Abstract: We study how transparent information about effort impacts the allocation of earnings in a dictator game experiment. We manipulate information about the respective contributions to a joint endowment that a dictator can keep or share with a counterpart…
Employees within an organization are sensitive to whether they are being treated fairly. Greater organizational fairness is shown to improve job satisfaction, reduce employee turnover, and boost the organization’s reputation. To study how transparent information impacts fairness perceptions, we conduct a dictator game with a jointly earned endowment.
The endowment is earned by completing a real effort task in the experiment, an analog to the labor employees contribute to employers. First, two players work independently to create a pool of money. Then, the subject assigned the role of the “dictator” allocates the final earnings between them.
In the transparent treatment, both dictators and recipients have access to complete information about their own effort levels and contributions, as well as those of their counterparts. In the non-transparent treatment, dictators have full information about the relative contributions of both players, but recipients do not know how much each person contributed to the endowment. The two treatments allow us to compare the behaviors of dictators who know they could be judged and held to reciprocity norms with dictators who do not face the same level of scrutiny.
*drumroll* results:
This graph shows the amount of money the dictators take from the recipient contribution, in cents. There are two ways to look at this. Notice the spike next to zero. Most dictators do not take much from what their counterpart earned. They are *dictators*, meaning they could take everything. Most take almost nothing, regardless of the treatment. We interpret this to mean that they are acting out of a sense of fairness, and we apply a humanomics framework to explain this in the paper.
Also, there is significantly more taken in non-transparency. When the worker does not have good information on the meritocratic outcome, then some dictators feel like they can get away with taking more. Some of this happens through what we call “shading down” of the amount sent by the dictator under the cover of non-transparency.
There is more in the paper, but the last thing I’ll point out here is that the “worker” subjects (recipients) anticipate that this will happen. The recipients forecast that the dictator would take more under non-transparency. In our conclusion, we mention that, even though the dictator seems to be at an advantage in a non-transparent environment, the dictator still might choose a transparency policy if it affects which workers select into the team.
View and download your article* This hyperlink is good for a limited number of free downloads of my paper with Demiral and Saglam, says Springer the publisher. Please don’t waste it, but if you want the article I might as well put it out there. I posted this on 11/2/2024, so there is no guarantee that the link will work for you.
Parkinson’s Law, the principle that “work expands to fill the time available for its completion,” was originally intended as a satirical observation on bureaucratic inefficiencies. However, it has broader applications, especially in academic life. When preparing to teach an intermediate microeconomics class, for example, I often find that Parkinson’s Law applies: no matter how much time I dedicate, there’s always more content, illustrative examples, and analysis that could be included. The time invested in preparation creates a tradeoff between covering the broad spectrum of microeconomic theories versus delving deeply into a few core concepts. Either approach can be effective, but Parkinson’s Law reminds me that more preparation doesn’t always imply improvement.
Teaching intermediate microeconomics presents a natural tradeoff between breadth and depth. The course covers foundational concepts like consumer and producer theory, market structures, and welfare economics, and each of these areas is rich with intricate details, special cases, and real-world applications. A broader approach would expose students to more topics, providing a more comprehensive view of microeconomics. Exploring fewer topics fosters more critical thinking and analytical skills. Too much preparation on one topic can detract from time that could be spent introducing other essential concepts… Or other classes for that matter.
Let’s say I have a few hours to prepare for a Monday lecture on consumer theory. I might fall into a spiral of over-preparation: digging into endless variations of consumer surplus or finding additional applications that illustrate price elasticity. This is precisely what Parkinson’s Law warns against; if I pour time into my preparation, then the lecture becomes denser beyond the ideal for my students’ comprehension.
The extra hours may result in a more detailed presentation, but this doesn’t necessarily mean better learning outcomes. A concise, well-planned lecture is often just as effective—if not more so—than one crammed with detail. Overwhelming students with information that won’t stick is bad pedagogy.
I doubt anyone has been waiting for my take on the Trump and Harris economic plans to decide their vote. More than that, it is entirely reasonable to vote based on things other than their economic plans entirely- like foreign policy, character, or preservingdemocracy. But either Trump or Harris will soon be President, and thinking through their economic plans can help us understand how the next 4 years are likely to go.
The bad news is that both campaigns keep proposing terrible ideas. The good news is that, thanks to our system of checks and balances, most of them are unlikely to become policy. The other good news is that our economy can handle a bit of bad policy- as Adam Smith said, there’s a lot of ruin in a nation. After all, the last Trump admin and the Biden-Harris admin did all sorts of bad economic policies, but overall economic performance in both administrations was pretty good; to the extent it wasn’t (bad unemployment at the end of the Trump admin, bad inflation at the beginning of Biden-Harris), Covid was the main culprit.
Note that this post will just be my quick reactions; the Penn Wharton Budget Model has done a more in-depth analysis. They find that Harris’ plan is bad:
We estimate that the Harris Campaign tax and spending proposals would increase primary deficits by $1.2 trillion over the next 10 years on a conventional basis and by $2.0 trillion on a dynamic basis that includes a reduction in economic activity. Lower and middle-income households generally benefit from increased transfers and credits on a conventional basis, while higher-income households are worse off.
We estimate that the Trump Campaign tax and spending proposals would increase primary deficits by $5.8 trillion over the next 10 years on a conventional basis and by $4.1 trillion on a dynamic basis that includes economic feedback effects. Households across all income groups benefit on a conventional basis.
We are already running way too big a deficit; candidates should be competing to shrink it, not make it worse. This isn’t just me being a free-market economist; Keynes himself would be saying to run a surplus in good economic times so that you have room to run a deficit in the next recession.
Now for my lightning round of quick reactions:
No tax on tips: both campaigns are now proposing this; it is a silly idea, there is no reason to treat tips differently from other income. The good news is that this almost certainly won’t make it through Congress.
Taxes: Trump’s Tax Cuts and Jobs Act of 2017 is set to expire in 2025. He says he wants to renew it and add more tax cuts, though he will need a friendly Congress to do so. Harris wants to let most of it expire, but renew and expand the Child Tax Credit while raising taxes on the wealthy and corporations. There’s a good chance we end up with divided government, in which case probably only the most popular parts of TCJA (increased standard deduction and child tax credit) get renewed and no big new changes happen.
Price controls: both campaigns, especially Harris‘, have talked about fighting ‘price gouging’, leading economists to worry about the price controls (any intro micro class explains why these are a bad idea). My guess is that no real bill gets passed, President Harris gets the FTC to make a show of going after grocery stores but nothing major changes.
Tariffs: Harris would probably leave them where they are; Trump is promising to raise them 10-20% across the board and 60% on China. This would lead to higher prices for US consumers and invite retaliation from abroad; we saw the same things when Trump raised tarriffs in his first term, but he is promising bigger increases now. This is worrisome because the President has a lot of power to change tariffs unilaterally; it would take a bill getting through Congress to stop this, and I don’t see that happening.
Regulation / One in two out: The total amount of Federal regulation stayed fairly flat during the Trump administration thanks to his one in two out rule, while regulation increased during the Biden-Harris administration. I expect that a second Trump admin would behave like the first here, while a Harris admin would continue the Biden-Harris trend.
Antitrust: FTC and DOJ have been aggressive during the Biden-Harris administration, blocking reasonable mergers and losing a lot in court. But Trump’s VP candidate JD Vance thinks FTC Chair Lina Khan is “doing a pretty good job”, so we could see this poor policy continue either way. More generally, voters should consider what a Vance presidency would look like, because making him Vice President makes it much more likely (Trump is 78 and people keep trying to shoot him; plus VPs get elected President at high rates).
Immigration: Immigration rates have been high under the Biden-Harris admin, while Trump’s top two planks in his platform are “seal the border” and “carry out the largest deportation operation in American history”. Economically, this would lead to a reduction in both supply and demand in many sectors, with the relative balance (so whether prices go up or down) depending on the sector. The exclusion of Mexican farmworkers in the 1960’s led to a huge increase in mechanization, to the point that domestic farmworkers saw no increase in their wages; presumably this also limited the potential harm to the food supply.
Crypto: The Biden admin has been fairly negative on crypto; both Harris and Trump are making pro-crypto statements in their campaigns, particularly Trump.
Marijuana: The Biden admin is in the process of rescheduling marijuana to no longer be in the most restricted category of drugs. I think Trump would probably see the process through, while Harris definitely would.
Elon Musk / Civil Service: Elon Musk has thrown his support hard behind Trump, spending lots of money, tweeting continuously, and attending rallies. It’s hard to know how much of this is genuine support for a range of Trump’s policies, how much is to get the Federal government to stop suing his companies so much, and how much is to get himself a direct role in government. In any case, it is a safe bet that more Federal civil servants get fired in a Trump admin than in a Harris admin. What’s much harder to say is how many get fired, and what proportion of firings come from a genuine attempt to improve efficiency vs a purge of those Trump sees as disloyal. Personally I think government could stand to treat its employees a bit more like the private sector, making it easier to fire people for genuine poor performance (not political views), but also allowing for more flexibility on improved pay, benefits, and the ability to focus on achieving goals more than following the way things have always been done. But I doubt that’s on the table either way.
CFTC/ Prediction Markets: The Biden CFTC has tried to crack down on prediction markets, though they have mostly failed in the courts, and the growth of Kalshi and Polymarket mean that prediction markets are now bigger than ever. Most of the anti-prediction-market decisions have been 3-2 votes of the democrats vs the republicans, so a new republican appointee could lock in the legal gains prediction markets have made, though this is far from guaranteed (not all Rs support this).
Final Thoughts: So much of how things turn out will depend not just on who wins the Presidency, but on whether their party wins full control of Congress. Because the Democrats have a lot more Senate seats up for grabs this year, Harris is much more likely to be part of a divided government (especially once you consider the Supreme Court).
Because of this, and because of the ability of the President to raise tariffs unilaterally, I see Trump as the bigger risk when it comes to economic prosperity, as well as non-economic issues. Harris with a Republican Senate is the best chance of maintaining something like the status quo, whereas a Trump victory is likely to see bigger changes, many of them bad.
That said, predicting the future is hard, and this applies doubly to Presidential terms. I’m struck by how often in my lifetime the most important decisions a President had to make had nothing to do with what the campaign was fought over. Who knew in 1988 that the President’s biggest task would be managing the breakup of the Soviet Union? In 2000, that it would be responding to 9/11? Bush specifically tried to distinguish himself from Gore as being the candidate more against “nation-building”, then went on to try just that in Afghanistan and Iraq. In 2004, who knew that the biggest issue of the term would be not Social Security or foreign policy, but a domestic financial crisis and recession? In 2016, who knew that they were voting on the President that would respond to the Covid pandemic? In 2020, who knew that they were voting on who would respond to Russia’s invasion of Ukraine?
The most important issue for the next President could easily be how they address China or AI, because those are clearly huge deals. I won’t vote based on this, because I don’t know who has the better plan for them, because I have no idea what a good plan looks like. Or the most important issue could be something that comes completely out of left field, like Covid did. Not even the very wise can see all ends.
What I do know is that, while much of the Libertarian Party has recently gone from its usual “goofy-crazy” to “mean-crazy“, Chase Oliver is so far the only candidate pandering to me personally. But it’s not too late for other politicians at all levels to try the same.
See you all again next Thursday, by which time the election will, I hope, be over.
In Fiscal Year 2019, the US federal government spent $4.45 trillion dollars. In Fiscal Year 2024, spending was $6.75 trillion, or an increase of $2.3 trillion dollars. If you adjusted the 2019 number for inflation with the CPI, it would only be about $1 trillion more. Where did that additional $2.3 trillion go?
It will probably not surprise you that most of the increase in spending went to the largest categories of spending. Historically these have been health, Social Security, and defense, but now we must also include interest spending (roughly equal in size to defense and Medicaid in 2024). Indeed, with these areas of spending, 72 percent of the increase is accounted for. Add in the next three functions, and we’ve already accounted for over 90 percent of the increase.
Importantly, most of these categories are outside of the annual federal budget process, meaning that Congress does not need to approve new spending each year (Congress could change them, just as it could change any law, but it’s not part of the annual budgeting process). The “mandatory” categories, as they are called in federal law, are shaded red. I’ve striped with red and blue the health and income security functions, because some of this is subject to the annual budget process, but most of it is not. For example, Medicaid is not subject to the budget process (biggest part of the “health” function) and SNAP is not subject to the budget process (a big part of income security — it is set by the Farm Bill, usually on a five-year cycle).
So, when we talk about the $2 trillion increase since 2019, or the roughly $2 trillion cuts that would be needed to balance the budget, keep in mind that most of this is not subject to the annual budget process. It would require Congress to consider them specifically to enact cuts — though some big categories, such as Social Security, would be automatically cut under current law once their trust funds are exhausted (coming up on about a decade for the Social Security Old-Age Trust Fund).
This week at my university the Economics Department Co-hosted an event titled “Interest Rates and Student Loans” with the Office of Financial Aid. I discussed interest rates for a broad audience of young adults. Below is my talk on interest rates.
If you want to know how many pigs were killed in the United States yesterday, the USDA has the answer. But if you want to know how many humans were killed in the US this month, the FBI is going to need a year or two to figure it out. The new Real Time Crime Index, though, can tell you much sooner, by putting together the faster local agency reports:
Trends currently look good, though murders still aren’t quite back to pre-2020 levels.
In addition to graphing top-line state and national trends, the Real Time Crime Index also offers the option to download a CSV with city-level data going back to 2018. This seems like a great resource for researchers, worthy of adding to my page of most-improved datasets.
As the presidential race finishes out the last two weeks, it’s clearly a close race. In the past I have recommended prediction markets, and right now these are giving Trump about 60% odds. There have been lately a few big bettors coming into the markets and primarily betting on Trump, so there has been speculation of manipulation, but even at 60-40 the race is pretty close to a toss-up.
Another tool many use to follow the election are prediction models, which usually incorporate polling data plus other information (such as economic conditions or even prediction markets themselves). One of the more well-known prediction models is from Nate Silver, who right now has the race pretty close to 50-50 (Trump is slightly ahead and has been rising recently).
But Silver’s model, and many like it, is likely very complicated and we don’t know what’s actually going into it (mostly polls, and he does tell us the relative importance of each, but the exact model is his trade secret). I think those models are useful and interesting to watch, but I actually prefer a much simpler model: Ray Fair’s President and House Vote-Share Models.
The model is simple and totally transparent. It uses just three variables, all of which come from the BEA GDP report, and focuses on economic growth and inflation (there are some dummy variables for things like incumbency advantage). Ray Fair even gives you a version of the model online, which you can play with yourself. Because the model uses data from the GDP report, we still have one more quarter of data (releasing next week), and there may be revisions to the data. So you can play with it (and one of the variables uses the 3 most recent quarters of growth), but mostly these numbers won’t change very much.
I don’t like to follow politics, much less politics in another country. Policy on the other hand? I’m always hooked.
Most of us have heard of President Javier Milei by now. He became Argentina’s president in December of 2023. Prior, he had been in charge of a private pension company, a university professor who taught macroeconomics, had hosted a radio show, and has written several books. See his Wikipedia entry for more.
What makes him worth talking about is that he appears a little… unique. He’s boisterous and rattles off economic stories and principles like he wants you to get up and do something about it. To anyone in the US, he looks and behaves like a weird 3rd-party candidate – sideburns and all. He’s different. Here he is bombastically identifying which government departments he would eliminate:
I’ve enjoyed the spectacle, but haven’t paid super close attention. I know that he is libertarian in political outlook, drops references to Austrian economists and their ideas by the handful, and doesn’t mince words. Here he is talking at the Davos World Forum (English & Dubbed).
So what?
Argentina has a long history of high inflation and debt defaults. Every president always says that they’ll fix it, and then they don’t. There have been periods of lower inflation, but they don’t persist. Among Milei’s stated goals was to end that cycle and bring down inflation. His plan was to substantially reign in deficit spending by eliminating entire areas of government. We’re now approaching a year since Milei took office, and I thought that I would check in. Below is the CPI for Argentina since 2018. As soon as Milei took office prices spiked, but have started coming down more recently. Similarly, the Argentine Peso has fallen in value by 50% since he’s taken office. Ouch!
Kalshi just announced that they will begin paying interest on money that customers keep with them, including money bet on prediction market contracts (though attentive readers here knew was in the works). I think this is a big deal.
First, and most obviously, it makes prediction markets better for bettors. This was previously a big drawback:
The big problem with prediction markets as investments is that they are zero sum (or negative sum once fees are factored in). You can’t make money except by taking it from the person on the other side of the bet. This is different from stocks and bonds, where you can win just by buying and holding a diversified portfolio. Buy a bunch of random stocks, and on average you will earn about 7% per year. Buy into a bunch of random prediction markets, and on average you will earn 0% at best (less if there are fees or slippage).
This big problem just went away, at least for election markets (soon to be all markets) on Kalshi. But the biggest benefit could be how this improves the accuracy of certain markets. Before this, there was little incentive to improve accuracy in very long-run markets. Suppose you knew for sure that the market share of electric vehicles in 2030 would over 20%. It still wouldn’t make sense to bet in this market on that exact question. Each 89 cents you bet on “above 20%” turns into 1 dollar in 2030; but each 89 cents invested in 5-year US bonds (currently paying 4%) would turn into more than $1.08 by 2030, so betting on this market (especially if you bid up the odds to the 99-100% we are assuming is accurate) makes no financial sense. And that’s in the case where we assume you know the outcome for sure; throwing in real-world uncertainty, you would have to think a long-run market like this is extremely mis-priced before it made sense to bet.
But now if you can get the same 4% interest by making the bet, plus the chance to win the bet, contributing your knowledge by betting in this market suddenly makes sense.
This matters not just for long-run markets like the EV example. I think we’ll also see improved accuracy in long-shot odds on medium-run markets. I’ve often noticed early on in election markets, candidates with zero chance (like RFK Jr or Hillary Clinton in 2024) can be bid up to 4 or 5 cents because betting against them will at best pay 4-5% over a year, and you could make a similar payoff more safely with bonds or a high-yield savings account. Page and Clemen documented this bias more formally in a 2012 Economic Journal paper:
We show that the time dimension can play an important role in the calibration of the market price. When traders who have time discounting preferences receive no interest on the funds committed to a prediction-market contract, a cost is induced, with the result that traders with beliefs near the market price abstain from participation in the market. This abstention is more pronounced for the favourite because the higher price of a favourite contract requires a larger money commitment from the trader and hence a larger cost due to the trader’s preference for the present. Under general conditions on the distribution of beliefs on the market, this produces a bias of the price towards 50%, similar to the so-called favourite/longshot bias.
We confirm this prediction using a data set of actual prediction markets prices from 1,787 market representing a total of more than 500,000 transactions.
Hopefully the introduction of interest will correct this, other markets like PredictIt and Polymarket will feel competitive pressure to follow suit, and we’ll all have more accurate forecasts to consult.
You may have heard that there are roughly 7 million men of working age that are not currently in the labor force — that is, not currently working or looking for work. The statistic has been produced in various ways using slightly different definitions by different researchers, but the most well-known is from Nicholas Eberstadt who uses the age cohort of 25-55 years old and gets about 7 million (in 2015). More recently and perhaps more prominently is from Senator JD Vance, and as with almost all issues he has tied this to illegal immigration.
The 7-million-men statistic is true enough, and if we limit it to native-born American men with native-born parents (I assume this is the group Vance is concerned about), we can get right at 7 million non-working men in 2024 by expanding the age cohort slightly to 20-55 year olds.
Why are these men not working? According to what they report in the CPS ASEC, here are the reasons broken down by 5-year age cohort (I drop 55-year-olds here to keep the group sizes equal, which shrinks the total to 6.7 million men):
By far the largest reason given for not working is illness or disability, which is 42% of the total for all of these men, the largest reason for every age group except 20-24 (who are mostly in school if they aren’t working), and it’s the majority for workers ages 30-54 (about 56% of them report illness or disability). Slightly less than 10% report “could not find work” as the reason they weren’t working, which is about 650,000 men in this age group (and are native-born with native-born parents). And over half of those reporting that they couldn’t find work are under age 30 — for those ages 30-54, it’s only about 7% of the total.
More men report that they are taking care of the home/family (800,000) than report not being able to find work (650,000). And a lot more report that they are currently in school — almost 1.5 million, and even though they are mostly concentrated among 20–24-year-olds, about one-third of them are 25 or older.
It’s certainly true that the number of working age men in the labor force has fallen over time. In 1968, 97% of men ages 20-54 had worked at some point in the past 12 months (that’s for all men regardless of nativity, which isn’t available back that far in the CPS ASEC). In 2024, that was down to about 87%. But even if we could wave a magical wand and cure all of the men that are ill or disabled, this would add less than 3 million people to the labor force, not nearly enough to make up for all of the immigrants that Vance and others are suggesting have taken the jobs of native-born Americans.
Economist Writing Every Day 