1 new technologies and inequality within u.s. occupations peter b. meyer us bureau of labor...

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New technologies and inequality New technologies and inequality within U.S. occupationswithin U.S. occupations

Peter B. MeyerPeter B. MeyerUS Bureau of Labor StatisticsUS Bureau of Labor Statistics

(but none of this represents official measurement or policy;(but none of this represents official measurement or policy;Views and findings are those of the author not the agency)Views and findings are those of the author not the agency)

SHOT, Lisbon, Oct 12, 2008SHOT, Lisbon, Oct 12, 2008

Outline1.1. Technological opportunity, uncertainty, and Technological opportunity, uncertainty, and

turbulenceturbulence2.2. Measures of inequality by occupationMeasures of inequality by occupation3.3. “ “Superstars” phenomenonSuperstars” phenomenon4.4. Discussion / summaryDiscussion / summary

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Technological opportunity, uncertainty and turbulence

In the field of semiconductors and computers since 1960 there has been great opportunity to create new goods and improve old ones.

Technological uncertainty means not knowing exactly what the future production technology will be.

Intense creation and destruction in this environment Explosive business successes happen Roles, skills, and businesses and can obsolesce rapidly. Occupations are conceptually expanded.

These can increase inequality-within-occupation I’m studying measures of that, over time

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An environment with uncertainty and gambling

Big business and technical opportunities appear. Novel successes redefine the IT field and expand its relevance across society. Computer hardware categories: Minicomputers and disk drives, 1960s.

Personal computer market 1970s; Networks, laptops, CDs, handhelds, touchscreens.

Software: microcomputer operating systems, spreadsheets, word processors, desktop publishing, GUIs, computer graphics.

The Web, e-commerce, social software: Ebay, Yahoo, Amazon, Google, downloads

Mobile phones, now with third-party hardware and software New technologies drive down the value of previous jobs roles. In the IT fields, such events are chronic, or recurring. These events add noise -- risky-gambling outcomes to income

distribution. Maybe there are bursts of higher income inequality. The zone over which IT designers are relevant has expanded, and

so has the diversity of their work. “Electrical engineers” cover not only power but also chip design using software

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56

78

1960 1980 2000 2020

Electrical engineers

90%ile ln-wkly earned $, Census 10%ile ln-wkly earned $, Census

90%ile ln-wkly earned $, CPS 10%ile ln-wkly earned $, CPS

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Graphs by Occupation

Inequality within an occupation

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0020

0030

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1960 1980 2000 2020

Electrical engineers

90%ile wkly earned $, Census 10%ile wkly earned $, Census

90%ile wkly earned $, CPS 10%ile wkly earned $, CPS

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Income data are from 10-year U.S. Census (1960-2000), and annual “March CPS” (1968-2006). We have salary+self-employment income, with high incomes censored (“top-coded”). We want stock options income too, but don’t have it. At left, top lines have the 90th percentile monthly income. The bottom lines have the 10th percentile monthly income. The growth of incomes creates a big trend that looks like more inequality. It is standard in this field to study log-incomes instead as at right.

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The “Moore’s Law occupations”

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67

84

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1960 1980 2000 2020

1960 1980 2000 20201960 1980 2000 2020

Electrical engineers Computer analyst/admin/scientistsElectrical eng technicians

Software developers Data processing equipment repairers



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Background problem: Occupation categories 1960-

present US Census changes occupation categories every decade So long-term comparisons are difficult to arrange. This study 387 harmonized occupation categories for 1960-to-

present defined by Meyer and Osborne (2005). Those categories are based on the 1990 Census definitions and

usually assign a best-match from the occupations in other Censuses. Categories do not extend the whole period

More work is needed to improve those occupation-assignments, using other data on the respondents.

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“Superstars” occupation idea

Imagine 100 separate local markets with one musician each musicians have similar wages

Now radio, tapes, CDs, downloads are invented. This puts them in one “market for music services”.

In unified market, the musicians now compete with one another. A few become “stars”, whose income rises. Others are outcompeted and incomes don’t rise. Inequality of wages rises.

This was algebraically modeled by Rosen (1981) as an effect of - Imperfect substitutability (in quality or type)- Joint consumption of services (e.g. by broadcasting)

Expanding markets from invention and globalization raise inequality in some jobs because small variations in worker can have a big effect on market share.

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Superstars effect – some evidence

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68

1960 1980 2000 2020

Athletes, sports instructors, referees



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24

68

1960 1980 2000 2020

Art and craft makers



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34

56

7

1960 1980 2000 2020

Photographers



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24

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1960 1980 2000 2020

Musicians and composers



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“Media-amplified” occupations

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0-5

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10

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51

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1960 1980 2000 2020 1960 1980 2000 2020

1960 1980 2000 2020 1960 1980 2000 2020

Writers and authors Designers Musicians and composers Actors, directors, and producers

Art and craft makers Photographers Dancers Art and entertainment performers

Editors and reporters Athletes, sports instructors, referees



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Nurturing occupations; “care work” Many occupations experience technological uncertainty or

superstars effects a little. Can we examine occupations at the other extreme? England et al defined “care work” occupations, in which there is

one-on-one care/nurturing of the recipient. We do see the opposite effect. In these occupations the income

distribution is slightly compressing over time.

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1960 1980 2000 2020

Registered nurses



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There are many “nurturing occupations”

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1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020

1960 1980 2000 2020 1960 1980 2000 2020

Physicians Dentists Optometrists Podiatrists Other health and therapy jobs Registered nurses Respiratory therapists

Occupational therapists Physical therapists Speech therapists Therapists, n.e.c. Physicians' assistantsEarth, environmental, and marine science instructorsBiology instructors

Chemistry instructors Physics instructors Psychology instructors Economics instructors History instructors Sociology instructors Engineering instructors

Math instructors Education instructors Law instructors Theology instructors Home economics instructors Humanities instructors Other academic subject instructors

Kindergarten and earlier school teachersPrimary school teachers Secondary school teachers Special education teachers Other teachers, pre-collegeVocational and educational counselors Librarians

Social workers Recreation workers Clergy and religious workers Dental hygenists Licensed practical nurses 387 Dental assistants

Health aides, except nursing Child care workers



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For large scale analysis, use linear regressions

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Method for large scale analysis:

linear regressions Find what predicts “inequality within an occupation-year” Inequality is measured by the standard deviation of log-

incomes These categories predict trends in that variable.

Holding levels of inequality by year and job fixed, the trends are: Inequality rose over time in media-amplified jobs (9 of them) Rose slightly over time in high-tech jobs (5 of them) Fell slightly over time in the care-work jobs (~30) Was very slightly falling in the (~330) remaining occupations.

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Can reject some alternative hypotheses

Maybe technical jobs in general had rising inequality? No, as a group they don’t. The Moore’s Law ones do.

Maybe professionals at large experienced a superstars effect? Not much. Doctors, lawyers, and managers for example did not

experience rising inequality as strongly as the media-amplified occupations.

Maybe inequality trends between industries are strong? In my past research – no. Why? Industries have similar occupation

mixes; they all have secretaries and accountants and sales. Workers can jump between industries relatively easily which maintains an equilibrium of wages.

Is years-of-education a strong predictor of this? I don’t think so. Different paradigm. Education levels of Bill Gates,

Steve Jobs, etc, is not very relevant. Education content changes over time. Education signals ability as well as improving ability.

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Tentative findingsTentative findingsOccupations in general have stable levels of inequality• In occupations designing or fixing Moore’s Law devices, inequality rose over time• Media-amplified occupations had rising earnings inequality• “Care work” occupations declined in inequality.

ImplicationsImplications• Waves of new technology raise earnings inequality (temporarily?)• Inequality changes may measure how fast an economy adapts to technological uncertainty and opportunity.• Such findings can support narratives treating IT field as distinctive.• IT-historical people & firms affect, and are affected by, these forces.

1 new technologies and inequality within u.s. occupations peter b. meyer us bureau of labor...

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