Book review: Empire of Guns

My ECONS101 class discussed the Industrial Revolution in class last week, focusing on why it happened first in England, rather than France or China or elsewhere. By coincidence, I was just finishing up reading the book Empire of Guns by Priya Satia. A conventional view of factors underlying the Industrial Revolution in England, such as the explanation by the economic historian Bob Allen, focuses on the role of changes in wages and coal prices, and individual entrepreneurship and invention. Satia instead focuses on the role of government in driving investment in manufacturing, and in particular the manufacturing of guns. As Satia explains:

The story of Britain's transformation from a predominantly agrarian, handicraft economy to one dominated by industry and machine manufacture - the commonly accepted story of the industrial revolution - is typically anchored in images of cotton factories and steam engines invented by unfettered geniuses. The British state has little to do in this version of the story. For more than two hundred years, that image has powerfully shaped how we think about stimulating sustained economic growth - development - the world over. But it is wrong: state institutions drove Britain's industrial revolution in crucial ways.

The book moves through four sections, motivated by a story of Samuel Galton Jr. (grandfather of the statistician and eugenicist Francis Galton), and a disagreement with his fellow Quakers over his gun-making business (which was inconsistent with the Quakers' ethic of nonviolence). In the first section, Satia tells the story of the British gun trade from 1688 to 1815. The second section then discusses how guns "migrated from being an instrument of terror specifically relevant to contests over property to a weapon for new kinds of impersonal violence on the battlefield and in the streets". The third section takes the story up to the present, looking at how failures to regulate gun manufacture and trade has "distorted the theory and practice of economic development".

The book is exhaustively researched, with 67 pages of endnotes and an extensive bibliography. However, with so much material at hand, it can be difficult to keep the narrative flowing. Satia falls into this trap, with the same ground covered from multiple angles in several sections of the book. It is difficult to see how this could be avoided, but I found it distracting. The text is also quite dense and at times overly descriptive, again as can be expected given the large amount of source material. This is definitely not a lightweight and breezy treatment of its topic.

I did learn some interesting things from the book. For example, 'trade guns' were a common form of 'money', when coins and other mediums of exchange were scarce. However, guns were not a perfect form of money, as:

...guns' market value and metallic content made them a practical monetary instrument, but their perishability undermined both those sources of value, reducing them to mere commodities after all.

Given the focus on the (British) trade in guns, I was also disappointed to see only a single mention of the musket wars in New Zealand. Satia notes that it:

...consumed large quantities of British guns, killing a third to a half the Maori population.

That aspect of the story is left at that. I may be biased, but that sentence alone screams for some further exposition. I wasn't really convinced by the last section of the book either. Like the earlier sections, it was interesting, but in my view, it lacked a strong narrative thread and seemed more like a collection of related anecdotes.

Overall, this book would be a good read for a student of history, particularly those interested in a broader understanding of the context of the Industrial Revolution. But for others with a more general interest in history or economic history, there are better and more readable book-length treatments available.

Is poverty a driver of crime?

This week my ECONS101 class covered, among other things, the difference between causation and correlation. When two variables appear to move together, the relationship might be causal. We might even be able to tell a good story of why the relationship is causal. However, there may be other explanations for the relationship.

For example, take the relationship between poverty and crime. It is well established that poor people commit more crime. Is that because poverty causes people to commit more crime? Many people think so. Being poor means that people lack access to resources, and they may try to obtain those resources through crime. Alternatively, perhaps being poor leads to anger, frustration, or resentment, which leads poor people to commit more crime.

On the other hand, perhaps there is reverse causation - people who commit more crime may make themselves poorer. Being caught and punished through fines or imprisonment will reduce a person's financial resources, making them more likely to be poor. Or, perhaps there is some confounding (or a common cause), and both poverty and crime are related to some third variable. Education is a possibility, since people with more education tend to earn more (and be less at risk of poverty), and also commit less crime. The local unemployment rate might also be a confounder, since when unemployment is higher, poverty will also be higher, and unemployment is also associated with crime.

Putting all of that together, it isn't clear that there is a causal relationship between poverty and crime. We would need some careful research to try and identify whether the relationship is causal. Fortunately, we have this 2023 NBER working paper by David Cesarini (New York University) and co-authors, to provide us with some evidence. Cesarini et al. look at the impact of winning the lottery in Sweden on criminal convictions. They are fortunate in two ways. First, they have data from the register of criminal convictions on all convictions between 1975 and 2017. And second and more importantly, they are able to match the conviction data to four samples of lottery players. Their sample includes over 350,000 lottery wins by over 280,000 individuals. They also look at the effects on children (of their parents winning the lottery), where they have a sample of over 100,000 children.

The cool thing about this analysis is that Cesarini et al. can look at what happens to criminal behaviour, comparing people who are otherwise similar but win the lottery (and therefore are less poor) with those who did not win the lottery (and therefore are just as poor as before). This analysis should establish the causal impact of financial resources on crime (and therefore by extension also the causal impact of income or wealth on crime).

For the adult analysis, Cesarini et al. find:

...a positive but statistically insignificant effect of lottery wealth on criminal behavior. The point estimate of our main outcome of interest — conviction for any type of crime within seven years of the lottery event — suggests 1 million SEK (about $150,000) increases conviction risk by 0.28 percentage points (10.2%). The 95% confidence interval allows us to reject reductions in conviction risk larger than 0.16 percentage points (5.8%). We find no clear evidence of differential effects across types of offenses.

In other words, lack of financial resources does not cause crime in this sample. If it did, then the increase in financial resources arising from the lottery win would lead to less crime. Or, lack of financial resources does cause crime, the effect is very small. Turning to the effect on children, Cesarini et al. find:

...an effect of parental financial resources on child delinquency close to zero, but non-trivial effects in either direction cannot be ruled out. The 95% confidence interval for the effect of 1 million SEK ranges from a 1.36-percentage-point reduction (12.9%) to a 1.54-percentage-point (14.6%) increase in conviction risk.

Again, the central estimate of the effect of financial resources on crime is zero, albeit with less certainty in the result. The takeaway is that a lack of parental financial resources does not cause crime among children. Both results point to a lack of a causal impact of financial resources on crime. Cesarini et al. conclude that:

Our results therefore challenge the view that the relationship between crime and economic status reflects a causal effect of financial resources on adult offending.

It is likely, then, that the observed correlation between poverty and crime arises as a result of reverse causation (in my view possible, but unlikely), or confounding. That has clear implications for policy, because it suggests that focusing on reducing poverty is unlikely to have any impact on crime. Now, this study was conducted in Sweden, and these results might not hold in other contexts. However, they should make us question more strongly the prevailing view that poverty is a driver of crime.

[HT: Marginal Revolution, back in December 2023] 

This week in research #83

For the last two days, I've been at the New Zealand Population Conference in Wellington. There was some really interesting research presented, but the biggest talking point (unsurprisingly) was the recently announced changes to the Census. Here are some of the highlights I found from the conference:

• Irina Grossman presented a fast-paced keynote on small-area population projections, noting that machine learning methods do not systematically outperform simpler methods (and also noting that while many end-users say that they want measures of the uncertainty associated with projections to be reported, very few of them actually use those measures!)
• Rosemary Goodyear and Miranda Devlin presented new data on severe housing deprivation and homelessness in New Zealand which, among other things, showed that homelessness has been increasing in every Census since 2001, and that severe housing deprivation is highest among Pacific Peoples (but being 'without shelter' is highest among Māori)
• Jacques Poot reviewed various methods of modelling internal migration, using data from Australia, and similar to Grossman he concluded that more complex methods don't systematically outperform simpler methods
• Ji-Ping Lin shared details about a free dataset, the Taiwan Indigenous Peoples Open Research Data (which can be found here)
• John Bryant retraumatised the audience by talking about excess mortality during COVID-19 in New Zealand, where excess deaths were low, but interestingly mortality hasn't completely returned to the pre-COVID trend
• Andrew Sporle shared news about a forthcoming data portal for New Zealand that will include 25 years of data on 'amenable mortality' (preventable mortality)
• Marion Burkimsher summarised data on fertility change in New Zealand, noting that the fertility curve for New Zealand in 2024 most resembles the curve for England and Wales
• Several Stats NZ staff tried (with little success) to sell the audience on the administrative and survey data that will replace the five-yearly Census, although Hannes Diener clearly undersold the value of the experimental Administrative Population Census (which one of my PhD students has been working with)

Aside from the conference, here's what caught my eye in research over the past week:

• Bajaj, Jena, and Reilly (open access) use data from a now-defunct online gambling platform that created 'share prices' for soccer players, and find that the skin tone of the player matters with the darker the shade, the lower the player’s online purchase price
• Guthmann and Scheidel (with ungated earlier version here) develop a theoretical model of the economics of Greco-Roman slavery in the ancient world
• Wang, Sarker, and Hosoi (open access) find a positive and statistically significant effect of investment in analytics on NBA team performance
• Nilsson and Biyong (with ungated earlier version here) find that training hairdressers to be mental health first responders improved hairdresser-customer interactions, but had no effect on the mental health of customers, and worsened mental health outcomes for hairdressers

This couldn't backfire, could it?... Spanish slugs edition

My ECONS102 class covered unintended consequences this week. So, this story from YLE in Finland last month seemed very timely:

The population of the invasive Spanish slug (Arion vulgaris) has exploded in Finland, prompting four cities to offer six euros per litre for dead slugs.

Known as a highly destructive garden pest and even nicknamed the "killer slug" following reports of it preying on bird chicks, this species thrives in wet summers, with each individual capable of laying hundreds of eggs.

To combat infestations this summer, the cities of Lappeenranta, Turku, Kerava and Jämsä are encouraging locals to get the Crowdsorsa app, which allows residents to earn money by helping remove invasive species...

Getting a payout requires a few more steps. To earn a reward, slug killers must film a video showing the slugs being packed into one-litre containers (like milk cartons) sealed with tape and disposed of in designated bins.

The final step is uploading a video of the packing and disposal process to the Crowdsorsa app, and if everything is done correctly, the payment gets credited to the user's account.

These sort of bounty programmes have a habit of backfiring, though. The emblematic example of this is a story I wrote about back in 2015:

The government was concerned about the number of snakes running wild (er... slithering wild) in the streets of Delhi. So, they struck on a plan to rid the city of snakes. By paying a bounty for every cobra killed, the ordinary people would kill the cobras and the rampant snakes would be less of a problem. And so it proved. Except, some enterprising locals realised that it was pretty dangerous to catch and kill wild cobras, and a lot safer and more profitable to simply breed their own cobras and kill their more docile ones to claim the bounty. Naturally, the government eventually became aware of this practice, and stopped paying the bounty. The local cobra breeders, now without a reason to keep their cobras, released them. Which made the problem of wild cobras even worse.

So, how long will it be before an enterprising Finn realises that they can make money by breeding these Spanish slugs? Particularly since:

Hunting down the slugs is not always easy, and the pest can be confused with the homegrown Limax cinereoniger, or ash-black slug.

They would be much easier to hunt down if you are farming them yourself! And, when you are farming the right type of slug, there's no risk of confusing them with a local slug. It would be so much easier to claim the bounty that way, than by fossicking around hunting wild slugs. How long will it be before the Finns work this out?

[HT: Marginal Revolution]

Read more:

• This couldn't backfire, could it?... Ragweed edition
• This couldn't backfire, could it?... Possum bounty edition
• This couldn't backfire, could it?... Cane toad edition
• This couldn't backfire, could it?... Dead possums edition