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The Effect of Labor Cost Behavior on Labor Investment Efficiency

i

Abstract

The Effect of Labor Cost Behavior on Labor Investment Efficiency

Jeong, Junyoung

College of Business Administration

The Graduate School

Seoul National University

Labor investment is one of the firms’ important decisions that has significant influence over firm value (Lee and Yu 2017). However, how labor cost behavior of firms affect labor investment efficiency has rarely been examined. According to Oi (1962), costs regarding labor employment doesn’t only include wage payment but also includes costs regarding hiring and training activities, which enhances potential labor productivity. This study investigates the association between cost behavior of these labor costs and the labor investment efficiency. Following model of Oi (1962) augmented with Greenward and Stiglitz (1988), this paper test the hypotheses that optimal level of labor cost is associated with efficiency of labor investment. Empirical results support the hypotheses: the level of hiring cost, training cost, and wage is positively associated with labor investment efficiency only they successfully contribute to labor productivity and firm value.

Keywords: Labor cost, Labor investment efficiency, Cost behavior, Cost stickiness, Training and education, Research and development, Information asymmetry

Student Number: 2016-20621

ii

Table of Contents

1. INTRODUCTION………………………………………………….1 2. HYPOTHESES DEVELOPMENT…………………………….....4

2.1. Wage and labor investment efficiency………………………………9

2.2. Training cost and labor investment efficiency…………………….10

2.3. Hiring cost and labor investment efficiency……………………….12

3. DATA AND RESEARCH DESIGN…………………………...…14

3.1. Data Description…………………………………………………….14

3.2. Measure of Labor Investment Efficiency………………………….17

3.3. Test of Hypotheses…………………………………………………..19

4. EMPIRICAL RESULTS………………………………………….24

4.1. Regression Results…………………………………………………..25

4.2. Additional Test………………………………………………………28

5. CONCLUSION……………………………………………………31

………………………………………………………… 45

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List of Figures and Tables

REFERENCES………………………………………………………34

TABLE 1. Variable Description…………………………………….37 TABLE 2. Descriptive statistics…………………………….……….38

TABLE 3. Test of H1……………….……………………….……….39

TABLE 4. Test of H2……………….……………………….……….40

TABLE 5. Test of H3……………….……………………….……….41

TABLE 6. Test of Hypotheses in Merged Regression Model……..42

TABLE 7. Stickiness of Labor Costs……………………………….44

1

1. INTRODUCTION

Investment efficiency refers to a firm’s ability to exercise

investment close to profit-maximizing level determined by firm-

specific factors. Market frictions such as information asymmetry and

agency problems can make a firm’s investment deviate from optimal

level and lead to either overinvestment or underinvestment. If a firm is

in overinvestment, the firm may exercise negative NPV project that

deteriorates firm value. On the other hand, firms in underinvestment

may pass up investment opportunities that would have positive NPV

and also harm long-term firm value (Verdi 2006).

Labor investment shares similarities with capital investment.

Employment of labor is an investment in human capital made with the

expectation of creating future firm value. Therefore, it is obvious that

there exists an optimal level of labor investment decision. Similar to

capital investment, suboptimal labor investment can also deteriorate

firm value. A firm’s labor investment is efficient when it is hiring

decision is close to optimum level, which is determined by firm’s

economic fundamentals. Overinvestment in labor means hiring too

much workers than optimal level while underinvestment in labor means

hiring too less workers than optimal level. In these cases, the firm’s

labor investment becomes less efficient. Overinvestment in labor

2

increases operating leverage and may decrease firm value (Rosett 2001;

Lee and Yu 2017). Underinvestment in labor also negatively impacts

firm value because it restricts firms’ growth opportunities (Lee and Yu

2017).

Prior research has extensively researched issues in investment

efficiency (e.g. Biddle et al. 2009; Verdi 2006). However, these

researches examine non-labor investments such as capital investment or

R&D and thus efficiency of labor investment has rarely been examined.

This is to say, despite employment decisions being a critical component

of a firm’s total investment decisions, limited evidence exists within

accounting literature on efficient labor investments.

Nevertheless, there have been several researches about labor

investment efficiency. Some studies examined the influence of labor

investment efficiency. Lee and Yu (2017) investigated Korean listed

firms and found evidence that labor efficiency is positively associated

with the subsequent year’s firm performance. Other stream of

literatures examined which factor affects efficient investment on labor.

Notably, using models of Pinnuk and Lilis (2007) to capture the

degree of labor investment efficiency, Jung et al. (2014) found out that

firms with higher level of financial accounting quality has better labor

investment efficiency. Ben-Nasr and Alshwer (2016) documented that

stock price informativeness positively affects labor investment

3

efficiency.

Even though financial information factors are important in

efficiency of labor investments, costs regarding labor should be also

considered. However, relationship between labor costs and labor

investment has not been thoroughly investigated. To the best of our

knowledge, this paper is the first study examining the association

between labor cost behavior and labor investment efficiency. For this

purpose, we follow Oi (1962) and define labor costs as costs take place

following labor investment. Hiring cost, training cost and wage are

representative examples of labor cost. We hypothesize that the level of

labor cost spending affects firm’s labor investment efficiency.

Moreover, we conjecture that labor investment efficiency is affected by

not only the level but also the effectiveness of labor costs.

To examine the effectiveness of labor costs, we use Human

Capital Corporate Panel (HCCP) dataset which is a survey-based panel

data. This dataset allows us to observe qualitative aspects of firms’

labor investment decision, which are hard to be observed from financial

statements. Our empirical investigation suggests that when labor costs

are close to optimum, the level of labor costs is positively associated

with labor investment efficiency. On the other hand, we also found that

when labor costs are far from optimum, the level of labor costs is

negatively associated with labor investment efficiency.

4

This paper has several contributions. Firstly, this paper

contributes to research stream about labor investment which has not

been studied enough, and investigate how firm’s cost behavior

regarding labor costs is associated with labor investment effieciency.

Moreover, while the financial dataset used by previous studies has only

limited information about firms’ specific cost behavior, this paper uses

unique survey-based panel dataset so that be able to look over firm’s

inside cost behavior more closely. Lastly, findings of this paper

emphasize that not only the level but also the optimization of labor

costs is important on improving labor investment efficiency, and

therefore firms should closely take care of their labor cost behavior.

2. HYPOTHESES DEVELOPMENT

Classical economic theory considers labor as a purely variable

factor that is free from adjustment costs (Dixit 1997). According to this

view, the level of employment is determined based on the change in

production demand, and hiring decision is not affected by inflation or

other market frictions. In the classical economic model, hiring decision

is determined at the level that makes marginal productivity of labor

(MPL) same with marginal cost of labor (MCL). Equation (1) explains

the condition. With this condition, labor market is in equilibrium and

hiring is efficient.

5

However, hiring decision becomes less efficient if market

friction exists. One reason of market friction is information asymmetry.

Different qualities among laborforce cause information asymmetry

between workers and a company since a company makes hiring

decisions through utilizing limited, ex-ante information on employee

qualification. In this situation, labor market may suffer from adverse

selection problem and hiring level is suboptimal (Greenward and and

Stiglitz 1988). Another reason of market friction is rigidity or stickiness

of labor costs. Anderson et al. (2003) documented that selling, general

& administrative costs are sticky, which means those costs increase

more with an activity increase than they decrease with an activity

decrease of the same magnitude (Banker et al. 2006). With cost

stickiness, downward adjustment of labor costs is harder than upward

adjustment. If labor costs are rigid downwards and firms cannot reduce

labor costs even if the firm performance declines, marginal cost of

labor exceeds marginal product of labor and hiring labor becomes

suboptimal. Especially in Korea, seniority-based compensation plan is

more pervasive than in other countries, which can be a potential cause

of labor cost rigidity (Ahn and Nam 2008).

In a similar vein, Oi (1962) documented that costs regarding

labor employment has quasi-fixed factors, so that the costs are not

adjusted proportionally with the level of change in production demand.

6

According to Oi (1962), wage, hiring cost, training cost and wage are

representative examples of labor cost. Hiring cost is a cost that occurs

during recruiting process. Training cost occurs with a firm’s training or

education program for their employees. Lastly, wage represents

payments for a flow of productive services. Oi (1962) posited that

training cost is an investment in the human agent and improve worker’s

productivity, but thought that hiring cost has no effect on worker’s

productivity. Equation (2) describes a condition for equilibrium of Oi

(1962)’s model. H, K and W denote hiring cost, training cost and wage

per worker, respectively. Training cost creates incremental productivity

( ) and the level of increment is determined solely by the level of

training cost. Optimal level of the sum of labor costs (H+K+W) is determined

at the same level with the sum of original marginal productivity and the

increment due to training.

However, Greenward and Stiglitz (1988) suggest the possibility

that hiring cost can also create incremental productivity. When

information asymmetry between a firm and laborforce exists, firms

may set higher hiring standards and spend more hiring cost to reduce

information asymmetry and hire more qualified workers. In this case,

hiring cost can enhance productivity of labor since productivity of

qualified workers is higher than unqualified workers and spending

additional hiring cost increases the possibility of distinguishing

7

qualified workers. Following this suggestion, the condition for

equilibrium is as in equation (3). In equation (3), is created not

only by training cost but also by hiring cost. and

denotes incremental productivity created by training and hiring,

respectively. Optimal level of the sum of labor costs (H+K+W) is

determined at the same level with the sum of original marginal

productivity and the increment due to training and hiring.

Equation (4) extends model of equation (3) from single-period

to multi-period. Hiring cost occurs at once, while training cost and

wage occurs during a worker’s entire tenure (t). r denotes the rate at

which future costs are discounted.1

(1)

(2)

(3)

(4)

Note again that equations (1) to (4) are conditions for

equilibrium labor cost. Optimal level of labor cost spending discussed

above is associated with equilibrium in labor market (Borjas 2016). In a

competitive labor market, labor demand curve slopes downward due to

1 See equation (7) of Oi (1962).

8

diminishing returns of labor. Therefore, optimal level of hiring is

decided where marginal productivity of additional laborforce is same

with marginal cost of it. In this level, a firm achieves both optimal level

of labor cost and optimal level of hiring. However, if labor cost is not in

optimal level, conditions for equilibrium (in equations above) doesn’t

hold anymore and labor market friction occurs. As an example, assume

that a firm cannot adjust its labor cost easily due to wage rigidity or

cost stickiness. In this case, even if a firm faces economic downturn

and its value of marginal product declines, its marginal labor cost

cannot be easily reduced, so that its level of labor cost spending

becomes suboptimal. This deviation also causes friction in labor market

so that the firm’s hiring decision becomes suboptimal, and its labor

investment efficiency is deteriorated. Many economic studies including

Christoffel and Linzert (2005) have theoretically and empirically

discussed about the association between wage rigidity and labor market

frictions.

Our hypotheses come from the idea that if current level of labor

costs is not in profit-maximizing level, it leads to suboptimal level of

employment since optimality of labor costs and hiring level are

interrelated. Following Oi (1962), we classify labor cost as three factors

(hiring cost, training cost and wage) and explore how their cost

behavior is associated with labor investment efficiency.

9

2.1 Wage and labor investment efficiency

Wage is a typical type of labor cost and takes the biggest

portion of it in general. How wage is associated with labor investment

efficiency depends on the level and characteristics of wage payment. If

a firm is paying wage to its employees that is close to an optimal level,

its hiring decision would be more close to equilibrium level and its

labor investment efficiency would increase. A natural question that

arises is how to determine whether a firm’s wage expense is close to

being efficient or optimal. Profit-maximizing level of wage

compensation is decided in the level same with marginal productivity

of labor. In other words, optimal level of wage compensation should

reflect the productivity of a worker who receives it. However, in a real

world situation, wage compensation doesn’t fully reflect worker’s

performance and easily deviates from optimal level. One reason of it is

wage rigidity. The possibility of wage rigidity is introduced by Keynes

(1934) at first, and Stiglitz (1984) and many other researchers argued

that the wage is rigid downwards so that downward adjustment of wage

is harder than upward adjustment of it due to implicit contract and

efficiency wage effect. In this case, wage level cannot fully reflect

marginal productivity of labor. Moreover, especially in Korea and other

East Asian countries, many firms have seniority-based compensation

10

system other than performance-based compensation system (Ahn and

Nam 2008; Kim et al 2004). This kind of pay structure also deviates

wage compensation from the worker’s performance and leads to

inefficiency. Previous empirical studies also support this idea. Looking

over more than 16,000 managers, Abowd (1990) found that

performance-based managerial performance is positively related to

following corporate performance.

To sum up, firms with a compensation structure that is more

related with worker’s productivity are more likely to achieve optimal

hiring. In these firms, the level of wage per workers would be

positively associated with labor investment efficiency. On the other

hand, when firms have a pay structure which less reflects worker’s

productivity, the level of wage per workers would be negatively

associated with labor investment efficiency (LIE).

H1: The level of wage per workers is positively associated with LIE

when a firm’s compensation to employees is closely related to their

performance.

2.2 Training cost and labor investment efficiency

As Oi (1962) mentioned, training costs are investments in the

11

human capital to improve a worker’s future productivity. Using British

panel data, Dearden et al. (2006) showed that work-related training is

associated with significantly higher productivity.

However, the level of training costs itself is not enough to tell

whether it improves future productivity or not. That is, monetary input

or time spending are not the only factors affecting the effectiveness of

training and education program. Other factors such as trainee attitudes,

culture, organization characteristics are also crucial to training

effectiveness (Noe 1986; Black and Mendenhall 1990; Burke et al.

2008). To illustrate a case in point, the possibility of inefficient training

cost spending same amount of spending can cause different results.

These findings imply that the association between training cost and

future productivity is not linear and there are contingent factors that

influence the association between training costs and future productivity

enhancement. The effectiveness of training is more important to future

productivity than the level of training cost itself. A firm’s training can

be effective and successfully improve future labor productivity.

However, there also exists a possibility that a firm’s training is not

effective and fail to improve future productivity. In this case, the firm’s

labor cost spending is excessive and is deviated from optimal level.

This can be also explained theoretically with equation (4). If a

firm’s training cost spending successfully improve future labor

12

productivity, in equation (4) would be positive

and would be same with present value of training cost spending,

(which is in LHS of equation (4)). In means the firm’s

training cost is in optimal, profit-maximizing level. On the other hand,

if a firm’s training program is not effective and fail to improve future

productivity, is less than and

training cost becomes far from optimum. As mentioned above, optimal

level of labor cost leads to optimal level of hiring. Therefore, we can

safely assume that the association between training cost and hiring

efficiency is different between firms with successful training and firms

with unsuccessful training. Firms with successful training would show

more positive association between training cost and labor investment

efficiency, while the association can be negative with firms with

unsuccessful training and spending excessive labor costs.

H2: The level of training cost per workers is positively associated with

LIE in the firms whose training & education investment successfully

improves worker’s productivity.

2.3 Hiring cost and labor investment efficiency

Greenward and Stiglitz (1988) argued that there exists

13

information asymmetry between a firm and laborforce. The reason

firms set higher hiring standards and spend more hiring cost is because

they want to reduce that asymmetry and hire more qualified workers.

Schlicht (2005) empirically showed that recruiting standards are more

demanding when heterogeneity and mobility of workers are high. In

line with this notion, Sedláček (2014) found that hiring standard

explains significant portion of match efficiency fluctuation in U.S.

labor market.

In this regard, whether the high level of hiring cost is

justifiable or not depends on the degree of information asymmetry

firms facing. If there is no information asymmetry, hiring costs are

spent just once and does not affect worker’s productivity. In this case,

firm’s optimal decision is to minimize hiring cost as far as possible and

excessive level of hiring cost leads to inefficiency. However, if firms

are facing information asymmetry problem, insufficient hiring standard

and hiring costs can deteriorate future firm value since they can hire

workers with less productivity. Especially, firms in high-tech industry

tend to have highly sophisticated job functions and hiring unqualified

workers can be crucial to firm performance. These firms have higher

demand of information asymmetry mitigation and high level of hiring

cost is necessary to improve firm value. In this case,

in RHS of equation (4) is positive and the optimal level of

14

hiring cost is decided where .

Therefore, we can assume that the efficiency of hiring cost is

different between firms facing higher degree of information asymmetry

and firms facing lower degree of information asymmetry. Optimal level

of hiring cost of the firms facing serious information asymmetry

problem (i.e. high-tech firms) is higher than the firms less concern

about information asymmetry (i.e. firms providing simple goods or

services). In other words, in the firms with higher demand of

information asymmetry mitigation, high level of hiring cost compared

to firm size is more likely to be optimum. In the firms with lower

demand of information asymmetry mitigation, high level of hiring cost

compared to firm size is more likely to excessive and inefficient, which

can lead to inefficiency of hiring.

H3: The level of hiring cost per workers is positively associated with

LIE in the firms with higher demand of information asymmetry

mitigation.

3. DATA AND RESEARCH DESIGN

3.1 Data Description

15

We can achieve labor cost data from the firm’s financial

statements, but it is hard to capture qualitative characteristics of them.

Human Capital Corporate Panel (HCCP) dataset is a survey-based

panel data and sheds light to qualitative aspects of firm’s hiring

decision.

Human Capital Corporate Panel (HCCP) dataset is a survey-

based panel data exercised by Korea Research Institute for Vocational

Education and Training (KRIVET) and officially approved by Korean

Ministry of Employment and Labor. It is a biannual panel data from

2005. It consists of 450-500 firms selected by stratified sampling

method and therefore relatively free from selection bias.

Question categories of HCCP survey consist of question

categories for managers about general business profiles, human

resource department, human resource management, human resource

development, current situation of employment and also category for

employees. These categories include questionnaire items about firms’

monetary spending on hiring procedure, training program and wage

payment. There are also questionnaire items about manager’s

satisfaction on training program, size and power of labor union, pay

difference between employees, etc. These items allows us to examine

the level, effectiveness, and the characteristics of labor costs and hiring

decisions.

16

In this study, we use survey results of 5 years (2006, 2008, 2010,

2012 and 2014) which consist of 2,389 firm-years. We exclude non-

listed firms since their market price data are hard to get, and also

exclude firms in financial industry since their characteristics are

different from non-financial firms. Therefore, our final dataset consists

of 1,168 firm-years. Financial statements data are obtained from TS-

2000 database and stock price data are obtained from DataGuide. We

match financial data and stock price data with HCCP data based on

stock code.

There are several benefits of using HCCP dataset. First of all, it

is able to capture qualitative information about firms’ labor investment

decision, which cannot be captured from financial statements. Also,

some financial data (e.g. actual spending for hiring procedure) can only

accessible from HCCP. Some firms don’t report their income statement

by the nature of expenses; about 17% of all listed firm-years in our

sample period don’t report ‘education and training expense’ or ‘wage

expense’ (4,470 out of 26,246 firm-years) and there may be the concern

of selection bias. For these reasons, we believe that our unique dataset

can contribute to close examination of the qualitative characteristics of

labor cost.

17

3.2 Measure of Labor Investment Efficiency

Our empirical model measuring labor investment efficiency

follows Pinnuk and Lillis (2007), Li (2011) and Jung et al. (2014).

Pinnuk and Lillis (2007) used the percentage change in the number of

employees ( ) as a proxy for labor investment. Following

Pinnuk and Lillis (2007) and other studies, we regress on

variables representing firm’s economic fundamentals:

where:

NET_HIRE = the percentage change in a firm’s employees.

SALEGRW = the percentage change in sales, and is included to

control firm’s profitability.

ROA = net income / total assets at the beginning of the year.

18

RETURN = the annual stock return for year t.

SIZE = natural logarithm of market value of equity at the

beginning of the year.

QUICK = quick ratio (quick assets divided by current liabilities

at the end of the year)

LEV = long-term debt at the beginning of the year / total assets

at the beginning of the year.

LOSSBIN variables are indicators for each 0.005 interval of

prior year’s ROA from 0 to -0.025.2

From the model in equation (5), the error term is

interpreted as net hiring which is not explained by firm’s fundamental

economic factors. Therefore, following Jung et al. (2014) and Pinnuk

and Lillis (2007), we define a variable Abnormal_Hiring as an error

term derived from equation (5). Absolute value of error term,

|Abnormal_Hiring|, means a degree of abnormal hiring. Note that

|Abnormal_Hiring| and labor investment efficiency has inverse

relationship: higher |Abnormal_Hiring| means a firm’s labor

investment efficiency is lower. We can also divide our sample with

2 Empirical model of equation (5) is first developed by Pinnuk and Lilis (2007), and explanations about variables in this paper are majorly from Jung et al. (2014).

19

overinvesting group and underinvesting group. A firm overinvesting in

labor has positive abnormal hiring and holds. For

overinvesting group, we define absolute value of error term

(|Pos_Abnormal_Hiring|) as a degree of overinvestment. A firm

underinvesting in labor has negative abnormal hiring and

holds. For underinvesting group, we define absolute value of error term

(|Neg_Abnormal_Hiring|) as a degree of underinvestment.

3.3 Test of Hypotheses

Regression model for testing H1 is as follows:

Our dependent variable Abnormal hiring can be any of

|Abnormal_Hiring|, |Pos_Abnormal_Hiring| |Neg_Abnormal_Hiring|.

Dependent variable is |Abnormal_Hiring| when testing full sample,

|Pos_Abnormal_Hiring| when testing overinvesting subsample, and

|Neg_Abnormal_Hiring| when testing underinvesting subsample.

TA_Avg_Wage denotes wage per workers scaled by total asset. High

level of TA_Avg_Wage means that a firm is paying higher wage in

average relative to its size. Paydiff denotes relative compensation of

20

high-performance workers compared with low-performance workers. It

is achieved from HCCP survey results from a questionnaire item

“Compensation level of workers evaluated as highest performance

group compared with compensation level of workers evaluated as

lowest performance group in the same position, setting lowest

performance group’s compensation as 100”. Paydiff is calculated as the

response divided by 100. 3 The bigger the Paydiff is, wage

compensation is more closely related with worker’s performance. On

the other hand, lower level Paydiff implies that the wage compensation

is less related with performance due to other factors such as downward

rigidity or seniority-based pay structure. Coefficient of TA_Avg_Wage

captures the association between wage level and abnormal hiring.

Coefficient of interaction term, TA_Avg_Wage*Paydiff captures the

effect of wage level compounded with pay difference to abnormal

hiring.

We expect that the coefficient of TA_Avg_Wage*Paydiff as

negative because wage level would be more close to optimum in firms

whose wage compensation is closely related to performance, and thus

their hiring would be more efficient. However, without reflecting pay

difference, relatively higher wage level is likely to cause hiring

3 Therefore, the minimum value of Paydiff is 1, which means the compensation of high-performance group and low-performance group are equal.

21

inefficiency. To be specific, firms paying high wage relative to their

size are hard to adjust it downwards and may hire less than optimal

level. Therefore, we expect the effect of TA_Avg_Wage to abnormal

hiring to be positive.

Regression model for testing H2 is as follows:

TA_Avg_Training denotes firms’ spending on training program

achieved by HCCP survey results, and it is scaled by total asset at the

end of the year. Effective is an indicator variable which is 1 if a firm

answered that their training program was helpful on enhancing workers’

productivity.4 Therefore, Effective = 1 can be interpreted as a firm’s

training program was effective and successfully enhanced its worker’s

productivity, while Effective = 0 means the firm’s training program was

not successful.

As it is described in H2, we expect the coefficient of an

interaction term TA_Avg_Training * Effective as negative, which means

4 The questionnaire item is as follows: “Please indicate how much your firm’s training program exercised during the last 1 year improved employee productivity.” The answer consists of four options: ‘Rarely improved’, ‘Improved a little’, ‘Fairly improved’ and ‘Improved a lot’. We define Effective variable as 1 for samples answered ‘Fairly improved’ and ‘Improved a lot’, and as zero otherwise.

22

the relative level of training cost is more likely to be optimal and

mitigates abnormal hiring when the firm’s training program effectively

enhance worker’s productivity. On the other hand, we expect that the

coefficient of TA_Avg_Training as positive, which means when training

program fails to enhance productivity, training cost relative to firm size

is less likely to be optimum and deviates hiring decision from efficient

level.

Regression model for testing H3 is as follows:

For the model3, we conjecture that the demand of information

asymmetry mitigation is higher in R&D intensive firms. For this

purpose, we use a rate of R&D department workers (RnDWorker) as a

proxy of R&D intensity. Since R&D-intensive firms have higher

demand on skilled and qualified workers, they would consider

information asymmetry as more serious problem and would have more

incentive to invest in sophisticated hiring procedure. Consistent with

this conjecture, Moen (2005) found out that mobility of technical

personnel can be a source of R&D spillover, and R&D intensive firms

compensate long tenured technical staff to avoid their migration to

23

other companies. Moreover, Aboody and Lev (2000) found out that

R&D-intensive firms face higher degree of information asymmetry

between inside members and outside investors, and they are associated

with higher level of insider trading.

There might be concerns about variables since using rate of

R&D department workers as a proxy of information asymmetry is not

based on previous studies. However, we believe that R&D intensity is

at least a good proxy of a firm’s demand of highly qualified workers

since job function of R&D needs highly experienced and educated

workers. Moreover, considering there had been no consensus on the

best proxy measuring the degree of information asymmetry between

firm and potential workers in (to the best of our knowledge), we believe

that our proxy is justifiable.

Similar to models for H1 and H2, we expect that the coefficient

of TA_Avg_Hire * RnDWorker as negative and the coefficient of

TA_Avg_Hire as positive. This is because the level of hiring cost

relative to firm size is more likely to be optimal within firms that need

more qualified workers, while it is less likely to be optimal within firms

that are not in urgent need of qualified workers.

We also merge equation (6) to (8) and include all of our

variables of interest in a single model. This is to identify whether our

24

results still hold even after testing H1 to H3 simultaneously. The

merged regression model is as follows;

In models from equation (6) to (9), control variables are also

included. The control variables are majorly based on Jung et al (2014).

AQ which means accounting quality measured by Dechow et al. (2002)

is included since Jung et al (2014). found that labor investment

efficiency is higher for firms with better accounting quality. We also

include inflation and GDP growth rate to control macro-economic

effect to employment decision. Explanations for all variables are listed

in Table 1.

(TABLE 1 here)

4. EMPIRICAL RESULTS

(TABLE 2 here)

25

We present descriptive statistics for the variables in Table 2.

Table 2 shows summary statistics for the variables of interest and

control variables. Among labor costs, wage takes the largest portion and

training cost is the second. Mean value of Effective is 0.55, which

means 55% of our samples answered that their training program

effectively increased the productivity of their workers while 45% of our

samples thought their training program was not satisfactory.

Union_rate shows that 20% of employees in sample firms are joined in

labor union. Paydiff shows that the average compensation gap between

low-performance worker and high-performance worker in the same

position was about 27%.

4.1 Regression Results

(TABLE 3 here)

Table 3 illustrates the regression results from equation (3). The

second and third column of Table 3 show the regression results from

full sample (dependent variable = |Abnormal_Hiring|). The fourth and

26

fifth column show the regression results from positive abnormal hiring

samples (dependent variable = |Pos_Abnormal_Hiring|), meaning the

samples are in overinvestment. In the same way, the sixth and seventh

column of Table 3 show the regression results from underinvestment

samples (dependent variable = |Neg_Abnormal_Hiring|).

Looking at the results from negative abnormal hiring

subsample (underinvesting firms), our variable of interest,

TA_Avg_Wage, is positive and significant. This result implies that firms

paying higher wage relative to their size tend to underinvestment on

labor and hire less than expected level. On the other hand, other

variable of interest, TA_Avg_Wage * Paydiff is negative and significant.

This implies that for firms whose wage is closely related with worker’s

performance, the association between wage payment and

underinvestment is mitigated, consistent with our hypothesis.

Notably, both variables show no statistical significance in

positive abnormal hiring subsample. This can be explained by

downward rigidity or ‘stickiness’ of labor costs. When labor costs are

sticky, downward adjustment of labor costs are harder than upward

adjustment. In this case, inefficiency of labor costs is majorly caused by

excessive spending, and therefore underinvestment in labor is more

pervasive than overinvestment in labor. Consistent with this conjecture,

the association between wage level and abnormal hiring posted in Table

27

3 is more pronounced in underinvesting firms than in overinvesting

firms.

(TABLE 4 here)

Table 4 illustrates the test results of H2. Similar to the results

of H1, coefficient of TA_Avg_Training * Effective is negatively

significant and coefficient of TA_Avg_Training is positively significant

in negative abnormal hiring subsample, consistent with our hypothesis.

In positive abnormal hiring subsample, the significances disappear.

(TABLE 5 here)

Regression results of H3 are posted in Table 5. Both in full

sample and negative abnormal hiring subsample, TA_Avg_Hire shows

positive and significant coefficient. This means that the more a firm

spends on hiring relative to its size, its labor investment becomes less

efficient. On the other hand, interaction term TA_Avg_Hire*

Avg_RndPeople shows negative and significant coefficient, implying

that the association is mitigated in the R&D intensive firms that need

28

highly qualified workers and consider information asymmetry problem

more serious.

(TABLE 6 here)

To test H1 to H3 at the same time instead of testing them

separately, we regress merged model in equation (9). The results are

posted in Table 6. Main results from Table 3 to Table 5 still hold

especially in underinvesting firms. Some findings in control variables

are also notable. Consistent with Jung et al. (2014), accounting quality

(AQ) is negatively associated with abnormal hiring since accounting

quality improvement reduced information asymmetry between

management and investors. Firms that drastically change their hiring

level (high STD_Net_Hire) tend to have less labor investment

efficiency. Inflation also affects labor investment efficiency: firms

abnormally increase its hiring level in the year with higher inflation rate,

implying the possibility of ‘money illusion’ effect suggested by Keynes

(1936).

4.2 Additional Test

To examine the robustness of our test results, we exercise

29

several additional tests. First, we use R&D investment divided by total

asset (TA_RnD) instead of the rate of R&D workers (RnDWorker) as a

proxy of R&D intensity. Untabulated results show that our findings still

holds.

Second, we test whether labor costs are really ‘sticky’ in

Korean firms. From the discussion above, we conjectured that

stickiness of labor costs can be the potential reason deviating labor

costs from optimum. Examining the existence of labor cost stickiness is

important because it can strengthen the theoretical background of labor

cost inefficiency and hiring inefficiency. For the test, we follow

Anderson et al. (2003) and construct a model below:

(10)

where labor cost can be either wage or training cost. The level

of wage and training cost are achieved from financial statements.

Hiring cost is not included since it is not available in financial

statements and only available in biannual survey data, so that

can’t be calculated. Dec is a dummy variable which is

equal to 1 when sales decrease in period t, and 0 otherwise. Total sample used

consists of 19,958 non-financial firm-year data from year 2000 to 2016.

30

(TABLE 7 here)

Results of equation (10) are documented in Panel A of Table 7.

For both wage and training cost, coefficient of is

positively significant and coefficient of is

negatively significant. The only difference between two terms is that

the latter includes sales decrease dummy. Therefore, the results show

that the change rate in labor cost is between sales upturns and sales

downturns and the rate of change during sales downturn is smaller. In

conclusion, labor costs show asymmetric response consistent with cost

stickiness.

To examine the robustness of the result, we add control variables

other than sales to reflect firm-specific fundamentals. The control variables

include log of net hiring, firm size, leverage and ROA. Controlled model is as

in equation (11) and the results are documented in Panel B of Table 7. Our

variables of interest, and , still remains significant and the signs are

not changed.

31

5. CONCLUSION

Labor investment is one of the firms’ important decisions that

has significant influence on firm value (Lee and Yu 2017). However,

the determinants and factors of labor investment efficiency have not

been investigated enough. This study suggests labor cost behavior as

one of the important determinants of labor investment efficiency.

According to Oi (1962), costs regarding labor employment doesn’t only

include wage payment but also includes costs regarding hiring and

training activities. This study investigates the association between cost

behavior of these labor costs and labor investment efficiency.

Following Oi (1962)’s model augmented with Greenward and Stiglitz

(1988), this paper hypothesize that optimality of labor cost is associated

with efficient labor investment. Empirical results of this paper support

the hypothesis. The level of hiring cost, training cost, and wage is

positively associated with efficient labor investment only if they

successfully contribute to labor productivity and firm value.

Our study has several limitations. First of all, this study relies

on survey data with relatively small sample. Therefore, concern about

32

response bias or selection bias may arise. Nevertheless, we believe that

the benefit of using survey data is larger than its cost since selection

bias is mitigated by stratified sampling method and the dataset includes

qualitative cost information which is hard to be achieved from financial

statements.

Other limitation is about the adequacy of our model and proxy

variables. One may concern that our measure of labor cost optimality

and labor investment efficiency are hard to rely on, since many of them

are not used in previous studies. This is the common concern about the

studies about cost optimality or labor investment efficiency, since

generally accepted methodologies about them are scarce. Even though

some of our models are not commonly used, we still believe they are

worth it because they have solid theoretical background from previous

studies and based on acknowledged suggestions.

Despite those limitations, this paper has several contributions.

First, this paper empirically examines the association between labor

cost behavior and labor investment efficiency which has rarely been

investigated. Also, this paper uses unique survey-based panel data and

shed light on the qualitative aspects of labor costs. Finally, this paper

gives insight to management decision makers that optimization of labor

costs is important on improving labor investment efficiency and firms

should closely take care of their labor cost behavior. We hope that this

33

paper helps facilitating further studies about this issue with improved

theory and methodology.

34

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37

TABLE 1. Variable Description

AQ Accounting quality measured by accruals quality from Dechow and Dechev (2002) model augmented with Jones(1991)

Union_Rate Union member / Total employees (=0 if there is no union)

Union_Power =1 if a firm answered that it makes agreement with union ahead of labor adjustment

Inflation Inflation rate

GDPGRW GDP growth rate

MTB MV / BV of common equity

Size Log (market value of equity)

Quick Quick asset / Current liability

Lev Non-current liability / Total asset

Divdum =1 if a firm pays dividend, o/w =0

STD_CFO 5 year standard deviation of cash flow from operations

STD_Sales 5 year standard deviation of sales

Tangible Tangible asset / Total asset

Loss =1 if a firm’s Net income is less than o/w =0

STD_Net_Hire 5 year standard deviation of NET_HIRE

Laborintensity Total employees / Total asset

Yeardum Year dummy

38

TABLE 2. Descriptive statistics

N Mean Std. 1th-quantile Median 3th-

quantile TA_Avg_Wage 1176 0.000368 0.000371 0.000114 0.000266 0.000489

TA_Avg_Hire 1005 3.39E-06 6.88E-06 1.67E-07 7.77E-07 3.00E-06

TA_Avg_Training 1153 2.35E-06 4.00E-06 3.46E-07 9.75E-07 2.45E-06

RndWorker 1204 0.06873 0.086188 0 0.038622 0.100846

Effectiveness 1211 0.55161 0.497535 0 1 1

Paydiff 1211 1.269232 0.633427 1 1 1.25

Union_rate 1211 0.199246 0.267326 0 0 0.43878

Union_power 1211 0.088357 0.28393 0 0 0

AQ 1211 0.018038 0.041799 0.003019 0.007111 0.018329

Inflation 1211 0.027933 0.011233 0.022 0.022 0.036

GDPGRW 1211 0.032744 0.014948 0.029 0.037 0.039

MTB 1170 1380.11 971.5125 722.485 1157.38 1738.47

Size 1170 11.36516 1.495734 10.34487 11.06082 12.02944

Quick 1211 1.597252 1.579922 0.669669 1.063052 1.792376

Lev 1211 0.105878 0.086513 0.037539 0.082711 0.154997

Divdum 1211 0.480595 0.49983 0 0 1

STD_OCF 1211 32266753 1.11E+08 3319397 6303030 15777751

STD_Sales 1211 98347464 3.41E+08 8737209 18911844 49915573

Tangible 1211 0.326202 0.169245 0.209794 0.316377 0.433507

Loss 1211 0.180017 0.38436 0 0 0

STD_Net_Hire 1136 0.122592 0.123124 0.044894 0.08585 0.15207

Laborintensity 1171 2.75E-06 2.00E-06 1.37E-06 2.33E-06 3.60E-06

39

TAB

LE

3. T

est o

f H1

Full

Sam

ple

Posi

tive A

bnor

mal

Hiri

ng

Neg

ativ

e Abn

orm

al H

iring

Inde

pend

ent v

aria

bles

C

oeff.

Est

imat

e t-v

alue

C

oeff.

Est

imat

e t-v

alue

C

oeff.

Est

imat

e t-v

alue

Inte

rcep

t 0.

0629

0.

88

0.

2846

1.

90

* -0

.133

1 -1

.84

*

TA_A

vg_W

age

51.5

413

2.

17

**

-44.

6378

-0

.78

99.8

466

4.60

**

* TA

_Avg

_Wag

e*Pa

ydiff

-7

.950

3

-0.5

8

59

.657

4 1.

38

-2

1.48

53

-2.1

4 *

* Pa

ydiff

0.

0003

0.

04

-0

.009

0 -0

.58

-0

.001

1 -0

.22

Uni

on_r

ate

-0.0

211

-1

.19

-0.0

199

-0.5

6

-0.0

177

-1.0

1

U

nion

_pow

er

-0.0

169

-1

.14

-0.0

169

-0.5

7

-0.0

190

-1.3

1

A

Q

-0.1

218

-1

.21

-0.1

119

-0.6

2

-0.1

251

-1.1

7

In

flatio

n 1.

4118

1.

59

2.

7187

1.

63

1.

1106

1.

22

G

DPg

row

th

-1.2

728

-1

.60

-2.4

844

-1.6

2

-0.2

146

-0.2

7

M

TB

0.00

00

-0.2

5

0.

0000

-1

.44

0.

0000

1.

74

* Si

ze

0.00

37

0.71

-0.0

106

-0.9

4

0.01

44

2.84

**

* Q

uick

0.

0052

1.

75

* 0.

0074

1.

03

0.

0040

1.

53

L

EV

-0

.090

6

-1.6

2

-0

.144

5 -1

.31

-0

.047

1 -0

.87

Div

dum

-0

.036

9

-3.3

0 *

**

-0.0

544

-2.6

5 **

* -0

.000

6 -0

.05

STD

_OC

F 0.

0000

-0

.79

0.00

00

-0.0

1

0.00

00

-1.0

2

ST

D_S

ales

0.

0000

0.

58

0.

0000

0.

61

0.

0000

0.

35

Ta

ngib

le

0.00

23

0.08

0.03

53

0.65

-0.0

262

-0.9

5

L

oss

0.02

66

2.37

**

0.

0432

2.

02

**

0.01

92

1.66

*

STD

_Net

_Hir

e 0.

1704

4.

81

***

0.19

22

2.98

**

* 0.

1387

3.

82

***

Lab

orIn

tens

ity

-554

4.79

34

-2.0

6 *

* -9

699.

8895

-1

.71

* -2

992.

7483

-1

.19

. *,

**,

and

***

den

ote

statis

tical

sig

nific

ance

bet

wee

n th

e tw

o su

bsam

ples

at

the

10%

, 5%

, an

d 1%

lev

els,

resp

ectiv

ely.

40

TAB

LE

4. T

est o

f H2

Full

Sam

ple

Posi

tive A

bnor

mal

Hiri

ng

Neg

ativ

e Abn

orm

al H

iring

Inde

pend

ent v

aria

bles

C

oeff.

Est

imat

e t-v

alue

C

oeff.

Est

imat

e t-v

alue

C

oeff.

Est

imat

e t-v

alue

Inte

rcep

t 0.

0066

0.

08

**

0.02

41

0.14

**

-0

.002

7 -0

.03

TA_A

vg_T

rain

ing

4120

.871

7

2.33

**

10

276.

0000

1.

01

17

41.2

697

2.70

**

* TA

_Avg

_Tra

inin

g*E

ffect

ive

-334

7.28

85

-1.5

1

-1

0235

.000

0 -0

.29

-7

33.1

483

-2.3

8 *

* E

ffec

tive

0.00

52

0.52

0.01

67

0.86

-0.0

035

-0.3

4

U

nion

_rat

e -0

.019

4

-1.0

8

-0

.023

5 -0

.67

-0

.010

4 -0

.56

Uni

on_p

ower

-0

.017

0

-1.1

2

-0

.015

6 -0

.52

-0

.020

4 -1

.32

AQ

-0

.144

8

-1.4

3

-0

.143

5 -0

.79

-0

.158

2 -1

.41

Infla

tion

-0.0

305

-0

.04

-0.1

839

-0.1

1

0.05

52

0.06

GD

Pgro

wth

3.

2716

1.

98

**

6.91

11

2.16

**

1.

0761

0.

62

M

TB

0.00

00

-0.7

3

0.

0000

-1

.41

0.

0000

0.

54

Si

ze

-0.0

011

-0

.24

-0.0

119

-1.1

7

0.00

37

0.79

Qui

ck

0.00

67

2.24

**

0.

0094

1.

32

0.

0059

2.

12

**

LE

V

-0.0

930

-1

.64

-0.1

114

-1.0

1

-0.0

701

-1.1

8

D

ivdu

m

-0.0

422

-3

.72

***

-0

.063

3 -3

.09

***

-0.0

112

-0.8

7

ST

D_O

CF

0.00

00

-0.6

7

0.

0000

0.

05

0.

0000

-0

.76

STD

_Sal

es

0.00

00

0.66

0.00

00

0.67

0.00

00

0.47

Tang

ible

0.

0108

0.

38

0.

0452

0.

84

-0

.014

1 -0

.48

Los

s 0.

0267

2.

36

**

0.03

89

1.84

*

0.01

64

1.34

STD

_Net

_Hir

e 0.

1838

5.

07

***

0.21

26

3.26

**

* 0.

1525

3.

83

***

Lab

orIn

tens

ity

-457

9.61

71

-1.7

2 *

-8

072.

0399

-1

.44

-1

146.

3876

-0

.43

. *,

**,

and

***

den

ote

statis

tical

sig

nific

ance

bet

wee

n th

e tw

o su

bsam

ples

at

the

10%

, 5%

, an

d 1%

lev

els,

resp

ectiv

ely.

41

TAB

LE

5. T

est o

f H3

Full

Sam

ple

Posi

tive A

bnor

mal

Hiri

ng

Neg

ativ

e Abn

orm

al H

iring

Inde

pend

ent v

aria

bles

C

oeff.

Est

imat

e t-v

alue

C

oeff.

Est

imat

e t-v

alue

C

oeff.

Est

imat

e t-v

alue

In

terc

ept

0.08

03

1.22

0.22

60

1.74

*

-0.0

099

-0.1

4

TA

_Avg

_Hir

e 16

99.0

003

2.

07

**

-245

0.34

71

-1.5

1

37

98.7

885

4.27

**

* TA

_Avg

_Hir

e*R

ndW

orke

r -1

0153

.000

0

-1.6

5 *

-1

904.

5634

-0

.19

-181

11.0

000

-2.4

2 *

* R

ndW

orke

r -0

.029

6

-0.5

0

-0

.026

9 -0

.28

-0

.024

5 -0

.33

Uni

on_r

ate

-0.0

240

-1

.33

-0.0

306

-0.9

2

-0.0

161

-0.8

1

U

nion

_pow

er

-0.0

136

-0

.87

-0.0

066

-0.2

3

-0.0

240

-1.4

2

A

Q

-0.2

981

-2

.06

**

-0.1

260

-0.6

7

-1.1

514

-4.0

2 *

**

Infla

tion

2.14

21

2.00

*

3.49

32

1.89

*

0.71

98

0.59

GD

Pgro

wth

0.

2090

0.

50

0.

3933

0.

52

-0

.198

9 -0

.42

MTB

0.

0000

-0

.99

0.00

00

-1.1

8

0.00

00

0.49

Size

-0

.001

8

-0.3

6

-0

.013

4 -1

.37

0.

0059

1.

16

Q

uick

0.

0033

1.

07

0.

0013

0.

19

0.

0025

0.

80

L

EV

-0

.090

2

-1.5

6

-0

.172

0 -1

.69

* -0

.042

0 -0

.64

Div

dum

-0

.030

8

-2.8

2 *

**

-0.0

475

-2.5

4 **

-0

.001

5 -0

.11

STD

_OC

F 0.

0000

-0

.29

0.00

00

0.22

0.00

00

-0.3

9

ST

D_S

ales

0.

0000

0.

15

0.

0000

-0

.14

0.

0000

0.

20

Ta

ngib

le

0.00

95

0.33

0.01

80

0.35

-0.0

272

-0.8

6

L

oss

0.01

13

1.00

0.01

28

0.63

0.00

38

0.30

STD

_Net

_Hir

e 0.

1717

4.

82

***

0.19

05

3.15

**

* 0.

1413

3.

38

***

Lab

orIn

tens

ity

-303

6.01

82

-1.1

2

-5

358.

2545

-0

.99

-6

59.3

361

-0.2

3

.

*, *

*, a

nd *

** d

enot

e sta

tistic

al s

igni

fican

ce b

etw

een

the

two

subs

ampl

es a

t th

e 10

%,

5%,

and

1% l

evel

s, re

spec

tivel

y.

42

TAB

LE

6. T

est o

f Hyp

othe

ses i

n M

erge

d R

egre

ssio

n M

odel

Full

Sam

ple

Posi

tive A

bnor

mal

Hiri

ng

Neg

ativ

e Abn

orm

al H

iring

In

depe

nden

t var

iabl

es

Coe

ff. E

stim

ate

t-val

ue

Coe

ff. E

stim

ate

t-val

ue

Coe

ff. E

stim

ate

t-val

ue

Inte

rcep

t -0

.050

3

-0.6

2

0.

0749

0.

47

-0

.203

4 -2

.28

**

TA_A

vg_W

age

63.9

866

2.

54

**

-39.

7102

-0

.70

106.

2039

3.

99

***

TA_A

vg_W

age*

Payd

iff

-19.

3684

-1

.41

56.6

802

1.

36

-3

1.07

28

-2.3

7 *

* Pa

ydiff

0.

0023

0.

32

-0

.007

4

-0.5

1

0.00

22

0.21

TA_A

vg_H

ire

1254

.73

1.

45

-3

393.

2761

-1

.93

* 34

19.0

669

3.67

**

* TA

_Avg

_Hir

e*R

ndW

orke

r -1

0566

.000

0

-1.6

9 *

-200

8.50

35

-0.2

-169

31.0

000

-2.1

9 *

* R

ndW

orke

r -0

.028

9

-0.4

8

-0.0

202

-0

.21

-0

.009

1 -0

.12

TA

_Avg

_Tra

inin

g 81

24.0

810

3.

55

***

1275

0.00

00

2.89

**

* 53

30.2

230

2.21

**

TA

_Avg

_Tra

inin

g*E

ffect

ive

-793

0.06

65

-3.0

8 **

* -1

1972

.000

0

-2.5

5 **

-6

665.

3260

-2

.18

**

Eff

ectiv

e 0.

0090

0.

86

0.

0155

0.

81

0.

0081

0.

7

. *,

**,

and

***

den

ote

statis

tical

sig

nific

ance

bet

wee

n th

e tw

o su

bsam

ples

at

the

10%

, 5%

, an

d 1%

lev

els,

resp

ectiv

ely.

43

TAB

LE

6. T

est o

f Hyp

othe

ses i

n M

erge

d R

egre

ssio

n M

odel

(Con

tinue

d)

Full

Sam

ple

Posi

tive A

bnor

mal

Hiri

ng

Neg

ativ

e Abn

orm

al H

iring

Inde

pend

ent v

aria

bles

C

oeff.

Esti

mat

e t-v

alue

C

oeff.

Esti

mat

e t-v

alue

C

oeff.

Esti

mat

e t-v

alue

U

nion

_rat

e -0

.018

64

-1

-0

.019

0

-0.5

4

-0.0

152

-0.7

5

Uni

on_p

ower

-0

.012

21

-0.7

6

-0.0

095

-0

.32

-0

.019

7 -1

.15

A

Q

-0.2

900

-2

.00

**

-0.1

399

-0

.73

-0

.999

9 -3

.41

***

Infla

tion

1.87

00

1.68

*

3.63

40

1.86

*

0.68

26

0.54

GD

Pgro

wth

0.

2641

0.

62

0.

2419

0.

31

-0

.122

2 -0

.26

M

TB

0.00

00

0.01

0.00

00

-0.6

7

0.00

00

1.72

*

Size

0.

0069

1.

19

-0

.003

0

-0.2

6

0.01

85

2.98

**

* Q

uick

0.

0020

0.

61

0.

0011

0.

15

0.

0008

0.

24

L

EV

-0

.086

7

-1.4

5

-0

.140

2

-1.3

2

-0.0

404

-0.6

Div

dum

-0

.033

6

-2.9

7 *

**

-0.0

581

-2

.93

***

0.00

37

0.28

STD

_OC

F -0

.000

0 -0

.24

0.

0000

0.

12

-0

.000

0 -0

.55

ST

D_S

ales

-0

.000

0 -0

.15

-0

.000

0 -0

.14

-0

.000

0 -0

.01

Ta

ngib

le

0.02

13

0.73

0.04

07

0.77

-0.0

146

-0.4

5

Los

s 0.

0075

0.

65

0.

0122

0.

57

0.

0018

0.

14

ST

D_N

et_H

ire

0.19

61

5.21

**

* 0.

2165

3.

31

***

0.16

08

3.72

**

* L

abor

Inte

nsity

-4

636.

0394

-1

.61

-4

263.

3487

-0

.72

-2

702.

8836

-0

.91

.

*, *

*, a

nd *

** d

enot

e sta

tistic

al s

igni

fican

ce b

etw

een

the

two

subs

ampl

es a

t th

e 10

%,

5%,

and

1% l

evel

s, re

spec

tivel

y.

44

TABLE 7. Stickiness of Labor Costs

Panel A

y = y =

Independent variables

Coeff. Estimate t-value Coeff.

Estimate t-value

Intercept 0.0367 18.33 *** 0.0041 0.42 Log_Salesgrw 0.0198 16.14 *** 0.2830 8.43 ***

Log_Salesgrw*Dec -0.1300 -12.74 *** -0.2034 -3.95 ***

Panel B

y = y =

Independent variables

Coeff. Estimate t-value Coeff.

Estimate t-value

Intercept 0.0412 1.95 * -0.0748 0.42 Log_Salesgrw 0.1659 24.42 *** 0.4506 8.43 ***

Log_Salesgrw*Dec -0.0999 -10.04 *** -0.0963 -3.95 * Log_NET_HIRE -0.1977 -36.65 *** -0.6109 -22.23 ***

Size -0.0002 -0.19 0.0042 0.73 Lev 0.0022 0.29 0.0366 1.05

ROA 0.0226 3.14 *** 0.0346 1.04

*, **, and *** denote statistical significance between the two subsamples at the 10%, 5%, and 1% levels, respectively.

45