“ … the wage distribution. Mark Williamson (Advisor: Ercan Karadas ) Union College Economics Department, … ”
Abstract
Throughout the past two decades, e-commerce has exploded in the United States, with Amazon leading the way. Following criticism from the media and politicians, Amazon instituted a nationwide minimum wage of $15 per hour for all employees at the end of 2018. Since Amazon fulfillment and sortation centers employ approximately twenty percent of all warehouse employees in the United States, one must ask whether or not their recent institution of a $15 minimum wage in 2018 has improved overall local warehousing wages in counties with Amazon warehouses present. This paper will answer this question by conducting a quarterly county-level analysis on the American warehousing industry from 2016 to 2020 using QCEW data from the Bureau of Labor Statistics website, comparing counties with Amazon warehouse to counties without them. This paper finds that Amazon's minimum wage increase in 2018 resulted in lower overall warehouse wages for counties with an Amazon warehouse present due to the increase in lower-level employees in the industry. Moreover, this paper will consider the spillover effects of the minimum wage change onto both employment in the warehousing industry as well as the retail industry. Lastly, the spillover effects of Amazon Air on air travel costs at the airports served by Amazon will be analyzed.
“ … bottom of the wage distribution. Mark Williamson (Advisor: Ercan Karadas ) Union College Economics Department … ”
Abstract
Throughout the past two decades, e-commerce has exploded in the United States, with Amazon leading the way. Following criticism from the media and politicians, Amazon instituted a nationwide minimum wage of $15 per hour for all employees at the end of 2018. Since Amazon fulfillment and sortation centers employ approximately twenty percent of all warehouse employees in the United States, one must ask whether or not their recent institution of a $15 minimum wage in 2018 has improved overall local warehousing wages in counties with Amazon locations present. This paper will answer this question by conducting a quarterly county-level analysis on the American warehousing industry from 2016 to 2020 using QCEW data from the Bureau of Labor Statistics website, comparing counties with Amazon warehouse to counties without them. This paper finds that Amazon's minimum wage increase in 2018 resulted in lower overall warehouse wages for counties with an Amazon warehouse present due to the increase in lower-level employees in the industry. Moreover, when looking at employment spillover effects, counties with an Amazon warehouse saw an increase in warehousing employment as well as a decrease in clothing retail employment after 2018.
“ … Nowcasting Using Google Trends Shizhe Li Supervisor: Ercan Karadas ********* Department of Economics Union College … ”
Abstract
This paper incorporates Google Trends in addition to other macroeconomic predictors to nowcast
the regional-level Consumer Price Index in the United States by using the Artificial Neural
Network (ANN). In order to assess the contribution of adding Google Trends, we first consider a
benchmark forecasting model to regional inflation. The benchmark model solely utilizes panel
data of macroeconomic and financial variables such as employment, money supply M2, gold
price, and bond yield. And then we extend the model by incorporating regional Google Trends
into the macroeconomic panel. Google trends data is not available at the regional level.
Therefore, I first construct regional Google Trends data by aggregating state-level Google Trends
in which each state receives a weight proportional to its GDP. The results show that the ANN
model with Google trends data nowcasts U.S regional level CPI more accurately based on the
MAE and MSE measures on a 12-month horizon testing period ranging from Feb 2022 to Jan
2023. Finally, I present regional-level CPI nowcasts for the period Feb 2023-April 2023 with the
ANN model using Google trends data and a panel data of macro-financial data.
“ … Acknowledgements: I would like to thank my advisors, Dr. Ercan Karadas and Dr. John Rieffel, for their guidance … ”
Abstract
Google's Android Operating System (OS) is open-source software available for public use. Several smartphone manufacturers (Original Equipment Manufacturers or OEMs) like Samsung and LG use and contribute to the Android Open-Source Project (AOSP). These contributions can be tracked using their commits to the AOSP, a public data set. This research will explore the relationship between a firm's or an OEM's contributions to AOSP and marketing a product based on the contribution type and date, which could ultimately contribute to the success of a phone model and the firm. These contributions to AOSP can be in the form of a firm's exploratory and exploitative learning, which can be related to the Time to Market (TTM) of a product, which would be a phone model in this research. I will use the mobile telecom data on Android commits (software contributions) to understand the difference between exploratory and exploitative learning of these Android OEMs. The dataset consists of the complete population of Android commits written by software engineers worldwide between 2008-2022 (approximately 16 million observations), which Prof. Dallas and his colleagues have collected over a period. Because Android is open source, this is free and open for public use. The data set highlights some handset (mobile phone) companies contributing to Android and to which release (different releases of Android), and how quickly they come up with a handset (or phone) after the new version is released that version of the Android OS. I will be re- searching the time and type (exploratory or exploitative) of these contributions and exploring what affects their TTM. This research could potentially lead to discovering strategic management techniques and timed marketing in the Information and Communications Technology (ICT) industry, focusing on firms that use and contribute to the Android OS.
“ … of Economics and Political Science, March 2022. ADVISORS: Ercan Karadas and Guillermina Seri This paper analyzes how … ”
Abstract
This paper, drawing on my Senior Thesis, analyzes how the U.S. Federal Reserve policies impact the economic conditions of countries around the world. In particular, my research focuses on eight countries considered as emerging market economies from 2000 to 2020. Country-specific macroeconomic and political variables help me examine how developing countries alter their short-term targets to avoid volatile spillover from Washington. Moreover, I use each country's sovereign nominal credit spread to assess its economic conditions, in particular its perceived risk premium or cost of borrowing. The countries examined in my thesis are Argentina, Brazil, Colombia, Mexico, Russia, Korea, Qatar, and Turkey. Using a two-way fixed effect model, I found that U.S. short-term rate hikes and heightened volatility in the S&P 500 increases the nation's cost of borrowing. Poor country-specific underlying macroeconomic conditions drive up the nation's risk premium, seen through a more extensive sovereign credit spread. Correspondingly, elevated domestic social and political vulnerability levels positively impact each nation's perceived risk alluded to decaying economic conditions. Complementing my econometric empirical results, my thesis provides four country case studies of Argentina, Brazil, Russia, and Turkey, highlighting historical macro management changes in policy targets to hedge against U.S. spillover. Understanding the role of the U.S. political-military hegemony in the context of the global credit cycle is vital, my presentation argues. Otherwise, a shortsighted technocratic perspective from monetary policymakers in Washington could put the landscape of international finance at risk of an eventual collapse.
