Category: Economics
Will we see less comovement in global economic growth?
That is the question behind my latest Bloomberg column. China is now, and looking forward, less of a common growth driver around the world. Oil price shocks may not be less important for humanitarian outcomes, but they matter less for many of the largest economies. America is now an oil exporter, and the EU just made some major adjustments in response to the Russia shock. More renewable energy is coming on-line, most of all solar.
The column closes with this:
In this new world, with these major common shocks neutered, a country’s prosperity will be more dependent on national policies than on global trends. Culture and social trust will matter more too, as will openness to innovation — and, as fertility rates remain low or decline, so will a country’s ability to handle immigration. A country that cannot repopulate itself with peaceful and productive immigrants is going to see its economy shrink in relative terms, and probably experience a lot of bumps on the way down.
At the same time, excuses for a lack of prosperity will be harder to come by. The world will not be deglobalized, but it will be somewhat de-risked.
Dare we hope that these new arrangements will produce better results than the old?
Or perhaps a more general rising tide was the only way many countries were going to make progress?
Rooftops
It’s not at all clear that after tax 1% inequality has gone up at all https://t.co/rJADPjCFSg
— Adam Ozimek (@ModeledBehavior) November 8, 2023
Urban sentences to ponder
Cities in the top decile of the city-size distribution have a 50% lower markup than cities in the bottom decile.
That is from the job market paper of Santiago Franco, who is on the job market from University of Chicago with one of this year’s most interesting papers. Does that stylized fact then mean there are too many firms and outlets in the major cities, and not enough in the lesser cities?
Autonomous Vehicles Lower Insurance Costs
The insurance giant Swiss RE did a study comparing human drivers with Waymo autonomous vehicles in the same zip-codes and found that autonomous vehicles generated significantly fewer insurance claims.
This study compares the safety of autonomous- and human drivers. It finds that the Waymo One autonomous service is significantly safer towards other road users than human drivers are, as measured via collision causation. The result is determined by comparing Waymo’s third party liability insurance claims data with mileage- and zip-code-calibrated Swiss Re (human driver) private passenger vehicle baselines. A liability claim is a request for compensation when someone is responsible for damage to property or injury to another person, typically following a collision. Liability claims reporting and their development is designed using insurance industry best practices to assess crash causation contribution and predict future crash contributions. In over 3.8 million miles driven without a human being behind the steering wheel in rider-only (RO) mode, the Waymo Driver incurred zero bodily injury claims in comparison with the human driver baseline of 1.11 claims per million miles (cpmm). The Waymo Driver also significantly reduced property damage claims to 0.78 cpmm in comparison with the human driver baseline of 3.26 cpmm. Similarly, in a more statistically robust dataset of over 35 million miles during autonomous testing operations (TO), the Waymo Driver, together with a human autonomous specialist behind the steering wheel monitoring the automation, also significantly reduced both bodily injury and property damage cpmm compared to the human driver baselines.
The Waymo vehicles are in San Francisco and Phoenix so this doesn’t mean that autonomous vehicles are better everywhere. Also, when we say autonomous vehicles we really mean the entire Waymo system including backup. In addition, there are some differences that are hard to account for such as human drivers use more freeways even in the same zip codes. Nevertheless, it is clear that autonomous vehicles are happening. I predict that some of my grandchildren will never learn to drive and their kids won’t be allowed to drive.
Persistence in policy: evidence from close votes
That is the job market paper by economist Zach Freitas-Groff of Stanford University. Here is the abstract:
Policy choices sometimes appear stubbornly persistent, even when they become politically unpopular or economically damaging. This paper offers the first systematic empirical evidence of how persistent policy choices are, defined as whether an electorate’s or legislature’s decisions affect whether a policy is in place decades later. I create a new dataset that tracks the historical record of more than 800 state policies that were the subjects of close referendums in U.S. states since 1900. In a regression discontinuity design, I estimate that passing a referendum increases the chance a policy is operative 20, 40, or even 100 years later by over 40 percentage points. I collect additional data on U.S. Congressional legislation and international referendums and use existing data on state legislation to document similar policy persistence for a range of institutional environments, cultures, and topics. I develop a theoretical model to distinguish between possible causes of persistence and present evidence that persistence arises because policies’ salience declines in the aftermath of referendums. The results indicate that many policies are persistently in place—or not—for reasons unrelated to the electorate’s current preferences.
Impressively original. Zach has several interesting papers (see the first link), some from an EA-adjacent point of view.
What the Kia-Hyundai Crime Wave Tells Us About the Long-Term Decline in Crime
Motor vehicle thefts (per capita) are about one third the level they were in the early 1990s, a drop which is consistent with the Great Crime Decline, the large fall in many crimes since the early 1990s. A lot of ink has been spent trying to explain the great crime decline–abortion legalization, lead abatement, increased imprisonment, more policing–these are just some of the leading theories.
In recent years, however, there has been a notable increased in motor vehicle theft–not back to earlier peaks–but a substantial increase. Theft hasn’t increased uniformly across the board, however. Thefts of Kias and Hyundais have seen massive increases–in some places thefts of these cars have increased by a factor of five or ten in just a few years. The reason is simple–most cars today have electronic immobilizers which mean that without the key present these cars can’t easily be hotwired. Some enterprising thieves, however, discovered that Kia and Hyundai neglected to install these devices and they spread the word through Tik-Tok videos about a method to quickly and efficiently jack these cars.
I propose that the micro can shed light on the macro. Consider the four theories for the great crime decline that I mentioned earlier–abortion legalization, lead abatement, increased imprisonment and more policing. The first two, abortion legalization and lead abatement, are theories about why there are fewer criminals–these theories say that people improved and that is why crime declined. But better people shouldn’t steal any car models, including Kias and Hyundais! Moreover, people haven’t suddenly become worse. Thus, offender-based theories cannot explain the sharp rise in motor vehicle thefts. There have been some changes in punishment, imprisonment and policing, in recent years but these have been slow moving and fairly small and in addition they also don’t explain the rise in Kia and Hyundia thefts in particular.
Obviously, what explains the rise in thefts of Kias and Hyundias in particular is the discovery that these vehicles were unguarded, unprotected and unsecured. Notice that being unguarded, unprotected and unsecured swamped any effect coming from abortion legalization, lead abatement, increased imprisonment or more policing.
The failure of the big four to explain the rise in Kia and Hyundai thefts isn’t proof that these theories are wrong. But lets ask the inverse question, can the rise in Kia and Hyundai thefts suggest an explanation for the great crime decline? In other words, can we explain the great crime decline by an increase in security. Begin with the most direct case, motor vehicle theft. Car immobilizes and other security devices began to be installed in the 1990s so the timing fits. Moreover, the timing fits multiple countries. One of the weaknesses of theories of the great crime decline such as increased imprisonment and policing is that these theories work for the United States but the crime decline occurred in many industrialized countries at about the same time. Canadian crime rates, for example, fall in near lockstep with US rates but with very different prison and policing strategies. Immobilizer technology, however, happened at similar times in similar places and where we saw delays or early adoption we also see delayed or early falls in motor vehicle theft. In addition, motor theft declined first for newer cars (secured) rather than for older cars despite the fact that the newer cars are the more desirable for thieves–again this fits the security hypothesis better than an offender or punishment hypothesis.
The security hypothesis fits motor vehicle theft but the connection is less clear with respect to other crimes. Home security devices have increased and become higher quality over time but the change was less rapid and less precisely timed to the early 1990s. The rise of credit cards and decline of cash could have reduced muggings, although again the timing doesn’t appear to be precise. Violent crime would seem even less likely to be security related–although cameras and lights surely matter–but keep in mind that a lot of violent crime is a side-effect of property crime. Vehicle thefts, muggings and drug deals turn into homicides, for example. In addition, there are “life of crime” or “career” effects. If you make motor vehicle theft and burglary less profitable that makes a life of crime less profitable which can reduce crime in general even without specific deterrence.
Overall, the security hypothesis carries some weight, especially in explaining multiple countries. I don’t fully discount any of the major theories, however. Multiple causation is important.
The main less I draw is this: The increase in Kia and Hyundai thefts suggests that the crime wave declined not because the ocean became more gentle but because we built more secure sea walls. The big waves are still out there in the vast ocean and if we lower the walls we shouldn’t be surprised if another big crime wave comes rolling in.
Das Adam Smith Problem
The second set of advocates for the book [Theory of Moral Sentiments] I usually find in media outlets, sophisticated media outlets at that, or I hear it over lunch table conversation. These claims suggest that Wealth of Nations covers the commercial, selfish side of human behavior, while Theory of Moral Sentiments is an account of the caring, empathetic side, or something like that. I wish I had a nickel for every time I read or heard that contrast. Maybe it is harmless enough, but – and I don’t completely understand why — it kind of makes me sick. It is simultaneously an attempt to claim a bland centrist middle ground, to snidely distance oneself from capitalism and selfishness, and reduce Smith to a series of empty clichés. It is trying to be pat rather than insightful. It is trying to give everything its place in a manner that we can then safely ignore.
Just for a start, I view Smith’s portrait of human nature in Wealth of Nations as rich and multi-faceted, a piece of behavioral economics, in modern terminology, rather than narrow, commercial, and purely selfish. And in Theory of Moral Sentiments yes people are empathetic, and show sympathy for others, but they are often caring in…pretty narrow and selfish ways. I just don’t think the “each book carves out its own sphere” understanding of the pair works very well.
My biggest takeaway from TMS is that humans beings make evaluations, including sympathetic evaluations but not only, based on local rather than global information. They put a lot of weight on what is right before their eyes and neglect the bigger picture. The very opening passage of TMS expresses how we can understand the emotions of others only through our own. We cannot look around corners to understand other minds directly, so we make inferences from our own experience. Smith demonstrates and then demonstrates that point again throughout the book.
That is a passage from my generative book, written by me, GOAT: Who is the Greatest Economist of all Time, and Why Does It Matter?
Does mobility make people nicer?
In much of modern life, cooperation takes the form of people engaging in costly behavior that helps another. It is easy to understand how cooperation might be achieved in small communities where members interact repeatedly. However, in our modern world, there is a high degree of relational mobility – where individuals can easily change locations and/or social groups. How does this affect cooperative behavior? I examine this question from both theoretical and empirical perspectives. I first develop a model of repeated prisoner’s dilemma. Individuals are matched but have some ability to leave the relationship. This is sufficient to show that, perhaps surprisingly, greater relational mobility actually leads to more cooperation in equilibrium, and the model predicts a stronger effect when players are more patient. I take these predictions to the data by first conducting a meta-analysis of twelve prisoner’s dilemma experiments that varied the amount of mobility in and out of relationships. I also examine the predictions of the model using data from the World Values Survey and Gallup World Poll. I find that looking across individuals, more relational mobility in the region is associated with greater cooperation and that this relationship is stronger when individuals are more patient. Both the experimental and the observational evidence are consistent with the theoretical mechanism.
That is from the job market paper of Ziqi Lu of Harvard economics. His entire portfolio looks interesting…
Child street vendors in India
Street vending is an important source of self-employment for the urban poor. I use primary observation, survey, and experimental data from Delhi to study this market. Partnering with street vendors to randomize both prices and the passersby they solicit to try to make sales, I find that even with identical goods, child vendors are 97% more likely to make a sale and earn more than twice that of adult vendors. Despite no differences in valuation for the goods, couples, and female customers are 90% and 27% more likely to buy than male customers. Females and couples are 50% more likely to be targeted by vendors than males and are charged higher prices on average (1.15-2 times) than males. I show that these findings are consistent with a model that incorporates altruism and a cost of refusal in the buyer’s decision-making. I find that passersby are more altruistic towards children than adults in an incentivized dictator game. Additionally, requesting passersby to buy, increases the purchasing probability twofold for adult vendors and fourfold for child vendors. Survey data confirms that vendors target females or couples, over males, because they consider who would find it harder to refuse. The paper demonstrates that sellers leverage insights into consumer social preferences to inform their selling strategies, which can be effective in markets with personal selling.
That is from the job market paper of Ronak Jain, job market candidate from Harvard, updated draft to be uploaded by mid-November.
Occupational dynasties
Children often follow their parents in the same occupation. The literature has previously documented occupational persistence, but whether it has economic implications remains an open question. Using administrative data from the Netherlands and a unique policy experiment, this paper documents the prevalence of occupational transmission and estimates its effects and selection for medical doctors. I find that children are twice as likely to enter a parent’s field, with this rate substantially increasing for those above the top quartile of the parental income distribution. In addition, OLS estimated returns to occupational persistence are 2.5%. I focus on the medical profession to decompose these “naive” returns into a treatment and a selection effect of occupational transmission. I find that ’dynastic’ doctors experience a 24% income boost relative to their ’non-dynastic’ counterparts, corresponding to 58% higher returns from the medical profession. Furthermore, I identify a substantial negative selection bias in the OLS estimates, explaining why naive returns considerably underestimate the effects of occupational persistence. The large treatment effect together with the unequal incidence along the income distribution highlights the critical role of occupational transmission in exacerbating inequalities.
That is from Maria Ventura, a job market candidate from LSE.
Income security for American workers has been rising
American workers are doing relatively well, but there is still a lot of anxiety about their plight. To many commentators, the US worker is suffering: Whether the culprit is outsourcing, trade with China, or the sheer daily turbulence of capitalism, that worker faces increasingly volatile income prospects. One political scientist even wrote a whole book about this worry.
Fortunately, the reality is much brighter. One study of this question, performed by a group of economists from Wharton, Stanford, the University of Minnesota and Brookings, suggests that income volatility has mostly been declining for the last seven decades — and especially for the last four. Whatever volatility risks remain, they used to be much worse.
One striking feature of these results, posted last week and based on data from the US Census Bureau and the Social Security Administration, is how widespread are the gains in job security. They are not going to just a scant few workers. They are long-running for both women (dating to the 1950s) and men (dating to the 1980s). They hold across most demographic groups and by gender, age, earnings level and cohort.
Here is the rest of my latest Bloomberg column.
Behavioral Economics and GPT-4: From William Shakespeare to Elena Ferrante
There is a new paper on LLMs by Gabriel Abrams, here is the abstract:
We prompted GPT-4 (a large language model) to play the Dictator game, a classic behavioral economics experiment, as 148 literary fictional characters from the 17th century to the 21st century.
Of literary interest, this paper analyzed character selfishness by century, the relative frequency of literary character personality traits, and the average valence of these traits. The paper also analyzed character gender differences in selfishness.
From an economics/AI perspective, this paper generates specific and quantifiable Turing tests which the model passed for zero price effect, lack of spitefulness and altruism, and failed for human sensitivity to relative ordinal position and price elasticity (elasticity is significantly lower than humans). Model updates from March to August 2023 had relatively minor impacts on Turing test outcomes.
There is a general and mainly monotonic decrease in selfish behavior over time in literary characters. 50% of the decisions of characters from the 17th century are selfish compared to just 19% of the decisions of characters from the 21st century. Overall, humans exhibited much more selfish behavior than AI characters, with 51% of human decisions being selfish compared to 32% of decisions made by AI characters.
Historical literary characters have a surprisingly strong net positive valence across 2,785 personality traits generated by GPT-4 (3.2X more positive than negative). However, valence varied significantly across centuries. The most positive century, in terms of personality traits, was the 21st — over 10X the ratio of positive to negative traits. The least positive century was the 17th at just 1.8X. “Empathetic,” “fair” and “selfless,” were the most overweight traits in the 20th century. Conversely, “manipulative,” “ambitious” and “ruthless” were the most overweight traits in the 17th century.
Male characters were more selfish than female characters: 35% of male decisions were selfish compared to just 24% for female characters. The skew was highest in the 17th century where selfish decisions for male and female were 62% and 20% respectively.
This analysis offers a specific and quantifiable partial Turing test. In a few ways, the model is remarkably human-like; The key human-like characteristics are the zero price effect, lack of spitefulness and altruism. However, in other ways, GPT-4 reflects unusual or inhuman preferences. The model does not appear to have human sensitivity to relative ordinal position and has significantly lower price elasticity than humans.
Model updates in GPT-4 have made it slightly more sensitive to ordinal value, but not more selfish. The model shows preference consistency across model runs for each character with respect to selfishness.
To which journal might you advise him to send this paper?
Evan Soltas on the job market from MIT
Here is his home page, the job market paper is “Tax incentives and the supply of low income housing”:
Subsidies to developers are a core instrument of housing policy. How do they affect housing markets, and who benefits? I assess their impacts and incidence with a dynamic model of housing markets and new data on developers competing for Low-Income Housing Tax Credits. I estimate the model using three sources of variation: quasi-random assignment of subsidies, shocks to subsidy generosity, and nonlinearities in scoring rules for subsidy applications. I find that, due to displacement of unsubsidized housing, subsidies add few net units to the housing stock and instead reallocate units progressively. Households benefit from developer competition for subsidies, but competition also results in high entry costs, and developers still capture nearly half of the welfare gains. In counterfactuals, a stylized voucher program can generate the same household benefits at less fiscal cost.
Recommended!
Minimum wages and rents
This topic remains underdiscussed in the minimum wage debates, here are some recent results from Atsushi Yamagishi:
I analyze the effect of minimum wage hikes on housing rents using exogenous variation in minimum wages across local labor markets in Japan. I estimate that in low-quality rental housing market, a 10% minimum wage increase induces a 2.5%–4.5% increase in rents. Minimum wage hikes benefit workers in light of a spatial equilibrium model showing that changes in housing market rents work as a sufficient statistic for measuring utility changes arising from changes in minimum wages. The increase in housing rents also implies an unintended benefit for homeowners.
Atsushi Yamagishi is from Princeton economics, but that is not his job market paper, here is the whole portfolio, which looks quite interesting.
Basil Halperin on the job market from MIT
Here is the home page, here is his job market paper (with Daniele Caratelli) “Optimal policy under menu costs”:
We analytically characterize optimal monetary policy in a multisector economy with menu costs, and show that inflation and output should move inversely after sectoral shocks. That is, following negative shocks, inflation should be allowed to rise, and vice versa. In a baseline parameterization, optimal policy stabilizes nominal wages. This nominal wage targeting contrasts with inflation targeting, the optimal policy prescribed by the textbook New Keynesian model in which firms are permitted to adjust their prices only randomly and exogenously. The key intuition is that stabilizing inflation causes shocks to spill over across sectors, needlessly increasing the number of firms that must pay the fixed cost of price adjustment compared to optimal policy. Finally, we show in a quantitative model that, following a sectoral shock, nominal wage targeting reduces the welfare loss arising from menu costs by 81% compared to inflation targeting.
Noteworthy!