Rationing access to beaches, in a time of physical distancing

Last week, my ECONS102 class covered common resources and the Tragedy of the Commons. Common resources are rival (one person's consumption reduces the amount of the good available for everyone else), and non-excludable (if the good is available to anyone, it is available to everyone, and you can't easily prevent people from having access to it). The problem with common resources is that, because they are non-excludable (and therefore open access), they are over-consumed relative to the socially efficient quantity. Essentially, there is a difference between the private incentives (to consume as much of the good as you want to), and the social incentives (to ensure that the good is shared in some fair and equitable way).

Now consider public parks and beaches. In normal times, parks and beaches are non-rival (and non-excludable), because there is plenty of space available for everyone. However, in peak season they are clearly rival, and because they are non-excludable as well, they are common resources and so they are subject to the common resource problem outlined above. Everyone wants to be at the beach (the private incentive), but by everyone being at the beach, the beach becomes overcrowded and everyone's experience is all the worse for it. Fortunately, this is only a problem at times of peak demand.

However, what constitutes peak demand at beaches is redefined when physical distancing is important, as Time reported back in May:

Last weekend, images and reports of glutted beaches and parks have spurred several governors to roll back access to parks and shorelines for fear of a surge in new COVID-19 infections. At a certain point, it becomes physically impossible to pack so many people into six-foot intervals.

As summer approaches, and demand for outdoor recreation skyrockets even further, public space stands to become what economists call a “common resource” — something that belongs to no one, like fish in a lake, but can be depleted without a form of rationing. Already, New York City Mayor Bill de Blasio said Thursday that the virus-stricken city may limit entry to some parks.

“Space is now a resource that, in the foreseeable future, we’re going to have to ration in a way we’ve never had to ration before,” says Clemson University economist Michael D. Makowsky. “The outdoors used to be an inexhaustible resource. Human beings now require a lot more volume than they used to.”

The solution to a common resource problem is to make the good excludable, rather than non-excludable - essentially, to move from a resources that is open access, to one that is closed access (or where access is restricted). In the case of parks or beaches, the government can achieve this by rationing access to the resource. In the case of parks and beaches, the Time article notes three potential options:

The first and simplest approach would be to limit access to public places, like parks and beaches, based on some form of lottery. On Mondays, for example, Yosemite National Park might be closed to families in which the head of household had a driver’s license number ending in a 1 or a 2. On Tuesdays, on 3 or 4, and so forth — effectively reducing the potential crowd by 20%...

A second strategy could involve issuing permits for controlled spaces with discrete access points that can be sold or exchanged. It’s another idea that has already been tested in environmental policy — specifically, the “cap and trade” system, which created a marketplace for companies to buy and sell emissions permits while attempting to incentivize emissions reductions.

In the case of space rationing, an analogous policy would aspire less to incentivize isolation than to fairly distribute the limited resource of open areas. Under such a framework, cities would issue free permits to all residents to be used for access to the most popular parks, beaches and other coveted public areas. A person could choose to sell unwanted permits, or trade them for a different kind of permit (to a different park, or for a different day, and so forth.)...

A third option would be to impose new taxes on certain privileges that are currently shut down in many parts of the country, like dining in at a restaurant. “If ever there was a time for a dine-in tax, it’s now,” Makowsky says. Such a tax could be proportional to demand, with higher levies on weekends and other peak times, he says.

The first option is clearly open to abuse, since households with multiple vehicles (with difference licence plates) could skirt around the restriction quite easily. The third option doesn't regulate the number of people going to the beach directly, but simply makes it more expensive to do so (in essence, this potential solution isn't about making the good excludable, but about making it non-rival). Unless the tax varies based on beach-going demand, the tax would be too high on bad weather days, discouraging beach-going on days when people don't want to go to the beach, while simultaneously being too low on good weather days, where people would be more willing to pay the tax and still go to the beach. It likely wouldn't solve the common property problem at all.

The second option seems most feasible from an economic standpoint, and is quite similar to the tradeable quotas that are used to manage fisheries (another common resource). It encodes a property right for everyone (the right to go to the beach on a particular day), and then lets people trade between themselves to determine who actually takes up the right. The number of permits can easily be limited to ensure the 'right' number are available to ensure physical distancing can be maintained each day.

To be efficient (welfare-maximising), a property rights system needs to have four features:

1. Universal - In this case, everyone who wants to go to the beach must have a permit;
2. Exclusive - Only permit-holders are allowed to go to the beach, and all the costs and benefits of beach-going must accrue to the permit-holder;
3. Transferable - Permits must be able to be transferred in a voluntary exchange; and
4. Enforceable - There must be penalties in place that are sufficient to deter people without permits from attempting to go to the beach.

If those four features are in place, then the permit-based system is an efficient solution to the common property problem of how to make the beach (or park) closed access. The big problem then becomes, how do you allocate the permits in the first place? And of course, how do you administer the system? Those problems would first need to be solved in order for this system to be workable.

[HT: Marginal Revolution]

Coronavirus and the market for oranges

Last week, the New Zealand Herald reported:

Kiwis desperate to stay healthy in the midst of the Covid 19 pandemic have been buying up vitamin C in whatever form they can get it.

Since the virus hit our shores in early March, sales of oranges, kiwifruit and vitamin C supplements have surged.

Citrus New Zealand domestic market lead James Williams said sales of New Zealand navel oranges were 10-20 per cent higher than in previous years meaning Kiwis gobbled between 800,000 and 1.6 million kilograms of extra oranges.

He said pre-packed bags of the fruit had proved especially popular during the peak of the outbreak in New Zealand because there was less human interaction involved...

The popularity of the fruit this season meant consumers would have seen the price of the fruit go up in the last seven-10 days as the supply of the fruit dwindled, Williams said.

Consumer tastes and preferences are one of the factors underlying the demand curve. As consumers' preferences shift towards oranges, they demand more of them at each and every price. As shown in the diagram below, the market for oranges was initially in equilibrium where demand (D0) met supply (S0), at a price of P0, and Q0 oranges were traded. The increase in demand to D1 increases the quantity of oranges traded to Q1, and increases the price to P1.

The same effect is happening in other markets mentioned in the article, including kiwifruit (also increased demand because of high Vitamin C content) and avocados (because people have more time for cooking their own meals, they seem to be demanding more avocados).

Transition to Daylight Saving Time and student academic performance

The transition to Daylight Saving Time (DST) caught me out this weekend. Fortunately, I don't feel like I lost sleep as I was already exhausted from teaching this trimester. Putting aside teacher performance, the idea that losing an hour's sleep, or disruptions to the body's natural circadian rhythm, affects students' academic performance is a common working hypothesis in education circles. So, I was interested a couple of weeks ago to read this 2017 article by Stefanie Herber, Johanna Sophie Quis, and Guido Heineck (all University of Bamburg, in Germany), published in the journal Economics of Education Review (ungated earlier version here).

They used data from 22,000 European fourth-grade students from six countries, drawn from the 2011 waves of the Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS) studies (see here for more on those studies). The interesting thing about the 2011 waves is that, in the countries that Herber et al. look at, they know on what date the students were assessed, and they can identify some students who were assessed just before a change into DST, and other students who were assessed just after the change into DST. Since schools were effectively randomly assigned to when their students were assessed, this provides a useful quasi-experiment of the effect of DST on student academic performance.

Herber et al. looked at how maths, reading, and science performance differed by DST status for these students, and found that:

...students scored about 4 points lower in both math and science when tested during the week after the clock change. Given a standard deviation of about 72 and 70 points, respectively, test scores in the week after the clock change drop by roughly 6% of a standard deviation. These effects are not substantial in terms of either statistical significance or magnitude.

That quote doesn't make it particularly clear, but the effects were both tiny and statistically insignificant. In other words, DST made no difference to how well students performed on these low-stakes tests in maths, reading, or science. Herber et al. also looked at the effects on performance for eighth-graders, and again found effects that weren't consistently statistically significant and were small in magnitude (albeit on a smaller sample of countries). They conclude that:

This might be due to the fact that one hour of sleep loss is not enough to unbalance circadian clocks to the degree that performance within the following week measurably declines...

Based on our research, it is fair to say that neither parents, nor children, nor competence testing agencies (or even policy makers) have reason to worry about the alleged harmful effects of the clock change on low-stakes test performance.

So, we should probably stop worrying (if we ever were) about the effect of Daylight Saving Time on student academic performance.

Is there an acne premium as well as a beauty premium in labour markets?

I've written a number of times about the beauty premium in the labour market (see here and here, as well as here for my review of Daniel Hamermesh's excellent book Beauty Pays). More attractive people earn more. Hamermesh notes a number of potential mechanisms that might underlie the beauty premium.

However, in an interesting twist, a 2019 article by Hugo Mialon of Emory University (whose previous work includes the economics of faking orgasms) and Erik Nesson (Ball State University) looks at whether there is a premium associated with having acne in high school. They use data from over 43,000 adolescents in the Add Health survey, and find that:

...having acne in high school is positively associated with overall grade point average (GPA), mathematics GPA, and science GPA in high school; positively associated with earning an A in high school math, science, history/social studies, and English; and positively associated with completing a bachelor’s degree. The associations are generally stronger for women than for men... We also find some weak evidence that acne is associated with higher future personal labor market earnings for women.

Mialon and Nesson try to argue that acne is exogenous, meaning not related to any variable that in turn is related to student academic outcomes or later earnings. They show that having acne is:

...not related to measures of socioeconomic status, including parental education levels or most measures of family structure.

I don't find those particular results convincing. Parental education is likely to be measured with error (particularly since the data are drawn from the adolescents, not from the parents), and Mialon and Nesson include "don't know" and "missing" as categories in their education variable, which isn't a particularly robust way of handling it. So, I don't think we can interpret these results as necessarily causal, even though Mialon and Nesson provide some evidence of a plausible mechanism:

In theory, having acne may reduce feelings of being socially accepted, thereby reducing time spent socializing and increasing time spent studying, which may be conducive to educational attainment.We find strong evidence that having acne is associated with feeling less socially accepted and less attractive. Interestingly, we also find that acne is associated with reduced participation in sports clubs and increased participation in nonsports clubs, suggesting a possible shift from physical to intellectual pursuits.

Perhaps that explanation is true. Or perhaps adolescents whose parents had acne, and whose parents also achieved better in school, spend more time on academic pursuits. Is the causality occurring in this generation, or the previous generation? Or the one before that? It's an interesting question, but this should be far from the last word on this topic.

[HT: Marginal Revolution, last year]

Read more:

• Blonds have more fun (or rather, they get paid more)
• The beauty premium in undergraduate study is small, and more attractive women major in economics
• The beauty premium in the LPGA
• The beauty and height premiums in the labour market

The optimal amount of screen time for kids

At the start of the year, Cyril Morong (The Dangerous Economist) had a post titled "Are kids getting "too much" screen time?":

Kids might be spending alot [sic] of time on their phones, computers tablets, etc. watching videos, texting, etc. But is it too much?

A person can do too much (or too little) of anything. The optimal amount is found where marginal cost (MC) equals marginal benefit (MB). It is a good idea to keep doing something if the MB of the next unit is greater than the MC. In fact, you keep doing it right up to where they are equal (there is a graph below to illustrate this).

I want to pick up where Cyril left off, because there's some key points missing from his explanation. Anyway, the relevant diagram is laid out below. Marginal benefit (MB) is the additional benefit of one additional hour of screen time. In the diagram, the marginal benefit of screen time is downward sloping - the more screen time a kid gets, the less additional benefit another hour provides. The first hours of screen time each day are cool and exciting (maybe?), but once you've watched some the cool factor starts to wear off. Even kids get satiated (they call it 'bored'). Marginal cost (MC) is the additional cost of one additional hour of screen time. The marginal cost of screen time is upward sloping - the more screen time the kid engages in, the higher the opportunity costs (they have to give up more valuable alternative activities they could be engaging in, like homework, or sleep). The 'optimal quantity' of screen time (from the perspective of the kid) occurs where MB meets MC, at Q* hours of screen time. If the kid watches more than Q* hours (e.g. at Q2), then the extra benefit (MB) is less than the extra cost (MC), making them worse off. If the kid watches less than Q* hours (e.g. at Q1), then the extra benefit (MB) is more than the extra cost (MC), so one more hour of screen time would make them better off.

So there is an optimal amount of screen time for kids and, unless the benefit of the very first hour is less than the cost, the optimal amount of screen time is not zero. But it's probably not all-day-every-day either.

Wading through sludge

In Saturday's book review post for Sendhil Mullainathan and Eldar Shafir's book Scarcity, I noted that the book fell a little bit short in terms of the linkages with behavioural economics. That was further highlighted to me when reading this new article by Cass Sunstein (co-author with Richard Thaler of the book Nudge), published in the journal Behavioural Public Policy. Sunstein highlights the role of 'sludge', which he defines as:

...‘a viscous mixture’, in the form of excessive or unjustified frictions that make it difficult for consumers, employees, employers, students, patients, clients, small businesses and many others to get what they want or to do as they wish...

When a student seeking financial aid has to fill out an elaborate application form with dozens of difficult questions, that is sludge. When a firm requires a consumer to call to cancel a subscription, and keeps them waiting on hold for a long time before they can deal with a customer service representative, that is sludge. When a potential immigrant has to jump through multiple hurdles, filling out endless paperwork and providing copious amounts of information, a lot of that is sludge. Dealing with sludge is frustrating, disheartening, and demotivating. It can easily lead the student to give up on applying for financial aid, the consumer to give up on cancelling their subscription, or the potential immigrant giving up on their dreams of immigration.

Sometimes the creation of sludge is inadvertent, but often it is intentional. You can imagine that the example of the difficulties in cancelling a subscription may well involve an intentional action on the part of the firm. At the very least, they won't be looking to make it easier to cancel a subscription.

Reducing sludge should be an important goal. As Sunstein notes:

Simplification and burden reduction do not merely reduce frustration; they can change people’s lives... An underlying reason for this is that our cognitive resources are limited...

It is at this point that the link to scarcity is apparent. Sunstein, to his credit, does reference Mullainathan and Shafir on this point. When people are dealing with limited cognitive resources - when they are facing extreme scarcity - burdening them with sludge is simply going to exacerbate their problems. Think about all the times where we burden the poor with excessive paperwork or require costly or time-consuming in-person appointments with case managers at Work and Income or StudyLink - applying for welfare benefits, food grants, student loans, etc. Is all of this necessary, or is the government simply putting obstacles in the way and preventing the people who need assistance from obtaining the assistance that they need? Sunstein argues for what he terms 'sludge audits'. It's an idea worth thinking about.

Sunstein's article makes some important points, and should be required reading for public policy students and for those working in the government sector.

[HT: Marginal Revolution, last year]

Book review: Scarcity

I just finished reading Scarcity by Sendhil Mullainathan and Eldar Shafir. The subtitle is "The new science of having less and how it defines our lives". Scarcity is of course the central theme of economics, and features in many textbooks' definitions of the discipline. However, there is a key difference between the sort of scarcity that economics considers, being the idea that we have limited resources with which to satisfy unlimited wants and desires, and the sort of scarcity that Mullainathan and Shafir are discussing. 

The scarcity that the book considers is much more extreme and in-your-face. It doesn't simply enforce a need to make choices, it defines the alternatives that people choose. In Mullainathan and Shafir's view, it is the "feeling of scarcity" that "taxes our bandwidth", capturing the minds of those facing scarcity and limiting their ability to achieve their goals across multiple domains. People facing scarcity have a tendency to "tunnel", focusing intently on their scarcity. This can have positive effects (a "focus dividend") as it concentrates our cognitive resources on the task at hand. However, it causes us to neglect things that are outside the tunnel. As Mullainathan and Shafir note:

Scarcity alters how we look at things; it makes us choose differently. This creates benefits: we are more effective in the moment. But it also comes at a cost: our single-mindedness leads us to neglect things we actually value.

Much of the book is set up to describe how scarcity affects people's lives. This information was interesting, but I felt it was drawn out a bit too much. It culminates in a description of "scarcity traps", which Mullainathan and Shafir neatly summarise:

Tying all this together, we see that scarcity traps emerge for several interconnected reasons, stretching back to the core scarcity mindset. Tunneling leads us to borrow so that we are using the same physical resources less effectively, placing us one step behind. Because we tunnel, we neglect, and then we find ourselves needing to juggle. The scarcity trap becomes a complicated affair, a patchwork of delayed commitments and costly short-term solutions that need to be constantly revisited and revised. We do not have the bandwidth to plan a way out of this trap. And when we make a plan, we lack the bandwidth needed to resist temptations and persist. Moreover, the lack of slack means that we have no capacity to absorb shocks. And all this is compounded by our failure to use the precious moments of abundance to create future buffers.

I found myself nodding along with many of the points that the book makes. However, I'm not sure that I have fully absorbed all of the key points. I think one key takeaway that I wish the book had made would be to be kind to one another - we don't know what is taxing other people's bandwidths. When someone lets us down or is unable to perform as we think they should (like our students, for those of us who are teachers), it may reflect only their capacity in the moment, and not their general capability. That suggests we should perhaps adopt a more compassionate approach than many of us do (and is something my wife is much better at in her teaching, than I am in mine).

If there was one disappointment with the book, it was the final sections, which attempted to provide some solutions to the problems associated with feelings of scarcity. The solutions seemed incredibly context-specific and not at all generalisable - for example, at times the solution to tunneling is to add things into the tunnel, but at other times the solution is to get things out of the tunnel. I guess this just reflects the state of the literature at the moment, and that even if we know that scarcity is a problem, we haven't found workable general solutions as yet.

Another aspect of the book that could have been strengthened was the links to behavioural economics. These were few and far between, but it seems to me that this was an opportunity lost. In particular, the focus of the latter sections could easily have been reframed to take advantage of additional perspectives. I'll note some more on this in a follow-up post shortly.

Having said that, the book was definitely thought-provoking and an interesting read. I do enjoy reading books in the intersection of psychology and economics, and this book fits right into that field.

Assar Lindbeck, 1930-2020

I was saddened to read today of the passing of the famed Swedish economist Assar Lindbeck last week. The American Institute for Economic Research has a good summary of Lindbeck's contributions. My ECONS102 students might recognise the Insider-Outsider model as one of the reasons for job rationing in the labour market:

His most substantive contribution to the body of economic knowledge is the Insider-Outsider model of labor markets that he developed together with Dennis Snower. In several much-cited publications in the 1980s, Lindbeck and Snower showed that those already employed (and those in labor unions) are uninterested in expanding jobs for those outside the labor market. The negotiation of wages between labor unions and firms – the “insiders” – thus set up barriers and exclude those about to enter the labor market or for some reason have been unable to get a job – the “outsiders.” Labor unions, in other words, are not nice, benevolent constructions looking out for the little guy, but just another privileged interest group advancing the welfare of its members at the expense of outsiders. The model offers an explanation for involuntary unemployment and, like efficiency wages, a rationalization for above-market clearing wages.

My ECONS101 and ECONS102 students might recognise his arguments against rent control, especially this:

...one of Lindbeck’s most iconic and memorable quotes are called for: 

“Rent control appears to be the most efficient technique presently known to destroy a city—except for bombing.”

I had no idea how instrumental Lindbeck was in the development of the Nobel Prize in Economics, although on reflection it makes a lot of sense. He obviously made many more contributions than I realised. In the AIER article, I particularly liked this bit:

Perhaps becoming publicly loved is out of reach for economists, but Lindbeck at least managed a wide enough respect and recognition that almost everyone knew his name.

That might be all that any economist could ask for. Lindbeck will be missed.

[HT: Marginal Revolution]

 

The toilet paper crisis and the market for bidets

The coronavirus pandemic and associated lockdowns led to shortages of toilet paper earlier this year (which I've discussed before here). An interesting aspect of that is the effect on the market for bidets, as Business Insider reported in March:

The ongoing coronavirus outbreak has caused a toilet paper panic-buying frenzy, with customers flooding large retailers like Costco and even becoming unruly over fears that essential items – like toilet paper – may soon deplete.

But it also seems that many people have been outfitting their bathrooms with another option: the bidet. Home product company Brondell, which sells various types of bidet toilet seats and attachments as well as heated toilet seats, has seen an increase in sales over the last few days, company spokesperson Daniel Lalley told Business Insider.

Lalley said Brondell is selling a bidet on Amazon every two minutes, or about one thousand units per day. The company earned $US100,000 in one day this week through Amazon sales, an “exponential” increase over an average day, according to Lalley.

The company’s direct sales have also spiked, and overall sales demand across all of the company’s retail channels has increased by about 300%, Brondell president Steven Scheer told Business Insider over email.

Toilet paper and bidets are substitutes. With toilet paper becoming scarcer, the 'full cost' of obtaining toilet paper increased (once you factor in the difficulty of finding toilet paper when the shelves of many stores are empty). That means that, in terms of full cost, bidets became a relatively cheaper option than toilet paper for some people, increasing the demand for bidets. As shown in the diagram below, the market for bidets was initially in equilibrium where demand (D0) met supply (S0), at a price of P0 and Q0 bidets were traded. The increase in demand to D1 increases the quantity of bidets traded to Q1, and increases the price to P1. 

It seems likely that bidet sellers may have been one of the few big winners from the coronavirus pandemic.

[HT: Marginal Revolution]

How insurers can use data to beat adverse selection and moral hazard

This week, my ECONS102 class has been covering the economics of information. In particular, we focus on the problems of information asymmetry, and we spend a fair amount of time talking through problems of adverse selection. Adverse selection arises when one of the parties to an agreement (the informed party) has private information that is relevant to the agreement, and they use that private information to their own advantage at the expense of the uninformed party.

A classic example of adverse selection, which I've blogged about many times, occurs in the market for insurance (regardless of whether we are discussing home insurance, car insurance, health insurance, or even life insurance). The insured person knows whether they are high risk or low risk, but the insurer doesn't know - risk is private information. Since the insurer doesn't know how risky any person applying for insurance is, their best option is to assume that everyone is high risk. We refer to this as a pooling equilibrium - all insurance applicants are pooled together as if they are the same risk. The insurer then sets the insurance premium on the basis of the risk pool they think they have (high risk). The low risk people will (rightly) identify that the insurance premium is too high for them, and they drop out of the market, leaving only high risk people buying insurance. The insurance market for low risk people fails - they can't by insurance if they can't credibly convince the insurer that they are low risk. This problem is referred to as adverse selection, because the people who select into applying for insurance are the people that the insurer least wants to insure!

As you know, we do have insurance markets that cater to low risk people, so the markets must have adapted to deal with this adverse selection problem. This involves the private information (about the level of risk) being credibly revealed to the uninformed party (the insurer). If the insurer tries to reveal the private information, or tries to induce the informed party (the person applying for insurance) to reveal the private information, this is referred to as screening.

Insurers can screen applicants on the basis of their demographic and other information that they provide when they apply for insurance, their insurance history or credit history, and details about what they are insuring (house, car, health, life, etc.). However, insurers are increasingly using online data to screen applicants and determine their risk. Take this example, from The Wall Street Journal (gated) last year:

"Did you document your hair-raising rock-climbing trip on Instagram? Post happy-hour photos on Facebook? Or chime in on Twitter about riding a motorcycle with no helmet? One day, such sharing could push up your life insurance premiums.

In January, New York became the first state to provide guidance for how life insurers may use algorithms to comb through social media posts—as well as data such as credit scores and home-ownership records—to size up an applicant’s risk. The guidance comes amid expectations that within years, social media may be among the data reviewed before issuing life insurance as well as policies for cars and property.

If you're not thinking about how much information you reveal on social media, perhaps you should be now that it might cost you more in terms of insurance (on the other hand, if you are a low risk person, then perhaps your social media posts will earn you a lower insurance premium). However, that isn't the end of insurance companies' use of data.

Another information asymmetry problem in insurance happens after the insurance contract is agreed. This is the problem referred to as moral hazard - this problem arises when one of the parties to an agreement has an incentive, after the agreement is made, to act differently than they would have acted without the agreement. In the case of insurance, the insured party might act in a more risky manner when they are insured than they would have acted without insurance. They can do this because they have passed some of the (financial) risk of their actions onto the insurer.

One solution to moral hazard problems is for the uninformed party (the insurer) to monitor the actions of the informed party (the insured) more closely. And, you guessed it - insurers are looking at data to deal with moral hazard problems. As one example, Sven Tuzovic (Queensland University of Technology) wrote in The Conversation last year that:

...wearable devices are not only being embraced by consumers, but also across insurance industries. Health and life insurance companies collect data from fitness trackers with the goal of improving business decisions.

Currently, these business models work as a “carrot” incentive. That means consumers can benefit from discounts and cheaper premiums if they are willing to share their Fitbit data.

But we could see voluntary participation become mandatory, shifting the incentive from carrot to stick. John Hancock, one of the largest life insurance companies in the United States, has added fitness tracking with wearable devices to all of its policies. Though customers can opt out of the program, some industry experts argue that this “raises ethical questions around privacy and equality in leaving the traditional life insurance model behind”.

In terms of moral hazard, the insured is less likely to engage in risky behaviour if they know that their insurer is watching their every move. Insurers can use the data they collect from devices like Fitbit to not only monitor the insured, but also to determine their risk and adjust future premiums. It potentially solves both moral hazard and adverse selection problems at the same time.

And this is just the beginning. Insurers may turn to more sophisticated artificial intelligence tools in the near future, as David Tuffley (Griffith University) wrote in The Conversation last year:

Then you have a car accident. You phone your insurance company. Your call is answered immediately. The voice on the other end knows your name and amiably chats to you about your pet cat and how your favourite football team did on the weekend.

You’re talking to a chat-bot. The reason it “knows” so much about you is because the insurance company is using artificial intelligence to scrape information about you from social media. It knows a lot more besides, because you’ve agreed to let it monitor your personal devices in exchange for cheaper insurance premiums.

This isn’t science fiction. More than three-quarters of insurance executives believe artificial intelligence will revolutionise the industry within a few years. By 2030, according to McKinsey futurists, artificial intelligence will mean your car and life insurance premiums could change based on whether you decide to take one route or another.

If you're starting to think that there is nowhere to hide, you're right. Even if you refuse to let your insurer access your data, you're simply suggesting to the insurer that you are high risk. The insurer may frame it as if those agreeing to share data are receiving a discount, but really they are applying a higher premium to the high risk people who are least likely to want to share their data.

Should we be worried? Arguably no, unless we are high risk people wanting to pass ourselves off to insurers as low risk. Otherwise, we get insurance priced at premiums that is actuarially fair and accurately reflects our level of risk. However, as Tuffley notes, we should be concerned about what happens to the data that insurers collect about us:

An insurer might also be tempted to use the data for purposes other than assessing risk. Given its value, the data might be sold to third parties for various purposes to offset the cost of collecting it. Advertisers, marketers, lobbyists and political parties are all insatiably hungry for detailed demographic data.

It pays to read the fine print on contracts, and if insurers are going to collect much more data about us in the future, we should at least be aware of the risks of what will happen to that data.

[HT: The Dangerous Economist last year, for the Wall Street Journal article]

Read more:

• Your Fitbit will betray you
• The most surprising thing I learned about home insurance this year
• Could your social media posts make insurance more expensive?
• Why your Fitbit or Apple Watch could soon get you cheaper health insurance
• Could technology eliminate moral hazard in car insurance?
• Is there adverse selection in the life insurance market?

Columbia University students are willing to pay less for online tuition than for studying in person

Going into lockdown forced university teaching online. We heard a lot about how students were unhappy with online learning (e.g. see here and here). Students felt shortchanged by the new learning environment. So, if students prefer studying in person, we would expect them to be willing to pay for in-person classes compared with online study.

A new working paper by Zafar Zafari (University of Maryland), Lee Goldman, Katia Kovrizhkin, and Peter Muennig (all Columbia University), looks at exactly that question. They surveyed 46 Columbia University public health students, and the study had two interesting parts to it. First, they asked students to trade off the risk of becoming infected by coronavirus against attending classes in person. Second, they asked students how much they were willing to pay for online classes, in comparison with face-to-face classes. They found that:

On average, students were willing to accept a 23% (SE = 4%) risk of infection on campus over the semester in exchange for the opportunity to attend class in-person. Of the 46 students, 37 (80%) were willing to accept a >1% chance of infection and 3 (7%) were willing to accept a 100% chance of infection. One student was not willing to attend classes in-person unless the risk was 0%, and 9 (20%) were willing to attend in-person classes if the risk was less than 1%.

With respect to costs, students were willing-to-pay an average of only 48% (SE: 3%) of their tuition if courses were held exclusively online. No student was willing to pay full price for exclusively on-line instruction, and the maximum reported willingness-to-pay for online-only courses was 85% of standard tuition.

In other words, students in this sample are willing to accept a fairly high risk of coronavirus infection in exchange for attending classes in person, and they're willing to pay much less for online studying (and, by extension, willing to pay much more for the opportunity to attend classes in person). Of course, this should not be the last word on this topic. It was a study of just 46 students, and the methods are not what I would have used.

In fact, I wouldn't read much at all into the willingness-to-pay results - they simply asked students what they were willing to pay, which we know will be biased downwards (people will always say they are willing to pay less than they actually are, if only just in case they are later asked to actually pay!). They also anchored the willingness to pay by giving students a value first, then asking them what they would be willing to pay. It should be a surprise that the average result is about half of what they started with. It seems to me that a student, not knowing how much they would actually be willing to pay but knowing for sure that they wouldn't want to pay the full price, is likely to choose half price. And that's what they did.

A contingent valuation approach or a discrete choice experiment, where student respondents were asked to choose across a range of scenarios incorporating different levels of coronavirus risk, tuition costs, and whether studying was online or in person (and maybe other factors such as class size), would lead to much more plausible and defendable results. Hopefully, someone else is doing research along those lines.

[HT: Marginal Revolution]

Air pollution and the clean democracy hypothesis

Following on from Tuesday's post on the pollution haven hypothesis, I read this new article by Andreas Kammerlander and Günther Schulze (both University of Freiburg), published in the European Journal of Political Economy (sorry, I don't see an ungated version online). The paper tests the pollution haven hypothesis, but its main focus is the 'cleaner democracy hypothesis' - the idea that democracies are better at protecting the environment. As Kammerlander and Schulze explain:

According to this theory, there are five related causal mechanisms through which democracy leads to a cleaner environment: First, democracies allow a freer flow of information, and, therefore, environmental lobby groups are more effective in informing the population and raising awareness than in autocracies that censor information. Second, democracies protect the rights of civil society through freedom of speech and freedom of association, which makes it easier for environmental interest groups to organize and exert influence on the political process. Third, democracies are more responsive to demands of the electorate as incumbents are more accountable through free elections and environmental interests can seek political representation... Fourth, democracies are more cooperative and tend to honor environmental agreements as they are bound by the rule of law... Fifth, the members of the ruling elite in autocracies are less inclined towards environmental protection than the democratic public as they have a bigger share in the national income and the costs of environmental protection would therefore be higher for them...

They use cross-country data for 137 countries over the period from 1970 to 2012, to test whether there are robust correlations between measures of democracy and ten different pollutants:

These pollutants can be separated into gaseous air pollutants and aerosols. The available gaseous air pollutants are carbon monoxide (CO), nitrogen oxide (NOx), sulfur dioxide (SO2), non-methane volatile organic compounds (NMVOC), and ammonia (NH3). The aerosols in the dataset are black carbon (BC), organic carbon (OC), fine particle matter smaller than 10 μm (PM10), and fine particulates smaller than 2.5 μm (PM2.5), which are further differentiated between those originating from burning fossils and those from organic matter (PM2.5_fossil and PM2.5_bio).

If the cleaner democracy hypothesis is true, then you would expect to find similar effects for all pollutants, or at least not wildly divergent effects. Unfortunately, the news is not good for the hypothesis:

We find no consistent effect of democracy on pollution levels, neither in the regressions with country and time FE [Fixed Effects], nor in the regressions with only time FE. We do not even find that democracy has a consistent positive effect on the environmental quality for richer countries (as well-off citizens could have been hypothesized to be more likely to demand environmental control).

The results are robust (in the sense that alternatives also don't show any discernible pattern) to a battery of different specifications and the inclusions of different variables. They also show results that don't support the pollution haven hypothesis, unlike the paper I discussed on Tuesday, even with the use of various different measures of globalisation and trade intensity.

The problem with this analysis is that there is likely a lot of endogeneity in the regression models. Democracy might have causal effects on pollution, but there might also be other factors that cause both increases in democratic institutions and lower pollution - for instance, state capacity, social capital, or simply cultural differences in preferences for political institutions might be correlated with preferences for environmental quality. Also, there is likely to be a fair degree of multicollinearity in the model, as many of the variables will be correlated with each other. In the pollution haven hypothesis paper I discussed on Tuesday, Faqin Lin used an instrumental variables approach to overcome some of these issues, but that seems more difficult here, unless an instrument that affects democracy but not pollution (or preferences for environmental quality) could be identified. It is challenging but not impossible - this 2003 paper uses religion and socialist tradition, among other variables, as instruments for democracy. Having said that, I think it will be quite challenging, even with a better econometric approach, to revive the cleaner democracy hypothesis.

Read more:

• Trade openness, air pollution, and the pollution haven hypothesis

Meta-analytic results support some positive effects of using videos on student learning

The coronavirus pandemic and associated lockdowns forced teaching online. Teachers at all levels had to adapt their teaching to online delivery basically overnight. For most, that included both asynchronous recorded video 'lectures' that students watch in their own time, or synchronous video classrooms or workshops using videoconferencing tools like Zoom or Teams. Since the lockdowns have been relaxed, many teachers have continued to use videos in their teaching (in many cases, including at my institution, this was forced on teachers). A reasonable question, then, is what impact the use of videos has on student learning.

A new working paper by Michael Noetel (Australian Catholic University) and co-authors provides a fairly thorough answer, in terms of the impact of asynchronous, pre-recorded video content (there is also a non-technical summary of the research available on The Conversation). I say that this was a thorough answer because the authors conducted a meta-analysis of 105 different studies, with a combined 7,776 student research participants. Meta-analysis involves combining the results of many studies quantitatively, in order to determine more precisely any underlying relationship, and if it is executed well it can take account of publication bias (such as when statistically significant results are more likely to be published than statistically insignificant results). An added mark of quality in this particular meta-analysis is that they limited the included studies to randomised trials, which are more likely to establish causal estimates than observational or quasi-experimental studies.

The 105 studies included in the meta-analysis all compared the impact of either replacing face-to-face classes with video, or adding video to face-to-face classes, on student learning, measured as differences in grades or examination results or some other measure of academic performance (not differences in subjective measures, such as student evaluations of their own learning). All were conducted in higher education settings.

There are a lot of important results to unpack in this paper. First, I'll focus on the results that relate to replacing content with videos. The headline result is that:

...replacing other teaching with video had a significant positive effect on student learning...

I guess, in spite of my scepticism on the impacts of online learning (see this post, and the links at the bottom of this post), maybe there are advantages to it. In terms of the size of the effect, it was reasonably large, with students exposed to video performing about 0.28 standard deviations better. To provide some additional context, in my ECONS101 class in A Trimester last year, a 0.28 standard deviation increase in grade would be 6.4 percentage points, or a bit more than one grade point.

In the conclusion, Noetel et al. provide some theoretical foundation for their positive result:

The finding that video was superior to even face-to-face classes may be explained in a few ways. It may be due to the increased ability for students to manage their own cognitive load (e.g., by pausing and rewinding) or because teachers can better optimise cognitive load through editing.

Essentially, giving students more control over their own pace of learning is a good thing, as is editing the video content to focus more on the key points. Noetel et al. also note that videos perform better even when the face-to-face classes had more time:

...because teachers are prioritising relevant content, by editing out discussions and details that are not important for the learning objectives.

I'm not sure that all teachers would see that as a good outcome. Also, it appears that not all video content is created equal. Extending their analysis a bit further, they find that:

...half the implementations of exchanging other learning for video will be helpful (50% of true effects, 95% CI [40%, 59%]). A small proportion of implementations may be unhelpful for student learning (19% of true effects, 95% CI [13%, 25%]) with the rest having negligible influences.

That suggests that there is a large amount of heterogeneity in the impact of video. So, it is worth considering what conditions make video content more effective. On that question, they find that:

...effects were not significantly different between studies that used videos in lectures, tutorials, or homework... In contrast, the comparison condition was a significant moderator... when video replaced static media (e.g., text) it was significantly more effective... than when video replaced a teacher...

So, the comparison really matters here. Replacing the textbook with video led to a 0.51 standard deviation increase in performance, but replacing a teacher (presumably, a face-to-face lecture or tutorial) with video led to a barely statistically significant 0.18 standard deviation increase in performance. The type of content also appears to matter:

Video was more effective when students were assessed on skill acquisition... compared with assessments of their knowledge...

In other words, a 'how-to' style video helping students develop skills was more effective than a video delivering knowledge, skills-development videos increasing performance by 0.44 standard deviations while knowledge videos increased performance by a barely statistically significant 0.18 standard deviations. It may be that videos help students to develop particular skills (Noetel et al. use the example of learning how to calculate a t-statistic), but don't really help in terms of developing a broader knowledge base. That probably also explains two other results from the paper. First:

The number of minutes of the educational intervention did not moderate effects... and there were no significant differences in effects when the video intervention was applied to a single topic or a whole course... In other words, there was no significant dose-response effect.

If one video is good for learning, more videos should be better. However, that is not what they find. Perhaps there are substantial diminishing marginal returns to the use of video in teaching and learning, and all of the benefits of using video are exhausted after the first topic worth of videos is made available to students? That seems unlikely. More likely is that each intervention replaced all the skills-development content with video first (since those are the easiest videos to create), quickly exhausting all of the gains from the transition to video content. However, that doesn't quite explain why there would be no difference between using video in a single topic or a whole course. Definitely, this is something that needs further exploration. Second:

The relative interactivity was a significant moderator of effects... There was no benefit to video when the control condition was afforded more interactivity... Videos were effective when both conditions were given equivalent opportunities for interactivity... Effects were particularly large when videos were presented in an interactive context (e.g., co-viewing with a peer) that was not available to the control condition...

Interactivity really matters. Simply replacing face-to-face classes (or a textbook) with video content that lacks interactive elements does not afford students with the same learning opportunities - the effect of non-interactive video content was small and statistically insignificant. Since interactivity in 'knowledge videos' is more difficult to pull off than in skills-development videos (where students can be encouraged to follow along and practice their skills), that may help explain the type of video content that works best.

Finally, when video doesn't replace the traditional content but is instead added as supplementary material, the effect is large - a 0.88 standard deviation increase in performance, and almost all implementations of adding video increase student performance, which is much less heterogeneity than observed for replacing content with video. These particular results seemed to hold equally for both knowledge and skills-development videos. However, again there appeared to be no dose-response relationship. The takeaway from this is that, at the minimum, once face-to-face teaching returns we should be routinely recording our existing lectures and making those recordings available to students. Teachers need to get over their fear that making recorded lectures available somehow makes students worse off, because it clearly is not the case.

Noetel et al. note in the Discussion section of their paper:

As universities move toward online learning through multimedia, some academics may fear that students will be receiving an inferior learning experience compared with traditional methods. Our review suggests those intuitions are unfounded.

I don't think I would go so far as to say that the intuitions are unfounded. Video clearly adds value in some circumstances, but it is not clear that it dominates face-to-face learning. If the focus of a particular class is on developing particular skills, video is a good tool. Otherwise, unless the teacher has a particular tool or method for engaging interactively with students during their video watching (and I'm still waiting to see robust evaluations of such methods), it isn't clear that there is much advantage over face-to-face teaching. Certainly, this paper doesn't suggest that we should be abandoning face-to-face teaching in favour of video.

Read more:

• Online vs. blended vs. traditional classes
• Flipped classrooms work well for top students in economics
• Flipped classrooms are still best only for the top students
• Online classes lower student grades and completion
• Online classes may also make better students worse off

Trade openness, air pollution, and the pollution haven hypothesis

Until a couple of years ago, in my ECONS102 class (at that time, it would have been ECON110) I used to go through a fairly detailed summary of the evidence in favour of, and against, globalisation. The 'globalisation debate' material covered many dimensions, one of which was the environment. Among the arguments against globalisation in terms of its environmental impacts is the pollution haven hypothesis. When a globalised firm has choices over which country to locate manufacturing operations in, they are likely to choose the location with the lowest levels of environmental protection, because that would entail the lowest cost to the firm. So, if this results in a relocation of manufacturing from high-cost, high-environmental-protection countries to low-cost, low-environmental-protection countries, it effectively exports pollution to the low-environmental-protection countries. Those countries provide a 'haven' for pollution.

Evidence that would support this hypothesis would include countries (or parts of countries) that are more open to trade experiencing higher levels of pollution. So, I think that is why I had this 2017 paper by Faqin Lin (Central University of Finance and Economics, China), published in the journal China Economic Review (sorry, I don't see an ungated version online), waiting on my pile of papers to read. Lin uses Chinese prefecture-level data on exports and imports, and pollution data from NASA (to overcome any data-quality issues related to using Chinese pollution data) over the period from 2004 to 2011. Using distance to the coast as an instrument for trade allows Lin to extract plausibly causal estimates of the impact of trade openness on pollution. They find that:

...the coefficients for trade openness show that a 1% expansion in trade openness quantitatively raises NO2 (Aerosols) concentration by approximately 0.736–1.383% (0.723–0.806%) on average...

In other words, trade openness causes higher levels of pollution in China. The results are robust to alternative data sources (including Chinese pollution data), and different specifications produce similar results. That includes Lin's preferred analysis where they first use distance to the Huai River as an instrument for pollution (because of differences in access to coal-fired heating between the north and south of China) to account for reverse causation, then use the residuals from that analysis as the measure of trade openness. I'm less convinced by this analysis, but the results are at least consistent with the others.

Overall, this research provides some support in favour of the pollution haven hypothesis that differs from the usual cross-country analyses, and therefore doesn't suffer from being confounded by unobserved differences between countries (although you may argue that there are unobserved differences between Chinese prefectures, at least the regulatory system is plausibly consistent).