Abstract:

Many theoretical models of labor market search imply a tight link between worker flows (hires and separations) and job gains and losses at the employer level. Partly motivated by these theories, we exploit establishment-level data from U.S. sources to study the relationship between worker flows and job flows in the cross section and over time.  We document strong, highly nonlinear relationships of hiring, quit and layoff rates to employer growth in the cross section.  Simple statistical models that capture these cross-sectional relationships greatly improve our ability to account for fluctuations in aggregate worker flows.  We also evaluate how well various theoretical models and views fit the patterns in the data.  Aggregate fluctuations in layoffs are well captured by micro specifications that impose a tight cross-sectional link between worker flows and job flows.  Aggregate fluctuations in quits are not.  Instead, quit rates rise and fall with booms and recessions across the distribution of establishment growth rates, but more so at shrinking employers.  Finally, we use our preferred statistical models – in combination with data on the cross-sectional distribution of establishment growth rates – to construct synthetic JOLTS-type measures of hires, separations, quits and layoffs back to 1990.

http://papers.nber.org/papers/W17294

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

This paper studies the long-term effects of the business cycle on workers’ future promotions and wages. Using the Swedish employer-employee matched data, we find that a cohort of workers entering the labor market during a boom gets promoted faster and reaches higher ranks. This pro-cyclical promotion cohort effect persists even after controlling for workers’ initial jobs, and explains at least half of the wage cohort effects that previous studies have focused on. We repeat the same analyses using personnel records from a single US company, and obtain the same qualitative results.

We document that trust in public institutions–and particularly trust in banks, business and government–has declined over recent years. U.S. time series evidence suggests that this partly reflects the pro-cyclical nature of trust in institutions.  Cross-country comparisons reveal a clear legacy of the Great Recession, and those countries whose unemployment grew the most suffered the biggest loss in confidence in institutions, particularly in trust in government and the financial sector.  Finally, analysis of several repeated cross-sections of confidence within U.S. states yields similar qualitative patterns, but much smaller magnitudes in response to state-specific shocks.

http://papers.nber.org/papers/W16891

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.