Archive for category Labor Market Issues 2000 to 2012
This article is an update on the labor force participation rate (LFPR), adding information through November, 2012. In July I posted a long article about the LFPR. If anything, the situation has gotten worse. Let’s look at some updated data and graphs.
The two youngest groups (16-24, 25-34) continue their downward trend:
The data for 2012 covers the first three quarters of 2012. The 25-34 age group stayed just about constant compared to 2011: 81.52% in 2012 vs. 81.53% in 2011. To get some perspective on the 16-24 group, here’s the annual data for the last five years:
That’s a decrease of 11.11 percentage points. Once again, it is even clearer that we are losing a generation of workers. Economists know that getting a job for the first time is one of the best predictors of long-term career success.
There is one canard that needs to be dealt with as soon as possible. Some comments in the press have alleged that the decline in the LFPR has been caused by people in my generation retiring. Luckily I have the data for the 65 and older age group:
Quite the obvious. Not only are we not retiring, we’re re-entering the labor force.
I’m preparing a much longer piece that will investigate issues around discouraged workers and others marginally attached to the labor force (as the BLS puts it). Stay tuned.
It’s been nine days since the September jobs report was released by the Bureau of Labor Statistics. I’ve been working on a much longer article, but decided to post this abbreviated version to pull together some analysis.
Did the U.S. economy really gain 873,000 jobs in September, 2012? Was the unemployment rate really 7.8%? Economists have reacted to these numbers with a peculiar mixture of disbelief and defensiveness. No sane economist believes these numbers represent the current state of the U.S. economy. A quick-and-dirty estimate says that real GDP would have to grow at a 4 – 5% annual rate to add that many jobs. Actual GDP growth in recent quarters has been below 2.5%.
But, at the same time, we economists are vehemently defending the statisticians and economists at the Bureau of Labor Statistics (BLS). (Technical note: we should also be defending the Census Bureau because those folks conduct the “household survey” under contract from BLS. Technically the household survey is called the current population survey, CPS, while the establishment survey is called current employment statistics, CES.)
Before going any further, I have to say that there’s a good chance that the 873,000 increase in jobs is simply a statistical fluke. Remember, total employment is estimated using a sample of 60,000 households. There is a large margin of error. More details are in the next section of this paper where I look at the numbers and analyze this possibility.
The purpose of this report is to reconcile those two conflicting viewpoints: the jobs number seems unbelievable, but I remain fully confident in the integrity of the number wonks at BLS. And there is also a contribution from the vagaries of the seasonal adjustment process the BLS uses, specifically the treatment of those between ages 20 and 24. (For those who are interested, Catherine Rampell has an excellent discussion in her blog at the New York Times website (may be behind a paywall). I have included Ms. Rampell’s numbers with a few additions as the last worksheet in the Excel workbook for this report.)
A Quick Look at the Numbers
Before heading into the analysis, I have to mention a few facts about the data. Since January, 2002 the month-to-month change in employment has had a standard deviation of 356,510 and a mean of only 56,820. This is a very imprecise number with huge month-to-month volatility. It has been alleged that the 853,000 employment increase in September, 2012 was the largest increase in 29 years. Not according to the data: since January, 2002 (and including September, 2012) there have been nine months when employment increased by more than 500,000 and three months in which employment gained more than 750,000 (January, 2012, +847,000 and January, 2003 with a whopping +991,000). At best, this is the largest increase in 108 months. The point is that this number moves all over the place. We shouldn’t take the +873,000 figure any more seriously than, say, the job loss of 1,141,000 in January, 2009.
If you want to stop reading right now, that’s fine with me. But I think you may find parts of this revealing and/or instructive. Some parts may even be mildly entertaining.
Statistical Issues and Seasonal Adjustment: Half the Gain
There have been steady changes in the number of people ages 20 − 24 who are employed each September. According to Ms. Rampell, since 1948 employment in this group fell by an average of 398,000 in September. In September, 2012, employment of these folks increased by 101,000. After processing with the standard seasonal adjustment software, the actual seasonally adjusted increase was 368,000, about 42% of the total increase in September. That leaves 853,000 − 368,000 = 485,000 new jobs still to be explained. Read on.
Note, however, that the 485,000 figure is well within 1.5 standard deviations of the mean since 2002. That’s a bit more evidence that the number is simply a statistical fluke.
Before going further, it’s important to understand how things are measured. Much of the following is from the Bureau of Labor Statistics’ Handbook of Methods. (On the BLS website, the handbook is available chapter-by-chapter as separate web links. Click here to download a copy as a single pdf file. And, as always, my methodology is transparent. Click here to download the Excel workbook with the gruesome details. This is an Excel 2011 workbook.)
An individual in the CPS sample is employed if, “during the reference week, (1) did any work at all as paid employees, worked in their own business or profession or on their own farm, or worked 15 hours or more as unpaid workers in a family-operated enterprise; and (2) all those who did not work but had jobs or businesses from which they were temporarily absent due to illness, bad weather, vacation, childcare problems, labor dispute, maternity or paternity leave, or other family or personal obligations—whether or not they were paid by their employers for the time off and whether or not they were seeking other jobs. Each employed person is counted only once, even if he or she holds more than one job. Included in the total are employed citizens of foreign countries who are residing in the United States, but who are not living on the premises of an embassy. Excluded are persons whose only activity consisted of work around their own home (such as housework, painting, repairing, and so forth) or volunteer work for religious, charitable, and similar organizations.” (BLS Handbook of Methods, chapter 1, p. 6)
An individual who did one hour of work for pay during the reference week counts as employed. The “reference week” is the week of the month that includes the 12th day.
Individuals in the sample are unemployed if they “1) had no employment during the reference week; 2) were available for work, except for temporary illness; and 3) had made specific efforts, such as contacting employers, to find employment sometime during the 4-week period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed.”
This definition, of course, creates the “discouraged worker” phenomenon, along with its impact on the unemployment rate. (Those interested should read my blog post about the labor force participation rate.)
The labor force is the sum of the number of people employed and the number of people unemployed. The unemployment rate is the number unemployed divided by the labor force. So simple, yet with much hidden complexity.
Now that you understand who is employed, who is unemployed, and who is not in the labor force, let’s turn our attention to sampling methodology.
BLS – Census Methodology
There are 60,000 households surveyed each month by the Census Bureau for the Current Population Survey (CPS, usually called the “household survey.”) That translates into 155,400 individuals using the Census figure of 2.59 people per household. Of those 155,400 individuals, 78.79% will be age 16 or over. Even though Census questions those age 15, the only data reported for purposes of the jobs report is on those 16 and over. With an unemployment rate of 8.11%, we expect 9,932 individuals in the household survey to be unemployed.
Now set a target unemployment rate, say 7.8%. That implies 9,551 of those surveyed need to be unemployed, a decrease of only 381 people compared to the 7.11% unemployment rate. Scary, isn’t it? Such are the vagaries of projecting a relatively small sample onto a large population. (BLS and Census know this. There are warnings all over their websites and in the BLS Handbook.)
Census conducts the survey during the week of the month that contains the 19th of that month. Respondents are asked about their employment status for the preceding week, the week that includes the 12th. There is a rather complicated pattern of rotation in and out of the sample.
“Rotation of sample. Part of the sample is changed each month. Each monthly sample is divided into eight representative subsamples or rotation groups. A given rotation group is interviewed for a total of 8 months, divided into two equal periods. The group is in the sample for 4 consecutive months, leaves the sample during the following 8 months, and then returns for another 4 consecutive months. In each monthly sample, 1 of the 8 rotation groups is in the first month of enumeration, another rotation group is in the second month, and so on. (The rotation group in the fifth month of enumeration is returning after an 8-month break.) Under this system, 75 percent of the sample is common from month to month and 50 percent is common from year to year for the same month. This procedure provides a substantial amount of month-to-month and year-to-year overlap in the sample, thus yielding better estimates of change and reducing discontinuities in the series of data without burdening sampled households with an unduly long period of inquiry.” (BLS Handbook of Methods, chapter 1, p. 7)
There’s a reason economists like me make a fairly good living. We’re willing to dig into the numbers and the underlying assumptions. If you found this persuasive and/or interesting, you may have the economist gene.
In this post I’ll take a closer look at the labor force participation rate (LFPR). Specifically, I will show that the Great Recession has had dreadful consequences for younger workers. The U.S. is on the verge of losing a generation of young adults who simply cannot find work and have given up looking. And, at the same time, I’ll debunk one widespread myth: that the LFPR is declining because baby boomers are retiring. In fact, a quick look at the data reveals exactly the opposite: the LFPRs for older workers are rising, not falling.
The graphs below were compiled from data at the Bureau of Labor Statistics. Curiously, the only data I could find on the participation rate did not go back very far. Luckily, the BLS provides breakdowns of both the civilian noninstitutional labor force and total population by age bracket all the way back to 1960. So, with the help of our old pal Mr. Excel™, we can grow our own participation rate. (As always, I value transparency. Click here to download the Excel workbook with all the data. For technical reasons, this file is in Excel 2007 format.)
Background on the Labor Force Participation Rate
If you’ve been reading this blog for a while you know that I’ve written about the labor force participation rate before. The definition is simple: the number of people in the labor force divided by total population. The major complicating factor is the definition of the labor force. To be in the labor force someone must be either employed or unemployed. They are counted as employed if they did any work for pay during the previous four weeks. This is complication number one: many people counted as employed have part-time jobs, but would like to have full-time jobs. The unemployed are those who did no work for pay during the previous four weeks and are actively seeking a job. Those who have given up looking for work are not part of the labor force. A significant increase in the number of these discouraged workers leads to a drop in the labor force participation rate.
The LFPR has been declining since the mid‑1990s. There was an uptick in the mid‑00′s. But today the LFPR is at the lowest level since 1988. This is important. People who are unemployed for an extended period have increasing difficulty obtaining jobs. Indeed, there is quite a bit of evidence that their working skills deteriorate. In general, the longer someone is out of work, the harder it becomes for them to find a job and, if successful, keep the job after returning to work. After we look at the data, I’ll summarize the research.
Before we get to the data, a word of warning. The vertical axes on the graphs have different scales. Since some groups have LFPRs in the neighborhood of 80% while others are down around 10%, I decided this was the best way to proceed. If you don’t like what I’ve done, download the Excel file and DIY.
Let’s begin by looking at the LFPR over the last 53 years.
The U.S. LFPR hit a low of about 54% in 1975, reflecting baby boomers into the population over age 16. So let’s look at what’s happened to the 16-24 age group:
This is where the catastrophe begins. Between 2000 and 2011 the LFPR for this group fell from about 66% to 55%. While I would like to believe this reflects more people going to college and even graduate school, I worry that is not the case. If this precipitous decline is being caused by people dropping out of the labor force because they cannot find work, we are condemning our youngest generation to an increased likelihood of reduced lifetime earnings.
Well, that’s depressing. Let’s look at ages 25 to 34:
The news here is a little better, but only a little. The LFPR has declined from 85% to about 81%. At least the decline isn’t as great as it is for younger workers. The next age group is 25-34:
That’s more like it. The age group 25-44 is actually doing pretty well. These are the folks who have jobs or are looking aggressively. What about 45-54?
Holding steady at about 82%.
Rather than discussing the last two groups individually, let’s look at both graphs:
Aha! People over age 54 are staying in the labor force longer. I should know — I’m one of them.
This is even more bad news Younger workers are dropping out of the labor force and, to a certain extent, they are being replaced by older workers hanging on to their jobs. This does not bode well for the future. Stay tuned to these pages. In the coming weeks I’ll look at the duration of unemployment broken down the same way.
Summary of the Economic Research
The most concise summary of the effects of long-term unemployment is from Aaronson, Mazumder, and Schechter (2010):
” As we entered 2010, the average length of an ongoing spell of unemployment in the United States was more than 30 weeks—the longest recorded in the post-World War II era. Remarkably, more than 4 percent of the labor force (that is, over 40 percent of those unemployed) were out of work for more than 26 weeks—we consider these workers to be long-term unemployed. In contrast, the last time unemployment reached 10 percent in the United States, in the early 1980s, the share of the labor force that was long-term unemployed peaked at 2.6 percent. Although there has been a secular rise in long‑term unemployment over the last few decades, the sharp increases that occurred during 2009 appear to be outside of historical norms. Further, this trend may present important implications for the aggregate economy and for macroeconomic policy going forward.
The private cost of losing a job can be sizable. In the short run, lost income is only partly offset by unemployment insurance (UI), making it difficult for some households to manage their financial obligations during spells of unemployment (Gruber, 1997; and Chetty, 2008). In the long run, permanent earnings losses can be large, particularly for those workers who have invested time and resources in acquiring knowledge and skills that are specific to their old job or industry (Jacobson, LaLonde, and Sullivan, 1993; Neal, 1995; Fallick, 1996; and Couch and Placzek, 2010). Health consequences can be severe (Sullivan and von Wachter, 2009). Research even suggests that job loss can lead to negative outcomes among the children of the unemployed (Oreopoulos, Page, and Stevens, 2008) and to an increase in crime (Fougère, Kramarz, and Pouget, 2009).
All of these costs are likely exacerbated as unemployment spells lengthen. The probability of finding a job declines as the length of unemployment increases. Although there is some debate as to exactly what this association reflects, it is certainly plausible that when individuals are out of work longer, their labor market prospects are diminished through lost job skills, depleted job networks, or stigma associated with a long spell of unemployment (Blanchard and Diamond, 1994). For risk-averse households that cannot insure completely against a fall in consumption as they deplete their precautionary savings, the welfare consequences of job loss rise as unemployment duration increases. Welfare implications are particularly severe during periods of high unemployment for individuals with little wealth (Krusell et al., 2008).”
Aaronson, D., B. Mazumder, and S. Schechter (2010), ” What is behind the rise in long-term unemployment?” Economic Perspectives, Federal Reserve Bank of Chicago, 2Q/2010, 23-51.
Blanchard, O. and P. Diamond (1994), ” Ranking, Unemployment Duration, and Wages.” Review of Economic Studies (1994) 61, 417-434.
Thomsen, Stephan L. (2009), “Explaining the Employability Gap of Short-Term and Long-Term Unemployed Persons.” Kyklos, August 2009, v. 62, iss. 3, pp. 448-78.
Ochsen, C. and H. Welsch (2011), “The Social Costs of Unemployment: Accounting for Unemployment Duration.” Applied Economics, November 2011, v. 43, iss. 25-27, pp. 3999-4005.
 For examples, see Thomsen, Stephan L (2009); Ochsen, C. and H. Welsch (2011); Blanchard, O., and P. Diamond (1994); and Aaronson, D., B. Mazumder, and S. Schechter (2010).
 Aaronson, et. al., op. cit., p. 23.