Academic journal article Economic Quarterly - Federal Reserve Bank of Richmond

Heterogeneity in Sectoral Employment and the Business Cycle

Academic journal article Economic Quarterly - Federal Reserve Bank of Richmond

Heterogeneity in Sectoral Employment and the Business Cycle

Article excerpt

(ProQuest: ... denotes formulae omitted.)

This paper uses a factor analytic framework to assess the degree to which agents working in different sectors of the U.S. economy are affected by common rather than idiosyncratic shocks. Using Bureau of Labor Statistics (BLS) employment data covering 544 sectors from 1990-2008, we first document that, at the aggregate level, employment is well explained by a relatively small number of factors that are common to all sectors. In particular, these factors account for nearly 95 percent of the variation in aggregate employment growth. This finding is robust across different levels of disaggregation and accords well with Quah and Sargent (1993), who perform a similar analysis using 60 sectors over the period 1948-1989 (but whose methodology differs from ours), as well as with Foerster, Sarte, and Watson (2008), who carry out a similar exercise using data on industrial production.1

Interestingly, while common shocks represent the leading source of variation in aggregate employment, the analysis also suggests that this is typically not the case at the individual sector level. In particular, our results indicate that across all goods and services, common shocks explain on average only 31 percent of the variation in sectoral employment. In other words, employment at the sectoral level is driven mostly by idiosyncratic shocks, rather than common shocks, to the different sectors. Put another way, it is not the case that "a rising tide lifts all boats." Moreover, it can be easy to overlook the influence of idiosyncratic shocks since these tend to average out in aggregation.

Despite the general importance of idiosyncractic shocks in explaining movements in sectoral employment, we nevertheless further document substantial differences in theway that sectoral employment is tied to these shocks. Specifically, we identify sectors where up to 85 percent of the variation in employment is driven by the common shocks associated with aggregate employment variations. Employment in these sectors, therefore, is particularly vulnerable to the business cycle with little in the way of idiosyncratic shocks that might be diversified away. These sectors are typically concentrated in construction and include, for example, residential building.

More generally, our empirical analysis indicates that employment in goods-producing industries tends to more tightly reflect changes in aggregate conditions relative to service-providing industries. However, even within the goods-producing industries, substantial heterogeneity exists in the way that sectoral employment responds to common shocks. For instance, the durable goods and construction industries are significantly more influenced by common shocks than the nondurable goods and mining industries. Among the sectors where employment is least related to aggregate conditions are government, transportation, and the information industry.

Finally, we present evidence that the factors uncovered in our empirical work play substantially different roles in explaining aggregate and sectoral variations in employment. Although the findings we present are based on a three-factor model, our analysis suggests that one factor is enough to explain roughly 94 percent of the variation in aggregate employment. At the same time, however, that factor appears almost entirely unrelated to employment movements in specific sectors such as in natural resources and mining or education and health services. Interestingly, the reverse is also true in the sense that the analysis identifies factors that significantly help track employment movements in these particular sectors but that play virtually no role in explaining aggregate employment fluctuations.

This article is organized as follows. Section 1 provides an overview of the data. Section 2 describes the factor analysis and discusses key summary statistics used in this article. Section 3 summarizes our findings and Section 4 offers concluding remarks. …

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