Your next car will probably cost you more

The cost of fuel has been in the news recently, due to large increases resulting from the war in Ukraine. The war isn't only going to affect fuel prices though, as the New Zealand Herald reported yesterday:

For more than a year, the global auto industry has struggled with a disastrous shortage of computer chips and other vital parts that has shrunk production, slowed deliveries and sent prices for new and used cars soaring beyond reach for millions of consumers.

Now, a new factor — Russia's war against Ukraine — has thrown up yet another obstacle. Critically important electrical wiring, made in Ukraine, is suddenly out of reach. With buyer demand high, materials scarce and the war causing new disruptions, vehicle prices are expected to head even higher well into next year.

The war's damage to the auto industry has emerged first in Europe. But US production will likely suffer eventually, too, if Russian exports of metals — from palladium for catalytic converters to nickel for electric vehicle batteries — are cut off...

In the United States, the average price of a new vehicle is up 13 per cent in the past year, to $45,596, according to Edmunds.com. Average used prices have surged far more: They're up 29 per cent to $29,646 as of February.

Before the war, S&P Global Mobility had predicted that global automakers would build 84 million vehicles this year and 91 million next year. (By comparison, they built 94 million in 2018.) Now it's forecasting fewer than 82 million in 2022 and 88 million next year.

Mark Fulthorpe, an executive director for S&P, is among analysts who think the availability of new vehicles in North America and Europe will remain severely tight — and prices high — well into 2023. Compounding the problem, buyers who are priced out of the new-vehicle market will intensify demand for used autos and keep those prices elevated, too — prohibitively so for many households. 

It's easy to explain what is going on here with simple supply and demand models of the new and used car markets. First, let's start with the new car market, as shown in the diagram below. The market last year was at equilibrium, with demand D0 and supply S0, leading to an equilibrium price of P0 ($40,350, based on the 13 percent increase noted in the quote above) and Q0 (94 million, assuming a similar number to 2018) new cars traded. The cost of car production has increased, because of the shortage of computer chips (increasing their price) and now the same for electrical wiring. This decreases the supply of new cars to S1, increasing the price to P1 ($45,596 in the quote), and decreasing the quantity of new cars traded to Q1 (82 million in the quote). [*]

Now consider the market for used cars, as shown in the diagram below. Again, the market last year was at equilibrium, with demand DB and supply SA, leading to an equilibrium price of PB ($22,981, based on the 29 percent increase noted in the quote above) and QB used cars traded. Used cars and new cars are substitutes. With new cars increasing in price, they became relatively more expensive than used cars, so car buyers have shifted to buying used cars instead of new cars. The demand for used cars increased to DA, increasing the price to PA ($29,646 in the quote), and increasing the number of used cars traded to QA.

So, if you are looking for a car any time soon, you can expect to pay a higher price for the car, as well as a higher price at the pump.

*****

[*] For simplicity, I've ignored the increase in demand for new cars alluded to in the article. The overall effect of that, combined with the decrease in supply, would be equilibrium price increasing by more, but an ambiguous effect on the equilibrium quantity of new cars traded. Given that the quantity of new cars traded has likely decreased, the increase in demand must be relatively smaller than the decrease in supply (and much smaller, given the large decrease in new cars traded).

Try this: Tradle

How much do you know about international trade? If you think you're a trade whiz, then test yourself on Tradle. Yes, it is a blatant rip-off of Wordle. Each day, you get presented with a graphic (a 'treemap') showing the exports of a particular country. You get six guesses to guess which country it is. After each guess, Tradle will tell you how far away, and in which direction, your selected country is from the country you are trying to guess. With a combination of geographical and international trade knowledge, you can get there.

Today's [*] Tradle was this one:

By way of bragging, I got it in three guesses. However, it was a total guess, because who knew that country's biggest export category was integrated circuits?

Tradle is brought to you by the same folks behind the 'Atlas of Economic Opportunity' (that I referenced in this 2017 post), and now called the Observatory of Economic Opportunity (OEC). Enjoy!

[HT: Jack Tame in this New Zealand Herald article]

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[*] I'm a little worried that Tradle doesn't get updated every day, as when I tried to share the results, it looked like it was posted on 6 March (at least, it was labelled "#Tradle #29 3/6"). Anyway, even playing it once was fun for a quick diversion. [Update: Tradle does appear to be updating each day. I didn't do so well today (5 April), essentially choosing every country bordering the correct answer along the way!]

Funny paper titles may be worth it

The title of a research paper matters because it signals to potential readers the content of the paper, and helps them to determine whether it is worthwhile to read (or cite). The title of a research paper may therefore affect how often the paper is cited. As I've noted before, papers with shorter titles tend to gather more citations. However, there is also research that says the opposite, and there is no consensus on this point (e.g. see here).

The research I cited in this earlier post suggested that shorter titles might lead to more citations because they are more memorable. That may be the case, but in my experience an even better way to make a paper memorable is to make the title funny. I'll not easily forget "Riccardo Trezzi is immortal" (which I blogged about here), or "Japan's Phillips Curve Looks Like Japan". Of course, in both cases the content of the paper was funny, and that helped to make the title more memorable.

How important is humour in a paper title? That is the research question addressed in this new working paper by Stephen Heard (University of New Brunswick), Chloe Cull (Concordia University), and Easton White (University of New Hampshire). They collected data on 2439 papers in top ecology and evolution journals published in 2000 or 2001, and asked 11 volunteers to rate the titles in terms of the author's attempt at humour (rather than whether they raters thought the title was actually funny or not). Heard et al. then limited the sample to the 414 titles where at least one of the raters rated humour in the title at greater than zero (on a zero to six scale), as well as 650 randomly-selected articles rated as not-humorous. They then looked at how humour related to subsequent citations, and found that:

After we controlled for other predictors, total citations declined with average title humour...The effect was relatively small, with a decrease of 4% in total citations for each 1 point increase in average humour score, but this equates to a difference of 20.4% between the least and most humorous titles. There is, however, an important qualification: the pattern was similar, but much stronger, for self citations, with an 82% decrease for the most humorous titles... Thus, after correcting for underlying paper importance, funny title are cited more, not less... with a 23% increase for each 1 point increase in humour score.

So, funny paper titles get fewer citations, but Heard et al. infer that that is because researchers attach funny titles to papers that they think are less important. That's because (emphasis is theirs):

...papers with funnier titles are subsequently cited less by their own authors. Since authors don’t need titles to alert them to their own papers, self-citation provides a title-independent estimator of importance – unlike other citations.

Correcting for importance (self-citations), Heard et al. found that there was a positive relationship between humour and the number of citations. However, the way that they corrected for self-citations was to create a dependent variable that was total citations divided by self-citations. That seems a bit odd to me. Why not simply include self-citations as an additional explanatory variable, and subtract self-citations from the total citations in the dependent variable?

However, taking their results at face value, Heard et al. conclude that:

Advice to avoid humour in paper titles... is thus not well founded in evidence – at least, not if the concern is citation impact.

That may be true. It would be interesting to see whether these results extend into other fields. After all, ecologists might not be the most humorous of researchers. Economists are clearly much funnier. On a more serious note, it would be interesting to see whether humour has differential impacts at different points in the quality distribution. It wouldn't surprise me to find that a lower-quality paper gets a citation boost from having a funny title, while a higher-quality paper suffers a humour-related penalty.

Finally, Heard et al. titled their paper "If this title is funny, will you cite me? Citation impacts of humour and other features of article titles in ecology and evolution", about which they lament:

...funny titles increase impact. We regret, therefore, being unable to think of a funnier title for this paper.

[HT: Marginal Revolution]

Read more:

• Title length and research quality

Decomposing the difference in life expectancy between rich and poor

An under-recognised aspect of socioeconomic inequality is not measured in income or wealth, it is measured in disparities in length of life. There are essentially two ways that we can conceptually look at this issue. One is in terms of lifespan inequality - essentially how big are disparities in the length of life across the whole distribution of lifespan. I have a PhD student working on a number of research questions related to lifespan inequality and its relationship with income inequality. The second way is in terms of inequality in life expectancy - differences in the average length of life between different groups, such as the difference between the rich and poor. The two ways of looking at this issue are related, but they are not the same - one looks at differences group-level averages, while the other looks at individual differences.

So, with a PhD student working on this topic, I've had good reason to delve into the literature (which is something I have been wanting to do for some time). And we aren't the only ones looking at this issue - there's a growing literature on inequality in length of life. Most use fairly standard inequality measurements. However, this recent working paper by Gordon Dahl (University of California, San Diego), Claus Kreiner, Torben Nielsen, and Benjamin Serena (all University of Copenhagen) takes things in a different direction. They compare inequality in life expectancy between rich and poor for the US and Denmark, first noting that:

The gap in life expectancy between rich (top tertile) and poor (bottom tertile) males is around 8 years in both the US and Denmark in 2001. Over the short period from 2001 to 2014, this inequality increased by 1.7 years in the US and 0.9 years in Denmark. The gap between rich and poor females stayed constant in Denmark over this period, but also increased by about 1.8 years in the US.

Dahl et al. then decompose those differences in life expectancy into different mortality trends between rich and poor, and a common mortality trend for all. They refer to the common mortality trend as 'survivability', and it measures:

...the likelihood of surviving until a given age multiplied by the expected remaining life years after surviving this age.

Essentially, this decomposition recognises that differences in age-specific mortality have different effects on life expectancy, depending on what proportion of the population live to each age. Using this decomposition method, Dahl et al. find that:

In the US, half of the rise in inequality for forty-year old males... is due to larger reductions in mortality rates for the rich than the poor, while the other half is due to differences in their survivability. For Danish males... life expectancy inequality increased, even though mortality rates have fallen more for the poor. The explanation for this apparent puzzle is that survivability strongly favored the rich, more than offsetting the effect of differential mortality rate changes. For females in both countries, survivability plays a similarly important role.

They also find similar results to Denmark for nine other western European countries (Austria, Belgium, England/Wales, Finland, France, Italy, Spain, Sweden, and Switzerland). Why does all this matter? Dahl et al. argue that:

Trends in age-specific mortality rates of the rich and poor are informative about changes in underlying health status, while trends in life expectancy are a relevant measure of the associated welfare effects. Looking at each of these in isolation misses an important link - survivability - and, as demonstrated by our empirical results, can lead to misleading conclusions.

For example, say that there is a change in age-specific mortality at older ages arising from some new health procedure or treatment. This change appears to favour the poor because it affects the age-specific mortality of poor to a larger extent than the rich. However, this new treatment might lead to a larger increase in the life expectancy of the rich than the life expectancy of the poor, if a greater proportion of rich than poor live to the ages where the treatment has the most positive effects.

None of this is particularly surprising once you get your head around it, but nevertheless it is important to recognise. As we are finding in our own research, the greatest decreases in lifespan inequality (and likely in differences in life expectancy between groups) happen when mortality reduces at young ages.

[HT: Marginal Revolution, last year]