Stereotypes, Asian Americans, and Wages: An Empirical Strategy Applied to Computer Use at Work

This article examines the effect on wages of the Asian-American stereotype as mathematically and technically adept, and the role this stereotype may play in explaining racial wage differences. We propose an empirical strategy to examine the influence of stereotypes on labor market outcomes, with a specific application to the wage premium associated with computer use at work. Using Current Population Survey data, ordinary least squares estimates do not provide compelling evidence that a positive stereotype affects wages for Asian Americans.     

Differences in Food Preparation by Race and Ethnicity: Evidence from the American Time Use Survey

This paper examines factors that influence individuals’ time use decisions regarding food preparation. Using American Time Use Survey data, Tobit estimates confirm that time spent on food preparation differs by race, ethnicity and socio-economic status. In general, hours worked, age, and education are negatively associated with time spent preparing food-cooked-at-home, while family care and leisure time are positively associated with it. Time spent purchasing prepared-food, on the other hand, has a positive relation to hours worked, family care, leisure time, education, and income, although its effects significantly differ by race and ethnicity. Estimates further show a positive relationship between time spent preparing food-cooked-at-home and family size, supporting the Barten theory of scale economies in home production.     

Labor market outcomes under digital platform business models in the sharing economy: the case of the taxi services industry

Business Economics - This research investigates the effects of ride-sharing online platforms on the taxi and limousine industry. It also compares and contrasts labor market outcomes between...     

Encoding of high-frequency order information and prediction of short-term stock price by deep learning

Predicting the price trends of stocks based on deep learning and high-frequency data has been studied intensively in recent years. Especially, the limit order book which describes supply-demand balance of a market is used as the feature of a neural network; however these methods do not utilize the properties of market orders. On the other hand, the order-encoding method of our prior work can take advantage of these properties. In this paper, we apply some types of convolutional neural network architectures to order-based features to predict the direction of mid-price trends. The results show that smoothing filters which we propose to employ rather than embedding features of orders improve accuracy. Furthermore, inspection of the embedding layer and investment simulation are conducted to demonstrate the practicality and effectiveness of our model.