Knowledge that Transforms

As result of public housing reform and welfare reform, the operating environment of public housing authorities has changed significantly. Given these policy initiatives, housing mobility programs represent viable strategies for providing public housing residents with access to economically healthy, integrated neighborhoods. In this paper we present a decision support methodology to assist the design of housing mobility programs. This methodology incorporates economic models for estimating dollar-valued impacts associated with tenant relocation, and a multiobjective optimization model for generating alternative relocation schemes associated with various objective function weights. Using data for Lake County, Illinois and Chicago, we demonstrate that nondominated allocations represent significant trade-offs between dollar-valued and non-dollar-valued policy objectives; existing distributions of subsidized housing represent suboptimal solutions to the housing relocation problem; and increases in available rental housing can result in housing dispersion schemes that have positive net economic benefits relative to the status quo.

In the supply-chain literature, an increasing body of work studies how suppliers can use incentive schemes such as quantity discounts to influence buyers' ordering behaviour, thus reducing the supplier's (and the total supply chain's) costs. Various functional forms for such incentive schemes have been proposed, but a critical assumption always made is that the supplier has full information about the buyer's cost structure. We derive the optimal quantity discount policy under asymmetric information and compare it to the situation where the supplier has full information.

This paper studies a key driver of the demand for the products and services of the global IT industry—returns from IT investments. We estimate an intercountry production function relating IT and non-IT inputs to GDP output, on panel data from 36 countries over the 1985–1993 period. We find significant differences between developed and developing countries with respect to their structure of returns from capital investments. For the developed countries in the sample, returns from IT capital investments are estimated to be positive and significant, while returns from non-IT capital investments are not commensurate with relative factor shares. The situation is reversed for the developing countries subsample, where returns from non-IT capital are quite substantial, but those from IT capital investments are not statistically significant. We estimate output growth contributions of IT and non-IT capital and discuss the contrasting policy implications for capital investment by developed and developing economies.

Arguments can be made on both sides of the question of whether a stringent global corporate environmental standard represents a competitive asset or liability for multinational enterprises (MNEs) investing in emerging and developing markets. Analyzing the global environmental standards of a sample of U.S.-based MNEs in relation to their stock market performance, we find that firms adopting a single stringent global environmental standard have much higher market values, as measured by Tobin's q, than firms defaulting to less stringent, or poorly enforced host country standards. Thus, developing countries that use lax environmental regulations to attract foreign direct investment may end up attracting poorer quality, and perhaps less competitive, firms. Our results also suggest that externalities are incorporated to a significant extent in firm valuation. We discuss plausible reasons for this observation.

We propose and test a new pricing procedure for solving large-scale structured linear programs. The procedure interactively solves a relaxed subproblem to identify potential entering basic columns. The subproblem is chosen to exploit special structure, rendering it easy to solve. The effect of the procedure is the reduction of the number of pivots needed to solve the problem. Our approach is motivated by the column-generation approach of Dantzig-Wolfe decomposition. We test our procedure on two sets of multicommodity flow problems. One group of test problems arises in routing telecommunications traffic and the second group is a set of logistics problem which have been widely used to test multicommodity flow algorithms.

When rational risk-averse agents must choose among and share monetary risks, it is known that efficient sharing is typically nonlinear, even with common beliefs. Wherever it is, the sharing rule may affect the choice, randomized choice may allow everyone to gain, and indeed a randomized choice between unacceptable risks may be acceptable. An important exception occurs if the agents' utility functions are all exponential, all logarithmic, or all the same power (HARA). Then choices should accord with a group utility function of the same form independent of the sharing rule, randomization never helps, and all efficient sharing rules are linear. This self-contained paper simplifies, refines, and completes earlier analyses, identifying all exceptions; they are the linear sharing rules that make the agents' utilities agree. Aside from HARA, this can only occur for precisely one linear sharing rule or, in periodic versions of HARA, countably many.

Researchers examining loosely coupled systems, knowledge management, and complementary practices in organizations have proposed, informally, that the complexity of a successful business strategy can deter imitation of the strategy. This paper explores this proposition rigorously. A simple model is developed that parametrizes the two aspects of strategic complexity: the number of elements in a strategy and the interactions among those elements. The model excludes conventional resource-based and game-theoretic barriers to imitation altogether. The model is used to show that complexity makes the search for an optimal strategy intractable in the technical sense of the word provided by the theory of NP-completeness. Consequently, would-be copycats must rely on search heuristics or on learning, not on algorithmic “solutions,” to match the performance of superior firms. However, complexity also undermines heuristics and learning. In the face of complexity, firms that follow simple hill-climbing heuristics are quickly snared on low “local peaks,” and firms that try to learn and mimic a high performer's entire strategy suffer large penalties from small errors. The model helps to explain why some winning strategies remain unmatched even though they are open to public scrutiny; why certain bundles of organizational practices diffuse slowly even though they lead to superior performance; and why some strategies yield superior returns even after many of their critical ingredients are adopted by competitors. The analysis also suggests roles for management science and managerial choice in a world of complex strategies.

We study the problem of determining production quantities in each period of an infinite horizon for a single item produced in a capacity-limited facility. The demand for the product is random, and it is independent and identically distributed from period to period. The demand is observed at the beginning of a time period, but it need not be filled until the end of the period. Unfilled demand is backordered. A base stock or order-up-to policy is used. The shortfall is the order-up-to level minus the inventory position. The inventory system is easily understood and managed if we know the distribution of the shortfall. We develop a new approximation for this distribution, and perform extensive computational tests of existing approximations. Our new approximation works extremely well as long as the coefficient of variation of the demand is less than two. For practical applications this is by far the most interesting case. No known approximations work well consistently when the coefficient of variation of the demand is greater than two.

We study the robustness of empirical efficiency valuations of production processes in an extended Farrell model. Based on input and output data, an empirical efficiency status—efficient or inefficient—is assigned to each of the processes. This status may change if the data of the observed processes change. As illustrated by a capacity planning problem for hospitals in Germany, the need arises to gauge the robustness of empirical efficiency valuations. The example suggests to gauge the robustness of the efficiency valuation for a process with respect to perturbations of prespecified elements of the data. A natural measure of robustness is the minimal perturbation, in terms of a suitable distance function, of the chosen data elements that is necessary to change the efficiency status of the process under investigation. Farrell's (1957) efficiency score is an example of such a robustness measure. We give further examples of relevant data perturbations for which the robustness measure can be computed efficiently. We then focus on weighted maximum norm distance functions, such as the maximal absolute or percentage deviation, but allow for independent perturbations of the elements of an arbitrary a priori fixed subset of the data. In this setting, the robustness measure is naturally related to a certain threshold value for a linear monotone one-parameter family of perturbations and can be calculated by means of a linear programming–based bisection method. Closed form solutions in terms of Farrell's efficiency score are obtained for specific perturbations. Following the theoretical developments, we revisit the hospital capacity planning problem to illustrate the managerial relevance of our techniques.

This paper describes, analyzes and evaluates an algorithm for estimating portfolio loss probabilities using Monte Carlo simulation.Obtaining accurate estimates of such loss probabilities is essential to calculating value-at-risk, which is a quantile of the loss distribution. The method employs a quadratic (“delta-gamma”) approximation to the change in portfolio value to guide the selection of effective variance reduction techniques;specifically importance sampling and stratified sampling.If the approximation is exact, then the importance sampling is shown to be asymptotically optimal.Numerical results indicate that an appropriate combination of importance sampling and stratified sampling can result in large variance reductions when estimating the probability of large portfolio losses.