Do the Industry Characteristics Influence the Impact of Employee Downsizing on Firm Performance

Author: Nilasari TivenStudent Number: 411541

Master Thesis

Program: Accounting, Auditing, and ControlErasmus School of Economics

Supervisor: Drs. R.H.R.M. Aernoudts

Do the Industry Characteristics Influence the Impact of Employee Downsizing on Firm Performance

Abstract

Employee downsizing is a company’s strategy aimed at reducing the number of employees. This strategy has become common practice in U.S business. Employee downsizing is typically undertaken as a company endeavours to improve firm performance. Empirical research that relates to employee downsizing had shown little agreement on how this strategy affects firm performance. Additionally, empirical research has not thoroughly investigated other factors that may influence the impact of employee downsizing on firm performance. Based on contingency theory the effectiveness of a strategy depends on a company its characteristics. If the strategy does not fit a company its characteristics, then the outcome may not satisfy a company its expectations. This thesis aims to examine the difference between labour intensity and capital intensity in moderating the employee downsizing effect on firm performance. This study uses archival data of U.S companies from 2010 - 2014. This thesis found that, overall employee downsizing has a negative impact on firm performance. Moreover, this negative impact is greater when a firm is labour intensive than when a firm is capital intensive. According to contingency theory, organization’s strategic posture either enhances or reduces the impact of human resources practice on firm performance. This suggests that employee downsizing is not a proper strategy for a company with labour intensive characteristic, because it magnify the negative impact of employee downsizing on firm performance. In conclusion, labour intensive strategy reduces the impact of human resources practice on firm performance.

Keywords: Employee downsizing, Firm performance, Industry characteristics

Table of Contents

Abstract.........................................................................................................................................1 Introduction...........................................................................................................................1

1.1 Scientific relevance...........................................................................................................41.2 Practical relevance............................................................................................................41. 3 Thesis outline...................................................................................................................5

2 Literature Review..................................................................................................................62.1 Employee downsizing theory and past research............................................................7

2.1.1 Summarizing the findings......................................................................................122.2 Employee Downsizing and Different Industry Characteristics....................................12

3 Research Method and Hypothesis Development................................................................183.1 Research Method..........................................................................................................183.2 Hypothesis development..............................................................................................21

3.2.1 Return on Asset.....................................................................................................213.2.2 Profit Margin.........................................................................................................233.2.3 Cost of Goods Sold................................................................................................24

3.3 Measurement................................................................................................................263.3.1 Dependent variable................................................................................................263.3.2 Independent variable.............................................................................................263.3.3 Moderating variable...............................................................................................273.3.4 Control variable.....................................................................................................28

3.4 Sample and Data collection..........................................................................................29

4 Result and Data Analysis....................................................................................................314.1 Descriptive statistic......................................................................................................314.2 Correlation test.............................................................................................................324.3 OLS Regression Test....................................................................................................334.4 Chow-test.....................................................................................................................36

4.4.1 ROA Chow-test.....................................................................................................374.4.2 Profit Margin Chow-test........................................................................................394.4.3 COGS Chow-test...................................................................................................41

5 Conclusion..........................................................................................................................446 Discussion and Limitation..................................................................................................48Reference:.................................................................................................................................50

Table of Figures and Tables

Figure 1 Libby Boxes........................................................................................................................................ 18

Table 1 Descriptive Statistics......................................................................................................................... 31Table 2 Correlation Test...................................................................................................................................33Table 3 OLS Regression...................................................................................................................................35Table 4 Chow test regression for ROA.......................................................................................................38Table 5 Chow calculation for ROA..............................................................................................................39Table 6 Chow test regression for profit margin........................................................................................40Table 7 Chow Calculation for profit margin.............................................................................................41Table 8 Chow test regression for COGS.....................................................................................................42Table 9 Chow calculation for COGS...........................................................................................................42Table 10 Predicted sign and results..............................................................................................................46

1 Introduction

Over the past few decades, the U.S market has become viciously competitive and it

seems as if there is no other way to keep a company profitable other than executing a

significant change of action. The industry and technology development has rapidly changed,

which at the same time causes a company inevitably to adjust to these changing economic

conditions. Due to economic and market conditions, employee downsizing has become a

common action for American companies. In the most recent economic recession, downsizing

in global scope, there are approximately 8.5 million layoffs in the United States and more

than 50 million worldwide (Cascio and Reynolds, 2015). According to Datta and Basuil

(2015) employee downsizing has become a fact of organizational life, not just in the U.S. but

also in other countries.

Since the 1980s, the downsizing strategy started to become a popular strategy (Allen et

al (2001). Employee downsizing is one of many organizations restructuring approaches

(Bowman, Singh, Useem, and Bhadury, 1999). Downsizing is a broad term that can include

any number of combinations of reductions in a firm’s use of assets – financial, physical such

plant property and equipment, or informational (databases) (Cascio and Reynolds, 2015).

Datta et al (2010) defines employee downsizing as “a planned set of organizational policies

and practices aimed at workforce reduction with the goal of improving firm performance” (p.

282). The term layoff is also used as a term to describe employee downsizing (Cascio and

Reynolds, 2015) in the scientific literature, within this thesis, downsizing and layoffs are

terms used interchangeably to describe the same concept.

The idea is that by reducing the number of employees, companies are able to reduce

fixed costs and thus this in turn leads to a reduction of expenses; which in turn may lead to a

higher profit. In the case of an economic crisis, companies are often forced to make instant

cost cuts whenever revenues fall, otherwise they would suffer losses. Based on Datta and

Basuil (2015) “Headcount reduction is often viewed as an easy solution. In contrast, other

forms of change are seen as being too “long-term” in meeting investor expectations related to

instant returns” (p. 199).

Since 1999, multiple studies that are related to employee downsizing have been

conducted, the results show little agreement on the effects of employee downsizing on firm

performance (Datta and Basuil, 2015). Most of the research results reveal negative impacts of

downsizing on firm performance. For example, Luan et al (2013) and Cascio and Young

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(2003) found that employee downsizing has a negative effect on return on assets. These are in

contrast to the companies’ expectations with regard to the effect of downsizing. The

contradictions between the positive and negative impacts of downsizing on firm performance

have been explained in DeMeuse and Dai (2013) and Cascio and Young (2003). They assert

that there were negative or no impacts during the base year and several consecutive years after

the base year, but after a certain period of time there are also positive performance

improvements. For example, they show that in comparison to a non-downsizing company, a

downsizing company yields lower return on assets and EPS 2 years post downsizing and do

not show any significant improvement, however their performance improved in the third year.

This may be explained by a lag of the effect, during the base year until 2 years, after the

downsizing a company suffers direct and indirect costs which outweigh the benefits. As a

result, the benefits from employee downsizing will occur 3 years after downsizing, thus after

a time lag.

Furthermore, most empirical studies compare firm performance based on downsized

companies and non-downsized companies as summarized in Datta et al (2010). However,

there is very little evidence on how other factors can moderate the relation between

downsizing and firm performance. Therefore, researchers are motivated to find the factors

that may moderate the effects in order to explain the different impacts of downsizing on firm

performance.

Moreover, it is important to study other factors, as it is expected that the impact of

layoffs on firm performance is different across different industries with different

characteristics. This aligns with the contingency theory, which suggests that an organization’s

strategic posture either enhances or reduces the impact of human resources practice on firm

performance (Youndt et al., 1996). Because industry characteristics are one part of an

organization its strategic posture, therefore it might influence the effectiveness of human

resources practice, in this case employee downsizing on firm performance.

Scholars who investigate other factors that may moderate the effect of downsizing on

firm performance are Guthrie and Datta (2008); Love and Noharia (2005); Yu and Park

(2006) and Chadwick et al (2004). They found that indeed moderating factors could influence

layoff impact on firm performance. However, only Guthrie and Datta (2008) study industry

characteristics as moderating factor.

Indeed, there is only one study that uses industry characteristic specifically capital

intensity as one of the moderating variables between layoffs and the firm performance

relation. However, it has been argued that industry characteristic do have individual impact on

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layoff itself and on firm performance. For example, Coucke, Pennings, and Sleuwaegen,

(2007) found that industry characteristics affects the tendency of a firm to effectuate an

employmee downsizing strategy and Wagar, (1997) found that industry characteristic affects

the magnitude of the layoffs. Moreover, industry characteristics also affect firm performance

individually, for example Ballesta, Livnat, and Sinha (1999) found that changes in the

magnitude of capital or labour intensity affect firm performance. The evidence suggests that

capital intensity or labour intensity has an impact on both the magnitude of downsizing and

firm performance. Therefore, from a theoretical perspective one can assume that labour

intensity and capital intensity may moderate the impact of employee downsizing on firm

performance.

Moreover, it is argued that downsizing has a negative effect on the psychology of the

survivor employee (Travaglione and Cross, 2006; Cascio, 2002; and Wagar, 1998). Therefore

firms that depend more on labour may suffer more, because their production process is

disrupted by the negativity from the remaining employees.

In contrast, production processes in capital intensity firms depend more on machines

and as a result the negative attitudes from remaining employees and other indirect cost will be

less harmful for these firms. So according to contingency theory, labour intensity or capital

intensity plays a role as a company characteristic in enhancing or reducing the effectiveness

of companies strategy. If the strategy does not fit its characteristic, then the result may not

satisfy a company its expectation (Donaldson, 2001). Subsequently, in order to have positive

results from an employee downsizing strategy, firstly, firms must understand whether their

characteristics may lead to the negative layoff impact to a more positive outcome or a

negative outcome.

Therefore, examining the difference of how labour intensity and capital intensity as a

moderating factor affect firm performance may provide insight into why there are different

findings in regard to the impact of downsizing on firm performance. Moreover, these studies

also aim to examine which industry characteristics fit better with an employee downsizing

strategy. Lastly since there is very limited evidence on this topic, it motivates this study to

examine other factors that may influence layoffs and the relation with firm performance.

In sum, this thesis investigates whether there is a different effect between labour

intensity and capital intensity that might influence layoff effect on firm performance and

followed by the attempt to answer the following research question:

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“Do industry characteristics, specifically labour intensity and capital intensity, affect the

negative effect of employee downsizing events on subsequent firm performance?”

This study aims to investigate how other factors can moderate a layoff effect on firm

performance. Moreover, the aim is to see whether the two identified industry characteristics

from prior literature influence the layoff and firm performance relation differently.

According to De Meuse and Dai (2013), Luan et al (2013), Yu and Park (2006), and

Guthrie and Datta (2008), layoffs have significant impact on ROA. Meanwhile, Chalos and

Chen (2002) and Chen et al (2001) report significant relation between layoff and cost of

goods sold. Profit margin has also been shown to have a significant relation with employee

downsizing (De Meuse et al 2004).

To investigate the downsizing effect, this thesis examines firm performance from US

companies over 4 year period, from 2011 – 2014. This study focuses on employee downsizing

strategy during economic prosperity, between 2010 and 2011, while during 2007 – 2008 the

U.S. was undergoing an economic crisis and after 2010 US GPD growth is positive, which

signals that the U.S economy already has already recovered from the crisis (U.S Bureau of

Economic Analysis). Data of employee downsizing and firm performance are obtained from

Compustat.

1.1 Scientific relevance

This thesis contributes to the growing literature on the impact from company

restructuring (downsizing), specifically with regard to moderating factor forms that moderate

the relation between downsizing and firm performance. The scientific relevance of this thesis

lies in the importance to answer the research question, because there only four studies in this

literature that examine moderating factor. One of the four studies, investigate industry

characteristic as a moderating factor of employee downsizng influence. However, none of the

studies that compared how two contrast characteristic moderates employee downsizing impact

on firm performance. By comparing these two contrast characteristic may shed a light why

there is a different finding in employee downsizing impact on firm performance. Additionally

this study elaborates more on how contingency factor play an important role in determining

the successfulness of one strategy. Specifically this thesis contributes to provide the evidence

of how labour intensive and capital intensive influence the negative impact of employee

downsizing effect on firm performance after U.S crisis in 2007 – 2008.

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1.2 Practical relevance

This study is relevant and beneficial for management as it provides a basis of

consideration before effectuating an employee-downsizing strategy. It is also relevant for

investors to understand how industry characteristic may moderate the relation between

employee downsizing and firm performance. Investors will have the ability to better forecast

the future impact of a downsizing strategy, and it aids them to decide whether they will or will

not support a company’s chosen downsizing strategy.

In general, employee downsizing results in no impact or a negative impact on firm

performance and a positive impact after 3 years after the employee downsizing event. This

study helps practitioners to better understand how firm characteristic as a contingency factor

that influence the effectiveness of a strategy. Therefore this study can provide basis for

practitioners such as management to decide on wheteher management decide to implement an

employee-downsizing strategy or not.

1. 3 Thesis outline

To provide an introduction to the topic, this thesis begins by explaining the theoretical

background, presented in the next section. Theoretical background section explains empirical

researches that have been conduct which related to employee downsizing impact on firm

performance. This section also elaborates the theories that underlie the research question in

this study. The third chapter then explains the research design, hypotheses developments,

measurement, sample and data collection. This chapter explains the method uses to test the

hypothesis and how hypotheses relates to theoretical background that has been explained in

chapter 2. The fourth chapter provides the results and data analysis. Furthermore the final

chapter provides conclusion, discussion, limitation and suggestion for future research.

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2 Literature Review

This section explains the theoretical concept underlying the research question, by

elaborating on empirical findings with regard to the topic. Firstly, this thesis incorporates

empirical findings to explain how employee downsizing affects firm performance. By

carefully analysing previous findings, this thesis is able to assess the effects of employee

downsizing and its impact on firm performance, which further can be used as a basis

assumption.

Secondly this thesis explains underlying theory on how labour intensity and capital

intensity can moderate employee-downsizing effects on firm performance. Furthermore, basic

assumptions with regard to layoffs and the firm performance relation are compared when

labour intensity or capital intensity is introduced as a moderating factor. Consequently, this

thesis is able to ascertain which of the two characteristics that influence downsizing impact on

firm performance in a positive or negative way. As a result, it can be concluded which of the

two characteristics fits better with employee downsizing strategy.

Datta et al (2010) and Datta and Basuil (2015) provide a synthesis of prior research

that summarizes all the studies in this literature over the past 25 years, which contains 91

empirical studies that examine employee downsizing. This study is the first source of

reference to examine before examining other empirical studies in regard to the topic. Most

studies with regard to the topic were found in the journal of management. Subsequently, in

order to find a theory that fits with how industry characteristic can influence the success of a

company its strategy, this thesis aims to find it in strategic human resource management

(SHRM) literature, while this literature is firstly identified in the Guthrie and Datta (2008)

and Datta et al (2005) studies. Those studies are further used as main source to find other

studies in (SHRM). This literature has argued how human resource management may affect

firm performance. Therefore, it is used as a basis reasoning how labour intensity and capital

intensity can affect an employee downsizing strategy. Since the difference between labour and

capital is in how big company contingent on labour sources.

To provide a comprehensive literature review, this thesis not only includes empirical

studies that have been mentioned in previous studies. This review aims to include other

studies that are related to an employee downsizing effect on firm performance using Google

Scholar. In order to find other studies that may be relevant, keywords that are used to identify

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additional studies are downsizing, employee downsizing, employee downsizing on firm

performance, employee downsizing and industry, downsizing in different industry, layoff,

layoff and firm performance, layoffs in different industry.

The first paragraph begins by explaining employee downsizing theory and past

research in regard to the impact of employee downsizing on firm performance. The second

subsection explains how industry characteristics may influence an employee downsizing

effect on firm performance.

2.1 Employee downsizing theory and past research

Over the past decades, downsizing has occurred in all industries and sectors of the

economy, affecting business, governments, and individuals around the world (Cascio, 2012;

Gandolfi, 2007). The trend of employee downsizing in the US began in the early 1980s

because of the economic recession. During an economic downturn, the laying off of

employees is a form of company response to declining revenue. Low profitability, declining

productivity, shareholder value and sales positively influence adoption of employee

downsizing (Budros, 2000; Yoo and Moody, 2000; Ahmadjian and Robinson, 2001; Baumol,

Blinder and Wolff, 2003). Based on Guthrie and Datta (2008), “Even after economic

recovery, however, the ensuing years witnessed continued and significant job loss” (p. 109).

Recently, downsizing has not only been a reaction to economic crisis, as was often the case in

the early 1990s (IBM laid off 23% of staff between 1990 and 1992), but also as company

strategy to (proactively) prevent certain circumstances, such as in the case of Goldman Sachs,

who reduced their workforce by 9% from 2012 to 2013 (workforce.com).

In addition, some theories argue that downsizing also occurs because of mimetic

behaviour among companies in the same industry. Based on institutional theory, companies

often mimic each other when some practices become widely accepted (Budros 1997, 2000;

Bebbington, Higgins, and Frame, 2009; Ahmadjian and Robinson, 2001). Furthermore

Budros (2002) found that mimicking positively impacts voluntary downsizing. Therefore,

employee downsizing has become a common practice nowadays.

Overall there are three main reasons a company may apply an employee downsizing

strategy, namely a response to an economic crisis, a proactive strategy, or institutionalized

practices.

From an economic perspective, by choosing an employee downsizing strategy,

companies are able to reduce cost of labour and if other things remain equal, overall expenses

will decrease resulting in higher profit. Other things remaining equal, in this case refers to the

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amount of revenue that is stable. However, it is difficult to maintain such a stable performance

in the dynamic US market (Cascio and Reynolds, 2015).

Moreover, the cost arising from downsizing itself often exceeds the payroll expenses a

company is aiming to cut. There are two cost categories that arise from using a downsizing

strategy. Based on Cascio (2010) these are direct and indirect costs resulting from employee

downsizing. Direct costs or short-term consequences consist of severance pay, supplemental

employee benefits, outplacement, pension and benefit payouts, and administrative processing

costs. Indirect costs or long-term consequences include survivor employees having low

morale and being risk-averse, decreased productivity among remaining employees, voluntary

termination of contracts of those employees that remain after the effectuation of the

downsizing strategy, loss of trust in management, and even potential lawsuits or strikes

instigated by labour unions. Consequently, an employment downsizing strategy does not

always result in a positive effect on firm performance.

Pfeffer (1998) also pointed out that one fallacy of businesses is believing that cost

cutting is the only way to increase profits, thus companies often rely on downsizing because

future costs are more predictable than revenue (Cascio and Reynolds, 2015). Predictability in

this case refers to the difficulty of managing costs, which is easier to manage than revenue,

because revenue is not only driven by a firm’s performance but by market conditions. Most

companies often perceive employees as a cost rather than a source of value creation (Cascio

and Reynolds, 2015), therefore they see it as a quick cost cutting strategy and a way of

generating an instant higher return for investors (Datta and Basuil, 2015).

According to Guthrie and Datta (2008), “workforce reductions and employment

instability negatively impact organizational functioning” (p. 109). From a psychological

perspective, there is some implicit psychological contract between employer and employee.

De Meuse et al. (2004) define a psychological contract as a mutual relationship between

employer and employee. A downsizing strategy is a violation of this contract; therefore

employees may reduce their organisational contributions (DeMeuse and Dai, 2013).

According to Pfeffer (1998), organisations rarely realise the long-term consequences, as

remaining employees become distrustful and uncommitted to a company that considers them

as a disposable commodity.

Travaglione and Cross (2006) stated, “It is widely accepted that downsizing has a

drastic effect on employee morale and often leaves organizations populated with depressed

survivors” (p. 2). Employees who remain, often express their disappointment through

decreased commitment, motivation, and productivity. In the worst cases, downsizing will

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trigger the remaining employees to leave the company. The negative attitudes that emerge are

commonly known as survivor syndrome.

Travaglione and Cross (2006), Cascio (2002), Wagar (1998), and Luthans and

Sommer (1999) provide evidence that employee commitment, effort, performance,

productivity, satisfaction, and workgroup trust decreases after the employee downsizing

event. Trevor and Nyberg (2008) also provide the same evidence. They show that downsizing

is positively associated with voluntary employee turnover rates, and this relationship is

mediated by organisational commitment. Furthermore, survivors of an employee downsizing

strategy express the most negative reactions when they know or have a relationship with

layoff victims, and will exhibit a greater negative response if they perceive the layoff

procedure as unfair (Brockner et al., 1994). This suggests that negative behaviour is mainly

due to the survivors’ perspective of justice. Meanwhile, Mirabal and De Young (2005) argues

that the decrease of performance and productivity is caused by frustration due to increases in

job tasks.

An economic perspective suggests the benefit of employee downsizing to a company

will be a reduction in expenses and higher profits. However, as explained, an employee

downsizing strategy has numerous costs. The effectiveness and efficiency of layoffs is not

instantly visible in firm performance; it takes time for surviving employees to adjust to the

new situation. Consequently, at the base year and one or two years after downsizing, the costs

outweigh the benefits of the effectiveness and efficiency of the new structure. At this point,

the economic perspective theory becomes unclear as to whether employment downsizing

benefits firm performance, or even if there is any effect at all, because the eliminated cost is

equal to the costs that arise. Additionally, a psychology perspective suggests that there is a

negative relation between downsizing and firm performance.

A number of studies have been conducted in the years after, yet the impacts of

downsizing remain unclear (Guthrie and Datta, 2008). Some results show no impact or a

negative impact. Some studies found positive impacts with non-downsized companies that

performed better, contrary to what is expected when examining the topic from an economic

perspective.

Many researchers have found negative impacts of downsizing on firm performance.

Cascio et al. (1997) found that employee downsizing resulted in negative changes in ROA at

the occurrence year and the following year, and was significantly lower than non-downsizers.

Wagar (1998) found that permanent employee reduction resulted in lower employer

efficiency. McElroy, Morrow and Rude (2001) tested three employee reduction approaches: ,

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involuntary turnover, voluntary turnover, and reduction-in-force They explain that both

involuntary turnover and reduction-in-force is caused by company’s will, unlike voluntary

turnover that caused by employee’s will. However, they explain that the difference between

involuntary turnover and reduction-in-force is whether company replaced the eliminated

employee or not. If company replaced the eliminated employee then it is categorise as

involuntary turnover, and reduction-in-force otherwise. They found that dismissal resulted in

significant negative performance for two years following the layoff event, involuntary

turnover negatively affect customer satisfaction, and reduction in force turnover was

negatively associated with profitability, customer satisfaction, and productivity. Luan et al.

(2013) also provide evidence of a negative relation between employee downsizing and firm

performance. They tested whether employees, wages, and slack downsizing can improve firm

performance during an economic crises. The impact of employee downsizing on firm

performance was found to be negative, regardless of the economic situation.

Despite numerous studies finding a negative effect, there are also studies that

discovered no effect at all on performance from employee downsizing. For example, Suárez-

González (2001) found no difference in labour productivity between downsized and non-

downsized companies. Baumol, Blinder and Wolff (2003) also concluded no relation between

layoffs and firm performance. They found no direct association between downsizing and total

factor productivity growth. Said, Le Louarn and Tremblay (2007) conclude that downsizers

do not show any significant changes in labour productivity compared to non-downsizers.

They argue that the payroll savings gained from employee downsizing were compensated by

direct costs such severance payments for terminated employees, voluntary turnover expenses

and other unexpected costs that related to organization structure redesign.

Although great costs arise from employee downsizing strategy, there are also many

benefits. The most apparent are cost reduction and efficiency growth. Firstly, from a basic

economic perspective, removing employees enables companies to reduce payroll expenses.

As a result, they can maintain their profit targets. Secondly, employee downsizing allows

companies to eliminate human resources that contributed least to their core operations, thus

making them more efficient in performing their business activity.

Firms undertake downsizing with the expectation that they will achieve financial and

organisational benefits (Bruton, Keels, and Shook, 1996). Despite numerous studies finding

negative relations between employee downsizing and firm performance, a large number of

studies have found positive effects. Palmon, Sun and Tang (1997) found that downsizing with

the aim to enhance efficiency positively affects ROA and ROE, and sales increase one year

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prior to downsizing until three years post downsizing. Chalos and Chen (2002) found that

employee downsizing for cost cutting or revenue refocusing results in significant increases in

ROA, operating cash flow, and sales. They explained that firm engage in employee

downsizing with the idea to refocusing the revenue may resulted in reducing company’s

excessive diversification. Perry and Shivdasani (2005) found that downsized firms with an

outside board saw positive changes in ROA. Yu and Park (2006) found that, Chen (2001), and

Brookman et al. (2007) also found layoffs positively impacted on asset turnover, operating

income to total assets, labour productivity, and ROA. All these studies argues that company

can benefit from downsizing if its increases effectiveness and if other things remain equal. As

explained before, other things remaining equal means if the income and other expenses

remaining equal. By the reducing the number of employee will result in the decreases of

labour expenses, therefore, generating better financial income.

In addition to employee downsizing literature, there are also many studies comparing

firm performance between downsized and non-downsized firms. For example, De Meuse et al

(2013) examined the effect of employee downsizing on firm performance during a period of

economic prosperity. They found that a downsized company has a positive effect on ROA,

Profit Margin, and EPS, but a negative effect on revenue growth. Differences in financial

performance get smaller over time because the downsized company improved after three

years of employee downsizing. Cascio and Young (2003) tested the impact of employee

changes on ROA and return on common stock. The results showed a negative relation at

current year and several consecutive years. Although downsizers did increase their ROA over

time, it never exceeded stable employers. Moreover, Espahbodi et al. (2000) found that firms

who are laying off exhibit lower pre-tax profits for the first two years following downsizing,

but higher in the third and fourth years. These studies point out that it takes time for a

downsized company to gain a competitive advantages. They also argue that companies that

downsize to cut cost quickly as a response to changes in the economic situation are less likely

to be successful than companies that engage in employee downsizing to restructure their

streamline. The term streamline focuses on a company’s business process that are aligned. In

conclusion these study suggests that the negative and positive impacts of employee

downsizing on firm performance also depend on a firm’s strategy, which may be reactionary

or proactive.

Overall, the comparison between non-layoff firms and layoff firms suggests that the

benefits of downsizing are not immediately apparent in financial performance but improve

over time. Most comparisons between downsized and non-downsized firms show that the

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downsized company is always outperformed by the non-downsized. The reason is that the

company that did downsize has been underperforming since the beginning, otherwise it would

not have downsized, and as mentioned, at the base year and over the following two years, the

company is still in the adjusting phase. As a result, the improvement is not immediately

reflected in firm performance.

2.1.1 Summarizing the findings

To summarise, there are two findings that are prevalent in these studies. First,

downsizing has no impact or a negative impact on firm performance, and second, downsizing

may have a positive impact. Most of the negative results can be explained due to the fact that

these studies are not longitudinal studies; therefore they reflect the short-term/immediate

consequences of downsizing for firm performance. In the short-term, there are several direct

costs a company must bear because of employee downsizing. As a result, firm performance

improvements materialise after three years of downsizing. Based on short-term and long-term

consequences as mentioned in Cascio and Young (2003), De Meuse and Dai (2013), and

Espahbodi et al. (2000) studies, they all use a longitudinal approach to study the impact of

downsizing over a longer period.

Other studies from Cascio and Young (1997), Guthrie and Datta (2008), and Chadwick

et al (2004) used mean from multiple years following employee downsizing events. The main

reason is, they are able to see overall impact on firm performance, while, longitudinal studies

or single year studies resulted in each year pattern of firm performance. This study aims to see

the overall main effect of employee downsizing on firm performance when labour intensity

and capital intensity is introduced as a moderating factor.

2.2 Employee Downsizing and Different Industry Characteristics

Human resource management (HRM) has been frequently linked to firm performance.

According to Huselid (1995), HRM literature suggests that a “firm’s current and potential

human resources are an important consideration in development and execution of a strategic

business plan” (p. 636). There are two primary perspectives to explain how HR can influence

firm performance. First is the universal theory. This theory implies a direct relation between

human resources and firm performance (Youndt et al., 1996). The second is contingency

theory. Contingency theory implies that an organisation’s strategic posture either enhances or

reduces the impact of human resources practice on firm performance. Basically, contingency

theory suggests that there is no best way of organising (Morgan, 1986) and the appropriate

way is dependent on the environment the company is dealing with. Tosi and Slocum (1984)

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mention this as a contingency factor. Therefore, the effectiveness of strategy execution

depends on whether it is aligned with an organisation’s culture, characteristics, and

environment. If the strategy fits with the company’s characteristics, then the strategy will

benefit its performance. Although both theories seem contrary to one another, analytically this

distinction can be explained as thus: universal theory sees the main effect, while contingency

theory sees the moderation effect (Youndt et al., 1996).

Since the 1960s, a number of seminal studies related to contingency theory have been

conducted (Burns and Stalker, 1961; Woodward, 1965). Moreover, Volbreda (2012) stated,

“High performance is a consequence of co-alignment between a limited number of

organizational and environmental factors” (p. 1042). Consequently, when there is a misfit, the

strategy negatively impacts on firm performance. For example, Burton, Lauridsen, and Obel

(2002) show that ROA declines if there are misfits between the chosen strategy and company

characteristics. They proposed several characterisics based on environment, strategy,

technology, management style, and company’s ownership. One example of misfits between

strategy and company characteristic is a misfits of prospector strategy with company that have

high formalization. High formalization means that company with more rigid structure and

business process. Keller (1994) also tests contingency theory; he shows that technology

influences the information processing requirements of an organisation. Furthermore,

Venkatraman and Prescott (1990) show that co-alignment between environment and strategy

results in positive performance. Burton, Lauridsen, and Obel (2004) state “quantitative studies

have already shown that misalignment or misfit among a firm’s strategy, technology,

management, environment, size, and climate will damage financial performance” (p. 79).

Zajac et al. (2000) show how a strategy can be beneficial to a company. First, when

the strategy fits with multiple relevant contingencies, and does not decrease other important

dimensions, then it will likely benefit the company. Second, contingencies vary over time;

therefore a strategy that benefits the company at one time will not necessarily benefit it at

another. Third, strategic choice is not unidirectional or discrete. Direction and magnitude will

vary depending on relevant contingencies. Industry characteristic is one of the contingencies a

company needs to consider before implementing a strategy. Guthrie and Datta (2008) argue

that a firm’s industry context will influence the effectiveness of human resources strategy,

such as employee downsizing.

HRM literature has accentuated the influence of environmental conditions on HR

effectiveness. However, most studies investigate HR strategies that enhance employee skills

(Huselid, 1995; Terpstra and Rozell, 1993). There is also much research into how industry

13

characteristics influence human resource strategy, particularly employee downsizing. Datta et

al. (2010) synthesised all studies related to employee downsizing between 1984 and 2008.

The objective was to review employee-downsizing literature, help point out the gaps among

the studies, and explore possible causes of equivocal findings. Based on their study, a number

of studies have examined how the relation between employee downsizing and firm

profitability is mixed, as explained in the previous section. The lack of agreement in this

stream has motivated some researchers to find a moderator variable that can explain

performance differences.

Based on Datta et al (2010), there are only four studies in total looking at moderator

variables to find out whether employee downsizing or firms’ financial performance is driven

by other factors. Love and Noharia (2005) found that the benefits of employee downsizing for

firm performance is greater when downsizing is a proactive strategy, when it has a broader

scope, and when a firm is more slack. Moreover, Yu and Park (2006) found that downsizing

resulted in ROA improvement if the firms did not experience losses in the three years before

layoff events. Guthrie and Datta (2008) tested whether industry characteristics moderate

employee downsizing and firms’ financial performance. They found the negative impact of

downsizing is driven by high R & D intensity, low capital intensity, and growth. Chadwick et

al (2004) examined how HR policies can moderate the impact of layoffs on financial

performance. They found HR policies a have positive influence on the relation between

layoffs and financial performance.

As mentioned, an industry characteristic is one contingency factor a company needs to

consider before implementing employee- downsizing strategy. Therefore this thesis aims to

study to what extent labour intensity and capital intensity affect layoff and firm performance

relations. There are also economic and psychological theories that suggest different effects on

firm performance. Therefore, this study does not focus on one, but three measurements to be

able to conclude how a firm’s financial performance is affected. Those measurements are

ROA, profit margin, and cost of goods sold. These measurements are explained further in the

next section.

Furthermore, based on the Resource-Based View (RBV) of the firm, resources viewed

as a firm’s competitive advantage could be interpreted as its strengths and weaknesses

(Wernerfelt, 1984 and Barney, 1986 ). Wernerfelt (1984) also believes a firm’s ability to keep

profitable is dependent on its ability to manage an advantageous position in underlying

resources. A firm’s resources can be divided into two categories: tangible and intangible

assets. Examples of tangible assets include plant, property, and equipment, while intangible

14

assets include goodwill, and skill and knowledge of the employee. Practically, a firm

combines all its resources to produce an end product. According to Conner (1991), “In a

neoclassical model, firms produce by teaming two inputs: Labour and Capital” (p. 123).

However, in practice the role of labour and capital is not equal, considering each industry has

its own characteristics. For example, the service industry needs more human resources

compared to manufacturing, where machines play a big role in production. Both human

resources and capital resources have their own advantages.

Employee contributions to companies come not only in the form of skills and

knowledge, but also commitment, effort, and loyalty. According to Barney and Hesterly

(2005), to value a resource as a competitive advantage it has to fulfil four criteria, which are

Valuable Rare Imitability and Organisation (VRIO). The first criterion is valuable: a resource

must create value for a company, either by outperforming competitors or by reducing a

company’s weaknesses. The second criterion is rarity To fulfil this a resource must only be

controlled by a few companies. Third criterion is imitability, i.e. a resource that would be

difficult and costly to duplicate. The last criterion is organisation, which means the resource

are readily available to be organised by a company.

In this framework, employees can fulfil all the criteria that are required to provide a

competitive advantage. Employees and capital are valuable, because they create value for the

company. According to Cascio and Reynolds (2015) as mentioned, an employee is actually

create a value added for a company, but companies often see them as a cost. Employees’

skills, commitement and loyalty is considered as rare assets. Therefore they can only be

controlled by a company who hires and treats them well. However, capital is not rare, because

it is a tangible asset, and it is not unique; therefore it can easily be acquired and controlled by

a company. Employees’ skills and knowledge are hard to imitate. Every person develops their

own ability, which cannot be easily imitated. Meanwhile , capital can be easily imitated. For

example, a mining company could acquire the same machines as their competitor have, in a

same or different quality, simply because it is available in the market. The last is employees

and capital are readily available to a company.

Overall, employees as well as capital are the factors that determine a company’s

performance. They are a contingency factor, because a firm’s performance is dependent on

labour and capital. As a result, each firm is driven by a different combination of competitive

advantage, which means layoffs impact firm performance in different ways. Some companies

derive their competitive advantage largely from labour; others from capital.

Capital-intensive industry is where the company requires a large degree of capital.

15

Capital refers to cash and fixed assets such as machines. In this industry, capital plays a

dominant role in production and generating income. As mentioned in Guthrie and Datta

(2008), “higher levels of capital intensity reduce the performance effects associated with

variability in a firm’s stock of human capital” (p.113). Terpstra and Rozell (1993) define

capital intensity as “greater constraints placed upon employee performance by the degree of

task structure or the degree of automation of the production technology”. As a result, capital-

intensive companies are less dependent on human resources. For example, car manufacturers,

oil and gas companies, and chemical companies. These companies require large amounts of

expensive equipment to produce their products.

According to Shahidul and Shazali (2011), labour-intensive industries are “industries

where labour costs are more important than capital costs. More specifically, labour intensity

means use of manpower in the production process, with little support of technology”. To

produce their product, labour-intensive industries are dependent on a great number of human

resources. Some examples of labour-intensive industries are the garment industry and

construction. Employees are one of their biggest resources, therefore employee downsizing

will have a big impact.

Labour intensity is used to describe any production process that requires higher labour

than capital costs. As mentioned, from an RBV perspective, to produce a product a company

requires a combination of labour and capital input. If labour costs outweigh the cost of capital

than it can be categorised as labour-intensive, and vice versa. Labour-intensive industries

allow companies to adjust the number of employees or salary expenses to control costs when

needed. This is one of the advantages of labour- compared to capital-intensive, because for

the latter companies normally have high fixed costs.

Roca-Puig et al. (2012) argue a “less labour-intensive firm, characterized by a

mechanized production system, offers few opportunities to its employees to improve their

labour productivity levels through greater commitment and dedication in their jobs.

Conversely, labour-intensive production systems provide employees with more opportunities

to make suggestions and innovations” (p. 942). Moreover, Tepstra and Rozell (1993) point

out that capital-intensive industries are unable to take full advantage of their employee’s skills

and knowledge, because of automated production systems. But in labour-intensive industries,

employees are valuable strategic assets. Therefore, employee downsizing strategy is usually

associated with labour-intensive industries, because, as argued by Roca-Puig et al. (2012),

Tepstra and Rozell (1993), and Pfeffer (1998), employees are highly advantageous for

company competitive strategy.

16

Capital intensity and labour intensity are two different industry characteristics. It is

expected that these contradictory characteristics will have a different effect in moderating the

relation between downsizing and firm performance. To distinguish whether a company is

capital- or labour intensive, the first step is to calculate asset to sales ratio and employee to

sales ratio. Secondly, compare ratios. If a company is capital-intensive, then the proportion of

asset to sales is higher than employee to sales, and vice versa.

To summarise, employee downsizing is one of company strategy, which involving

human resource. According to contingency theory, the result of the strategy may fulfill a

company its expectations if it fits with company characteristics (Donaldson, 2001). Moreover,

based on Travaglione and Cross (2006); Cascio (2002); and Wagar (1998) survivor

employees develop a negative attitude with regard to a layoff strategy. Therefore firms with

labour intensity may show more negative results in comparison to firms with a high capital

intensity, since labour intensity production depends more on employees rather than machines.

Finally, the goal of this thesis is to examine how labour intensity or capital intensity influence

the downsizing effect on firm performance.

17

3 Research Method and Hypothesis Development

3.1 Research Method

The predictive validity framework (“Libby boxes”) is presented in the following

figure to show the conceptual relations that are examined in this thesis. Figure 1 Libby Boxes

This study investigates how industry characteristic, more specifically labour intensity

and capital intensity moderate the impact of an employee downsizing strategy on firm

performance. Further details about measurement and sample are explained in the next

subsection.

Data was obtained from Compustat and KLD database, therefore this study is

categorized as quasi experimental or archival study. In archival study the process of sample

selection is not random. For example, assigning 1 and 0 in downsizing dummy variable is not

random. It is based on the reduction in the numbers of employee, which may have an effect

on both downsizing and firm performance. Change in the number of employee often called z

factor. Every Z factor should be controlled to avoid, non random selection bias.

Based on Mitchel and Janina (2010) “General idea of what makes a measurement

sensitive is validity and reliability. First, the most valid measure is the one in which scores

are least affected by factors that are irrelevant to what you are trying to measure. Second, the

18

less a measure’s scores are affected by irrelevant factors, the more it will be sensitive to

changes in the relevant factor”. Accounting measures are the most common and readily

available means of measuring organizational performance (Richard et al, 2008). Danielson

and Press (2003) found that the correlation between accounting and firm rates of return was

above 0.75; this suggests that accounting measures highly sensitive with firm performance.

Therefore using ROA, profit margin and COGS as a proxy for firm performance will have

high construct validity and reliability.

According to Mitchel and Janina (2010) internal validity refers to how well a study

captures a causal effect after eliminating the alternative causal relation. They mention that to

establish internal validity the cause must come before its effect, a study must establish that

changes in the treatment came before changes in the outcome variable. This study used

downsizing and control variables data for year 2010 and all the dependent varables is 2011-

2014. Therefore, the reverse causal will not be a problem to the model, because employee

downsizing as a cause occur before firm performance (ROA, profit margin, and COGS).

The benefit from archival study is high external validity because the use of a real-time

data and a large number of sample, so it can be generalized to the population (Mitchel and

Janina, 2010). Different from lab experiment, in archival study treatment to the sample occur

naturally and cannot be manipulated, therefore, the result can be generalized to real world

setting.

This study uses a break test approach to see how the downsizing pattern changes when

labour-intensive and capital-intensive is examined. By taking this approach, it can be clearly

seen which characteristic influences the downsizing effect on firm performance to a more

positive or more negative direction.

First, this study test the impact of downsizing on firm performance without including a

labour/capital intensity variable. To see whether downsizing has an impact on ROA, profit

margin, and COGS. Second, this study test the differences of how labour intensity and capital

intensity influences the relation between downsizing and firm performance (ROA, profit

margin, and COGS). To test for these differences, Chow break tests (Heij et al (2004) are

applied to the model.

A Chow test is often applied to see a structural change (Davidson and MacKinnon,

1993) because this test allows determining whether the coefficients of a regression model are

identical between two groups. Therefore by applying this test, it enables identification

whether the actual parameters before the sample divided into two groups are changing and

how the difference between the two groups may drive employee downsizing coefficient.

19

To test the differences between labour intensive and capital intensive this thesis

separates the sample based on those two characteristic. The Labour intensity variable is

measured as 1 if company is labour intensive and 0 if the company is capital intensive. The n

observation is split into two groups, the first group consisting N1 observations which is firm

with labour intensity companies and the second group consist the remaining observation

which is N2 consisting of firms that are capital intensive.

Under the null hypothesis the F value will follow the F(K, N1+N2-K) distribution . If

the result of the F value of the Chow test is greater than the critical F value, than there is a

significant difference between labour intensity and capital intensity. However if the F value of

the Chow test is smaller than critical value, then there are no differences across labour

intensity and capital intensity in moderating downsizing impact on firm performance.

Furthermore, it can be concluded which of the labour intensive and capital intensive that

drives downsizing effect on firm performance to a more positive or negative direction.

If the result from Chow test suggest that there is no significant difference of labour

intensity and capital intensity influence on downsizing impact on each firm performance

measurement, then the hypothesis will be reject.

The Chow test is operationalized using the following equation:

F=

S 0−S 1−S2K

S 1+S 2N 1+N 2−2 K

Index:

S0 = Residual sum of squares under the null hypothesis

S1 = Residual sum of squares under labour intensity group

S2 = Residual sum of squares under labour intensity group

K = Total variable in the model includes intercept

N1 = Total observation for labour intensity group

N2 = Total observation for capital intensity group

In order to conduct the Chow test, there are 3 linear regression that are run before

calculating the Chow test. First linear regression is with the entire sample and without the

labour intensity variable. The model is as the following:

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ROA/PM/COGSt+1 = α + β2 EMPDWNSZt + β4 DSIZEt + β5 FIRMSIZEt + β6

ASSETCHANGEt + β7 LABOURUNIONt + β8 INDUSTRYt + εit

The second and third linear regression use the same model but it is separated into 2

groups which are labour intensity and capital intensity groups respectively. Further Chow

tests are calculated where S0 is the residual sum of squares under the null hypothesis / first

model (obtained by regression over the full sample of observations (N = N1 + N2). S1 and S2

is obtained is the residual sum of squares under labour intensity group and capital intensity

group respectively. K is the number of total number of variable including constant. N1 and

N2 is the total number of observation in Labour intensity group and Capital intensity group.

The result of Chow break test are compared to the F test critical value. If the Chow

break test is greater than the F test critical value than this suggests that there is a structural

change between labour intensive and capital intensive groups. As a result there is a difference

between labour intensity and capital intensity in moderating layoff and firm performance

relation. However if the Chow break test result is lower than the critical value than there is no

significant difference between labour and capital in terms of influencing layoff and firm

performance. This test is used repeatedly for ROA, profit margin and COGS.

Moreover to study layoff impact on firm performance, this thesis poses 6 hypotheses.

Which are elaborated in the following paragraph.

3.2 Hypothesis development

3.2.1 Return on Asset

ROA is used as one of operating measures because it is a standard accounting measure

of return that has been used in several studies of post downsizing performance (e.g., Cascio et

al., 1997; Palmon et al., 1997; Espahbodi et al., 2000, Guthrie and Datta, 2008) and it is

considered as a key of financial indicator (Bruton et al., 1996; Stickney et al., 1999).

As discussed in previous section Cascio et al (1997) found that ROA decline directly

and 2 consecutive years after employee downsizing. Luan et al (2013) provide negative

evidence of employee downsizing on ROA. However, Chen et al (2001) shows that ROA is

greater than industry adjusted and non-layoff firms for periods 3 years after employee

downsizing. Palmon, Sun and Tang (1997), found that downsizing for efficiency enhancement

has positive changes in ROA. Moreover, Chalos and Chen (2002) also found significant

21

increases in ROA. Further, these contradictory findings are explained by in Cascio and Young

(2003), De Meuse and Dai (2013), Espahbodi et al (2000) studies. They find that firm

performance improvements materialize after 3 years of downsizing. To see overall effect of

employee downsizing on firm performance, this study follow Guthrie and Datta (2008) using

the mean of ROA from several years. In this thesis, mean ROA is calculated by aggregating 4

years ROA from 2011-2014.

According to Contingency theory, organization’s strategic posture either enhances or

reduces the impact of human resources practice on firm performance. In this case, labour and

capital structure of organization, play important role in determining successful outcome of

employee downsizing strategy. Additionally, downsizing will be more detrimental to labour-

intensive industry, because of survivor syndrome. Survivor syndrome will result in lower

productivity, which further will lower down the net income. Even though labour expenses

decreases but it is also companied by lower revenue from lower productivity, and also several

direct and indirect costs from downsizing. Direct cost example is severance pay,

unemployment benefit, pension cost, administrative processing cost. Meanwhile indirect cost

consists of new hiring cost, survivor syndrome, voluntary termination from survivor, lack of

staff, potential strikes from labour unions. As a result, ROA decreases in will be bigger in

labour intensity rather than in capital intensity.

In a capital-intensive industry, production does not depend on labour. Capital plays a

big role in generating income. As a result downsizing will be less detrimental under these

circumstances. Employees in a capital intensive industry are used to work efficiently,

therefore survivor employees will more quickly adjust to a new company structure.

Consequently, this thesis predicts firms that are labour intensive that the influence of an

employee downsizing effect on firm performance to a more negative direction rather than

capital intensity, which leads to the first hypothesis:

H1a: The negative effect of employee downsizing strategy on ROA is greater when a

firm is labour intensive

H1b: The negative effect of employee downsizing strategy on ROA is lesser when a firm

is capital intensive

To test the first hypothesis, this study includes mean ROA as dependent variable,

employee downsizing as independent variable, capital intensity and labour intensity as

moderating variable. Additionally the model consists of firm size and asset change as control

22

variable. Cascio (2003) suggests to control firm asset’s changes. This is because firm’s asset

changes influence firm performance, specifically ROA. Guthrie and Datta (2008) also point

out that controlling fixed asset changes can isolate its influence on layoff and firm

performance relation.

Recall from the research design, to test whether there is a break point in the model when

labour and capital intensity sample are divided into two groups, firstly this thesis run the

linear regression the null hypothesis, where there is no diffrences between labour and capital

intensity. Thereby, the main effect of downsizing on firm performance appears. Secondly, the

sample is divided into two groups which are a labour intensity group and capital intensity

group. Thirdly, after the sample has been divided, regressions are run on each group. Fourth,

this thesis calculates the breaks using chow equation that has been mentioned in research

design part. The first hypothesis are tested with the following models:

MEANROAt+1 = α + β1 EMPDWNSZt + β2 DSIZEt + β3 FIRMSIZEt + β4

ASSETCHANGEt + β5 LABOURUNIONt + β6 INDUSTRYDUMMYt + εit

3.2.2 Profit Margin

Profit margin basically indicates how much profit generated from sales price. It is

consist net income and total revenue or total sales. Palmon, Sun and Tang (1997) found

positive improvement in profit margin. Moreover, Chen et al (2001) also found greater

operating earnings to sales (profit margin). Suárez-González (2001) found lower return on

sales (ROS / profit margin) than non-layoff firms. Profit margin is also used as one of firm

performance measurement because company cut employee expense cost in order to increase

their profit. Therefore, if a company employed layoff strategy it should directly affect their

profit margin.

Furthermore, according to the analysis from economic theory and psychological

theory, layoff has a negative influence because direct cost of employee downsizing may

exceed company’s salary expense that eliminated. Therefore for both labour intensity and

capital intensity, net income will be lower after downsizing. In addition, lower productivity

because of survivor syndrome will lead to lower sales. However, profit margin changes

depend on whether the changes in net income exceed the changes in total revenue. If both

nominator and denominator is decreasing, the effect may not be significant in PM changes.

Furthermore, company who deals with more survival syndrome tends to have slow

improvement, because it is difficult to motivate employee performance, effort and

23

commitment once their trust is broken. Labour intensity company, are dealing with more

employee than capital intensity. Therefore employee downsizing will be more detrimental for

labour intensity firm than capital intensity. As a result labour intensity wil have more negative

influence on employee dowsizing and profit margin relation.

Moreover, the number of employees in capital intensity industry is usually smaller

than labour intensity, therefore leads to the smaller size of downsizing. As a result, negative

effect from survivor syndrome will be smaller than labour-intensive. Consequently, sales and

net income will not effected as much as labour-intensive. Therefore this study predicts profit

margin in labour-intensive industry will be lower than capital-intensive industry. This leads to

second hypothesis:

H1a: The negative effect of employee downsizing strategy on profit margin is greater

when a firm is labour intensive

H1b: The negative effect of employee downsizing strategy on profit margin is lesser

when a firm is capital intensive

In second hypothesis, this study used profit margin to measure firm performance.

Therefore, profit margin is placed as dependent variable. All independent variable for second

hypothesis are the same with the first hypotheses and as well as for control variable.The

testing treatment for the second model are the same as the first model. Second hypothesis are

tested with the following models:

MEANPMt+1 = α + β1 EMPDWNSZt + β2 DSIZEt + β3 FIRMSIZEt + β4

ASSETCHANGEt + β5 LABOURUNIONt + β6 INDUSTRYt + εit

3.2.3 Cost of Goods Sold

Although many research has been conducted, only little of empirical studies that

provide the evidence how employee downsizing affect cost of goods sold. One of the

researches that implicitly include COGS in their research is Cascio and Young (2003). They

calculate ROA with Operating Income Before Depreciation, Interest and Taxes (OIBDP),

which consist COGS in it. However, the impact of employee downsizing that they show is

through ROA not directly to COGS. Another evidence of COGS was provides by Chen et al

(2001) and Chalos and Chen (2002). They found that COGS are lower and not significant

after employee downsizing. Therefore in this study are motivated to test how layoff affect

24

COGS. COGS is employed as one of firm performance measurement, its because COGS

represent all the cost that allocated to each product, and labour cost is one of them. Therefore

if a company implement employee downsizing strategy, the basic assumption is, the labor

cost will be decreasing and leads to COGS decreases. Direct impact of employee downsizing

to COGS is one of consideration why COGS is employed as one of firm performance

measurement.

Practically by reducing the number of employee, the labour cost that allocated in each

product also decrease. Therefore if the price of the product holds, each product can generate

higher profit. In labour-intensive industry indeed the cost of goods sold are mainly driven by

cost of labour, as a result cost of goods sold in this industry will be lower than capital

industry. Whilst in capital-intensive industry, cost of goods sold are driven by heavy

equipment cost. As a result, employee downsizing would not reduce cost of goods sold that

much in capital intensity. Considering that labour-intensive industry are highly depending on

its human resources, therefore this study predict that the impact of employee downsizing on

cost of goods sold in labour-intensive industry are higher than capital-intensive industry. As a

result, labour salary expense decreases is much bigger in labour intensity rather than capital

intensity, which leads to the following hypothesis:

H1a: The negative effect of employee downsizing strategy on COGS is greater when a

firm is labour intensive

H1b: The negative effect of employee downsizing strategy on COGS is lesser when a

firm is capital intensive

In these hypothesis, cost of goods sold will be placed as dependent variable to measure

firm performance from expenses side. The more negative COGS will provide more positive

evidence of employee downsizing impact on firm performance, because it means that labour

expense decrease a great part of cost that are spend for producing the product. Similar to

previous hypothesis, the models used to test third hypothesis also consist the same

independent, moderating, and control variable, only dependent variable that change. Third

hypothesis are tested with the following models:

MEANCOGSt+1 = α + β1 EMPDWNSZt + β2 DSIZEt + β3 FIRMSIZEt + β4

ASSETCHANGEt + β5 LABOURUNIONt + β6 INDUSTRYt + εit

25

3.3 Measurement

3.3.1 Dependent variable

To see overall layoff impact on firm performance, this study choose to measure firm

performance on 3 measurements. Those measurements are return on asset (ROA), profit

margin, and cost of goods sold (COGS).

Return on Assets (ROA) is measured as net income divided by total assets. ROA is an

indicator for how much profit that company generates for each cent they invested in assets. To

calculate ROA this thesis obtained net income and total asset from Compustat. Net Income is

one of Compustat item under income statement option. Moreover total asset is denote as AT

in Compustat, which can be found under balance sheet items.

Profit margin is also commonly known as Return on Sales (ROS). Profit margin is

measured as net income divided by sales. This ratio shows much profit company can generate

from each sales they made. Sales data is denotes as sale in compustat. This study used the

same net income data that previously been used to calculates ROA.

Cost of goods sold (COGS) is measuring all costs directly allocated by the company to

production, such as material, labour and overhead. Since labour cost is one of Cost of Goods

Sold part, therefore if company reduce their number of employees, as a result their cost

should be decreasing. Therefore cost of goods sold in labour intensity industry should be

more affected if its compared to capital intensity industry. This study obtain COGS from

Compustat under COGS item.

Finally all the dependent variable are calculated in mean form from 2011-2014.

Moreover, to all dependent variable is volatile, therefore to diminish its skewness all the

dependent variable is measured in natural logarithm.

3.3.2 Independent variable

Independent variable for all hypotheses is the occurrence of employee downsizing

during 2010 – 2011. Cascio et al (1997); Osterman (2000); Ahmadjan and Robinson (2005);

Lorente and Gonzalez (2007); Le Louarn and Tremblay (2007) pointed out that 5% reduction

in the number of employee can be categorize as layoff strategy. Similar to previous research,

this study used employee reduction above 5%. According to Guthrie and (2008), using higher

percentage of employee reduction tend to eliminate many firms that actually did employee

downsizing.

Moreover, to measured employee reduction in US company. Firstly, this study

obtained total number of employee in 2010 and 2011 from Compustat under EMP item.

26

Secondly, to distinguish whether company did employee downsize or not, this study

calculates the percentage of changes in number of employee from 2010 to 2011. Thirdly, this

thesis use binary variable to differentiate between downsized company and non-downsized

company, 1 if company undertook employee downsize strategy, and 0 otherwise.

3.3.3 Moderating variable

The main objective of this study is to investigate whether labour intensity or capital

intensity influence the relation between employee downsizing and firm performance.

Moderating variable is labour intensity. Labour intensity is used to describe any production

process that requires higher labour cost than capital cost. As mentioned before in resource

based view (RBV), to produce a product company requires a combination of labour and

capital input. If the costs of labour outweigh the cost of capital than it can be categorize as

labour intensity, and vice versa. Labour intensity industry allows company to adjust the

number of employee or the salary expense to control the cost when it is needed to. This is one

of the advantages of labour intensity in compared to capital intensity, because in capital

intensity, normally company has high fixed cost.

Roca-Puig et al (2012) define labour intensity as the higher proportion of labour cost

relative to plant and machinery cost. They measured labour intensity using Schmenner (1986)

measurement, which by calculating the ratio of labour cost to total fixed asset. In addition,

Dewenter and Paul (2001) measures labour intensity using two approaches. The first approach

is employee cost to sales, and the second approach is the ratio employee cost to fixed asset.

This study choose to follow Dewenter and Paul (2001), which is employee cost to total sales,

because there is no clear ratio threshold that define how company can be more to labour

intensity side or capital intensity side. Meanwhile the approach that this study choose can

clearly answer how much employee cost that company spent can generate return in sales.

Employee cost is measured as total employee salaries including bonus and compensation.

Data is obtained from Compustat under staff expense item, meanwhile sales under item

named sale.

On the other hand capital intensity is measured as the ratio of fixed asset to sales.

Align with previous research Chang and Singh (1999); Datta et al (2005) and Guthrie and

Datta (2008). Subsequently, to categorize whether a company is labour intensive or capital

intensive, this study compare both ratio. This study used binary variable to differentiate

between labour intensity and capital intensity, 1 for labour and 0 otherwise.

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3.3.4 Control variable

Based on econometric theories, if there is a variable that influence both independent

and dependent variables, it should be included as control variable, otherwise the result will be

bias due to omitted variable. All hypotheses are tested with the same control variable. There

are five control variables that are included in all models. First control variable is the size of

downsizing. The magnitude of employee downsizing has been frequently tested, because of

its ability to influence the strength of layoff impact on firm performance. For example, Cascio

and Young (2003) argues that De Meuse et al (2004) find that the bigger the size of

downsizing the lower firm performance. Lee (1997) also argues that the magnitude of

employee downsizing is signaling the level of firm performance severity. Larger size of layoff

also means larger direct and indirect cost will affect firm performance. Therefore the size of

employee downsizing is included as control variable in this study.

The second control variable is firm size. Based on Guthrie and Datta (2008) firm size

can influence human resource strategy, which further can affect firm performance. Moreover,

Wagar (1998) also used firm size as control variable. He argues that firm size and industry

sector may be associated with company effectiveness. In consistence with Guthrie and Datta

(2008) this study measured firm size as natural logarithm of total assets during downsizing

period.

The third control variable is asset change. Asset change has been frequently argued

that this variable has a strong influence on the impact of layoff. For example, Cascio and

Young (2003) found that firm performance of employee downsizers is lower than company

who did employee downsizing with asset changes. Moreover, asset changes also influence

company’s ROA. Additionally, companies with capital intensity are more driven by their

fixed asset. Therefore, the impact of employee downsizing in these industry also influence by

asset changes. Asset changes measured as the percentage of fixed asset changes during

downsizing period.

The fourth control variable is labour union. Recall from the second section, it has been

argued that companies that layoff its employee, often suffer a negative impact from survivor

employees in the company. The negativity comes from their productivity, effort, commitment

and loyalty that decrease due to employee downsizing. One of the indirect cost that has been

mentioned before is potential lawsuit or strikes from remaining employee of from labour

union. Therefore the existence of labour union in the company might influence employee

downsizing impact on firm performance. One might argue that, if there is labour union in the

company that undertook layoff strategy, they might suffer more than company that did not

28

have labour union. Moreover, labour union is noted as 1 is there is labour union exist in the

company and 0 otherwise.

The fifth control variable is industry. The study includes industry to control industry

effect on employee downsizing relation with firm performance. Some industry may have

labour intensity or capital intensity characteristic, therefore this variable may have influence

on layoff and firm performance relation. Industry type also influence firm performance,

because each indutry has their own performance range, which cannot be compare to each

other. Moreover, Wagar (1997) found that industry affect the level of employee downsizing.

Filatotchev et al (2000) found that industry condition is positively associated with employee

downsizing. Industry variable is denotes using SIC codes. Standard Industrial Classification

(SIC) codes are four-digit numerical codes assigned by the U.S. government to identify the

primary business of company. This study use the first two digits of SIC codes to identify the

industry. There are 10 industry based on two digits SIC codes. Those industries are

agriculture, mining, construction, manufacturing, transportation and public utility, wholesale

trade, retail trade, finance, insurance and real estate, services and the last is public

administration.

3.4 Sample and Data collection

This study uses archival data collected from Compustat. This study chooses U.S

companies that undertook employee downsizing during economic prosperity. Many empirical

researches argue that employee downsizing is often used as the company’s defense during

economic downturn (Eg. Luan et al (2012), therefore, the effectiveness of employee

downsizing strategy can clearly be seen during economic downturn period. However, the

impact on firm performance is not clear whether it is because of employee downsizing

strategy or it is because of the improvement of economic condition itself. Therefore it is best

to test the impact of employee downsizing effect on firm performance during economic

prosperty.

De Meuse and Dai (2013) also argue that economic conditions influence a company’s

ability to rebound. Therefore to eliminate the chance of economic volatility its influence on

the layoff and firm’s performance relation, this study focuses on the effects of employee

downsizing strategy during a period of economic prosperity, which is between 2010 and

2011, because during 2007 – 2008 U.S. was under economic crisis and before 2010 US GPD

growth is still negative due to crisis. From 2010 US GDP growth is positive (www.bea.gov),

therefore during the 2010 – 2011 period economic control will be not necessary. Moreover

29

this rational is also based on Luan et al (2013), who found that employee downsizing will

have a negative impact regardless of economic condition.

Firstly, this study only targets U.S listed public companies. Secondly, all the data is

obtained from Compustat except for the Labour Union variable, which is obtained from the

KLD database. To create an industry dummy this study obtained the SIC codes for each firm.

Using two digits SIC codes this study separates the dummy variable for industry into 10

industries, as explained in the control variable section. Thirdly, the number of firms in the

sample is different across the models because of the incompleteness of the data.

All financial proxies were chosen because theoretically employee downsizing has a

direct impact to those measurements. Direct impact of the reduction in number of employee to

a certain account such cost of sales and sales, may have significant effect on the chosen

measurement. For example COGS, cost of sales is directly affected when a company reduce

the number of employees and therefore salary expense in which allocated to cost of sales is

decreases.

To be able to test how labour intensity and capital intensity influence these measures,

first the goal is to assure that downsizing has a significant impact on those measures,

otherwise the differences between labour intensive and capital intensive will not be of use

considering that employee downsizing thus has no significant impact. There are 304

companies included in the ROA model, 131 companies in the model for profit margin, and

lastly there are 496 companies in the sample for COGS. A previous study by Guthrie and

Datta (2008), operationalizes ROA using the natural logarithm because of its skewness.

Therefore to diminish skewness the dependent variable is measured using the natural

logarithm. This study also eliminates the outliers to obtain more accurate results from the

OLS regression.

30

4 Result and Data Analysis

The first task is to identify the number of observations, mean, standard deviation, and

the minimum and maximum values of all variables. This thesis provides a statistical

summary, which is presented in table 1. Secondly, this thesis examines the correlation

between each variable using a Pearson correlation method, which is presented in table 2. In

order to see whether downsizing has a significant impact on firm performance during the four

year period after downsizing, this thesis also performs a regression analysis for each

dependent variable, with and without a labour or capital intensity variable. This test is to

examine in the first instance how downsizing affects firm performance. This third test is

presented in table 3. Finally, to see how labour and capital intensity impact the employee

downsizing effect for each dependent variable, this thesis applies a Chow break test, as

explained previously the in research design section.

4.1 Descriptive statistic

Table 1 presents the descriptive statistics for each model. Panel A shows that ROA has

a total of 344 observations, with a mean of -4.031. The ROA that was used for the test is the

natural logarithm of the mean ROA from 2011-2014. Table 1 also shows the standard

deviations and the minimum and maximum values of the observations. Panel B shows that

there are 131 observations for the profit margin variable. Profit margin was also calculated

using a natural logarithm. Table 1 shows that the profit margin mean is -2.612. Finally, Panel

C shows that there are 495 observations for the cost of goods sold (COGS) and that the COGS

mean is 5.055.

Table 1 Descriptive Statistics

Panel A : ROA

Variable

Observatio

n Mean

Std.

Dev. Min Max

ROA 344 -4.031 1.144 -6.694 -0.598

DOWNSIZING 344 0.090 0.287 0 1

LABORINTENSITY 344 0.410 0.493 0 1

DSIZE 344 0.059 0.178 -0.381 2.225

FIRMSIZE 344 8.697 1.997 3.816 15.326

ASSETCHANGE 344 0.092 0.287 -0.442 4.622

LABORUNION 344 0.869 0.338 0 1

31

Panel B : Profit Margin

Variable

Observatio

n Mean

Std.

Dev. Min Max

PROFITMARGIN 131 -2.612 0.799 -5.668 -0.957

DOWNSIZING 131 0.137 0.346 0 1

LABORINTENSITY 131 0.267 0.444 0 1

DSIZE 131 0.071 0.241 -0.381 2.225

FIRMSIZE 131 8.084 2.087 3.816 13.912

ASSETCHANGE 131 0.119 0.422 -0.442 4.622

LABORUNION 131 0.740 0.440 0 1

Panel C : COGS

Variable

Observatio

n Mean

Std.

Dev. Min Max

COGS 495 5.055 2.611 -0.186 12.132

DOWNSIZING 496 0.131 0.338 0 1

LABORINTENSITY 496 0.395 0.489 0 1

DSIZE 496 0.094 0.435 0.530 6.450

FIRMSIZE 496 8.089 2.300 0.237 15.326

ASSETCHANGE 496 0.122 0.688 -0.633 11.081

LABORUNION 496 0.877 0.329 0 1

4.2 Correlation test

A Pearson correlation test was employed to measure the linear correlation between

two variables. The result of the correlation test will be between -1 and +1, where 1 is

interpreted as a positive correlation, 0 is no correlation and -1 is a negative correlation. Table

2 presents the Pearson correlations for each model. Aligned with the hypothesis, employee

downsizing (DOWNSIZING) has a negative impact on ROA, profit margin and COGS.

Table 2 also indicates that the labour intensive strategy (LABOURINTENSITY) has a

positive correlation with employee downsizing (DOWNSIZING). This suggests that a firm

with a high labour intensity characteristic is associated with a tendency to implement an

32

employee downsizing strategy. Meanwhile, a change in the number of employees (DSIZE)

has a negative correlation with downsizing. This is because when a firm reduces the number

of employees, this does not necessarily mean that a firm is implementing downsizing,

however, if the decreases are above 5% of a total number of employees, it can be categorized

as employee downsizing. Firm size (FIRMSIZE) is here measured as the total assets at the

beginning of the downsizing year. Firm size has a negative correlation with employee

downsizing, because if a company had low assets at the beginning of the year, there is a

tendency to reduce the number of employees to make the company more effective. Asset

change (ASSETCHANGE) has a negative correlation with employee downsizing. According

to Cascio and Young (2003), company downsizing is usually accompanied by a reduction in

assets. Finally, labour union (LABOURUNION) has positive correlation with downsizing.

Table 2 Correlation Test

Panel A: ROA 1 2 3 4 5 6 7

1 ROA 1

2 DOWNSIZING -0.0411 1

3 LABORINTENSITY -0.1562* 0.0061 1

4 DSIZE 0.028

-

0.3146* 0.038 1

5 FIRMSIZE -0.2248*

-

0.1105* 0.0945 -0.1148* 1

6 ASSETCHANGE 0.0759 -0.0558 0.0674 0.7895*

-

0.1031 1

7 LABORUNION -0.1819* 0.0017 0.1305* 0.0424

-

0.0087 -0.0029 1

Panel B: Profit Margin 1 2 3 4 5 6 7

1 PROFITMARGIN 1

2 DOWNSIZING -0.1970* 1

3 LABORINTENSITY -0.0572 0.1098 1

4 DSIZE 0.1117

-

0.3440* 0.1304 1

5 FIRMSIZE 0.0732

-

0.1948* -0.1893* -0.1146 1

6 ASSETCHANGE 0.0363 -0.0501 0.1478 0.8314*

-

0.1271 1

7 LABORUNION 0.1624 0.034 0.0033 0.0891 - 0.0311 1

33

0.1481

Panel C: COGS 1 2 3 4 5 6 7

1 COGS 1

2 DOWNSIZING -0.0409 1

3 LABORINTENSITY -0.0662 0.0283 1

4 DSIZE 0.0027 -0.2106 -0.0026 1

5 FIRMSIZE 0.5211 -0.1154 0.0398 -0.1089 1

6 ASSETCHANGE -0.0265 -0.0968 0.0481 0.2835

-

0.1347 1

7 LABORUNION -0.2303 0.0001 0.1143 -0.1379 -0.043 0.0088 1

Correlation are significant at p<0.05 (*)

4.3 OLS Regression Test

Table 3 presents regressions for all 3 models to test whether employee downsizing has

a significant impact on each measurement. Each model is tested with and without the labour

intensity variable to see if the employee downsizing coefficient changes when there labour

intensity is included in the model.

Due to industry variation in the sample, this study controls for each industry because

different industry environments can influence a company’s operating performance. To control

for industry differences in the sample, this study includes an industry dummy. Industry

dummy variables are separated into ten industry categories based in two-digit SIC codes.

According to Luan et al. (2013), industry attributes can influence firm performance.

Furthermore, there are some industries that have labour intensive characteristics, for example,

agriculture. Meanwhile, there are other industries that have capital intensive characteristics,

for example, manufacturing. Therefore it is important to control for the type of industry, as

explained in the control variable section. In total, there are ten industries dummies, due to

incomplete data for the variables however, some samples were eliminated and as a result, only

seven industries remain in the sample. Furthermore, due to multicollinearity, some samples

were omitted from the regression models. In models 1 and 2, a construction dummy was

omitted. In models 3 and 4, finance, insurance and real estate were omitted. Finally, in models

5 and 6, mining was omitted from the model. Due to multicollinearity some industries must

be eliminated.

Although it is unlikely that labour intensive versus capital intensive characteristics

will have a significant impact on firm performance in driving employee downsizing, a Chow-

test is nonetheless performed to see if any such difference exists. Before this is performed

34

however, it is important to ensure that downsizing has a significant impact on firm

performance. All hypotheses in this study are aligned with the proposition that employee

downsizing has a negative impact on firm performance.

Table 3 shows that employee downsizing has a negative impact on ROA, profit

margin and COGS with and without the labour intensity variable. Employee downsizing has a

significant negative impact on ROA as model 1 (β = -0.313, p <0.05) and model 2 (β = -

0.308, p <0.05) indicate. Employee downsizing also has a significant negative relation with

profit margin as model 3 (β = -0.444, p <0.05) and model 4 (β = -0.461, p <0.05) show. Both

the ROA and profit margin relationships with employee downsizing are aligned with

hypotheses 1 and 2. However, employee downsizing does not exhibit a significant impact on

COGS, even though it has a negative effect on COGS, as shown in model 5 (β = -0.065) and

model 6 (β = -0.055). Chalos and Chen (2002) found that employee downsizing has an

insignificant effect on COGS in three types of employee downsizing, which are revenue

refocusing, cost cutting and plant closure. Due to a lack of empirical evidence of COGS, the

possible explanation is that labour costs for most companies in the sample do not play a big

role in driving total COGS, even though it still reduces COGS. A big part of COGS may be

driven instead by raw material cost..

Table 3 OLS Regression

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

VARIABLES ROA ROAPROFIT PROFIT

COGS COGSMARGIN MARGIN

DOWNSIZING -0.313** -0.308** -0.444** -0.461** -0.065 -0.055

LABORINTENSITY -0.164* 0.099 -0.214*

DSIZE -0.708 -0.732 -0.032 -0.060 0.155 0.158

FIRMSIZE -0.108*** -0.104*** 0.033 0.037 0.822*** 0.824***

ASSETCHANGE 0.414 0.447 0.028 0.029 0.045 0.051

LABORUNION -0.018 0.006 0.295* 0.298* -0.426** -0.396**

MINING -0.679 -0.713 -0.381 -0.339 - -

CONSTRUCTION - - -0.959* -0.948* 1.458** 1.483**

MANUFACTURING 0.011 0.036 -0.756** -0.757** 0.840*** 0.915***

TRANSPORTATION

AND PUBLIC UTILITY-0.123 -0.126 -0.966** -0.950** 1.228*** 1.247***

RETAIL TRADE -1.790*** -1.757*** -0.912** -0.875** -3.043*** -2.980***

FINANCE, INSURANCE -0.690 -0.624 - - -0.662** -0.553*

35

AND REAL ESTATE

SERVICES -0.571 -0.523 -0.589 -0.594 0.768** 0.856***

Constant -1.819*** -1.837*** -2.258*** -2.326*** 0.079 0.064

Observations 344 344 131 131 495 495

R-squared 0.481 0.486 0.151 0.153 0.774 0.774

Coefficient are significant at *** p<0.01, ** p<0.05, * p<0.1

Labour intensity characteristic, which represented as 1 in the LABOURINTENSITY

variable has a negative and significant relation with ROA (β = -0.164, p <0.1) and COGS (β =

-0.214, p <0.1). This suggests that if a firm is labour intensive, it will have a lower ROA and

COGS than a capital intensive firm. However, labour intensity does not have a significant

relationship with the firm profit margin.

The downsizing magnitude (DSIZE) variable is insignificant in all models, but firm

size (FIRMSIZE) has a significant relationship with ROA (β = -0.108, p <0.01 and β = -0.104,

p <0.01) and COGS (β = 0.882, p <0.01 and (β = 0.824, p <0.01). Firm size (FIRMSIZE)

shows a negative and highly significant relationship with ROA. As mentioned previously,

firm size is measured as the firm’s total assets at the beginning of the downsizing year. In

calculating ROA, total assets are the denominator; therefore if the dominator increases and the

nominator is assumed to be constant, this will result in a lower ROA. Furthermore, firm size

has a positive impact on COGS. A positive relationship with COGS means in practice a

negative impact; because a higher COGS means a company has higher costs or expenses. The

higher the total assets, the higher the costs that a company has to spend for maintain those

assets.

Apparently, asset change does not show a significant relationship in any model, but it

has a positive effect on ROA, profit margin and COGS. Finally, labour union has a significant

relationship only with COGS and profit margin, but not with ROA. Labour union has a

positive effect on profit margin (β = 0.295, p <0.1 and β = 0.298, p <0.1). This suggests that if

a firm has a labour union, it will contribute to a higher profit margin. This is probably because

the closer relationship between employees and a higher sense of belonging, as a result of

which they feel more responsible for increasing the company’s performance. It is also aligned

with the labour union effect on COGS. Labour intensity has negative effect on COGS (β = -

0.426, p <0.05 and β = -0.396, p <0.05). Negative in COGS means more positive firm

performance due to lower costs. This suggests that if a firm has a labour union, it can reduce

36

costs. The possible explanation for this is that because of the closer relations between

employees, the more effective and efficient they are in performing their jobs.

In conclusion, this test attempted to test whether employee downsizing has a negative

and significant impact to support the original hypotheses. This study found that downsizing

only has a significant impact on ROA and profit margin, but not impact COGS. As explained

previously, the possible explanation for this is that labour costs for most companies in the

sample do not play a big role in driving total COGS, even though it still reduces COGS. A big

part of COGS may be driven by raw material costs.

4.4 Chow-test

A Chow test is often applied to identify a structural change (Davidson & MacKinnon,

1993) because this test allows determination of whether the coefficients of a regression model

are identical between two groups. To perform this test, the labour intensity

(LABOURINTENSITY) variable will be excluded from the model to see the main effects of

downsizing on firm performance. The labour intensity variable will act as a company’s

identification to separate between the two groups, which are labour intensive and capital

intensive. If a firm is coded as 1 in the labour intensity variable, then it is categorized as a

labour intensive group, and if a firm coded as 0 then it categorized as a capital intensive

group. These two groups will be compared using a Chow-test. A higher Chow-test value than

the critical value indicates that there is a structural change between a sample that is labour

intensive and a sample that is capital intensive.

First of all, this thesis needs to test the whole sample with OLS regression before

separating the samples it into two groups. Secondly, this study performs OLS regression to

test the two groups individually. Thirdly, for each regression this study can obtain residuals of

the sum of the squares, the number of observations, and the total variables, including those

which are constant, that are employed in the model. It is important to note that to be able to

obtain non-biased results, all three regressions have to employ the same variables. Therefore,

this study needs to omit one or two variables in order to obtain the same total number of

variables. The coefficient of the variables in the regression that uses the whole sample will be

slightly different from the previous regression because the omitted variables are different.

Furthermore, the calculation of the Chow-Test for all measurements is presented in the next

subsection.

4.4.1 ROA Chow-test

Table 4 presents the regressions for employee downsizing on ROA for the whole

37

sample, labour intensive group, and capital intensive group. Table 4 suggests that employee

downsizing is not significant when the sample is separated into two groups. This is signalling

that labour intensity and capital intensity do not play a significant role in influencing

employee downsizings impact on ROA. Recalling the hypotheses H1a and H1b, the negative

effect of employee downsizing strategy on ROA is greater when a firm is labour intensive and

lesser when a firm is capital intensive. This is aligned with the employee downsizing results

given in table 4. The employee downsizing coefficient is more negative when a company is

labour intensive (from β = -0.311, p <0.05 to β = -0.354) and more positive when a company

is capital intensive (from β = -0.311, p <0.05 to β = -0.239). However, the result does not

show a significant impact. Furthermore, the Chow-test calculation also revealed that there is

no significant difference.

Table 5 provides further calculations of Chow-tests using data that was obtained from

the regression in Table 4. Table 5 shows that if the value from a Chow-test is lower than the

critical F value (1.452577883 < 1.81844592), this suggests that there is no structural change

between labour intensive and capital intensive effects. The results failed to prove H1a and

H1b that there is a significant difference between labour intensive and capital intensive

industries in moderating the employee downsizing effect on firm performance, even though

the direction of the employee downsizing coefficient changed, as predicted by hypotheses

H1a and H1b.

Table 4 Chow test regression for ROA

ROA ROA

VARIABLES ROA LABOUR CAPITAL

DOWNSIZING -0.311** -0.354 -0.239

DSIZE -0.799* -1.668** -0.337

FIRMSIZE -0.107*** -0.0876*** -0.126***

ASSETCHANGE 0.429 0.806** 0.165

LABORUNION -0.0195 -0.146 0.0139

CONSTRUCTION 0.0778 -1.110 0.339

TRANSPORTATION AND PUBLIC UTILITY -0.0402 0.600 -0.211

RETAIL TRADE -1.706*** -1.616*** -1.697***

FINANCE, INSURANCE AND REAL

ESTATE

-0.607*** -0.242 -0.920**

SERVICES -0.474** -0.0474 -0.675**

Constant -1.910*** -2.186*** -1.682***

38

Observations 344 141 203

R-squared 0.477 0.539 0.450

MINING and MANUFACTURING was omitted due to autocorrelation

Significant at *** p<0.01, ** p<0.05, * p<0.1*** p<0.01, ** p<0.05, * p<0.1

It is argued that downsizing will be more detrimental to labour intensive industries

because of survivor syndrome. Survivor syndrome will result in lower productivity, which

will further lower the net income. Since labour intensive industry depends more on labour, the

survivor syndrome disrupts the production process, which in turn leads to lower sales and net

profit. Meanwhile, in a capital intensive industry, production does not depend on labour but

on capital such as machinery to generate income. As a result, downsizing will be less

detrimental under these circumstances. However, the results show that there is no significant

difference between labour intensive and capital intensive industries in driving employee

downsizing effect on ROA.

Employee downsizing has a negative effect on net profit for both labour intensive and

capital intensive industries However ROA is driven by net profit as the nominator and total

assets as the denominator. A possible explanation is that employee downsizing affects the

nominator equally between labour intensive and capital intensive industries, without

influencing the denominator as well as in this case the total assets,. This suggests that between

the labour intensive sample and the capital intensive sample, there are no significant changes

in their total assets accompanying employee downsizing. Therefore, the downsizing

coefficient in the OLS regression after the sample is separated into two groups is

insignificant. Morover, the Chow-test supports the case that there is no difference between

labour intensive and capital intensive industries in influencing the effects of employee

downsizing on ROA.

Table 5 Chow calculation for ROA

ROA Residual Observation

General 235.041 344

Labour 87.104 141

Capital 136.825 203

K 11

Critical F value 1.81844592

39

Result 1.452577883 < 1.81844592

F =

(S0 - S1 - S2)/ K

(S1 + S2) / (N1 + N2 -

2*K)

F =(235.041 - 87.104 - 136.825) / 11

(87.104 + 136.825) / (141 + 203 - 2 * 11)

F = 1.452577883

4.4.2 Profit Margin Chow-test

Table 6 provides the evidence that labour intensive and capital intensive industries

influence the employee downsizing effect on the profit margin. First, before examining in

more detail the actual Chow-test calculation, focusing on the employee downsizing

coefficient in each regression, it can be seen that the negative effect of employee downsizing

is greater when a firm is labour intensive (from β = -0.439, p <0.05 to β = -0.450, p<0.1) and

lesser when a firm is capital intensive (from β = -0.439, p <0.05 to β = -0.345, p<0.1). Results

from table 6 align with hypotheses H2a and H2b. For further detail regarding the test, table 7

provide results for the Chow-test calculation.

Table 7 shows that the F value from the Chow-test is greater than the critical F value

(2.042944278 > 1.8775524). This suggests that there is a structural change between the labour

intensity and capital intensity groups. This means that labour and capital intensities have a

significant influence on the employee downsizing effect on profit margin.Table 6 Chow test regression for profit margin

PROFIT LABOR CAPITAL

VARIABLES MARGIN INTENSIT

Y

INTENSTY

DOWNSIZING -0.439** -0.450* -0.345*

DSIZE 0.0775 -1.519 0.801

FIRMSIZE 0.0372 -0.0381 0.0833*

ASSETCHANGE 0.00188 0.524 0.830

LABORUNION 0.294* 0.751* 0.345*

CONSTRUCTION -0.163 -2.348** 0.134

MANUFACTURING 0.0413 -0.591 0.0149

TRANSPORTATION AND PUBLIC UTILITY -0.168 -1.528 -0.0247

40

FINANCE, INSURANCE AND REAL

ESTATE

0.807** -0.443 1.447***

SERVICES 0.199 -0.213 0.233

DOWNSIZING -3.094*** -1.960* -3.711***

Observations 131 35 96

R-squared 0.138 0.402 0.230

MINING and RETAIL TRADE was omitted due to autocorrelation

Significant at *** p<0.01, ** p<0.05, * p<0.1

As discussed in previous sections, the reason for this is that a company which deals with a

higher level of survival syndrome tends to have a slower improvement, because it is difficult

to motivate employees to perform and commit once their trust is broken. In a labour intensive

company, there are more employees than in a capital intensive company. Therefore employee

downsizing will be more detrimental for a labour intensive firm than a capital intensive firm.

As a result, labour intensity will have a more negative influence on employee downsizing and

profit margin.

Profit margin is driven by net profit as a nominator and the total sales as the

denominator. Recall that employee downsizing negatively affects the net profit equally

between labour intensive and capital intensive industries, because all of the direct and indirect

costs that arise with this strategy occur in both types of industry. However, employee

downsizing effects on sales is different between labour intensive and capital intensive

industries. Labour intensive industries will have a lower productivity and a higher level of

survivor syndrome as mentioned previously. Table 7 Chow Calculation for profit margin

Profit Margin Residual Observation

General 71.594 131

Labour 15.337 35

Capital 44.020 96

K 11

Critical F value 1.8775524

Result 2.042944278 > 1.8775524

F =(S0 - S1 - S2)/ K

(S1 + S2) / (N1 + N2 -2*K)

F = (71.594 - 15.337 - 44.020) / 11

41

(60.483 + 128.534) / (141 + 203 - 2 * 11)

F = 2.042944278

4.4.3 COGS Chow-test

Table 8 provides the evidence that if an employee downsizing effect on COGS is not

significant even if tested with the whole sample, than the effect of a labour intensive or capital

intensive industry will be insignificant also. However, table 8 shows that labour intensity

magnifies the negative effect of employee downsizing on COGS (from β = -0.0651 to β = -

0.286) compared to capital intensity (from β = -0.0651 to β = 0.131). By focusing on the

coefficient changes, this result aligns with hypotheses H3a and H3b. The more negative result

means that a firm with a labour intensive characteristic can reduce higher costs in COGS

compared to firms, which are capital intensive by implementing an employee downsizing

strategy. To ensure that there is a difference, table 9 provides Chow-test calculation results.

Table 9 shows that the F value of the Chow-test is slightly above the critical F value

(1.796053747 > 1.77274649). This suggests that there is a structural change between the

labour intensive group and the capital intensive group. However, since employee downsizing

does not have a significant impact on COGS, the difference between labour intensive and

capital intensive industries is also not significant in the regression presented in table 8.

Table 8 Chow test regression for COGS

(1) (2) (3)

VARIABLES MEANLNCOGS MEANLNCOGS MEANLNCOGS

DOWNSIZING -0.0651 -0.286 0.131

DSIZE 0.155 0.193 0.178

FIRMSIZE 0.822*** 0.851*** 0.801***

ASSETCHANGE 0.0452 0.0458 0.0363

LABORUNION -0.426** -0.396 -0.450*

CONSTRUCTION 1.458** 2.233* 1.308*

MANUFACTURING 0.840*** 1.165* 1.024***

42

TRANSPORTATION AND PUBLIC UTILITY 1.228*** 2.363*** 1.071***

RETAIL TRADE -3.043*** -2.862*** -2.843***

FINANCE, INSURANCE AND REAL ESTATE -0.662** 0.0107 -0.992**

SERVICES 0.768** 1.356** 0.626

Constant 0.0790 -0.714 0.311

Observations 495 196 299

R-squared 0.772 0.857 0.740

MINING was omitted due to autocorrelation

Significant at *** p<0.01, ** p<0.05, * p<0.1*** p<0.01, ** p<0.05, * p<0.1

Table 9 Chow calculation for COGS

COGS Residual Observation

General 766.124 495

Labour 170.015 196

Capital 562.586 299

K 12

Critical F value 1.77274649

Result 1.796053747 > 1.77274649

F =(S0 - S1 - S2)/ K

(S1 + S2) / (N1 + N2 -2*K)

F =(766.124 - 170.015 - 562.586) / 12

(170.015 + 562.586) / (196 + 299 - 2 * 12)

F = 1.796053747

In conclusion, the result is aligned with the expectations of H3a and H3b, however,

since it is not significant, this result failed to support H3a and H3b. A possible explanation is

that labour costs for most companies in the sample, does not play a big role in driving total

COGS, even though employee downsizing reduces COGS. COGS may be driven by other

factors, for example raw material costs. Therefore employee downsizing has no significant

effect on COGS.

43

5 Conclusion

Multiple studies that are related to employee downsizing have been conducted

showing little agreement on how employee downsizing affects firm performance. Datta et al.

(2010), who reviewed the employee downsizing literature, have claimed that there are diverse

relationships between employee downsizing and firm performance. Therefore, researchers are

motivated to find the factors that may moderate the effects in order to explain the different

44

impacts of downsizing on firm performance. The impact of layoffs on firm performance is

different across different industries with different characteristics. This aligns with the

contingency theory, which suggests that an organization’s strategic posture either enhances or

reduces the impact of human resources practice on firm performance (Youndt et al., 1996).

Because industry characteristics are one part of an organizations strategic posture, they might

influence the effectiveness of human resources practice and the effets of, employee

downsizing on firm performance. This study aims to investigate how other factors can

moderate a layoff effect on firm performance. Moreover, the aim is to see whether labour

intensive and capital intensive characteristics can influence the layoff effect on firm

performance.

This study used 3 measurements, ROA, profit margin and COGS. To examine how

labour and capital intensity affect firm performance, first this study tested the impact of

downsizing on firm performance without including a labour/capital intensity variable. To see

whether downsizing has an impact on ROA, profit margin, and COGS, OLS regression was

used. Second, this study tested the differences concerning how labour intensity and capital

intensity influence the relationship between downsizing and firm performance (ROA, profit

margin, and COGS). To test for these differences, Chow break tests (Heij et al., 2004) were

applied to model. A Chow-test is often applied to see a structural change (Davidson and

MacKinnon, 1993) because this test allows determination of whether the coefficients of a

regression model are identical between two groups. Therefore, by applying this test,

identification of whether the actual parameters before the sample was divided into two groups

were changing, and how the difference between the two groups may drive the employee

downsizing coefficient could be established.

There are several findings of this study. First, this study found that the employee

downsizing strategy has a negative and significant impact on ROA and profit margin, which

is aligned with previous studies from Cascio et al. (1997), Luan et al. (2013) and Suárez-

González (2001). Cascio et al. (1997) found that employee downsizing resulted in negative

changes in ROA at the occurrence year and the following year. Luan et al. (2013) provided

negative evidence of employee downsizing on ROA and Suárez-González (2001) found a

lower return on sales (ROS/profit margin). Overall, this is aligned with the basic assumption

that an employee downsizing strategy has a negative impact on firm performance. The

negative impact on ROA and profit margin is because of the cost arising from downsizing

itself, which often exceeds the payroll expenses a company is aiming to cut. As a result, there

is a decrease in net profit, which leads to negative ROA and negative profit margin.

45

However, this study found that employee downsizing has negative impact in COGS

but that it is not significant. This suggests that downsizing has no impact on COGS. Although

the main reason for a company implementing an employee downsizing strategy is because the

company wants to reduce its expenses, employee downsizing is supposed to have a direct

impact on COGS. However, the results failed to support a negative effect of employee

downsizing on COGS. A possible explanation is that labour costs for most companies in the

sample do not play a big role in driving total COGS, even though employee downsizing

reduces COGS. COGS may be driven by other factors, for example, raw material costs.

Therefore employee downsizing has no significant effect on COGS. This finding is also

aligned with previous studies from Chen et al. (2001) and Chalos & Chen (2002). They found

that COGS are lower and not significant after employee downsizing.

Furthermore, the Chow-test result revealed that there is no structural change between

labour intensive and capital intensive groups in moderating the employee downsizing effect

on ROA. This suggests that the employee downsizing effect on ROA is the same across these

two characteristics. This result does not support hypotheses H1a and H1b. A possible

explanation is that employee downsizing affects the nominator equally between labour

intensive and capital intensive industries, without influencing the denominator as well as in

this case the total assets, there is no diffrences between labour intensive and capital intensive

industries in moderating the downsizing effect on ROA. This suggests that between labour

and capital intensive samples, there are no significant changes in total assets that accompanied

employee downsizing. Therefore, the downsizing coefficient in the OLS regression after the

total sample was separated into two groups was insignificant.

However, the Chow-test shows that there is a structural change between labour

intensive and capital intensive groups in moderating the employee downsizing impact on

profit margin. The employee downsizing coefficient in each regression shows the negative

effect of employee downsizing is greater when a firm is labour intensive and lesser when a

firm is capital intensive. These results support hypotheses H2a and H2b. One possible

explanation is that profit margin is driven by net profit as a nominator and the total sales as a

denominator. Recall that employee downsizing negatively affects the net profit equally

between labour intensive and capital intensive industries, because all of the direct and indirect

costs that arise with this strategy occur in both characteristic industries. However, the

employee downsizing effect on sales is different between labour intensive and capital

intensive industries. Labour intensive industries will have lower productivity and higher

survivor syndrome, as previously before.

46

Lastly, this study found that labour intensive industries magnify the negative effect of

employee downsizing on COGS compared to capital intensive industries, although the result

is not significant. Therefore this test failed to prove H3a and H3b. A possible explanation why

there is no significant difference between labour intensive and capital intensive industries in

moderating employee downsizing on COGS is because employee downsizing itself does not

have a significant relation with COGS for the whole sample. Therefore, when the samples are

separated into two groups, which are labour intensive and capital intensive, the result was not

significant also. Table 10 Predicted sign and results

No. Hypotheses Moderating Variable

Predicted Sign

Result Significant Level

H1a The negative effect of employee downsizing on ROA

Labour intensive

- - Not Significant

H1b The negative effect of employee downsizing on ROA

Capital intensive

+ + Not Significant

H2aThe negative effect of employee downsizing on profit margin

Labour Intensive

- - *

H2bThe negative effect of employee downsizing on profit margin

Capital intensive

+ + *

H3a The negative effect of employee downsizing on COGS

Labour Intensive

- - Not Significant

H3b The negative effect of employee downsizing on COGS

Capital Intensive

+ + Not Significant

The main research question is:

“Do industry characteristics, specifically labour intensity and capital intensity, affect the

negative effect of employee downsizing events on subsequent firm performance?”

The Answer from the research question is:

Labour intensity drives employee-downsizing impacts on firm performance in a more

negative direction. Meanwhile, capital intensity drives employee-downsizing impacts on firm

performance to a more positive direction. This study thus concludes that firms with labour

intensive characteristics experience more negative effects from an employee downsizing

strategy than firms, which are capital intensive. According to Contingency theory, an

organization’s strategic posture either enhances or reduces the impact of human resources

practice on firm performance. This study found that a labour intensive characteristic reduces

the impact of an employee downsizing strategy on firm performance. Based on Volbreda’s

47

(2012) statement, “High performance is a consequence of co-alignment between a limited

number of organizational and environmental factors” (p. 1042). The employee downsizing

strategy does not co-align with a labour intensive characteristic, as a result, this strategy does

not enhance firm performance if a firm is a labour intensive. However, the result shows that

the negative effects of employee downsizing on firm performance are less when a company is

capital intensive. This suggests that an employee downsizing strategy co-aligns with a capital

intensive characteristic.

Nevertheless, capital and labour intensities as contingency factors do not drive

employee downsizing impacts on firm performance for all variables. Although the results

show labour and capital intensities drive employee downsizings impact as this study expected,

this effect is only significant for the profit margin measurement.

6 Discussion and Limitation

The objective of this study is to contribute to the growing literature on the effect of

employee downsizing on firm performance. From research point of view, this study adds the

important of industry characteristic as a contingency factor in influencing employee

downsizing and firm performance relation. The finding also highlights that industry

characteristic is an important factor to consider before implementing a strategy.

On the other hand, for managerial point of view, this study elaborates how industry

characteristic affects the successfulness of employee downsizing strategy. This thesis

48

indicates that employee-downsizing strategy will be more detrimental to a certain firm

characteristic. Overall this thesis, support the notion that employee downsizing has negative

impact on firm performance, and it is magnified if the company labour intensive rather than

capital intensive.

Based on Guthrie and Datta (2008) “If samples are drawn from single or a limited set

of industries, then the particular features of these industries may have undue influence on the

performance effects associated with downsizing” (p. 119). This study uses all US data and

controlled for the all industry sector. As a result, employee-downsizing impact on firm

performance examined in this study is clear from undue influence.

Although this study provides insightful result that explain other factor that can

moderate the effectiveness of employee downsizing strategy, but the finding is associated

with some limitation.

First, this study do not have a direct measure of employee downsizing, instead this

study measure the difference of the total number of employee between two timeframe.

However, this thesis provide strong evidence that support this measurement reflect employee

downsizing strategy. For other measures this study also provide strong reason and also based

on previous study support.

Second, one of firm performance proxy, which is profit margin, only has small

number of observation due to sample limit. The result from this particular measurement

cannot be generalized to other companies.

Third, this study does not control for prior performance in the model. Prior

performance has a high level of serial autocorrelation, thus prior performance will already

explain current values well. This will hinder us from investigating the relationship of other

explanatory variables. Future research might figure out how to control for prior performance,

as prior performance could be influence the tendency of firm to take employee downsizing

strategy. Prior performance also has been argued can moderate employee downsizing impact

on firm performance. For example, Yu and Park (2006) find that improvement in ROA is

greater for a firm that did not experience loss than a firm that experience loss before

downsizing.

Fourth, this study does not take into account each year performance improvement.

Therefore, the time-lagged theory about positive impact of employee downsizing cannot be

seen in this study. Therefore, future research might take into account each year performance

after employee downsizing events.

This thesis examine contingency factor as industry level, future research can explore

49

the contingency factor in a firm level. For example, as explained before, employee

downsizing has negative effect on firm performance because employees perceive it as

violation to the implicit contract between employee and employer. This perception could

moderate the effectiveness of downsizing, because employees in a loss company might have a

different perception than employees in a healthy company.

This study provides the evidence that employee-downsizing strategy has negative

impact on firm performance; however, it is not necessarily all the firms that employed

downsizing strategy would result in negative performance. It is also depend on how

management of the company can mitigate the impact. Much work remains for future research

in identifying other factor that may influence employee-downsizing impact on firm

performance. Hopefully this study will be beneficial for practitioners and future academic

research.

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