In wildfires, people prefer to save people rather than endangered species

If you were an incident controller who needed to deploy firefighting resources in a wildfire, how would you decide where to distribute those resources? If there is not enough firefighting to cover all areas at once, which areas should receive priority? Saving human lives seems like it should be a priority, but what about animal lives? What about preserving biodiversity, or saving endangered species from the fire? What about built infrastructure? What about important cultural artifacts? Some of these questions may seem easy to resolve, but there are important trade-offs, and understanding those trade-offs is important.

That is where this 2024 article by John Woinarski, Stephen Garnett, and Kerstin Zander (all Charles Darwin University), published in the journal Conservation Biology (open access, with non-technical summary on The Conversation), comes in. They surveyed a sample of over 2000 Australians, asking them to repeatedly make best-worst choices among five different alternatives (of eleven total). As they explain:

...respondents are asked to state which item among a set of items they consider as best and worst... In our survey, best meant the asset the respondent most wanted to save and worst meant the asset the respondent least wanted to save.

By getting the research participants to repeat this task many times (eleven times, in fact), with different sets of items to choose from, Woinarski et al. develop a good picture of the relative ranking of each of the eleven items, both for each research participant and for the sample overall. This best-worst scaling (BWS) method is a form of non-market valuation, since it essentially works out the relative value (in terms of ranking) of the different options that research participants are presented with. [*]

The eleven options that research participants were ranking overall were:

1. A person with a car stuck behind a fallen tree, whom you know had not received advice to evacuate;
2. A person with car stuck behind a fallen tree, whom you know had ignored repeated advice to evacuate beforehand;
3. A house that you know has no people in it;
4. A farm shed with some hay bales and a tractor;
5. A flock of 50 sheep—a few of which will be killed by fire, but survivors are likely to be badly injured;
6. A population of 50 koalas—a few of which will be killed by fire, but survivors are likely to be badly injured;
7. The last population of a native snail species for which the fire will kill all individuals, thereby causing the species’ extinction;
8. The last population of a small native shrub, for which the fire will kill all plants, thereby causing the species’ extinction;
9. One of only two populations of a rare wallaby for which the fire will kill all individuals of one of the populations (but not affect the other), thereby making it more endangered;
10. Ancient rock art that will be destroyed if fire gets into the weeds now growing in the rock shelter; and
11. An old tree with an ancient Aboriginal carving on the trunk.

The results are interesting, if not terribly surprising:

In terms of relative importance, saving a person who ignored evacuation advice was rated 57% as important as saving a person who had not received warnings... Saving the koala population was rated slightly lower (56% as important as saving a person who had not received warnings). Saving the wallaby population was 45% as important as saving a person who was not warned. Saving the house and shed had the lowest rankings (14% and 9%, respectively, as important as saving a person who was not warned).

For completeness, compared with saving a person who had not received warnings, saving the shrub was rated as 25% as important. Saving the sheep was rated as 26% as important, saving the snails was rated as 25% as important, saving the ancient rock art was rated as 15% as important, and saving the carved tree was rated 12% as important, respectively. Woinarski et al. bemoan that no one loves snails, but I also think the loss of the cultural artifacts would be a tragedy as well. I guess that reflects that each of us would place different weightings on things, and come out with different rankings. And that is what Woinarski et al. look at next, finding that:

Female respondents placed higher importance than male respondents on the protection of the rare wallaby population, the koala population, the sheep, and the tree carving and lower importance than male respondents on the protection of the house, shed, native shrub, and rock art... Older respondents (>65 years) rated protecting people more highly than younger respondents, but rated the tree carving less highly than younger respondents...

Respondents who self-identified as Indigenous placed a higher score on protecting the rock art and tree carvings than those identifying as non-Indigenous.

Those differences may not come as a surprise either. Now, in my ECONS102 class, when we discuss non-market valuation (specifically in the context of estimating the value of a statistical life), I point out that personal experience of the risk makes a difference. And that is true in this case as well. Woinarski et al. find that:

Survey respondents affected by wildfires and those assessing themselves as being prepared for wildfires were less likely to save a person who had not received warnings... Those who rated themselves as prepared for wildfire were also less likely to save a person who ignored warnings, whereas those who had been affected by wildfire were more likely to do so.

It is interesting to consider what the differences mean here. If a person has personal experience of wildfires, then they know how devastating they can be, and how unpredictable and fast-moving. In my mind, that should make them more likely to want to save a person who has not received warnings, but instead they are less likely. On the other hand, it does make sense that they would be more likely to save someone who ignored warnings. Woinarski et al. don't provide a good explanation for that result (although, to be fair, they are focused on the results related to conservation, rather than humans!). On the other hand, people who are well prepared being less willing to help those who ignored warnings makes some sense.

The takeaway message from this paper, though, is that people prefer to save people, rather than endangered species. Especially snails.

*****

[*] If one of the options had been monetary, Woinarski et al. could have used their results to work out the rough monetary value of each option.

Onshore windfarms vs. birds

Several times recently, I've had conversations with others about environmental objections to windfarms, and specifically about their impacts on birds. I've expressed surprise that anyone could believe that large, slow-moving wind turbines could be a threat to birds. It turns out, there is research that supports the negative impacts of wind turbines on birds (see here or here), but that research doesn't actually demonstrate that wind turbines cause a decrease in bird populations. The problem, of course, is that it isn't feasible to run a randomised controlled trial with wind turbines due to cost - placing wind turbines at random across some areas and not others, and comparing the effect on bird populations in both areas. The cost of such an experiment would be enormous.

Fortunately, there are statistical methods that we can use to try and estimate the causal effects. And that is what this new article by Meng et al., published in the Journal of Development Economics (ungated earlier version here) attempts to do. Specifically, they look at the effect of onshore windfarms on bird biodiversity at the county level in China. They have two measures of biodiversity:

Bird abundance is the average number of birds of a given species per checklist observed at a county-month-year-species level. Species richness is the total number of unique species observed in a given county at the month-year level, which better reflects the diversity of the bird populations.

To establish causality, they use a difference-in-differences (two-way fixed effects) model, which essentially compares the difference in bird biodiversity before and after a windfarm is installed, between counties with and without windfarms. However, Meng et al. go a step further, using an instrumental variables approach, instrumenting for the location of windfarms by the interaction between national-level growth in windfarms interacted with county-level average windspeed at 100 metres. That instrumental variables approach should mitigate issues arising from the correlation of wind turbine location and bird biodiversity.

The novelty of this paper is not just in the methods, but in the data that Meng et al. employ. To measure bird biodiversity, they make use of data:

...from the China Birdwatching Report (CBR, similar to the eBird Reference Dataset), a citizen science dataset consisting of reports from users, including information on individual bird trips and associated characteristics, such as the specific date and time, location of a specific trip, as well as species and quality of birds encountered...

Their dataset covers the period from 2015 to 2022, and includes data collated from over 33,000 checklists. They also control for a variety of other variables:

...including average bird observed duration, average temperature, average visibility, average wind speed, total precipitation, average ozone, percentage of natural park areas in the county, average population density, and average night light value.

Using this data and the two-way fixed effects approach, Meng et al. find that:

A one standard-deviation increases in wind turbines (approximately 84 turbines)... in a given county leads to a 9.75% decrease in bird abundance per checklist from the mean value of 5.38...

...while a one standard-deviation increases in wind turbines (approximately 84 turbines) in a given county decreases the number of unique bird species by 17.67% from the mean value of 66...

Meng et al. also find evidence that the impacts are greater on migratory birds than on resident birds (important given that China is a major migration pathway for migratory birds), and that the effects are larger in forested and urban/farmland than for grassland. There is also evidence that the impact is greatest for the largest bird species.

Finally, Meng et al. show that there are effects of windfarms on neighbouring counties (as well as the counties in which the windfarms are located), and that those effects are somewhat smaller in size. That made me wonder why those analyses were not the primary results in the paper, since it seems obvious that birds may move across county borders.

So, it does appear that windfarms might cause a decrease in bird biodiversity. Meng et al. even address a bunch of concerns that jumped out at me as I was reading the paper, especially that birdwatchers, anticipating that there would be fewer birds near windfarms, do less birdwatching in those locations. On that point, Meng et al. note that:

We do not find a significant impact of wind turbine installations on birdwatcher behaviors regarding the submitted number of checklists...

And they further support that with detailed mobile phone GPS data, showing that there were not fewer trips made to the areas of windfarms, relative to areas further away. However, a couple of concerns do remain, but they are rather technical. First, I wondered why Meng et al. used the interacted variable (national growth in windfarms interacted with windspeed), rather than just windspeed alone. They describe this as a "Bartik-like variable", but we should be cautious about whether Bartik instruments are appropriate (see here). Also, two-way fixed effects models have also come in for criticism recently (see here and here). I'm not going to drag you into the technical details (read the links if you're interested). But suffice to say, this will not be the last word on whether windfarms negatively impact bird biodiversity. However, the best quality study we have so far seems to suggest they do.

When analogies fail... Marine Protected Areas edition

In The Conversation last week, Mark John Costello (Nord University) wrote an article explaining the economic benefits of Marine Protected Areas (MPAs). MPAs are areas where fishing is prohibited. The article is interesting, and in short it makes the case that MPAs have unrecognised (or under-recognised) economic benefits, in terms of positive spillover effects on fishing and tourism.

However, this bit struck me as really odd:

Although it may seem counterintuitive that a full restriction of fishing in an area will result in more fish elsewhere, this happens because MPAs act like a reservoir to replenish adjacent fisheries.

In financial terms, the capital is invested and people benefit from the interest on the investment. To count the establishment of an MPA as a cost to fisheries is like claiming that interest earned on money is a cost.

I had to read that second paragraph a couple of times, because it didn't make sense to me. And it didn't make sense to me because it doesn't work as an analogy. Let me labour that point by unpacking the analogy.

First, think about a financial investment. The return on the financial capital that the investor employs is the interest that they receive. The less financial capital they invest, the less interest they will receive. Now think about a fishery. The return on the natural capital is the value of the fish that the fishermen take from the fishery. The less natural capital in the fishery, the less fish the fishermen can take.

Now think about a marine protected area. The MPA sets aside some of the fishery (some of the natural capital). Lower natural capital means that the fishermen will be able to take less fish. The fishermen will receive a lower return from their fishing. Now go back to the financial investment. The equivalent of an MPA for the financial investment is setting aside some of the financial capital. Lower financial capital means that the investor will receive less interest. The investor will receive a lower return from their investing.

The establishment of a MPA really is a cost to fisheries. Fishermen can take less fish. Unlike Costello's claim, this is nothing at all like "claiming that interest earned on money is a cost". It is more like claiming that 'losing interest that you would have otherwise earned if the financial capital hadn't been set aside and paying no interest is a cost'. Which it is. It is what economists refer to as an opportunity cost. Costello's analogy fails.

The rest of the article is interesting and does raise some important and valid points. There may be spillover benefits to fishermen outside of MPAs because the MPA can act as a nursery for young fish. Focusing solely on the fish that were not taken in the MPA area would overstate the cost to fishermen, if they are able to take more fish from outside of the MPA. Tourist operators also benefit from the MPA, because tourists like to look at wildlife, including fish. The case that MPAs have significant benefits is strong. It's a pity that the analogy that was employed to make part of the case was much weaker.

Taxing carbon emissions in agricultural markets with exports

In my previous post, I discussed the effect of an agricultural emissions tax. The effect of the tax would be to reduce New Zealand production of agricultural goods, and less production of agricultural goods in New Zealand means less agricultural emissions originating in New Zealand. However, as I noted in the footnotes to the post, the analysis left out the market effects of international trade. I argued that:

...the omission of trade doesn't affect the conclusions we draw from the model, that taxing agricultural emissions would increase the domestic price and decrease the quantity of dairy products traded.

Let's look at the market again in this post, but with international trade included. Before we get that far, it is worth revisiting the effect of international trade in an exporting country. This is shown in the diagram below. This is the market for an exporting country, which means that this country has a comparative advantage producing the agricultural good. That means that this country can produce the agricultural good at a lower opportunity cost than other countries, which is represented on the diagram by the domestic market equilibrium price of (PD) being below the price on the world market (PW). Because the domestic price is lower than the world price, if the country is open to trade there are opportunities for traders to buy the agricultural good in the domestic market (at the price PD), and sell it on the world market (at the price PW) and make a profit (or maybe the suppliers themselves sell directly to the world market for the price PW). In other words, there are incentives to export the agricultural good. The domestic consumers would end up having to pay the price PW for the agricultural good as well, since they would be competing with the world price (and who would sell at the lower price PD when they could sell on the world market for PW instead?). At this higher price, the domestic consumers choose to purchase Qd0 of the agricultural good, while the domestic suppliers sell Qs0 of the agricultural good (assuming that the world market could absorb any quantity of agricultural goods that was produced). The difference (Qs0 - Qd0) is the quantity of agricultural goods that is exported. Essentially the demand curve with exports follows the red line in the diagram.

Now consider what happens if the government decides to tax production of the agricultural good. For the moment, let's assume that the tax doesn't affect the world price (we'll come back to that later). Sellers who sell to the world market receive the world price PW, but then must pay a tax (T) to the government, which leaves them with (PW - T). There is less incentive to sell the agricultural good, so less will be produced. The quantity of the agricultural good supplied decreases to Qs1. The domestic consumers must still compete with the world market, so they still face the price of PW, so the quantity of the agricultural good demanded remains Qd0. So, as we saw in my previous post, the agricultural emissions tax would reduce domestic production of the agricultural good.

But what happens if the reduction in domestic production is large enough that it reduces global supply of the good? Then, we are back to the analysis in the previous post, where the price increases in the domestic market (because the world price increases and domestic consumers must also pay the world price), and production of the agricultural good decreases.

So, in markets where New Zealand is a large producer and has an appreciable effect on the world price, an agricultural emissions tax would reduce New Zealand production. And, in markets where New Zealand is a small producer and has no effect on the world price, an agricultural emissions tax would reduce New Zealand production and New Zealand emissions. Whether the tax would reduce global emissions, though, is a much more complicated question. It depends on all of the factors I highlighted in the previous post, and we don't have a complete answer to it yet (however, as I noted, it is likely that global emissions will be somewhat lower). I'll reiterate that there is more research needed in this area.

Read more:

• The question of carbon emissions 'leakage' is not as simple as some people would have you believe

The question of carbon emissions 'leakage' is not as simple as some people would have you believe

Agricultural emissions policy has been in the news recently (see here and here), with the National Party in favour of pricing agricultural emissions in such a way that there is "no leakage". What do they mean by leakage? As this RNZ article explains:

...Aotearoa is already one of the most efficient producers of meat and dairy products globally. If we reduce emissions here, will that not simply lead to other, less efficient countries picking up the lost production, while our farmers pay the price?

This idea is known as "carbon leakage" and is often used as an argument against any domestic policy that could result in reduced agricultural production. The issue is important as New Zealand depends heavily on agricultural exports. In 2022, of all merchandise trade, 65 percent were agricultural commodities.

How plausible is carbon emissions leakage as a result of reducing agricultural production in New Zealand? Let's focus on New Zealand production of dairy products [*]. We'll use a basic supply and demand model to explain what is happening. The diagram below shows the domestic market for dairy products [**]. If the market were left alone with no emissions price, it would operate at equilibrium with a price of P0, and Q0 dairy products would be traded. When the emissions price (effectively a type of excise tax on dairy products) is imposed, we represent that with the new curve S+tax. The price the consumer pays for dairy products increases to PC, but the effective price for the seller decreases to PP (which is the consumer's price PC, minus the amount of the emissions tax paid to the government). The quantity of dairy products traded decreases to QT. And, because less dairy products are traded, fewer cows are needed, and agriculture-related emissions are decreased.

Notice that there's no emissions leakage in this model. Nothing else is going on. That's because we're only looking at a model of partial equilibrium. We are looking at the domestic market for dairy products, in isolation. New Zealand is producing less dairy products, but the world still wants lot of dairy products. So, the argument goes, other producers will increase production to fill that demand.

However, that ignores the fact that New Zealand is a huge producer of dairy products for export. Higher prices of New Zealand dairy products (because of the agricultural emissions tax) will increase the global price of dairy products. When the global price goes up, dairy consumers will demand less dairy products. So, we can expect fewer dairy products to be produced globally.

Even that conclusion is incomplete though, because it ignores the dynamics. When the price of dairy products is high, profits are higher, and that might encourage more producers to enter the market, increasing supply. That would lower the global price of dairy products, and increase the quantity of dairy products demanded.

We're still not done though. Those new dairy products manufacturers will be higher-cost than New Zealand producers (if they weren't, then they would be producing dairy products already). So, the increase in global supply won't fully offset the reduction in New Zealand supply. And, an increase in global supply of dairy products must mean a decrease in the global supply of some other agricultural commodity that those new suppliers were previously producing. To understand emissions leakage, we need to get a handle on these general equilibrium and dynamic effects.

And finally, we get to the question of whether the new global dairy product suppliers have higher carbon emissions than New Zealand producers, as well as whether producing dairy products has higher emissions than whatever it was that they were previously producing.

So, you can see, the question of whether pricing agricultural emissions in New Zealand leads to emissions leakage doesn't have a straightforward answer. It requires a substantial understanding of global and local agricultural markets, agricultural supplier response to price changes, and the substitutability of agricultural land between different uses, and the carbon emissions of different land uses, both in New Zealand and overseas.

Some smart economists are working on understanding this though. As the same RNZ article notes:

It's difficult to know exactly what might happen in agriculture, as emissions pricing on agricultural products has not yet been used elsewhere. There is no historical evidence to draw on.

International modelling studies present a mixed picture of the likelihood of leakage: an OECD study estimated 34 percent of agricultural emissions would be leaked, mostly to developing countries.

Recent modelling for New Zealand examines a series of scenarios of domestic pricing on its own as well as international pricing. The results show that for the current proposal where only 5 percent of emissions are priced to begin with, with a 1 percent increase each year, New Zealand's production of meat and dairy products could decline by 2050...

This shows leakage may occur, with reductions in production of New Zealand dairy products. But global meat and dairy production by 2050 would be considerably lower than without the policy, which would have a positive overall impact on the climate.

Perhaps we can take away from the evidence so far that there is some emissions leakage, but it's not complete. Not all emissions reductions are leaked overseas. So, pricing agricultural emissions in New Zealand would likely lead to lower global emissions in total. Clearly, there is more research needed in this area, but based on what we know so far, I think it's ridiculous that we give one of the largest emitting sectors a free pass on contributing to climate change. If anyone tells you that all of our emissions would simply move overseas, you should probably examine their motivations for doing so.

*****

[*] The case is much simpler for the production of other agricultural products, where New Zealand production doesn't affect the global price. If a reduction in New Zealand production is so small that it doesn't affect the world price, then it doesn't create any incentive for other countries to produce more. So, there is unlikely to be any emissions leakage of note.

[**] For simplicity, I'm ignoring two things in this market diagram. First, the diagram doesn't show a negative externality, for example the impact of dairy emissions on the climate. So, the supply curve shouldn't be equal to marginal social cost (MSC), as MSC should also include the social cost of the emissions. However, correcting for that wouldn't change the general conclusion, that taxing agricultural emissions would increase the domestic price and decrease the quantity of dairy products traded. It would simply move the market closer to the socially-optimal quantity. Second, the diagram doesn't include international trade, which has important effects on the New Zealand domestic market for dairy products. However, including trade in a diagram of the dairy products market for New Zealand is not as simple as including the world price (as in this post, for example), because the world price of dairy products is directly affected by New Zealand production. One way of solving that problem is to assume that demand in the New Zealand market reflects global (rather than just domestic) demand, and then the diagram looks much as we have drawn it. Again, the omission of trade doesn't affect the conclusions we draw from the model, that taxing agricultural emissions would increase the domestic price and decrease the quantity of dairy products traded.

More evidence that Julian Simon was lucky in the Simon-Ehrlich bet

Last October, I wrote a post about the Simon-Ehrlich best:

...the famous bet between University of Maryland professor of business administration Julian Simon and Stanford University biology professor (and author of the influential book The Population Bomb) Paul Ehrlich. Ehrlich had argued that resources were becoming scarcer. Simon pointed out that, if increased scarcity were true, then the price of resources would be increasing. He challenged Ehrlich to choose any raw material, and a date more than a year away, and Simon would bet that the price would decrease over that time rather than increasing.

Ehrlich accepted the bet, choosing copper, chromium, nickel, tin, and tungsten as the raw materials on which the bet would be based. The bet was formalised on 29 September 1980, and was evaluated ten years later. The price of all five materials fell between 1980 and 1990, and Simon won the bet.

The purpose of that post was to highlight this 2010 article that demonstrated that Julian Simon might just have gotten lucky. Now, a new article by Ross Emmett (Arizona State University) and Jesse Grabowski (Université Paris 1 Panthéon-Sorbonne), published in the Journal of Ecological Economics (sorry, I don't see an ungated version online) follows up on the question of whether Simon was lucky, using the tools of financial economics. They argue that financial economics is the appropriate way to analyse the bet, because:

...the Simon-Ehrlich bet [can be] recast as an investment portfolio, specifically a forward contract, with Simon in a short position as an investor and Ehrlich in a long position. 

In other words, Simon's short position makes money if the value of the commodities decreases (which is what happened), while Ehrlich's long position makes money if the value of the commodities increases. Now, recast as an investment, Emmett and Grabowski employ a number of financial economics tools, including portfolio diversification, market beta, the Sharpe ratio, and mean-variance efficiency. I'm not going to go into detail about what each of those methods entails, but in terms of portfolio diversification, Emmett and Grabowski note that:

...Simon should have proposed a wager against a basket of as many resources as possible, to reap the benefits of portfolio diversification.

Simon's offer to bet on any number of resources opened him up to a lot of risk, since a diversified portfolio of resources is more likely to follow the overall price trend (which he believed was downwards), whereas any single (or five) resources could more easily go against that trend, simply due to luck. On the other hand, from Ehrlich's perspective:

A diversified portfolio would still have lost the bet, but it might have saved Ehrlich a few hundred dollars.

So, Emmett and Grabowski believe that Ehrlich was in trouble regardless of portfolio choice. Moving on to their other methods, Emmett and Grabowski find that:

...Ehrlich’s portfolio did not contain as much market trend exposure, as measured by CAPM beta, as it could have, which is surprising given his boisterous confidence in price trends. Luckily for him, however, an explicit strategy of maximizing portfolio beta ended up in failure. When risk profile was held constant at the level he himself chose, however, we found he could have done better given the information available by moving to a mean-variance efficient portfolio.

So, as they suspected, Simon would have lost the bet almost regardless of which resources he chose. However, that doesn't answer the underlying question of whether Simon was simply lucky. If you look at the final value of a $1000 investment in the five resources, for any ten-year period between 1903 and 2015, you get Figure 4 from Emmett and Grabowski's paper:

The red shaded areas show points in time where if the ten-year period ended then, Simon would win the bet, while the green shaded areas show points in time where Ehrlich would win. It is clear that Ehrlich would have won slightly more often over the past 110 years (similar to the findings in the earlier paper). Emmett and Grabowski then go a step further, creating and running a Monte Carlo simulation of the change in resource prices over time, and find that:

From our simulation, we estimate that, were they able to repeatedly make the same bet, Simon would win only 37% of the time, while Ehrlich would win 63%.

So, I guess we can take this as further (and more detailed) evidence that Julian Simon was just a little bit lucky in the Simon-Ehrlich bet.

Read more:

• Was Julian Simon just lucky in the Simon-Ehrlich bet?

Was Julian Simon just lucky in the Simon-Ehrlich bet?

You may have heard of the famous bet between University of Maryland professor of business administration Julian Simon and Stanford University biology professor (and author of the influential book The Population Bomb) Paul Ehrlich. Ehrlich had argued that resources were becoming scarcer. Simon pointed out that, if increased scarcity were true, then the price of resources would be increasing. He challenged Ehrlich to choose any raw material, and a date more than a year away, and Simon would bet that the price would decrease over that time rather than increasing.

Ehrlich accepted the bet, choosing copper, chromium, nickel, tin, and tungsten as the raw materials on which the bet would be based. The bet was formalised on 29 September 1980, and was evaluated ten years later. The price of all five materials fell between 1980 and 1990, and Simon won the bet.

However, was he just lucky? This 2010 article by Katherine Kiel, Victor Matheson, and Kevin Golembiewski (all College of the Holy Cross), published in the journal Ecological Economics (ungated earlier version here), argues that he was. Kiel et al. look at all of the possible ten-year periods from 1900 to 2008 (there are 99 decades from 1900-1910, to 1998-2008), and find that Ehrlich would have won the bet in 61.6 percent of decades. Simon was lucky that the decade the bet was placed in, starting in 1980, was one where resource prices generally declined (and having a large recession just at the time when the bet was being evaluated probably helped).

Kiel et al. evaluated the bet using annual data. It would be interesting to see how the bet would fare using higher-frequency data. How sensitive was the outcome of the bet to the recessionary state of the economy at the end of the bet period, for example? By taking such a long time period into account, Kiel et al. ignore the endogeneity - if the bet had been placed in 1900, it's unlikely that Ehrlich would have chosen those five raw materials - he may well have chosen five completely different raw materials.

The Simon-Ehrlich bet was important in demonstrating that the neo-Malthusian view of the world is overly pessimistic. However, we need to be careful not to overstate its significance. Julian Simon got a little bit lucky.