PhD researchers examine minimum-wage
employment, online shopping habits
Doctoral students Evan Totty, economics, and Na Zhang, management information systems, were honored with the 2014-15 Robert W. Johnson Award for Distinguished Research Proposal. They exemplify how Krannert researchers push the boundaries of knowledge and address some of the world’s most complex issues.
Doctoral student Na Zhang's research examines online shopping habits and crowd funding websites. (Stock photo)
In his proposal “Minimum Wages and Employment: A Factor Model Analysis,” Totty examines how the recently developed factor model approach allows economists to model the effect of unobservable characteristics and events on observed data.
Totty says it is particularly well-suited to address the issues in the minimum wage-employment debate because it allows researchers to account for the fact that counties and states that are geographically near each other do not necessarily have similar economic dimensions. With this method, researchers can control for unobservable factors without having to make restricting assumptions such as similarities among bordering counties and states.
Totty focuses specifically on the effect of minimum wage on restaurant employment and teenage employment because restaurants employ a higher fraction of workers at the minimum wage level than any other industry and teenagers are more likely to earn the minimum wage than adults.
“From a policy perspective, my findings suggest that raising the minimum wage is not as harmful on employment as is often believed,” Totty says. “This could be used to support raising minimum wages, but there are still many other important considerations. From an academic perspective, factor model methods should receive more attention in future work.”
Zhang‘s proposal “Essays on Nudging Consumers’ Behavior: Evidence from Online Grocery Shopping” examines how the advent of “Big Data” has revealed new dimensions of consumer purchasing habits and how vendors can use this information to their advantage.
Zhang focuses on three distinct areas: identifying and ranking customer purchasing potential, comparing coupon distribution versus product information distribution, and comparing project reward structures on crowd-funding platforms such as Kickstarter.
“I’m fascinated by information technology’s ability to transform different industries, like online grocery shopping and the fundraising industry,” Zhang says.
Developing a method to rank customer propensity to purchase would allow retailers to target which consumers should receive coupons, which are limited. “Future online grocery stores would feature personalized recommendations to customers and make their shopping experience much easier and more enjoyable,” Zhang says.
Zhang’s research also looks at crowd-funding and its infinitely customizable project reward structures. Kickstarter, for example, collected $480 million for over 19,000 projects in 2013.
To collect the vast amount of data on such websites, Zhang uses a “web crawler” robot that automatically searches the sites’ pages for project data. She then uses multiple programs and scripts to analyze it.
Zhang’s findings could help entrepreneurs rid their projects of unhelpful reward choices and maximize their project’s total revenue.
“Our study, using data from Kickstarter, shows that entrepreneurs could strategically and dynamically modify the project reward options to make the fundraising process more successful,” she says.