Why are Long-Run Inflation Expectations Falling?

Randal Verbrugge and I have just published a Federal Reserve Bank of Cleveland Economic Commentary called "Digging into the Downward Trend in Consumer Inflation Expectations." The piece focuses on long-run inflation expectations--expectations for the next 5 to 10 years-- from the Michigan Survey of Consumers. These expectations have been trending downward since the summer of 2014, around the same time as oil and gas prices started to decline.  It might seem natural to conclude that falling gas prices are responsible for the decline in long-run inflation expectations. But we suggest that this may not be the whole story.

First of all, gas prices have exhibited two upward surges since 2014, neither of which was associated with a rise in long-run inflation expectations. Second, the correlation between gas prices and inflation expectations (a relationship I explore in much more detail in this working paper) appears too weak to explain the size of the decline. So what else could be going on?

If you look at the histogram in Figure 2, below, you can see the distribution of inflation forecasts that consumers give in three different time periods: an early period, the first half of 2014, and the past year. The shaded gray bars correspond to the early period, the red bars to 2014, and the blue bars to the most recent period. Notice that there is some degree of "response heaping" at multiples of 5%. In another paper, I use this response heaping to help quantify consumers' uncertainty about long-run inflation. The idea is that people who are more uncertain about inflation, or have a less precise estimate of what it should be, tend to report a round number-- this is a well-documented tendency in how people communicate imprecision.

The response heaping has declined over time, corresponding to a fall in my consumer inflation uncertainty index for the longer horizon. As we detail in the Commentary, this fall in uncertainty helps explain the decline in the measured median inflation forecast. This is a remnant of the fact that common round forecasts, 5% and 10%, are higher than common non-round forecasts.

There is also a notable change in the distribution of non-round forecasts over time. The biggest change is that 1% forecasts for long-run inflation are much more common than previously (see how the blue bar is higher than the red and gray bars for 1% inflation).  I think this is an important sign that some consumers (probably those that are more informed about the economy and inflation) are noticing that inflation has been quite low for an extended period, and are starting to incorporate low inflation into their long-run expectations. More consumers expect 1% inflation than 2%.

The Economics of Crime

On September 28, the Economics Department at Haverford College will hold its annual alumni forum. The topic this year is "The Economics of Crime and Incarceration." Our panelists will be
Eric Sterling (Haverford class of '73), Executive Director of the Criminal Justice Policy Foundation, and Mark Kleiman (class of '72), Director of the Crime and Justice Program at New York University’s Marron Institute of Urban Management. In anticipation of the event, especially for any Haverford students who might be reading my blog, I wanted to do a quick survey of the literature on the economics of crime and some of the major topics and themes in this literature.

Why are crime and incarceration economics topics? In other words, given that there is an entire field--criminology--devoted to the study of crime, why are economists studying it as well?  Gary Becker suggested in 1968 that "a useful theory of criminal behavior can dispense with special theories of anomie, psychological inadequacies, or inheritance of special traits and simply extend the economist's usual analysis of choice" (p. 170).  In other words, he believed that criminal behavior could be modeled as a rational response to incentives; that the private and social costs of crime, and the costs of apprehension and conviction, could be quantified; and that a socially "optimal" (likely non-zero) level of crime could be computed.

How does the criminal justice system affect the incentives for crime, and, in turn, criminal behavior? Causal effects are quite challenging to study empirically. For example, consider the question of whether a larger police force deters crime. Suppose the data shows a positive correlation between crime rates and size of police force. While it is possible that larger police forces cause more crime, it is also possible that causality runs in the reverse direction: cities with higher crime rates hire more police. Steven Levitt, whose "Freakonomics" fame came in part from his clever approaches to these types of questions, has looked for "instruments," or ways to identify exogenous variations in criminal justice policies.

It is also difficult to identify causal effects of incarceration on criminal recidivism and other outcomes. Prison sentences are not "randomly assigned." So if we see that people who spend longer in prison are more likely to commit a second crime, we can't say whether the extra time in prison had a causal influence on the recidivism. A recent working paper by Manudeep Bhuller, Gordon B. Dahl, Katrine V. Løken, and Magne Mogstad exploits the random assignment of criminal cases in Norway to judges who differ in their stringency of sentencing. They find that imprisonment discourages further criminal behavior. This decline in recidivism is driven by people who were unemployed before incarceration, and who participated in programs in prison aimed at increasing employability. The authors conclude that "Contrary to the widely embraced 'nothing works' doctrine, these findings demonstrate that time spent in prison with a focus on rehabilitation can indeed be preventive." But since not all prison systems have a focus on rehabilitation, they add that "It is important to recognize that our results do not imply that prison is necessarily preventative in all settings. While this paper establishes an important proof of concept, evidence from other settings or populations would be useful to assess the generalizability of our findings."

Some dimensions of crime can be difficult to measure. Many crimes go unreported or undetected. Black market activity, by its very definition, is hidden. Economists have also tried to come up with ways to measure illegal production or trade. See, for example, this study of elephant poaching and ivory smuggling. Online black markets, and other types of crime and fraud committed online, are also the subject of a growing economics literature.

Network economics is also applicable to the study of crime, since it can help with understanding the formation and workings of criminal networks.

Studies of the economics of crime are nearly always controversial. In part, this is because criminal justice itself is so controversial, so whenever an economic study draws implications about criminal justice, it is sure to find some resistance. In addition, many people find Becker's description of crime as a purely rational response to incentives to be lacking. Recall, for example, the controversy surrounding Roland Fryer's recent working paper on racial differences in police use of force. I think part of what people were uncomfortable with was the incorporation of racial discrimination into the utility function, and part was the distinction he made between "statistical discrimination" and racial bias.

I anticipate an interesting discussion on Wednesday and will try to update the blog with my impressions following the forum.