Psychology at work

We compare three wage–effort psychological games. In the first game, the agent’s motivation hinges on a degree of altruism influenced by the surprise generated by the principal’s wage offer. The agent works harder when the wage is greater than expected and vice versa when the wage is smaller than expected. Consistent equilibrium beliefs oblige the principal to randomize in order to surprise the agent, which results in the principal being worse off than if she were dealing with an unemotional agent. We then consider an intention-based reciprocity model and a model of guilt aversion. We find that guilt aversion may potentially yield the best outcome for the principal.     

Global Income Divergence, Trade, and Industrialization: The Geography of Growth Take-Offs

This article formalizesthe theoretical interconnections among four post–industrialrevolution phenomena—the industrialization and growth take-offof rich northern nations, massive global income divergence, andrapid trade expansion. In stages-of-growth model, the four phenomenaare jointly endogenous and are triggered by falling trade costs.In the first growth stage (with high trade costs) industry isdispersed internationally, and growth is low. In the second (mediumtrade costs), the North industrializes rapidly, growth take-off,and the South diverges. In the third (low trade costs), highgrowth and global divergence become self-sustaining. In the fourthstage, when the cases of ``trading' ideas decreases, the Southquickly industrializes and converges.     

Returns to Scale on Nonparametric Deterministic Technologies: Simplifying Goodness-of-Fit Methods Using Operations on Technologies

Thepurpose of this short article is to simplify goodness-of-fitmethods to obtain qualitative information about returns to scalefor individual observations. Traditional and new goodness-of-fitmethods developed for estimating returns to scale on nonparametricdeterministic reference technologies are reviewed. Using compositionrules for technologies with specific returns to scale assumptions,we show how these goodness-of-fit methods can be simplified inthe case of convex technologies (Data Envelopment Analysis (DEA)models).