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, and there is some relation to new information technology There is also a general historical question about whether and how new technologies affect earnings inequality Hypotheses in this paper: – tech uncertainty raises earnings inequality [yes] –superstars effect [yes] –other occupations exhibit these some too –but nurturing and physical production work do not

Technological uncertainty 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 Discussed by Dosi (1988), Rosenberg (1996), and others, but not much in formal models or statistics. It follows from this condition that efforts to experiment with the technology are chancy. They are gambles. Likewise a casino would generate sudden “income inequality”. 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.

Superstars idea Imagine 100 similar villages with one musician each in separate markets => musicians have similar wages Now changes in communications and transportation unify the villages into one market for music services, with tapes, CDs, downloads, bigger concerts Same villages, same musicians, in one market puts them in competition with one another. A few become “stars”. Others are outcompeted and fall to the bottom. This was modeled by Rosen (1981) as an effect of - Imperfect substitutability (in quality or type) - Joint consumption of services (through manufacture or transmission) Expanding markets raise inequality because small variations could have a big effect on market share. Regressions here will attempt to detect this on those occupations that involve working with novel, incomplete, or malfunctioning computer systems.

Data sources and variables Current Population Surveys, (“CPS”) Decennial Censuses , from IPUMS. Inequality measured by coefficient of variation of wage-and-salary earnings within an occupation Occupation categories come from Meyer and Osborne (2004). –Which is still expanding –Is similar to IPUMS occ1950 classification –but centered on 1990’s Census list.

Proximate sources of uncertainty Rapid price declines Price volatility New programming languages Object-oriented languages Concept of programming in hardware Opportunities therefore appear and evaporate –Original opportunity to be Apple –Or Microsoft / Bill Gates –Or Ebay, Yahoo, Amazon

Discussion These effects don’t seem closely related to the USE of computers in special 1984 CPS survey. this helps us document that the work of adapting to moore’s law and other radical technological changes is detectable and stressful. If that could be measured / systematize we’d have a kind of economic measure of the existence of novelty and uncertainty. Technologically uncertain job-holders are doing us all a service by adapting truly new technology, and squaring off again the unknowns. Some economies respond quickly to technological change compared to others. This kind of adaptation may be essential to being quick about it. There exists models of wage or price dispersion in which workers vary in ability/skill/competence. an alternative here is that the environment is noisy, or that the workers are positioned partly at random. Greenwood-Yorukoglu (1997) model seems to work best but there is no great fit.

Extended occupational category system