Knowledge that Transforms

The impending elimination of mandatory retirement for tenured faculty on January 1, 1994 (Public Law 99-592, 1986), has raised the visibility of a number of questions about faculty retirement behavior. What are the factors that influence individual faculty members' retirement decisions? How important are financial and nonfinancial considerations? Why do faculty in private institutions work to a later age, on average, than their colleagues in public institutions? Are there other systematic (for example, genderor discipline-related) differences as well? This paper is an attempt to answer such questions about individual faculty retirement behavior. The paper utilizes data collected as part of a comprehensive national study that projected faculty retirements through the year 2003 for over 35,000 faculty at 101 doctoral research, comprehensive, and general baccalaureate institutions (see our 1990 paper). The discussion that follows uses data from that broader institutional survey and from a survey of 747 faculty members age 55 and over who had separated from this same set of 101 institutions. Among the information provided by the 518 usable responses were data on factors that influence faculty members' decisions regarding the appropriate time to retire.

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Gary Becker and Kevin Murphy (1988) present a theoretical model of rational addiction that requires information on past, present and future prices. They do not study illegal drugs empirically in this and their related papers. A major data source is the annual survey of high school seniors (HSS), also known as Monitoring the Future. This work is summarized in L. Johnston et al. (1988). They find a growing use of drugs (measured by monthly, annual, and lifetime prevalence) over time, and differences by region and sex. Cocaine use showed marked increases from 1976, though this levelled off from 1986 to 1987. They often rely on cross-tabs that leaves many variables uncontrolled and the results subject to omitted variable bias. J. Bachman et al. (1984) use ordinary least squares (OLS) regressions in which the drug use in the three years post-high school is related to various characteristics such as living arrangements. While an improvement over cross-tabs, OLS applied to categorical dependent variables yields inefficient estimates (see M. Nerlove and S. Press, 1973). Johnston et al. used follow-up surveys of a subsample drawn from each cohort. They find the use of some drugs decline at older ages, say 35, though they do not determine if the heavy users have died, dropped of the sample, or have been rehabilitated.' The HSS's initial restriction to high school seniors removes about 30 percent of the population who drop of high school perhaps because of taking drugs. Studies based on the National Institute of Drug Abuse (NIDA) sample of people 12 and older show some heavy drug use of people less than 17 years old. (See J. D. Miller et al., 1982, and NIDA, 1985.) Richard Clayton (1985) using the 1980 HSS presents univariate regressions that shows the frequency of use of cocaine is positively related to lifetime marijuana use and days of school in the past month, but negatively related to high school grade point average. H. Abelson and Miller (1985), using the NIDA surveys covering 1974-82, show differences in the percentage using cocaine by education and race-for lifetime, 12 months, and last month measures. They find strong trends. D. Kandel (1980) presents a recent survey of drinking and drug use among youth. People in their late 30's mature out of heroin use rather than die.

This paper reexamines observed international differences in the price of services and the positive correlation of these with per capita GDP differences. Using a general trade model with a nontraced sector, the authors find that differences in countries' factor endowments, populations, trade policies, and trade balances will have ambiguous and sometimes opposite effects on their service prices and real incomes. Estimating the service-price equation using recent data suggests that larger endowments of agricultural land, minerals, and capital, larger trade deficits, and higher prices for tradables increase service prices. Conversely, larger populations and labor forces reduce service prices.