Over the course of the Great Recession, rates of job loss in the US reached record highs. As the recovery continues, understanding the nature and speed of labour market adjustment is more important than ever. At the national level, much of the public attention and media coverage has been on overall levels of job creation and economic activity. However, variation in the severity of the downturn across labour markets points to a need to focus the policy discussion on local areas.
There is a considerable amount of research on local labour market adjustment processes. Well-known work by Blanchard and Katz (1992) originally emphasised the importance of labour mobility as opposed to local job creation, showing that local unemployment rates primarily adjust through the migration of workers to areas with more jobs. Building on this idea, Bound and Holzer (2000) measured the responsiveness of specific populations to labour market shocks. They show that low-skilled workers, particularly low-skilled black workers, migrate infrequently relative to other groups in response to labour market shocks. One reason for these differential rates of mobility across socioeconomic groups may be due to the extent to which different types of workers bear the incidence of a local downturn. As shown by Notowidigdo (2013), low-skilled workers may be less likely to migrate because they are disproportionately impacted by increases in public assistance and decreases in housing costs brought about by adverse labour demand shocks.
Migration is by no means the only type of labour force adjustment. Unemployed workers discouraged by a lack of opportunities may turn to permanent sources of income in the form of retirement and Social Security Disability Insurance (SSDI). The role of retirement and social security as alternatives for workers in economic downturns has been studied extensively (Black et al. 2002, Burkhauser 2004, Autor and Duggan 2003, Chan and Stevens 2001).
In addition to migration and public assistance programmes, labour force non-participation has become an increasingly important channel of labour force exit, especially since the Great Recession. In hard economic times, unemployed workers may become discouraged and stop looking for work (Erceg and Levin 2013). In the Great Recession, the labour market saw a surge in exits due to discouraged workers, only half of whom eventually re-entered the labour market (Ravikumar and Shao 2014, Kwok, Daly and Hobijn, 2010). On a national level, rates of labour force participation have declined precipitously over the past 10 years, reaching lows not seen since the 1970s.
A focus on mass layoffs
In recent research (Foote et al. 2015), we contribute to this literature in two key ways.
- First, we unify the disparate observations of the many types of labour market exits and thus compare their relative magnitude as channels of adjustment.
- Second, we focus on mass layoffs as the measure of local labour market shock, which is a marked improvement over other measures of local downturns such as the unemployment rate.
Our empirical strategy focuses on mass layoff events that represent a sizeable deviation from a county’s economic trends. When at least 50 workers file unemployment insurance claims against a single employer, the Bureau of Labour Statistics (BLS) contacts the employer to confirm a mass layoff or downsizing. We combine the resulting dataset, which includes information on mass layoffs measured at the county level for the years 2001-2011, with various county-level indicators. In every year from 2001 to 2011, between one quarter and one half of all US counties had mass layoffs affecting at least 1% of their labour force. During the recent Great Recession, layoffs surpassing 5% of a county’s labour force became more common. While large layoff events were concentrated in the Rust Belt area, large events occurred across the country, in both urban and rural counties.
We measured how a layoff event affects the size of the labour force, as well as different types of labour force exit: in-migration, out-migration, retirement, and Social Security Disability Insurance. We compiled county-level data from a variety of sources. We calculated migration rates using county data from the Internal Revenue Service, and used retirement and disability insurance counts from publicly available resources at the Social Security Administration. We compiled county labour market and demographic statistics from the BLS Local Area Unemployment Statistics and from the National Cancer Institute Surveillance, Epidemiology and End Results (SEER) programme.
Figure 1. Estimated effect of a layoff of 2% of the labour force on outmigration
Notes: Estimate and 95% pointwise confidence interval shown. Sample limited to counties that experienced just one event of at least 2% of the labour force between 2001 and 2007. More details on estimation methodology available in Foote, Grosz and Stevens (2015).
Mass layoffs have a long-term impact
- We find that when 1% of a county’s labour force is laid off, the county’s total labour force shrinks by 0.15 percentage points within three years.
Between 2001 and 2011, internal migration, take-up of disability insurance, and early retirement account for three-quarters of the decline in the labour force following a significant economic downturn, with internal migration accounting for more than half. This is consistent with the findings in Blanchard and Katz (1992). Labour force non-participation accounts for the remaining quarter of the decline.
As the country experienced the Great Recession, patterns of labour force exit changed. Workers were twice as likely to exit the labour force following mass layoffs after the start of the Great Recession than before. Even so, the relative importance of migration as the mechanism for the labour force reduction declined substantially, comprising less than a fifth of all exits from 2007-11.
This implies that the importance of non-participation has grown, accounting for 60% of exits in the period from 2007 to 2011. Non-participants account for a growing segment of labour force exits, yet we know little about their economic activities. A recent article in the New York Times, for example, discussed this phenomenon, but also pointed to how little is known about the daily lives of these non-participants and the reasons for their long-term exits from the labour force (Cox 2014).
Following a mass layoff, applications for disability insurance increase as well, particularly for workers over 55 years old. This finding is driven by a larger response to mass layoffs during the Great Recession. Our results show that this effect can explain five percent of the change in the labour force following a mass layoff.
Finally, we find significant geographic differences in the relative importance of different types of labour force exit. In urban counties, rates of labour force exit are a third as high as in rural counties, reflecting the higher density of employment opportunities in cities. In rural counties, workers were less likely to move away than in urban counties, and were more likely to become non-participants.
In light of the recent Great Recession, we continue to learn about how large economic downturns directly affect workers in a variety of ways. Here, we document that following an adverse demand shock, individuals exit local labour markets primarily through migration, although that has become less prominent in the Great Recession. Faced with declining economic prospects, workers are becoming more likely to stay put, without re-entering the labour market. While our research documents the increase in non-participation following adverse labour demand shocks, more needs to be done to understand what effect this phenomenon has on the broader economy. In particular, little research has been done on the effect of non-participation on wages and employment prospects for those seeking work, or on the long-term effects labour force exit has on a worker’s human capital.
Authors’ note: The research described in this article was undertaken while Andrew Foote was at University of California, Davis. Any opinions and conclusions expressed herein are those of the author and do not necessarily represent the views of the U.S. Census Bureau. The research in this article does not use any confidential Census Bureau information.
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