Fewer US homeless?

When the Great Recession began in 2008, housing experts expected a rise in homelessness when millions of Americans lost their jobs. To the surprise of many social scientists, estimates for the number of homeless actually dropped to 610,042 in 2013 from 664,414 in 2008. As described in chapter 3 of The Data Game, measuring homeless is tricky. But the numbers, published by the US Department of Housing and Urban Development, don't show a recession-induced bump; instead homelessness declined steadily, if slowly, since 2008.

It may take a while to understand these statistics. The federal government report credits a 2010 program called Opening Doors as a reason for fewer homeless. Similarly, the Bush administration attributed the 2007 homelessness decline to its "housing first" policies. As with the unanticipated decline in the US crime rate (see chapter six), there are likely many factors at work. And, there are anomalies in the data. The federal report indicates that the decline is exclusively in the number of people who do not seek shelter. Those in shelters actually remained nearly constant since 2008, while there has been an increase in the number of people doubling up with friends and relatives. There are local reports that contradict the rosier national picture. We will need more statistics and careful analysis to fully understand why economic troubles didn't lead to more homelessness.

Hat tip to Tim Taylor for his blog post.

More students going to college? not by much

Australian economist John Quiggin corrects the often-stated statistic that more US students are attending college. While it is true that more Americans have a college degree, now over 30 percent, up from less than 20 percent in the early 1980s, the reason isn't that students are now attending college in greater numbers. What has happened is that many people educated before World War II, when few people finished college, are no longer living.  As a result, the current population contains a larger percentage of people who graduated from college in the 1950s and 1960s when, indeed, college attendance sharply increased.

However, since about 1970, the proportion of young people graduating from college has been relatively stable. Looking at the single age group, 25 - 29, those with a college degree has remained at about 25 to 30 percent since then-- although there have been notable increases in college graduation by women and some minority groups.

These statistics demonstrate the importance of taking into account cohorts (that is age groups) as well as the entire population. In this case, the rising number of past college graduates masks a leveling off in attendance by the more recent generation. These statistics are important for debates about the role of college as vehicle for economic advancement.

Is the unemployment rate a good measure of job market strength?

Mark Thoma argues it is not, because the official unemployment rate changes with both the number of unemployed AND the number of discouraged workers.

The problem with using changes in the unemployment rate as the most important measure of the job picture is that it can fall even when labor market conditions get worse. When the unemployment rate declines because more jobless people find work, that reduction reflects a positive development in the labor market. But the unemployment rate also falls when people get discouraged about their prospects for finding a job and drop out of the labor force.

Thus, to properly interpret a change in the unemployment rate, it's important to know whether the change is due to a change in the proportion of people finding jobs or from a change in the labor force participation rate.

This is especially important recently because there has been a large change in labor force participation, meaning the number of working-age people who are employed or who are looking for work. The civilian labor force participation rate has fallen from 66 percent at the start of the recession in December 2007 to 62.8 percent in October of this year.

According to the Bureau of Labor Statistics, there are 2.3 million people among those who have dropped out of the labor force who wanted and are available for work. These individuals are not counted as unemployed because they have not searched for work in the previous four weeks. If all of these workers had been counted as part of the labor force, the unemployment rate in October would have been 8.6 percent instead of 7.2 percent, a major difference.

One big unknown for the future is how many of these workers will return to the labor force once labor market conditions improve. If they return in substantial numbers, it would make it harder for the unemployment rate to fall.

Thoma goes on to suggest that the employment-to-population ratio be given more attention. He also notes, "Of course, no single measure of labor market conditions is perfect, so it's best to look at the full range of indicators rather than focusing on any one measure."

Possible discussion questions:
- Why do the rates of employment and unemployment sometimes tell conflicting stories?
- What other issues should be considered when interpreting changes in the unemployment rate?

How big is the gay population?

A new working paper by Katherine B. Coffman, Lucas C. Coffman, and Keith M. Marzilli Ericson provides new evidence on how difficult it can be to determine the true size of the gay population (discussed in chapter 2 of The Data Game). From the abstract:

Measuring sexual orientation, behavior, and related opinions is difficult because responses are biased towards socially acceptable answers. We test whether measurements are biased even when responses are private and anonymous and use our results to identify sexuality-related norms and how they vary. We run an experiment on 2,516 U.S. participants. Participants were randomly assigned to either a “best practices method” that was computer-based and provides privacy and anonymity, or to a “veiled elicitation method” that further conceals individual responses. Answers in the veiled method preclude inference about any particular individual, but can be used to accurately estimate statistics about the population. Comparing the two methods shows sexuality-related questions receive biased responses even under current best practices, and, for many questions, the bias is substantial. The veiled method increased self-reports of non-heterosexual identity by 65% (p<0.05) and same-sex sexual experiences by 59% (p<0.01). The veiled method also increased the rates of anti-gay sentiment. Respondents were 67% more likely to express disapproval of an openly gay manager at work (p<0.01) and 71% more likely to say it is okay to discriminate against lesbian, gay, or bisexual individuals (p<0.01). The results show non-heterosexuality and anti-gay sentiment are substantially underestimated in existing surveys, and the privacy afforded by current best practices is not always sufficient to eliminate bias. Finally, our results identify two social norms: it is perceived as socially undesirable both to be open about being gay, and to be unaccepting of gay individuals.

Possible discussion questions:
- It is well known that there may be bias in survey questions about sensitive issues. With what other issues might we expect to find similar bias? Why is this bias problematic for researchers? For policymakers?
- In addition to bias because of a reluctance to answer questions about sensitive issues accurately, what other problems may lead to an inaccurate count of the gay population?

How should we measure poverty?

The Numbers Guy's column this week looked at how we measure poverty (discussed in chapter 8 of The Data Game). Although most economists agree that the current measure is problematic, finding a replacement is politically difficult since any change will lead to 'winners' and 'losers' relative to the current measure. Bialik notes that some states (such as New York) have started coming up with their own measures, which may put some pressure on the feds to establish a better national instrument.

Possible discussion questions:
- What are some of the problems with the existing poverty measure? What are some of the advantages and disadvantages of the Supplemental Poverty Measure?
- Why might it be problematic for each state to develop their own poverty measure? Why do we need a national measure?