Morale and Debt Dynamics

This paper shows that debt undermines relational incentives and harms worker morale. We build a dynamic model of a manager who uses limited financial resources to simultaneously repay a creditor and motivate a worker. If the manager can divert or misuse revenue, then debt makes the manager less willing to follow through on promised rewards, leading to low worker effort. In profit-maximizing equilibria, the firm prioritizes repaying its debts, leading to gradual increases in effort and wages. These dynamics can persist even after debts have been fully repaid. Consistent with this analysis, we document that a firm’s financial leverage is negatively related to measures of employee morale, wages, and productivity. This paper was accepted by Joshua Gans, business strategy.

Sales Effort Management Under All-or-Nothing Constraint

We consider a sales effort management problem under an all-or-nothing constraint. The seller will receive no bonus/revenue if the sales volume fails to reach a predetermined target at the end of the sales horizon. Throughout the sales horizon, the sales process can be moderated by the seller through costly effort. We show that the optimal sales rate is nonmonotonic with respect to the remaining time or the outstanding sales volume required to reach the target. Generally, it has a watershed structure, such that for any needed sales volume, there exists a cutoff point on the remaining time above which the optimal sales rate decreases in the remaining time and below which it increases in the remaining time. We then study easy-to-compute heuristics that can be implemented efficiently. We start with a static heuristic derived from the deterministic analog of the stochastic problem. With an all-or-nothing constraint, we show that the performance of the static heuristic hinges on how the profit-maximizing rate fares against the target rate, which is defined as the sales target divided by the length of the sales horizon. When the profit-maximizing rate is higher than the target rate, the static heuristic adopting the optimal deterministic rate is asymptotically optimal with negligible loss. On the other hand, when the profit-maximizing rate is lower than the target rate, the performance loss of any asymptotically optimal static heuristic is of an order greater than the square root of the scale parameter. To address the poor performance of the static heuristic in the latter case, we propose a modified resolving heuristic and show that it is asymptotically optimal and achieves a logarithmic performance loss. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Robust Stochastic Models for Profit-Maximizing Hub Location Problems

This paper introduces robust stochastic models for profit -maximizing capacitated hub location problems in which two different types of uncertainty, including stochastic demand and uncertain revenue, are simultaneously incorporated into the problem. First, a two-stage stochastic program is presented in which demand and revenue are jointly stochastic. Next, robust stochastic models are developed to better model uncertainty in the revenue while keeping the demand stochastic. Two particular cases are studied based on the dependency between demand and revenue. In the first case, a robust stochastic model with a min-max regret objective is developed assuming a finite set of scenarios that describes uncertainty associated with the revenue under a revenue-elastic demand setting. For the case when demand and revenue are independent, robust stochastic models with a max-min criterion and a min-max regret objective are formulated considering both interval uncertainty and discrete scenarios, respectively. It is proved that the robust stochastic version with max-min criterion can be viewed as a special case of the min-max regret stochastic model. Exact algorithms based on Benders decomposition coupled with a sample average approximation scheme are proposed. Exploiting the repetitive nature of sample average approximation, generic acceleration methodologies are developed to enhance the performance of the algorithms enabling them to solve large-scale intractable instances. Extensive computational experiments are performed to consider the efficiency of the proposed algorithms and also to analyze the effects of uncertainty under different settings. The qualities of the solutions obtained from different modeling approaches are compared under various parameter settings. Computational results justify the need to solve robust stochastic models to embed uncertainty in decision making to design resilient hub networks.

An Experimental Pricing Framework for E-Commerce

Characterizing the demand curve of products is important for pricing them optimally. However, in deriving empirical demand curves, econometricians have to contend with identification issues. Furthermore, theoretical demand curves derived using standard economic theory are divorced from empirical realities: firms rarely have information on customers’ budget constraints; theoretical utility functions are seldom derived empirically. Recognizing these issues, we propose an experimental approach for determining a product’s demand curve and, in turn, its profit-maximizing price in online environments. The proposed approach yields precise estimates and is quick and inexpensive to implement.

AN ECONOMIC RESEARCH OF BROILER PROJECTS FOR SOME PROVINCES IN THE MIDDLE OF IRAQ IN 2019

This research was aimed to estimate the long-run cost and supply functions and to calculate the optimum profit-maximizing level of production, optimum capacity for broiler projects. The preliminary data were obtained by questionnaires that were distributed to project owners in Qadisiyah, Babil and Wasit governorates. A total of 80 projects amounted for 15% of the total projects in these governorates were included. The results indicated that the optimal profit-maximizing and actual production were 25,337, 34,737, and 21.25 tons respectively. The optimum production capacity was 9.9 thousand birds, while the actual capacity was 8.3 thousand chicken The cost elasticities were 0.936, 1.0 and 1.23 at the actual, optimal and profit maximizing production, respectively. The supply function had low elasticity indicating that the producers face great difficulty in controlling production in case of price changes. From these results, it can be concluded that government support is required for productive inputs, facilitating loans, preventing poultry importing, and adoption of strategic policy for the agricultural sector in general and poultry production in particular.

Implications of Competitor Representation for Profit-Maximizing Design

Design optimization studies that model competition with other products in the market often use a small set of products to represent all competitors. We investigate the effect of competitor product representation on profit-maximizing design solutions. Specifically, we study the implications of replacing a large set of disaggregated elemental competitor products with a subset of competitor products or composite products. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors when using logit and nested logit models (where preferences are homogeneous). However, this relationship differs in the case of random-coefficients logit models (where preferences are heterogeneous), and we demonstrate that profit-maximizing design solutions using latent-class or mixed-logit models can (but need not always) depend on the representation of competing products. We discuss factors that affect the magnitude of the difference between models with elemental and composite representations of competitors, including preference heterogeneity, cost function curvature, and competitor set specification. We present correction factors that ensure models using subsets or composite representation of competitors have optimal design solutions that match those of disaggregated elemental models. While optimal designs using logit and nested logit models are not affected by ad-hoc modeling decisions of competitor representation, the independence of optimal designs from competitors when using these models raises questions of when these models are appropriate to use.