2 resultados para DEMAND FOR PHDS IN STATISTICS

em DRUM (Digital Repository at the University of Maryland)


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This dissertation examines the price sensitivity of demand for higher education among non-traditional students in the United States. Chapter 1 discusses the issues related to the demand for higher education. It presents the recent trends and reviews the literature addressing these issues. A major conclusion that emerges from this chapter is that the price sensitivity of demand for higher education appears to depend on the source of the variation in price and the characteristics of the students who face the price change. The baseline estimate for the price sensitivity of demand is that a $1,000 (in year 2000 dollars) decrease in tuition costs should result in a 4 percentage-point increase in enrollment for the traditional 18- to 24-year-old student. Chapter 2 examines the price sensitivity of demand for higher education for military spouses resulting from variation in tuition due to military-mandated moves across states. The data suggest that a $1,000 (in year 2000 dollars) decrease in the cost of 2-year schools is associated with a 1--1.5 percentage-point increase in the probability of attending college. This estimate is less than half the previous estimates due to in-state tuition price differences faced by the civilian 18- to 24-year-old population on a percentage-point basis. However, this represents a 7--10 percent increase for this population, and the magnitude of this metric is in line with previous estimates. This suggests tuition assistance can be an effective means of increasing enrollment for military spouses, but other barriers to education for this population may also need to be addressed. Chapter 3 examines the impact of a change in the tax treatment of savings set aside for higher education by those who decide to suspend their education and enter the workforce. The taxation of these funds appears to have increased the rate at which these funds are included in an employee's initial contract and the quantity of funds allocated. These results are counterintuitive if the tax preference was the primary reason for the savings plan. However, these results suggest the rationale for the savings plan was to offer targeted additional compensation to recruits with greater negotiating power. Taxation of funds previously set aside did not appear to have a statistically significant impact on their utilization. Point estimates of the price sensitivity of demand from changes in the out-of-pocket costs for higher education induced by the taxation of these funds were small and often not statistically significant. The results from this dissertation show responses to changes in the net cost of college that differ by the source of price variation and the population experiencing them. This is consistent with the previous literature. This dissertation contributes to the literature by providing estimates for the price sensitivity of demand for higher education to previously understudied non-traditional students.

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Datacenters have emerged as the dominant form of computing infrastructure over the last two decades. The tremendous increase in the requirements of data analysis has led to a proportional increase in power consumption and datacenters are now one of the fastest growing electricity consumers in the United States. Another rising concern is the loss of throughput due to network congestion. Scheduling models that do not explicitly account for data placement may lead to a transfer of large amounts of data over the network causing unacceptable delays. In this dissertation, we study different scheduling models that are inspired by the dual objectives of minimizing energy costs and network congestion in a datacenter. As datacenters are equipped to handle peak workloads, the average server utilization in most datacenters is very low. As a result, one can achieve huge energy savings by selectively shutting down machines when demand is low. In this dissertation, we introduce the network-aware machine activation problem to find a schedule that simultaneously minimizes the number of machines necessary and the congestion incurred in the network. Our model significantly generalizes well-studied combinatorial optimization problems such as hard-capacitated hypergraph covering and is thus strongly NP-hard. As a result, we focus on finding good approximation algorithms. Data-parallel computation frameworks such as MapReduce have popularized the design of applications that require a large amount of communication between different machines. Efficient scheduling of these communication demands is essential to guarantee efficient execution of the different applications. In the second part of the thesis, we study the approximability of the co-flow scheduling problem that has been recently introduced to capture these application-level demands. Finally, we also study the question, "In what order should one process jobs?'' Often, precedence constraints specify a partial order over the set of jobs and the objective is to find suitable schedules that satisfy the partial order. However, in the presence of hard deadline constraints, it may be impossible to find a schedule that satisfies all precedence constraints. In this thesis we formalize different variants of job scheduling with soft precedence constraints and conduct the first systematic study of these problems.