Wednesday, August 31, 2016

More geography of police shootings

In July, I had a post on state differences in the rate of fatal shootings by police, which differ by a lot--the rate in New Mexico or Wyoming is about ten times the rate in New York and Connecticut.  Peter Moskos, a professor at John Jay College of Criminal Justice, also noted the regional differences (I got the reference from Andrew Gelman's blog) and suggested that they resulted from differences in the training and procedures in different police departments.  That suggests that we should go to the level of individual cities.  I took the 100 largest cities and calculated the expected number of deaths if they were proportional to population (which comes to about 1 per 135,000 in the period covered by the data).  The deviations were statistically significant by any standard you want (chi-square of about 267 with 99 degrees of freedom), so the differences in the rates are not just a matter of chance.

The cities with the highest ratio of actual to predicted deaths:

                Deaths  Predicted
Miami              13       3.2
San Bernardino      5       1.6
St Louis            7       2.3
Orlando             6       2.0
Baton Rouge         5       1.7
Bakersfield         8       2.7
Las Vegas          13       4.6
Reno                5       1.8
Norfolk             5       1.8
St Paul             6       2.2
Albuquerque        10       4.1

For the lowest ratio, it's a tie among eight cities--Hialeah, Irvine, Jersey City, Lexington, Lubbock, Plano, Riverside, and Winston-Salem--which had no fatal shootings.  Those are all in the lower reaches of the top 100 in population, and the expected numbers are about two in each.  With those expected values, a zero can easily come up by chance, so we can't be sure that the actual risk in those cities is actually different from the average.  But the next two lowest ratios are in big cities:  New York, with eight actual deaths and 63.6 expected, and Philadelphia, with three actual and 11.5 expected.  Those differences definitely cannot be attributed to chance.  

There seems to be a lot of geographical clustering--New York and Philadelphia are less than 100 miles apart, and the #41, 44, 45, 46, and 47 cities are all in Texas.  Maybe there is some general cultural similarity in regions that makes a difference, or maybe police departments just tend to model themselves after departments in nearby cities.  But whichever it is, there is something that needs to be explained.

Thursday, August 25, 2016

Down the home stretch

Currently Hillary Clinton leads Donald Trump by about seven percentage points in the polls (48.5% to 41.5%).  That's a good lead, but not an overwhelming one--it's a little smaller than the lead that Barack Obama had over Mitt Romney at this time in 2012.  I looked at the polls going back to 1952, picking the one or two that were closest to August 25.*  The closest race was in 1960, when Kennedy and Nixon were tied at 46%.  The most lopsided was in 1964, when 67% said they were for Johnson and only 26% said they were for Goldwater.  The difference between Democratic and Republican shares in the actual votes (V) could be predicted from the difference in the polls in August (A) by:
For example, in 1952, Eisenhower led Stevenson by 55%-38% in August, so A was -17.  The predicted margin in the actual vote was .63*(-17)=-10.7, which was almost exactly equal to the actual margin (55.2% to 44.3%).  Clinton's +7 leads to a predicted margin of 4.4%.  The standard error is about 4.2.

The biggest residual was in 1980, when Carter and Reagan were tied in August, but Reagan went on to win easily (about 51%-41%).  I think that even the final polls showed a close race, but I also recall than Carter's campaign seemed to be floundering in the last few months.  So probably some of it was survey error but some of it was a real change.  The other two large residuals were in 2008, when the polls were pretty much tied in August, and in 1956, when Eisenhower was ahead by 13 in August and increased the margin to 15 in September.  The explanation for 2008 is obvious--the financial crisis that started in September 2008.  I don't know anything about the details of the 1956 campaign, but my guess is that since it was a rematch of 1952, people made their minds up earlier than usual.

The experience of 1952-2012 suggests that a Trump victory is unlikely but not impossible (maybe 15%).  A Clinton landslide (say a margin of 10 or more) is also unlikely.

The raw data:

     Aug  Final
1952 -17 -11
1956 -13 -15
1960   0   0
1964  41  24
1968  -6  -1
1972 -37 -23
1976   9      2
1980   0 -10
1984 -26 -18
1988  -8  -8
1996  15   9
2000   5   1
2004  -1  -2
2008   0   7
2012   9   4

*I omitted 1992. because Ross Perot had dropped out, but resumed his campaign in September.

[Data from iPOLL, Roper Center for Public Opinion Research]

Thursday, August 18, 2016

Looking in the Mirror

A 2012 Pew Global Attitudes survey asked people about their opinions of various countries.  Their were twelve cases in which they asked people about their opinion of their own country.  The averages, with "very favorable," "somewhat favorable," "somewhat unfavorable," and "very unfavorable" counted as +2,+1,-1, and -2:

*India         1.59
*Pakistan      1.59
*China         1.49
*Russia        1.14
*United States 1.07
Turkey         1.01
Germany        0.84
Britain        0.83
*Greece        0.67
France         0.31
Italy          0.20
Spain         -0.16

An obvious follow-up question would be how the opinion within the country compared to opinions in other countries.  The list of countries that were asked about differed among nations, making it difficult to do a rigorous comparison.  I indicated countries whose people regarded themselves a lot more favorably that people in other countries did with an asterisk.  Turkey could arguably be included in that group.

As far as ratings of one's own country, there seems to be a strong negative relationship to GDP--people in poorer countries have a higher opinion of their own country (the United States is an outlier in this respect).  Of course, GDP isn't necessarily the cause--another possibility is that richer countries tend to have more freedom of the press, and as a result  people become more aware of the problems of their own country.

Monday, August 1, 2016

In the long run, we are still not all free traders

Few people in politics are speaking up for trade agreements lately.  Hillary Clinton and Tim Kaine have switched from support to opposition on the Trans-Pacific Partnership.  Donald Trump has always been opposed to the TPP, and says he would even scrap NAFTA.  Greg Mankiw has a piece in the NY Times proposing that public support for free trade will increase over the long run as average levels of education increase.  His rationale (drawing on research by Edward Mansfield and Diana Mutz), is that more educated people are more internationalist and less ethnocentric, and therefore more likely to support free trade.  I think this is true, and there's also another reason that he doesn't mention:  more educated people are more favorable to markets generally (I've discussed that in several blog posts and this article).

However, Mankiw overlooks an important point, which is that support for trade agreements is not all that strong even among people with high levels of education.  For example, in a 2009 question about whether trade agreements like NAFTA and the policies of the World Trade Organization have been good or bad for the United States,  net favorability (good minus bad) was +24,+9,+5, and +11 among people with no high school diploma, high school diploma, some college, and college graduate respectively.*  Among people with a college degree (or more), 44% said "good thing," 33% "bad thing," and 22% that they didn't know.

Why is there substantial opposition to trade agreements, even among educated people?  I think that it's because many people see economics in moral terms--they regard making tangible things, especially things that are important for life, as more valuable than other activities.  So economists can talk about comparative advantage all they want, but for many people the loss of manufacturing jobs matters more than any gains in services and finance.  It's possible that this is just a historical legacy--people are thinking of the kind of jobs their fathers or grandfathers had as the standard--but my guess is that it goes deeper. 

*That looks like no relationship at all, but if you control for race and ethnicity, there is some association.