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Motivation: What is Management?

MANAGEMENT AS A TECHNOLOGY? Nick Bloom (Stanford), Raffaella Sadun (HBS) and John Van Reenen (LSE). Motivation: What is Management?. Evidence of massive national and plant spread in TFP: e.g. Hall and Jones (1999) Syverson (2004) Can management explain any of this: 1%, 10% or 100%?

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Motivation: What is Management?

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  1. MANAGEMENT AS A TECHNOLOGY? Nick Bloom (Stanford), Raffaella Sadun (HBS) andJohn Van Reenen (LSE)

  2. Motivation: What is Management? • Evidence of massive national and plant spread in TFP:e.g. Hall and Jones (1999) Syverson (2004) • Can management explain any of this: 1%, 10% or 100%? • Two main theories take opposite positions: • Management as Technology (MAT): management key driver of performance gaps (Walker, 1887, Marshal 1887) • Management as Design (MAD): management optimized so less important (Woodward, 1958, core Org-Econ)

  3. MAT v MAD – are management difference optimal? Ohio, USA Maharashtra, India

  4. Summary of the paper • Management data from ≈ 15,000 firms in 30 countries • US highest (unweighted) average management score • US lead ≈50% larger if size weight (more reallocation) • Are these variations technology (MAT) or optimal (MAD)? Develop three predictions on: performance, competition and reallocation, and find best fit for MAT • Given MAT, then estimate management can account for possibly ≈ 25% of plant and country spread in TFP

  5. Management Data Management Models Testing the models Management and cross-country and firm TFP

  6. Survey methodology (following Bloom & Van Reenen (2007)) 1) Developing management questions • Scorecard for 18 monitoring and incentives practices in ≈45 minute phone interview of manufacturing plant managers 2) Getting firms to participate in the interview • Introduced as “Lean-manufacturing” interview, no financials • Official Endorsement: Bundesbank, RBI, PBC, World Bank etc. 3) Obtaining unbiased comparable responses, “Double-blind” • Interviewers do not know the company’s performance • Managers are not informed (in advance) they are scored

  7. Example monitoring question, scored based on a number of questions starting with “How is performance tracked?”

  8. Example incentives question, scored based on questions starting with “How does the promotion system work?”

  9. Survey randomly drawn firms from the population of larger (50 to 5,000 employee) manufacturers across countries (including public and private firms) Locations of plants surveyed 2004-2008

  10. Wide spread of management: US, Japan & Germany leading, and Africa, South America and India trailing Data includes 2013 survey wave as of 9/20/2013. Africa data not yet included in the paper

  11. Some quotes illustrate the African management approach Interviewer “What kind of Key Performance Indicators do you use for performance tracking?” Manager: “Performance tracking? That is the first I hear of this. Why should we spend money to hire someone to track our performance? It is a waste of money!” Interviewer “How do you identify production problems?” Production Manager: “With my own eyes. It is very easy”

  12. Average management scores across countries are strongly correlated with GDP Data includes 2013 survey wave as of 9/20/2013. Africa data not yet included in the paper

  13. Like TFP management also varies within countries Data includes 2013 survey wave as of 9/20/2013. Africa data not yet included in the paper

  14. Management Data Management Models Testing the models Management and cross-country and firm TFP

  15. Two perspectives on these management differences in economics • Management as a Technology (MAT) • Management part of firm’s TFP, often said to explain a firm’s TFP fixed effect (Mundlak, 1961) • Think of improvements as “process innovations”, hence the sense that management is a technology

  16. Management as a technology: Francis Walker “It is on account of the wide range [of ability] among the employers of labor that we have the phenomenon in every community and in every trade some employers realizing no profits at all, while others are making fair profits; others, again, large profits; others, still, colossal profits.” Francis Walker (QJE, April 1887) Walker ran the 1870 US Census, and was the founding president of the AEA and president of MIT

  17. Management as a technology: Alfred Marshall “I am very nearly in agreement with General Walker’s Theory of profits….the earnings of management of a manufacturer represents the value of the addition which his work makes to the total produce of capital and industry....” Alfred Marshall (QJE, July 1887)

  18. Two perspectives on these management differences in economics • Management as a Technology (MAT) • Management part of firm’s TFP, often exampled as explaining firm’s TFP fixed effects (Mundlak, 1961) • Think of improvements as “process innovations”, hence the sense that management is a technology • Management as Design (MAD) • Management is optimally chosen • Popular in Organizational Economics (Gibbons and Roberts HOE, 2013), and other fields like Strategy and Management (Contingency theory, Woodward 1958)

  19. So we define 2 highly stylized management models Production Function: Y=AKαLβG(M), M = management Flavor1: Management as Technology (MAT) G(M)=Mγ; pay for M which depreciates (like R&D) Flavor 2: Management as Design (MAD) G(M) = 1/(1+|M*-M|); M* = “optimal” choice, M is free • Otherwise common set of assumptions • Changing M & K involves adjustment costs (L flexible) • τ % of sales lost to distortions (bribes, regulations etc) • Monopolistic competition (Isoelastic demand) • Sunk entry cost (E) & fixed per period operating cost (F)

  20. We simulate these two management models • Entrants pay a sunk cost E for a draw on (A,M,τ), with rate of entry determined by the free entry condition • All firms get a shock to TFP: At=ρAt-1 + εt • Pay fixed operating cost F per period (or exit) • Invest in M & K, plus choose optimal labor

  21. Result 1: Performance (size, TFP, profits) increase in management with MAT but not MAD MAT: Y=AKαLβMγ MAD: Y=AKαLβ/(1+|M-M*|) Notes: Results from simulating 2,500 firms per year in the steady state taking the last 5 years of data. Simulations are identical except that the production functions differ as outlined above. Plots normalized log(management) in the simulation data onto a 1 to 5 scale and log(sales) onto a 0 to 1 scale. Lowess plots shown with Stata defaults (bandwidth of 0.8 and tricube weighting).

  22. Result 2: Average Management increases with competition (elasticity) with MAT but not MAD MAT: Y=AKαLβMγ MAD: Y=AKαLβ/(1+|M-M*|) Comp increasing Comp increasing Notes: Results from simulating 2,500 firms per year in the steady state taking the last 5 years of data for each level of elasticity. Simulations are identical for MAT and MAD except that the production functions differ as outlined above. Plots normalized ln(management) in the simulation data onto a 1 to 5 scale.

  23. Result 3: Sales weighted management is higher in less distorted economies with MAT but not MAD MAT: Y=AKαLβMγ MAD: Y=AKαLβ/(1+|M-M*|) US India Distortions increasing (% sales lost) Distortions increasing (% of sales lost) Notes: Plots log(management) scores weighted by firm sales. Results from simulating 2,500 firms per year in the steady state taking the last 5 years of data for each level of elasticity. Simulations are identical for MAT and MAD except that the production functions differ as outlined above. ln(management) in the simulation data is normalized onto a 1 to 5 scale.

  24. Very stylized models with obvious extensions • Dynamics: maybe management also changes adjustment costs, information (forecasting) and factor prices • Multi-factor: currently 1-dimensional management, but design view model could also be about sub-components • Spillovers: Technology is (partially) non-rival so we should see learning, information spillovers, clustering etc. • Governance/ownership issues: family firms, FDI, etc. • Co-ordination: e.g. Gibbons & Henderson (2012)

  25. Measuring Data Management Models Testing the models • Performance • Competition • Reallocation Management and cross-country and firm TFP

  26. TABLE 2: Performance is robustly correlated with management (consistent with MAT) M, Management Index is average of all 18 questions (sd=1). Other controls include % employees with college degree, average hours worked, firm age, industry, country & time dummies & noise (e.g. interviewer dummies). Standard errors clustered by firm. Notes: Regressions includes controls for country, SIC3 & year, dummies. Firm-age, skills, noise controls etc. SE clustered by firm.

  27. TABLE 3: Performance: use the Great Recession as a natural experiment and find again well managed firms perform better Notes: SHOCK is a binary indicator for a fall in exports or fall in sales in the SIC3 by country cell from 2007 to 2009. All columns include controls for skills, firm and plant size, noise, country and industry dummies. Management from 2004-2006

  28. Table 4: Contingent performance: not consistent with MAD (best to be at optimal industry/country point) Notes: Regressions includes controls for country, SIC4 & year dummies, firm-age, average hours, % with degrees, noise controls etc. SE clustered by firm. Productivity columns from regression of ln(sales) as dependent variable with controls for ln(labor) and ln(capital). Only cells with 2+ observations used.

  29. Measuring Data Management Models Testing the models • Performance • Competition • Reallocation Management and cross-country TFP

  30. TABLE 5: Competition improves management (consistent with MAT) Notes: Includes SIC-3 industry, country, firm-size, public and interview noise (interviewer, time, date & manager characteristic) controls. Col 1,2, 4 & 5 clustered by industry*country, cols 3 & 6 by firm

  31. Measuring Data Management Models Testing the models • Performance • Competition • Reallocation Management and cross-country TFP

  32. Table 6: More reallocation to better managed firms in the US where markets generally less distorted (consistent with MAT) Notes: Includes year, country, three digit industry dummies, # management questions missing, firm age, skills and noise controls.

  33. Table 7: Reallocation also stronger in countries with lower labor and trade regulations (consistent with MAT) Notes: OLS, clustered by firm; dependent variable is firm employment; Domestic firms only. Controls for firm age, skills, noise, SIC3, country dummies, EPL(WB) is “difficulty of hiring” from World Bank (1=low, 100=high). Trade cost from World Bank (0=low,6=high). Last columns tariffs are in deviations from industry and country specific mean, from Feenstra and Romalis (2012).

  34. Measuring Data Management Models Testing the model • Performance • Competition • Reallocation • Extensions: skills Management and cross-country TFP

  35. Management and Education: use UNESCO World Higher Education Database university locations (N=9,081)

  36. Distance to the nearest university seems to matter for firm skills and management Notes: Clustered by 313 regions. In final column proportion skilled is instrumented with distance to university. Include industry, regional and full set of firm and noise controls.

  37. Measuring Data Management Models Testing the model • Performance • Competition • Reallocation • Extensions: skills Management and cross firm and country TFP

  38. Following MAT we can estimate rough contribution of management to country TFP spread • Estimate country differences in size weighted management • Impute impact of this on differences in TFP Requires many assumptions, so only magnitude calculation

  39. First calculate the employment weighted difference in management (from the US as baseline) Management gap with the US Reallocation Within Firm Notes: Total weighted mean management deficit with the US is the number on top of bar. This is decomposed into (i) reallocation effect (blue bar) and (ii) unweighted average management scores (red bar) . Domestic firms, scores corrected for sampling bias

  40. First calculate the employment weighted difference in management (from the US as baseline) 30% of US-Greece management gap due to better US reallocation Management gap with the US Reallocation Within Firm Notes: Total weighted mean management deficit with the US is the number on top of bar. This is decomposed into (i) reallocation effect (blue bar) and (ii) unweighted average management scores (red bar) . Domestic firms, scores corrected for sampling bias

  41. Second, estimate impact of management on TFP using result from micro regressions and field experiments result: ↑1 SD management ≈ ↑ 10% TFP Assume one sd increase in management increases TFP by 10%. Regressions suggest about 5% to 15% depending on specification. TFP data from Jones and Romer (2010).

  42. Interestingly, also get similar 25% share for cross firm contribution of management to TFP spread • Typical 4 digit industry in US has TFP ratio of 2:1 for 90-10 (Syversson, 2004) • 90-10 for management is about 2.6 standard deviations • Using a causal effect of 10% this implies management causes ≈ ¼ of TFP dispersion

  43. CONCLUSIONS • Large spreads in size weighted management across firms and countries, with about 1/3 of US lead due to reallocation • Micro data consistent with a model in which this variation in management is technology, with better & worse practices • Estimate variations in management accounts for ≈25% of plant and country variation in TFP

  44. MY FAVOURITE QUOTES: The difficulties of defining ownership in Europe Production Manager: “We’re owned by the Mafia” Interviewer: “I think that’s the “Other” category……..although I guess I could put you down as an “Italian multinational” ?” Americans on geography Interviewer: “How many production sites do you have abroad? Manager in Indiana, US: “Well…we have one in Texas…”

  45. MY FAVOURITE QUOTES: Don’t get sick in Britian Interviewer : “Do staff sometimes end up doing the wrong sort of work for their skills? NHS Manager: “You mean like doctors doing nurses jobs, and nurses doing porter jobs? Yeah, all the time. Last week, we had to get the healthier patients to push around the beds for the sicker patients” Don’t do Business in Indian hospitals Interviewer: “Is this hospital for profit or not for profit” Hospital Manager: “Oh no, this hospital is only for loss making”

  46. MY FAVOURITE QUOTES: • Don’t get sick in India • Interviewer : “Do you offer acute care?” • Switchboard: “Yes ma’am we do” • Interviewer : “Do you have an orthopeadic department?” • Switchboard: “Yes ma’am we do” • Interviewer : “What about a cardiology department?” • Switchboard: “Yes ma’am” • Interviewer : “Great – can you connect me to the ortho department” • Switchboard?: “Sorry ma’am – I’m a patient here”

  47. “OLLEY PAKES” (OP) DECOMPOSITION OF WEIGHTED AVERAGE MANAGEMENT SCORE (M) IN GIVEN COUNTRY Size of firm i Overall management z-score of firm i Covariance (Olley-Pakes, 1996, reallocation term) Unweighted mean of management score

  48. DECOMPOSING THE RELATIVE MANAGERIAL DEFICIT BETWEEN COUNTRY j AND THE US ECONOMY Difference in reallocation (between firm) Difference in aggregate share-weighted Management scores Difference in unweighted Means (within firm)

  49. TAB 3: DECOMPOSING AGGREGATE MANAGEMENT GAP INTO REALLOCATION & UNWEIGHTED MEAN DIFFERENCE

  50. INFORMATION: Are firms aware of their management 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

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