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Hair inequality

Explore the rise in inequality between firms, reasons for segregation, and implications on income distribution. Study by researchers from SSA, Stanford, and UCLA at NYU CV Starr Center. Analyzing US worker-firm database from 1978-2013 to uncover patterns in firm behavior and income disparities.

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Hair inequality

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  1. Firming Up InequalityJae Song (Social Security Administration)David Price (Stanford)Fatih Guvenen (Minnesota)Nicholas Bloom (Stanford)Til von Wachter (UCLA)NYU CV Starr Center, April 28th 2016

  2. Hair inequality $500k $5bn

  3. Even Ted Cruz has been getting in on the act

  4. Much of the research on inequality looked within & between observable groups: education, occupation, sex, race, age etc This paper studies the link between firms and inequality Between firms (e.g. are top firms are paying more?) Within firms (e.g. is CEO pay higher than median pay?) Motivation is to understand inequality

  5. 1) How much of the rise in inequality is between firms? Almost all Except: (A) the Top 1%, and (B) Mega firms (10k+ employees) 2) Why has inequality risen so much between firms? Massive rise in segregation (well paid clustering in some firms) 3) Why is segregation rising so strongly in US firms? Inequality is rising (skill-biased technical change) But inequality constrained in firms by fairness & tax So firms reorganizing (through outsourcing) to focus on core activities and segregate by income We build a new worker-firm database from 1978-2013 covering the US to investigate 3 questions

  6. The story in company logos…….. Catering & facilities Security IT services HR Real estate

  7. Consistent with timing of the rise in outsourcing Notes: CPS, share of workers in outsourcing industries (personnel services, trucking, warehousing, facilities services, security, cleaning and food service preparation” Notes: Google Ngrams

  8. Also matches rising focus on “core competencies”

  9. And since US firm size is roughly constant firms are reorganizing (rather than atomizing)

  10. Research builds on prior finding an important role for firms in rising inequality (US, 1977–1992, manufacturing)Between-establishment dispersion of earnings (Dunne, Foster & Haltiwanger 2004). (US: 1992–2007, 9 states) Between-establishment inequality 2/3 rise in inequality (Barth, Bryson, Davis & Freeman 2014). UK (1984–2001), Mueller, Ouimet and Simintizi (2016), Faggio, Salvanes and Van Reenen (2007) Germany (1985–2009), Card, Henning and Kline (2013) Brazil (1996–2012), Alvarez, Engbom and Moser (2015) Sweden (1986–2008), Håkanson, Lindqvist & Vlachos (2015) So whatever is driving this seems to be global

  11. Should we care inequality increasing across firms? Spreads inequality to benefits (e.g. health, pensions, tuition support etc) Could facilitate rising inequality – lower paid workers out of sight are easier to ignore? But could also be good – maybe lower paid workers less likely to compare?

  12. Global income distribution – comparisons matter global percentile Source: BrankoMilanovic (2014, World Bank)

  13. The SSA database Non-parametric results on inequality Basic result Robustness of the basic result Top 1% Firm size (Mega firms (≥10k employees) and the rest) More formal econometric approach (AKM and CHK) Why is this happening - the changing structure of firms Outline

  14. Data: SSA Master Earnings File (MEF) Universe of all W-2s from 1978 to 2013 For each job: SSN, EIN and total compensation: “Total compensation includes: wages, salaries, tips, restricted stock grants, exercised stock options, severance payments, & all other types of income considered remuneration for labor services by the IRS.”

  15. Data: SSA Master Earnings File (MEF)

  16. Data: SSA Master Earnings File (MEF) Universe of all W-2s from 1978 to 2013 For each job: SSN, EIN and total compensation: “Total compensation includes: wages, salaries, tips, restricted stock grants, exercised stock options, severance payments, & all other types of income considered remuneration for labor services by the IRS.” For each SSN: age, sex, place of birth, date of death For each EIN: 4-digit SIC (industry) code, location No top-coding, no survey response error, every firm & employee

  17. What is an EIN (Employer Identification Number)? Any firm with an employee (so issues a W-2) must have an EIN Bureau of Labor Statistics uses the EIN as its definition of a firm Many organizations have one (e.g. Facebook, Walmart Stores) Others have many, e.g. Stanford has 4 EINs (1 for the university, 1 for each hospital and 1 for the bookstore) The 6165 public companies in D&B have 19,969 EINs

  18. Our core dataset Firms with 20+ employees (to get within firm inequality) and workers at those firms. Exclude government & education sector. Our data covers 1.1m firms (18% of total), 103 million workers (73% of total) and $5.4tn in earnings (80% of total).

  19. Earnings cumulative distribution(10%=$10k, 50%=$40k, 90%=$100k, 99%=$400k)

  20. Firm size cumulative: unweighted & emp weighted Median= 3 employees Median= 1000 employees

  21. The SSA database Non-parametric results on inequality Basic result Robustness of the basic result Top 1% Firm size (Mega firms (≥10k employees) and the rest) More formal econometric approach (AKM and CHK) Why is this happening - the changing structure of firms Outline

  22. Income data is multi dimensional so various ways of showing a time trend in inequality Decomposition of variance Percentiles over time Change by initial percentile I will show each in turn, all with the same message that the majority of the increase in inequality is between firms

  23. 1. Decomposition of Variance

  24. Decomposition of Variance – about 25% increase is within firms (75% between firms)

  25. 2. Percentiles over time: the classic inequality figure Individuals Note: Change in log real annual earnings by employee earnings percentile

  26. Now with average firm earnings for each individual Their Firms Note: Change in average log real annual firm earnings by employee’s earnings percentile

  27. Now with individual/(average firm) earnings Individuals/Their Firms Note: Change (log employee – average firm) earnings by employee’s earnings percentile

  28. 3. The cross-sectional change 1981-2013 Note: Change in average log real annual pay for individuals in each percentile

  29. 3. The cross-sectional change 1981-2013 Note: Change in average log real pay for firms employing individuals in each percentile

  30. 3. The cross-sectional change 1981-2013

  31. The SSA database Non-parametric results on inequality Basic result Robustness of the basic result Top 1% Firm size (Mega firms (≥10k employees) and the rest) More formal econometric approach (AKM and CHK) Why is this happening - the changing structure of firms Outline

  32. Robustness: different sub-groups look similar Location (region and county) Gender Age Industry (SIC 1-digit and SIC 4-digit average) Continuing firm 5-year earnings Minimum earnings cutoff Healthcare

  33. Similar by region West North East South Mid West

  34. Within county (after removing county average)

  35. Similar by 1-digit SIC industry Manufacturing Utilities Services Finance, Insurance & Real Estate Retail and Wholesale Ag, Mining and Construction

  36. Similar using 5-year average earnings

  37. Suggests similar if we include firm healthcare Source: Burkhauser and Simon (2010, NBER WP15811), data for 2008

  38. The SSA database Non-parametric results on inequality Basic result Robustness of the basic result Top 1% Firm size (Mega firms (≥10k employees) and the rest) More formal econometric approach (AKM and CHK) Why is this happening - the changing structure of firms Outline

  39. Zooming in on the top 1% +470% +225% +210%

  40. S&P 1500 CEOs are part of this trend – but there are only 1500 of them… +330% (2014 vs 1993) Source: Execucomp, in real 2013 $ values, salary+bonus+stockgrants+options+ltips.

  41. Although some of them are paid amazing amounts so attract plenty of media attention $3.3bn (2013) $0.5bn (2007) $0.4bn (2011) $0.3bn (2006)

  42. The SSA database Non-parametric results on inequality Basic result Robustness of the basic result Top 1% Firm size (Mega firms (≥10k employees) and the rest) More formal econometric approach (AKM and CHK) Why is this happening - the changing structure of firms Outline

  43. Decomp. of variance: firms20≤employees<10k 90% of the increase in inequality is between firms

  44. Decomp. of variance: firms10k≤employees 50% increase is between firms

  45. Why has inequality risen in large firms?Driven by changes at both the top end (top 1% of earners) and bottom end (lower 50% of earners)

  46. Firms 100≤employees<1k, percentiles since 1981 +49% +30%

  47. Firms 10k≤employees, percentiles since 1981 +245% +51% -5%

  48. Top end: likely to be partly stock grants & options

  49. Bottom End: part of this appears to be low-skill wage premium in largest firms is vanishing High-school (or less) pay premiums in larger firms College (or more) pay premiums in larger firms

  50. The SSA database Non-parametric results on inequality More formal econometric approach (AKM and CHK) Why is this happening - the changing structure of firms Outline

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