Of the copious research on the labor market returns to college, very little has adequately modeled the pathways of non-completers or compared their outcomes with those of award holders. In this paper, we present a novel method for linking non-completers with completers according to their program of study. This method allows us to calculate the labor market returns to programs of study, accounting both for those who obtain an award and those who do not. We use a large dataset of community college transcripts matched with earnings data. We find that different classification systems-by algorithm, intent, or goal-yield very different enrollment patterns across programs. These classifications make a substantial difference to earnings patterns. Our results show that returns vary not only by program completion, but they also vary by program non-completion. Consequently, combining completers and non-completers yields a new pattern of returns. For some awards, this leads to wider earnings differentials. However, we find that the variance in returns by subject of study is reduced when we combine data on completers and non-completers. Finally, we find that progression in a program per se does not lead to higher earnings for students who do not complete (even as it demonstrably does for students who complete their program). If validated, these findings have significant implications for policies on program choice and on student retention policies. 1. Introduction 1 2.
Objective: Excess credits earned by college students, over and above those required to complete their programs of study, have become increasingly a subject of interest and concern. There has been almost no research on the extent of these credits. This study focuses on all of the associate degree programs within one state's community college system and measures the extent of excess credits within each program. Method: I created a measure of the number of excess credits earned relative to all credits earned and measured the extent to which colleges and programs vary in the levels of excess credits. Within particular academic programs, such as business, nursing, and general studies, I generated measures of the extent to which programrelated and general education courses create excess credits. I examined the transcripts of some students who have earned excess credits, to see specifically what they did. Results: I take no normative position on the usefulness or harm of excess credits, but instead explore some of the factors that may generate them. Contribution: I suggest some policies that might be implemented to reduce excess credits, if desired.
This chapter describes community college STEM programs, including transfer‐oriented science and engineering (S&E) programs and workforce‐oriented technician programs, and the characteristics and educational pathways of the students who enroll in these programs.
A simple dynamic model of agent operation of an infrastructure system is presented. This system evolves over a long time scale by a daily increase in consumer demand that raises the overall load on the system and an engineering response to failures that involves upgrading of the components. The system is controlled by adjusting the upgrading rate of the components and the replacement time of the components. Two agents operate the system. Their behavior is characterized by their risk-averse and risk-taking attitudes while operating the system, their response to large events, and the effect of learning time on adapting to new conditions. A risk-averse operation causes a reduction in the frequency of failures and in the number of failures per unit time. However, risk aversion brings an increase in the probability of extreme events.
In this paper we explore the interaction between a dynamic model of the power transmission system (OPA) and a simple economic model of power generation development. Despite the simplicity of this economic model, complex dynamics both in the economics (prices, market share etc) and in the transmission system characteristics (blackouts, reliability etc) are found. Depending on the values of the control parameters (the price enhancement factor, the critical margin and the Minimal Acceptable Rate of Return) the system can be in various states with vastly differing properties. These states are characterized by power law tails in the failure sizes in one limit and exponential tails with extremely high frequency of failures in the other limit. At least some of these control parameters can be thought of as regulatory based and could therefore be directly influenced by reliability considerations.
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