Measuring & explaining management practices

Measuring & explaining management practices Nick Bloom (Stanford & NBER)based on work with Raffaella Sadun (HBS) & John Van Reenen (LSE) MIT/Harvard Org Econ Lecture 1 (February 2010)

Two part lecture course • Lecture 1: Measuring and explaining management practices • Overview what are management practices, how we measure them, why they vary and what effect they have on performance • Highlight how little is rigorously known – management is one of the major holes in social science, and a great research area • Lecture 2: Measuring and explaining organizational practices • As above overview what are organizational practices, how we measure them, why they vary and what effect this has • Again, large holes in rigorous large sample causal evidence

Other points • Meeting up: I am around until late Thursday so e-mail me if you would like to talk for individually about work or topics • Lectures: I will post all the lectures on my website (teaching page) on Thursday PM • http://www.stanford.edu/~nbloom/ (or Google Nick Bloom) • Breaks: I’ll take a 10 minute break at 10:30 • Questions: Please feel free to ask any questions and/or make comments. I’ve not prepared material assuming this

Lecture 1: Overview • Motivation: productivity across firms and countries • Measuring management practices • Management practices across firms and countries • Explaining why management practices vary • The effect of management practices on performance

Large GDP & TFP differences across countries Average US worker makes more in 2 weeks than a Tanzanian in 1 year Source: Jones and Romer (2009). US=1

These TFP & GDP differences are often persistent Source: Maddison (2008) Data is smoothed by decade

Productivity differences across firms are also large US plants at 90th percentile have 4x higher labor productivity than plant at the 10th percentile (Syverson, 2004 REStat) Controlling for other inputs, TFP difference is about 2:1 In China and India this gap is about 5:1 (Hsieh and Klenow, 2009 QJE) Not just mismeasured prices: in detailed industries with plant level prices like white pan bread, block ice, concrete productivity differences still 2:1 (Foster et al, 2008 AER)

TFP dispersion is particularly large in low competition industries Low competition High competition Source: Syverson (2004, JPE)

Productivity difference between firms in one obvious motivation for looking at management Persistent TFP differences a key part of many Macro, Trade, IO and Labor models – but these are typically silent on the casues Could this be in part because of differences in management – even Adam Smith’s 1776 wealth of nations suggests this matters Today will present a bunch of evidence showing that management differences do seem to be a major factor driving TFP differences Of course, might also be interesting in management for a range of other reasons around growth, strategy and theory of the firm

Note, good productivity overview paper, Syversson (2010, NBER WP) Since completing your lecture outline noticed a very nice overview of the productivity literature has been complied by Syversson “What determines productivity”, NBER WP 15712 and forthcoming in the Journal of Economic Literature http://home.uchicago.edu/~syverson/productivitysurvey.pdf

Before discussing “management practices”, want to point out that this is different from “managers” There is also a large literature looking at CEOs (managers) – for example Jack Welch, Bill Walsh or Alex Ferguson Best known paper is Bertrand and Schoar (2003, QJE) They build a panel dataset tracking managers across US firms over time, and allow for firm and manager fixed effects Focus on large US publicly traded firms – average of about 10,000 employees – so represents impact of strategy by the top manager

Summary of Bertrand and Schoar (2003) • Interesting results, and highly cited, finding: • Manager fixed effect exist, particularly for M&A, dividend policy, debt ratios and cost-cutting • Managers have styles - more/less aggressive and internal/external growth focus • Managers are also absolutely “better” or “worse” – performance fixed effects exist, and linked to compensation and governance • These styles and fixed effects also correlated with manager characteristics – particularly CEO age and having an MBA

Measuring management practices • Also a literature on management practices, which I will focus on in these lectures, as these are more about firms than individuals • Historically been strongly case study based – e.g. Ford, GM, Toyota, GE, Mayo Clinic, Citibank, Dabbawala etc. • Case-studies helpful for intuition and illustration, but potentially misleading because very selected sample – e.g. Enron • More recently work has focused on trying to systematically measure management practices in large samples of firms • First generation, single country studies & direct questions • Second generation, international studies & indirect questions

Challenges to measuring management practices • Despite sounding easy, “measuring management” is fraud with • difficulties, which has held back research. • How to quantify (as in put numbers on) management practices • How to get data from firms – surveys are tough to do • How to get the truth – will badly managed firms ‘fess-up’ • Building a representative population – e.g. not just targeting Compustat firms – especially important for cross-country work

First generation surveys: single-country focus with direct survey techniques • Black and Lynch (2001, REStat) is a good example of a well • executed single country management survey • Surveyed about 3,000 establishments with the US Census bureau • Quantify: Asked a series of questions on employee recruitment, work organization, meetings and modern production practices • Get data: Administered by the US Census Bureau • Truth: Told respondents their answers were confidential • Population: stratified from the Census establishment database • Found large variations in management, and strong correlation of • management practices and performance

Second wave surveys: cross countries and tries to address response bias with indirect surveys • Cross country comparisons: identification of many factors driving management typically require cross-country data • Problems with direct surveys: unfortunately people typically do not tell the complete truth in open surveys: • Schwartz (1999, American Pschologist) • Opinion poll-evidence • Bertrand and Mullainathan (2001, AER P&P). • Bloom and Van Reenen (2007, QJE) is a good example of a second wave of management survey, which I’ll cover in detail

The Bloom and Van Reenen (2007) approach • 1) Quantifying: use scoring grid from a consulting firm • Scores 18 monitoring, targets and incentives practices • ≈45 minute phone interview of manufacturing plant managers • 2) Truth: use “Double-blind” • Interviewers do not know the company’s performance • Managers are not informed (in advance) they are scored • All interviews run from a single location with rotation by country • 3) Getting data: a variety of tricks • Introduced as “Lean-manufacturing” interview, no financials • Official Endorsement: Bundesbank, PBC, CII & RBI, etc. • Run by 75 MBAs types (loud, assertive & business experience) • 4) Population: sample randomly medium and large firms (100-5000 employees) from population databases across countries

Monitoring - i.e. “How is performance tracked?”

Survey Video

Getting representative cross country samples • So far interviewed about 7,000 firms across about 20 countries • Obtained 45% coverage rate from sampling frame (with response rates uncorrelated with performance measures) • Currently being extended to Charities, Hospitals, Law Firms, Retail firms, PPPs, Schools and Tax Collection Agencies • So basic concept easily transported across industries

Internal survey validation – useful exercise suggesting double-blind methodology may work Re-interviewed 222 firms with different interviewers & managers Firm average scores (over 18 question) Firm-level correlation of 0.627 2nd interview 1st interview

External survey validation – another useful exercise suggesting double-blind methodology may work Performance measure country c management (average z-scores) ln(capital) other controls ln(labor) ln(materials) • Use most recent cross-section of data (typically 2006) • Note – not a causal estimation, only an association

External validation: better performance is correlated with better management Includes controls for country, with results robust to controls for industry, year, firm-size, firm-age, skills etc. Significance levels: *** 1%, ** 5%, * 10%. Sample of all firms where accounting data is available

External validation – robustness across countries (the “ooh la la” question) • Performance results robust in all main regions: • Anglo-Saxon (US, UK, Ireland and Canada) • Northern Europe (France, Germany, Sweden & Poland) • Southern Europe (Portugal, Greece and Italy) • East Asia (China and Japan) • South America (Brazil)

Consistent with Helpman, Melitz and Yeaple (2004, AER) well managed firms also export more Share of firms exporting Management score (rounded to nearest 0.5)

1 point higher management score associated with about 20% less energy use Energy use, log( KWH/$ sales) Management Well managed firms also more energy efficient • Many US firms not operating using energy efficient technology (De Cannio, 1993 EP ), due in large part to a mix of management problems (Howarth, Haddad and Paton, 200 EP) • We find similar results in our management data (below) Source: Bloom, Genakos, Martin and Sadun, (2010, EJ). Analysis uses Census of production data for UK firms

US management practices score highest on average, with developing countries lowest Also have data from Chile, Mexico and New Zealand, but not yet publicly released Average Country Management Score

Variation even greater across firms than across countries Firm-Level Management Scores

Relative management practices also vary by country Relatively better at ‘operations’ management (monitoring, continuous improvement, Lean etc) Relatively better at ‘people’ management (hiring, firing, pay, promotions etc) People management (hiring, firing, pay & promotions) – operations (monitoring, continuous improvement and Lean)

So why does management vary across countries and firms? • I will discuss five factors that seem important • Competition • Family firms • Multinationals • Labor market regulations • Education • But, before that, I that want to raise one informational constraint for why every firm does not adopt best practices

Wanted to find out if firms were aware of their practices being good/bad? We asked: “Excluding yourself, how well managed would you say your firm is on a scale of 1 to 10, where 1 is worst practice, 5 is average and 10 is best practice” We also asked them to give themselves scores on operations and people management separately

To the extent they are honest, most managers seem to think they are well above average “Worst Practice” “Average” “Best Practice”

The Brazilians and Greeks overscored the most, the US and French the least Self score (normalized to 1 to 5 scale) – Management score

These self-scores also appear not only too high on average, but also uncorrelated with actual practices Correlation 0.032* Labor Productivity Self scored management * In comparison the management score has a 0.295 correlation with labor productivity

So seems many firms are unaware of their poor management, consistent with a range of evidence that management practices are a type of technology A number of studies, including several I will discuss later on this lecture, provide evidence that management is a technology Innovations include the American System of Manufacturing, Scientific Management, Mass Production, M-form firm, Quality Movement and Lean So one reason for bad management is like any other technology there is a diffusion curve, with many firms below the curve Even so, many well informed firms are badly managed, hence why I will discuss a range of other factors

Lecture 1: Overview • Motivation: productivity across firms and countries • Measuring management practices • Explaining why management practices vary • Competition • Family firms • Multinationals • Labor market regulations • Education • 4. The effect of management practices on performance

Tough competition appears strongly linked to better management practices 1 1-Rents = 1- (operating profit – capital costs)/sales2 Includes 108 SIC-3 industry, country, firm-size, public and interview noise (analyst, time, date, and manager characteristic) controls3 S.E.s in ( ) below, robust to heteroskedasticity, clustered by country-industry

We also have some management panel data, and find similar results 1 1-Rents = 1- (operating profit – capital costs)/sales S.E.s in ( ) below, robust to heteroskedasticity, clustered by country-industry UK, US, France and Germany only

Competition also appears linked to selection An additional point on the management score is associated with an increase of employment US 715 more workers UK 546 more workers India 263 more workers Competitive forces of reallocation weak in India compared to US

So appears to be a mix of ways competition can improve management “Incentives” (e.g. “Boot up the ass effect”) – competition forces badly managed firms to improve performance “Selection” – competition selects out badly managed firms “Learning” – competition provides more firms in and industry, increasing experimentation and learning.

Studies on TFP find similar results of competition on performance Syversson (2004, JPE) looks at the concrete industry and finds that more competitive markets had higher average levels of TFP and less dispersion. Pavcnik (2002, REStud) and Olley-Pakes (1996, Econometrica) also at changes in competition from trade-reforms and deregulations respectively, finding this weeds out low TFP firms Schmitz (2005, JPE) shows great lakes iron-producers responded heavily to import competition Nickell (1996, JPE) shows changes in competition lead to faster TFP growth within a panel of firms

Management practices vary strongly with ownership, even after controlling for industry, country, skills, size etc. Distribution of firm management scores by ownership. Overlaid dashed line is approximate density for dispersed shareholders, the most common US ownership type Average Management Score

share family CEO (2nd+ generation) share founderCEO (1st generation) share government owned Ownership differences are another factor behind cross-country variations in management practices share of ownership (for types associated with low management scores)

Results again consistent with other studies using other performance metrics • One nice study is by Perez-Gonzalez (2006, AER) looking at the impact of a family CEO • Finds stock prices fall the day a firm’s founder announces they are passing the CEO position down to one of their kids • Drops particularly for hand-downs to kids who went to non-selective schools • Another clever study by Bennedsen, Nielson, Perez-Gonalez, and Wolfenzon (2007, QJE) on family firms and performance • Use gender of the first born to instrument for family control • Find family CEOs reduce profitability and growth rates

Multinationals appear to always be well managed, consistent with selection and most trade models Foreign multinationals Domestic firms Average Management Score

Measuring & explaining management practices