We use a quasi-experimental research design to study the effect of giving workers feedback on their relative performance. The setting is a firm in which workers are paid piece rates and where, for exogenous reasons, management begins to reveal to workers their relative position in the distribution of pay and productivity. We find that merely providing this information leads to a large and long-lasting increase in productivity that is costless to the firm. Our findings are consistent with the interpretation that workers' incipient concerns about their relative standing are activated by information about how they are performing relative to others.
A U.S. law mandating nonintrusive imaging and radiation detection for 100% of U.S.-bound containers at international ports has provoked widespread concern that the resulting congestion would hinder trade significantly. Using detailed data on container movements, gathered from two large international terminals, we simulate the impact of the two most important inspection policies that are being considered. We find that the current inspection regime being advanced by the U.S. Department of Homeland Security can only handle a small percentage of the total load. An alternate inspection protocol that emphasizes screening—a rapid primary scan of all containers, followed by a more careful secondary scan of only a few containers that fail the primary test—holds promise as a feasible solution for meeting the 100% scanning requirement.
We analyze how entrepreneurial opportunity cost conditions performance. Departing from the common practice of using survival as a measure of entrepreneurial performance, we model both failure and cash-out (liquidity event) as conditioned by the same underlying process. High-opportunity-cost entrepreneurs prefer a shorter time to success, even if this also implies failing more quickly, whereas entrepreneurs with fewer outside alternatives will choose less aggressive strategies and, consequently, linger on longer. We formalize this intuition with a simple model. Using a novel data set of information security start-ups, we find that entrepreneurs with high opportunity costs are not only more likely to cash out more quickly but are also more likely to fail faster. Not only is survival a poor indicator of performance, but its use as one obscures the relationship between entrepreneurial characteristics, entrepreneurial strategies, and outcomes.
The academic and practitioner literature justifies firms' use of product costs in product pricing and capacity planning decisions as heuristics to address an otherwise intractable problem. However, product costs are the output of a cost reporting system, which itself is the outcome of heuristic design choices. In particular, because of informational limitations, when designing cost systems firms use simple rules of thumb to group resources into cost pools and to select drivers used to allocate the pooled costs to products. Using simulations, we examine how popular choices in costing system design influence the error in reported costs. Taking information needs into account, we offer alternative ways to translate the vague guidance in the literature to implementable methods. Specifically, we compare size-based rules for forming cost pools with more informationally demanding correlation-based rules and develop a blended method that performs well in terms of accuracy. In addition, our analysis suggests that significant gains can be made from using a composite driver rather than selecting a driver based on the consumption pattern for the largest resource only, especially when combined with correlation-based rules to group resources. We vary properties of the underlying cost structure (such as the skewness in resource costs, the traceability of resources to products, the sharing of resources across products, and the variance in resource consumption patterns) to address the generalizability of our findings and to show when different heuristics might be preferred.
Platform competition is ubiquitous, yet platform market structure is little understood. Theory models typically suffer from equilibrium multiplicity—platforms might coexist or the market might tip to either platform. We use laboratory experiments to study the outcomes of platform competition. When platforms are primarily vertically differentiated, we find that even when platform coexistence is theoretically possible, markets inevitably tip to the more efficient platform. When platforms are primarily horizontally differentiated, so there is no single efficient platform, we find strong evidence of equilibrium coexistence.
This paper develops a real options model to understand two distinct roles played by intellectual capital in corporate financing decisions. Whereas limiting a firm's debt capacity because of its low liquidation value, intellectual capital enhances a firm's debt capacity through its positive impact on earnings. Our model shows that the former dominates or is dominated by the latter, depending on whether the rate of dissipation of intellectual capital upon default is larger or smaller than a critical level, respectively. Using patent-based and research-and-development-based variables as proxies for intellectual capital, we find robust evidence that the relation between intellectual capital and leverage is positive. Specifically, a one-standard-deviation increase in the level of a firm's intellectual capital is associated with an increase of 6.6% to 21.1% in its market leverage. We further find this positive relation to be stronger for biotechnology firms.
The conventional method of estimating a probability prediction model by maximum likelihood (MLE) is a form of maximum score estimation with economic meaning. Of all the probabilities that a given model might have produced, those obtained by MLE yield maximum in-sample betting return to a log utility investor. Recognition of this affinity between MLE and log utility begs the wider methodological question of whether different decision makers benefit in different degrees from different probabilities. Probabilities produced by MLE can be either too conservative or too bold relative to those found by maximizing utility under more risk-tolerant or risk-averse score functions. A very (not very) risk-averse user, who bets characteristically small (large) fractions of wealth based on a conservative forecast, is bound to make a rapidly (slowly) increasing bet as the forecast probability becomes progressively bolder or more distant from the market probability. The effect of this interaction between risk aversion and forecast is that a highly risk-averse user may need a much bolder forecast to obtain the same certainty equivalent as a more risk-tolerant investor. It follows more broadly that professional forecasters should anticipate how a client with given risk aversion expects to gain from any given forecast, or forecast revision, before committing resources toward making a better informed (but still honest) forecast.
Fruit and vegetable marketing organization the Greenery has experienced various governance structure changes, like horizontal merger, forward integration, and the emergence of grower associations. A multilateral incomplete contracting model is presented to account for these changes by analysing the interactions between pooling, access, and countervailing power. This model does not only explain the changes at the Greenery, but it contributes also to the design of efficient channel governance.
This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects.
Prior firm experience, firm capabilities, and the industry environment are known to be important determinants of new-venture performance. We hypothesize that firm experience prior to setting up a new venture influences the ability to learn from experience after start-up (which is a key capability), and that this relationship is moderated by the importance of learning by doing within the new venture's industry (which is a critical aspect of the industry environment). We argue that together, these relationships influence performance differences among new plant ventures of incumbents, diversifying entrants, and entrepreneurial (de novo) entrants. Using data on 47,915 new plant ventures in U.S. manufacturing, we find that incumbents and diversifying entrants establish significantly more productive new plants than de novo entrants, and that this advantage significantly increases with the importance of learning by doing in an industry (industry learning intensity). These productivity differences appear to be driven more by learning subsequent to plant start-up than by initial disparities in productivity. Together, these findings strongly suggest that pre-start-up experience adds to the process of post-start-up learning, and that the industry learning environment plays an important role in whether entrepreneurial firms can achieve a competitive advantage over existing firms.