Labor Unemployment Risk and Sticky Cost Behavior full paper-2_final.pdf · Labor Unemployment Risk...

Labor Unemployment Risk and Sticky Cost Behavior

Jeong-Bon Kim

City University of Hong Kong

[email protected]

Ke Wang

City University of Hong Kong

[email protected]

January 2014

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Labor Unemployment Risk and Sticky Cost Behavior

Abstract

This paper presents large-sample evidence that firms consider labor unemployment risk when

setting their resource adjustment policies. Prior studies find that costs rise more in response to

sales increases than they fall in response to sales decreases. Anderson, Banker, and Janakiraman

(2003) term this phenomenon “cost stickiness” and attribute it to managers’ deliberate

adjustment to committed resources in the existence of adjustment costs. We argue that workers’

unemployment costs constitute non-trivial costs of downsizing labor forces in particular and

adjusting resources downward in general. To the extent that state unemployment insurance

benefits offset the costs borne by involuntarily displaced workers, the generosity of

unemployment insurance can inversely capture the heterogeneous levels of workers’

unemployment costs and in turn firms’ downward adjustment costs. We predict and find that

more generous unemployment insurance benefits lead to lower stickiness of selling, general, and

administrative (SG&A) costs. This finding is robust to controlling for firm-level determinants of

cost stickiness, state-wide economic conditions, and unobservable time-invariant state

characteristics. Additional analysis shows that the results for SG&A costs are also applicable to

other cost accounts. Overall, our study enriches the understanding of sticky cost behavior and

takes an early step to examine the accounting implications of labor unemployment risk.

Keywords: unemployment risk; sticky costs; unemployment insurance; resource adjustment;

adjustment costs

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1. Introduction

Unemployment risk significantly concerns workers. Prior research in labor economics

and other social sciences documents ample evidence on not only pecuniary (e.g., Gibbons and

Katz 1991; Gruber 1997; Ruhm 1991) but also psychological (e.g., Clark and Oswald 1994;

Winkelmann and Winkelmann 1998) and social (e.g., Kalil and Ziol-Guest 2008; Rege et al.

2011) costs borne by the jobless. To the extent that workers are essential in the value creating

process, their concerns about unemployment could play an important role in shaping corporate

financing, investment, and accounting policies. Starting with Titman (1984), financial

economists investigate whether the potential loss of firm-specific human capital caused by

bankruptcy affects the optimal level of financial leverage. In a recent study, Agrawal and Matsa

(2013) argue and find that leverage is positively related to the generosity of unemployment

insurance (hereafter UI), consistent with UI reducing workers’ unemployment costs and in turn

the firm’s indirect cost of bankruptcy. These authors conclude that firms take into account labor

unemployment risk when setting financial policies. Prior accounting studies are, however,

largely silent about the implication of unemployment risk or UI on corporate accounting

decisions. As a result, little is known about whether and how labor unemployment risk impacts

accounting choices and/or the associated economic consequences. To fill this void, our study

investigates the impact of unemployment risk on the behavior of selling, general, and

administrative (SG&A) costs.

Anderson, Banker, and Janakiraman (2003, hereafter ABJ) provide first large-sample

evidence that SG&A costs respond more to sales increases than to sales decreases. They term

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this phenomenon “cost stickiness” and attribute it to managers’ deliberate adjustment to

committed resources in the existence of adjustment costs. To support a rise in activity level,

managers must expand committed resources. On the contrary, when the activity level declines,

managers can trade off the costs of holding unutilized resources against the costs of cutting

committed resources. When the latter dominates the former, managers have tendency to retain a

certain level of idle capacity. Viewed in this manner, one can infer that the degree of cost

stickiness is likely to vary with resource adjustment costs.

As discussed in Banker et al. (2011), testing this inference is interesting and important

but involves practical complication, since it is difficult to directly measure the adjustment costs.

Nevertheless, existing studies have made considerable effort to capture adjustment costs and

corroborate the ABJ interpretation of cost stickiness. In one of their tests, ABJ find that the

degree of cost stickiness increases with asset intensity and employee intensity, which are indirect

measures of adjustment costs. However, this finding should be interpreted with cautions, because

the degree of cost stickiness and the asset and employee intensity could be simultaneously

determined by unobservable factors. Later studies shed more light on the effect of adjustment

costs on the degree of cost stickiness in intra-firm (Balakrishnan and Gruca 2008) and

international (Banker et al. 2013; Calleja et al. 2006) settings. Unlike these studies, we

investigate whether the magnitude of cost stickiness at the firm level varies with labor

unemployment risk. In so doing, we maintain that the more generous are state UI benefits, the

lower is the labor unemployment risk. Under this maintained hypothesis, labor unemployment

risk can be captured inversely by the generosity of state UI benefits. It should be noted here that

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given that UI benefits at the state level can be viewed as largely exogenous to a firm, the impact

of state UI benefits on cost stockiness is unlikely to be endogenous.

In labor market equilibrium, wage differentials compensate workers for bearing

unemployment risk (Topel 1984a). Stated differently, ex post costs suffered by involuntarily

displaced workers are reflected in ex ante premiums in wages or benefits offered by the firm,

which constitute non-trivial costs of adjusting resources downward. In addition, prior studies find

that UI substantially reduces workers’ unemployment costs and in turn narrow down wage

differentials that can be explained by unemployment risk (e.g., Gormley et al. 2010; Gruber 1997;

Topel 1984a). As a result, firms have lower costs of cutting labor when UI benefits become more

generous.1

In this sense, the generosity of UI benefits can inversely measure downward

adjustment costs. We therefore predict that firms located in states with more (less) generous UI

benefits have a smaller (larger) degree of cost stickiness or the asymmetry in SG&A costs.

Using a large sample of 104,135 observations for 12,906 unique firms in the US from

1979 to 2010, we test the above prediction and find that the generosity of state UI benefits

significantly lowers the stickiness of SG&A costs. This finding is robust to controlling for firm-

level determinants of cost stickiness, state-wide economic conditions, and unobservable time-

invariant state characteristics. We further find that the above finding remains unchanged when

we exclude from the sample firms in the retail, wholesale, and transport industries where firms

are likely to be located across different states. This finding suggests that our results are unlikely

1 An implicit assumption underlying this argument is that UI benefits are not completely financed by employers. In

Section 2, we discuss the institutional background of state UI benefits in the US and, therein, show that this

assumption is realistic.

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to be driven by potential errors in measuring UI benefits for firms with geographically dispersed

labor force. Our approach circumvents potential endogeneity issues to a large extent, since we

identify state UI generosity as an exogenous variable that captures costs of adjusting resources

downward. Firms could exert influence on state government policy on UI through lobbying

activities, but it is unlikely that any change in state-level UI policy is made for the purpose of

shifting the firm-level cost behavior. In addition, our falsification test shows that our result is

hardly driven by regional economic factors that potentially have an effect on both state UI

generosity and firm-level cost behavior. The above findings, taken together, support the view

that higher (lower) adjustment costs bring about a larger (smaller) degree of cost stickiness, and

are in line with the ABJ interpretation of sticky cost behavior.

Banker, Byzalov, Ciftci, and Mashruwala (2012a, hereafter BBCM) show that the degree

of cost stickiness or anti-stickiness is conditional on managerial expectation about future sales

based on the prior sales change.2 Costs are sticky when managers are optimistic but anti-sticky

when managers are pessimistic. We show that more generous UI benefits result in lower

stickiness of SG&A costs under optimism and larger degree of cost anti-stickiness under

pessimism. This further supports our prediction, showing that the degree of cost stickiness

decreases with the generosity of state UI benefits even after controlling for managerial

expectation about future sales.

We also study which cost subcategories drive the impact of the UI generosity on the

stickiness of SG&A costs and whether our main finding on SG&A costs can be extended to other

2 Weiss (2010) is the first study that terms costs “anti-sticky” if they respond less to sales increases than to sales

decreases.

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categories of costs. We find that more generous UI results in lower stickiness of research and

development (R&D) cost and other SG&A costs,3 but not advertising cost. In addition, cost of

goods sold (COGS) and operating costs4 are less sticky when the UI benefits become more

generous. This is also applicable to total costs.5 Finally, we use a reduced sample to provide

evidence suggesting that the stickiness of labor cost decreases with the generosity of state-level

UI benefits.

Our study contributes to existing literature in the following ways. First, to our knowledge,

this study is one of the few, if not the first, to investigate the accounting implication of labor

unemployment risk and UI.6 In particular, we provide the first piece of evidence that labor

unemployment risk, inversely measured by the generosity of UI, is an important determinant of

sticky cost behavior. As noted by ABJ, cost stickiness reflects resource adjustment decisions

made by managers. Our findings suggest that firms take labor unemployment risk into account

when choosing their resource adjustment policies.

Second, we employ a novel setting to test the hypothesis that the degree of cost stickiness

increases with the magnitude of adjustment costs. ABJ provides strong evidence on the SG&A

asymmetry with respect to sales increase and sale decrease for U.S. firms, but no study has yet

tested the ABJ interpretation of cost stickiness, namely, the asymmetric impact of resource

3 Other SG&A costs are defined as SG&A costs excluding R&D cost and advertising cost. See the Appendix for

variable definitions. 4 Operating costs are calculated by the difference between sales and operating income. As a result, operating costs

include both COGS and SG&A costs. 5 Total costs are calculated by the difference between sales and income before extraordinary items. 6 In a concurrent paper, Devos and Rahman (2013) find that the generosity of UI is positively related to corporate

tax aggressiveness.

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adjustment costs on SG&A cost stickiness for U.S. firms in a direct and reliable manner. We

argue and find that the generosity of state UI benefits has a positive impact on the degree of cost

stickiness.

Third, our study is related to the international study conducted by Banker, Byzalov, and

Chen (2013, hereafter BBC). These authors show that stricter country-level employment

protection legislation engenders larger degree of cost stickiness. Interestingly, the U.S. has the

least strict provisions on employee protection among 19 OECD countries covered by their

sample, and thus, implies that U.S. firms should have a relatively small degree of cost

stickiness.7 Our study shows that the costs of cutting committed resources have a significant

impact on sticky cost behavior even in a country like U.S. that exhibits low stickiness compared

with other countries. Further, BBC use the time-invariant country-level employment protection

legislation as a proxy for labor adjustment costs, ignoring the possible change in adjustment

costs over time. By contrast, state UI generosity experiences significant changes over time and

across states. More importantly, these changes are not synchronized among different states.8 To

sum up, our evidence on the impact of state-level UI benefits on SG&A cost stickiness of U.S.

firms lends further support to the ABJ concept of cost stickiness on the top of BBC in particular

and the extant literature in general.9

7 In an international comparison, Calleja et al. (2006) find that costs are less sticky in the US and the UK than in

Germany and France, lending partial support to this inference. 8 See Figure 2 of Agrawal and Matsa (2013) for a visualized demonstration of the states’ changes in UI benefit laws

relative to peer states. 9 BBC include a time-invariant country-level measure of UI benefits as a control variable in their robustness tests. In

their Table 6, they show that the generosity of UI benefits increases the degree of cost stickiness, especially in the

sub-period of 1990–2000. This result is, however, inconsistent with their argument. Specifically, these authors argue

that costs of hiring labor are higher the more generous unemployment benefits are, because the recipients of

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Finally, traditional managerial accounting textbooks view costs as symmetric in

responding to activity levels. That is, the relation between changes in costs and changes in

activity levels is not conditional on the direction of the changes. Recent studies, however,

provide evidence on sticky cost behavior, inconsistent with the traditional view (e.g., ABJ;

Noreen and Soderstrom 1997). Our study add to the managerial accounting literature by

providing evidence suggesting that labor unemployment risk is an important determinant of cost

behavior.

The remainder of this paper proceeds as follows. In the next section, we review related

literature and develop our hypothesis. Section 3 presents the sample selection procedure, defines

the key variable, and specifies the empirical models. In Section 4, we report the empirical results.

Section 5 concludes.

2. Related literature and hypothesis development

2.1 Sticky cost behavior

Inconsistent with the model of cost behavior in standard managerial accounting textbooks,

recent research documents the sticky or asymmetric cost behavior.10

Noreen and Soderstrom

(1997) are the first to examine whether the response of costs to changes in activity is conditional

on the direction of the changes. Using data for hospitals in Washington, these authors find that

generous unemployment benefits are under less pressure to seek jobs (see their Footnote 33). Higher hiring costs

should result in less response to activity increases and in turn smaller degree of cost stickiness. As a result, the

implication of the authors’ argument is that the generosity of UI benefits decreases the degree of cost stickiness,

although the authors do not clearly state this prediction. Compared with BBC, our paper has a different focus, makes

a specific prediction, uses a different setting, adopts a different measure of UI generosity (see Section 3 for our

measure), and documents empirical results of the opposite direction. The above differences indicate that our work

complements (rather than refutes) theirs. We leave more detailed discussions on the extant literature to Section 2. 10 See Banker et al. (2011) for a thorough discussion on the economic theory underlying sticky cost behavior and an

excellent review of the extant literature.

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the great majority of cost accounts tend to increase more under activity increases than they tend

to decrease under activity decreases, although only a few of the differences are statistically

significant. ABJ provides the first piece of strong, large-sample evidence that supports the sticky

cost behavior. They study more than 7,000 US firms over a 20-year period and show that SG&A

costs increase by 0.55% in response to a 1% increase in sales revenue but decrease by 0.35% in

response to a 1% decrease in sales revenue. Subsequent studies provide evidence on the sticky

behavior of various cost accounts in different settings (e.g., Balakrishnan and Gruca 2008; BBC;

Calleja et al. 2006; Chen et al. 2012; Dierynck et al. 2012; Subramaniam and Weidenmier 2003).

This evidence corroborates the finding of ABJ that SG&A costs respond more to activity

increases than to activity decreases.

Why do costs behave asymmetrically? What are the main factors that drive the cross-

sectional variation in sticky cost behavior? Prior studies provide theoretical discussions and

empirical evidence on these issues.11

In their seminal study, ABJ attribute cost stickiness to

managers’ deliberate adjustment to committed resources in the existence of adjustment costs.

They argue that managers expand committed resources to accommodate increased sales but

maintain unutilized resources when facing decreased sales. This occurs because it is normally

expensive to cut committed resources and, if sales rebound later, replace those resources. It is

11 Prior studies find that the degree of cost stickiness can vary with asset intensity and employee intensity (ABJ;

Subramaniam and Weidenmier 2003), capacity utilization (Balakrishnan et al. 2004), the strictness of employee

protection legislation (BBC), managerial optimism or pessimism (BBCM), managerial incentives to meet or beat

earnings targets (Dierynck et al. 2012; Kama and Weiss 2013), the empire building incentives (Chen et al. 2012),

CEO compensation structure (Banker et al. 2012b), and the quality of internal control systems (Kim et al. 2012). In

this study, we mainly focus on the factors related to costs of adjusting committed resources and optimal resource

adjustment decisions. The misaligned interests between shareholders and managers are beyond the scope of this

paper.

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true that firms also incur costs when adjusting resources upward, but the economics literature

shows that upward adjustment costs are smaller than downward adjustment costs for many

resources (e.g., Bentolila and Bertola 1990; Cooper and Haltiwanger 2006; Goux et al. 2001;

Jaramillo et al. 1993; Palm and Pfann 1998; Pfann and Palm 1993). As a result, costs exhibit

stickiness in general.12

More importantly, an important implication from the ABJ concept of

sticky cost behavior is that the magnitude of adjustment costs determines the degree of cost

stickiness.13

Costs are more sticky if downward adjustment costs are higher. On the contrary,

costs are less sticky, or more anti-sticky, if downward adjustment costs are lower.14

As discussed in Banker et al. (2011), empirical tests for the impact of adjustment costs on

cost stickiness are practically complicated, because adjustment costs are difficult to measure

directly. Prior studies make some efforts to study this issue. For example, ABJ and Subramaniam

and Weidenmier (2003) find that the stickiness of SG&A costs and COGS, respectively,

increases with asset intensity and employee intensity, which are used as firm-level proxies for

downward adjustment costs. Asset intensity and employee intensity could capture adjustment

12 Costs can exhibit stickiness in general even when downward adjustment costs are not larger than upward

adjustment costs, because sales change is predominantly positive. For example, sales decreases account for 31.3% of

firm-years in our sample, compared with 68.7% for sales decreases (see Table 1, Panel A). As a result, managers

expect more subsequent sales increases than decreases. To avoid current costs of cutting resources and future costs

of replacing those resources, managers tend not to immediately adjust resources downward in reaction to sales

decreases. While not focusing on the reasons why costs are generally sticky, we refer readers to Banker et al. (2011)

for more detailed discussions. 13 Hereafter we follow most prior studies and focus on costs of adjusting resources downward. 14 Indeed, downward adjustment costs may affect decisions on both downward and upward resource adjustments.

When expanding resources to accommodate increased activity, managers need to consider the expected costs of

cutting those resources in case of subsequent activity decreases. The effect of downward adjustment costs on

downward resource adjustments, however, dominates the effect on upward resource adjustments, because (i) the

probability of subsequent activity decreases is less than one and (ii) future adjustment costs are discounted.

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costs, because adjustment costs tend to be higher for firms that rely more on assets and

employees than on purchased materials and services.

However, these two factors and the degree of cost stickiness are likely to be

simultaneously determined by omitted firm characteristics. Business strategy could be one

possible factor that affects both asset and employee intensity and the sticky cost behavior. In a

department-level study, Balakrishnan and Gruca (2008) use data for Ontario hospitals to show

that costs exhibit greater stickiness in an organization’s core functions than in ancillary and

supporting ones. It is reasonable to argue that the adjustment costs are higher for core functions

than for other functions, but the intra-firm setting limits the generality of the authors’ work.

Further, Calleja et al. (2006) make an international comparison of cost behavior and find that

operating costs are more sticky in French and German firms than in U.K. and U.S. firms. They

attribute this finding to higher costs of downsizing labor forces in particular and more emphasis

put on the interests of stakeholder groups (e.g., workers) in general in France and Germany than

in the U.K. and the U.S. In a similar vein, BBC use data from 19 OECD countries and find that

the degree of cost stickiness increases with the strictness of country-level employee protection

legislation. According to Table 4 of BBC, the U.S. has the least strict employment protection

legislation among the 19 countries, implying a relatively small degree of cost stickiness in the

U.S.

The U.S. provides a unique setting to study whether the degree of cost stickiness

increases with the magnitude of downward adjustment costs for the following reasons. First, the

first piece of large-sample evidence of sticky cost behavior is documented by ABJ in the U.S.

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setting. Studying other economies or making international comparisons cannot show whether the

cost stickiness found in the U.S. data is really attributable to managers’ deliberate decisions on

resource adjustments as interpreted by ABJ. Second, as inferred from BBC and Calleja et al.

(2006), costs exhibit relatively low, if not the lowest, stickiness in the U.S. If the degree of cost

stickiness increases with downward adjustment costs even in a country with generally low

stickiness, one can be more convinced of the ABJ notion of cost stickiness. Third, the U.S. has

rich state-level data available to the public so that one can find exogenous variables that capture

both cross-sectional and time-series variations of downward adjustment costs. In this paper, we

use the generosity of state UI benefits as an inverse measure of downward adjustment costs, or

more specifically, firing costs.15

While international studies cannot easily control for

unobservable time-varying cross-country differences, our study is less subject to this problem.

2.2 Labor unemployment risk and UI

Workers are highly concerned about unemployment risk, because unemployment brings

significant costs in many dimensions. These include psychological costs (e.g., Clark and Oswald

1994; Winkelmann and Winkelmann 1998) and social costs (e.g., Kalil and Ziol-Guest 2008;

Rege et al. 2011) in addition to monetary costs (e.g., Gibbons and Katz 1991; Gruber 1997;

Ruhm 1991). In labor market equilibrium, workers are compensated by a premium in wages or

benefits for bearing unemployment risk (Topel 1984a). Put it another way, the ex post

unemployment costs borne by the unemployed are reflected in the ex ante wage premiums paid

by the firm. Financial economists support this argument by investigating the interplay between

15 The rationale of UI generosity decreasing firing costs and in turn the degree of cost stickiness is discussed later in

this section.

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human capital and corporate financial policies. As aggressive financial policies increase the

likelihood of financial distress or bankruptcy, workers in high-leverage firms are exposed to high

unemployment risk. Analytical studies show that human capital costs can be significant indirect

costs of financial distress and decrease the optimal level of debt in capital structure (Berk et al.

2010; Titman 1984). Further, prior studies provide evidence that employee-friendly firms

maintain lower leverage than other firms (Bae et al. 2011; Verwijmeren and Derwall 2010), and

that leverage has a significantly positive impact on employee pay (Chemmanur et al. 2013).

The extant literature suggests that UI substantially reduces costs borne by the jobless.

Gruber (1997) documents a non-trivial role of UI in smoothing the unemployed workers’

consumption. Moreover, Gormley et al. (2010) show that generous UI benefits increase stock

market participation by U.S. households. More related to our study, Topel (1984a) provides

evidence that UI has a powerful equalizing effect on the wage differences caused by

unemployment risk.16

Despite of the evidence that the generosity of UI benefits impacts

unemployed workers’ wealth, whether it affects employers’ firing costs and in turn layoff

behavior depends on who pays the benefits ultimately. If individual employers are completely

liable for any benefit payments received by their former employees, the benefit generosity does

not have the potential to affect firing costs. On the contrary, if UI benefits are subsidized at least

partially, generous UI benefits lower firing costs to the firms and thus may incentivize firms to

engage in layoffs more often than otherwise.

16 The generosity of UI benefits can also affect workers’ job searching behavior. Prior studies find that UI reduces

unemployed workers’ incentives to search for new employment and in turn lengthens the duration of unemployment

spells (e.g., Meyer 1990, 1995; Meyer and Mok 2007; Moffitt 1985). In this paper, however, we focus on the role of

UI in reducing workers’ unemployment costs.

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2.3 Institutional background of state UI benefits in the US

In the U.S., eligible workers who are involuntarily displaced are provided unemployment

benefits by the Federal-State Unemployment Insurance Program. Although governed by the

Federal law, each state administers its own UI program. The benefit payments are made from

state government funds and, if state funds are exhausted, from federal funds. The UI provisions

are financed by a payroll tax imposed on employers. The amount of this tax is determined by an

experience rating method, with the intent of holding individual firms liable for UI benefits

claimed by their former workers. Specifically, higher tax liabilities are imposed on firms that

have generated higher unemployment in the past. The experience-rated UI financing is, however,

imperfect for at least two reasons. First, the UI tax rates have upper and lower bounds, meaning

that the tax liabilities are insensitive to slight shift in layoff behavior for employers that currently

pay the maximum or minimum rate. Second, even for firms with current tax rates falling between

the maximum and the minimum, time lags exist between the current increase in UI benefit

payments and the subsequent increase in UI tax liabilities without any interest charged. This no-

interest loan type of arrangement incentivizes layoffs, since the incremental firing costs are lower

than the incremental benefit payments after taking into account the time value of money. Overall,

the individual employers are only partially liable for UI benefit payments, with the remaining

part of benefits subsidized.17

2.4 The effect of UI on firm decisions

17 See Topel (1983, 1984b) for more detailed discussion.

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As discussed above, the institutional feature of UI benefits in the U.S. suggest that the

benefit generosity can affect employers’ costs associated with firing employees, and thus, shape

corporate layoff policy or other firm policies related to layoffs. Supporting this argument, Topel

(1983) shows that employers initiate more unemployment spells when UI benefits are more

generous and both employees and employers bear lower unemployment costs. In addition,

Agrawal and Matsa (2013) find that more generous UI benefits lead to higher leverage,

consistent with corporate financing policies being partly shaped by workers’ exposure to

unemployment risk. In this paper, we study whether corporate resource adjustment policies are

also affected by workers’ unemployment concerns: when making decision to adjust committed

resources, firms have to account for firing and hiring costs. In this sense, labor unemployment

risk and UI benefits are likely to affect resource adjustment policies. Next we develop a

hypothesis on this under-researched issue.

2.5 Hypothesis development

When a firm responds to activity decreases by cutting committed resources, the

downsizing of labor forces is often unavoidable. If the firm usually cuts more labor in reaction to

sales declines, its workers are exposed to higher unemployment risk. Rational workers demand a

wage differential for bearing higher risk and therefore firms also incur unemployment costs in an

indirect manner. We argue that the cross-firm wage differential constitutes a significant cost of

firing workers in particular and adjusting committed resources downward in general. An

important implication is that workers’ exposure to unemployment risk is likely to shape

corporate policies on resource adjustments, and thus, determine the degree of cost stickiness. To

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the extent that UI reduces the costs of unemployment borne by both workers and their employers

(Topel 1983), firms can adjust committed resources downward at lower costs when facing sales

declines. Assuming the generosity of UI benefits does not significantly change the hiring costs,

we predict that more generous UI benefits lead to smaller degree of cost stickiness.18

To provide

systematic evidence on this unexplored issue, we test the following hypothesis in alternative

form: .

H1: More generous unemployment insurance benefits lead to smaller degree of

cost stickiness, other things being equal.

3. Sample selection, variable definition, and model specification

3.1 Sample and data

In our empirical analysis, we obtain firm-level financial data from Compustat, data for

state UI benefits manually collected from the “Significant Provisions of State Unemployment

Insurance Laws” of the U.S. Department of Labor, and data for state-level economic conditions

from the US Bureau of Economic Analysis and the U.S. Bureau of Labor Statistics. Following

ABJ, we choose the fiscal year of 1979 as the start of our sample period. Our initial sample

covers all firms available from Compustat for the period 1979–2010. We then delete financial

and utilities companies. In addition, we exclude the observations with missing or non-positive

total assets. The observations with non-positive sales revenue or SG&A costs in the current year

or prior two years are also discarded. Further, we delete the observations with more than 50%

18 In fact, more generous UI benefits may increase the hiring costs, because the unemployed workers have lower

incentives to return to work if they receive more generous UI. As a result, firms are likely to respond less to sales

increases when UI benefits are more generous. For brevity, we exclude this effect from our story, although taking

the effect into account does not change our prediction for the impact of UI generosity on cost stickiness.

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sales increases or more than 33% sales decreases, in order to rule out the possible influence of

mergers or divestitures on the results (e.g., Banker et al. 2013). Finally, we delete the firm-years

with missing data for constructing the variables used in the main tests. Our final sample includes

104,135 firm-year observations for 12,906 unique firms in the U.S. To mitigate the effect of

extreme values, the firm-level continuous variables used in the regression analysis are

winsorized at the top and bottom 1%.

3.2 Key variable

The key variable of interest for this study is the generosity of state UI benefits. Three key

parameters that determine the generosity of UI payments, which are governed by the State

legislation, are the eligibility for UI benefits, the weekly benefit amount, and the benefit duration.

Normally, all workers that are unemployed through no fault of their own are eligible for the UI

benefits. Following prior studies, we rely on the other two parameters (i.e., benefit amount and

duration) in constructing our measure of UI generosity (e.g., Agrawal and Matsa 2013).

The data for the benefit amount and duration are available from the “Significant

Provisions of State Unemployment Insurance Laws” of the U.S. Department of Labor from 1937

to the present. Since 1965, the Provisions have been semiannually updated in every January and

July. We download all of these Provisions and manually record the information on weekly

benefit amount and the number of covered weeks. These two parameters are normally set in form

of a range, with a minimum and a maximum provided in the Provisions. To capture the

generosity of each state’s UI policy, we focus on the upper bounds of the parameters. The key

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variable, UI, is constructed by taking the natural logarithm of the product of the maximum

weekly benefit amount and the maximum number of weeks covered by the benefits.19

3.3 Empirical models

We start our empirical analysis by estimating the ABJ baseline model. The model links

the change in SG&A costs to the change in sales revenue and differentiate between sales

increases and sales decreases, as specified below:

ΔlogSGAi,t = α0 + α1 ΔlogSALEi,t + α2 ΔlogSALEi,t × DECi,t + µ i,t , (1)

where, for firm i and year t, ΔlogSGA is change in the natural logarithm of SG&A costs

(#xsga);20

ΔlogSALE is change in the natural logarithm of sales (#sale); and DEC is an indicator

variable that equals one for sales decreases, and zero otherwise. SG&A costs normally change in

the same direction as the change in sales revenue and therefore we predict α1 > 0. More

importantly, we predict α2 < 0, consistent with SG&A costs responding more to sales increases

than to sales decreases.

To test our hypothesis, we augment Model (1) by including the interaction terms of the

state UI generosity. In addition, we explicitly control for firm-level characteristics that are

known to affect the degree of cost stickiness. Further, the interaction terms of two state-level

economic factors, i.e., GDP growth and unemployment rate, are also included in the model.

Specifically, to test the impact of state UI generosity on cost stickiness, we specify our main

model as below.

19 In unreported robustness tests, we also use the natural logarithm of maximum weekly benefit amount as an

alternative proxy for state UI generosity and find qualitatively similar results. 20 # denotes Compustat data item.

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ΔlogSGAn,i,t = β0 + (β1 + β2 UIn,t + β3 GDPGROWTHn,t + β4 UNEMPLOYMENTn,t

+ β5 AINTn,i,t + β6 EMPINTn,i,t) × ΔlogSALEn,i,t

+ (β7 + β8 UIn,t + β9 DECn,i,t-1 + β10 GDPGROWTHn,t

+ β11 UNEMPLOYMENTn,t + β12 AINTn,i,t

+ β13 EMPINTn,i,t) × ΔlogSALEn,i,t × DECn,i,t + εn,i,t ,

(2)

where the subscripts n, i, and t index state, firm, and year, respectively; UI is the generosity of

state UI benefits as defined earlier; GDPGROWTH is the state GDP growth rate;

UNEMPLOYMENT is the state unemployment rate; AINT is asset intensity defined as total assets

(#at) divided by sales (#sale); EMPINT is employee intensity defined as the number of

employees (#emp) divided by sales (#sale); and all the other variables are defined in the same

way as in Model (1).

Recall that a negative sign of the coefficients of ΔlogSALEn,i,t × DECn,i,t indicates the

sticky behavior of SG&A costs. In the above model, the negative (positive) coefficient on a

three-way interaction term is consistent with the prediction that the degree of cost stickiness is

accentuated (attenuated) by a particular factor of interest. Our hypothesis H1 is supported if the

estimated β8 is significantly positive. In addition, we expect β9 > 0, since SG&A costs are likely

to exhibit lower stickiness when sales decrease in the recent two consecutive years. As noted by

ABJ and BBCM, when the economic condition is strong (poor), managers are likely to regard

sales decreases as transitory (permanent) and adjust committed resources downward to a smaller

(larger) degree. As a result, we expect β10 < 0 and β11 > 0. Finally, we expect β12, β13 < 0: asset

intensity and employee intensity are likely to increase the degree of cost stickiness, to the extent

20

that both capture the magnitude of downward adjustment costs (e.g., ABJ; Subramaniam and

Weidenmier 2003).

4. Results

4.1 Summary statistics

Table 1 presents the summary statistics of our research sample. In Panel A, we report the

descriptive statistics for the firm- and state-level variables in the whole sample. The mean

(median) of ΔlogSGA is 0.068 (0.071), indicating that the mean (median) percentage increase in

SG&A costs is 7.0 (7.4). The mean (median) of ΔlogSALE is 0.060 (0.068), suggesting that the

mean (median) percentage increase in sales revenue is 6.2 (7.0). In our sample, 31.3% of the

firm-year observations experience sales declines. This figure is comparable to that of prior

studies in the U.S. setting (e.g., ABJ). Further, UI has a mean (median) of 8.846 (8.842),

showing that the mean (median) value of maximum total UI benefit payments is $6,947 ($6,919).

On average, the sample firm-years see a state GDP growth of 6.1% and a state unemployment

rate of 6.0%. To earn $1,000 sales revenue, our sample firm-years need a mean (median) of

$1,141 ($804) assets and 10 (7) employees.

Panel B of Table 1 presents the descriptive statistics for major variables by state. The

number of observations varies widely across states. While more than one-third of the sample

firm-years are headquartered in California, New York, or Texas, only less than 100 observations

are from Alaska, Maine, North Dakota, and Wyoming each.21

The range of the state averages of

21 California, New York, and Texas could have a disproportional influence on our regression analysis. As robustness

tests, we exclude the firm-years headquartered in these three states separately. The results of our main tests are

qualitatively unchanged. We also discard all observations from these three states and re-estimate the model using the

21

change in SG&A costs and sales revenue appears reasonable: The smallest (largest) average

change in SG&A costs is 4.5% (16.5%) for Hawaii (Alaska), while the smallest (largest) average

change in sales is 3.0% (10.0%) for West Virginia (Alaska). The top three states in terms of the

maximum total UI benefit payments are Massachusetts ($15,899), Washington ($10,970), and

Rhode Island ($10,084), while the bottom three are Alabama ($4,289), Missouri ($4,484), and

Arizona ($4,645). As for the state economic conditions, the average GDP growth ranges from 2.8%

for Alaska to 8.8% for Wyoming, and the average unemployment rate ranges from 3.4% for

Nebraska to 8.5% for West Virginia.

[INSERT TABLE 1 ABOUT HERE]

4.2 Main results

Table 2 presents the results of our main analysis.22

In column 1, we report the results of

estimating our baseline regression in Model (1). The coefficient of ΔlogSALEn,i,t × DECn,i,t is

negative and highly significant (-0.194 with t = -13.98), consistent with the generally sticky

behavior of SG&A costs. In column 2, we add the key variable, UIn,t, to the model. The

coefficient of the three-way interaction term is positive and highly significant (0.230 with t =

11.17). This result is consistent with H1, suggesting that the degree of cost stickiness decreases

with the generosity of UI benefits.

In column 3, we estimate Model (2), which includes not only the key variable but also the

firm- and state-level control variables. The coefficient on the three-way interaction term of UIn,t

remaining two-third observations. The results are again similar. For brevity, We do not tabulate the results of these

robustness tests, but they are available on request. 22 For expressional convenience, we omit the state and firm indices for the variables in the remaining tables.

22

is positive and remains highly significant (0.116 with t = 4.62) even after these additional

controls are accounted for. In addition, we find that the coefficients on other three-way

interaction terms are all significant at less than the 1% level with expected signs. Specifically,

the degree of cost stickiness increases with state GDP growth, asset intensity, and employee

intensity, but decreases with state unemployment rate. Moreover, managers tend to regard sales

decreases in two consecutive years as permanent and therefore adjust committed resources

downward to a larger degree. In column 4, we add the state dummies to the model to control for

the unobservable state-level time-invariant factors that may affect both UI generosity and cost

behavior. Like the key variable and other control variables, the state dummies are interacted with

both ΔlogSALEn,i,t and ΔlogSALEn,i,t × DECn,i,t. The results are very similar to those of column 3.

The results of regressions in Model (2) to (4), taken together, support our hypothesis H1,

suggesting that more generous UI benefits lead to smaller degree of cost stickiness, and this

finding is robust to different model specifications.


One potential concern about our main results is that we inaccurately measure the

generosity of state UI benefits for firms with geographically dispersed workforce across different

states. While workers are eligible to the UI benefits of the state where they work, we measure the

UI generosity based on their employer’s headquarter state. Following Agrawal and Matsa (2013),

we exclude retail, wholesale, and transport firms from our sample and repeat all the tests

presented in Table 2 using the reduced sample. As shown in Table 3, the regression results are

largely unchanged in all columns. The statistical significance and the magnitude of our variable

23

of interest, i.e., ΔlogSALEn,i,t × DECn,i,t × UIn,t, are both comparable to those in Table 2. This

suggests that our full-sample results in Table 2 are unlikely to be driven by potential

measurement errors associated with the UI generosity.


4.3 Additional tests

4.3.1 Endogeneity issues

One advantage of our study is that state UI generosity is largely exogenous. The UI

policies help us capture costs of adjusting committed resources downward or more specifically,

firing costs to the firm. The generosity of state UI benefits is not chosen by individual firms, but

decided by state legislation. To some extent, firms can exert influence on state government

policy on UI via lobbying efforts. It is unlikely, however, that any policy shift in terms of UI is

made for the purpose of changing the firm-level behavior of SG&A costs. In this sense, our

approach mitigates concerns about potential endogeneity with respect to the UI generosity.

One may argue that unobservable time-varying local economic factors may drive both the

UI generosity and the SG&A cost stickiness. In such a case, the observed relation between the

two could be spurious. To alleviate this concern, we conduct a falsification test by adding the

median level of UI generosity in the bordering states (UIn,tBordering state

) to the model. If our main

results are, indeed, driven by unobservable local economic factors, one can expect that the

coefficient on the variable of interest, ΔlogSALEn,i,t × DECn,i,t × UIn,t, to be insignificant after

controlling for UIn,tBordering state

. This is so because the bordering states are likely to share the same

regional economic factor. As shown in Table 4, we find that the coefficient on ΔlogSALEn,i,t ×

24

DECn,i,t × UIn,t is positive remain significant at the 1% level, even after UIn,tBordering state

is

explicitly controlled for. The magnitude of this coefficient is similar to that in column 4 of Table

2. Moreover, we find that the coefficient on ΔlogSALEn,i,t × DECn,i,t ×UIn,tBordering state

is

insignificant. In short, our results in Tale 4 suggest that our main results in Table 2 are unlikely

to be driven by unobservable time-varying local economic factors.


4.3.2 The BBCM framework

Next, we examine whether our results are robust even after controlling for managers’

expectation on future sales based on the direction of prior sales changes. We start by estimating

Model A of BBCM. The model discriminates between sales increase and sales decrease in the

prior year as follows.

ΔlogSGAn,i,t = δ0 + (δ1OPT

INCi,t-1 + δ1PES

DECi,t-1) × ΔlogSALEi,t

+ (δ2OPT

INCi,t-1 + δ2PES

DECi,t-1) × ΔlogSALEi,t × DECi,t + ζi,t , (3)

where INC is an indicator variable that equals one for sales increases, and zero otherwise; and all

the other variables are defined in the same way as in Model (1). In this model, δ1OPT

and δ1PES

capture α1 of Model (1) in the subsample of prior sales increase and prior sales decrease,

respectively. Likewise, δ2OPT

and δ2PES

capture α2 of Model (1) in the subsample of prior sales

increase and prior sales decrease, respectively. We expect both δ1OPT

and δ1PES

to be positive,

reflecting the upward adjustment of committed resources when sales increase. In addition, we

predict that δ1OPT

is larger than δ1PES

, since managers are likely to interpret two-year consecutive

sales increases in an optimistic way but interpret sales increase that follows prior sales decrease

25

in a pessimistic way. More importantly, we expect δ2OPT

to be negative but δ2PES

to be positive,

since sales decrease is probably regarded as transitory (permanent) if it follows prior sales

increase (decrease) and therefore managers tend to adjust resources downward to a smaller

(larger) degree. Stated another way, SG&A costs are expected to be sticky (anti-sticky) when the

firm experiences prior sales increase (decrease). We then augment Model (3) by accounting for

the effect of state UI generosity and firm- and state-level control variables on the sticky behavior

of SG&A costs as follows.

ΔlogSGAn,i,t = γ0 + (γ1OPT

INCn,i,t-1 + γ1PES

DECn,i,t-1 + γ2OPT

INCn,i,t-1×UIn,t

+ γ2PES

DECn,i,t-1×UIn,t + γ3 GDPGROWTHn,t

+ γ4 UNEMPLOYMENTn,t + γ5 AINTn,i,t

+ γ6 EMPINTn,i,t) × ΔlogSALEn,i,t

+ (γ7OPT

INCn,i,t-1 + γ7PES

DECn,i,t-1 + γ8OPT

INCn,i,t-1×UIn,t

+ γ8PES

DECn,i,t-1×UIn,t + γ9 GDPGROWTHn,t

+ γ10 UNEMPLOYMENTn,t + γ11 AINTn,i,t

+ γ12 EMPINTn,i,t) × ΔlogSALEn,i,t × DECn,i,t + νn,i,t ,

(4)

where all variables are defined in the same way as in Model (1) to (3). The variables of interest

are the four-way interaction terms of UIn,t. If both γ8OPT

and γ8PES

are positive, one can conclude

that more generous UI benefits lead to less sticky SG&A costs under optimism and more anti-

sticky SG&A costs under pessimism, corroborating the results of our main tests.

As shown in column 1 of Table 5, the estimated results for Model (3) are consistent with

our prediction. The coefficients of ΔlogSALEn,i,t × INCn,i,t-1 and ΔlogSALEn,i,t × DECn,i,t-1 are both

positive and statistically significant, with the former larger than the latter. Further, the coefficient

of ΔlogSALEn,i,t × DECn,i,t × INCn,i,t-1 is negative and that of ΔlogSALEn,i,t × DECn,i,t × DECn,i,t-1 is

positive, suggesting that SG&A costs are sticky (anti-sticky) when sales increase (decrease) in

26

the prior year. This result is consistent with the main finding of BBCM. In column 2, we add the

key variable of interest, UIn,t, to the model, and find that the four-way interaction terms of UIn,t

are both positive and statistically significant. When sales increase (decrease) in the prior year, the

generosity of state UI benefits decreases (increases) the stickiness (anti-stickiness) of SG&A

costs. In columns 3 and 4, we show that the results are qualitatively unchanged after controlling

for the firm- and state-level variables and the state dummies. To conclude, our main results are

robust even when the direction of prior sales changes is accounted for.


4.3.3 Other costs

Up to now, our analysis has been focused on SG&A costs. In this final part, we

investigate which subcategories of SG&A costs drive our main results. We also study whether

the results for SG&A costs can be extended to other cost accounts.

Table 6 presents the results of testing the impact of state UI generosity on the behavior of

various types of costs. Columns 1–3 show that more generous UI benefits lead to lower

stickiness of R&D costs and other SG&A costs but not advertising costs. One possible

explanation is that advertising is normally a purchased service and the costs of adjusting its level

are trivial. Moreover, making adjustment to advertising is less likely to involve cutting labor than

doing so to other activities. In column 4, we find that the stickiness of another important cost

item, that is costs of goods sold (COGS) also decreases with the generosity of UI benefits.

As suggested in column 5 and 6, our main results can also be applied to broader cost

categories (than SG&A costs), i.e., operating costs and total costs. Finally, in column 7, we show

27

that more generous UI benefits lead to lower stickiness of labor costs, using the reduced sample

(N = 6,060). This evidence is important, since labor costs are the most relevant to labor

unemployment risk and UI. However, this result should be interpreted cautiously because the

large number of missing data for labor costs from Compustat may limit the generality of the

finding. To sum up, our main results for the impact of unemployment risk on SG&A costs are

applicable to other cost accounts as well, and therefore provide strong evidence that firms take

into consideration labor unemployment risk when setting their resource adjustment policies.


5. Concluding remarks

Using a large sample of 104,135 observations for 12,906 unique U.S. firms for the period

of 1979–2010, we provide strong and reliable evidence that more generous UI benefits lead to

lower stickiness of SG&A costs. This finding is robust to controlling for firm-level determinants

of cost stickiness, statewide economic conditions, and state dummies. The result is also robust to

whether or not we exclude from our sample retail, wholesale, and transport firms, whose labor

force is likely to be geographically dispersed. In addition, we show that our results are not likely

to be driven by unobservable time-varying local economic factors. Further, our results are robust

to whether or not managers’ expectation based on the direction of prior sales change is controlled

for. We further find that after decomposing SG&A costs into subcategories, the impact of the UI

generosity on the sticky behavior of SG&A costs is driven by R&D costs and other SG&A costs

but not by advertising costs. Finally, we show that the main results for SG&A costs are also

applicable to COGS, operating costs, and total costs. Overall, these results indicate that expected

28

unemployment cost constitute significant costs of cutting labor in particular and adjusting

committed resources downward in general, and that firms take labor unemployment risk into

account when setting their resource adjustment policies.

Our study corroborates the ABJ concept of sticky costs by exploiting state UI benefits as

an exogenous source of variation in resource adjustment costs. To our knowledge, our study is

one of the few to examine the accounting implications of labor unemployment risk. However,

our study focuses on state-level rather than firm-specific unemployment risk. Further, it does not

provide general evidence on the behavior of labor cost due to data availability. Given the scarcity

of empirical evidence on labor unemployment risk in the accounting literature, this could be a

promising area for future research.

29

Appendix

Variable Definitions

Variable Definition

UI The generosity of state UI benefits, calculated as the natural logarithm

of the product of maximum weekly benefit amount and maximum

number of covered weeks at the state level. Data are obtained from the

“Significant Provisions of State Unemployment Insurance Laws” of the

US Department of Labor.

UIBordering state

The median level of UI in the bordering states

GDPGROWTH The state GDP growth rate obtained from the US Bureau of Economic

Analysis

UNEMPLOYMENT The state unemployment rate obtained from the US Bureau of Labor

Statistics

ΔlogSGA The change in the natural logarithm of selling, general, and

administrative (SG&A) costs (#xsga)

ΔlogSALE The change in the natural logarithm of sales revenue (#sale)

DEC An indicator variable that takes the value of one if sales decrease, and

zero otherwise

INC An indicator variable that takes the value of one if sales increase, and

zero otherwise

AINT Asset intensity defined as total assets (#at) divided by sales (#sale)

EMPINT Employee intensity defined as the number of employees (#emp) divided

by sales (#sale)

ΔlogADV The change in the natural logarithm of advertising costs (#xad), with

missing values from Compustat set to zero.

ΔlogRND The change in the natural logarithm of R&D costs (#xrd), with missing

values from Compustat set to zero.

30

ΔlogOSGA The change in the natural logarithm of other SG&A costs, i.e., SG&A

costs (#xsga) excluding advertising costs (#xad) and R&D costs (#xrd).

ΔlogCOGS The change in the natural logarithm of cost of goods sold (#cogs)

ΔlogOC The change in the natural logarithm of operating costs, calculated by

the difference between sales (#sale) and operating income (#oiadp).

ΔlogTC The change in the natural logarithm of total costs, calculated by the

difference between sales (#sale) and income before extraordinary items

(#ib).

ΔlogLC The change in the natural logarithm of labor costs (#xlr)

31

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34

Table 1

Descriptive Statistics

This table presents summary statistics of our sample (Panel A) and the sample distribution across state (Panel B). The

sample covers firm-years available from Compustat for the period 1979–2010. Financial and utilities companies are

excluded. The subscripts n, i, and t denote state, firm, and year indices, respectively. ΔlogSGA is the log-change in

selling, general, and administrative costs. ΔlogSALE is the log-change in sales. DEC is an indicator variable that takes

the value of one if sales decrease, and zero otherwise. UI is the natural logarithm of maximum unemployment

insurance benefit at the state level. GDPGROWTH is the state GDP growth rate. UNEMPLOYMENT is the state

unemployment rate. AINT is total assets divided by sales. EMPINT is the number of employees divided by sales.

ΔlogSGA, ΔlogSALE, AINT, and EMPINT are winsorized at the top and bottom 1%. All variables are defined in details

in the Appendix.

Panel A

Variable N Mean S.D. 10% 25% Median 75% 90%

ΔlogSGAn,i,t 104,135 0.068 0.205 -0.152 -0.023 0.071 0.167 0.284

ΔlogSALEn,i,t 104,135 0.060 0.161 -0.157 -0.030 0.068 0.164 0.268

DECn,i,t 104,135 0.313 0.464 0.000 0.000 0.000 1.000 1.000

UIn,t 104,135 8.846 0.437 8.269 8.566 8.842 9.158 9.367

GDPGROWTHn,t 104,135 6.132 3.432 2.364 4.172 5.881 8.216 10.095

UNEMPLOYMENTn,t 104,135 5.953 1.839 3.942 4.650 5.600 6.925 8.283

AINTn,i,t 104,135 1.141 1.261 0.401 0.566 0.804 1.212 2.060

EMPINTn,i,t 104,135 0.010 0.010 0.002 0.004 0.007 0.012 0.020

Panel B

State Number of

observations

Average

ΔlogSGAn,i,t

Average

ΔlogSALEn,i,t

Average

maximum UI

benefit ($)

Average

GDP growth

Average

unemployment

rate

Alaska 30 0.153 0.095 7,990.667 2.795 7.194

Alabama 536 0.070 0.058 4,289.030 5.607 6.315

Arkansas 363 0.088 0.073 7,001.807 5.635 6.382

35

Arizona 1,266 0.068 0.072 4,645.395 7.679 5.820

California 15,002 0.072 0.062 6,920.392 6.110 6.976

Colorado 2,915 0.071 0.057 7,580.967 7.038 5.271

Connecticut 2,747 0.061 0.053 9,270.207 6.580 4.999

District of Columbia 157 0.106 0.093 7,919.796 5.841 7.550

Delaware 301 0.048 0.033 6,350.997 7.749 4.916

Florida 4,294 0.066 0.057 6,257.938 6.749 5.508

Georgia 2,819 0.069 0.062 5,747.051 6.771 5.422

Hawaii 213 0.044 0.063 8,110.047 6.013 4.542

Iowa 550 0.068 0.063 6,805.713 4.977 4.954

Idaho 243 0.077 0.049 6,415.901 6.508 5.792

Illinois 4,388 0.058 0.057 8,826.129 5.222 6.452

Indiana 1,498 0.066 0.056 5,746.746 5.408 6.083

Kansas 732 0.066 0.058 6,942.462 5.469 4.736

Kentucky 619 0.073 0.068 6,357.483 5.081 6.869

Louisiana 596 0.052 0.043 5,624.436 5.396 6.962

Massachusetts 4,937 0.077 0.067 15,898.980 6.007 5.318

Maryland 1,604 0.079 0.067 6,111.945 6.531 5.086

Maine 99 0.090 0.084 7,433.636 6.427 5.694

Michigan 2,255 0.064 0.064 7,391.967 4.777 7.287

Minnesota 3,711 0.080 0.074 8,679.691 6.004 4.806

Mississippi 1,856 0.067 0.059 4,783.454 5.509 5.843

Missouri 212 0.051 0.058 4,484.387 5.253 7.201

Montana 109 0.099 0.058 6,733.174 5.002 5.418

North Carolina 2,190 0.064 0.055 7,419.557 7.069 5.584

North Dakota 64 0.096 0.080 5,509.563 5.521 4.443

Nebraska 364 0.069 0.067 5,638.857 5.290 3.429

New Hampshire 511 0.065 0.060 6,100.129 6.844 4.322

New Jersey 5,932 0.064 0.056 8,415.010 6.426 5.944

New Mexico 116 0.081 0.076 5,532.190 7.469 6.833

Nevada 805 0.055 0.044 6,715.106 8.347 6.146

36

New York 10,190 0.064 0.055 7,019.617 5.828 5.522

Ohio 3,920 0.053 0.050 8,792.703 4.921 6.394

Oklahoma 939 0.072 0.062 6,346.658 5.566 5.412

Oregon 1,022 0.081 0.066 8,159.319 6.426 6.867

Pennsylvania 4,768 0.070 0.061 9,010.118 5.348 6.439

Rhode Island 410 0.062 0.060 10,084.390 5.970 6.124

South Carolina 561 0.058 0.048 5,322.446 6.350 6.187

South Dakota 108 0.096 0.085 4,892.574 5.892 3.888

Tennessee 1,454 0.085 0.078 5,333.666 5.986 6.377

Texas 9,780 0.067 0.059 6,693.804 6.826 6.202

Utah 849 0.055 0.045 7,592.250 7.360 4.690

Virginia 2,726 0.073 0.064 5,950.309 6.905 4.499

Vermont 121 0.086 0.077 5,997.835 6.031 4.461

Washington 1,299 0.069 0.075 10,970.480 6.045 7.252

Wisconsin 1,786 0.060 0.058 6,888.408 5.536 5.421

West Virginia 131 0.047 0.030 7,346.763 4.470 8.537

Wyoming 37 0.060 0.042 5,665.189 8.837 5.126

37

Table 2

Unemployment Insurance and the Asymmetry of Selling, General, and Administrative Costs

This table presents the results of testing the impact of state unemployment insurance on the asymmetric behavior of

selling, general, and administrative (SG&A) costs. The subscript t denotes time index, while state and firm indices are

omitted for brevity. The dependent variable is ΔlogSGA, the log-change in SG&A costs. Column 1 replicates the main

finding of Anderson et al. (2003). Column 2 adds the key variable UI. Column 3 controls for the firm characteristics

and the state economic conditions. Column 4 further controls for state dummies, although estimates are not reported for

brevity. ΔlogSALE is the log-change in sales. DEC is an indicator variable that takes the value of one if sales decrease,

and zero otherwise. UI is the natural logarithm of maximum unemployment insurance benefit at the state level. All

variables are defined in the Appendix. All models include an unreported intercept. The t-statistics reported in

parentheses are based on standard errors clustered by firm. In this table, *, **, and *** denote statistical significance at

the 10%, 5%, and 1% levels, respectively.

Exp. sign (1) (2) (3) (4)

ΔlogSALEt + 0.699***

1.716***

1.457***

2.054***

(104.12) (20.93) (12.47) (13.24)

ΔlogSALEt×DECt – -0.194***

-2.227***

-1.214***

-2.051***

(-13.98) (-12.13) (-4.97) (-5.90)

ΔlogSALEt×UIt -0.115***

-0.082***

-0.144***

(-12.40) (-6.81) (-9.22)

ΔlogSALEt×DECt×UIt + 0.230***

0.116***

0.214***

(11.17) (4.62) (6.13)

ΔlogSALEt×GDPGROWTHt 0.006***

0.004**

(4.04) (2.53)

ΔlogSALEt×UNEMPLOYMENTt -0.017***

-0.022***

(-6.92) (-8.32)

ΔlogSALEt×AINTt -0.009 -0.003

(-1.46) (-0.51)

ΔlogSALEt×EMPINTt 2.667***

1.976***

(5.28) (3.77)

ΔlogSALEt×DECt×DECt-1 + 0.437***

0.433***

(25.36) (25.03)

38

ΔlogSALEt×DECt×GDPGROWTHt – -0.021***

-0.016***

(-6.42) (-4.49)

ΔlogSALEt×DECt×UNEMPLOYMENTt + 0.016***

0.025***

(3.19) (4.51)

ΔlogSALEt×DECt×AINTt – -0.084***

-0.089***

(-9.30) (-9.51)

ΔlogSALEt×DECt×EMPINTt – -5.008***

-4.293***

(-3.99) (-3.30)

Interaction terms with state dummies No No No Yes

Observations 104,135 104,135 104,135 104,135

Adjusted R2 0.240 0.242 0.259 0.260

39

Table 3

Unemployment Insurance and the Asymmetry of Selling, General, and Administrative Costs: Excluding

Industries with Geographically Dispersed Labor Force


selling, general, and administrative (SG&A) costs after excluding retail, wholesale, and transport firms from our

sample. The subscript t denotes time index, while state and firm indices are omitted for brevity. The dependent variable

is ΔlogSGA, the log-change in SG&A costs. Column 1 replicates the main finding of Anderson et al. (2003). Column 2

adds the key variable UI. Column 3 controls for the firm characteristics and the state economic conditions. Column 4

further controls for state dummies, although estimates are not reported for brevity. ΔlogSALE is the log-change in sales.

DEC is an indicator variable that takes the value of one if sales decrease, and zero otherwise. UI is the natural

logarithm of maximum unemployment insurance benefit at the state level. All variables are defined in the Appendix.

All models include an unreported intercept. The t-statistics reported in parentheses are based on standard errors

clustered by firm. In this table, *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels,

respectively.

Exp. sign (1) (2) (3) (4)


1.715***

1.446***

2.080***

(91.10) (18.98) (11.20) (11.78)


-2.358***

-1.133***

-2.195***

(-13.13) (-11.92) (-4.34) (-5.74)


-0.083***

-0.150***

(-11.42) (-6.27) (-8.59)


0.110***

0.229***

(11.00) (4.10) (6.11)


0.005**

(3.72) (2.39)


-0.022***

(-5.98) (-7.30)

ΔlogSALEt×AINTt -0.005 0.002

(-0.73) (0.29)


1.681***

(4.22) (2.66)

40


0.445***

(24.32) (23.96)


-0.017***

(-6.23) (-4.23)


0.028***

(2.86) (4.59)


-0.091***

(-9.04) (-9.26)


-6.327***

(-4.93) (-4.23)


Observations 85,891 85,891 85,891 85,891

Adjusted R2 0.225 0.227 0.246 0.247

41

Table 4

Falsification Test: Unemployment Insurance and the Asymmetry of Selling, General, and

Administrative Costs after Controlling for Unemployment Insurance in Bordering States

This table presents the results of testing the impact of state unemployment insurance on the

asymmetric behavior of selling, general, and administrative (SG&A) costs after controlling for

unemployment insurance in bordering states. The subscript t denotes time index, while state and

firm indices are omitted for brevity. The dependent variable is ΔlogSGA, the log-change in

SG&A costs. ΔlogSALE is the log-change in sales. DEC is an indicator variable that takes the

value of one if sales decrease, and zero otherwise. UI is the natural logarithm of maximum

unemployment insurance benefit at the state level. UI Bordering state

is the median UI in bordering

states. All variables are defined in the Appendix. The model includes an intercept and state

dummies, although estimates are not reported for brevity. The t-statistics reported in parentheses

are based on standard errors clustered by firm. In this table, *, **, and *** denote statistical

significance at the 10%, 5%, and 1% levels, respectively.

Exp. sign (1)


(12.39)


(-5.14)


(-3.19)

ΔlogSALEt×UItBordering state

-0.040

(-1.03)


(2.88)

ΔlogSALEt×DECt ×UItBordering state

-0.017

(-0.19)

ΔlogSALEt×GDPGROWTHt 0.004**

(2.46)


(-8.45)

ΔlogSALEt×AINTt -0.003

(-0.55)


(3.60)


(25.06)


(-4.52)

42


(4.37)


(-9.46)


(-3.27)

Interaction terms with state dummies Yes

Observations 103,892

Adjusted R2 0.261

Table 5

Unemployment Insurance and the Asymmetry of Selling, General, and Administrative Costs: the BBCM Framework


selling, general, and administrative (SG&A) costs after controlling prior sales change following the BBCM framework

(Banker et al. 2012a). The subscript t denotes time index, while state and firm indices are omitted for brevity. The

dependent variable is ΔlogSGA, the log-change in SG&A costs. Column 1 replicates the main finding of Banker et al.

(2012a). Column 2 adds the key variable UI. Column 3 controls for the firm characteristics and the state economic

conditions. Column 4 further controls for state dummies, although estimates are not reported for brevity. ΔlogSALE is

the log-change in sales. DEC (INC) is an indicator variable that takes the value of one if sales decrease (increase), and

zero otherwise. UI is the natural logarithm of maximum unemployment insurance benefit at the state level. All

variables are defined in the Appendix. All models include an unreported intercept. The t-statistics reported in

parentheses are based on standard errors clustered by firm. In this table, *, **, and *** denote statistical significance at

the 10%, 5%, and 1% levels, respectively.

Exp. sign (1) (2) (3) (4)

ΔlogSALEt×INCt-1 + 0.781***

1.638***

1.189***

1.619***

(121.03) (20.05) (10.34) (10.15)

ΔlogSALEt×DECt-1 + 0.353***

1.325***

0.879***

1.226***

(26.67) (5.84) (3.66) (4.34)

ΔlogSALEt×DECt×INCt-1 – -0.483***

-2.396***

-1.178***

-1.650***

(-29.60) (-10.00) (-4.03) (-4.08)


-1.230***

0.032 -0.762

(19.41) (-3.77) (0.09) (-1.64)

ΔlogSALEt×INCt-1×UIt -0.097***

-0.053***

-0.102***

(-10.49) (-4.52) (-6.53)

ΔlogSALEt×DECt-1×UIt -0.110***

-0.066**

-0.090***

(-4.31) (-2.49) (-2.89)

ΔlogSALEt×DECt×INCt-1×UIt + 0.217***

0.114***

0.197***

(8.06) (3.69) (4.77)

ΔlogSALEt×DECt×DECt-1×UIt + 0.182***

0.074* 0.136

***

(4.97) (1.89) (2.79)


0.007***

44

(5.15) (3.92)

ΔlogSALEt×UNEMPLOYMENTt -0.004* -0.008

***

(-1.78) (-2.81)

ΔlogSALEt×AINTt 0.003 0.008

(0.44) (1.34)


1.890***

(4.95) (3.69)


-0.018***

(-6.83) (-5.04)

ΔlogSALEt×DECt×UNEMPLOYMENTt + 0.004 0.011*

(0.79) (1.91)


-0.100***

(-10.56) (-10.66)


-4.303***

(-3.82) (-3.30)


Observations 104,135 104,135 104,135 104,135

Adjusted R2 0.264 0.265 0.273 0.275

45

Table 6

Unemployment Insurance and the Asymmetry of Other Categories of Costs


other categories of costs. The subscript t denotes time index, while state and firm indices are omitted for brevity. The

dependent variables are ΔlogADV, the log-change in advertising costs, ΔlogRND, the log-change in research and

development (R&D) costs, ΔlogOSGA, the log-change in other selling, general, and administrative costs, ΔlogCOGS,

the log-change in cost of goods sold, ΔlogOC, the log-change in operating costs, ΔlogTC, the log-change in total costs,

and ΔlogLC, the log-change in labor costs, respectively. ΔlogSALE is the log-change in sales. DEC is an indicator

variable that takes the value of one if sales decrease, and zero otherwise. UI is the natural logarithm of maximum

unemployment insurance benefit at the state level. All variables are defined in the Appendix. All models include an

intercept, firm characteristics and state economic factors as control variables, and state dummies, although estimates are

not reported for brevity.. The t-statistics reported in parentheses are based on standard errors clustered by firm. In this

table, *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Dependent variable

ΔlogADVt ΔlogRNDt ΔlogOSGAt ΔlogCOGSt ΔlogOCt ΔlogTCt ΔlogLCt

(1) (2) (3) (4) (5) (6) (7)

ΔlogSALEt 0.204 2.380***

2.147***

1.280***

1.904***

2.183***

1.031**

(0.34) (4.93) (13.57) (9.38) (18.92) (17.89) (2.38)

ΔlogSALEt×DECt 0.112 -3.120***

-2.148***

-0.859**

-1.239***

-1.754***

-1.794

(0.07) (-3.20) (-5.86) (-2.41) (-4.93) (-5.47) (-1.24)

ΔlogSALEt×UIt 0.023 -0.257***

-0.147***

-0.023 -0.101***

-0.128***

-0.019

(0.39) (-5.27) (-8.81) (-1.62) (-9.38) (-9.81) (-0.40)

ΔlogSALEt×DECt×UIt 0.195 0.445***

0.214***

0.082**

0.122***

0.153***

0.262*

(1.34) (4.41) (5.77) (2.16) (4.57) (4.54) (1.71)

Control variables Yes Yes Yes Yes Yes Yes Yes

Interaction terms with

state dummies Yes Yes Yes Yes Yes Yes Yes

Observations 38,021 47,261 103,857 104,091 104,132 103,932 6,060

Adjusted R2 0.082 0.055 0.217 0.474 0.571 0.429 0.397

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