Creative Small Businesses and Their Economic Impact on New York City's Neighborhoods

Richard Florida’s idea on “creative class” in the year 2002 led many researchers from diverse disciplines to seek the value of “creativity.” In his study, Florida argued that the creative class is a potential engine for metropolitan economic growth. This thesis is founded on this earlier concept and investigates the extent to which the growth of creative small businesses can impact the economic conditions of neighborhoods in New York City. Rather than assessing the issue from a metropolitan scale, however, this study zooms in and focuses on the neighborhood level in order to detect impacts at a micro-level. Through the mapping of creative small businesses (CSB) in New York City neighborhoods over time, this study found that there was a strong growth of creative small businesses in Brooklyn from 2000 to 2012. Decreases in unemployment rates and increases in median rent values were evaluated through mapping and statistical analyses to find that CSB might affect the decline of unemployment rates at the neighborhood boundary level. Also, median rent changes studied at the borough level showed an inverse relationship between creative small business growth and increase in rent values in Brooklyn, while the opposite was found for the Bronx and Queens. Since original property characteristics and rent values are much distinct in each borough and yield divergent results from the analysis, this thesis found that it is important to understand the relationship between CSB growth and rent values at the boroughs separately

Entrepreneurship and the Barrier to Exit: How Does an Entrepreneur-Friendly Bankruptcy Law Affect Entrepreneurship Development at a Societal Level?

Does an entrepreneur-friendly bankruptcy law encourage more entrepreneurship development at a societal level? How does bankruptcy law affect entrepreneurship development around the world? Drawing on a real options perspective, we argue that if bankrupt entrepreneurs are excessively punished for failure, they may pass potentially high-return but inherently high-risk opportunities. Amassing a longitudinal, cross-country data base from 35 countries spanning ten years, we find that a lenient, entrepreneur-friendly bankruptcy law encourages entrepreneurs to take risks and thus let entrepreneurship prosper. Components of an entrepreneur-friendly bankruptcy law are: (1) the availability of a reorganization bankruptcy option, (2) the time spent on bankruptcy procedure, (3) the cost of bankruptcy procedure, (4) the opportunity to have a fresh start in liquidation bankruptcy, (5) the opportunity to have an automatic stay of assets, (6) the opportunity for managers to remain on the job after filing for bankruptcy, and (7) the protection of creditors at the time of bankruptcy.

Cuban Tourism: Facing Opportunities of a New Era

Seung Hyun “Jenna” Lee is an Assistant Professor at East Carolina University. Jenna became inspired to instill knowledge to the next generation of leaders while working in the hospitality industry in Las Vegas. She attended graduate school at University of Nevada Las Vegas and University of Central Florida where she obtained her Masters of Science and Ph.D. in Hospitality Management. Her main area of research has been revenue management, market trends, and guest perceptions. Marketa Kubickova is an Assistant Professor at the University of South Carolina. Prior to joining academia, she held number of managerial positions in several upscale international hotels. Her research interests mainly focus on competitiveness, hotel sustainability, the role of government, with special emphasis on developing countries.Passport to Research (Visual Papers

Comparing Sample-wise Learnability Across Deep Neural Network Models

Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind of focus on individual samples invokes the concept of sample-wise learnability: How easy is it to correctly learn each sample (cf. PAC learnability)? In this paper, we approach the sample-wise learnability problem within a deep learning context. We propose a measure of the learnability of a sample with a given deep neural network (DNN) model. The basic idea is to train the given model on the training set, and for each sample, aggregate the hits and misses over the entire training epochs. Our experiments show that the sample-wise learnability measure collected this way is highly linearly correlated across different DNN models (ResNet-20, VGG-16, and MobileNet), suggesting that such a measure can provide deep general insights on the data's properties. We expect our method to help develop better curricula for training, and help us better understand the data itself.Comment: Accepted to AAAI 2019 Student Abstrac

Exchange rate regimes and international business cycle transmission revisited

노트 : A paper prepared for the conference on 'Korea and the World Economy', 21-22 July 2002, Seoul, South Korea