American Inequality in Historical Perspective

Economics 2333 Class 10 Spring 2014Professor Robert A. Margo

General Outline

• Background• Goldin and Katz: The “Race” Between

Technology and Education• Katz and Margo: A Unified View of the Relative

Demand for Skills Across the C19 and C20• Goldin and Margo: The Great Compression of

the 1940s• If time: Frydman and Saks on executive pay

Background: American Inequality in Historical Perspective

• Approaches to inequality: functional versus individual (or household) distribution.• Functional: factor shares. For most of American history, little change in labor’s

share (slight increase). Major change was in land vs. capital – land’s share decreased from C19 to C20, capital’s share increased. BUT recently capital’s share has increased – see new book by Thomas Piketty.

• Individual (household) distribution: human capital (Goldin and Katz), gender/race/ethnic discrimination, institutional factors (unions, minimum wage).

• Kuznets curve (AER 1955) – inequality vs. per capita income follows an inverted U. Model involves shift of labor out of agriculture. Inequality is much higher in non-farm economy because of diversity of skills and (initially) inelastic supplies of skills. As labor shifts out of agriculture, inequality increases. Supply of skills catches up and government may engage in redistribution, producing downward segment of the Kuznets inverted U.

• Large literature in development on the Kuznets curve. Historical literature is mostly Williamson and various co-authors.

Summary: Broad History of US Inequality

• C19: Williamson and Lindert argue that inequality rises, starting around 1820. Evidence is drawn from available wage series by occupation and also wealth data.

• According to WL, skill premium (artisan/common labor wage ratio) increased sharply from 1820 to 1860. However, errors in WL (see Margo 2000). Archival wage series by Margo (2000) finds decrease or no trend in artisan/unskilled wage ratio and slight increase in clerk/unskilled; see below.

• Wealth inequality does appear to increase over the C19 but further work is needed.• VERY important: previous work on C19 ignores slaves and Native Americans before Civil

War BUT post Civil War data will include former slaves (and Native Americans). New project by Williamson and Lindert attempts to correct for this but not ready for prime time, IMHO.

• In C20, inequality declines in the first half of the century and then rises in the second half. Based on occupational wage data, Iowa state census of 1915, 1940-present IPUMS and CPS, IRS tax records, SSA master file, SEC filings (Frydman and Saks).

• Moral: not a Kuznets curve, but possibly a cycle. Cycle appears more dramatic in the C20 than in the C19.

The Race Between Technology and Education

• New technology is frequently “embodied” in capital goods (e.g. computers). In 20th century capital and skilled (educated) labor are relative complements. Fall in price of capital → increase in relative demand for skilled labor.

• If relative supply of skill keeps up, relative wage of skilled labor won’t change. But if supply lags behind demand, relative wage of skilled labor increases.

• Important book by Goldin and Katz (2008) shows that, over the course of the 20th century in the US, relative supply of skilled labor grew more quickly than demand in the first half of the 20th century, but slower in the second half. So, relative wage of skilled labor fell during first half, but rose in second. Rise in second is part of the rise of the “one percent”.

• Relative demand for skilled labor increased in every decade of the 20th century at about the same pace, except for the 1940s when it was slower. More on the 1940s later.

• Goldin and Katz argue that capital-skill complements originates with diffusion of electrical power. Electricity permits many new skill intensive technologies and also eliminates many unskilled jobs on the shop floor.

Main Points, Katz and Margo• Standard economic history/labor history view: Technology

and “skill” are relative complements in C20/C21 but capital deepening is “de-skilling” in C19.

• KM emphasize the continuity between the first (C19) and second “industrial revolution” (C20) in US economic development.

• Link (informal): task-based framework of Acemoglu-Autor. In both IRs new technology embodied in capital goods alters the allocation of labor of different levels of education/skill to tasks. This alters the relative demand for different levels of education/skill.

Deep Background• Williamson & Lindert (1980). Assumed capital-skill complementarity in C19

manufacturing. Claimed to find evidence of an American inverted-U: rising skilled blue collar-unskilled wage premium before 1860.

• Upward ante-bellum trend in artisan/unskilled wage ratio disputed by Margo (2000) who finds upward trend in white collar/unskilled wage premium before Civil War.

• James & Skinner (1983). Capital complementary to natural resources but not skill.

• Goldin & Sokoloff (1982): relative use of women and children increases with establishment size, 1820-1850.

• Standard labor history perspective: diffusion of factory system is “de-skilling”: percent artisan ↓. Atack, Bateman, and Margo (2004) show negative relationship between average establishment wage and size.

• Goldin & Katz (1998): origins of technology-skill complementarity for production workers found in diffusion of electrical power. Wage-firm size relationship turns positive.

Task-Based Perspective I• Dramatic capital-deepening in C19 century manufacturing. Takes

the form of “special purpose, sequentially-implemented” machinery. • Translation: machine (partially) substitutes for skilled artisan in

production. Today, the computer substitutes for mid-level white collar.

• BUT skilled labor designs the machines and skilled labor installs and maintains the machines. Eg. Steam-powered plants hire “machinists” and/or “engineers”. Today, IT designs and implements software, maintains system.

• In C19 firms become much larger. Why? Improved transportation (Adam Smith) plus mechanization (steam). Impact on white collar demand.

Task-Based Perspective II• Prediction (from slightly modified Goldin and

Katz 1998 framework): – As firms become bigger from access to cheaper

motive power (steam power), % skilled artisan ↓ and % operative ↑.

– BUT as firms get bigger, managerial tasks increase in number & complexity & % white collar (arguably) ↑.

– Division of labor in manufacturing “hollows” out the middle (skilled blue collar) in favor of the low-skill (operative) and high-skill (white collar) jobs.

Katz-Margo: Evidence for 19th Century Manufacturing

• In manufacturing, labor shifts towards larger, more capital intensive establishments. Firm size increases, economies of scale.

• Using 1850-80 censuses of manufacturing, KM show that capital intensive, steam-powered establishments used relatively more unskilled labor. Effect of steam/capital is mostly explained by larger firm size. Indirect implication is that capital deepening encouraged more division of labor in production.

Was Technical Change “De-Skilling” in C19 America? Occupation Distributions

• Evidence from manufacturing censuses suggests 19th century technical change was “de-skilling” but does not directly address white collar skills.

• KM look at occupation by industry starting in 1850. Compute a very broad occupation distribution for manufacturing – white collar, skilled artisan, and operative/unskilled – and somewhat more detailed classification for overall economy.

Results: Occupation Distributionsfor 1850 to 1910

• Manufacturing: clear evidence of “hollowing-out”. Share artisan decreases, while share white collar and share operative/unskilled increase.

• Overall: (a) no long term trend in share artisan (or, modest U-shape) (b) share white collar increases (c) share operative/unskilled decreases.

• Why the difference? (a) manufacturing sector grows, relatively intensive in artisan labor despite hollowing out (b) construction sector is an increasing share of GNP, intensive in artisan labor (c) share operative/unskilled rises in manufacturing but declines in overall economy because of shift of labor out of agriculture.

• Bottom line #1: C19 manufacturing was “de-skilling” in the artisan sense but not white-collar sense. Makes sense in task framework.

• Bottom line #2: NO de-skilling in the aggregate economy. Increase in relative demand for educated (white collar) labor extends backward in time from Goldin-Katz to at least 1850.

Supply vs. Demand: Relative Wages• Relative wage of educated labor declines first half of C20,

rises during second half (Goldin and Katz 2008). Pattern is due to supply shifts; relative demand for skilled/educated labor increases rapidly & steadily.

• What about C19? Cannot say about returns to schooling directly because C19 censuses only collected data on literacy and nothing on earnings/income.

• KM use archival data to construct new time series of wages for unskilled labor, artisans, and white collar workers. Evidence suggests that relative wage of white collar workers increases, flat for artisans.

New Wage Series

• We extend Margo (2000) by presenting national wage series for common labor, artisans and white collar for 1866-1880.

• Same data source (Reports of Persons and Articles Hired) and similar methods of construction (hedonic wage regressions)

• Caveat: series are first produced at region-occupation level and then aggregated using region-occupation weights. Some inconsistencies in weighting pre vs. post-bellum.

White Collar (Clerk) Earnings Increase relative to Common Laborers and Artisans from 1820s to 1870s

U-shaped earnings of Artisans relative to Common Laborer ends up atAbout same places in 1870s as in the 1820s

Occupational Change: 1920 to 2010

• Monotonic secular skill upgrading from 1920 to 1980 in overall economy and manufacturing

• Polarization of employment growth from 1990 to 2010 – Seen in declining share of “middle skill” jobs– Continued rise in prof/managerial share– Rise in in-person service employment and low-skill

share for overall economy in 2000s

Table 6A: Occupational Change from 1920 to 2010Aggregate Economy, Civilian Employment, 16+

Skill Groups 1920 1940 1960 1980 1990 2000 2010

High Skill (Prof/Tech/Manager) 12.4 15.7 20.7 27.8 33.3 37.6 39.4

Middle Skill (Clerical/Sales/Farmer/Craft)

43.6 38.9 41.0 39.3 36.9 34.6 31.6

Low Skill (Operative/Laborer/Farm Laborer/Service)

44.1 45.4 38.3 32.9 29.9 27.7 29.0

Table 6B: Occupational Change from 1920 to 2010Manufacturing, Civilian Employment, 16+

Occupational Groups 1920 1940 1960 1980 1990 2000 2010

White Collar 14.8 21.5 28.4 33.5 39.3 41.5 45.6

High Skill (Prof/Tech/Manager) 6.1 7.9 13.0 18.1 23.9 27.6 31.7

Clerical/Sales 8.7 13.6 15.4 15.5 15.4 13.9 13.9

Skilled Blue Collar (Craft) 24.8 18.9 20.1 19.3 19.0 18.0 15.8

Middle Skill (Clerical/Sales/Craft) 33.5 32.5 35.5 34.8 34.4 31.9 29.7

Low Skill (Operatives/Laborers/Service)

60.4 59.5 51.4 47.2 41.7 40.5 38.6

Summary: Katz and Margo• Capital deepening relentless in US economic history. Alters

task assignments in production process, which affects relative demand for different types of skills.

• In C19 manufacturing capital deepening displaced tasks performed by skilled artisans for those performed by operatives + specialized machines, especially if powered by steam. Hollowing-out of occupation distribution in C19 manufacturing is conceptually similar to today (computers vs. mid-level white collar).

• There is NO de-skilling in aggregate economy post-1850 and likely earlier (but not that much earlier).

• In 19th century race was slightly won by technology like second half of 20th century, and unlike first half of 20th century.

The Great Compression• Sharp reduction in inequality in the 1940s. “Great Compression” is an

(obvious) play on “Great Depression”.• But GD does not appear to be the cause of the GC. Inequality rises in

the early 1930s but by 1940 is back to where it was at the start of the decade.

• GC reflects forces associated with WW2 and various post-war institutional changes (GI Bill, minimum wage, unions). Also some role for narrowing of geographic differences in school quality. Change in inequality is so large that it mostly remains in place until 1970.

• Important side effect of GD: narrowing of black-white income differences in 1940s, rivals change during Civil Rights movement of the 1960s (Margo 1995). Flip side is rising wage inequality since 1970s has impeded black-white convergence.

38

39

Trends in wage dispersion

40

41

42

Frydman and Saks

• Executive pay has risen sharply, absolutely and relative to the average worker in recent decades. Are the recent trends in the level and structure of executive pay unusual?

• Are the determinants of the recent trends in compensation similar to the factors that shaped compensation in earlier periods?

A competitive labor market for executives (scale)

Rent extraction by CEOs (corporate governance)

Managerial incentives (pay-to-performance)

Changes in managerial skills

New dataset on executive compensation

Executive compensation:

1936 – 1992: annual data from historical proxy statements and 10-K reports- 50 largest firms in 1940, 1960 and 1990 (total of 101 firms)- 3 highest-paid executives in each firm

1992 – 2005: annual data from Compustat’s Executive Compensation Database - also based on proxy statements- 3-highest paid executives in the same 101 firms

Other firm-level data:- Market value from CRSP - Other firm-level variables from Moody’s Manuals (1936-1950) and Compustat (1950-2005)

Sample Summary Statistics

1936-2005

Total # of person-year observations 15883Total # of executives 2862Average # of firms in each year 76Average # of years each executive is observed 5.6

Fraction CEO, president or chairman of the board

47.5%

Fraction director 84.7%

Fraction of observations in firms with market value:

Ranked 1-50 39.0%Ranked 50-100 19.6%Ranked 100-200 19.1%Ranked 200+ 22.1%

Representativeness of the Sample

Potential issue: - (Small) Unbalanced panel of firms that are successful at some point

Compare to other samples: - Forbes surveys (800 firms since 1970) - Hall & Liebman (475 firms, 1980 to 1994)

Use weighting schemes: - inversely proportional to probability of being selected among the 500 largest firms in each year - inversely proportional to firm’s market share or the firm’s share

of aggregate sales among the 500 largest firms

Other uses of our data: - firm in sample only since year of selection - use years close to 1940, 1960, and 1990 only

Conclusion: - our sample is representative of the largest 300 public firms in the economy in each year

- no significant bias for using the whole time span for each firm

Median Real Value of Total Compensation, 1936-2005

Mill

ions

of 2

000

Dol

lars

(log

sca

le)

year

salary+bonus sal.+bonus+long-term pay sal.+bonus+ltp+options granted

1940 1950 1960 1970 1980 1990 2000

1.00

2.00

3.00

4.00

5.00

Median Real Value of Total Compensation, 1936-2005

Mill

ions

of 2

000

Dol

lars

(log

sca

le)

year

salary+bonus sal.+bonus+long-term pay sal.+bonus+ltp+options granted

1940 1950 1960 1970 1980 1990 2000

1.00

2.00

3.00

4.00

5.00

Note: Based on the three highest-paid officers in the largest 50 firms in 1940, 1960 and 1990.

Average and Median Total Compensation,

1936-2005M

illio

ns o

f 200

0 D

olla

rs(lo

g sc

ale)

year

mean compensation median compensation

1940 1950 1960 1970 1980 1990 2000

1

3

5

7

91113

Distribution of Total Compensation,

1936-2005

10th percentile

25th percentile

50th percentile

75th percentile

90th percentile

Mill

ions

of 2

000

Dol

lars

(log

scal

e)

year

1940 1950 1960 1970 1980 1990 2000

1.00

5.00

10.00

20.00

30.00

Median Compensation of CEOs and Other Top Officers, 1936-2005

Mill

ions

of 2

000

Dol

lars

(log

scal

e)

year

CEOs other top officers

1940 1950 1960 1970 1980 1990 2000

2

4

6

8

10

Total Compensation and S&P Index

1936-2005M

illio

ns o

f 200

0 D

olla

rs

year

S&

P In

dex

Rel

ativ

e to

CP

I (20

00=1

)

Median Compensation (left) S&P Index (right)

1940 1950 1960 1970 1980 1990 2000

1.00

2.00

3.00

4.00

5.00

0.10

0.20

0.40

0.60

0.80

1.001.20

Total Compensation in the FirmRelative to its Market Value and Sales,

1936-2005M

edia

n A

cros

s Fi

rms (

1936

=1)

year

Relative to Market Value Relative to Total Sales

1940 1950 1960 1970 1980 1990 2000

.1

.4

.7

1

1.3

1.6

Correlation of Compensation and Firm Size

0 1

2

3

( ) ( )

( )

( ) ( ) ( )

ijt t

j

jt t j

ijt

Ln Compensation Ln S

Ln S

Ln S Ln S Ln S

Where Sjt is firm size measured by the firm’s market value.

Correlation of Compensation and Firm Size

Standard errors shown in parentheses, clustered by firm. Value in brackets show fraction of total variance explained by each independent variable. Size is measured as ln(real market value).

Dependent variable: ln(real total compensation)ijt Δln(comp.)ijt

1945-75

1976-05

1945-75

1976-05

1945-75

1976-05

1945-75

1976-05

(1) (2) (3) (4) (5) (6) (7) (8)

Average size in year t .137(.025)[.020]

.935(.035)[.332]

.134(.024)

.970(.037)

.033(.031)

.736(.082)

-- --

Average firm size .212(.032)[.164]

.292(.032)[.135]

-- -- -- -- -- --

Size - Firm avg.- Year avg.

.200(.041)[.036]

.265(.032)[.043]

-- -- -- -- -- --

Size - Year avg. -- -- .219(.040)

.313(.028)

.224(.039)

.304(.027)

-- --

Δ(Avg. size in year t) -- -- -- -- -- -- .004(.030)

.221(.077)

Δ(Size) – Δ(Year avg.) -- -- -- -- -- -- .095(.029)

.269(.035)

Firm FE No No Yes Yes Yes Yes No NoTime Trend No No No No Yes Yes No NoNo. obs. 6944 6938 6944 6938 6944 6938 5328 5213

Total Compensation and S&P Index

1936-2005M

illio

ns o

f 200

0 D

olla

rs

year

S&

P In

dex

Rel

ativ

e to

CP

I (20

00=1

)

Median Compensation (left) S&P Index (right)

1940 1950 1960 1970 1980 1990 2000

1.00

2.00

3.00

4.00

5.00

0.10

0.20

0.40

0.60

0.80

1.001.20

Total Compensation in the FirmRelative to its Market Value and Sales,

1936-2005M

edia

n A

cros

s Fi

rms (

1936

=1)

year

Relative to Market Value Relative to Total Sales

1940 1950 1960 1970 1980 1990 2000

.1

.4

.7

1

1.3

1.6

Correlation of Compensation and Firm Size

0 1

2

3

( ) ( )

( )

( ) ( ) ( )

ijt t

j

jt t j

ijt

Ln Compensation Ln S

Ln S

Ln S Ln S Ln S

Where Sjt is firm size measured by the firm’s market value.

Correlation of Compensation and Firm Size

Standard errors shown in parentheses, clustered by firm. Value in brackets show fraction of total variance explained by each independent variable. Size is measured as ln(real market value).

Dependent variable: ln(real total compensation)ijt Δln(comp.)ijt

1945-75

1976-05

1945-75

1976-05

1945-75

1976-05

1945-75

1976-05

(1) (2) (3) (4) (5) (6) (7) (8)

Average size in year t .137(.025)[.020]

.935(.035)[.332]

.134(.024)

.970(.037)

.033(.031)

.736(.082)

-- --

Average firm size .212(.032)[.164]

.292(.032)[.135]

-- -- -- -- -- --

Size - Firm avg.- Year avg.

.200(.041)[.036]

.265(.032)[.043]

-- -- -- -- -- --

Size - Year avg. -- -- .219(.040)

.313(.028)

.224(.039)

.304(.027)

-- --

Δ(Avg. size in year t) -- -- -- -- -- -- .004(.030)

.221(.077)

Δ(Size) – Δ(Year avg.) -- -- -- -- -- -- .095(.029)

.269(.035)

Firm FE No No Yes Yes Yes Yes No NoTime Trend No No No No Yes Yes No NoNo. obs. 6944 6938 6944 6938 6944 6938 5328 5213

Fraction of Executives Granted and Holding Stock Options

Frac

tion

of E

xecu

tives

year

fraction granted stock options fraction holding stock options

1940 1950 1960 1970 1980 1990 2000

0

.2

.4

.6

.8

1

Creating a Broad Measure of Compensation

Changes in executive wealth relevant for correlation between pay and firm performance

A comprehensive measure of compensation: Total direct compensation + Revaluation of Stock and Stock Option Holdings

Two methodologies to calculate revaluations: Realized change, using stock and option holdings at

beginning of year Ex-ante change, calculating the option’s delta for each

particular portfolio of stock options (Core and Guay 2002)- Ex-ante measures only useful if changes in an executive’s wealth can be approximated by the revaluations

How to measure pay-to-performance?

Jensen-Murphy statistic Value of equity stakes

Definition dollar change in executive wealth per $1,000 change in firm value

dollar change in executive wealth for a 1% change in firm value

Appropriate measure of incentives if…

managerial decisions have same dollar effect on firms of different size

managerial decisions have same percentage effect on firms of different size

Examples of managerial actions

buying a corporate plane

restructuring the company

Estimation strategy

Or ex-ante revaluation using Core & Guay measure

Or ex-ante revaluation using Core & Guay measure

Correlation with firm size

Negative (Schaeffer 1998)

Positive (Baker & Hall 2004)

ijtJMt

JMt

Exec

jt

ijt

Value)r Shareholde(

Wealth).(

ijtESt

ESt r

Exec

jt

ijt Wealth).(

Correlation of Executive Wealth with Firm Performance

Regression results are based on median regressions estimated separately for each decade. Standard errors are given in parentheses and are clustered by firm.

1936-

1939

1940-1949

1950-1959

1960-1969

1970-

1979

1980-

1989

1990-1999

2000-

2005

Dollar change in wealth for $1,000 dollar change in firm market value

Regression coef. of change in wealth

1.140(0.66)

0.380(0.121)

0.359(0.096)

0.292(0.125)

0.128(0.048)

0.258(0.072)

0.774 (0.270)

0.474(0.092)

Ex-ante revaluation of stock + option holdings

1.35 0.399 0.452 0.675 0.470 0.551

0.946 1.08

Dollar change in wealth for a 1 percent increase in firm’s rate of return

Regression coef. of change in wealth

18,075(5,122)

7,738(1,867)

23,378(2,865)

40,269(7,067)

22,822(3,710)

37,086(5,151)

135,527(22,986)

151,508(30,123)

Ex-ante revaluation of stock + option holdings

18,670

6,814 13,975 38,978 21,743

34,679

120,342

227,881

Adjusting the Correlation of Pay to Performance for Changes in Firm Size over Time

where t is a series of overlapping 4-year windows, and f(Firm Size) is a spline on the quintiles of distribution of firm size in each 4-year period, and It equals 1 for the second half of the 2-year estimation period.• Predicted change in Pay-to-Performance for each executive is the 2-year growth rate, based on the size of his firm.

• Obtain a measure of how Pay-to-Performance changed from one period to the next controlling for firm size (but not the level)

ijtjts

s

tijt FirmSizefIePerformanctoPay )(

1*100)(% s

s

ePerformanctoPay

Size-Adjusted Pay-to-Performance Correlations

Dol

lar c

hang

e in

wea

lth fo

r a1

perc

ent i

ncre

ase

in fi

rm ra

te o

f ret

urn

year

$100

0 in

crea

se in

mar

ket v

alue

Dol

lar c

hang

e in

wea

lth fo

r a

Equity at stake (left) Jensen-Murphy (right)

1940 1950 1960 1970 1980 1990 2000

4000

54000

104000

154000204000254000304000

1

3

5

7

911

The Strength of Managerial Incentives: Wealth at Stake for a Change in Firm Performance from 50th to 70th Percentile

Median Across ExecutivesDollar change in wealth for moving from 50th to

70th percentile in distribution of firms rate

of return

Percent change in wealth =

(1) / (total comp + change in wealth at 50th rate of return)

Elasticity =(2) / (rate return 70th – rate return

50th )

(1) (2) (3)1930s 265,270 28.6 2.001940s 96,947 9.2 0.64

1950s 199,046 22.9 1.601960s 556,247 50.7 3.54

1970s 312,559 29 2.03

1980s 496,266 27.6 1.921990s 1,720,953 52.3 3.66

2000s 3,212,822 59 4.12

Reassessing current theories (I)

New facts concerning the level and composition of executive pay:

► Relative stability in the level of total compensation from the mid-1930s to the mid-1970s, followed by sustained growth at an increasing rate.

► Stock options and incentive pay have been growing shares of total pay since the 1950s. These forms of pay are not unique to the 1990s, but are part of a long-run trend in the forms of managerial compensation.

These findings are hard to reconcile with rent seeking explanation as factor driving the trends in executive compensation

Sharp change in growth rate around 1970s does not match changes in managerial skills explanation

Reassessing current theories (II)

Correlation of Pay-to-Performance:► Adjusting for changes in the size of firms over time,

the correlation of executive wealth with firm performance followed a particular pattern over time: about the same level in 1950s, 1960s, and 1980s, but relatively lower in the 1930s, 1940s and 1970s. Since the 1990s, it has been higher than at any other point in the century.

► Measures of pay-to-performance have no correlation with the level of pay of the executives.

It is not obvious that recent soaring trends respond to an increase in the level of managerial incentives

Reassessing current theories (III)

The level of pay and firm size:► The correlation of compensation with the size of the

own firm has been relatively stable over time…► …but the effect of the size of the market on executive

pay has varied substantially.

A simple competitive labor market story is not sufficient to account for the long-run trends in executive compensation.