Management Practices, Workforce Selection, and Productivity

Management Practices, Workforce

Selection, and Productivity

2016 Conference of the Global Forum on Productivity

Lisbon, 7-8 July 2016

Stefan Bender (Bundesbank), Nicholas Bloom (Stanford),

David Card (UC Berkeley), John Van Reenen (LSE),

Stefanie Wolter (IAB)

Disclaimer: Any opinions expressed in this paper are those of the authors and do not necessarily reflect the views

of the Deutsche Bundesbank or the Institute for Employment Research.

Are Management practices just due to

folks like these?

MOTIVATION I

• Big dispersion in firm productivity (e.g. Syverson, 2011)

• Management practices matter a lot for productivity

– Personnel Economics (Ichniowski, Shaw &

Prennushi, 1997; Lazear, 2000; HLE, 2011)

– World Management Survey (WMS): linked to firm total

factor productivity [TFP] (Bloom & Van Reenen, 2007;

Bloom et al, 2013) & country TFP (e.g. Bloom et al,

2015 find ~30% of TFP gaps with US management

related)

MOTIVATION II

• Do “good management practices” simply reflect human

capital: e.g. more talented CEOs (Lucas, 1978), senior

managers, or employees in general?

• Or are these firms more than just the sum of the

“atoms” of human capital of managers– e.g. Toyota

corporate culture persists when managers leave or

founder dies?

IEB: Data: German Employer-Employee Panel

Management Practices & Human Capital

Productivity

WMS Data: World Management Survey

Selection – Inflows & Outflows

Extensions & Robustness

World Management Survey (12,342 firms, 4 major waves:

2004, 2006, 2009, 2014; 34 countries)

Medium sized manufacturing firms(50-5,000 workers, median≈250)

Now extended to Hospitals, Retail, Schools, etc.

1) Developing management questions

• Scorecard for 18 monitoring (e.g. lean), targets & people (e.g.

pay, promotions, retention and hiring). ≈45 minute phone

interview of manufacturing plant managers

2) Obtaining unbiased comparable responses (“Double-blind”)

• Interviewers do not know the company’s performance

• Managers are not informed (in advance) they are scored

• Run from LSE, with same training and country rotation

3) Getting firms to participate in the interview

• Introduced as “Lean-manufacturing” interview, no financials

• Official Endorsement: Bundesbank, Bank of England, RBI, etc.

• Run by 200 MBA types (loud, assertive & business experience)

BLOOM - VAN REENEN (2007) SURVEY METHODOLOGY

Score (1): Measures

tracked do not

indicate directly

if overall

business

objectives are

being met.

Certain

processes

aren’t tracked at

all

(3): Most key

performance

indicators

are tracked

formally.

Tracking is

overseen by

senior

management

(5): Performance is

continuously

tracked and

communicated,

both formally and

informally, to all

staff using a range

of visual

management tools

MONITORING – e.g. “HOW IS PERFORMANCE TRACKED?”

8

Note: All 18 questions and over 50 examples in Bloom & Van Reenen (2007) &

Appendix

http://worldmanagementsurvey.org/

http://worldmanagementsurvey.org/

Average Management Scores by Country

Note: Unweighted average management scores (raw data) with number of observations. All waves pooled

(2004-2014)

0.5

11.5

20

.51

1.5

20

.51

1.5

20

.51

1.5

20

.51

1.5

20

.51

1.5

2

1 2 3 4 5

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

1 United States 2 Japan 3 Germany 4 Sweden 5 Canada 6 Great Britain

7 France 8 Australia 9 Italy 10 Mexico 11 Poland 12 Singapore

13 New Zealand 14 Northern Ireland 15 Portugal 16 Republic of Ireland 17 Chile 18 Spain

19 Greece 20 China 21 Turkey 22 Argentina 23 Brazil 24 Vietnam

25 India 26 Colombia 27 Kenya 28 Nigeria 29 Nicaragua 30 Myanmar

31 Zambia 32 Tanzania 33 Ghana 34 Ethiopia 35 Mozambique

De

nsi

ty

Firm Average Management ScoreGraphs by country_rank

Firms with 50 to 5000 employees randomly surveyed from country population. Mar 2014.

Large variation of firm management within countries

IEB Data: German Employer-Employee Panel


Productivity

WMS Data: World Management Survey



Matching IEB administrative data to WMS data

• Link WMS to IEB data via names, company ID, address

– Found 361 of the 365 WMS firms

• Sample includes everybody who worked at least one

day in these firms between 1992 to 2010

• Match 88% of employees in our 361 firms

‐ 98% of relevant population in firms (full-time

employed, age 20-60). ~200,000 employee

• “Employee ability”: Av. employee FE by firm-year

• Assume managers in upper part of firm wage hierarchy

– “Managerial ability”: av. employee FE in the top

quartile of wages (compare other cut-offs like decile)

Proxying Employee Ability (German IEB Panel Data)

Data: German Employer-Employee Panel


Productivity

Data: World Management Survey



Fig 1: Firms with high average employee

ability have higher management scores

Notes: 590 firm-year observations across 355 firms; employee ability & management

are z-scored. Ability is firm average of employee FE from CHK & in vingtile groups



Productivity




17

Firm sales

ln ln(L ) ln(K )jt M jt L jt K jt z jt jtY M z u

Capital services

WMS Management

(z-score each question,

average & z-score again) Labor services Other controls

• M, Management Index is average of all 18 questions (sd=1)

• z : firm age, industry & time dummies, ownership, competition, “noise”

• What are labor services, L?

– Total #employee hours & observable characteristics (e.g. college %)

– Average employee unobserved ability

– Average Managerial unobserved ability

PRODUCTION FUNCTIONS

5.5

66

.57

lpro

d

-2 -1 0 1 2zmanagement_sub1

Lab

or

Pro

ductivity

Management Z-score

Fig 2: Productivity is increasing in WMS

management scores in our German sample

Management is an average of all 18 questions (set to sd=1). Productivity is ln(sales/worker) N=588

67

89

10

lpro

d

-2 -1 0 1 2zpeff_9602_mean_june

Lab

or

Pro

duct

ivity

Av. employee FE in firm

Fig 3: Productivity is increasing in employee

ability (especially for top talent)

Productivity is ln(sales/worker) N=588; Employee FE computed from CHK 1996-2002 &

standardized

-.5

0.5

zm

ean

_zm

ana

ge

men

t_an

-2 -1 0 1 2zfirm_eff_9602

Man

ag

em

en

t S

co

re

Firm FE

55

.56

6.5

77

.5

lpro

d

-2 -1 0 1 2zfirm_eff_9602

Lab

or

Pro

ductivity

Firm FE

Fig 4: Firm Fixed Effect (in wages) correlated with

(a) WMS management scores (b) productivity

(a) WMS Management Score & Firm FE (b) Productivity & Firm FE

Productivity, Management Practices & Ability

Analysis of Productivity with the straightforward production function

Partial correlations of the WMS management score

A.0.26 without controlls

B.0.20 if we control for average employee ability

C.0.15 if we control for average employee ability and managerial ability

D.0.13 if we control for average employee ability and managerial ability

and the share of college-educated employee

One-half of the (relatively large) effect on management

scores on productivity is explained by the fact that firms

with more advanced management practices hire better

quality workers.

Broad similar pattern, if we use TFP.



Productivity




Why do “better managed” firms have higher

ability employees?

• Several Possible mechanisms:

1. Higher ability employees are selected into better

managed firms

– Look at the ability of inflows (again, ability estimated

from wage data using CHK 1996-2002)

2. Exit of lower ability employees from better managed

firms

3. Changing/training the quality of employees while they

are in the firm

Variables

Inflows to our

firms from the

specified labor

market state

Outflows

from our firms

to the specified

labor market

state

Unemployment 16% 30%

Jobs 58% 57%

Other sources 27% 13%

Total 122,436 132,600

Tab 1B: Inflows and outflows into the WMS-

IEB matched data, 2004-2010

• Focus on inflows from unemployment.

Inflows: Firms with higher management scores

select more able employees

-In every specification the coefficient on the management score

is positive at every ability percentile, but particularly strong for

workers in the top of the distribution.

Outflows: Firms with higher management scores

exit less likely their relatively high-ability workers

Clear mechanism, but it would take about 9 years for a firm to

move from the bottom 90% into the top decile of WMS

management score to converge to the average employee

ability score by improving the quality of the inflows and

outflows.

Conclusions I

• We combine:

– WMS data on management & firm performance

– IEB data on near population of German workers

1975-2011. Use Abowd et al (1999) approach to

recover employee fixed effects (“ability”) & firm FE

• We find: Firms with high WMS management scores

have more talented managers & workers (observable &

unobservable human capital). Also higher firm wage FE

– Partly via selection of employee inflows & outflows

Conclusions II

• Also find: ~ ¼ to ½ of firm TFP-WMS management

practices correlation is because of human capital (esp.

managerial talent)

– Consistent with important role for practices over and

above human capital

• Managerial human capital is important for the ability to

sustain successful mangement practices

• We found an effect of „corporate culture“, because there

is information in the management practice scores that

predicts productivity

Economy & Finance

Management Practices, Workforce Selection, and Productivity