Gross Job Creation and Destruction: Microeconomic Evidence and Macroeconomic Implications

Questions & answers

Q1. What data set underlies the paper’s measurements, and what specifically do “gross job creation” and “gross job destruction” mean here?

The paper uses the Longitudinal Research Data file (LRD), a comprehensive, establishment-level panel drawn from the Annual Survey of Manufactures, covering “approximately 860,000 annual observations and 3.4 million quarterly observations on 160,000 different manufacturing establishments” between 1972 and 1986 (p. 124, Sec. 2.1). Gross job creation in a sector-period is defined as the sum of employment gains across all expanding and newly born establishments in that sector, and gross job destruction as the sum of employment losses across all shrinking and dying establishments, each expressed as a rate by dividing by a measure of sector size (Sec. 2.2, pp. 129-130); the authors note this measure “directly reflect[s] the reallocation of employment positions or jobs, and not the reallocation of workers,” and that because only point-in-time plant-level employment is observed, these measures represent lower bounds on the true magnitude of job creation and destruction (p. 130).

Q2. How large are gross job creation and destruction in U.S. manufacturing, and how much of this reflects persistent rather than transitory establishment-level change?

Based on March-to-March establishment-level employment changes, the paper calculates that manufacturing’s rates of gross job creation and destruction averaged 9.2 percent and 11.3 percent per year respectively, with quarter-to-quarter rates of 5.37 percent and 5.62 percent (p. 124). A second basic fact is that most of this creation and destruction is persistent: 73 percent of jobs created between March 1974 and March 1975 still existed in March 1976, and 72 percent of jobs lost over that same interval were still lost a year later, with average one-year persistence rates of 68 percent for annual job creation and 81 percent for annual job destruction (p. 125).

Q3. What role do establishment births and deaths play in total job creation and destruction?

The paper reports that “establishment deaths account for 25% of annual gross job destruction over the sample period, while establishment births account for 20% of annual gross job creation,” and more broadly that establishment-level employment changes “exhibit considerable discreteness” (p. 125) – that is, a meaningful share of gross flows reflects whole plants opening or closing rather than continuing plants expanding or contracting.

Q4. How does gross job reallocation vary over the business cycle, and what drives that time variation?

The quarterly gross job reallocation rate (the sum of creation and destruction) for manufacturing “ranges from a low of 6.9% in 1979:1 to a high of 15.4% in 1975:1,” with a simple correlation between net employment growth and gross job reallocation of -0.57 using March-to-March changes and -0.51 using quarter-to-quarter changes (p. 125) – a strongly countercyclical pattern. Decomposing this time variation into idiosyncratic, sectoral, and aggregate-time components, the paper finds “the overwhelming bulk of time variation in gross job reallocation is accounted for by time variation in the idiosyncratic component,” with aggregate-time and sector-time effects accounting for only a small fraction, and this idiosyncratic component itself displays “a strong pattern of countercyclic movements with respect to own-sector and total manufacturing net employment growth rates” (p. 126); the authors report these results hold “in both annual and quarterly data and for every sectoral classification scheme we consider.”

Q5. What does the paper’s theoretical model predict about how aggregate demand disturbances versus allocative disturbances affect job creation and destruction differently?

In the model, adverse aggregate shocks “tend to increase job destruction and decrease job creation,” but because the endogenous timing of reallocation interacts with labor-market frictions, adverse aggregate shocks also “interact with frictions in the labor market to induce an accelerated pace of reallocation” – a mechanism the authors label a “reallocation timing effect,” since depressed current demand lowers the opportunity cost (in forgone production) of reallocating resources now (p. 126). In contrast, an increased intensity of allocative shocks “increases job destruction and eventually increases job creation,” with job creation’s response lagging for several compounding reasons: new-job creation and worker reallocation are themselves time-consuming, persistence in the intensity of allocative disturbances raises uncertainty about the returns to current specific-capital investment and so discourages immediate job creation, and (outside the formal model) macroeconomic externalities from final-demand spillovers could temporarily depress job creation even as destruction rises (p. 126).

Q6. What empirical method does the paper use to recover the dynamic effects of aggregate versus allocative shocks, and what is its central finding?

Adapting a methodology from Blanchard and Diamond’s (1989) study of unemployment and vacancy dynamics, the authors estimate the joint dynamics of job creation and destruction, use theoretical identifying restrictions to recover innovations to the underlying allocative and aggregate shocks, and trace out their dynamic effects (Sec. 4, p. 127). Their main finding is “the large contribution that allocative shocks make to movements in job creation and destruction over short-, medium-, and long-forecast horizons,” with the implied contribution of allocative shocks to movements in manufacturing employment growth large over medium- and long-forecast horizons – a result the authors note “contrast[s] sharply with Blanchard and Diamond’s conclusion that allocative shocks play a small role in the dynamics of unemployment and vacancies over short and medium horizons.”

Q7. What relationship does the paper find between job reallocation and the manufacturing unemployment rate, and how confident are the authors in a causal reading?

Using quarterly regressions of unemployment on alternative measures of job reallocation (including a decomposition into a part associated with observed allocative shocks such as oil-price growth and a part reflecting unobserved allocative shocks or reallocation-timing effects, and a separate VAR-based decomposition), the authors report “a strong positive effect of job reallocation on unemployment in all specifications we consider” (p. 128). They add an explicit qualification: “allocative disturbances have a statistically significant effect on unemployment both directly and through reallocation timing channels, but some specifications suggest that the direct contribution of allocative disturbances to unemployment movements is small,” meaning much of the link runs through the endogenous timing channel rather than a direct, mechanical effect of allocative shocks on unemployment (p. 128).

Q8. What are the paper’s “five main messages,” as the authors themselves summarize them in the conclusion?

The authors summarize: first, “there is tremendous heterogeneity of establishment-level employment changes,” with associated large rates of gross job creation, destruction, and reallocation; second, “the magnitude of heterogeneity varies significantly over time and in a way that is intimately related to aggregate fluctuations,” and this comovement is driven by the idiosyncratic component specifically, not by differing mean sectoral responses to common shocks (Sec. 6, p. 166). Third, drawing on the broader literature on the costs of job loss and specific capital, they state they “are doubtful that a satisfactory understanding of aggregate fluctuations will emerge from theories that ignore these frictions”; fourth, their model is offered as “suggestive of how both aggregate and allocative disturbances can drive fluctuations in job creation and destruction, unemployment, productivity, and output,” with the simple model seen as extensible to richer search and investment technologies; and fifth, their empirical results support “the view that allocative disturbances were a major driving force behind movements in job creation, job destruction, job reallocation, and net employment growth” in U.S. manufacturing from 1972 to 1986, with unemployment regressions suggesting allocative disturbances played an important role both directly and via reallocation timing (p. 167).