Why have some nations produced enormous material wealth, while others have not? For my Senior Thesis in Economics I chose to engage with this question which has such profound impact on billions of people every day.
Current literature on this subject varies greatly, with focuses ranging from the neoclassical growth model, cross-country regressions, and institutional quality within a country. I approached this question from the later angle, trying to think about how different institutions affect economic growth. We generally think of market structure, governmental structure, the judicial system, and the regulatory environment as the societal entities which comprise a nation’s institutions.
My thesis proposal is that a key determiner for whether an institutional setup is conducive to economic growth is if the institutions in question provide an economic environment with clearly defined risks, or if those risks are ambiguous. That is, when economic risks are clear and tractable, like predicting how many fair coin flips end up being heads, people invest more than when risks are uncertain and not easily discernable (to continue with the coin example, think about trying to predict how many fair coin flips end up being heads, if the coin has an unknown weighting to land on a given side).
To bring empirical evidence to bear on this theory, I conducted an experimental survey on Amazon’s Mechanical Turk platform. Within a Qualtrics survey, 415 subjects were treated to different types of risk environments and asked to choose between investing (with a bonus payment based on the current risk environment), or working (with a clearly defined bonus payment). This study produced statistically significant evidence that people are more willing to invest when risks are clearly defined and tractable, as opposed to when those same risks are ambiguous. This potentially has important policy implications to bear when discussing institutional arrangements.
The CURF grant directly allowed me to fund a significant portion of my subject acquisition costs, which increased the statistical power of the study noticeably. Participating in this research allowed me to work through an IRB-approved micro-economic study from end-to-end, a capstone moment in my undergraduate experience at Penn. I was able to experience the challenges of designing a survey-based study, and was able to apply skills from my statistics course work to appropriately analyze the collected data. Learning how to use the tools necessary for the data collection, namely Qualtrics and Amazon’s Mechanical Turk platform, was extremely valuable. Lastly, through this work I engaged more deeply than ever with the field of economics about which I am most passionate. Many thanks to CURF for supporting this valuable experience.