Donald Trump's overturn of the DACA program has been unpopular, and reaction to the Cotton-Perdue plan to change immigration law has been lukewarm. The lack of enthusiasm is not surprising: surveys show strong support for allowing people who were brought here as children (or even adults who have been here for a while) to stay, and a fairly even division of opinion on whether the number of legal immigrants should be reduced . Immigration was Trump's signature issue--did it actually help him? And if so, how?
I think the answer can be found in a survey sponsored by CNBC and conducted in late October 2016. It asked "If Donald Trump/Hillary Clinton is elected president, do you think the number of illegal immigrants who come to the United States will increase, stay about the same, or decrease?" The results:
Clinton Trump
Increase 42% 6%
Same 45% 31%
Decrease 10% 61%
In 2009, a CNN/ORC poll asked "Would you like to see the number of illegal immigrants currently in this country increased, decreased, or remain the same?" Only 3% wanted to see it increased, and 73% wanted a decrease. So Trump had a big advantage on this issue. By comparison, here is what people expected on some other things that Trump had talked about.
"If ... do you think your federal income taxes would increase, stay about the same, or decrease? "
Clinton Trump
Increase 43% 29%
Same 42% 42%
Decrease 6% 19%
"If ... do you think that our trade agreements with other countries will become more favorable to US interests, stay about the same, or become less favorable to US interests?"
Clinton Trump
More favorable 19% 32%
Same 45% 18%
Less favorable 28% 41%
There was also a question on "which candidate for president would you say has the better policies and approaches to ...Increase your wages," and 46% said Clinton, against 32% for Trump.
It seems that most people thought that Trump would vigorously enforce existing immigration law and Clinton would not. The Republican platform talked a lot about the need to enforce the law--"our highest priority, therefore, must be to secure our borders and all ports of entry and to enforce our immigration laws"--and said nothing about changing them. Trump frequently talked about how we had "open borders" and "people pouring across the border." Clinton and the Democrats did little to counter this picture. The Democratic platform spoke of "our broken immigration system" and talked about the need for "comprehensive immigration reform," but their only comment on enforcement was that it "must be humane and consistent with our values." This raises a question of why they didn't point to the substantial rise in deportations under the Obama administration. I will take that up in a future post.
[Data from the Roper Center for Public Opinion Research]
Saturday, September 30, 2017
Monday, September 25, 2017
The owl of Minerva, part 3
In May I had a post about factors associated with support for Donald Trump in the presidential election. This post elaborates about one of those factors, income. I used American National Election Studies data to do a series of (binary logistic) regressions on income controlling for various factors. Here are the estimated effects of income, with a positive sign meaning that higher income goes with a greater chance of voting for Trump:
Controls estimate se
1. none .007 .005
2. black, white,
Hispanic, other -.014 .006
3. plus gender -.016 .006
4. plus education .000 .006
5. plus married -.013 .007
So conclusions about the effect of income depend on what you control for. If you just compare people with higher incomes to people with lower incomes, it seems those with higher incomes were more likely to vote for Trump. But if you compare people of the same ethnicity, gender, education, and marital status, it seems those with higher incomes were less likely to vote for Trump. I think that the second comparison is more meaningful, because we know that ethnicity, education, gender, and marital status made a difference in voting. However, income doesn't make much difference either way, and is not statistically significant in 1, 4, and 5 (which is why I just say "it seems"). The income variable had 28 categories, and an estimate of -.013 means that going from an income of 25-27,000 (category 8) to 100-109,000 (category 23) would change the probability of supporting Trump vs. Clinton from .5 to .452.
By comparison, here are the estimates for the other control variables:
White 0.73
Black -2.32
Hispanic -.90
Female -.19
Education -.15
Married .63
Education had 16 categories, and the impact of going from a high school graduate with no college (9) and a college graduate (13) was 4*.15=0.6, which is bigger than the impact of going from the lowest to highest income categories (28*.013)=.36.
The basic conclusion is that income was not an important factor in the choice between Trump and Clinton; education was. This is not surprising, given what is known about the relationship between education and political opinions. What is surprising for me is that marital status was also an important factor--the difference between married and unmarried people was about the same as the difference between college graduates and people with just a high school diploma. I knew that marital status was a factor in Democratic vs. Republican support in recent elections, but thought that it was on the same order as gender.
PS: Data from exit polls shows some increase in support for Trump as income increases. The difference between the ANES and exit poll data is statistically significant. My guess is that the ANES estimates are more accurate, partly because the response rate is probably higher, and partly because the exit poll sample is not designed to be representative with respect to anything except which candidate people voted for. The practical reason I use ANES data is that the individual-level data for the exit polls hasn't been released yet. But it's safe to say that controlling for the factors discussed here would push the exit poll estimates towards zero.
Controls estimate se
1. none .007 .005
2. black, white,
Hispanic, other -.014 .006
3. plus gender -.016 .006
4. plus education .000 .006
5. plus married -.013 .007
So conclusions about the effect of income depend on what you control for. If you just compare people with higher incomes to people with lower incomes, it seems those with higher incomes were more likely to vote for Trump. But if you compare people of the same ethnicity, gender, education, and marital status, it seems those with higher incomes were less likely to vote for Trump. I think that the second comparison is more meaningful, because we know that ethnicity, education, gender, and marital status made a difference in voting. However, income doesn't make much difference either way, and is not statistically significant in 1, 4, and 5 (which is why I just say "it seems"). The income variable had 28 categories, and an estimate of -.013 means that going from an income of 25-27,000 (category 8) to 100-109,000 (category 23) would change the probability of supporting Trump vs. Clinton from .5 to .452.
By comparison, here are the estimates for the other control variables:
White 0.73
Black -2.32
Hispanic -.90
Female -.19
Education -.15
Married .63
Education had 16 categories, and the impact of going from a high school graduate with no college (9) and a college graduate (13) was 4*.15=0.6, which is bigger than the impact of going from the lowest to highest income categories (28*.013)=.36.
The basic conclusion is that income was not an important factor in the choice between Trump and Clinton; education was. This is not surprising, given what is known about the relationship between education and political opinions. What is surprising for me is that marital status was also an important factor--the difference between married and unmarried people was about the same as the difference between college graduates and people with just a high school diploma. I knew that marital status was a factor in Democratic vs. Republican support in recent elections, but thought that it was on the same order as gender.
PS: Data from exit polls shows some increase in support for Trump as income increases. The difference between the ANES and exit poll data is statistically significant. My guess is that the ANES estimates are more accurate, partly because the response rate is probably higher, and partly because the exit poll sample is not designed to be representative with respect to anything except which candidate people voted for. The practical reason I use ANES data is that the individual-level data for the exit polls hasn't been released yet. But it's safe to say that controlling for the factors discussed here would push the exit poll estimates towards zero.
Tuesday, September 19, 2017
They did it their way
Since my last post was long and complicated, I thought I should follow with something short and simple. In 1987, the Roper Organization asked "Thinking about the way your own life has turned out so far, would you say it has been primarily a matter of luck or fate, or has it been more a matter of factors which are within your control?" The same question was asked in CBS News polls in 1996 and 2016. The results
Luck Your Control Both DK
1986 22% 66% 9% 3%
1996 18% 72% 6% 4%
2016 27% 60% 9% 4%
The differences in the relative frequencies of luck and own control are statistically significant. It seems possible that opinions on this are affected by economic conditions--when people experience bad things like unemployment or reduced income, they are likely to say it's luck. However, as I recall economic conditions in 1986 and 1996 were roughly like they were in 2016--pretty good but not outstanding.
[Data from the Roper Center for Public Opinion Research]
Luck Your Control Both DK
1986 22% 66% 9% 3%
1996 18% 72% 6% 4%
2016 27% 60% 9% 4%
The differences in the relative frequencies of luck and own control are statistically significant. It seems possible that opinions on this are affected by economic conditions--when people experience bad things like unemployment or reduced income, they are likely to say it's luck. However, as I recall economic conditions in 1986 and 1996 were roughly like they were in 2016--pretty good but not outstanding.
[Data from the Roper Center for Public Opinion Research]
Sunday, September 17, 2017
More old news
About six months ago, I saw several stories saying that "Having just one black teacher can keep black kids in school," to quote NPR's summary. They all noted the magnitude of the effect: almost 40% reduction in dropout rates for low-income black boys. I located the paper on which the stories were based and thought about posting on it, but it was a long paper by the time I got around to reading it, the attention seemed to have passed. However, last week's NY Times magazine had a list of statistics on education, and one of them was "exposure to at least one black teacher in Grades 3 to 5 reduced the probability of low-income black male students dropping out of school by almost 40%." So that led me back to the paper.
The thing that originally attracted my attention was not the general idea that having a black teacher would help to keep black children in school, which seemed plausible, but that it could reduce dropouts by 40% for any group. There is a lot of data on basic educational outcomes like finishing school, and by the standards of social science it's high quality data. Moreover, there are a lot of people who have studied the issue, so it seems that any simple and straightforward way to dramatically reduce dropout rates would have been discovered long ago.
The paper reports that the estimated effect on dropout rates is -.04 for all black students, -.06 for persistently low-income black students, and -.12 for persistently low income black male students. Since about half of students are boys, that suggests that the estimated effect on persistently low income black female students would be about zero, and indeed they report an estimate of 0.00 for that group. So the issue was treating only the big estimate as worthy of interest. If you believe that there are differences in the effects on boys and girls (and the difference appears to be statistically significant), both of the estimates are equally important; if you don't, you should just report the estimate for boys and girls combined. The differences between persistently low income students and other students don't appear to be statistically significant (it's hard to tell from the tables), so maybe you should just report the estimate for all students.
There's also a more complex issue which relates to the way that they got the estimate. The simple approach would be to do a regression with dropping out as the dependent variable, and having a black teacher plus some other variables as independent variables. But the authors say that those estimates "are likely biased by unobserved student characteristics that jointly predict classroom assignments and long-run outcomes, even after conditioning on the basic socio-demographic controls in X and school FE (Rothstein 2010). For example, students with lower achievement (Clotfelter, Ladd & Vigdor, 2006) and greater exposure to school discipline (Lindsay & Hart, 2017) are more likely to be matched to black teachers, and these factors likely affect long-run outcomes as well." That is, black teachers tend to be given the kind of students who are at higher risk of dropping out. The authors had an idea on how to eliminate this potential bias. They had multiple students from each school, which means that they could include a dummy variable for each school. That's a reasonable thing to do, since it's generally agreed that some schools are more effective than others. They also had five different classes of students: those who started third grade in 2000, 2001, 2002, 2003, and 2004. Because of new hires, departures, and leaves, the percent of the teaching staff that was black could change from year to year. Those personnel changes would depend on idiosyncratic individual factors--getting pregnant, reaching retirement age, having a spouse get a job offer in another state--so they would be random from the point of view of the students. So you can use within-school variation in the racial composition of the teaching staff over time as a substitute ("instrument") for the original variable (having a black teacher or not) and get unbiased estimates.
This approach strikes me as clever but not very convincing. Teachers' decisions to stay or go will depend partly on how rewarding it is to work in a school. That could depend on student performance (teachers like it when their students do well) or on things that might affect student performance, like discipline problems, or how well teachers get along with the administration. Things get more complicated because what matters is differential effects on black and white teachers, but I can think of possibilities here too: for example, black teachers may be particularly interested in how the black students are doing. I think I might trust the simple results more than the results from their method--at any rate, I'd like to see them, but they aren't reported in the paper.
This isn't a straightforward mistake, but the sort of difference of judgment that often comes up with research, and the authors could probably say more in defense of their approach. But I will stick with my original feeling that a 40% reduction in dropout rates for anyone is too big to be believed.
The thing that originally attracted my attention was not the general idea that having a black teacher would help to keep black children in school, which seemed plausible, but that it could reduce dropouts by 40% for any group. There is a lot of data on basic educational outcomes like finishing school, and by the standards of social science it's high quality data. Moreover, there are a lot of people who have studied the issue, so it seems that any simple and straightforward way to dramatically reduce dropout rates would have been discovered long ago.
The paper reports that the estimated effect on dropout rates is -.04 for all black students, -.06 for persistently low-income black students, and -.12 for persistently low income black male students. Since about half of students are boys, that suggests that the estimated effect on persistently low income black female students would be about zero, and indeed they report an estimate of 0.00 for that group. So the issue was treating only the big estimate as worthy of interest. If you believe that there are differences in the effects on boys and girls (and the difference appears to be statistically significant), both of the estimates are equally important; if you don't, you should just report the estimate for boys and girls combined. The differences between persistently low income students and other students don't appear to be statistically significant (it's hard to tell from the tables), so maybe you should just report the estimate for all students.
There's also a more complex issue which relates to the way that they got the estimate. The simple approach would be to do a regression with dropping out as the dependent variable, and having a black teacher plus some other variables as independent variables. But the authors say that those estimates "are likely biased by unobserved student characteristics that jointly predict classroom assignments and long-run outcomes, even after conditioning on the basic socio-demographic controls in X and school FE (Rothstein 2010). For example, students with lower achievement (Clotfelter, Ladd & Vigdor, 2006) and greater exposure to school discipline (Lindsay & Hart, 2017) are more likely to be matched to black teachers, and these factors likely affect long-run outcomes as well." That is, black teachers tend to be given the kind of students who are at higher risk of dropping out. The authors had an idea on how to eliminate this potential bias. They had multiple students from each school, which means that they could include a dummy variable for each school. That's a reasonable thing to do, since it's generally agreed that some schools are more effective than others. They also had five different classes of students: those who started third grade in 2000, 2001, 2002, 2003, and 2004. Because of new hires, departures, and leaves, the percent of the teaching staff that was black could change from year to year. Those personnel changes would depend on idiosyncratic individual factors--getting pregnant, reaching retirement age, having a spouse get a job offer in another state--so they would be random from the point of view of the students. So you can use within-school variation in the racial composition of the teaching staff over time as a substitute ("instrument") for the original variable (having a black teacher or not) and get unbiased estimates.
This approach strikes me as clever but not very convincing. Teachers' decisions to stay or go will depend partly on how rewarding it is to work in a school. That could depend on student performance (teachers like it when their students do well) or on things that might affect student performance, like discipline problems, or how well teachers get along with the administration. Things get more complicated because what matters is differential effects on black and white teachers, but I can think of possibilities here too: for example, black teachers may be particularly interested in how the black students are doing. I think I might trust the simple results more than the results from their method--at any rate, I'd like to see them, but they aren't reported in the paper.
This isn't a straightforward mistake, but the sort of difference of judgment that often comes up with research, and the authors could probably say more in defense of their approach. But I will stick with my original feeling that a 40% reduction in dropout rates for anyone is too big to be believed.
Saturday, September 9, 2017
Alternate history
This came up several weeks ago, but I hadn't gotten around to posting on it. On July 26, Elizabeth Hinton (a professor of History and African-American Studies at Harvard) reviewed three books on race and policing in the New York Times. She said that one of the books (Chokehold, by Paul Butler), "demonstrates that when citizenship rights are extended to African-Americans, policy makers and officials at all levels of government historically used law and incarceration as proxy to exert social control in black communities. Black Codes, convict leasing and Jim Crow segregation followed Emancipation; overpolicing and mass incarceration followed the civil rights movement." This reminded me of the figures on crime and prison that I showed in a previous post. I concentrated on crime rates there, so here is more detail about imprisonment. First, the figure for 1929-86, which was the period covered in the data source (it kept going up after 1986):
The next one focuses on the period during and after the civil rights movement:
The rate of imprisonment didn't start rising until 1973, when the movement had either faded away or become mainstream (that is, not really a "movement"). During the period of peak activity of the civil rights movement, the rate of people in prison declined or stayed about the same, although crime was increasing. In a literal sense, the rise in imprisonment did follow the civil rights movement, but the suggestion of cause and effect is not very credible.
The next one focuses on the period during and after the civil rights movement:
The rate of imprisonment didn't start rising until 1973, when the movement had either faded away or become mainstream (that is, not really a "movement"). During the period of peak activity of the civil rights movement, the rate of people in prison declined or stayed about the same, although crime was increasing. In a literal sense, the rise in imprisonment did follow the civil rights movement, but the suggestion of cause and effect is not very credible.
Saturday, September 2, 2017
Respect
The Pew survey I mentioned in my previous post had a series of questions about "how much respect do you think Donald Trump has" for various groups "a great deal, a fair amount, some, or none at all." Then the same questions were asked about how much respect Hillary Clinton had for the groups. The averages, ranked from greatest to least average respect for the group:
Trump Clinton
White people 3.23 3.03
Men 3.21 2.77
Veterans 2.84 2.73
Women 2.19 3.16
Blue collar workers 2.64 2.66
Black people 2.31 2.84
Evangelical Christians 2.66 2.42
Hispanic people 2.16 2.85
Immigrants 2.00 2.93
Muslims 1.86 2.93
People who support [opponent] 1.91 1.91
The standard errors are about .03 or .04. The ratings aren't surprising--Trump is seen as having substantially less respect for women, black and Hispanic people, immigrants, and Muslims, and somewhat more respect for white people, men, veterans, and evangelical Christians. However, it's noteworthy that Trump and Clinton are rated almost exactly the same in respect for blue collar workers--this is one of many pieces of evidence that contradicts the popular story that working class voters turned to Trump because they thought that liberals were condescending to them. It's also notable that Clinton was seen as having pretty high respect across the board--her perceived respect for Evangelicals, which was lower than her perceived respect for any group except Trump supporters, was higher than Trump's perceived respect for six of the groups.
What difference did these perceptions make? I regressed intended vote on each candidate's perceived respect for the groups (one half of the sample was asked about women, men, whites, blacks, Hispanics, and veterans; the other about Muslims, evangelicals, immigrants, blue collar workers, and people who supported the other candidate). The logistic regression coefficients, with positive values meaning that more perceived respect for the group goes with more support for the candidate (standard errors are typically about .2 or .3, and standard errors of the differences about .3):
Trump Clinton
White people -0.31 -0.03
Men 0.12 -0.09
Veterans 0.66 1.14
Women 1.13 0.32
Blue collar workers 1.10 1.19
Black people 1.17 1.06
Evangelical Christians 0.34 0.91
Hispanic people 0.74 0.62
Immigrants 0.57 0.04
Muslims 1.53 0.72
People who support [opponent] -0.13 0.77
I don't think that perceptions of respect are necessarily causes of the way that people vote: to some extent, probably a large extent, people are rationalizing the way they voted. But the way that people rationalize their actions is still interesting.
A large coefficient could mean that a group is held in high esteem (that people think it should be respected) or regarded as important in some sense. But from that point of view, the coefficients for white people and men are puzzling. Another factor could be whether respect from the candidate in question could be taken for granted. For example, there wasn't much doubt that Hillary Clinton respected women, and it didn't make much difference in support for her; there was a lot of doubt about whether Trump did, and it made a lot of difference in support for him. So the fact that perceived respect for men and white people didn't matter could be because most people thought they'd be all right regardless of which candidate won (this contradicts another popular story, about how Trump supporters were motivated by perceived threats against whiteness or masculinity).
The combination of these principles seems to make sense of the coefficients, with one exception: the difference in the effect of Trump and Clinton's perceived respect for supporters of their opponent. Clinton gained from being seen as respecting Trump voters; Trump didn't gain from being seen as respecting Clinton voters. This pattern suggests there's a bit of truth in the "liberal condescension" story--that on the average, people cared more about whether Clinton respected them than whether Trump did.
[Data from the Roper Center for Public Opinion Research]
Trump Clinton
White people 3.23 3.03
Men 3.21 2.77
Veterans 2.84 2.73
Women 2.19 3.16
Blue collar workers 2.64 2.66
Black people 2.31 2.84
Evangelical Christians 2.66 2.42
Hispanic people 2.16 2.85
Immigrants 2.00 2.93
Muslims 1.86 2.93
People who support [opponent] 1.91 1.91
The standard errors are about .03 or .04. The ratings aren't surprising--Trump is seen as having substantially less respect for women, black and Hispanic people, immigrants, and Muslims, and somewhat more respect for white people, men, veterans, and evangelical Christians. However, it's noteworthy that Trump and Clinton are rated almost exactly the same in respect for blue collar workers--this is one of many pieces of evidence that contradicts the popular story that working class voters turned to Trump because they thought that liberals were condescending to them. It's also notable that Clinton was seen as having pretty high respect across the board--her perceived respect for Evangelicals, which was lower than her perceived respect for any group except Trump supporters, was higher than Trump's perceived respect for six of the groups.
What difference did these perceptions make? I regressed intended vote on each candidate's perceived respect for the groups (one half of the sample was asked about women, men, whites, blacks, Hispanics, and veterans; the other about Muslims, evangelicals, immigrants, blue collar workers, and people who supported the other candidate). The logistic regression coefficients, with positive values meaning that more perceived respect for the group goes with more support for the candidate (standard errors are typically about .2 or .3, and standard errors of the differences about .3):
Trump Clinton
White people -0.31 -0.03
Men 0.12 -0.09
Veterans 0.66 1.14
Women 1.13 0.32
Blue collar workers 1.10 1.19
Black people 1.17 1.06
Evangelical Christians 0.34 0.91
Hispanic people 0.74 0.62
Immigrants 0.57 0.04
Muslims 1.53 0.72
People who support [opponent] -0.13 0.77
I don't think that perceptions of respect are necessarily causes of the way that people vote: to some extent, probably a large extent, people are rationalizing the way they voted. But the way that people rationalize their actions is still interesting.
A large coefficient could mean that a group is held in high esteem (that people think it should be respected) or regarded as important in some sense. But from that point of view, the coefficients for white people and men are puzzling. Another factor could be whether respect from the candidate in question could be taken for granted. For example, there wasn't much doubt that Hillary Clinton respected women, and it didn't make much difference in support for her; there was a lot of doubt about whether Trump did, and it made a lot of difference in support for him. So the fact that perceived respect for men and white people didn't matter could be because most people thought they'd be all right regardless of which candidate won (this contradicts another popular story, about how Trump supporters were motivated by perceived threats against whiteness or masculinity).
The combination of these principles seems to make sense of the coefficients, with one exception: the difference in the effect of Trump and Clinton's perceived respect for supporters of their opponent. Clinton gained from being seen as respecting Trump voters; Trump didn't gain from being seen as respecting Clinton voters. This pattern suggests there's a bit of truth in the "liberal condescension" story--that on the average, people cared more about whether Clinton respected them than whether Trump did.
[Data from the Roper Center for Public Opinion Research]
Saturday, August 26, 2017
What went wrong?
Two years ago, not many people thought that Donald Trump would get the Republican nomination. In late July, a McClatchy/Marist College polls asked Republicans "Do you think Donald Trump is a serious presidential candidate, or a distraction from the presidential primary process?" 44% said a serious candidate, and 51% said that he was a distraction. On August 13, 2015, I had a post that offered probabilities of getting the nomination: 40% for Jeb Bush, 20% for Marco Rubio, 15% for John Kasich, 12.5% for other declared candidates, and 12.5% for someone else. If I'd gone farther, I think I would have put Trump at or near the top of the others, with maybe 5%. I give myself some credit for not being impressed with the field--this was when people were still talking about the "deep bench." But no matter how you slice it, I thought Trump had little chance of winning. Sometimes unusual things happen--someone who had little chance of winning could make it because of a lot of unpredictable things. But looking at the campaign, I don't think that Trump got all that many lucky breaks, so I'm not going to try to defend my prediction that way. I think that a large part of the reason I didn't give him much chance was that I thought he wouldn't stay in very long--that the first time he encountered adversity he would quit, complaining that the rules were rigged against him. But why was I confident of that? I was certainly familiar with Donald Trump--I'd been hearing his name since the early 1980s. But I didn't really know much about him--I hadn't read any of his books, or biographies of him, or watched his media appearances. So my mistake was in taking general familiarity for real knowledge about him--I should have started by thinking about what would happen if he was serious about the race, and if I had I would have rated him higher.
The same post offered some ideas about the sources of his support at the time. I said that a large part of his appeal was negative--people distrusted politics and politicians, and he was an outsider. An alternative is that he made a positive connection to a large number of voters. Reporters who attended his rallies were often struck by the energy, and many people talked about an "enthusiasm gap" in favor of Trump during the general election campaign. A Pew survey in October 20-5, 2016 asked separate questions about whether Trump and Clinton would be: great, good, average, poor, or terrible. The results:
Trump Clinton
Great 9% 10%
Good 17% 28%
Average 15% 18%
Poor 12% 12%
Terrible 47% 31%
That's not much enthusiasm for Trump, but it includes both supporters and opponents. If we limit it to people who said they would support the candidate:
Trump Clinton
Great 21% 19%
Good 39% 54%
Average 30% 25 %
Poor 7% 2%
Terrible 2% 0%
At least in this respect, Clinton supporters were move favorable about their candidate than Trump supporters were about theirs. In fact, 10% of the people who said they would vote for Trump thought that he would be a poor or terrible president.
On a possibly related note, of the people who had the same expectations of Trump and Clinton (e. g., said both would be average), 75% said they would vote for Trump. I haven't investigated, but one possibility is that they were Republicans who figured that even if he wouldn't be especially good, Republicans in Congress would get their way if he were president. So I think my original analysis was correct on that point--Trump got the Republican nomination more because of his opponents' weakness than because of his strength. After he got the nomination, party loyalty kept him close enough to have a chance.
[Data from the Roper Center for Public Opinion Research]
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