United States Department of Agriculture

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Economic

Research

Service

Economic

Research 

Report 

Number 172

August 2014

United States Department of Agriculture

Rural Employment Trends in 

Recession and Recovery

Tom Hertz

Lorin Kusmin

Alex Marré

Tim Parker

August 2014

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err172

Tom Hertz, Lorin Kusmin, Alex Marré, and Tim Parker

Rural Employment Trends in Recession

and Recovery

What Is the Issue?

In December 2007, 6 years of economic growth ended as the U.S. economy entered the most severe 

recession since the Great Depression. Eighty-two percent of U.S. counties experienced job losses as 

a result of the recession, but some places were hit much harder than others, and some have recovered more rapidly. This report describes the nature and causes of this geographic variation, which 

include differences in the mix of industries that support the local economy, in population growth 

trends, and in the demographics of the local workforce. How did these factors lead to differences in 

employment outcomes between urban (metropolitan) and rural (nonmetropolitan) areas and among 

nonmetro counties? What explains geographic differences in the severity of unemployment during 

the recession and the pace of job growth since the end of the recession?

What Did the Study Find?

National nonfarm employment fell by 6.3 percent from its peak in January 2008 to its nadir in 

February 2010. In May 2014, fully 5 years after the official end of the recession, the number of 

nonfarm jobs has finally reached its pre-recessionary peak level. Yet, given slow but steady population growth over the intervening years, the share of the adult population that was employed in 

May 2014 remains 4 percentage points below its pre-recessionary level and 9.8 million people (or 

6.3 percent of the U.S. workforce) remain unemployed, 3.4 million of whom having been out of 

work for more than 6 months.

Regional differences in the effects of the recession are striking, with the column of States running 

from North Dakota to Texas faring much better in terms of employment and unemployment rates 

than most other regions. The Great Plains States in particular experienced smaller spikes in 

unemployment largely because their initial industrial composition was skewed toward relatively 

stable economic sectors (in particular, agriculture) and away from some of the hardest hit sectors 

(e.g., manufacturing).

Employment losses from their peak values were slightly larger in nonmetro than metro counties 

and began a year earlier, in 2007. Employment recovered over the course of 2010, growing at a 

comparable pace in metro and nonmetro counties. But nonmetro employment growth slowed in 

2011 and fell to zero or slightly below thereafter. Our statistical analysis suggests that about half of 

this employment growth deficit is due to nonmetro counties having slower population growth. In 

addition, fewer jobs are being created in areas that have older and less well-educated workforces. 

Together, these effects outweighed the benefits of nonmetro counties’ more favorable mix of 

industries, in particular their higher employment shares in agriculture and the robust extractive 

industries (mining, oil, and gas).
The most rural nonmetro counties were less affected by employment losses and unemployment increases during 

the recession. Counties in the lowest population-density category saw average employment levels fall by just 1.3 

percent during the recession, versus 5 to 6 percent for medium- and high-density nonmetro counties. Again, these 

differences were driven in part by differences in the local mix of industries. At the start of the recession, the more 

rural counties had much lower shares of employment in manufacturing, an industry that suffered some of the most 

rapid job losses during the recession. More rural counties also had higher shares of farm employment and Federal 

employment, sectors that, in general, did not shed jobs during the recession. 

The employment effects of the recession were more pronounced in nonmetro counties with large African-American 

populations. These areas saw employment fall by 7.9 percent, compared to 4.1 percent for counties with no large 

minority populations. This pattern could not readily be explained by the types of industries found in counties with 

large African-American populations, by the educational status of their workforces, or by differences in age structure 

or prior population and employment growth trends. Instead, the most important explanatory factor appears to be that 

these counties were located in Southeastern States that lost employment across the board, in counties with both high 

and low African-American population shares. Still, when we include a county-level variable that measures the share 

of the population that is African-American, this variable is statistically associated with an average 1.3 percentage 

points of the excess job losses experienced by these counties. The causal mechanism behind this result is not clear. In 

particular, these results are not direct evidence of racial discrimination in the labor market, which cannot be assessed 

using county-level analysis such as this. Instead, race may be serving as a proxy for other measures of socioeconomic 

and labor market disadvantage that led to below-average outcomes during the recession. Regardless, these results 

provide evidence of a disparate impact of the recession on areas with large African-American populations, holding 

a number of other important factors equal. 

Nonmetro counties with large Hispanic populations actually managed to add jobs during the recession (employment increased by 0.2 percent), and employment has grown by 4.6 percent during the 4 years of economic 

recovery, compared to 1.8 percent for nonmetro counties without large minority populations. Here, industrial 

composition proves most important; Hispanic counties had much lower shares of employment in manufacturing 

than did counties without large minority populations (5 percent versus 13 percent), which insulated them from the 

steep manufacturing employment decline. Although counties with large Hispanic populations continue to grow 

faster than average during the recovery, below average levels of educational attainment—as in counties with large 

African-American populations—have slowed employment growth during the 4-year recovery period. 

How Was the Study Conducted?

The primary sources of data for this analysis were the Census Bureau’s Current Population Survey and American 

Community Survey and the Bureau of Labor Statistics’ Local Area Unemployment Statistics and Quarterly Census 

of Employment and Wages datasets. These were used for descriptive and multivariate regression analysis. Secondary 

sources were used in discussing macroeconomic trends, household debt burden, the causes and consequences of 

long-term unemployment, and other factors.

In interpreting this report’s findings, two caveats need to be recognized. First, the report focuses exclusively 

on employment outcomes and ignores many other factors that influence economic well-being, such as wage 

levels, income, poverty, and other factors affecting living standards. A second important caveat concerns the 

multivariate regression analyses used in this study, the findings of which depend on the accuracy and specificity 

of the underlying data on county characteristics, and on the choice of regression specification, both of which 

have limitations. In particular, data on industry shares are available for all counties at only a fairly high level of 

aggregation, limiting their explanatory power. The regression specifications are chosen to work with the available data in order to shed light on some of the broad factors that may have contributed to geographic variation 

in employment outcomes. As always with regression analysis, their precision should not be overstated, and the 

possibility of biased estimation results cannot be dismissed. 

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