Asymmetric Cost Behavior in Local Public Enterprises:

Exploring the Public Interest and Striving for Efficiency㛗⃝ ᪼ᖹ

Springer 発行
Journal of Management Control 掲載
2018年12月 Vol.29 No.3-4This is a post-peer-review, pre-copyedit version of an article published in Journalof Management Control. The final authenticated version is available online at:
http://dx.doi.org/10.1007/s00187-018-0269-x

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Table of Contents

1 Introduction ........................................................................................................................................3
2 Background and Literature Review……………………………………………………………………7
2.1 Characteristics of LPEs……………………………………………………………………………7
2.2 Institutional Constraints of LPEs………………………………………………………………….9
2.3 Cost Behavior of Public Sector Organizations…………………………………………………...11
2.4 Hypothesis Development………………………………………………………………………....13
3 Research Method and Sample Selection…………………………………………………………..….16
3.1 Research Method…………………………………………………………………………………16
3.2 Sample Selection and Descriptive Statistics……………………………………………………..17
4 Results………………………………………………………………………………………………...18
5 Conclusions………………………………………………………………………………………...…23
References……………………………………………………………………………………………27
Appendix……………………………………………………………………………………………..31

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Abstract

Asymmetric cost behavior, which was first identified in Germany in the 1920s, has attracted theattention of researchers over the last two decades. Cost management is essential not only for commercial
enterprises (CEs) but also for public organizations. Therefore, in this research, I focus on local public
enterprises (LPEs), one type of public organization in Japan, and clarify their cost behavior. Then,
taking the perspective of institutional theory, I compare LPEs with CEs. Because LPEs are required tobehave according to the restrictions of LPE law, they are more vulnerable to institutional pressure.
Specifically, LPEs have two normative institutional constraints: (1) efficiency and (2) the public interest(i.e., the responsibility to support people’s everyday lives). Therefore, LPEs must provide certain
services even if they are unprofitable. To explore whether normative institutional pressure causes LPEs
to be cost inefficient, I compare the cost behavior of these enterprises with that of CEs in five ways. I
analyze (1) panel data covering 40 years, (2) the change over time, (3) the differences by industry type,(4) the relationship with population changes, and (5) the effect of political influence. I find that LPEs’
cost management is not necessarily cost inefficient; however, their ability to adjust costs may be lost in
the future due to the influence of institutional constraints. I therefore assert that LPE administrators
must constantly struggle to balance the institutional constraints of the public interest and efficiency
since these factors require long-term, stable management.

Keywords: local public enterprises, sticky costs, anti-sticky costs, asymmetric cost behavior, public
interest, efficiency, institutional constraints
JEL codes: H83, M41

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1 Introduction
After World War II, public enterprises (PEs) were created in both developed and developing
countries to address market deficits and capital shortfalls, promote economic development, reduce massunemployment and/or ensure national control over the overall direction of the economy (UN, 2008).
Over the long term, PEs provided public services that were directly managed by governments. However,management inefficiencies, overstaffing, inflation and rising current account deficits in the 1980s
exposed serious “government failures” and the limitations of PEs as major players in economic
development (UN, 2008). Subsequently, new public management (NPM) led public organizations(including PEs) to change their behavior from reflecting administrative aspects to reflecting managerial
aspects (Van Genugten 2008; Pérez-López et al. 2015). From the perspective of fiscal finance, theoperations of public organizations switched from recognizing soft budget constraints to recognizing
hard budget constraints (Bertero and Rondi 2000). Furthermore, in the 1990s, many public services
provided by public sector organizations were outsourced or the organizations were privatized andbecame commercial enterprises (CEs) because of pressure to improve their efficiency and effectiveness(Hefetz and Warner 2007). Thus, public service costs in public sector organizations were initiallyreduced through outsourcing or privatization (Domberger and Jensen 1997; Domberger and Rimmer
1994; Hodge 2000), but the cost reduction effects gradually decreased over the long term (Bel andCostas 2006; Dijkgraaf and Gradus 2011). Therefore, in the 2000s, the responsibility for outsourced
public services shifted again to corporatized PEs, which emphasize efficiency and have greater
independence from the government than PEs that are directly managed by governments (Hefetz andWarner 2007; Grossi and Reichard 2008; Wollmann et al. 2010). Currently, various public services areprovided by corporatized local public enterprises (LPEs) in every region of the world (Saussier andKlien 2013) (Table 1).
[Insert] Table 1. LPEs in selected countries
Recently, corporatized LPEs 1 have been found to be more efficient than LPEs directly managed by
local governments (Voorn et al. 2017). Nevertheless, LPEs are generally considered to be more costinefficient than CEs since the former face stronger institutional pressure (i.e., normative, coercive, andmimetic) than CEs (Frumkin and Galaskiewicz 2004). In particular, from the viewpoint of normative
institutional constraints, LPE administrators are pressured by law to achieve efficiency 2 and serve thepublic interest 3. However, it is very difficult for LPE administrators to do both simultaneously. If LPE
administrators prioritize cost reductions due to the influence of efficiency pressures, the risk of1

Hereafter, in Section 1, “LPEs” refer to corporatized LPEs.
The concept of efficiency is used differently in each study focusing on the public sector (Voorn et al.
2017). In this article, efficiency refers to cost efficiency.
3 The concept of the public interest can be defined not only as a specific conceptualization of the term
“public interest” but also with a variety of meanings from very specific to very broad definitions (Pesch2005; Van Genugten 2008). Therefore, in this research, following De Bruijn et al. 2004, “public interest”
is defined as both the importance of services (i.e., necessary and convenient for everyday lives) and theroles and responsibilities of governments.
2

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declining public service quality increases. Conversely, pursuing the public interest can lead LPE
administrators to manage their costs more inefficiently. Thus, LPE administrators must strike a balance
between efficiency and the public interest under the pressure of these two normative institutional
constraints (Kawarata 2005). By contrast, CE managers aim only to maximize profits; since they are
subject to fewer institutional pressures than LPEs, they have greater flexibility in making managementchanges (Eldenburg et al. 2004; Balakrishnan et al. 2010; Holzhacker et al. 2015). However, to date,research on whether public services are more inefficiently performed by LPEs than CEs is lacking.
Therefore, my research question is whether LPEs manage their costs more inefficiently than CEs. In
this research, I focus on LPEs in Japan and clarify their cost management. In addition, I compare myresults with those for CEs based on the theoretical background of institutional theory. I choose Japanese
LPEs for two reasons. First, the number of LPEs in Japan is very high compared to the number
worldwide (Table 1). In Japan, the Local Public Enterprise Law was enacted in 1948, after World War II,
and subsequently, many LPEs were established in each municipality. Therefore, it is possible to collectdata from a large cross-sectional sample, making this empirical research more robust. Second, LPEs are
consistently the main bodies providing public services and have been continuously engaged in thisimportant role supporting civil life in Japan over the long term. Therefore, it is possible to collectconsistent, long-term time series data. The accounting system for LPEs remained unchanged until 2014 4.
Therefore, in this research, I was able to collect fiscal data from 1974 5 to 2013 and verify the long-termchanges in cost management alongside the global trends for each period, for example, the trends in
NPM since the 1980s, outsourcing or privatizing into CEs since the 1990s, and the revival of LPEs sincethe 2000s.
Additionally, I discuss how LPEs’ cost management should be sustainably controlled in the future
not only in theory but also in practice. LPEs in Japan have encountered two main issues in recent years
that have intensified the institutional constraints of achieving efficiency and serving the public interest:
population changes and a deteriorating financial situation. According to Japan’s population census, thecountry’s population had reached its upper limit and entered a stage of decline (Figure 1). In Japan, theproportion of elderly people in the total population exceeded 14% in 1995, and Japan became an aging
society. Furthermore, in 2007, this proportion exceeded 21%, representing a super-aging society. In
conjunction with this shift, the population of youth and of those in the productive ages has continued to
decline. Additionally, Japan’s suburban population has decreased dramatically. The Japanese
government reported that the percentage depopulated areas 6 of Japan has increased from 40.7% in 1972
4 LPEs in Japan adopted almost the same bookkeeping method as CEs beginning in 1966. After 2014,
the accounting standards of LPEs have changed. Many of them are based mainly on changes in thebalance sheet that this research does not pay attention to. On income statements (P/L) that I pay
attention to in this study, the method of amortizing fixed assets when purchased with subsidies has beenchanged. Before 2013, the amortizing fixed assets were accounted for only in expenses; on the other
hand, after 2014, the amortizing fixed assets were accounted for not only in expenses but also inrevenue, as the long-term advances received.
5 1974 is the first year for which data collection was possible.
6 The depopulated areas in Japan are defined in the Act on Special Measures for Promotion for
Independence for Underpopulated Areas. There are many requirements for specifying depopulated

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to 58.7% in 2015. The number of depopulated municipalities also increased from 32.3% in 1972 to
46.4% in 2015. LPEs must continue their businesses despite the institutional constraint of serving the
public interest, even if the costs of idle capacity rise due to a declining number of users caused bypopulation decreases. Conversely, the aging population, who need more public services (e.g., medical
services, care services) at a low cost, will continue to increase in the future.
[Insert] Figure 1. Population changes in Japan
A final issue is the difficulty LPEs experience in repaying bonds (Figure 2 Panel A). LPEs issue
bonds to finance new public service projects (including both maintenance and renovation projects) or to
improve the quality or expand the quantity of public services. In examining LPEs’ financial statements,
although operating revenues and expenses may be in surplus, non-operating revenues and expenses
often show deficits (Figure 2 Panel B and Panel C). This difference is due mainly to the repayment ofbonds and interest payments. Since interest payments are a fixed cost, LPE administrators must reduceother variable costs. However, cost adjustment flexibility decreases with increases in LPE bonds.
Namely, the repayment of LPE bonds requires LPE administrators to further enhance their organizations’efficiency.
[Insert] Figure 2. LPE bonds, operating and non-operating revenues and expenses
For LPEs to improve their efficiency, it is essential to consider further developing their costmanagement. Thus, clarifying LPEs’ cost behavior and understanding its movement is important for
improving LPEs’ cost management (Murray 1975; Rainey et al. 1976). In research on cost behavior,
German studies identified “Kostenremanenz” in the 1920s. Over the past 20 years, this phenomenon has
again attracted the attention of empirical researchers in management accounting (Noreen andSoderstrom 1997) and is now known as “sticky costs (cost stickiness)” (Anderson et al. 2003). Sticky
costs increase proportionally as activities increase, but when activities decrease, the costs do notdecrease symmetrically. In subsequent studies, sticky costs were found to exist in each region, country
and industry (Calleja et al. 2006; He et al. 2010; Subramaniam and Weidenmier 2016). Conversely, it
has also been verified that a change in cost may exceed the change in activity (Weiss 2011). Subsequentempirical research showed that cost behavior includes not only sticky costs but also anti-sticky, i.e.,
asymmetric, costs when activity increases and decreases (Banker and Byzalov 2014). However, mostprevious studies have focused on CEs (Malik 2012; Günther et al. 2014), and only a few studies havefocused on public sector organizations’ cost behavior (Yasukata et al. 2011; Bradbury and Scott 2014;
Cohen et al. 2014; Holzhacker et al. 2015). Therefore, the goal of this research is to examine LPEs’ costbehavior, which has not yet been analyzed. In addition, I examine whether LPEs’ cost behavior reflects
high or low sticky costs when compared to CEs from the viewpoint of institutional theory through aareas: one is that the population declined more than 33%.

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long-term empirical analysis.
Through this study, I contribute five findings to the cost behavior research. First, I find that LPEs’
cost management is not necessarily inefficient compared to CEs from the perspective of cost behavior.
Namely, I find that sticky costs exist in CEs’ cost behavior, and conversely, anti-sticky costs are
revealed in LPEs through a panel data analysis covering 40 years. In addition, I discovered that LPEs’
cost behavior contrasts with that of CEs. However, these results also contrast with the expectedconclusions in general. I believe that the lack of support for this expectation might be driven byaccounting system (regulations on dividends and retained earnings) and management system(redundancies; e.g., preparation for disasters) differences between CEs and LPEs.
Second, I discovered that after a certain period of time has passed from LPEs’ establishment,
inefficient risks in LPEs’ cost management are caused by institutional pressure to protect the public
interest. Through a timeline (year by year) analysis over 40 years, I find that LPEs’ cost behavior
gradually shifted from anti-sticky costs to sticky costs. This result also contrasts with CEs’ cost behavior,
which did not drastically change. I discovered that the adjustment ability of management resources in
LPEs was gradually lost over the long term. From the viewpoint of securing the public interest, obsolete
equipment must be repaired or replaced to maintain the quality of public services, even if revenues
decrease. I conjecture that cost-inefficient risk is affected by an increase in the costs of facilities andequipment.
Third, through an analysis by industry type, I find various characteristics of LPEs’ cost behavior ineach industry type, including high material resource industries and high human resource industries. The
diversity of cost behavior in LPEs might be caused by the resource adjustment costs in various businessenvironments and the various institutional restrictions, including the non-exclusion of public services
and the influence of monopolies.
Fourth, I discovered that depopulation and structural changes in the population influence LPEs’ costbehavior. Since population change is closely related to public service demand, the administrators of
LPEs need to manage those costs that respond sensitively to population changes. I can show how public
service providers should adjust their costs due to population changes, which suggests that the influenceof population changes must be taken into consideration to preserve LPEs’ cost adjustment ability.
Finally, I clarify how LPE administrators adjusted their costs based on changing activity levels overfour years, which equals politicians’ term in office, and verify the differences between LPEs and CEs. I
find the cost behaviors’ differences in both the speed of change and the direction of movement can becompared. Regarding the changing speed of cost behavior, LPE administrators try to adjust their costs
so that they remain proportional over four years, as they aim to operate their services in a stable mannerand attempt to balance the public interest and efficiency sustainably. Regarding the direction of
movement, one might assume that LPE administrators are subject to institutional pressure frompoliticians, who respond to public opinion, and social demands, which require the enrichment of public
services rather than excessive cost efficiency. I conjecture that LPE administrators intend to adjust theircosts to balance their proportions during politicians’ term in office.
In addition, by understanding the characteristics of LPEs’ cost behaviors from an academic
perspective, it will be possible to contribute to public administrators’ ability to manage their future costs.

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I also contribute to practical aspects of LPE cost management in the future sustainability plans called theCompact City and Intermunicipal Cooperation.
The article proceeds as follows. Section 2 discusses the characteristics of LPEs from the viewpointof institutional theory, reviews the literature on public organization cost behavior and develops myresearch hypotheses. In Section 3, the research methodology is described, including the sample data, thevariable measures, and the models. Section 4 presents and discusses the results. Finally, Section 5summarizes the results and concludes with a discussion of the limitations of this study and suggestions
for future research.
2 Background and Literature Review
2.1 Characteristics of LPEs
Since World War II, LPEs have been an important public service provider not only in developed
countries throughout the world but also in developing countries (UN, 2008). LPEs are called various
names within each country and region, such as “municipally owned enterprises”, “municipal
corporations”, “local public companies”, “municipal corporatizations”, and “state-owned enterprises”(Collin et al. 2009; Saussier and Klien 2013; Voorn et al.2017).
A UN (2008) report defined public enterprises as follows: a “public enterprise can be considered anorganization established by the government under public or private law, as a legal personality which isautonomous or semi-autonomous, that produces/provides goods and services on a full or partial
self-financing basis, and in which the government or a public body/agency participates by way of
having shares or representation in its decision-making structure”.
However, in the academic field, there is no definite and common definition of a public enterprise todate (Collin et al. 2009; Saussier and Klien 2013) because LPE regulations differ from country tocountry and LPEs’ service content differs from region to region. Thus, it can be stated that LPEs exist in
an institutional twilight area, as they are both public administrators and private companies (Collin et al.
2009). Because of the existence of various forms and types of LPEs in each country and region,
academics to date have not recognized common LPE issues. Based on a taxonomy, Saussier and Klien(2013) classified LPEs based on decision-making rights, organizational control, and property rights.
They distinguished between directly managed LPEs and corporatized LPEs. Additionally, Voorn et al.
(2017) described the unique features of directly managed LPEs and those of corporatized LPEs (Table
2).
[Insert] Table 2. Characteristics of directly managed LPEs and corporatized LPEs
Saussier and Klien (2013) explained that Japanese LPEs are part of the local public government andare not independent organizations. Therefore, they argued that Japanese LPEs are not suitable assubjects of empirical research because they are not financially and organically separate from local
public governments. LPEs in Japan are certainly a type of public organization owned by local
governments. However, I assert that the researchers’ argumentation is partly correct and partly wrong.
According to their taxonomy, LPEs in Japan are classified into directly managed LPEs and corporatized8

LPEs. The former are part of local public bodies, as these authors claim, but the latter are run
independently. The services provided by corporatized LPEs are funded by user fees, and the entities
must be profitable independently of local public bodies. Thus, corporatized LPEs have weakerregulations than directly managed LPEs and can be managed flexibly using their income from utilities.
It is expected that the efficiency and effectiveness of the services provided by corporatized LPEs will bepromoted over services provided by directly managed LPEs (Oshima 1971). Therefore, I argue that
corporatized LPEs in Japan are suitable for empirical analysis because they are financially andorganically separate from local public governments.
In Japan, legislation established LPEs in each municipality after World War II. The number of LPEs
increased with the increase in population: there were 6,995 enterprises in 1974, 12,629 enterprises in
2002, when they reached a peak, and recently, after a decline due to privatization or amalgamation, there
were 8,712 enterprises in 2013 (Figure 3). In addition, there are more directly managed LPEs thancorporatized LPEs. However, the number of directly managed LPEs has decreased substantially since2004 under the influence of privatization based on the institutional pressure of NPM. By contrast, thenumber of corporatized LPEs has not changed drastically for 40 years. I argue that corporatized LPEs
are also appropriate for empirical analysis because the number of such organizations is larger than thatin other countries, and data collection is possible over a longer period. For this reason, I focus on
corporatized LPEs for this analysis.
[Insert] Figure 3. Trends in the number of LPEs in Japan
Corporatized LPEs (hereafter, LPEs) are governed by an administrator appointed by the mayor andapproved by congress for a four-year term in office. Dismissal is restricted during this term. The
administrator has decision rights regarding the management of an LPE. Therefore, the administrator issimilar to the CEO of a CE. However, unlike CEOs, LPE administrators are not allowed to receive
dividends from the organization’s profits. Therefore, from the perspective of agency costs, there is little
incentive for administrators to declare a high amount of dividends (Eldenburg and Krishnan 2003).
However, if the administrator achieves a high level of performance (e.g., high evaluation of the service,cost reductions), the mayor can reappoint the administrator. Therefore, the administrators of LPEs may
strive to achieve high performance with regard to serving the public interest and achieving efficiency. In
other words, administrators may be indirectly influenced by politics (congress and the mayor).
Additionally, LPEs’ budget must be approved by both congress and the mayor, which means that LPE
administrators are accountable to both parties. Therefore, the administrators of LPEs may faceinstitutional pressure from stakeholders such as congress and the mayor.
LPEs are responsible for various public service businesses that complement the public services
offered by local governments (Ooshima 1971; Kawarata 2005). More specifically, LPEs in Japan arebusinesses that act under the LPE law and municipal ordinances. Examples of businesses in which LPEs
operate include residential water supply, industrial water supply, transportation (e.g., tramway, bus, andsubway), electricity, gas power, hospitals, and other businesses that are run by local governments
according to their own rules (Table 1). These businesses not only require a large amount of investment

9that cannot be procured by the private sector but also will not necessarily be profitable for CEs.
Therefore, LPEs provide essential, lifesaving activities that cannot be managed as CEs based on
economic principles. For these reasons, administrators must attempt to recover the invested funds
appropriately and make decisions that prevent losses (Yasukata et al. 2011). Additionally, they must be
accountable to congress and the mayor in terms of securing profits and improving benefits for the public(Eldenburg and Krishnan 2008).
2.2 Institutional Constraints of LPEs
Institutions are social structures consisting of symbols, social actions and objectives, but institutions
are formed not only through social structures but also through the activities in which norms and rules
are produced. In its present form, the new institutionalism in organizational analysis provides a wide
range of theoretical and methodological benefits (Scott 2001). Neo-institutional theorists, e.g., Meyer
and Rowan (1977), noted that organizations engage in normative organizational behavior based on rules,laws, customs, traditions, and regulations with an emphasis on legitimacy, satisfactory behavior,
structural decoupling, and symbols. They also explained that organizations pursue practices that may beof little relevance to maximizing efficiency and that organizations constantly seek ways to respond topressure from external scrutiny and regulations rather than improving their performance. DiMaggio andPowell (1983) identified three forces that drive institutionalization: (1) coercive isomorphism, whichstems from political influence and the need for legitimacy; (2) mimetic isomorphism, which results fromstandard responses to uncertainty; and (3) normative isomorphism, which is associated withprofessionalization. Among them, normative institutional pressure constrains both decision-making andorganizational behavior (Balakrishnan et al. 2010; Holzhacker et al. 2015).
Public organizations promote mainly normative institutionalization in for-profit and nonprofit
organizations since public organizations can establish rules, laws, and regulations and provide licenses
and inspections. However, public organizations experience strong institutional pressure with regard totheir role governing profit and non-profit organizations (Frumkin and Galaskiewicz 2004). Balakrishnan
et al. (2010) also argued that the influences of institutional constraints are stronger for publicorganizations than for for-profit organizations. The authors showed that normative institutional
constraints include political pressure, legal compatibility, the corporate governance system, andfinancial support. As evidence of normative institutional pressure that constrains both decision-making
and organizational behavior, Wollmann (2000) explained that local German governments have changed
their organizational structures based on the institutional pressure of NPM. One of the reasons for thestrong influence of institutional constraints is that public organizations must respond tomultidisciplinary evaluations at all times due to the existence of an unspecified number of stakeholders(Rainey 1997). Therefore, these organizations act to acquire legitimacy by observing institutional norms
such as rules, laws, and regulations (Oliver 1991; Nee and Cao 2005), which makes them sensitive tonormative institutional pressure (Frumkin and Galaskiewicz 2004).
For LPEs, there are two behavioral standards (codes of conduct) mandated by LPE law to stabilize
public services and to continue the business over the long term: first, fulfilling public demands to satisfy
the public interest, and second, pursuing appropriate profits by focusing on profitability and optimizing

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costs by improving efficiency. LPEs must adopt a strict code of behavior and conduct their businesswhile confronting these two normative pressures. In particular, from the perspective of the public
interest, LPEs offer public services that are essential to citizens’ lives. This system covers the provisionof public goods and services in a comprehensive manner that complements the public services provided
by local governments from the public interest perspective. In addition, the level of public service must
always be kept constant since declining quality can threaten livelihoods. Thus, LPEs have a
responsibility to support everyday lives and provide improved public benefits through theirorganizational behavior. Additionally, the evaluation of public services is conducted by all citizens, that
is, an unspecified number of people. Because such evaluations are multifaceted, as Rainey (1997) noted,
the administrators of LPEs must be concerned about serving the public interest. Thus, LPEs must
provide public services even if they are unprofitable (Ooshima 1971; Kawarata 2005). As a result,institutional pressures also affect the cost-management decisions made by the administrators ofgovernment hospitals, which are a type of public organization (Balakrishnan et al. 2010). However,
because the public interest must be balanced with efficiency, administrators cannot prioritize one over
the other (Eldenburg et al. 2004). Conversely, with regard to efficiency, LPEs must provide services
more economically, effectively, and efficiently than local municipalities (Kawarata 2005), which meansthat they must operate with limited assistance from the government. Moreover, raising public utility fees
is not easy because it will be opposed by residents. Therefore, LPE administrators must manage theirorganizations to avoid service charge increases as much as possible. As a result, they may have anxiety
due to the need for cost management and efficiency.
Because of these normative institutional constraints, LPEs’ organizational behavior differs greatly
from that of CEs. CEs act to maximize profits; because they are subject to fewer institutional pressures,
they have greater flexibility when making changes (Eldenburg et al. 2004; Balakrishnan et al. 2010;
Holzhacker et al. 2015). Therefore, institutional constraints more strongly affect the cost behavior of
public organizations than that of for-profit organizations (Holzhacker et al. 2015). To confirm thecharacteristics of public organizations, research methods that compare these organizations with a control
group, either for-profit or nonprofit organizations, are generally adopted (Sørensen 2007; Balakrishnan
et al. 2010; Holzhacker et al. 2015). Therefore, I verify LPEs’ cost behavior by comparing theseorganizations to CEs from the perspective of institutional constraints. Table 3 summarizes thedifferences in the institutional pressure experienced by LPEs and CEs according to Eldeburg et al.
(2004) and Balakrishnan et al. (2010).
[Insert] Table 3. Organizational type and expected influence on cost behavior
In their governance systems, LPEs have fewer executives than CEs. Thus, LPEs usually have only
one administrator and a few vice administrators. For this reason, the pressure from stakeholders isconcentrated on the administrators; therefore, the administrators may focus on maintaining public
service standards at a low cost in order to gain legitimacy. In terms of legal compliance, unlike CEs,
which aim only to maximize profits, LPEs are required to pursue both the public interest and efficiency.
Furthermore, in terms of political pressure, LPE administrators are accountable to residents, the local

11parliament and the mayor with regard to public service quality and cost management. If LPE
administrators prioritize cost reductions due to the influence of efficiency pressures, the risk ofdeclining public service quality will increase. Conversely, pursuing the public interest can lead LPE
administrators to manage their costs more inefficiently. Thus, LPE administrators must govern theirorganizations while considering both the public interest and efficiency, and they must behave in amanner that ensures business continuity (Kawarata 2005; Martinsons et al. 2007).
2.3 Cost Behavior of Public Sector Organizations
The concept of cost stickiness originated in the latter half of the 1920s. In Germany, Brasch (1927)
termed this phenomenon “Kostenremanenz”, and this notion was clarified through the direct observationof companies’ cost information. Recently, “Kostenremanenz” has attracted the attention of empiricalanalysts; the German term has since been translated to “cost stickiness” (“sticky costs”) by Anderson etal. (2003). Those authors examined 7,629 firms over 20 years, from 1979 to 1998, using annual
Compustat data. In addition, they verified firms’ cost behavior using models based on publishedfinancial data to determine the rate of change in net sales revenue (a proxy for the activity level as an
explanatory variable) and the rate of change for selling, general and administrative expenses (a proxyfor cost variables and the dependent variable). They found that the rate of change for costs when theactivity level decreases is smaller than it is when the activity level increases (Figure 4).
[Insert] Figure 4. Image of sticky costs and anti-sticky costs
Figure 4 shows that cost and revenue change proportionately and linearly with respect to the normal
t-1 phase of the slope from the t-1 to the t period, but sticky costs result in a slope that is less steep than
the slope near the t-1 period. Thus, “Kostenremanenz” is empirically confirmed as “cost stickiness”.
With regard to additional evidence of cost stickiness, since Anderson et al. (2003), sticky costs havebeen verified through additional empirical research using those authors’ model and have also been
confirmed to exist in other scenarios, such as inter-industry and inter-country scenarios.
In a study focused on inter-industry scenarios, Subramaniam and Weidenmier (2016) examined costbehavior by industry using Compustat data from 1979 to 2000. They showed that cost stickiness isstronger in the manufacturing industry, which has more fixed assets, than in the merchandising, service
and finance industries. However, He et al. (2010) examined the cost behavior of Japanese CEs byindustry type from 1975 to 2000 using the PACAP database. They showed that the merchandisingindustry has stickier costs than the service and manufacturing industries. As described above, various
cost behaviors have been confirmed for each industry for CEs. In addition, sticky costs were confirmed
not only in industries with high material resources but also in industries with high human resources.
In studies focused on inter-country scenarios, Calleja et al. (2006) performed an analysis using
financial data for US, UK, German, and French firms from 1988 to 2004. Their findings confirmed that
German and French firms demonstrate stronger sticky costs than firms in the UK and US. The authors
noted the possibility that differences in corporate governance and managerial oversight driven by theregulation laws in each country and the characteristics of each firm and each type of industry may also

12affect sticky costs. Using Compustat data from 1988 to 2008, Banker et al. (2013) showed that thedifferent worker protection regulations in 19 OECD countries affected labor adjustment costs. Thesestudies suggested that as industries become more regulated by law, their cost adjustment flexibility
decreases. LPEs that are highly subject to legal institutional restrictions may have a lower degree of
freedom regarding cost management than CEs. In previous studies targeting CEs, the analysis period has
mainly been set at approximately 20 years or less. Since public service providers are required to havestable management over the long term (longer than 20 years), it is necessary to further understand theirlong-term cost behavior.
Researchers have classified cost behavior for not only sticky costs but also anti-sticky costs (Weiss2011). Figure 4 shows that anti-sticky costs also result in a slope that is initially steeper but that grows
less steep as it approaches the t period. Thus, anti-stickiness results when the slope of costs forincreasing activities is lower than the slope of costs for decreasing activities. Dalla Via and Perego(2014) confirmed the existence of anti-cost stickiness for small and medium-sized enterprises. At thesame time, they noted that cost stickiness increases in large firms. Likewise, Sepasi and Hassani (2015),
and Boshch and Blandon (2011) also showed that cost stickiness is higher in large enterprises whencomparing large enterprises to small and medium-sized enterprises. These studies show that sticky costs
increase when the adjustment costs (committed capacity costs) for capacity resources such as
high-intensity assets or labor in large companies are high. That is, when the resource adjustment cost is
high, it is difficult to adjust costs according to changes in the activity level (Banker et al. 2014a).
Conversely, since the capacity resources of small and medium enterprises consists mainly of variable
costs, anti-sticky costs emerge. Günther et al. (2014) organized and described the relationship between
holding costs and adjustment costs based on the prior cost stickiness literature. The authors explainedthat the factors influencing cost stickiness can be classified into three relationship types: (1) highadjustment costs attributable to legal requirements or economic and psychological issues; (2) highholding costs attributable to opportunity costs; and (3) high holding costs attributable to social issues.
To date, most studies have focused only on CEs, and only a few empirical studies of cost behavior
have focused on public organizations. Bradbury and Scott (2014) conducted an empirical analysis of thecost behavior of New Zealand’s public municipalities from 2008 to 2012. In New Zealand,cost-management methods similar to those used by CEs have been introduced into public organizations
since the 1980s as part of an NPM plan to improve the effectiveness and efficiency of administrative
activities. With thirty years having passed since 1980, these authors examined whether cost management
improved after 2008. However, the research showed that sticky costs continued to exist in New
Zealand’s local governments and that the efficiency of local government activities had not yet improved.
Cohen et al. (2017) investigated the cost behavior of Greek local governments, which was a cause of the
Greek fiscal crisis. These authors verified asymmetric cost behavior for different cost categories.
Specifically, they focused on the difference between administrative costs and the costs of service
provision by empirically describing the cost behavior. They found that the costs of service provision (acore competence of local governments) were sticky, and administrative costs were anti-sticky. Theseauthors asserted that this asymmetric cost behavior was influenced by the decisions of local government
administrators, who were pressured by politicians and stakeholders. Additionally, they argued that local

13government administrators cannot decrease the cost of service provision in response to externalpressures, even if revenues have decreased because of a fiscal crisis. Holzhacker et al. (2015) focusedon the differences between the institutional pressures on government hospitals and those on for-profit
and nonprofit hospitals and found differences in cost behaviors. Specifically, sticky costs were prevalent
in government hospitals, which were subject to strong institutional pressures. The authors argued that
one reason for their research results is that government hospitals need to take normative actions becauseof stakeholders’ excessive pressure. The taxes, subsidies or donations from stakeholders such as local
communities or citizens’ groups force government hospitals to behave for the public interest. Yasukata
et al. (2011) showed the existence of sticky costs in the Japanese National Hospital Organization,suggesting that sticky costs appeared within labor costs because the Japanese National Hospital
Organization was strongly influenced by institutional pressures to not dismiss employees.
In analyses of these public organizations, there has been no focus to date on LPEs. LPEs haveunique characteristics among public organizations because they are required to act not only in the public
interest (similar to public organizations) but also in the interest of efficiency (similar to CEs). Therefore,
it is academically interesting to investigate how LPEs’ cost behavior has changed because such changes
reflect the pressure to act in the interest of both the public and efficiency (Figure 5).
[Insert] Figure 5. The causal relationship between the institutional constraints on and the cost behaviorof LPEs
2.4 Hypothesis Development
Based on the model developed by Anderson et al. (2003), asymmetric cost behavior, especiallysticky costs, has been evaluated in empirical studies focused on CEs. Using the same method, theasymmetric cost behavior of local governments was confirmed by Bradbury and Scott (2014) and Cohenet al. (2017). Holzhacker et al. (2015) found that the degree of sticky costs was greater in public
hospitals than in private hospitals because for-profit organizations have fewer institutional restrictions
than do public organizations. Therefore, the latter can change their governance or cost structure to
respond flexibly to increase their efficiency (Eldenburg et al. 2004; Eldenburg and Krishnan 2008;
Balakrishnan et al. 2010; Holzhacker et al. 2015). Further, public organizations are more strongly
influenced by institutional pressure than CEs (Frumkin and Galaskiewicz 2004). Therefore, it istheorized that sticky costs can be confirmed in LPEs, given that these organizations have characteristics
similar to both public and private organizations. Additionally, LPEs are subject to the institutional
restrictions that service levels must be maintained without generating profits. Therefore, sticky costs
will be more prevalent in LPEs than in CEs. Thus, the first hypothesis is as follows:
Hypothesis H1: Sticky costs are more prevalent in local public enterprises than in commercial
enterprises.
Günther et al. (2014) argued that asymmetric cost behavior is affected by adjustment costs, such aslegal requirements. LPEs are legally required by LPE law both to work in the public interest and to14

maximize efficiency. In addition, LPE administrators are influenced by various stakeholders against thebackground of the two normative institutional constraints. Therefore, they are required to maintain the
public service level at a low, stable cost. In other words, pressures to prioritize efficiency will weaken
the sticky costs of LPEs from the cost behavior perspective. Conversely, pressures to prioritize thepublic interest will boost LPEs’ sticky costs because public service quality must be maintained, even ifrevenues decrease. To maintain their service level, LPEs must renew or replace aging facilities over thelong term, and they must plan for these costs without increasing their service charges. When LPE
administrators are subject to strong institutional constraints, they cannot make decisions quickly(Martinsons et al. 2007) and will put off these problems to the future. Sometimes, facilities can be
repaired early in the business cycle, but after many years, it is often better to replace these facilities thanto repair them. In these cases, the replacement or repair costs may drastically increase, and LPEs’
resource adjustment ability will gradually be lost. Thus, it is believed that their cost behavior willchange based on the influence of institutional constraints, especially the requirement to protect the
public interest. Therefore, LPEs may take more time to balance their obligations due to the institutional
constraints of both protecting the public interest and achieving efficiency. Thus, the next hypothesis is asfollows:
Hypothesis H2: Institutional pressures are associated with the change in local public enterprises’
cost behavior over time, in contrast to that of commercial enterprises.
Subramaniam and Weidenmier (2016) revealed that sticky costs are stronger in manufacturingindustries with more fixed assets than in the commercial, service and finance industries. By contrast, He
et al. (2010) showed that the commercial industry’s sticky costs are higher than those of the service andmanufacturing industries. As described above, various asymmetric cost behaviors have been confirmed
for each type of industry for CEs, including cases with both high material resources (high fixed assets)
and high human resources (high labor costs). Anderson et al. (2003) argued that sticky costs will
increase when asset intensity and labor costs are high. LPEs’ businesses include not only high asset-typeindustries, such as water supply and sewerage, but also high labor cost-type industries, such astransportation and hospitals. Moreover, due to institutional constraints, various asymmetric costbehaviors should appear in all businesses, as LPEs must balance serving the public interest andachieving efficiency rather than only aiming to maximize profits, which is the goal of CEs. I conjecturethat sticky costs in LPEs will increase when these firms are pressured from the institutional constraint ofserving the public interest; conversely, LPEs’ sticky costs will decrease when they are pressured fromthe institutional constraint of achieving efficiency. Thus, the next hypothesis is as follows:
Hypothesis H3: Similar to that of commercial enterprises, local public enterprises’ cost behavior isassociated with the type of industry.
Banker et al. (2014b) found that sticky costs increase when demand uncertainty or the downside riskof demand increases. The demand for public services depends on population changes (Nakai 1988;

15

Nakano 2016). For this reason, the administrators of LPEs are required to predict changes in public
service demand based on population changes (Nishioka et al. 2007). In Japan, the population structure
has changed significantly since 1995. The population of youth and those of production age isdecreasing; conversely, the elderly population is increasing. Furthermore, the economy and demand areexperiencing a depression, and CEs are withdrawing from depopulated regions due to a lack ofprofitability. Even if public demand decreases due to the declining population, LPEs cannot stop
providing services because of the institutional pressure to serve the public interest. In other words, fromthe perspective of the public interest, LPEs cannot reduce the quality of their public services. In addition,with the increase in elderly people, whose income is derived primarily from pensions, LPEs must
maintain the same level of public services at low prices because of the institutional pressure to achieve
efficiency. LPEs may experience increased sticky costs due to the downside risk of public demand andpublic demand uncertainty. By contrast, the market demand for CEs is affected not only by domestic
trading but also by overseas trading, so they are less affected by population changes than LPEs. I
theorize that LPEs’ cost behavior will be more strongly influenced by population changes than that ofCEs. Thus, the next hypothesis is as follows:
Hypothesis H4: Local public enterprises’ sticky costs are strongly influenced by population changes
since 1995 in relation to commercial enterprises.
As noted by Bradbury and Scott (2014) and Cohen et al. (2017), local government administrators are
influenced by public opinion (demand for both low-cost and high-quality services) when they make costmanagement decisions. Public organizations, including LPEs, must respond to multidisciplinary
evaluations at all times due to the existence of an unspecified number of stakeholders (Rainey 1997). In
particular, LPE administrators are appointed by the mayor and approved by congress, who are, in turn,elected by citizens. Therefore, the administrators may be sensitive to not only public opinion but also
political opinion (from mayors and local councils) if they wish to be reappointed for the next term, andthey may strive to achieve a high level of performance with regard to protecting the public interest andachieving efficiency. As a result, LPE administrators may act to control and adjust their asymmetric costbehavior in the direction of symmetric cost behavior during the political term of mayors and local
councils, which is 4 years in Japan. Thus, LPE administrators must aim for a long-term balance between
protecting the public interest and achieving efficiency due to political pressure. Conversely, CEs’
business managers may decide to control and adjust their costs with a focus on securing profits asquickly as possible, and they may not be as strongly affected by political pressure as LPEs. Thus
because of institutional constraints, LPEs’ long-term cost adjustments may be more controlled and move
more slowly than those of CEs. As a result, it is hypothesized that the administrators of LPEs makedecisions that result in asymmetric cost behavior that gradually transforms into a proportional
relationship over the long term. The final hypothesis is as follows:
Hypothesis H5: Local public enterprise administrators make decisions that result in the long-term,
proportional stabilization of cost behavior within a 4-year election period in16

relation to commercial enterprises.
LPEs are characterized by serving the public interest and achieving efficiency. Thus, LPEs’ costbehavior is presumed to change in the context of the tradeoff between the public interest and efficiency.
Because of the need to run businesses in a stable manner, LPE administrators make deliberate decisions
from a different perspective than that of CE managers.
3 Research Method and Sample Selection
3.1 Research Method
The analytical model of Anderson et al. (2003) is the basis of recent empirical studies of costbehavior; it was adopted in studies following Anderson et al. (2003) and recently used by Bradbury and
Scott (2014), Cohen et al. (2017), and Holzhacker et al. (2015) to analyze the cost behavior of publicorganizations. Therefore, this study assumes that the model can also be applied to the analysis of LPEs’
cost behavior. Thus, to verify hypotheses 1 to 3, I adopt model 1. To examine hypothesis 1, all thesamples are analyzed through panel data analysis using model 1. Next, to verify hypothesis 2, theyear-to-year changes in cost behavior are analyzed through OLS analysis using model 1. OLS analysis
was adopted to clarify the cost behavior in prior studies (Anderson and Lanen 2007; Zanella et al. 2015).
Thus, I intend to use not only panel data analysis but also OLS analysis to verify the existence of sticky
costs. Finally, for hypothesis 3, the samples for each type of industry are analyzed through panel data
analysis using model 1.
model 1ln �

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡
� = 𝛽𝛽0 + 𝛽𝛽1 ∗ ln �� + 𝛽𝛽2 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 ∗ ln �� + 𝜀𝜀𝑖𝑖,𝑡𝑡
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡−1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1

LPEs’ operating expenses are substituted for Cost. Additionally, Revenue takes operating revenues

as a proxy for the activity amount. Decrease Dummy is a dummy variable that takes the value of 1 when
operating revenue decreases between the t period and the previous period and 0 otherwise. All the data
are natural logarithms.
Using this model, it can be confirmed that when operating revenue increases by 1%, the costchanges by the value indicated by β1. Additionally, because of the Decrease Dummy, when operating
revenue decreases by 1%, the cost decreases by β1 + β2, whereas β2 indicates the value of the sticky or
anti-sticky costs. Therefore, when there is cost stickiness, β2 will be negative, and when cost stickinessis not present (anti-sticky costs), β2 will be positive.
To examine hypothesis 4, I clarify the influence of the total population change and the population
structure on cost behavior. Therefore, I focus on population data from a report on population movement
based on a basic resident registration system database 7. In particular, it is necessary to clarify the7

Population data in each municipality is published as “Basic Resident Register Annual Population
Report” by statistics bureau, ministry of internal affairs and communications in Japan.

17influence of depopulation and the increasing ratio of the aging population on the cost behavior of LPEs.
For this reason, I collect population data from 1995, which is the year Japan started to become an aging
society. The population data were divided into three stages: 0-14 years old, 15-64 years old, and 65
years old and over. To evaluate hypothesis 4, I adopt the following model 2.

model 2ln �

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡
� = 𝛽𝛽0 + 𝛽𝛽1 ∗ ln �� + 𝛽𝛽2 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 ∗ ln ��
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡−1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−16+ � 𝛽𝛽𝑛𝑛 𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖,𝑡𝑡,𝑛𝑛 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 ∗ ln �
𝑛𝑛=3

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡
� + 𝜀𝜀𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1

The total population represents the natural logarithms of the year-over-year comparison. The young
population, the productive age population, and the elderly population are natural logarithms of each
respective proportion of the total population.
Next, to examine hypothesis 5, it is necessary to confirm the relationship between operating
revenues over 4 years and changes in operating expenses. I extend the model of Anderson et al. (2003)
and verify the hypothesis using the following model 3.
model 3ln �

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡
� = 𝛽𝛽0 + 𝛽𝛽1 ∗ ln �� + 𝛽𝛽2 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 ∗ ln ��
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡−1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1+𝛽𝛽3 ∗ ln �+𝛽𝛽5 ∗ ln �+𝛽𝛽7 ∗ ln �

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1
� + 𝛽𝛽4 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡−1 ∗ ln ��𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−2
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−2

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−2
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−2
� + 𝛽𝛽6 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡−2 ∗ ln ��𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−3
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−3

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−3
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−3
� + 𝛽𝛽8 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡−3 ∗ ln �� + 𝜀𝜀𝑖𝑖,𝑡𝑡
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−4
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−4

If asymmetric cost behavior terminates over time, the sticky costs value will gradually approach 0. If
cost stickiness is confirmed by β2, it should change, β2 < β4 < β6, with time, since LPE administrators
are subject to institutional restrictions and will only gradually overcome the sticky costs. In particular,
political pressure is strengthened by politicians’ 4-year term. Additionally, local elections for congress
and the mayor of each municipality in Japan are held almost simultaneously on the same day. Therefore,
LPEs’ cost behavior may be influenced by political pressure. The analysis begins at t=0, which is anelection year, and elections are held in t=0, 4, 8, 12, etc.
3.2

Sample Selection and Descriptive Statistics
No empirical analysis of LPEs’ cost behavior has been previously performed. This research is18therefore the first to examine LPEs’ cost behavior. To obtain robust results, as much cross-sectional dataas possible should be used. I collected non-consolidated fiscal accounting data on all LPE businesses
from LPEs’ yearbooks 8. Thus, the sample population for this analysis is all local public enterprisebusinesses that are classified as corporatized LPEs. The data include 10 industry types (residential watersupply, industrial water supply, sewage, transportation, electric power, gas power, hospitals, wholesalemarket, toll road, and car parking). In addition, observations must be made over a long period toconfirm how cost behavior has changed in accordance with changes in Japan’s social environment.
To verify LPEs’ cost behavior, long-term cost data are necessary. Therefore, in this study, theanalysis period is the 40 years from 1974 to 2013, which is a longer period than that analyzed by any
previous empirical studies on cost stickiness. LPEs are legally obligated to release annual financial
reports. The financial reporting method has not changed over the 40 years under study, making it
possible to collect fiscal data over a very long period. The collected data represent 120,317 firm-years.
To control for the effect of outliers, I removed (deleted) the largest and smallest 1 percent ofobservations (outliers). I used list-wise case deletion without winsorized data to delete the observations.
That is, if there is even a single outlier in one sample, all the data from that sample are deleted (cleared).
This approach is rather conservative as a statistical method, but since there are numerous samples, I
contend that this approach is a valid statistical processing method to obtain robust analysis results. The
final sample includes 115,929 firm-years. Therefore, the sample consists of unbalanced panel data.
Additionally, to create a comparison with LPEs over the same period, I collected data provided by
Nikkei NEED-FinancialQUEST on CEs listed on the Tokyo Stock Exchange. LPEs’ financial statements
provide non-consolidated accounting data for various industry types, such as water supply and hospitals,
so I also collected CE non-consolidated accounting data from the Annual Securities Reports forcomparison. The collected data represent 85,705 firm-years. After excluding (deleting) outliers, thesample includes 84,343 firm-years. The descriptive statistics are calculated after the exclusion ofoutliers.
[Insert] Table 4. Descriptive statistics
4 Results
In panel data analysis, there is a process for choosing the optimal result from the model of pooled
estimates, fixed effects, and random effects. I describe all the analysis results and explain the optimal
results. First, in all panel data analyses, I used an F-test to determine whether a pooled model and afixed/random effects model is more suitable. The result confirms that the fixed/random effects model ismore suitable than the pooled model. In addition, I also conducted the Hausman test to confirm whichmodel, the fixed effects or random effects model, is suitable. In addition, I confirmed the influence of

8 LPEs’ yearbooks are edited annually by the ministry of internal affairs and communications in Japan.
They include the annual financial statement of each LPE in each municipality. The financial statements
include B/S, P/L, the detail information of expenses, etc.; these data are found in electronic databases
after 1999.

19

serial correlation through the Durbin-Watson ratio. The influence of serial correlation is low in all theanalyses.
To test hypothesis 1 using model 1, I analyzed panel data for 40 years. The results showed that LPEs’
cost actions demonstrate asymmetric cost behavior (Table 5 Panel A). Namely, β2 was 0.0791 (fixed
effects), and the positive value indicates anti-sticky costs. Conversely, the CE analysis resulted in a β2
value of -0.0978 (fixed effects), and the negative value indicates sticky costs (Table 5 Panel B). Thus,
hypothesis 1 was not supported.
[Insert] Table 5. Cost behavior based on the panel data analysis using model 1
Under institutional constraints, it was predicted that sticky costs would increase because LPEs are
subject to stronger institutional pressures than CEs. However, the analysis resulted in the opposite
conclusion, which was not expected. In previous studies, no research showed that public organizations’cost behavior was anti-sticky (Yasukata et al. 2011; Bradbury and Scott 2014; Cohen et al. 2017;
Holzhacker et al. 2015). Additionally, Banker and Byzalov (2014) argued that CEs’ cost behavior
generally indicated sticky costs on average. Clearly, this result is a new discovery that contrasts withprevious studies.
This result signifies that LPE administrators actively manage their resource-adjustable costs when
their operating revenue decreases and the pressure for low-cost economic efficiency increases. I believethat the lack of support for this hypothesis might be driven by the accounting (regulations on dividends
and retained earnings) and management system (redundancies, i.e., preparation for disasters such as astandby isolated power unit and food stockpiled for emergencies) differences between CEs and LPEs.
Namely, the anti-sticky costs are induced by resource-adjustable costs, which imply that there are
redundant resources caused by LPEs’ accounting and management systems.
Regarding the accounting system, I focus on the appropriation of retained earnings and the net
income of LPEs. The retained earnings of CEs are often allocated to stakeholders, such as shareholders,managers, or workers. Unlike CEs, LPEs are subject to legal restrictions regarding how they canappropriate retained earnings. Namely, it is unnecessary for LPEs to distribute their final profits tostakeholders, such as shareholders, managers, and workers. Additionally, because they can receive
preferential treatment regarding corporate tax and property tax, their retained earnings may often be
generated. However, LPEs are required to operate with moderate profits and not to maximize their net
income. Therefore, I conjecture that LPE administrators intend to ensure their management resource
slack so that they can adjust quickly when operating revenue declines. Because the slack resources in
LPEs are oriented toward preventing disasters, they are not necessary for normal operations. Therefore,
there is a great deal of room for discretion; thus, it is easy to reduce these resources. In other words,
LPE administrators may increase their management resources, thus increasing their operating expenses,
in order to avoid significantly increasing their operating profits. In fact, as shown in Panel B of Figure 2,
operating expenses and operating revenues show very similar, consistent movements over the long term.
LPEs thus may accumulate excessive management resources rather than repaying their bonds. Because
LPEs have little risk of bankruptcy, they may not make the effort to repay their debt; on the contrary, it20

is possible that they intend to bear the cost of procuring excessive management resources accordingly.
Therefore, they can use their profit for management resources instead of bond repayment.
Next regarding the management system, I focus on public sector management, especially the
redundancy of management resources. Cyert and March (1963) argued that organizations use internal
rules for different purposes to compensate for environmental changes. In public sector management,
retaining slack management resources is explained as a necessary cost “redundancy” to prepare fordisaster (Koike et al. 2015), such as retaining emergency equipment or facilities that can provide public
services in a disaster such as an earthquake, typhoon, eruption, or flood. Therefore, LPEs are allowed toretain slack management resources as redundant management resources because LPE administrators can
explain that it is necessary to secure slack resources for the public interest. That is, they earn legitimacy
for their spending by retaining slack resources as redundant resources. LPE administrators can therefore
adjust their costs for redundancy; in other words, they can increase the slack resources that aredesignated redundant resources when operating revenue is likely to exceed operating expenses;
conversely, they can easily decrease the slack resources designated as redundant resources when their
net income is in deficit and the disaster does not occur. I believe that when operating revenue isdeclining, it might actively reduce the holding costs of these slack resources, and therefore, I conjecturethat anti-sticky costs appear in LPEs. Thus, I believe that LPE administrators may avoid sticky costs andobtain legitimacy for their spending by retaining redundant management resources and adhering toregulations for the disposal of net profits.
To verify hypothesis 2, I analyzed the cross-section of cost behavior using the data for each year
separately and verified that the change was dynamic over time (Table 6). When the β2 coefficient was
found to be not significant through the t-test of an OLS analysis, I used linear interpolation to show the
movement of β2 and added the approximated curve (Figure 6). It is possible to confirm the tendency ofthe change in cost behavior through time based on the approximated curve 9. Two characteristics—sticky
costs and anti-sticky costs—were confirmed by the dynamic analysis. Panel A of Table 6 and Panel A of
Figure 6 show that β2 changed from a positive to a negative value for LPEs’ cost behavior, that thedeviations of the β2 values were large and that the year-to-year change in β2 had a negative slope. Thus,
the results robustly show that anti-sticky costs gradually weakened. Especially from 1975 to 2002, β2
had primarily positive values, indicating anti-sticky costs. However, the degree of anti-sticky costs
gradually decreased, especially after 2004, when β2 was primarily negative, indicating sticky costs. In
contrast, in the analysis of CEs, β2 was primarily negative in Panel B of Table 6 and Panel B of Figure 6.
The average cost stickiness changed slightly with time but, in contrast to the results for the LPEs, there
was no significant change in the value of β2 for CEs over time. Thus, institutional pressures wereassociated with the change in LPEs’ cost behavior over time, in contrast to that of CEs; hypothesis 2 waspartly supported for LPEs after around the year 2000.
9This result was equivalent and consistent with the results using panel data analysis with the time trend
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅

dummy variable: ln �𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖,𝑡𝑡 � = 𝛽𝛽0 + 𝛽𝛽1 ∗ ln �𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑖𝑖,𝑡𝑡 � + 𝛽𝛽2 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 ∗ ln �𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑖𝑖,𝑡𝑡 � + 𝛽𝛽3 ∗𝑖𝑖,𝑡𝑡−1

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝛽𝛽4 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 ∗ ln �𝑖𝑖,𝑡𝑡−1

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1

� ∗ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝜀𝜀𝑖𝑖,𝑡𝑡 .

𝑖𝑖,𝑡𝑡−121[Insert] Table 6. The results for individual years based on OLS analysis using model 1[Insert] Figure 6. β2 value (sticky cost value) changes in each year
Considering the change in LPEs’ long-term cost behavior, one can assume that the asymmetric costbehavior changed substantially after around the year 2000. LPEs gradually lost redundancy due tosurplus profits and, simultaneously, the potential loss of cost adjustment flexibility. Additionally, LPEs
and CEs had significantly different cost behavior characteristics. I hypothesized that these different costbehavior characteristics were caused by institutional constraints, especially those serving the “public
interest”. LPEs provide services in a constant and stable manner, and the quality of the public services
must be maintained over the long term. For this purpose, LPEs must always maintain their facilities andequipment. For example, if the LPE is operating a water supply project, it will be necessary toconstantly update the water pipeline and maintain the dam facility. However, in a long-term business,
obsolete equipment must be repaired or replaced, even if revenues decrease. Moreover, it is difficult toincrease utility fees. Since repair or replacement costs, as substantial fixed costs, increase with thepassage of time 10, I suggest that increases in repair or replacement costs for large-scale facilities
gradually lead LPEs to lose redundant management resources and cost adjustment flexibility. As a result,
I assume that LPE administrators cannot gain gradual control over the efficiency of their services. In
other words, LPE management is strongly affected by institutional pressure to protect the public interest.
Therefore, I conjecture that this inefficiency risk is affected by an increase in reinvestment(replacement) costs for large-scale facilities or equipment.
Next, to verify hypothesis 3, I analyzed each industry type (Table 7) using model 1. I foundsignificant results for all industries except for the toll road business. The results show that the presenceof not only sticky costs but also anti-sticky costs was confirmed. Various cost behaviors appeared in
LPEs for each industry. Based on these results, hypothesis 3 was partially supported. I found that similar
to CEs, LPEs demonstrated diverse cost behaviors in each industry. In particular, considering theindustry types with a high ratio of human resources 11, transportation businesses’ cost behavior reflectedanti-sticky costs (β2 was 0.0693 (fixed effects)), while hospital businesses’ cost behavior reflected
sticky costs (β2 was -0.1640 (fixed effects)). For the industry types with a high ratio of material
resources 12, residential water supply, industrial water supply, and gas power businesses’ cost behavior
reflected anti-sticky costs (β2 was 0.2908 (fixed effects), 0.0565 (random effects), and 0.3996 (fixed
effects)), while electricity and sewage businesses’ cost behavior reflected sticky costs (β2 was -0.1473
10

Repair costs (including replacement costs) increased by a factor of 7.6 times from 1974 to 2013.
According to the LPEs’ yearbook in 2013, labor cost ratios are as follows: residential water supply is
32.7%, industrial water supply is 39.6%, sewerage is 44.0%, transportation is 25.7%, electric power is
26.2%, gas power is 13.0%, and hospitals are 6.5%.
12 According to the LPEs’ yearbook in 2013, depreciation cost ratios are as follows: residential watersupply is 12.5%, industrial water supply is 11.9%, sewerage is 6.4%, transportation is 33.3%, electric
power is 25.1%, gas power is 8.5%, and hospitals are 46.5%.
11

22(random effects) and -0.2656 (fixed effects)).
[Insert] Table 7. Cost behavior of each industry based on the panel data analysis using model 1
The various cost behaviors suggest that there are factors other than the influence of the adjustment
cost for human resources and material resources. It is possible that the non-exclusion of public services
and the influence of monopolies also exert an influence on cost behaviors. Public services provide
essential, lifesaving activities that cannot be managed based on CEs’ economic principles. For example,
it is impossible to cut off the electric power supply of people who do not pay their bills or to fail toprovide medical services to those who cannot pay for them. Thus, these businesses would not be
profitable for CEs. In LPE businesses with sticky costs, I conjecture that these non-exclusionary public
services (welfare services for free) make LPEs’ cost management less flexible from the perspective of
institutional constraints, especially in terms of protecting the public interest. On the other hand, these
LPEs’ businesses are projects that require substantial investment and that cannot be procured by theprivate sector; therefore, the market share ratio of LPEs is generally high 13. In these high market sharebusiness environments, it may be possible to manage their costs by accurately forecasting the necessary
resources for the future without idle capacity costs. Therefore, I believe that the evidence of anti-sticky
costs in the residential water supply business and the industrial water supply business originates frommanaging the supply based on the accurate prediction of demand.
Next, to verify hypothesis 4, I analyzed whether population changes impact LPEs’ cost behavior.
Table 8 shows the results of model 2. In Panel A of Table 8, β3 indicates the influence of the total
population and is -0.3080 (fixed effects); β4 shows the influence of the youth population (0-14 years
old) and is 0.5216 (fixed effects); β5 shows the influence of the productive age population and is not
significant; and β6 indicates the effect of the elderly population and is -0.0901 (fixed effects). In
particular, it should be noted that the changes in the total population (β3) and the elderly population (β6)
may have had negative impacts on LPEs’ cost behavior after 1995. Conversely, the youth population
acted to strengthen the anti-sticky costs.
[Insert] Table 8. Population changes and cost behavior based on the panel data analysis using model 2
In contrast, population changes also impact CEs’ cost behavior, as shown in Panel B of Table 8. The
influence of the total population β3 is 2.4228 (fixed effects), the influence of the youth population β4 is-0.2005 (fixed effects), the influence of the productive age population β5 is 0.8441 (fixed effects), andthe influence of the elderly population is 0.5842 (fixed effects). Thus, the results confirm that population
changes affect cost management for not only LPEs but also CEs. Furthermore, population changes,
except in the youth population, positively influence CEs’ cost management. I argue that cost
13 According to the LPEs’ yearbook in 2013, the market share ratios are as follows: residential watersupply is 99.5%, industrial water supply is 99.9%, sewerage is 91.3%, transportation (railway) is 13.4%,
electric power is 1.0%, gas power is 2.3%, and hospitals are 12.3%.

23

management corresponding to population changes is important for both CEs and LPEs. In particular,
since 1995, LPEs have had to consider that changes in the total population and the elderly population
affect cost management. Thus, Hypothesis 4 was almost supported.
Next, using model 3, I verified that LPEs’ long-term cost management was performed over 4-year
periods, verifying hypothesis 5. Thus, LPE administrators decide to control costs under normative
institutional constraints from the local parliament and mayor. The results of the analysis are shown in
Table 9, and the changes in the asymmetry of LPEs’ and CEs’ cost behaviors over 4 years are shown in
Figure 7. In the analysis of model 3, the β2 value is the rate of change from t-1 to t, which indicates
whether the asymmetric cost behavior involved sticky costs or anti-sticky costs. Additionally, the β4, β6,and β8 values represented the annual change in asymmetric cost behavior for t-1/t-2, t-2/t-3, and t-3/t-4,
respectively. The result of the analysis of LPEs in Panel A of Table 9 shows that β2 was 0.1157 (fixed
effects), and the positive value indicates that anti-sticky costs were observed over the short term.
However, the asymmetric cost behavior values (β4, β6, and β8) gradually approached zero through eachperiod and were 0.0226, -0.0179, and -0.0158 (fixed effects), respectively, and the change from a
positive value to a negative value occurred over 4 years. It can be theorized that the anti-sticky value
gradually shifted in the direction of the value of sticky costs within 4 years. Thus, the administrators of
LPEs managed their costs to approximate a proportional relationship throughout the four years, with thegoal of stable operations. Therefore, hypothesis 5 was supported.
[Insert] Table 9. Cost behavior over 4 years based on the panel data analysis using model 3[Insert] Figure 7. β2, β4, β6, and β8 value (sticky cost value) change in each 4-year period
The analysis of CEs in Panel B of Table 9 contrasts with the analysis of LPEs. β2 was -0.0685 (fixed
effects), and the negative value indicates sticky costs. Additionally, the value changed in the subsequent
period. The three asymmetric cost behavior values (β4, β6, and β8) were 0.0505, 0.0244, and -0.0070(fixed effects), respectively, and the change from a negative value to a positive value occurred over 4
years. Therefore, CE managers returned costs to a proportional relationship to secure profits as quickly
as possible.
LPEs balance the public interest and efficiency, and this process requires sustainable management.
Therefore, with a focus on the four-year change in cost behavior, it was theorized that administrators
decide to maintain their costs after anti-sticky costs are observed. Additionally, they decide to improve
services in the public interest instead of pursuing excessive efficiency, which may be due to institutionalpressure stemming from the election of a local parliament and mayor.
5 Conclusions
This study verified the long-term cost behavior of Japanese LPEs by comparing these firms with
CEs. We found five primary results. First, it was generally believed that LPEs are less efficient thanprivate enterprises (CEs), but when examining the cost behavior change, the results revealed that LPEs
are not necessarily inefficient with regard to cost stickiness. A panel data analysis covering 40 years24

showed contrasting results. The results indicated that sticky costs were confirmed for CEs, whereas
anti-sticky costs were present among LPEs. I believe that the lack of support for this expectation might
be driven by differences between CEs’ and LPEs’ accounting and management systems. In terms of thedifference in accounting systems, the regulations on dividends and retained earnings mark a differencein accounting systems. LPEs are also subject to legal restrictions regarding how they can appropriate
retained earnings, though they may often be generated since they can receive preferential treatment
regarding corporate tax and property tax. Furthermore, LPE administrators are not allowed to receive
dividends from the organization’s profits. Therefore, it is possible that LPE administrators intend toensure that they have slack management resources because LPEs are required to operate with moderate
profits and not to maximize their net income. In terms of the difference in management systems,
securing slack resources is different in CEs and LPEs. LPEs earn legitimacy for their spending byretaining slack resources as facilities for disasters (i.e., redundancies) because they must serve the public
interest. For this reason, I believe that it is possible that profits may be allocated to the expenses ofredundancies if the LPE administrator predicts an increase in profits. I also suggest that compared toCEs, LPEs have more redundancies that allow them to adjust their management resources. Based on the
results of the analysis, I argue that choosing LPEs as public service providers over outsourcing andprivatization was a successful decision in terms of cost management, and it was not a mistake since
LPEs can manage their costs by maintaining the flexibility of cost adjustment.
Second, the cross-sectional analysis for each year shows that the timeline transition of cost behavior
is different between LPEs and CEs. Namely, LPEs’ anti-sticky costs have shifted to sticky costs eventhough CEs’ cost behavior remained unchanged. Therefore, the fluctuation of LPEs’ cost behavior
suggests that LPE administrators gradually lost the flexibility to adjust costs around the year 2000. In
other words, LPEs are gradually losing redundancy due to surplus profits. I suppose that this trend has
occurred because LPEs have experienced strong institutional pressure to protect the public interest fromthe viewpoint of maintaining public service quality. Namely I conjecture that this inefficiency risk isaffected by increases in repair and replacement costs. Obsolete equipment must be repaired or replacedin order to maintain public service quality, even when revenues decrease. Since repair and replacement
costs, as fixed costs, increase with the passage of time, I suggest that increases in these costs graduallylead LPEs to lose cost adjustment flexibility. Therefore, LPEs’ business must be continually managed toreduce their costs by maintaining their ability to adjust management resources. In other words, LPE
administrators must carry out cost management that is always conscious of taking measures to maintainthe ability to adjust management resources. For this reason, LPE administrators should always be
careful to maintain a balance between efficiency and the public interest. These findings are confirmedby clarifying the change in long-term cost behavior over 40 years. Regarding the cost behavior of public
sector organizations, I argue that it is necessary to verify their cost management based on long-term
empirical analysis because of the premise that public organizations must operate stably over the longterm.
Third, in the analysis by industry, LPEs’ cost behavior showed not only anti-sticky costs but also
sticky costs. LPEs’ anti-sticky costs differ from findings in previous studies to date. Therefore, it ispossible that the panel data analysis results of the 40 years are influenced and distorted by the type of25

industry. In addition, the results of this analysis indicate a conclusion that differs from previous studies:anti-sticky costs are stronger in projects with high resource adjustment costs, such as high-intensity
assets. In other words, for projects with substantial physical assets, such as the residential water supply,
industrial water supply and gas businesses, the presence of anti-sticky costs was confirmed. Especiallyin industries with high fixed assets, market monopoly rates are also high 14 . These industries’
administrators may be able to adjust their management resources according to accurate future demand
forecasts. Therefore, anti-sticky costs appear in these industries despite high fixed assets. Conversely, in
LPE businesses with sticky costs, I conjecture that these non-exclusionary public services (welfare
services for free) make LPEs’ cost management less flexible from the perspective of institutional
constraints, especially in terms of protecting the public interest. I suggest that the influence ofmonopolies and the non-exclusionary nature of public services also influence cost behaviors. For thoseindustries in which anti-sticky costs appeared, further detailed research that focuses on thecharacteristics of each of these industries is needed. Furthermore, it is also important to clarify how
administrators can maintain cost adjustment abilities over the long term.
Fourth, I clarified the relationship between population changes and LPEs’ cost behavior. Population
changes drive changes in the demand for public services. In Japan, the increasing number of elderly
people and the decreasing population are major demographic issues. In order for LPEs to maintainstable cost management in the future, LPE administrators must engage in cost management in response
to population changes. This analysis confirmed that the population changes, the increasing elderly
population, and the decreasing total population have had a negative influence on LPEs’ cost behavior,suggesting that the impact of population changes must be taken into account when considering
management needs. Forecasting future population changes will provide accurate demand forecasts for
management. A declining population and an increasing number of elderly people are a problem not onlyin Japan but also across developed countries. I believe that determining how to reduce surplus capacity
costs based on population changes has become an important issue for LPEs throughout the world.
Fifth, I verified that asymmetric cost behaviors were resolved over subsequent periods in the 4-yeartime frame because of institutional pressure from politicians. Clearly, both LPE administrators and CE
managers acted to resolve asymmetric cost behaviors. However, there were differences in the speed ofchange and the direction of movement. In CEs, business managers promptly adjusted their costs toacquire cost management flexibility when sticky costs were present. In contrast, when anti-sticky costs
were present in LPEs, administrators managed their costs subtly and slowly, and cost behaviors
gradually shifted toward a proportional relationship over four years. Because LPEs must supply theirservices stably and sustainably, one might assume that LPE administrators should avoid responding
promptly and suddenly controlling their costs and instead attempt to balance the public interest and cost
efficiency. Regarding the direction of movement of cost behaviors over four years, LPE administrators
chose to improve services in the public interest instead of pursuing efficiency. The examination of the
four-year change in cost behaviors shows that the LPE administrators decided to maintain their costs
after anti-sticky costs were observed. One might assume that LPE administrators are subject to
14

Refer to footnote 13.

26institutional pressure from the politicians who insist on responding to public opinion and social demands
that require the enrichment of public services rather than excessive cost efficiency. Conversely, CE
managers may aim to adjust their costs promptly to be able to manage them flexibly. Thus, from adecision-making perspective, I believe LPE administrators must aim for a long-term balance between
protecting the public interest and achieving efficiency due to institutional pressure from politicians. In
contrast, CEs’ business managers may decide to control and adjust their costs with a focus on securing
profits as quickly as possible.
Management accounting research can provide information about cost behavior and propose effective
cost management strategies not only in theory but also in practice. In public organizations, including
LPEs, it is important to understand how cost behavior will change in the future. Currently, Japan’snational and local governments are promoting two plans to resolve the two main issues of population
changes and a deteriorating financial situation. The first plan is called the Compact City Plan. It intends
to concentrate urban functions, such as public service systems, in central urban areas, thus improving
the efficiency of cost management in depopulated areas. Examples include district development plans toincrease the public transportation network of central urban areas and a renewed maintenance plan toconstruct a single building that houses many types of public services together. The second plan is theIntermunicipal Cooperation Plan, in which public services will be combined through amalgamation or
joint ventures to improve efficiency with economies of scale. By reaching agreements with different
public organizations, separately managed entities can be consolidated into one organization. For
example, in the water supply business, several LPEs can jointly develop large dams and provide water
services for a wide area that spans multiple municipalities.
Although the expectations for the Compact City Plan and the Intermunicipal Cooperation Plan are
high, the effects and benefits of these policies, such as improved public services and reduced costs, havenot been adequately explained. In addition, because the Compact City Plan and the Intermunicipal
Cooperation Plan have not been studied sufficiently in either an academic or a real-world context, we do
not know whether they will improve efficiency. Therefore, it is extremely important to understand thefuture cost behavior of public organizations to determine whether the Compact City or Intermunicipal
Cooperation Plans will provide the effective management of public organizations in the context of adeclining population and depopulated areas. Having reached these five conclusions, my research
explored how public organization administrators have made long-term cost management decisions.
In the future, research should examine the factors influencing LPEs’ asymmetric cost behavior, as
noted by Günther et al. (2014), including both internal and external factors. Especially for industries inwhich sticky costs have been confirmed, we need to determine how to maintain cost flexibility over thelong term. In contrast, in industries with anti-sticky costs, we must learn how to maintain costadjustment flexibility. It is conceivable that LPEs may be subject not only to institutional constraints,
such as achieving efficiency and protecting the public interest, but also to the non-exclusionary nature of
public services and the influence of monopolies. There is a continuing need for detailed investigationsof and research on public organizations’ asymmetric cost behavior, especially that of LPEs.

27

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31

Appendix
Variable

Definition

Cost

Operating expenses

Revenue

Operating revenues
The natural logarithm of total population deflated by the previous year's total

Pop _Total

population
The natural logarithm of the youth population (aged 0-14) deflated by the total

Pop_Youth

population
The natural logarithm of the middle-aged population (aged 15-64) deflated by

Pop_Middle

the total population
The natural logarithm of the elderly population (aged 65 and over) deflated by

Pop_Elder

the total population

Figure 1. Population changes in Japanper 1,000 persons
140,000

120,000

100,000
84,11580,00090,077

94,30280.0%
128,057
126,926
123,611 125,570
127,768
127,095
121,049
70.0%
117,060
111,940
104,665
60.0%
99,20950.0%

72,14740.0%

60,00030.0%
40,00020.0%

20,0000

10.0%

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Total population

0-14 age ratio

Created based on Japan’s population census for the year 2015.

15-64 age ratio

0.0%

65 age ratio

32

Figure 2. LPE bonds, operating and non-operating revenues and expenses
Panel A: LPEs’ bonds
per 100,000,000 Yen
60,000700,000

600,000

50,000500,000

40,000400,000
30,000300,000
20,000200,000

10,000

0

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

100,000

Amount of issued debt securities outstanding

0

Bond issuance volume

Created with reference to LPEs’ yearbooks
Panel B: LPEs’ operating revenues and expenses
per 1,000 Yen

12,000,000

10,000,000

8,000,000

6,000,000

4,000,000

0

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

2,000,000

Operating revenues

Created with reference to LPEs’ yearbooks

Operating expenses

33

Panel C: LPEs’ non-operating revenues and expenses
per 1,000 Yen

2,000,000
1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000

0

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

200,000

Non-operating revenues

Non-operating expenses

Created with reference to LPEs’ yearbooks
Figure 3. Trends in the number of LPEs in JapanThe number of LPEs
14,00012,00010,000

8,000

6,000

4,000

0

1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

2,000

Corporatized LPEs

Created with reference to LPEs’ yearbooks

Directly managed LPEs

34

Figure 4. Image of sticky costs and anti-sticky costs

t periodof costs

t-1 periodof costs

Cost function observed when revenue decreases fromthe previous year.
[Sticky line]
If revenuet-1< revenuet and β1＋β2< β1[Anti-sticky line]
If revenuet-1< revenuet and β1＋β2> β1

Sticky line

β1

β1＋β2

Cost function observed whenrevenue increases from theprevious year. It increases
proportionally.

Normal line
Anti-sticky linet -1 period ofrevenuest period ofrevenues

Created with reference to Yasukata (2012)

Figure 5. The causal relationship between the institutional constraints on and the cost behavior of LPEs
Institutional constraints
Publicinterest

Management decision

Economic
efficiency

Cost behavior

35

Figure 6. β2 value (sticky cost value) changes in each year 15
Panel A: LPEs
0.8
0.6
Linear Approximationy = -0.0161x + 0.4056

0.4β2-value

0

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

0.2

-0.2
-0.4
-0.6
Panel B: CEs
0.8
0.6
0.4

0.0

β2-value

1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

0.2

-0.2
-0.4

Linear Approximation y = -0.0029x - 0.0341

-0.6

15

The results in 2012 and 2013 in Panel A and between 2010 and 2013 in Panel B were not significant,so the interpolation method could not be adopted.

36

Figure 7. β2, β4, β6, and β8 value (sticky cost value) change in each 4-year period
0.15000.10000.05000.00001 year

2 year

3 year

-0.0500-0.1000
LPEs

CEs

4 year37

Table 1. LPEs in selected countries
Number ofmunicipalities

Number of
LPEs*

Austria

2,359

149

Electricity, gas, heating, public transport, water, sewerage, waste,telecommunications, public equipement, cemeteries, public areas, health

Belgium

589

243

Electricity, gas, communication networks, funding, economic
development, water, waste, health, social care

Czech Republic

6,258

339 Public transport, electricity, gas, heating, water, waste

Denmark

275

224

Economic development, electricity, gas, heating, water, waste, public
transport, leisure, computing, housing

Estonia247

224

Electricity, gas, water, housing, public transport, heating, health, social
services, trade, waste

Finland448

944

Economic development, energy, water, sewerage, waste, public
transport, ports, telecommunications

France36,565

Tourism, planning, housing, public transport, economic development,
1,198 water, sewerage, waste, environment, leisure, culture,telecommunications, parking spaces

Germany

13,8543,500

Greece900

1,116 Water, sewerage, culture, tourism, training, careers

Italy

8,101

Regions: economic development, planning, public equipment, public
963 transport, Provinces: commercial events, tourism municipalities: energy,
water, waste, pharmacies, cemeteries

Japan

1,727

Residential water, industrial water, transport, electricity, gas, hospitals,
9,379 and other businesses that are run by each local government according
to its own rules

Latvia547

Health, heating, waste, real estate operations, sport, public transport,
669 pharmacies, water, social care, radio & TV, auditing, training, tourism,
electricity

New Zealand

85

257 -

Poland

2,489

2,415

Water, construction, waste, real estate operations, electricity, gas,
heating, public transport, trade, leisure, culture, sport

Portugal

4,037

76

Energy, public transport, tourism, environment, planning, commercial
and industrial infrastructures, health, education, food industry

Slovakia

2,920

239

Waste, water, sewerage, heating, public spaces, health, public transport,
public lighting, sport, housing, cemeteries, local television, tourism

Slovenia

193

60

Water, waste, road, cemeteries, public transport, public spaces,
electricity, heating

South Korea

232

306 -

Spain

8,106

770

Municipalities and provinces: public transport, water, real estate,planning, economic development, cemeteries

Sweden290

1,750

Energy, water, waste, public transport, housing, tourism, economic
development

United Kingdom

326

185 Economic development, tourism,public equipment, health, social care

Country

Sectors of activities**

Energy, economic development, water, waste, public transport, public
equipment, housing, banks, telecommunications

Created with reference to Saussier and Klien (2013)* LPEs include not only corporatized LPEs but also directly managed LPEs.
** Sources: Dexia Crediop (2004)

38

Table 2. Characteristics of directly managed LPEs and corporatized LPEs
Directly managed LPEs

Corporatized LPEs

Decision rights

Local government

LPE management

Legal status

Local government

Corporation

Governed under

Public municipal law

Public municipal law and municipal ordinances

Organisation form

Multi-purpose

Single-purpose

Governed by

Local bureaucracy

Appointed executive board

Funded through

Taxes

User fees

Cooperative flexibility

MediumHigh

Created with reference to Saussier and Klien (2013) and Voorn et al. (2017)

Table 3. Organizational type and expected influence on cost behavior
Institutional Pressures

GovernancesystemCEs

Importantconstituencies

Rural community members /residents / service user

Shareholders

Board composition

Officer or publicly elected figure

Business people

Board size

Small

Large

Public interests and efficiency

Profit maximization

Maximum pressure

Minimal pressure

Access to capitalFees, donations, bonds
and limited tax support

Net sales, debt and equity
financing

Emphasis on economic
returns

Soft budget constraints

Rewards for efficiency

Legal compliance
Political pressure

Financialperformance

LPEs

Lack of cost only for indigent
residents
Created with reference to Eldenburg et al. (2004), Balakrishnan et al. (2010)* No compensation service for needy persons
Charity service*Preference for profits over charity
for indigent people

39

Table 4. Descriptive statistics
Panel A: LPEs
(Scale: 1,000 Yen)

Total

Mean

Standard
deviation

Minimum

Lower
quartile

MedianUpper
quartile

Maximum

Cost *

1,946,197

7,320,193

382

148,701

441,239

1,512,568

295,467,927

Revenue **2,083,008

9,354,756

75

170,870

477,245

1,547,037

355,330,535

0.04101

0.09656-0.48719-0.00904

0.02957

0.08073

0.57912

Ln costt / costt-1
Ln revenuet / revenuet-1

Residential
water
supplyIndustrial
water
supplySewerage

0.04341

0.10744-0.56932-0.00933

0.021140.07433

0.66314

Cost *

1,065,668

6,774,393

2,160

107,613

221,621603,446

295,467,927

Revenue **1,300,173

8,365,265

294

130,266

272,108735,249

355,330,535

Ln costt / costt-1

0.04577

0.10358-0.48290-0.01010

0.031160.089020.57874

Ln revenuet / revenuet-1

0.04900

0.11070-0.56601-0.00895

0.020350.07403

0.66286

Cost *

476,548889,671

1,677

62,770214,673

463,819

8,042,787

Revenue **611,846

1,125,119

708

67,160265,268

615,598

11,326,896

Ln costt / costt-1

0.019800.11230-0.48719-0.03042

0.011170.063900.56784

0.023610.10851-0.56583-0.00629

0.001770.03059

0.64851

Cost*

5,322,071

18,203,066

1,086

201,342

682,4542,744,434

225,035,329

Revenue**

7,300,351

28,571,454

75

101,462555,047

3,217,774

344,008,013

0.03273

0.08959-0.47635-0.00871

0.01830

0.05856

0.57912

Ln revenuet / revenuet-1

Ln costt / costt-1
Ln revenuet / revenuet-1

Transportation

0.04654

0.10907-0.55551-0.00635

0.02001

0.07392

0.66314

Cost *

8,925,017

19,250,696

382

348,485

1,779,477

6,132,778

149,541,551

Revenue **8,705,197

21,195,366

1,741

306,571

1,570,819

5,174,318

163,824,708

0.01174

0.08491-0.48158-0.02899

0.011530.049290.57121

Ln costt / costt-1
Ln revenuet / revenuet-1

Electric
power

Gas power

Hospitals

WholesalemarketToll road

Car parking

0.01463

0.09550-0.53314-0.02770

0.00457

0.04682

0.65543

Cost *

1,685,005

1,401,034

7,198

715,088

1,359,032

2,324,393

7,926,889

Revenue **2,265,455

1,792,677

13,700948,625

1,911,831

3,179,656

9,605,919

Ln costt / costt-1

0.03174

0.07822-0.40621-0.01232

0.025700.069650.45241

Ln revenuet / revenuet-1

0.023990.07932-0.37420-0.01410

0.00869

0.046830.62548

Cost *

1,263,644

3,293,313

27,840

198,447

447,234

954,152

40,287,262

Revenue **1,377,663

3,505,486

27,253215,862

487,177

1,064,206

40,270,247

Ln costt / costt-1

0.04504

0.09517-0.36607-0.01443

0.02992

0.09005

0.56286

Ln revenuet / revenuet-1

0.04829

0.09993-0.28446-0.01263

0.024640.08038

0.58733

Cost *

3,090,634

3,621,221

28,828726,049

1,687,856

4,030,324

31,602,391

Revenue **2,790,521

3,360,561

700

621,644

1,463,985

3,637,399

32,298,365

Ln costt / costt-1

0.04084

0.07661-0.47925-0.00031

0.03384

0.07567

0.57704

Ln revenuet / revenuet-1

0.040110.10112-0.56932-0.00902

0.03386

0.08350

0.65367

Cost *

1,900,347

3,679,698

42,733

235,815496,231

1,091,269

16,949,597

Revenue **1,718,437

3,206,354

58,783

188,243

477,556

1,372,835

14,497,486

Ln costt / costt-1

0.01971

0.07123-0.41089-0.01870

0.014120.050880.43474

Ln revenuet / revenuet-1
Cost *

0.01658
497,635

0.06003481,220-0.42070
22,252-0.01496
195,7020.00482
290,3470.03430
642,2190.51378
2,372,781

Revenue **692,079

884,575

22,778

186,634

342,516

826,0084,569,640

Ln costt / costt-1

0.028850.13502-0.39079-0.03376

0.022080.08266

0.53742

Ln revenuet / revenuet-1

0.02526

0.15362-0.56601-0.03861

0.020850.078890.62754

Cost *

82,441

68,813

2,222

38,621

74,155

108,933

372,239

Revenue **127,903112,924

4,366

51,00898,883

174,920563,130

Ln costt / costt-1

0.00743

0.13908-0.44680-0.05085

0.00225

0.05782

0.57128

Ln revenuet / revenuet-1

0.00069

0.12528-0.53474-0.04949

0.00000

0.05577

0.66278* Operating expenses, ** Operating revenues

SampleSize
115,929

64,675

7,296

4,525

2,677

1,261

2,274

32,066

516

270

369

40

Panel B: CEs(Scale: 1,000,000 Yen)

Cost *
Revenue **Total
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Agriculture and Revenue **fishery
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Revenue **MiningLn costt / costt-1
Ln revenuet / revenuet-1
Cost *
Revenue **Petroleum
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Revenue **Construction
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Revenue **Foods
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Tectiles, pulp and Revenue **paper
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Revenue **Chemicals
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Resources and Revenue **materials
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Machinery and Revenue **electric machinery Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Automobiles andRevenue **transportation
Ln costt / costt-1
equipment
Ln revenuet / revenuet-1
Cost *
Revenue **Financial
Ln costt / costt-1
Ln revenuet / revenuet-1
Cost *
Broadcasting,
Revenue **software,Ln costt / costt-1
commercial etc.
Ln revenuet / revenuet-1* Operating expenses, ** Operating revenues

MeanStandard
Minimum
deviation

Lower
quartile

MedianUpper
quartile

Maximum

125,774
131,5210.03958
0.03871
94,881
96,7470.03889
0.0393160,597
62,7700.036110.03559
57,586
76,7920.046120.03331132,019
136,9410.026900.02572
72,22075,4460.060450.06093
43,36545,521
0.051200.05190
60,83866,0400.043840.04249
100,727
105,601
0.02205
0.02044
97,397
101,3240.035250.03442
136,215
141,568
0.035150.03423
363,828
369,269
0.04457
0.04371
118,766
132,2100.041430.04007648,568
659,166
0.141100.15540
152,120
153,9140.16101
0.18943
22,065
22,7790.17392
0.19552
53,898
94,1130.20656
0.23462
237,300
246,680
0.13266
0.13736
212,318
220,709
0.154420.16605
100,497
105,2420.171110.18957
122,718
131,9440.12206
0.13302294,103
306,000
0.130310.14584
374,006
382,156
0.153590.18155
531,671554,6630.130220.14288
1,715,798
1,720,356
0.15367
0.16007383,299
435,4120.120230.12611

9,127
9,817
-0.02781
0.10157
4,295
4,419-0.03284-0.03497
52,86655,544-0.05549-0.06487
10,215
15,943-0.05296-0.08351
21,094
21,748-0.04520-0.04859
5,542
6,162
-0.01157-0.01254
3,610
3,923
-0.03426-0.03882
7,817
8,578
-0.01651-0.01987
9,626
10,254-0.04353-0.05065
8,861
9,415
-0.04333-0.05260
11,284
12,113-0.02943-0.03243
18,53020,414-0.02835-0.03005
9,540
10,141-0.01427-0.01583

23,643
24,9780.03373
0.03435
17,99820,4800.028160.02480
67,05468,5660.023990.02476
55,35460,5550.022960.03760
47,33349,0450.028960.02892
16,471
17,5530.039190.03795
10,185
10,9080.033100.03393
19,003
20,4780.034510.03496
22,14323,4200.024380.02428
19,44920,626
0.038140.04087
26,03027,1800.033360.03415
59,05361,3120.03425
0.03524
24,001
25,1720.03405
0.03397

68,73772,599-0.03163
0.10635
138,252
140,4810.08908
0.09267
74,24276,6590.10066
0.09957
72,511
84,6340.131360.14985
132,397
135,4200.101850.10341
56,06359,3750.112290.11462
31,93833,3950.118770.1272154,03658,0240.092640.09632
70,41973,8370.08901
0.09562
49,93652,6400.116010.12769
63,67066,1380.096100.09974
147,589
152,949
0.11203
0.11322
63,34367,917
0.09108
0.09146

21,359,227
21,403,613
0.81494
0.85349
602,390
612,8880.73166
0.81866
101,943
104,996
0.58887
0.65909
218,544452,2280.805110.75472
2,015,551
2,168,285
0.68698
0.75482
3,464,264
3,480,490
0.814940.85105
1,045,802
1,100,228
0.792490.84832
1,563,564
1,602,062
0.803150.85053
3,678,713
3,753,397
0.788460.82038
4,862,221
4,994,719
0.80750
0.85349
10,970,663
12,079,264
0.813120.84630
21,359,227
21,403,613
0.812100.83715
6,034,976
6,371,287
0.80006
0.831822
3-0.73556-0.77351
2
3-0.58739-0.70995261
180-0.64688-0.64940
2,769
4,638
-0.35237-0.66545
174
212-0.67110-0.7425627
22-0.72516-0.76474167-0.71247-0.7734941
10-0.70580-0.73760
158
236-0.72685-0.77351
1021-0.73094-0.7730823
6-0.73524-0.7652375
51-0.73556-0.75328101
94-0.71695-0.73672

SampleSize
84,343

426

82

65

5,272

6,679

6,287

10,2798,040

14,947

11,402

8,920

11,944

41

Table 5. Cost behavior based on the panel data analysis using model 1
Panel A: LPEs
Predicted
sign

β0

PooledFixedeffects

Randomeffects

0.0200 ***67.38

0.0207 ***68.24

0.0200 ***
67.20

β1

＋

0.5077 ***195.840.4952 ***183.96

0.5077 ***195.33β2－0.06770.0791

0.0677

***

***

***

9.74

10.69

9.71

Adj.R2

0.33890.33550.3389N

115,929

115,929

115,929

DW

2.11382.18192.1138

Statistic

H-TestP-value(Degree of freedom)

393.42(2)0

PooledFixedeffects

Randomeffects

0.0023 ***8.49
0.8704 ***463.940.0026 ***9.23
0.8647 ***413.830.0028 ***7.44

Panel B: CEs
Predicted
sign

β0
β1

＋

β2

－

-0.1008***-0.0978***

0.8682 ***448.82-0.1045***-28.85-24.92-28.740.83790.84670.8337

N

84,34384,34384,343DW

1.64871.8114

1.6913

Adj.R

2

H-TestStatistic(Degree of freedom)

20.48

(2)

For β0 , β1 , and β2, upper data indicate coefficient estimates;
lower data indicate t-statistics, *significant at the 10% level,**significant at the 5% level, ***significant at the 1% level,
Adj.R2 =Adjusted R2 , N=Number of Observations,
DW=Durbin-Watson ratio, H-Test=HausmanTest,
β2 indicates the value of the sticky or anti-sticky costs.

P-value042Table 6. The results for individual years based on OLS analysis using model 1
Panel A: LPEs
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

β０

β２

β１

0.0834***0.0864***0.0860***0.0528***0.0667***

0.0916***0.0476***0.0255***0.0246***0.0297***0.0317***0.0197***0.0143***0.0242***0.0243***0.0384***0.0400***0.0306***0.0280***0.0231***0.0132***0.0130***0.0156***0.0105***0.0091***0.0049***0.0114***-0.0038**-0.0032**-0.0028*-0.00250.00170.0032**0.0033**-0.0040***-0.0040***0.0052***0.0061***0.0085***0.2884***0.2572***

0.3476***

0.3991***

0.4025***0.3922***0.3327***0.3887***0.4699***0.4181***0.4139***0.4338***0.5593***0.5104***0.5686***0.5471***0.5447***0.07650.5313***0.5212***

0.4643***0.5559***0.02300.4663***0.4735***0.4898***0.5487***0.5338***0.4594***0.4173***0.5785***-0.0134

0.6758***0.8186***

0.5754***0.4978***0.04390.4470***-0.02400.5068***0.4387***0.5021***0.3067***0.3630***F-statistic

Prob(F-statistic)

2,656

504.750N

Adj.R20.27510.4668***0.6934***0.27302,723

512.0000.4302***0.27012,802

519.3300.2634***0.2763

2,858

546.3000.2336***

0.26482,886

520.6600.3728***0.35132,890

783.2800.2413***0.25482,934

502.4500.1806***

0.25942,962

519.6400.1482***0.29882,991

637.9900.0902*0.2515

3,028

509.4200.2455***0.2602

3,050

537.2100.2608***0.26803,075

563.7800.1810***0.34883,080

825.5900.1485***0.28683,110

626.0300.04300.3046

3,119683.800-0.01700.26253,130557.86

00.2582

3,137

546.720-0.1058**

0.25503,163

542.2800.1578***0.29283,179

658.970-0.1466***

0.19533,180

386.7400.26373,200

573.7800.2584***0.27673,204613.6000.2397***0.2874

3,218

649.5900.2175***0.2764

3,219

615.4900.1725***0.27423,230

610.9000.1411***0.27183,218

601.3400.2003***0.21713,224

447.96

00.1568***0.21933,232

454.8400.2736

3,198

602.980-0.2361***0.30582,821

622.120-0.3796***

0.43852,454

958.940-0.2197***

0.19882,725

338.9300.24482,708

439.7900.24272,710

435.200-0.1284***

0.2081

2,698

355.350-0.1333***

0.14142,723

225.060-0.2221***0.14722,687

232.8700.03090.10482,740

161.250-0.05930.0799121.07

0

2,767
2

2

*significant at the 10% level, **significant at the 5% level, ***significant at the 1% level, Adj.R =Adjusted R , N=Number of Observations

43

Panel B: CEs
Year
1975
1976
1977
1978
1979

β０

β１

0.0298***0.0168***0.0123***0.0119***-0.0017

F-statistic

Prob(F-statistic)

1,053

6,966.39

00.9092

1,065

5,330.40

00.92361,090

6,579.05

0N

Adj.R2β２0.8926***-0.1076***0.8543***-0.0567**0.8500***-0.0206

0.9113***0.00970.9397***0.9050***0.8879***0.9295***0.92980.9332

1,149

8,025.62

0-0.1150***

0.9321

1,367

9,374.25

00.1452***0.9241

1,385

8,428.48

00.1137***0.9383

1,406

10,678.92

0-0.0363*0.94001,436

11,249.80

00.0069***0.0137***

1982

0.0096***1983

0.0059 ***0.9583 ***-0.1630 ***0.9342

1,479

10,497.84

01984

0.0033 ***0.9371 ***-0.0730 ***0.95091,525

14,756.17

01985

0.0024**

0.9439***-0.0812***0.9506

1,575

15,138.00

00.0065***0.9357***-0.0535***

0.9432

1,621

13,444.14

00.9704***-0.0997***

0.9364

1,673

12,319.27

00.9274***-0.0356*

0.9156

1,720

9,322.80

00.8948***0.0722***0.9404

1,759

13,874.49

00.9445***-0.1287***

0.9125

1,837

9,573.52

00.9377***-0.0722***0.9292

1,916

12,558.01

00.9458***-0.0883***

0.9367

1,981

14,654.93

00.9829***-0.2312***0.9381

2,053

15,552.19

0***-0.1195***

0.93042,107

14,068.74

0

1980
1981

1986
1987
1988
1989
1990
1991
1992
1993

0.0014-0.0027**

0.0035***0.0040***0.0073***0.0062***0.00071994-0.00140.95391995-0.0025 ***0.9156 ***-0.0387 ***0.92942,150

14,142.35

00.9173***-0.0425**0.92562,214

13,765.3400.9506***-0.1833***

0.90182,320

10,654.80

00.9527***-0.1180***0.9396

2,398

18,638.26

00.9371***-0.1462***0.92152,487

14,597.35

00.9057***-0.0351**0.89972,554

11,446.83

00.9058***-0.3004***

0.8385

2,637

6,845.2500.8670***-0.1067***

0.86902,730

9,054.0300.8663***-0.0434**

0.8287

2,869

6,938.02

00.8287***0.0063

***

1996
1997
1998
1999
2000
2001
2002
2003
2004

0.0009-0.0018*

0.0023***-0.0095***-0.0055***-0.0052***

0.0021-0.0057-0.0005

0.81952,929

6,647.120***0.81042,963

6,331.68

02005

0.00230.8583

2006

0.0044 ***0.8663 ***-0.1177 ***0.81282,974

6,456.73

02007

0.0056 ***0.8797 ***-0.2054 ***0.7544

3,007

4,617.78

02008

0.0120***0.7870***-0.0728***

0.69373,054

3,457.98

00.0061***0.8248***-0.1069***0.74443,098

4,511.85

0-0.0211***0.7556***-0.02370.7156

3,112

3,914.5800.7059***-0.01110.6902

3,183

3,545.08

00.6528***0.00770.64263,206

2,881.67

00.7256***-0.0174

0.69403,698.40

0

2009
2010
2011
2012
2013

-0.0009
0.00640.0012***-0.1164

3,261
2

2

*significant at the 10% level, **significant at the 5% level, ***significant at the 1% level, Adj.R =Adjusted R , N=Number of Observations

2

2

Fixed

0.01381.37
0.4316 ***5.90-0.1073-0.79
0.1781270
1.7883(2)0.01311.33
0.4400 ***6.18-0.1208-0.92
0.1951270
1.77900.42

Toll road

Pooled

0.01311.31
0.4400 ***6.12-0.1208-0.91
0.1951270
1.77900.81

RandomFixed-0.0215 **-2.44
0.7247 ***8.28-0.7056 ***-4.94
0.1646369
2.2731(2)-0.0197 **-2.27
0.7101 ***8.28-0.6605 ***-4.75
0.1690369
2.22952.10

Car parking

0.0174 ***9.61
0.6705 ***43.62
0.3996 ***6.56
0.58922,274
2.3448(2)

Pooled

0.0170 ***9.50
0.6776 ***45.03
0.3908 ***6.56
0.59582,274
2.33028.68

0.0168 ***6.79
0.5476 ***18.55-0.1326-1.60
0.26951,261
2.4553(2)0.0165 ***6.76
0.5541 ***19.24-0.1473 *-1.83
0.28291,261
2.43861.64

0.0165 ***6.70
0.5541 ***19.06-0.1473 *-1.81
0.28291,261
2.43860.44

Fixed

PooledGas power

Random

0.0135 ***9.21
0.3101 ***21.44
0.05001.56
0.09277,296
2.1746(2)

Fixed0.0135 ***9.40
0.3111 ***22.33
0.0565 *1.83
0.10047,296
2.1257
1.80

Fixed

Random-0.0197 **-2.26
0.7101 ***8.25-0.6605 ***-4.74
0.1690369
2.22950.35

Random

0.0170 ***9.42
0.6776 ***44.67
0.3908 ***6.50
0.59582,274
2.3302
0.01

Random

0.0135 ***9.36
0.3111 ***22.23
0.0565 *1.83
0.10047,296
2.12570.41

Industrial water supply

Electric power

0.0254 ***58.85
0.4820 ***137.030.2748 ***23.69
0.318964,6752.20750.00

PooledPooled

0.0261 ***59.80
0.4719 ***131.640.2908 ***24.29
0.311564,6752.2369(2)

0.0254 ***59.17
0.4820 ***137.780.2748 ***23.82
0.318964,6752.2075245.02

Random

0.0127 ***33.69
0.6315 ***170.84-0.1581 ***-20.34
0.596032,0661.9019109.05Pooled

0.0098 ***6.71
0.4224 ***33.71-0.2731 ***-7.63
0.21804,525
1.8467
16.18

Pooled

0.0128 ***33.33
0.6279 ***166.43-0.1640 ***-20.380.595432,066
1.9436(2)

Fixed

Hospitals

0.0107 ***7.24
0.4059 ***31.17-0.2656 ***-7.27
0.2304
4,525
1.9222(2)

Fixed

SewageAdj.R2=Adjusted R2, N=Number of Observations, DW=Durbin-Watson ratio, H-Test:Hausman Test, β2 indicates the value of the sticky or anti-sticky costs.

Upper data indicate coefficient estimates; lower data indicate t-statistics, *: significant at the 10% level, **: significant at the 5% level, ***: significant at the 1% level,

Adj.R
N
DW
H-testβ2

β1

β0

Adj.R2N
DW
H-test

β2

β1

β0

Adj.R
N
DW
H-test

β2

β1

β0

Fixed

Residential water supply

Pooled

0.0127 ***33.66
0.6315 ***170.71-0.1581 ***-20.330.596032,0661.90190.00

Random0.0100 ***6.05
0.4179 ***33.18-0.2713 ***-7.58
0.21204,525
1.86790.00

Random

0.0134 ***3.87
0.5407 ***9.49
0.2506 *1.85
0.2622516
2.0174
1.74

Pooled

0.0065 ***3.54
0.4322 ***20.75
0.04281.07
0.25632,677
2.0214
8.62

Pooled

0.0135 ***3.88
0.5297 ***9.14
0.2464 *1.80
0.2556516
2.0448(2)

FixedWholesale market

0.0073 ***3.94
0.4159 ***19.27
0.0693 *1.66
0.25452,677
2.0660(2)

FixedTransportation
Random

0.0134 ***3.85
0.5407 ***9.45
0.2506 *1.85
0.2622516
2.0174
0.42

Random

0.0065 ***3.54
0.4322 ***20.72
0.04281.07
0.25632,677
2.02140.01

44

Table 7. Cost behavior of each industry based on the panel data analysis using model 1

Panel A: LPEs

20.0041 ***3.23
0.8520 ***102.46-0.1697 ***-9.37
0.78516,679
1.7382(2)0.0030 **2.54
0.8671 ***123.78-0.1764 ***-11.410.7747
6,679
1.577814.22

0.0040 ***2.64
0.8624 ***117.60-0.1771 ***-10.980.7627
6,679
1.61970.00

RandomFixed

0.0034 ***5.41
0.8267 ***200.41-0.0676 ***-9.31
0.8961
14,9471.8500(2)

Pooled

0.0036 ***5.94
0.8280 ***219.89-0.0638 ***-9.76
0.892014,9471.72046.44

0.0032 ***4.27
0.8283 ***215.04-0.0688 ***-10.180.8913
14,9471.7463
0.04

RandomMachinery and electric machinery

Fixed

Foods

Pooled0.0017 **2.12
0.8627 ***135.15-0.2071 ***-16.130.7481
10,2791.505535.10

0.0080 ***3.93
0.8091 ***97.56-0.1092 ***-6.56
0.7376
6,287
1.54950.00

0.0025 ***4.27
0.8849 ***197.99-0.0695 ***-8.53
0.882911,402
1.6846
3.12

PooledRandom

0.0025 ***3.22
0.8835 ***193.04-0.0739 ***-8.78
0.8806
11,402
1.7211
0.21

Fixed

0.0021 ***3.44
0.8861 ***
182.17-0.0797 ***-8.82
0.8885
11,402
1.8344(2)-0.0009-0.97
0.9211 ***143.12-0.1518 ***-12.420.81128,920
1.7796
1.54

PooledPooled-0.0109-0.46
0.8512 ***7.28-0.4249 **-2.07
0.576765
2.1331
9.74

PooledRandomAutomobiles and transportation equipment

0.0075 ***4.84
0.7865 ***83.75-0.0721 ***-3.92
0.77966,287
1.7361(2)

Fixed

Tectiles, pulp and paper
0.0032 **2.20
0.8277 ***104.93-0.1247 ***-7.75
0.75786,287
1.480026.63

Pooled

0.00590.52
0.8134 ***12.64-0.0254-0.21
0.811282
1.63980.91

Random

-0.0008-0.76
0.9179 ***127.14-0.1515 ***-10.890.81438,920
1.8728(2)

FixedFinancial

0.0030 ***3.54
0.8503 ***121.25-0.1780 ***-12.440.7667
10,2791.6892(2)

FixedChemicals

0.00490.21
0.7625 ***6.51-0.2344-1.11
0.623765
2.3279(2)

FixedPetroleum

Adj.R2=Adjusted R2, N=Number of Observations, DW=Durbin-Watson ratio, H-Test:Hausman Test, β2 indicates the value of the sticky or anti-sticky costs.

Upper data indicate coefficient estimates; lower data indicate t-statistics, *: significant at the 10% level, **: significant at the 5% level, ***: significant at the 1% level,

Adj.R2N
DW
H-testβ2

β1

β0

Adj.R2N
DW
H-test

β2

β1

β0

Adj.R
N
DW
H-testβ2

β1

β00.00580.47
0.8132 ***11.42-0.0289-0.21
0.806882
1.6473(2)0.00591.11
0.7735 ***22.42-0.0613-0.90
0.7885426
2.1566(2)

0.00801.69
0.7606 ***28.42-0.0244-0.47
0.7818426
1.98591.10

0.00590.52
0.8134 ***12.79-0.0254-0.21
0.811282
1.63980.19

Fixed

0.00841.37
0.7539 ***27.05-0.0202-0.37
0.7751426
2.0054
0.58

MiningPooledFixedRandomAgriculture and fishery

Pooled-0.0008-0.75
0.9207 ***140.29-0.1547 ***-12.370.80808,920
1.7943
0.46

Random

0.0030 ***2.48
0.8573 ***130.29-0.2038 ***-15.370.7406
10,2791.56480.00

Random-0.0109-0.49
0.8512 ***7.72-0.4249 **-2.20
0.576765
2.1331
0.01

RandomFixed

Random

0.0021 *1.81
0.8574 ***138.79-0.0610 ***-5.71
0.86388,040
1.71860.00

0.0010 **1.98
0.9363 ***218.09-0.1186 ***-14.270.8793
11,944
1.9776
53.60

Pooled0.0010 *1.75
0.9331 ***198.05-0.1265 ***-13.640.8792
11,9442.0349(2)

Fixed0.0010 **1.98
0.9363 ***218.04-0.1186 ***-14.270.8793
11,944
1.9776
0.00

RandomBroadcasting, software, commercial etc.

0.0021 ***2.65
0.8592 ***136.03-0.0574 ***-5.22
0.87288,040
1.8629(2)

Random0.0011 *1.85
0.9576 ***205.95-0.0308 ***-3.67
0.95565,272
2.0396
0.64
Resources and materials

0.0023 ***2.99
0.8546 ***138.10-0.0535 ***-5.08
0.86208,040
1.6629108.33PooledFixed

0.0012 **2.02
0.9563 ***196.60-0.0283 ***-3.18
0.95595,272
2.1086(2)0.0011 *1.87
0.9576 ***-3.68-0.0306 ***-3.68
0.95575,272
2.03520.90

Construction
Pooled45

Panel B: CEs

46

Table 8. Population changes and cost behavior based on the panel data analysis using model 2
Panel A: LPEs
The effect of youth (0-14 years) population change

The effect of total population change

Pooled
0.0041 ***11.38
0.5321 ***96.27-0.0347 ***-3.57-0.4190 ***
-3.10

β0
β1
β2
β3

Fixed
effects

Randomeffects

0.0041 ***10.66

0.0041 ***
11.30

β0

0.5227 ***84.53

0.5321 ***95.56-0.0401 ***-3.60-0.0347 ***-3.54-0.3080-2.17**-0.4190
-3.08

Fixed
effects

Randomeffects

0.0041 ***11.38

0.0041 ***10.80

0.0041 ***
11.30

β1

0.5321 ***96.33

0.5212 ***84.34

0.5321 ***
95.66

β2

0.6357 ***7.44

0.9987 ***9.89

0.6357 ***7.39

Pooled

***

β3

β4

β4

β5

β5

β6

β6

0.33727.91***

0.521610.36***

0.3372
7.85

Adj.R20.24580.2346

0.2458Adj.R20.24650.23610.2465

N

55,97655,97655,976

N

55,97655,97655,976DW

2.17752.32502.1775DW

2.17452.32272.1745

H-TestStatistic
56.96(Degree of freedom)

P-value
0(3)

The effect of middle-aged (15-64 years) population change

β0
β1
β2

PooledFixedeffects

Randomeffects

0.0041 ***11.33
0.5322 ***96.29-0.0329-0.76

0.0041 ***10.60
0.5227 ***84.53-0.0861 *-1.67

0.0041 ***11.25
0.5322 ***95.58-0.0329-0.76

H-Test

β0
β1
β2
β3

β4

β4
0.00530.06-0.0954-0.90

0.0053
0.06

Adj.R
DW

H-Test0.24570.23450.2457

Adj.R

55,97655,97655,976N

2.1768DW

2.1768
Statistic
57.83

2.3245(Degree of freedom)(3)

PooledFixedeffects

Randomeffects

0.0042 ***11.41
0.5320 ***96.25-0.1367 ***-3.48

0.0041 ***10.72
0.5224 ***84.47-0.1721 ***-3.70

0.0041 ***11.33
0.5322 ***95.54-0.1367 ***-3.45-0.0901-0.0695
-2.66

β6

N

0

(3)

β5

β6
2

P-value(Degree of freedom)

102.74The effect of elderly (65+ years) population change

β3

β5

Statistic***

P-value
02

H-Test

***

***

***

-2.91

-0.0695-2.64

0.24580.2346

0.245855,97655,97655,9762.1768
Statistic

2.3246(Degree of freedom)

56.72

(3)

2.1768P-value
0

From β0 to β6 , upper data indicate coefficient estimates; lower data indicate t-statistics, *significant at the 10% level,**significant at the 5% level, ***significant at the 1% level, Adj.R2 =Adjusted R2 , N=Number of Observations,
DW=Durbin-Watson ratio, H-Test=HausmanTest, β3 means Pop_total, β4 means Pop_youth, β5 means Pop_middle and β6 means Pop_elder

47

Panel B: CEs
The effect of total population change

PooledThe effect of youth (0-14 years) population change

Fixedeffects

Randomeffects
0.0010 **2.08
0.8265 ***297.84-0.0839 ***-10.101.32832.71

β00.00040.94

0.0021 ***
5.01

β1

0.8338 ***312.88β2

0.4892 ***11.81

0.8049 ***253.81-0.0567 ***-10.10β3

1.348914.01

2.4228
2.71

***

β0
β1
β2

PooledFixedeffects

Randomeffects

0.00041.07
0.8336 ***312.80-0.5776 ***-16.990.0023 ***5.58
0.8047 ***254.01-0.3699 ***-10.660.0013 ***2.81
0.8256 ***298.87-0.5211 ***-15.51

β3

β4

β4

β5

β5

β6

β6

-0.3122-14.56***-0.2005
-9.21

***

***

-0.2791-13.18Adj.R20.79640.80750.7872

Adj.R20.79640.80780.7884

N

53,14653,14653,146

N

53,14653,14653,146DW

1.64781.85691.7017DW

1.64781.8523

1.6941

H-TestStatistic

P-value(Degree of freedom)248.89

H-Test0(3)

The effect of middle-aged (15-64 years) population change

PooledFixedeffects

Randomeffects

Statistic

P-value(Degree of freedom)

397.680(3)The effect of elderly (65+ years) population change

PooledFixedeffects

Randomeffects

β00.00040.94

0.0023 ***5.49

0.0013 ***
2.71

β0

0.00041.11

0.0023 ***5.59

0.0013 ***
2.71

β1

0.8338 ***312.880.8258 ***298.97β1

0.8336 ***312.780.8047 ***254.020.8258 ***298.97β2

0.4892 ***11.81

0.8048 ***254.030.3057 ***7.28

0.4299 ***
10.51

β2

1.6929 ***14.06

1.1022 ***9.02

0.4299 ***10.51

0.5842

β3

β3

β4

β4
1.3489
14.01

β5

***

0.8441
8.65

***

***

1.1996
12.63

2

8.65

0.7883

N

53,14653,14653,146DW

1.64781.8525

1.6941

0.7883

Adj.R

N53,14653,14653,146DW

1.64781.8525

1.6941

Statistic
401.70(3)

P-value
0***0.80770.8077

H-Test***0.7964

0.7964(Degree of freedom)***

1.199612.63

0.8986
14.79

β6

β6
Adj.R

β5

2

H-TestStatistic(Degree of freedom)

393.74(3)

P-value
0

From β0 to β6 , upper data indicate coefficient estimates; lower data indicate t-statistics, *significant at the 10% level,**significant at the 5% level, ***significant at the 1% level, Adj.R2 =Adjusted R2 , N=Number of Observations,
DW=Durbin-Watson ratio, H-Test=HausmanTest, β3 means Pop_total, β4 means Pop_youth, β5 means Pop_middle and β6 means Pop_elder

48

Table 9. Cost behavior over 4 years based on the panel data analysis using model 3
Panel A: LPEs

β0

PooledRandomeffects

PooledFixedeffects

Randomeffects

0.0113 ***0.0122 ***0.0113 ***0.0010 ***0.0015 ***0.0011 ***

31.30

30.76

31.10

4.03

4.45

3.78

β1

0.4764β2

0.0972(t/t-1)

12.26

***

0.4693***0.1157141.08β30.05860.0137β6-0.0226

24.95

β7

***

0.09720.05500.0226

***

***

2.76
0.0690 ***
23.75

***

-0.0179***

0.8748***-0.0719140.16

17.18
*

1.74
0.0710 ***(t-2/t-3)

0.4764

13.89
***

β5

(t-1/t-2)***

135.2918.74

β4Panel B: CEs

Fixedeffects

0.8760***-0.0685442.75***0.8744 ******-0.0714428.33444.22***12.18
0.0586 ***-21.400.0352 ***-19.310.0346 ***-21.220.0349 ***

18.62

18.09

17.41

18.07

*

0.01371.73
0.0710 ***
24.78

**

***

13.72
0.0229 ***
12.25

***

-0.0226

0.0470***0.02160.0505***14.23
0.0213 ***
11.15

***

0.0244***0.047413.90
0.0226 ***
12.18

***

***

0.0217-2.94
0.0460 ***-2.23
0.0456 ***-2.92
0.0460 ***6.44
0.0224 ***7.02
0.0217 ***6.50
0.0221 ***

18.13

17.58

18.01

13.52

12.45

13.40

**

**

**

**

**

**

β8

-0.0156(t-3/t-4)-2.15
0.3194-2.09
0.3104-2.13
0.3194-2.40
0.8926-2.12
0.8956-2.44
0.8904N
DW

100,923

100,923

100,923

72,814

72,814

72,814

H-TestStatistic
138.30Adj.R2

2.1958-0.0158

2.2610(Degree of freedom)(8)-0.0156-0.0075

2.19581.9671

P-value

Statistic
285.450-0.0070

2.1173(Degree of freedom)(8)

From β0 to β8 , upper data indicate coefficient estimates; lower data indicate t-statistics,*significant at the 10% level, **significant at the 5% level, ***significant at the 1% level
Adj.R2 =Adjusted R2 , N=Number of Observations, DW=Durbin-Watson ratio, H-Test=HausmanTest,
β2 value is the rate of change from t-1 to t, β4, β6, and β8 values represented the annual change in asymmetriccost behavior for t-1/t-2, t-2/t-3, and t-3/t-4, respectively.

-0.0076

1.9863P-value0