More on the switching costs of online subscriptions

On Thursday, I posted about switching costs in the context of online subscriptions, and noted that sellers can take advantage of customers that are locked into buying from them because of high switching costs. However, the idea that subscriptions lock customers in was based on the theoretical notion that the more difficult (costly) it is to cancel a subscription, the more likely we consumers are to simply keep the subscription in place. This is backed up by anecdotal experience, so it would be sensible to question whether there is real empirical evidence to support this.

It turns out that there is, as noted by Tim Harford in this article in the Financial Times earlier this month:

A new working paper from economists Liran Einav, Benjamin Klopack and Neale Mahoney attempts an answer. Using data from a credit and debit card provider, they examine what happens to subscriptions for 10 popular services when the card that is paying for them is replaced. At this moment, the service provider suddenly stops getting paid and must contact the customer to ask for updated payment details.

You can guess what happens next: for many people, this request reminds them of a subscription they had stopped thinking about and immediately prompts them to cancel it. Relative to a typical month, cancellation rates soar in months when a payment card is replaced — from 2 per cent to at least 8 per cent. Einav and his colleagues use this data to estimate how easily many people let stale subscriptions continue. Relative to a benchmark in which infallible subscribers instantly cancel once they decide they are no longer getting enough value, the researchers predict that subscribers will take many extra months — on average 20 — to get around to cancelling.

Don’t take the precise numbers too seriously — as with most social science, this is not a rigorously controlled experiment but an attempt to tease meaning out of noisy real-world data. What you should take seriously is the likelihood that you are swimming in barely noticed subscriptions, some of which you would choose to cancel if you were forced to pay attention to them for a few minutes.

The NBER Working Paper by Einav et al. is available here (ungated version here). So, there is empirical evidence that supports the idea that subscriptions lock consumers into buying, because in that research, as soon as the lock-in was broken, many consumers stopped buying. Subscriptions clearly do provide a source of customer lock-in.

Read more:

• The subscription economy as price discrimination
• The switching costs of online subscriptions

Effective marginal tax rates, and work incentives for older people

Tax rates matter for work incentives. When tax rates are high, there is less incentive for people to work. They may pass up additional work and choose leisure time instead. However, it isn't just taxes that matter. It is the loss of other entitlements as well. All of these are bound up in what is called the effective marginal tax rate (EMTR), which is the amount of the next dollar of income a taxpayer earns that would be lost to taxation, decreases in rebates or subsidies, and decreases in government transfers (such as benefits, allowances, pensions, etc.) or other entitlements.

Because they relate not just to tax rates, but to the loss of other entitlements, EMTRs can get very high (sometimes over 100 percent), and present a strong disincentive for work. This article in The Conversation this week, by Peter Martin (Australian National University) presents one example for Australia:

Pensioners who do go over the $227 per week limit lose half of every extra dollar they earn in a cut to their pension.

Plus tax, this means they lose a total of 69% of what they earn over the limit where their tax rate is 19%, and 82.5% on the portion of earnings taxed at 32.5%.

And this is after the boost designed to “incentivise pensioners into the workforce”.

So, the EMTR for older people in Australia could be as high as 82.5 percent. And the consequence of this, in comparison with New Zealand (where there is no reduction in national superannuation for older people who work):

In Australia, 15.1% of the population aged 65 and older are in some kind of paid work, up from 14.7% a year earlier.

In contrast, in New Zealand the proportion has just hit 26%. That’s right: more than one-quarter of New Zealanders aged 65 and older are employed.

That's a substantial difference, that is almost certainly explained in part by the difference in EMTRs between New Zealand and Australia. Now, sometimes government may have a good reason for high EMTRs and the work disincentives they create. For example, tertiary students in New Zealand who receive a student allowance face an EMTRs of 100 percent beyond the first $258.08 of additional work earnings. That ensures that students don't spend so much time working that they can't concentrate on their studies. However, it's hard to make a similar argument for older people. Would the Australian government want a high EMTR on older people who that they don't spend so much time working that they can't concentrate on their retirement?

Most governments want older people to work more, not less, in order to mitigate labour force shortages (e.g. see the Older Workers Employment Action Plan for New Zealand). Australia seems to be getting this wrong. As Martin concludes:

...New Zealand is certainly making it easier for retirees to work legitimately, rather than stay at home or accept cash in hand.

The switching costs of online subscriptions

Switching costs provide sellers with a lot of opportunity to extract additional profits from consumers. That's because high switching costs create customer lock-in - customers are unwilling to change provider, or stop buying, because they would then face the costs of switching. For example, there may be a disconnection fee if you try to change your mobile phone service. Or, it may simply be difficult to make a change - perhaps you have to fill in some forms, and go into a store with valid photo identification, in order to change to the new mobile phone service. Those sorts of costs can be quite effective in keeping customers locked in.

Switching costs and customer lock-in are important aspects of business strategy, especially for firms offering subscription services, who want to ensure that their customers remain buying from them over the long term. The Financial Times had a good article back in June related to this (ironically, paywalled):

Put your hand up if you have looked at a credit card statement recently and spotted a charge for a subscription that you had forgotten signing up for.

You’re not alone. The number of new subscriptions per US consumer peaked last year and cancellations are now outpacing new sign-ups. But for many services, getting out can be a lot more complicated than getting in, as I discovered when I tried to end my monthly payment to Amazon’s Audible recorded books membership.

If I cancelled, the app warned, I would lose the three book credits that I have already paid for but not used. Instead, it touted a “pause” button that would put off the next payment for three months. Not wanting to set that money on fire, I dutifully obliged and set a calendar reminder to cancel in October.

The more difficult (costly) it is to cancel a subscription, the more likely we consumers are to simply keep the subscription in place. That creates an incentive for sellers to make the cancellation process as onerous as possible, to increase the switching cost, and ensure that we continue to subscribe. The seller can also use our locked-in status in order to sell us other products or services. However, regulators have recently started to push back:

In the EU, pressure from Brussels led Amazon to begin allowing customers to end their Prime subscription with just two clicks using a clearly labelled “cancel” button. It also changed its UK policies around that time, but only altered US cancellations this year, ahead of the FTC lawsuit. The company, which plans to fight the case, insists that its cancellation procedures are “clear and simple . . . by design”.

A simple 'cancel' button effectively minimises the switching costs, allowing consumers to free themselves from the shackles of an ongoing subscription. However, sellers have no incentive to offer this unless they are forced to by regulators. And, there is little to stop the seller from sending consumers to a new screen after they click 'cancel', pointing out some special offer that the consumer is missing out on, in the hopes that they will re-subscribe. And, they have consumers' contact details, so no doubt they will continue to spam their former subscribers unless they separately follow the procedures to 'unsubscribe' from the mailing list.

Subscriptions are very profitable for sellers, and are only growing in importance in the modern economy. We can expect sellers to try their best to keep the switching costs high.

Consumer preferences may explain the pricing puzzle that is diamond engagement rings

On the Marginal Revolution blog last month, Alex Tabarrok presented a bit of a puzzle:

DeBeers also produces lab-grown diamonds and they have a very strange pricing strategy:

De Beers started selling its own lab-grown diamonds in 2018 at a steep discount to the going price, in an attempt to differentiate between the two categories. The company expects lab-grown prices to continue to tumble, in what it sees as a tsunami of more supply coming on to the market, Rowley said. That should create an even bigger delta in prices between natural diamonds and lab grown, helping differentiate the two products, he said.

What? Ordinarily, the bigger the price between a competitor and its substitute the greater pressure on the competitor to lower prices! Yet DeBeers is gambling that the bigger the difference in price between natural and lab grown diamonds the bigger the demand for natural diamonds! Strange. The only way I see this working is if the fiancée knows the price of the ring, which maybe they do! In that case, the buyer still has to spend 10k and doesn’t care whether it’s 10k on synthetic diamonds or 10k on natural grown diamonds. But 10k on synthetic diamonds will get you more carats so we need an equilibrium in which a smaller diamond signals more expensive. But that runs against hundreds of years of expectations! And remember natural and lab grown diamonds are indistinguishable by the naked eye. It’s one thing for the fiancée to know the price of the diamond but surely her friends judge by what they can see, namely the size of the ring. Which signal is the most important to send?

The quote embedded above is from this Bloomberg article. In producing its own lab-grown diamonds, De Beers is competing with itself (as I noted in this post in 2018). In that post, I noted that De Beers was engaged in rent-seeking behaviour:

Synthetic diamonds are a close substitute for natural diamonds, and natural diamonds represent a highly profitable business for De Beers. If some of De Beers' competitors (like Chatham or Diamond Foundry) start producing high-quality synthetic diamonds and selling them relatively cheaply, then some diamond consumers will be induced to switch from natural diamonds to synthetic diamonds, and De Beers will lose profits. By selling synthetic diamonds itself at a very low price, clearly De Beers' goal is to make it unprofitable for the synthetic diamond producers to operate, by seriously undercutting their prices - a tactic known as predatory pricing. The synthetic diamond competitors won't be able to compete with De Beers for long at the low prices, and will soon go out of business (at least, that is what De Beers is hoping). At that point, De Beers can quietly exit the synthetic diamond industry and resume claiming high profits from natural diamonds (having taken a bit of a hit to its profits from both synthetic and natural diamonds in the meantime).

I noted that the problem with that strategy for De Beers is that there needs to be some barrier to entry to keep competitors out once De Beers had exited the synthetic diamond industry and wanted to resume high profits in the natural diamond industry. Tabarrok presents a different problem, and potential puzzle: if consumers can't tell a lab-grown (synthetic) diamond and a natural diamond apart, why would lab-grown diamonds sell at a discount?

I thought that the solution related to signalling, as Tabarrok suggested. That is, until I started writing this post, and then ended up back at the puzzle. But I think I have a solution. So, let's work it through. As I noted in this 2015 post, spending on engagement rings (and weddings, as I noted here) is a signal. In fact, there are two potential signals. But first, let's take a step back and think about why signalling is necessary.

Signalling is a solution to an adverse selection problem. Adverse selection arises when one of the parties to an agreement has private information that is relevant to the agreement, and they use that information to their own advantage and to the disadvantage of the other party. Adverse selection is a problem of 'pre-contractual opportunism', and can cause markets to fail. Signalling is one way that markets have adapted to deal with adverse selection problems. With signalling, the informed party finds a way to credibly reveal the private information to the uninformed party (a signal). There are two important conditions for a signal to be effective: (1) it needs to be costly; and (2) it needs to be costly in such a way that those with lower quality attributes would not want to attempt the signal (and one way this criteria is fulfilled is if it is more costly for those with lower quality attributes).

In the case of engagement rings, there are two adverse selection problems that the engagement ring signal might solve. First, the person proposing marriage has private information about their quality as a future partner. The person receiving the proposal may not know this information. If the proposer is a low-quality partner, they have an incentive to hide this information until it is too late and they are already married! The engagement ring provides an effective signal, that reveals the quality of the proposer. An engagement ring is an effective signal because it is costly, and because it is costly in a way that a low-quality partner would not want to attempt. That's because the proposer would lose their investment in the ring if they are low quality and the relationship breaks up. Going one step further, the more valuable the ring (relative to the income of the proposer), the higher the quality of the signal.

The second adverse selection problem arises because the couple have private information about their quality as a couple (as I noted in this post about the cost of weddings). The people attending their wedding likely do not know this information. The couple receives gifts at their wedding, and wedding attendees are more likely to want to provide gifts to couples who will be successful. So, low-quality couples have an incentive to hide from wedding attendees that they are low quality. Is an engagement ring a good signal here? I'm not so sure. Yes, the engagement ring is costly. But is it costly in such a way that a low-quality couple wouldn't want to attempt? A low-quality couple could buy a wedding ring, receive the wedding gifts, then divorce and sell the engagement ring to re-coup their initial outlay. It seems to me that this signal is not nearly as effective as the overall cost of a wedding (which is not recoverable).

Ok, so I think we have established that an engagement ring is an effective signal from one partner to another, but not necessarily from a couple to others. A more valuable ring provides a higher quality signal. Does whether the diamond is natural or lab-grown matter? In theory, it shouldn't - they are indistinguishable to the untrained eye. However, in practice they may be very different. Natural diamonds are scarce. Lab-grown diamonds are less scarce. Perhaps, in the minds of consumers, a natural diamond is a more desirable product, even if they are indistinguishable? After all, a motivated consumer can find out (through an expert appraisal) whether a diamond is natural or lab-grown. In that case, the perceived quality of the diamond really matters - a smaller natural diamond would provide the same quality of signal as a larger lab-grown diamond. And for two diamonds of the same size, a natural diamond would be priced higher than a lab-grown diamond. In other words, consumer preferences (for natural rather than lab-grown diamonds) would explain this pricing puzzle.

Book review: Chip War

Semiconductors are central to the modern economy. Every electronic product that we buy, and many products that we wouldn't expect, has a microchip, or many, embedded inside. Semiconductors have also been in the news a lot recently, both as the key focus of a trade war between the US and China, and as a component in the ongoing development of artificial intelligence. So, I was interested to read more about how semiconductors became some central to the global economy and the risks that poses, and picked up a copy of Chris Miller's recent book Chip War.

The book provides a run-through of the historical development of the semiconductor industry, in the US and worldwide, from the development of the first computers and up to the present day. Miller paints a picture of an industry that has gone through an ongoing process of concentration over time, with small firms increasingly giving way to larger and larger firms, that have the resources necessary to fund the research and development, the infrastructure, and the processes, necessary to produce the most advanced chips. These processes of concentration have occurred globally, resulting in an industry where the fabrication of chips is undertaken mostly in Asia, and concentrated in Taiwan.

As you would expect, the book also highlights the international tensions that have occurred over time, between the US and Russia, the US and Japan, and most recently the US and China. That the semiconductor industry is so heavily reliant on Taiwan, an island that is at constant risk of Chinese aggression, is the key issue that Miller presents. He also outlines the loss of competitiveness of the US semiconductor industry in key areas, alongside the increasingly aggressive approach to international cooperation and intellectual property adopted by China. For example, while the US has fallen behind:

...China is pouring billions of dollars into its chip industry while pressuring foreign companies to turn over sensitive technology. For every major chip firm, the Chinese consumer market is far more important a customer than the U.S. government.

However, at the same time that Miller presents Chinese behaviours as an issue, Miller also outlines the ways in which this issue may not be problematic, due to recent trade restrictions imposed on China by the US. For example, Miller notes that:

For China, which lacks competitive firms in many parts of the supply chain, from machinery to software, technological independence is even more difficult. For complete independence, China would need to acquire cutting-edge design software, design capabilities, advance materials, and fabrication know-how, among other steps. China will no doubt make progress in some of these spheres, yet some are simply too expensive and too difficult for China to replicate at home.

And in relation to Chinese aggression and any potential takeover of semiconductor manufacturing in Taiwan, Miller notes that:

The idea that China would simply destroy TSMC's fabs out of spite doesn't make sense, because China would suffer as much as anyone, especially since the U.S. and its friends would still have access to Intel's and Samsung's chip fabs. Nor has it ever been realistic that Chinese forces could invade and straightforwardly seize TSMC's facilities. They'd soon discover that crucial materials and software updates for irreplaceable tools must be acquired from the U.S., Japan, and other countries. Moreover, if China were to invade, it's unlikely to capture all TSMC employees. If China did, it would only take a handful of angry engineers to sabotage the entire operation.

The book overall strikes me as having been overtaken a little by recent events. It would have presented a much more challenging and scary picture if the US hadn't enacted trade restrictions in recent times, making very apparent the fragility of Chinese goals. Nevertheless, it was an interesting read, especially for the historical detail, which Miller has clearly investigated in depth.

However, I found the style of writing to be less engaging than it could otherwise have been. The book is made up of very short chapters, which are more-or-less chronological. That is good in a way, but each chapter reads a bit like a standalone article, and that means that there is fair amount of repetition. There is even repetition within particular chapters, which I found a bit frustrating. For someone who reads a chapter at a time, this might be ok, but a book that keeps repeating itself will struggle to maintain the attention of a dedicated reader like myself. Nevertheless, if you are interested in the semiconductor industry, this book may be an interesting read for you.

No, you don't need an income of $193,000 in order to be happy

You may have seen a news story in the last few weeks, saying that in order to be happy in New Zealand, you need a household income of $193,000 (see Stuff here, or the New Zealand Herald here, or here). The problem is, that framing is a misrepresentation of what the original research actually found.

The $193,000 estimate comes from this blog post by S Money, which updated figures from this 2018 research article by Andrew Jebb, Louis Tay (both Purdue University), Ed Diener (university of Illinois, Urbana-Champaign), and Shigehiro Oishi (University of Virginia), published in the journal Nature (sorry, I don't see an ungated version online). However, that article isn't about the amount of income required to be happy, it is an estimate of the amount of income, beyond which additional income isn't associated with higher levels of happiness (or life satisfaction) - what the authors refer to as 'income satiation'.

So, if we take the S Money number at face value, at household incomes below $193,000, higher income is associated with more happiness, but at household incomes above $193,000, higher income is not associated with more happiness. That doesn't at all mean that you need a household income of $193,000 in order to be happy. S Money has completely misrepresented what the research is finding (their key findings mention the "cost of happiness" several times, even though that's not what this is about.

Let's take New Zealand as an example. S Money lists New Zealand in the "Countries with the highest cost of happiness", and writes that:

Meanwhile, New Zealand and Israel suffer a high cost of happiness despite not being among the top earners. 

In fact, you could easily interpret this 'high cost of happiness' as positive for New Zealand, rather than negative. If you think of the relationship between income and happiness as causal (which is not established in the Jebb et al. paper - it is just a correlation), then in New Zealand, earning a higher income increases your happiness, and continues to increase your happiness all the way up to an income of US$114,597 (or about NZ$193,000). So, there is a good reason to earn more money, since it will make you happier. For other countries with a lower 'cost of happiness', happiness peaks much earlier, and the incentives to earn more disappear at a much lower income. It is little wonder that Sierra Leone remains a poor country, if earning any more than US$8,658 doesn't make you any happier. What a desperately awful place to live (sorry, Sierra Leone).

The S Money figures tell us the level of income at which income satiation occurs, but not the level of happiness (or life satisfaction) at which income satiation occurs. If happiness peaks at a fairly low level, then a low 'cost of happiness' is simply representing a very unhappy place to live. The level of happiness at income satiation isn't reported by S Money, and it isn't reported separately for New Zealand in the Jebb et al. paper. However, for Australia and New Zealand combined, life satisfaction peaks at about 8 (on a 1-10 scale), which is much higher than for any other world region. In Africa (including Sierra Leone), life satisfaction peaks at about 6 on the same 1-10 scale. The difference between 6 and 8 doesn't seem like much, but is actually very substantial (about 1.2 standard deviations). So, while the 'cost of happiness' is much lower in Sierra Leone than in New Zealand, the happiness that you would be 'buying' is much less.

Many of the news stories also breathlessly referred to the saying that 'money can't buy you happiness'. However, the original research actually finds evidence that supports exactly the opposite conclusion. Up to the point of 'income satiation', income is associated with greater happiness (again, that doesn't mean that higher income causes greater happiness, only that they are correlated with each other).

This news story is complete rubbish, and I didn't even have to get into the problems with life satisfaction measures (see here or here). It does no credit to the journalists who have simply repeated this story and the reported figures without considering what they really mean. Sadly, on the New Zealand Herald Front Page podcast, Robert MacCulloch (University of Auckland) looked at the general question of the relationship between income and happiness, and missed the real point, which is that this particular news story is trash.

Read more:

• Money can't buy happiness, but it can buy life satisfaction
• More evidence that money can't buy happiness, but it can buy life satisfaction

When you increase landowners' costs, the value of land goes down

Last week, the New Zealand Herald reported:

As of October 19, landowners and foresters who participate in the Emissions Trading Scheme (ETS) will be charged an additional annual fee of $30.25 per hectare in perpetuity.

A Ministry for Primary Industries (MPI) spokesperson says the $29.8m it costs each year to run the forestry ETS has been 100 per cent taxpayer-funded until now.

But from October 19, 63 per cent of those “administrative costs” will instead fall to ETS participants, with the remaining 37 per cent still funded by the taxpayer.

It seems fair enough to me that the landowners who benefit from the ETS should pay the costs of administering the system that they benefit from. However, what caught my eye in the article was this bit:

To cover that cost, [New Zealand Institute of Forestry president James] Treadwell says foresters will have to pass the problem on to farmers.

“I’ve got one farmer who is wanting to sell off about 120 hectares of their farm to cover their interest rates. I had to tell him the price has just dropped by $500 a hectare. He was pretty upset,” said Treadwell.

Why would a small charge of $30.25 per hectare per year lead to a $500 reduction in land values? It's because the value of the land is based on how profitable it is to own land. With the new annual fee, land is less profitable by $30.25 per hectare per year. And that translates into $500 in land value. The Efficient Markets Hypothesis suggests that new information is incorporated into the value of an asset immediately. And this appears to be what has happened here.

It's interesting to consider what that implies about the discount rate for landowners (or land buyers). Using the formula for the present value of an annuity, setting the value of the annuity to $500, and the value of the annual payment to $30.25, and solving for the discount rate R, I get an implied discount rate of 6.05 percent. [*]

That implied discount rate is interesting for a few reasons. First, it's very similar to current interest rates for farm loans. For example, ANZ currently has business term loans (including for farms) with a floating rate of 7.05 percent. At a discount rate of 7.05 percent, that $30.25 annual fee should reduce land prices by $429 per hectare. So, landowners (or land buyers) are reducing land prices by slightly more than expected given current interest rates. That may be because, while the annual fee is currently proposed to be $30.25, there is little stopping the government from increasing that fee later (at a rate that is higher than the rate of inflation), which would increase the amount of compensation, in terms of lower land prices, required now in order to offset the annual fee. So, perhaps that additional risk is being priced into land prices, and being picked up in the annuity calculation as a lower discount rate. [**]

Second, the discount rate is far higher than the social discount rate that is used in climate change (and many other) applications, which is often 3 percent (see here for more on this). If we applied the 3 percent social discount rate, the $30.25 annual fee should be reducing land values by $1008 per hectare. That suggests that landowners (and land buyers) are more focused on the present than the social discount rate implies, which is consistent with the idea of present bias from behavioural economics. Alternatively, we could interpret this as implying that society as a whole is much more focused on the future (as captured by the social discount rate) than individual landowners (or land buyers) are. Short-term profit motives strike again?

Finally, and perhaps most surprisingly to me, the implied discount rate is not wildly high (or low), implying that landowners (and land buyers) are not seriously overreacting to the introduction of this fee. Not only is the Efficient Markets Hypothesis working, but it appears that the landowners (and land buyers) have been relatively rational in terms of their response (as picked up in the change in land value). I guess it's not always the case that asset markets are crazy.

*****

[*] Ok, I admit that I didn't solve this by hand. It's not possible to do so easily. I used the 'solver' function in Excel, and set the number of periods to 10,000 (so, it's really a solution to the question: what is the discount rate that equalises an annual value of $30.25, paid every year for 10,000 years, and a one-time payment today of $500?

[**] If we used $30.25 per year for the first five years, then increasing to $37.25 for every year after that, then the implied discount rate that equates the annuity and the decrease in land values is 7.05 percent, the same as the ANZ farm loan rate. So, perhaps it is some calculation like that which explains the decrease in land values being higher than implied by current interest rates.

Could cash grants solve the homelessness problem?

New Zealand has a serious homeless problem. We could argue about the numbers of homeless people, but it is clear that it is a serious problem when even small-town New Zealand is experiencing rising homeless numbers. And homelessness is a particular problem for Māori. However, there appears to be no easy solution. If there was, we would have done it already.

Over the last decade in Hamilton, the People's Project has adopted the Housing First approach, funded by the Ministry of Housing and Urban development. While this approach has shown some promising results, it doesn't take much walking around downtown Hamilton or Hamilton East to realise that it is far from perfect.

What if we could do better by simply offering a grant to homeless people, that they can use in any way that they wish, including obtaining housing? Would that be an effective way to reduce homelessness? That is essentially the research question addressed in this recent article (open access) by Ryan Dwyer (University of British Columbia) and co-authors, published in the Proceedings of the National Academy of Sciences (see also this non-technical summary on The Conversation). Dwyer et al. use a randomised controlled trial to evaluate the impact of giving homeless people in Vancouver a one-off grant of CDN$7.500. As they explain:

We conducted a preregistered cluster-randomized controlled trial where individuals experiencing homelessness were randomly assigned to receive a one-time unconditional cash transfer of CAD$7,500. This amount equaled the annual income assistance in British Columbia in 2016 and represented 59.6% of the average personal annual income ($12,580) of our participants. The cash transfer was provided in a lump sum to enable maximum purchasing freedom and choice (e.g., rent, durable goods), whereas smaller repeated transfers would not. To avoid benefits cliff, we established an agreement with the BC provincial government that ensured the cash transfer did not impact participants’ existing or future benefits.

Dwyer et al. limited the pool of potential cash recipients to homeless people aged 19 to 65 who had been homeless less than two years, and who did not have serious issues with mental health or substance abuse (including alcohol). The final sample included 115 homeless people, 65 in the control condition (who did not receive the cash), and 50 in the treatment condition (who did receive the cash). They randomised treatment at the homeless shelter level (so all participants from the same shelter were either in the treatment group, or the control group) to limit risks to those who received the cash. More specifically:

There were four conditions in the study: two cash and two noncash. Based on past studies showing that motivational training can help improve cognitive and behavioral outcomes for those living in poverty... we provided workshop and coaching supports in addition to the cash transfer... Workshop consisted of a 1-h session every 3 mo for 1 y, where participants were guided to complete self-affirmation, goal-setting, and plan-making exercises to help participants brainstorm strategies to regain stability in their lives...Coaching consisted of three 45-min phone calls per month for 6 mo with a certified coach trained to help participants learn from their own experiences to increase self-efficacy in developing life skills and strategies to achieve their life goals.

In condition 1, 25 participants (nshelters = 5) were provided with a one-time cash transfer of $7,500, workshop, and coaching. In condition 2, 25 participants (nshelters = 5) were provided with the cash transfer and workshop but no coaching. In condition 3, 19 participants (nshelters = 5) were provided with workshop and coaching, but no cash transfer. In condition 4, 46 participants (nshelters = 6) were not provided with the cash transfer, workshop, or coaching.

The primary outcomes that Dwyer et al. were interested in were subjective wellbeing and cognitive outcomes one month after receiving the cash grant. However, they found that there was:

...no significant interaction effect for any of the preregistered outcomes. Specifically, cash recipients did not differ from noncash participants in terms of cognitive and subjective well-being outcomes from baseline to 1 mo; cash recipients with coaching did not differ from cash recipients without coaching; and noncash participants with workshop and coaching did not differ from noncash participants without any supports.

In other words, there was no effect on what Dwyer et al. expected. However, they then explore some other outcomes, and over a longer time period of up to one year, finding that:

 Over the year, cash recipients spent 99 fewer days homeless (e.g., shelter, streets) and 55 more days in stable housing (e.g., apartment) on average than control participants... For finances, cash recipients retained more savings ($1,160) and increased monthly spending more ($429) on average than control participants... Importantly, spending on temptation goods (i.e., alcohol, drugs, cigarettes) was not different between groups.

Those are potentially important results, although we should discount them somewhat because they were not part of the pre-registered study. However, this part is potentially the most important:

By reducing time in shelters, the cash transfer was cost-effective. The societal cost of a shelter stay in Vancouver is estimated at $93 per night... so fewer nights in shelters generated a societal cost savings of $8,277. After accounting for the cost of the cash transfer, the reduced shelter use led to societal net savings of $777 per person a year. Alternatively, freed-up shelter beds can be reallocated, so the benefits can trickle down by helping others avoid sleeping on the street.

If the benefits of this intervention (in terms of cost savings from shelter stays) is greater than the costs (in terms of the cash payment, plus any administrative costs [which are not included in the calculations in the paragraph quoted above]), then that seems like a win to me. However, before we get too carried away, the authors also report on the outcomes at various time points in between one month and one year after the cash grant, where they find that:

...the overall effects were primarily driven by impacts within the first 3 mo after the cash transfer.

Looking at the number of days spent homeless, the effect is relatively large (-0.95 standard deviations) after one month, and remains high after three months (-0.94 standard deviations), but declines steadily after that, decreasing by two thirds (to -0.3 standard deviations) and becoming statistically insignificant by one year after the cash grant.

So, it is likely that there would need to be additional cash grants each year in order to keep these homeless people out of shelters. And note that these aren't the highest risk homeless people - they have only been homeless less than two years, and aren't suffering from serious mental health or substance abuse problems. On the other hand, perhaps this is the group that government really should be targeting, before they progress to serious mental health or substance abuse problems?

Anyway, a nice aspect of the study is that Dwyer et al. then went on to explore the beliefs and biases of the general population in relation to homelessness, finding that there is:

...a public mistrust of individuals experiencing homelessness in their ability to manage money. This mistrust can be a barrier for establishing cash transfers as a homelessness reduction policy.

However, when the general public is given information demonstrating the research results that homeless people do not spend the cash grant on 'temptation goods' (like alcohol, drugs, or cigarettes), or information about the cost-effectiveness of the cash grant, then people were more likely to support the cash grant policy. That was my experience in reading the research as well - the cost-effectiveness results were the results that most caught my attention. Dwyer et al. concludes that:

These two messages can be used to boost public support for a cash transfer policy to reduce homelessness.

Overall, this seems like a promising approach that is worth trying in other areas, including New Zealand. And importantly, Dwyer et al. have shown that it may be possible to get public buy-in to cash grants as a potential solution to homelessness.

Using economic incentives to reduce food waste

In an interesting article in The Conversation this week, Trang Nguyen and Patrick O-Connor (both University of Adelaide) looked at food waste. I found this bit of particular interest:

Currently, Australians pay for waste management through their council rates. This is a “pay-as-you-own” system.

The cost is determined by the property’s value, regardless of the amount of waste generated. Renters indirectly contribute to this cost by paying rent.

Neither owner-occupiers nor renters have any incentive to reduce waste generation when the cost is levied on property value rather than the amount of waste.

An alternative approach gaining momentum in other parts of the world is the “pay-as-you-throw” approach, such as Stockholm and Taipei. This system charges households based on the weight of their waste, usually the general waste that needs to be discarded in landfill, while the collection of food waste and other recyclables remains free to encourage waste sorting.

Recent research in Italy shows pay-as-you-throw schemes result in significant reductions in both the quantity of waste and costs associated with waste disposal in many Italian municipalities.

That a change from 'pay-as-you-own' to 'pay-as-you-throw' would reduce the amount of food waste would come as no surprise to economists. In the first week of an introduction to economics course, students learn that people respond to incentives. When the (marginal) benefit of doing something increases, we tend to do more of it. When the (marginal) cost of doing something increases, we tend to do less of it. In general, people try to avoid costs and capture benefits.

In this case, the 'pay-as-you-own' model leads to a marginal cost of zero for food wastage. It literally costs a household nothing at all to throw out a little bit more wasted food. When the marginal cost is zero, we can expect the quantity of food waste to be high. In contrast, the 'pay-as-you-throw' model explicitly increases the marginal cost of wasting food, since households need to pay for each additional kilogram of waste. When the marginal cost of wasting food goes up, people will waste less food.

This is a lesson that a lot of local government authorities need to learn. Many local authorities in New Zealand, for instance, have only a general charge for rubbish collection. There is no cost per bag (or per kilogram) of rubbish. The amount of rubbish (not just food waste) going to landfill could be reduced by charging per bag (or per kilogram). Many local authorities make residents buy a sticker for each rubbish bag. However, an even better approach would be to collect wheelie bins and to weight each bin as it is collected. The technology for this must already exist, surely.

Similarly, many local authorities in New Zealand have only a general charge for water. There is no cost per litre (or cubic metre) of water use, or per litre (or cubic metre) of water discharge. The amount of water usage could be reduced by charging per litre (or cubic metre) of water use or water discharge. Given that many local authorities face water shortages over summer, and that water infrastructure in New Zealand needs billions of dollars of new investment, we should be looking for ways to ensure that water users are adequately paying for their use.

Economic incentives are an effective tool that governments can (and should) use to change behaviour. Increasing the marginal cost could help to achieve goals of reducing food waste, rubbish, and water use.

Ten years of Sex, Drugs and Economics

This week marks the ten-year anniversary of this Sex, Drugs and Economics blog. Here's my first post (from 8 October 2013), briefly explaining my rationale for blogging, including:

...this blog is a way for me to create a discussion space for my students, and help them to recognise the value in the economic approach to looking at real-world situations.

I think it has been relatively successful in that. My blog is often mentioned in student comments in the end-of-trimester teaching evaluations, as one of the most important aspects of my teaching. Around half of the posts in recent years have been tailored towards illustrating first-year economics concepts in action in the real world (that's why there are so many posts that have graphs). Most of the rest of the posts discuss recent economics research, making the blog is complementary to the Waikato Economics Discussion (EDG) group (on Facebook here), which meets every couple of weeks during teaching to discuss recent economics research.

While I had hoped to create a discussion space for students, it has turned out that much of the discussion doesn't happen on the blog itself. The comments section is pretty quiet. However, I do know that the blog is relatively widely read among my current and former students and others, and discussed inside and outside of class.

Finally, here's a few statistics on the blog's first ten years. The blog is closing in on half a million page views (498,709 as of right now), and tends to get between 4000 and 6000 page views per month on average (more during teaching time, especially around assessments, for obvious reasons). The blog currently has 27 followers on Follow.it (you can subscribe to get email updates using the box on the right). There would probably be more followers, but we had to reset when Feedburner was killed off in July last year. Also, others may follow the blog through Feedly or other aggregators, or via my posts to the EDG Facebook group.

This is my 1698th post on the blog. I usually post around 20 times a month. My most read post of all time is this one from 2014 on the economics of drug development and pricing, with over 3000 page views. Next are this one on the cobweb model of supply and demand, this one on the deadweight loss of rent control, this one on Pigovian taxes and tradeable pollution permits, and this one on advertising and consumer choice. They all have between 1700 and 2200 page views, no doubt because of their relevance for my first-year classes.

My favourite topics to post on are demand and supply, rational behaviour, and market power (because of their relevance to teaching), as well as teaching and learning, and alcohol. The latter reflects one of my key research interests. I have also posted 129 book review posts (some of which have covered more than one book). I know that the book reviews are read by at least some of my most keen students, and have led to reading recommendations in both directions.

I also have to acknowledge my students for sharing interesting material with me, that has directly led to several of my posts. And a special mention to my wife, the incomparable Gemma Piercy-Cameron, who reads some of my more difficult posts in order to ensure that they are as understandable as possible, as well as holding me back from saying things that are truly stupid. Without that help, I'm sure the blog would not be nearly as readable as it is.

Overall, the blog is still going strong. One of my favourite blogs and a source of inspiration for many of my posts, Marginal Revolution, recently celebrated twenty years of blogging (see here and here). My blog is almost exactly half as old as theirs. If I'm still feeling the energy to do this in another ten years, that will be a great achievement. We will see how I go!

Nobel Prize for Claudia Goldin

I was very excited to read that Claudia Goldin (Harvard University) was awarded the 2023 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (aka the Nobel Prize in Economics) on Monday, "for having advanced our understanding of women’s labour market outcomes". There are several reasons I was excited. First, it is welcome and well-deserved recognition for another female economist (only the third to win the Nobel Prize, and the first to win it alone). Second, although David Card won the award a couple of years ago, I still feel that labour economics has been under-represented among Nobel Prize winners, and many of Goldin's contributions have been in labour economics. Third, and most importantly (to me, at least), I've been routinely picking Goldin as a Nobel winner (jointly with Larry Katz) in the Waikato Economics Discussion Group annual poll for the last several years (and this is proof that a stopped clock will eventually be right).

The Nobel Committee's summary of Goldin's work focuses very much on her work on gender, but I see her contributions as much broader than that. In particular, I have greatly appreciated her work on education and inequality, as beautifully captured in her book with Katz, The Race between Education and Technology (which I reviewed here). That book, along with the broader research, was one of the contributing sources to the topic on inequality and social security in my ECONS102 class, which we covered this week.

Having said that, the work on gender has been particularly important to economics, and it is no wonder that the Nobel Committee focuses on that. Along with Goldin's research on female work and labour market outcomes, she also established the Undergraduate Women in Economics (UWE) Challenge, as a way of addressing the gender gap in economics. As I have noted before (most recently here, and in the links at the end of that post), the gender gap remains a serious problem. Goldin has also mentored or otherwise supported many researchers investigating the gender gap, including the authors of many papers that I have written about on this blog. The impact of this work is likely to have a long-run positive effect on the economics profession, and for Goldin's contributions in this space, we can be very thankful.

On The Conversation, Leonora Risse (RMIT University) wrote a great summary of Goldin's contributions to economics. Alex Tabarrok at Marginal Revolution points to a number of video sources on Goldin and her work. The Financial Times has an excellent write-up (paywalled), as does the New York Times (also possibly paywalled). This was a well-deserved award, and in my view overdue. Oh, and a fourth reason to be excited: Goldin's latest NBER Working Paper, published on the morning of the Nobel Prize announcement, is titled "Why Women Won". Very fitting.

The minimum wage and homelessness

The minimum wage has a number of effects, both inside and outside of the labour market (see the links at the end of this post for more). Low-income workers (such as many of those working for the minimum wage) are particularly vulnerable to any negative effects it may have. For that reason, I was really interested to read this recent working paper by Seth Hill (University of California San Diego), on the relationship between minimum wages and homelessness.

There are a couple of reasons to expect that higher minimum wages might increase homelessness. If minimum wages decrease employment (a result that is contested, but I believe it is likely given the galaxy of literature we have to date; again, see the links at the end of this post), then higher minimum wages may directly increase the risk of people becoming homeless. That's because when low-income people workers lose their jobs, they may no longer be able to afford to pay rent, and may lose their homes. Second, if minimum wages increase incomes for those that are not made unemployed, they may increase the demand for housing, pushing up rents. This may indirectly increase the risk of people becoming homeless, who can no longer afford the higher market rent.

Hill looked at municipality-level changes in minimum wages in the U.S., coupled with municipality-level data on homelessness from the Department of Housing and Urban Development's Annual Homeless Assessment Reports (which provides data on the number of homeless in each municipality in January each year). Hill used multiple different estimation methods, including: (1) an event study design; (2) a stacked regression estimator; and (3) a local projections difference-in-differences (DiD) estimator. For our purposes, we don't need to get too much into the details about the different methods, since they all point in the same direction. Hill finds that, in the event study analysis:

Municipalities that increased minimum wages by up to $2.50 per hour from 2013 to 2018 saw an average increase of 14 percent in homeless counts in the years 2014 to 2019 relative to municipalities with no nominal change in the minimum wage (real decline) or with changes pegged to inflation (real no change). Municipalities that increased minimum wages by more than $2.50 per hour from 2013 to 2018 saw an average increase of 23 percent in homeless counts in the years 2014 to 2019 relative to municipalities with no change. A dynamic version of this analysis suggests the increase in homeless counts increases as time passes.

For the stacked regression estimator:

Increases of $0.75 or more in local minimums increased relative homeless counts by about 25 percent in the years following the increase. All results hold with controls for changes in local income and local population.

And finally, for the local projections DiD estimator:

...when cities raise their minimum wage by 10%, relative homeless counts increase by three to four percent.

Overall, all of the results suggest that when the minimum wage increases, homelessness increases. Hill then goes on to consider the mechanisms that might explain this relationship, and finds that:

Using the event-study estimator to evaluate mechanisms, I find that increases in the minimum wage decreased employment among low-skill workers and increased costs of local rental housing in my sample.

That seems to support the theoretical direct and indirect effects of minimum wages on homelessness that I outlined at the start of the post. The minimum wage may make some workers better off, but it has unintended consequences. Here is another consequence that policy makers and others need to take account of.

[HT: Marginal Revolution]

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data

What workers are willing to pay for jobs at safer times of the day

Not all jobs pay the same. Some jobs are pleasant, fun, and/or clean. Other jobs are unpleasant, boring, and/or dirty. Because more people want to do the pleasant, fun, and clean jobs, the wages for those jobs tend to be lower (holding other characteristics of jobs the same). And because fewer people want to do the unpleasant, boring, and dirty jobs, the wages for those jobs tend to be higher (holding other characteristics of jobs the same). This is an explanation of what economists call compensating differentials. Jobs with desirable non-monetary characteristics tend to have lower wages than jobs with undesirable monetary characteristics. Workers are essentially compensated for taking on jobs with undesirable non-monetary characteristics.

One of the important non-monetary characteristics of a job is how safe or risky the job is. Jobs that are risky tend to pay more than jobs that are safe. Usually, economists think about this in terms of how safe or risky the job itself is. However, the same theory should apply to jobs in risky locations, compared with jobs in safer locations, or to jobs at risky times of the day, compared with jobs at safer times of the day.

How much are workers willing to sacrifice to work at a safer time of the day? That is the question that was addressed in this recent article by Oscar Becerra and José-Alberto Guerra (both Universidad de los Andes), published in the Journal of Economic Behavior and Organization (sorry, I don't see an ungated version online). They used an experimental approach, where they offered:

...participants a one-hour job and elicit individual preferences about alternative arrangements affecting the job’s safety perception by exploiting the time of the day (early versus late shift) and location (online versus on-site). In the first round, we offer students to participate in a second round in which they must perform an unspecified simple task at the university campus, which is located in a neighborhood generally perceived as more unsafe than most of the city. Participants are then allowed to choose between an early shift (9–10 a.m.) and a late shift (8–9 p.m.), given a compensation schedule of 11 fixed payments. We told participants, without deception, that we would invite them for this future one-time job under the proposed payment scheme. Based on their choices, we recover a measure of the willingness to pay for the early on-site shift. In the second round, a subsample of subjects from the first round faced a similar setting but now the future task could be completed remotely, which removes safety concerns related to the workplace’s neighborhood and commute. 

The research participants were all students at Universidad de los Andes in Colombia. Becerra and Guerra document that there are negative perceptions of safety around the university campus at night, and then based on their experiment they find that:

...safety concerns about the late shift on campus and gender differences (beyond those in safety concerns) are the main determinants of willingness to pay for the early on-site shift. The unconditional difference in willingness to pay between individuals who consider the late shift as riskier than the early shift is 3,721 Colombian Pesos (COP) –about USD 2.8 PPP and 11%–15% of the baseline payment. Similarly, we also find an unconditional gender gap in willingness to pay for the early on-site shift of 2,843 Colombian Pesos (COP) –USD 2.1 PPP and 8%–11% of the baseline payment. Using regression analysis, controlling for safety concerns reduces the gender gap by 20%.

So, workers are willing to sacrifice a relatively large proportion of pay in order to work in a job at a safer time of the day. Workers who feel less safe are willing to sacrifice more if they have high safety concerns, and women are willing to sacrifice more than men (even accounting for women having higher safety concerns than men). Becerra and Guerra then go on to show that there is likely to be external validity for their results, because based on administrative data from the university:

...students whose willingness to pay for the safer shift is larger than the median participant were more likely to exit University premises before 5 p.m., and they were also less likely to enroll in courses with lectures occurring after that time.

In other words, the experimental results were consistent with actual behaviour, in relation to being on campus in the evenings. So, as expected it appears that workers are willing to pay (11-15 percent of their wage) for jobs at safer times of the day. That means that there should be a substantial compensating differential for jobs that require working at unsafe times.

The gender difference in willingness-to-pay for working at safer times of the day has interesting implications. It means that, given a particular wage on offer, women are less likely to accept a job at an unsafe time (like evening or night work), so we should expect to see more men than women working at those times. It also means that women would be willing to accept a lower wage for working at safer times of the day than men do, since women are willing to give up more wages to work at those times than men. This could be an unrecognised contributor to the gender wage gap in locations where personal safety is generally low (or where risks are particularly high at certain times of the day). This is definitely something that is worth further exploring in other settings.

The likely consequences of New Zealand's new value of a statistical life

This past week, my ECONS102 class covered health economics. As part of that topic, we cover the value of a statistical life (VSL) [*]. When I was looking up the current VSL for New Zealand, I realised that a pretty important change earlier this year had completely passed me by. As reported in this Newsroom article:

Over the past 30 years, there's a strong economic argument to be made that government has not valued human life highly enough; it failed to acknowledge that New Zealanders placed greater value on saving their friends, family and neighbours from injury or death than they did on shaving a few seconds off their morning commute.

Last month, that changed. With no fanfare, no press release, no ministerial statement, the transport agency Waka Kotahi published a document entitled Monetised benefits and costs manual v1.6 April 2023. The 429-page manual is a dense compendium of tables, formulae and, to the layperson, impenetrable economic justifications for obscure policies.

But buried in this manual are two big changes to v1.5, published two years earlier. It raises the value we place on saving time stuck behind a wheel driving to and from work, from $7.80/hr to $19.53 an hour – an increase by a factor of 2½. That figure increases to as much as $36.18/hr if that's what it costs to avoid being stuck in congestion.

And at the same time, it increased an esoteric number called the VoSL – the Value of a Statistical Life – from $4.88m to a somewhat breathtaking $12.5m. That's an even bigger increase.

That's not an inconsequential change (for reasons I will come to a bit later), but it is very surprising. And that's because:

In 1991 government researchers completed a survey of 700 New Zealanders to find out what value they placed on safety. 

They wanted to measure the amount society would pay for the avoidance of one premature statistical death – and they did it by asking individuals the amount they would pay for safety improvements. They came up with a Value of a Statistical Life of $2m.

A new more thorough survey in 1998 doubled the figure to $4 million – but the government of the day refused to adopt it, seemingly dismayed at the cost implications for road, rail and aviation infrastructure.

So it is that the flawed 1991 survey result has been updated in line with wage inflation, every subsequent year. Extraordinarily, that outdated and discredited survey was still used to decide whether or not to build transport and other infrastructure – until now.

That's right. Until earlier this year, the VSL that was used in government decision-making was based on a survey of 700 people conducted in 1991. Among other uses, the VSL is a number that is used to measure the benefits of road safety improvements. If straightening a road, or reducing the speed limit, or installing median barriers, would save on average one life per year, then the value of those benefits was equal to $4.88 million per year. And now, that value has jumped to $12.5 million.

The Newsroom article argues that this means that more road safety projects would be funded. It isn't quite that simple. The government (or, rather, Waka Kotahi NZ Transport Agency) calculates benefit-cost ratios for each potential project, then ranks those projects from those with a high benefit-cost ratio, to those with a low benefit-cost ratio. Not all projects with a benefit-cost ratio greater than one (that is, those with benefits that outweigh costs) will be funded, as the roading budget doesn't stretch that far. Some projects with benefit-cost ratios less than one (that is, those with benefits that are smaller than costs) may be funded, depending on the political priorities of government. [**]

If all potential roading projects have similar road safety improvements and travel time savings, then the ranking of those projects wouldn't change. So, even though the benefit-cost ratios would be more favourable, there would be no additional roading projects funded, and the projects that were funded would be no different. However, to the extent that not all projects result in the same road safety improvements, the change in the VSL will tend to shift benefit-cost ratios in favour of projects with greater statistical lives saved.

Moreover, because the change in the VSL is greater than the change in the value of travel time savings, the benefit-cost ratios will tend to shift more in favour of projects that result in road safety improvements, and less in favour of projects that result in travel time savings. And that means that we can expect more roads with median barriers and slower speed limits in the future, and fewer road changes that result in travel time savings.

*****

[*] This is now often termed the 'value of a preventable fatality'. The difference is mostly semantic, but I buy into the argument that the old terminology feels somewhat uncomfortable when discussing with non-economists.

[**] Many times, politics trumps good economics.

Bitcoin still isn't money, because people aren't using it as if it was

Back in 1875, the economist William Stanley Jevons described the functions of money as [*]:

1. It is a medium of exchange - you give it up when you buy goods or services, and you can receive it when you sell goods or services;
2. It is a unit of account - you can measure the value of something using the amount of money it is worth; and
3. It is a store of value - you can keep it and it will retain its value into the future.

Since the introduction of Bitcoin in 2009 (if not earlier), there has been an ongoing debate about whether Bitcoin (and other cryptocurrencies) are money. I've blogged on this topic before (see here and here), and to me it is pretty clear that cryptocurrencies are not money. For one thing, their value is too volatile to act as a unit of account. Second, whether they are a good store of value is questionable. Just ask anyone who held TerraUSD or Luna cryptocurrencies in 2022. And, does anyone use Bitcoin as a medium of exchange?

That last question was addressed in this recent article in The Conversation by John Hawkins (University of Canberra):

The whole point of Bitcoin, as its creator “Satoshi Nakamoto” stated in the opening sentence of the 2008 white paper outlining the concept, was that:

A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution.

The latest data demolishing this idea comes from Australia’s central bank.

Every three years the Reserve Bank of Australia surveys a representative sample of 1,000 adults about how they pay for things. As the following graph shows, cryptocurrency is making almost no impression as a payments instrument, being used by no more than 2% of adults.

You can go to the article to see the graph that Hawkins refers to, but the point is clear. While around 65 percent of Australians have used PayPal to make a payment in the previous 12 months, only 2 percent of Australians have used cryptocurrency to do so. But it's not just Australia:

These findings confirm 2022 data from the US Federal Reserve, showing just 2% of the adult US population made a payment using a cryptocurrrency, and Sweden’s Riksbank, showing less than 1% of Swedes made payments using crypto.

So, if people aren't using Bitcoin as money, why do they have it? Hawkins notes that:

But most people buying Bitcoin essentially as a speculative token, hoping its price will go up, are likely to be disappointed. A BIS study has found the majority of Bitcoin buyers globally between August 2015 and December 2022 have made losses...

UK government research published in 2022 found that 52% of British crypto holders owned it as a “fun investment”, which sounds like a euphemism for gambling. Another 8% explicitly said it was for gambling.

 Bitcoin still isn't money. People simply aren't using it in that way.

*****

[*] Jevons noted a fourth function, a standard of value (a way of valuing debts), which we now consider to be much the same as a unit of account.

Read more:

• You may be able to buy Coke with Bitcoin, but that still doesn't make Bitcoin money
• No, El Salvador hasn't just turned Bitcoin into money

The political cost of inflation in India

The costs of inflation is a common topic in introductory macroeconomics. We cover it in ECONS101. The challenge in recent years has been getting students to understand inflation at all. After around three decades of low inflation, it just hasn't been really relevant until quite recently. Now, the costs of inflation are becoming all too apparent.

However, one cost that we tend to overlook is the political cost. High inflation can be quite damaging to the political party in power at the time, as increases in the cost of living cut into voters' pay and erode their savings. This has been the case in New Zealand, but appears to be even more the case in India, as noted in this Financial Times article (paywalled) from July:

Higher food prices have in the past proved politically precarious for incumbent Indian governments, with analysts attributing famous election upsets to anger over high onion prices.

India’s opposition has seized on the latest surge to attack Modi’s government. Mallikarjun Kharge, president of the Indian National Congress, the main opposition party, blamed vegetable price inflation on the BJP’s “loot” and “greed”.

“The public has become aware and will answer your hollow slogans by voting against the BJP,” he said this month...

The BJP remains the favourite in national elections, which are due in the first half of next year, but faces a series of potentially tough state polls in Rajasthan and Madhya Pradesh later this year. It suffered a major setback in May, losing control of Karnataka to Congress.

A disgruntled voting public creates a risk for the government, particularly in an election year. India's ruling BJP party have responded by implementing a rice export ban (in order to lower the domestic price of rice), as well as subsidies, as well as giving out free rice. However:

The BJP’s restrictions on rice exports, designed to appease consumers, have upset another powerful constituency: farmers, many of whom stood to benefit from higher prices.

Swamy K, a 68-year-old rice farmer in a village near Mysuru, said he remained loyal to Modi even though he loathed Karnataka’s erstwhile BJP government. But he said his patience with the party was running thin.

“Politicians keep saying that farmers are the backbone of the country, but that backbone has long been broken,” he said. “They put us on posters, but give us nothing.”

It seems that they are forgetting that farmers are voters too. Or perhaps they are relying on the urban population being much larger, and more likely to vote, than rural farmers. India goes to the polls for their general election in April or May 2024 (the date is yet to be set). However, there are many other countries suffering high inflation (albeit perhaps not as high as India), with elections to come later this year, including the Netherlands (3 percent inflation rate), Poland (10.8 percent inflation rate), and of course, New Zealand (6.0 percent inflation rate). It will be interesting to see what price the incumbent governments pay (pun intended) for the higher-than-usual inflation rates that their voters have been experiencing.

Read more:

• Fiscal drag as a cost of inflation

Book review: Information Rules

It's been a while since I posted a book review, as I've been busy with teaching and various research evaluation activities (this year I'm a panellist for both the Royal Society's Marsden Fund, and the Health Research Council). However, recently I did manage to finish reading Information Rules, by Carl Shapiro and Hal Varian. The book was published in 1999, but it maintains currency because information is so central to the modern economy. So, although the examples are dated (although, I appreciated many of the references from a time when I worked in business and internet consultancy), the underlying economic theory, business strategy, and government policy implications remain highly relevant. In fact, some sections, such as those that relate to the 'economics of attention', are arguably even more relevant now than they were in 1999.

Shapiro and Varian are strong on linking economic theory to real-world context. The book doesn't attempt to create a grand theory of the information economy, instead relying on existing economic theory, particular in relation to network externalities, switching costs and lock-in, and engaging with customers and competitors. These are all concepts that would be familiar to my ECONS101 students. Interesting, and perhaps surprisingly for a book on this topic, Shapiro and Varian shied away from making grand predictions of the future of the information economy. The book is all the more readable 24 years later as a result of that choice.

Much of the details in the book were not new to me, although I appreciated picking up some additional ideas on business economics that I can bring into my ECONS101 class. Nevertheless, there were also a few surprising bits, such as this on offering different versions of a product (e.g. software):

Then, when you are ready to develop the product for the lower-end markets, just start turning features off. Take the high-resolution images and produce low-resolution versions. Put wait states in you program to slow it down. Remove the buffering. Do whatever it takes to make the product relatively unattractive to the high willingness-to-pay users but still attractive to the next group down.

This hadn't occurred to me before, but makes complete sense for a firm that wants to price discriminate between consumers with high willingness-to-pay and consumers with lower willingness-to-pay. There are also some humorous bits, such as this:

Although software producers don't hang around outside of schoolyards pushing their products (yet), the motivation is much the same. If you can get someone to use your product when he or she is a student, you've got a good chance of building a loyal customer down the road.

I enjoyed this book, and it's a shame that it hasn't been updated since. It would be really good to have Shapiro and Varian's take on the rise of social media, smartphones, and artificial intelligence. Of course, there are plenty of other books on those topics, but it seems like a natural extension of this one.

Many readers won't get as much out of this book as I did. However, if you want to understand the information economy, then putting aside the dated examples, this is a good book to read.