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.”

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.


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.


Origins of the alphabet

This chart shows the origins of the Phoenician, Hebrew, Greek, and Latin alphabets. The Phoenician alphabet is adapted from Egyptian hieroglyphs, but the exact pictorial origins are rather uncertain. The specific Phoenician alphabet used is from here. The chart was produced using R.


WSC: final Cruiser results

Based on the official results, the chart below (click to zoom) shows the final scores for the WSC Cruiser class. Each team has three coloured bars: first the number of person-kilometres, which should be large (black icons show occupied seats and white icons empty seats), then the energy usage, which should be small (number of charges, which is 6 in each case, times battery capacity), and finally the overall efficiency score, which should be large again (it is the ratio of those first two numbers). The rule for the efficiency score bar is: first bar divided by second bar, then scale so that the largest result is 80%. The scaled practicality scores out of 20 (grey bars) are then added. Eindhoven is the clear winner, with Bochum second.

The chart below (click to zoom) shows the raw practicality scores for all Cruisers (finishing, non-finishing, and non-starting).


WSC: Challenger class charts

Based on data from the WSC web site, the final race chart above (click to zoom) shows Challenger-class timings (for the cars that did not trailer). It is drawn with reference to a baseline speed of 83.89 km/h. This is the speed that would complete the race (to “end of timing”) in 4 days and 5 hours. The left vertical axis shows how far behind the baseline cars are driving. Straight lines represent cars driving at a consistent speed. The right vertical axis shows arrival time at “end of timing” in Darwin time (Adelaide time is an hour later). The twists and turns of the lines here reveal many of the dramatic events of the race, such as the spate of bad weather. The chart below shows average speeds.


WSC: Challenger and Cruiser charts

Based on data from the WSC web site, the chart above (click to zoom) shows Challenger-class timings. It is drawn with reference to a baseline speed of 83.89 km/h. This is the speed that would complete the race (to “end of timing”) in 4 days and 5 hours (that’s a substantially slower baseline than I used in 2015). The left vertical axis shows how far behind the baseline cars are driving. Straight lines represent cars driving at a consistent speed. The right vertical axis shows arrival time at “end of timing” in Darwin time (Adelaide time is an hour later).

Open coloured circles show simplistically extrapolated arrival times. For example, I would expect Nuon to arrive a little after 2 pm Darwin time (3 pm Adelaide time) tomorrow. The rest of the top five should arrive later in the afternoon, or early Friday morning. All the other Challengers are potentially in trouble, but it’s impossible to say for certain.

The grey stripe on the right (11:00 to 2:00 on Friday) shows the permitted Cruiser class arrival window. Eindhoven (40) and Bochum (11) are on track to arrive inside this window. Arrow (30) and HK IVE (35) will do so if they speed up a little. Minnesota (94) and Apollo (95), travelling at 60 km/h and 58 km/h respectively, are in serious trouble, timewise.

The chart above (click to zoom) shows Cruiser scores as at Kulgera (which lets us compare apples with apples). Each team has three coloured bars: first the number of person-kilometres, which should be large (black icons show occupied seats and white icons empty seats), then the energy usage, which should be small (number of charges, which is 4, times battery capacity), and finally the overall score, which should be large again (it is the ratio of those first two numbers). The black number inside the final bar shows the ranking. All bars are scaled to a percentage of the maximum, because the exact numbers do not matter – only the relative relationships. The rule for the final score bar is: first bar divided by second bar, then scale so that the largest result is 80%. The practicality score (out of 20) will be added to that final result.