Tag Archives: statistics

US government (and “statistical bureaucracy”) looking to measure well-being

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The federal government is looking into ways to measure well-being as a new indicator of social life:

Funded by the U.S. Department of Health and Human Services, a panel of experts in psychology and economics, including Nobel laureate Daniel Kahneman, began convening in December to try to define reliable measures of “subjective well-being.” If successful, these could become official statistics.

But as the United States ventures into the squishy realm of feelings, statisticians will first have to define happiness and then how to measure it. Neither is a trivial matter. There is even some doubt whether people, when polled, can accurately say whether they are happy…

The panel, organized by the nonprofit National Academies, has already met with two of the key figures in the U.S. statistical bureaucracy: Robert Groves, the director of the U.S. Census Bureau, and Steve Landefeld, the director of the Bureau of Economic Analysis, the federal agency that puts out the gross domestic product figures.

According to proponents, a measure of happiness could help assess the success or failure of a range of government policies. It could gauge the virtues of a health benefit or establish whether education has more value than simply higher incomes. It might also detect extremes of inequality or imbalances in how people divide their time between work and leisure.

I’m not sure why there is opposition to this. There are plenty of social scientists who study this topic and have developed established measures of “happiness.” I’ve written on this topic a number of times looking at the effect of income on happiness, how religion leads to greater life satisfaction through interaction with others, and an argument that we need to study flourishing rather than happiness. As I’ve noted before, measuring happiness requires looking at both short- and long-term satisfaction. This panel may have to work on applying these measures onto a national scale but they are not creating a whole new field of study.

The cost issue may be driven more by the current budget troubles than anything else. If you are studying the effectiveness of programs and policies, why not include a measure of well-being? We tend to measure many things in terms of economics and pragmatic factors alone. Overall, it could make government statistics more holistic. A measure of well-being doesn’t have to be the only number that matters in the future but it can play an important role.

Three other thoughts:

1. The panel might consider avoiding the term “happiness” as this seems too subjective to a lot of people. In popular usage, the emotion is considered to be ephemeral. Instead, stick with well-being or life satisfaction.

2. Tying this panel to the idea of the “pursuit of happiness” in the Declaration of Independence seems silly. This doesn’t provide evidence for or against this sort of panel.

3. I’m very amused at the mention of a “statistical bureaucracy.” This might be the worst nightmare for some people: statistics plus government. Just a reminder: one member of the bureaucracy, Robert Graves at the Census Bureau, is a sociologist with a lot of experience with surveys.

 

The median college loan debt: $12,800

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Growing calls for ways to deal with college loan debt can lead to a statistical question: just how much does “the average” college student owe? Here are some of the figures:

Meet Kelli Space. She went to Northeastern University to get a degree in sociology. And she graduated in $200,000 of student loan debt. In the economy’s newest trillion-dollar crisis, she is the 1 percent.

Kelli is not the face of America’s student debt problem. Among the 37 million people in this country with student loans to pay off, the median balance is $12,800. A whole 72 percent of borrowers have less than $25,000 left in debt, according to data from the Federal Reserve Bank of New York.

No, students like Kelli are the rarities, the white rhinos. Only about 5 percent of borrowers owe more than $75,000.

The key figures for me: the median is $12,800, meaning that half of people with student loans have less than this figure, half have more. Yet, nearly three-quarters of those with loans have less than $25,000 to pay off. Only 11% have more than $50k in debt.

So why do we keep hearing stories about those who owe mega amounts of money? Perhaps we might think of them as canaries in the mine shaft, students who show how bad the college finance system might be today. But, on the other hand, the statistics suggest that these students are rarities, people who have unusual debt. From these anecdotal and relatively rare stories, it seems like there is a pattern: a student goes to a prestigious school banking on the name of the school to pay off. (One common argument you will find online is that the major should be blamed – this usually puts more creative disciplines, the humanities, and subjects like sociology at the center of blame.) But, the name doesn’t always pay off, the student can’t find a good enough job to start paying off these debts, and the interest just continues to grow.

Overall, we need to work with the statistics more than the anecdotes: most college students do not have more than $25,000 of debt. This is not a small amount but it can be tackled (though the economy doesn’t help).

We need a more complex analysis of how taxes affect income inequality

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One current blogosphere discussion about whether taxes could help reduce income inequality would benefit from more complex analyses. Here is the discussion thus far according to TaxProf Blog:

There have been a number of reports published recently that purport to show a link between rising inequality and changes in tax policy — especially tax cuts for the so-called rich. The latest installment comes from Berkeley professor Emmanuel Saez, Striking it Richer: The Evolution of Top Incomes in the United States.

Saez and others who write on this issue seem so intent on proving a link between tax policy and inequality that they overlook the major demographic changes that are occurring in America that can contribute to — or at least give the appearance of — rising inequality; a few of these being, differences in education, the rise of dual-earner couples, the aging of our workforce, and increased entrepreneurship.

Today, we will look at the link between education and income. Recent census data comparing the educational attainment of householders and income shows about as clearly as you can that America’s income gap is really an education gap and not the result of tax cuts for the rich.

The chart below shows that as people’s income rise, so too does the likelihood that they have a college degree or higher. By contrast, those with the lowest incomes are most likely to have a high school education or less. Just 8% of those at the lowest income level have a college degree while 78% of those earning $250,000 or more have a college degree or advanced degree. At the other end of the income scale, 69% of low-income people have a high school degree or less, while just 9% of those earning over $250,000 have just a high school degree.

This analysis starts in the right direction: looking at a direct relationship between two variables such as tax rates and income inequality is difficult to do in isolation of other factors. While some factors may be more influential than others, there are a number of reasons for income inequality. In other words, graphs with two variables are not enough. Pulling out one independent variable at a time doesn’t give us the full picture.

But, then the supposedly better way is that we were just looking at the wrong variable’s influence on income and should have been looking at education instead! So after saying that the situation was more complex, we get another two variable graph that shows that as education goes up, so does income so perhaps it really isn’t about taxes at all.

What we need here is some more complex statistical analysis, preferably including regression analysis where we can see how a variety of factors at the same time influence income inequality. Some of this might be a little harder to model since you would want to account for changing tax rates but arguing over two variable graphs isn’t going to get us very far. Indeed, I wonder if this is more common now in debates: both sides like simpler analyses because it allows each to make the point they want without considering the full complexity of the matter. In other words, easier to make graphs line up more with ideological commitments rather than an interest in truly sorting out what factors are more influential in affecting income inequality.

Credibility, statistics, and the legal profession

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Elie Mystal at Above the Law has this take on a recent story involving credibility, statistics, and the legal profession:

This week, the law schools at Columbia, NYU, and Fordham have come under fire for their allegedly inflated employment statistics. A story in the New York Post specifically called out the top New York-area law schools for shady reporting of graduate outcomes when it comes to graduates employed by the schools….

I want to take a step back and look at what we’re really fighting about here: some of the best law schools in New York City have put out a statistic about how many graduates get jobs, and the New York Post and a bunch of other people immediately called “bulls**t.” Think about that. Even if the law schools can somehow convince people that, technically, their published information isn’t riddled with lies, we’re living in a world where such data can be assumed to be false absent a long and detailed explanation and discussion from the law schools. When somebody notices a discrepancy between a school’s numbers and what’s in the newspaper, we assume the school was full of crap, not that the newspaper got it wrong.

I suppose this isn’t very flattering to either newspapers or law schools.  Perhaps Americans now trust journalists more than lawyers (or at least legal educators)?

Commenting a few months ago on a scandal within academic sociology, Brian suggested several approaches to dealing with uncertain statistics:

This reminds me of Joel Best’s recommendations regarding dealing with statistics. One common option is to simply trust all statistics. Numbers look authoritative, often come from experts, and they can be overwhelming. Just accepting them can be easy. At the other pole is the common option of saying that all statistics are simply interpretation and are manipulated so we can’t trust any of them. No numbers are trustworthy. Neither approaches are good options but they are relatively easy options. The better route to go when dealing with scientific studies is to have the basic skills necessary to understand whether they are good studies or not and how the process of science works [emphasis added].

Brian’s point is a good one.  Unfortunately, it’s not possible to implement his “third way” here because the root problem is the lack of raw information rather than the inability to duplicate experimental/study results.  The question is not, in the theoretical abstract:  how many law students will get jobs when the economy is in condition X?  The question is rather:  as a matter of historical fact, how many 2010 law school graduates (or 2011, or 2009, or whatever) actually had jobs by date Y?

Elie faults the American Bar Association, which oversees and accredits law schools, for the current disaster of unreliable data:

The ABA is supposed to represent lawyers and law schools to the public. It’s supposed to relegate them so that the public can trust that moral and ethical standards are being upheld and enforced. And on that scale, the ABA has been an unmitigated failure. It’s done a disservice to all law schools. Nobody can trust any law school because the ABA has failed to impose effective oversight over all of them.

That’s tragic. A society is supposed to be proud of its institutions of higher learning, but the ABA has robbed us of that pride in our nation’s law schools. We no longer get to feel like our justice system is populated by people trained to the highest ethical standards, because we can’t even trust our law schools to tell us the truth about how many people got hired.

If the numbers published by law schools under the oversight of the ABA are unreliable, it goes without saying that it’s very difficult to derive these numbers through other means, especially in a form that allows for legitimate comparisons between schools and over time.  There are workarounds, of course, like journalistic attempts to compile and/or verify employment statistics independently of the law schools.  But those are obviously imperfect solutions, as The Economist recently noted in its analysis of the (surprisingly analogous) problem of Argentinan inflation statistics:

Since 2007 Argentina’s government has published inflation figures that almost nobody believes. These show prices as having risen by between 5% and 11% a year. Independent economists, provincial statistical offices and surveys of inflation expectations have all put the rate at more than double the official number (see article). The government has often granted unions pay rises of that order….

We [The Economist] hope that we can soon revert to an official consumer-price index for Argentina. That would require INDEC to be run by independent statisticians working unhindered. Until then, readers are better served by a credible unofficial figure than a bogus official one.

Unfortunately, for the foreseeable future, I think we’re going to need to start seeing a lot more credible unofficial figures out there, both for Argentinian inflation and for law school placement statistics.

“Copyright math”

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Since this blog regularly covers issues ranging from intellectual property law to statistics, Rob Reid’s recent TED talk on “Copyright Math” seems particularly salient:

http://www.youtube-nocookie.com/embed/GZadCj8O1-0

Why two media sources ranking the world’s wealthiest people is a good thing

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While Forbes had the corner on the market for years in compiling a ranking of the world’s richest people, there is now another option: this week Bloomberg released its Billionaires Index. One commentator thinks we don’t need both Forbes and Bloomberg examining this topic:

The Forbes list, available online today, is published every March. (Its companion, the “Forbes 400” list of richest Americans published in September.) It’s hard to not feel that Bloomberg’s outing takes some of the air out of Forbes usually-hyped cover story on who are the world’s richest people. This year’s edition proves unexciting not only because there were few shake-ups in the top spots from 2011’s list, but also because these rankings don’t appear all that different from Bloomberg’s.

Highlights from 2012’s version: With $69 billion, Mexico’s Carlos Slim Helu ranks No. 1 again for the third year in the row. (The magazine also profiled him.) Helu was followed by another 1,225 billionaires, starting with Bill Gates, Warren Buffett, and Bernard Arnault (of Louis Vuitton fame), who were also two through four last year. But beside no one being knocked off the top of this year’s Forbes list, it’s markedly similar to how rich Bloomberg News told us these folks were. Here’s a side-by-side comparison, with Forbes on the left and Bloomberg on the right.

So there are slight differences. Bloomberg has Arnault one spot lower and places fashion mogul Amancio Ortega down to seventh. Bloomberg puts the Koch brothers in the top 10, whereas Forbes had them both pegged at 12th. But isn’t this hair-splitting? If anything, the discrepancies show how hard it is to measure rich people’s riches.

What today’s Forbes list shows more than anything is that we don’t need two billionaires lists reminding us how wealthy the wealthy are. If we had to choose one, we’d go with Bloomberg’s, since it’s updated daily instead of once a year. But we doubt that will stop Forbes from producing its longstanding annual issue as long folks keep buying it.

I disagree. Here is why: I think that having two media sources looking at this topic will actually give readers better information. With two publications tackling the subject, I hope this improves their measurement of wealth for both publications. Perhaps we could average the rankings across the publications to get a more accurate assessment of what is going on. In the end, two sets of people looking at the data is better than one. Because Bloomberg is updating this list daily, perhaps this will push Forbes to update their lists more frequently and move away from a magazine era schedule to an Internet era schedule. The two lists do have some differences and this is not inconsequential. Lots of people are interested in this list and I’m sure some of the people at the top of the list have some interest in where they rank. Of course, these differences can indicate “how hard it is to measure rich people’s riches” but this doesn’t mean we should just throw up our hands and go with one list. Just because these people are really wealthy doesn’t mean that we shouldn’t have more fine-grained analysis of their financial holdings. (This sometimes seems to happen quite a bit in sociology: we assume we know about the elites and so spend more time studying marginalized groups but we have fewer in-depth studies of the elites who do have a lot of influence in society.)

A second issue: Bloomberg obviously thinks there is a market for another list that is updated daily and so this is a market decision as well as a journalistic interest in updating this information more frequently. The Forbes list always gets a lot of attention and Bloomberg probably wants to draw away some of that market. I imagine there is enough room in the market for both lists to survive, particularly as the two could serve different markets. However, it will be interesting to see how the rest of the media responds to changes in the Bloomberg list: if someone moves up from #3 to #2 in the next few days, will there be news stories about it? Will journalists providing background information about the wealthy reference the Forbes or the Bloomberg list?

Santorum claims college pushes people away from religion, experts push back

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Republican presidential candidate Rick Santorum recently suggested that going to college pushes people away from the church and faith. Those who study the subject disagree:

Santorum told talk show host Glenn Beck on Thursday that “62% of kids who go into college with a faith commitment leave without it.”

Thom Rainer, president of LifeWay Christian Resources, a Nashville evangelical research and marketing agency, said, “There is no statistical difference in the dropout rate among those who attended college and those that did not attend college. Going to college doesn’t make you a religious drop out.”…

The real causes [of leaving the faith]: lack of “a robust faith,” strongly committed parents and an essential church connection, Rainer said.

“Higher education is not the villain,” said sociologist William D’Antonio of Catholic University of America. Since 1986, D’Antonio’s surveys of American Catholics have asked about Mass attendance, whether they rate their religion as very important in their life, and whether they have considered leaving Catholicism. The percentage of Catholics who scored low on all three points hovers between 18% in 1993 and 14% in 2011. But the percentage of people who are highly committed fell from 27% to 19%.

Recent research also disputes this: several 2011 studies found that those with education are actually more religious than those with less education.

So what was Santorum getting at with his statement? Three thoughts:

1. Conservative Christians commonly cite alarmist statistics to show that the church needs to redouble its efforts or to demonstrate that the church is under attack. See this classic article “Evangelicals Behaving Badly with Statistics,” a good article titled “Curing Christians’ Stats Abuse,” and the book Christians are Hate-Filled Hypocrites…

2. He is hitting back against “elitist academia,” responding to but also feeding the perception college classrooms are filled with atheists and agnostics who want to disabuse students of their faith. Of course, there are many people of faith in academia. This is a larger battle over a perceived liberal, atheist elite versus a faith-filled “average America.”

2a. If Santorum were correct, does this mean that people of faith should not send their kids to college? Or alternatively, do these ideas continue to boost attendance at religious colleges?

3. To compound matters, Santorum was talking to Glenn Beck and this argument was aimed at Beck’s audience. At the same time, it appears Santorum made this a more general argument on the campaign trail:

“President Obama once said he wants everybody in America to go to college. What a snob,” Santorum said Saturday at a campaign stop in Troy, Mich. “There are good, decent men and women who go out and work hard every day and put their skills to test that aren’t taught by some liberal college professor that [tries] to indoctrinate them.”

In the end, this seems like another plank in a moral argument, rather than a political or social argument, for Republicans.

Using a list of “sleep-deprived professions to illustrate statistical and substantive significance”

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I ran into a list of “sleep-deprived jobs” yesterday and I think it is a useful tool for illustrating what significance means. The top five sleep deprived jobs (starting with the least rested): home health aide (6 hours, 57 minutes), lawyer, police officers, physicians/paramedics, and economists. The top five jobs with the most sleep (starting with the most rested): forest/logging workers (7 hours, 22 minutes), hairstylists, sales representatives, bartenders, and construction workers. Here is where the data from the list came from:

The lists are based on interviews with 27,157 adults as part of the annual National Health Interview Survey, conducted by a division of the Centers for Disease Control and Prevention. Sleepy’s says its rankings were based on two variables: 1) average hours of sleep that respondents said they got in a 24-hour period, and 2) respondents’ occupations, as they would be classified by the Department of Labor.

Let’s talk about significance. First, statistical significance. The lower value is 6 hours and 57 minutes and the highest value is 7 hours, 22 minutes. We would need to know how the data is clustered, meaning does it look like a normal distribution (meaning most jobs are clumped in the middle) or it is a broader distribution? With a standard deviation, we could figure out how far these highest and lowest values are from the mean and whether they are outside 95% of all the cases.

Perhaps more interesting in this case is the second aspect of significance: even if a case is significantly different from the other cases, is this a meaningful difference in the real world? Just looking at the ten occupations at the top and bottom of this list, the top and bottom are separated by 35 minutes. Would roughly a half hour of sleep really change the quality of life or health between home health aides and forest/logging workers? Of course, sleep might not be the only factor that matters here but is this a meaningful difference? The Mayo Clinic recommends 7-9 hours a night for adults, the National Sleep Foundation also says 7-9 hours a night, and both agree that there are a lot of other factors involved. On the whole then, it appears that the average American (who is in an occupation) is on the low end of recommended sleep (a recurring theme in news stories over the years).

It appears that this list isn’t that helpful if everyone is relatively clustered together. But if we had a little more information, we could know more and determine whether there are (statistically and substantively) significant occupations.

Making sure the graph of mean age of marriage covers a broad enough period of history

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Two days ago, I noted a new report that said that fewer adult Americans, 51%,  are married than ever before. One of the markers of this trend is the rising mean age for a first marriage. But, a sociologist points out that it is important to have a broad enough context for the mean age of marriage:

But Philip N. Cohen, a sociologist at the University of North Carolina at Chapel Hill, reminds us that using 1960 as a reference point can be misleading.

In 1960, the median age of first marriage was near a record low, having bottomed in about 1956. If you check out the trends going back to 1890, however, you get a much different picture…

“[T]he 1950s,” Professor Cohen writes, “doesn’t represent the ‘traditional’ family.”

The graph is pretty clear: the 1950s represent a clear dip in the mean age of marriage between 1890 and today. Suggesting there is a sharp rise since the 1960s in the mean age is not incorrect but it masks the bigger context.

This leads me to an interesting idea: while the 1960s are often considered an unusual decade and perhaps one whose reverberations are still being felt (consequences of foreign wars, sexual revolution, rise of youth culture, Baby Boomers, challenges to political authority, etc.), perhaps it is the 1950s that is the real unusual decade in the United States. The swift movement of people to the suburbs, a large crop of war veterans going back to school and starting families, an expanding economy, and relative peace (with the US as a clear superpower) represents an unusual period. Perhaps then the 1960s were simply the beginning of the unraveling of that “golden decade.”

Official existing home sales statistics to be revised downward

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The National Association of Realtors announced that existing home sales figures for recent years will be rechecked and revised downward after some errors in counting were discovered:

Data on sales of previously owned U.S. homes from 2007 through October this year will be revised down next week because of double counting, indicating a much weaker housing market than previously thought.

The National Association of Realtors said a benchmarking exercise had revealed that some properties were listed more than once, and in some instances, new home sales were also captured.

“All the sales and inventory data that have been reported since January 2007 are being downwardly revised. Sales were weaker than people thought,” NAR spokesman Walter Malony told Reuters…

Early this year, the Realtors group was accused of overcounting existing homes sales, with California-based real estate analysis firm CoreLogic claiming sales could have been overstated by as much as 20 percent.

So if you thought the existing real estate market was in bad shape in recent years, it was actually worse than you thought. However, it will be interesting to see how much these statistics are revised and then how these changes affect things like the stock market. A little change may not matter much.

This is a reminder about trusting “official” figures too much. On one hand, it would be hard for the average citizen to gather this information. Therefore, we have to trust certain data sources. On the other hand, official measurements can be affected by a variety of factors and always should be considered probabilistic since they are often based on surveys and not 100% counts or “proof.”