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THE PAY EQUITY DILEMMA WOMEN FACE AROUND THE WORLD
Title
THE PAY EQUITY DILEMMA WOMEN FACE AROUND THE WORLD.
Creator
McMurray, Lana D, Yayla-Kullu, Muge, University of Central Florida
Abstract / Description
In this research, I examine the pay equity dilemma women face around the world and how it is different in various regions of the world. My research question focuses on "how a nation's cultural characteristics affect pay equity?" It is already documented that men are paid more than women. The goal of this study is to explain how individual characteristics of national culture (such as masculinity, individualism, power distance, and uncertainty avoidance) impacts this inequality. By increasing...
Show moreIn this research, I examine the pay equity dilemma women face around the world and how it is different in various regions of the world. My research question focuses on "how a nation's cultural characteristics affect pay equity?" It is already documented that men are paid more than women. The goal of this study is to explain how individual characteristics of national culture (such as masculinity, individualism, power distance, and uncertainty avoidance) impacts this inequality. By increasing the understanding of pay inequality, changes can be made that will improve the lives of not just women but the families of those women and the world overall. We use data from Geert Hofstede's national culture dimensions and the Global Gender Gap Report by the World Economic Forum. Our results suggest that gender gap reduces in low power distance cultures, in high individualistic cultures, in low masculine cultures, and in low uncertainty avoidance cultures. Our results provide evidence that the economic prosperity of women around the world is significantly impacted by cultural dimensions.
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Date Issued
2018
Identifier
CFH2000372, ucf:52906
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH2000372
STOCHASTIC OPTIMIZATION AND APPLICATIONS WITH ENDOGENOUS UNCERTAINTIES VIA DISCRETE CHOICE MODELSl
Title
STOCHASTIC OPTIMIZATION AND APPLICATIONS WITH ENDOGENOUS UNCERTAINTIES VIA DISCRETE CHOICE MODELSl.
Creator
Chen, Mengnan, Zheng, Qipeng, Boginski, Vladimir, Vela, Adan, Yayla Kullu, Muge, University of Central Florida
Abstract / Description
Stochastic optimization is an optimization method that solves stochastic problems for minimizing or maximizing an objective function when there is randomness in the optimization process. In this dissertation, various stochastic optimization problems from the areas of Manufacturing, Health care, and Information Cascade are investigated in networks systems. These stochastic optimization problems aim to make plan for using existing resources to improve production efficiency, customer...
Show moreStochastic optimization is an optimization method that solves stochastic problems for minimizing or maximizing an objective function when there is randomness in the optimization process. In this dissertation, various stochastic optimization problems from the areas of Manufacturing, Health care, and Information Cascade are investigated in networks systems. These stochastic optimization problems aim to make plan for using existing resources to improve production efficiency, customer satisfaction, and information influence within limitation. Since the strategies are made for future planning, there are environmental uncertainties in the network systems. Sometimes, the environment may be changed due to the action of the decision maker. To handle this decision-dependent situation, the discrete choice model is applied to estimate the dynamic environment in the stochastic programming model. In the manufacturing project, production planning of lot allocation is performed to maximize the expected output within a limited time horizon. In the health care project, physician is allocated to different local clinics to maximize the patient utilization. In the information cascade project, seed selection of the source user helps the information holder to diffuse the message to target users using the independent cascade model to reach influence maximization. \parThe computation complexities of the three projects mentioned above grow exponentially by the network size. To solve the stochastic optimization problems of large-scale networks within a reasonable time, several problem-specific algorithms are designed for each project. In the manufacturing project, the sampling average approximation method is applied to reduce the scenario size. In the health care project, both the guided local search with gradient ascent and large neighborhood search with Tabu search are developed to approach the optimal solution. In the information cascade project, the myopic policy is used to separate stochastic programming by discrete time, and the Markov decision process is implemented in policy evaluation and updating.
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Date Issued
2019
Identifier
CFE0007792, ucf:52347
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007792