Do the rich give to charity?

A couple of months ago, Ken Stern had an article in the Atlantic called "Why the rich don't give to charity."  He said   "In 2011, the wealthiest Americans—those with earnings in the top 20 percent—contributed on average 1.3 percent of their income to charity. By comparison, Americans at the base of the income pyramid—those in the bottom 20 percent—donated 3.2 percent of their income. "  He didn't give any details about the source of these figures, but in a letter to this month's Atlantic, Jonathan Meer (a professor of economics at Texas A&M) made a convincing case that they couldn't be believed:  basically, the pattern followed from the way the sample was selected. 

In 1996, the General Social Survey asked a representative sample of Americans about how much they gave to various charitable causes.   I combined those causes into two groups, religious and non-religious (which included health, education, human services, the arts, and others), and divided the totals by reported income.  On the average, people reported giving about 9% of their income to religious causes and 5% to non-religious.  These figures seem likely to be overestimates.  However, reported giving was very skewed:  the median contribution to both was 0%, while a few people claimed to give over 100% of their income.   I suspect that most of the very high figures were people who did donate generously but inadvertently exaggerated or counted one donation in several categories.  

I then divided people into groups of approximately the lowest 20%, middle 60%, and highest 20% of household income.  The results:

                 Non-Religious Donations            Religious Donations
               Mean    Median  75 percentile   Mean     Median    75 percentile
Lowest 20%     5.4%     0%         1.8%        11.9%      0%      0%
Middle 60%     4.6%     0.6%       4.4%         7.2%      0%      3.6%
Top 20%        9.8%     1.5%       6.2%        15.8%    1.3%      8.9%

The means don't show a clear pattern, but they are strongly affected by the few people who claimed extremely large donations relative to their income.  The medians and 75th percentiles both show a clear tendency for more affluent people to give a larger share of their income.  I also did an analysis using a more sophisticated technique that essentially involves predicting ranks (proportional hazards regression).  According to that, more affluent people definitely gave a larger share of their income to non-religious causes (a t-ratio of 3.7); there was some evidence, but not definitive evidence, that they gave a larger share to religious causes (t-ratio of 1.9).  

The measurement of income in the GSS isn't precise enough to distinguish the truly rich (the top category was $75,000 and up, which would be about $111,000 in today's dollars).  But it's pretty clear that "affluent" people give more of their incomes to charity than poor people do.  Of course, this doesn't necessarily mean that they are more generous:  you could certainly argue that a person who gives 1% of $10,000 is making a bigger sacrifice than someone who gives 5% of $100,000 (and gets a tax deduction).  But more affluent people have more money to spare after paying the bills.  

Happy and unhappy countries

There has been a lot of comparative research on "subjective well-being."  Usually it is measured by a question about how happy you are, using a three- or four-category scale ("very happy," "pretty happy," "not too happy," and sometimes "not happy at all").  Sometimes people are asked to rate their satisfaction with life.  A less common way is to ask people about whether they have felt various emotions recently.  The ratio or difference between prevalence of positive and negative emotions can then be used to make a measure of subjective well-being.  The 1990 World Values Survey asked all three kinds of questions:  for the exact questions about emotions, see this post.   They fell pretty clearly into two groups:  excited, proud, pleased about accomplishing something, "on top of the world," and "things are going your way" versus restless, bored, lonely, depressed, and upset at criticism.  I took the logarithm of the ratio of positive to negative emotions.

At the national level, there are substantial correlations among the three measures, but they are far from identical.  Ratings of happiness and satisfaction have a higher correlation than either one has with the measure of good to bad emotions.  This figure shows the averages for happiness (higher indicates more unhappy) vs. emotions.  There are a number of cases for which they don't match up that well.  The biggest discrepancies are for Slovenia, Bulgaria, and Latvia, which are among the least happy countries but towards the middle in terms of emotions, and Turkey and Chile, which are about average in happiness but well below average in terms of emotions.

Putting all three indicators together, the countries that were highest in subjective well-being are Switzerland, Sweden, Iceland, Denmark, and Ireland.  Going on self-rated happiness alone, the leaders were the Netherlands, Iceland, Ireland, Denmark, and Belgium (Switzerland was seventh).  Either way, countries of the former Soviet Union and eastern Europe dominate the lower ranks.

A cure for cancer?

In 1949, the Gallup Poll asked "Do you think a cure for cancer will be found in the next fifty years?"  88% thought that a cure would be found, and only 7% did not.  The same basic question has been asked a number of times since then with only minor differences in wording, but with time horizons varying from 10 years to a century.  I estimated the relationship between the time horizon and the percent agreeing (the reciprocal seemed to give the best fit--that is, going from 10 to 20 years made about as much difference as going from 50 to 100).  The adjusted percentages of people agreeing (as a percentage of those who agreed or disagreed) are shown in the figure.  The adjustment estimates the percent that would have agreed if the question had asked about the next 50 years.  There is a definite downward trend, but a substantial majority still expects to see a cure.

The hot hand

This is a departure from my usual topics, but it's something that I wanted to get off my chest.  Last week, the New York Times had an article on the economist Larry Summers, which quoted him talking to the Harvard basketball team:  "Then Summers paused and asked the assembled players a rhetorical question: Did they believe a shooter could get a 'hot hand' and go on a streak in which he made shot after shot after shot? All the players nodded uniformly. Summers paused again, relishing the moment. 'The answer is no,' he said. 'People apply patterns to random data.' A statistical analysis of player performance reveals that streaks are random events."  That reminded me of something I've noticed before:  many sophisticated observers seem to think that the "hot hand" has been definitively refuted, not just called into question.  A few days later, the Times had an article on some research suggesting that hot hands do exist (at least in free-throw shooting and bowling).  But even that referred to "the magisterial past research on hot hands."  

In fact, the research has a serious flaw, at least as applied to things like field goal shooting in basketball.  This is the assumption that "chance" can be represented by a model in which the probability of success in each trial is independent and identically distributed.  For example, if a player is a 50% shooter,  he has a probability of .5 of making every shot.  

The probability of success is clearly not identically distributed:  there are easy shots and hard shots.  The paper that started the "hot hand" research (Gilovich, Vallone, and Tversky, "The Hot Hand in Basketball," Cognitive Science 1985), acknowledged this point but said that the model of an identical distribution was "indistinguishable" from a more realistic model in which "each player has an ensemble of shots that vary in difficulty . .  . and each shot is randomly selected from that ensemble."   

But in fact, random variation in the difficulty of shots will tend to hide any evidence of a "hot hand."  For example, one way of trying to detect the hot hand is to look at the occurrence of streaks:  are they more common than would be predicted by the model of an identical distribution?  Suppose that someone takes two shots and has a .5 chance of making each one.  If the chance of making the second is independent of success on the first, there is a .25 chance of two misses, and a .25 chance of two hits.  Suppose that someone takes two shots, an easy one (.9 probability of success), and a difficult on (.10 probability).  Then he has a .09 chance of making both and a .09 chance of missing both.  That is, the chance of a "streak" is .5 if the shots don't vary in difficulty and .18 if they do, even though the average chance of success is the same in both cases. 

Random variation in the difficulty of shots will also tend to drive any correlation between success in successive shots toward zero.  I did a simulation in which a player had three states:  "hot" (field goal percentage of about 75%), ordinary (.5), and "cold" (about 25%).  There is a 90% chance that a player will be in the same state as he was on the last shot:  otherwise, he randomly shifts to a new state with the probability of 80% normal, 10% hot, and 10% cold.  This degree of variation seems large enough to make a practical difference, but it turns out to be hard to detect if you allow for random variation in shot difficulty.  

I assumed that shot difficulty followed a normal distribution with mean 0 and standard deviation of 1, and that the probability of making a shot was (exp(x+s)/(1+exp(x+s)), where x is the difficulty variable and s is -1,0, or 1 depending on whether a player is cold, normal, or hot.   Then the correlation between success and success in the previous shot is only about .02; the correlation between success and the number of successes in the three previous shots is a little less than .04.  It takes a sample of about 3,000 shots to have a 50% chance of getting a statistically significant association between success and the number of successes in the last three (around 10,000 to have a 50% chance of a statistically significant association between success in two successive shots).

Of course, I have no particular reason to think that the difficulty of successive shots is normally distributed with a standard deviation of 1, but that's the general problem:  results are sensitive to the assumptions we make about an unobserved variable.    And this is assuming that the difficulty of successive shots is independent.  Suppose there's a slight negative correlation between them (maybe a player who just missed a shot gets more selective).  That would make it even harder to detect a hot hand.

The research questioning hot hands made a valid point:  that people have a tendency to overinterpret data and think there's a reason for differences that are really just due to chance.   But I think the conviction that hot (and cold) hands simply don't exist reflects social scientists' love for reaching counter-intuitive conclusions rather than a justified inference from the data.

A Dog that Didn't Bark

In June 1968, a Gallup Poll had the following question:  "Some people are calling this country a 'sick society.' Do you agree or disagree with them?"  36% said they agreed, 58% disagreed, and 6% weren't sure.  In December 1985, a Los Angeles Times poll asked the same question: 39% agreed and 55% disagreed, again with 6% not sure.  This is a case where the absence of a difference is interesting.  The 1968 poll was taken just a week after the assassination of Robert Kennedy and a few months after the assassination of Martin Luther King, with the Vietnam war at its height.  In contrast, 1985 was "morning in America":  Ronald Reagan had been re-elected in a landslide, the economy was doing well, and tensions between the United States and Soviet Union seemed to be easing.

The question in the LA Time survey followed a fairly long series of questions about AIDS, which may have led people to focus on the negative.  But it still seems surprising.  

Looking good

The New York Times recently had a story on a Dove advertisement about women's views of their own appearance.  According to the story, "Dove executives said the campaign resulted from company research that showed only 4 percent of women consider themselves beautiful."  It didn't say anything more about the research, but a 1999 Gallup poll asked "If you had to describe yourself to someone who didn't know you, how would you describe your physical appearance? Would you say you are Beautiful or handsome; Attractive or above average; Average; Somewhat below average in attractiveness; or Unattractive."  The percentages for men and women:

                                        Women      Men 

Beautiful or Handsome    4%          12%

Attractive                         40%         31%

Average                             53%         54%

Below average                    2%           3%

Unattractive                       1%           0%

Only 4% of women said "beautiful", exactly as Dove's research found, but 44% said they were above average and only 3% thought they were below average.  That is, people are almost as positive about their looks as they are about their driving ability.    More men put themselves in the top category, but it seems to me that "beautiful" is a stronger term than "handsome." 

The Gallup survey followed with two other questions about looks:  "All in all, are you satisfied with how attractive you are, or do you often wish you could be more attractive?" and "All in all, would you say you are generally pleased with the way your body looks, or not?"

On these, there were some differences:  26% of women and only 17% of men said they often wished they could be more attractive, while 80% of men and 66% of women said they they were generally pleased with the way their body looks.  But substantial majorities of women chose the positive response on each one.

A forgotten turning point

Rand Paul recently gave a speech in which he asked "How did the Republican Party, the party of the Great Emancipator, lose the trust and faith of an entire race?"  Charles Blow replied that this was an easy question, and offered a number of causes beginning with Richard Nixon's "Southern Strategy."  Oddly, he left out the first and most important:  the 1964 presidential election, when the Republicans nominated Barry Goldwater, who had not only opposed the Civil Rights Act of 1964, but denounced it as unconstitutional.

According to the American National Election Studies, the Republican candidate averaged 27% of the black vote in the 1952-60 presidential elections, and 0% in 1964.  Of course, that's just the ANES sample, which included only 94 blacks in 1964.  Goldwater undoubtedly got some black support, but it was almost certainly less than 5% (if he actually got 5%, there's a 99% chance that he would get at least one vote in a random sample of 94 from that population).   

Blow isn't alone here.  Goldwater's nomination is widely remembered as a key moment in the development of modern conservatism, but its role in the development of racial divisions in voting often seems to be overlooked.  If the Republicans had nominated someone who had supported the Civil Rights Act, as the great majority of leading Republicans did, they would probably have much better support among black voters--not as much as they'd had in 1952-60, but enough to maintain a foothold.  As far as why the 1964 election has been overlooked, it may be because Goldwater had rehabilitated his reputation pretty effectively by the late 1970s.  He was out of the Senate between 1964 and 1968, and after he returned he took on an "elder statesman" role (for example, endorsing Gerald Ford over Ronald Reagan in 1976 in the interests of party unity).   Nixon was discredited by Watergate, so he made a better villain.