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Social scientists have recently turned their attention to the important consequences of industrial districts or so-called agglomeration economies on economic growth and firm performance. This paper explores an important but unanswered question involving agglomeration economies: does geographic location within an agglomeration affect firm performance? I assess this question by examining the effects of different geographic office locations (by zip code) on the failure rates of all corporate law firms located in Silicon Valley from 1969 to 1998. Empirical estimates reveal that Silicon Valley corporate law firms benefit from the increased volume of client referrals that comes from being near mutualistic firms that offer a different range of legal services, the lower labor costs and more specialized division of labor that come from being near a large joint supply of lawyers, and the increased business that comes from being near important clients (i.e. venture capital firms).

In addition, corporate law firms that locate in certain municipalities of Silicon Valley, including Palo Alto, San Jose, and Santa Clara, have significantly increased failure rates, even controlling for many firm-specific differences. Younger corporate law firms (under the age of 11 years) are helped disproportionately by being near important environmental resources and harmed disproportionately by being in certain perilous areas of Silicon Valley. All told, a law firm’s office location within Silicon Valley has significant consequences for its survival.

The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.

Some consumer durables, such as automobiles, involve warranties involving two attributes. These are time elapsed since the sale of the product and the usage of the product at a given point in time. Warranty may be invoked by the customer if both time and usage are within the specified warranty parameters and product failure occurs. In this chapter, we assume that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior pattern. Further, product failure rate is influenced by the usage rate and product age. Of importance to the organization is to contain expected warranty costs and select appropriate values of the warranty parameters accordingly. An avenue to impact warranty costs is through research on product development. This has the potential to reduce the failure rate of the product. The objective then becomes to determine warranty parameters, while constraining the sum of the expected unit warranty costs and research and development (R&D) costs per unit sales, under a limited R&D budget.

Learning theory explains how organizations change as a result of experience, and can be used to predict the competitive strength of individual organizations and competitive pressures in organizational populations. We review extant learning theoretical propositions on how competitive strength is affected by experienced competition, founding conditions, and observed failures of other organizations. In addition, we propose that niche changes are an important source of learning. We test these propositions on data from the Norwegian general insurance industry. We find that historical density increases failure rates, contrary to some earlier findings, and also that the effect of founding density supports the density delay rather than trial-by-fire hypothesis. We find that failures of others before and during the lifetime of the organization reduce failure rates, and niche changes reduce failure rates for joint-stock companies but not for mutual firms. Overall the findings suggest that organizations learn more cheaply from the failures of others than from their own experiences, and that the stresses of competition can overwhelm the learning effects of competition.

This chapter considers warranty policies involving two attributes, such as the time elapsed since sale of the product and product usage at a given point in time. Examples of such policies are found for automobiles, where warranty may be invoked by the consumer if both time and usage are within specified warranty parameters when a product failure occurs. Here, we assume that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior patterns. Furthermore, product failure rate is influenced by the usage rate and product age as well as research and development expenditures per unit. It is assumed that, in production, there is a learning effect with time. The attained market share of a product will be influenced by the warranty policy parameters of warranty time and usage limit and also by the product price and product quality. An integrated model is developed to address multiobjective goals such as attainment of a specified level of market share and net profit per unit when manufacturing and warranty costs are taken into account. The impact of the goal priorities are investigated on the attained warranty policy parameters.

Two-attribute warranty policies are considered that incorporate, for example, the time elapsed since sale of the product and product usage at a given point in time. Such policies occur in consumer products, such as automobiles, where warranty may be exercised if both time and usage are within specified warranty parameters when a product failure occurs. In this chapter, it is assumed that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior patterns. Product quality is modeled through the product failure rate, which is influenced by unit research and development expenditures as well as the usage rate and product age. The attained market share of the product is modeled as a function of the warranty policy parameters of price, warranty time, and usage limit, with product quality also having an influence. Attainment of single and multiple objectives are explored. Such objectives encompass expected total unit costs as a proportion of unit product price and market share.

For certain consumer durables, such as automobiles, warranty policies involve two attributes. These could be the time elapsed since sale of the product and usage of the product at a given point in time. Warranty may be invoked by the consumer if both time and usage are within specified warranty parameters when a product failure occurs. In this chapter, we assume that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior patterns. Additionally, product failure rate is influenced by the usage rate and product age. The integrated model includes expected unit warranty costs, expected unit research and development costs, and expected unit production costs. It is assumed that in production, there is a learning effect with time. A multiobjective model is incorporated with the objectives being market share and proportion of expected warranty costs relative to total manufacturing expenditures per unit. The goals could be conflicting in nature. The problem then is to determine the warranty policy parameters while attaining certain desirable values of the two objectives.

A warranty policy involving two attributes, for example time and usage, is considered. Usage is assumed to be related to time through the usage rate, which is considered to be a random variable satisfying a specified probability distribution. The paper analyzes a policy where warranty is not renewed on product failure, within the specified time period and amount of usage, but is minimally repaired. Unit cost of minimal repair, conditional on the usage rate, is assumed to be a non-linear function of the two warranty parameters. Expressions for the expected warranty costs per unit sales are derived. Applications of the results are presented through sample computations. The results demonstrate the use of warranty cost information in selecting the parameters of the warranty policy.

Warranty policies for certain products, such as automobiles, often involve consideration of two attributes, for example, time and usage. Since consumers are not necessarily homogeneous in their use of the product, such policies provide protection to users of various categories. In this chapter, product usage at a certain time is linked to the product age through a variable defined as usage rate. This variable, usage rate, is assumed to be a random variable with a specified probability distribution, which permits modeling of a variety of customer categories. Another feature of the chapter is to model the propensity to execute the warranty, in the event of a failure within specified parameter values (say time or usage). In a competitive market, alternative product/warranty offerings may reduce the chances of exercising the warranty. This chapter investigates the impact of warranty policy parameters with the goal of maximizing market share, subject to certain constraints associated with expected warranty costs per unit not exceeding a desirable level.

The successful culmination of missions based on Unmanned Aerial Vehicles (UAV) can be measured with two main parameters: (1) successful mission completion: all objectives of the mission (e.g., maneuvering and navigation, reconnaissance and targeting or search and rescue, and return) were accomplished and (2) safety: no damage to the vehicle and no fatalities or injuries to any human were sustained throughout the mission. Automation of the UAV's control and operations increasingly becomes a determining factor in successful mission completion and increased safety. However, in this day and age of automatically launched and retrieved swarms of UAVs, the human operator still has a critical role. Human-controlled UAVs will persist for a long time and human error is a factor that still needs addressing in the age of automation. Even a single person, who has flown radio-controlled model aircraft as a hobby since childhood, can still cause the crash of an expensive UAV in a matter of seconds. Moreover, there are aspects of human error in UAV control that can have important implications to the implementation of automation and to keeping the human operator in the control loop.