Reviews the book "The Growth Of Arthur Andersen & Co. 1928-1973. An Oral History by Leonard Spacek," edited by Paul Briggs.

In this paper, we implement a methodology to identify and measure premia in the pricing of forward foreign exchange that involves application of signal‐extraction techniques from the engineering literature. Diagnostic tests indicate that these methods are quite successful in capturing the essence of the time‐series properties of premium terms. The estimated premium models indicate that premia show a certain degree of persistance over time and that more than half the variance in the forecast error that results from the use of current forward rates as predictors of future spot rates is accounted for by variation in premium terms. The methodology can be applied straightforwardly to the measurement of unobservables in other financial markets.

In this paper, we implement a methodology to identify and measure premia in the pricing of forward foreign exchange. The methodology involves application of signal-extraction techniques from the engineering literature. Diagnostic tests indicate that these methods are quite successful in capturing the essence of the time-series properties of premium terms. The estimated premium models indicate that premia show a certain degree of persistence over time and that more than half the variance in the forecast error that results from the use of current forward rates as predictors of future spot rates is accounted for by variation in premium terms. The methodology can be applied straightforwardly to the measurement of unobservables in other financial markets.

We estimate how cost sharing, the portion of the bill the patient pays, affects the demand for medical services. The data come from a randomized experiment. A catastrophic insurance plan reduces expenditures 31 percent relative to zero out-of-pocket price. The price elasticity is approximately -0.2. We reject the hypothesis that less favorable coverage of outpatient services increases total expenditure (for example, by deterring preventive care or inducing hospitalization).

Measurement error may be an important source of bias in studies using self-reported health indicators to explain work behavior. As a test of measurement error, the tetrachoric correlation coefficient is used to examine the relationship between two alternative measures of arthritis, a standard self-reported measure and a simulated clinical measure. While the two measures are highly correlated, measurement error is found. Regression analysis demonstrates that it varies systematically across different socioeconomic groups. In particular, individuals who are not working tend to report their health incorrectly, perhaps owing to social pressure to justify not having a job. Coauthors are Richard V. Burkhauser, Jean M. Mitchell, and Theodore P. Pincus.