Précis
Measures and models
Cancer survivors keep working
How can an economy, one as dynamic as the U.S. economy has been throughout the past 12 months, be measured and modeled? In his remarks before the National Association for Business Economics, Alan Greenspan provides some suggestions on meeting the challenge: "Should we endeavor to continue to refine our techniques of deriving maximum information from an existing body of data? Or should we find ways to augment our data library to gain better insight into how our economy is functioning? Obviously, we should do both, but I suspect greater payoffs will come from more data than from more technique." He acknowledges that U.S. statistical systems are "world class…and set the world standard." Yet, the time has arrived to implement even more statistical resources to better understand "the complexities of the newer technologies that confront analysts."
The types of problems that these analysts must confront relate to the notion of a unit of output, and exactly how an economist defines output. For decades, any attempt to define output—and with it, price—centered on the product produced. For example, "an average price of hot rolled steel and a corresponding total tonnage was precise enough for most analytic needs. By the same token, tons of steel per work hour in a rolling mill yielded rough approximations of underlying productivity for most purposes."
Chairman Greenspan believes that a new model may be necessary to answer the question of output in today’s ever-changing economy. Using computer software as an example, we see that the issues have grown more complex, for while a dollar value can be applied to the application, when the economist compares software-application values over time, how much of the change is related to volume and how much to price? "The answer… requires judgments about very fundamental issues in measurement…. Problems that were always latent in defining steel prices and quantities but rarely rose to this level of significance are threatening to seriously challenge our measurement systems in the age of the microprocessor, fiber optics, and the laser."
Another area of pricing he discusses is surgical procedures and the best way "to capture changes in the mix of inputs used to treat a given disease." As an example, he cites the changes in inpatient and outpatient procedures and how earlier techniques of measuring prices no longer seem to be able to capture "the appropriate degree of productivity advance in medicine." The old ways of measuring no longer seem valid, particularly the price deflators currently employed, and Chairman Greenspan notes that progress is visible, yet challenges remain.
He concludes with a thought about just what is actually being measured. "The measured characteristics may be acting only as proxies for the qualities of the services that buyers ultimately value. This...raises the difficult question of whether the correct approach may be to move toward directly pricing the services we obtain from our information processing systems rather than pricing the individual hardware components and the software."
When people take ill with a serious disease, the last thing they need to worry about is losing their jobs. In "Breast Cancer Survival, Work, and Earnings," (National Bureau of Economic Research Working Paper 8134), Cathy J. Bradley, Heather Bednarek, and David Neumark study whether employers discriminate against cancer survivors and if they do so, then why; whether health effects, motivational effects, or other incentives related to health insurance retention cause labor supply to shift; and what policies might mediate some of the more negative outcomes for cancer survivors. The last is particularly interesting for it can compel people to remain in jobs that they are ill-suited for or do not like, for the primary purpose of maintaining health insurance.
The authors use data from the first wave of the Health and Retirement Study to determine if breast cancer influences labor market decisions and outcomes. They also supplement the data with an additional sample of cancer survivors from Detroit, Michigan, and a comparison sample from Detroit residents who responded to the 1999 March supplement of the Current Population Survey (CPS).
What they found was surprising. Women who have survived breast cancer tend to work longer hours than women who have not had cancer, a pattern that continues in subsequent years. They do not find any evidence to support that this continued working is due to a lack of health insurance. These women also often earn more, which may be a result of the increased working hours. Why should these women choose to continue their careers? The authors posit: "By virtue of being a ‘survivor’ women could approach their careers with more vigor than they had previously had."
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