Do gun laws save lives?

Do gun laws save lives? The chart above shows homicide rates for U.S. states (data from here) together with an A to F ranking of state gun laws from the Giffords organisation. As with my post from 2017, there is actually no statistically significant correlation (this is particular noticeable among the F’s, which include both the seven states with the highest murder rate and the two states with the lowest). In other words, the answer seems to be no.

Rather, it seems that guns don’t kill people, people kill people. The murder rate in the U.S. is driven by social factors which differ from state to state – factors which make New Hampshire and Maine pretty safe, but which produce a murder rate ranging from 14 per 100,000 to 20.5 per 100,000 in Missouri, Alabama, Louisiana, and Mississippi. For comparison, New Hampshire has a murder rate similar to that of Australia, but Louisiana and Mississippi, if they were countries, would rank among the most murderous 20 countries in the world.

There is some evidence that keeping guns out of the hands of criminals would reduce the murder rate in the U.S., but this is extremely difficult to do. The U.S. has a lengthy, porous southern border, across which there is a free flow of people, guns, and illegal drugs.

In addition, a concept from catastrophe theory is useful here. In the cusp pictured below, it is possible to “drop” from the top of the fold to the bottom, but a long roundabout journey would be required to get back up. Similarly, it is very easy to introduce guns into a society, but very difficult to remove them. Although such removal has been done elsewhere, laws forbidding gun ownership are likely to be ignored by precisely those violent criminals that one would not wish to carry them. And, of course, there is the 2^nd Amendment.

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Voting patterns in the United States

Because of my interest in social statistics, I’ve been exploring county-level results from the US 2016 presidential election. The map below (click to zoom) summarises the results (blue for Democrat and red for Republican, coloured according to how strongly counties voted one way or the other). Nine of the more extreme counties are highlighted.

Most of the variance in this data can be explained by demographic factors such as race, age, education levels, local unemployment, rural-urban continuum code, and median household income. The latter is particularly interesting, and the chart below provides a summary.

On a scale from −0.5 being 100% Republican to +0.5 being 100% Democrat, the curve shows the average vote of counties by 2016 median household income (where the averages are weighted by county population sizes, and LOESS smoothing is used to draw the curve). Overlaid on the diagram is a bar chart for the total population of different median household income groups (the scale for this bar chart is on the right).

It can be seen that, at the very poorest end, votes are balanced between Democrat and Republican. For example, here are the 7 poorest counties in the United States, by median household income. They are all rural:

• Holmes County, MS: $22,045 (mostly Black, strongly Democrat)
• Buffalo County, SD: $22,500 (mostly the Crow Creek Indian Reservation, Democrat)
• Owsley County, KY: $23,115 (mostly White, strongly Republican)
• Wilcox County, AL: $24,216 (mostly Black, Democrat)
• McDowell County, WV: $24,460 (mostly White, strongly Republican)
• Clay County, KY: $24,901 (mostly White, strongly Republican)
• Stewart County, GA: $24,945 (mostly Black, Democrat)

Above that bottom end, votes trend Republican, with the peak Republican vote occurring at median household incomes around $40,000. Above about $56,000, counties swing Democrat, and the Democrat vote increases with increasing wealth. For example, here are the 4 richest counties in the United States, by median household income. They are all part of the Baltimore–Washington metropolitan area:

• Fairfax County, VA: $115,518 (Democrat)
• Falls Church, VA: $118,035 (strongly Democrat)
• Howard County, MD: $119,386 (Democrat)
• Loudoun County, VA: $134,609 (Democrat)

Essentially, the Republican Party seems to have become the party of the poor, particularly the rural poor. Indeed, the (population-weighted) median of 2016 median household incomes for Democrat-majority counties was $61,042; while for Republican-majority counties it was $52,490 ($8,552 less). For a visual perspective, the map below limits the previous one to counties with a 2016 median household income below $56,000. It is precisely because the Republican Party has become the party of the rural poor that these maps are mostly red. I had not fully appreciated this before analysing the data (although others have), but it certainly explains much of recent politics in the US. However, predicting the results of the next presidential election would require some solid demographically linked polling data as well as a good voter turnout model.

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Support for Kavanaugh vs support for Trump

Brett Kavanaugh has been in the news rather a lot lately. The chart above shows support for his appointment to the US Supreme Court, for various demographic groups, as per a 1 October Quinnipiac University Poll. This is compared to the 2016 Trump vote for those same groups, as per CNN exit polls (in both cases, some missing information had to be inferred using the data provided plus census data). The area of the circles shows the size of the various groups.

Responses to Kavanaugh seemed largely to follow partisan lines. Democrats mostly went one way, Republicans the other. However, white women seemed to support Kavanaugh less than expected, perhaps because they were more likely to believe the accusations made against him. Minority groups, on the other hand, were more supportive of Kavanaugh than of Trump, perhaps because of concerns about evidence, corroboration, and due process. Overall, it seems to almost balance out, though – I must say that I can’t see any support here for a “blue wave” at the November elections.

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Reflections on school performance in the US

The US has just had a release of the 2017 National Assessment of Educational Progress (NAEP) results. They are not good. Of grade 8 pupils in public schools, 65% failed to meet proficiency standards in reading, and 67% failed to meet proficiency standards in mathematics. This is a serious problem, and it is worth getting to the bottom of it.

Doing a multiple regression on average state grade 8 reading scores, the politics of the state governor has no effect (p = 0.67). States vary enormously in the money they spend on education, ranging from $6,575 per pupil in Utah to $21,206 per pupil in New York. This makes no difference either (p = 0.93). What does make a difference is the state poverty rate (R² = 0.49, p = 0.000000014).

For grade 8 mathematics scores, the story is similar. Politics of the state governor (p = 0.76) and money spent on education (p = 0.51) have no effect, but the state poverty rate does (R² = 0.55, p = 0.0000000008).

Clearly, poor children do much less well in school, and spending money on schools does not address the problem. Why do poor children do less well in school? Research shows that on day one, poor children have a cognitive and behavioural disadvantage. Poor children eat less well. Poor children are starved of words, because their parents, on average, spend less time talking, singing, and reading to them.

The problems lie at home; the solutions must also lie at home. Rather than spending more money in schools, the US seems to need more assistance to parents at home. For example, the State Library of Queensland has started a wonderful Dads Read programme in Australia. Bookstart in the UK offers a free pack of books to children at 0–12 months and at 3–4 years. Also helpful would be guides to teaching number skills, guides to nature walks, discounts for families at museums, and other assistance in STEM areas (I’m start to feel like it’s time to write another children’s book). Surely this problem with reading and mathematics needs to be addressed with urgency!

Story time at the Dover Air Force Base library, Delaware (USAF photo by Roland Balik)

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Democracy, Religion, and Same-Sex Marriage in Australia

The results of the postal survey are in, and Australia has voted 61.6% “Yes” to same-sex marriage. Or rather, it seems that two Australias voted. The official results have been made available by electorate, which means that they can be correlated with demographic factors (and my readers know that I love doing that). The average age of each electorate had no effect, but religious composition certainly did.

According to the 2016 census, Australia’s stated religious composition looks like this (where the 33.3% “Secular” includes Agnostic, Atheist, Humanist, New Age, and Unitarian Universalist):

The chart below shows a strong correlation (0.82) between the percentage of “Secular” people in an electorate, and the size of the “Yes” vote. If all the “Secular” people voted “Yes” (as seems likely), this means that 58% of the religious people voted “No.” Doing some simple multiple linear regression, there was a statistically significant link between religion and voting “No” for every major religious group. This link was strongest for Muslims, Hindus, Buddhists, Orthodox, the Uniting Church, and other non-Anglican Protestants. It was a little weaker for Anglicans and even more for Catholics, although the Anglican link was quite strong in Victoria, South Australia, New South Wales, and Queensland. The Catholic link was quite strong in the last three of those states.

Electorates in the chart are coloured according to the largest religious group within them. Sydney is 52.7% Secular, for example (as well as 8.6% Buddhist, 1.7% Muslim, 1.7% Hindu, 1% Jewish, 17.9% Catholic, 2.4% Orthodox, 13.5% Protestant, and 0.5% Other Religion). It voted 83.7% “Yes.”

Blaxland is 32.2% Muslim (as well as 9% Buddhist, 3.3% Hindu, 21.2% Catholic, 5.5% Orthodox, 13.2% Protestant, 0.7% Other Religion, and 14.9% Secular). It voted 73.9% “No.”

Auburn Gallipoli Mosque in Blaxland (photo: Maksym Kozlenko)

McMahon is 39% Catholic (as well as 5.9% Buddhist, 12.4% Muslim, 2.9% Hindu, 6.9% Orthodox, 18.5% Protestant, 1.4% Other Religion, and 13.2% Secular). It voted 64.9% “No.”

Barton is multi-religious with 28.1% Secular being the largest group (as well as 5.6% Buddhist, 8.4% Islam, 5.6% Hindu, 0.2% Jewish, 22.6% Catholic, 15.7% Orthodox, 13.3% Protestant, and 0.5% Other Religion). It voted 56.4% “No.”

It does seem that there is a secular Australia, which voted overwhelmingly “Yes,” and a religious Australia of twice the size, which voted mostly “No.” If the disparate religious communities in Australia realise that they have more in common than they have thought, that could have quite a significant influence on Australian politics in the future.

A (distorted) geographical view of the postal survey results

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Guns, education, religion, and suicide

My earlier post indicated that gun laws in the US had little impact on the homicide rate, when demographic factors were taken into account. This makes sense – if I want to kill somebody, the lack of a gun will merely prompt me to choose another weapon. But what about suicide? The impulse to suicide is often brief, and easy access to a gun during a suicidal episode may increase the chance of dying.

To test this, I extended my previous dataset with data on educational attainment, data on religiosity, registered gun ownership data from the ATF, age-adjusted suicide rates from the CDC, poverty rates, unemployment rates, and other demographic data. I ran all that through a regression tree analysis, using R.

Suicide rates in the chart (click to zoom) are indicated by colour, ranging from 8 per 100,000 for New Jersey and New York (yellow) to 23.7 for Montana (black). Having a college degree seems to have a protective effect – states on the right of the chart, with more college degrees, had lower suicide rates. This may relate to the higher employability of college graduates. However, states at the top of the chart, with higher high school graduation rates, had higher suicide rates. I am not sure why this is the case.

Among the states with fewer college graduates, religion had a protective effect (this is consistent with other studies). States where 77% or more of the population said that religion was “somewhat important” or “very important” to them are indicated on the chart by triangles. For the states with fewer college graduates, the suicide rate was 13.6 per 100,000 for religious states, and 17.5 for less religious ones.

Finally, the highest-risk states (fewer college graduates and less religious) split according to gun ownership. States with more than 0.008 registered guns per capita are marked on the chart with an inner dot. Among the highest-risk states, the suicide rate increased from 13.9 per 100,000 to 18.6 when more guns were present. This group included Alaska (23.2 per 100,000), Arizona (17.5), Idaho (19.2), Maine (17), Montana (23.7), Nevada (18.6), North Dakota (17.3), Oregon (16.8), and Utah (21.4). Among the more religious states, registered gun ownership did not seem to have an effect (although, of course, registered gun ownership is a poor indicator of true gun ownership).

Thus the data does seem to suggest a link between gun ownership and suicide risk, but only when other risk factors are present (low religiosity and no college degree). This is exactly what we expected, and it means that suicidal (or potentially suicidal) people need to be kept away from guns.

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Do gun laws save lives?

Somebody pointed me at this interesting data the other day. The chart above (click to zoom) combines the “Gun Law Score Card” from the Law Center to Prevent Gun Violence in the US with homicide rate data from Wikipedia and voting results from the last US election. Do gun laws reduce the chance of being murdered?

Obviously, “Blue” states tend to have stricter gun laws than “Red” states (an average of B− vs D−). “Blue” states also have lower homicide rates than “Red” states (4.5 vs 5.9), and this is statistically significant (p = 0.012). There is a weak (R² = 6%) correlation between gun laws and homicide rates, but this relationship is not statistically significant.

Whatever it is that makes you less likely to be murdered in some states than others, it does not primarily seem to be the gun laws. Poverty may be one of the relevant factors, however – median household income explains 22% of the variance in homicide rates, and when this is taken into account, any effects due to gun laws or election results disappear. “Red” states are, on the whole, simply poorer (and, conversely, poor states are more likely to vote Republican and have weak gun laws). Other demographic factors, such as the number of people with college degrees, also seem to have explanatory value as far as the murder rate is concerned. However, the phenomenon of murder does not seem to be understood as well as it could be.

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Troubles at Evergreen

The Evergreen State College (photo: “kelp”)

I’m sure that everyone is familiar with the protests at The Evergreen State College in Washington State, which were triggered when biology professor Bret Weinstein objected to the 2017 version of a college event called “Day of Absence.” This event was described by organisers with the words “We are having people of color stay on campus and we are encouraging white staff, faculty, and students to go off campus in order to make the space at Evergreen more centered around people of color.” Weinstein, who is Jewish, objected to this in an internal email (using what seems to me very polite language), noting that “On a college campus, one’s right to speak – or to be – must never be based on skin color.” No doubt he saw some disturbing historical precedents.

Protests snowballed, however, taking a rather anti-White, anti-Jewish, anti-Asian, and anti-Science turn. One of the activists suggested that “Hopefully, long-term we can just weed out people like Bret.” It’s not clear to me what “people like Bret” means. I do note, however, that protestors vandalised both the college’s natural history museum and its scientific computing facilities, so I suspect that not only Weinstein, but scientists in general, were a target. That seems an unfortunate situation in an educational institution (even one that accepts 98.9% of applicants). No doubt there’s more here than meets the eye.

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Gender and glaciers?

There has been some controversy about the 2016 NSF-funded paper “Glaciers, gender, and science: A feminist glaciology framework for global environmental change research” (see here for a detailed analysis). The paper refers, inter alia, to the Forbes/Tyndall debate of the century before last (although I believe it is misinterpreting that saga). But, interesting as that episode was in the history of science, it has little to say about the epistemology of modern glaciology. In the 1800s, observing glaciers required extensive (perhaps even “heroic”) mountain climbing. Today, remote sensing methods and computer models are also important, and we understand glaciers much better than either Forbes or Tyndall did.

I don’t think that the gender studies lens adds anything to our understanding of glaciers. And I suspect that Elisabeth Isaksson, Moira Dunbar, Helen Fricker, Julie Palais, Kumiko Goto-Azuma, or Jemma Wadham would not think so either. Nor are race relations particularly important in studying ice. And as to “alternative ways of knowing,” I would prefer to stick with the scientific method – it’s worked very well so far (didn’t we just have a march against “alternative facts”?). Indeed, to subordinate science to the modern politicised humanities would be to abandon the concept of scientific truth, and to make it impossible to gain widespread agreement on the crises currently facing humanity.

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Marching for Science #7

Interesting summary of the Science March from STAT:

• Yes, it was a partisan anti-Trump event – “critics of the march who worried that it could turn scientists into an interest group to be isolated and ignored will likely feel their concerns validated after the event.”
• It was mostly white – “There were [speakers] who were immigrants, trans, gay, Native American, black, Latino, young, and old. … But that audience itself was largely white.”
• Industry science wasn’t there – “companies that are now marketing their ‘bold’ work in scientific discovery and developing new treatments largely lacked an official presence at the marches.”
• People had fun – “lots of kids, dogs, and people dressed as dinosaurs. … and plenty of off-rhythm dancing to funk bands.”
• What comes next is uncertain – “Will the march make a difference? Or will it end up as a historical footnote?”

March for Science, Washington, DC (photo: Becker1999)

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