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My research focus is in experimental economics and behavioral economics. I use a combination of theoretical analysis and carefully designed laboratory experiments to study how individuals make economic decisions.
Click here for a short description of laboratory experiments in economics.
If you are unfamiliar with the field of experimental economics, you may find the concept of a "laboratory experiment in economics" to be a little curious. To be clear, we don't wear lab coats or safety goggles (although I suppose you could). Our "laboratory" is just a controlled environment in which we can vary some variable of interest while holding all else constant. (For example, we might vary the price of a good and study how demand for the good changes as a result.)
Economic experiments are often conducted in computer labs, where subjects essentially play economic computer "games". (I put "games" in quotes because people typically associate games with being fun, and these games usually won't meet that requirement.) In the Department of Agricultural and Resource Economics at UMD, we use our Symons Hall Experimental Laboratory (SHEL) to conduct our studies.
Similar to how lab experiments are used in the physical sciences to improve our understanding of the physical world beyond the lab, economic experiments play an important role in understanding how economic agents make decisions in real-world settings. Although the experimental setting of laboratory studies may seem unrepresentative of the "real world", the increased level of control in the lab allows researchers to identify and study the underlying mechanisms driving behavior. This information helps to improve our economic models and deepens our understanding of how individuals make decisions.
I am currently studying decision-making in the context of charitable giving and the interactions between risk, uncertainty, and ambiguity.
Click here for a short description of charitable giving.
Charitable giving research seeks to understand why individuals give to charity, and what factors influence their decision of whether and how much to donate. Charitable giving is an interesting phenomena because donors typically don't directly benefit from their donations. That is, unlike money spent on a typical good or service, money spent on charity usually goes towards providing others with some good or service. In the cynical world of economics, such behavior is often considered to be irrational. And yet, people choose to donate substantial amounts to all sorts of causes (charitable giving accounts for more than 2% of GDP in the US).
A better understanding of the mechanisms driving charitable giving can aid charitable organizations in their fund-raising efforts. Furthermore, insights regarding charitable giving also have meaningful implications for public policy and tax policy. To the extent public goods can be funded via voluntary contributions (i.e., donations), increasing charitable giving could allow for lower taxes and greater individual autonomy regarding which causes to support.
Click here for a
short medium description of risk, uncertainty, and ambiguity.
Risk, uncertainty, and ambiguity are closely related concepts in economics. Depending on who you ask, different definitions may be provided, but the following are common definitions for each of the three terms.
Both risk and uncertainty refer to situations where some future outcome has multiple possibilities. The distinction made between the two is that risk is used to describe situations where the possible future states occur with known probabilities, while uncertainty refers to situations where the probabilities associated with each possible state are unknown (and the set of all possible outcomes may itself not be known). For example, gambling on the outcome of a flip of a "fair" coin would be considered to entail risk because the probabilities of winning and losing are known, while gambling on a horse race would entail uncertainty because you presumably have no way of identifying the probabilities associated with each outcome.
In the case of uncertainty (such as gambling on a horse race), you may still be able to make some judgments, such as, "Horse A is more likely to win than Horse B," despite not being able to identify exact probabilities. That is, you're not necessarily completely in the dark regarding the probability of each outcome. In fact, depending on the assumptions you're willing to make, it is possible to use an individual's preferences over uncertain gambles to derive their subjective probabilities for each outcome (see L.J. Savage's The Foundations of Statistics (1954)). That is, theoretically you can present an individual with different possible gambles on the horse race and, using their preferred choice in each of the gambles, back out the probability they implicitly assign to each outcome. In the extreme scenario in which an individual is completely agnostic regarding the relative likelihood of all events, they will always prefer the bet that pays more (irrespective of which event the payout is contingent upon), and their preferences over gambles will imply a uniform distribution over all possible outcomes.
It can be argued that, in the real world, all probabilities are subjective probabilities. For instance, in the example of the "fair" coin, the probabilities are only "known" because the coin is assumed to be "fair," which is equivalent to assuming that the odds of flipping heads or tails is 50-50. In reality, no coin will actually provide exactly 50-50 odds (beyond unavoidable imperfections in the construction of the coin, the odds will also depend on the method used to flip it, etc.). Thus, the concept of "known probabilities" is really only a theoretical idealization. And as a result, the distinction made between risk and uncertainty is really only a matter of degree. That is, risk refers to situations where we are relatively confident about the probabilities associated with each possible outcome (such as the flip of a coin), and uncertainty refers to situations where we are relatively unconfident about the probabilities associated with each possible outcome (such as a horse race).
In this formulation of risk and uncertainty all gambles exist on a spectrum, with certain probabilities on one end (representing risk) and completely uncertain probabilities on the other end (representing extreme uncertainty). Where we are on this spectrum is sometimes referred to as the level of ambiguity. That is, ambiguity measures the amount of uncertainty with respect to the true probabilities associated with each outcome. For a gamble on the flip of a coin, while we may never know the exact odds, we can be pretty confident that the odds are close to 50-50. Thus, there is little ambiguity associated with a gamble on the flip of a coin. However, for a gamble on a horse race, while we may have a general idea about which horses are most likely to win, we will typically have little confidence that our estimated probabilities accurately reflect the true probabilities (assuming we are even able to derive estimated probabilities). Therefore, a gamble on a horse race will embody much more ambiguity.
The extent to which an individual seeks to avoid ambiguity is sometimes referred to as ambiguity aversion.
My current research projects are listed below. If you have any questions or comments regarding my work, please feel free to email me.
⚠️ Some of my projects are supported by a National Science Foundation (NSF) Doctoral Dissertation Research Improvement Grant (DDRIG) in Economics (Award #2315706). Projects receiving support from the NSF are marked below.
Do Matches Really Outperform Rebates? New Evidence from a Novel Experiment
Zedekiah G. Higgs and Neslihan Uler [NSF DDRIGE Award #2315706]
Working Paper 2023
(Job market paper)
How Donor Uncertainty Affects Their Response to Matches
Zedekiah G. Higgs [NSF DDRIGE Award #2315706]
Work in Progress
Can Loss Aversion Explain Ambiguity Aversion? Theory and Experiments
Zedekiah G. Higgs
Work in Progress
College Expenditures and Federal Aid Policy in the Market for Higher Education
Zedekiah G. Higgs
Archived Project 2019
"Pure logical thinking cannot yield us any knowledge of the empirical world; all knowledge of reality starts from experience and ends in it. Propositions arrived at by purely logical means are completely empty as regards reality."