (total factor productivity, profitability, growth rates, and Tobin’s Q and survival rates). These correlations are not causal but do suggest that HR practices that reward effort and performance are associated with better firm performance. Other research shows that these practices are also associated with better patient outcomes in hospitals (Bloom, Propper, Seiler and Van Reenen, 2009) and improved work-life balance indicators (Bloom, Kretschmer and Van Reenen, 2009).
Figure 2. 3 shows the distribution of these people management practices across countries. The US clearly has the highest average scores for people management. Bloom, Genakos, Sadun and Van Reenen (2009) show that this appears to be due to a combination of the US being absolutely good at 6 managing firms across all 18 questions on average, and also having a particular advantage in people (HR) management. Other countries with light labor regulation like Canada, Great Britain and Northern Ireland also display relatively strong HR management practices.
Interestingly Germany and Japan also fare well, in large part reflecting the fact that these countries have generally well managed manufacturing firms. Figure 2. 4 breaks out the people management score into three of the key areas in the overall people management score, which are promotions, fixing/firing underperformers and rewards. What is clear is that US firms have the globally highest scored practices across all three dimensions, but are particularly strong on “fixing/firing” practices.
That is, in the US employees who underperform are most likely to be rapidly “fixed” (dealt with through re-training or rotated to another part of the firm where they can succeed), or if this fails “fired” (moved out of the firm). In contrast in countries like Greece and Brazil underperforming employees are typically left in post for several months or even years before any action is taken to address them. In sub-section 4. 1 we discuss reasons for these patterns. Broadly speaking, the high levels of competition and low incidence of family firms are the main contributing factors to the leading position of the US in overall management.
On top of this, high levels of education and weaker labor regulations give American firms a particular advantage in the HR aspect of management. Figure 2. 5 displays the firm level distributions within each country for these management practices, showing there is a wide dispersion of practices within every country. The US average score is the highest because it has almost no firms with weak HR management practices, while Brazil and Greece has a large tail of firms with poor HR management practices. This wide variation within each country is what most of the prior micro literature has focused on, with Figure 2.
5 showing this variation is common across every country we have investigated. 2. 1. 2. Measuring Incentive Pay through indirect methods The indirect method has been common in labor economics mainly due to data constraints. Essentially this method examines the correlation of workers’ remuneration with firm-specific characteristics that should be important if pay is contingent on performance such as profitability, market value, etc. For example, if there are profit-related pay schemes, increases in firm profits should cause increases in worker pay.
If pay was set solely on the external labor market, it should be unrelated to idiosyncratic 7 changes in the firm’s financial position. An advantage of this approach over the direct approach is that many of the incentive schemes may not be explicitly written down as contracts. A disadvantage is that the correlations between firm performance and pay we observe may be unrelated to incentive schemes for econometric reasons – e. g. a positive demand shock may simultaneously raise a firm’s profitability and mean it hires workers of an unobservably higher skill level.
Further, to the extent we do credibly identify a causal effect of firm performance on worker pay we cannot discern easily whether this is due to explicit contracts, implicit contracts, union bargaining4 or some other model. Having said this, there is substantial evidence that firm performance does matter a lot for worker remuneration. This is clearest in the many studies of matched worker firm data which generally shows an important role for firm characteristics in determining worker wages (e. g. Abowd, Kramarz and Margolis, 1999).
Simple OLS regressions of changes of wages on changes of firm’s profitability tend to find a positive effect (e. g. Blanchflower, Oswald and Sanfrey (1996), but these are likely to be downward biased as shocks to wages will tend to reduce profitability. Using trade-based (Abowd and Lemieux, 1993) or technology-based (Van Reenen, 1996) instrumental variables tends to significantly increase the effect of firm performance on wages as we would expect. Matched worker-firm data is now commonly available in a large number of countries (see the collection of papers in Lazear and Shaw, 2008, for example).
In the US, for example, Abowd, Haltiwanger and Lane (2008) use the LEHD (Longitudinal Employer- Household Dynamics Program) covering about 80% of all employees. They show that about one half of all individual wage variance is associated with individual characteristics and about a half due to firm effects. Although the focus of the literature has mainly been on explaining the distribution of wages at a point in time Dunne, Foster, Haltiwanger and Troske (2004) show that between firm effects are important in understanding the growing inequality of wages over time in the US.
Faggio, Salvanes and Van Reenen (2007) also find this for the UK and furthermore, offer evidence that the association of firm performance with wages has grown stronger over time. This is consistent with the more direct evidence discussed above that performance pay (explicit or implicit) may be more prevalent in recent years. 2. 2. Productivity dispersion Abowd (1989) looks at unexpected changes to wages and finds that shareholders wealth falls by an equal and opposite amount. He interprets this as consistent with strongly efficient bargaining over the rents between unions and firms. 4 8
Research on firm heterogeneity has a long history in social science. Systematic empirical analysis first focused on the firm size distribution measured by employment, sales or assets. Most famously, Gibrat (1931), characterized the size distribution as approximately log normal and sought to explain this with reference to simple statistical models of growth (i. e. Gibrat’s Law that firm growth is independent of size). In the 1970s as data became available by firm and line of business, attention focused on profitability as an indicator of performance (e. g. Kwoka and Ravenscraft, 1986).
Accounting profitability can differ substantially from economic profitability, however, and may rise due to market power rather than efficiency. In recent decades the development of larger databases has enabled researchers to look more directly at productivity. The growing availability of plant-level data from the Census Bureau in the US and other nations combined with rapid increases in computer power has facilitated this development. Bartelsman, Haltiwanger and Scarpetta (2008) offer many examples of the cross country microdatasets now being used for productivity analysis.
One of the robust facts emerging from these analyses is the very high degree of heterogeneity between business units (see Bartelsman and Doms, 2000). For example, Syverson (2004a) analyzes labor productivity (output per worker) in US manufacturing establishments in the 1997 Economic Census and shows that on average, a plant at the 90th percentile of the productivity distribution is over four times as productive as a plant at the 10th percentile in the same four digit sector. Similarly, Criscuolo, Haskel and Martin (2003) show that in the UK in 2000 there is a fivefold difference in productivity
between these deciles. What could explain these differences in productivity, and how can they persist in a competitive industry? One explanation is that if we accounted properly for the different inputs in the production function there would be little residual productivity differences5. It is certainly true that moving from labor productivity to total factor productivity (TFP) reduces the scale of the difference. For example, in Syverson (2004) the 90-10 productivity difference falls from a factor of 4 to a factor of 1. 9, but it does not disappear.
This is analogous to the historical debate in the macro time series of productivity between Solow, who claimed that TFP was a large component of aggregate growth and Jorgenson who claimed that there was little role for TFP when all inputs were properly measured (see Griliches, 1996). A similar debate is active in “levels accounting” of cross-country TFP (e. g. Caselli, 2005). 5 9 These differences show up clearly even for quite homogeneous goods. An early example is Salter (1960) who studied the British pig iron industry between 1911 and 1926.
He showed that the best practice factory produced nearly twice as many tons per hour as the average factory. More recently, Syverson (2004b) shows TFP (and size) is very dispersed in the US ready mix concrete industry. Interestingly, the mean level of productivity was higher in more competitive markets (as indicated by a measure of spatial demand density) and this seemed to be mainly due to a lower mass in the left tail in the more competitive sector. Studies of large changes in product market competition such as trade liberalization (e.
g. Pavcnik, 2002), foreign entry into domestic markets (Schmitz, 2005) or deregulation (e. g. Olley and Pakes, 1996) suggest that the subsequent increase in aggregate productivity has a substantial reallocation element6. A major problem in measuring productivity is the fact that researchers rarely observe plant level prices so an industry price deflator is usually used. Consequently, measured TFP typically includes an element of the firm-specific price-cost margin (e. g. Klette and Griliches, 1994).
Foster, Haltiwanger and Syverson (2009) study 11 seven-digit homogeneous goods (including block ice, white pan bread, cardboard boxes and carbon black) where they have access to plant specific output (and input) prices. They find that conventionally measured revenue based TFP (“TFPR”) numbers actually understate the degree of true productivity dispersion (“TFPQ”) especially for newer firms as the more productive firms typically have lower prices and are relatively larger7. Higher TFP is positively related to firm size, growth and survival probabilities.
Bartelsman and Dhrymes (1998, Table A. 7) show that over a five year period around one third of plants stay in their productivity quintile. This suggests that productivity differences are not purely transitory, but partially persist. Analysis of changes in aggregate productivity over time has shown that this productivity dispersion is also important in explaining economic growth. For example, Baily, Hulten and Campbell (1992) find that half of the change in US industry-level productivity is due to the reallocation of output from lower productivity plants to those with higher productivity.
This reallocation effect is partly due to the shift There is also a significant effect of such policy changes on the productivity of incumbent firms. Modelling the changing incentives to invest in productivity enhancing activities, such as R&D, is more difficult in heterogeneous firm models, but some recent progress has been made (e. g. Aw, Roberts and Xu, 2008). 7 Foster et al (2009) show that measured revenue TFP will in general be correlated with true TFP but also with the firm specific price shocks.
Hsieh and Klenow (2007) detail a model where heterogeneous TFPQ produces no difference in TFPR because the more productive firms grow larger and have lower prices, thus equalizing TFPR. In their model intra-industry variation in TFPR is due to distortions as firms face different input prices. 6 10 in market share between incumbents and partly due to the effects of exit and entry. Bartelsman, Haltiwanger and Scarpetta (2008) show that the speed of reallocation is much stronger in some countries (like the US) than others. There is also significant sectoral variation.
For example, Foster, Krizan and Haltiwanger, 2006, show that reallocation between stores accounts for almost all aggregate productivity growth in the US retail sector. In summary, there is a substantial body if evidence of persistent firm-level heterogeneity in firm productivity (and other dimensions of performance) in narrow industries in many countries and time periods. Differential observable inputs, heterogeneous prices and idiosyncratic stochastic shocks are not able to adequately account for the remarkable dispersion of productivity.
So what could account for this? One long suggested factor is management practices, with authors going back at least to Walker (1887) suggesting that management practices play an essential role in explaining differences in performance across firms. 8 3. The effects of HRM on productivity So the question is do variations in variations in HRM practices play a role in driving differences in and productivity? We find that the answer is “probably, yes”, although the empirical basis for this which we survey in detail is surprisingly weak given the importance of the topic.
In fact, as Syversson (2010) notes in discussing management as a driver of productivity “no potential driving factor of productivity has seen a higher ratio of speculation to empirical study”. We should also state in advance that in this section we focus on productivity as the key outcome. Many studies look at other outcomes such as worker turnover, absenteeism, worker perceptions, etc. These are useful, but if they have no effect on productivity then in our view they are second order – generally studies use them because they have no direct evidence on productivity (e. g. Blasi et al, 2009:4).
We do not focus on measures of worker wellbeing such as job satisfaction or wages. Lazear and Shaw (2008) suggest that some of the dramatic increase in wage inequality in the US, UK and other country since the late 1970s is due to HRM practices. Lemieux et al (2009) and Guadalupe and Cunat (2009a) also take this position, although the current state of the evidence is still limited. These 8 Walker was an important character in the early years of the economics discipline as the founding president of the American Economics Association, the second president of MIT, and the Director of the 1870 Economic Census.
11 are interesting outcomes in their own right, and may also feed through into productivity, but we are space constrained and refer the reader to the wider literature were relevant. An important issue is the correct way to econometrically estimate production functions and TFP. Ackerberg et al (2007) have surveyed such methods in a recent Handbook chapter, and this is a lively (but still unsettled) area of research. Many of the issues on econometric identification of the parameters of conventional factors of production (such as labor or capital) are the same as those that will be discussed in sub-section 3.
2 below. There is also a growing literature on examining the impact of worker characteristics (or “human resources” such as skills, gender, race, seniority and age) on productivity through direct estimation in production functions rather than the traditional approach of looking at these indirectly through including them in wage equations. Interested readers are referred to recent examples of this approach in Moretti (2004), Hellerstein et al (1999) and Dearden et al (2006). 3. 1 Why should we expect to see an impact of HRM on productivity?
Before discussing issues of identification and the results from these studies, it is worth asking some basic questions: (a) why is this an interesting empirical question? and (b) why would we expect to see any positive average effect of HRM practices on productivity? Note that the answer to this question is not specific to human resources, but any endogenously chosen organizational design of the firm. One response is that we should not expect to see any effects. The design perspective on HRM (discussed more fully in Section 3 below) assumes that all firms are optimizing their HRM practices.
This may vary between firms because of different environments – for example, variations in technologies across industries – but each firm is still optimizing. Externally manipulating the firm to “force” it to do something sub-optimal (e. g. adopt incentive pay schemes) can only harm the firm’s performance. By contrast, using actual changes in the firm’s choices of HRM (such as Lazear’s (2000) Safelite Glass paper discussed below) will show that firms improve productivity as they will be optimizing so we expect any change to produce a positive outcome on average.
An important rejoinder to this is that firms maximize discounted profits, not productivity. It may increase productivity to introduce a given HRM practice, but this may still reduce profits, which is why firms have chosen not to adopt. One example is Freeman and Kleiner, 2005, who found that the abolition of piece rates reduced productivity but increased profits as quality rose in the absence of 12 piece rates.
This is analogous to any factor input such as capital – increasing capital per hour will increase output per hour, but the firm already takes this into account in its maximization program. Thus, just as we are interested in estimating the parameters of a conventional production function for capital and labor, we may be interested in the parameters associated with an HRM augmented production function even if all management practices are chosen optimally. A second reason for studying the effect of HRM on productivity is that if