The Information Content of Cost Behavior Components:
Evidence from Labor Market Flows

Abstract
We examine the information content of cost behavior components in firms’ asymmetric costfunction, namely, aggregate-level elasticities of costs with respect to sales increases vs. decreases.
We show that the persistence of the elasticity of costs is higher for sales increases than for sales
decreases. Using business-level job flows from the Business Employment Dynamics (BED)
dataset, which has recently been made available by the BLS, we find that, after accounting for
GDP growth and other macroeconomic indicators, the aggregate elasticity of costs with respect tosales increases explains gross job inflows, but not gross job outflows. On the other hand, theaggregate elasticity of costs with respect to sales decreases explains gross job outflows, but notgross job inflows. When we include both elasticities in the regression, both are significant but with
opposite signs. We obtain similar results in vector autoregression (VAR) models. Additional tests
indicate that: (a) the effect of aggregate elasticity of costs with respect to sales decreases is more
pronounced in periods with high uncertainty; and (b) asymmetric cost models explain more of thevariation in job outflows than models that assume symmetric cost responses.

11. Introduction
A growing body of literature, beginning with Anderson, Banker, and Janakiraman (2003)(hereinafter ABJ), studies cost behavior and assumes a non-symmetric relation between costs andactivities: costs increase more when activity rises than they decrease when activity falls by anequivalent amount. In particular, the literature estimates a log-linear cost function to capture theasymmetry, with a separate coefficient for firms that experienced an increase in sales (hereinafter
βSU) and an incremental coefficient for firms that experienced a decrease in sales during the period(hereinafter βSD). These coefficients are interpreted in the literature as the elasticity of costs toincreasing or decreasing sales. In this paper, we study the information content of both coefficientsat the aggregate level, by focusing on business-level job-flows.
We use the Business Employment Dynamics (BED) quarterly data on job flows provided
by the Bureau of Labor Statistics (BLS). While traditional employment data, which are based on
the Current Population Survey (CPS), are gathered at the worker level, the BED data are collected
at the business (i.e., the firm or establishment) level.1 Boon, Carson, Faberman, and Ilg (2008)
argue that, compared with other sources of employment data, the BED data are more appropriate
for analyzing the business side of the labor market because they provide information on positions
and not employees. Furthermore, the BED data provide flow information, in particular, grossinflows and outflows of jobs each quarter. Studying job inflows and outflows separately allows us
to better evaluate the differential impact of firms’ cost behavior components. We also note that the
BED data are available with a lag of three quarters and hence are less timely than the CPS data,
which are compiled every month and released at the beginning of the following month. Because1

The data cover all establishments covered by State unemployment insurance programs.

2

our focus in this paper is to understand how cost behavior components relate to the labor market,
rather than forecasting of unemployment per se, the BED data fit our purpose.
We estimate quarterly time-series of aggregate-level βSU and βSD for all public firms in the
United States for the period Q3:1992 to Q2:2017. Prior literature suggests that the two coefficients
capture different aspects of firms’ cost behavior. βSU captures the elasticity of changes in costs toincreases in sales, with a greater coefficient reflecting more elastic (less rigid) cost structure (Lev
1974; Noreen 1991; Kallapur and Eldenburg 2005; Aboody, Levi and Weiss 2018). As such, it
reflects managers’ long-term capacity and production-technology choices and cannot be easily
altered in the short-run. βSD captures the incremental resource retention when sales decrease—that
is, the difference between change in costs when sales increase and change in costs when sales
decrease by an equivalent amount. By its very nature, this coefficient reflects the level ofadjustment costs to reduce or restore committed resources as well as short-term managerial
expectations (ABJ 2003). Both coefficients are determined by the current production technology,
capacity choices, level of adjustment costs, and managers’ discretion when making decisions about
resources. Yet, βSU is likely to reflect the elasticity of the production technology and managers’
long-run operational and capacity choices to a larger extent, while βSD is more likely to capture
adjustment costs and managerial short-term expectations.2
We begin our analysis by studying the time-series properties of each coefficient, by
estimating its persistence over time. We expect the persistence of the aggregate elasticity of costswith respect to sales increases (βSU) to be higher than the persistence of the aggregate elasticity of
costs with respect to sales decreases (βSD), for the following reasons. First, sales decline is typically2

There are vast literatures on βSD, the stickiness coefficient, and also on the coefficient of a symmetric cost function.
βSU, per se, has not been studied extensively, and hence we draw from the literature on firms’ cost structure when weinterpret this coefficient.

3

only temporary. Hence, at the aggregate level, the composition of firms experiencing declining
sales is likely to change substantially from one quarter to the next. Second, at the firm level, βSU
is likely to reflect long-term decisions, such as the choice of capacity and production technology,
while the decision to cut down resources, reflected in βSD is more short-term in nature and istypically made only after reduced demand realization is observed. Our findings indicate that bothcoefficients are highly persistent, which is to be expected because they reflect aggregate
elasticities. Consistent with our expectations, we also find that the persistence of βSU is higher thanthe persistence of βSD.
We next examine the explanatory power of each coefficient individually. We regresscontemporaneous gross job inflow and outflow rates on the aggregate βSU and βSD coefficients andcontrol variables. We find that βSU is related to gross job inflow rate, but not gross job outflow
rate. Conditional on GDP growth and other macroeconomic indicators, a larger βSU entails a greater
increase in resources, and hence greater job inflows. βSU fails to predict the gross job outflow rate
due to the asymmetric nature of cost behavior. Specifically, the actual reduction in resources islikely to be different from the one predicted by βSU, due to cost stickiness. We also find that βSD isnegatively related to gross job outflows but is not related to inflows, consistent with βSD reflecting
resource retention decisions. Conditional on macroeconomic indicators, a larger βSD entails greater
resource retention and hence less job outflows.
When we include both coefficients in the regression, we find that they both predict gross
job inflow and outflow rates, but with opposite signs. βSU is positive, whereas βSD loads negatively.
Conditional on the inclusion of βSD, GDP growth, and other macroeconomic indicators, a larger
βSU indicates greater job inflows and job outflows because of the greater elasticity of the costfunction. Conditional on the inclusion of βSU and macroeconomic indicators, a larger βSD indicates4

less job outflows, but also less job inflows due to the higher level of adjustment costs to reduce orrestore committed resources that affect both contraction and expansion. Moreover, the coefficienton βSU is always larger in absolute value than the coefficient on βSD. This result is consistent withthe greater persistence of βSU compared with βSD.
Having documented the information content of the two macro-level elasticities using
contemporaneous job flows, we now examine their relationship to future job flows. Given the highpersistence of both coefficients, we expect that they will explain not only contemporaneous flows,
but also future ones. Indeed, both coefficients predict future flows up to four quarters ahead.
We next examine second moment properties of the βSD coefficient. We interpret thestandard error of the estimated βSD coefficients—SE(βSD)—as indicating uncertainty. A higher
value of this standard error implies a less precise βSD estimate, which indicates greater dispersion
among firms in their level of resource retention, which in turn reflects different opinions about
future macroeconomic conditions. We examine whether the explanatory power of βSD varies withuncertainty. We find that the standard error of the estimated βSD coefficient is higher at the onsetof recessionary periods, consistent with the higher level of uncertainty, and that, conditional on
βSD, higher standard error of βSD is associated with less job outflows, indicating greater employee
retention in periods of high uncertainty.
Finally, we compare the model assuming an asymmetric cost behavior (i.e., sticky costs)
to a traditional model that assumes symmetry. To do so, we estimate the time series of βSYM, whichassumes a symmetric relation between change in costs and increases and decreases in sales. We
then use βSYM in the regressions of gross job inflow and outflow rates. We find that, for a given
level of GDP growth and conditional on macroeconomic indicators, a larger βSYM is associatedwith greater job outflows as well as greater job inflows.

5We compare the explanatory power of a model that includes βSYM to a model that includes
both βSD and βSU, by examining the R-squared of the two models. Vuong (1989) tests indicate astatistically significant improvement in the R-squared of the asymmetric cost model when
explaining the gross and net job outflow rates, but not the gross job inflow rate, consistent with
the information content of βSD reflecting the sticky nature of costs.
We augment the OLS results by estimating vector autoregression (VAR) models. The VAR
approach takes into account time-series interdependencies of the different variables of interest.
One major disadvantage of VAR models, however, is the need to estimate a very large number ofcoefficients (Robertson and Tallman 1999). In particular, compared to an OLS regression, thenumber of cross-sectional predictors that a VAR model can handle is relatively limited, because
the number of coefficients to be estimated grows exponentially with the number of variables. We
run a reduced-form VAR, which builds on Okun’s law (Okun 1963). Okun’s law documents arobust negative relationship between output (GDP) growth and unemployment rate changes. We
decompose the change in unemployment rate into gross job inflow rate and gross job outflow rate,
and we include the βSU and βSD coefficients. Results from the VAR analysis are in line with theconclusions from the OLS regressions.
This paper contributes to several streams of literature. First, we contribute to the costaccounting literature on sticky costs (Anderson, Banker, and Janakiraman 2003; Banker and Chen
2006; Anderson, Banker, Huang, and Janakiraman 2007; Weiss 2010; Kama and Weiss 2013;
Rouxelin, Wongsunwai, and Yehuda 2018). To the best of our knowledge, we are the first to
examine the differential information content (with respect to the labor market) of aggregate costelasticities to sales increases (βSU) and sales decreases (βSD). Our study employs a relatively new
dataset that has been understudied in the literature—the BED dataset—and includes information

6

about job flows at the firm level and hence is more suitable to analyze firms’ cost structure.
Furthermore, we compare the symmetric and the asymmetric costs models at the aggregate leveland show the incremental informativeness of the asymmetric cost behavior model.
Second, we contribute to the macroeconomics literature on unemployment rate. We show
how the aggregate cost elasticity to sales increases (βSU) and sales decreases (βSD) relate to job
inflows and outflows in business establishments. Recent research on unemployment forecastingemphasizes the importance of separately predicting each type of flow. Using the refined BED dataat the establishment level, we are better able to assess separately the effect of each of thecoefficients βSD and βSU.
Third, our paper integrates a cost accounting research topic, asymmetric cost behavior,
with the financial accounting literature on the usefulness of aggregate accounting information inpredicting the macroeconomy (e.g., Jorgensen, Li, and Sadka. 2012; Konchitchki and Patatoukas
2014; Gallo, Hann, and Li 2016; Nallareddy and Ogneva 2017). The importance of integrating
insights from financial and managerial accounting research and other literatures has long been
acknowledged (e.g., Hemmer and Labro 2008; Banker and Byzalov 2014).
The remainder of the paper proceeds as follows. We review related literature and develop
our main prediction in Section 2. Section 3 describes the data and provides descriptive statistics.
Sections 4, 5 and 6 present the results of the analysis. Section 7 concludes.

7

2. Related Literature and Main Prediction
2.1

The Information Content of Cost Behavior Components
A growing body of literature, beginning with ABJ (2003), studies cost behavior andassumes a non-symmetric relation between costs and sales: costs increase more when activity rises
than they decrease when activity falls by an equivalent amount. In particular, the literature captures
the asymmetry by estimating a log-linear cost function, with a separate coefficient for firms thatexperienced an increase in sales (βSU) and an incremental coefficient for firms that experienced adecrease in sales during the period (βSD).
Prior research has studied the coefficients at the firm level. βSU is typically interpreted aselasticity of cost to increases in demand — that is, the percentage change in costs for a 1 percent
increase in sales.3 A higher slope indicates a more elastic cost structure with a lower proportion of
fixed costs and a higher proportion of variable costs, or a lower operating leverage (Lev 1974;
Cooper and Kaplan 1987; Noreen and Soderstrom 1994, 1997; Kallapur and Eldenburg 2005;
Banker, Byzalov, and Plehn-Dujowich 2013). The lower the slope coefficient, the more rigid isthe firm’s cost structure. Additionally, βSU can also be interpreted as the ratio of marginal cost to
average cost (Noreen and Soderstrom 1994) or the ratio of variable costs to total costs if total costs
are linear in volume (Kallapur and Eldenburg 2005).
A more elastic cost structure offers companies greater flexibility because it involves fewer
upfront cost commitments (i.e., fewer fixed costs). A firm’s cost structure is determined by its
production technology. Yet, firms can transform fixed costs into variable costs via a process ofvariabilization. Under this process, activities involving higher fixed costs are outsourced. Recent
managerial accounting papers study how the business environment in which the firm operates
3

A large literature studies this coefficient. Yet, this literature assumes a symmetric cost function. The cost stickiness
literature, on the other hand, has mainly studied the properties of the incremental βSD coefficient.

8affects its cost rigidity. Using data from the hospital industry, Kallapur and Eldenburg (2005) andHolzhacker, Krishnan, and Mahlendorf (2015) identify resource procurement choices made byhospitals facing higher demand uncertainty. These choices include the degree of outsourcing, theproportion of leased equipment versus owned equipment, and the proportion of contract laborversus fixed-term salaried employees.
In contrast, Banker, Byzalov, and Plehn-Dujowich (2013) demonstrate that, in response tohigher demand uncertainty, firms choose a more rigid short-run cost structure. They assume a
production technology with a fixed capacity resource that is chosen in advance and a variable
resource that is chosen after the demand is realized. When demand is unusually high relative tocapacity, the firm bears high congestion costs due to strained capacity. Consequently, managers
prefer to commit sufficient capacity in advance to avoid excessive congestion costs, which canconsiderably reduce the firm’s profitability.
The βSD coefficient has also been studied in the literature on cost stickiness. ABJ (2003)
argue that this coefficient captures the effect of deliberate managerial decisions about committed
resources when there is uncertainty about future demand for their firms’ products. In particular,
greater magnitude of adjustment costs leads to greater cost stickiness because the firm’s behavior
under optimal decision-making is asymmetric. With labor, severance and training costs can besignificant. Faced with declining sales, managers are reluctant to fire workers because retaining
the unused resources helps avoid the large staff termination costs and future training costs whenrehiring. Conversely, when activity increases, although managers may be reluctant to hire moreworkers because of the adjustment costs, the increase in current sales can only be achieved if
additional workers are hired, thus the reluctance effect is likely to be more muted (Banker andByzalov 2014; Banker, Byzalov, and Chen 2013).

9

The literature has offered additional explanations for this coefficient. First, cost stickinessis indicative of managerial expectations regarding future demand for the firm’s products (Banker,
Byzalov, Ciftci, and Mashruwala 2014). Managerial optimism weakens cost response to current
sales decreases and amplifies cost response to current sales increases, thereby resulting inincreased cost stickiness. Second, cost stickiness can be the result of managers’ empire-building
incentives to maximize resources under their control. Larger resources can give managers more
power, potentially higher compensation, and other perquisites (Anderson, Banker, andJanakiraman 2003; Chen, Lu, and Sougiannis 2012; Kama and Weiss 2013). Empire-building
managers will cut resources only moderately when sales decrease and will expand resources
excessively when sales increase.

2.2

Empirical Prediction
We expect the two beta coefficients to capture different aspects of firms’ cost behavior.

βSU captures the elasticity of changes in costs to increases in sales, with a greater coefficientreflecting more elastic (less rigid) cost structure. This coefficient is the result of managers’ longterm decision about capacity and production technology, and cannot be easily altered. βSD captures
the incremental resource retention when sales decrease—that is, the difference between change incosts when sales increase and change in costs when sales decrease by an equivalent amount. As
such, this coefficient reflects the level of adjustment costs to reduce or restore committed resources
as well as managerial discretion (ABJ 2003). Moreover, the decision to scale down resources isoften made after observing demand realization, and hence this decision is more short-term innature.
Both coefficients are determined by the current production-technology, capacity level,
adjustment cost level, and managers’ discretion when making resource-decisions. Yet, βSU is likely
10to reflect the elasticity of the production technology to a larger extent, while βSD is more likely tocapture adjustment costs and short-run managerial expectations. This is because the cost structureof the firm (i.e., βSU) is the result of managers’ long-term capacity and operational choices. Indeed,
Holzhacker, Krishnan, and Mahlendorf (2015) argue that changes to a firm’s cost structure usuallyrequire modifications to the firm’s operations that are not easily reversible. In contrast, cost
stickiness reflects managers’ short-term decisions to retain unused resources, and hence is more
likely to capture timelier managerial expectations (Banker, Byzalov, and Plehn-Dujowich 2013).

3. Data
3.1

The Business Employment Dynamics (BED) Data
We obtain quarterly data on job inflows and outflows from the Business Employment

Dynamics (BED) dataset provided by the Bureau of Labor Statistics (BLS). The BED contains
longitudinally-linked administrative records for all establishments covered by state unemployment
insurance agencies.
Spletzer, Faberman, Sadeghi, Talan, and Clayton (2004) argue that, while the traditional
unemployment rate identifies the overall growth or decline of the labor market, it does not convey
the underlying heterogeneity of job inflows and job outflows at the establishment level. Job inflows
are the sum of all employment increases at opening or expanding establishments. Job outflows are
the sum of all employment losses at closing or contracting establishments.

11

3.2

The Sample
Our sample period is Q3:1992 to Q2:2017. We begin our sample in Q3:1992 because thisis the earliest quarter covered by the BED dataset.4 We collect quarterly financial statement datafrom the Compustat North America Quarterly Database. Since the BLS compiles the BED data on
a quarterly basis, using quarterly data sources provides a natural alignment with the data generation
process for the key dependent variables in our analyses.
We implement the methodology in ABJ (2003) to estimate βSU and βSD, by regressing thesum of SG&A expenses (Compustat data item xsgaq), and cost of goods sold (cogsq) on sales
revenue (data item saleq). We combine expenses for COGS and SG&A to proxy for labor cost.
We use available data for all U.S. industrial companies available on Compustat. In line with priorresearch (e.g., Banker and Byzalov 2014), we winsorize all continuous variables at 0.5% tails
within each quarter and 2-digit SIC industry.
We estimate the following regression each quarter:
log

&
&

𝛽

𝛽 log

𝛽 𝐼_𝐷𝑒𝑐𝑟𝑒𝑎𝑠𝑒

log

𝜀

(1)

In each calendar quarter q, we estimate regression (1) using Compustat firm-quarter datawith fiscal quarters ending in [q–3, q]. We seasonally adjust changes in costs and sales. The
indicator variable I_Decrease takes value 1 if seasonal change in quarterly sales (sales reported in
the current quarter compared to four quarter ago) is negative and 0 otherwise. βSU is equivalent tostandardized β1 (i.e., after subtracting sample mean of the estimated β1 values and dividing by the
sample standard deviation). βSD is calculated as standardized β2 multiplied by −1, to facilitate easierinterpretation (higher value of βSD indicates more sticky costs).

4

The BED dataset first became publicly available in 2004. The data are updated on a quarterly basis, with a lag of threequarters.

12

3.3

Descriptive Statistics
Panel A of Table 1 reports descriptive statistics for the main variables used in the analysis.

The gross job inflow rate, expressed as a percentage of total employment, had a mean value of
7.11% for our sample period, with a standard deviation of 0.82%. The gross job outflow rate, on
the other hand, averaged 6.79% with 0.75% standard deviation.
We estimate β1 and β2 coefficients by running rolling OLS regression models following
equation (1), using all Compustat industrial firms. The β1 and β2 estimates have means of 0.410
and -0.053, respectively. Thus, for a given quarter, firms report an average increase of 0.41% intheir operating costs for every 1% increase in sales revenue, whereas firms report a cost decreaseof only 0.36% (0.410% − 0.053%) per 1% decrease in sales revenue.
We next present descriptive statistics for the four sets of control variables that are known
to explain the change in the level of unemployment (Rouxelin, Wongsunwai, and Yehuda 2018).
All variables are defined in Appendix A. The first set of control variables captures the overallmacroeconomy and includes the growth in real GDP (GDP), aggregate GAAP earnings (Earn),change in earnings (ΔEarn), stock market return (MktRet) and the industrial production index (IPI).
We use estimates of real GDP growth rate as released by the Bureau of Economic Analysis. Over
our sample period, this variable averaged 2.53% but with substantial variation (standard deviationof 1.95%). We also control for aggregate earnings (Earnt), and stock market return (MktRett). The
industrial production index (IPIt), published by the Federal Reserve Board, measures the real
output of all manufacturing, mining, and electric and gas utility establishment in the U.S.
Our second set of controls are proxies for different explanations of cost stickiness that havebeen identified in prior literature: (a) BBD Economic Policy Uncertainty (EPU) index, which
captures the level of policy-related economic uncertainty (Baker, Bloom, and Davis 2014; Bloom

13

2014). This index is likely to be correlated with uncertainty about future activities, a potential
driver of cost stickiness; (b) the University of Michigan Consumer Sentiment Index (CSI). This
index gauges consumers’ level of optimism or pessimism, which is likely to be correlated withmanagers’ level of optimism or pessimism, another potential explanation for cost stickiness. The
third set of control variables includes federal funds (i.e., interest) rate (IR) and inflation rate (Inf),
following Taylor’s (1993) rule.
Our final set of controls includes factors that have been proposed in prior literature aspredictors of unemployment specifically (rather than of the macroeconomy as a whole), consistingof labor-force flows and labor reallocations. 5 The four-week average change in initial
unemployment insurance claims (UIC) (people who filed for unemployment benefits for the firsttime during the previous month) and the composite Help-Wanted Index (HWI) (percentage of job
openings or vacancies out of the total labor force) are proxies for labor flows, often used inpolicymaking (e.g., Barnichon 2010). Employment growth dispersion (Lilien 1982) and return
dispersion (Loungani, Rush, and Tave 1990; Brainard and Cutler 1993; Nallareddy and Ogneva2017) are proxies for performance dispersion.
Panel B of Table 1 presents the correlation matrix between the BED job flow rates and thechange in the traditional unemployment rate, which is based on CPS responses. Change inunemployment rate is positively and significantly associated with net job outflow rate (difference
between gross job outflow rate and gross job inflow rate). Furthermore, gross job inflow rate (gross
job outflow rate) is negatively (positively) correlated with change in unemployment rate. Finally,
there is a positive and significant correlation between gross job inflow rate and gross job outflow

5

Lilien (1982) and Davis (1987) argue that unemployment is, in part, the result of worker turnover from declining to
expanding sectors of the economy. Due to labor reallocation frictions related to job search, retraining, or physicalrelocation, changing jobs takes time, which leads to higher unemployment in the interim. High performance dispersion
implies that some firms lay off employees while others recruit new workers.

14

rate, suggesting that periods of increased job inflows and outflows occur simultaneously and are
therefore indicative of overall job redistributions in the economy.

4. The Information Content of Cost Behavior Components
4.1

Persistence over time
We begin by estimating the persistence of both beta coefficients, βSU and βSD, using AR(1)regressions of the coefficient on its lagged value. As both coefficients capture aggregate
persistence, we expect the AR(1) coefficient to be statistically significant and close to 1. Yet, we
expect the persistence of βSU to be higher than the persistence of βSD for the following reasons:First, a reduction in sales is likely to be temporary. Hence, the composition of firms experiencing
such a decline is likely to change considerably form one quarter to the next, affecting theintertemporal variation of βSD. Second, the decision to reduce committed resources when sales
decrease, reflected in βSD, is typically made after observing demand realization, and hence is likely
to vary more over time compared with the decisions reflected in βSU, which are more long-run innature, and reflect decisions such as capacity and choice of production technology.
Table 2 presents the results. Column I shows the estimation of βSU’s persistence. The
magnitude of the coefficient is 0.99 and it is highly statistically significant. Column II shows thatthe persistence of βSD is 0.93, and it is also statistically significant. The difference in coefficients
is significant at the 5% level.

15

4.2

OLS Regressions

4.2.1

Contemporaneous Gross Job Inflow Rate
Next, we run OLS regressions of job inflows on the two beta coefficients, βSU and βSD. We

include a battery of control variables. Additionally, because the beta coefficients are standardized,
the interpretation of their incremental explanatory power is straightforward. We estimate three
OLS regressions, nested in the following model:
Job flow rate = α1 + α2k βSU + α3k βSD + α Controls + ε,(2)where βSU and βSD are estimated in the current quarter q using data from public filings by listed
U.S. firms with fiscal quarters ending in [q-3, q].6 Job flow rate is either gross job inflow rate or
gross job outflow rate. We apply the Newey-West procedure in order to obtain consistent standard
errors in the presence of autocorrelation. We use a truncation parameter (or lag) of 3.7
Table 3, Column I, presents the results of a regression of the gross job inflow rate on βSU
and control variables. Consistent with our prediction, we obtain a positive association between βSU
and the contemporaneous gross job inflow rate. The coefficient is both economically andstatistically significant. A one-standard-deviation-higher βSU is associated with an increase of 38
basis points in the gross job inflow rate (the dependent variable, measured in percentage points).
This effect is economically significant, given that the model includes all the control variables andthe mean gross job inflow rate during the sample period is 7.11%.
We include a battery of control variables, known to explain the change in the level of
unemployment (Rouxelin, Wongsunwai, and Yehuda 2018). These coefficients generally enter the6

We use quarterly data from quarter t-3 to quarter t to address potential complications due to seasonality, which affects
many businesses. In robustness tests, we only use quarterly data in quarter t and our conclusions remain unchanged.

7

The choice of 3 lags is based on the usual rule of thumb: T0.25, where T is the number of observations. In our sample
of 100 quarterly observations, this suggests a truncation parameter of 3. In robustness tests, we allow for different lag
lengths and our conclusions remain unchanged.

16regression with their expected sign. For example, the coefficients on GDP is positive andsignificant, consistent with Okun’s law.8 The coefficient on MktRet is negative and significant,contrary to our expectation, possibly as a result of the correlation between this variable and theother variables capturing the state of the overall economy (GDP, Earn, ∆Earn). The coefficient on
IPI is negative and significant, consistent with greater uncertainty reducing future job inflows. CSI
loads positively, confirming the positive relation between job inflows and consumer sentiment.
Ret_Disp is strongly positive, consistent with labor reallocation frictions resulting in greater
movement in the labor market.
In Column II, we estimate the regression with βSD and control variables as the independent
variables. The coefficient on βSD is not significant at conventional levels, suggesting that there is
no relationship between βSD and the gross job inflow rate, consistent with the hypothesized
information content of this coefficient.
Colum III presents the results of a regression which includes both beta coefficients. We
find that βSD loads positively, while βSD loads negatively. Conditional on the inclusion of βSD, GDP
growth, and other macroeconomic indicators, a higher βSU indicates more job inflows, because of
the greater elasticity of the cost function. Conditional on the inclusion of βSU and macroeconomic
indicators, a larger βSD indicates less job inflows due to the higher level of adjustment costs to
reduce or restore committed resources that affect both contraction and expansion.
Both coefficients are larger in magnitude compared to the regressions in the first twocolumns, in which they are included individually, indicating that the inclusion of both coefficients
increases both their economic and statistical significance. For example, the coefficient on βSU is
almost twice as large as its magnitude in Column I: conditional on βSD, a one-standard-deviation-

8

Okun (1963) documents a negative relation between unemployment and real GDP growth. In Table 3, we examine
job inflows, which are positively related to real GDP growth.

17higher βSU is associated with an increase of 63 basis points in the gross job inflow rate, comparedwith an increase of 38 basis points documented in Column I.
Finally, the coefficient on βSU is larger than the coefficient on βSD. This finding is consistent
with the greater explanatory power of βSU. Moreover, βSU is more persistent than βSD because therelative difficulty in adjusting the elasticity of costs with respect to sales increases at the firm level
and at the economy level.
Overall, the results in Table 3 lend strong support to the different information content of
βSU and βSD.

4.2.2

Contemporaneous Gross Job Outflow Rate
Table 4 repeats the analysis for the gross job outflow rate. Column I shows the results of aregression of the contemporaneous gross job outflow rate on βSU and control variables. The
coefficient of βSU is not significant at conventional levels, suggesting that βSU by itself does not
explain the gross job outflow rate. This finding is consistent with aggregate-level cost stickiness—that is, for a given growth in GDP, firms are not likely to cut jobs at the level predicted by βSU (inabsolute value) because of adjustment costs and managerial discretion.
In Column II, we examine the information content of βSD. The coefficient of βSD is negativeand significant, indicating that higher cost stickiness is associated with lower job outflows. The
coefficient is also economically large. Conditional on GDP growth and other macroeconomic
indicators, a one-standard-deviation-higher βSD is associated with a decrease of 19 basis points in
the gross job outflow rate. This effect is economically significant, given that the model includes
all the control variables and the mean gross job outflow rate during the sample period is 6.79%.
As before, we include a battery of control variables, known to explain the change in the18

level of unemployment. These coefficients generally enter the regression with their expected sign.
For example, the coefficients on Earn, ∆Earn and MktRet are negative and significant, indicatinga negative relation between the state of the economy and job outflows, as we would expect. The
coefficients on EPU and IPI are negative and significant, suggesting that in periods of highuncertainty, job outflows are smaller. We explore this issue in detail in section 4.3.
Unemployment insurance claims (UIC) is positively related to job outflows. Finally, thepositive coefficient obtained for IR indicates a rise in unemployment following an interest-rateincrease.
In Column III, we include both elasticities βSU and βSD. The coefficient on each of theelasticities is larger compared with Column I and II. Moreover, both coefficients are statisticallysignificant. However, while βSU is positively related to the gross job outflow rate, βSD is negatively
related to job outflows. Conditional on the inclusion of βSD, GDP growth, and othermacroeconomic indicators, a higher βSU indicates a more elastic cost structure and hence more job
outflows. Conditional on the inclusion of βSU and macroeconomic indicators, a larger βSD indicatesless job outflows due to cost stickiness, which implies a lower decline in resources when sales
decline, compared to the situation in which sales increase by an equivalent amount. As in
Column III of Table 2, the coefficient on βSU is larger than the coefficient on βSD consistent withhigher persistence and greater explanatory power of βSU compared with βSD.

4.2.3

Future Gross Job Outflow and Inflow Rates
In Table 5, we present the prediction analysis. Panel A shows results for predicting the job

inflow rate in the subsequent four quarters. The control variables are included in the regressions
but are not tabulated for parsimony. The evidence in Column I indicates that the coefficient on βSU

19

is positive and significant at all horizons. This coefficient increases monotonically in quarters t+1
to t+3, and decreases in quarter t+4. The coefficient on βSU is also economically large. Conditional
on GDP growth and other macroeconomic indicators, a one-standard-deviation-higher βSU is
associated with an increase of 33 basis points in the gross job inflow rate in the next quarter, 36
basis points two quarters ahead and 37 basis points three quarters ahead.
Column II confirms that βSD is not a predictor of gross job inflows at any prediction
horizon, consistent with the results in Table 3. Column III shows results using both elasticities.
The coefficients on both βSU and βSD are economically and statistically significant. Moreover,consistent with Panel A, βSU loads positively, while βSD loads negatively. The coefficient on βSU,
conditional on the inclusion of βSD in the regression, is larger compared to Column I. Furthermore,
the coefficient on βSU is always larger than the coefficient on βSD. This finding is consistent withthe short-term nature of the information in the elasticity of costs with respect to sales decreases.
In Panel B, we explore the prediction of job outflow rates. Column I confirms the findingsin Table 4 and indicates that βSU does not predict gross job outflow rates. Column II indicates thatβSD predicts future job outflows at all horizons examined. The coefficient on βSD decreases
monotonically in quarters t+1 to t+3, and then increases. In Column III, when both coefficients areincluded in the regression, both load significantly, but with opposite signs at all horizons except
for quarter t+4, when only the coefficient on βSD is statistically significant at conventional levels.
Collectively, Tables 3 to 5 document the different information content of the aggregatelevel elasticities of costs relative to sales increases vs. decreases. Moreover, when both areincluded in the same regression, these elasticities have an economically large explanatory power.
The explanatory power of the elasticity of costs with respect to sales increase is larger than theelasticity with respect to sales decreases.

20

4.3

Standard Error of βSD
Having studied the explanatory power of both the βSU and βSD coefficients, we next examine

whether the explanatory power is greater in periods of high uncertainty. We use the second momentof the estimated βSD coefficient—that is, the standard error, SE(βSD)—as a measure of the level of
uncertainty.
A higher standard error of βSD implies: (a) a less precise βSD coefficient; and (b) a higher
dispersion in the level of resource retention across firms, reflecting different expectations about
future macroeconomic conditions. Under explanation (a), we expect a positive and significant
interaction between βSD and its standard error, consistent with less pronounced effect of βSD on job
outflows when the estimation of βSD is less precise. Under (b), we expect a negative and significant
interaction term, indicating that the impact of βSD on job outflows is more pronounced in periodsof high uncertainty.
Figure 1 depicts SE(βSD) and the BBD Economic Policy Uncertainty Index. SE(βSD) peaks
during recessions, then declines when recovery occurs.
In Table 6, we regress gross job outflow rate on βSD, SE(βSD), and their interaction.
SE(βSD)—top decile (quartile) is a dummy variable that takes a value of 1 if SE(βSD) is in thehighest decile (quartile) and 0 otherwise. As before, the coefficient on βSD is negative andsignificant, suggesting that the higher βSD, the higher is the employee retention by firms, and hence
the lower are job outflows. The coefficient of SE(βSD), the standard error of βSD, is also negativeand significant, consistent with a greater standard-error corresponding to a higher level of
uncertainty.
The interaction term between βSD and SE(βSD) quantile is negative and significant,suggesting that the relationship between βSD and job outflows is stronger when there is greater

21

uncertainty. The results in column I show that, in quarters with high uncertainty (standard error ofβSD is in the highest decile), a one-standard-deviation-higher βSD is associated with a decrease of
46.5 (-0.217 – 0.248) basis points in the gross job outflow rate (the dependent variable, measured
in percentage points). Column II shows similar results when using quartiles of SE(βSD) to construct
the dummy variable.

4.4

Comparing the Symmetric and Asymmetric Cost Behavior Models
In Table 7, we compare the asymmetric cost model, introduced to the literature by ABJ(2003), to a symmetric cost model, which assumes the same response of costs (in absolute value
terms) to a 1 percent increase or decrease in sales. We run the following seasonally-adjusted
quarterly regression:
log

&
&

𝛽

𝛽 log

𝜀

(3)

We label the standardized β3 coefficient from this regression βSYM.
Table 7 compares the explanatory power of βSYM to the explanatory power of both βSU andβSD for different job flow rates. Specifically, we compare the difference in adjusted R-squared
between the two classes of models, and present Vuong (1989) Z-statistics to indicate whether thisdifference is statistically significant.
In Columns I and II we compare the two models’ performance in explaining the gross job
inflow rate. The coefficient of βSYM in column II loads positively and significantly, suggesting thata higher βSYM, conditional on GDP growth and other macroeconomic indicators, is associated witha greater job inflow rate. We do not find any difference between the adjusted R-squared of thesymmetric and asymmetric models.
Columns III and IV repeat the analysis for gross job outflow rate. In this case, the22coefficient of βSYM is also positive and statistically significant, suggesting that a higher βSYM,
conditional on GDP growth and other macroeconomic indicators, is associated with a greater job
outflow rate. Yet, the adjusted R-squared of the symmetric model is lower than the adjusted Rsquared of a model that includes both βSU and βSD, suggesting that the asymmetry of cost behavior
is important in predicting gross job outflows. The difference in the adjusted R-squared between
the two models is highly statistically significant (1.2% level).
Finally, Columns V and VI repeat the analysis for explaining the net job outflow rate. As
was the case for gross job outflows, we find that the adjusted R-squared of the symmetric model
is lower than the adjusted R-squared of the asymmetric model. This difference is statisticallysignificant with a p-value of 2.3%.
Overall, the findings in Table 7 provide strong support for the importance of theasymmetric cost model in explaining job outflows. Using both βSU and βSD explains a greater
proportion of job outflow variation compared with βSYM.

5. Vector Autoregression (VAR) Analysis
We augment the OLS regression analysis with reduced-form vector autoregression (VAR)
models corresponding to the six regressions in Tables 3 and 4. The VAR models build on Okun’slaw, which predicts a negative relationship between unemployment and real GDP growth. We
decompose the unemployment rate into the gross job inflow rate and the gross job outflow rate,
and add the variables of interest — that is, βSU, and βSD. We use four lags. We add βSU, and βSD to
the VAR individually as well as jointly. We estimate different VAR models as follows:
AZt = ϕZt–k + εt,where 𝑍𝐽𝑜𝑏 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒 , 𝐺𝐷𝑃 , 𝛽

(4)
, , 𝛽

is a vector of variables that includes (in this,23

order) job-flow rate, real GDP growth rate (GDP), βSU, and/or βSD.
In Panel A of Table 8, we consider the gross job inflow rate, modeled using two separate
VAR systems. The fist system includes βSU and the second includes βSD.
We present two orthogonalized impulse-response graphs. The first graph depicts the impactof an exogenous shock to βSU on the gross job inflow rate. A one-standard-deviation shock to βSUleads to an increase in the gross job inflow rate of 7 basis points. The shock persists (i.e., is reliably
positive within a 95% confidence band) for up to 4 quarters. The second graph depicts the impactof an exogenous shock to βSD on the gross job inflow rate. We find that βSD has no significant
impact on the gross job inflow rate.
In Panel B we consider a VAR system that includes both coefficients (i.e., βSU and βSD).
The first graph indicates that a one-standard-deviation shock to βSU leads to an increase in the gross
job inflow rate of 8 basis points. The shock persists for up to 4 quarters. At the same time, as
indicated in the second graph of panel B, a one-standard-deviation shock to βSD leads to a reduction
in gross job inflow rate of 5 basis points. This shock lasts for 2 quarters and then dissipates.
Overall, the findings in Table 8 are consistent with the findings in Table 3 and provide
strong support for the different information content of βSU and βSD in explaining the gross job
inflow rate.
In Table 9, we repeat the analysis in Table 8, with future gross job outflow rate as the job
flow variable in the VAR model. In Panel A, we consider the gross job outflow rate, modeled using
two separate VAR systems, including βSU and βSD, respectively, while in Panel B, we include bothvariables in the same system.
The first graph in Table 9, panel A shows that there is no significant relationship between
βSU and gross job outflow rate. In the second graph, a one-standard-deviation exogenous shock to24

βSD leads to a decrease in the gross job outflow rate of 5 basis points, which lasts up to 2 quarters.
In Panel B, a one-standard-deviation shock to βSU leads to a 7 basis points increase in gross job
outflow rate, which persists for up to 3 quarters. At the same time, a one-standard-deviation shock
to βSD leads to a reduction in gross job outflow rate of 6 basis points, lasting for up to 2 quarters.
Overall, the findings in Table 9 are consistent with the findings in Table 4 and provide
strong support for the different information content of βSU and βSD in explaining the gross job
outflow rate. Moreover, the impact of βSU and βSD on the gross job outflow rate is less persistentthan the impact of these coefficients on the gross job inflow rate.

6. Aggregate Cost Behavior, Job Flow Rates and The Adoption of Wrongful-Discharge
Laws (WDLs)
Next, we compare the explanatory power of βSU and βSD in different states. In particular,
we classify states according to the adoption of Wrongful Discharge Laws (WDLs). The U.S. haslong had a legal presumption that workers can be dismissed at will—that is for any reason(without having to establish “just cause” for termination), and without warning. Since the 1970s,
the vast majority of U.S. states have adopted common law exceptions to the employment-at-will
doctrine. 9 These exceptions are part of the common law, that is, law created by court decisions(in this case, state courts). We refer to these common-law exceptions as wrongful-discharge laws
(hereinafter WDLs). The exceptions are:(1) The public policy exception, which provides workers with protections against
discharges that would inhibit them from acting in accordance with public policy, such as
performing jury duty, filing a worker’s compensation claim, reporting an employer’s

9

See Walsh and Schwarz (1996), Autor (2003), Autor, Donohue, and Schwab (2006), Littler (2009) and Serfling (2016) for
detailed discussion.

25

wrongdoing, or refusing to commit perjury.
(2) The good faith exception, which forbids employers from firing workers for ‘badcause.’(3) The implied contract exception, which becomes effective when an employer implicitly
promises not to terminate a worker without good cause.
WDLs impose substantial firing costs on employers and hence increase the level of laboradjustment costs. We employ the state-level BED data and estimate the gross job inflow andoutflow rates in states that adopted WDLs and states that did not. In determining the dates inwhich each state has passed each of the above exceptions, we follow Serfling (2016).
We test the ability of aggregate-level βSU and βSD to explain the job flows in the two typesof states. We expect that the ability of βSU to explain gross job inflow rates is greater in states that
did not adopt the WDLs, as firms in states with WDLs are more likely to consider the potential
firing costs and hence, for a given growth rate in GDP, limit the number of newly-hired
employees, compared with the number implied by βSU. In contrast, we expect that the ability ofβSD to explain gross job outflow rates is greater in states that adopted these laws. In these states,
employers are more likely to retain more employees, due to the larger firing costs.10
Table 10 reports the results. Panel A compares the ability of βSU to explain the gross job
inflow rates in states that adopted (Column I) and did not adopt (Column II) the WDLs. We findthat the coefficient of βSU is larger in states that did not adopt these laws, in line with our10
We expect that WDLs are likely to affect the calculation of βSU and βSD of firms that reside in states that did and did not
adopt these laws. To avoid this potential circularity, we use the elasticities estimated for the entire sample as the independent
variables in our analysis.

26

expectations. The difference in βSU coefficients between the two types of states is statisticallysignificant at the 6% level.
In Panel B, we conduct a parallel analysis comparing the ability of βSD to explain gross job
outflow rates between the two types of states. Confirming our expectations, we find that thecoefficient of βSD is larger in states that adopted these laws (Column I) compared with sates that
did not (Column II). The difference is statistically significant at the 6% level.

7. Summary and conclusions
A growing body of literature, beginning with ABJ (2003), studies cost behavior andassumes a non-symmetric relation between costs and activities: costs increase more when activity
rises than they decrease when activity falls by an equivalent amount. We examine the information
content of the two aggregate-level elasticities of costs with respect to sales in firms’ asymmetriccost function. Both aggregate-level coefficients are highly persistent. Additionally, Consistent
with our expectations, we find that the persistence of the aggregate elasticity of costs is higher forsales increases (βSU) than for sales decreases (βSD).
Next, we examine information content with respect to the labor market, using the Business
Employment Dynamics (BED) quarterly data on job flows. These data were recently madepublicly available by the Bureau of Labor Statistics (BLS). We find that, conditional on the growth
in GDP and other macroeconomic indicators, the aggregate elasticity of costs with respect to sales
increases (βSU) explains the gross job inflow rate, but not the gross job outflow rate. Conversely,
the aggregate elasticity of costs with respect to sales decreases (βSD) explains the gross job outflow
rate, but not the gross job inflow rate. When we include both elasticities in the regression, both aresignificant, but with opposite signs. Moreover, both economic and statistical significance increase27

in the combined model. We repeat the analysis using vector autoregression (VAR) models anddocument similar results.
Next, we examine whether the explanatory power of the elasticity of costs with respect toa decrease in sales (βSD) is greater in periods of high uncertainty. We measure the level of
uncertainty using the standard error of the estimated βSD coefficient (SE(βSD)). The higher thisstandard error, the less precise is βSD and the higher is the dispersion among different firms in theirlevel of resource retention, reflecting different opinions about future macroeconomic conditions.
We find that, conditional on the level of βSD, a higher standard error of βSD is associated with alower gross job outflow rate, indicating greater employee retention in periods of high uncertainty.
Finally, we compare the model assuming asymmetric cost behavior (i.e., sticky costs) to a
traditional model that assumes symmetry. To do so, we estimate the time series of βSYM, whichassumes a symmetric relation between the change in costs and the change in sales. Our findings
indicate that, conditional on macroeconomic indicators and for a given level of GDP growth, alarger βSYM is associated with both greater job outflows and job inflows. Moreover, the explanatory
power of a model that includes βSYM is lower than the explanatory power of a model that includes
both βSD and βSU, when explaining the gross job outflow rate, but not the gross job inflow rate,consistent with the information content of βSD reflecting the sticky nature of costs.

28

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Appendix A
Definitions of key variables
Estimated coefficients obtained from running the following
ordinary least squares regression cross-sectionally eachquarter q using Compustat quarterly data for quarters[q–3, q]:β1 and β2 coefficient estimates

log

𝐶𝑂𝐺𝑆 𝑆𝐺&𝐴
𝑙𝑎𝑔 𝐶𝑂𝐺𝑆 𝑆𝐺&𝐴
𝛽

𝛽 log

𝑆𝐴𝐿𝐸𝑆
𝑙𝑎𝑔 𝑆𝐴𝐿𝐸𝑆𝛽 𝐼_𝐷𝑒𝑐𝑟𝑒𝑎𝑠𝑒

β3 coefficient estimates

βSD
βSYM
Gross job inflow/outflow rate
Net job outflow rate
Real GDP growth rate (GDP)

Aggregate earnings (Earn)

Market return (MktRet)
Industrial production index(IPI)

BBD Economic Policy
Uncertainty (EPU) index

𝑆𝐴𝐿𝐸𝑆
𝑙𝑎𝑔 𝑆𝐴𝐿𝐸𝑆𝜀

Estimated coefficients obtained from running the following
ordinary least squares regression cross-sectionally eachquarter q using Compustat quarterly data for quarters[q–3, q]:
log

βSU

log

𝐶𝑂𝐺𝑆 𝑆𝐺&𝐴
𝑙𝑎𝑔 𝐶𝑂𝐺𝑆 𝑆𝐺&𝐴

𝛽

𝛽 log

𝑆𝐴𝐿𝐸𝑆
𝑙𝑎𝑔 𝑆𝐴𝐿𝐸𝑆𝜀β1 coefficient estimates, standardized by subtracting sample
mean and dividing by standard deviation.
β2 coefficient estimates × –1, standardized by subtracting
sample mean and dividing by standard deviation.
β3 coefficient estimates, normalized by subtracting its sample
mean and dividing by standard deviation.
Gross job inflow or outflow for the total private sector in the
U.S. as a percentage of total employment in this sector,
retrieved from the Bureau of Labor Statistics.
Gross job outflow rate minus gross job inflow rate.
Real GDP growth rate.
Value-weighted average earnings (scaled bycontemporaneous sales) in each quarter, with weights basedon market capitalization as of the beginning of the quarter.
Aggregate GAAP earnings each quarter are the crosssectional value-weighted averages of earnings (scaled bycontemporaneous sales), following the procedure described inKonchitchki and Patatoukas (2014).
Equal-weighted average quarterly market return for the stocks
in the sample.
Industrial production index from the Federal Reserve Bank of
St. Louis.
Economic policy uncertainty index obtained fromwww.policyuncertainty.com and based on Baker, Bloom, andDavis (2014). This index is constructed from three underlying
components—disagreement among economic forecasters, thenumber of federal-tax-code provisions set to expire in futureyears, and newspaper coverage of policy-related economic
uncertainty.
32

Consumer Sentiment Index(CSI)
4-week average of
unemployment insurance
weekly claims (UIC)
Help-Wanted Index (HWI)

Employment growthdispersion (EmpG_Disp)

Stock return dispersion(Ret_Disp)
Effective Federal Funds rate
(IR)
Inflation (Inf)

Index of consumer sentiment based on surveys of consumers
by the University of Michigan. This index is constructed froma national representative survey based on telephonichousehold interviews and captures short-term consumer
attitudes about the business climate, spending, and personal
finance.
4-week average of unemployment insurance weekly claims
released by the U.S. Department of Labor Employment &Training Administration.
Composite Help-Wanted Index, captures the job opening rate,
which equals the number of job openings or vacancies
divided by the size of the labor force.
Sector-level employment growth dispersion available inquarter t, measured as residual from an AR(2) model:
AggEmpGDispt = r0 + r1 AggEmpGDispt–1 +r2 AggEmpGDispt–2 + et,where AggEmpGDispt–k is aggregate employment growthdispersion estimate for quarter t–k. (See Nallareddy andOgneva 2017).
Stock return dispersion available in quarter t, measured as theresidual from an AR(2) model:
AggRetDispt = r0 + r1 AggRetDispt–1 +r2 AggRetDispt–2 + et,where AggRetDispt–k is aggregate stock return dispersion
estimate for quarter t–k.
Federal funds rate released by the Federal Reserve Bank of
New York.
Quarterly average of monthly annualized changes in chainweighted GDP price index.

33

Table 1. Descriptive statistics
Panel A: Summary statistics
Gross job inflow rate %Gross job outflow rate %Net job outflow rate %β1 coefficient estimates
β2 coefficient estimates
β3 coefficient estimates
GDP
Earn
∆Earn
MktRet
IPI
EPU
CSI
UIC
IR
Inf
HWI
EmpG_Disp
Ret_Disp

Mean
7.107
6.790
-0.3170.410-0.053
0.392
2.531
0.072
-0.1010.029
92.290106.053
87.491
0.357
2.673
1.920
2.797
-0.083
0.019

Median7.000
6.800-0.4000.385-0.064
0.375
2.612
0.082
-0.061
0.031
95.21797.85590.000
0.340
2.302
1.950
2.827
-0.136
0.009

SD
0.823
0.746
0.597
0.108
0.088
0.089
1.952
0.041
0.220
0.125
11.33331.722
12.264
0.071
2.264
0.840
0.599
0.289
0.080

Panel B: Pairwise Pearson correlations – job flow rates and unemployment rate change

ChUR
Net job outflow rate
Gross job inflow rate
Gross job outflow rate

ChUR
1
0.816***-0.226**0.404***Net job outflow
rate

Gross job inflow
rate

Gross job outflow
rate

1-0.486***0.265***

1
0.714***

1

Table shows descriptive statistics for the main variables. The sample consists of 100 calendar quarterly observations
ranging from Q3:1992 to Q2:2017. Composite Help-Wanted Index is available until Q4:2016, we use the latest
updated value of the index for Q1:2017 and Q2:2017. Panel A shows summary statistics for the main variables.
Panel B shows pairwise correlations between the BED job flow rates and the change in the traditional unemployment
rate (ChUR), which is based on the Current Population Survey. Variables are as defined in Appendix A. ***, **, and* indicate two-tailed statistical significance at levels 1%, 5%, and 10%, respectively.

34

Table 2. The persistence of aggregate cost behavior
I
βSU t+1βSU t

IIβSD t+1

0.990***(53.008)

βSD t
Intercept‐0.022(‐1.064)0.931***(18.811)0.001(0.020)Adjusted R-squared
Observations

0.973
100

0.864
100

Suest chi2-statistic(p-value)(I) vs (II): 3.65**[0.056]

Table reports the results of OLS regressions of aggregate cost behavior components on its own lags. We compare thestatistical difference in the magnitude of βSU and βSD at the bottom of the table. Variables are as defined in Appendix A.
t-statistics shown in parentheses underneath the estimated coefficients are based on Newey and West (1987)
heteroscedasticity and autocorrelation-consistent standard errors with three lags. ***, **, and * indicate two-tailed
statistical significance at levels 1%, 5%, and 10%, respectively.

35

Table 3. Association between aggregate cost behavior and the gross job inflow rate-Contemporaneous relationship
I
Gross job inflow rate
βSU

II
Gross job inflow rate

III
Gross job inflow rate

0.021
(0.423)0.083***(3.638)0.653(0.691)-0.199(-1.104)-0.878**(-2.329)-0.037***(-10.448)
0.001(0.387)0.021***(4.648)0.807(0.892)0.071(0.574)-0.127(-1.167)
1.544***(3.589)0.093***(3.804)0.082**(2.134)7.410***(9.815)0.904
1000.634***(8.112)-0.184***(-3.959)
0.054***(2.854)0.800(0.944)-0.160(-0.933)-0.386*(-1.723)-0.014***(-3.465)0.001(0.468)0.007*(1.881)
0.002(0.003)0.085(0.916)-0.062(-0.821)0.812**(2.322)0.022(1.205)0.084***(2.705)7.034***(8.997)0.935
1000.380***(3.849)

βSD
GDP
Earn
∆Earn
MktRet
IPI
EPU
CSI
UIC
HWI
EmpG_Disp
Ret_Disp
IR
InfIntercept
Adjusted R-squared
Observations

0.065***(3.413)0.775(0.754)-0.160(-0.837)-0.689*(-1.922)-0.020***(-4.169)0.000(0.387)0.014***(2.894)
1.554**(2.239)0.104(1.113)-0.104(-1.305)
1.244***(2.945)0.035*(1.667)0.069**(2.031)6.382***(9.576)0.924
100

Table reports the results of OLS regressions of gross job inflow rate on βSU and βSD estimated in the current quarter q,
using public firms’ accounting data from quarter [q-3, q]. Variables are as defined in Appendix A. t-statistics shownin parentheses underneath the estimated coefficients are based on Newey and West (1987) heteroscedasticity andautocorrelation-consistent standard errors with three lags. ***, **, and * indicate two-tailed statistical significance atlevels 1%, 5%, and 10%, respectively.

36

Table 4. Association between aggregate cost behavior and the gross job outflow rate-Contemporaneous relationship
I
Gross job outflow rate
βSU

II
Gross job outflow rate

III
Gross job outflow rate

-0.187***(-3.971)-0.023(-1.145)-3.187***(-2.843)-0.367***(-2.720)-0.705*(-1.920)-0.026***(-6.992)-0.003*(-1.978)0.012*(1.678)3.869***(3.810)0.047(0.363)0.148(1.018)0.851(1.292)0.161***(7.323)0.095**(2.147)6.537***(5.910)0.866
1000.316***(2.774)-0.289***(-4.583)-0.037*(-1.750)-3.114***(-2.776)-0.348**(-2.527)-0.460(-1.429)-0.015**(-2.564)-0.003*(-1.915)0.005(0.683)3.468***(3.379)0.054(0.394)0.181(1.170)0.486(0.838)0.126***(5.862)
0.096**(2.167)6.350***(5.628)0.874
100-0.083(-0.752)

βSD
GDP
Earn
∆Earn
MktRet
IPI
EPU
CSI
UIC
HWI
EmpG_Disp
Ret_Disp
IR
InfIntercept
Adjusted R-squared
Observations-0.021(-0.955)-3.153**(-2.294)-0.348**(-2.029)-0.937**(-2.068)-0.025***(-4.564)-0.003*(-1.830)
0.016*(1.779)5.908***(5.675)0.084(0.635)0.115(0.797)
1.166(1.500)0.147***(7.138)0.073(1.448)5.325***(4.880)0.842
100

Table reports the results of OLS regressions of gross job outflow rate on βSU and βSD estimated in the current quarterq, using public firms’ accounting data from quarter [q-3, q]. Variables are as defined in Appendix A. t-statistics shownin parentheses underneath the estimated coefficients are based on Newey and West (1987) heteroscedasticity andautocorrelation-consistent standard errors with three lags. ***, **, and * indicate two-tailed statistical significance atlevels 1%, 5%, and 10%, respectively.

37

Table 5. Association between aggregate cost behavior and future gross job flow rate--Prediction
Panel A: Association between βSU, βSD and future gross job inflow rates

t+1
βSU

0.327***(3.953)I
Gross job inflow ratet+k
t+2
t+3
0.357***(3.341)0.372***(3.039)

t+4

Yes
0.919
96

Yes
0.914
96

Yes
0.910
96

t+4

t+1

0.001
(0.012)
Yes
0.899
96

0.593***(7.101)-0.170***(-4.077)
Yes
0.928
96

0.328***(2.666)βSD
Control Variables
Adjusted R-squared
Observations

t+1

II
Gross job inflow ratet+k
t+2
t+3

Yes
0.912
96

0.009
(0.233)
Yes
0.906
96

0.009
(0.193)
Yes
0.898
96

0.001
(0.011)
Yes
0.893
96

III
Gross job inflow ratet+k
t+2
t+3
0.653***(7.349)-0.189***(-3.986)
Yes
0.925
96

0.706***(7.005)-0.213***(-3.839)
Yes
0.924
96

t+4
0.621***(6.469)-0.187***(-3.454)
Yes
0.923
96

Panel B: Association between βSU, βSD and future gross job outflow rates

t+1
βSU

-0.042(-0.273)I
Gross job outflow ratet+k
t+2
t+3-0.058(-0.425)-0.044(-0.362)

t+4

Yes
0.809
96

Yes
0.801
96

Yes
0.783
96

t+4

t+1

-0.198**(-2.619)
Yes
0.836
96

0.494***(3.307)-0.342***(-4.595)
Yes
0.856
96

-0.177(-1.393)βSD
Control Variables
Adjusted R-squared
Observations

t+1

II
Gross job outflow ratet+k
t+2
t+3

Yes
0.811
96

-0.193***(-3.694)
Yes
0.839
96

-0.169***(-3.009)
Yes
0.823
96

-0.167**(-2.493)
Yes
0.804
96

III
Gross job outflow ratet+k
t+2
t+3
0.394*(1.837)-0.288***(-2.728)
Yes
0.833
96

0.414*(1.910)-0.293**(-2.437)
Yes
0.816
96

t+4
0.252
(1.573)-0.274**(-2.488)
Yes
0.839
96

Table reports the results of OLS regressions of future gross job flow rate on βSU and βSD estimated in the current quarter q, using public firms’ accounting data from quarter[q-3, q]. Panel A reports the results of OLS regressions of future gross job inflow rate on βSU and βSD up to 4 quarters. Panel B reports the results of OLS regressions of
future gross job outflow rate on βSU and βSD up to 4 quarters. Variables are as defined in Appendix A. t-statistics shown in parentheses underneath the estimated coefficientsare based on Newey and West (1987) heteroscedasticity and autocorrelation-consistent standard errors with three lags. ***, **, and * indicate two-tailed statisticalsignificance at levels 1%, 5%, and 10%, respectively.

38

50

.009

100

Uncertainty Index
150

200

.01
.011
.012
.013
Standard Errors of Beta Coefficients

250

.014

Figure 1. Standard errors of βSD and the Economic Policy Uncertainty index

1993q1

1998q1

2003q1

2008q1

2013q1

2018q1Figure shows BBD Economic Policy Uncertainty Index (in solid line) and standard errors of βSD (in dash line) forthe period Q3:1992 to Q2:2017.

39

Table 6. Association between βSD, standard error of βSD, and gross job outflow rate
I
II
Gross job outflow rate Gross job outflow rate
-0.217***(-4.764)-0.329**(-1.997)-0.248**(-2.430)

βSD
SE(βSD)—top decile
βSD × SE(βSD)—top decile
SE(βSD)—top quartile
βSD × SE(βSD)—top quartile
GDP-0.018(-0.907)-3.122***(-2.763)-0.368***(-2.691)-0.719**(-2.026)-0.029***(-7.208)-0.003*(-1.970)0.010(1.405)3.464***(3.064)0.194(1.232)0.136(0.892)0.761(1.246)0.134***(5.914)0.111**(2.468)6.798***(6.342)
0.873
100

Earn
∆Earn
MktRet
IPI
EPU
CSI
UIC
HWI
EmpG_Disp
Ret_Disp
IR
InfIntercept
Adjusted R-squared
Observations-0.208***(-5.683)-0.295***(-3.879)-0.103*(-1.846)-0.042**(-2.188)-3.151***(-2.990)-0.354***(-2.844)-0.441(-1.269)-0.030***(-10.105)-0.002(-1.507)
0.011(1.651)
3.068***(2.976)0.151(1.227)0.129(0.925)0.473(0.811)0.136***(6.345)0.080**(2.029)7.143***(7.335)0.880
100

Table reports the results of OLS regressions of job outflow rates on βSD, the standard error of βSD, their interactions,and control variables. βSD is estimated for each quarter q using public firms’ accounting data from quarters [q-3, q].
SE(βSD)—top decile (quartile) is a dummy variable that takes a value of 1 if SE(βSD) is in the highest decile (quartile)and 0 otherwise. Other variables are as defined in Appendix A. t-statistics shown in parentheses underneath theestimated coefficients are based on Newey and West (1987) heteroscedasticity and autocorrelation-consistent standard
errors with three lags. ***, **, and * indicate two-tailed statistical significance at levels 1%, 5%, and 10%,respectively.

40

Table 7. A comparison of the symmetric and asymmetric cost models
I
Gross job
inflow
rate
βSU
βSD
βSYM
GDP
Earn
∆Earn
MktRet
IPI
EPU
CSI
UIC
HWI
EmpG_Disp
Ret_Disp
IR
InfIntercept
Adjusted R-squared
Observations
Vuong z-statistic(p-value)II
Gross job
inflowrate

III
Gross job
outflowrateIV
Gross job
outflowrate

V
Net job
outflowrate

VI
Net job
outflow
rate

0.634***(8.112)-0.184***(-3.959)

0.316***(2.774)-0.289***(-4.583)-0.318**(-2.488)-0.105*(-1.726)

0.551***(7.989)0.054***
0.053***(2.854)(2.777)0.800
0.940(0.944)(1.114)-0.160-0.186(-0.933)(-1.116)-0.386*-0.313(-1.723)(-1.461)-0.014***-0.013***(-3.465)(-3.451)
0.001
0.001
(0.468)(0.553)0.007*
0.006(1.881)(1.474)0.002-0.556(0.003)(-0.789)
0.085
0.081
(0.916)(0.845)-0.062-0.049(-0.821)(-0.660)
0.812**
0.746**(2.322)(2.029)0.022
0.017(1.205)(0.978)0.084***
0.091***(2.705)(2.839)7.034***
7.307***(8.997)(10.608)
0.935
0.935
100
100
(I) vs (II): 0.355[0.722]0.285**(2.259)-0.037*-0.041*(-1.750)(-1.893)-3.114***-2.973**(-2.776)(-2.078)-0.348**-0.331*(-2.527)(-1.857)-0.460-0.620(-1.429)(-1.607)-0.015**-0.009(-2.564)(-1.434)-0.003*-0.003(-1.915)(-1.627)
0.005
0.007
(0.683)(0.752)
3.468***
5.551***(3.379)(4.886)0.054
0.100(0.394)(0.667)0.181
0.157(1.170)(0.966)0.486
0.709(0.838)(1.075)0.126***
0.092***(5.862)(3.254)
0.096**
0.072(2.167)(1.434)6.350***
4.927***(5.628)(4.355)
0.874
0.851
100
100
(III) vs (IV): 2.519**[0.012]-0.267*(-1.872)-0.091***-0.093***(-3.731)(-3.601)-3.914***-3.914***(-4.170)(-3.631)-0.188-0.145(-1.325)(-0.953)-0.074-0.307(-0.203)(-0.787)-0.001
0.005(-0.163)(0.684)-0.003*-0.003*(-1.946)(-1.723)-0.0020.001(-0.279)(0.158)3.466***
6.108***(3.128)(5.388)-0.0320.019(-0.255)(0.135)0.242
0.207(1.439)(1.152)-0.326-0.037(-0.504)(-0.048)0.104***
0.075***(4.492)(2.989)0.012-0.019(0.288)(-0.379)-0.684-2.380**(-0.708)(-2.274)
0.742
0.695
100
100
(V) vs (VI): 2.276**[0.023]

Table reports the results of OLS regressions of job flow rates on βSU, βSD and βSYM, estimated in the current quarter q,
using public firms’ accounting data from quarters [q-3, q]. z-statistics following Vuong tests are presented at thebottom of each pair of columns to show the differences in R-squared between models that include βSU and βSD andmodels that include βSYM. p-values are shown in square brackets underneath the z-statistics. Variables are as definedin Appendix A. t-statistics shown in parentheses underneath the estimated coefficients are based on Newey and West
(1987) heteroscedasticity and autocorrelation-consistent standard errors with three lags. ***, **, and * indicate twotailed statistical significance at levels 1%, 5%, and 10%, respectively.

41

Table 8. Association between βSU, βSD and future gross job inflow rates –Vector autoregression models
Panel A: Impulse-response graph to shock in cost behavior components— βSU and βSD separately
AZt = ϕZt–k + εt, where 𝑍

𝐽𝑜𝑏 𝐼𝑛𝑓𝑙𝑜𝑤 𝑅𝑎𝑡𝑒 , 𝐺𝐷𝑃 , 𝛽Impulse: βSD --- Response: Job-inflow rate

.15

.15

.1

.1

.05

.05

Percent

Percent

Impulse: βSU --- Response: Job-inflow rate

/𝛽

0

0

-.05

-.05

-.1

-.1
0

2

4

6

8

10

12

0

2

4

Quarter

6

8

10

12

Quarter

Panel B: Impulse-response graph to shock in cost behavior components— βSU and βSD jointly
AZt = ϕZt–k + εt, where 𝑍

𝐽𝑜𝑏 𝐼𝑛𝑓𝑙𝑜𝑤 𝑅𝑎𝑡𝑒 , 𝐺𝐷𝑃 , 𝛽Impulse: βSD --- Response:Job-inflow rate

.15

.15

.1

.1

.05

.05

Percent

Percent

Impulse: βSU --- Response: Job-inflow rate

,𝛽

0

0

-.05

-.05

-.1

-.1
0

2

4

6

8

10

12

0

Quarter

2

4

6

8

10

12

QuarterTable reports the results of reduced-form VAR models with 4 lags including the following variables: gross job inflow
rate (Job Inflow Rate), real GDP growth rate (GDP), βSU, and βSD. Panel A shows the impulse-response function (IRF)
graphs for the response of gross job inflow rate to a one-standard-deviation shock to βSU and βSD from VAR models
that include βSU and βSD separately. Panel B shows the impulse-response function (IRF) graph for the response of gross
job inflow rate to a one-standard-deviation shock to βSU and βSD from a VAR model that includes βSU and βSD jointly.
95% confidence bands are also presented (shaded areas). Variables are defined in Appendix A.

42

Table 9. Association between βSU, βSD and future gross job outflow rates –Vector autoregression models
Panel A: Impulse-response graph to shock in cost behavior components—βSU and βSD separately
AZt = ϕZt–k + εt, where 𝑍

𝐽𝑜𝑏 𝑂𝑢𝑡𝑓𝑙𝑜𝑤 𝑅𝑎𝑡𝑒 , 𝐺𝐷𝑃 , 𝛽Impulse: βSD --- Response: Job-outflow rate

.15

.15

.1

.1

.05

.05

Percent

Percent

Impulse: βSU --- Response: Job-outflow rate

/𝛽

0

0

-.05

-.05

-.1

-.1
0

2

4

6

8

10

12

0

2

4

Quarter

6

8

10

12

Quarter

Panel B: Impulse-response graph to shock in cost behavior components—βSU and βSD jointly
AZt = ϕZt–k + εt, where 𝑍

𝐽𝑜𝑏 𝑂𝑢𝑡𝑓𝑙𝑜𝑤 𝑅𝑎𝑡𝑒 , 𝐺𝐷𝑃 , 𝛽Impulse: βSU --- Response: Job-outflow rate,𝛽

Impulse: βSD --- Response: Job-outflow rate

.15
.15
.1

.05

Percent

Percent

.1

0

.05

0

-.05

-.05

-.1

-.1
0

2

4

6

8

10

12

0

Quarter

2

4

6

8

10

12

QuarterTable reports the results of reduced-form VAR models with 4 lags including the following variables: gross job outflow
rate (Job Outflow Rate), real GDP growth rate (GDP), βSU, and βSD. Panel A shows the impulse-response function(IRF) graphs for the response of gross job outflow rate to a one-standard-deviation shock to βSU and βSD from VAR
models that include βSU and βSD separately. Panel B shows the impulse-response function (IRF) graph for the responseof gross job outflow rate to a one-standard-deviation shock to βSU and βSD from a VAR model that includes βSU andβSD jointly. 95% confidence bands are also presented (shaded areas). Variables are defined in Appendix A.

43

Table 10. The association between aggregate cost elasticities and job flow rates— Different state-level labor protection
Panel A: βSU and gross job-inflow rate
State-level labor protection
βSU
GDP
Earnt
∆Earnt
MktRett
IPIt
Uncert
CSIt
UICt
HWIt
EmpG_Dispt
Ret_Dispt
IRt
InftIntercept
Adjusted R-squared
Observations
Suest chi2-statistic(p-value)

I
High

II
Low

0.386***(2.747)
0.066**(2.220)1.686(1.027)-0.298(-0.950)-0.835(-1.539)-0.024***(-3.193)-0.000(-0.168)
0.024***(3.181)2.140**(2.053)0.096(0.609)-0.197*(-1.714)1.520**(2.399)0.046(1.543)0.112*(1.883)-1.592(-1.425)0.887
100

0.490***(4.457)0.083***(4.171)0.520(0.447)-0.149(-0.689)-0.697*(-1.712)-0.025***(-4.813)
0.002(1.171)0.014***(2.671)
1.392*(1.686)0.123(1.135)-0.053(-0.572)
1.380***(2.925)0.046*(1.833)0.070*(1.964)-0.404(-0.542)0.930100(I) vs (II): 3.44*[0.064]44

Panel B: βSD and gross job-outflow rate
State-level labor protection
βSD
GDP
Earnt
∆Earnt
MktRett
IPIt
Uncert
CSIt
UICt
HWIt
EmpG_Dispt
Ret_Dispt
IRt
InftIntercept
Adjusted R-squared
Observations
Suest chi2-statistic(p-value)

I
High

II
Low

-0.283***(-3.626)-0.024(-0.734)-3.384**(-2.052)-0.523*(-1.781)-0.859(-1.343)-0.033***(-4.565)-0.005**(-2.511)0.012(0.995)3.968**(2.376)-0.103(-0.452)0.323(1.473)1.088(1.242)0.243***(7.209)0.137*(1.825)0.810(0.415)0.822
100-0.212***(-3.250)-0.035(-1.409)-4.721***(-4.031)-0.406**(-2.390)-0.878*(-1.926)-0.036***(-7.209)-0.003(-1.518)0.019**(2.152)5.853***(4.438)0.159(0.922)0.216(1.171)
1.182(1.430)0.199***(6.029)0.131**(2.343)-0.993(-0.710)0.876100(I) vs (II): 3.59*[0.058]

Table reports the results of OLS regressions of job flow rates of firms headquartered in states with high and low laborprotection laws. A high labor-protection state is defined as a state that has adopted the good faith, implied contract,and public policy exceptions as of a given quarter. βSU and βSD estimated in the current quarter, using public firms’
accounting data from quarter [t-3,t]. Panel A reports the results of OLS regressions of gross job inflow rates on βSU,
while Panel B reports the results of regressing gross job outflow rates on βSD. We compare the statistical difference inthe explanatory power of βSU and βSD between the two types of states at the bottom of each panel. Variables are definedin Appendix A. t-statistics shown in parentheses underneath the estimated coefficients are based on Newey and West
(1987) heteroscedasticity and autocorrelation-consistent standard errors with three lags. ***, **, and * indicate twotailed statistical significance at levels 1%, 5%, and 10%, respectively.

45