The Choice is Yours: How Pension System Decisions Might Shape the Teacher Workforce

Current interest in teacher pension reform stems from the poor financial condition of many states' pension systems. The costs of these systems were not fully capitalized into the cost of education in the past, which is putting pressure on current finances, and policy-makers may wantto reduce the likelihood of this occurring in the future. Moreover, there are concerns that many states' traditional defined benefit plans may not distribute compensation in a way that optimally attracts and retains the best teachers. In considering pension reforms, such as shifting towards defined contribution structures, it is important to gain insights into teachers' preferences for different types of plans. Washington State's experience of creating a hybrid pension plan can provide useful information to policy makers dealing with these issues. The "big picture" policy implication of the experience in Washington State is that teacher pension systems can be reformed in a way that is attractive to both teachers and states. The proportion of teachers choosing to transfer to the hybrid plan (75 percent) or enroll as a new hire (60 percent) suggests that there was substantial win-win territory to be taken by restructuring the pension system. As stated previously, creating a new pension system does not by itself reduce unfunded liabilities associated with an existing DB system. However, a state can reduce the financial risk associated with its exposure to those liabilities by inducing employees to voluntarily transfer to the new system. Our analysis of the 1997 transfer decision illustrates a situation in which a large proportion of teachers in a traditional DB plan were willing to transfer to a hybrid pension system, and that the decision to transfer was influenced by financial incentives and factors related to risk preferences (particularly age and income). While these findings cannot be generalized to hybrid plans as a whole (we only observe choice between two specific plans), they do indicate the potential to induce a large proportion of transfersto a suitably structured plan.Our analysis of pension choice among new hires in the 2007 choice cohort indicates that a popular hybrid pension plan can be created at comparable cost to a traditional DB plan and with lower financial exposure for taxpayers. Excepting teacher age, new hires' pension preferences were not related to observable teacher and work-environment characteristics. It appears unlikely that the introduction of TRS3 significantly altered the composition of the teacher workforce interms of attracting new hires. Furthermore, we find no evidence that transitioning to the hybrid system negatively affected the quality of the teacher workforce. In fact, more effective teachers were slightly more likely to choose the hybrid plan. As unfunded pension obligations compete for many state's current education dollars, there is likely to be increasing pressure to enact reforms that will prevent the recurrence of such problems in the future. Given the stakes involved, pension reform is inevitably a contentious process, but the findings from Washington State suggest changes can be made to pension systems that make both teachers and taxpayers better off

Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach

We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system's data inputs that causally drives the decision (i.e., changing the inputs in the set changes the decision) and is irreducible (i.e., changing any subset of the inputs does not change the decision). We (1) demonstrate how this framework may be used to provide explanations for decisions made by general, data-driven AI systems that may incorporate features with arbitrary data types and multiple predictive models, and (2) propose a heuristic procedure to find the most useful explanations depending on the context. We then contrast counterfactual explanations with methods that explain model predictions by weighting features according to their importance (e.g., SHAP, LIME) and present two fundamental reasons why we should carefully consider whether importance-weight explanations are well-suited to explain system decisions. Specifically, we show that (i) features that have a large importance weight for a model prediction may not affect the corresponding decision, and (ii) importance weights are insufficient to communicate whether and how features influence decisions. We demonstrate this with several concise examples and three detailed case studies that compare the counterfactual approach with SHAP to illustrate various conditions under which counterfactual explanations explain data-driven decisions better than importance weights

Counterfactual Explanations for Data-Driven Decisions

Users’ lack of understanding of systems that use predictive models to make automated decisions is one of the main barriers for their adoption. We adopt the increasingly accepted view of a counterfactual explanation for a system decision: a set of the system inputs that is causal (meaning that removing them changes the decision) and irreducible (meaning that removing any subset of the inputs in the explanation does not change the decision). We generalize previous work on counterfactual explanations in three ways: we explain system decisions rather than model predictions; we do not enforce any specific method for removing inputs, and our explanations can incorporate inputs with arbitrary data structures. We also show how model-agnostic algorithms can be tweaked to find the most useful explanations depending on the context. Finally, we showcase our approach using a real data set to illustrate its advantages over other explanation methods when the goal is to understand system decisions better

Soil-based septic system decisions in Oklahoma

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Configurations Driving NPD Performance Fit with Market Demands and Time Constraints

The research reported in this paper is aimed at developing knowledge on organizing NPD systems to optimize their contribution to performance. To this end, a systems approach to fit is used to explain the context-structure-performance relationships for NPD performance, specifically in terms of fit with market demands of the product concept and fit with time constraints of the development process. From a sample of 164 US firms, the top 15 % performers in terms of both fit with market demands and fit with time constraints have been identified. An optimized ‘Ideal Profile’ for the organization of NPD systems, formed by a consistent pattern of: NPD Process, NPD Project Structure and Management, Innovation Climate, and NPD Goal Setting and Portfolio Management, followed from the analysis of the NPD configuration of these top performers. For the calibration sample (the other 85%) significant deviation from the ideal profile on all elements of the configuration was found, the correlations between NPD Performance Fit with Market Demands and Fit with Time Constraints and total Euclidean distance are also significant. Overall, these results provide evidence for the proposition that (1) new product success is a function of a set of NPD development system decisions and (2) to truly understand the impact of those decisions, they must be considered as a holistic system.\ud The contribution of this research is in the empirical validation of the internal consistency of an ideal organizational profile for NPD systems achieving both a high NPD performance in terms of market acceptance of their new products as well in terms of the satisfactory level of the development times of those products. By also examining ideal profiles for each of these NPD performance dimensions separately, the conflicting demands created by multiple performance metrics are highlighted as well as the organizational trade-offs necessary for optimal performance. In terms of managerial implications, this also gives direction for organizational redesign to firms either wanting to maximize their product concept (Fit with Market Demands) or development process (Fit with Time Constraints) performance

An algorithm of a costing system for electroplating

Businesses are increasingly competitive and rigorous and it is essential the existence of a strong, effective and realistic costing system. Decisions taken on the basis of the costing are very important, they can make the difference between success and failure of the organization. This paper explains the construction of an initial algorithm that aims to calculate the costs associated with electroplating of metal articles, in a specific company. This work can act as a possible guide for the construction of the costing system algorithms in this type of industry.info:eu-repo/semantics/publishedVersio