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Employment Protection Legislation, Adjustment Costs

and Cross-country Differences in Cost Behavior

Rajiv D. Banker* Dmitri Byzalov† Lei (Tony) Chen‡

Abstract: Central to the economic theory behind sticky costs is the proposition that

managers take into account adjustment costs when changing resource levels. Few studies

have been able to test this theoretical prediction empirically due to the difficulty of

measuring adjustment costs directly. Adjustment costs associated with firing employees

that arise due to employment protection legislation (EPL) in different countries provides

an apt setting to test this prediction because prior research in economics provides strong

support for linking EPL with adjustment costs for labor. Using a sample of 15,833 firms

in 19 OECD countries during 1990-2008, we test the association between firm-level cost

stickiness and country-level EPL measures. We find that the degree of cost stickiness is

increasing in the strictness of EPL provisions, consistent with our hypotheses based on

adjustment costs, and supporting the theory that sticky cost behavior is driven by

managers’ deliberate resource commitment decisions in the presence of adjustment costs.

Keywords: Sticky costs; Employment protection legislation; Labor adjustment costs.

* Fox School of Business, Temple University, 461 Alter Hall, Philadelphia, PA 19122. E-Mail: banker@temple.edu. Phone: (215) 204-2029. Fax: (215) 204-5587. † Fox School of Business, Temple University, 452 Alter Hall, Philadelphia, PA 19122. E-Mail: dbyzalov@temple.edu. Phone: (215) 204-3927. Fax: (215) 204-5587. ‡ Guanghua School of Management, Peking University, Beijing, 100871. E-mail: chentl@gsm.pku.edu.cn. Phone: (8610) 62753707. Fax: (8610)62756991.

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1. Introduction

Recent studies have documented strong evidence of asymmetric cost behavior. Costs

are sticky when the cost response to a decrease in activity is smaller than the cost

response to an equivalent increase in activity (Anderson et al., 2003, Balakrishnan et al.,

2004, Banker et al., 2010b, Weiss 2010). The prevalence of cost stickiness calls into

question the validity of the traditional cost model which implies a mechanical symmetric

relationship between changes in activity and changes in costs. Recognizing this potential

source of asymmetry in cost behavior and hence in earnings changes has also been shown

to be informative in forecasting earnings and understanding earnings management in

financial accounting research (e.g., Banker and Chen, 2006, Weiss, 2010).

Prior research suggests that the key to understanding sticky cost behavior is to view

many costs as arising from deliberate resource commitment decisions made by managers,

and speculates that adjustment costs play a central role in these decisions (e.g., Anderson

et al., 2003). Prior research also informally describes the basic tradeoff that managers

would face in the presence of adjustment costs. In particular, in deciding how much to cut

committed resources when activity levels drop, managers would weigh the benefits of

more efficient operations against the adjustment costs to be incurred. This would lead

them to deliberately retain some of the underutilized resources in order to save on the

adjustment costs. At the same time, managers have much less discretion about acquiring

required resources when activity increases. Thus, to the extent that managers recognize

the role of adjustment costs and the associated tradeoffs, adjustment costs are expected to

moderate the extent of resource reductions for activity decreases without a commensurate

effect for activity increases, leading to cost stickiness (Anderson et al., 2003).

While the sticky costs literature alludes to the potential role of adjustment costs and

deliberate managerial decisions, the issue of optimal decisions in the presence of

adjustment costs has been explored in much greater depth in the literature on dynamic

factor demand in economics (e.g., Hamermesh, 1990, Bentolila and Bertola, 1990,

Caballero, 1991, Abel and Eberly, 1994, Hamermesh and Pfann, 1996, Dixit, 1997,

Eberly and Van Mieghem, 1997, Palm and Pfann, 1997, Goux et al., 2001). This

economics literature explicitly models the dynamic optimization problem faced by

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forward-looking firms in the presence of adjustment costs. As we discuss in section 2, the

key tradeoff in this dynamic optimization is between the adjustment costs incurred for a

marginal unit of committed resources and the present value of net cash flows this

marginal unit is expected to generate over its useful life with the firm (e.g., Bentolila and

Bertola, 1990, Abel and Eberly, 1994). In other words, the firm will optimally increase

committed resources as long as the expected net present value for each added unit of

resources outweighs the upward adjustment costs. Conversely, it will optimally reduce

committed resources as long as the expected net present value for each eliminated unit of

resources is negative and large enough (in absolute value) to outweigh the downward

adjustment costs.

Besides providing a more nuanced view of the dynamic tradeoffs involved in

resource commitment decisions, the dynamic factor demand literature also shows that

asymmetry in the adjustment costs leads to asymmetry in the optimal decision rules. For

example, Caballero (1991) points out that “in general, for the asymmetric case, the stock

of capital responds more to ‘good’ than to ‘bad’ realizations” (page 284). Bentolila and

Bertola (1990) obtain similar analytical predictions for labor. Thus, the dynamic factor

demand literature in economics explicitly derives the cost accounting notion of cost

stickiness as a direct consequence of forward-looking optimal decisions in the presence

of asymmetric adjustment costs.1

The economic theory of optimal resource commitment decisions in the presence of

adjustment costs provides a plausible and theoretically sound potential explanation for

the widely-documented empirical patterns of cost stickiness. We will term this potential

explanation for cost stickiness the “economic theory of sticky costs.” However, there are

other plausible explanations for cost stickiness. For example, stickiness may arise for

purely mechanical reasons, since managers can reduce activity levels without cutting the

unused resources but they cannot similarly increase activity levels without acquiring the

required additional resources. Such physical asymmetry may lead to stickiness even in

the absence of any adjustment costs. Alternatively, stickiness may arise due to managers’

1 The dynamic factor demand literature focuses primarily on the implications of adjustment costs for macro outcomes like aggregate employment levels and aggregate business cycle dynamics. The implications for cost stickiness are mostly a useful by-product that has not been emphasized in this literature.

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empire-building behavior (Chen et al., 2011). As we discuss in section 2, while any of

these mechanisms can generate cost stickiness in the data, they have different practical

implications. Therefore, it is important to directly examine the underlying fundamental

mechanism behind cost stickiness.2

In this paper, we test the central observable implication of the economic theory of

sticky costs. As we show in section 2, if cost stickiness reflects deliberate resource

commitment decisions by forward-looking managers who recognize the tradeoffs arising

due to adjustment costs, then the degree of cost stickiness should be increasing in the

magnitude of downward adjustment costs. On the other hand, alternative explanations for

cost stickiness do not imply any systematic relationship between adjustment costs and the

degree of cost stickiness. This allows us to test the economic theory of sticky costs

against alternative explanations.

Despite the importance of adjustment costs in the economic theory of sticky costs,

the relationship between adjustment costs and cost stickiness has yet to be tested

empirically. The lack of large-sample empirical evidence on this issue is primarily due to

the difficulty in measuring adjustment costs. Unlike costs actually incurred to provide

productive capacity, adjustment costs are typically opportunity costs not recorded in the

accounting system. Thus, direct measurement is hardly feasible, if not impossible, for

researchers. Moreover, it is not easy to find suitable research settings in which broad and

reliable proxies for the magnitude of adjustment costs are readily available.3

In this study, we exploit the provisions of employment protection legislation (EPL)

as a source of considerable adjustment costs for labor. As previous studies in labor

economics have demonstrated, EPL imposes substantial firing costs on firms (e.g., Long

and Siebert, 1983, Pissarides, 1999). This allows us to use indexes of EPL strictness,

2 There have been some recent claims that the findings of cost stickiness in the literature may be spurious findings that do not reflect actual asymmetries in cost behavior. Banker et al. (2010a) show that because of methodological errors in these studies their claims are unfounded. 3 Empirical studies of dynamic factor demand in economics (e.g., Bentolila and Bertola, 1991, Palm and Pfann, 1997, Goux et al., 2001) do not even attempt to find any observable proxies for adjustment costs. Instead, they assume that the observed resource adjustment decisions are optimal within a dynamic optimization model with adjustment costs, and back out the shape and magnitude of adjustment costs based on this assumption. This approach does not allow them to test the underlying theory of dynamic optimization with adjustment costs.

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which are compiled and reported for most OECD countries, as reliable exogenous

empirical proxies for the adjustment costs associated with firing workers.

We develop hypotheses on the relationship between the degree of cost stickiness and

EPL strictness in different OECD countries. Specifically, we predict that, under the

economic theory of sticky costs, a firm operating in a country with more stringent EPL

provisions (i.e., greater downward adjustment costs for labor) would exhibit a greater

degree of cost stickiness, i.e., a greater degree of asymmetry in cost response to increases

and decreases in sales.

We conduct empirical tests of the relationship between EPL strictness (our empirical

proxy for labor adjustment costs) and cost stickiness using a large sample of publicly

listed companies from 19 OECD countries. The empirical results support our hypotheses

and are consistent with the economic theory of sticky costs. In other words, the

relationship between cost stickiness and EPL strictness that we observe in the data is

consistent with the theory that cost stickiness reflects the outcomes of deliberate resource

commitment decisions made by managers who recognize the dynamic tradeoffs that arise

because of adjustment costs.

We contribute to the growing literature on cost behavior by demonstrating that the

degree of cost stickiness varies across countries as a function of the strictness of EPL – a

widely-available empirical measure that has been shown in prior research in economics

(e.g., Long and Siebert, 1983, Lazear, 1990, Pissarides, 1999, Blanchard and Portugal,

2001) to be a reliable exogenous proxy for the adjustment costs which are central to the

economic theory of sticky costs. Our results show that a full understanding of cost

behavior in general and of cost stickiness in particular requires careful analysis not only

of the firm-specific factors analyzed in prior literature but also of the economy-wide

structural forces that shape managers’ resource adjustment decisions.

This paper is structured as follows. In section 2, we describe the economic theory of

sticky costs and employment protection legislation, and derive the empirical hypotheses.

In section 3, we describe the data and the empirical models. Section 4 presents the

empirical results, and section 5 concludes.

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2. Sticky costs and employment protection legislation

While the traditional textbook view of cost behavior implies a symmetric mechanical

relationship between changes in activity and changes in costs, recent research on sticky

costs (e.g., Anderson et al., 2003, Balakrishnan et al., 2004, Banker et al., 2010a, Weiss,

2010) documents pervasive patterns of asymmetric cost behavior (stickiness and anti-

stickiness4) which are inconsistent with the traditional view.

In an effort to explain the source of such asymmetries in cost behavior, the sticky

costs literature suggests that many costs arise as a result of deliberate resource

commitment decisions made by managers. When managers adjust committed resources,

they have to incur adjustment costs such as hiring and firing costs for labor, or

installation and disposal costs for equipment. The sticky costs literature speculates that

adjustment costs play a central role in cost behavior, and alludes to the dynamic tradeoffs

that managers would face in the presence of adjustment costs. In particular, in deciding

how much to adjust committed resources in response to a given contemporaneous change

in activity, managers would weigh the profit consequences of carrying too much or too

little capacity against the adjustment costs that would have to be incurred when changing

the committed resources in the current period and in the future (e.g., Anderson et al.,

2003). To the extent that managers recognize this tradeoff in their decisions, it would

introduce more complex dynamics in cost behavior,5 giving rise to patterns of stickiness

and anti-stickiness.

While the sticky costs literature informally describes some of the dynamic

considerations that arise because of adjustment costs, this dynamic tradeoff has been

modeled formally in the literature on dynamic factor demand in economics (e.g.,

Hamermesh, 1990, Bentolila and Bertola, 1990, Caballero, 1991, Abel and Eberly, 1994,

4 Formally, we say that costs are “sticky” if they increase more for a one-percent increase in activity than they decrease for an equivalent decrease in activity. Conversely, costs are “anti-sticky” (Weiss, 2010) if they increase less for a one-percent increase in activity than they decrease for an equivalent decrease in activity. 5 Specifically, managers’ resource adjustment decisions would be driven not only by the contemporaneous changes in activity, but also by the level of capacity carried over from the prior period (since it affects the amount of adjustment costs that would have to be incurred to achieve the desired capacity level in the current period) and by managers’ expectations for future activity levels (since they affect the amount of adjustment costs that would have to be incurred to achieve the desired levels of capacity in the future).

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Hamermesh and Pfann, 1996, Dixit, 1997, Eberly and Van Mieghem, 1997, Palm and

Pfann, 1997, Goux et al., 2001). This economics literature explicitly models the dynamic

optimization problem faced by forward-looking firms in the presence of adjustment costs.

At the optimal level of committed resources in this dynamic optimization, the marginal

adjustment costs incurred per unit of committed resources in the current period should be

equal to the present value of expected net cash flows generated by the marginal unit of

committed resources over its useful life with the firm (Bentolila and Bertola, 1990, Abel

and Eberly, 1994). For example, in the context of optimal hiring and firing decisions, the

present value of net cash flows generated by a marginal worker consists of the marginal

revenue product she is expected to generate over her tenure with the firm net of her

wages over the same period, and net of the expected future firing costs that will be

incurred at the (random) end of her tenure, all discounted to the present period (Bentolila

and Bertola, 1990). The firm will optimally hire additional workers as long as the present

value of expected net cash flows of the marginal worker exceeds the hiring costs (upward

adjustment costs per worker). Conversely, the firm will optimally fire workers if the

present value of net cash flows of the marginal worker is negative and large enough (in

absolute value) to exceed the firing costs (downward adjustment costs). In other words, it

will fire workers only if it is costlier for the firm to keep them than to fire them.

Furthermore, after the firm has fired the optimal amount of workers, the net present value

of the marginal surviving worker will still be negative (equal to the firing cost with a

minus sign), i.e., the firm will optimally retain some of its underutilized workers in order

to save on the firing costs.

One key insight from the dynamic factor demand literature is that asymmetry in the

adjustment costs translates into asymmetry in the optimal resource commitment decisions

(e.g., Bentolila and Bertola, 1990, Caballero, 1991). For example, in the context of labor

resource adjustment decisions, if the firing costs per worker are larger than the hiring

costs, then the firm will be more reluctant to fire workers when demand drops than to hire

workers when demand increases by an equivalent amount. In particular, the firm will hire

additional workers only if the favorable demand shock is strong enough to increase the

net present value of its marginal worker to above the hiring threshold, equal to the hiring

cost per worker. Likewise, the firm will fire workers only if the adverse demand shock is

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strong enough to reduce the net present value of its marginal worker to below the firing

threshold – a negative number equal to minus the firing cost per worker.6 If the firing

costs are larger than the hiring costs, the adverse demand shocks that trigger the firing

decision would have to be relatively more severe than the favorable demand shocks that

trigger the hiring decision. In other words, the firm will be less likely to fire workers for

activity decreases than to hire workers for equivalent activity increases. Thus, the

dynamic factor demand literature in economics explicitly derives the cost accounting

notion of cost stickiness as a direct consequence of forward-looking optimizing behavior

in the presence of asymmetric adjustment costs.

The economic theory of optimal resource commitment decisions in the presence of

adjustment costs provides a plausible and theoretically sound potential explanation for

the widely-documented empirical patterns of cost stickiness. We will term this potential

explanation for cost stickiness the “economic theory of cost stickiness.” However, there

are other plausible explanations for cost stickiness. For example, stickiness may arise for

purely mechanical reasons, since it is physically impossible to increase activity levels

without acquiring the required additional resources, but at the same time it is physically

possible to reduce the activity levels without cutting the unused resources. Furthermore,

cutting the unused resources requires substantial managerial discretion and effort (e.g.,

Anderson et al., 2003). This may lead to stickiness, even in the absence of any adjustment

costs, if managers do not exercise sufficient discretion and effort in identifying and

eliminating the unused resources. Alternatively, stickiness may arise due to managers’

empire-building behavior (Chen et al., 2011), since empire-building managers will be

eager to expand the resources under their control when activity increases but reluctant to

cut back unused resources when activity declines. This may also lead to cost stickiness

even in the absence of adjustment costs.

While any of these explanations may lead to cost stickiness in the data, they have

very different implications. In particular, if stickiness arises due to mechanical reasons or

due to empire-building behavior, it would reflect the consequences of wasteful

6 With linear hiring and firing costs, there is also a region of inaction. Specifically, if the net present value of the marginal worker is above the firing threshold and below the hiring threshold, then the firm will not adjust its work force.

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managerial behavior that reduces firm value. On the other hand, under the economic

theory of sticky costs (i.e., dynamic optimization by managers in the presence of

adjustment costs), the same observed pattern of stickiness would represent the

consequences of desirable managerial behavior that increases firm value. Therefore, it is

important to examine the underlying fundamental mechanism behind cost stickiness.7

In this paper, we exploit the central testable implication of the economic theory of

sticky costs in order to test it against alternative explanations. As we discuss next, the

economic theory of sticky costs implies a systematic positive relationship between the

magnitude of downward adjustment costs and the degree of cost stickiness, which can be

tested using appropriate empirical proxies for the magnitude of adjustment costs. At the

same time, alternative explanations mentioned above do not imply any clear relationship

between the magnitude of adjustment costs and the degree of cost stickiness.

In deriving our predictions, we build on prior literature on dynamic factor demand in

economics (e.g., Bentolila and Bertola, 1990, Caballero, 1991, Abel and Eberly, 1994).

While this prior literature has focused primarily on the macroeconomic implications of

adjustment costs for aggregate employment levels and business cycle dynamics, we

leverage their insights to generate our predictions for firm-level resource commitment

decisions made by managers and their implications for firm-level cost behavior. For

clarity of exposition, we describe the relationship between downward adjustment costs

and stickiness in the context of labor resources, where the adjustment costs are the firing

and hiring costs per employee (the intuition for other capacity resources is similar). When

the firing costs per employee are higher, it affects the hiring and firing decisions through

two channels. First, conditional on the original level of the work force carried over from

the prior period, higher firing costs moderate the magnitudes of both the hiring decisions

for activity increases and the firing decisions for activity decreases. As we explain below,

this moderating effect of higher firing costs is disproportionately larger for activity

decreases than for activity increases. As a result, conditional on the original level of the

7 The underlying mechanism behind cost stickiness also has important implications for the design of managers’ incentives and performance evaluation. For example, if stickiness arises due to mechanical reasons or empire-building behavior, then the optimal incentive contract would involve penalizing managers for retaining slack resources in a downturn. On the other hand, under the economic theory of sticky costs, the same incentive contract would reduce firm value by forcing managers to reduce slack resources below the optimal level from the dynamic optimization perspective.

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work force, higher firing costs increase the degree of cost stickiness. Second, higher

firing costs affect the typical level of the work force (and hence the typical level of slack

labor) carried over from the prior period. As we explain below, higher firing costs reduce

the average amount of slack labor carried over from the prior period, which further

amplifies cost stickiness.

The intuition for the first channel is as follows. In its hiring decisions, the firm will

keep adding workers as long as the net present value of the marginal worker is positive

and above the hiring cost per worker (e.g., Bentolila and Bertola, 1990, Goux et al., 2001).

Similarly, in its firing decisions, the firm will keep laying off workers as long as the net

present value of the marginal worker is negative and below the firing cost per worker

(with a minus sign8). In other words, the firm will fire workers if the cost of keeping them

(i.e., negative present value) exceeds the cost of firing them. When the firing costs per

worker are higher, it reduces the net present value of the marginal worker by making it

costlier for the firm to lay off this worker in the future if necessary.9 In other words, by

increasing the anticipated cost of future layoffs, higher firing costs make the marginal

worker less valuable for the firm, both in the context of hiring decisions and in the

context of firing decisions (e.g., Bentolila and Bertola, 1990). As a result, for the same

increase in activity (and for the same original level of the work force), the firm will

optimally hire fewer additional workers, since the (unchanged) hiring costs per worker

are now traded off against lower net present value generated by this worker. In the

context of firing decisions, higher firings costs have a similar negative effect on the net

present value of the marginal worker, making it less desirable for the firm to keep the

marginal worker. However, unlike in the hiring context, higher firing costs have an

additional direct effect of making it even less desirable for the firm to lay off the marginal

worker. The latter effect will dominate since the firing costs have to be incurred

immediately and with certainty when laying off workers, while the anticipated future

firing costs embedded in the net present value of the marginal worker will be incurred

only in the future (and therefore are discounted), and they will be incurred only if future

8 The sign is reversed for firing decisions since the firm gives up the net present value of the fired workers. 9 Recall that the net present value of the marginal worker consists of all expected net cash flows during her tenure with the firm, including the firing costs to be incurred at the (random) future end of her tenure, all discounted to the present period (e.g., Bentolila and Bertola, 1990).

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shocks turn out to be sufficiently adverse to necessitate layoffs (and not with certainty).

As a result, for the same decrease in activity (and for the same original level of the work

force), the firm will optimally lay off fewer workers when the firing costs are higher,

since the costs associated with firing workers increase to a greater extent than the costs

(negative present value) associated with retaining underutilized workers. Thus, for a

given original level of the work force carried over from the prior period, higher firing

costs will moderate both the hiring decisions for activity increases and the firing

decisions for activity decreases, and this moderating effect will be disproportionately

stronger for the firing decisions.10 This will increase the degree of cost stickiness (or

reduce the degree of anti-stickiness in situations in which labor is anti-sticky11).

Second, higher firing costs will affect the typical levels of slack labor carried over

from the prior period, which will further affect the degree of cost stickiness (e.g.,

Balakrishnan et al., 2004). Firing costs have two offsetting effects on the amount of slack.

On the one hand, by moderating the firing decisions for activity decreases, higher firing

costs increase the amount of slack labor (e.g., Bentolila and Bertola, 1990). On the other

hand, by moderating the hiring decisions for activity increases, higher firing costs

decrease the amount of slack labor. On average, we expect the latter effect to dominate

since activity increases are much more common in the data than activity decreases (64.1

percent increases vs. 35.9 percent decreases in our sample). This will result in less slack

carried over from the prior period on average. In turn, this will further moderate the firing

decisions for activity decreases, since there are fewer underutilized workers to begin with.

At the same time, less slack will amplify the hiring decisions for activity increases, since

managers will have to hire more additional workers to accommodate a given activity

increase. Consequently, by reducing the average amount of slack, higher firing costs will

further increase the degree of cost stickiness (or reduce the degree of anti-stickiness in

situations in which labor is anti-sticky).

Summing up, under the economic theory of sticky costs, higher firing costs will

increase the degree of cost stickiness (or reduce the degree of anti-stickiness) for labor,

10 Bentolila and Bertola (1990) derive this prediction formally in an analytical dynamic model of optimal labor demand with firing and hiring costs. 11 Banker et al. (2010b) show that costs are likely to be anti-sticky in periods when managers’ expectations for future activity levels are sufficiently pessimistic.

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via both channels discussed above. The same intuition applies more generally for any

type of capacity resources that are costly to adjust. Higher downward adjustment costs

will increase the degree of cost stickiness (or reduce the degree of cost anti-stickiness),

while higher upward adjustment costs will reduce the degree of stickiness (or increase the

degree of anti-stickiness). This implication is central to the economic theory of sticky

costs, and adjustment costs are one of the key determinants of cost behavior if this theory

is valid.

At the same time, alternative explanations for cost stickiness do not imply any

systematic relationship between the magnitude of adjustment costs and the degree of cost

stickiness. For example, if cost stickiness arises for mechanical reasons due to managers’

failure to identify and eliminate unused resources, we would not expect any systematic

relationship between the magnitude of adjustment costs and the degree of managerial

inattention that is driving stickiness under this explanation. Similarly, if cost stickiness

arises due to managers’ empire-building behavior, we would not expect any systematic

relationship between the magnitude of adjustment costs and the intensity of empire-

building incentives. This difference allows us to test the observable implications of the

economic theory of sticky costs against alternative explanations.

Despite the importance of adjustment costs in the economic theory of sticky costs,

few studies have been able to test the relationship between adjustment costs and cost

stickiness empirically. The main complication in testing this relationship is that

adjustment costs are hard to measure directly. As Hamermesh and Pfann (1996) point out,

many of the adjustment costs are implicit costs of lost output,12 not explicit monetary

costs captured in the accounting system. Despite this complication, several studies have

been able to test this relationship by relying on observable firm-level proxies for the

magnitude of adjustment costs such as asset and employee intensity (Anderson et al.,

2003) or sales volatility (Kama and Weiss, 2010), and by linking these proxies to firm-

level variation in cost stickiness. In contrast, in this paper we exploit country-level

proxies for adjustment costs for labor, based on the strictness of employment protection

12 For example, after firing workers, the firm may have to reassign some of the surviving workers to new tasks, temporarily reducing their productivity. Likewise, after hiring new workers, more experienced (and more productive) workers may have to spend time training them, resulting in lower productivity. Similarly, productivity may decline temporarily if the firm added new equipment or got rid of its existing equipment.

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legislation in each country, and we link them to cross-country variation in cost stickiness

(in addition, we also control for firm-level drivers of cost stickiness following prior

literature). Compared to proxies for adjustment costs used in prior literature such as asset

intensity or sales volatility, one key advantage of employment protection measures is that

they are exogenous with respect to managers’ resource commitment decisions.13

Employment protection legislation (EPL), our empirical proxy for labor adjustment

costs, is a central part of firms’ institutional environment. EPL describes rules about

dismissal of employees, such as restrictions on firing after some period of service, the

compulsory payment of redundancy payments and the length of notice required before

dismissal. Acting as a tax on dismissals, employment protection imposes substantial

firing costs on employers (Long and Siebert, 1983, Pissarides, 1999, OECD, 2004).14 As

we document in section 3, there are large differences in EPL strictness across the

countries in our data, providing a rich source of exogenous cross-country variation in the

firing costs.

Prior research in labor economics has documented extensively that EPL plays a

substantial role in macroeconomic outcomes such as labor force participation, job

creation and destruction, unemployment and productivity growth. Lazear (1990),

Bentolila and Bertola (1990), Hopenhayn and Rogerson (1993), Mortensen and Pissarides

(1999), Heckman et al. (2000), Blanchard and Portugal (2001), Besley and Burgess

(2004), Botero et al. (2004), Boeri and Jimeno (2005), Haltiwanger et al. (2006), Micco

and Pages (2006), Autor et al. (2007), Kugler and Pica (2008), Schivardi and Torrini

(2008) document large significant effects of EPL strictness on the long-run aggregate

employment levels and other labor market outcomes, for a wide variety of developed and

developing countries. DeFreitas and Marshall (1998), Besley and Burgess (2004), Micco

and Pages (2006), Pierre and Scarpetta (2006), Autor et al. (2007), Cingano et al. (2008)

and Bassanini et al. (2009) document significant effects of EPL strictness on long-term

productivity growth. Mortensen and Pissarides (1999), Burgess et al. (2000), Barone

13 In contrast, measures like asset intensity or sales volatility are in part outcomes of prior managerial decisions in response to shocks that may still have a persistent direct effect on cost behavior in the current period. 14 For example, in Portugal, which has the strictest EPL in our data, mandated severance pay is equal to 3 months of regular pay for a worker with 9 months of tenure, and it increases to 20 months of regular pay for a worker with 20 years of tenure (OECD, 2004).

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(2001) and Caballero (2004) find that stricter EPL reduces firms' ability to adjust to

shocks.

Thus, prior research in labor economics documents that (1) EPL is a major

determinant of firing costs, and (2) it has important effects for a wide range of

macroeconomic outcomes. We exploit the same cross-country institutional variation in

EPL, however our main focus is different: we are interested in the role of EPL in firm-

level cost behavior (which is of interest to firm managers in planning resource levels and

costs) rather than its role in macroeconomic outcomes (which is of interest for policy-

makers in formulating broad macroeconomic policy). Despite the different focus of our

study, we are able to leverage the key insights from these previous studies regarding the

impact of EPL on adjustment costs to develop our empirical hypotheses.

As we discuss above, the economic theory of sticky costs implies that higher

downward adjustment costs should lead to more stickiness in resource adjustment. Since

stricter EPL increases the magnitude of firing costs (downward adjustment costs for

labor), we expect it to increase the degree of stickiness for labor costs, and, since direct

and indirect labor costs account for a large fraction of operating costs, we expect it to

increase the degree of stickiness for operating costs.15 This leads us to our first empirical

hypothesis:

Hypothesis 1: Stricter employment protection legislation (EPL) is associated with a

higher degree of cost stickiness for operating costs.

2.1. Differential effects of EPL for regular and temporary employees

Overall employment protection legislation provisions consist of two distinct

components: employment protection for regular employees and regulation of temporary

forms of employment (see Table 1 and section 3 for details). As we show in section 3,

there are significant cross-country differences in relative employment protection for

15 We focus on operating costs, as opposed to more detailed measures of labor costs, due to data limitations for labor costs in Compustat. Specifically, labor costs have a high proportion of missing data (69 percent missing in our full sample), and even when data is available, it is less reliable than data on operating costs. Among firms that report labor costs, they account for 28 percent of total operating costs on average.

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temporary vs. regular employees, i.e., some countries have strict EPL for regular but not

for temporary employees and vice versa.

Both forms of EPL are likely to affect the magnitude of downward adjustment costs

for labor. In particular, EPL for regular workers specifies the severance pay levels, which

gives rise to explicit monetary costs associated with firing regular workers (e.g., OECD,

2004). It also specifies the regulations and procedures that constrain employer’s ability to

fire workers, such as notice length and notification procedures before dismissal or the

rules related to unfair dismissal, which give rise to additional, non-monetary, firing costs.

EPL for temporary employees also affects labor adjustment costs, but through a different

channel. In particular, it constrains employers’ ability to use temporary workers (who

have low firing costs) instead of regular workers (who have much higher firing costs).

Thus, stricter EPL for temporary employees increases the effective monetary and non-

monetary firing costs that employers face, by shifting the composition of their work force

from temporary to regular employees.

Stricter EPL for regular workers and stricter EPL for temporary workers both

increase the downward adjustment costs for labor. Therefore, based on the economic

theory of sticky costs, we expect both forms of EPL to increase the degree of cost

stickiness for labor costs, and, since labor costs represent a large fraction of total

operating costs, we expect them to increase the degree of stickiness for operating costs.

This leads us to the following hypotheses:

Hypothesis 2a: Stricter employment protection for regular employees is associated

with a higher degree of cost stickiness for operating costs.

Hypothesis 2b: Stricter employment protection for temporary employees is

associated with a higher degree of cost stickiness for operating costs.

The relative magnitudes of the effects of regular and temporary EPL on cost

stickiness are a priori ambiguous. For example, companies may use temporary employees

in all areas of operations, in which case they will be able to accommodate most of the

fluctuations in their labor requirements by adjusting the number of temporary workers,

16

taking advantage of the much lower firing costs for them relative to regular workers. In

this case, even though temporary employees account for a relatively small fraction of the

total work force, they would account for most of the changes in individual companies’

work force. If so, EPL for temporary workers would have a much greater effect on the

degree of cost stickiness than EPL for regular workers. Conversely, the use of temporary

workers may be confined to a relatively narrow subset of operations (for example,

generic tasks that can be accomplished without much firm-specific human capital and

experience). If so, labor force adjustments would involve hiring and firing regular

workers alongside temporary workers, in which case EPL for regular workers might have

a much stronger effect on cost stickiness than EPL for temporary workers. Thus, a priori

either form of EPL (regular or temporary) may have a dominant effect on cost stickiness.

3. Research methodology

We empirically examine the relationship between country-specific employment

protection legislation and cost stickiness for firms in OECD member countries.16 We

choose this research setting primarily for two reasons: (1) OECD includes all the major

industrialized nations with a free market economy, and (2) measures of country-specific

EPL and other labor market characteristics are reliably and systematically reported. In

addition, as discussed in the previous section, there is a rich literature in labor economics

that has examined various aspects of EPL and other labor market characteristics for

OECD countries (e.g., Lazear, 1990, Bentolila and Bertola, 1990, Hopenhayn and

Rogerson, 1993, Mortensen and Pissarides, 1999, Heckman et al., 2000, Botero et al.,

2004, and many others). Since our main goal in this study is to explore the relationship

between EPL and cost behavior, we are able to leverage this literature both in formulating

our empirical hypotheses and in identifying the appropriate empirical measures of EPL

and additional labor market control variables.

16 At the beginning of our sample period (1990), OECD consisted of 24 member countries. Six more countries joined OECD during the sample period 1990-2008 (Czech Republic, Hungary, Mexico, Poland, South Korea and Slovakia), and four more countries joined after 2008 (Chile, Estonia, Israel and Slovenia).

17

3.1. Measures of employment protection legislation

To measure the strictness of employment protection legislation for regular and

temporary employees, we use indexes defined and reported in OECD (2004). These

indexes are based on the legislative provisions governing the firing of workers (such as

the length of the notice period before dismissal, or the level of severance pay), as well as

regulations related to temporary forms of employment (such as the maximum duration of

successive fixed-term contracts, or restrictions on employment through temp agencies).

For each country, OECD (2004) characterizes employment protection legislation for

regular and temporary employees along 14 basic items, and then combines them into

summary indexes of EPL strictness for regular employees REGEPLn and temporary

employees TEMPEPLn for country n (see Table 1 for details). These indexes are

normalized to range from 0 to 6, with higher scores representing stricter regulation.

Table 3 presents the EPL indexes for the 19 OECD countries in our main sample

(Australia, Austria, Belgium, Canada, Denmark, Finland, Germany, Ireland, Italy, Japan,

Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK and US).17 There is

substantial cross-country variation in EPL strictness for regular employees, with lowest

scores for the US, UK and Switzerland (0.2, 0.9 and 1.2 respectively on a scale from 0 to

6), and the highest scores for Portugal, Netherlands, Austria and Sweden (4.3, 3.1, 2.9

and 2.9 respectively).18 Countries with stricter EPL for regular workers typically have

stricter EPL for temporary workers as well (the correlation between the two EPL indexes

is 0.789), but there is also some meaningful independent variation in EPL for temporary

workers relative to regular workers. For example, France and Italy have moderate levels

of EPL for regular employees (1.8 and 2.3 respectively, vs. the median level of 2.3), but

they have stricter EPL for temporary employees than any other country in the sample (3.3,

vs. the median of 1.6). At the opposite extreme, the Netherlands has one of the strictest 17 We do not include countries that joined the OECD after the beginning of our sample period, since most of them are the transition economies in Eastern Europe. We also discard OECD countries with missing data on labor market characteristics (Iceland, Luxembourg, Mexico, New Zealand and Turkey). 18 To illustrate how substantial this variation is, we also examine the mandated severance pay regulations since they give rise to well-interpretable monetary firing costs. Mandated severance pay for a worker with 4 years of tenure ranges from zero in the US to 2.6 months of regular pay in Spain and 4 months of regular pay in Portugal. For a worker with 20 years of tenure, mandated severance pay ranges from zero in the US to 12 months of regular pay in Spain and 20 months of regular pay in Portugal (OECD, 2004).

18

levels of EPL for regular employees (3.1), but below-median levels of EPL for temporary

employees (1.2, vs. the median of 1.6).

3.2. Sample selection and descriptive statistics

We start by drawing the full sample of publicly-listed non-financial firms from

Compustat (Global and North America) between 1988-2008 for the 19 OECD countries

we focus on.19 We deflate sales and operating costs to control for inflation, using country-

specific GDP deflators. We discard firm-years if: (1) sales or operating costs are missing

or negative in current or two prior years, (2) operating costs are less than 50% or more

than 200% of sales in current or two prior years, or (3) assets are missing or negative in

current year. We also discard firms reporting in a non-native currency (for example,

European firms reporting in US dollars).20 After that, we discard 1% outliers on each tail

for the dependent variable (log-change in deflated operating costs) and for the continuous

firm-level explanatory variables (log-change in deflated sales and asset intensity). We

also discard firm-years if deflated sales increased by over 50% or dropped by over 33%

in current or prior year,21 since such extreme year-on-year changes in sales mostly reflect

mergers or divestitures. The final sample in estimation consists of 128,333 observations

for 15,833 firms in 19 OECD countries between 1990-2008.22

We merge this sample with the EPL data described earlier and additional country-

level control variables from several sources. The data on annual GDP growth rates and

inflation rates (GDP deflators) for each country is from the World Bank Databank.23 The

data on labor market control variables in robustness checks (union density, bargaining

coordination and centralization index and unemployment benefits index) is from OECD

(2004) and Nickell et al. (2005). The variable definitions in estimation are summarized in

Table 2. 19 For most OECD countries in Global Compustat, data is available only starting from 1988. For consistency, we start the sample in 1988 for all countries in our data. 20 The results are similar when we do not discard such firms. 21 These percentage cutoffs (-33% and +50%) are symmetric when transformed into the log-change form (ln(2/3) and ln(3/2) respectively) that we use in estimation. 22 The first two lags in the raw data are used up in computing the first differences and preparing the control variables, so the final sample in estimation starts in 1990 rather than in 1988. 23 http://databank.worldbank.org/ddp/home.do

19

The descriptive statistics are presented in Table 3. Besides large differences in EPL

discussed in the previous subsection, there are important cross-country differences in

other variables. For example, average annual GDP growth ranges from 1.1 percent in

Italy and 1.3 percent in Japan to 6.2 percent in Ireland. The average annual log-change in

deflated sales ranges from 0.022 (2.2 percent a year) for Japanese firms to 0.051 (5.1

percent) for Swedish firms and 0.053 (5.3 percent) for Irish firms. As Anderson et al.

(2003) and Banker et al. (2010b) show, managers’ optimism and pessimism regarding

future sales play an important role in sticky cost behavior. Thus, the cross-country

differences in growth rates are likely to lead to large cross-country differences in cost

stickiness due to differences in managers’ expectations. Therefore, in the empirical

analysis, we include empirical proxies for managers’ optimism and pessimism following

Anderson et al. (2003) to control for cross-country and within-country differences in

managers’ expectations regarding future sales.

3.3. Empirical model

Our empirical estimation is based on a hierarchical linear model on which the

behavior of a level-1 outcome (i.e., firm-level cost behavior) is postulated to be a

function of level-2 explanatory variables (i.e., country-level characteristics) and firm-

level control variables. We next specify the level-1 and level-2 models and discuss the

model structure in detail.

We begin with the following firm-level model of cost behavior linking annual

changes in deflated operating costs (XOPR) to contemporaneous changes in deflated

sales revenue (SALE), which follows the sticky costs model of Noreen and Soderstrom

(1997) and Anderson et al. (2003)

tintintintintintintin uSALEDECSALEXOPR ,,,,,,,,,2,,,,,10,, lnlnln (1)

where ∆lnXOPRn,i,t represents the log-change in deflated operating costs for firm i in

country n in year t, ∆lnSALEn,i,t is the log-change in deflated sales, DECn,i,t is a binary

variable equal to one if deflated sales decreased in year t and zero otherwise, un,i,t is an

error term that has mean zero and is independent of the explanatory variables, and the

slopes α1,n,i,t and α2,n,i,t are specified in detail below. In this specification, the slope α1,n,i,t

20

approximates the percentage change in costs for a one percent increase in sales, and

α1,n,i,t+α2,n,i,t approximates the percentage change in costs for a one percent decrease in

sales. The cost stickiness coefficient α2,n,i,t captures the degree of asymmetry in cost

behavior (stickiness if α2,n,i,t is negative, and anti-stickiness if α2,n,i,t is positive).

We introduce the level-2 model by specifying the firm-level slopes α1,n,i,t and α2,n,i,t in

model (1) as a function of country-level explanatory variables, firm-level control

variables following prior studies (e.g., Anderson et al., 2003), and additional country-

level random effects. In the basic model (Model A), we specify the slope coefficients as

nnntintntin vEPLLAWAINTGDPGROWTH ,154,,3,21,,,1 (2a)

nnntintntintin vEPLLAWAINTGDPGROWTHDEC ,21110,,9,81,,76,,,2 (2b)

where GDPGROWTHn,t represents real GDP growth rate in country n in year t, AINTn,i,t

represents asset intensity (log ratio of total assets to sales) for firm i in year t, LAWn is a

binary variable equal to one for common-law countries, DECn,i,t-1 is a binary variable

equal to one if sales decreased in prior period, EPLn is the employment protection

legislation index for country n, and v1,n, v2,n are country-level random effects. Following

prior literature (e.g., Anderson et al., 2003), we use GDP growth (GDPGROWTHn,t) and

a dummy for successive decreases in sales (DECn,i,t-124 ) as empirical proxies for

managers’ optimism or pessimism regarding future sales, and we use asset intensity

(AINTn,i,t) as an empirical proxy for the magnitude of adjustment costs facing the firm.25

We include the common-law dummy LAWn (equal to one for Australia, Canada, Ireland,

UK and US), since prior research in economics (e.g., La Porta et al., 1997, 1998, 2000;

Djankov et al., 2007) has found that the legal origin of a country (common law vs. code

law) is one of the primary drivers of cross-country differences in corporate governance,

access to external finance, business regulation and other outcomes that likely play an

24 Since DECn,i,t-1 enters the slope only for current decreases in sales (DECn,i,t=1), it is relevant only for successive decreases taking place both in year t and in year t-1. 25 We do not include employee intensity as a control variable in our main specification, since the number of employees variable has a high proportion of missing observations, and since cross-country comparisons of employee intensity are contaminated by cross-country differences in labor productivity and fluctuations in market exchange rates. In untabled robustness checks, we get similar results after controlling for employee intensity.

21

important role in firm-level cost behavior.26 The country-level random effects v1,n, v2,n

capture the cross-country differences in cost behavior that are not accounted for by the

observable explanatory variables. By construction, v1,n, v2,n have mean zero and they are

independent of the explanatory variables in the regression. The main parameter of interest

in estimation is β11, which captures the relationship between the strictness of EPL and the

degree of cost stickiness. Hypothesis 1 implies that β11 should be negative, i.e., stricter

EPL should be associated with a higher degree of cost stickiness (a more negative

α2,n,i,t).27

By combining equation (1) with (2a) and (2b), we obtain our basic estimation model:

Model A

tintintinn

ntintntin

tinnntintntin

SALEDECEPLLAWAINTGDPGROWTHDEC

SALEEPLLAWAINTGDPGROWTHXOPR

,,,,,,11

10,,9,81,,76

,,54,,3,210,,

ln)(

ln)(ln

(3)

where εn,i,t is the error term, which combines the residuals from equations (1), (2a) and

(2b), and the rest of the terms have been described above. The error term εn,i,t can be

rewritten as

tintinntinntintin SALEDECvSALEvu ,,,,,2,,,1,,,, lnln (4)

where the original random shocks un,i,t, v1,n, v2,n from equations (1), (2a), (2b) have mean

zero and are independent of the explanatory variables in the regression. Consequently, the

combined error term εn,i,t in equation (3) has zero mean for any value of the explanatory

variables, and therefore ordinary least squares (OLS) yields unbiased and consistent

estimates of the parameters in equation (3). The inclusion of country-level random effects

v1,n, v2,n introduces cross-sectional correlation in εn,i,t across firms within each country. It

also introduces heteroskedasticity, since the random shocks v1,n, v2,n are multiplied by

∆lnSALE and DEC×∆lnSALE respectively. To deal with these issues, we use clustered

standard errors (Rogers, 1993) with clustering by country.28

26 Calleja et al. (2006) also find (for a limited sample of four developed countries) that legal origin plays a role in cost behavior. 27 We also control for EPL strictness in the slope for increases α1,n,i,t since EPL strictness is likely to affect firms’ hiring decisions for activity increases. However, as we discuss in section 2, the expected sign of this effect is a priori ambiguous. 28 Alternatively, instead of using OLS with appropriately clustered standard errors, we could estimate Model A as a random-coefficients model using maximum likelihood. However, this approach would be less

22

We also estimate an extended model (Model B), in which we replace the aggregate

EPL index (EPLn) with two more detailed indexes, which measure the strictness of EPL

separately for regular and temporary employees. The slope coefficients in Model (B) are

specified as:

nnnntintntin vTEMPEPLREGEPLLAWAINTGDPGROWTH ,1654,,3,21,,,1 (5a)

nnn

ntintntintin

vTEMPEPLREGEPLLAWAINTGDPGROWTHDEC

,21312

11,,10,91,,87,,,2

(5b)

where REGEPLn represents the index of regular employment protection in country n,

TEMPEPLn is the index of temporary employment regulation in country n, and the rest of

the terms have been described above. The main parameters of interest in estimation are

β12 and β13. Hypotheses 2a, 2b imply that β12 and β13 should both be negative, i.e., an

increase in strictness of EPL for regular or temporary employees should be associated

with a higher degree of cost stickiness (a more negative stickiness coefficient α2,n,i,t).29

Proceeding similarly to Model A above, we obtain the estimation equation for Model

B by combining equation (1) with equations (5a), (5b):

Model B

tintintinnn

ntintntin

tinnn

ntintntin

SALEDECTEMPEPLREGEPLLAWAINTGDPGROWTHDEC

SALETEMPEPLREGEPLLAWAINTGDPGROWTHXOPR

,,,,,,1312

11,,10,91,,87

,,65

4,,3,210,,

ln)(

ln)(ln

(6)

We estimate Model B using OLS with standard errors clustered by country.

In robustness checks, we introduce additional labor market control variables (union

density, collective bargaining coordination and centralization index, and unemployment

benefits index) in the slope coefficients α1,n,i,t, α2,n,i,t. The final estimation models in these

robust, since it would require us to impose additional distributional assumptions on the error terms in equations (1) and (2a), (2b), including their time-series structure, and it would yield consistent estimates only if these additional distributional assumptions hold in the data. When we impose these additional assumptions and estimate Model A as a random-coefficients model using maximum likelihood, the main results are similar. 29 We also control for REGEPLn and TEMPEPLn in the slope for increases α1,n,i,t since these variables are likely to affect firms’ hiring decisions for increases in sales. As we discuss in section 2, the expected sign of this effect is a priori ambiguous.

23

robustness checks are analogous to Models A and B above, with additional interaction

terms for the labor market controls.

4. Empirical results

We present the main estimation results for Model A in column (a) of Table 4. The

coefficients on the control variables (asset intensity, GDP growth and a dummy for

successive decreases in sales) have the expected signs and are consistent with the

findings in prior literature for the US data (e.g., Anderson et al., 2003). The main

parameter of interest is β11, which captures the association between the strictness of

employment protection legislation EPLn and the degree of cost stickiness. The estimate is

β11=-0.044 (t=-4.03), negative and significant at the 1 percent level. Thus, stricter EPL in

our data is associated with a higher degree of cost stickiness (i.e., a more negative

stickiness coefficient α2,n,i,t), lending strong support to Hypothesis 1.

In addition to being statistically significant, the magnitude of β11 is also

economically significant. For example, if we compare Switzerland (the country with the

least strict EPL in continental Europe) and Portugal (the country with the strictest EPL in

continental Europe), the predicted cost stickiness coefficient for a firm in Portugal is 0.11

higher (in absolute value) than the stickiness coefficient for an equivalent firm in

Switzerland. For comparison, the average degree of cost stickiness in the full sample

is -0.090, i.e., the degree of cross-country variation in cost stickiness driven by EPL has

the same order of magnitude as the average cost stickiness coefficient.

As a first robustness check, we split the full sample period (1990-2008) into two

shorter time periods, 1990-2000 and 2001-2008,30 and re-estimate Model A separately for

each subsample (columns (b) and (c) in Table 4). The estimates of β11 are negative and

significant at the 5 percent level in both subsamples (β11=-0.046, t=-2.14 between 1990-

30 The end of 2000 is a natural break point for sub-period analysis, since it is close to several major economic events, such as the introduction of the Euro at the wholesale level in early 1999, the bursting of the dot-com bubble in 2000, the collapse of Enron and related scandals that significantly increased the scrutiny on corporate governance starting in 2001. Another important factor over the same period was the Y2K concerns, which led companies to upgrade or replace their IT systems. In addition to addressing the Y2K bug, this large investment in IT allowed companies to interact in new ways with their customers and suppliers, redefining business processes and relationships (e.g., Anderson et al., 2006), which also affected cost behavior.

24

2000, and β11=-0.056, t=-2.34 between 2001-2008), lending additional support to

Hypothesis 1. In another robustness check, we re-estimate the model after discarding data

for the US firms. US data accounts for 35 percent of observations in the full sample, and

therefore it could have a disproportionate effect on the estimates. The estimation results

after discarding the US are in column (d) of Table 4. The point estimates and their

significance level (β11=-0.048, t=-4.15) are very similar to our original estimates for the

full sample. In another robustness check, we add control for other country-level labor

market characteristics (trade union density, an index of collective bargaining coordination

and centralization, and an index of unemployment benefits) that are likely to affect the

magnitude of adjustment costs for labor and consequently affect the degree of cost

stickiness. The estimation results are presented in Table 6. The estimates of β11 are close

in magnitude and significance levels to the original estimates in Table 4, indicating that

stricter EPL is associated with a significantly higher degree of cost stickiness even after

controlling for the main labor market characteristics in each country, lending additional

support to Hypothesis 1. Since cost stickiness may be affected by corporate governance

variables (e.g., Chen et al., 2011), we also add control for creditor rights index from

Djankov et al. (2007) and shareholder protection index from LaPorta et al. (2006) in one

specification, and an alternative shareholder protection index from Djankov et al. (2007)

in another specification. The main results (untabled) are similar, both with and without

control for the main labor market characteristics. Since cost stickiness may depend on the

long-term growth prospects of the firm, we also add control for long-term firm-level

growth rates,31 with similar results (untabled). In another robustness check, we replace

our main EPL strictness index from OECD (2004) with an alternative employment

protection index from Botero et al. (2004), with similar results (untabled). In a further

robustness check, we add country fixed effects and year effects, to control for unobserved

country-specific factors and time-varying unobserved global shocks. The main results

(untabled) are similar.

31 In one specification, we compute the growth rate for firm i as the average log-change in its deflated sales over the entire sample period. In another specification, we compute the growth rate for firm i in year t as the average log-change in its deflated sales between year t-4...t.

25

4.1. Results for regular and temporary employment protection measures (Model B)

As we discuss in subsection 3.1, our measure of overall employment protection

legislation in Model A (EPLn) combines two more detailed EPL indexes, employment

protection for regular employees (REGEPLn) and regulation of temporary forms of

employment (TEMPEPLn), which may have important differential effects.

We use Model B to examine the effects of regular and temporary EPL indexes on

cost stickiness. The main estimation results are presented in column (a) of Table 5. The

coefficients on the control variables (GDP growth, asset intensity and a dummy for

successive decreases in sales) have the expected signs and are consistent with the

findings in prior literature for the US data (e.g., Anderson et al., 2003). The main

parameters of interest are β12 (the coefficient on regular employment protection index

REGEPLn) and β13 (the coefficient on temporary employment regulation index

TEMPEPLn). The coefficient for regular EPL (β12) is negative but not significant at the

10 percent level (β12=-0.015, t=-1.54), while the coefficient for temporary EPL (β13) is

negative and significant at the 0.001 percent level (β13=-0.027, t=-6.20). These smaller

insignificant estimates for regular EPL suggest that companies are able to successfully

bypass most of the EPL restrictions on firing regular workers by relying on temporary

workers to accommodate most of the changes in their work force. 32 However, the

insignificant estimates for regular EPL could also be due to multicollinearity (the

correlation between regular and temporary EPL in our sample is 0.789). While the

estimates indicate that REGEPLn and TEMPEPLn are jointly significant and affect the

degree of cost stickiness in the direction consistent with Hypotheses 2a, 2b, the

inferences regarding the role of each EPL variable separately are less informative due to

multicollinearity. To get a better sense of the effects of independent variation in

REGEPLn and TEMPEPLn, we do the following. First, we regress TEMPEPLn on

REGEPLn, and compute the residual rTEMPEPLn in this regression. By construction, this

residual captures the portion of variation in TEMPEPLn that is orthogonal to

32 This does not mean that stricter regular EPL has no adverse effects for companies. For example, if temporary workers are less productive than regular workers, then stricter regular EPL will reduce the average productivity of companies’ work force by forcing them to rely more on temporary workers.

26

(uncorrelated with) REGEPLn.33 After that, we re-estimate Model B after replacing the

original TEMPEPLn with the residual rTEMPEPLn.34 The estimate of β12 in this modified

regression captures the effect of common variation in REGEPLn and TEMPEPLn, while

the estimate of β13 captures the effect of independent variation in TEMPEPLn. The

coefficients on both variables in this modified regression are negative and significant at

the 1 percent level (β12=-0.035, t=-3.17, β13=-0.027, t=-6.20), indicating that both the

common variation in regular and temporary EPL and the independent variation in

temporary EPL have a significant association with the degree of cost stickiness,

supporting Hypotheses 2a and 2b.

This association is also economically significant. For example, if regular and

temporary EPL indexes change from their levels in Switzerland (the country with the

lowest EPL in continental Europe) to their levels in France (a country with relatively

stringent EPL), it would increase the cost stickiness coefficient by 0.084 (in absolute

value), which is nearly the same as the average cost stickiness coefficient in our sample

(-0.090).

As a first robustness check, we split the full sample period (1990-2008) into two

shorter time periods, 1990-2000 and 2001-2008, and re-estimate Model B separately for

each subsample (columns (b) and (c) in Table 5). The estimates of β13 (the coefficient on

TEMPEPLn) are negative and significant in both subsamples (β13=-0.036, t=-2.45

between 1990-2000, and β13=-0.020, t=-2.84 between 2001-2008), while β12 is negative

and significant between 2001-2008 but not between 1990-2000 (β12=-0.041, t=-2.19

between 2001-2008, and β12=-0.008, t=-0.54 between 1990-2000). As discussed above,

since EPL indexes for regular and temporary employees are strongly positively correlated,

the interpretation of individual coefficients is complicated by multicollinearity.35 When

we redefine the EPL variables to separate common variation in regular and temporary

EPL from independent variation in temporary EPL using the procedure described above,

both coefficients are negative and significant at the 5 percent level in both subsamples

33 This is a general property of OLS regression: the regression residual is orthogonal to all the explanatory variables. 34 By construction, this regression is equivalent to the original Model B, since the new explanatory variable rTEMPEPLn is a linear combination of the original explanatory variables. 35 On the other hand, their combined effect is identified reliably even in the presence of strong multicollinearity.

27

(β12=-0.037, t=-2.02 and β13=-0.036, t=-2.45 between 1990-2000; β12=-0.056, t=-2.46

and β13=-0.020, t=-2.84 between 2001-2008), lending further support to Hypotheses 2a,

2b.

Since the US data accounts for 35 percent of the full sample and therefore could have

a disproportionate impact on the results, we also re-estimate the model after discarding

the US from the sample. The estimation results without the US are very similar in

magnitude and significance (column (d) in Table 5). In another robustness check, we add

control for other country-level labor market characteristics (trade union density, an index

of collective bargaining coordination and centralization, and an index of unemployment

benefits) that are likely to affect the magnitude of adjustment costs for labor and

consequently affect the degree of cost stickiness. The estimation results are presented in

Table 7. The estimates of β12, β13 are again very close in magnitude and significance

levels to the original estimates in Table 5. Like in the original estimates, the coefficient

on temporary EPL is negative and significant at any reasonable significance level

(β13=-0.023, t=-5.35) while the coefficient on regular EPL is negative but insignificant

(β12=-0.011, t=-1.24). As discussed above, the insignificant estimates for regular EPL

suggest that companies are able to bypass most of the EPL restrictions for regular

workers by relying on temporary workers, however this could also reflect

multicollinearity. When we redefine the EPL variables to separate between common

variation in regular and temporary EPL and independent variation in temporary EPL

using the procedure described above, both coefficients are negative and significant

(β12=-0.029, t=-2.65 and β13=-0.023, t=-5.35). Thus, both the common variation in

regular and temporary EPL and the independent variation in temporary EPL significantly

increase the degree of cost stickiness even after controlling for the main labor market

characteristics, further supporting Hypotheses 2a, 2b. Since cost stickiness may be

affected by corporate governance variables (e.g., Chen et al., 2011), we also add control

for creditor rights index from Djankov et al. (2007) and shareholder protection index

from LaPorta et al. (2006) in one specification, and an alternative shareholder protection

index from Djankov et al. (2007) in another specification. The main results (untabled) are

similar, both with and without control for the main labor market characteristics. Since

cost stickiness may depend on the long-term growth prospects of the firm, we also add

28

control for long-term firm-level growth rates,36 with similar results (untabled). In another

robustness check, we add country fixed effects and year effects. The main results

(untabled) are similar.

Summing up, the estimation results for a single aggregate measure of EPL (Model

A) indicate that stricter EPL is associated with a higher degree of cost stickiness,

supporting our main empirical hypothesis. When we decompose the overall EPL index

into two more detailed measures of EPL strictness, those for regular and temporary

employees (Model B), the estimates for both measures confirm our main findings. The

estimates for Model B also indicate that the association with cost stickiness is stronger for

temporary EPL than for regular EPL, which may suggest that companies are able to

successfully bypass many of the EPL restrictions for regular workers by relying more on

temporary workers.37 In both models, the association between EPL strictness and cost

stickiness is significant both statistically and economically, and the results are robust to

alternative specifications. These results support the central premise of the economic

theory of sticky costs, that cost behavior reflects deliberate resource-adjustment decisions

by forward-looking managers who recognize the dynamic tradeoffs associated with

adjustment costs, in the empirical context of labor resources.

5. Conclusion

In this study we investigated the relationship between employment protection

legislation (EPL) in different countries and sticky cost behavior. The basic premise in the

economic theory of sticky costs is that many costs arise as a result of deliberate resource

commitment decisions made by forward-looking managers in the presence of adjustment

costs. Adjustment costs play a central role in this theory, giving rise to dynamic patterns

of sticky cost behavior which are inconsistent with the standard textbook view of cost

36 In one specification, we compute the growth rate for firm i as the average log-change in its deflated sales over the entire sample period. In another specification, we compute the growth rate for firm i in year t as the average log-change in its deflated sales between year t-4...t. 37 Due to the high correlation between regular and temporary EPL, the estimates of their separate effects are less precise than the estimates of their combined effect. Therefore, more caution is required in interpreting their relative magnitudes.

29

behavior. A central testable implication of this theory is that the degree of cost stickiness

should be increasing in the magnitude of (downward) adjustment costs for capacity

resources. However, empirical tests of this theory have been hampered by the fact that

adjustment costs are hard to measure directly. In this study, we leveraged prior literature

in labor economics on the structural features of the labor market, and exploited cross-

country variation in the strictness of employment protection legislation for OECD

countries to test the theory of sticky costs. Cross-country variation in EPL strictness

provides a reliable exogenous source of variation in adjustment costs for labor resources,

since EPL is the primary source of firing costs for employers (e.g., Long and Siebert,

1983, Pissarides, 1999). Based on the economic theory of sticky costs, we hypothesized

that firms in countries with stricter employment protection should exhibit a greater degree

of cost stickiness.

We tested our hypotheses using a large sample of firms in 19 OECD countries. The

empirical results strongly support our hypotheses, reinforcing the notion that observed

cost behavior is driven by deliberate resource commitment decisions made by forward-

looking managers who recognize the role of adjustment costs. The relationship between

the strictness of EPL and the degree of cost stickiness is also highly economically

significant, and the estimation results are robust to alternative model specifications.

Our study is the first in the literature to explicitly consider and test the link between

economy-wide structural variables and sticky cost behavior. Prior literature on cost

behavior was conducted almost exclusively using samples of firms from a single country,

and as such has largely ignored the impact of economy-wide structural variables on cost

behavior.38 Our results show that a full understanding of cost behavior in general and of

cost stickiness in particular requires careful analysis not only of the firm-specific factors

but also of the economy-wide structural forces that shape managers’ resource adjustment

decisions.

38 The only exception we are aware of is Calleja et al. (2006).

30

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34

Table 1. Calculation of the summary indicators of EPL strictness reported in OECD (2004)

For each country, EPL is characterized along 14 basic items described in Panels A and B below. As a first step in computing these indexes in OECD (2004), each basic item is renormalized into a

cardinal score ranging from 0 to 6, where higher values represent stricter regulation. After that, the summary indices of EPL are computed as a weighted average of individual items, with weights described in Panel C below.

Panel A. EPL for regular employees (source: Table 2.A1.1 in OECD 2004) assignment of numerical scores of strictness short description 0 1 2 3 4 5 6

Item 1 Notification procedures

Scale 0-3 0 when an oral statement is enough; 1 when a written statement of the reasons for dismissal must be supplied to the employee; 2 when a third party (such as works council or the competent labor authority) must be notified; 3 when the employer cannot proceed to dismissal without authorization from a third party.

Scale (0 – 3) × 2

Item 2 Delay involved before notice can start

Days Estimated time includes, where relevant, the following assumptions: 6 days are counted in case of required warning procedure, 1 day when dismissal can be notified orally or the notice can be directly handed to the employee, 2 days when a letter needs to be sent by mail and 3 days when this must be a registered letter.

≤2 <10 <18 <26 < 35 <45 ≥45

Item 3 Length of the notice period at

9 months tenure Months 4 years tenure Months 20 years tenure Months

0 0

<1

≤0.4 ≤0.75 ≤2.75

≤0.8 ≤1.25

<5

≤1.2 <2 <7

<1.6 <2.5 <9

<2 <3.5 <11

≥2 ≥3.5 ≥11

Item 4 Severance pay at

9 months tenure Months pay 4 years tenure Months pay 20 years tenure Months pay

0 0 0

≤0.5 ≤0.5 ≤3

≤1 ≤1 ≤6

≤1.75 ≤2 ≤10

≤2.5 ≤3 ≤12

<3 <4 ≤18

≥3 ≥4

>18 Item 5 Definition of justified or unfair dismissal

Scale 0-3 0 when worker capability or redundancy of the job are adequate and sufficient ground for dismissal; 1 when social considerations, age or job tenure must when possible influence the choice of which worker(s) to dismiss; 2 when a transfer and/or a retraining to adapt the worker to different work must be attempted prior to dismissal; 3 when worker capability cannot be a ground for dismissal.

Scale (0 – 3) × 2

Item 6 Length of trial period

Months Period within which, regular contracts are not fully covered by employment protection provisions and unfair dismissal claims can usually not be made.

≥24 >12 >9 >5 >2.5 ≥1.5 <1.5

Item 7 Compensation following unfair dismissal

Months pay ≤3 ≤8 ≤12 ≤18 ≤24 ≤30 >30

Item 8 Possibility of reinstatement following unfair dismissal

Scale 0-3 The extend of reinstatement is based upon whether, after finding of unfair dismissal, the employee has the option of reinstatement into his/her previous job, even if this is against the wishes of the employer.

Scale (0 – 3) × 2

35

Panel B. EPL for temporary employees (source: Table 2.A1.1 in OECD 2004) assignment of numerical scores of strictness short description 0 1 2 3 4 5 6

Item 9 Valid cases for use of fixed-term contracts (FTC)

Scale 0-3 0 fixed-term contracts are permitted only for “objective” or “material situation”, i.e. to perform a task which itself is of fixed duration; 1 if specific exemptions apply to situations of employer need (e.g. launching a new activity) or employee need (e.g. workers in search of their first job); 2 when exemption exist on both the employer and employee sides; 3 when there are no restrictions on the use of fixed-term contracts.

6 – scale (0 – 3) × 2

Item 10 Maximum number of successive FTC

Number no limit

≥5 ≥4 ≥3 ≥2 ≥1.5 <1.5

Item 11 Maximum cumulated duration of successive FTC

Months no limit

≥36 ≥30 ≥24 ≥18 ≥12 <12

Item 12 Types of work for which temporary work agency (TWA) employment is legal

Scale 0-4 0 when TWA employment is illegal; 1-3 1 to 3 depending upon the degree of restrictions; 4 when no restrictions apply. 6 – Scale (0 – 4) × 6/4

Item 13 Restrictions on number of renewals

Yes/no - - No - Yes - -

Item 14 Maximum cumulated duration of TWA contracts

Months no limit

≥36 ≥24 ≥18 ≥12 ≥6 <6

36

Panel C. EPL summary indicators at different levels of aggregation and the weighting scheme

(source: Table 2.A1.2 in OECD 2004)

Level 4 Scale 0-6

Level 3 Scale 0-6

Level 2 Scale 0-6

Level 1 Scale 0-6

Procedural inconveniences (1/3)

1. Notification procedures (1/2) 2. Delay to start a notice (1/2)

Notice and severance pay for no-fault individual dismissals (1/3)

3. Notice period after 9 months (1/7) 4 years (1/7) 20 years (1/7) 4. Severance pay after 9 months (4/21) 4 years (4/21) 20 years (4/21)

Regular contracts REGEPLn (1/2)

Difficulty of dismissal (1/3)

5. Definition of unfair dismissal (1/4) 6. Trial period (1/4) 7. Compensation (1/4) 8. Reinstatement (1/4)

Fixed term contracts (1/2) 9. Valid cases for use of fixed-term contracts (1/2) 10. Maximum number of successive contracts (1/4) 11. Maximum cumulated duration (1/4)

Overall summary indicator EPLn

Temporary contracts TEMPEPLn (1/2)

Temporary work agency employment (1/2)

12. Types of work for which is legal (1/2) 13. Restrictions on number of renewals (1/4) 14. Maximum cumulated duration (1/4)

37

Table 2. Variable definitions

n – country index. i – firm index. t – year index. XOPRn,i,t – operating costs for firm i in country n in year t, deflated to control for inflation. SALEn,i,t – sales revenue for firm i in country n in year t, deflated to control for inflation. GDPGROWTHn,t – real GDP growth in country n in year t. DECn,i,t – binary variable equal to one for sales decreases. AINTn,i,t – asset intensity for firm i in country n in year t, computed as the log ratio of assets to sales,

ln(AT/SALE). LAWn – binary variable equal to one for common-law countries (Australia, Canada, Ireland, UK and US),

zero otherwise. REGEPLn – index of employment protection legislation (EPL) for regular employees in country n in late

1990-s, from Table 2.A2.4 in OECD (2004). REGEPLn ranges from 0 to 6, and higher values correspond to stricter EPL.

TEMPEPLn – index of employment protection legislation (EPL) for temporary employees in country n in late 1990-s, from Table 2.A2.4 in OECD (2004). TEMPEPLn ranges from 0 to 6, and higher values correspond to stricter EPL.

EPLn – aggregate index of employment protection legislation (EPL) in country n in late 1990s, computed as (REGEPLn+TEMPEPLn)/2 following OECD (2004).

TUDn – trade union density in country n in 2000, from OECD (2004). BCCn – bargaining coordination and centralization index for country n in late 1990s, from OECD (2004). BNFTn – unemployment benefits index for country n in late 1990-s, from Nickell et al. (2005).

Sample selection criteria: We start by drawing the full sample of publicly-listed non-financial firms from Compustat (Global

and North America) between 1988-2008 for the 19 OECD countries we focus on. We discard firm-years if: (1) sales or operating costs are missing or negative in current or two prior years, (2) operating costs are less than 50% or more than 200% of sales in current or two prior years, or (3) assets are missing or negative in current year. We also discard firms reporting in a non-native currency (for example, European firms reporting in US dollars). After that, we discard 1% outliers on each tail for the dependent variable (log-change in deflated operating costs) and for the continuous firm-level explanatory variables (log-change in deflated sales and asset intensity). We also discard firm-years if deflated sales increased by over 50% or dropped by over 33% in current or prior year, since such extreme year-on-year changes in sales likely reflect mergers or divestitures. The final sample in estimation contains 128,333 observations for 15,833 firms in 19 OECD countries between 1990-2008.42

42 The first two lags in the original data is used up to compute first differences and control variables, so the final sample in estimation starts in 1990 rather than in 1988.

38

Table 3. Descriptive statistics

country number of observations

average ∆lnCOST

average ∆lnSALE

average GDP growth

regular EPL (REGEPL)

temporary EPL

(TEMPEPL) Australia 3,798 0.045 0.041 3.5 1.5 0.9 Austria 755 0.051 0.048 2.4 2.9 1.5 Belgium 894 0.031 0.029 2.1 1.7 2.6 Canada 4,651 0.046 0.042 2.7 1.3 0.3 Denmark 1,254 0.038 0.036 2.0 1.5 1.4 Finland 1,192 0.050 0.049 2.9 2.3 1.9 France 5,941 0.042 0.040 1.9 2.3 3.6 Germany 5,613 0.030 0.029 1.6 2.7 2.3 Ireland 478 0.053 0.053 6.2 1.6 0.3 Italy 1,743 0.035 0.031 1.1 1.8 3.6 Japan 37,094 0.022 0.022 1.3 2.4 1.6 Netherlands 1,516 0.035 0.035 2.7 3.1 1.2 Norway 960 0.040 0.042 2.8 2.3 3.1 Portugal 414 0.027 0.029 1.9 4.3 3 Spain 1,187 0.043 0.042 3.2 2.6 3.3 Sweden 2,096 0.050 0.051 2.5 2.9 1.6 Switzerland 1,898 0.031 0.030 1.7 1.2 1.1 UK 12,286 0.035 0.032 2.5 0.9 0.3 US 44,563 0.041 0.039 2.8 0.2 0.3

The variable definitions are described in Table 2.

39

Table 4. Estimates of the relationship between the aggregate EPL index and cost stickiness

The estimation model is

tintintinn

ntintntin

tinnntintntin

SALEDECEPLLAWAINTGDPGROWTHDEC

SALEEPLLAWAINTGDPGROWTHXOPR

,,,,,,11

10,,9,81,,76

,,54,,3,210,,

ln)(

ln)(ln

where ∆lnXOPRn,i,t is the log-change in deflated operating costs for firm i in country n in year t, ∆lnSALEn,i,t is the log-change in deflated sales, GDPGROWTHn,t is the real GDP growth rate in country n in year t, AINTn,i,t is asset intensity (log ratio of assets to sales), LAWn is a binary variable equal to one for common-law countries, EPLn is the aggregate employment protection legislation index for country n, DECn,i,t is a binary variable equal to one if deflated sales decreased in year t, and εn,i,t is an error term. The sample selection criteria are described in Table 2.

main sample 1990-2008

subsample 1990-2000

subsample 2001-2008

main sample excluding US exp.

sign (a) (b) (c) (d)

β0 0.001 (0.92)

0.000 (0.07)

0.002*** (3.13)

0.000 (-0.30)

β1 + 0.913*** (62.68)

0.887*** (29.86)

0.913*** (56.90)

0.935*** (106.22)

β2 + 0.004** (2.00)

0.005 (1.12)

-0.010* (-1.83)

0.005 (1.56)

β3 – -0.033*** (-4.01)

-0.028*** (-2.72)

-0.033*** (-4.14)

-0.032** (-1.99)

β4 0.021 (0.83)

0.095* (1.87)

-0.013 (-0.54)

0.007 (0.27)

β5 0.019* (1.65)

0.049** (2.34)

0.010 (0.82)

0.004 (0.28)

β6 – -0.081*** (-6.21)

-0.057** (-2.35)

-0.057** (-2.08)

-0.103*** (-11.24)

β7 + 0.134*** (7.74)

0.124*** (3.58)

0.134*** (12.02)

0.111*** (10.32)

β8 – -0.001 (-0.28)

-0.008 (-1.47)

0.025*** (3.46)

0.001 (0.35)

β9 – -0.080*** (-6.36)

-0.091*** (-5.09)

-0.079*** (-5.76)

-0.075*** (-3.51)

β10 -0.125*** (-5.22)

-0.175*** (-4.10)

-0.123** (-2.52)

-0.127*** (-5.42)

β11 (EPL) – -0.044*** (-4.03)

-0.046** (-2.14)

-0.056** (-2.34)

-0.048*** (-4.15)

N 128,333 63,751 64,582 83,649 adj. R2 0.7925 0.8144 0.7722 0.7964 t-values in parentheses. The t-values are computed using clustered standard errors (Rogers 1993) with

clustering by country. *, ** and *** indicate significance at 10, 5 and 1 percent levels respectively.

40

Table 5. Estimates of the relationship between EPL indexes for regular and temporary employees and cost stickiness

The estimation model is

tintintinnn

ntintntin

tinnnntintntin

SALEDECTEMPEPLREGEPLLAWAINTGDPGROWTHDEC

SALETEMPEPLREGEPLLAWAINTGDPGROWTHXOPR

,,,,,,1312

11,,10,91,,87

,,654,,3,210,,

ln)(

ln)(ln

where ∆lnXOPRn,i,t is the log-change in deflated operating costs for firm i in country n in year t, ∆lnSALEn,i,t is the log-change in deflated sales, GDPGROWTHn,t is the real GDP growth rate in country n in year t, AINTn,i,t is asset intensity (log ratio of assets to sales), LAWn is a binary variable equal to one for common-law countries, REGEPLn is the employment protection legislation index for regular employees for country n, TEMPEPLn is a measure of temporary employment regulation in country n, DECn,i,t is a binary variable equal to one if deflated sales decreased in year t, and εn,i,t is an error term. The sample selection criteria are described in Table 2.

main sample 1990-2008

subsample 1990-2000

subsample 2001-2008

main sample excluding US exp.

sign (a) (b) (c) (d)

β0 0.001 (0.94)

0.000 (0.08)

0.002*** (3.14)

0.000 (-0.27)

β1 + 0.914*** (60.35)

0.887*** (30.24)

0.912*** (54.08)

0.940*** (186.56)

β2 + 0.004** (2.01)

0.005 (1.12)

-0.010* (-1.81)

0.006** (2.02)

β3 – -0.033*** (-3.98)

-0.028*** (-2.68)

-0.033*** (-4.10)

-0.033** (-2.07)

β4 0.019 (0.71)

0.094* (1.83)

-0.011 (-0.44)

-0.018 (-0.91)

β5 0.006 (0.59)

0.022 (1.29)

0.007 (0.59)

-0.028*** (-2.76)

β6 0.012* (1.95)

0.027** (2.13)

0.004 (0.50)

0.011* (1.88)

β7 – -0.083*** (-5.74)

-0.059*** (-2.59)

-0.051* (-1.85)

-0.104*** (-10.63)

β8 + 0.134*** (7.77)

0.123*** (3.58)

0.134*** (11.78)

0.111*** (10.30)

β9 – -0.001 (-0.28)

-0.007 (-1.36)

0.026*** (3.40)

0.000 (0.09)

β10 – -0.079*** (-6.23)

-0.090*** (-5.01)

-0.080*** (-5.66)

-0.074*** (-3.45)

β11 -0.122*** (-4.59)

-0.169*** (-4.14)

-0.133*** (-2.65)

-0.119*** (-4.34)

β12 (REGEPL) – -0.015 (-1.54)

-0.008 (-0.54)

-0.041** (-2.19)

-0.016 (-1.13)

β13 (TEMPEPL) – -0.027*** (-6.20)

-0.036** (-2.45)

-0.020*** (-2.84)

-0.026*** (-6.21)

N 128,333 63,751 64,582 83,649 adj. R2 0.7925 0.8144 0.7722 0.7966

t-values in parentheses. The t-values are computed using clustered standard errors (Rogers 1993) with clustering by country. *, ** and *** indicate significance at 10, 5 and 1 percent levels respectively.

41

Table 6. Estimates of the relationship between the aggregate EPL index and cost stickiness, after controlling for other labor market characteristics

The estimation model is

tintintinnn

nnntintntin

tinnn

nnntintntin

SALEDECEPLBNFTBCCTUDLAWAINTGDPGROWTHDEC

SALEEPLBNFTBCCTUDLAWAINTGDPGROWTHXOPR

,,,,,,1716

151413,,12,111,,109

,,87

654,,3,210,,

ln)(

ln)(ln

where ∆lnXOPRn,i,t is the log-change in deflated operating costs for firm i in country n in year t, ∆lnSALEn,i,t is the log-change in deflated sales, GDPGROWTHn,t is the real GDP growth rate in country n in year t, AINTn,i,t is asset intensity (log ratio of assets to sales), LAWn is a binary variable equal to one for common-law countries, TUDn is trade union density in country n, BCCn is the bargaining centralization and coordination index for country n, BNFTn is the unemployment benefits index for country n, EPLn is the aggregate employment protection legislation index for country n, DECn,i,t is a binary variable equal to one if deflated sales decreased in year t, and εn,i,t is an error term. The sample selection criteria are described in Table 2.

main sample 1990-2008

subsample 1990-2000

subsample 2001-2008

main sample excluding US exp.

sign (a) (b) (c) (d)

β0 0.001 (1.00)

0.000 (0.17)

0.002*** (3.01)

0.000 (-0.18)

β1 + 0.912*** (54.14)

0.885*** (40.19)

0.891*** (34.98)

0.939*** (60.96)

β2 + 0.004** (1.96)

0.005 (1.36)

-0.011** (-2.00)

0.005 (1.55)

β3 – -0.033*** (-3.94)

-0.026*** (-2.73)

-0.033*** (-4.32)

-0.032** (-1.97)

β4 0.009 (0.39)

0.060* (1.94)

0.004 (0.16)

-0.010 (-0.39)

β5 0.025 (0.55)

0.132** (2.49)

-0.045 (-1.22)

-0.010 (-0.33)

β6 -0.007 (-1.20)

-0.026*** (-3.18)

0.014** (1.96)

-0.008 (-1.24)

β7 0.029 (1.55)

0.068* (1.79)

-0.002 (-0.08)

0.035** (2.23)

β8 0.013 (1.18)

0.033** (2.17)

0.011 (0.81)

-0.004 (-0.36)

β9 – -0.062*** (-2.96)

-0.063** (-2.05)

0.017 (0.40)

-0.074*** (-3.83)

β10 + 0.134*** (7.75)

0.123*** (3.56)

0.134*** (11.63)

0.111*** (9.96)

β11 – 0.000 (-0.05)

-0.007 (-1.43)

0.029*** (3.90)

0.003 (0.55)

β12 – -0.080*** (-6.48)

-0.093*** (-5.67)

-0.079*** (-5.96)

-0.075*** (-3.58)

β13 -0.121*** (-4.78)

-0.132*** (-3.68)

-0.171*** (-3.58)

-0.129*** (-5.10)

β14 -0.006 (-0.17)

-0.064 (-1.06)

0.008 (0.28)

-0.035 (-1.48)

β15 0.001 (0.17)

0.030*** (2.80)

-0.029*** (-2.89)

0.000 (0.06)

β16 -0.043*** (-2.59)

-0.107*** (-3.81)

0.008 (0.30)

-0.032 (-1.60)

β17 (EPL) – -0.036*** (-3.26)

-0.028 (-1.50)

-0.060*** (-3.19)

-0.046*** (-4.42)

N 128,333 63,751 64,582 83,649 adj. R2 0.7926 0.8147 0.7723 0.7965 t-values in parentheses. The t-values are computed using clustered standard errors (Rogers 1993) with

clustering by country. *, ** and *** indicate significance at 10, 5 and 1 percent levels respectively.

42

Table 7. Estimates of the relationship between EPL indexes for regular and temporary employees and cost stickiness, after controlling for other labor market characteristics

The estimation model is

tintintinnnn

nnntintntin

tinnnn

nnntintntin

SALEDECTEMPEPLREGEPLBNFTBCCTUDLAWAINTGDPGROWTHDEC

SALETEMPEPLREGEPLBNFTBCCTUDLAWAINTGDPGROWTHXOPR

,,,,,,191817

161514,,13,121,,1110

,,987

654,,3,210,,

ln)(

ln)(ln

where ∆lnXOPRn,i,t is the log-change in deflated operating costs for firm i in country n in year t, ∆lnSALEn,i,t is the log-change in deflated sales, GDPGROWTHn,t is the real GDP growth rate in country n in year t, AINTn,i,t is asset intensity (log ratio of assets to sales), LAWn is a binary variable equal to one for common-law countries, TUDn is trade union density in country n, BCCn is the bargaining centralization and coordination index for country n, BNFTn is the unemployment benefits index for country n, REGEPLn is the employment protection legislation index for regular employees for country n, TEMPEPLn is the employment protection regulation index for temporary employees in country n, DECn,i,t is a binary variable equal to one if deflated sales decreased in year t, and εn,i,t is an error term. The sample selection criteria are described in Table 2.

main sample 1990-2008

subsample 1990-2000

subsample 2001-2008

main sample excluding US exp.

sign (a) (b) (c) (d) β0 0.001

(1.01) 0.000 (0.20)

0.002*** (3.02)

0.000 (-0.15)

β1 + 0.912*** (52.16)

0.883*** (40.55)

0.889*** (34.33)

0.945*** (58.78)

β2 + 0.004** (1.99)

0.005 (1.36)

-0.011** (-1.99)

0.006* (1.91)

β3 – -0.033*** (-3.96)

-0.027*** (-2.75)

-0.033*** (-4.24)

-0.033** (-2.03)

β4 0.007 (0.32)

0.058* (1.82)

0.007 (0.25)

-0.029 (-1.28)

β5 0.034 (0.74)

0.156*** (2.72)

-0.055 (-1.25)

0.001 (0.05)

β6 -0.007 (-1.18)

-0.025*** (-3.01)

0.014** (2.02)

-0.007 (-1.15)

β7 0.025 (1.30)

0.060 (1.48)

0.003 (0.08)

0.023** (2.10)

β8 0.002 (0.28)

0.006 (0.41)

0.010 (0.86)

-0.028*** (-2.89)

β9 0.011 (1.35)

0.027** (2.33)

0.001 (0.12)

0.007 (1.14)

β10 – -0.063*** (-2.70)

-0.062* (-1.95)

0.021 (0.50)

-0.074*** (-3.46)

β11 + 0.134*** (7.76)

0.123*** (3.56)

0.134*** (11.51)

0.111*** (10.01)

β12 – 0.000 (-0.06)

-0.007 (-1.39)

0.030*** (3.86)

0.002 (0.37)

β13 – -0.079*** (-6.39)

-0.093*** (-5.56)

-0.080*** (-5.84)

-0.075*** (-3.53)

β14 -0.118*** (-4.18)

-0.126*** (-3.34)

-0.177*** (-3.91)

-0.124*** (-4.28)

β15 -0.019 (-0.58)

-0.102* (-1.81)

0.029 (0.70)

-0.041* (-1.73)

β16 0.001 (0.16)

0.030*** (3.16)

-0.029*** (-3.08)

0.000 (0.08)

β17 -0.037** (-2.30)

-0.094*** (-3.22)

-0.003 (-0.09)

-0.028 (-1.30)

β18 (REGEPL) – -0.011 (-1.24)

0.004 (0.33)

-0.041*** (-2.85)

-0.017 (-1.45)

β19 (TEMPEPL) – -0.023*** (-5.35)

-0.029*** (-2.64)

-0.021** (-2.08)

-0.025*** (-4.58)

N 128,333 63,751 64,582 83,649 adj. R2 0.7926 0.8147 0.7723 0.7966

t-values in parentheses. The t-values are computed using clustered standard errors (Rogers 1993) with clustering by country. *, ** and *** indicate significance at 10, 5 and 1 percent levels respectively.