2022 AugustVol. 12 No. 1
h t t p : / / re a l o p n . j p

RealOptions
特集
in Global Perspectives
巻頭言
In Pursuit of Wisdom for Sustainability ［Hiroshi Takamori］

1

査読 Real Options and the Design of Global Public Policy Interventions［Linda Peters］

2

An Event Study of the Impact of Corporate Social Responsibilityon the Corporate Values: A Case of Kao Corporation［Miho Tanaka］

12

論文

Small Businesses’Awareness, Experience and Attitude towards Microfinancein Asmara, Eritrea［Fiyori Afeworki Negash］
22
On the Correlation between Market Risk Premiums and SDGs:
Implications to Corporate Value［Haruyoshi Ito］

編集後記

35

第12巻 第1号
目次

巻頭言
In Pursuit of Wisdom for Sustainability ………………………………………………… Hiroshi Takamori

1

特集：「Real Options in Global Perspectives」
査読論文
Real Options and the Design of Global Public Policy Interventions…………………………………………………………………………………………………………………………………………………… Linda Peters

2

An Event Study of the Impact of Corporate Social Responsibilityon the Corporate Values: A Case of Kao Corporation ……………………………… Miho Tanaka 12
Small Businesses Awareness, Experience and Attitude towards Microﬁnancein Asmara, Eritrea……………………………………………………………………………………… Fiyori Afeworki Negash 22
On the Correlation between Market Risk Premiums and SDGs:
Implications to Corporate Value ………………………………………………………………………… Haruyoshi Ito 35〈学会ニュース〉
学 会だより …………………………………………………………………………………………………………………………………………… 51
編集後記 ……………………………………………………………………………………………………………………………………………… 51
日本リアルオプション学会法人会員リスト ……………………………………………………………………………………………… 51

日本リアルオプション学会 機関誌

リアルオプションと戦略

第 12 巻 第 1 号<Prefatory Note>

In Pursuit of Wisdom for Sustainability
Hiroshi Takamori(Professor Emeritus, Aoyama Gakuin University )I was part of the group that founded the Japan
Association of Real Options and Strategy(JAROS) in 2006. During the phenomenal eraof Japanese economic growth in the 1990s, ourfinancial circles matured tremendously andrecognized the sophisticated technologies ofderivative assets. We were particularly attracted
to the options that are now present in almost
every risk-hedging arrangement. We believedthat the principles underlying such financial
instruments could be applied to assess risk andvalue involving real or tangible assets such asreal estate, investment projects, and evencorporate assets. Real options analysis
essentially addresses the valuation of business
opportunities that harbor uncertain and risky
outcomes. Risk and uncertainty present achallenge to all human endeavors, especiallywhen it comes to investment, a basic humanactivity that involves deferring the pleasure oftoday for the potential security and benefits of
tomorrow. We have called for the positive
valuation of the choices that may arise as anenterprise evolves, instead of being held backby opacity.
Our association has been fortunate enough tobe supported by corporate members of goodstanding and repute. Starting in 2016, the“Innovation and Strategy for Value” study
group has held monthly seminars in which top
executives from various sectors gave an
in-depth talk on their corporate experiences andstrategic outlooks for their future direction.
Articles outlining these valuable discussions
are documented in this journal.
Looking back at the past 15 eventful andturbulent years, the sub-prime crisis
precipitated by the fall of Lehman Brothers in2008 stood out as one of the most damaging
incidents to the world economy. The East Japan

Earthquake and the subsequent nuclear
accident in 2011 not only devastated Japan but
also prompted serious soul-searching regarding
our nation’s energy security. The COVID-19
catastrophe is also another stark reminder of
how vulnerable and helpless we are to viral
infections. Combined, these calamitousdevelopments have clearly shown us thatsociety and humanity are severely threatenedby systemic risks that any individual,
corporation, or country cannot hope to copewith alone. Today, the most uncertain issue we
confront is whether mankind can overcome
such systemic and global crises.
The problems we face, including poverty,
inequality, climate change, and environmental
failure, are an adverse legacy of the 20th
century capitalistic, market-based economics.
Even though we owe our prosperity today tothis paradigm, we must still be held
accountable for the problems and threats it hasleft behind. It is thus encouraging that theworld is now in the process of redressing thepitfalls of old capitalism. For example, therecent call for environmental, social, andgovernance (ESG) is a promising initiative forcompanies to be examined not just by theirfinancial performance, but also theirsustainability measures such as emissions,water, and energy consumption. Withsustainability becoming an increasingly
important aspect for investors, the worldwide
campaign for Sustainable Development Goals(SDGs) will hopefully gather enoughmomentum to end poverty, address inequality,
and save our planet.
I believe our original aspiration for wisdom tocreate value is still valid, with the added caveatof now being aligned with the on-going effort
to make our planet more sustainable.
1

Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

<Reviewed Paper / Accepted: July 6th, 2019>

Real Options and the Design of Global Public Policy Interventions
Linda Peters (University of Antwerp, Belgium)
Abstract: Global public policy is an unexplored, but very promising area for the application of real options. Global
public policy interventions could be valued through the use of real options, like an investment. This assessment could
be deployed strategically such as in case of making interventions more attractive for the potential participants or topre-assess the effectiveness of different policy measures. These possibilities have been already recognized andaccepted in the literature of public policy, but not very well-known to the field of global public policy. In this paper
it is demonstrated why real options is very suitable for global public policy and this is demonstrated on the basis ofthree specific domains: global warming, migration and privacy.

Keywords：global public policy; global public policy networks; real options; strategy(Farazmand, 2018). This refers to domestic policy that
attempts to achieve public goals, such as safety, health andsocial protection. At global level policy relates to issues
that transcend national borders, such as climate change,
trade, terrorism, financial markets and human development.
Even more than within public policy, there is a highuncertainty within the global arena. The complexity
increases, there are more factors and actors that have to be
dealt with and there is less enforceability present. The
aforementioned factors are referring to some differences
between public and global. Consequently, even more thanon public policy, real options is a powerful tool for global
public policy interventions.
In this paper, we will further elaborate on this idea. It isdemonstrated how real options are used on the field of
public policy and how real options valuation could be
deployed with regards to global public policy making. It isshown that the use of real options is particularly promisingfor strategic matters. This is illustrated on the basis of the
topics privacy, migration and global warming, while themodelling itself is subject to future research. The rationale
behind this is to first conceptualize the idea withoutdrowning into the technical details, as has often happened
before, which could deter potential users from using real
options. This is based on the fact that practitioners often
find it difficult to understand and implement real options
due to the complex mathematics involved (He, 2007).
Section 2 describes public policy instruments and insection 3 it is illustrated how real options is applied tocommonly used public-private partnerships. Section 4shows the difference between public policy and global
public policy and section 5 explores the applications forpolicy makers on the fields of global warming, migrationand privacy. Section 6 provides some concluding remarks.

1. Introduction
One crucial aspect of new global governance structures
is the ascendancy of economic modes of thinking within
the policy process (Kennett, 2008). An example of thisinvestment thinking is the widespread embrace of costbenefit analysis (CBA) as decision making tool in policy.
CBA is the practical use of welfare economics to assess
potential projects (Rosen and Gayer, 2010). This toolcompares the costs and benefits of global public policy
projects and projects where the benefits exceed the costs
should be carried out. In practice, CBA is mainly
performed by using net present value (NPV) and internalrate of return (IRR). Global organizations such as theWorld Bank used to heavily rely on traditional valuation
approaches, such as the NPV, for the evaluation of
investment decisions (IEG World Bank, 2010). However,
nowadays the World Bank is reconsidering the policy
towards the traditional cost and benefit analysis andseeking for alternatives. Traditional models, such as NPV
and IRR do not capture the value of flexibility to adapt andrevise future investment decisions to respond to thisuncertainty. In contrast, Real Options Analysis (ROA)
acknowledges the fact that many projects containembedded options and provides adequate tooling toincorporate and correctly value these options.
It is not surprising that there is a lot of literature
available on the application of real options to public policy(e.g. Chen et al., 2012). However, it is surprising to acertain extent that there is little literature available on the
combination of real options and global public policy (GPP).
Public policy can be defined as purposive action that isostensibly in the public interest and has been legitimatedby governments or quasi-governmental organizations

2

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第 12 巻 第 1 号

3. Using Real Options for Public-Private
Partnerships

2. Public Policy Instruments
From an economic point of view, market failure is themost important justification for public policy interventions(Dollery and Worthington, 1996). Bator (1958) describes
market failure as the failure of the price-market system ‘tosustain "desirable" activities or to estop "undesirable"
activities’ (p. 351), where activity is referring to all
consumption and production and the ‘desirability’ of anactivity is determined by its relative share in solving amaximum-welfare problem. An important case of market
failure are externalities with global warming as a standard
example. We speak of externalities when there is adiscrepancy between private and social costs and/or
between private and social benefits (Kennett, 2008).
Policy makers have two types of instruments at theirdisposal: economic instruments and command-and-control
instruments (Hepburn, 2006). Economic instruments
consist of price-based instruments, such as taxes andsubsidies, and quantity-based instruments, such as capand-trade. Command-and-control instruments focus on theenforcement of compliance of firms or individuals tocertain standards, such as environmental standards. Highquality information, such as on the consequences for firms
and individuals, is needed for command-and-control
instruments. In case this information is missing, it would
be better for a policy maker to leave the determination of
the equilibrium to the market forces and monitor the priceby using economic instruments.
The way in which some of these public policy
instruments are implemented, and then in particular on thefield of public infrastructure, is more and more throughpublic-private partnerships (PPP) (Osei-Kyei and Chan,
2015). Within a PPP, public and private sectors are working
together on large-scale projects (Chen et al., 2012). A key
characteristic of PPP is the risk allocation and risk sharing(Osei-Kyei and Chan, 2015). The starting point here is thatthe party who is best capable of managing risks is also
going to bear them (Shan et al., 2010). However, there areenough problems with PPPs (Chen et al., 2012). PPP
projects often have a long duration, which is accompanied
by a lot of uncertainty. Poor risk management andunrealistic projections often lead to the failure of PPP
projects. In addition, the management of PPPs arehampered, because PPP arrangements can contain either
explicit contractual options or implicit options not stated in
the contract (Krüger, 2012).

In situations where there is a lot of uncertainties and a
lot of operational flexibility present, such as in the context
of PPPs, real option analysis is a very suitable tool (e.g.
Brach, 2003; Copeland and Antikarov, 2000). Real options
analysis is a valuation method that is based on financial
option theory. Real options approach acknowledges thefact that many projects contain embedded options andprovides adequate tooling to incorporate and correctly
value these options (Dixit and Pindyck, 1994; Peters, 2016).
There is a problem with traditional valuation approaches,such as in the case of Net Present Value. These methods do
not consider the options in the decision-making process,and therefore underestimate flexibility of projects. They
assume that the results of these projects are static and thatdecisions of these projects are irreversible. In contrast to
the traditional valuation approaches, real options analysis
does capture the value of managerial flexibility to adapt
and revise decisions in response to unexpected market
decisions (Trigeorgis, 1996). And therefore real options
analysis maximizes potential gains and minimizes
downside losses.
In the literature, ROA is often used to value PPPs(e.g. Alonso-Code et al., 2007; Brandao and Saraiva, 2008;
Shan et al., 2010). Incentives, such as guarantees andsubsides, that are used in PPPs could be interpreted as
options (Chen et al., 2012; Cheah and Liu, 2006). ROA
provides a way of modeling the flexibility-type incentives
for PPP projects and is able to optimize the functionality of
the incentives. Furthermore, the context of PPP proves tobe suitable for the use of ROA, because of the highuncertainty, the complexity, the different stages together
with their related risks and the interdependence of thevariables (Alonso-Conde et al., 2007).
A real option gives the right, but not the obligation, tobuy (call-option) or sell (put-option) the real underlying
asset (stock) at an agreed price (strike price or exercise
price) during a specific period or at a predetermined
expiration date. As an example Cheah and Liu (2006)
explain that a minimum revenue guarantee effectively is aput option and that a revenue cap could be regarded as acall option. Chen et al (2012) provide an overview on theuse of different type of options. In case of PPP, primarily
combinations of simple call and simple put options are3Communications of the Japan Association of Real Options and Strategy
used. For example, Shan et al. (2010) use collar options. In
case of a collar option the concessionaire buys a floor (aput option) from the underwriter to receive the protection
against revenue below the floor, and simultaneously sells acap (a call option) to the underwriter to defray the cost of
the floor.
Alongside the valuation of PPP, real options are also
useful regarding the design of the PPP incentive itself. By
employing real options strategically, the design of PPPs
could be significantly improved (Chen et al., 2012).
Different (combinations of) incentives could be valued
with the use of real options, and the (combination of)
incentives that results in the highest value proves to be thebest PPP arrangement. We will notice that this strategic
function becomes more and more important as soon as we
make the transition from public policy within a nation state
to public policy at a global level.

Vol. 12 No.1

sovereignty is missing (Reinicke, 1997). Therefore, thesuccess of global public policy is dependent uponcompliance and good international citizenship (Stone,2008). However, it is expected that ‘interests and concerns
will vary and cooperation will not be easy to achieve due
in part to differences in policy priorities and otherpreferences—perhaps often simply due to lack of
information and mutual understanding and trust’ (Kaul etal. (2003), p.15). Furthermore, global public policy making
could take a long time, sometimes several decades (Soroos,1986). In summary, processes within global public policy
demonstrate a high level of complexity, contingency andambiguity (Farazmand, 2018).
At the same time the extent of flexibility changes as well.
Due to the fact that policy makers have to seek cooperation
with people, governments, companies and nongovernmentorganizations across and between local, national, andinternational levels, other actors are given more flexibility
by policy makers within the policy instruments.
Participation in policy instruments could be stimulated byproviding different options to participants, such as the
freedom to start or exit a project whenever they feel isnecessary. For this reason, the flexibility of policy makers
is reduced, while the flexibility of other actors increases.
Through Global Public Policy Networks (GPPNs),
policy makers have found a way to deal with global public
policy problems that are characterized by the global scope,
speed and multidisciplinarity (Reinicke, 1999). Streck(2002) describes GPPNs ‘as multisectoral partnerships
linking different sectors and levels of governance andbringing together governments, international organizations,
corporations, and civil society’ (p. 3). Their particularstrength is inclusiveness (Detomasi, 2007) and threedifferent types of networks are distinguished: negotiation
networks, coordination networks, implementation
networks (Benner et al., 2002). An example of a GPPN is
the Clean Development Mechanism (CDM) (Streck, 2002).
CDM is one of the three market-based instruments under
the Kyoto protocol that came into effect in 2005. CDM isa mechanism that should act as a carrot to incentivize
developing countries to invest in climate mitigation
projects. This market-based offset mechanism enables
industrial countries, in addition to the emission reductionsin their own country, to reduce the emission of CO2 byinvesting in sustainable projects in developing countries.
Globalization and participation in GPPNs also hasconsequences for public administrators. Since the role ofthe state is transforming from a welfare state to a corporate

4. Going Global
Reinicke (1997) describes globalization as ‘theintegration of a cross-national dimension into the very
nature of the organizational structure and strategic
behavior of individual companies’ (p. 127). This microeconomic process will go well beyond just theinterdependence of sovereign states at macro-economic
level and, among other things, means that certain policy
problems will have to be solved at global level (Farazmand,
2018). The crucial difference between policy at nation statelevel and at global level is the lack of an authority(Detomasi, 2007), i.e. there is only ‘soft law’ (Stone, 2008).
There is no global sovereign that has both the authority andthe means to enforce policy compliance. This means achanging role for the policy maker, where the policy arena
consists of an increasing number of terrains and actors. In
addition, the boundaries between public and private
spheres are less precise and there is the transition fromgovernment to governance (Kennett, 2008). Governancerefers to a way to make policies and to exercise power. This
could be performed by a government, but could also bedone by multiple parties. Therefore, governance has amuch wider application than in case of governments.
Globalisation leads to a situation where governments nolonger have the full power, but where governance is shared
among multiple parties.
We could notice that uncertainty is increasing due toglobalization. At global level, policy makers do not havethe same power as in the nation state, since the internal4日本リアルオプション学会 機関誌

リアルオプションと戦略

第 12 巻 第 1 号

state (Farazmand, 1999) and because more and morecooperation is required with international businesses,
investment thinking becomes more and more important(Kaul and Conceicao, 2006). Investment thinking is not
only necessary to speak the language of the corporate
sector, but also to convince them to participate in GPPNs.
Therefore, an attractive and reliable financial picture is ofvital importance.
The motivations of the use of real options with regard to
PPPs could be extrapolated to GPPNs. ROA is very usefulto provide justification for investments under highuncertainty and high flexibility and therefore very suitable
for the valuation of participation in GPPNs. In case of
GPPNs, the strategic use of real options is of vital
importance. With the use of ROA, different flexibilities ofpolicy instruments are valued under high uncertainty.
Accurate valuations result in more reliable predictions of
the financial impact for companies and governments,
which reduces the barrier to participate in GPPNs.
However, ROA is little applied to global public policy
interventions. For example, CDM has been a subject ofinterest to ROA scholars (e.g. Lee et al., 2013), but istreated as a regular investment without providing attention
to the global aspect of CDM. This could be due to the factthat GPP processes can be highly abstract and vague(Soroos, 1986) and investment valuations are only
becoming relevant as soon as policies become clearer.
Initially, ROA seems to do well with implementation
networks, and to a lesser extent in case of negotiation
networks or coordination networks.
However, it is clear that many global public policy
mechanisms consist of different phases. And indeed, thesemechanisms provide participants the possibility to maketheir own decisions regarding when to enter or to exit the
project. The Brucellosis Vaccine Prize a $30 million
financial instrument with the objective to incentivize
animal health companies to develop a vaccine against
Brucella melitensis. The Brucellosis Vaccine Prize has amultistage character and a company has the possibility tostart at different phase of vaccine development, therefore
the pharmaceutical company has the flexibility to obtain an
optimal result 1 . Some companies that already have avaccine on the shelf could possibly start at another phasein comparison to companies who have not developed avaccine yet. Other companies might even skip anintermediate stage, because they do not want to show what

they have done so far, and compete only for the Final Prize.
Because of this sequential design, a sequential option, suchas in case of a compound option, is a suitable option withrespect to GPP interventions. A second feature that isobserved in case of GPP interventions is the competitionelement for which there are also specific real options
models present, which for example make use of gametheory (Azevedo and Paxson, 2014). An example of acompetition element concerning a GPP intervention
mechanism is the Brucellosis Vaccine Prize competition
where companies compete for obtaining a prize when they
have successfully delivered a vaccine that meets therequirements of the competition. The more companies
participate in the competition, the less likely it is to beeligible for winning a prize. However, at the same time
more competitors increase the probability of finding aworking vaccine.

In this paper we would like to explore GPP as a new
application for real options. However, we do not want toexperience the same pitfalls as has been encountered
before with the use of real options. There are several
criticisms on real options (Peters, 2016). The first criticism
on real option analysis comes from practitioners who often
find real options difficult to understand and to implement
due to the complex mathematics involved. Managers
simply feel confused by implementing a method they
perceive as a black box, and why should they use tools they
do not fully understand? Related criticism arises from thefact that ROA is a heuristic based on the logic of financial
options. The assumptions that are applicable to financial
option valuation do not necessarily apply to real options. A
consequence of this is that these models tend to reflect
perfection rather than economic reality (Triantis, 2005),
which creates a gap between theory and practice. Sanders
et al. (2013) argues for example that even though a deferral
option is a preferred solution in theory, in practice this is
not always the case, taking in account projects where
investors need to compete in order to obtain a project, suchas a tender. The wait-and-see approach might not be themost optimal solution, since timing is crucial in these kindsof situations. In these situations there is not always time towait, because a competitor might act faster and in that casethe opportunity of obtaining a profitable project might be

1 https://brucellosisvaccine.org/assets/Uploads/1Brucellosis-

Competition-Rules-18Nov16-0020.pdf

5. Exploring three applications of ROA to GPP

5Communications of the Japan Association of Real Options and Strategy
lost. Furthermore, gathering information could require
such a huge investment that doesn’t add any value to gather
more information again.
Since we would like to explore a new field of application
for real options, and at the same time do not want toincrease the gap between theory and practice, anillustration regarding the added value of real options isprovided on the basis of three applications. We avoid theuse of complex mathematical models, but instead provide
an indication on how real options could be applied on thefield of GPP. In this manner, we attempt to elaborate theapplication of real options to GPP interventions without
scaring practitioners away or losing credibility by making
unrealistic assumptions. Furthermore, on the field of GPP,
we often have to deal with the lack of reliable and integral
sources of competitive information. Instead of worrying
about data as input for complicated real options models, we
explore the possibilities of application of real options to
GPP.

Vol. 12 No.1

emission reduction targets under the Kyoto Protocol. Since
the end of 2012, Clean Development Mechanism market is
not performing as was expected. After remaining around€10 –15 per metric ton for most of 2009, 2010, and the firsthalf of 2011, CDM prices fell steadily to less than €1 in
November and December 2012. (Newell et al., 2013).
The project cycle of a CDM project from project design
to CER issuance can be broken down into five main steps.
As a consequence, we can identify the CDM project cycle
as a sequential, multi-staged project, in which, at eachstage, a cost is paid to enter the stage, and the successful
completion of one stage (which is not guaranteed) provides
the investor with the opportunity to move to the next stage,again at a particular cost. Only successful completion of
the final stage will provide the investor with an uncertain
stream of future revenues, depending on the CER market
price at that moment. This multi-staged process can becharacterized as a compound options, i.e. a chain of options
on options. Starting a CDM project provides a compound
option on generating CER revenues. In this entire process
there are two major types of risks that influence the valueof this project. The first one is the probability, in each stage,that stage will not be completed successfully, and theinvestor will not be able to move to the next stage of theprocess. The other major risk, is the uncertainty related tothe amount of revenues, mainly due to uncertainty about
future CER prices.
The start of a CDM project could be valued as acompound option, such as with a closed-form solution (e.g.
Cassimon et al., 2011) or through the use of a binomial
option pricing model (e.g. Lynch and Shockley, 2016). At
the same time, real options could also be used for strategic
purposes by policy makers. Real options provide policy
makers the possibility to value the participation into
different designs of a mechanism such as CDM and provide
an estimation of the attractiveness and risks of such amechanism. Possibly, another design, such as the one thatis proposed by Lee et al. (2013), could have tempered theimpact regarding the drop in the CER price.

5.1 Global warming

Global warming is a textbook example of a global publicbad (Arrow, 2007). Emissions of CO2 and other trace gases
almost irreversible. Emissions today also have
implications for humanity in the distant future. Also, thescale of the externality is truly global. This means thatnation-states are limited in their individual power andinternational cooperation is needed.
In 1997 the Kyoto Protocol was adopted, where 37
industrialized countries and the European Community
committed themselves to reducing emissions of carbon
dioxide and other greenhouse gases. A number of possible
instruments have been considered and many of them havebeen discarded (Molle, 2013). Regulation was considered
as politically infeasible and taxation wasn’t an option for
countries as they are unwilling to give up their sovereignty
in matters of taxation. In the end, it was decided to use
market type instruments, compromising the setting of
maximum pollution levels and the trading of emission
rights. The Clean Development Mechanism (CDM) is oneof the three flexible mechanisms of the Kyoto Protocol thatallows global public-private networks to develop, execute,finance, and supervise projects (Streck, 2002). This
market-based offset mechanism allows developing
countries to earn certified emission reduction (CER)
credits, each equivalent to one ton of CO2, by investing involuntary emission reduction projects. These CERs can be
traded and sold to industrialized countries to offset their

5.2 Migration

A crucial element of globalization is movements ofpeople and a major cause of migration is the growing
inequality in incomes and human security between moreand less-developed countries (Castles, 2013). Sjaastad(1962) was the first to acknowledge that migration could
be viewed as an investment and Burda (1993, 1995) was
the first to model migration as a real option. His work6

日本リアルオプション学会 機関誌

リアルオプションと戦略

found that when a migration decision is postponed, it
generates a positive value if there is uncertainty about
future wage differences. Locher (2001) explores the same
concept as Burda (1995) in a two-period framework, using
data on ethnic German migration from CIS countries(Russian Commonwealth). Khwaja (2002) has extended
the framework of Burda (1993, 1995) by describing therole of uncertainty in the migration decision. Bayer andJuessen (2012) model internal migration decisions in the
United States.
Most literature models a migration decision as a simple
call option, where the migrant has the possibility, but does
not have the obligation to migrate. The migrant has theoption to wait if relevant information can be expected toreveal itself over time. It could for example be profitable
to postpone the migration decision, because the migrant is
expecting ‘bad news’, or because the opportunity cost
could be decreasing. These models, however, do not meetthe requirements to model the decision-making process of
mixed migrants. This flow of migrants consists of a varietyof migrants such as asylum seekers, migrants andeconomic migrants, moving to more-developed countries
through authorized as well as irregular channels for various
reasons, such as during the European migrant crisis of 2015.
In 2015 the European migrant crisis began when theflow of migrants had increased dramatically, from 153
thousand in 2008 to more than one million in 2015. Thiswas mainly due to the growing number of Syrians, Iraqis,
Libyans, Afghans and Eritreans fleeing war, ethnic conflictor economic hardship, who entered Europe throughdifferent routes. Migrants have to make a careful
deliberation whether or not to make the crossing, at whichtime it happens, in what way, according to which route etc.
In order to do so migrants keep themselves informed of thedevelopments that could have an impact on their journey
and adjust their decisions on a real-time basis (Malakooti,2015). Their journey contains a mix between involuntaryand voluntary decisions, involves multiple legs, throughseveral countries, proceed either through regular or
irregular channels or both, where at each stage the migrant
has to make a trade-off between the costs and benefits,
uncertainty and duration, in order to choose the best routefor reaching the desired destination. In contrast to thesimple option model, where the journey consists of only
one leg, the journey during the European migrant crisis
consists of multiple legs, which is referred to as transit
migration. In addition there is also technical risk, such as
the probability of arrival for each leg and uncertainty of

第 12 巻 第 1 号

benefits at the final destination. These characteristics
demand for a more complex real options model, i.e. amultiphase real option model.
Suppose that the migrant has already decided to migrate,
but has to choose between different routes through whichto migrate to a desired destination. A multiphase real
options framework could be used in order to model eachroute, where a leg of a route is a phase of the model. A
journey is then a sequential option on the benefits in thedesired destination. The choice for the best route between
alternative routes by the migrant is analogous to making a
selection between several investments under uncertainty,
where uncertainty amongst others is caused by thechanging political situation in the desired destination.
Through the valuation of a route, we are able to quantify
the popularity of that route. The migration route with thehighest option value would be the best choice and the mostappealing route for the migrant, so in this specific case weare not so much interested in the absolute value as in therelative value between the multiple routes. Policy makers
could use this information to forecast which route migrants
are going to take given the current situation, but also prior
to the implementation of policy measures. They have theability to calculate different possible scenarios and on thebasis of that analysis to take most effective measure. For
example, is setting a daily cap on the number of asylum
seekers, as was the case with Austria, a meaningful way toaddress the migration influx? Does this measure have ameaningful impact or does Austria remain a favored transit
country even after such a measure? The most effective
measure will depend on the policy maker. In case of theEuropean migrant crisis, we could notice that nation states
took their own measures to cease the migrant flow. From aEuropean point of view, rather the objective is to equallydistribute the influx among the different nation states.
5.3 Privacy

Because of the technological developments and thedigitalization of the economy, an increase in the demandfor consumers’ personal information has occurred, which
for example enables sectors leveraging personal data toleap ahead of the rest of the economy and produce €1trillion in corporate profits in Europe by 2020.
(Spiekermann et al., 2015). In response to this, privacy andits protection is rapidly emerging as one of the mostsignificant public policy issues (Acquisti et al., 2016).
Since the issue of privacy protection is a global challenge,a global response is required, where governments,
7

Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

businesses, and civil society together are achieving dataprotection (Doneda and Almeida, 2015).
In 2016, the European Commission and the UnitedStates adopted the Privacy Shield, a new framework fortransatlantic exchange of personal data, as a substitute for
the Safe Harbour framework (Monteleone and Puccio,
2018). The Privacy Shield allows personal data to betransferred from the EU to a company in the United States,
provided that the company processes (e.g. uses, stores andfurther transfers) personal data according to a strong set ofdata protection rules and safeguards (European
Commission, 2016). Already existing transfer tools, suchas the binding corporate rules (BCR) or standard
contractual clauses (SCC), still apply. Furthermore, acompany has the possibility to select one of the available
options (Privacy Shield, BCR or SCC) to access privacy
data. BCRs are sets of good business practice guidelines
adopted by companies voluntarily and applied throughout
their branches, regardless of where the branches are located(Kulesza, 2011). In addition, the EU adopted a set ofstandard contractual clauses for accepted transfers from an
EU data exporter to a non-EU data importer (Bender andPonemon, 2006). In summary, an international company
has the possibility to choose from at least three different
options to transfer personal data from the EU to the UnitedStates.
If the Privacy Shield is used, U.S. companies must firstsign up to this framework with the U.S. Department ofCommerce (European Commission, 2016). This
Department is responsible for managing and administering
the Privacy Shield and ensuring that companies live up to
their commitments. In order to be able to certify,
companies must have a privacy policy in line with thePrivacy Shield Principles, a detailed set of requirements
based on privacy principles such as notice, choice, access,and accountability for onward transfer. They must recertify to the Privacy Shield on an annual basis. The costs
consist of a one-time contribution to the Privacy ShieldArbitral Fund and annual costs for Privacy Shield
Certification. Finally, there is also an annual enrollment fee
in one of the Privacy Shield dispute resolution programs.
A company could obtain approval for a binding
corporate rule in five steps 2. The advantage here is that acompany is allowed to design its own BCRs and therefore
has the flexibility to optimize the design as much as

possible. The problem is that approval from one national
data protection commissioner is not binding on othernational data ombudsmen, leaving a company to struggle
through difficult and costly international administrative
procedures. In order to simplify these procedures, a mutual
recognition procedure has been agreed between 21 EU
countries. Under this procedure, once the lead authority
considers that binding corporate rules meet therequirements, the data protection authorities under mutual
recognition accept this opinion as sufficient basis forproviding their own national permit or authorization. A
straightforward application will take up to twelve months
and the process does require significant investment up
front (both in terms of finance and resourcing) (Allen &Overy, 2016).
Standard contractual clauses were meant to simplify theprocess of crafting data transfer agreements (Schwartz,2012). Rather than use attorneys to draft contracts fromscratch, a company can use the pre-approved SCCs andtheir "off-the-rack" language. Therefore, no license isneeded for the use of SCCs. A disadvantage is that large
companies have to add many clauses, which results in highcosts and maintenance.
Through The Privacy Shield, BCRs or SCCs aninternational company receives the authorization to accessprivacy data. This could be interpreted as a (call) option on
privacy data. It could be noticed that The Privacy Shield,
BCRs and SCCs have different structures. BCRs contain aphased approval process, but after that BCRs are free to be
used. Participation in a Privacy Shield has to be renewed
annually, with the risk that it will not be extended. In bothcases the structure of a compound option could berecognized. SCCs are the simple options. They are morestraightforward and for occasional use, but could result invery high costs afterwards, which reduces the profitabilityof privacy data. Furthermore, the value of privacy data
itself is also uncertain. Personal data could become
obsolete or irrelevant.
By regarding Privacy Shield, BCRs and SCCs as options
on privacy data, real options could be used for its valuation.
This provides policy makers insight in which type of accessto privacy data is the most attractive for companies. It gives
policy makers also the possibility to influence the choices
through the so-called value drivers of real option
valuation.Value drivers are the input variables, which

2 https://ec.europa.eu/info/law/law-topic/dataprotection/international-dimension-data-protection/binding-corporate-rules-bcr_en8日本リアルオプション学会 機関誌

リアルオプションと戦略

determine the value of an option, such as costs, benefits,
uncertainties, etc. By influencing one or more drivers of adata access program, a policy maker is able to influence thevalue of such a program and therefore the attractiveness ofthat particular program. For example, policy makers havethe ability to increase the cost of BCRs and evaluate its
impact on the attractiveness of BCRs. For this reason, real
options could support the policy makers as such in a
strategic way by directing companies to the desired variant.
Also other options could be recognized. Some companies
could for example already have BCRs or SCCs. Would it
be still attractive for them to switch to The Privacy Shield(switching option)? And finally, there might also becompanies that are willing to use personal data, but who
are not willing to take the official route. This could be
regarded as a criminal option (Engelen, 2004). Generally
speaking there are more than sufficient possibilities for theapplication of real options to privacy policy.

第 12 巻 第 1 号

options models and to raise GPP to a new and fully-fledged
area of application for real options.

7. References
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6. Conclusion
We have demonstrated that GPP is a promising field of
application for real options. Investment thinking, highuncertainty and high complexity come together in thecontext of GPP, which is caused by the large playing field
and the lack of enforcement. Real options could be brought
to its full potential here, as well as for valuations andstrategic considerations. Consecutively, we have explored
the application of real options to three GPP matters. On thefield of global warming, real options could be deployed toverify whether or not a market-based instrument, such as
Clean Development Mechanism, is profitable for potential
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could be taken for making it profitable. In case of migration,
we have explored how real options could be used toquantify the relative attractiveness of a given migrationroute. On the basis of this information, policy makers havethe ability to assess which routes will become more
attractive, even prior to taking policy measures. Finally, we
have demonstrated that real options are very suitable on thefield of privacy and could be employed to assess which of
the possible frameworks a company will use in order tohave access to personal data. Policy makers have theopportunity to influence the value drivers of the different
frameworks in order to direct the company to the intended
variant. Currently, we have focused to explore the possible
applications of real options within GPP. It is subject tofuture research to sophisticate these ideas into usable real

9Communications of the Japan Association of Real Options and Strategy
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Corporate Rules: A Critical Look at International
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New Challenges. Journal of Economic
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リアルオプションと戦略

https://ideas.repec.org/a/aea/jecper/v27y2013i1p
123-46.html
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and strategy in resource allocation. MIT press.

11Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

<Reviewed Paper / Accepted: February 8th, 2020>

An Event Study of the Impact of Corporate Social Responsibility on
the Corporate Values: A Case of Kao Corporation
Miho Tanaka (International University of Japan)
Abstract: The purpose of this research is to examine how awards and indexes related to Corporate Social
Responsibility (CSR) affect the corporate values of Kao, which is one of the biggest chemical manufacturer in Japan.
We conduct event study to validate that an abnormal return occurs on the day Kao wins awards or Kao's stock isdesignated as investment indexes. We extract all information related to CSR awards and indexes as much as we
could search on Kao's web site. In case that Kao won awards and designations in a row, we conduct a retrospective
review of the award history. We conduct comprehensively event study on 38 items including domestic and foreign
investment indexes, commendation by Japanese and international organizations, and internal evaluation such as
Kao's CSR reports. As a result, out of 105 of the event study, we find two statistically significant abnormal returns.
One is positive in FTSE4Good 2011, and one is negative in Kao's CSR report 2018. It is more natural to think thatthe two significant abnormal results are observed coincidentaly as we set the significance level as 5%. Two events
out of 105 events could show the statistically significant result at 5% coincidentally. We could not conclude that
CSR activities positively affect the corporate values of Kao. In the future, it is also necessary to verify CSR activities
from the reduction of risk and the improvement of brand values through the increase in reputation.

Keywords： CSR, ESG, Event Study, Corporate Values, Kao corporation

1. Introduction

can say CSR has effects on the corporate values of Kao.

1.1 Research objectives
The purpose of this research is to examine how
awards and indexes related to Corporate Social
Responsibility (CSR) affect the corporate values of Kao,
one of the major chemical companies in Japan. This
research aims to gauge the impact of CSR on corporate
values comprehensively, so event studies are conducted
on internal and external evaluations of Kao's CSR
activities.
Our event studies are divided into different
categories, such as the registration days of indicators-Environment, Social and Governance (ESG) investment;
the registration days of brands by the Japanese
government 1; and the days Kao won CSR awards whichwere selected by Japanese and foreign organizations. As
for the internal evaluation of Kao's CSR, we also
conduct event studies on the publication day of Kao's
CSR reports. Note that because Kao changed their nameof CSR report four times since 2001, we use “Kao’s CSR
report” for all titles for convenience.
By conducting event studies on multilateral andcomprehensive evaluations, we clarify whether CSR
activities have effects on the corporate values of Kao. If
we figure out the significant result of event studies, we

1.2 Background of the study
Kao is one of the major chemical companies inJapan. Kao has four business fields: Cosmetics, Skin
Care & Hair Care, Human Health Care, and Fabric &Home Care. Kao conducts business in Japan, Asia, theAmericas, Europe, and other parts of the world. Kao
positions ESG activities as an investment in the futureand is a company actively engaged in CSR activities.
Kao positions ESG activities as an investment in the
future. Kao is a company actively engaged in CSR
activities and plans to further accelerate efforts toachieve a global presence by 2030.
1.3 Research questions
We focus on Kao, and comprehensively examine
whether evaluations of CSR activities affect corporate
values. We conduct event studies on CSR-related awards
and index registrations as long as we find them on Kao's
CSR PR news page. We comprehensively examine theimpact of CSR activities on corporate values of Kao, andnot restricted to the content of CSR activities. As far as
we can check all information on the Kao's website, wefind 38 awards or indexes which are related to CSR
activities. Since some of the awards or indexes are

1 Japanese government and the Tokyo Stock Exchange(TSE) jointly selected listed enterprises that are outstandingby category: such as encouraging the empowerment of

women in the workplace (Nadeshiko brand), and strategicallyfocusing on employee health management from a managerial
perspective.
12

日本リアルオプション学会 機関誌

リアルオプションと戦略

selected every year, we also research all historicalinformation. We include the years when Kao missed
awards or was not chosen for our event study because
we consider that investors might pay attention more to
the case of missing awards or indexes than the fact ofwinning awards or indexes continuously. Then, weclarify the impact of CSR on the corporate values of Kao
by categories: the investment indexes in Japan andoverseas, the award by the Japanese government,commendation in Japan, commendation from foreign
institutions, and the internal evaluation of Kao. We use
Kao's CSR reports as internal evaluation. we examine
whether CSR activities are affecting corporate values
positively or negatively. If we figure out significant
results, we try to analyze what kind of CSR activities
Kao needs to focus on.

第 12 巻 第 1 号

remaining 14% mixed correlations.
In Japan, there are fewer studies of CSR activities
from the perspective of corporate profit in Japan
compared to the United States. As far as we know, theresults of research about the relationship between CSR
and corporate values in Japan are also mixed. For
researching the relationship between CSR efforts andeconomic performance, Sudo, Mashiko, and Wakazono(2006) select companies which include four significant
Socially Responsible Investment (SRI) indexes andcompared their performances with non-SRI corporategroups. Although the result is not statistically significant,companies with a clear CSR policy generally show lessvolatility in earnings and higher market value. On theother hand, Sudo, Mashiko, and Wakazono point out that
CSR activities which aimed at reducing reputation risk
may gain profitability.
Endo (2013) tries to measure the effects on
corporate values for the 147 manufacturing companies
listed on the Tokyo Stock Exchange by using the overall
score of Environmental Management Survey conductedby Nihon Keizai Shimbun as Corporate Social
Performance (CSP) indexes. Endo confirms a positive
relationship between CSP and corporate values, but thestatistical significance of his studies was low. He could
not conclude that CSP is enhancing corporate values.
As far as we know, we could not find any research
focusing on one company in particular and mentioning
the impact of index registration on a corporate value inJapan. Our study is unique in this sense.

1.4 Significance of the study
Kao is a very large global company, and wecomprehensively examine the impact of social
assessment on corporate values. In the stock market, we
believe that registration and awarding related to CSR
generates important investment factors for investors. If
this statement is true, after the news related to CSR,
stock prices could increase significantly. In other words,
CSR related activities might improve corporate values.
If the stock price does not change or if it falls, it implies
investors do not pay full attention to CSR activities, nor
CSR related activities affect corporate values. In ourpaper, we would like to discuss whether evaluations
related to CSR activities have effects on the corporate
values or not. If we can figure out that CSR related
activities improve the corporate values, we can say that
our research leads to motivation significantly to do CSR
activities for Japanese companies.

3.
METHODOLOGY
3.1 Data collection and objectives of study
Based on the announcements posted on Kao's
CSR news page, we extract all information related to
CSR awards and indexes as much as we could find on
Kao's web site. We aim to exhaustively verify how CSR
activities have effects on the corporate values of Kao. Incase that Kao won awards and designations in a row, we
conduct a retrospective review of the award history as
much as possible.
As a result, we choose 38 items meaning that the
number of titles, indexes, or awards which we choose forthis report. Then we categorize them as follows: (1)Domestic ESG investment indexes, (2) Foreign
investment indexes, (3) Awards by Japanese government,(4) Commendation in Japan, (5) Commendation fromforeign institutions, (6) Internal evaluation in Kao. Table

2.

LITERATURE REVIEWThe previous papers which analyze therelationship between CSR and corporate values show
mixed results. For example, Peloza (2009) reviews
previous studies on the relationship between CSR and
Corporate Social Performance (CSP), which is definedas accounting-based performance, market-based
performance, operational performance, perceptual
performance, growth metrics, risk measures, and theperformance of ESG portfolios. He finds that 59% of theprevious studies show positive relationship between
CSR and CSP, 27% a negative correlation, and the13Communications of the Japan Association of Real Options and Strategy
1 shows all event studies in this paper by category. In
addition, we collect adjusted stock price of Kao andTokyo Stock Price Index (TOPIX 2) from Yahoo finance.
We also use Kao's financial data, market capital,
liabilities, and cash flow from Datastream.

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 = 𝛼𝛼 + 𝛽𝛽 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟= 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑜𝑜𝑜𝑜 𝐾𝐾𝐾𝐾𝐾𝐾 – 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅(2)Equation (3) shows that the abnormal returns fromday -5 to day 5 (11 days in total) as a cumulative
abnormal return (CAR).
(3)𝐶𝐶𝐶𝐶𝐶𝐶 = ∑𝑑𝑑𝑡𝑡=−5 𝐴𝐴𝐴𝐴
where, d is the duration of event window and AR
is abnormal returns. After derivation of the CARs, we
perform a two-tailed t-test of the statistical significanceof each CAR.

Table 1: All event studies in this paper by category
Category(1) Domestic ESG investment indexes(2) Foreign investment indexes(3) Awards by Japanese government(4) Commendation in Japan(5) Commendation from foreign institutions(6) Internal evaluation in Kao
Total

Numberof items
4
5
6
7
15
1

Number of event
studies
8
29
14
18
33
3

38

105

Vol. 12 No.1

𝑇𝑇ℎ𝑒𝑒 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑡𝑡 − 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 =𝐶𝐶𝐶𝐶𝐶𝐶�𝑆𝑆 2 ∗𝑑𝑑

(4)

where, s is the standard error, and d is the durationof event window. We also assume that the sampling
distribution follows the t-distribution after applying theformula. The Equation (4) converts CAR to a value
following to the t-distribution. According to the resultsof the t-test, we use the degree of freedom (148, which
is derived from 150 – 2: 150 as number of days for
estimate window: 2 as parameters estimated in Equation(4)). Finally, we calculate two-tailed probability of CAR
by using Excel sheet. Before conducting event studies,
we set 5% at two-tailed probability of CAR as thesignificance level. The alternative hypothesis is that twotailed probability of CAR is different from 0.05, and thenull hypothesis is that the two-tailed probability of CAR
is not different from 0.05.

Note: items means the number of title of indexes or awards which
we choose for this report.

3.2 The period of estimation window and event
window
In this paper, we call the event day when Kao won
awards related to CSR activity, or was designated as
investment indexes, as "day 0". We also call the one day
before the event day as "day -1", and the day after “day0” as "day 1". The event window, which represents thewindow of days surrounding of “day 0”, is set from day-5 to day 5 (11 days in total), to measure change in the
short term. The estimation window; the window of days
significantly prior to “day 0”, is used as a basis tomeasure long term change compared to “day 0” and its
event window, from the day -200 to day -51 (150 days intotal). We calculate on business day basis.
3.3 Methods of event study
We perform a regression analysis of Kao's stock
prices on TOPIX during the estimation window. First,
we calculate the daily-basis-change-rate of TOPIX andKao's stock prices during the estimation window,
respectively. After performing the regression, we
calculate abnormal returns by a stock return of Kao
relative to its expected stock return. Expected return isgiven by equation (1). We multiply the coefficient of
Kao and the coefficient of TOPIX and add the change
rate of TOPIX to calculate expected return an event
window (1). We also estimate an abnormal return (2).

4. DATA ANALYSIS AND RESULT
In our paper, we conduct 105 event studies on 38
items meaning that the number of titles, indexes, orawards included are comprehensive. As a result, only
FTSE4Good 2011 and Kao's CSR report 2018 in which
two-tailed probability of CAR show statisticallysignificant results at 5% level. We consider that
abnormal results might happen incidentally because we
set the significance level at 5%. The two-tailed
probability of CAR of FTSE4GOOD in 2011 showsabnormal return with significant level at 0.042. Kao has
been awarded FTSE4Good continuously since 2008 andhas not been able to show a clear reason to respondsignificantly only in 2011. In 2008, when Kao waselected to FTSE4 GOOD for the first time, the number

2 TOPIX is a stock price quantified by a fixed calculation
method. It covers multiple stock prices, such as an entire
exchange or a series of stocks, rather than individualcompany’s stock price. By looking at TOPIX, it is possible tocomprehensively read the movements of the stock market as awhole.
14

日本リアルオプション学会 機関誌

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リアルオプションと戦略

of registered companies was 707 in total, of which 195
were Japanese companies 3. In 2018, Kao was registered
FTSE4GOOD index for 10 years in a row. However,based on the results of our event study, there is no
characteristic response in 2008 and 2018.

difference between the 2018 version and different year
versions. We also presume that investors can collectinformation related CSR at stockholders' general
meeting before publishing CSR report. Kao has received
the 2016 Grand Prize for the Corporate Value
Improvement Award, implemented by the Tokyo Stock
Exchange. This award commends listed companies
carrying out management with a strong focus on
investors.
Aoki (2017) appreciates the Kao's Sustainability
Report and uses Kao's reports as one of the examples tointroduce how Japanese companies disclose nonfinancial information. Kao was not able to get Corporate
Value Improvement Award 2018, but it is natural forinvestors because Tokyo Stock Exchange chooses
different companies every year. According to the criteriaof Corporate Value Improvement Award, there is no
regulation to exclude the previous winning company.
However, we can assume that investors understand thatthe previous winning company will not reserve next year
based on the historical information which is available
since 2016 on the Tokyo Stock Exchange web site.

4.1 Abnormal return on FTSE4Good in 2011
The announced day was May 10, 2011, and Table
2 shows the daily results of FTSE4Good in 2011.
Actually, two-tailed probability of CAR is less than 0.05
only on May 5, 2011, which is Day -4, and May 15, 2011,
which is Day 5. The value of two-tailed probability of
CAR from day -3 to day 5 is not significant. We cannot
prove CSR has an effect on the corporate values of Kaofrom only this result. It seems to be more natural to thinkthat it just happens to be this way.
Table 2: The result for FTSE4Good 2011
Day

AR

CAR

t_CAR

p_CAR

-5

0.009

0.009

1.094

0.354

-4

0.033

0.042

3.521

0.039

-3

-0.018

0.024

1.666

0.194

-2

-0.001

0.023

1.381

0.261

-1

0.030

0.053

2.813

0.067

Day

AR

CAR

t_CAR

p_CAR

0

-0.010

0.043

2.065

0.131

-5

-0.040-0.040-3.685

0.035

**

1

-0.015

0.028

1.247

0.301

-4

-0.041-0.081-5.227

0.014

**

2

0.016

0.044

1.851

0.161

-3

0.012

-0.069-3.649

0.036

**

3

-0.011

0.034

1.320

0.278

-2

-0.018-0.087-3.991

0.028

**

4

0.022

0.055

2.069

0.130

-1

0.006

-0.081-3.313

0.045

**

5

0.041

0.096

3.421

0.042

0

0.013

-0.068-2.547

0.084

*

1

-0.013-0.081-2.792

0.068

*

2

-0.012-0.093-3.011

0.057

*

3

0.005

-0.088-2.683

0.075

*

4

-0.012-0.100-2.886

0.063

*

5

-0.017-0.117-3.231

0.048

**

**

Table 3: The result for Kao's CSR report in 2018**d

Note: CAR = ∑ AR , d = the duration of event window, andt = −5

AR = abnormal return. 𝑡𝑡_𝐶𝐶𝐶𝐶𝐶𝐶 =𝐶𝐶𝐶𝐶𝐶𝐶√𝑆𝑆 2 ∗𝑑𝑑

, 𝑆𝑆 =standard error.

p_CAR is the two-tailed probability of CAR. ** and * indicate
5% and 1% level of significance, respectively.

d

Note: CAR = ∑ AR , d = the duration of event window, and

4.2 Abnormal return on Sustainability report in 2018
The two-tailed probability of Sustainability
Report in 2018 shows abnormal return with significant
level as 0.05 We compare the material of Kao's CSR
report 2018 to other year versions, but as far as wechecked we could not figure out any significant

t = −5

AR = abnormal return. t_CAR =CAR√𝑆𝑆 2 ∗𝑑𝑑

, 𝑆𝑆 =standard error.

p_CAR is the two-tailed probability of CAR. ** and * indicate
5% and 1% level of significance, respectively.

Table 3 shows the result for the Sustainability

3 Available at FTSE4 GOOD Web site,
https://www.ftse.com/products/downloads/ftse-russell-esg-

presentation-for-jp.pdf

15

Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

carry out verification in different industries too.

Report in 2018. Looking at the two-tailed probability of
CAR, only Day 5 to Day 1 and Day 5 show 0.05 or less.
Abnormal return did not occur five days from Day 0 to
Day 4. And CAR value shows minus during the event
window. Abnormal return occurred before publishing the
CSR report in 2018. We believe that the CSR report does
not have a negative impact on the corporate values ofKao.
5. CONCLUSION
In this paper, we conduct comprehensively theevent study on 38 items including domestic and foreign
investment indexes, commendation by Japanese andinternational organizations, and internal evaluation as
Kao's CSR reports. The oldest data used in our event
study is from July 2003, when Kao was registered asMorningstar Socially Responsible Investment Index 4 .
And the latest data used in our event study until the day
when Kao was selected as supplier engagement leader
by Carbon Disclosure Project (CDP 5 ) in February 2019.
Appendix shows all 105 events which we examined.
As a result, two-tailed probability of CAR showed
less than 0.05 only in FTSE4Good 2011 and Kao's CSR
report 2018 of 105 event studies. These abnormal results
which happened unexpectedly may be due to the fact that
we set the significance level at 5%. The result of ourevent studies supports Endo (2013), who concluded that
there is insufficient evidence to prove that CSR activities
increase corporate values.
In our event studies, we set the event window as
11 days from Day-5 to Day5 and the estimate window as
150 days from Day-200 to Day-51. Since Kao hasreceived numerous CSR-related awards, we consider
that changing this date setting might be a good idea.
In addition, our study only examined only thechanges in stock prices for the winning award or index
registration from 2003 to 2019. This paper may seem toanalyze only the movement of the stock prices several
days after CSR activities, the concept of Corporate Valueis inclusive, and is essentially the same as the “reductionof risk and the improvement of brand values regarding
reputation”. On this note, it would be necessary to verify
the above in future research. Finally, we targeted only
one company for this paper, but it would be necessary to

REFERENCE
1. Aoki, T. (2017). The Ideal Way of Information
Disclosure of Japanese Companies Aiming at
Improvement of Corporate Value and ESG activities:
Case of Kao Corporation and Vision Inc. Journal of
University of Hyogo, Vol. 69, No. 12, pp. 1-12.
2. Endo, K. (2013). Does Stakeholder Welfare EnhanceFirm Value? Economic Today, Vol. 34, No.2, pp. 126.
3. Kao Corporation. (2015). Kao Sustainability Report2015. Retrieved from Kao Corporation Website:
https://www.kao.com/content/dam/sites/kao/wwwkaocom/jp/ja/corporate/sustainability/pdf/sustainability
2015_book.pdf.
4. Kao Corporation. (2016). Kao Sustainability Report2016. Retrieved from Kao Corporation Website:
https://www.kao.com/content/dam/sites/kao/wwwkaocom/jp/ja/corporate/sustainability/pdf/sustainability
2016_all.pdf.
5. Kao Corporation. (2017). Kao Sustainability Report2017.
Retrieved
from
Kao
Corporation
Website:https://www.kao.com/content/dam/sites/kao/www-kaocom/jp/ja/corporate/sustainability/pdf/sustainability
2017-all.pdf.
6. Kao Corporation. (2018). Kao Integrated Report
2018.
Retrieved
from
Kao
Corporation:
https://www.kao.com/content/dam/sites/kao/wwwkao-com/global/en/investor-relations/pdf/reportsfy2018e-all-002.pdf.
7. Kao Corporation. (2019). News Release. Retrieved
from
Kao
corporation
Web
site:
https://www.kao.com/global/en/news/
8. Peloza, J. (2009). The Challenge of Measuring
Financial Impacts from Investments in Corporate
Social Performance. Journal of Management, Vol. 35,
No. 6, pp. 1518-1541.
9. Sudo, M., Mashiko, N., & Wakazono, T. (2006).
Corporate Social Responsibility (CSR) Activities
and performance: Corporate revenue and Risk.

4 Morningstar is a company that provides analysts and otherglobal financial and economic information to institutional andsemi-professional individual investors, focusing on rating
evaluation of mutual funds. https://www.morningstar.com/

5 CDP is an NGO established in the UK in 2000 and operates
a global information disclosure system for investors,
businesses, cities, countries and regions to manage
environmental impacts. https://www.cdp.net/en16日本リアルオプション学会 機関誌

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リアルオプションと戦略

Waseda University Institute of Finance Working

Paper Series WIF-06-002.

APPENDIX
Within this section are several tables that hold too much information and/or data to place within the text without
distracting or burdening the reader. Below, one will find that many of the tables are complete lists in regards to the resultsof event studies in this paper by category, which are included as they could be thought to be related or correlated with
Corporate Social Responsibility.
Table A.1: Result of event studies for domestic ESG investment indexes
Name

Year

FTSE Blossom Japan Index

2018
2017

CAR

t_CAR

p_CAR

0.016

0.351

0.749

0.000

0.000

1.000

0.021

0.690

0.540

-0.012-0.2980.785

MSCI Japan Empowering Women Select Index

2018-0.042-0.886

0.441

MSCI Japan ESG Select Leaders Index

2017-0.054-1.1690.327

S&P/JPX Carbon Efficient Index

2018-0.022-0.4880.659

Morningstar Socially Responsible Investment Index

2003-0.028-0.6760.548dNote: CAR = ∑ AR , d = the duration of event window, and AR = abnormal return.
t = −5tailed probability of CAR.

17

t_CAR =CAR

√𝑆𝑆 2 ∗𝑑𝑑

, 𝑆𝑆=standard error. p_CAR is two-Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

Table A.2: Result of event studies for foreign investment indexes
Name

Year

CAR

t_CAR

p_CAR

DJSI World2018*-0.056-1.215

0.311

2017

-0.031-0.936

0.419

2016

0.027

0.689

0.540

2015

-0.040-0.812

0.476

2014

-0.062-1.7270.183-0.012-0.262

0.810

0.000

0.000

1.000

0.052

1.738

0.181

-0.017-0.411

0.709

0.044

1.002

0.390

0.045

0.980

0.399

-0.019-0.405

0.713

0.048

1.025

0.381

-0.063-1.7590.177-0.029-0.6430.566-0.019-0.462

0.675

0.054

1.678

0.192

-0.033-1.098

0.353

0.004

0.098

0.928

0.096

3.421

0.042

-0.002-0.065

0.952

0.002

0.040

0.971

-0.031-0.5850.600-0.036-0.522

0.638

0.009

0.120

0.912

-0.107-2.0810.129-0.095-2.422

0.094

FTSE4Good

2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008

Bloomberg Gender-Equality Index

2019-0.093-2.0320.135

ECPI Ethical Index

2016-0.021-0.4900.658dNote: CAR = ∑ AR , d = the duration of event window, and AR = abnormal return. 𝑡𝑡_𝐶𝐶𝐶𝐶𝐶𝐶 =t = −5tailed probability of CAR.

𝐶𝐶𝐶𝐶𝐶𝐶√𝑆𝑆 2 ∗𝑑𝑑**

, 𝑆𝑆=standard error. p_CAR is the two-

We out * on the year which Kao was excluded from registrations, and ** on the result which is lower than

0.05.

18

日本リアルオプション学会 機関誌

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リアルオプションと戦略

Table A.3: Result of event studies for awards by Japanese government
Name

Year

CAR

t_CAR

p_CAR

Cabinet Office Special Ministers' Commendation

2018

0.031

0.531

0.531

Health Management Excellent Corporation-White 500

2018-0.016-0.416

0.706

2017

-0.016-0.430

0.696

The 100 Diversity Management Companies

2013

0.046

1.411

0.253

Offensive IT Management Brand

2016

0.044

0.975

0.401

Health Management brand2018-0.016-0.416

0.706

2017

0.022

0.475

0.667

2016

0.099

1.975

0.143

2015

-0.031-0.757

0.504

2017

0.054

1.497

0.231

2016*

0.062

1.382

0.261

2015

0.024

0.527

0.635

2014*

0.032

0.703

0.533

2013

0.030

0.924

0.424

Nadeshiko Brandd

Note: CAR = ∑ AR , d = the duration of event window, and AR = abnormal return. 𝑡𝑡_𝐶𝐶𝐶𝐶𝐶𝐶 =t = −5tailed probability of CAR. We put * on the year which Kao missed the awards or designations.

𝐶𝐶𝐶𝐶𝐶𝐶√𝑆𝑆 2 ∗𝑑𝑑

, 𝑆𝑆=standard error. p_CAR is the two-

Table A.4: Result of event studies for commendation in Japan
Name

Year

CAR t_CAR p_CAR

Corporate Value Improvement Award

2016

0.094

2.055

0.132

CSR ranking by Toyo Keizai

2019 3rd place

0.034

0.781

0.492

2018 5th place-0.003 -0.080

0.941

2017 9th place

0.053

1.172

0.326

2016 18th place-0.060 -1.239

0.304

2015 27th place

0.063

1.548

0.219

2014 42nd place

0.048

1.104

0.350

2013 46th place-0.050 -1.519

0.2262012 37th place

0.000

0.009

0.994

2011 32nd place-0.003 -0.093

0.932

2010 38th place-0.059 -1.111

0.3482009 33rd place

0.034

0.398

0.717

The 50th Ichimura Industrial Award Contribution Award2018-0.083 -2.284

0.107

The 49th Japan Chemical Industry Association Technology Award

2017

0.040

0.930

0.421

Special award of DBJ Health Management Rating

2017

0.048

1.064

0.366

2012

0.045

1.400

0.256

2014

-0.029 -0.777

0.494

0.087

0.142

Excellent Award at HR Award 2014 by I-Q Co., Ltd.

BEST 100 companies that women play an active part by Nikkei WOMAN 2017dNote: CAR = ∑ AR , d = the duration of event window, and AR = abnormal return. 𝑡𝑡_𝐶𝐶𝐶𝐶𝐶𝐶 =t = −5tailed probability of CAR.

19

𝐶𝐶𝐶𝐶𝐶𝐶√𝑆𝑆 2∗𝑑𝑑

1.979, 𝑆𝑆=standard error. p_CAR is the two-Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

Table A.5: Result of event studies for commendation from foreign institutions
Name

Year

CAR t_CAR p_CAR

CDP A list of water security

2016-0.074 -1.639

0.200

CDP supplier engagement leader

2019

0.042

0.942

0.416

2018

0.063

1.784

0.172

Corporate Knights, Global 100 Most Sustainable Corporations in the World

2007

0.000

0.003

0.998

EcoVadis, Gold class2018-0.024 -0.667

0.553

Ethisphere Institute, World's Most Ethical Companies

2019-0.004 -0.082

0.940

World's Most Ethical Companies

2019

0.026

0.577

0.605

2018

-0.003 -0.080

0.941

2016

-0.047 -0.978

0.400

2017

0.026

0.585

0.600

2016

-0.025 -0.527

0.635

2015

0.063

1.548

0.219

2014

0.088

1.968

0.144

2013

0.037

1.133

0.340

2012

0.002

0.041

0.970

2011

-0.001 -0.016

0.988

2010

-0.009 -0.144

0.895

2009

0.060

0.753

0.506

2008

0.018

0.377

0.731

Industrial Estate Authority of Thailand (IEAT), Amata Best Waste Management
2017
Awards 2017

0.022

0.475

0.667

Intellectual.Asset.Management (IAM) Asia.IP.Elite

2015

0.027

0.689

0.540

2014

-0.044 -1.158

0.331

2013

0.022

0.481

0.664

Ministry of Industry of Thailand Eco Factory Award2017-0.050 -1.547

0.220

Oekom research, Prime class2006-0.021 -0.578

0.604

Pentaward 2017, Bronze Pentaward 20172017-0.061 -1.877

0.157

Responsible Care Management Committee of Thailand(RCMCT), Responsible
2017
Care Silver Award

0.022

0.475

0.667

RobecoSAM, Gold class at CSR ranking

2018

0.074

2.068

0.131

2017

0.040

0.868

0.449

2015

0.067

1.879

0.157

RobecoSAM Industry Leader at Personal Products

2015

0.068

1.403

0.255

Thomson Reuters Diversity & Inclusion Index

2018

0.001

0.017

0.988

Thomson Reuters Top100 Global Innovator 20142014

-0.050 -1.026

0.380

Ethisphere Institute, World's Most Ethical CompaniesdNote: CAR = ∑ AR , d = the duration of event window, and AR = abnormal return. 𝑡𝑡_𝐶𝐶𝐶𝐶𝐶𝐶 =t = −5

two-tailed probability of CAR.

20𝐶𝐶𝐶𝐶𝐶𝐶√𝑆𝑆 2 ∗𝑑𝑑

, 𝑆𝑆=standard error. p_CAR is the日本リアルオプション学会 機関誌

第 12 巻 第 1 号

リアルオプションと戦略

Table A.6: Result of event studies for internal evaluation in Kao
Name

Year

Sustainability Report2018-0.117-3.231

0.048

2017

0.053

1.172

0.326

2016

0.011

0.257

0.813

d

CAR

Note: CAR = ∑ AR , d = the duration of event window, and AR = abnormal return. 𝑡𝑡_𝐶𝐶𝐶𝐶𝐶𝐶 =t = −5t_CAR

𝐶𝐶𝐶𝐶𝐶𝐶√𝑆𝑆 2∗𝑑𝑑

tailed probability of CAR. We put ** on the result which is lower than 0.05 as a significant result.

21

p_CAR
**

, 𝑆𝑆=standard error. p_CAR is the two-Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

<Reviewed Paper / Accepted: October 15th, 2020>

Small Businesses’ Awareness, Experience and Attitude towards
Microfinance in Asmara, Eritrea
Fiyori Afeworki Negash (Hokkaido University)
Abstract: It is universally accepted that Micro Finance Institutions (MFIs) have a significant contribution in
supporting Small and Medium Enterprises (SMEs) and buttressing the economy of developing countries. This study
attempts to identify the problems that small businesses in Eritrea face while also measuring their awareness andattitudes towards microfinance. We collected data through questionnaires from 145 small business owners, classified
into beneficiaries and non-beneficiaries of MFI loans. We find that there is considerable lack of awareness about andpositive attitude from the beneficiaries towards MFI. The biggest challenge facing SMEs is financial constraints. We
find that MFI is helpful in addressing the financial limitations faced by the beneficiaries. These findings underline
the need to ramp up sensitization efforts and to increase access to loans for SMEs by privatizing and proliferating
MFIs in Eritrea.

Keywords: Eritrea, Microfinance Institutions (MFIs), Saving and Micro Credit Program (SMCP), Small businesses, Impact,
Awareness, Positive Attitude.

1. Introduction

However, despite the potential merits of small
business both at the individual and national level, thereality of launching and running it presents its own
challenges. One of the main problems is the lack ofawareness among small business owners with regards tothe existence and activities of financial institutions. In
many corners of the globe, where MFIs are available, they
are not properly utilized as expected. And usually the main
reason for this is the lack of adequate knowledge about
MFIs.
Entrepreneurs in the SME sector have revealed that
there is great reliance on credit as a tool for businessgrowth and development. However, most entrepreneurs
asserted the fact that they are still faced with the challenge
of inadequate capital in their businesses and this inhibits
their growth. From another perspective, MFIs believe thatcredit obtained by entrepreneurs is misappropriated.
Another constraint to most SMEs is the lack of managerial
and business skills. There is a glaring need to build thesecapacities in addition to opening access to financial
resources in order to achieve growth. The smallness ofloans and savings, the absence of asset-based collateraland the simplicity of operations are the three distinctive
characteristics of the financial services provided by MFIs.
Eritrea is a small country in the eastern part of Africa,
along the Red Sea coast line. Despite the strategic
importance of the country’s geographic location and thebusiness opportunities that this presents, extraordinarily
little has been done. In Eritrea, the history of small
business shows a track record of financial affliction
resulting from different problems not unlike small
businesses throughout the world. In addition to this, thepolitical instability of Eritrea (i.e. colonialism) had adebilitating role in Eritrean small business firms. Since theThis research attempts to investigate the generalawareness and attitude of small business owners towards
MFI in Eritrea. Attitude in this context mean ‘The beliefof small businesses in the importance of MFIs to thegrowth of their Business.’ The study is highly expected tocontribute valuable information to the government andmicrofinance in Eritrea on how to develop awareness in
SME owners about Microfinance in Eritrea and to makerecommendations to allow and encourage private
investors in Microfinance. Available evidence indicatesthat small businesses played a major role in the growthand development of all leading economies. And there is
no doubt that most developing countries depend on theirsmall businesses in driving their economies forward.
However, most of these small businesses struggle withgetting financing. Thanks to the modern microfinance’s
existence, small businesses can access and receive
adequate finance and increase their working capital.
However, in Eritrea most small businesses are notaccessing MFI. One of the reasons is their lack ofawareness of the existence of MFI and its services.
However, even if they know about MFIs, they may not get
the access due to several reasons.
In this study we incorporate a statistical regression
analysis to identify the factors that affect small businesses’
awareness of MFIs and whether they become beneficiaries.
Small businesses constitute the lifeblood of any
growing economy. Not only do they boost the national
economy, but they also allow a significant segment of the
population, who would have remained idle or relatively
less productive, to engage in the business world. In
addition to that, many small businesses are the big
business organizations of tomorrow.
22日本リアルオプション学会 機関誌

リアルオプションと戦略

Eritrean economy was at its lowest level there was noavailability of financial sources especially for small firms.
Owing to this problem and other external factors many
individuals were hindered from starting their own
business.
The state of small business in Eritrea has not been as
bad over the past 26 years of independence whencompared to the previous decades. Small businesses havebeen gearing forward as a development vehicle inproviding job opportunities to the underprivileged such as
women and the poor. In Eritrea one way of encouraging
small business has been through the Savings and Micro
Credit Program (SMCP). The SMCP is an MFI operating
throughout the State of Eritrea. Most of the Eritrean
population does not have access to the conventional
financial institutions basically due to lack of collateral andfinancial records. Moreover, the Eritrean financial sector
is small and underdeveloped offering only a limited rangeof financial services. SMCP was, therefore, established in
1996 as part of the Eritrean Community Development
Fund and it provides savings and micro credit services tothe poor section of the population, which the conventional
financial institutions have failed to serve. The institution
enables its beneficiaries to successfully deal withenvironmental hazards, economic failures as well as
personal and family problems. According to many studies,challenges faced by SMEs may emanate from financial
challenges, management or marketing limits. Small
Businesses fail either at the start-up stage or at their earlyphases of operation. Even after the SMCP is established,most of the small business owners’ knowledge falls
beneath expectations.
1.1. Importance of the Study
Small business has been the backbone of almost any
economy, particularly in a small and young country like
Eritrea, its importance remains to be contested. In Eritrea,
a few decades ago, farming was the primary income of thenationals. Nowadays, however, it is no more, because theSahara Desert keeps extending into the young nation. For
this, small business has almost become the only option
where people can turn. Consequently, there is an urgent
need to understand the attitude of the people, who were
initially immersed in traditional farming activities,
towards small businesses and the microfinanceinstitutions available. Secondly, the next critical focus tobe addressed is the awareness of the society about theavailability and operations of those microfinanceinstitutions. Unfortunately, due to many confounding
factors, these basic concepts had never been studied
scientifically. The above-mentioned points became thepurpose of my research.
Considering the current state of affairs in Eritrea, it isalmost impossible to find adequate economic data.
Therefore, this being the first scientific research to be
conducted in this field, the study continues to be

第 12 巻 第 1 号

appreciated by all three stakeholders of small businesses
in Eritrea, namely the society, the government, and themicrofinance institutions. The research work dug out
significant findings that would help both the governmentof the state of Eritrea and the microfinance institutions not
only to amend their policies but also to come up with new
strategies that would, in a way, tackle the problems
identified in the research. On top of that, the study will
inevitably be referenced by small business owners andmicrofinance institutions that might be launchedsubsequently in Eritrea.
In addition to the merits previously mentioned, theresearch work would ignite other researchers to conduct
similar research in other parts of the country and in otherrelated issues that need to be addressed scientifically.
Besides, the work will serve as a pioneer reference to be
used by other researchers in future studies.
Most importantly, the research revealed and clearlystated problems that small businesses face both inlaunching and running their business, especially in the lineof finances. For this, the study came up with researchbased recommendations that both the government of thestate of Eritrea and microfinance institutions could use asalternative strategies to bring the general knowledge andawareness of the society up to the desired level.
1.2. Research questions of the study
1) What are the factors that determine theawareness of SMEs about MFIs?
2) What are the factors that determine an SME
becoming a beneficiary?
3) Do MFI fulfill beneficiaries’ expectations?
4) Do MFI increase beneficiaries’ business capital?
1.3. Objectives of the Study
The general objective of the study is to makesuggestions to the government regarding the policy ofprivatization of microfinance institutions in Eritrea.
Specific Objectives:
To identify the difficulties small businesses face when
launching their business.
2. To evaluate the awareness of small business owners about
micro finance institutions in Asmara, Eritrea.
3. To identify the relationship between small businessowner’s knowledge about and utilizations of microfinanceinstitution in Asmara, Eritrea.
4. To assess the attitude of small business owners towards
microfinance institutions in Asmara, Eritrea.
5. To identify the role/contribution of micro financeinstitutions on small business in Eritrea.
To achieve the above objectives, 4 research hypotheses
were formulated:
1) The nature of the business, challenges faced during thestart of the business, the objectives for establishment,
method of financing, the type of loan requirements needed,
1.

23

Communications of the Japan Association of Real Options and Strategy
and whether they have adequate capital for the businesscan significantly determine whether a small businessbecomes a beneficiary of an MFI.
2) The nature of the business, challenges faced during thestart of the business, the objectives for establishment,
method of financing, the type of loan requirements needed,
and whether they have adequate capital for the businesscan significantly determine whether a small business willknow about MFIs.
3) Beneficiaries of MFI do not believe MFIs fulfill their
expectation (there is no positive attitudes)
4) Beneficiaries of MFI do not believe MFIs increase theirbusiness capital.

Vol. 12 No.1

sector by encouraging the development and expansion ofsmall business, assisting individuals to increase theirincome generating ability and to improve their earningand the prosperity of their communities and the nation asa whole.
To achieve its objectives SMCP has three operational
strategies. The first strategy is to provide access to saving
and credit to individuals. The second is to strengthencommunity representative structures, from village tohigher levels, and to involve communities in thedevelopment and sustainability of the program. Lastly, toestablish a legal, regulatory and judicial framework for the
microfinance sector of Eritrea so that SMCP can be asustainable autonomous financial institution. SMCP
targets any citizen that has limited or no access to credit
from a formal financial institution, provided they agree tocomply with the required terms of credit. SMCP isgrassroots-oriented; it is a group-based lending programthat substitutes collateral with joint liability principles.
Small business firms constitute the bulk of businessactivities in the country. Thus, it is no wonder that
securing the well-being of these small business firms andfinancing them would be of great interest.
In Eritrea too, many researchers have focused theirstudies on the topic of microfinance. SMCP has asignificant effect on the livelihood of households in therural areas. Rural livelihoods have the effect of reducing
rural-urban migration and relieves the urban area’spressure. Micro financial resources in rural areas promote
development and socio-economic justice in Eritrea andother developing countries according to [Habte et al.
2017: 73-107].
Asgedom and Muturi [2014] focused on the repayment
performance of the SMCP loans in Eritrea. They foundthat there is no significant relationship between the socioeconomic factors and the repayment performance. But ingeneral there is a high level of repayment performance.
Lensink and Mehrteab [2003] addressed Risk behavior
and Group formation in Microcredit groups in Eritrea.
And they find evidence that borrowers in large groups willtake more risk than borrowers in smaller groups.
Yet, for SMEs to identify potential suppliers offinancial services, there must be an information flow tosmall business owners. Information asymmetries are
actually concerned with the two players in the financial
market. Given two projects with equal expected value, thelender prefers the safest one and the borrower the riskiest.
This is because the borrowers know more about theirbusiness and find it easy to hide the true nature of theirproject to exploit the lender’s lack of information. It
entails the lack of timely, accurate, and complete
information regarding the ability of the applicants to repay
back the loan and to access financial products from thebanking institutions. [Postle, P. 2005: 136–8.]
Sharma and Deshmukh [2013] studied MFIs facilities
and the awareness of people about MFIs. Poor married

2. Literature Review

Microfinance allows people to safely take on
reasonable small business loans in a manner that is
consistent with ethical lending practices. Although they
exist all around the world, the majority of microfinancing
operations occur in developing nations. Micro lending
starts in small villages where family and friends get
together in money sharing groups. These have their own
names. For example, in West Africa, they were called‘‘tontines’’, in Bolivia, ‘‘pasanaku’’, in Latin America,‘‘tandas,’’ in Africa and the Caribbean, ‘‘Susu’’ and in
Eritrea, ‘‘Eukub.’’ While the concept has been used
globally for centuries, The 2006 Nobel Prize winner Dr.
Muhammad Yunus, the founder of the modern
microfinance, has helped to push the industry further into
the spotlight.
Ashamu [2014] noted that the operations of MFIs havegrown in the recent years in Nigeria. Providing funds toemerging micro and small scale enterprises has been theengine of growth. In spite of challenges of different kinds
regarding policy implementation framework and an
urgent need for approval, the number of MFI branches andtheir employees increased five and ten folds respectively,
whereas their asset base and clients increased six and sixty
seven folds respectively. Growth is defined in relation tochanges in micro credit, and training provided by MFIs
assures that microfinance services had a strong positive
impact on the growth of SMEs. The research was basedon two hypotheses, namely whether there is a difference
between beneficiaries and non-beneficiaries and also theeffect of microfinance on poverty alleviation.
Mewl, which is equivalent to Bangladesh’s Grameen
Bank, was started six years after Eritrean independence,in 1996. At the beginning its purpose was to help
interested Diaspora returnees, War veterans and women tolaunch their own businesses and also to help rebuild
Eritrea’s
war-devastatedinfrastructure.
Eritreanmicrofinance is called SMCP [Mehrteab 2005]. The main
aim of the SMCP or Mewl is to provide financial services
to the vulnerable groups in both rural and urban areas of
the country who have no access to commercial banks. A
corollary to this would be the promotion of the private
24日本リアルオプション学会 機関誌

リアルオプションと戦略

第 12 巻 第 1 号

women are the target consumers. Two hypotheses
underpinned the data analysis of the research. First, Urban
Poor people are aware of the concept of micro finance, andthe second is that People of Nagpur city are Aware about
various schemes under micro finance. The analysis
yielded the following result.
They provide loans during their starting or existing
business, for the purchase of commercial vehicles andpersonal two wheelers. In the study most of the poor
people of Nagapur city are aware about micro saving
schemes. Out of 552 respondents 473 respondents, which
is 90.6% stated that they were aware about microfinance.
Regarding the awareness about micro-credit, insurance,saving and employment schemes, 17.2%, 9.2%, 55% and18.6% of the respondents have an awareness about thementioned schemes respectively. The high level ofawareness has been proved in the paper.
Most of the research on Eritrea focused on the roles andactivities of Microfinance Institutions and their impact on
small businesses. To mention some:- Asgedom and Muturi [2014] focused on therepayment performance of the SMCP loans in
Eritrea.
- Lensink and Mehrteab [2003] addressed Risk
behavior and Group formation in Microcredit
groups in Eritrea.
- [Habte et al. 2017: 73-107] studied the SMCP
effect on the livelihood of households in therural areas of Eritrea.
None of them has attempted to study the awareness of
small business owners towards SMCP in Eritrea. Filling
this gap is the ultimate purpose of the research.

farmers are largely dependent on rain-fed agriculture, andgrowth in this and other sectors is hampered by lack of adependable water supply. Asmara is known for its wellpreserved colonial Italian modernist architecture. The

3. Methodology

official currency of Eritrea is the Nakfa. In the city of
Asmara there are many SMEs of all kind the poor and richalike, compared to other cities and they can be a look to
the SMEs in other cities.

3.1. Study Population
The target population for the study comprised all
registered small and Medium Scale Enterprises in Asmara.
Asmara is located in the Maekel Region near the center ofEritrea. Asmara is the capital city and largest settlementin Eritrea with a population of around 649,000 inhabitants
making it the largest city in Eritrea. As the capital city andlargest settlement of Eritrea, most Eritrean businesses
have their headquarters in Asmara.
The city was once a factory town. During the colonial
period, it was an administrative and commercial center of
Italian East Africa. When the British entered the country
in 1941, many businesses were closed down or relocatedoutside of the city. This trend continued under Ethiopian
occupation. Textile clothing, footwear, processed meat,beer, soft drinks, and ceramics are Asmara’s majorindustrial products. The city is a marketplace for
agricultural products, and a center for tanning hides. The
Eritrean economy is largely based on agriculture, which
employs 80 percent of the population but contributes little
percent to gross domestic product. Agricultural exports
include cotton, fruits and vegetables, hides, and meat, but

Figure 1. Map of Eritrea.

3.2. Data Collection Tools and Sampling Technique
After developing the research proposal and the reliabledata-collecting tool that passed through rigorous statisticalprocedures, we successfully incorporated all the
stakeholders, and data was collected firsthand. I traveled
to Eritrea to collect the required data directly from thestudy participants. The data was collected at Asmara,which is the capital of Eritrea. The study participants were
small business owners of various economic and social
strata. These included experienced business owners,
young people who just launched small businesses, andindividuals who sold a few items on the streets of Asmara.
The main tool for collecting primary data in this study
was questionnaire. In order to draw the samples randomly,
stratified random sampling technique has been used. The
two strata are beneficiaries and non-beneficiaries of loans
from MFIs. The sampling frame for SMEs was taken from
the Ministry of Trade and Industry and SMCP in Asmara
25Communications of the Japan Association of Real Options and Strategy
Eritrea. The number of businesses samples were taken
proportionally according to the number of SMEs. Aftercompensating for non-responses, the final sample size
calculated was 145. Out of 145 only 141 replied positively
to our questionnaires and the rest 4 were not responsive.

(33%) and services (43%). Manufacturing, which has ahigh potential to create more employment opportunities
was lower in proportion at 7%. The study found that the
majority of non-beneficiaries were active in commerce(64.4%) and services (25.7%) and active in farming wasvery low at 4%.

3.3. Data Analysis
Most of the survey variables were categorical andfrequency distributions were used to describe variables ofinterest. Chi-square goodness of Fit Test was also used to
calculate the attitude of the respondents, and its
subsequent impact on utilization of loans from MFIs. To
know the factors that determine whether being abeneficiary or not and whether non-beneficiaries know
about MFI or not, this study made further use of twomodels of Logistic Regression Analysis:
ln �

𝑝𝑝𝑖𝑖

1−𝑝𝑝𝑖𝑖Vol. 12 No.1

Table 1: Socio-economic Characteristics of the SMEs in Asmara,Eritrea.
Non-Beneficiaries Beneficiaries
Socio-economic variable
n (%)
n (%)
GenderMale

42(41.6)22(55.0)

Female59(58.4)

18(45.0)

Age Category� = 𝛽𝛽0 + 𝛽𝛽1 𝑥𝑥1,𝑖𝑖 + ⋯ + 𝛽𝛽𝑘𝑘 𝑥𝑥𝑘𝑘,𝑖𝑖 ,𝑝𝑝𝑖𝑖 = Pr (𝑌𝑌𝑖𝑖 = 1),

where Υ is the dependent variable indicating whether a
respondent was a beneficiary or not (Beneficiary=1 / NonBeneficiary=0 or whether non beneficiary was
knowledgeable of MFI=1 or Not knowledgeable =0),𝛽𝛽𝑘𝑘 is a coefficient associated with the independent
variable 𝑥𝑥𝑘𝑘 .
In the first model, if the coefficient is positive(negative), the corresponding variable supports he isbeneficiary (non-beneficiary). In the second model, if thecoefficient is positive (negative), the corresponding
variable supports that the non-beneficiary knows (does notknow) MFI.

18-30

14(13.9)

14(35.0)31-45

28(27.7)21(52.5)

46-60

42(41.6)4(10.0)Above 60

17(16.8)22(55.0)

Level of Education
Illiterate2(2.0)

1(2.5)

Elementary
JuniorandHigh School

22(21.8)8(20.0)60(59.4)

26(65.0)

Diploma Level9(8.9)

4(10.0)Degree Level

6(5.9)

1(2.5)

Above Masters2(2.0)-

Subject's Background

4. Data Presentation and Analysis

4.1. Socio-economic Characteristics of SMEs in Asmara,Eritrea.
The data collected for this study indicates that the
majority of beneficiaries of Microfinance are male (55%)
as compared to females (45%).
On the contrary, the majority of non-beneficiaries werefemale (58.4%) as compared to males (41.6%), whichindicates that more needs to be done to make credit
available to female entrepreneurs for equitabledevelopment.
The study also found that the age of the greater
majority of beneficiaries was in the range of 46-60 (55%)
followed by those in the age group 31-45 (35%).
Meanwhile, young adults aged 18-30 were very few
comprising 2.5%. The non-beneficiaries were also mostly
between the age of 46 and 60 (41.6%), followed by the 3145 age group (27.7%).
The study found that 35% of beneficiaries werefarmers, 47.5% were ordinary citizens, 15% were postwar victims while the rest 2.5% were orphanages.
Glancing at the business activities, we find that the
majority of beneficiaries were involved in commerce

Farmer4(4.0)

14(35.0)

Orphanage
Post-war
victim-1(2.5)

13(12.9)6(15.0)Ordinary

84(83.2)

19(47.5)

Nature of Business
Manufacturing9(8.9)

3(7.5)

Commerce

65(64.4)

13(32.5)

Service26(25.7)

17(42.5)

Others1(1.0)
7(17.5)
N.B. 145 questionnaires distributed and 141 received. (101=Non-Beneficiaries and 40= Beneficiaries)

4.2. Case studies of SMEs in Asmara, Eritrea
The data collected indicates that 75% ofbeneficiaries had faced financial challenges. Meanwhile,
12.5% of the beneficiaries of MFI faced social challenges,
15% faced business location challenges, while 5% faced
challenges related to rules and regulations. It is clear thatthe challenges faced by SMEs are mainly financial innature. Similarly, non-beneficiaries of MFI were also
26

日本リアルオプション学会 機関誌

faced with financial challenges-- a majority of 61.4%
stated that the main challenge to start their business was
financial. Social, business location, regulatory challenges
accounted for 9.9%, 25.7% and 10.9% respectively.
Another 12.9% of the respondents stated that they faced
no challenges at all.
Also, 35% of SMEs benefiting from MFI indicatedthat they started business through self-finance compared
to 30% who commenced with credit from MFI. 51.5% and44.6% of non-beneficiaries commenced their businessfrom self-finance and family and Colleagues respectively.

65% of beneficiaries followed by 67.3% of nonbeneficiaries started their business for the purpose ofgenerating income to help their family. 52.5% ofbeneficiaries and 64.4% of non-beneficiaries said therequirements of getting loans from Bank is Collateral
Security. This is very true and that is one of the main
reasons MFI existed, to help those small businesses who
could not provide collateral.
Table 3: The Contribution of MFIs in the operations of SMEs’ inAsmara, Eritrea
Non-Beneficiaries Knowledge and Beneficiaries Expectation
about MFI

Table 2: Case-studies of the SMEs in Asmara, Eritrea
Non-Beneficiaries,
Beneficiaries,
Variables
n (%)
n (%)
Challenges in starting businessSocial10 (9.9)

5 (12.5)

Financial

62 (61.4)

30 (75.0)

Location26 (25.7)

6 (15.0)

Rules and regulations

11 (10.9)

2 (5.0)

Others13 (12.9)

4 (10.0)

Generate income
Create employment
opportunities

68(67.3)

26(65)

9(8.9)

1(2.5)

Self-employment

21(20.8)

7(17.5)Serve Community2(2.0)

1(2.5)

Growth in Economy

21(20.8)

6(15)52(51.5)

14(35.0)45(44.6)

10(25.0)

Partnership
Loans from banks andfinance institutions-2(5.0)

6(5.9)

2(5.0)

MFI-

12(30.0)

Others3(3.0)

4(10.0)65(64.4)

21(52.5)

Good will2(2.0)

3(7.5)

License28(27.7)

10(25.0)

Others10(9.9)6(15.0)22(21.8)38(95.0)1(1.0)

37(92.5)

In contrast, 70.3% of non-beneficiaries had noknowledge about MFI operations at all when compared to29.7% who stated otherwise. An overwhelming 95% of all
beneficiaries stated that MFI fulfilled their expectations,while the rest (5%) found that the MFI did not meet their
expectations.
Beneficiaries were queried if they saw an increase in
their capital as a result of the loans and a large majority of
beneficiaries (92.5%) stated that they saw an increase in
their capital as a result of the loans compared to a meager
7.5% who observed no increase in capital.
4.3. Small Businesses Challenges at the Start-up andfactors that determine being a beneficial or not
We conducted multivariable logistic regression
analysis to estimate coefficients associated with MFI’s
socioeconomic profile and the answer of the case study,
where the independent variable is whether a respondent
was a beneficiary (1) or not (0). Table 4 shows the resultof analysis (see Table A1 at the last of this paper for detailquestionnaire). In order to avoid multicollinearity problem,
we eliminated one variable of each pair that has over ±0.4
correlation coefficient. As we can see McFadden’s Pseudo
R2 = 0.49855, the result is rather effective.
In order to determine the efficacy of MFI in thebusiness operations of SMEs, it is imperative to identify
the main challenges that confront these enterprises fromthe start. All SME owners participating in the study, whoconstitute beneficiaries and non-beneficiaries of MFI,
were queried on the challenges they faced in starting andrunning their business. According to the data collectedfrom both beneficiaries and non-beneficiaries of MFI,

Possession of Adequate Capital
Yes

Beneficiaries, n (%)

30(29.7)

No100(99.0)3(7.5)
N.B. 145 questionnaires distributed and 141 received.
(101=Non-Beneficiaries and 40=Beneficiaries)

Requirements of getting loans from Bank
Collateral Security

Non-Beneficiaries, n (%)

Yes

Yes

Source of Finance at startandVariablesNo
71(70.3)2(5.0)
Ever applied for credit (Non-Beneficiaries) and Increase in
Capital from loan (Beneficiaries)

Objectives for establishment

Self-FinanceFamilyColleagues

第 12 巻 第 1 号

リアルオプションと戦略

14(35.0)No79(78.2)
26(65.0)
N.B. 145 questionnaires distributed and 141 received.
(101=Non-Beneficiaries and 40=Beneficiaries)
Multiple response allowed and percentages represent a
proportion of cases
27Communications of the Japan Association of Real Options and Strategy
four major factors have been identified as challenges
during the setting up of their businesses. These variables
are: financial, social, location and regulatory.
Financial challenges mean the inability of the small
businesses to have enough finance. This finding echoed
what the World Bank Enterprise Survey (2013) stated that
financial limitation as the primary barrier to the growthand development of small businesses. Next to the financial
problem is the social problem. Moving from a traditional
lifestyle such as farming that has been practiced for many
centuries to business is not welcomed by many. In
societies like Eritrean where the approval of the family
and the community at large has a great impact in every
move an individual member, diverting from the traditional
way of living to launching and running small businessVol. 12 No.1

leaves many small businesses to social pressures such as
rejection, discouragement, and so on. Another problem
people face when they start their business is lack of getting
a strategic business location; meaning that either by nothaving any place at all or a place that doesn’t suit thebusiness type. In addition to that sometimes thegovernment doesn’t let some type of businesses toestablish in a location where the owners preferred. Last
but not least one of the identified challenges is regulatory
obstacles. By regulatory obstacles means any sort ofgovernmental regulations that somehow negatively affectthe small business. These obstacles include prolonged
time to get legal permit to start a business, and this is
usually due to long bureaucratic procedures.

Table 4: Logistic Regression Analysis of factors associated with being a beneficiary or not. (In order to avoid multicollinearity problem,
we eliminated one variable of each pair that has over ±0.4 correlation coefficient.)
Variable
Coefficient
Std. Error
Z-value
Pr(>|Z|)
Socioeconomic Profile
B11
Manufacturing
1.31820
1.19679
1.101
0.27070
B13Services
2.086640.76430
2.730
0.00633***
B2
Gender
0.995280.70638
1.409
0.15884
B31
Age (18-30)-1.82834
1.36670-1.3380.18097
B33
Age (45-60)-0.47479
0.75674-0.6270.53039
B41
Illiterate
0.07160
2.047150.035
0.97210
B42
Elementary
0.62567
0.94882
0.659
0.50963
B44
Diploma
1.15597
1.100741.050
0.29364B45
Degree-0.73637
1.50164-0.4900.62386
B51
Farmer
2.398251.220311.965
0.04938**
B52
Orphanage
20.32975
6522.63877
0.003
0.99751
B53
Post-war
0.21489
0.87286
0.246
0.80553
Case Study of Selected SMEs
A1
Business duration-0.01867
0.02419-0.7720.44028
A21
Societal challenges
0.20116
1.04279
0.193
0.84704
A23
Location challenges-0.77097
0.86873-0.8870.37483
A24
Regulation challenges
0.02859
1.007480.028
0.97736
A25
Other challenges-0.19443
0.99759-0.1950.84547
A31
Family0.26085
0.74703
0.349
0.72695
A32
Employment
0.74606
1.218630.612
0.54040
A34
Community
0.800531.68059
0.476
0.63383
A4
People employed-0.39264
0.21836-1.7980.07216*
A52
Friends & relatives-0.75255
0.65769-1.1440.25253
A53
Partnership18.78651
4153.08491
0.005
0.99639
A54
Banks-0.63014
1.29610-0.486
0.62684
A55
MFI
21.29206
1523.48593
0.014
0.98885
A62
Good will
1.70728
1.52973
1.116
0.26440
A63
License-1.16889
1.01641-1.1500.25014
A64
Other requirements
0.33886
1.150540.295
0.76836
A7Adequate capital
1.691440.71349
2.371
0.01776**
Criteria
Number of observations
141
AIC

144.34

McFadden’s Pseudo R2

0.49855

Significance codes: ‘***’ 0.01 ‘**’ 0.05 ‘*’ 0.1

As we can see earlier the foremost challenge faced byboth beneficiaries (75%) and non-beneficiaries (61.4%) of

MFIs is financial. The data acquired found that 10.9% of
non-beneficiaries found that rules and regulations was a28

日本リアルオプション学会 機関誌

リアルオプションと戦略

challenge, while 5% of beneficiaries listed regulation as achallenge. Equally challenging to non-beneficiaries was thelocation of the business (25.7) but only 15% of beneficiaries

第 12 巻 第 1 号

identified this as a challenge. On the contrary, 12.5% ofbeneficiaries faced social challenges to starting theirbusiness compared to just 9.9% of non-beneficiaries.

Table 5: Logistic Regression Analysis of factors associated with whether non-beneficiaries know about MFI or not. (In order to avoid
multicollinearity problem, we eliminated one variable of each pair that has over ±0.4 correlation coefficient.)
Variable
Coefficient
Std. Error
Z-value
Pr(>|Z|)
Socioeconomic Profile
B11
Manufacturing
2.36876
1.26456
1.873
0.06104*
B13
Services
0.10487
0.91738
0.114
0.90899
B2
Gender
0.187830.809200.232
0.81644
B31
Age (18-30)
0.022111.23295
0.018
0.98569
B33
Age (45-60)
0.54889
0.81956
0.670
0.50303
B41
Illiterate-15.75758
1546.29763-0.0100.99187
B43
Junior high1.00209
1.04573
0.958
0.33793
B44
Diploma-0.34995
1.92391-0.1820.85567
B45
Degree3.38398
1.68552
2.008
0.04468**
B51
Farmer
1.195712.65400
0.451
0.65233
Case Study of Selected SMEs
A1
Business duration
0.044930.024321.847
0.06471*
A21
Societal challenges-4.30483
4.55128-0.946
0.34422
A23
Location challenges-0.46727
0.91814-0.5090.61080
A24
Regulation challenges
2.47246
1.03768
2.383
0.01719**
A25
Other challenges
0.31363
1.06194
0.295
0.76774
A32
Employment
1.043541.20707
0.865
0.38730
A33
Self employed
0.03061
1.14328
0.027
0.97864
A34
Community
1.43228
4.621530.310
0.75663A35
Growth of economy-2.15635
1.13088-1.907
0.05655*
A4
People employed
0.458910.246131.864
0.06225*
A52
Friends & relatives
0.316640.786700.402
0.68732
A54
Banks
3.29156
1.684431.954
0.05069*
A56
Others-0.05296
1.26576-0.0420.96662
A62Good will-0.22678
3.76780-0.0600.95200
A63
License0.75234
0.888300.847
0.39703
A64
Other requirements-2.21374
1.59639-1.3870.16553
A7
Adequate capital
2.092170.98357
2.127
0.03341**
Criteria
Number of observations 101
AIC

125.1

McFadden’s Pseudo R2

0.43768

Significance codes: ‘***’ 0.01 ‘**’ 0.05 ‘*’ 0.1

While all challenges faced during the start of thebusiness (A21-A25) are not statistically significant, we canfind that some other factors have significant impact onwhether a respondent was a beneficiary or not. Those who
has background as a farmer (B51; β=2.39825**), whosebusiness nature is service (B13; β=2.08664***) are more
likely to be beneficiaries. Next, those who have more
employee (A4; β=-0.39264*) are less likely to bebeneficiaries, that is, those who have large employment
seem not to have necessity for MFI’s help. Lastly, those whohave adequate capital for your business (A4; β=1.69144**)are more likely to be beneficiaries. This result echoes theexistence of MFI to minimize the SMEs financial distress.
Hypothesis 1 was partially accepted because the results
provide evidence that only nature of the business and having

adequate funds for daily activities can determine whether asmall business will become a beneficiary of an MFI, noneof the other stated factors were statistically significant.
4.4. Awareness of non-beneficiaries about MFI andfactors that determine their knowledge:
Indeed, it would be obsolete to talk about the awarenessof non-beneficiaries about MFIs. As we can see from table
2, they were asked whether they had knowledge about
microfinance prior to the study. When we look at the figures
regarding non-beneficiaries, we observe that the majority(n=71, 70.3%) have no awareness of MFIs when comparedto those who do.
To determine the factors associated whether theysignificantly affect non-beneficiaries to know about MFI or
29

Communications of the Japan Association of Real Options and Strategy
not we incorporate logistic regression analysis in the same
way as the previous section.
According to the result derived from Table 5, those
whose nature of business is manufacturing (B11;
β=2.36876*) and whose education is degree level (B45;
β=3.38398**) are significantly more likely to know about
MFI. So, the nature of the business could determine whethera small business knew about MFI.
Next, those who had regulation challenges at the start oftheir business (A24; β=2.47246**) were significantly more
likely to know about MFI. Those who started their businessto serve growth economy (A35; β=-2.15635*) were lesslikely to know about MFI.
The method of financing at the start of the businesscould determine whether a small business knew about MFI.
Those who used banks (A54; β=3.29156*) weresignificantly more likely to know MFI. Lastly, those who
had adequate capital (A7; β=2.09217**) were significantly
more likely to know about MFI.
Hypothesis 2 was fully accepted except for the type ofloan requirements needed because the results in eachcategory provide evidence that is statistically significant.
The nature of the business, the challenges faced during thestart of the business, the objectives for establishment,
method of financing, and whether they have adequate
capital for the business are all factors that can significantly
determine a small business’ awareness about MFI.

Vol. 12 No.1

Table 6: Chi-Square Test of goodness fit to measure beneficiaries’
expectation
Variables
Observed Expected(O-E)2/E
High expectation
38
20
16.2
No expectation
2
20
16.2
Total
40
40
32.4
N.B. Number of samples: 40=Beneficiaries.

4.6. Impact of MFI Loans on Business Capital
To identify the role/contribution of micro financeinstitutions on small business in Eritrea. The beneficiaries
were asked whether the loans acquired from the MFIs led to
an increase in their capital. To this, 37 beneficiaries (92.5%)
responded positively while a minority of 3 beneficiaries
responded negatively. Beneficiaries were asked whether theloans acquired lead to increase in their capital. This is shownin Table 7.
Table 7: Chi-Square Test of goodness fit to measure the Impact
MFI made to Beneficiaries
Variables
Observed Expected(O-E)2/E
Yes
37
20
14.45
No
3
20
14.45
Total
40
40
28.9
N.B. Number of samples: 40=Beneficiaries. Chi Square test results
are unreliable because not all observed frequencies were greaterthan 5.

This hypothesis is specific to beneficiaries who alreadyuse fund for their business from MFI. Apart from thedescriptive statistics that indicate a majority of beneficiaries
witnessing an increase in their capital as a result of MFIloans, a chi-square test of goodness fit has also shown thatthe impact of MFI loans is statistically significant as can beseen in table7 calculated value (χ 2c) 28.9> Table value (χ2t)
3.84, then Hypothesis 4 is rejected.

4.5. Attitude towards MFIs:
We have already seen that the level of awareness about
MFIs among non-beneficiaries is comparatively low. But it
can be said that the attitude of non-beneficiaries about the
impact of prospective loans on their businesses was
relatively proportional with 55.4% of respondents having a
positive attitude and 44.6% giving negative responses. On
the other hand, the majority of beneficiaries (95%) said that
MFIs fulfilled their expectations. Contextual meaning of
attitude is ‘The belief of small businesses towards theimportance of MFIs to the growth of their business. And
when we say positive attitude it means when beneficiaries
are satisfied with the services and products of MFI.
To know the attitude of SMEs towards MFIs different
questions were asked to beneficiaries and non-beneficiaries.
Beneficiaries were asked whether MFIs fulfilled their
expectations and non-beneficiaries were asked whether
their business would be improved if they had loan from
SMCP.
To further gauge the significance of attitudes towards
MFIs among beneficiaries, a chi-square test of goodness fit
was conducted. The results have nullified the hypothesisthat there is no positive attitude on expectations from MFIs.
The test value (χ2c) 32.4> Table value of chi-square (χ2t)
3.84. This indicates that MFIs indeed helped those who
benefited from them. Therefore, reject Hypothesis 3. See
Table 6 for detail.

5. Discussion

The results revealed profoundly important findings thatwould serve as useful information to small business owners
and help them connect with the microfinance institutions.
Additionally, this would also help the microfinanceinstitutions to improve their activities.
The predominant challenge for both beneficiaries andnon-beneficiaries of MFI in Asmara is financial constraint.
And it is precisely for this reason that the beneficiaries
sought funds from MFIs. Not surprisingly, the vast majorityof these beneficiaries (92.5%) reported a positive impact on
their businesses and an increase in their capital as aconsequence of the credit from MFI. This would inevitably
lead us to question why the non-beneficiaries did not follow
suit to address their financial problems.
Those who had financial challenges at the start up weresignificantly less likely to know about MFI and those whohave adequate capital were significantly more likely toknow about MFI. The result may look contradictory, but itsuggests that there are other, possibly more pressing reasons,30日本リアルオプション学会 機関誌

リアルオプションと戦略

why a non-beneficial would remain a non-beneficiary
despite having financial challenges.
It seems that the non-beneficiaries’ lack of awarenessabout MFIs. The fact that more than two-thirds of the nonbeneficiaries who participated in this study were not aware
about MFIs and their activities compels us to look at theconcept of information asymmetry from a different
perspective. Asymmetry of information is the inability offinancial institutions to determine whether the businesses
are able to repay their loans. Asymmetric information iswhen a borrower once in possession of borrowed funds, may
divert them (misappropriate) to other personal purposes
other than the business activities. However, this study has
found that it is not the lack of readily available detailed
information about the performance and operations of small
businesses that has been a bottleneck to the provision oroffering of credit to the non-beneficiaries. On the contrary,
it is the total lack of awareness of the majority of nonbeneficiaries about the very existence of the MFIs that hasdenied them the much-needed funds for the growth of theirbusiness and augmentation of their capital.
The reason why information asymmetry with regards to
MFIs is heavily skewed towards the non-beneficiaries may
be attributed to various factors upon deeper study. However,
the evidence at hand points inevitably to the inability of the
SMCP, the primary and only MFI in Eritrea, to sufficiently
reach out to its intended beneficiaries. Indeed, as mentioned
earlier, the SMCP, at least on paper, targets a vast portion of
the Eritrean population, particularly focusing on
disadvantaged segments like war veterans, returnees fromthe diaspora and women. One of the three operational
strategies that are supposed to realize this mission is thestrengthening of representative structures across all
administrative levels in the country. However, the empiricalfindings of this study suggest that there may be a certain
measure of complacency on the part of the SMCP in its
implementation of this strategy. After all, you cannot blame
the non-beneficiaries for not knowing about the SMCP if it
does not make efforts to advertise its services and reach out
to potential clients.
This lack of awareness in business owners in Asmara,Eritrea is in stark contrast to the level of awarenessdescribed in other studies. For instance, Sharma and
Deshmukh [2013], has found that awareness about MFIs
was rather high at 90.6% among the poor of Nagapur city inIndia. Not only had that but her study showed that theawareness extended to the different activities and offers of
MFIs in varying degrees.
If businesses in Eritrea, which are already operating
despite challenges, have such a low level of awareness about
MFI, one can assume how a lack of knowledge in thegeneral population is precluding many disadvantaged
individuals from starting their own SMEs and contributing
to the growth and development of the economy.
Would the non-beneficiaries have utilized loans of MFIs
had they been aware? This is not a hypothetical question.

第 12 巻 第 1 号

The study found that 0.99% of non-beneficiaries had everapplied for a loan from SMCP. In plain terms, two-thirds ofthe non-beneficiaries did not know about the MFI and thus
they never requested loans. Also, it has been found that theawareness about MFIs has a strong statistical relationshipwith the utilization of loans.
However, another factor that requires consideration is
the skepticism of approximately 45% of non-beneficiaries
towards loans. This does not mean that these beneficiaries
were only averse to loans from MFIs as opposed to otherfinancial institutions; rather, data indicates that 53.4% of
non-beneficiaries cited banks as their primary source for
additional capital. This leads us to surmise that nonbeneficiaries could have made use of loans from MFIs ifthey had been aware of its availability, its ease of access andpotential rewards.

6. Conclusion

31

By investigating both beneficiaries and nonbeneficiaries, this study has been able to bring forthconclusive evidence that, when accessed, MFI loans have asignificant positive impact on the growth of SMEs.
However, it has also found that there is a big lack ofawareness about MFIs and the services that they provide. It
is this lack of awareness that has prevented the nonbeneficiaries from utilizing loans from MFIs. On top of this,the negative attitude towards loans presents an additional
challenge in helping SMEs to address financial shortages
and problems in running their businesses. This clearly
indicates that not only the non-beneficiaries but also people
in general need to be made aware about the MFIs to increase
the role of SMEs in the development of the Eritrean
economy.
In this study the statistical logistic regression analysis
reveals that those who has farmer background and whose
nature of business is service are significantly more likely tobe beneficiaries, on the other hand those who have more
employees are less likely to be beneficiaries, that is, those
who have large employment seem not to have necessity for
MFI’s help. On top of that the study found out that those
who had adequate capital were more likely to bebeneficiaries. This supports the proponents of MFI whoargue that MFI can be a real solution to small businessfinance constraints.
The study also found out that those who had regulation
challenges at the start of their business were significantly
more likely to know about MFI. And also, those whose
nature of business manufacturing, those whose education isdegree level and those who had adequate capital are
significantly more likely to know about MFI. But those whostarted their business to serve growth economy were lesslikely to know about MFI. This result suggests the need forresearch into the possible reasons for these observations.
At the outset, this study attempted to fill a gap inresearch on MFIs in general. This gap pertained to the levelsof awareness and the attitudes of SMEs towards MFIs.

Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

Promotion Foundation for providing me with the financial
assistance needed to complete this project. I also appreciate
the Savings and Microcredit Program (SMCP) managers of
the head office and Zoba-Maekel for their welcoming andimmense support during the data collection.

However, while reviewing previous literature, it has beenfound that the gap is far wider than previously conceived.
Researchers have tended to focus their studies on the limited
MFI activities being undertaken, particularly thebeneficiaries. It cannot be maintained that socio-economic
justice and development are being promoted by MFIs
[Habte et al. 2017] while the majority of SMEs remain nonbeneficiaries. Also, narrow research perspectives on thecharacteristics and behaviors of the beneficiaries or groupsof beneficiaries fail to provide a fuller picture of the realsituation [Asgedom & Muturi. 2014; Lensink & Mehrteab.
2003]. Therefore, future research on the domain of SMEs
and MFIs needs to follow a more holistic approach.
This study confirms that, even in Eritrea, MFI help tostimulate growth of their beneficiaries through the provisionof dearly needed resources. It was shown here that 93% of
SMEs benefiting from access to MFI credit have seen an
increase in their capital and were satisfied with MFI as their
expectations had been met. Nonetheless, a worrying
revelation is the fact that the awareness about the existenceof MFI is exceptionally low in the case of Eritrea.

References

Ali, A. H., A. O. Abu-Hadi and A. Y. S. Ali. 2013. (1)International Journal of Humanities and Social
Science, 3(11):172-180.
Asgedom, Y. G. and W. Muturi. 2014. Socio-Economic
Determinants of Eritrea’s Savings and Micro
Credit Program Loan Repayment Performance: A
Case of the Dekemhare Sub-Zone
Ashamu, S. O. 2014. The Impact of Micro-Finance on Small
Scale Business in Nigeria. Journal of Policy andDevelopment Studies, 9(1):179-193.
Ashamu, S. O. and O. J. Ayodele. 2015. An Assessment of
Impact of Microfinance Banks on Poverty
Alleviation in Nigeria: An Empirical Investigation.
Journal of Policy and Development Studies, 9(2):83-89.
Asor, A. E., M. E. Essien & N. Ndiyo. 2016. The Impact ofMicrofinance Banks on Small Scale Businesses in
Cross River State: A Case Study of Calabar
Metropolis.
Gulani, M. G. and A. Usman. 2012. Financing Small andMedium Scale Enterprises (SMEs): A Challenge
for Entrepreneurial Development in Gombe State.
Asian Journal of Business and Management
Sciences, 2(9).
Gulima, H. A. 2016. The Impact of Training on Performanceof Micro and Small Enterprise Operators in
Ethiopia. Journal of Policy and Development
Studies, 10(3):92-103.
Habte, A., K. Visser and M. K. Ocran. 2017. The impact of
microfinance on household livelihoods: evidence
from rural Eritrea Development Finance (pp. 73107): Springer.
Hadi, N. A. and A. Kamaluddin. 2015. Social collateral,
repayment rates, and the creation of capital among
the clients of microfinance. Procedia Economics
and Finance, 31: 823-828.
Kaburi, S. N., B. B. Ombasa., D. N. Omato., V. O. Mobegi
and F. Memba. 2013. An Overview of the Role ofMicrofinance in Eradicating Poverty in Kenya: A
Lesson to be Learnt from the Emerging
Economiesǁ. International Journal of Arts andCommerce, 2(5):Khan, M. A. and M. A. Rahaman. 2007. Impact of
microfinance on living standards, empowerment
and poverty alleviation of poor people: a case study
on microfinance in the Chittagong District ofBangladesh: Handelshögskolan vid Umeåuniversitet.

7. Recommendations

Solving the problem of lack of awareness about MFIs
and the negative attitude towards loans rests primarily on
the shoulders of the SMCP. Serious consideration must be
given by the SMCP to take the following measures:
Introducing extensive sensitization programs on MFI
activities and their benefits to SMEs
- Galvanizing SMCP operational strategies; in particular,
strengthening cooperation with stakeholders at alllevels of the community to ensure wider outreach in the
population- Co-opting successful beneficiaries in the sensitization
programs and encouraging them to share their practices
At the same time, the government must promulgate
policies and create conducive grounds for the privatizationand proliferation of MFIs in Eritrea.
Researchers can also contribute positively to these efforts
by making studies to identify who and how more people canbecome beneficiaries of MFIs. More studies are also needed
to identify challenges to SMEs on a broader scale and how
they can be tackled.
Acknowledgement
I would like to thank two anonymous referees. I renderspecial appreciation to my dependable supervisor, Professor
Makoto Goto. His constructive criticisms, suggestions andadvice helped make this work a masterpiece. I would also
like to thank professor Hiroto Suzuki for his technicaladvice in the logistic regression analysis. His contribution
to this manuscript is much appreciated.
Many thanks are due to MEXT (The Japanese Ministryof Education, Culture, Sports, Science and Technology,
Monbukagakusho),
OtsukaToshimiScholarshipFoundation and the Ishii Memorial Securities Research
32日本リアルオプション学会 機関誌

リアルオプションと戦略

Kiiru, J. M. and M. Kenia. 2007. The impact of
microfinance on rural poor households’ income
and vulnerability to poverty: Case Study ofMakueni District, Kenya. Unpublished PhD thesis
University of Bonn.
Lensink, R. and H. T. Mehrteab. 2003. Risk behaviour andgroup formation in microcredit groups in Eritrea.
Mbithe, N. 2013. The effects of microfinance service on thegrowth of small and medium enterprises in
Machakos County: Award of Masters of business
Administration (MBA), school of business,
University of Nairobi.
Mehrteab, H. T. 2005. Adverse selection and moral hazard
in group-based lending: Evidence from eritrea.
Centre for Development Studies, University ofGroningen.
Ogubazghi, S. K. and W. Muturi. 2014. The Effect of Ageand Educational Level of Owner/Managers on

第 12 巻 第 1 号

SMMEs’ Access to Bank Loan in Eritrea: Evidence
from Asmara City. American journal of industrial
and business management, 4(11): 632.
Olowe, F. T., O. A. Moradeyo and O. A. Babalola. 2013.
Empirical Study of the Impact of MicrofinanceBank on Small and Medium Growth in Nigeria.
International Journal of Academic Research in
Economics and Management Sciences, 2(6): 116.
Sharma, S. and A. Deshmukh. 2013. A Study ofMicrofinance Facilities and Analysing theAwareness Level of People About Micro Financein Nagapur City. International Journal of Social
Science and Interdisciplinary Research 2(3):16.
Vogelgesang, U. 2001. The impact of microfinance loans on
the clients' enterprises: Evidence from Caja Los
Andes, Bolivia. GK working paper series:1.

33

Communications of the Japan Association of Real Options and Strategy

Appendix
Table A1: Detail questionnaireSocioeconomic Profile
B1
What is the nature of your Business?
B11
Manufacturing
B12
Commerce
B13
Services
B14
OthersB2
Kindly indicate your gender. (Male:1, Female: 0)
B3
Kindly tell us your age.
B31
18-30
B32
31-45
B33
45-60
B34
Above 60
B4What is the level of your Education?
B41
Illiterate
B42
Elementary
B43
Junior high school
B44
Diploma level
B45
Degree level
B46
Above master’s level
B5
Background of the owner.
B51
FarmerB52
Orphanage
B53
Post-war victim
B54
Ordinary
Case Study of Selected MFIs
A1
How long have you kept your business?
A2
The challenges you face when you start your busines.
A21
Societal
A22
Financial
A23
Location
A24
Rules and regulation
A25
Other challenges
A3
What was the objective for establishing your business?
A31
To generate income to support their family
A32
To create employment opportunities for others
A33
To be self employed
A34
To serve the community
A35
For better growth of economy
A4
How many people have you employed?
A5
How did you finance when you start your business?
A51
Self
A52
Friends & relatives
A53
Partnership
A54
Loans from banks & finance institutions
A56
OthersA6
To get loan from bank what are the requirements?
A61
Collateral security
A62
Good will
A63
LicenseA64
OthersA7
Do you have adequate capital for your business?

34

Vol. 12 No.1

日本リアルオプション学会 機関誌

リアルオプションと戦略

第 12 巻 第 1 号<Reviewed Paper / Accepted: August 8th, 2022>

On the Correlation between Market Risk Premiums and SDGs:
Implications to Corporate Value
Haruyoshi Ito

Associate Professor of Finance
Graduate School of International Management
Aoyama Gakuin University
Abstract: There are plethora of papers analyzing the correlation between CSR / SDGs / ESG and corporate values /stock prices / stock returns, however, a few of them focus on how CSR / SDGs / ESG factors improve or destroy the
corporate value and only a very limited number of papers analyze the market risk premium as factor of corporate
value. This paper focuses on how the macro level SDG indices are associated with market risk premiums and thecorporate value. We show that contributions to SDGs related activities pursuing to improve the overall SDG Index
Score as well as SDG 3 (Good Health and Well-Being) and 13 (Climate Action) scores could increase the corporate
value via the reduction of the market risk premium. On the other hand, SDG 15 (Life on Land) and 16 (Peace, Justiceand Strong Institutions) scores are not correlated with market risk premiums, while an SDG 9 (Industry Innovation
and Infrastructure) score is positively correlated with market risk premiums. We need further analysis to conclude
the relationship between market risk premiums and SDG 1,2, 4-8, 10-12, 14, and 17.

Keywords：SDG Index, SDGs, ESG, CSR, Market Risk Premium, Corporate Valueis the expected value calculator under the physicalprobability measure, CFt is the free cash flow to thecorporation at time t, n is the expected life span of thecorporation, WACC is the weighted average cost of capital,
D is the market value of the debt, E is the market value ofthe equity, τ is the effective tax rate, rD is the cost of debt,
rE is the cost of equity, rf is the risk free rate, β is themeasure of market risk, and rm is the return on the market
portfolio.
As seen in equation (1), if contributions to SDGs
reduce the market risk premium (MRP) defined as rm - rf,
the value of the corporation improves. A plethora of papers
analyze the impact of Corporate Social Responsibility(CSR) / Environment, Social, and Governance (ESG) /SDGs factors on the corporate performance. However, as
far as we know, few papers focus on the correlation
between SDGs and MRPs. The only exceptions are Buhr etal. (2018), Kling et al. (2018), and Nemoto and Liu (2020).
Buhr et al. (2018) and Kling et al. (2018) show thatenvironmental consideration would reduce the country risk
premium using the data of government bonds issued in 46
countries. However, they focus on the climate related
indices and their impacts on the sovereign bond yields.
Nemoto and Liu (2020) use country level ESG scores inorder to analyze the correlation between these ESG scoresand costs of sovereign debt. While Nemoto and Liu (2020)
use the overall social score, they did not utilize moredetailed social scores.

1. Introduction
This paper analyzes whether there are significant
correlations between Sustainable Development Goals(SDGs) and market risk premiums. We find that the SDGs
related activities or projects improving the overall SDG
Index Score as well as SDG 3 (Good Health and WellBeing) and 13 (Climate Action) scores could increase thevalue of the corporation via the reduction of the market risk
premium. On the other hand, the correlation between
market risk premiums and SDG 15 (Life on Land) and 16(Peace, Justice and Strong Institutions) scores are notstatistically significant at 10% level. An SDG 9 (Industry
Innovation and Infrastructure) score is positively
correlated with market risk premiums. We need further
analysis to conclude the relationship between market risk
premiums and SDG 1, 2, 4-8, 10-12, 14, and 17.
Descriptions of 17 SDGs are shown in Table 1.
In this paper, the value of the corporation is definedas equation (1).

V0=

E P ( CF1 )+1(1 + WACC )E P ( CF2 )(1 + WACC )+ ... +
2DErE
(1 − τ ) rD +
E+D
E+D
rE =+
rf β rm − rf

WACC
=

(

E P ( CFn )(1 + WACC )

n

(1)

)

where, V0 is the value of the corporation at t = 0, EP()

35

Communications of the Japan Association of Real Options and Strategy
Table 1: Descriptions of 17 SDGs

Vol. 12 No.1

create or destroy the value of the corporation. According toequation (1), SDGs have a positive impact on the value ofa corporation if 1. the expected free cash flows are
improved, 2. the risk premium is reduced, or 3. theexpected life span of the corporation is improved by SDGs.
First, CSR could improve the value via the
improvement of cash flow. Khan, Serafeim, and Yoon
(2016) show that the corporations which have highinvestment in material sustainable issues have higher
growth in returns on sales two years or later from the time
corporations invested in material sustainable issues using
U.S.A. data. As a result, monthly stock returns are higher
for corporations which have high investment in material
sustainability issues due to the higher alpha controlled by
the Fama-French three-factor model (Fama and French1993). On the other hand, Ogata (2016) shows thatprofitability and environmental, social, and governancescores (ESG scores) are not positively correlated withprofitability measures such as ROE. Ogata (2016) also
shows that Socially Responsible Investment (SRI) funds inJapan select corporations with higher ROE in the portfolio
at first but the difference in ROE between firms in SRI
funds and firms excluded from them diminish after five
years or later from the time of selection. Goss and Roberts(2011) show that over emphasis on CSR might destroy thefirm value due to the problem of over-investment.
On the other hand, the impact of CSR or ESG
consideration on the risk premium is less controversial. For
example, Lins, Servaes, and Tamayo (2017) show thatfirms concerning CSR have higher returns during the crisis
period by 4-7 %, that is, lower risks in the crisis period.
Orlitzky and Benjamin (2001) and Jo and Na (2012) showthat investments in CSR reduce both the market risk
measured by beta and the firm-specific risk. Shirasu (2011)using Japanese data shows that the average beta ofcorporations included in SRI funds is lower in crisis period
than in normal period while average beta of other types ofcorporations is higher in crisis period.
Additionally, CSR / ESG / SDGs considerations
could lead to lower yield spread. Crifo, Diaye, andOueghlissi (2017) show that emphasis on ESG decreases
the government bond spreads.
As for the analysis on the correlation between CSR /ESG / SDGs and MRPs, a very limited number of papers
address these issues. As far as we know, Buhr et al. (2018),
Kling et al. (2018), and Nemoto and Liu (2020) are the only
exceptions. Buhr et al. (2018) and Kling et al. (2018) showthat countries which are vulnerable to climate change tend

SDGs
Descriptions
SDG 1 No Poverty
SDG 2 Zero Hunger
SDG 3 Good Health and Well-Being
SDG 4 Quality of Education
SDG 5 Gender Equality
SDG 6 Clean Water and Sanitation
SDG 7 Affordable and Clean Energy
SDG 8 Decent Work and Economic Growth
SDG 9 Industry Innovation and Infrastructure
SDG 10 Reduced Inequalities
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action
SDG 14 Life below Water
SDG 15 Life on Land
SDG 16 Peace, Justice and Strong Institutions
SDG 17 Partnership for the Goals
Source: The United Nations’ website (https://sdgs.un.org/goals,
accessed July 10th, 2022).

On the other hand, this paper uses SDG indicesrelated to all 17 goals including both social andenvironmental issues of all countries where these data are
available. We use 790 samples in total for five years (158countries’ data per year on average) in terms of the overall
SDG Index Score. This paper provides broader
perspectives on how the contributions to SDGs have theimpact on the market risk premiums.
This paper is organized as follows. The second
section reviews the related literature. The third section
describes the data employed. The fourth section shows thedescriptive statistics and analyzes the correlation between
the MRPs and the SDG indices. The fifth section concludes
the paper.

2. Literature Review
A plethora of papers analyze the relationshipbetween CSR / ESG / SDGs factors and the value of thecorporation. However, the impact of CSR / ESG / SDGs
consideration on corporate performance is controversial.
Friede, Busch, and Bassen (2015) point out 62.6%, 6.9%,
and 30.5% of 1,902 meta-analyses studies find positive,
negative, or neutral correlation between corporate
performances and CSR activities, respectively. WhileFriede, Busch, and Bassen (2015) claim that approximately
90% papers show non-negative results, the fact that 37.4%
papers showing non-positive results is not ignorable.
Additionally, it is important to consider how SDGs

36

日本リアルオプション学会 機関誌

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to have higher yield on country sovereign bonds. However,they use indices related to climate change only but do not
utilize the indices related to social issues included in SDGs.
Nemoto and Liu (2020) using country level ESG scoresshow that overall ESG scores are negatively correlatedwith sovereign debt costs. However, they just use one index
for each social and environmental aspect while this paper
uses all 17 SDGs indices including both environmental andsocial aspects in addition to one overall country level SDG
Index Score.
Unique contributions of this paper to the literatureare summarized as follows. First, this paper uses indicesrelated to all 17 SDGs including both environmental andsocial issues in order to examine what kind of SDGs related
activities have a positive, a negative, or no impact on
MRPs. Second, we use both developing and developed
countries’ data to see how SDG indices are related to MRPs.
Third, we use the MRP defined as rm - rf in equation (1), asa proxy of country risk premium instead of sovereign bondyield. Using MRP as a proxy of country risk premium
allows investors to understand how the SDGs have impact
not only on rf but also on rm in equation (1). Lastly, we use
three kinds of MRPs in order to examine whether estimatedimpacts of SDGs related activities on MRPs are robust ornot.
We explain details of data related to MRPs andSDGs in Section 3.

第 12 巻 第 1 号

ratings as MRP (CR), and those estimated from the credit
spreads as MRP (CS) hereinafter.
As the determination of the best one among three
types of MRPs is beyond the scope of this paper, we utilize
all of these three types of MRPs. By doing so, we could
check the robustness of the correlation between SDG
indices and MRPs by seeing how many of them aresignificant. If all analyses show consistent results no matterthe types of MRPs, we can reasonably conclude that
correlations between SDGs and MRPs found by our
analysis are more believable. If only one of the analyses
shows the statistically significant result, the statisticalsignificance might be just coincidentally observed.
We use MRPs instead of the spreads of bonds tradedin the market because the impact of social andenvironmental issues such as climate risk on the financemarket should be observed in both green and non-green
bonds as these social and environmental issues are
externalities (Preclaw and Bakshi 2015). In other words,
both green and non-green bonds and even stocks should
benefit from the less climate risk environment. It isdifficult to extract the benefit (e.g. green premium)analyzing the difference of spread between green and nongreen bonds. We assume that MRPs reflect the externalities
of social and environmental issues within the country. We
could also argue that a country’s positive contribution tosociety or the environment could benefit other countries.
Implications from our analysis just focus on theexternalities which do not beyond the domestic
environmental and social issues.
Second, as for SDG indices, we utilize Sachs et al.
(2016-2020). Sachs et al. (2016-2020) report SDG indicesfor 17 goals and overall SDG Index Score for each countryin June or July since 2015. We exclude the 2015 report,
which only reports the indices for 34 industrializedcountries. After that, SDG indices are reported for 149, 157,
156, 162, and 166 countries in 2016, 2017, 2018, 2019, and2020, respectively. SDGs indices are scaled from 0 (worst)
to 100 (best). Sachs et al. (2016-2020) note that they
construct these indices based on the several indicators
related to SDGs such as poverty headcount ratio at$1.90/day (% population). They refine the methodologies
to calculate these SDG indices and claim that these indicescannot be compared across the report. However, we
assume that change in the ranking or scores due to the

3. Data
We use three types of MRPs as proxies of the country
risk premium and 18 types of SDG indices in order toconsider how the environmental and social aspects have an
impact on the market risk premium.
First of all, as for MRPs, we utilize the survey
conducted by Fernandez (Fernandez et al. 2016-2021) andthe data provided by Damodaran 1. He updates the market
risk premiums 2 in January every year except 2020. As for
2020, the premiums in April are reported. That is, thepremiums estimated incorporate the impact of COVID-19.
Damodaran estimates the premiums by two methodologies.
One uses the credit ratings of the sovereign bonds and theother one uses the credit spreads implied by the credit
default swaps. We call MRPs reported by Fernandez et al.
(2016-2021) as MRP (F), those estimated from the credit
1 Data is retrieved from Damodaran’s website(http://pages.stern.nyu.edu/~adamodar/, accessed on July 10th,2022)

2 Damodaran defines rm - rf as equity risk premium (ERP). For
simplicity, we call this ERP as MRP in this paper.

37

Communications of the Japan Association of Real Options and Strategy
change in the methodology is not significant compared to
the real improvement in the SDGs contribution to eachcountry. We decided to use all the data from 2016 to 2020
in order to analyze the correlation between MRPs and SDG
indices.
Third, we also use the control variables to see
whether the statistical significance observed from theregression analysis is robust or not. Control variables
employed to the regression analysis are Gross Domestic
Product (GDP) per capita, Democracy Index, andGovernment Debt / GDP. In this sense, we exclude acontrol variable from the regression model if thecorrelation between the control variable and an SDG index
is not statistically significant at 10% level. We retrieve
GDP per capita from the World Bank, Government Debt /GDP from International Monetary Fund (IMF), and the
Democracy Index from the Economist Intelligence Unit 3.
The Democracy Index measures the state of democracy for
165 independent states and two territories based on fivecategories, namely, electoral process and pluralism, civil
liberties, the functioning of government, politicalparticipation, and political culture.
We report the descriptive statistics and results ofregression analysis in section 4.

Vol. 12 No.1

Table 2: Descriptive Statistics of MRPs
MRP (F)
All
2021
2020
2019
2018
2017
Mean
7.90% 7.62% 8.22% 8.19% 7.68% 7.68%
Median 7.00% 6.85% 7.20% 7.50% 6.90% 6.70%
s.d.
2.82% 2.44% 2.90% 2.99% 3.06% 2.74%
Min
5.10% 5.20% 5.60% 5.60% 5.10% 5.60%
Max 23.70% 19.80% 23.10% 23.70% 22.10% 17.40%
N
330
86
79
67
57
41
MRP (CR)
All
2021
2020
2019
2018
2017
Mean 10.24% 8.55% 12.56% 10.76% 8.94% 10.36%
Median 9.24% 8.21% 11.51% 10.13% 7.96% 9.24%
s.d.
4.13% 3.35% 5.05% 3.84% 3.06% 3.73%
Min
4.72% 4.72% 6.01% 5.96% 5.08% 5.69%
Max 28.10% 23.90% 24.52% 28.10% 16.60% 19.90%
N
675
140
140
135
131
129
MRP (CS)
All
2021
2020
2019
2018
2017
Mean
7.73% 6.33% 9.21% 8.35% 6.38% 8.16%
median 6.76% 5.50% 7.63% 7.40% 5.97% 7.15%
s.d.
3.50% 2.02% 4.44% 2.49% 1.39% 4.83%
Min
4.72% 4.72% 6.01% 5.96% 5.08% 5.69%
Max 43.15% 12.99% 31.78% 15.68% 11.28% 43.15%
N
353
74
76
76
63
64
Note: MRP (F) indicates market risk premiums estimated by
Fernandez et al. (2016-2021), MRP (CR) and MRP (CS) are
estimated by Damodaran and based on the credit ratings and thecredit spreads, respectively. s.d. indicates standard deviation. N
indicates the number of samples.

4. Analysis

Table 3: Correlations among MRPs (F, CR, and CS)
Raw Values
MRP (F) MRP (CR) MRP (CS)MRP (F)
1.00***
0.89***
0.75*********MRP (CR)
0.89
1.00
0.83*********MRP (CS)
0.75
0.83
1.00***
Year-to-Year Differences
MRP (F) MRP (CR) MRP (CS)MRP (F)
1.00***
0.52***
0.41*********MRP (CR)
0.52
1.00
0.77***MRP (CS)
0.41***
0.77***
1.00***
Note: MRP (F) indicates market risk premiums estimated by
Fernandez et al. (2016-2021), MRP (CR) and MRP (CS) are
estimated by Damodaran and based on the credit ratings and thecredit spreads, respectively. *** indicates 1% level of statisticalsignificance.

4.1 Descriptive Statistics

Table 2 shows the descriptive statistics of MRPs.
MRP (F), MRP (CR), and MRP (CS) are different in termsof the absolute values but trends of these values are moreor less the same. Additionally, Table 3 reports thecorrelation among these three MRPs. Raw values as well
as differences of these MRPs are highly correlated witheach other.
As these MRPs are not perfectly correlated, we canreasonably say these MRPs capture different perspectivesof country risk premiums. Using these MRPs aremeaningful to examine the correlation between
contributions to SDGs and MRPs.

3 Data is retrieved from The Economist Intelligence Unit’s
website (https://www.eiu.com/n/campaigns/democracy-index-

2020/, accessed July 10th, 2022)

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日本リアルオプション学会 機関誌

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Table 4: Descriptive Statistics of the SDG indices andControl Variables
Panel A: SDG Indices

Mean
Median
s.d.
Min
Max
N

Mean
Median
s.d.
Min
Max
N

Mean
Median
s.d.
Min
Max
N

Mean
Median
s.d.
Min
Max
N

Mean
Median
s.d.
Min
Max
N

Mean
Median
s.d.
Min
Max
N

Overall SDG Index Score
2020
2019
2018
2017
66.77 65.99
64.90 64.84
68.73 68.01
66.06 65.97
9.96 10.03
10.18 10.80
38.54 39.08
37.66 36.75
84.72 85.22
84.98 85.61
166
162
156
157
SDG 1 (No Poverty)
All
2020
2019
2018
2017
79.98 73.83 74.40
86.25 85.22
96.70 90.62 90.40
98.61 98.23
28.74 32.16 31.50
23.77 24.50
0.00
0.00
0.00
0.00
0.00
100.00 100.00 100.00 100.00 100.00
616
154
151
156
155
SDG 2 (Zero Hunger)
All
2020
2019
2018
2017
53.91 55.19 53.56
54.42 52.40
53.62 56.55 53.70
52.57 51.43
11.65 10.62
9.79
13.57 12.30
14.74 20.76 19.01
14.74 21.84
83.23 80.24 77.87
83.23 80.42
641
166
162
156
157
SDG 3 (Good Health and Well-Being)
All
2020
2019
2018
2017
70.25 69.30 70.04
70.99 70.74
76.19 75.58 75.62
76.66 76.19
19.72 20.10 20.11
19.07 19.70
15.41 15.41 17.59
21.75 24.55
97.89 97.08 97.89
97.05 97.58
641
166
162
156
157
SDG 4 (Quality Education)
All
2020
2019
2018
2017
74.83 78.69 76.90
71.13 72.34
83.14 87.94 85.86
79.39 80.24
23.26 22.68 23.37
23.61 22.76
1.64
1.64
8.42
4.77
3.91
99.94 99.91 99.92
99.94 99.84
639
164
162
156
157
SDG 5 (Gender Equality)
All
2020
2019
2018
2017
61.30 61.04 60.17
64.18 59.87
64.10 63.44 62.71
67.91 63.83
16.25 16.39 16.19
15.72 16.50
6.72
6.72 10.39
15.01 14.16
92.60 91.23 89.24
91.89 92.60
641
166
162
156
157
All
64.28
65.92
11.36
26.10
85.61
790

第 12 巻 第 1 号

SDG 6 (Clean Water and Sanitation)
All
2020
2019
2018
2017
2016
Mean
72.25 67.86
67.64 75.60 78.32 NAMedian74.05 69.56
69.20 80.99 82.94 NA
s.d.
16.76 14.72
16.44 16.96 16.35 NA
Min
27.47 32.71
27.47 28.85 29.16 NA
Max
100.00 94.95
96.98 100.00 98.50 NA
N
641
166
162
156
157
NA
SDG 7 (Affordable and Clean Energy)
All
2020
2019
2018
2017
2016
Mean
68.78 71.66
71.13 66.00 66.09 NA
Median81.84 87.15
86.49 79.60 78.56 NA
s.d.
27.94 27.67
28.14 27.98 27.73 NA
Min
0.00
0.11
0.00
0.00
0.00 NAMax
99.91 99.71
99.37 98.89 99.91 NA
N
641
166
162
156
157
NA
SDG 8 (Decent Work and Economic Growth)
All
2020
2019
2018
2017
2016
Mean
67.65 71.77
71.63 64.90 61.92 NA
Median70.60 72.60
72.57 64.53 61.85 NA
s.d.
14.83 10.56
10.43 16.37 18.18 NA
Min
11.95 37.47
36.48 11.95 17.05 NAMax
95.76 91.85
90.61 92.93 95.76 NA
N
641
166
162
156
157
NA
SDG 9 (Industry Innovation and Infrastructure)
All
2020
2019
2018
2017
2016
Mean
36.47 41.81
35.06 35.29 33.46 NAMedian29.83 35.04
29.32 28.93 26.35 NA
s.d.
25.03 27.22
23.72 24.09 24.22 NA
Min
0.85
1.81
1.87
0.85
1.69 NAMax
98.76 98.76
93.31 92.83 93.86 NA
N
641
166
162
156
157
NA
SDG 10 (Reduced Inequalities)
All
2020
2019
2018
2017
2016
Mean
58.91 56.96
59.12 57.28 62.52 NAMedian60.59 57.10
60.68 60.49 66.23 NA
s.d.
23.92 23.98
24.36 23.15 24.06 NA
Min
0.00
0.00
0.00
0.00
0.00 NAMax
100.00 100.00 100.00 100.00 100.00 NA
N
596
149
148
156
143
NA
SDG 11 (Sustainable Cities and Communities)
All
2020
2019
2018
2017
2016
Mean
71.95 71.58
71.81 70.81 73.60 NA
Median77.23 77.01
77.43 75.44 80.11 NA
s.d.
17.85 16.76
16.10 15.87 22.10 NA
Min
0.00 17.71
27.76 26.42
0.00 NAMax
100.00 100.00
98.35 97.28 100.00 NA
N
641
166
162
156
157
NA
SDG 12 (Responsible Consumption and Production)
All
2020
2019
2018
2017
2016
Mean
73.10 76.45
77.43 69.24 68.94 NA
Median76.30 81.38
82.80 73.30 73.10 NA
s.d.
16.15 17.45
19.00 12.49 12.51 NA
Min
17.77 17.77
22.17 28.86 24.31 NAMax
99.29 96.21
99.29 93.69 93.57 NA
N
641
166
162
156
157
NA

2016
58.41
59.33
13.80
26.10
84.53
149
2016
NA
NA
NA
NA
NA
NA
2016
NA
NA
NA
NA
NA
NA
2016
NA
NA
NA
NA
NA
NA
2016
NA
NA
NA
NA
NA
NA
2016
NA
NA
NA
NA
NA
NA

39

Communications of the Japan Association of Real Options and Strategy
SDG 13 (Climate Action)
2020
2019
2018
2017
2016
Mean
83.29 86.61
82.33 78.78 NA
Median90.00 90.91
85.61 81.66 NA
s.d.
18.78 13.35
11.65 11.93 NA
Min
13.31 33.41
23.30 30.14 NAMax
99.91 99.43
95.93 95.00 NA
N
166
162
156
157 NASDG 14 (Life below Water)
All
2020
2019
2018
2017
2016
Mean
51.28 60.71 50.51
48.65 45.53 NAMedian51.50 61.24 52.32
48.42 45.81 NA
s.d.
13.43 11.30 15.05
10.15 12.48 NA
Min
5.80
5.80
8.67
10.67 10.53 NA
Max
83.07 83.07 81.30
81.54 75.98 NA
N
526
126
126
156
118
NA
SDG 15 (Life on Land)
All
2020
2019
2018
2017
2016
Mean
62.80 65.88 64.81
60.38 59.86 NA
Median63.01 65.25 65.38
60.74 60.72 NA
s.d.
14.60 14.05 14.64
13.06 15.68 NA
Min
11.08 25.25 23.50
25.66 11.08 NAMax
97.84 97.84 93.31
90.71 90.44 NA
N
641
166
162
156
157
NA
SDG 16 (Peace, Justice and Strong Institutions)
All
2020
2019
2018
2017
2016
Mean
64.89 66.25 66.01
63.84 63.33 NAMedian64.88 67.40 67.44
64.08 63.02 NA
s.d.
13.67 14.03 14.05
13.62 12.78 NA
Min
29.90 29.90 31.07
36.40 31.21 NA
Max
94.65 94.65 93.05
93.09 92.37 NA
N
641
166
162
156
157
NA
SDG 17 (Partnership for the Goals)
All
2020
2019
2018
2017
2016
Mean
63.41 62.15 64.46
62.06 65.02 NA
Median61.93 61.75 63.04
59.77 62.87 NA
s.d.
15.24 14.34 14.79
15.06 16.65 NA
Min
0.00 30.93 27.24
0.00
0.00 NAMax
100.00 100.00 100.00 100.00 100.00 NA
N
641
166
162
156
157
NA

Vol. 12 No.1

Democracy Index (DIt)
All
2020
2019
2018
2017
2016
Mean
5.44
5.36
5.43
5.47
5.46
5.51
Median
5.70
5.72
5.65
5.66
5.69
5.73
s.d.
2.22
2.25
2.25
2.21
2.20
2.21
Min
1.08
1.08
1.08
1.08
1.08
1.08
Max
9.93
9.81
9.87
9.87
9.87
9.93
N
820
164
164
164
164
164
Government Debt / GDP (%, Debtt)
All
2020
2019
2018
2017
2016
Mean
58.20 67.95
56.90 55.59 55.34
55.30
Median49.22 58.91
49.06 48.18 47.44
46.71
s.d.
37.26 42.82
35.90 34.87 35.16
35.85
Min
2.58
2.86
2.58
2.59
2.83
3.00
Max
272.92 272.92 200.35 199.72 202.54 194.60
N
831
165
166
166
167
167
Source: We retrieve SDG indices from Sachs et al. (2016-2020),
GDP per capita (GDPt) from the World Bank, the Democracy
Index (DIt) from The Economist Intelligence Unit, andGovernment Debt / GDP (Debtt) from International Monetary
Fund (IMF),
Note: Sachs et al. (2016) does not report the SDG index for each
goal.

All
82.79
87.04
14.53
13.31
99.91
641

As shown in Panel A of Table 4, mean and median ofeach SDG index do not change significantly each year.
Their standard deviations are also consistent each year.
While the methodologies to calculate the SDG indices are
modified year by year, we can reasonably make use of
these indices to compare with MRPs.
According to Panel B of Table 4, as for GDPt, there
are huge differences between median and mean each year.
It indicates that GDP per capita might not follow thenormal distribution. We exclude the outliers when applying
panel regression models. Details of the method applied to
exclude outliers are explained in section 4.2. On the other
hand, as for DIt, its median and mean values are more orless same for each year. Lastly, as for Debtt, it has
significantly increased in 2020. It could be due to theimpact of COVID-19. We exclude Debtt from the analysisof SDG 1, 2, 5, 6, 8, 10, 13, and 15 as these SDG indices
are not correlated with Debtt. In other words, inclusion of
Debtt does not have the impact on the estimation ofcorrelations between SDG indices and MRPs. Main
purpose of this paper is estimation of these correlations but
not the construction of the statistical model to forecast
these MRPs.
In the next subsection, we run the regression in orderto estimate the correlations between SDG indices and
MRPs.

Panel B: Control Variables
GDP per capita (USD, GDPt)
All
2020
2019
2018
2017
2016
Mean
20,251 20,233 21,431 20,758 19,893 18,939
Median 13,142 13,026 14,099 13,555 12,771 12,224
s.d.
20,717 21,118 21,717 21,150 20,347 19,320
Min
771
771
783
780
774
797
Max 119,415 118,504 119,415 117,245 114,986 113,365
N
912
180
183
183
183
183

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リアルオプションと戦略

4.2 Regression Analysis

Table 5: Results of the Hausman Test (χ2)

In order to analyze the correlation between MRPs
and SDGs indices, we have applied the panel data
regression model. First of all, we have conducted theHausman test to see which is more appropriate, fixed effectmodel or random effect model for all regression
specifications. If the Hausman test rejects the null
hypothesis, results from the fixed effects and the random
effects model are significantly different. In this case,results from fixed effects models are more appropriate.
However, even if the Hausman test does not reject
the null hypothesis, it does not necessarily mean the
random effect model is more appropriate. According toOkui (2005), results of the fixed effects model areappropriate no matter the results of the Hausman test while
results of random effect model are appropriate only if theHausman test does not reject the null hypothesis.
Additionally, statistical tests such as the Hausman test
sometimes do not reject the null hypothesis even if the null
hypothesis is not appropriate, that is, a fixed effect model
could be appropriate. Furthermore, it is hard to prove that
individual-specific effects e.g. country specific effects are
not correlated with explanatory variables although we needthis assumption in order to use a random effects model.
Lastly, a fixed effects model could avoid the omitted
variable bias.
In this paper, if the Hausman test rejects the null
hypothesis at the 5% significance level, we just report theresults of the fixed effects model. If not, we report resultsfrom both fixed and random effects models. In this case, if
both models show the consistent results, we can reasonably
conclude the results are robust. However, if these results
are inconsistent with each other, we could not conclude
what is the true correlation between the SDG index and
MRPs. Results of the Hausman test are reported in Table 5.
Table 5 shows that we conclude the fixed effects
model is appropriate for 65% (35 out of 54) of model
specifications. We report results of both fixed and random
effects models for 19 model specifications; we could notconclude which model is more appropriate.
Additionally, a problem of autocorrelation andcross-sectional dependence is observed in our analysis. We
have corrected these issues using Driscoll and Kraay(1998) Robust Covariance Matrix Estimator. We also
correct the heteroskedasticity using the HC3 estimator
proposed by Davidson and MacKinnon (1993). HC3
estimator of the variance-covariance matrix of theregression coefficient is given by equation (2).

MRP (F) MRP (CR) MRP (CS)SDG Index Score
9.96***
2.86***
8.97*********
SDG 1 (No Poverty)
29.56
92.06
2.44*********
SDG 2 (Zero Hunger)
27.87
24.74
63.44***SDG 3 (Good Health16.50***
24.02***
28.41***and Well-Being)
SDG 4 (Quality of
14.06***
11.37***
182.83***
Education)
SDG
5(Gender
8.32**
22.11***
13.44***
Equalities)
SDG 6 (Clean Water
525.22***
15.19***
57.81***and Sanitation)
SDG 7 (Affordable and8.21***
44.73***
9.16***
Clean Energy)
SDG 8 (Decent Work
7.04***
5.12***
663.76***and Economic Growth)
SDG
9(IndustryInnovation
and50.68*** 2873.20***
40.63***
Infrastructure)
SDG 10 (Reduced
11.70***
12.71***
8.08***
Inequalities)
SDG 11 (Sustainable
Citiesand
7.67***
1.56***
21.29***
Communities)
SDG 12 (Responsible
Consumption
and8.62***
1.71***
81.45***
Production)
SDG
13(Climate75.90***
10.05***
249.35***
Action)SDG 14 (Life below
26.66***
5.76***
5.94***
Water)
SDG 15 (Life on Land)27.71***
83.30***
4.65***
SDG 16 (Peace, Justice11.81***
47.25***
11.24***and Strong Institutions)
SDG 17 (Partnership
10.76***
0.45***
5.24***
for the Goals)
Note: MRP (F) indicates market risk premiums estimated by
Fernandez et al. (2016-2021), MRP (CR) and MRP (CS) aremarket risk premiums estimated by Damodaran and based on thecredit ratings and the credit spreads, respectively. ***, **, and *indicate 1%, 5%, and 10% level of statistical significance,
respectively.

 e2 
−1−1
HC 3 = ( X′X ) X′diag  i  X′ ( X′X ) (1 − hii ) (2)

where, X is an n × (p + 1) matrix of independent
variable values in the regression model including a columnof ones for the regression constant where n is the numberof samples and p is the number of independent variables. ei
is a sample residual. hiis are leverage values and diagonal
elements in the hat matrix, H, defined by equation (3)H = X ( X′X ) X′
−1

(3)

Lastly, we detect outliers of both dependent and41

Communications of the Japan Association of Real Options and Strategy
independent variables conducting Rosner's generalized
extreme Studentized deviate test by setting alpha as 1%.
We remove these outliers for further analysis.
Applying these corrections of heteroskedasticity andcross sectional and time series correlation as well as
removing outliers as mentioned above, we run the
regression using the equation (4).

MRPj ,t =ai , j + β1,i , j SDGi ,t + β 2,i , j GDPt + β 3,i , j DI t
+ β 4,i , j Debtt + β 5,i , j SDGi ,t ×GDPt

Vol. 12 No.1

on the types of the model, fixed or random, and MRPs used
to estimate. These results suggest that improvements ofoverall SDG Index Score at the national level could
improve the corporate value via the reduction of the market
risk premium shown in equation (1). If the coefficient of-0.055 which is estimated by the fixed effect model using
MRP (CR) as in Table 7, one point improvement in overall
SDG score reduces MRP (CR) by 5.5 basis points. In otherwords, if a country or a company in the country wants to
reduce MRP by 1%, overall SDG Index Score has to be
improved by approximately 18 points.

(4)

where, MRPj,t is market risk premium provided byeither Fernandez (MRP (F)), or Damodaran. Damodaranprovides two types of MRPs, a MRP based on credit ratings(MRP(CR)) and the other MRP based on credit spreads(MRP(CS)) for each year, SDGi,t is the overall SDG Index
Score or an SDG index of each goal minus the global
average of overall SDG Index Score or the SDG index ofeach goal for each year, GDPt is GDP per capita (inthousand USD) minus global average of GDP per capita (inthousand USD) for each year, DIt is democracy index
provided by Economists Intelligence Unit for each year,
and Debtt is Government Debt / GDP for each year. We use
the deviation from the average for SDGi,t and GDPt in order
to avoid multicollinearity as we utilize the interaction termof SDGi,t and GDPt.
We include the interaction term of SDGi,t and GDPt
as the impact of SDGs on the MRPs might depend on the
GDP per capita. We exclude Debtt if it is not correlated with
SDGi because our interest is not the creation of the model
which explains the variations in MRPj,t the most but thecorrelations between SDGi,t and MRPj,t and how control
variables affect their correlations.
A summary of the results of the regression are shownin Table 6. Details of results from the fixed effects model
are reported in Table 7. Results from the random effectmodel are reported in Table 8 in case that the Hausman test
does not reject the null hypothesis at 5% level, that is, it
could not conclude that the fixed effects model isappropriate.
According to Table 6, relationships between SDG
indices and MRPs vary depending on the type of MRPs
utilized. However, some SDG indices show the consistent
results no matter the types of MRPs we use.
All correlations between overall SDG Index Scoreand all types of MRPs are negative and statisticallysignificant at least at 5 % level (Table 7 and 8). As shownin Table 7 and 8, coefficients of overall SDGs Index Score
are negative and ranging from -0.036 to -0.121 depending

Table 6: Summary of Results from Regression Model ofSDGs Indices on Market Risk Premiums (MRPs)
SDG Index (β1,i,j)
MRP

Dam
(CR)

Dam
(CS)

Interaction (β5,i,j)
MRP

Dam
(CR)

Dam
(CS)

SDG Index
Score
Neg.
Neg.
Neg.
Pos.
NS
Inc.
SDG 1
Pos.
NS
Neg.
Neg.
NS
NS
SDG 2
NS
NS
Pos.
Neg.
Pos.
Neg.
SDG 3
Neg.
Neg.
Neg.
Pos.
Pos.
Pos.
SDG 4
NS
Pos.
Neg.
NS
Pos.
Pos.
SDG 5
NS
NS
Pos.
NS
NS
Neg.
SDG 6
NS
Neg.
Neg.
NS
NS
NS
SDG 7
NS
Neg.
Inc.
NS
Pos.
Pos.
SDG 8
NS
NS
Pos.
NS
NS
Pos.
SDG 9
Pos.
Pos.
Pos.
Pos.
NS
NS
SDG 10
Pos.
Pos.
Neg.
Neg.
NS
Pos.
SDG 11
NS
Inc.
Pos.
NS
Inc.
NS
SDG 12
NS
Pos.
Pos.
NS
NS
Neg.
SDG 13
Neg.
Neg.
Neg.
Pos.
NS
NS
SDG 14
Neg.
Inc.
Inc.
Neg.
NS
NS
SDG 15
NS
NS
NS
Pos.
Pos.
Inc.
SDG 16
NS
NS
NS
Pos.
Pos.
Pos.
SDG 17
Inc.
Inc.
Inc.
Inc.
Inc.
NS
Note: This table shows the sign of a coefficient of an SDG index
and an interaction term of the SDG index and GDP per capita, e.g.
β1,i,j and β5,i,j in equation (4). If β5,i j is not statistically significant,β1,i,j is estimated by MRPj,t = ai,j + β1,i,jSDGi,t + β2,i,jGDPt + β3,i,jDIt+ β4,i,jDebtt. MRP (F) indicates market risk premiums estimatedby Fernandez et al. (2016-2021), MRP (CR) and MRP (CS) aremarket risk premiums estimated by Damodaran and based on thecredit ratings and the credit spread, respectively. Description of
SDGs are as follow. SDG 1: No Poverty, SDG 2: Zero Hunger,
SDG 3: Good Health and Well-Being, SDG 4: Quality of
Education, SDG 5: Gender Equality, SDG 6: Clean Water andSanitation, SDG 7: Affordable and Clean Energy, SDG 8: Decent
Work and Economic Growth, SDG 9: Industry Innovation andInfrastructure, SDG 10: Reduced Inequalities, SDG 11:
Sustainable Cities and Communities, SDG 12: Responsible
Consumption and Production, SDG 13: Climate Action, SDG 14:
Life below Water, SDG 15: Life on Land, SDG 16: Peace, Justiceand Strong Institutions, and SDG 17: Partnership for the Goals.
Neg. indicates a coefficient of the SDG index or the interaction
term was negative at 5% level of statistical significance, Pos.
indicates the coefficient is positive at 5% level of statisticalsignificance, NS indicates not statistically significant at 5% level,
and Inc. indicates the sign of the coefficient is inconclusive.

42

日本リアルオプション学会 機関誌

Panel D: SDG 3 (Good Health and Well-Being)

Table 7: Fixed Effect Panel Data Regression Model ofSDGs Indices on Market Risk Premium (MRP)
Panel A: Overall SDG Index Score
Overall SDG Index Score
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
Overall SDG Index Score
× GDPts.d.
Fp (F)Adj. R2
N

SDG 3
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 3 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.044*** -0.055*** -0.061***0.020***
0.010***
0.027*********-0.019-0.164-0.091***
***
***
0.004
0.055
0.046***-0.691*** -0.333*** -0.864***0.153***
0.197***
0.239***
***
***
0.011
0.030
0.032***
***
***
0.001
0.013
0.016***
0.001***
0.000***
2.927***
0.014***-0.332***
283

NS
NS
8.704***
0.000***-0.194***538

0.001***
0.002***
2.323***
0.044***-0.288***
320

SDG 4
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 4 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)0.027***
0.000*** -0.037***
***
0.002
0.005***
0.016*********-0.106-0.300-0.157***
***
***
0.016
0.080
0.047*********-0.848-0.533-0.771***
***
***
0.159
0.237
0.123***
NA
NA
NA
NA
NA
NA
***
0.001
NS
NS
0.000***
NSNS
5.498*** 11.861***
4.126***
0.000***
0.000***
0.007*********-0.357-0.262-0.364***
267
475
273

MRP (F) MRP (CR) MRP (CS)
0.007***
0.016*** -0.018***0.006***
0.005***
0.006***-0.018*** -0.327*** -0.246***
0.007***
0.085***
0.060*********-0.948-0.474-1.157***
***
***
0.172
0.262
0.278***
***
***
0.011
0.007
0.016***
***
***
0.002
0.017
0.021***
***
NS
0.003
0.004***
***
NS
0.001
0.001***
4.492***
8.420***
3.148***
0.002***
0.000***
0.009*********-0.365-0.227-0.340***
244
433
262

Panel F: SDG 5 (Gender Equality)
SDG 5
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 5 × GDPt
s.d.
F
p (F)
Adj. R2N

Panel C: SDG 2 (Zero Hunger)
SDG 2
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 2 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.046*** -0.050*** -0.353***0.014***
0.023***
0.100*********-0.162-0.407-0.447***
***
***
0.012
0.072
0.064*********-0.819-0.498-0.821***
***
***
0.167
0.254
0.254***
***
***
0.006
0.002
-0.001***0.002***
0.019***
0.021***
0.005***
0.006***
0.012***
***
***
0.000
0.003
0.003***
***
***
6.284
8.264
8.321***
***
***
0.000
0.000
0.000*********-0.275-0.229-0.182***
245
434
263

Panel E: SDG 4 (Quality Education)

Panel B: SDG 1 (No Poverty)
SDG 1
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 1 × GDPt
s.d.
F
p (F)
Adj. R2N

第 12 巻 第 1 号

リアルオプションと戦略

MRP (F) MRP (CR) MRP (CS)
0.000***
0.022***
0.107***
0.007***
0.010***
0.027***-0.029*** -0.292*** -0.132***0.020***
0.069***
0.056*********-0.758-0.469-0.706***
***
***
0.186
0.255
0.176***
NA
NA
NA
NA
NA
NA
-0.001***0.002*** -0.002***0.000***
0.001***
0.001***
3.638*** 11.635***
4.631***
***
***
0.007
0.000
0.001*********-0.398-0.223-0.321***
273
487
283

43

MRP (F) MRP (CR) MRP (CS)
0.010***
0.008***
0.100***
***
***
0.014
0.009
0.009*********-0.031-0.299-0.142***
***
***
0.012
0.084
0.066***-0.771*** -0.492*** -1.032***
0.170***
0.276***
0.220***
NA
NA
NA
NA
NA
NA
NS
NS
-0.003***NS
NS
0.000***
***
***
3.600
12.792
3.399***
***
***
0.015
0.000
0.010***-0.418*** -0.245*** -0.352***
273
487
283

Communications of the Japan Association of Real Options and Strategy
Panel G: SDG 6 (Clean Water and Sanitation)
SDG 6
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 6 × GDPt
s.d.
F
p (F)
Adj. R2
N

Vol. 12 No.1

Panel J: SDG 9 (Industry Innovation and Infrastructure)

MRP (F) MRP (CR) MRP (CS)
0.001*** -0.025*** -0.040***0.005***
0.007***
0.009*********-0.033-0.293-0.176***
***
***
0.014
0.076
0.062*********-0.763-0.471-0.920***
***
***
0.173
0.281
0.235***
NA
NA
NA
NA
NA
NA
NS
NS
NS
NS
NS
NS
3.522*** 15.421***4.887***
0.016***
0.000***
0.003*********-0.420-0.220-0.339***
273
487
283

Dependent Variable
SDG 9
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 9 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)0.024***
0.072***
0.053***
***
***
0.002
0.018
0.018*********-0.030-0.266-0.165***
***
***
0.006
0.074
0.052*********-0.964-0.549-1.236***
***
***
0.148
0.233
0.271***
0.013***
0.013***
0.016***
0.003***
0.016***
0.023***
***
0.001
NS
NS
0.000***
NS
NS
4.486*** 13.085***
3.162***
0.001***
0.000***
0.015*********-0.337-0.188-0.353***
245
434
263

Panel H: SDG 7 (Affordable and Clean Energy)

Panel K: SDG 10 (Reduced Inequalities)

Dependent Variable
SDG 7
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 7 × GDPt
s.d.
F
p (F)
Adj. R2
N

Dependent Variable
SDG 10
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 10 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.021*** -0.019*** -0.084***0.015***
0.007***
0.013***-0.020*** -0.360*** -0.307***0.009***
0.092***
0.093*********-0.963-0.520-1.322***
***
***
0.165
0.247
0.299***
***
***
0.012
0.003
0.004***
***
***
0.003
0.017
0.021***
***
NS
0.004
0.005***
NS
0.001***
0.002***
4.545***
8.167***
3.300***
***
***
0.002
0.000
0.007*********-0.362-0.231-0.333***
245
434
263

MRP (F) MRP (CR) MRP (CS)0.014***
0.008*** -0.014***0.009***
0.004***
0.006***-0.017*** -0.299*** -0.276***
0.016***
0.087***
0.075*********-0.782-0.494-0.839***
***
***
0.177
0.253
0.211***
NA
NA
NA
NA
NA
NA
-0.001***NS
0.003***
0.000***
NS
0.001***
3.743*** 11.479***
4.948***
***
***
0.006
0.000
0.001*********-0.411-0.275-0.330***
262
463
268

Panel I: SDG 8 (Decent Work and Economic Growth)Panel L: SDG 11 (Sustainable Cities and Communities)

Dependent Variable
SDG 8
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 8 × GDPt
s.d.
F
p (F)
Adj. R2
N

Dependent Variable
SDG 11
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 11 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.007***0.010***
0.024***
***
***
0.011
0.009
0.012*********-0.037-0.291-0.174***0.009***
0.071***
0.062***-0.772*** -0.442*** -0.888***
0.160***
0.240***
0.223***
NA
NA
NA
NA
NA
NA
NS
NS
NS
NS
NS
NS
3.793*** 13.165***
3.376***
0.011***
0.000***
0.019***-0.414*** -0.241*** -0.368***
273
487
283

44

MRP (F) MRP (CR) MRP (CS)
-0.002***0.003***
0.016***
***
***
0.006
0.001
0.005*********-0.020-0.270-0.158***0.008***
0.081***
0.054***-0.945*** -0.522*** -1.238***
0.168***
0.271***
0.279***
***
***
0.012
0.011
0.018***
***
***
0.002
0.016
0.022***
***
NS
0.001
NS
NS
0.000***
NS
4.398***
6.737***
2.575***
0.002***
0.000***
0.039***-0.366*** -0.256*** -0.370***
245
434
263

日本リアルオプション学会 機関誌

Panel M: SDG 12 (Responsible Consumption and
Production)
Dependent Variable
SDG 12
s.d.
GDPt
s.d.
DIt
s.d.
Debtt
s.d.
SDG 12 × GDPt
s.d.
F
p (F)
Adj. R2N

Panel P: SDG 15 (Life on Land)
Dependent Variable
SDG 15
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 15 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.007***0.047***
0.084***
***
***
0.006
0.014
0.025***-0.028*** -0.212*** -0.150***
0.010***
0.055***
0.038*********-0.944-0.459-1.115***
***
***
0.168
0.269
0.259***
***
***
0.011
0.014
0.018***
***
***
0.003
0.017
0.021***
NS
NS-0.001***NS
NS
0.000***
4.532*** 12.339***
4.016***
***
***
0.002
0.000
0.002*********-0.362-0.198-0.309***
245
434
263

Dependent Variable
SDG 16
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 16 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.037*** -0.031*** -0.027***0.005***
0.012***
0.014*********-0.032-0.312-0.192***
***
***
0.018
0.082
0.065***-0.907*** -0.596*** -1.056***
0.161***
0.220***
0.227***
NA
NA
NA
NA
NA
NA
0.001***
NS
NS
0.000***
NSNS
6.782*** 18.998***
4.235***
0.000***
0.000***
0.006***-0.313*** -0.188*** -0.351***
273
487
283

Panel O: SDG 14 (Life below Water)
Dependent Variable
SDG 14
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 14 × GDPt
s.d.
F
p (F)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.004*** -0.006*** -0.020***0.007***
0.004***
0.014*********-0.043-0.311-0.189***
***
***
0.012
0.080
0.067*********-0.757-0.495-0.917***
***
***
0.176
0.273
0.223***
NA
NA
NA
NA
NA
NA
0.0004*** 0.0006*** 0.0006***
0.0001*** 0.0003*** 0.0002***3.039***
9.977***
2.250***
***
***
0.019
0.000
0.065*********-0.416-0.243-0.382***
273
487
283

Panel Q: SDG 16 (Peace, Justice and Strong Institutions)

Panel N: SDG 13 (Climate Action)
Dependent Variable
SDG 13
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 13 × GDPt
s.d.
F
p (F)
Adj. R2N

第 12 巻 第 1 号

リアルオプションと戦略

MRP (F) MRP (CR) MRP (CS)
-0.004*** -0.005***
0.002***
***
***
0.002
0.013
0.003*********-0.021-0.266-0.175***
***
***
0.006
0.072
0.060*********-0.912-0.394-0.452***
***
***
0.198
0.269
0.162***
***
***
0.003
-0.010-0.016***0.003***
0.015***
0.014***-0.0002***
NS
NS
***
0.0001NS
NS
3.021***
5.032***
1.405***
0.013***
0.001***
0.235*********-0.430-0.369-0.458***
220
364
241

45

MRP (F) MRP (CR) MRP (CS)
-0.004*** -0.011*** -0.021***0.005***
0.010***
0.016***-0.031*** -0.301*** -0.195***
0.008***
0.085***
0.057*********-0.912-0.439-1.013***
***
***
0.162
0.304
0.296***
***
***
0.014
0.010
0.015***
***
***
0.003
0.017
0.023*********
0.00050.001
0.001*********
0.00020.000
0.001***
3.677***
7.054***
2.109***
0.004***
0.000***
0.066*********-0.368-0.250-0.375***
245
434
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Communications of the Japan Association of Real Options and Strategy

Vol. 12 No.1

Panel R: SDG 17 (Partnership for the Goals)

Panel B: SDG 1 (No Poverty)

Dependent Variable
MRP (F) MRP (CR) MRP (CS)SDG 170.013*** -0.008*** -0.006***s.d.
0.014***
0.004***
0.005***
***
***
GDPt
-0.016-0.277-0.167***
***
***
s.d.
0.006
0.080
0.056***
***
***
DIt
-0.992-0.503-1.172***
***
***
s.d..
0.189
0.291
0.276***
Debtt
0.013***
0.009***
0.017***
s.d.
0.004***
0.017***
0.022*********
SDG 17 × GDPt
-0.00020.0001NS
s.d.
0.0004*** 0.0003***NSF
3.693***
6.640***
2.466***p (F)0.003***
0.000***
0.047***
2******
Adj. R-0.367-0.258-0.373***
N
245
434
263
Note: This table shows the parameter estimates from the fixed
effect panel regression models shown in equation (4). If a
coefficient of the interaction term of SDGi,t and GDPt, β5,i,j inequation (4), is not statistically significant and if Debtt iscorrelated with SDGi, β1,i,j is estimated by MRPj,t = a + β1,i,jSDGi,t+ β2,i,jGDPt + β3,i,jDIt + β4,i,jDebtt. If Debtt is not correlated with
SDGi,t, β1,i,j is estimated by MRPj,t = ai,j + β1,i,jSDGi,t + β2,i,jGDPt+ β3,i,jDIt. We use three types of market risk premiums, MRP (F),MRP (CR), and MRP (CS), as dependent variables. MRP (F)indicates market risk premiums estimated by Fernandez et al.
(2016-2021). MRP (CR) and MRP (CS) are market risk premiums
estimated by Damodaran and based on the credit ratings and thecredit spread, respectively. NA indicates correlation between
Debtt and SDGi,t is not statistically significant at 5% level, thus,
Debtt is excluded from the regression model. NS indicates a
coefficient of the interaction term of SDGi,t and GDPt is notstatistically significant at 5% level, thus, the interaction term is
excluded from the model. In case that the Hausman test does not
reject the null hypothesis and β5,i,j of the random effect model isstatistically significant at 5% level, the interaction term is
included in the fixed model.

MRP (CS)
Overall SDG Index Score-0.055***s.d.
0.012***
GDPt-0.045***s.d.
0.005***
DIt-0.342***s.d..
0.052***
Debtt
NA
s.d.
NA
Overall SDG Index Score
× GDPtNS
s.d.
NSIntercept
11.150***s.d.
0.874***
2
χ
103.455***2
p (χ )
0.000***
Adj. R2
0.35516***
N
273

Panel C: SDG 7 (Affordable and Clean Energy)
SDG 7
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 7 × GDPt
s.d.
Intercepts.d.
χ2
p (χ2)
Adj. R2N

Table 8: Random Effect Panel Data Regression Model ofSDGs Indices on Market Risk Premiums (MRP)
Panel A: Overall SDG Index Score
Overall SDG Index Score
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
Overall SDG Index Score
× GDPts.d.
Intercepts.d.
χ2
p (χ2)
Adj. R2NMRP (F) MRP (CR) MRP (CS)
-0.036*** -0.046*** -0.121***0.018***
0.015***
0.051*********-0.055-0.094-0.070***
***
***
0.004
0.009
0.007*********-0.342-0.364-0.415***
***
***
0.085
0.103
0.079***
0.004***
0.017***
0.013***
0.006***
0.012***
0.005***MRP (F) MRP (CS)
-0.004***0.021******
0.007
0.023******-0.047-0.134******0.004
0.037******-0.363-0.563******0.085
0.099******
0.006
0.012***
0.005***
0.006***
NS
0.004***
NS
0.001******
10.518
10.639******0.617
0.942******
90.33098.087******0.000
0.000******
0.534
0.344***
245
263

Panel D: SDG 8 (Decent Work and Economic Growth)
SDG 8
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 8 × GDPt
s.d.
Intercepts.d.
χ2
p (χ2)
Adj. R2N

0.002***
NS
0.005***
***
0.000
NS
0.001*********
10.48011.85010.540***
***
***
0.642
1.123
0.732*********
86.642200.026138.081***
***
***
0.000
0.000
0.000***
0.515***
0.326***
0.400***
283
538
320

46

MRP (F) MRP (CR)-0.013*** -0.017***0.009***
0.013******-0.046-0.093******0.003
0.010******-0.300-0.410******0.078
0.094***
NA
NA
NA
NA
NS
NS
NS
NS
10.424*** 12.832***
0.539***
1.301******
96.235180.814***
***
0.000
0.000******
0.530
0.312***
273
487

日本リアルオプション学会 機関誌
Panel E: SDG 10 (Reduced Inequalities)
SDG 10
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 10 × GDPt
s.d.
Intercepts.d.
χ2
p (χ2)
Adj. R2N

Panel H: SDG 14 (Life below Water)

MRP (CS)-0.017***0.005***-0.070***0.017***-0.536***0.103***
NANA
0.002***
0.000***
11.632***
1.118***
76.549***0.000***
0.307***
268

Dependent Variable
SDG 14
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 14 × GDPt
s.d.
Intercepts.d.
χ2
p (χ2)
Adj. R2N

Panel F: SDG 11 (Sustainable Cities and Communities)
SDG 11
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 11 × GDPt
s.d.
Intercepts.d.
χ2
p (χ2)
Adj. R2NMRP (CR) MRP (CS)
-0.019*** -0.021***0.003***
0.005******-0.098-0.061******0.008
0.008******-0.495-0.472******0.062
0.084******
0.016
0.007***
0.010***
0.006***
NS
NS
NS
NS
12.548*** 11.225***0.849***
1.072******
167.866
68.501******0.000
0.000******
0.406
0.314***
364
241

Panel I: SDG 15 (Life on Land)

MRP (F) MRP (CR)0.000*** -0.014***0.002***
0.005******-0.048-0.097******0.003
0.009******-0.371-0.487******0.066
0.080******
0.006
0.017******
0.005
0.010***
NS
0.0004***NS
0.0001******
10.54412.374******0.562
1.071******
89.126
187.453***
***
0.000
0.000******
0.532
0.341***
245
434

Dependent Variable
SDG 15
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 15 × GDPt
s.d.
Intercepts.d.
χ2
p (χ2)
Adj. R2N

Panel G: SDG 12 (Responsible Consumptions andProduction)
SDG 12
s.d.
GDPt
s.d.
DIt
s.d..
Debtt
s.d.
SDG 12 × GDPt
s.d.
Intercepts.d.
2χp (χ2)
Adj. R2N

第 12 巻 第 1 号

リアルオプションと戦略

MRP (F) MRP (CR)-0.015***0.023******
0.009
0.013******-0.056-0.085***0.010***
0.005***-0.395*** -0.480***
0.095***
0.073******
0.005
0.018******
0.004
0.010***
NS
NS
NS
NS
10.722*** 12.279***
0.665***
1.024******
93.013
191.643***
***
0.000
0.000******
0.537
0.346***
245
434

47

MRP (CS)
0.003***
0.003***-0.064***0.009***-0.445***0.097***
NANA-0.0004***
0.0002***
11.423***1.228***
70.274***0.000***
0.289***
283

Communications of the Japan Association of Real Options and Strategy
Panel J: SDG 17 (Partnership for the Goals)Vol. 12 No.1

corporate value by the reduction of the market risk
premium.
Table 7 shows that regression coefficients of SDG 3(Good Health and Well-Being) on MRPs are -0.046 for
MRP (F), -0.050 for MRP (CR), and -0.353 for MRP (CS).
Magnitudes of the impact of improvements in the SDG 3
are widely varied. Managers of the corporation considering
to pursue SDG 3 need to carefully estimate its impact on
the reduction in the risk premium of the corporation.
Additionally, as for SDG 3 (Good Health and WellBeing), the level of GDPt is correlated with the impact ofSDGi on MRPs as the coefficients of interaction terms(β5,i,j) in equation (4) are positive and statisticallysignificant according to Table 7 and 8. Equation (5) shows
how the impact of SDGi,t on MRPs varied depending on thelevel of GDPt.

Dependent Variable
MRP (F) MRP (CR) MRP (CS)SDG 170.015***
0.014***
0.010***
***
***
s.d.
0.004
0.003
0.003***
***
***
GDPt
-0.049-0.100-0.065***
***
***
s.d.
0.003
0.009
0.010***
***
***
DIt
-0.385-0.511-0.529***
***
***
s.d..
0.074
0.092
0.102***
Debtt
0.006***
0.018***
0.012***
s.d.
0.005***
0.010***
0.005*********
SDG 17 × GDPt
-0.0003-0.0004NSs.d.
0.0001*** 0.0001***NSIntercept
10.614*** 12.500*** 11.366***s.d.
0.604***
1.133***
1.159***
2
***
***
χ
94.916189.584
76.381***2******
p (χ )
0.000
0.000
0.000***
Adj. R2
0.541***
0.343***
0.305***
N
245
434
263
Note: This table shows the parameter estimates from the random
effect panel regression models shown in equation (4). These
parameters are reported in the table only when the Hausman test
does not reject the null hypothesis, that is, only when the fixed
effect model is not necessarily appropriate. If a coefficient of theinteraction term of SDGi,t and GDPt, β5,i,j in equation (4), is notstatistically significant and if Debtt is correlated with SDGi,t, β1,i,j
is estimated by MRPj,t = ai,j + β1,i,jSDGi,t + β2,i,jGDPt + β3,i,jDIt+ β4,i,jDebtt. If Debtt is not correlated with SDGi,j, β1,i,j is estimatedby MRPj,t = ai,j + β1,i,jSDGi,t + β2,i,jGDPt + β3,i,jDIt. We use three
types of market risk premiums, MRP (F), MRP (CR), and MRP(CS), as dependent variables. MRP (F) indicates market risk
premiums estimated by Fernandez et al. (2016-2021). MRP (CR)and MRP (CS) are market risk premiums estimated byDamodaran. MRP (CR) and MRP (CS) are estimated based on thecredit ratings and the credit spread, respectively. NA indicatescorrelation between Debtt and SDGi,t is not statisticallysignificant at 5% level, thus, Debtt is excluded from theregression model. NS indicates a coefficient of the interaction
term of SDGi,t and GDPt is not statistically significant at 5% level,
thus, the interaction term is excluded from the model.

()

MRPj ,t =ai , j + β1,i , j + β 5,i , j GDP SDGi ,t + β 2,i , j GDPt
+ β 3,i , j DI t + β 4,i , j Debtt(5)

That is, the negative impact of SDG 3 (Good Healthand Well-Being) on MRP is diminishing as the magnitude
of GDPt is increasing. According to equation (5), if GDPtis higher than - (β1,i,j / β5,i,,j), the impact of SDG 3 on MRPs
will be positive. As GDPt is defined as the deviation fromthe average GDP per capita of all countries for each year,
the average in 2020 is 20,233 USD as shown in Table 4,
and - (β1,i,j / β5,i,,j) for MRP (F), MRP (CR), and MRP (CS)are -8.435, -9.064, and -28.804, respectively, thresholds ofthe GDP per capita for MRP (F), MRP (CR), and MRP (CS)are 28,658 USD, 29,287 USD, and 49,027 USD,
respectively. GDP per capita of Japan in 2020 is 41,732
USD. As this value is bigger than the threshold estimatedby the models using MRP (F) and MRP (CR) but smaller
than the model using MRP (CS). Implications of the
pursuant of SDG 3 for Japanese corporations have to befurther analyzed. The improvement in SDG 3 could reduce
MRPs in developing countries whose GDPt is less than
28,658 USD but the improvement in SDG 3 could increase
MRPs in developed countries whose GDPt is higher than
49,027 USD.
On the other hand, the correlations between SDG 9(Industry Innovation and Infrastructure) and MRPs are
positive. That indicates that the higher SDG 9 score, thehigher the market risk premiums. SDG 9 score consists ofinvestment in research and development. We could inferthat these countries achieving higher SDG 9 grow further
by taking risks. In other words, the increase in theeconomic growth of the country could be bigger than the

While we need further analysis to conclude whether
there is causality between SDG indices and MRPs, we canreasonably say if the corporation could commit to improveoverall SDG Index Score in the country, its corporate value
could be increased by the reduction of the market risk
premium.
However, we could not conclude whether the impactof overall SDG Index Score on MRPs depends on thelevel of GDPt as the coefficient of the interaction term,
β5,i,j in equation (4), could be negative, positive, or
neutral depending on the types of MRPs used. Impact ofoverall SDG Index Score could be inflated or deflated,depending on the level of GDPt.
Similarly, the relationship between SDG 3 (Good
Health and Well-Being) & SDG 13 (Climate Action) and
MRPs are negative (Table 6-8). These correlations arerobust. A corporation which commits to improve the healthof people or solve climate issues could improve their48

日本リアルオプション学会 機関誌

第 12 巻 第 1 号

リアルオプションと戦略

increase in the MRPs. We would like to leave the analysisof the correlation between economic growth of the country
and SDGs for future research.
Table 6 also shows that SDG 15 (Life on Land) &SDG 16 (Peace, Justice and Strong Institutions) are notcorrelated with MRPs. Companies could commit activities
related to SDG 15 and 16, however, managers of these
corporations should understand these activities are notnecessarily improve the corporate value by the reductionof the market risk premium. That is, these managers need
the consensus from the investors if these activities arecostly and could damage their corporate values.
Additionally, as for SDG 16 (Peace, Justice andStrong Institutions), the coefficients of interaction (β5,i,,j) inequation (4) and (5) are positive and statistically significant
according to Table 6 and 7. That is, if GDPt is higher(lower), the impact of SDG 16 on MRPs would be morepositive (negative). - (β1,i,,j / β5,i,,j) for MRP (F), MRP (CR),and MRP (CS) are 9.452, 9.305, and 32.503, respectively.
Thresholds of GDP per capita for MRP (F), MRP (CR), and
MRP (CS) are 29,685 USD, 29,538 USD, and 52,736 USD,
respectively. It could imply a developing country whose
GDP per capita is less than 29,685 USD is willing to pursue
a peaceful society while a developed country whose GDP
per capita is higher than 52,736 USD could prefer lesspeaceful situations as they reduce the market risk premium.
The development of the financial market could help toachieve a peaceful situation for developing countries while
we need some schemes in addition to the financial market
to achieve SDG 16 for developed countries. This result is
consistent with Jha and Shayo (2019) which show that
increase in the exposures to financial assets shifts thevoting behavior towards parties pursuing peace fromexperiments in Israeli.
We need further analysis to conclude the relationshipbetween MRPs and SDG 1,2, 4-8, 10-12, 14, and 17.

score is positively correlated with MRP. We need further
empirical analysis to conclude the impact of SDG 1, 2, 48, 10-12, 14, and 17 on MRPs
Thus, pursuant of SDGs improving overall SDG
Index Score and SDG 3 (Good Health and Well-Being) &13 (Climate Action) scores in the country could improvethe corporate value by the reduction of MRPs. We would
like to study further what exactly kind of activities could
improve the overall SDG Index Score and SDG 3 & 13scores and result in improvement of the corporate value.

Reference
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6.

5. Conclusion
This paper analyzes the correlation between MRPs
and the SDG indices to see whether the contributions to
SDGs could improve the corporate value or not. Ourfindings suggest that there is strong negative correlation
between overall SDG Index Score and MRPs. Furthermore,
SDG 3 (Good Health and Well-Being) & 13 (Climate
Action) scores are negatively correlated with MRPs. On
the other hand, SDG 15 (Life on Land) & 16 (Peace, Justiceand Strong Institutions) scores are not correlated with
MRPs and SDG 9 (Industry Innovation and Infrastructure)

7.

8.

49

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50

日本リアルオプション学会 機関誌

リアルオプションと戦略

第 12 巻 第 1 号

学会だより


JAROS2022 研究発表大会について
日時： 2022 年 12 月 3 日、4 日 （土日 2 日間 開催）
場所： 東京理科大学神楽坂キャンパスもしくはオンライン（ハイブリッド）
大会実行委員長： 東京理科大学 髙嶋 隆太 教授
早割 受付中【大会参加】 （早割：11/6 日まで）
一般研究報告セッション：エントリー受付中（9/12 月まで）
（一般研究報告は、既に他の学会等で発表したものでも、発表可能です。）

編集後記
リアルオプション学会の機関誌「リアルオプションと戦略」第 12 巻第 1 号をお届けいたします。まず、第11 巻第 2 号が 2020 年 3 月に刊行されてから 2 年以上経っての発行となったことにつきまして、深くお詫びを申し上げます。機関誌をお読み頂いている会員の方々、論文を投稿頂いた方にご迷惑をお掛けしまして申し訳ございませんでした。今後は年に 2 回をめどに定期的に刊行できるように体制を整えて参りたく存じますので引き続きお力添えを賜ることができましたら幸甚です。
本号は、初めての試みとして”Real Options in Global Perspectives”というテーマで、英語で執筆された査読論
文 4 編を刊行致しました。リアルオプション学会の取り組みを国内外に発信することにより、リアルオプシ
ョン学会会員の方々の便益を向上することができればと考えております。ご高見がございましたら編集委員
会までお知らせ頂ければ幸甚です。
刊行が大幅に遅れましたこと深くお詫び申し上げます。今後も会員の方々に資するリアルオプション関する学術あるいは実務的な論文や論考を定期的に刊行して行きたく存じます。引き続きましてご厚誼の程、何卒宜しくお願い致します。
担当編集委員 伊藤晴祥

日本リアルオプション学会法人会員リスト
日本リアルオプション学会は以下の法人の方からのサポートを受けています。感謝申し上げます。

株式会社 シーエスデー
株式会社 アーク情報システム
株式会社 構造計画研究所
日本管理センター 株式会社株式会社 サンセイランディック
株式会社 翻訳センター
ダイドーグループホールディングス 株式会社株式会社 大和コンピューター
日東精工 株式会社株式会社 エフアンドエム
株式会社 エムティーアイ
モリト 株式会社

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᪥ᮏࣜ࢔ࣝ࢜ࣉࢩࣙࣥᏛ఍ ᶵ㛵ㄅࣜ࢔ࣝ࢜ࣉࢩࣙࣥ࡜ᡓ␎

➨12ᕳ ➨1ྕ

2022ᖺ8᭶31᪥ Ⓨ⾜(ᶵ㛵ㄅ⦅㞟ጤဨ఍)ጤဨ㛗㸸ఀ⸨ᬕ㷋ጤဨ 㸸㧘᳃ᐶࠊబ⸨Ύ࿴ࠊ᳃ᖹ∝୍㑻൅ߨॶ᪥ᮏࣜ࢔ࣝ࢜ࣉࢩࣙࣥᏛ఍

THE JAPAN ASSOCIATION OF REAL OPTIONS AND STRATEGY஦ົᴗົᢸᙜ㸸
ࠛ104-0033
ᮾி㒔୰ኸ༊᪂ᕝ2-22-4 ᪂ඹ❧ࣅࣝ2F
㟁ヰ㸸 03-3551-9893 FAX: 03-3553-2047

Reviewed Papers, Vol. 12, No. 1
Real Options and the Design of Global Public Policy Interventions［Linda Peters］

2

An Event Study of the Impact of Corporate Social Responsibilityon the Corporate Values: A Case of Kao Corporation［Miho Tanaka］

12

Small Businesses’Awareness, Experience and Attitude towards Microfinancein Asmara, Eritrea［Fiyori Afeworki Negash］
22
On the Correlation between Market Risk Premiums and SDGs:
Implications to Corporate Value［Haruyoshi Ito］

http://realopn.jp

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