Measuring Organizational Performance: A Case for Subjective Measures

We review the organizational performance (OP) measurement literature highlighting the limitations of both objective and subjective measures of performance. We argue that, with careful planning, subjective measures can be successfully employed to assess OP. This is because often consistent, reliable and comparable compatible objective data on OP measures – particularly across countries and sectors – is difficult to come by. Considering that an inflated OP measure can be cross‐checked with the use of secondary data, managers have little incentive to report such figures. As a result, when quizzed over the stand‐alone performance measures of their organizations or vis‐à‐vis their rivals, managers accurately assess and respond to questions on the performance of their organizations. An in‐depth statistical exercise conducted on the subjective measures of OP as reported by managers of four sets of companies in four separate countries, show consistent results, thus lending support to this premise.     

Irregular Identification,Support Conditions,and Inverse Weight Estimation

In weighted moment condition models, we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models. In particular, if it is necessary for (point) identification that the weights take arbitrarily large values, then the parameter of interest, though point identified, cannot be estimated at the regular (parametric) rate and is said to be irregularly identified. This rate depends on relative tail conditions and can be as slow in some examples as n^−1/4. This nonstandard rate of convergence can lead to numerical instability and/or large standard errors. We examine two weighted model examples: (i) the binary response model under mean restriction introduced by Lewbel (1997) and further generalized to cover endogeneity and selection, where the estimator in this class of models is weighted by the density of a special regressor, and (ii) the treatment effect model under exogenous selection (Rosenbaum and Rubin (1983)), where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score. Without strong relative support conditions, these models, similar to well known “identified at infinity” models, lead to estimators that converge at slower than parametric rate, since essentially, to ensure point identification, one requires some variables to take values on sets with arbitrarily small probabilities, or thin sets. For the two models above, we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to Andrews and Schafgans (1998) for the sample selection model.     

Inference in Censored Models with Endogenous Regressors

This paper analyzes the linear regression model y = xβ+ε with a conditional median assumption med (ε| z) = 0, where z is a vector of exogenous instrument random variables. We study inference on the parameter β when y is censored and x is endogenous. We treat the censored model as a model with interval observation on an outcome, thus obtaining an incomplete model with inequality restrictions on conditional median regressions. We analyze the identified features of the model and provide sufficient conditions for point identification of the parameter β. We use a minimum distance estimator to consistently estimate the identified features of the model. We show that under point identification conditions and additional regularity conditions, the estimator based on inequality restrictions is normal and we derive its asymptotic variance. One can use our setup to treat the identification and estimation of endogenous linear median regression models with no censoring. A Monte Carlo analysis illustrates our estimator in the censored and the uncensored case.     

Estimation and Confidence Regions for Parameter Sets in Econometric Models1

This paper develops a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities and equalities. Applications of this framework include the analysis of game‐theoretic models, revealed preference restrictions, regressions with missing and corrupted data, auction models, structural quantile regressions, and asset pricing models. Specifically, we provide estimators and confidence regions for the set of minimizers Θ_I of an econometric criterion function Q(θ). In applications, the criterion function embodies testable restrictions on economic models. A parameter value θthat describes an economic model satisfies these restrictions if Q(θ) attains its minimum at this value. Interest therefore focuses on the set of minimizers, called the identified set. We use the inversion of the sample analog, Q_n(θ), of the population criterion, Q(θ), to construct estimators and confidence regions for the identified set, and develop consistency, rates of convergence, and inference results for these estimators and regions. To derive these results, we develop methods for analyzing the asymptotic properties of sample criterion functions under set identification.     

Bounds on Parameters in Panel Dynamic Discrete Choice Models

Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using these insights, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest that the model restricts the parameter to lie in a region that is very small in many cases, and the failure of point identification may, therefore, be of little practical importance in those cases. Although the emphasis is on identification, our techniques are constructive in that they can easily form the basis for consistent estimates of the identified sets.     

Coordination and Priority Decisions in Hybrid Manufacturing/Remanufacturing Systems

Companies are increasingly realizing the need to coordinate their manufacturing and remanufacturing operations. This can be a challenge due to the inherent variability in the condition and amount of returns, which has a direct impact on remanufacturing costs and leadtimes. In this paper, we develop a modeling framework to compare two alternative strategies that use either manufacturing or remanufacturing as the primary means of satisfying customer demand. Of course, in the event that the demand cannot be met by the prioritized process, the secondary process is used as a contingency. In our basic model, the priority decisions are made at the component level in replenishing the serviceable inventory, while the disposal and new component ordering decisions are made independently. The second model represents the coordination of remanufacturable and new component inventory control decisions. Using simulation‐based optimization on a large number of experiments, we observe that when prioritization is in the upstream echelon and there is no coordination in managing component stocks, there exists a critical return ratio, below which it is beneficial to give priority to manufacturing and above which it is beneficial to give priority to remanufacturing. We also see that coordinated control of the component inventories considerably reduces the importance of prioritization. These observations remain valid when congestion in the shop floor is also taken into account. We also study the benefits of state‐dependent dispatching policies in a realistic case.     

An Analysis of Monopolistic and Competitive Take‐Back Schemes for WEEE Recycling

We study two prevailing types of take‐back schemes for electrical and electronic equipment waste recycling: monopolistic and competitive. We address key market and operating factors that make one scheme preferable to the other from the viewpoints of recyclers, manufacturers, and consumers. To this end, we model competitive decision making in both take‐back schemes as two‐stage sequential games between competing manufacturers and recyclers. Deriving and computing equilibria, we find that the competitive take‐back scheme often accomplishes a win–win situation, that is, lower product prices, and higher recycler and manufacturer profits. Exceptionally, recyclers prefer the monopolistic scheme when the substitutability level between the manufacturers' original products is high or economies of scale in recycling are very strong. We show that consolidation of the recycling industry could benefit all stakeholders when the economies of scale in recycling are strong, provided that manufacturer's products are not highly substitutable. Higher collection rates also render recycler consolidation desirable for all stakeholders. We also identify a potential free rider problem in the monopolistic scheme when recyclers differ in operational efficiency, and propose mechanisms to eliminate the discrepancy. We show that our results and insights are robust to the degree of competition within the recycling industry.     

Selling with Money‐Back Guarantees: The Impact on Prices,Quantities, and Retail Profitability

In this paper, we consider a retailer adopting a “money‐back‐guaranteed” (MBG) sales policy, which allows customers to return products that do not meet their expectations to the retailer for a full or partial refund. The retailer either salvages returned products or resells them as open‐box items at a discount. We develop a model in which the retailer decides on the quantity to procure, the price for new products, the refund amount, as well as the price of returned products when they are sold as open‐box. Our model captures important features of MBG sales including demand uncertainty, consumer valuation uncertainty, consumer returns, the sale of returned products as open‐box items, and consumer choice between new and returned products and possibility of exchanges when restocking is considered. We show that selling with MBGs increases retail sales and profit. Furthermore, the second‐sale opportunity created by restocking returned products enables the retailer to generate additional revenues. Our analysis identifies the ideal conditions under which this practice is most beneficial to the retailer. Offering an MBG without restocking increases the new product price. We show that if the retailer decides to resell the returned items as open‐box, the price of the new product further increases, while open‐box items are sold at a discount. On the other hand, customers enjoy more generous refunds along with lower restocking fees. The opportunity to resell returned products also generally decreases the initial stocking levels of the retailer. Our extensive numerical study substantiates the analytical results and sharpens our insights into the drivers of performance of MBG policies and their impact on retail decisions.     

Weighted L1-estimates for the First-order Bifurcating Autoregressive Model

We developed robust estimators that minimize a weighted L₁ norm for the first-order bifurcating autoregressive model. When all of the weights are fixed, our estimate is an L₁ estimate that is robust against outlying points in the response space and more efficient than the least squares estimate for heavy-tailed error distributions. When the weights are random and depend on the points in the factor space, the weighted L₁ estimate is robust against outlying points in the factor space. Simulated and artificial examples are presented. The behavior of the proposed estimate is modeled through a Monte Carlo study.     

Corporate Governance and Tobin's Q as a Measure of Organizational Performance

This empirical study examines the relationship between corporate governance and organizational performance (OP), measured using Tobin's Q (TQ) in the context of an emerging economy for which, as yet, only a handful of studies have been conducted. We employ a system generalized method of moments approach controlling for endogeneity and test it on a newly created dataset comprising 324 listed firms in Pakistan. We find that board size, number of board committees and ownership concentration are positively linked with high TQ ratio, whilst board independence and CEO duality display a negative relationship. In terms of moderating effects, we find that ownership concentration negatively moderates the relationship between board independence and OP, as well as that of CEO duality and OP. The relationship between the number of board committees and OP is positively moderated by ownership concentration. Our findings contribute towards a better articulation and application of a more concrete measure of OP ? that of the TQ ratio ? whilst, at the same time, testing the board composition–performance relationship in the context of an upcoming and increasingly important emerging market. Wider applicability of results and policy implications are discussed.