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American Inequality in Historical Perspective Economics 2333 Class 10 Spring 2014 Professor Robert A. Margo.

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Presentation on theme: "American Inequality in Historical Perspective Economics 2333 Class 10 Spring 2014 Professor Robert A. Margo."— Presentation transcript:

1 American Inequality in Historical Perspective Economics 2333 Class 10 Spring 2014 Professor Robert A. Margo

2 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

3 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.

4 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.












16 The Race Between Technology and Education New technology is frequently “embodied” in capital goods (e.g. computers). In 20 th 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 20 th century in the US, relative supply of skilled labor grew more quickly than demand in the first half of the 20 th 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 20 th 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.




20 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.

21 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.

22 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.

23 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.

24 Katz-Margo: Evidence for 19 th 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.

25 Was Technical Change “De-Skilling” in C19 America? Occupation Distributions Evidence from manufacturing censuses suggests 19 th 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.

26 Results: Occupation Distributions for 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.




30 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.

31 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.

32 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 at About same places in 1870s as in the 1820s

33 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

34 Table 6A: Occupational Change from 1920 to 2010 Aggregate Economy, Civilian Employment, 16+ Skill Groups1920194019601980199020002010 High Skill (Prof/Tech/Manager)12.415.720.727.833.337.639.4 Middle Skill (Clerical/Sales/Farmer/Craft) 43.638.941.039.336.934.631.6 Low Skill (Operative/Laborer/Farm Laborer/Service) 44.145.438.332.929.927.729.0

35 Table 6B: Occupational Change from 1920 to 2010 Manufacturing, Civilian Employment, 16+ Occupational Groups1920194019601980199020002010 White Collar14.821.528.433.539.341.545.6 High Skill (Prof/Tech/Manager)6.17.913.018.123.927.631.7 Clerical/Sales8.713.615.415.515.413.9 Skilled Blue Collar (Craft)24.818.920.119.319.018.015.8 Middle Skill (Clerical/Sales/Craft)33.532.535.534.834.431.929.7 Low Skill (Operatives/Laborers/Service) 60.459.551.447.241.740.538.6

36 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 19 th century race was slightly won by technology like second half of 20 th century, and unlike first half of 20 th century.

37 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.

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39 39 Trends in wage dispersion

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43 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

44 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)

45 Sample Summary Statistics 1936-2005 Total # of person-year observations15883 Total # of executives2862 Average # of firms in each year76 Average # of years each executive is observed5.6 Fraction CEO, president or chairman of the board47.5% Fraction director84.7% Fraction of observations in firms with market value: Ranked 1-5039.0% Ranked 50-10019.6% Ranked 100-20019.1% Ranked 200+22.1%

46 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

47 Median Real Value of Total Compensation, 1936-2005

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

49 Average and Median Total Compensation, 1936-2005

50 Distribution of Total Compensation, 1936-2005 10 th percentile 25 th percentile 50 th percentile 75 th percentile 90 th percentile

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

52 Total Compensation and S&P Index 1936-2005

53 Total Compensation in the Firm Relative to its Market Value and Sales, 1936-2005

54 Correlation of Compensation and Firm Size Where S jt is firm size measured by the firm’s market value.

55 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 1945-751976-051945-751976-051945-751976-051945-751976-05 (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 FENo Yes No Time TrendNo Yes No No. obs.69446938694469386944693853285213

56 Total Compensation and S&P Index 1936-2005

57 Total Compensation in the Firm Relative to its Market Value and Sales, 1936-2005

58 Correlation of Compensation and Firm Size Where S jt is firm size measured by the firm’s market value.

59 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 1945-751976-051945-751976-051945-751976-051945-751976-05 (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 FENo Yes No Time TrendNo Yes No No. obs.69446938694469386944693853285213

60 Fraction of Executives Granted and Holding Stock Options

61 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

62 How to measure pay-to-performance? Jensen-Murphy statisticValue of equity stakes Definitiondollar 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 planerestructuring the company Estimation strategy Or ex-ante revaluation using Core & Guay measure Correlation with firm sizeNegative (Schaeffer 1998) Positive (Baker & Hall 2004)

63 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 0.4520.6750.4700.551 0.9461.08 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 18,6706,81413,97538,97821,74334,679120,342227,881

64 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 I t 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)

65 Size-Adjusted Pay-to-Performance Correlations

66 The Strength of Managerial Incentives: Wealth at Stake for a Change in Firm Performance from 50 th to 70 th Percentile Median Across Executives Dollar change in wealth for moving from 50 th to 70 th percentile in distribution of firms rate of return Percent change in wealth = (1) / (total comp + change in wealth at 50 th rate of return) Elasticity = (2) / (rate return 70 th – rate return 50 th ) (1)(2)(3) 1930s265,27028.62.00 1940s96,9479.20.64 1950s199,04622.91.60 1960s556,24750.73.54 1970s312,559292.03 1980s496,26627.61.92 1990s1,720,95352.33.66 2000s3,212,822594.12

67 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

68 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

69 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.

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