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Contests are a historically important and increasingly popular mechanism for encouraging innovation. A central concern in designing innovation contests is how many competitors to admit. Using a unique data set of 9,661 software contests, we provide evidence of two coexisting and opposing forces that operate when the number of competitors increases. Greater rivalry reduces the incentives of all competitors in a contest to exert effort and make investments. At the same time, adding competitors increases the likelihood that at least one competitor will find an extreme-value solution. We show that the effort-reducing effect of greater rivalry dominates for less uncertain problems, whereas the effect on the extreme value prevails for more uncertain problems. Adding competitors thus systematically increases overall contest performance for high-uncertainty problems. We also find that higher uncertainty reduces the negative effect of added competitors on incentives. Thus, uncertainty and the nature of the problem should be explicitly considered in the design of innovation tournaments. We explore the implications of our findings for the theory and practice of innovation contests. This paper was accepted by Christian Terwiesch, operations management.

The typical cost analysis of an environmental regulation consists of an engineering estimate of the compliance costs. In industries where fixed costs are an important determinant of market structure, this static analysis ignores the dynamic effects of the regulation on entry, investment, and market power. I evaluate the welfare costs of the 1990 Amendments to the Clean Air Act on the U.S. Portland cement industry, accounting for these effects through a dynamic model of oligopoly in the tradition of Ericson and Pakes (1995). Using the two-step estimator of Bajari, Benkard, and Levin (2007), I recover the entire cost structure of the industry, including the distributions of sunk entry costs and capacity adjustment costs. My primary finding is that the Amendments have significantly increased the sunk cost of entry, leading to a loss of between $ 810M and $ 3.2B in product market surplus. A static analysis misses the welfare penalty on consumers, and obtains the wrong sign of the welfare effects on incumbent firms.

Unusual clusters of disease must be detected rapidly for effective public health interventions to be introduced. Over the past decade there has been a surge in interest in statistical methods for the early detection of infectious disease outbreaks. This growth in interest has given rise to much new methodological work, ranging across the spectrum of statistical methods. The paper presents a comprehensive review of the statistical approaches that have been proposed. Applications to both laboratory and syndromic surveillance data are provided to illustrate the various methods.

The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. A few papers have proposed control charts that monitor this normalized measure of dispersion. This paper suggests a new method to monitor the CV by means of two one-sided EWMA charts of the coefficient of variation squared γ 2 . Tables are provided for the statistical properties of the EWMA-γ 2 when the shift size is deterministic or unknown. An example illustrates the use of these charts on real data gathered from a metal sintering process.

We analyze the duration and outcomes of patent examination at the European Patent Office utilizing an unusually rich data set covering a random sample of 215,265 applications filed between 1982 and 1998. In our empirical analysis, we distinguish between three groups of determinants: applicant characteristics, indicators of patent quality and value, and determinants that affect the complexity of the examination task. The results from an accelerated failure time model indicate that more controversial claims lead to slower grants but faster withdrawals, whereas well-documented applications are approved faster and withdrawn more slowly. We find strong evidence that applicants accelerate grant proceedings for their most valuable patents, but that they also prolong the battle for such patents if a withdrawal or refusal is imminent. This paper develops implications of these results for managerial decision making in research and development and innovation management.

The majority of labor transactions throughout much of history and a significant fraction of such transactions in many developing countries today are \"coercive,\" in the sense that force or the threat of force plays a central role in convincing workers to accept employment or its terms. We propose a tractable principal-agent model of coercion, based on the idea that coercive activities by employers, or \"guns,\" affect the participation constraint of workers. We show that coercion and effort are complements, so that coercion increases effort, but coercion always reduces utilitarian social welfare. Better outside options for workers reduce coercion because of the complementarity between coercion and effort: workers with a better outside option exert lower effort in equilibrium and thus are coerced less. Greater demand for labor increases coercion because it increases equilibrium effort. We investigate the interaction between outside options, market prices, and other economic variables by embedding the (coercive) principal-agent relationship in a general equilibrium setup, and studying when and how labor scarcity encourages coercion. General (market) equilibrium interactions working through the price of output lead to a positive relationship between labor scarcity and coercion along the lines of ideas suggested by Domar, while interactions those working through the outside option lead to a negative relationship similar to ideas advanced in neo-Malthusian historical analyses of the decline of feudalism. In net, a decline in available labor increases coercion in general equilibrium if and only if its direct (partial equilibrium) effect is to increase the price of output by more than it increases outside options. Our model also suggests that markets in slaves make slaves worse off, conditional on enslavement, and that coercion is more viable in industries that do not require relationship-specific investment by workers.

We propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. We define a new equilibrium concept that we call oblivious equilibrium, in which each firm is assumed to make decisions based only on its own state and knowledge of the long-run average industry state, but where firms ignore current information about competitors' states. The great advantage of oblivious equilibria is that they are much easier to compute than are Markov perfect equilibria. Moreover, we show that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain \"light-tail\" condition, then oblivious equilibria closely approximate Markov perfect equilibria. This theorem justifies using oblivious equilibria to analyze Markov perfect industry dynamics in Ericson and Pakes (1995)-style models with many firms.

In the past few decades multistore retailers, especially those with 100 or more stores, have experienced substantial growth. At the same time, there is widely reported public outcry over the impact of these chain stores on other retailers and local communities. This paper develops an empirical model to assess the impact of chain stores on other discount retailers and to quantify the size of the scale economies within a chain. The model has two key features. First, it allows for flexible competition patterns among all players. Second, for chains, it incorporates the scale economies that arise from operating multiple stores in nearby regions. In doing so, the model relaxes the commonly used assumption that entry in different markets is independent. The lattice theory is exploited to solve this complicated entry game among chains and other discount retailers in a large number of markets. It is found that the negative impact of Kmart's presence on Wal-Mart's profit was much stronger in 1988 than in 1997, while the opposite is true for the effect of Wal-Mart's presence on Kmart's profit. Having a chain store in a market makes roughly 50% of the discount stores unprofitable. Wal-Mart's expansion from the late 1980s to the late 1990s explains about 40-50% of the net change in the number of small discount stores and 30-40% for all other discount stores. Scale economies were important for Wal-Mart, but less so for Kmart, and the magnitude did not grow proportionately with the chains' sizes.

Traditional discrete-choice models assume buyers are aware of all products for sale. In markets where products change rapidly, the full information assumption is untenable. I present a discrete-choice model of limited consumer information, where advertising influences the set of products from which consumers choose to purchase. I apply the model to the U.S. personal computer market where top firms spend over $2 billion annually on advertising. I find estimated markups of 19% over production costs, where top firms advertise more than average and earn higher than average markups. High markups are explained to a large extent by informational asymmetries across consumers, where full information models predict markups of one-fourth the magnitude. I find that estimated product demand curves are biased toward being too elastic under traditional models. I show how to use data on media exposure to improve estimated price elasticities in the absence of micro ad data.

Nonparametric control charts are useful when the underlying process distribution is not likely to be normal or is unknown. In this paper, we propose two nonparametric analogs of the CUSUM and EWMA control charts based on the Wilcoxon rank-sum test for detecting process mean shifts. We first derive the run-length distributions of the proposed control charts and then compare the performance of the proposed nonparametric charts to (1) CUSUM and EWMA control charts on subgroup means and (2) the median chart and the Shewhart-type nonparametric control chart based on Mann-Whitney test. We show that the charts proposed herein perform well in detecting step mean shifts and perform almost the same as the parametric counterparts when the underlying process output follows a normal distribution and better when the output is nonnormal. We also study the effect of the reference sample size and the subgroup size on the performance of the proposed charts. A numerical example is also given as an illustration of the design and implementation of the proposed charts.