Management and Inequality

Nick Bloom (Stanford)Scott Ohlmacher (Census)

Cristina Tello-Trillo (Census)

ASSA January 4th 2019

Disclaimer: Any opinions and conclusions expressedherein are those of the author and do not necessarilyrepresent the views of the US Census Bureau. All resultshave been reviewed to ensure that no confidentialinformation is disclosed.

Long history of work on management in economics e.g. Walker (1887)

Francis Walker (1840-1897) was the founding President of the AEAWalker ran the 1870 and 1880 Census, claiming management was the major source of performance differences across US firms in Walker (1887)

But he had no management data – this was pretty much pure speculation

So the US Census ran the Management and Organizational Practices Survey (MOPS) in 2010 and 2015 (and in preparation for 2020)

Initial work on the MOPS management data looked at plant performance, e.g.

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Source: Bloom, Brynjolfsson, Foster, Jarmin, Patnaik, Saporta-Eksten & Van Reenen (forthcoming AER)

What about Management & Inequality?

Many claim that aggressive management practices only enrich CEOs and managers - presumably raising inequality

Maybe the rise of more structured management (private equity, multinationals etc) is driving the rise in inequality?

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Data – management and worker earnings

Management and Inequality

Management and Earnings Volatility

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Management & Organizational Practices Survey 2010

It was delivered to ~50,000 manufacturing plants in 2011 (asking about 2010) and in 2016 (asking about 2015)

This was quick and easy to fill out - and mandatory - so 74% of plants responded.

In 2010: covering 5.6m employees (>50% of US manufacturing employment)

MOPS contacts were mostly senior managers

MOPS asks about performance monitoring e.g.

Examples of monitoring– manufacturing

Example of no performance metrics: Textile Plant

Examples of monitoring: hotels (from a prior ASSA)

MOPS also asks about incentives e.g.

Examples of incentives - performance reviews

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Each of the 16 questions is assigned a value from 0 (least structured) to 1 (most structured)

16 Management

8 Monitoring

8 Incentives

4 Bonus

2 Promotions

2 Reassignment/Dismissal

Overall management score displays a wide spread

Note: The management score is the average of the scores for each of the 16 questions

Longitudinal Employer-Household Dynamics (LEHD)

Linked employer-employee quarterly wage data for all workers in state unemployment insurance records

Use workers with quarterly earnings at least full-time federal minimum wage ($3,800) around 2010 (2009Q4-2011Q1)

Use firm-state (SEIN) manufacturing with 20+ employees

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Data

Management and Inequality

Management and Earnings Volatility

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Correlation of management and within firm inequality is…. Decreasing in Structured Management (binscatter)

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Correlation of management and within firm inequality is strongly negative Decreasing in Structured Management (binscatter)

Maybe this is all due to industry, regional, size, age or some other variation?

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Dependent Variable Log(90th Percentile) ‐ Log(10th Percentile)(1) (2) (3)

Management ‐0.1447*** ‐0.1066*** ‐0.057***(0.0185) (0.0192) (0.019)

Log(Emp) ‐0.0312*** ‐0.013***(0.0026) (0.003)

Log(Capital/Emp) ‐0.0207*** ‐0.016***(0.0032) (0.003)

Log(VA/Emp) 0.0084** 0.015***(0.0038) (0.004)

Share of Employees w/ a Bachelor's Degree

0.2027*** 0.201***(0.0203) (0.020)

Firm Age 0.001(0.000)

Log(Firm Employment) ‐0.022***(0.002)

Observations (Firm‐State) 17,000 17,000 17,000Number of Firms (Clusters) 11,000 11,000 11,000Fixed Effects Industry, State Industry, State Industry, State23

No – the Management and within firm inequality correlation is very robust

This negative management & within-firm inequality correlation driven by the greater rise in lower half earnings at firms with more structured management

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The 90-10 Earnings Differential is Strongly Decreasing in the 8 Monitoring questions

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The 90-10 Earnings Differential is Weakly Increasing in 8 Incentives questions

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Dependent Variable Log(90th Percentile) ‐ Log(10th Percentile)(1) (2)

Monitoring ‐0.146*** ‐0.143***(0.018) (0.018)

Incentives 0.049***(0.014)

Bonuses 0.035***(0.009)

Promotions ‐0.018*(0.010)

Reassignment/Dismissal 0.020***(0.007)

Observations (Firm‐State) 17,000 17,000Number of Firms (Clusters) 11,000 11,000Fixed Effects Industry, State Industry, State

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Within incentives bonuses and reassignment (or dismissal) the most linked to inequality

Note: Includes controls for log(SEIN employment), log(parent firm employment), log(capital/employment), log(VA/emp), employee share with a degree and firm age.

Robustness: look at longer-run pay for workers in the firm 2009-2011, finding similar resultsDependent Variable Log(90th) ‐ Log(10th) Percentile

(1) (2) (3)Management ‐0.071***

(0.022)Monitoring ‐0.179*** ‐0.176***

(0.020) (0.020)Incentives 0.058***

(0.016)Bonuses 0.038***

(0.009)Promotions ‐0.008

(0.011)Reassignment/Dismissal 0.016**

(0.007)Observations (Firm‐State) 14,500 14,500 14,500Num Firms (Clusters) 10,000 10,000 10,000Fixed Effects Industry, State Industry, State Industry, State

28Note: Includes controls for log(SEIN employment), log(parent firm employment), log(capital/employment), log(VA/emp), employee share with a degree and firm age. Uses earnings from 2009Q1 to 2011Q4.

More generally find a weak negative link between performance and inequality

Dependent Variable Log(90th Percentile) ‐ Log(10th Percentile)(1) (2) (3)

Log(Firm Employment) ‐0.027***(0.002)

Log(Shipments/Emp) ‐0.011***(0.004)

Log(Profit/Shipments) ‐0.024***(0.007)

Largest Plant TFP

Observations (Firm‐State) 17,000 17,000 17,000Number of Firms (Clusters) 11,000 11,000 11,000Fixed Effects Industry, State Industry, State Industry, State

Data

Management and Inequality

Management and Earnings Volatility

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Well known inequality exists within and between firms (and is increasing in both) – motivating this paper

Source: Song, Bloom, Guvenen, Price and Von Wachter (2019, QJE)

Less well known: US earnings volatility is falling

LEHD data,(Abowd and McKinney, 2019)

SSA data,(Bloom, Guvenen, Pistaferri, Sabelhaus, Salgado & Song, 2018)

So what about management and earnings volatility – maybe good management reduces inequality but increase volatility?

Measure variance of the four quarters of 2010 earnings growth for each employee, then average at the SEIN (firm-state) level

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Earnings volatility small positive correlation with management (negative for monitoring and positive for incentives)

Dependent Variable Variance in Log(Quarterly Worker Earnings)(1) (2) (3)

Management 0.005**(0.002)

Monitoring ‐0.012*** ‐0.011***(0.002) (0.002)

Incentives 0.011***(0.001)

Bonuses 0.015***(0.001)

Promotions ‐0.004***(0.002)

Reassignment/Dismissal ‐0.001(0.001)

Observations (Firm‐State) 17,000 17,000 17,000Num Firms (Clusters) 11,000 11,000 11,000Fixed Effects Industry, State Industry, State Industry, State

34Note: Includes controls for log(SEIN employment), log(parent firm employment), log(capital/employment), log(VA/emp), employee share with a degree and firm age.

One mechanism is simply 4th quarter bonuses

Dependent Variable Firm‐State Mean of (Log Q4 Earnings ‐ Average Log Earnings for Q1‐Q3)(1) (2) (3)

Management 0.020**(0.008)

Monitoring & Targeting ‐0.021*** ‐0.019**(0.008) (0.007)

Incentives 0.028***(0.006)

Bonuses 0.031***(0.004)

Promotions ‐0.003(0.004)

Reassignment/Dismissal ‐0.003(0.003)

Obs (Firm‐State) 17,000 17,000 17,000Num Firms (Clusters) 11,000 11,000 11,000Fixed Effects Industry, State Industry, State Industry, State

Note: Includes controls for log(SEIN employment), log(parent firm employment), log(capital/employment), log(VA/emp), employee share with a degree and firm age.

Dependent Variable Average Variance in Log(Quarterly Worker Earnings)(1) (2) (3)

Management 0.007***(0.002)

Monitoring & Targeting ‐0.010*** ‐0.009***(0.002) (0.002)

Incentives 0.012***(0.001)

Bonuses 0.013***(0.001)

Promotions ‐0.003**(0.001)

Reassignment/Dismissal ‐0.000(0.001)

Obs (Firm‐State) 14,500 14,500 14,500Num Firms (Clusters) 10,000 10,000 10,000Fixed Effects Industry, State Industry, State Industry, State

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But not just individual 4th quarter bonuses as results are similar in the 3 year panel 2009-2011

Note: Includes controls for log(SEIN employment), log(parent firm employment), log(capital/employment), log(VA/emp), employee share with a degree and firm age.

Conclusions

1) Structured management practices (and better firm performance) are correlated with lower within-firm inequality

2) Offsetting effects: Monitoring is correlated with less within firm inequality

(and lower volatility) Incentives - particularly bonuses & firing - correlated

more within firm inequality (and higher volatility)

Next: (A) panel data (2015 MOPS), and (B) some causality….

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Thank you

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Performance and Inequality

Dependent Variable Log(90th Percentile) ‐ Log(10th Percentile)Log(Firm Employment) ‐0.014***

(0.003)Log(Emp) ‐0.022***

(0.002)Average Annual Employment Growth, 2005‐2010 (Winsorized)

‐0.034***(0.011)

Log(Capital/Emp) ‐0.013***(0.003)

Share of Employees w/ a Bachelor's Degree

0.201***(0.020)

Firm Age 0.000(0.000)

Observations (Firm‐State) 17,000Number of Firms (Clusters) 11,000Fixed Effects Industry, State

Monitoring Question Examples

Return

Targeting Question Examples

Return

Bonus Question Examples

Return

Promotion Questions

Return

Reassignment & Dismissal Question Example

Return

Establishment-Level Results from Bloom et al. (2013)

Dependent Variable Log(VA/Emp)Log(Profit/Shipments)

(1) (2) (3)Management 1.272*** 0.498*** 0.058***

(0.05) (0.037) (0.01)Log(Emp) ‐0.035*** 0.001

(0.006) (0.002)Log(Capital/Emp) 0.179*** 0.01***

(0.007) (0.002)Share of Employees w/ a Bachelor's Degree

0.418*** 0.004(0.041) (0.011)

Observations (Firm‐State) 32,000 32,000 32,000Number of Firms (Clusters) 18,000 18,000 18,000Fixed Effects None Industry Industry

Return

Structured Management Strongly Correlated with Performance (Bloom et al. 2019)

Dependent Variable Log(VA/Emp)Log(TFP of

Largest Plant)Log(Shipments

/Emp)Log(Profit/Shipments)

(1) (2) (3) (4) (5)Management 1.281*** 0.620*** 0.075*** 0.691*** 0.064***

(0.052) (0.044) (0.029) (0.038) (0.022)Log(Emp) 0.012 ‐0.005 0.004**

(0.008) (0.007) (0.002)Log(Capital/Emp) 0.002** 0.002*** ‐0.000

(0.001) (0.001) (0.000)Share of Employees w/ a Bachelor's Degree

0.673*** 0.637*** ‐0.024(0.052) (0.045) (0.044)

Observations (Firm‐State) 17,000 17,000 17,000 17,000 17,000Number of Firms (Clusters) 11,000 11,000 11,000 11,000 11,000Fixed Effects None Industry, State None Industry, State Industry, State

Descriptive Statistics

MeanStandard Deviation

25th Percentile

75th Percentile

Log(90th Percentile) ‐ Log(10th Percentile) 0.975 0.305 0.761 1.152

Log(90th Percentile) ‐ Log(50th Percentile) 0.617 0.244 0.446 0.748

Log(50th Percentile) ‐ Log(10th Percentile) 0.359 0.141 0.257 0.439

Average Variance inLog(Quarterly Worker Earnings) 0.033 0.032

Management Score 0.658 0.136 0.581 0.757

Monitoring & Targeting Score 0.698 0.153 0.604 0.813

Incentives Score 0.607 0.185 0.500 0.739

Bonuses Score 0.413 0.285

Promotions Score 0.858 0.257

Reassignment/Dismissal Score 0.632 0.347

Log(Emp) 4.882 1.065

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Again, relationship particularly driven

Dependent Variable Log(90th Percentile) ‐ Log(50th Percentile) Log(50th Percentile) ‐ Log(10th Percentile)(1) (2)

Monitoring & Targeting ‐0.094*** ‐0.053***(0.015) (0.008)

Incentives 0.042*** 0.007(0.012) (0.006)

Bonuses

Promotions

Reassignment/Dismissal

Log(Emp) ‐0.014*** 0.002*(0.002) (0.001)

Log(Capital/Emp) ‐0.019*** 0.005***(0.003) (0.001)

Log(VA/Emp) 0.009*** 0.006***(0.003) (0.002)

Share of Employees w/ a Bachelor's Degree

0.087*** 0.116***(0.016) (0.010)

Firm Age 0.000* 0.000(0.000) (0.000)

Log(Firm Employment) ‐0.020*** ‐0.000(0.002) (0.001)

Observations (Firm‐State) 17,000 17,000Number of Firms (Clusters) 11,000 11,000Fixed Effects Industry, State Industry, State

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Linking LEHD & MOPS

Aggregate MOPS (& ASM) to the firm-state (SEIN) level

Employment-weighted mean of management scores

Sum of shipments, employment, etc.

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