Technological uncertainty and superstardom: two sources of inequality within occupations Peter B. Meyer Office of Productivity and Technology, U.S. Bureau of Labor Statistics Jan 9, 2005 SGE session, ASSA meetings, Philadelphia

Subject: effects of new technology Earnings dispersion has risen in U.S. since 1973, partly driven by new information technology Possibly previous waves of new technology raised 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] –Physical production work did not

Technological uncertainty 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.

Superstars idea Imagine 100 separate markets with one musician each => musicians have similar wages Now radio, tapes, CDs, downloads are invented. These changes in communications and transportation unify them into one market for music services In unified market, the musicians now compete with one another. A few become “stars”. Others are outcompeted and fall to the bottom. Inequality of wages rose. This was modeled by Rosen (1981) as an effect of - Imperfect substitutability (in quality or type) - Joint consumption of services (e.g. by broadcasting) Expanding markets raise inequality because small variations could have a big effect on market share. Regressions here will attempt to detect this in media- amplified occupations.

Data sources and variables 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 –Categories do not extend the whole period Earnings are measured by wage and salary income, not self-employment or stock options.

Proximate sources of uncertainty Price volatility of components –Prices declining quickly –Availability varies Opportunities therefore appear and evaporate quickly –Original opportunity to be Apple –Microsoft / Bill Gates –Ebay, Yahoo, Amazon –To make an important invention or product Waves of entry and exit (e.g. dot-coms) High job turnover Key workers Turnover of top products – explosive sales surprises and disappointments Brief monopolies Opportunity to set engineering standards Customers don’t know what’s possible New programming languages –Object-oriented languages –Of browsers –Of hardware

Discussion 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.

Conclusion 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 Skill bias does not cover the whole rise in inequality related to technical change. Randomness, positioning, and institutional constraints are involved.