Management as a technology nick bloom stanford raffaella sadun hbs and john van reenen lse
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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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Management as a technology nick bloom stanford raffaella sadun hbs and john van reenen lse
MANAGEMENT AS A TECHNOLOGY? Nick Bloom (Stanford), Raffaella Sadun (HBS) andJohn Van Reenen (LSE)


Motivation what is management
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)



Summary of the paper
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


Management Data

Management Models

Testing the models

Management and cross-country and firm TFP


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


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


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


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


Wide spread of management us japan germany leading and africa south america and india trailing
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


Some quotes illustrate the African management approach Africa, South America and India trailing

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”


Average management scores across countries are strongly correlated with gdp
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


Like tfp management also varies within countries
Like TFP management also varies within countries correlated with GDP

Data includes 2013 survey wave as of 9/20/2013. Africa data not yet included in the paper


Management Data correlated with GDP

Management Models

Testing the models

Management and cross-country and firm TFP


Two perspectives on these management differences in economics
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


Management as a technology: Francis Walker economics

“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


Management as a technology: Alfred Marshall economics

“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)


Two perspectives on these management differences in economics1
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)


So we define 2 highly stylized management models
So we define 2 highly economicsstylized 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)


We simulate these two management models
We simulate these two management models economics

  • 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


Result 1 performance size tfp profits increase in management with mat but not mad
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).


Result 2 average management increases with competition elasticity with mat but not mad
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.


Result 3 sales weighted management is higher in less distorted economies with mat but not mad
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.


Very stylized models with obvious extensions
Very stylized models with obvious extensions distorted economies with MAT but not MAD

  • 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)


Measuring Data distorted economies with MAT but not MAD

Management Models

Testing the models

  • Performance

  • Competition

  • Reallocation

Management and cross-country and firm TFP


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.


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


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.


Measuring Data (best to be at optimal industry/country point)

Management Models

Testing the models

  • Performance

  • Competition

  • Reallocation

Management and cross-country TFP


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


Measuring Data MAT)

Management Models

Testing the models

  • Performance

  • Competition

  • Reallocation

Management and cross-country TFP


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.


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).


Measuring Data labor and trade regulations (consistent with MAT)

Management Models

Testing the model

  • Performance

  • Competition

  • Reallocation

  • Extensions: skills

Management and cross-country TFP


Management and education use unesco world higher education database university locations n 9 081
Management and Education: use UNESCO World Higher Education Database university locations (N=9,081)


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.


Measuring Data skills and management

Management Models

Testing the model

  • Performance

  • Competition

  • Reallocation

  • Extensions: skills

Management and cross firm and country TFP


Following mat we can estimate rough contribution of management to country tfp spread
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


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


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


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).


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


Conclusions
CONCLUSIONS contribution of management to TFP spread

  • 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


MY FAVOURITE QUOTES: contribution of management to TFP spread

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…”


MY FAVOURITE QUOTES: contribution of management to TFP spread

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”


  • 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”


“OLLEY PAKES” ( contribution of management to TFP spreadOP) 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


DECOMPOSING THE RELATIVE MANAGERIAL DEFICIT BETWEEN COUNTRY contribution of management to TFP spreadj AND THE US ECONOMY

Difference in reallocation

(between firm)

Difference in aggregate

share-weighted

Management scores

Difference in unweighted

Means (within firm)


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


Information are firms aware of their management practices being good bad
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


Self scores uncorrelated with productivity
Self-scores uncorrelated with productivity being good/bad?

Labor Productivity

Self scored management

* Insignificant 0.03 correlation with labor productivity, cf. management score has a 0.295


TABLE 11: COMPETITION AFFECTS FIRM’S SELF-PERCEPTIONS OF MANAGEMENT QUALITY

Notes: Controls include country & year dummies, public & interview noise (interviewer, time, date & manager characteristic). SEs clustered by firm.


Algorithm
ALGORITHM MANAGEMENT QUALITY

  • Discretise state space for M,K and A.

  • Value functions for entrants, incumbents. Numerical contraction mapping to obtain fixed point

  • Investment policy correspondences (for M & K). L defined optimally using first-order conditions

  • Simulate data and focus on steady states after 50 years for 10,000 firms

  • Change parameters & examine different steady states

    • Increase in competition

    • Increase in distortions

  • Compare actual and simulated data


Parameters
PARAMETERS MANAGEMENT QUALITY


TRANSITION MATRIX: MANAGEMENT, 2006-2009, 1600 firms MANAGEMENT QUALITY

TRANSITION MATRIX: TFP 1972-1977, BAILEY ET AL (1992)


Concerns with the cross country decomposition
CONCERNS WITH THE CROSS-COUNTRY DECOMPOSITION MANAGEMENT QUALITY

  • Re-weighting for sample responses. Use alternative covariate sets (e.g. just emp in selection in Table B1)

  • Using just labor as size measure (because cleanest). Use capital as well to get “input index” (Table B2)

  • Dropped multinationals because “size” unclear: what if we include (Table B3)

  • Only looking at firms between 50 to 5000 what about very small and very large firms? (Table B4)


Figure B1: Relative Management MANAGEMENT QUALITY(weighted by employment shares; only emp in selection equation)

Notes: These are the differences relative to the US of (i) the weighted average management scores (sd=1, blue bar) and (ii) reallocation effect (OP, light red bar). Domestic firms, . 2006 wave. Response bias corrections use country-specific employment only


Figure B2: Relative Management MANAGEMENT QUALITY(weighted by labor and capital inputs)

Notes: These are the differences relative to the US of (i) the weighted average management scores (sd=1, blue bar) and (ii) reallocation effect (OP, light red bar). Domestic firms, . 2006 wave. Response bias corrections use country-specific employment only


Figure B3: Relative Management MANAGEMENT QUALITY(weighted by employment shares; multinationals included)

Notes: These are the differences relative to the US of (i) the weighted average management scores (sd=1, blue bar) and (ii) reallocation effect (OP, light red bar). Domestic firms, . 2006 wave. Response bias corrections use country-specific employment only


Figure B4: Relative Management MANAGEMENT QUALITYscores. Correcting for missing very small and very large firms.

Notes: These are the differences relative to the US of the weighted average management scores. Response bias corrections. Blue bar is baseline results and red bar corrects for missing firms with under 50 or over 5000 employees. The correlation between the two bars is 0.95.


MANAGEMENT AS DESIGN: STYLES do differ systematically across firms and industries

Note: OLS with standard errors clustered by 156 three digit industries below coefficients. “Human Capital Management” (HC) is the average of the z-scores of questions 13,17 and 18 (and this overall average z-scored). “Fixed Capital Capital Management” (FC) is the average of the z-scores of questions 1, 2 and 4 (and this overall average z-scored). “Relative style of management” is the simple difference of HC and FC. All columns include year dummies, ln(firm size), ln(firm age) and a full set of country dummies. “% degree (firm)” is the proportion of the firm’s employees with a college degree. “% degree (industry-level in US)” is the proportion of workers in a three digit SIC with a college degree from the US Current Population Survey.


Management in the us vs developing countries
Management in the US vs developing countries firms and industries

US

Data includes 2013 survey wave as of 9/20/2013. Africa data not yet included in the paper


PERFORMANCE REGRESSIONS firms and industries

Performance measure

Management

(z-score each question,

average & z-score again)

Other controls

Capital

Labor

  • 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).


Education also important in accounting for management differences
Education also important in accounting for management differences

  • Important in both MAD and MAT models:

    • MAT: reduces the cost of good management

    • MAD: likely to change the optimal choice of practices

  • Empirically challenge is identifying causal effects, tried:

    • Variation in universities across locations by country

    • Using land-grant colleges in the US (Moretti, 2004)


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