Tuesday, September 20, 2016

More on police shootings

Over the summer, a paper by Roland Fryer got a lot of attention.  He summarized his findings:  "there are racial differences--sometimes quite large--in police use of force, even after accounting for a large set of controls ... Yet, on the most extreme use of force--offi cer-involved shootings--we are unable to detect any racial di fferences...."  You could restate this by saying that there is more anti-black bias in non-lethal force than in lethal force, and it's not clear if there is any bias (in either direction) in the use of lethal force.  The difference between lethal and non-lethal force was surprising to me--I figured that if there was bias, it would be more pronounced for the more extreme use of force.  I thought his paper was convincing on that point, partly because of the evidence he presented and partly because of a simple comparison to the data on fatal shootings by police that I've written about before.   Blacks comprise 27% of those fatally shot by the police.  This is considerably higher than their share of the total population, but not relative to other forms of negative involvement with the criminal justice system.  For example, blacks make up 39% of those arrested for violent crime.

The difficulty is in figuring out whether 27% is more, less, or about the same as what it would be if police shootings took place without regard to race--that is, if a white person and a black person in the same situation faced the same risk of being shot--which is why Fryer said "we are unable to detect any" racial differences rather than "there are no" or even "there appear to be no."

Although the data Fryer used has a lot more detail, the data I used also has some advantages:  it covers the whole nation and has more cases.  It includes a variable for whether the person who was killed was attacking a police officer, "other," or "unknown" and one for what kind of weapon, if any, they had.  I combined those into a new variable with three values:  people who were not attacking ("other" or "unknown") and unarmed (or "undetermined"), people who were armed but not attacking, and people who were attacking.  I'll call them low, medium, and high levels of apparent threat.  The breakdown of people killed by apparent threat:

            Black   Hispanic   White
low          35%      22%       39%
medium       26%      20%       48%
high         26%      16%       55%

There are statistically significant racial differences--the share of blacks and Hispanics is highest for the lowest threat level. You could also put it in terms of the chance that a person will be killed by the police when they are unarmed and not attacking:  blacks have about six times the risk of non-Hispanic whites, and Hispanics have about three times the risk.

The limitation of this comparison (and the ones Fryer did) is that we don't know the number of people who were in a comparable situation but were not fatally shot.  So it's possible that blacks and Hispanics were just less likely to be in the low-threat relative to the high threat situations.  That doesn't seem likely to me--the low-threat situations can include a wide variety of circumstances (e. g., bystanders who were killed by accident), so it seems the racial distribution of people in them in them should be closer to that in the general population.  It's also possible that police are unbiased in low-threat situations but less likely to kill blacks and Hispanics in high-threat situations.  However, the most plausible interpretation seems to be that there is some anti-black bias in fatal police shootings.

PS:  There were a total of 1,499 fatal shootings in the 18 months covered by the data: 133 low threat, 418 medium, and 948 high.

Monday, September 12, 2016

Cui bono?

In August 2008, a Gallup/USA Today poll asked "If _____ is elected president, who do you think his policies would benefit the most – the wealthy, the middle class, or the poor, or all about equally?" for John McCain and Barack Obama.  In late June and early July of this year, a survey sponsored by the American Enterprise Institute and Los Angeles times asked the same question about Donald Trump and Hillary Clinton.  The results:

                    McCain    Trump            Obama    Clinton
Wealthy              53%       54%               16%      36%
Middle Class         19%       15%               33%      19%
Poor                  1%        2%               22%      12%
All equally          25%       20%               25%      25%

The distribution of answers is almost the same for Trump as it was for McCain, but the distribution for Clinton is quite a bit different from what it had been for Obama--fewer saying the poor or middle class, and more saying the wealthy.  The 2008 survey also asked about voting intention (the 2016 survey did not).  As you might guess, people who thought a candidate would benefit the middle class or everyone about equally were a lot more likely to support him than those who thought he would benefit the rich.  Things were more complicated with the poor--the few people who thought McCain's policies would benefit the poor were overwhelmingly in favor of him (88%, although it was only eight people); the larger number who thought Obama's policies would benefit the poor were strongly against him (26%).

If you combine the 2008 estimates with the 2012 opinions, perceptions of who benefits from Clinton's policies are costing her about 7 percentage points compared to Obama.  Although I wouldn't take the exact number very seriously, it seems safe to say that she's not getting as much benefit as he was.

Why would Clinton be viewed so much differently than Obama was?  One possibility is that it's a fixed part of her image--maybe people are thinking of the well-compensated speeches she's made to Wall Street firms.  Another possibility is that the contrast with Bernie Sanders made people think of her as more favorable to rich, and that as people start focusing on the contrast with Trump perceptions will change (or maybe already have changed).  The fact that Trump is not seen as much different from McCain is interesting, since claims that he would help the working and middle classes have been a big part of his campaign.

[Data from the Roper Center for Public Opinion Research]

Tuesday, September 6, 2016

Are they blue?

One of the major themes of election reporting in this campaign has involved "blue-collar" or "working class" support for Donald Trump.  But few surveys ask people about their occupations today, so usually journalists treat class as equivalent to education.  For example, a Los Angeles Times story entitled "How do Americans view poverty? Many blue-collar whites, key to Trump, criticize poor people as lazy and content to stay on welfare," said the racial difference in opinions about who had the greatest responsibility for helping the poor "lay almost entirely with blue-collar whites--those without college degrees."  Of course, education is associated with occupation, but how strong is the association?  The Current Population Survey contains information on both education and occupation.  Civilian occupations are classified into 22 groups:  

Management
Business and Financial Operations
Computer and mathematical science
Architecture and engineering
Life, physical, and social science
Community and social service
Legal
Education, training, and library
Arts, design, entertainment, sports, and media
Healthcare practitioner and technical

Healthcare support
Protective service
Food preparation
Building and grounds cleaning and maintenance
Personal care and service

Sales
Office and administrative support

Farming, fishing, and forestry
Construction and extraction
Installation, maintenance, and repair
Production
Transportation and material moving

I divided them into four groups, with divisions indicated by the blank lines.  The last group of occupations corresponds with what people normally call "blue-collar"--they involve making or extracting some tangible product.   The first and third groups would clearly be "white collar" jobs--the difference is that the first generally involves more skill and higher pay than the third.  The second group is hard to classify by the blue collar/white collar distinction--like most white-collar jobs, they produce services rather than goods, but in terms of skills and autonomy, they are closer to blue-collar jobs.  

The occupational distribution of people with different amounts of education (rearranging the order to put the two white collar groups together):

                         White Collar
                Manager/Prof  Other   Service  Blue-Collar               

Not HS graduate         7%    17%        34%       41%
HS only                19%    25%        22%       34%
Some College           29%    31%        21%       19%
College Grad           64%    22%         8%        7%
Master's               84%    10%         3%        3%
Professional/Doctoral  92%     4%         2%        1%

Few blue-collar workers have college degrees, but a lot of people without college degrees are NOT blue-collar workers.   Considering everyone without a college degree, only 29% are blue-collar workers in the narrow or traditional definition.  Another 23% are service workers, so on a broader definition you could say a little over half are blue-collar workers.  

The reason that lacking a college degree is not synonymous with having a blue-collar job is partly that there just aren't that many blue-collar jobs in the United States any more, and partly that even people with only a high school diploma have a decent chance of obtaining a white-collar job (which might involve supervising blue-collar workers).  

Looking at it from one direction, it's strange that given the contemporary interest in class, few surveys bother to ask people what kind of work they do.  Looking at it from another direction it's strange that when given a straightforward measure of education, people call it "class" rather than "education."







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:
V=.63*A
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.