Generosity and Vulnerability in Conditional Cooperation
Advisor: Dr. Zoey Wang
Internship Location: Carnegie Mellon University
Onsite Mentor: Dr. Russell Golman
Standard economic theory assumes that all agents are rational, acting in their own best interest. Yet, we often find real-life decisions to be far from rational as they are affected by our emotions and environment. Specifically, I am focusing on the factor of vulnerability through a virtual project with Carnegie Mellon University’s Social & Decision Sciences Department. Vulnerability has often been studied in psychology as a facilitator of trust and intimacy. Considering this, I am researching whether vulnerability can also induce trust in an economics setting through reciprocity, generosity, and cooperation. By creating my own economic game and distributing it through Prolific, an online site for completing surveys and conducting research, I am gathering a sample of subjects to participate in my study. In my game, there is a clear Nash equilibrium; however, due to the effects of vulnerability, some subjects may not choose the most rational choice for themselves. Instead, they may be more inclined to give to other people. I expect that vulnerability can induce connections between humans, which is extremely pertinent in real-life interactions, specifically in business through sales and marketing. The results of my research add an additional explanation for why real-life human behavior is not the same as that predicted by economic models, and more importantly, it may advise us on sales and marketing strategies that can induce customers to give back more to the business.
Project Blog
Week 1: Standard Economic Theory Is Flawed!
Hi readers!! I’m so excited to be taking you all through this journey with my Senior Project. For the next several weeks, I’ll be detailing my findings and progress here on this blog. By the end of these 12 weeks, I hope to have designed and conducted my own economic game. For this first post though, let’s start off by playing a simple economic game 😀
The Prisoner’s Dilemma
A classic. Most likely you know or have heard about this game before. Essentially, two prisoners are both given two choices: confess or stay silent. If both confess, both prisoners get 5 years in jail. If both stay silent, both prisoners get 2 years in jail. However, if one confesses and one stays silent, the one who confesses gets let off while the one who stays silent serves 10 years. (This is just one variation of the game; you might have seen it with different numbers involved).
To illustrate this in game theory, we utilize a payoff matrix, as shown below:
I color coded it so it’s easier to understand. Red corresponds to the payoffs for player 1, and blue corresponds to the payoffs for player 2. Each square corresponds to a set of strategies.
If you were one of the players, what would you choose? Confess or stay silent?
Intuitively, we might think: well, isn’t the outcome better for everyone if both just stayed silent?
Wrong. Well, it’s wrong according to standard economic theory at least, which assumes that perfectly rational decision makers are selfish, only looking out for their own best interests. Thus, it is in each player’s best interest to confess since that yields the highest payoff for him/herself regardless of what the other player chooses.
Nash Equilibrium
This static state of {confess, confess} where neither player has an incentive to change his or her playing strategy is known as the Nash equilibrium (termed after John Nash). For the purposes of behavioral economic research, Nash equilibrium is incredibly important as it can serve as a predictor of theoretical and rational behavior.
However, it has been observed in many economic games that real-life human behavior often does not align with these predictions from Nash (hence why standard economic theory is flawed!!), and I plan on exploring more instances of this through my project.
Behavioral Economics
The prisoner’s dilemma game is a classic example of strategic decision making where the predicted rational outcome ({confess, confess}) is not the socially optimal outcome ({stay silent, stay silent}). These deviations from standard economic theory led to contributions in the field of behavioral economics, which utilizes social preferences and utilities to explain why human behavior isn’t rational at times.
What does that have to do with my project?
As for my project, I’ll be specifically looking at how acts of generosity and vulnerability may influence people’s levels of cooperation. By designing my own game with a clear Nash equilibrium (I’m thinking a one-shot, two-stage game at the moment, but anything is up to change!), I’ll be able to study people’s deviations from Nash to see whether generosity and vulnerability actually influence people to be more cooperative. Perhaps my results can inform businesses about how they can better develop sales and marketing strategies to connect with their customers!
I hope to spend the next few weeks studying several economic game designs and behavioral economics concepts before delving into my own game.
I’m also planning on using Prolific to distribute my game and recruit participants. The online platform is fairly easy to use, and I can even distribute monetary bonuses on there.
I hope you enjoyed this tiny glimpse into game theory, behavioral economics, and my senior project! I’ll try to include a fun game for each blog that I post 😀
Until next time,
Cindy
Sources
Rabin, M. (1998). Psychology and Economics. Journal of Economic Literature, 36(1), 11–46. http://www.jstor.org/stable/2564950
Goeree, Jacob, K., and Charles A. Holt. 2001. “Ten Little Treasures of Game Theory and Ten Intuitive Contradictions.” American Economic Review, 91 (5): 1402-1422.
Holt, C. A., & Roth, A. E. (2004). The Nash equilibrium: A perspective. Proceedings of the National Academy of Sciences, 101(12), 3999–4002. https://doi.org/10.1073/pnas.0308738101
Week 2: Being Generous Is Bad, Economically??
This week, I began taking a look at which specific factors affect individual decision-making. I have come to a few conclusions:
Reference Points And Relativity Matter People Tend To Focus On Reference Levels Instead Of Absolute Levels. For Example, Loss Aversion Explains How We Dislike Same-Sized Losses More Than We Like Same-Sized Gains (Even Though They’re The Same In Absolute Terms).
There Is No Pure “Self-Interest” Sorry Adam Smith. If Everyone Was Strictly Looking Out For Self-Interest, No One Would Contribute To The Public Good. But The Thing Is, People Do. There Is, In Fact, Altruistic People!!
Heuristics And Biases Cloud Judgement Hindsight Bias, Confirmation Bias, Mental Shortcuts, Reliance On Past Precedence… There’s Just So Many Instances Where We Think We Are Correct And Rational But Instead We’re Just Blinded By Our Brain.
IRL, many of us are not perfectly rational according to economic theory. We care about social preferences—about how other people/society perceive our actions. We care about how other people treat us—about the overall fairness of a situation.
That sense of irrationality explains why people tip at restaurants or donate impulsively to charity causes. Technically, you’re not obligated to pay. Especially if you’re at a random restaurant you’ll never visit again, there’s no economic reason for you to tip. But you still end up tipping. Because you care about how others perceive you and the social norm. Maybe you sincerely want to thank your server. Or maybe tipping makes you feel morally right and you receive a boost of serotonin and inflated ego. That’s up to you to decide.
Altruism
Alright, since my project is partially studying generosity and how that induces cooperative behavior, I feel obligated to define altruism.
There’s actually two different types of altruism:
Simple altruism is when people value the utility of others, even when it may cut into their own utility.
We can model it using this equation
U1(x) = (1- r) * Π1(x) + r * Π2(x)
(Rabin, 1998)
where U1(x) is person 1’s utility function, Π1(x) is person 1’s well-being from outcome x, and Π2(x) is person 2’s well-being from outcome x. When r > 0, that indicates how our concern for other people affect our own utility and thus decision-making.
The second type of altruism is impure altruism, or warm-glow giving. This occurs when people are altruistic due to the social implications of doing so, whether that is due to social pressures, fear of being judged, guilt of not giving, or any other psychological factor.
Warm-glow giving has been studied extensively in public goods games, such as those conducted by James Andreoni.
Public Goods Game
This week’s game is the PGG! Again, there’s many variations, and I’m just using my own numbers again :0
Assume you’re in a group with 4 other players. Each of you receive 2 tokens at the beginning of each round (5 rounds total). When prompted, all of you are given the choice to contribute any number of tokens to the public good. (This decision is kept anonymous and no one knows how much each group member has contributed!)
Tokens in the public good are multiplied by 1.5 and distributed equally among everyone in the group. Tokens not contributed to the public good are kept by each player in every round.
Shown above is just one example of a round.
How many tokens would you contribute? 0, 1, or 2?
Answer
According to Nash, you should always contribute 0 and free-ride off of other people’s contributions. However, in reality, people still contribute tokens, even in the last round of the game, when there’s actually no incentive to contribute anymore. In early stages of the game, you might want to contribute to build up trust.
As a result, PGG can be a great way to study generosity and cooperation. I was considering something like this for my study, but there’s just so many confounding variables and difficulties, ugh.
First off, the game requires a group, and that certainly makes participant recruiting a pain. I’d much rather have a one-shot game between two participants. Second, each round of the game presents a new scenario and potentially, a new strategy for each player to take. Third, there’s no way to directly measure whether generosity induces cooperation.
Anywho, I’m going to keep looking at game designs and bringing some of my findings to this blog.
Until next time,
Cindy
Sources
Andreoni, J. (1988). Why free ride? Journal of Public Economics, 37(3), 291–304. https://doi.org/10.1016/0047-2727(88)90043-6
Andreoni, J. (1990). Impure altruism and donations to public goods: A theory of warm-glow giving. The Economic Journal, 100(401), 464. https://doi.org/10.2307/2234133
Dawes, R. M., Orbell, J. M., Simmons, R. T., & Van De Kragt, A. J. (1986). Organizing groups for collective action. American Political Science Review, 80(4), 1171–1185. https://doi.org/10.2307/1960862
Rabin, M. (1998). Psychology and Economics. Journal of Economic Literature, 36(1), 11–46. http://www.jstor.org/stable/2564950
Week 3: SVB Is Totally A Prisoner’s Dilemma
I just had an idea come to mind… I want to model my economic game off of the Silicon Valley Bank (SVB) bank failure. I imagine that you might have some questions about how I aim to do that, or even what the whole situation is about. I hope this blog post can address those questions~~
What happened?
The latest fiasco in the banking industry and possibly the largest ever bank run in United States’ history—SVB. Essentially, a bank run happens when many customers simultaneously withdraw their funds, leading to liquidity and insolvency issues for the bank. In the case of SVB, $42 billion of deposits were withdrawn on March 9th, and the bank was shut down by federal regulators on the following day.
What is SVB?
SVB was owned by parent company SVB Financial Group and it held $211 billion in total assets by the end of fiscal year 2022. SVB primarily catered to startups and venture-backed firms, which all had great amounts of deposits in the bank.
What caused SVB to collapse? T
he bank run was a huge contributing factor, but it was sort of the end result and doesn’t account for the factors that led up to it.
As SVB grew during the COVID-19 pandemic, it utilized its deposits to invest in long-term treasury bonds. These investments are generally low-risk since they are protected by the United States government.
However, with rising interest rates to combat high inflation, the value of the bonds that SVB invested into decreased. This is because newly issued bonds carry higher interest rates and are thus more enticing to investors than the pre-existing ones, which drives the value of already-issued bonds down. This propensity for bond value to change based on interest rates is known as duration.
Alright, so SVB’s portfolio of investments were decreasing in value. But could they have held onto those bonds until maturity and they would’ve been fine?
Yes, SVB would’ve been fine if they held onto those bonds, collected their coupons, and waited until maturity. However, the bank needed money to accommodate customer withdrawals, so it sold some of its treasury bonds at a value less than par value. This resulted in a $1.8 billion loss.
Why didn’t the bank have any cash in its reserves in the first place? This dates back to 2018, when President Donald Trump changed a provision in the Dodd-Frank Act. Originally, the act subjected banks with more than $50 billion in assets to tougher regulations and risk assessments. This was put in place after the 2008 financial crisis as a safety measure. Trump’s Economic Growth, Regulatory Relief, and Consumer Protection Act loosened the requirement to banks with over $250 billion in assets. As a result, SVB wasn’t under any scrutiny by federal regulators.
In addition, the COVID-19 pandemic witnessed a change in United States banking from a fractional reserve system to a no reserve system. In the past, banks were required to keep a portion of their deposits on hand; however, in an effort to boost the economy during the pandemic, reserve requirements were removed so banks could lend more money or invest in securities.
Well… SVB used the extra cash to invest in treasury bonds, and high interest rates led to an overall loss on those investments.
Okay, so it seems like high interest rates played a pretty big factor in SVB’s collapse. What led to high interest rates in the first place?
Raising interest rates is a strategy used by the Federal Reserve to combat high inflation. In order to slow the economy down, higher interest rates will lead to less consumer demand and spending as people borrow less money (the cost of borrowing is more expensive and the return on savings is greater).
Technically, the Fed is only changing the federal funds rate, which is the interest rate for commercial banks to borrow and lend amongst each other. However, this change has a rippling effect across consumer loans, such as mortgages, auto loans, and lines of credit.
Inflation. Every problem seems to come back to inflation. Is there even a root cause? No one can pinpoint a single event or moment in time that spurred high inflation in the macroeconomy. Inflation is a lagging indicator, meaning that it reflects past performance. We don’t really know whether we’re experiencing inflation until the high prices start to hurt our pockets.
The COVID-19 pandemic, government stimulus checks, supply-chain issues, the Russo-Ukrainian war… they can all be said to have contributed to high inflation.
Back to my project…
Ahahaha, that was a pretty big tangent, yet what I summarized is only the tip of the iceberg. But seriously, the whole situation is genuinely very interesting and I think we can extract a lot of things to be learned from this bank failure.
As I mentioned earlier, SVB would’ve been fine if they held onto their portfolio and if clients cooperated/did not withdraw everything all at once. Sound familiar? Yes, I think the situation is like a mass-scale prisoner’s dilemma where SVB and the clients chose the most rational choice for themselves, missing out on the socially-optimal choice for everyone.
That revelation sparked something in me LOL. If the choices leading up to SVB’s collapse can be explained by behavioral finance, I can model it using an economic game. I’m hoping to apply different treatments so I can study potential ways that SVB could’ve prevented the bank runs.
For example, perhaps not spooking out their customers and telling them they lost $1.8 billion? (Even though that’s not transparent and I am in no way advocating for that). In addition, the Fed came in too late to reassure clients that their deposits would be guaranteed. That’s also a potential problem I can explore.
Still a lot of thinking and brainstorming to be done… How will I create the game to best model SVB?
Dr. Wang and I are hoping to churn out some details at our meeting next week. This was not what I was thinking of originally (since I could not foretell the future back in December), but it actually fits quite well into what I wanted to study.
Fingers crossed,
Cindy
Sources https://www.axios.com/2023/03/11/the-largest-bank-run-in-history https://www.cnbc.com/2023/03/10/silicon-valley-bank-collapse-how-it-happened.html https://www.cnbc.com/2023/03/17/financial-psychology-and-bank-runs.html https://www.investopedia.com/terms/d/dodd-frank-financial-regulatory-reform-bill.asp https://www.investopedia.com/terms/r/requiredreserves.asp https://www.investopedia.com/what-happened-to-silicon-valley-bank-7368676
Week 4: The Bank Game (Internally Named)
This is going to be way harder than I expected.
There’s just so many questions/factors to consider…
WHAT EXACTLY ARE MY TREATMENTS??!?!?
How Am I Going To Design The Treatments So They Best Reflect SVB?
How Many People Am I Including In My Study?
Am I Running An Economic Experiment Or Survey?
Where Am I Getting Participants If It’s An Economic Experiment?
What Are My Incentives For Participation?
There’s more, but these are the most pressing considerations. I think I’m going to tackle them one by one during the next two weeks. You know, 12 weeks seemed like a long time when I first started working on my project, but we’re one-third of the way done already. Time flies.
The Bank Game!
Alright, as I mentioned in my last blog, I’m going to model SVB’s collapse using an economic game. I do not have a name for it yet. Any suggestions are appreciated <3 !!
I’m thinking it’ll be sort of like a reverse public goods game, where players have an initial amount of tokens in a “bank” and they can withdraw any number of tokens within a specific time frame. The tokens kept in the “bank” earn “interest” while those withdrawn can be used in a secondary market to trade. This emulates the clients of SVB, which are primarily business owners, as they can withdraw their money to spend it on any expenses incurred by their business.
Concern 1: Treatment Groups
The scenario listed above would be my control. It reflects the daily operations of a bank and its clients, modeling pre-collapse SVB.
So now my question becomes, what are my other treatment groups? To that, Dr. Wang and I came up with 3 scenarios:
An FDIC-Insured Treatment, Meaning Deposits Are Protected Up To A Certain Amount
A Government-Protected Treatment, Where All Principal Is Guaranteed
A No Protection Treatment, Which Sucks.
I’m not sure if I’ll be adding more treatments. The thing is, each additional treatment group requires an additional sample of participants. I also currently have no idea what other measures SVB could’ve put in place. If I think of an appropriate one though, I’ll add it to this list.
Concern 2: Accurate Modeling
This is so hard.
I’ve spent this week thinking over the game design and discussing it with my advisors, but we still haven’t finalized it. At first I was thinking that the game has a 100% bank failure component, but then I realized that the risk of leaving money in the bank is way too high and players don’t have an incentive to keep it there. So then we changed it to a scenario where banks have a 10% chance of losing all their money. This expected payoff would then balance with that from interest earnings.
Then the other issue became the time restraint. In order to correctly model a bank withdrawal, the game needs to take place within a specific time frame, such as two minutes for example. Recruiting for participants when multiple people have to be live at the same time is near impossible. There’s just too much time commitment. The easy way would just be to sit in the cafeteria and pull participants into my study (but there’s cons with that approach too). Or I can just conduct an economic survey and ask for what people would do in a hypothetical scenario (but that’s boring).
I should probably figure that out since it ties into concerns 3, 4 and 5! But regardless of what that answer is, I need my game design and instructions finalized, so I’ll be working on that next week.
Current Design
Honestly, I just need to figure out the design for one treatment, and the other two are simple modifications away. Let’s take the government-protected treatment first. (This is just the first draft of the game!!)
Everyone starts with, let’s say, 100 tokens in the bank. These tokens earn 10% interest if they are kept in the bank for the entire duration of the game, let’s say 2 minutes. Now, if money is withdrawn by 2 or more people, the bank will go bankrupt. Those who withdrew get their principal payment and a certain amount of interest (depending on what time they withdrew). Those who left their money in the bank will lose their interest, but the government will back their principal.
Since each participant only receives one treatment, this will be a between-subject design.
I’m going to start typing up the instructions this week. I can promise it’ll have to go through multiple rounds of revisions.
Until next time,
Cindy
Week 5: Game Design Finalized!!!
FINALLY.
This week, I finalized the game and started writing the instructions, but I still haven’t figured out whether I’m running a “lab” experiment or an online survey. Dr. Wang recommended that I spend a day or two in Google or Apple’s cafeteria if I am doing the in-person experiment. She said “they’ll find it cute and interesting and want to participate.”
Currently, I’m writing the instruction sheet as if I am doing the in-person run, but I need to make another copy of the instructions and game design in the form of a survey. There shouldn’t be that big of a difference between the two copies—just some minor changes here and there, especially with the incentives. In-person experiment will probably utilize a conversion rate between tokens and candy while online survey will utilize a conversion rate between tokens and money.
Instructions Rough Draft (No government protection treatment)
As I mentioned last week, my three treatments are no government protection, FDIC-insured, and government protected. Their general game design and instructions are very similar, so I’ll just explain one of them. This is also the first draft of the design, so there will likely be small changes compared to the final version!
“You will participate in an economic decision-making game with 4 other participants. Each one of you have 100 tokens saved in the public account at the beginning of the game. The game will last for 2 minutes. Within the 2 minutes, you are allowed to take any amount of tokens out of the public account or keep it in the account. Tokens taken out can be used to purchase candy in a market at the end of the game while tokens kept in the account receive a multiplier. If tokens are kept in the account for the entire duration of the game, the number of tokens in the account are multiplied by 1.2x. All tokens possessed at the end of the game can be redeemed for candy.
If a total of more than 50 tokens are taken out by the 5 participants, all tokens kept in the public accounts will disappear. Tokens that were already taken out of the account are safe and can be redeemed for candy. For example, if one participant takes out 35 tokens, another participant takes out 20 tokens, a third participant takes out 5 tokens, and the rest of the participants decide to keep their tokens in the public account, the three people receive their requested tokens; however, because the sum of the tokens taken out is greater than 50, the tokens that are still in the account will disappear and no one will have access to those tokens.
Even if 50 tokens or less are taken out, there is still a 20% chance that the public account will fail at the end of the game, meaning no one will have access to their tokens that are in the account. You will not know how many tokens other participants have taken out during the game.
Now, I will give you a response sheet. At the top left hand of the response sheet is your unique ID. If you wish to take tokens out, you must record the number on the response sheet. If you are not taking tokens out, please write down “0.”
Explanations
Originally I had 10 people in a single game, but that’s too much to manage and recruit in a single run so I’m going with 5 instead.
I also increased the multiplier and chance of failure from .1 to .2 so there’s more incentive there. For example, if I end up using a conversion ratio of 10:1 for tokens to candy, a 1.1 multiplier is just a difference of 1 candy—not a big difference :/.
The 50 token threshold is not a random number; it’s derived from a 10% required reserve ratio. Since there’s 500 tokens in the public account, I figured that the threshold where the bank would start defaulting would be 10% of that.
Please let me know if there’s anything confusing. The instructions need to be as clear and concise as possible so there’s no confounding errors or misunderstanding in the experiment.
Next week, I’m going to run some preliminary tests with friends and family to see whether I need to change anything. Weeks 7 and 8 I want to collect and analyze data. The rest of the weeks will be dedicated to writing up my findings and policy proposal!
Until next time,
Cindy
Week 6: Revise, Revise, And Revise
This week was pretty light in terms of workload but still extremely crucial.
I spent the majority of my time revising my instructions to be as clear and concise as possible. That means avoiding any bias or phrasing that may intentionally influence a participant to make a certain decision.
It also meant sending my instructions around for fresh pairs of eyes! I’ve been reading them over and over, and I know my game to the smallest detail. Everything makes perfect sense to me of course, but the game might be confusing to a first time reader. So, I got a few of my friends to look over my instructions and point out any questions/misunderstandings they had. Shoutout to them~~
I then revised my instructions with their feedback and with Dr. Wang. It was a pretty long process—moving pieces and bits around, changing the structure of a sentence, switching out conjunctions, editing punctuation, etc.
Here is the newest version that I’ll be using for my testing trials:
“Each one of you have 100 tokens saved in the public account at the beginning of the game. The game will last for 2 minutes. Within the 2 minutes, you are allowed to take any amount of tokens out of the public account or keep it in the account. Tokens taken out are kept by you until the end of the game, where they can be converted into money. Tokens kept in the account receive a multiplier, where the number of tokens in the account are multiplied by 1.2x. These tokens that are kept in the account, including the additional tokens received from the multiplier, can be converted into money at the end of the game.
However, if a total of more than 50 tokens are taken out by the 5 participants, the public account will fail and all tokens kept in the public accounts will disappear. Tokens that were already taken out of the account are safe and can be converted into money. For example, if one participant takes out 35 tokens, another participant takes out 20 tokens, a third participant takes out 5 tokens, and the rest of the participants decide to keep their tokens in the public account, the three people will receive their requested tokens; however, because the sum of the tokens taken out is greater than 50, the tokens that are still in the account will disappear and no one will have access to those tokens.
Even if 50 tokens or less are taken out, there is still a 20% chance that the public account will fail at the end of the game, meaning no one will have access to their tokens that are in the account. You will not know how many tokens other participants have taken out during the game.
All tokens possessed at the end of the game can be converted into money. Each token is equivalent to one cent.”
I also want to mention that I’ve decided to go with the online survey. In-person is more difficult to coordinate and I won’t be able to get a representative pool of participants. I would rather have the conveniency of a virtual experiment, where I can easily send monetary incentives, record responses, and simulate the bank run.
The biggest con is that I won’t be able to answer any questions the participants may have, which is why I’m trying to make the instructions as clear as possible. The other concern I have is timing. I set my game for a duration of two minutes, but there’s no way I can actually enforce that. Even if I set a timer, I can’t predict how long it takes for participants to read the instructions and then spend two minutes making their decision. I’m considering taking that part out entirely but we’ll see.
Some information about how I’ll recruit participants: I’ll be running my experiment on Prolific, which is an online site that allows me to distribute a survey, such as a Google Form, to participants around the world. I can adjust the sample I want and send monetary rewards as well. It’s a pretty nifty site for running my experiment, and I will explore its features a bit more next week.
I will also spend some time next week running preliminary experiments to make sure everything is working in preparation for the actual experiment. Can’t wait!
Until next time,
Cindy
Week 7: Nesting Box Breakthrough
Even weeks after the installation of nesting boxes in the exhibits, the lemurs had yet to actually enter the nesting boxes. Instead, they would use the top of the boxes as a perch. With the change from shaving to hay as a cushion, the lemurs have begun to reside inside the nesting boxes. While it is a possibility that seasons have influenced their behavior as they often give birth around spring and nests are used as a safe place for the babies to stay, such a sudden shift immediately after the change in materials suggests otherwise.
I’m not completely sure why straw would make such a huge change, and I could not find anything online that might explain it. Straw seems to be the standard over shaving, but it is also unclear why that is true. Ally enjoys munching on the straw, so that may play a part. While I might not be able to explain why the lemurs prefer it, it is certainly a welcome improvement. Such a small change can make a huge difference, which just proves the importance of every tiny adjustment.
Here are some photos of Ally enjoying the nesting box (featuring sleepy Razoky).
Unfortunately, I could not get any photos of Meva in the nesting box, but she has been sighted multiple times.
Week 7: Introducing Prolific
This week, I made some final touches in preparation for running the experiment. No major changes but I thought I’d update you guys:
Added 2 Demographic Questions About Age And Gender
Included Group Size Of 5 Participants At The Beginning Of The Instructions
Checked Instructions For Grammar Issues
Replaced 2 Minute Limitation With Whenever Participant Submits Survey
I sent my final version to Dr. Wang to look over one last time, but I anticipate being able to run my experiment in the coming days!
Prolific
To do so, I will be using Prolific, which makes it super easy to distribute surveys to users around the world. I spent some time exploring its interface, and it’s really convenient—you can set parameters for which participants you want to recruit and you can pay participants after the conclusion of the study.
I will have to create a name for my study and write a description so users on Prolific can see the experiment. I can also internally name the study which is really useful.
I then get to insert the link to my Google Form so participants can access the survey. There’s also a bunch of cool features that allow me to record Prolific IDs and track when a participant has finished.
Here I can choose how many participants I want, where I’m getting my sample, and how I want that sample to be distributed.
And lastly I can choose how much to pay participants based on an hourly wage and estimated duration of completion.
I did a few test runs with the survey, and 4 minutes seems enough to read through the instructions and answer all the items. I did a cost estimate using 4 minutes for estimated time of completion, $12.00/hr, and 105 recruited participants (35 for each study). It should be about $84 for participant fees plus some service fees to Prolific. Then for the experiment itself, the most that it would cost is $126. This is assuming none of the banks fail and all participants keep their tokens in the public account. That would be highly unlikely, so $126 is our upper bound.
Hopefully, I can launch the experiment next week and get some results!
Until next time,
Cindy
Week 8: Data Is In!
Woohoo! I finished running all three treatments on Prolific!
I ended up recruiting about 35 participants in a standard sample for each treatment and used $0.50 for the participation bonus ($10.00 hourly wage and 3 minutes estimated completion). I know $10.00 might sound way too low to be ethical, but that’s just in comparison to California standards. The federal minimum wage is actually $7.25.
Currently, I just have all the data in graphs and excel sheets (as shown below). The results do look slightly different from each other, but I need to analyze them next week to figure out whether there’s actually a statistically significant difference. Then I will (hopefully) be able to come to a conclusion regarding SVB and the Fed’s safety regulations!
No Government Protection
FDIC Protected
Government Protected
Until next time,
Cindy
Week 9: P = 0.061!!
I can’t believe we’re nearing the end of the blog and my project! Collecting the data and seeing my results have been so gratifying these last two weeks :D. As anticipated, the Fed recently released a huge document with their own findings and review of their banking regulatory measures, specifically regarding the regulation of SVB. I am still going through the massive 118-page pdf, but their main findings thus far are:
SVB’s Board And Management Failed To Manage Risks
Supervisors Failed To Address Problems + Vulnerabilities As The Bank Grew
Stronger Regulatory Framework Is Required Form The Fed
The report basically affirmed a lot of the explanations for why SVB failed. The bank’s portfolio was growing super quickly but its liquidity and the Fed’s regulations were not keeping up. One proposed solution is a faster transition to heightened regulations when a bank’s portfolio grows. The Fed is also creating a dedicated novel activity supervisory group, aimed at monitoring risks that may have fallen under the radar. In addition, the report suggested greater capital or liquidity requirements in certain cases.
Overall, the report is mostly addressing issues regarding governance and regulation whereas my research is looking at clientele behavior. I believe both sides should be examined when understanding and studying a bank run just because it can tell us the full picture of what went wrong and give us multiple angles to address the issue. It was pretty amusing to read in the report that any proposed changes, such as those to stress tests and capital requirements, wouldn’t even come into effect until years later. How many more banks will collapse due to interest rate risk as a result??
Sigh, even First Republic went down the beginning of this week. And all of these collapses just keep coming back to the Fed’s interest rate hikes. Everyone is trying to examine what went wrong and how to prevent it from happening again, but what if there’s not even a solution to combating these high interest rates?
That is beyond the scope of my project but just some food for thought ;).
Back to my project…
I spent this week organizing and analyzing my data on excel. I was also able to pay all participants with their bonus. It totaled to $71.06 for all three treatments.
In each treatment, there were 7 groups of 5 participants each, and all of the public accounts failed. This meant I never had to utilize the chance error of bank failure (unfortunate, because I wanted to run my random number generator). It also meant I never had to consider the multiplier, or interest rate, in any of my calculations.
Data Analysis
For preliminary analysis, I was primarily concerned with the average number of tokens taken out by each group and individual. There was definitely a trend going on.
From now on, I’ll be referring to the No Government Protection treatment as the Baseline, FDIC Insured as Treatment 1, and Government Protected as Treatment 2. From the chart, we can see how participants were taking less tokens out as they were guaranteed their deposits back.
Next, I looked at the number of participants who took out 10 tokens or less. In my experimental design, 10 is sort of the threshold for most tokens each participant in a group can take out to ensure that the bank does not fail.
I wouldn’t say this is that apparent of a trend, but it’s still there as a reference. More people are being conservative with the amount of tokens they choose to take out as they are insured with more money.
T-tests and Linear Regression
Now on to some more in-depth statistical analysis: t-tests and linear regression.
I did all of these on excel (<3), which was super convenient as I just had to organize the data and then let excel do the math.
I ran 3 t-tests, comparing Baseline to Treatment 1, Baseline to Treatment 2, and also Treatment 1 to Treatment 2. The t-test is basically just a way to compare the mean of two datasets. So earlier I showed a graph of the average number of tokens taken out by individuals in each treatment. The t-test is basically seeing whether those differences are caused by a difference in treatment or not.
Each t-test yields a p-value, which tells me how likely the results are due to chance. For the purposes of my experiment, I will be using an alpha level of 0.10. In the t-test comparing Baseline to Treatment 2, I got a value of p=0.061, which means that I can reject the null hypothesis!! There is a significant difference in the participants’ behavior when the entirety of their deposits are protected.
I want to explain why I chose to use the one-tail test instead of two-tail. The one-tail t-test obviously yields more significant results, but I figured it was applicable to the purposes of my study since I just wanted to see if the treatments would yield any change to withdrawal behavior. The two-tail test cares about directionality, so it considers the possibility that the treatment would lead to both a worsening and improvement in the number of tokens withdrawn. I simply only care about if the treatment leads to less tokens withdrawn (and not the other way around), which is why the one-tail is still okay :)
I then ran multiple linear regressions and simple linear regressions using factors such as age, gender, and treatment for my independent variable. Age and gender did not seem to have any sort of relationship with the number of tokens taken out.
I will still need to discuss with Dr. Wang next week about the implications of the linear regression results, but that is my conclusion so far :).
Until next time,
Cindy
Week 10: A Post Of Gratitude
Thank you so much to all for following my project these past few weeks! I don’t have major updates since I’m just working on the slides and presentation for May 20th, but I just wanted to give a shoutout to those who have made this project possible and helped me along this process. It has been so rewarding, learning more about finance and economics and being able to guide my own project with real-world applications.
Thank you Dr. Wang for advising me on this project; your help has been tremendous. Discussing the game design and our ideas every week was so inspiring and engaging, and I’m so thankful I was able to combine what I learned from Corporate Finance and Game Theory to create this Senior Project.
Thank you Professor Golman for introducing me to behavioral economics and experimentation. I was guided through a whole new field, learning about different topics and creating novel possibilities that I wouldn’t have thought of before. Under your guidance, I was able to come up with my own ideas and take charge of them fully.
Thank you Mr. Brady, our 2023 Senior Project advisor, for organizing this massive catalogue of Senior Projects.
Thank you BISV for giving us the opportunity to showcase our work.
And lastly, thank you readers, for showing curiosity and support. It truly means a lot and I’m glad people can walk away having learned something new.
I hope you are able to join me on the morning of May 20th for my Senior Project Showcase Presentation!
Cindy