Pushing the Boundaries of Economics

As a macroeconomist, I mostly research the types of concepts that are more traditionally associated with economics, like inflation and interest rates. But one of the great things about economics training, in my opinion, is that you receive enough general training to be able to follow much of what is going on in other fields. It is always interesting for me to read papers or attend seminars in applied microeconomics to see the wide (and expanding) scope of the discipline.

Gary Becker won the Nobel Prize in 1992 "for having extended the domain of microeconomic analysis to a wide range of human behaviour and interaction, including nonmarket behaviour" and "to aspects of human behavior which had previously been dealt with by other social science disciplines such as sociology, demography and criminology." The Freakonomics books and podcast have gone a long way in popularizing this approach. But it is not without its critics, both within and outside the profession.

For all that the economic way of thinking and the quantitative tools of econometrics can add in addressing a boundless variety of questions, there is also much that our analysis and tools leave out. In areas like health or criminology, the assumptions and calculations that seem perfectly reasonable to an economist may seem anywhere from misguided to offensive to a medical doctor or criminologist. Roland Fryer's working paper on racial differences in police use of force, for example, was prominently covered with both praise and criticism.

Another NBER working paper, released this week by Jonathan de Quidt and Johannes Haushofer, is also pushing the boundaries of economics, arguing that "depression has not received significant attention in the economics literature." By depression, they are referring to major depressive disorder (MDD), not a particularly severe recession. While neither of the authors holds a medical degree, Haushofer holds doctorates in both economics and neurobiology. In "Depression for Economists," they build a model in which individuals choose to exert either high or low effort; depression is induced by a negative "shock" to an individual's belief about her return to high effort.

In the model, the individual's income depends on her effort, amount of sleep, and food consumption. Her utility depends on her sleep, food consumption, and non-food consumption. She maximizes utility given her belief about her return to effort, which she updates in a Bayesian manner. If her belief about her return to effort declines (synonymous in the model to becoming depressed), she exerts less labor effort. Her total (food and non-food) consumption and utility unambiguously decrease, leading to "depressed mood." In the extreme, she may reduce her labor effort to zero, at which point she would stop learning more about her return to effort and get stuck in a "poverty trap."

The depressed individual's sleeping and food consumption may either increase or decrease, as consumption motives become more important relative to production motives. In other words, she sleeps and eats closer to the amounts that she would choose if she cared only about the utility directly from sleeping and eating, and not about how her sleeping and eating choices affect her ability to produce.

While this result does match the empirical findings in the medical literature that depression may either reduce or increase sleep duration and lead to either over- or under-eating, it seems implausible to me that depressed individuals sleep ten or more hours a day because they just love sleeping, or lose their appetite because they don't enjoy food beyond its ability to help them be productive. I'm not an expert, but from what I understand there are physiological and chemical reasons for the change in sleep patterns and appetite that could be independent of a person's beliefs about their returns to labor effort.

However, the authors argue that an "advantage of our model is that it resonates with prominent psychological and psychiatric theories of depression, and the therapeutic approaches to which they gave rise." They refer in particular to "Charles Ferster, who argued that depression resulted from an overexposure to negative reinforcement and underexposure to positive reinforcement in the environment (Ferster 1973)...Ferster’s account of the etiology of depression is in line with how we model depression here, namely as a consequence of exposure to negative shocks." They also refer to the work of psychiatrist Aaron Beck (1967), whose suggested that depression arises from "distorted thinking" motivates the use of Cognitive Behavioral Therapy (CBT), a standard treatment for depression.

The authors note that "Our main goal in writing this paper was to give economists a starting point for thinking and writing about depression using the language of economics. We have therefore kept the model as simple as possible." They also steer clear of suggesting any policy implications (other than implicitly providing support for CBT.) It will be fascinating to see whether and how the medical community responds, and also to hear from economists who have themselves experienced depression.

False Discoveries and the ROC Curves of Social Science

Diagnostic tests for diseases can suffer from two types of errors. A type I error is a false positive, and a type II error is a false negative. The sensitivity or true positive rate is the probability that a test result will be positive when the disease is actually present. The specificity or true negative rate is the probability that a test result will be negative when the disease is not actually present. Different choices of diagnostic criteria correspond to different combinations of sensitivity and specificity. A more sensitive diagnostic test could reduce false negatives, but might increase the false positive rate. Receiver operating characteristic (ROC) curves are a way to visually present this tradeoff by plotting true positive rates or sensitivity on the y-axis and false positive rates (100%-specificity) on the x-axis.

Source: https://www.medcalc.org/manual/roc-curves.php

As the figure shows, ROC curves are upward sloping-- diagnosing more true positives typically means also increasing the rate of false positives. The curve goes through (0,0) and (100,100), because it is possible to either diagnose nobody as having the disease and get a 0% true positive rate and 0% false positive rate, or to diagnose everyone as having the disease and get a 100% true positive rate and 100% false positive rate. The further an ROC is above the 45 degree line, the better the diagnostic test is, because for any level of false positives, you get a higher level of true positives.

Rafa Irizarry at the Simply Statistics blog makes a really interesting analogy between diagnosing disease and making scientific discoveries. Scientific findings can be true or false, and if we imagine that increasing the rate of important true discoveries also increases the rate of false positive discoveries, we can plot ROC curves for scientific disciplines. Irizarry imagines the ROC curves for biomedical science and physics (see the figure below). Different fields of research vary in the position and shape of the ROC curve--what you can think of as the production possibilities frontier for knowledge in that discipline-- and in the position on the curve.

In Irizarry's opinion, physicists make fewer important discoveries per decade and also fewer false positives per decade than biomedical scientists. Given the slopes of the curves he has drawn, biomedical scientists could make fewer false positives, but at a cost of far fewer important discoveries.

Source: Rafa Irizarry

A particular scientific field could move along its ROC curve by changing the field's standards regarding peer review and replication, changing norms regarding significance testing, etc. More critical review standards for publication would be represented by a shift down and to the left along the ROC curve, reducing the number of false findings that would be published, but also potentially reducing the number of true discoveries being published. A field could shift its ROC curve outward (good) or inward (bad) by changing the "discovery production technology" of the field.

The importance of discoveries is subjective, and we don't really know numbers of  "false positives" in any field of science. Some never go detected. But lately, evidence of fraudulent or otherwise irreplicable findings in political science and psychology point to potentially high false positive rates in the social sciences. A few days ago, Science published an article on "Estimating the Reproducibility of Psychological Science." From the abstract:

We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects.

As studies of this type hint that the social sciences may be far to the right along an ROC curve, it is interesting to try to visualize the shape of the curve. The physics ROC curve that Irizarry drew is very steep near the origin, so an attempt to reduce false positives further would, in his view, sharply reduce the number of important discoveries. Contrast that to his curve for biomedical science. He indicates that biomedical scientists are on a relatively flat portion of the curve, so reducing the false positive rate would not reduce the number of important discoveries by very much.

What does the shape of the economics ROC curve look like in comparison to those of other sciences, and where along the curve are we? What about macroeconomics in particular? Hypothetically, if we have one study that discovers that the fiscal multiplier is smaller than one, and another study that discovers that the fiscal multiplier is greater than one, then one study is an "important discovery" and one is a false positive. If these were our only two macroeconomic studies, we would be exactly on the 45 degree line with perfect sensitivity but zero specificity.

Consumption Contagion and Income Inequality

The trends of rising income inequality and the declining national savings rate since the early 1980s may be related, according to a paper by Marianne Bertrand and Adair Morse. The authors find that higher levels of visible consumption by increasingly better-off households at the top of the income distribution induces consumers in the lower parts of the income distribution to spend a higher share of their disposable income. From "Consumption Contagion: Does the Consumption of the Rich Drive the Consumption of the Less Rich?":

"Our empirical strategy exploits variation across geographic markets and over time to identify the effect of expenditures by the rich on that of the non-rich. We ask whether, everything else held constant, higher levels of consumption by the rich living in a household’s relevant market (which we define to be either a state or an MSA in a given year) predicts a higher propensity to consume out of disposable income for the non-rich household. After establishing that such vertical consumption correlations occur, we then explore possible mechanisms. Our results are most consistent with the view that visible increased consumption by the rich induces status-seeking or status-maintaining consumption by the less rich."

Bertrand and Morse define the rich households in each state as those with above the 80th percentile of income in that state. Their baseline regression shows that a 1 percent increase in consumption (excluding housing) among the rich in a particular state translates into a 0.07 percent increase in consumption among the less-rich. They find no evidence that this could be explained by the permanent income hypothesis; rising consumption by the rich in a particular state is not predictive of faster future income growth by the state's less-rich. Thus, they conclude that "Our preferred explanation for the vertical consumption spillovers we observed in our basic results is that low and middle income households witness the higher consumption levels by the rich and are tempted to also consume more."

To test this explanation further, they use data from the Consumer Expenditure Survey and use the Ori Heffetz (2011) index to rank goods into seven categories of increasing visibility. Highly visible consumption items include cars, clothing (except underwear!), shoes, and cigarettes; minimally visible consumption items include health or legal accounting services and, yes, underwear. They replicate the analysis by goods category, and find strongest effects in the most visible consumption categories, consistent with a "consumption contagion" explanation.

As another test, they replicate the analysis using Census Metropolitan Statistical Areas (MSAs) instead of states. They use a measure of community segregation, indicating how closely the rich live to the less-rich in each MSA. In MSAs where the rich and less-rich live closer together, there is more consumption contagion.

Overall, the authors estimate that the savings rate of median-income households would be one to two percentage points higher in the absence of this "consumption contagion" effect. This is non-trivial but also not huge. What is most important is the empirical support of a particular type of departure from the Permanent Income Hypothesis. Many types of departures have been hypothesized, but quantifying their relative importance and carefully tracing out their implications for macro models is an ongoing task.

Japan and the Formation of Inflation Expectations

With the Bank of Japan's adoption of a 2% inflation target making headline news, it seems like a good time to discuss some recent research on the psychology of inflation expecations by Berkeley Professor Ulrike Malmendier, who was recently awarded the 2013 Fischer Black Prize from the American Finance Association. This biennial prize honors the top finance scholar under the age of 40 years old. Malmendier works in the intersection between finance and behavioral economics and is known for her incredible creativity.

Here is the abstract of Malmendier's paper with Steven Nagel titled "Learning from Inflation Experiences":

How do individuals form expectations about future inflation? We propose that past inflation experiences are an important determinant absent from existing models. Individuals overweigh inflation rates experienced during their life-times so far, relative to other historical data on inflation. Differently from adaptive-learning models, experience-based learning implies that young individuals place more weight on recently experienced inflation than older individuals since recent experiences make up a larger part of their life-times so far. Averaged across cohorts, expectations resemble those obtained from constant-gain learning algorithms common in macroeconomics, but the speed of learning differs between cohorts.
Using 54 years of microdata on inflation expectations from the Reuters/Michigan Survey of Consumers, we show that differences in life-time experiences strongly predict differences in subjective inflation expectations. As implied by the model, young individuals place more weight on recently experienced inflation than older individuals. We find substantial disagreement between young and old individuals about future inflation rates in periods of high surprise inflation, such as the 1970s. The experience effect also helps to predict the time-series of forecast errors in the Reuters/Michigan survey and the Survey of Professional Forecasters, as well as the excess returns on nominal long-term bonds.

Malmendier and Nagel's paper over a time period covering several monetary policy regimes, which differ markedly from that of Japan. But a related paper by David Blanchflower and Conall Mac Coille focuses on the UK, which practices inflation targeting.  "The Formation of Inflation Expectations: an Empirical Analysis for the UK" (2009) includes a summary of why inflation expectations matter for monetary policy, and how this is relevant to inflation targeting: 

In the neo-Keynesian model (see, for example, Clarida et al. 2000), sticky prices result in forward looking behaviour; inflation today is a function of expected future inflation as well as the pressure of demand, captured in an output gap term. Thus, expectations are deemed to be an important link in the monetary transmission mechanism. Monetary policy can be more successful when long-term inflation expectations are well anchored. Hence, many studies have focused on the question of how to assess the response of inflation expectations to macroeconomic shocks, and whether this is likely to be lower in inflation targeting regimes. 

Blanchflower and Mac Coille also summarize three paths through which inflation expectations matter:

Wages are set on an infrequent basis, thus wage setters have to form a view on future inflation.  If inflation is expected to be persistently higher in the future, employees may seek higher nominal wages in order to maintain their purchasing power.  This in turn could lead to upward pressure on companies’ output prices, and hence higher consumer prices.  Additionally, if companies expect general inflation to be higher in the future, they may be more inclined to raise prices, believing that they can do so without suffering a drop in demand for their output.  A third path by which inflation expectations could potentially impact inflation is through their influence on consumption and investment decisions.  For a given path of nominal market interest rates, if households and companies expect higher inflation, this implies lower expected real interest rates, making spending more attractive relative to saving. 

In Japan, the third path may be most important. The higher inflation target is intended to lower real interest rates and boost consumption and investment. But there is a fourth reason, not listed by Blanchflower and Mac Coille, of particular relevance to Japan. Foreigners' expectations of future inflation affect the value of the currency. the Japanese Ministry of Finance recently revealed a 222.4 billion yen ($2.5 billion) current account deficit-- a measure of how much imports exceed exports. When people expect Japanese inflation to be higher in the future, the yen gets less valuable now, because it won't be able to buy as much stuff later; the yen weakens. But in this case, weakness is not necessarily bad. A weaker yen means that Japanese people will find it more expensive to import stuff, so they will import less. Likewise, people outside of Japan will find it cheaper to buy Japanese stuff, so Japan will export more. This helps shrink the current account deficit. And depending on the sizes of the income and substitution effects, Japanese consumers may buy more Japanese products.

Under inflation targeting in the UK, even though inflation expectations are reasonably well anchored, and median expectations are around the inflation target, there is substantial heterogeneity across agents in their inflation expectations. Malmendier and Nagel's paper provides a behavioral theory to explain part of this heterogeneity based on agents' heterogeneous past experiences of inflation. Heterogeneous inflation expectations have the potential to affect the workings of all the paths through which inflation expectations matter. We need to understand not only how Japan's inflation target will influence median inflation expectations, but also how it will affect expectations of price setters, wage setters, borrowers, savers, exporters, trade partners, etc. More than likely, these groups differ significantly in their demographics, have had different experiences, and thus form different expectations of inflation. (For reference, the graph below displays Japanese inflation, interest rates, real GDP per capita growth rate, and M2 growth rate. Japan has not seen 2% inflation since 1997.) Extensions of Malmendier's research to other countries and monetary regimes will be very useful in understanding the effects of monetary policy.