Amid this pandemic, the most common numbers being thrown around are case numbers and deaths. Every morning I get up and look at theNew York Times US statistics on the spread of the COVID-19, and a few days ago I found myself wondering, “what do these numbers really mean?” Because of the vast heterogeneity in how tragic each death is, it’s tough to know.

In one extreme scenario, almost everyone who died was going to die anyways in, say, less than a year. This would be those with serious conditions and of advanced age (often both). Alternatively, there may be a surprising number of deaths where the expected number of years that the now deceased individuals would have lived is over 10 years, or perhaps over 20 or 30. I don’t think anyone would argue with the claim that deaths of the latter type are, in a sense, less tragic.

Thus, I propose a new measure to calculate the damage. Total number of expected life-years lost as a result of the virus. This could also be transformed into average number of  expected life-years lost per death. To create this measure, ideally one would use individual-level data on demographics, family background, and medical history for each person who dies to predict how long this person would have lived in normal circumstances. The magnitude of the difference between age the person was when they died and the predicted age of death. This could then be summed up over all the people who died. Then median value, modal value, and average value of the measure could be calculated as well.

I am unaware of data that would make such a calculation doable, but if someone happens to see this who has such data, it would be great to see these numbers.

A few weeks ago, I listened to an interview between Matt Cadwallader and Dani Rodrik on the Harvard policy cast. In this interview, Dr. Rodrik made the claim that workers who compete with labor markets in other countries may be the victims of “unfair competition.” This is because hiring workers who are protected by policies that promote worker rights (i.e. minimum wage, non-pecuniary benefits, mandatory safety precautions, etc.) tends to be more expensive than hiring workers without such protections. Thus, workers with fewer protected rights will have an advantage when competing for jobs. To make matters worse, it is generally illegal for protected workers to make themselves “cheaper” by negotiating a contract with fewer protections.

Later that week, I encountered this argument in a different context. I was discussing immigration with a friend and during our conversation, I claimed that “Mexican immigrants often take jobs that Americans will not do.” He responded that “illegal immigrants had an unfair advantage on the labor market.” He went on to explain that Americans have a high opportunity cost because they have access to welfare programs while illegal immigrants do not. Furthermore, companies do not have to pay the same benefits to illegal workers, which makes them cheaper. Though the context was different, this was Dr. Rodrik’s argument repackaged. Differences in the laws that regulate workers can give certain groups an unfair advantage over others in the labor market.

As hard as this can be to acknowledge, worker protections can have negative unintended consequences, especially when globalization and immigration create groups of workers that are governed by different policies, yet expected to compete with one another. Because of this, free trade, open borders, and worker rights–three ideas that are commonly supported together–may be inconsistent and harm the very workers that legal protections are intended to help.

Many Americans feel that the political system is failing them. Many are wondering “why are the presidential candidates so much more extreme than me or anyone I know?” It is tempting to think that corruption and money in politics must be part of the problem; that greedy politicians like Donald Trump are born out of an unrestricted political system run amuck. But, what if it’s exactly the opposite? What if American politics needs more back room deals and pork barrel spending? Jonathan Rauch, a senior fellow in governance studies at the Brookings Institution, makes this counterintuitive argument in his paper “Political Realism: How hacks, machines, big money, and back-room deals can strengthen American democracy.” In the remainder of this post, I explain the basics of policial realism and allude to a relationship between realist theory and the economics of political decision making.

Political Realists assume that politicians are not solely interested in serving the public. Instead, a realist assumes that politicians, like ordinary people, are driven by wealth, power, and personal gain as well as service, charity, and moral principles. Furthermore, a realist is not interested in whether self-interest should be a political motive. What matters is that politicians are self-interested.

Realists also believe that the primary function of government is to achieve a political equilibrium. Equilibrium consists of satisfying the most political participants (politicians, voters, bureaucrats, big businesses, etc.) and minimizing dissent. In short, a government is working well if there are as few groups of people who are frustrated with the current functions and uses of government. This goal requires compromise above all else.

Politicians are often divided on important issues, making compromise difficult. Realists believe that the only way around this are incentives. Political actors need ways to motivate dissenters to come together if there is to be any progress. Realists view pork barrel spending, earmarks, endorsements, loyalty, back-room deals, and other frequently deemed “corrupt” actions as the means of motivation that facilitate political compromise.

As an economist, many of Rauch’s assumptions are appealing. In fact, Nobel Laureate James Buchanan’s seminal work in Public Choice economics echoes the key points in the political realism literature. I plan to investigate the relationship between political realism and economics (both theoretical and applied) in a later post. My suspicion is that political realism in conjunction with economics will be able to explain much of the current state of American politics and how the country can move forward.

 

Over the last two months, I discovered many online behavioral economics lectures given by Sendhil Mullainathan (Harvard), Eldar Shafir (Princeton), and David Laibson (Harvard). I have seen many economics presentations, but these stood out because what I learned from them changed my life Immediately. I am not exaggerating. Understanding how psychology interacts with decision making had instant impacts on my life. In particular, two ideas changed the way I organize my life: (1) that mental bandwidth is scarce and (2) that there are commitment tools that help people overcome their present bias.

The first idea, that the human mind’s multitasking ability is limited, seems obvious. But, Dr. Mullainathan and Dr. Shafir demonstrate that this matters in surprising ways. For instance, they show that being poor can occupy bandwidth and reduce IQ, that dieters cannot concentrate well when prompted to think about food, and that lonely people focus obsessively on social cues. Furthermore, Dr. Mullainathan frequently relates bandwidth to daily decisions. For example, if you have your phone with you at a social event, you may receive emails that occupy your thoughts even while you are not using the phone. Thus, your bandwidth will be partially occupied, making it difficult to focus on the people you are with. Bandwidth also matters if you are trying to change your behavior. For example, if you are trying to work out more frequently, having a complicated and ill-specified plan forces you to think about when in the day to work out, which occupies bandwidth. Instead, coming up with a rule like, “I will work out every morning at 9am” allows you to form a habit that does not require much thought.

In my case, I have taken this idea and organized the way I work out to preserve bandwidth. First, I have created times every day that I work out so that I don’t have to think about it, I just do it. Second, I enter the gym thirty minutes before the gym closes so that I stay focused. This second change is not obvious but valuable. Under time scarcity, I know that I must squeeze a good workout into thirty minutes, which incentivizes me to be very focused.

The second idea is not so obvious. Dr. Laisbon explains that people are present biased, and thus don’t feel the same about doing something now as opposed to later. In particular, humans tend experience future costs and benefits with less magnitude. This is why procrastination is a universal phenomenon. If there is a task that will take some effort, it is much easier to plan to experience that cost in the future instead of in the present. Present bias also explains our love of immediate gratification. If you have two tasks, one in which there is an immediate payoff, like scrolling through Twitter, versus one that has a longer term payoff, like writing a paper, people often choose the immediate payoff because they are present biased.

In my case, I took Dr. Laibson’s advice to get a web blocker. Web blockers give you a credible way to limit the amount of time you can access specified websites. The web blocker I found even has a setting that requires that I complete an annoying and timely task if I want to change the amount of time that I can spend on blocked sites. I conceive of tools like this as weapons against my future self, where I can create costs that restrict my future actions. In fact, I wrote this blog post at least partially because I have run out of my allotted time on Netflix.

Economics research can be overly technical, boring, and inaccessible to the average person. On the contrary, Dr. Mullainathan, Dr. Shafir, and Dr. Laibson all produce research that is thoughtful, stimulating, and immediately applicable. I recommend that anyone interested in life-changing work read them.

I was listening to a lecture Chad Syverson gave at the Becker Friedman Institute, and I was struck by a particular argument he made in the talk.  Dr. Syverson showed graphically (see below) that the efficiency of American sugar refining steadily increased until 1934 when the Sugar Act was passed. Sugar refining then steadily decreased until the act was repealed in 1974. Since the repeal, there has been a steady increase in efficiency, paralleling the upward trend before the sugar act was originally passed.

Dr. Syverson used this as support for his argument that government regulation can create incentives that are antithetical to efficiency. He went on to explain that the Sugar Act essentially paid farmers for producing sugar which created an incentive for sugar beet farmers to produce larger sugar beats which, in turn, decreased refining efficiency (for more details listen to minutes 51:00–54:25 of his talk). In effect, he was claiming that the sugar act caused the decrease in sugar refining efficiency that occurred for 40 years while the act was law. When I heard his argument I realized two things: (1) that I was pretty convinced by his causal claim and (2) that Dr. Syverson never presented a well-identified estimate of the causal effect of the Sugar Act on sugar refining efficiency (it is very possible that such an estimate exists, but it is irrelevant to my point). How was this possible? As an aspiring applied microeconomist, I have been trained to be skeptical of all unidentified causal claims, so how could this argument be convincing without treatment and control groups?

The obvious answer is that there are other ways to convince even the most rigorous applied economists that a claim is causal. In this case, Dr. Syverson presented two empirical facts that are consistent with a straightforward economic explanation. For him to be wrong, farmers would need to deviate from profit maximizing behavior, and the timing of these changes in efficiency trends must have been coincidental. The burden of proof would have been on me if I were skeptical of his claim.

This experience was a good reminder that causal claims rely on theoretical explanations and institutional details as well as causally identified coefficient estimates. In fact, sometimes no estimate is needed at all, as in this case, to make a convincing argument.

A few months ago I discovered, via Professor Chris Blattman’s blog, that the Red Cross diverted funds for news exposure while they were supposed to be helping victims of Hurricane Katrina. If this is true, the Red Cross certainly made a morally questionable decision here, but their decision is not surprising from an economic perspective.

A non-profit organization like the Red Cross cannot operate without funds; if a non-profit, humanitarian organization wants to expand, invest in new technology, or hire well-trained experts, that organization needs to raise money like any other business. In other words, non-profits have an incentive increase their revenue via donations even though they do not compete for profit on the market in the traditional sense. From this perspective, the Red Cross’s behavior after Hurricane Katrina makes sense. What better way to elicit donations than to appear to help during a tragedy and ensure that people know about it? To maximize their donations, the Red Cross needs to convince people that they are helping,  not actually help. What good are good deeds if no one continues to donate?

The reality is that the Red Cross needs money to help people. Without advertising their acts and getting donations, they could not help anyone. Thus, humanitarian non-profit organizations face conflicting incentives when they try to grow their donor base and aid people.  I call this the joint client problem of humanitarian organizations. I call it this because the two clients–donors and individuals in need–are the source of a humanitarian organization’s conflicting incentives.

Why do I call both donors and aid recipients clients? To figure out who a firm’s clientele are, we need to ask the following question: to whom do humanitarian organizations provide services? The standard answer is that they provide services to the people they help (victims of disasters for the Red Cross). Perhaps in an ideal world, this would be the clear answer, but it’s not. In reality, donors also receive services from these organizations. The donor pays the humanitarian aid company to do what it aims to do. If donors do not think the non-profit is accomplishing its mission, then the humanitarian organization’s funds will dry up. To exist, aid organizations need to answer to the people they seek to help and answer to people providing resources.

These conflicting incentives are not studied enough in economics, which is a shame because this may not be the most efficient incentive structure to aid the poor in times of crisis.

 

 

In a new working paper, Professor Roland Fryer, a prolific Harvard economist, attempts to determine whether police are biased against minorities when they use force. To practically everyone’s surprise, Fryer finds that there is no racial bias in police shootings, which is the type of force that the media focuses on most. I am working on my own review of his paper, but in the meantime, here is a link to a clear and concise summary. Also, the findings were featured in the NY Times.

If you live in the U.S., you are probably aware of the high-profile police brutality cases that have incited uproar across the nation. From an economist’s perspective, this is a difficult topic to analyze; there are many reasons why these cases occur and using general principles to understand these events seems impossible. But all is not lost. The key to using economic principles to understand brutality lies in the types of questions we ask. In general, economists can answer two kinds of questions: (1) is it likely that A caused B?  and (2) what reactions should we expect from rational agents in this context?

Do police brutality cases tend to increase or decrease crime? Do police brutality cases affect crime rates differently depending on the race of the officer or victim? Do police enforce the law differently after a brutality case? Do rates of police brutality vary by race of officer or victim? These are all examples of cause and effect questions. Though economists can never be sure whether their answers to any of these questions are correct, econometrics can show how likely a particular causal relationship is. For instance, one way of understanding the effect of police brutality cases on crime rates could be to compare the average crime rate in a city before and after a brutality event occurred. Answering this question would require data about the timing of brutality events in multiple cities.

Unfortunately, this is not a good way of answering the causal question of interest. There could be reasons other than the brutality incident that caused systematic changes in crime rates after an event. For example, what if cities that tend to have more police brutality also have larger police forces, so police curb increased crime in these cities more efficiently. Thus, police force size (or another characteristic that is correlated with police brutality) could confound the effect of interest. This issue is called the endogeneity problem, and good econometricians have to circumvent this problem to answer almost every interesting cause and effect question.

But how do economists narrow the field of possible cause and effect relationships to look for? This question is linked to the second type of question that economists can answer: what reactions should we expect from rational agents in this context? Economists (or non-behavioral microeconomists at least) base their causal predictions on what is called Rational Choice Theory. Rational Choice Theory assumes that people make decisions to maximize their well-being (“utility” in economics) and that they can decide between alternatives.

How does that help us understand police brutality? We can see the power of Rational Choice Theory if we imagine the decisions that a police department faces after an officer uses excess force. If this is a high-profile or racially charged case, how might we expect the police department to respond? If this event puts media and public scrutiny on the department, the cost of another mistake could be extremely high. Furthermore, if civilians are frustrated with the police department, the chance of retaliatory behavior against the police may go up. Rational Choice Theory dictates that police officers will “ease up” in an attempt to reduce high-cost errors (for an empirical analysis of this theory see this paper by Lan Shi). Further, police may not police as aggressively because the chance of being attacked goes up. Rational Choice Theory provides the insight required to predict how humans make decisions when costs and benefits change.

Now that we understand the types of questions that economists can answer, we have a framework for understanding the causes and effects of police brutality more clearly. I intend to propose multiple ways to analyze police brutality in a series of posts following this one. In particular, I will use the insights of Rational Choice Theory to decide whether certain proposed causal theories make sense, and then I will discuss testable predictions and the type of data that might be useful for supporting or falsifying that theory. Through this, I hope to both articulate my opinions and clarify my research ideas on this topic. I am not so confident as to think that I can fully understand these issues, so I appreciate any and all comments or corrections.