1 New technologies and inequality within U.S. occupations Peter B. Meyer US Bureau of Labor Statistics (but none of this represents official measurement or policy; Views and findings are those of the author not the agency) SHOT, Lisbon, Oct 12, 2008 Outline 1. Technological opportunity, uncertainty, and turbulence 2. Measures of inequality by occupation 3. “Superstars” phenomenon 4. Discussion / summary

2 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

3 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

4 Inequality within an occupation Income data are from 10-year U.S. Census ( ), and annual “March CPS” ( ). 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 90 th percentile monthly income. The bottom lines have the 10 th 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.

5 The “Moore’s Law occupations”

6 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.

7 “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.

8 Superstars effect – some evidence

9 “Media-amplified” occupations

10 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.

11 There are many “nurturing occupations” For large scale analysis, use linear regressions

12 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.

13 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.

14 Tentative findings Occupations 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.Implications 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.

15 Waves of new technology can raise earnings inequality Hypotheses in this paper:  Occupations experiencing strong technological uncertainty had rising earnings inequality [yes]  Media-amplified occupations had rising earnings inequality [yes]  Nurturing occupations did not [yes] By the definitions here, we can distinguish a class of occupations which experience superstars effects and another class experiencing intense technical change. The occupational definitions are subject to debate. Technological change / uncertainty involves turbulence to certain kinds of workers  Big opportunities  Rapidly depreciating opportunities  Qualitative change  Expansion of the kinds of work in the occupation  Breakdown of pre-existing product, industry, and occupation categories

16 Can standardize occupation categories over time that look like recent ones? Metric: Consider this class of dependent variables: Inequality of work-related income within an occupation-period (by various measures of income, inequality, occupation, and time) Turbulence, uncertainty, and opportunity should add noise/dispersion to this distribution. If the sector expands qualitatively or quantitatively this inequality measure should rise. Test: If the members of that occupation face uncertainty, obsolescence, and new opportunities, that distribution could widen as the workers expand their zone of qualitative work.

17 Discussed by Dosi (1988), Rosenberg (1996), and others, but not much in formal models or statistics. It’s a state in which people don’t agree on forecasts of the future technology of production.  Technology is changing quickly  The players have imperfect knowledge  Are positioned according to history and institutions Efforts to experiment with the technology are chancy. If they are gambles, this condition is a source of noise and turbulence in productivity, profits, asset prices, wages, etc. As in dot-com boom. Mechanisms include:  Big opportunities appear, in disequilibrium  Opportunities evaporate quickly  Content of work changes qualitatively Regressions here look for evidence in wages in those occupations that involve working with novel, incomplete, or malfunctioning semiconductor and computer systems

18 Current Population Surveys, (“CPS”) Decennial Censuses , from IPUMS. Occupation categories come from Meyer and Osborne (2004).  Is similar to IPUMS occ1950 classification  But centered on 1990’s Census list  And is still expanding– some observations from 1960 and from 2000 are not in the system yet Earnings are measured by wage and salary income, not self-employment or stock options.

19 These effects don’t seem closely related to the USE of computers in special 1984 CPS survey. Technologically uncertain job-holders are doing us all a service by adapting truly new technology, and squaring off again the unknowns. Adapting to Moore’s Law and other radical technological changes is stressful and productive in the aggregate. Some economies respond quickly to technological change compared to others. Adaptation to uncertainty through opportunism seems faster than other ways. Modeling: Skill bias models attribute the dispersion to differences among workers, exaggerated by technology change. The discussion illustrates other dimensions:  Workers are positioned institutionally, partly at random, in firms, locations, occupations.  There are special kinds of noise in the wage process for the high tech workers.  Opportunities are bigger and briefer than in other jobs. Greenwood-Yorukoglu (1997) model seems to work best but there is no great fit.

20 By the definitions here, we can distinguish a class of occupations which experience superstars effects and another class experiencing intense technical change. The occupational definitions are subject to debate. Technological change / uncertainty involves turbulence to certain kinds of workers  Big opportunities  Rapidly depreciating opportunities  Qualitative change  Expansion of the kinds of work in the occupation  Breakdown of pre-existing product, industry, and occupation categories Randomness, positioning, and institutional constraints are involved.