Macro Roundup

Matt Rognlie @ludwigstraub @a_auclert argue that it will take up to five years for excess savings to work though the systems as individuals with higher marginal propensity to consume generate income for individuals with higher savings rates.

“…Deficit-financed fiscal transfers generate excess savings. One person’s spending is another person’s income. As we show, taking this fact into account implies that excess savings from debt-financed transfers have much longer-lasting effects than a naive calculation would suggest. In a closed economy, unless the government pays down the debt used to finance the transfers, excess savings do not go away as households spend them down. Instead, the effect of excess savings on aggregate demand slowly dissipates as they “trickle up” the wealth distribution to agents with lower MPCs. Tight monetary policy speeds up this process, but this effect is likely to be quantitatively modest. The partial equilibrium scenario summarizes the conventional wisdom according to which the effect of excess savings will dissipate in a few quarters. By contrast, our benchmark scenario suggests that these effects will stick around for roughly 5 years….”

Core of paper

“…Because excess savings and their distribution across the population intuitively matter for aggregate demand, economists have paid a considerable amount of attention to estimating both. In this paper, we provide a tractable Heterogeneous Agent New Keynesian model that explicitly maps the distribution of excess savings to the path of output, and that explains the process by which their effect dissipates. We use this framework to estimate the likely contribution of excess savings to aggregate spending in the coming years under various assumptions about the marginal propensities to consume (MPCs) of agents holding the savings and scenarios for monetary policy. Our framework recognizes that one person’s spending is another person’s income. As we show, taking this fact into account implies that excess savings from debt-financed transfers have much longer-lasting effects than a naive calculation would suggest. In a closed economy, unless the government pays down the debt used to finance the transfers, excess savings do not go away as households spend them down. Instead, the effect of excess savings on aggregate demand slowly dissipates as they “trickle up” the wealth distribution to agents with lower MPCs. Tight monetary policy speeds up this process, but this effect is likely to be quantitatively modest….”

Implication For US Economy Post-Pandemic

“…Table 1 summarizes our results by displaying the duration of output and excess savings for the middle class and the rich under each of our scenarios. The partial equilibrium scenario summarizes the conventional wisdom according to which the effect of excess savings will dissipate in a few quarters. By contrast, our benchmark scenario suggests that these effects will stick around for roughly 5 years. These numbers are larger if MPCs are lower, and are robust to plausible alternative calibrations. Rational expectations about the future boom make the response much larger on impact due to current spending out of anticipated income, which turns out to speed up the trickling up process. Tight monetary policy, on the other hand, also speeds up tickling up, but it does so by mitigating the effects of excess savings on demand. In either case, however, the duration of excess savings and output remains more than twice as long as the conventional wisdom suggests….”

Adrien Auclert, Matthew Rognlie and Ludwig Straub, “The Tricking Up Excess Savings,” National Bureau of Economic Research, January 2023, https://www.nber.org/papers/w30900

Inquisitive Bird summarizing data suggesting criminal activity has a power law distribution, “…perhaps the most illustrative study is by Falk et al. (2014), who used Swedish nationwide data of all 2.4 million individuals born in 1958-1980 and looked at the distribution of violent crime convictions. In short, they found that 1% of people were accountable for 63% of all violent crime convictions, and 0.12% of people accounted for 20% of violent crime convictions….”

Inquisitive Bird, "When few do great harm,"Patterns in Humanity, April 23, 2023, https://inquisitivebird.substack.com/p/when-few-do-great-harm

When few do great harm

I summarize some data illustrating how few number of criminals are responsible for a large fraction of crime.

While the terms “power law” or “Pareto distribution” have precise mathematical definitions, colloquially they have come to be used to convey an important fact: in many contexts, few account for much. For example, a small fraction of people hold a large fraction of overall wealth. Here I will consider it in the context of crime, where a small minority of repeat offenders are responsible for a large fraction of all crimes.

Criminal and delinquent behavior approximately follow such power laws. It is observed for arrests, convictions and even self-reported delinquent behavior. For example, Cook et al. (2004) compared convictions in a UK study and self-reported delinquency from a US dataset and found that both were well-described by a power law. Other UK data show that 70% of custodial sentences are imposed on those with at least seven previous convictions or cautions, and 50% are imposed on those with at least 15 previous convictions or cautions (Cuthbertson, 2017).

But perhaps the most illustrative study is by Falk et al. (2014), who used Swedish nationwide data of all 2.4 million individuals born in 1958-1980 and looked at the distribution of violent crime convictions. In short, they found that 1% of people were accountable for 63% of all violent crime convictions, and 0.12% of people accounted for 20% of violent crime convictions.

Below the mean number of convictions by percentile among offenders is illustrated, a very Pareto-like distribution. At the extremes there are people who are not merely arrested but convicted of 10, 20 or even more than 50 violent crimes. High-persistence criminals were almost all male, they typically started their first crime young, and very often had major mental disorders and substance use disorders.

1 Pryor et al. (2018) find that 72.8% of federal offenders sentenced had been convicted of a prior offense. The average number of previous convictions was 6.1 among offenders with criminal history. More recently, Durose & Antenangeli (2023) used prison records to analyze arrest history of people admitted to state prison in the United States. The distribution of prior arrest counts is shown below.

In the original graph, “31 or more” is high because it is the aggregation of all arrest counts 31, 32, 33, and so on, until the largest existing number of prior arrests. To consider arrest numbers higher than 31, I used a simple extrapolation method

2. From this extrapolation I speculate that 1% of people admitted to state prison had approximately 46 or more prior arrests, and 1 in 1000 may have had as many as 64 or more prior arrests. The precise numbers, however, are not given in the report and these extrapolations should be taken with a grain of salt.

A different analysis by the National Institute for Criminal Justice Reform finds that for the crime of homicide: “Overall, most victims and suspects with prior criminal offenses had been arrested about 11 times for about 13 different offenses by the time of the homicide. This count only refers to adult arrests and juvenile arrests were not included.” Again, we see that homicide offenders typically have been arrested numerous times before they commit their first homicide.

The fact that a small minority is responsible for a large chunk of crime is true for shoplifting and burglaries as well, perhaps to an even greater extent. Data from New York City finds that a tiny number of shoplifters commit thousands of theft. The police stated that nearly a third of all shoplifting arrests in the city in 2022 involved just 327 people, who collectively were arrested and rearrested more than 6,000 times. Thus 0.00386% of New York City’s population (327 out of 8.468 million, 1 in ~26,000) accounted for nearly a third of all shoplifting arrests in the city. As illustrated by a different study, crime in New York City is not only disproportionately committed by few people, it also disproportionately affects specific local areas. They find that 14% of streets in the city produce 75% of property crime and 10% of streets produce 75% of violent crime.

A particular incident vividly illustrates of how few people can be responsible for a large fraction of crime. In Leinster, Ireland, the number of burglaries plummeted after three criminals died in a car crash. These three criminals together had more than 200 previous convictions.

An implication of the fact that most crimes are committed by repeat offenders is that there are more victims than offenders

3. More people get robbed than there are robbers, more victims of rape than there are rapists4, and so on. More generally, few people can cause great harm to society at large. Of course, the victims themselves experience this damage most directly. But the harms of crime extend beyond those directly affected. It is difficult to precisely estimate the financial costs of crime, however estimates are high (e.g., Wickramasekera et al., 2015; Anderson, 2021, Miller et al., 2021). Beyond direct tangible costs are also opportunity costs, including the value of institutions and services that people don’t have access to because of the prevalence of crime. The benefits of reducing crime, particular in the United States, are therefore great.

1 Lewis & Usmani (2022) show that, while the US has much higher incarceration rates than other highly developed nations, the incarceration rate is not necessarily very high relative to the rate of serious crime. On the other hand, they show that the nation is very under-policed, meaning a low number of police officers per-capita and particularly few on a per-crime basis.

2 I assumed that in the tail the frequency fell exponentially with increasing number of prior arrests by some rate r. Then I fitted r such that the entire distribution summed to 100% (the extrapolated tail filling the otherwise missing 4.6%). The real data along with the extrapolated estimates can be seen below.

4 Occasionally regarding the issue of rape, activists rhetorically ask something like “Why does every woman know another woman that was raped but no man knows a rapist?” The most obvious explanatory factor is that people are unlikely to publicly acknowledge their own crimes; crimes often committed behind closed doors. But it is also because there legitimately are more victims than offenders, because of the extreme repeat offender phenomenon discussed in this post.

John Burn Murdoch checks the pulse of millennials and finds that their inflation-adjusted per capita wealth is tracking boomers, but “boomers got there first and bought the best houses in a cheaper market.”

John Burn-Murdoch, "Millennials are not as badly off as they think — but success is bittersweet,"Financial Times, April 6, 2023, https://www.ft.com/content/6f7d7522-42e9-43cb-bd73-36eee6681f3e

Millennials are not as badly off as they think — but success is bittersweet

For most of the past two centuries, if you asked a young adult in the west whether they had a better standard of living than their parents enjoyed at the same age, you would have been drowned out by a unity of yesses. More money, more things to spend it on, longer and healthier lives to look forward to.

But that has all changed. Today, almost one in four US millennials — the cohort born between 1981 and 1997 — say their lives are materially worse than their parents’ were, a record high for any generation of Americans asked that question.

On the other side of the Atlantic, almost half of the UK population says today’s youth will have a worse life than their parents, up from 13 per cent 20 years ago, according to an Ipsos Mori survey. The malaise is clearly deeply felt, but does it reflect reality? One statistic that is often wheeled out in support of the millennial plight is the wealth deficit. A widely shared chart from the US shows that young adults held only 3 per cent of US household wealth in 2019, while baby boomers owned 21 per cent at roughly the same age.

At first glance it’s a slam dunk, but this example has a flaw, as first explained by Jeremy Horpedahl, an economist at the University of Central Arkansas. Boomers are so-called because they are a very large generation. At the time they held 21 per cent of the wealth, they made up almost twice as large a share of the adult US population as millennials do today. If we instead take Horpedahl’s suggestion and compare inflation-adjusted per capita wealth within each generation over time, millennials are in fact almost perfectly tracing boomers’ footsteps.

One area of undeniable progress is education. Millennials entered the labour market with more degrees under their belt than any generation before them, and they’re happy to acknowledge it. But where has this got them? Britain’s millennial workforce boasts twice as many graduates as Gen X at the same stage, yet its incomes are tracing exactly the same path. For boomers and Gen X, more education than their predecessors translated into higher incomes. For millennials these benefits are yet to emerge.

Those aggregates mask significant differences beneath the surface, too. Degree-educated millennials in London are 41 per cent less likely to own a home than degree-educated boomers were at the same age. And if you think that’s bad, pity the non-graduate under-40s in London, just 20 per cent of whom own a home (among non-graduate boomers of the same age, 60 per cent were homeowners).