Management Practices, Workforce Selection, and ... Practices, Workforce … · Management...

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Management Practices, Workforce Selection, and Productivity Rising wage inequality in Germany causes and links with productivity developments December 17th, 2018 Stefan Bender (Bundesbank), Nicholas Bloom (Stanford), David Card (UC Berkeley), John Van Reenen (MIT), Stefanie Wolter (IAB)

Transcript of Management Practices, Workforce Selection, and ... Practices, Workforce … · Management...

Page 1: Management Practices, Workforce Selection, and ... Practices, Workforce … · Management Practices, Workforce Selection, and Productivity Rising wage inequality in Germany – causes

Management Practices, Workforce Selection, and

Productivity

Rising wage inequality in Germany –

causes and links with productivity developments

December 17th, 2018

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

David Card (UC Berkeley), John Van Reenen (MIT), Stefanie

Wolter (IAB)

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Motivation

Titel der Folie 2

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 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)

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?

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Dynamics of large firm wage premium

IAB-OECD Seminar * Stefanie Wolter 3

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91 93 95 97 99 01 03 05 07 09 11 13 15Brazil Germany Sweden UK

Colonnelli et al., 2018

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Summary of the Paper

Titel der Folie 4

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

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

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Titel der Folie 5

IEB: Data: German Employer-Employee Panel

Management Practices & Human Capital

Productivity

WMS Data: World Management Survey

Selection – Inflows & Outflows

Extensions & Robustness

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Bloom - van Reenen (2007) Survey Methodology

Titel der Folie 6

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)

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

2004, 2006, 2009, 2014; 34 countries)

Titel der Folie 7

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

Now extended to Hospitals, Retail, Schools, etc.

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Average Management Scores by Country

Titel der Folie 8

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

(2004-2014)

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Large variation of firm management within

countries

Titel der Folie 9

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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

nsity

Firm Average Management ScoreGraphs by country_rank

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

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Titel der Folie 11

IEB: Data: German Employer-Employee Panel

Management Practices & Human Capital

Productivity

WMS Data: World Management Survey

Selection – Inflows & Outflows

Extensions & Robustness

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Proxying Employee Ability (German IEB Data)

Titel der Folie 12

Original idea by Abowd, Kramarz & Margolis (1999) adopted by

Card, Heining, Kline (2013) to analyze wage heterogeneity among

German workers

Linear models for 4 overlapping time periods 1985-2009 with

ln(wage) as dependent variable and individual and firm fixed effects

𝑦𝑖𝑡 = 𝛼𝑖 + 𝜓𝑗𝑱(𝑖,𝑡) + 𝑥′𝑖𝑡𝛽 + 𝑟𝑖𝑡

where:

J(i,t)… J unique establishments j employ worker i in year t

𝛼𝑖 … employee component (person effect)

𝜓𝑱(𝑖,𝑡) … establishment component (establishment/firm effect)

𝑥′𝑖𝑡𝛽 … time varying observable characteristics

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Recover employee (& firm) Fixed Effects using full IEB

Data

Titel der Folie 13

Individual controls (x) – Cubic in age fully interacted with

education; estimate separately for men & women

IEB data: Labor market biographies of West Germans 1975-

2011, East Germany 1992-2011

Estimate CHK model to recover individual employee fixed

effects (“ability”) & firm effects

Focus on 1996-2002 period for CHK (86m person-years) as this

is before our first 2004 WMS (but compare with other periods)

Connected set = 97% of employees; 90% of establishments

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Matching IEB administrative data to WMS data

Titel der Folie 14

Link WMS to IEB data via names, legal status, 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 to their FE

‐ 98% of relevant population in firms (full-time employed, age 20-60). ~200,000 employee FE

“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)

‐ Alternatively, use average employee fixed effects in “managerial” occupations

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Tab 1A: Firm level Descriptive Statistics on

WMS-IEB matched data

Titel der Folie 15

Mean SD

Firm age 64.34 62.79

Number of workers 440.02 642.87

Proportion Female 0.27 0.17

% Employees with college degree share 0.12 0.13

ln(capital) 9.89 1.69

ln(materials) 11.29 1.07

% with 5 or more competitors 0.59

% family owned 0.23

Notes: 590 firm-year observations 2004, 2006 & 2009 across 355 firms

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Titel der Folie 16

IEB: Data: German Employer-Employee Panel

Management Practices & Human Capital

Productivity

WMS Data: World Management Survey

Selection – Inflows & Outflows

Extensions & Robustness

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Fig 1: Firms with high average employee ability

have higher management scores

Titel der Folie 17

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

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

groups

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Correlations of WMS Management Scores with

average employee & managerial ability

Titel der Folie 19

Dependent Variable: WMS Management average firm z-score

Mean employee quality0.216*** 0.029 -0.093

(0.078) (0.090) (0.112)

Mean managerial quality0.294*** 0.277*** 0.258***

(0.071) (0.091) (0.095)

% Employees with1.022**

college(0.452)

Ln(Employees) 0.237*** 0.261*** 0.264*** 0.263***

(0.049) (0.048) (0.048) (0.050)

Observations588 588 588 588

Notes: SEs clustered by 354 firms; Dependent variable & ability measures are z-scored. All columns include East

Germany dummy, %female, ownership dummies, #competitors, firm age, 2 digit industry & time dummies.

Employee quality mean level of individual FE over 1996-2002. Managerial quality is employee quality in the top

quartile of the within firm distribution.

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Titel der Folie 20

IEB: Data: German Employer-Employee Panel

Management Practices & Human Capital

Productivity

WMS Data: World Management Survey

Selection – Inflows & Outflows

Extensions & Robustness

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Main Regressions

IAB-OECD Seminar * Stefanie Wolter 21

Simple model generates structural production relationship for

firm j in year t (WMS management score & managerial quality

can affect overall labor quality & TFP)

log 𝑌𝑗𝑡 = 𝑠′′0 + 𝑠𝐿 log 𝐿𝑗𝑡 + 𝑠𝐼 log 𝐼𝑗𝑡 + 𝑠𝐾 log 𝐾𝑗𝑡

+ 𝜋1 𝜂 𝑗 + 𝜋2 𝜓 𝑗 + 𝜋3 𝜂 𝑀𝑗 + 𝜋4Λ 𝑗 + 𝜖′𝑗𝑡

# workersCapital

services

Intermediate

Inputs

Output

Average

Employee

Quality

Firm effect

in wage

equation

Average

Managerial

Quality

WMS Management Score

(adoption of practices, avg. of 18

questions)

Other controls: firm age, industry & time dummies, ownership, competition

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Productivity, Management Practices & ability

IAB-OECD Seminar * Stefanie Wolter 24

Dep. Ln(sales) Ln(sales) Ln(sales) Ln(sales) Ln(sales)

Management Score 0.264*** 0.199*** 0.150*** 0.129*** 0.074*

(0.052) (0.046) (0.042) (0.042) (0.038)

Employee Ability 0.821*** 0.597*** 0.375*** 0.250**

(0.144) (0.101) (0.105) (0.098)

Managerial ability 0.363*** 0.329*** 0.184*

(0.107) (0.100) (0.099)

% Employees with 1.873*** 1.308***

College degree (0.642) (0.465)

Ln(Labor) 0.315*** 0.446*** 0.589*** 0.591*** 0.389***

(0.067) (0.067) (0.071) (0.071) (0.062)

Ln(Capital) 0.431***

(0.048)Notes: All SEs clustered by 229 firms. Management score & employee ability are standardized. All columns include

dummy for East German, %female, 5 ownership dummies, #competitors, firm age, a cubic in the coverage rate,

industry & time dummies. Employee quality is mean of individual fixed effect measured over 1996-2002 period.

Managerial quality is employee quality in the top quartile of the within firm distribution.

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Factor TFP specification

IAB-OECD Seminar * Stefanie Wolter 25

Dependent Variable: LnTFP LnTFP LnTFP LnTFP

Management Score 0.081*** 0.062*** 0.053*** 0.044**

(0.021) (0.020) (0.019) (0.018)

Mean Employee ability 0.176*** 0.113*** 0.103***

(0.025) (0.034) (0.033)

Mean Managerial ability 0.062* 0.059*

(0.035) (0.034)

Firm effect (in wages) 0.070***

(0.018)

Firms 229 229 229 229

Observations 378 378 378 378

Notes: All SEs clustered by 229 firms. Management score & employee ability are standardized. All columns include

dummy for East German, %female, 5 ownership dummies, #competitors, firm age, a cubic in the coverage rate,

industry & time dummies. Employee quality is mean of individual fixed effect measured over 1996-2002 period.

Managerial quality is employee quality in the top quartile of the within firm distribution.

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Titel der Folie 26

IEB: Data: German Employer-Employee Panel

Management Practices & Human Capital

Productivity

WMS Data: World Management Survey

Selection – Inflows & Outflows

Extensions & Robustness

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Why do “better managed” firms have higher

ability employees?

Titel der Folie 27

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

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Inflows and outflows into the WMS-IEB matched

data, 2004-2010

Titel der Folie 28

Variables

Inflows to our

firms from the

specified labor

market state

Outflows

from our firms

to the specified

labor market

state

Unemployment 19,013 40,093

Jobs 70,675 75,023

Non-

participation 32,748 17,684

Total 122,436 132,600

• Focus on inflows from unemployment as generally do not know

WMS scores in firms where employees came from (or outflowed

towards)

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Inflows: Firms with high management scores select

more able employees

Titel der Folie 29

Notes: 19,026 workers. SEs clustered by firm. Management score is standardized. Controls for east dummy,

competition, ownership, ln(firm age), %female & industry. Panel B has additional controls for age & college %

of inflows.

(1) (2) (3) (4) (5)

Dependent Variable:

Proportion of workers above different ability percentiles from total

inflow from unemployment distribution (19,026 workers)

Percentile 10% 25% 50% 75% 90%

Panel A. No Size Control

Management Score 0.003 0.003 0.006 0.016** 0.019***

(0.002) (0.004) (0.005) (0.006) (0.006)

Observations 355 355 355 355 355

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Outflows. Firms with high management scores exit

least able employees more aggressively

IAB-OECD Seminar * Stefanie Wolter 30

(1) (2) (3) (4)

Dependent

variable:

Ln(Average ability of outflow) – ln(Average ability of

incumbents)

Management -0.091* -0.115** -0.106* -0.133**

Score (0.053) (0.058) (0.060) (0.057)

Average age of 0.048*** 0.041***

outflows (0.016) (0.015)

% college of 4.887***

outflows (0.861)

Gen. Controls No Yes Yes Yes

Firms 347 347 347 347

Notes: 40,098 employees. SEs clustered by firm. Management score is standardized. “General controls” are East

Germany dummy, competition, ownership, ln(firm age), %female & industry. Panel B has additional controls for age

& college share of outflows.

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Extensions and Robustness

Titel der Folie 31

Firm Fixed effects (Table A8)

Wage growth (Table A6) – do better managed firms promote

high ability workers more quickly?

Alternative definitions of managers (e.g. occupation)

Alternative estimation period for FE

Flows from other states (job to job; non-participation: Tables A2-

A5)

Drop East German firms

Other ways of aggregating management practice index (e.g.

principal components)

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Firm Fixed Effect (in wages) correlated with (a) WMS

management scores (b) productivity

Titel der Folie 32

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Firm fixed effect is positively associated with

management practices

Titel der Folie 33

Dependent Variable: Firm effect

Management 0.215*** 0.150*** 0.134*** 0.110*** 0.108*** 0.0853*

Score (0.0482) (0.0396) (0.0424) (0.0414) (0.0414) (0.0438)

Ln(Labor) 0.0646 0.0922* 0.102 0.101*

(0.0470) (0.0503) (0.0632) (0.0577)

% Employees 1.077*** 0.678 0.510

with College (0.370) (0.632) (0.511)

Mean Employee 0.134 -0.0418

Quality (0.238) (0.252)

Mean Managerial 0.293**

quality (0.136)

General Controls No Yes Yes Yes Yes Yes

Observations 588 588 588 588 588 588

Notes: SEs clustered by firm. Management score & employee quality are standardized. “General controls”: East

Germany dummy, competition, ownership, ln(firm age), %female & industry.

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Firm fixed effect is positively associated with

Productivity

Titel der Folie 34

Dependent Variable: Ln(sales) Ln(sales) Ln(sales)

Firm fixed effect (in wages) 0.556*** 0.319*** 0.101**

(0.069) (0.068) (0.048)

Management Score 0.064*

(0.038)

Average incumbent 0.258**

quality (0.104)

Mean Managerial 0.139

quality (0.095)

% Employees 1.276***

with College (0.468)

Ln(Employees) 0.343*** 0.383***

(0.073) (0.062)

Ln(Capital) 0.427***

(0.048)

General controls Yes Yes Yes

Observations 560 560 560

Notes: SEs clustered by firm. Management score & employee quality are standardized.“General controls” are East

Germany dummy, competition, ownership, ln(firm age), %female & industry.

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Conclusion

Titel der Folie 35

Merge WMS management scores in Germany with IEB-based

estimates of manager & worker FE a la CHK

WMS Management Scores correlated with human capital –

particularly managerial ability

About ¼ to ½ of the relationship between productivity &

management can be accounted for by human capital

Well-managed firms have a larger stock of higher-ability workers

Able to recruit more able workers and dismiss those of lower ability

Managerial human capital is important to sustain successful

management practices

Evidence for something like corporate culture

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www.iab.de

Stefanie Wolter

contact: [email protected]

Thank you.

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Backup

Titel der Folie 37

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Dependent Variable: Ln(sales) Ln(sales) Ln(sales) Ln(sales)

Firm fixed effect (in wages) 0.556*** 0.319*** 0.101** 0.033

(0.069) (0.068) (0.048) (0.021)

Management Score 0.064* 0.0289*

(0.038) (0.017)

Average incumbent 0.258** 0.0736

quality (0.104) (0.073)

Mean Managerial 0.139 0.0765

quality (0.095) (0.048)

% Employees 1.276*** 0.165

with College (0.468) (0.227)

Ln(Employees) 0.343*** 0.383*** 0.126***

(0.073) (0.062) (0.027)

Ln(Capital) 0.427*** 0.175***

(0.048) (0.022)

Ln(Materials) 0.660***

(0.033)

Observations 560 560 560 378

Table A8: Firm fixed effect is positively

associated with Productivity