[I evaluate the effect of loyalty on individuals' portfolio choice using a unique dataset of retirement contributions. I exploit the statutory difference that, in 401(k) plans, stand-alone employees can invest directly in their division, while conglomerate employees must invest in the entire firm, including all unrelated divisions. Consistent with loyalty, employees of stand-alone firms invest 10 percentage points (75%) more in company stock than conglomerate employees. Support is also found using variation in loyalty between different groups of employees, across and within firms. The cost to employees of loyalty is large, amounting to nearly a 20% loss in retirement income.]

This paper uses a unique panel data set of an insurer's transactions with repeat customers. Consistent with the asymmetric learning hypothesis that repeated contracting enables sellers to obtain an informational advantage over their rivals, I find that the insurer makes higher profits in transactions with repeat customers who have a good claims history with the insurer, the insurer reduces the price charged to these repeat customers by less than the reduction in expected costs associated with such customers, and repeat customers with bad claim histories are more likely to flee their record by switching to other insurers.

This paper tests the predictions of adverse-selection models using data from the automobile insurance market. I find that, in contrast to what recent research suggests, the evidence is consistent with the presence of informational asymmetries in this market: new customers choosing higher insurance coverage are associated with more accidents. Consistent with the possibility of policyholders' learning about their risk type, such a coverage-accidents correlation exists only for policyholders with enough years of driving experience. The informational advantage that new customers with driving experience have over the insurer appears to arise in part from customers' underreporting their past claim history: policyholders switching to new insurers are disproportionately ones with a poor claims history, and new customers tend to underreport their past claims history when joining a new insurer.

George Kaufman and Cynthia Latta in “The Demand for Money: Preliminary Evidence from Industrial Countries, ” have presented econometric evidence that the money-demand function may shift with the development of financial markets. The thesis depends on the heightened cross-elasticities and lowered wealth-elasticities (or income-elasticities) that are supposed to attend the development of new near-money forms. Their evidence is based on a summary of statistics from money-demand equations for developed and less-developed countries.

Journal Article Deposit Demand and the Pricing of Demand Deposits: Reply Get access Bruce C. Cohen Bruce C. Cohen Northeastern University Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 86, Issue 1, February 1972, Pages 140–142, https://doi.org/10.2307/1880500 Published: 01 February 1972

The constraint on bank supply of demand deposits, 524. — Aggregative properties, 529. — Conclusions, 529.

I. Introduction, 456. —II. Input-output model tracing the impact of changes in the prices of imports on the prices of domestic products, 457.— III. Application of this model to Colombia, 458. — IV. Conclusion, 461.

Introduction, 604. —I. India's loss in expanding world markets, 606. — II. Empirical evidence that this declining market share was caused by an increase in the price of Indian exports relative to competitors' prices, 608. — III. Policies adopted by the Indian government to achieve goals with higher priority than export promotion, 611. — IV. Policy implications of the historical analysis if the Indian government should adopt a more vigorous export promotion policy, 617. —Appendix, 619.

This paper is concerned with the validity of the conventional t tests on regression coefficients when there is serious multicollinearity between the explanatory variables. It is well known that increasing multicollinearity causes the true standard errors of regression coefficients to rise. The crucial question, however, is whether the conventional formulas will in practice reflect this rise. The purpose of this note is to show that the conventional t tests will in practice reflect this rise. But this note also points out the danger involved in mechanically dropping variables from multiple regression equations by t tests because t values of the regression coefficients may not be significantly different from zero when the true (population) values of these coefficients are in fact not zero, if the explanatory variables are highly intercorrelated.