The Value of Bosses

Kathryn L. Shaw

SOLE, May 5, 2012

Productivity

Why are some workers more productive than others?

Why are some plants more productive than others?

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HR Practices

Incentives

(selection)

Productivity

Incentives

(behavioral)

Teams

Peer EffectsInformation

Technology

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HR Practices

Incentives

(selection)

Productivity

Incentives

(behavioral)

Teams

Peer EffectsInformation

Technology

Lazear (2000)

Ichniowski, Shaw, Prennushi (1997)

Bartel, Ichniowski, Shaw (2007)

Bandiera, Barankay, Rasul (2009)

Hamilton, Nickerson, Owan (2003)

Mas and Moretti (2009)

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http://www.nytimes.com/2011/03/13/business/13hire.html?pagewanted=1&_r=2

Google’s Quest to Build a Better BossNY Times, March 12, 2011

“The starting point was that our best managers have teams that perform better, are retained better, are happier — they do everything better,” Mr. Bock says. “So the biggest controllable factor that we could see was the quality of the manager, and how they sort of made things happen. The question we then asked was: What if every manager was that good? And then you start saying: Well, what makes them that good? And how do you do it?”

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Most research tends to focus on these guys..

And we still have limited evidence on the impact of CEOs on productivity.

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What about these guys…

A typical boss is a supervisor in retail trade.

The Importance of Bosses

A digression on retail trade

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Retail trade was growing…manufacturing declining.

Source: County Business Patterns 9

The dip in the recession…

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All Retail Occupations

15017310

1510230015177150

15369450

15516280

15642700 15641530

14974830

14622020

14000000

14200000

14400000

14600000

14800000

15000000

15200000

15400000

15600000

15800000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Em

plo

ym

en

t

All Occupations

Sales jobs fell…

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Retail Occupation Employment

5600000

5800000

6000000

6200000

6400000

6600000

6800000

7000000

7200000

7400000

7600000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Years

Em

plo

ym

en

t

Sales Non-sales Support

Supervisors remain… at over 1 million.

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Retail Occupation Employment

0

200000

400000

600000

800000

1000000

1200000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Years

Em

plo

ym

en

t

First-Line Supervisors/Managers of Retail Sales Workers Management Occupations

There is about one supervisor for every 7 sales workers.

Retail

Key Point 1: there are a lot of bosses.

Key Point 2: not all jobs are “bad” jobs; the variance of pay is high.

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Pay in retail is highly dispersed…

14Not all jobs are minimum wage jobs.

This is especially true for supervisors…

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Supervisors earn $48,000 at the 75th percentile; they earn $78,000 at the 90th percentile.Average pay is $34,000.

Supervisors come from all educational backgrounds; returns to education are high.

Pay Employment Share

Less than High School $16,236 9%

High School $28,799 39%

Some College $32,427 31%

Bachelor’s Degree $56,003 18%

Post-BA $89,838 3%

100%Source: CPS Data

Retail Supervisors’ Pay and Employment, 2010

Returns to firm size are also high. 16

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– One extreme: Bosses have little effect on worker productivity and bosses get their jobs through internal politics

– Another extreme: Workers are substitutable and output is determined by the quality of the supervisor

• Some bosses are earning a lot; are they worth it?

• New data allow us to estimate boss effects.

Do Bosses Matter?

The Value of Bosses

Edward P. Lazear

Kathryn L. Shaw

Christopher T. Stanton

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Questions

• How much do bosses influence workers’ productivity?– What is the marginal product of a boss compared to a worker?– What is the variance in bosses’ productivity? Do some bosses

raise worker output more than others?

• Why are some bosses more productive than others? – Do bosses teach or motivate?– Which bosses should be assigned to which workers?

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DATA

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Technology-based Service Jobs

• A technology-based service job is one in which the company uses some form of advanced IT system to record every transaction and how long it takes

• Examples: Skilled– insurance-claims processing– computer-based test grading– technical call centers– in-house IT specialists – technical repair workers– some retail sales

• Examples: Less skilled– airline gate agents– telemarketers – some cashiers

Summary Statistics

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Variable Obs Mean Std. Dev.

Output Per Hour 5,729,508 10.26 3.16Uptime 4,870,610 0.96 0.03Output Per Hour * Uptime 4,870,610 10.01 3.00Tenure 5,729,508 648.91 609.83

Worker as Unit of Analysis

Number of Unique Bosses Per Worker 23,878 3.99 2.78

Team as Unit of AnalysisDaily Team Size 633,818 9.04 4.54

Boss as Unit of Analysis

Number of Unique Workers Per Boss 1,940 49.15 35.41

       

HOW MUCH DO BOSSES MATTER?

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Estimation

• Estimation of boss effects δj from

qijt= Xitβ + αi + δj + εijt.

• Xit contains month dummies, day of week dummies, and a fifth order tenure polynomial

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Standard Deviations of Boss And Worker EffectsDependent Variable: Output-per-hour

OLS

Worker Fixed

Effects

Worker and Boss Fixed

Effects

R-squared 0.061 0.237 0.243

Standard Deviation of Worker Effects 1.52 1.45

Standard Deviation of Boss Effects *Avg Team Size (9.04) 4.61

Number of observations 5,729,508Number of workers 23,878

Number of bosses 1,940

The Variance of Boss Fixed Effects

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Is There Non-Random Assignment of Workers to Bosses? Very little.

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Number of Bosses Number of Bosses

Who Work with Stars and Laggards

Who Work with Stars but not with Laggards

Who work with Laggrds but not with Stars

1711 48 95

Number of Bosses

Who Work with New and Old Workers

Who Do Not Work with Old Workers

Who Do Not Work with New Workers

1724 146 70

On any given day, 98.4 % of bosses work with both stars and laggards.

On any given day, 89% of bosses work with both new and old workers.

Do the worst bosses leave?

• Picking a random day, the probability that a boss is present 1 year later is regressed on measures of boss quality: Bad bosses leave 64% more often.

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Table 6: The Probability that a Boss is Retained for 1 Year

Bottom 10% of Boss Effects -0.236 -0.239(.082)*** (.082)***

Top 10% of Boss Effects -0.044(.050)

Constant 0.645 0.645(.021)*** (.022)***

R-squared 0.018 0.018Number of Observations 1,444 1,444

Notes: The data are repeated cross sections of bosses, with their respected estimated boss fixed effect, on January 10 of each year. The dependent variable is an indicator that the boss is present in the data 1 year in the future. Year fixed effects are not displayed. The distribution of boss effects uses each unique boss

as the unit of analysis. Standard errors are in parentheses.

The Value of a Boss

• Suppose that the bottom 10% of bosses are about as good as the top 10% of workers.

• Setting the 10th percentile of bosses at 12 units/hour, the average boss produces about 18 units/hour.

The average boss is 76% more productive than the average worker.

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WHY DO BOSSES MATTER? TEACHING AND MOTIVATION

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Teaching and Motivation

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Define a boss to be a “teacher” if his impact persists after the worker moves on to later bosses. Define a boss to be a “motivator” if his impact is only current.

Estimate the output regression with lagged boss effects to identify “teaching” versus “motivation:”

λ is the persistent portion of the boss effect (teaching).

γ is the monthly rate of depreciation of past skills.

Teaching: 78% of the past boss’s influence persists to the present.

Skill depreciation: 13% of the past boss effect remains after one year.

HETEROGENEITY IN BOSS EFFECTS: MATCHING STAR WORKERS TO STAR BOSSES?

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Theory

Output is

q = H * E

with

where S is boss skill in teaching or motivating.

q

SE

H

SH

E

Si t m

i i i

,

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Assignment of Bosses and Workers: The Best Workers with the Best Bosses?

The estimating equation: where is a match effect.

A worker is a star if his is above the median. A boss is a star if his is above the median.

What happens when these are paired? • Star boss and star worker raises output by 1% relative to star boss

paired with laggard worker. • A 1% productivity gain is worth a lot in these jobs.

CONCLUSION

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Summary of Results

• How much? – The marginal product of a boss is 76% greater than a typical

worker, consistent with compensation ratios.– The variance in boss effects is large. Replacing the lowest

decile boss with the highest decile boss improves team productivity by as much as adding one team member (9 member team).

• Why? – Bosses teach and motivate, but teaching is the main role. – Comparative Advantage: Star bosses increase the output of Star

workers the most.

Conclusion

Key Point 3: To understand why some workers are more productive than other workers, study the boss-worker relationship.

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BACKUP MATERIAL

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Actual Variation or Sampling Error?

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Consider three bosses and their fixed effects:

δ1 estimated using 1000 worker days of data

δ2 estimated using 30 worker days of data

δ3 estimated using 300 worker days of data

Fixes: Maximum likelihood to purge sampling variation: assumes the

estimates are normally distributed, and separates sampling variation from true variation. Weighting by boss-worker days gives a similar estimate.

Random effects estimates via REML recovers the variance directly.

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Standard Deviations of Boss And Worker EffectsDependent Variable: Output-per-hour

OLS

Worker Fixed

EffectsBoss Fixed

Effects

Worker and Boss Fixed

Effects

Worker and Boss Effects with Mean

Team Output

R-squared 0.061 0.237 0.092 0.243 0.25

Standard Deviation of Worker Random Effects 1.52 1.45 1.42Standard Deviation of Worker Fixed Effects

Unweighted (1 observation per worker) 1.87 1.85 1.8 Weighted by worker-days 1.34 1.32 1.31 ML (1 observation per worker) 1.24 1.22 1.21

Standard Deviation of Boss Random Effects *Avg Team Size (9.04) 6.69 4.61 2.71Standard Deviation of Boss Fixed Effects *Avg Team Size (9.04)

Unweighted (1 observation per boss) 8.86 7.5 7.5 Weighted by worker*boss days 5.24 3.44 2.98 ML (1 obs. per boss) 5.6 3.53 3.07

Coefficient on team's mean output 0.16

Number of observations 5,729,508

Number of workers 23,878

Number of bosses 1,940

Percent of sample in largest connected group 99.99 99.99

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Regressions of output-per-hour with lags

         

Lag Type 14 Day Avg. 14 Day Average 1 Day 1 Day

Number of Lags 1 1 1 2

Worker Tenure No Yes Yes Yes

R-squared 0.2599 0.2617 0.2516 0.2558

Coefficient on the first lag 0.402 0.375 0.105 0.0968

Coefficient on the second lag 0.0749

Standard Deviation of Worker Fixed Effects

Weighted by frequency 0.79 0.83 1.19 1.09

Standard Deviation of Boss Effects Multiplied by Average Team Size (9.04)

Weighted by frequency 2.26 2.12 3.07 2.85

NPV of a Standard Deviation of Boss Effects for an Average Team

3.78 3.39 3.43 3.16

         

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How do boss effects compare to peer effects?

Boss effects are large.

Peer Effects

Estimated from

qijt= Xitβ + αi + δj + ξ pijt + εijt

where peer effect, pijt, is specified in several ways:

1. Mean contemporaneous productivity of co-workers on team – very upward biased by daily demand effects.

2. Mean fixed effects of co-workers on team, estimated via two-step non-linear least squares – yields negligible peer effects.

3. Mean of peer’s first few months of output as a proxy for the peer’s current output. Results show peer effects not economically significant

In contrast with the large boss effects, peer effects are close to zero.

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Table 8: The Effect of Peer Quality on Output-per-hour

Estimation method: OLS Joint NLS Peer Proxies

R-Squared 0.2504 0.2356 0.243

Coefficient on Peers' Mean Ability 0.16 0.001 -0.022

Standard Deviation of Peer Effects 0.063 0.022 0.009Standard Deviation of Boss Effects (Weighted by frequency) 0.33 0.31 0.38Standard Deviation of Worker Effects (Weighted by frequency) 1.31 1.32

Number of Workers 23,878 1679 23,878Number of Bosses 1,940 155 1,940Number of Observations 5,729,508 391,730 5,729,508

Teaching and Motivation

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Suppose prior bosses affect the current period. Then call that which persists “teaching” and that which is only contemporaneous “motivation”.

The term captures months since working for boss j.

γ is the monthly rate of geometric decay.

λ is the persistent portion of the boss effect (teaching).

is a matrix of current boss assignments

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Teaching and the Fadeout of Boss Effects     

Teaching (λ) 0.78

Monthly Rate of Decay (γ) 0.87

Amount of Boss Effect Remaining After 1 Year (γ^12)*λ 0.13

Standard Deviation of Worker Fixed Effects

Weighted by frequency 1.28  

Standard Deviation of Boss Effects Multiplied by Average Team Size (9.04)

Weighted by frequency 3.51

Number of Workers 1679

Number of Bosses 155

Number of Observations 391,730

     

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Assignment of Bosses and Workers: The Best Workers with the Best Bosses?

Output-per-hour Output-per-hourNo Match Effects With Match Effects

R-squared 0.297 0.313

Standard Deviation of Worker Random Effects 2.83 2.72

Standard Deviation of Boss Random Effects 0.61 0.53

Standard Deviation of Boss Random Effects Multiplied by Average Team Size 5.51 4.79

Standard Deviation of Match Effects 0.76

Means of Match Effects by Groups (Defined from Estimated Random Effects) Good Bosses and Good Workers 0.091 Good Bosses and Bad Workers -0.043 Bad Bosses and Good Workers 0.015 Bad Bosses and Bad Workers -0.067

Number of Observations 178,856 178,856Number of Workers 1137 1137Number of Bosses 105 105

The Importance of CEOs?

CEO pay in the 500 biggest companies in the U.S.

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The Importance of CEOs?CEO-to-worker compensation ratio, with options granted and options realized,1965–2011

Note: "Options granted" compensation series includes salary, bonus, restricted stock grants, options granted, and long-term incentive payouts for CEOs at the top 350 firms ranked by sales. "Options exercised" compensation series includes salary, bonus, restricted stock grants, options exercised, and long-term incentive payouts for CEOs at the top 350 firms ranked by sales.

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Robustness and Non-Random Assignment to Bosses

• Precision of the boss effect estimates– Bias if worker effects do not capture all ability– Worker trends or transitory shocks that are

correlated with boss assignment• Bias in estimation of the distribution of

boss effects– Omitted observations (treatment of the

treated)