Category: Data Source
Perceptions of Corruption
Transparency International produces a much cited index of corruption, the Corruption Perceptions Index (CPI). But here is something, shall we say… interesting.
"Transparency International commissions the CPI from Johann Graf Lambsdorff." Lambsdorff, who likes to be called the "father" of the CPI, has another kid on the side, a firm called Anti-Corruption Training and Consulting. And what does this firm do? Well I will let them speak for themselves:
Following an invitation of the Chinese Ministry of Supervsion Prof.
Graf Lambsdorff and Mathias Nell went to China from July 22 to July 29
2007. The trip encompassed anti-corruption consultations in Beijing,
Nanjing and Chengdu as well as the release ceremony at Tsinghua
University of the Chinese version of Prof. Graf Lambsdorff’s new book
“The Institutional Economics of Corruption and Reform: Theory, Evidence
and Policy”.
China, let us recall, scores a 3.5 out of 10 on TI’s Corruption Index where the most corrupt country in the world, Somalia, has a score of 1.4. Pretty corrupt, eh? Here is a picture, from the ACTC website illustrating some of ACTC’s consulting:
Hat tip to CPI-Watch.
The best news I’ve heard in ages
What’s an old person anyway? Does it depend on how many years are behind you, or how many years you still can expect to live? Here is John Shoven:
The current practice of measuring age as years-since-birth, both in
common practice and in the law, rather than alternative measures
reflecting a person’s stage in the lifecycle distorts important
behavior such as retirement, saving, and the discussion of dependency
ratios. Two alternative measures of age are explored: mortality risk
and remaining life expectancy. With these alternative measures, the
huge wave of elderly forecast for the first half of this century
doesn’t look like a huge wave at all. By conventional 65+ standards,
the fraction of the population that is elderly will grow by about 66
percent. However, the fraction of the population that is above a
mortality rate that corresponds to 65+ today will grow by only 20
percent. Needless to say, the aging of the society is a lot less
dramatic with the alternative mortality-based age measures. In a
separate application of age measurement…GDP would be between seven and ten percent higher by 2050 if retirement
lengths stabilize.
Here is the paper (I can’t find a non-gated version). Note that the entire increase of life expectancy of the twentieth century has been taken in the form of retirement, rather than extra work. Of course our social security and Medicare policies have encouraged early retirement, and we have not adjusted age eligibilities for longer life spans and better health. For fiscal reasons, we will likely have to increase eligibility ages; not only will we spend less money but it will encourage more work.
If you have been thinking that a demographically-based American economic collapse is virtually inevitable, this paper gives some grounds for hope. Here is further commentary.
United States fact of the day
In today’s America, there are more World of Warcraft players than farmers.
Hat tip to Paul Krugman.
Passing down temptation to the next generation
We find that the elasticity between mothers’ and children’s BMI has
increased since the 1970s, suggesting that shared genetic-environmental
factors have become more important in determining obesity.
Here is more.
How special is American inequality?
Will Wilkinson writes:
I was surprised to discover that U.S. market income (i.e., pre-tax) inequality is lower than the U.K.’s, the same as Germany’s, and only slightly higher than Sweden’s…
Check the graph at the link. Will continues:
This is from Brandolini and Smeeding’s 2007 “Inequality Patterns in Western-Type Democracies: Cross-Country Differences and Time Changes” [pdf]. While the U.S. pre-tax Gini is still on the high side of the median of these 16 OECD countries, it is remarkable how much differences in tax and transfer policies push the U.S. to the top in inequality in disposable income. This is striking to me because, at a glance, it suggests that the U.S. is not all that distinctive in the way the basic structure of the economy affects the distribution of market income. Unions in Germany and the U.K. are rather more powerful than in the U.S., but (again, at a glance) appear to do nothing to reduce inequality relative to the U.S. Of course, eyeball empiricism isn’t dispositive. But it seems to me to fit pretty well with the weak effect of the relationship between declining unions and rising inequality found in other research, and suggests that the structure of basic American political-economic institutions is not especially conducive to inegalitarian outcomes.
My take: This is all well worth knowing, and it does help counter the view that growing inequality of income is a poliical conspiracy. But oddly both the critics and the defenders here are missing one major inequality-related difference between Germany and the United States, namely social norms. We have weaker families, weaker social pressures to conform, deeper bayous, and as a result more flat out lunatics, losers, and violent psychopaths. (Did I mention we also have more innovation?) That’s inequality too, though the usual political recipes aren’t likely to provide the cure.
Addendum: One very eminent source emailed me and he wishes to stress that the (relatively) high level of the European Gini stems from higher levels of unemployment, whereas the relatively high level of the American Gini stems from the rich being very rich. He points out that although the final Ginis may be similar, the underlying patterns are very different and it would be misleading to conclude that America and Germany have ended up at the same pre-tax point. This is absolutely correct, my apologies if the post created a misleading impression.
Angola fact of the day
The International Monetary Fund projects a 24 percent economic growth this year – one of the fastest rates in the world.
Wow. Here is fact number two:
…the Catholic University of Angola’s research center say two in three
Angolans still live on $2 or less a day, the same percentage as in
2002…no one disputes that most Angolans face appalling living
conditions, sky-high infant mortality rates, dirty water, illiteracy
and a host of other ills.
If you hadn’t guessed: it’s oil money: "The government is taking in two and a half times as much money as it did three years ago."
How to Cite a Blog
Here’s a sign of the times, the NIH provides a style guide on how to cite a blog. Bizarrely, however, they include a space for "Place of Publication." It’s annoying enough that book citations require a location for the publisher – does anyone use this? Ever? We should not carry wasteful practices to the web.
Still, the idea that blogs can and should be cited is nice to see. The bottom line? Two r’s in Tabarrok.
Hat tip to Boing Boing Blog.
The Divorce Myth: What is Really Happening?
I began my week guest blogging by noting a widely under-appreciated point: that divorce is falling (here, continued here). Those posts led a bunch of folks, in the comments and elsewhere, to ask about recent trends, to question the possible confounding influence of changes in marriage rates, and for requests to actually show, rather than summarize the data.
Good news: Despite blogging all week, Betsey Stevenson and I have managed to put together a shortish paper describing the trends in marital stability over recent decades, drawing on most available data sources. Read away here. The paper is largely pictures and tables, so should provide useful grist for discussion. And of course, we are open to any useful suggestions.
How to sound smart around the water cooler
The baseball playoffs begin today. (Go Red Sox!) But if you haven’t been following the 162-game season, you may risk sounding foolish around the water cooler.
Here’s how to sound like an expert: Research tells us that prediction markets yield accurate forecasts. Indeed, a prediction market forecast is likely smarter than any expert. Simply point your browser to your favorite prediction market, and make the following observations confidently around the water cooler:
- Note that the American League looks much stronger than the National League. (HT: Mike Giberson at Midas Oracle.)
- Sigh, while you say that "Once again the American League race looks like being the Red Sox or the Yankees."
- State emphatically that "the National League is anyone’s race. Heck, even the come-from-behind Phillies are a chance." (Say this as though you didn’t already know they were the betting favorites)
That’s it. You are now an expert. (How else do you think an Aussie can keep up a conversation about U.S. sports? I’ve been faking it for years… but shhh, don’t tell David Stern.)
The Real Significance of Changes in the Gender Happiness Gap
A qualifier: None of these comparisons are entirely satisfactory. For instance, if you believe that there is very little variation in happiness across people, time, or states of the economy, then you would interpret the above comparisons as suggesting that the change in the female happiness gap is big, only when compared with small things.
Another qualifier: We only document changes in the measured gender happiness gap.
Any other ideas on how to describe the "oomph" (or economic significance) of changes in qualitative variables like happiness?
[Thanks to Betsey Stevenson for coauthoring this post.]
UPDATE 1: Steve Levitt chimes in.
UPDATE 2: Jezebel adds some perspective.
The Significance of Changes in the Gender Happiness Gap
- A
misunderstanding. I suspect that the
claim that happiness did not significantly change from 1972-2006 comes from the
fact that we did not include stars when reporting the implied gender gaps in Table
1 of our paper. Thus, the claim that
the ordered probit analysis found that the "Gender
happiness gap" was not statistically significant, either in 1972 or in
2006, even at the 0.10 level
is simply untrue. Here’s the relevant part of Table 1, which is
an ordered probit regression, of happiness on time trends by gender:
The right way to test for whether women
were, on average, happier at the start of the sample is to look at the “Female
dummy”, which is clearly significant. The right way to ask whether this gender gap has changed is to look at
the difference in trends, which is also clearly significant. The last two rows are regression-based predicted
values, so we didn’t think we should put stars next to these numbers.
- Statistical
mischief: When you want to make a result go away, throw away enough data,
and a result will become insignificant. For instance pooling all of the data gives us a useful 46,303
observations. Analyze any specific year,
and you are left with only 1,500-3,000 data points. Even so, let’s analyze only data from 1972
and 2006:- %Very happy = 28.7 + 3.1*Female +1.6*(Year2006)
– 2.4*(Female in 2006) - %Not too happy = 18.1 -3.2*Female – 5.5*(Year2006)
+ 4.1*(Female in 2006)
- %Very happy = 28.7 + 3.1*Female +1.6*(Year2006)
In the first case, no coefficients are
statistically significant, and in the latter, all are. In both cases, the estimates say that women
were once a fair bit happier than men, and this is no longer true. Comparing this regression with those in our
paper, we simply learn that a smaller sample yields similar estimates, but they
are less likely to be statistically significant.
- Looking for
a masterpiece, when we are doing collage. Sometimes studying social
phenomena is hard, and one draws on many data sources to put together a collage
of evidence. Our paper finds declining
happiness among women relative to men in: the General Social Survey (n=46,303
from 1972-2006); the Virginia Slims Poll (n=26,701 from 1972-2000); among U.S.
12th graders (Monitoring the Future; n=433,906 from 1976-2005); in the
United Kingdom (British Household Panel Study data from 1991-2004; n=121,135);
in Europe (the Eurobarometer analysis has n=636,400 from 1973-2002, covering 15
countries), and across developed countries (the International Social Survey
Program contains surveys 35 countries from 1991-2001 yielding n=97,462). The only dataset that does not yield clear
results of a decline in women’s happiness relative to men’s is the World Values
Survey, and even there, the data do not speak clearly.
Let me try to give a particularly transparent description of the data,
simply splitting the GSS data into two periods, 1972-1989 v. 1990-2006. There was a clear gender happiness gap in the
earlier period (34.3% of women were very happy v. 31.8% of men). This difference is clearly statistically
significant (t=4.1). In the later
period, 30.9% of women were very happy, compared with 31.1% of men. This recent gender happiness gap is
insignificant (t=-0.3). The decline in
the share of women who were very happy (34.3% v. 30.9%) is clearly significant
(t=5.9), while the corresponding changes for men were not (t=-1.1). The decline in the share of women who were
very happy relative to men is also significant (t=-3.1). Analyzing the share who are “not too happy”
yields a roughly similar pattern (but in reverse): an insignificant “unhappiness
gap” in the earlier period, but a significant gap emerged in the latter period. Interestingly, the “unhappiness gap” emerged
because as men became less likely to be unhappy, as women’s unhappiness
remained largely stable. The ordered
probit is a regression technique that allows one to make these happiness and
unhappiness comparisons all at the same time; these regressions tell us that
there was a gender happiness gap favoring women in the earlier period, and it
now favors men. For the
regression-heads, if your library subscribes can download the GSS data from the
ICPSR here. I’ll post some stata code in the comments.
This post only deals with whether the effects we
describe in
the paper are statistically significant. The other complaint is that
our results are too small to matter. Later today, I’ll turn to how we
think
about whether these are large or small effects.
[Written jointly with my coauthor Betsey Stevenson]
UPDATE: See discussion of "economic significance" here.
Intriguing numbers on conscientious objectors
The GAO reports only 425 applications for conscientious objector status from 2002-2006, compared with 2.3 million servicemembers (including Reserves). Just over half were approved. Read more here.
Hat Tip, Zubin Jelveh, who also notes:
For reference, the Vietnam war had about 200,000 such applications.
(Of course, Vietnam did have a draft.)
Facts and True Facts: More on Divorce
My
initial guest post noted that recent
divorce statistics were misinterpreted widely in both the media,
and by the academics interviewed by the press. The question is what went wrong with the latest data?
First, some necessary background. This
table was published by the Census Bureau counting the proportion of those
who had wed in each year who subsequently celebrated various
anniversaries. Here’s a quick test: Look
at the data, and decide for yourself what is happening to marital
stability. Or if you are lazier, let me
help with an example: the Census reported that 76.4% of men whose first wedding
occurred in 1985-89 had celebrated a tenth anniversary; this declined rather
dramatically to 70.0% among those who marrying in 1990-94. By jingo, it looks like recent marriages have
become much less stable!
Not so fast. The
marital history data were collected from July-September 2004, and hence those
who had married in, say, October 1994, simply
could not have reached their tenth anniversary by the survey date. Because this affected around one-in-ten of
those wed from 1990-94, this statistical factor alone explains what looked like
a decline in marital stability.
How do we interpret what happened?
- The
Census Bureau reported true and useful facts: The data are interesting, and
the table includes a small footnote, noting “Approximately 10 percent of the
cohort has not reached the stated age by the end of the latest specified time
period. Because of this, estimates for this group for the highest anniversary
are low.” With this qualification, one
should not conclude that divorce is rising. (But what should one conclude? No
guidance is given.) - The
Census Bureau reported true, but useless facts: The tables measure exactly
what it says it measures. The Census
Bureau is like Fox news:
We report, you decide. And we report,
even if the number we report is meaningless. - The Census
Bureau reported misleading facts: It is obvious that a qualifying footnote will
be ignored by most. Indeed, the New York
Times printed
the table but omitted the footnote. But
let’s not be too harsh on the NY Times: I talked about these data with several excellent
economists, and none even noticed the
footnote. Headline numbers deserve
headline qualifications. - The
survey was flawed: If the Census is interested in measuring the survival of
a set of marriages to their tenth anniversary, then failing to wait ten years after
a wedding to measure this is a surveying glitch.
So what is the mission of a statistical agency? If the Census’ job is to just report back
what we (the surveyed population) tell them, then they performed that task
adequately. If their job is to report
parameters – useful facts – then they failed miserably, as the data they
reported are hopeless biased indicators of marital stability. Alternatively, the question is: Does the
Census provide facts, or interpretation? I’m happy if they present only facts and leave the interpretation to experts. But is there an obligation to report only interpretable
facts?
Stephen Colbert’s term “truthiness“,
the reigning word of
the year, refers to what you
want the facts to be as opposed to what the facts are. I’m wondering, what is the right word is for something that is a fact but isn’t true? Untruthiness, anyone?
The Divorce Myth
I want to start my week guest blogging by talking about divorce. Betsey Stevenson and
I had an
op-ed in yesterday’s New York Times noting a very simple fact: those
married in the 1990s have proved less likely to divorce than those wed in the
1980s, which were less likely to divorce than those wed in the 1970s. The
Divorce Facts are that divorce is falling, and marriages are more stable.
What is surprising, is just how easily and how often the
Divorce Facts lose out to the Divorce Myth. The Divorce Myth is that divorce
is rising. When the latest
divorce numbers came out last week, they once again confirm this
quarter-century long decline in divorce, but the media (including the Times,
Post,
and the Inquirer)
chose instead to write (incorrectly) about rising divorce. (In their defense, the data were presented in
a way that invited misinterpretation, a subject that I shall return to in a
future post.)
Why the persistence of the Divorce Myth?
- Blame the
public for underestimating divorce: Tyler
has argued that Americans “underestimate the probability of divorce”, and
so when the statistics show that divorce is quite common, they infer divorce
must have risen. - Blame the
public for overestimating divorce: Greg
Mankiw thinks that this “seems be an example of what Bryan Caplan calls ‘the
pessimistic bias’, a tendency to overestimate the severity of economic problems.” - Blame the
press: Mankiw may be a bit unfair on Joe Citizen: the average person gets
their news from the press, and in this case, the press reported falsehoods as
facts. - Blame the
politics: We argued that “Reporting on our families is a lot like reporting
on the economy: statistical tales of woe provide the foundation for reform
proposals. The only difference is that
conservatives use these data to make the case for greater government
intervention in the marriage market, while liberals use them to promote
deregulation of marriage.” - Blame the
professors: Academics are meant to provide the facts offsetting the
political hacks. But we don’t. Economists have had too little respect for
simple facts; publication glory lies with grand theories. Ideologically-motivated profs teaching family
sociology or family law would rather reinforce the Myth than offset it.
Personally, I go for #4 causing #3, unchecked by #5, and
would love to see research by Bryan testing #1 v. #2. Your thoughts?
Heroes are not Replicable
You know the plot. Young, idealistic teacher goes to inner-city high school. Said idealistic teacher is shocked by students who don’t know the basics and who are too preoccupied with the burdens of violence, poverty and indifference to want to learn. But the hero perseveres and at great personal sacrifice wins over the students using innovative teaching methods and heart. The kids go on to win the state spelling/chess/mathematics championship. c.f. Stand and Deliver, Freedom Writers, Dangerous Minds etc.
We are supposed to be uplifted by these stories but they depress me. If it takes a hero to save an inner city school then there is no hope. Heroes are not replicable.
What we need to save inner-city schools, and poor schools everywhere, is a method that works when the teachers aren’t heroes. Even better if the method works when teachers are ordinary people, poorly paid and ill-motivated – i.e. the system we have today.
In Super Crunchers, Ian Ayres argues that just such a method exists. Overall, Super Crunchers is a light but entertaining account of how large amounts of data and cheap computing power are improving forecasting and decision making in social science, government and business. I enjoyed the book. Chapter 7, however, was a real highlight.
Ayres argues that large experimental studies have shown that the teaching method which works best is Direct Instruction (here and here are two non-academic discussions which summarizes much of the same academic evidence discussed in Ayres). In Direct Instruction the teacher follows a script, a carefully designed and evaluated script. As Ayres notes this is key:
DI is scalable. Its success isn’t contingent on the personality of some uber-teacher….You don’t need to be a genius to be an effective DI teacher. DI can be implemented in dozens upon dozens of classrooms with just ordinary teachers. You just need to be able to follow the script.
Contrary to what you might think, the data also show that DI does not impede creativity or self-esteem. The education establishment, however, hates DI because it is a threat to the power and prestige of teaching, they prefer the model of teacher as hero. As Ayres says "The education establishment is wedded to its pet theories regardless of what the evidence says." As a result they have fought it tooth and nail so that "Direct Instruction, the oldest and most validated program, has captured only a little more than 1 percent of the grade-school market."