Invent your own research

 On March 30, Nicholas Kristof had wrote   "Survey data indicates that married couples on average report more happiness, build more wealth, live longer and raise more successful children than single parents or cohabiting couples, though there are plenty of exceptions."  The most popular reader comment, with over 2000 likes, said
"I notice that you didn't talk about the research that shows differences in gender in marriage happiness levels.
    Marriage is generally GREAT for men, who report being far happier in marriage than being single.  Much research indicates the reverse is true for women.  Single women report being happier, in general, than married women are." 
Most of the other leading comments were along the same lines.  

Since the 1970s, the General Social Survey has asked whether "taken all together, how would you say things are these days."  41% of married women say that they are very happy, and 8% say they are "not too happy" (the rest chose "pretty happy"); among single (never married) women, 23% say they are very happy and 17% not too happy.  This is just one survey, but it's one that puts a lot of effort into selecting a representative sample and gets a high response rate, so it can be considered to be pretty definitive.  Of course, the difference in happiness between married and never-married women is not necessarily caused by marriage, but it's there.  

But maybe the difference is smaller for women--married women are somewhat happier than single women, but married men are a lot happier?  The figure shows the averages for married and never-married men and women over the years (higher numbers mean happier).  There's a lot of sampling variation, so I also show the smoothed averages.  The pattern seems similar for men and women.

The next figure shows the difference between married and never-married people among men and women:  

Up until about 2000, the difference was a little bigger for men, but since then it's been about equal.  

I looked to see if the gap varied by other characteristics--for example, education, race, political views.  To make a short story even shorter, I found nothing worth mentioning.  

In the course of doing this analysis, I noticed that the GSS had a question on whether you thought that married people were generally happier than unmarried people.  45% agreed (or strongly agreed), 24% disagreed (or strongly disagreed), and 32% chose "neither agree nor disagree."  There were some group differences in average views on this question:  men were more likely to agree than women; whites more likely to agree than blacks or people of other races; conservatives more likely to agree than liberals; married and widowed people more likely to agree than never-married, with divorced people least likely to agree.  Despite what is sometimes said about "elites," there was no discernible difference by education, and people in higher status occupations were more likely to agree.  The question was asked only four times, most recently in 2012, but it seems like the gender difference was growing:  the means for men were 3.6 in 1988, 3.43 in 1994, 3.47 in 2002, and 3.32 in 2012; the means for women were 3.50, 3.26, 3.17, and 2.97 (higher means more agreement)--that is, the gender difference went from .10 to .35.  There were also signs that the gender difference varied among groups--for example, agreement was particularly low among black women (20% agreed and 48% disagreed).

The problem is you?, part 2

 I won't try to give a table of regression coefficients in this post, just summarize the differences between the analysis of the geographical origins of January 6 insurrectionists by Pape, Larson, and Ruby and my re-analysis of their data.  

1.   Control variables:  my main change was to use the logarithm of population rather than population as a predictor variable, for reasons discussed in my previous post.  I also created a variable for people living within driving distance, which I defined as 700 kilometers (which includes Boston, Cincinnati, and Detroit) and an interaction between distance and that variable.  My idea was that (a) if you were in driving distance you could make the trip without spending much money and (b) with driving, the cost in time and money is strongly related to the distance; if you have to fly the relation is weaker (a lot depends on distance to the nearest airport, whether you can get a nonstop flight, and the mysterious pricing decisions of airlines).

2.  Points in common:  the number of insurrectionists increased with the percent of the county that was non-Hispanic white; decline in manufacturing employment didn't make any clear difference; number of insurrectionists was higher in urban areas (although the estimated effect was much smaller in my analysis).

3.  Points of divergence:  a decline in the white population led to more insurrectionists in their analysis but had no effect in mine; the percent who voted for Trump led to fewer insurrectionists in their analysis but more in mine.  They also considered the difference between percent for Trump in 2020 and percent for Romney in 2012--that is, voters who were specifically attracted by Trump.  That also didn't have an effect in their analysis.  I ran a model including both Romney support in 2012 and the difference, and found that they both had similar positive estimates.  I think this is important--it suggests that the insurrectionists were drawn both from new Trump followers and traditional Republicans.  However, if you think of the population at risk of being insurrectionists as Trump voters in a county, it would be log(np)=log(n)+log(p), where n is the number of people and p is the percent for Trump (of course, there are people who weren't eligible to vote and people who were eligible but didn't vote, but suppose they are roughly constant across counties).  That suggests that both log(n) and log(p) should have coefficients of about 1.0.  Log(n) did, but log(p) was about 0.55.  The fact that it's not zero is interesting, but so is the fact that it's less than 1.  My thought is that a lot of people, especially those who aren't very interested in politics, just go along with the local climate.  That is, if you're a Trump voter in Manhattan, you're probably highly committed; if you're a Trump voter in Wyoming, you may just be following the crowd.  So the proportion of Trump voters increases, the fraction who are highly committed may decline.  

Overall, they conclude that participation in the insurrection was largely a response to perceived ethnic threat, and that the sources of "violent populism" are very different from those of "electoral populism."  My conclusion is that the sources are similar--after you control for population and distance, the places where Trump got votes were also the placed where he got supporters on January 6.

The problem is you?, part 1

 The Atlantic recently published a critical review of the new book by Tom Schaller and Paul Waldman, White Rural Rage: the Threat to American Democracy. The review, by Tyler Austin Harper, concluded by saying that they were not just wrong, but had it backwards--the threat is from the cities and suburbs:  

"Schaller and Waldman are right: There are real threats to American democracy, and we should be worried about political violence. But by erroneously pinning the blame on white rural Americans, they’ve distracted the public from the real danger. The threat we must contend with today is not white rural rage, but white urban and suburban rage.

Instead of reckoning with the ugly fact that a threat to our democracy is emerging from right-wing extremists in suburban and urban areas, the authors of White Rural Rage contorted studies and called unambiguously metro areas 'rural' so that they could tell an all-too-familiar story about scary hillbillies. Perhaps this was easier than confronting the truth: that the call is coming from inside the house. It is not primarily the rural poor, but often successful, white metropolitan men who imperil our republic."

The report that Harper links to says:  "the more rural a county, the lower its rate of sending insurrectionists, a finding which is significant with a p-value <.01%."  A  just-published paper by Robert A. Pape, Kyle D. Larson, Keven G. Ruby in PS: Political Science and Politics gives a more detailed analysis.  The results are from a negative binomial regression in which the  dependent variable is the number of people from a county who were charged with crimes related to the January 6 attack on the Capitol.  The number is estimated to be 2.88 times as large in urban than in rural counties, controlling for other factors.  

Of course, the population of the county is one of the other factors.  But a negative binomial regression predicts the logarithm of the dependent variable and their control is population (in 100,000s).   The estimated coefficient for population is .148, meaning that the natural log of the predicted number of insurrectionists goes up by .148 for every 100,000 increase in county population.  If the natural log of the predicted number goes up by .148, the predicted number goes up by about 15%.*  If you're starting from a population of 1,000, an increase of 100,000 means that population goes up by a factor of of about 100; if you're starting from a population of 1,000,000, it's 10%; if you're starting from a population of 5,000,000, it's only 2%.  So the model controlling for population builds in a relationship between county population and the chance that a person will be an insurrectionist:  declining and then increasing.  The figure shows the nature and size of the relationship using their estimate:

The number 1 on the y-axis represents the rate in a county of average size (about 100,000).  In a county with population of 10,000, the rate is about 8.5; in a county with 500,000, it's about .4, and in one of 5,000,000, it's about 80.  The biggest county in the United States (Los Angeles) has a population of about 10,000,000, but I don't extend the x-axis that far because it would make the figure too hard to read.   Of course, there is no reason to expect that there really is a relationship of this form.

A straightforward alternative would be to model the rate--number of insurrectionists (x) divided by county population (n).  But log(x/n)=log(x)-log(n), so you could express that by a regression with log(x) as the dependent variable and log(n) as one of the predictors.  Then a coefficient of 1.0 on log(n) would mean that the rate was the same across different county populations; a coefficient of less than one would mean it was higher in counties with smaller populations and a coefficient of greater than 1.0 would mean it was higher in counties with larger populations.  

What happens if you use log(population) rather than population as a control variable?

                                                                        Population                    Log

% white population decline                            .111***                    .035
                                                                        (.019)                        (.020)

manufacturing employment decline                .011                            -.006
                                                                        (.0054)                        (.006)

extra Trump %                                                    -.039***                .003
                                                                            (.0081)                    (.0082)

% non-Hispanic white                                           .009***                .014***
                                                                            (.0033)                    (.003)

Metro county                                                        1.095***             .326*
                                                                           (.1335)                    (.135)

Distance to DC                                                    -.304***            -.210***
                                                                            (.0623)                (.051)

(log) population                                                    .148***            .999***
                                                                             (.0210)                (.056)

  The fit of the model with the logarithm as control is better.  Several of the estimates for the other variables change substantially.  The estimate for metro counties is still statistically significant, but not overwhelmingly so (p=.019), and is much smaller than when using population.  So I don't think that the evidence justifies sweeping condemnation of urban and suburban men.

I have experimented with other specifications of the model, but this is enough for one post.  

*My figures are from my analyses using their replication data file, which are slightly different from the numbers implied by their tables.  
  

Did he do it?

 

After O. J. Simpson's death last week, I looked for surveys that asked for views about whether he was guilty of the murders of Nicole Brown Simpson and Ronald Goldman.  The figure is based on surveys which offered options of definitely guilty, probably guilty, probably not guilty, and definitely not guilty (counted as +2,+1, -1, and -2).*  All of the figures are positive, meaning that more people thought him guilty than not guilty.  Over the whole period, there was a move towards thinking that he was guilty--the latest surveys found that about 40% thought he was definitely guilty and another 40% thought he was probably guilty.  I thought that the civil case that found he was responsible for the murders might have helped to move opinion in that direction, but if anything it seems to have been followed by a short-term shift away from thinking he was guilty.  One survey taken at the time of the controversy over the release and withdrawal of his book, "If I did it," showed a move towards thinking him guilty.  But basically, it seems to have been a gradual movement over a long period.  

The next figure is the same data, but limited to the period up to the end of the criminal trial.  I don't recall most of the details of the trial, but the way the story is often told now, there were a number of dramatic moments in which the defense succeeded in raising doubts about his guilt.  You don't see much sign of this in the survey results--there seems to have been a small move towards thinking him guilty during the trial, followed by a drop after the verdict.  The absence of ups and downs during the trial surprised me--a lot of people followed it closely, and although many had strong opinions, it wasn't a partisan issue, so I would expect them to be more open to changing their minds.  

During and after the criminal trial, many people noted that there was a large division by race--black people were a lot less likely to think that Simpson was guilty.  This raises the question of whether that was there from the beginning or emerged during the trial, and whether it remained in the later surveys.  I'll look at this issue in a future post.  

*Some asked if the charges were definitely true, probably true, probably not true, or definitely not true.  Immediately after he was found not guilty, some prefaced the question with "whether or not you agree with the jury's decision."  There was no evidence that these variations made a difference.  

The happiness gap

 Conservatives typically report being happier than liberals do, but the size of the gap appears to change over time.    In 2015, I looked at data from the GSS  and found that the "happiness gap" became larger during the GW Bush presidency, but fell in the Obama presidency.  A few days ago, Ross Douthat had a column called "Can the Left be Happy," which said that "the left-right happiness gap is wider than before"--that is, the relative happiness of the left has declined in the last decade or so.   He made a plausible case but didn't offer any systematic data, so I'll take another look.

The GSS asks people to rate their political ideology on a seven point scale, from very liberal to very conservative, which I collapsed into three groups:  extremely liberal or liberal; slightly liberal, moderate, or slightly conservative; conservative or extremely conservative.  The liberal and conservative groups are both about 15-20% of the sample.  

The figure is hard to interpret, partly because of sampling error in individual years and partly because of the big drop among all groups in 2021 and 2022, so here's a figure showing the difference between the averages for liberals and conservatives (positive numbers mean conservatives report being happier than liberals do):

The higher reference line is the average difference.  The gap was larger than average in 2000, 2002, 2004, 2006, and 2008, and then fell in 2010 (liberals were actually happier than conservatives).  It's generally remained smaller than average since then.  

Here's the corresponding figure for the moderate/conservative gap:

A similar story:  the gap became a lot smaller in 2010, and has generally remained below average since then.  

So it's not liberals who have become relatively unhappier in recent years, but conservatives.  Going back to the original figure, there was little or no happiness gap in the 1970s.  Conservatives pulled ahead in the 1980s, and the size of the gap seemed to be gradually increasing until 2008.  Then conservatives became less happy in 2010, and the gap has been smaller since then.  

You could say that the shifts in the 21st century are just another example of increasing political polarization:  liberals are relatively happy when a Democrat is in office, and conservatives are relatively happy when a Republican is.  But I don't think that fits the pattern very well.  Although there were signs of growing polarization under Bush, they were pretty small--you didn't get a big increase until the Obama years.  And although conservatives became relatively happier in 2018, the change was not that big, and the gap remained smaller than it had been under Bush. 

But considering the whole period suggests that there may be a connection to the general political and social climate.  In the 1970s, the tide seemed to be running to the left, but with the tax revolt of the late 1970s and the election of Ronald Reagan in 1980, things seemed to stabilize and maybe even reverse.  Before the 2008 election, conservatives could feel pretty good--Republicans had won five of the last seven presidential elections, and no Democrat had received a majority of the popular vote since Jimmy Carter got 50.1% in 1976.  Republicans had also gained parity in Congress, and the courts had moved to the right.  Then Obama was elected with a lot of popular enthusiasm, a solid Congressional majority, and an economic crisis that provided a rationale for vigorous government action.  Prominent conservatives reacted to the threat by a strategy of scorched-earth opposition--e. g. they denounced Obamacare not just as ineffective and expensive, but as the end of the American way of life.  Since then, conservatives have felt like they are on the defensive, even when Trump was president--fighting against the deep state, the media, Big Tech, etc.  So my idea is that although ratings of happiness are primarily affected by individual factors, there is some spillover from feelings about the general direction of society.

When I was young

 In 1951, the Gallup Poll asked "Comparing your present family circumstances with those when you were a child, would you say you are better off, or worse off, than your parents were then?"  They asked it again in 1991, and other organizations asked it in 1994 (twice) and 2016.  The results:

                    better      worse    same (vol.)
Feb 1951       60%         24%      14%
Dec 1991      78%         12%        9%
Aug 1994      65%        22%      11%
Sep  1994      72%        21%        6%
Dec  2016      72%        20%        7%

The distributions are all about the same except for 1991, when people were more positive.  I can't think of a plausible reason that opinions would change that much between December 1991 and 1994 (economic conditions were similar, but somewhat better in 1994), so I think that difference is at least partly sampling error.  The important point is that opinions weren't more pessimistic in 2016 than in 1950 or 1994.  It's often suggested that Americans always felt like they were making economic progress from generation to generation until recently, and that the loss of that sense has led to a variety of social and political problems, like "deaths of despair," the decline in rates of marriage and childbearing, and the rise of Trump.  But I don't think that's the case--people are discontented with some aspects of society, but most still believe that there's an upward trend in their standard of living.

[Data from the Roper Center for Public Opinion Research]