Agricultural and Food Policy, Research Methods/Statistical Methods

Agricultural and Food Policy, Research Methods/Statistical Methods

Financial institutions are financed by both investors and customers. Investors expect an appropriate risk-adjusted return for providing financing and risk bearing. Customers, in contrast, provide financing in exchange for specific services, and want the service fulfillment to be free of the intermediary's credit risk. We develop a framework that defines the roles of customers and investors in intermediaries, and use it to build an economic theory that has the following main findings. First, with positive net social surplus in the intermediary-customer relationship, the efficient (first best) contract completely insulates the customer from the intermediary's credit risk, thereby exposing the customer only to the risk inherent in the contract terms. Second, when intermediaries face financing frictions, the second-best contract may expose the customer to some intermediary credit risk, generating “customer contract fulfillment” costs. Third, the efficiency loss associated with these costs in the second best rationalizes government guarantees like deposit insurance even when there is no threat of bank runs. We further discuss the implications of this customer-investor nexus for numerous issues related to the design of contracts between financial intermediaries and their customers, the sharing of risks between them, ex ante efficient institutional design, regulatory practices, and the evolving boundaries between banks and financial markets.

There is often evidence of confusion between two forms of the constant percentage learning curve model: the cumulative average and the individual unit forms. Failure to distinguish between the two models can lead to their misuse and to potentially serious errors of estimation. A precise statement of the difference between the two clarifies the errors of misspecification. This note provides an analytical comparsion of the two models and addresses empirical estimation issues. Résumé. On a souvent la preuve de la confusion entre deux formules de modèle de la courbe d'apprentissage à pourcentage constant: celle de la moyenne cumulative et celle de l'unité individuelle. Le fait de ne pas distinguer les deux modèles peut conduire à une mauvaise utilisation et éventuellement à de sérieuses erreurs d'estimation. Un examen précis de la différence entre les deux peut clarifier les erreurs de spécification. Cet article présente une comparaison analytique des deux modèles et traite des questions d'estimation empirique.

Journal Article Rents, Rates and Incomes in Bristol Get access A. W. T. Ellis A. W. T. Ellis Bristol Search for other works by this author on: Oxford Academic Google Scholar The Review of Economic Studies, Volume 11, Issue 2, 1944, Pages 99–108, https://doi.org/10.2307/2295971 Published: 01 January 1944

The cycle, 153. — High-income products, 155. — Product variations, 158. — Economics of scale, 160. — Transportation and tariffs, 160. — Conclusions, 161.

Preliminary Report of Industrial Commission F. W. T. F. W. T. Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 14, Issue 3, May 1900, Pages 430–432, https://doi.org/10.1093/qje/14.3.430 Published: 01 May 1900

Journal Article The Theory of Wages Adjusted to Recent Theories of Value Get access T. N. Carver T. N. Carver Cornell University Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 8, Issue 4, July 1894, Pages 377–402, https://doi.org/10.2307/1885001 Published: 01 July 1894

We study individual portfolio choice in a laboratory experiment and find strong evidence for heuristic behavior. The subjects tend to focus on the marginal distribution of an asset, while largely ignoring its diversification benefits. They follow a conditional 1/ n diversification heuristic as they exclude the assets with an “unattractive” marginal distribution and divide the available funds equally between the remaining “attractive” assets. This strategy is applied even if it leads to allocations that are dominated in terms of first-order stochastic dominance and is clearly irrational. In line with these findings, we find that framing and problem presentation have substantial influence on portfolio decisions.

The 2009 CARD Act limited credit card lenders' ability to raise borrowers' interest rates on the basis of new information. Pricing became less responsive to public and private signals of borrowers' risk and demand characteristics, and price dispersion fell by one‐third. I estimate the efficiency and distributional effects of this shift toward more pooled pricing. Prices fell for high‐risk and price‐inelastic consumers, but prices rose elsewhere in the market and newly exceeded willingness to pay for over 30% of the safest subprime borrowers. On net, average traded prices fell and consumer surplus rose at all credit scores. Higher consumer surplus was partly driven by a fall in lender profits, and partly by the Act's insurance value to borrowers who could retain favorable pricing after adverse changes to their default risk. The relatively high level of pre‐CARD‐Act markups was crucial for realizing these surplus gains.