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?