By Michael Levin
Overcrowding in United States hospitals’ emergency departments (EDs) has been identified as a significant barrier to receiving high-quality emergency care, resulting from many EDs struggling to properly triage, diagnose, and treat emergency patients in a timely and effective manner. Priority is now being placed on research that explores the effectiveness of possible solutions, such as heightened adoption of IT to advance operational workflow and care services related to diagnostics and information accessibility, with the goal of improving what is called throughput efficiency. However, high costs of technological process innovation as well as usability challenges still impede wide-spanning and rapid implementation of these disruptive solutions. This paper will contribute to the pursuit of better understanding the value of adopting health IT (HIT) to improve ED throughput efficiency.
Using hospital visit data, I investigate two ways in which ED throughput activity changes due to increased HIT sophistication. First, I use a probit model to estimate any statistically and economically significant decreases in the probability of ED mortality resulting from greater HIT sophistication. Second, my analysis turns to workflow efficiency, using a negative binomial regression model to estimate the impact of HIT sophistication on reducing ED waiting room times. The results show a negative and statistically significant (p < 0.01) association between the presence of HIT and the probability of mortality in the ED. However, the marginal impact of an increase in sophistication from basic HIT functionality to advanced HIT functionality was not meaningful. Finally, I do not find a statistically significant impact of HIT sophistication on expected waiting room time. Together, these findings suggest that although technological progress is trending in the right direction to ultimately have a wide-sweeping impact on ED throughput, more progress must be made in order for HIT to directly move the needle on confronting healthcare’s greatest challenges.
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Advisors: Professor Ryan McDevitt, Professor Michelle Connolly | JEL Codes: I1, I18, O33
By Jenny Jiao
In the past decade, police departments have increasingly adopted predictive policing programs in an effort to identify where crimes will occur and who will commit them. Yet, there have been few empirical analyses to date examining the efficacy of such initiatives in preventing crime. Using police and court data from the second-largest police department in the country, this paper seeks to evaluate the pilot version of Chicago’s Strategic Subject List, a person-based predictive policing program. Using a boundary discontinuity design, I find that individuals eligible for the Strategic Subject List were 2.07 times more likely to be found not guilty of all charges in court than similarly situated individuals in the control group. Taking into account crime category heterogeneity, I find evidence that individuals previously arrested for drug crimes drive this result. This research sheds light on the potential unintended consequences of person-based predictive policing.
Advisors: Professor Patrick Bayer, Professor Bocar Ba | JEL Codes: K4, K42, O33
By Neelesh T. Moorthy
I assess whether forward citations—how often patents are cited by subsequent patents—reliably capture patent quality. A high-quality invention might lack forward citations if there are no competing, patenting firms. This introduces measurement error in using citations to measure patent value. I test whether greater competition makes forward citations better measures of patent quality, with eight and twelve-year patent renewal rates serving as my benchmark measures of patent quality. Patent data come from the manufacturing survey in Cohen, Nelson, and Walsh (2000). I conduct logit regressions of patent renewal on forward citations and the number of competitors faced by surveyed manufacturing labs. While the regression results do not support the competition hypothesis, they confirm that forward citations positively predict renewal. They also lend insight into firms’ strategic renewal decisions.
Advisors: Wesley Cohen and Michelle Connolly | JEL Codes: O31, O34
By Elizabeth Lim, Akshaya Trivedi and Frances Mitchell
On March 29, 2016, the FCC initiated its first ever two-sided spectrum auction. The auction closed approximately one year later, having repurposed a total of 84 megahertz (MHz) of spectrum. The “Incentive Auction” included three primary components: (1) a reverse auction where broadcasters bid on the price at which they would voluntarily relinquish their current spectrum usage rights, (2) a forward ascending clock auction for flexible use wireless licenses which determined the winning bids for licenses within a given geographic region, and (3) an assignment phase, where winning bidders from the forward auction participated in single-bid, second price sealed auctions to determine the exact frequencies individual licenses would be assigned within that geographic region. The reverse auction and the forward auction together constituted a “stage.” To guarantee that sufficient MHz were cleared, the auction included a “final stage rule” which, if not met, triggered a clearing of the previous stage and the start of a new stage. This rule led to a total of four stages taking place in the Incentive Auction before the final assignment phase took place. Even at first glance, the Incentive Auction is unique among FCC spectrum auctions. Here we consider the estimated true valuation for these licenses based on market conditions. We further compare these results to more recent outcomes in previous FCC spectrum auctions for wireless services to determine if this novel auction mechanism
impacted auction outcomes.
Advisor: Michelle Connolly | JEL Codes: L5, O3, K2, D44, L96
By Anna Katherine Kropf
Recent economic literature suggests that entrepreneurship in technological fields can spur economic growth, making it a popular topic for city development officials. Yet, this increasingly popular phenomenon is met by many economic questions. One of those questions is which characteristics of metropolitan areas are attractive to entrepreneurs. To answer the question of attractiveness on both the small business and corporate levels, I compare across two case studies: Amazon’s search for a second headquarters and Google’s tech hub network. Using principal component analysis, I statistically deduce seven components of attractiveness from an original 34 variables. These components are then weighted using three methods—a case study, a survey, and an empirical method—to produce comparable indices of attractiveness. Generally, I find that sizeable population and healthy economy are the strongest components. However, the statistically insignificant components that can change an urban area’s ranking considerably are talent and geographic network effects. Ultimately, creating policy to maximize these aspects can change a city’s innovative
Advisor: Dr. Charles Becker | JEL Codes: O, O3, R, R1, R11
What Fosters Innovation? A CrossSectional Panel Approach to Assessing the Impact of Cross Border Investment and Globalization on Patenting Across Global Economies
By Michael Dessau and Nicholas Vega
This study considers the impact of foreign direct investment (FDI) on innovation in high income, uppermiddle income and lowermiddle income countries. Innovation matters because it is a critical factor for economic growth. In a panel setting, this study assesses the degree to which FDI functions as a vehicle for innovation as proxied by scaled local resident patent applications. This study considers research and development (R&D), domestic savings, imports and exports, and quality of governance as factors which could also impact the effectiveness of FDI on innovation. Our results suggest FDI is most effective as inward direct investment in countries outside the technological frontier possessing adequate existing domestic investment capital and R&D spending to convert foreign investment capital and technological spillover into innovation. Nonetheless, FDI was not a consistent indicator for innovation; rather, the most consistent indicators across this study were R&D and domestic savings. Differences amongst income groups are highlighted as well as their varying responses to our array of causal factors.
Advisor: Lori Leachman | JEL Codes: A10, B22, C82, E00, E02, O10, O11, O30, O31, O32, O33, O34, O43
By Hong Zhu
M-PESA, the hugely popular mobile money system in Kenya, has been celebrated for its potential to “bank the unbanked” and increase access to financial services. This paper provides evidence to support this idea and explores mechanisms through which this might be the case. It specifically looks at the savings products held by individuals and how this changes in relation to M-PESA use. It then constructs an index for measuring the extent to which individuals are integrated into the formal financial sector. This paper argues that M-PESA’s effect on financial inclusion is a growing phenomenon, which suggests that keeping pace with the rapid evolutions of this mobile money system should be a high priority for researchers. As this paper elucidates, M-PESA has become notably more integrated with the formal financial sector in 2013 as compared to 2009, which holds implications for user behavior.
Advisor: Michelle Connolly, Xiao Yu Wang | JEL Codes: D14, E42, G21, G23, O1, O17, O16, O33 | Tagged: Financial Inclusion, Mobile Money, Savings,Technology
By Trent Chiang
In this paper I relate the numbers of university licenses and options to both university research characteristics and research expenditures from federal government or industrial sources. I apply the polynomial distributed lag model for unbalanced panel data to understand the effects of research expenditures from different sources on licensing activity. We find evidence suggesting both federal and industrial funded research expenditures take 2-3 years from lab to licenses while federal expenditures have higher long-term dynamic effect. Break down licenses by different types of partners, we found that federal expenditures have highest effect with small companies and licenses generating high income. Further research is necessary to analyze the reason for such difference.
Advisor: David Ridley, Henry Grabowski | JEL Codes: I23, L31, O31, O32, O38 | Tagged: I