Abstract:

We use data from the American Time Use Survey (ATUS), covering both the recent recession and the pre-recessionary period, to explore how foregone market work hours are allocated to other activities over the business cycle.  Given the short time series, it is hard to distinguish business cycle effects from low frequency trends by simply comparing time spent on a given category prior to the recession with time spent on that category during the recession.  Instead, we identify the business cycle effects on time use using cross state variation with respect to the severity of the recessions.  We find that roughly 30% to 40% of the foregone market work hours are allocated to increased home production.  Additionally, 30% of the foregone hours are allocated to increased sleep time and increased television watching.  Other leisure activities absorb 20% of the foregone market work hours.  We use our evidence from the ATUS to calibrate and test the predictions of workhorse macroeconomic models with home production.  We show that the quantitative implications of these models regarding the allocation of time over the business cycle matches reasonably well the actual behavior of households.

http://papers.nber.org/papers/W17259

Abstract:

We reformulate the Smets-Wouters (2007) framework by embedding the theory of unemployment proposed in Gali (2011a,b).  We estimate the resulting model using postwar U.S. data, while treating the unemployment rate as an additional observable variable.  Our approach overcomes the lack of identification of wage markup and labor supply shocks highlighted by Chari, Kehoe and McGrattan (2008) in their criticism of New Keynesian models, and allows us to estimate a “correct” measure of the output gap.  In addition, the estimated model can be used to analyze the sources of unemployment fluctuations.

http://papers.nber.org/papers/W17084

A step in the right direction. (This paper has been out for a while, but is finally an NBER working paper. )

The Great Recession from December 2007 to June 2009 is associated with a dramatic weakening of the labor market from which the labor market is now only slowly recovering.  The unemployment rate remains stubbornly high and durations of unemployment are unprecedentedly long.  I use data from the Displaced Workers Survey (DWS) from 1984-2010 to investigate the incidence and consequences of job loss from 1981-2009.  In particular, the January 2010 DWS, which captures job loss during the 2007-2009 period, provides a window through which to examine the experience of job losers in the Great Recession and to compare their experience to that of earlier job losers.  These data show a record high rate of job loss, with almost one in six workers reporting having lost a job in the 2007-2009 period.  The consequences of job loss are also very serious during this period with very low rates of reemployment, difficulty finding full-time employment, and substantial earnings losses.

http://papers.nber.org/papers/W17040

U.S. Migration Falls Sharply, by Conor Dougherty, WSJ:

Migration around the U.S. slowed to a crawl last year, especially for this decade’s boom towns, as a weak housing market and job insecurity forced many Americans to stay put.

Demographers say the dropoff in migration, shown in Census data to be released Thursday, is among the sharpest since the Great Depression. It marks the end of what Brookings Institution demographer William Frey calls a “migration bubble.”

As asset values rose fairly steadily in the past decade, Americans young and old moved around the country in search of jobs or better weather. In many cases, people living in higher-cost housing markets such as San Francisco and New York cashed in their real-estate winnings and moved to outlying counties, or to states like Florida and Nevada, hoping to find a cheaper house and pocket the difference. Now, “people are hanging tight; they’re too scared to do anything,” said Mr. Frey.

The data, covering the one-year period until July 1, 2008, show this effect across U.S. counties and metropolitan areas — another sign of how this recession has spared few industries or regions.

Migration typically slows during recessions. But in past downturns, the slowdown has been more regional in scope, with workers fleeing weaker job markets for places where companies were still hiring. In the deep 1980s recession, for instance, laid-off auto workers fled the industrial Midwest for energy-rich states in the South with more plentiful jobs.

What’s unique this time is migration has slowed almost everywhere. The sharpest year-to-year changes were among what demographers call “domestic migrants,” people who moved within the U.S. That doesn’t count population changes that result from births, deaths or immigration.

Although I agree with the trend behavior described above, Dougherty is incorrect about the cyclicality of geographic mobility. In fact, geographic mobility is moderately countercyclical—that is, more people move during recessions than during booms (relative to trend). This may seem counter-intuitive but makes economic sense.

Geographic mobility is a means of reallocating resources, in this case labor, to more efficient uses. In the past, 70 percent of people who move indicated having moved for economic reasons and up to 50 percent of those moves occurred because of a job separation [Lansing and Morgan (1967); Bartel (1979)]. In particular, there is a significant positive relationship between unemployment and geographic mobility [Bartel (1979); Schlottmann and Herzog Jr. (1981, 1984)]. Thus, countercyclical mobility is consistent with reallocation of idle workers across space.

I assess the cyclicality of geographic mobility in a recent working paper. I the measure the rate of geographic mobility as one minus the share of persons living at the same address one year later reported by the U.S. Census Bureau. These data come from the March supplement to the Current Population Survey, so the 2007 data do not reflect much of the distress in mortgage markets—and any concomitant effects on mobility—that began later in 2007.

Removing the low-frequency trend is important because it represents structural changes—such as demographic changes or, say, innovations in mortgage finance—that are unassociated with the business cycle. I isolate the component of the time series that moves at business cycle frequency using an unobserved components model (see paper for details). The figure below plots the cyclical component of the mobility rate together with that of the unemployment rate for comparison.

The cyclical component of mobility tends to follow the unemployment rate, indicating that more people move during recessions than during booms. This is consistent with geographic mobility as a means for reallocating idle labor to more productive uses. The contemporaneous correlation of the cyclical component of the mobility rate with the unemployment rate is 0.50, indicating moderate countercyclicality. Also note that mobility is substantially less volatile over the business cycle than unemployment.

Of course, the problems in the housing market beginning in 2007, notably the dramatic decline in prices, will undoubtedly reduce geographic mobility during this recession. This will further slow recovery because unemployed persons cannot move to areas with more favorable labor markets as easily or quickly as before.

Recently several news pieces have made the claim that if the unemployment rate were calculated as it was during the Great Depression, the current rate would be close to double what it is, and creeping toward the formidable rates back in the 1930s.

The first problem with this statement is that there was no official unemployment rate until the 1940s. The ones we use today were reconstructed after the fact. As unemployment ballooned during the Great Depression a number of ad hoc attempts were made to calculate the rate, and the widely divergent results led private researchers and some state and local governments to experiment with various sampling methods….

The second problem with the statement is that it’s just not true. Although the BLS has refined their surveys and made questions more specific, conceptually the unemployment formulas have not changed, and the BLS’s own analysis of test data shows that the impacts of several sets of changes on the overall numbers are minor.

The Liscio analysis is consistent with my encounters with government statisticians. They are not idealogues who arbitrarily change formulas for political expedience. They are serious scientists and statisticians who are unwaveringly committed to obtaining the best measurements possible of very hard to measure things, things that have a very real impact on policy. It is at times a thankless job, made even more so when they are forced to refute nonsense claims about their methodology.

This paper uses weekly data from the Survey of Income and Program Participation (SIPP) to estimate the role of time aggregation in measuring gross labor force flows and unemployment dynamics. Time aggregation is substantial: gross flows estimated from monthly data understate the true number of transitions by 15–24 percent. Time aggregation in both separations to unemployment and accessions from unemployment comoves positively with the business cycle. The effect from time aggregation on separations is roughly offset by its effect on accessions, however, creating no meaningful cyclical bias in measured gross flows or hazard rates. Contrary to claims by Hall (2006) and Shimer (2007), separation hazard rates calculated from the SIPP and the Current Population Survey are strongly countercyclical and remain so after adjusting for time aggregation. In addition, the separation hazard rate contributes fully one-half of the cyclical variance of the steady-state unemployment rate after adjusting for time aggregation.