does global integration make firms more socially...

DOES GLOBAL INTEGRATION MAKE FIRMS MORE SOCIALLY RESPONSIBLE?

THE EFFECT OF INTERNATIONAL TRADE NETWORKS ON STATE AND PRIVATE FIRM

CORPORATE SOCIAL RESPONSIBILITY SIGNALING

Luis Alfonso Dau

Northeastern University

309 Hayden Hall, Boston MA 02115

1-803-381-6204

[email protected]

Elizabeth M Moore


918 Renaissance Park, Boston MA 02115

1-508-369-4034

[email protected]

Margaret A Soto


918 Renaissance Park, Boston MA 02115

1-571-405-1397

[email protected]

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DOES GLOBAL INTEGRATION MAKE FIRMS MORE SOCIALLY RESPONSIBLE?

THE EFFECT OF INTERNATIONAL TRADE NETWORKS ON STATE AND PRIVATE FIRM

CORPORATE SOCIAL RESPONSIBILITY SIGNALING

This study examines the effects of global integration on firm-level corporate social responsibility

signaling. Building on complex interdependence theory and institutional theory, we propose that

increased global trade networks encourage firm-level corporate social responsibility signaling, resulting

from the heightened pressures of complex interdependence. Further, we argue that these global trade

networks encourage greater signaling from state firms than private firms, because they increase the

accountability of state firms to supranational institutions, whereas private firms are already accountable to

the state and domestic institutions. Analyses from 11,990 firms and 153 countries from 2000 to 2014

provide robust support for the arguments.

Keywords: corporate social responsibility; state-owned firms; institutional theory; complex

interdependence theory; international trade networks; global trade integration; globalization

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INTRODUCTION

As the world continues to globalize and trade networks increase among nations, the distance between

borders and economies continues to shrink (Cerny, 1996; Kwon, 2012; Stohl, 2004). Simultaneously, as

markets converge, governments are forced to recognize the realities of interdependence and integration as

a means to combat increasingly global problems such as the Great Recession, Ebola, Zika, environmental

pollution, global warming, and terrorism (Baden et al., 2014; Leipziger & Canuto, 2012). In an effort to

coalesce divergent state interests towards a common goal, global organizations such as the United

Nations, the World Trade Organization, and the World Bank are gaining attention and importance in both

the practical and scholarly worlds (Diehl & Frederking, 2010; Pegram, 2010). These transnational

organizations and their corresponding networks are combining to form global (or supranational)

institutions, establishing international rules to which states adhere and willingly relinquish portions of

their sovereignty (Bohman, 1998; Nye & Keohane, 1971; Singh, 2012). As states continue to join these

organizations and international networks, it is important to examine their impact on domestic actors,

including firms. This is particularly critical in light of the recent growth of anti-globalization sentiments

and debates that have sparked across the globe.

Amidst the re-shifting of global norms towards an emphasis on the triple bottom line (Elkington,

1998; Gimenez, Sierra, & Rodon, 2012; Hacking & Guthrie, 2008), corporate social responsibility (CSR)

is becoming a focal point for scholars of international business and international relations. However, there

has been limited focus on the effects of global integration on firm CSR and how this effect varies for

different types of firms. This is unfortunate because in light of globalization, it is critical to understand the

accountability structures to which firms are held.

We address this gap in the literature by asking: what impact does country-level integration into

global trade networks have on firm-level CSR signaling? In other words, does increased international

integration motivate companies to signal positive CSR intentions, and further, does it have an impact on

the timing and quality of the signal? Additionally, what impact does state ownership have on firm CSR

signaling in response to this integration?

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This paper contributes to the growing dialogue on CSR signaling and sustainable development in

a post Great Recession world. By combining institutional theory (Acs, Desia, & Hessels, 2008; North,

1990) with complex interdependence theory (Holsti, 1978; Keohane & Nye, 2001), this article examines

the impact that global networks have on firm-level signaling behavior and adds to institutional theory

through the dynamics of CSR signaling. By utilizing the complex interdependence framework for firms –

a theoretical framework that has received remarkably little attention in the international business literature

– this paper contributes to international business theory by introducing a discussion of global (or

supranational) institutions as opposed to country-level institutions, which are most commonly assessed in

the international business literature. Further, this article supplements the literature on state ownership

(Lawson, 1994) by suggesting that while national-level policies and institutions primarily serve to hold

privately owned companies accountable, international organizations and institutions are necessary to hold

state firms accountable as globalization continues.

The remainder of this article is organized as follows. Section two begins with a brief discussion

of global integration, specifically within trade networks, and CSR signaling in an increasingly globalizing

world. It then develops the theoretical arguments and hypotheses for the relationship between global

integration and firm-level CSR signaling. Section three describes the methodology, while section four

provides the results and robustness tests. Using firm level data from the United Nations Global Compact

Initiative spanning from 2000-2014 in junction with data from the World Trade Organization and United

Nations World Development Indicators database, these sections examine the effects of integration on

CSR signaling using various time-panel models and measures. In section five, the article concludes with

an analysis of the findings and a discussion of the different ways that the hypotheses and theories put

forth contribute to the literature and may be utilized in future research.

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CORPORATE SOCIAL RESPONSIBILITY SIGNALING AND GLOBAL INTEGRATION

Global Integration and National Trade Networks

Globalization is not a new phenomenon, it has been discussed for several decades in an array of different

disciplines, including international business, economics, sociology, and political science (Held, 1997;

Lechner & Boli, 2005; Stohl, 2004). As the world continues to globalize, nations inherently become more

interconnected and reliant on each other (Bhagwati, 2004; Frenk, Gómez-Dantés, & Moon, 2014; Guillen,

2001). This interconnectedness results in a smaller political and economic distance between countries,

despite geographic distance (Drake, 2010; Kwon, 2012; Stohl, 2004).

Through the lens of complex interdependence theory, stemming from international relations, it is

suggested that as the world globalizes and the economic and political distance between nations continues

to shrink, either through willingness or added pressures, countries work more closely with each other

(Keohane & Nye, 1978, 2000). Working together leads to more interactions and network ties among

countries. In response to these increased interactions, transnational organizations have been on the rise in

an attempt to create order and stability among states (Coicaud & Heiskanen, 2001; Iriye, 2004; Womack,

2010). For example, although states may agree that global solutions are needed for pollution and

environmental hazards or to prevent future wars from being fought, organizations like the United Nations

and the World Bank have been formed to maintain order and provide direction among nations and their

differing interests during the negotiation process (Park, 2005; Snidal, 1992; Williams, 2008). Although

these organizations are often criticized for “not having teeth,” (e.g., not being able to actually enforce any

legislation or rules created within them) scholars agree that these organizations, as demonstrated through

states’ willingness to join them, have a crucial hand in shaping the newly forming global institutions

(Kahler, 2013; Pegram, 2010; Reimann, 2006).

As famously asserted by North (1990), institutions set the rules of the game – they are guidelines

for actors to follow. Institutions can be formal (e.g., codified or written rules, laws, and regulations) or

informal (e.g., non-codified or unwritten rules such as shared values and beliefs) (North, 1990). This

paper focuses on formal institutions. These formal rules do not necessarily have to be followed and this

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does not suggest that certain actors do not break the rules. Nevertheless, the establishment of the rules is

critical as it creates a mechanism through which actors within the ‘game’ can attain legitimacy by being

held accountable and meeting expectations (Dimaggio & Powell, 1983; Scott, 1995; Suchman, 1995).

Institutions create benchmarks and help other actors identify who is, and who is not, following the rules at

any given time or place.

Complex interdependence theory asserts that inter-state accountability and embeddedness

increases as globalization foments increased formal and informal ties of varying weights across borders

(Holsti, 1978; Keohane & Nye, 2001; Wilde, 1991). As accountability increases, both economically and

politically, the chance for war and mutual destruction decreases, predicated on the assumption that states

are rational, self-interested actors (Keohane & Nye, 2000; Lechner & Boli, 2005; Oviedo, 2013). In other

words, it would be in a states’ best interest to promote the success and well being of another state if it is

economically and politically accountable to that state (Cerny, 1996; Gartzke & Westerwinter, 2016; Zhao

& Liu, 2010).

There has been significant debate within the fields of political science and international relations

regarding the motivations of states and whether or not complex interdependence implies that states are

seeking to benefit global interests or primarily their own national interests (Frenk et al., 2014; Hay, 2004;

Zurn, 2002). While this discussion deserves a space and a dialogue, it is outside the scope of this

particular research project. Regardless of the antecedent motivation for states to have increased interest

and accountability among each other, it is important to look at the impacts that these linkages and

networks have on actors (in this particular case firms) within states.

Theoretical Justification for Assessing Global Integration and Corporate Social Responsibility

Signaling

With no end to globalization in sight (Mahtaney, 2013; Stiglitz, 2012), it is imperative to understand its

different and complex outcomes. There are both positive and negative effects of globalization (Cleveland,

Rojas-Mendez, Laroche & Papadopoulos, 2015; Stallings, 2007). Negative effects range from damaging

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pollution, financial crises that result in the domino effect around the world, global health epidemics like

Zika and Ebola that spread rapidly among nations and through borders, and anti-immigration/anti-

globalization sentiments among those who lose their jobs to immigrant workers and foreign trade (Baden

et al., 2014; Kennedy & Nisbett, 2015; Wheeler, 2001). Positive effects include increased cooperation

across borders, spikes and upswings in global employment and migration over state lines, and cross-

cultural interaction leading to more innovation and creativity (Obstfeld & Taylor, 2004; Thorpe &

Prakash-Mani, 2003).

As a result of these consequences, both negative and positive, global norms have become

increasingly focused on the preservation and improvement of the triple bottom line of “people, planet and

profit” (Donno, 2010; Gimenez et al., 2012; Hacking & Guthrie, 2008). More than ever before,

environmental issues, labor protection rights, and fair and free trade have been gaining recognition and

becoming a focal point on the global agenda (Doh & Guay, 2006; Kahler, 2013). Countless summits

between state leaders and diplomats have been held, rallies among millions of individuals and groups

have been started, and international treaties have been signed with the intention of promoting social

responsibility and fomenting a better relationship between people, corporations, and the planet (Nikolaeva

& Bicho, 2011; Strong, 2003; Tashman & Rivera, 2010).

Global interactions, in the form of trade networks between states continue to grow both in terms

of volume and scope. New trade relations are continuously formed and deepened across countries, over

different types of goods and services, resulting in the shrinking of distance between nations despite

geography. As these interactions increase, global issues gain more attention, more resources, and more

time devoted to them. In other words, as global institutions have become increasingly prominent, social

responsibility has gained a stronger position in shaping the rules that states, and the actors within those

states, must follow if they want to participate and have a positive reputation in the international arena

(Milner, 1998; Nikolaeva & Bicho, 2011; Nikolaou, Evangelinos, & Allan, 2013).

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The Impact of Global Integration on Corporate Social Responsibility Signaling

Signaling theory attempts to make sense of an actor’s decision to put forth information for others to

receive (Bliegebird et al., 2005; Connelly & Certo, 2011). A critical assumption behind signaling theory

is that signalers send signals to receivers in order to reduce asymmetric information and risk among the

actors involved (Stiglitz, 2002; Taj, 2016). There are a myriad different payoffs that signalers can receive

for sending these signals out to other actors (Bliegebird et al., 2005; Spence, 2002; Stiglitz, 1985). Within

international business and economics, profit as a payoff has been a focal point. The underlying

assumption, stemming from classic rational choice economics, is that firms are self-interested profit

maximizing entities that make decisions for profit first (Schumpeter, 1947; 1949). From this perspective,

signals are used to gain investments and to signal the strength of a company or product to consumers in

hopes of increasing sales and reputation (Fischer & Reuber, 2007; Reuer & Ragozzino, 2012; Stiglitz,

2000). Through these signals, a firm can reduce perceived risks by defusing information and knowledge

among different receivers (Dale Stoel & Muhanna, 2012; Ndofor & Levitas, 2004). As globalization

proliferates and interdependence among states increases, however, it is important to examine other

potential sources, goals, and foci of the signals that firms send out.

Complex interdependence theory suggests that the economic and political distance between states

is shrinking despite actual geographic distances, and provides a framework for understanding how states

will operate in a globalizing world (Keohane & Nye, 2001; Mahtaney, 2013). One critical actor within

complex interdependence theory is an international organization. This is an organization made up of

different nations as members that function to address transnational issues and problems (Coicaud &

Heiskanen, 2001; Womack, 2010). When states join international organizations, such as the United

Nations, the World Bank, and the World Trade Organization, they willingly concede a portion of their

sovereignty to the organization through a shared commitment to work with other states towards collective

global objectives (Fang & Stone, 2012; Park, 2005; Roberts, 2008).

These organizations help frame global institutions and norms based on the collective desires of

the nations of which they are comprised (Bohman, 1998; Kahler, 2013). In other words, these

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organizations play a critical role in setting the global agenda for all actors. Although these organizations

face criticism for the lack of enforcement mechanisms, they do effectively promote accountability,

legitimacy, and trust through transparency (Frenk et al., 2014; Stoddard, 2002). For example, if a state

breaks a rule that the United Nations sets, even though the United Nations may not be able to enforce

punishment in the form of fines, it does have the ability to communicate the infraction to other states

(Churie, Sjostedt, & Corell, 2005; Nikolaeva & Bicho, 2011). As globalization deepens both the formal

and informal relations among states, international organizations are also influencing actors within states:

firms. Although firms do not directly join international organizations, they are embedded within the

networks created around the international organizations. Consequently, these organizations become an

important motivating factor behind the signals firms choose to transmit.

Social network theory, one branch of network theory, deals with the structural relationships

created among different social entities, such as the one between firms, states, and international

organizations (Barabasi, 2002; Burt, Kilduff, & Tasselli, 2013; Steyer, 2012). This theory suggests that as

ties between nodes increase in volume, new networks form in order to optimize the relationships between

different actors (Fox, 2000; Jackson, 2008; Latour, 2005). As new networks form, existing relationships

are optimized, and new potentially fruitful relationships are created (Fox, 2000). The result is increased

diffusion of innovations, news, and norms as facilitated by the increase in network ties (Christakis &

Fowler, 2009; Vasudeva, Zaheer, & Hernandez, 2013; Westaby, Pfaff & Redding, 2014). The increased

networks and ties allow for actors to send signals to each other. The signals sent through the network

serve as social mechanisms to build trust and foment positive relationships among different actors

(Granovetter, 1983; Jones, Hesterly, & Borgatti, 1997; Sih, Hanser, & McHugh, 2009).

From social network theory, the role that international organizations play for firms in an

interdependent world becomes clearer: these organizations foster the creation of new networks through

increased ties of varying weights. As these organizations gain reputation, there is a payoff actors receive

for aligning with the interests of the organizations (Nye & Keohane, 1971, Singh, 2012). As a result,

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firms build trust through the sending of signals that show alignment with the organizations within the

global social network.

In other words, if a state, and the firms within it, seeks to function within the international arena,

we propose that signaling that it is connected to more nodes and networks associated with global

integration increases its level of legitimacy, trust, and reputation. Bearing in mind complex

interdependence theory and the current shift of global norms towards social responsibility (Doh & Guay,

2006; Marano & Kostova, 2015; Nikolaou et al., 2013), it is important to highlight that these networks are

increasingly geared towards global integration and common goals. We assert that amidst globalization, it

is advantageous for firms to signal that they are acting in accordance with social responsibility principals

to gain trust and accountability. The preceding logic leads to the following hypothesis:

Hypothesis 1: Global integration and national trade networks have a positive effect on firm CSR

signaling.

Firm Ownership and the Effects of Global Integration on Corporate Social Responsibility Signaling

Institutional theory suggests that firms operate according to the rules of the game and follow the

established and accepted guidelines (Dimaggio, 1988; North, 1990; Scott, 2013). This is not to say that

firms never break these rules, but rather that these rules are in place and there are advantages and benefits

that firms receive for following them (Lawernce, Hardy, & Phillips, 2002; Suchman, 1995). The

international business literature has focused primarily on state regulations and national governments as

the main sources for establishing these institutions (Acs et al., 2008).

As the world continues to globalize and as states relinquish portions of their sovereignty to

international organizations, it is important to recognize that global norms and global (or supranational)

institutions must also be taken into consideration (Alexandroff & Cooper, 2010; Hacking & Guthrie,

2008; Vezirgiannidou, 2013). In order to flesh out this consideration it is important to look at the basic

ownership structure of firms by establishing the dichotomy between state-owned and privately owned

firms, based on their majority ownership. Although there are other categorizations of firms, this paper

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focuses on state vs. non-state owned firms in order to zoom in on the impact of state ownership in a

globalizing world.

Both categories of firms have different considerations, such as accountability and reputation, to

balance when making strategic decisions – in this case, the decision whether or not to implement CSR

signaling (Young & Marais, 2012). The international business literature suggests that private firms are

held accountable primarily by national governments and the corresponding institutions created by them

(Boardman & Vining, 1989; Claessens, Djankov, & Lang, 2000; Kornai, 1990). In other words, private

firms are more likely than state-owned firms to follow the rules established by national governments

because they face costs and consequences that state firms do not necessarily face (Dewenter & Malatesta,

2001; Krueger, 1974; Megginson & Netter, 2001).

Unlike private firms that are held accountable by national-level institutions, state firms are not

always held accountable to the same extent because, although the citizenry technically owns them,

politicians and government officials manage them. These officials may often act in accordance with

personal or special interests, or national interests that are not necessarily aligned with those of the firm or

the country’s citizens (Shleifer & Vishny, 1994, 1997). State owned firms face a series of pressures

ranging from individuals, unions, and policy-makers (Khanna, Kogan, & Palepu, 2006; La Porta, Lopez-

de-Silanes, & Shleifer, 1999; Lawson, 1994). These pressures revolve around many different interests and

have the capability of pulling state-owned firms in different directions. As a result of this array of

pressures, the international business literature suggests that state-owned firms are more likely to have to

make decisions that offer them higher political value than profit value in many circumstances (Cigler &

Loomis, 2006; Vickers & Yarrow, 1988). This is not to suggest that state-firms do not value profit or that

non-state firms are not accountable to political demands, but rather that state-firms are more bound to

political accountability than their non-state counterparts.

Bearing in mind the consequences of globalization and the implications of complex

interdependence theory (e.g., increased global integration), it is important to highlight that states are now

facing added pressures from other states to engage in socially responsible behavior (Gimenez et al., 2012;

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Tiberghien, 2014; Wapner, 1995). This increased global integration results in increased accountability

among states (Boli & Thomas, 1999; Moghadam & Elveren, 2008; Reimann, 2006). Although this

accountability is not enforceable by mandates and specific actionable tasks, it does aid in the construction

of global institutions and increased pressures to comply with them (Harf & Lombardi, 2007; Kahler,

2013; Swank, 2002).

As previously noted, state firms are often not held accountable to national-level governments and

institutions to the same extent as private firms, but make decisions conditioned for political value. As

states become accountable to actors beyond their borders, so too do their state-firms as they seek political

gains and reputation on a global scale. We thus assert that state-firms are more likely to implement CSR

signaling practices than non-state firms as a result of global integration through national trade networks.

The preceding logic leads to the following hypothesis:

Hypothesis 2: The effect of global integration and national trade networks on firm CSR signaling

is positively moderated by state ownership.

METHODOLOGY

Sources and Sampling of Data

To effectively test the hypotheses, we utilize a panel of all firms registered within the United Nations

Global Compact Initiative (11,990 firms across 153 countries) and all years of data available (2000-2014).

The database used within this research was compiled annually since 2000 and collected from the United

Nations development database archives. The database serves as one of the most complete sources of firm

level CSR signaling for countries across the globe. Other scholars have used the database in different

ways, such as to understand why some firms engage in CSR signaling (Runhaar & Lafferty, 2009;

Voegtlin & Pless, 2014), while others have done so to understand the different local, national, and global

pressures that firms experience when making the strategic decision to implement CSR signaling policies

(Cetindamar, 2007; Fritsch, 2008), as well as several case study analyses and reports carried out by the

United Nations Development Indicators division (United Nations Global Compact, 2016a, b). The

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database lists any firm in the world that has, at one point, signed onto the Global Compact Initiative. For

each firm the time at which the firm joined the initiative is listed first, followed by the level of CSR

engagement, ownership, firm, firm type, number of employees, country of origin, next due date for CSR

reporting, and industry. Further, if a firm is expelled from the initiative, it is listed when the firm was

expelled, the reason why, and if the firm petitioned to re-join. Unlike other CSR databases that focus on

specific business policies and firm footprints within particular countries, such as the Human Rights

Campaign Corporate Equity Index which focuses on United States’ firms, the United Nations Global

Compact Initiative is unique in that it has a global reach, allowing for a larger breadth of analysis, and

captures signaling data from the firm’s perspective rather than from the host-country perspective.

In addition to the United Nations Global Compact Initiative database, we also collected country-

level panel data from the World Trade Organization’s Regional Trade Alliance (RTA) Database. The

source represents the most official and comprehensive data on country-level trade alliances and networks

and has been collected annually since 1957. Other research using the RTA database has been carried out

on a variety of topics including the impact of alliances (Powers, 2004), the effects of multilateralism on

world trade (Goldstein, Rivers & Tomz, 2007), and the consequences of a shrinking world trade schema

on governance (O’Brien, 2000). The database registers the name of the RTA, the countries within the

alliance (including when each country signed on), the coverage of the alliance, the type of alliance, the

status of the alliance, and when the alliance was signed and then ratified by each of the members. Further,

the database records if and when a country leaves an RTA and if and when it returns to the RTA. We also

include country-level controls from the United Nations Development Indicators database.

After removing missing values, the final sample includes 11,990 firms, 153 countries, 15 years

(2000-2014), and 152,241 firm-year observations.

Variables and Measures

Table 1 provides a summary of the measures and data sources used in this research project.

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Dependent Variable. The dependent variable is CSR signaling, for which we use two measures:

Global Compact Member and Global Compact Level. We describe the first of these in this section and the

second in the robustness test section of the paper. The main measure we use to capture the dependent

variable is Global Compact Member, a dichotomous variable used to indicate whether or not a firm was

an active member of the Global Compact Initiative in a given year. Prior work has outlined the measure

as a representation of self-reporting of CSR at the firm level (Kell, 2005).

*** Insert Table 1 About Here ***

Independent Variable of Interest. The independent variable of interest is the degree of global

integration, for which we use two measures of network centrality. The first is Trade Network Ties, which

is a binary centrality measure that captures the number of trade ties that a given country (i.e., node) has

with other countries (i.e., nodes) in a given year. We use this measure and describe it in more detail in the

robustness test section of the paper. The second is Trade Network Weight, which is a valued centrality

measure that captures not only the number of ties but also the weight of those ties. We thus use this as the

primary measure and describe it here.

Trade Network Weight assesses the degree of economic integration of a country. This measure is

captured at the country level and recorded annually based on data from the World Trade Organization’s

RTA database from 1957-2014. The variable captures how integrated a country is in international trade

networks in any given year. That is, it captures how deeply integrated the country in which the firm

operates is to other countries with which it has agreements. For the purpose of this study, trade network

weight is based and aggregated on the levels of economic integration (see Figure 1): Preferential Trade

Agreement, Free Trade Area, Customs Union, Common Market, Economic Union, and Political Union

(Hill, 2013: 452-453; World Trade Organization, 2016). Each level of integration is defined as follows

based on the definitions from Hill (2013) and the World Trade Organization (2016). Preferential trade

agreements are trading blocs that allow preferential access of certain products between countries through

the reduction, not removal, of trade barriers (World Trade Organization, 2016). Free trade areas remove

barriers between the actors in the bloc, for example NAFTA and AFTA. Customs unions, such as

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Mercosur, abolish trade barriers and members within the bloc, and adopt common external trade policies.

Common markets build upon customs unions by allowing for the free movement of the factors of

production among member countries, as seen with CARICOM. Further, building upon common markets,

economic unions, like the European Union, mandate a common currency, standardized tax rates, and

common fiscal policies. The deepest of all integration levels, political unions, combine all of the

aforementioned features and include central political apparatuses and coordinated economic, social, and

foreign policy (Hill, 2013; World Trade Organization, 2016). Each level builds upon the prior, and

therefore represents a deeper trade network weight (see Figure 1). In order to capture this weight, each

level is given a point value from 0.5 to 3.0, building in increments of 0.5, starting at preferential trade

agreement and building to political union. For each trade agreement (trade network tie) registered through

the World Trade Organization, a value from 0.5 to 3.0 is recorded to account for the weight (or value) of

the trade network tie. These weights are then aggregated per agreement, per country, and per year, and

account for whether a country removes itself from an agreement or if an agreement has expired in a given

year. For this variable, the higher the level of Trade Network Weight, the higher the level of global

integration. For example, if country X in year XXXX is involved in 3 Free Trade Agreements and 1

Political Union, the Trade Network Weight would be 7 (see Figure 2).

*** Insert Figures 1 and 2 About Here ***

Moderating Variable. Additionally, this research centers around an analysis of state-owned and

non-state owned firms and how this ownership impacts the relationship between trade networks,

integration, and CSR signaling at the firm level. In order to measure ownership, we use the self-reported

classification that each firm provides to the United Nations Global Compact Initiative for whether or not

it is state-owned (i.e., state firm dummy), based on majority ownership.

Control Variables. In order to eliminate alternate explanations and account for other factors that

might impact CSR signaling, we add multiple control variables at both the country and firm level. First,

we control for the country to mitigate the different country contexts and factors that may affect firms’

CSR practices. Our analysis includes 153 countries (see Table 2 for a list). Second, we control for the

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geographic region to further abate the different contextual factors that might influence a firm’s strategic

choice to implement CSR signaling (see Table 2 for a breakdown of the countries by region). Within this

study, seven regional classifications are used, as defined by the World Bank. Third, we control for

industry by using the primary SIC classifications because different types of sectors may be more or less

inherently prone to CSR signaling than others (Caves, 1989)1. Fourth, we control for the year of analysis

to account for different events in a given year that may directly or indirectly influence CSR signaling.

Fifth, we control for firm size using the number of employees because larger firms with greater resources

may have an advantage and ability to implement CSR signaling policies than smaller firms. Fifth, we

control for whether a firm was publicly traded or not because such firms may experience uneven levels of

pressure to signal that they are socially responsible. Sixth, we control for the business cycle as represented

by the growth in the gross domestic product, as the cycle and growth of the economy and businesses is

likely to impact a firm’s decision to allocate funds to CSR signaling and policies (Martin & Parker, 1995).

Seventh, we control for the country’s level of economic development using the gross domestic product per

capita because firms from wealthier and more economically advanced countries may have more or less of

a tendency to promote CSR signaling. Eighth, we control for the regulatory quality and government

effectiveness of a country because the level of regulation and specific policies and reforms within a

country may have an impact on a firm’s decision to engage in CSR signaling. Finally, as is commonly

done in the literature, we lag the data by one year to allow for the impacts of integration to translate into

firm actions.


Research Design

To provide robust and reliable results, we utilize three different methods. We discuss the first of these

here and the other two in the robustness test section of the paper. The main method we use is a mixed

effects conditional logistic regression model with correction for panel-specific autocorrelation. This

1 Based on the primary SIC classification, the 11 sectors covered in the database are: Agriculture, Forestry, and Fishing; Mining;

Construction; Manufacturing; Transportation, Communications, Electric, Gas, Oil & Services; Wholesale Trade; Retail Trade;

Finance, Insurance, and Real Estate; Services; Public Administration; and Non-Classifiable

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method is suited to account for difficulties within the structure of panel data and accounts for the binary

nature of the dependent variable used (Baum, 2006; Zeger, Liang, & Albert, 1988).

The observations are structured in the following way: firm-year observations nested within firms,

which are in turn nested within countries. Due to the hierarchical nature of the panel data, the random

effects model is also useful to control for unobserved heterogeneity (Lee, 2002). Further, the random

effects model is selected because the individual specific effects are uncorrelated with the independent

variables (Greene, 2003; Woolridge, 2002).

In order to further ensure reliable results, we followed Bliese and Ployhart’s (2002) testing

sequence to construct four-levels of models each building upon the prior for the first test and then two

levels of models for the second and third tests. Building sequential models ensures that the results are not

impacted by missing data and mitigates the effects of violations of sphericity among the errors (Polyhart,

Holtz, & Bliese, 2002).

We carry out the testing of our models based on the guidelines established in Frazier, Tix and

Barron (2004). To mitigate the potential effects of multicollinearity, the continuous independent variables

are standardized (Frazier, Tix, & Barron, 2004; Lee, 2002). The data is lagged by one year to account for

the time it takes for the outcomes of trade networks to impact firm strategic decisions regarding CSR

signaling. The following is the specific model used:

CSR Signalingijkt = β0 + β1Global-Integrationkt=1

+ β2Moderating Variableijkt-1

+ β3Global-Integration

x Moderating Variableijkt-

+ βmControl Variablesijkt-1 + ε

When testing Hypothesis 1 the moderating variable (i.e., 2) and interaction term (i.e., 3) are not

included so we can assess the general impact of global integration and trade networks on firm CSR

signaling. In this model, if 1 is positive and significant, that suggests that global integration has a

17

positive effect on firm CSR signaling, without making a distinction between the two types of firms.

When testing Hypothesis 2, the full model above is used. It is important to note that the results of

this model are conditional on the categorical moderator and need to be interpreted accordingly (Frazier et

al., 2004; Woolridge, 2002). In this model, 1 captures the impact of global integration on CSR signaling

for the omitted baseline category, private firms. If 1 is positive and significant, that indicates that the

effect of global integration on CSR signaling is positive for private firms. On the other hand, 3 captures

the differential effect of global integration on CSR signaling between private firms (the baseline category)

and state firms. If 3 is positive and significant, that indicates that the effect of global integration on CSR

signaling is significantly greater for state firms than for private firms.

RESULTS

Table 3 provides the summary statistics and correlations of the variables. Global integration values range

from 0 to 106 with a mean of 41.33. The average firm in the sample has 5,347 employees and the average

regulatory quality of each country is 0.67. The correlations are generally low, except for the three last

variables in the table, economic development, business cycle, and regulatory quality. To address potential

issues with these high correlations, we removed these three control variables and reran the analyses,

obtaining results that are consistent with those of the main analyses. We also tested for multicollinearity

using variance inflation factor (VIF) tests and found that the average VIF is well above the recommended

cutoff of 10 (Kutner, Nachtsheim, Neter, & Li, 2004). This is not surprising given the number of control

variables included in the main analyses, especially the country and region dummies. We thus ran the

analyses without these control variables, obtaining once again results that are consistent with those of the

main analyses, and VIF scores well below the recommended cutoff of 10 (mean VIF 2.32). This suggests

that multicollinearity is not an important issue affecting the results. We kept the control variables in the

models because they are important theoretically.


18

Main Test Results

Table 4 shows the results of the mixed effects conditional logistic regression models for the impact of

global integration and trade networks on firm CSR signaling and how this varies for private and state

firms. The models are organized sequentially as variables are added to them (Frazier et al., 2004). Model

1 only includes the control variables. Model 2 adds the variable state firm. Model 3 adds global

integration, testing Hypothesis 1. Model 4 adds the interaction effect between state firm and global

integration, testing Hypothesis 2.


Model 3 yields a positive and highly statistically significant result for the coefficient of global

integration (1), suggesting that firms in countries that are more integrated in global trade networks are

more likely to engage in CSR signaling. Since the measure for global integration is standardized, the

magnitude of the coefficient suggests that an increase in one standard deviation of global integration

increases CSR signaling by 5.2 percentage points (Model 3). The results of this model thus provide

support for Hypothesis 1.

Model 4 provides the effects of the moderating relationship. As mentioned above, the coefficient

of global integration (1) indicates the effect on the baseline category, private firms, while the effect of

the interaction term (3) indicates the differential effect between private firms (the baseline category) and

state firms. The coefficient of global integration (1) is positive and highly statistically significant,

suggesting that the effect of global integration on CSR signaling is positive for private firms. Moreover,

the coefficient of the interaction term (3) is also positive and highly statistically significant, suggesting

that the effect of global integration on CSR signaling is significantly higher for state firms than for private

firms. In order to estimate the effect of global integration on CSR signaling of state firms, we calculate

the linear combination between these variables, 1.22, which is also positive and highly statistically

significant. Together, these findings provide further support for Hypothesis 1 and also support for

19

Hypothesis 2, suggesting that the effect of global integration on CSR signaling is positive for both private

and state firms, while the effect is greater for state firms.

Robustness Tests

We conducted several additional analyses, not presented here for the sake of page length, to corroborate

that our findings are not due to alternate explanations and to provide more robust findings. Our results

negate the alternative explanations and corroborate our findings relating to the positive impact of global

integration and trade networks on firm level CSR signaling, as well as the positive moderation of state

ownership.

Alternate Test Designs. As mentioned earlier, in addition to the mixed effects conditional logistic

regression model discussed in the main analyses, we also use two other methods. The first of these is a

random effects probit model with correction for panel specific autocorrelation. This method is utilized as

it assumes that there is a conditional probability of a positive outcome of the random effects of the

independent variable (Baum, 2006). Further, it has an assumed correlation structure over time that fits the

panel data while also yielding cluster-robust standard error results (Arellano, 2003; Baltagi, 2001; Hsiao,

2003). The second alternate method we use is a mixed-effects logit regression model with correction for

panel specific autocorrelation. This method appropriately fits the data as it accounts for multi-level

longitudinal panel data as well as the binary nature of the dependent variable, while assuming normally

distributed random effects for nonlinear slopes (Cameron & Trivedi, 2005; Greene, 2003; Woolridge,

2002). The results of these tests provide consistent support for the hypotheses.

Alternate Dependent Variable. To achieve robust results, we run the models using an alternate

measure for the dependent variable, CSR Signaling. In the main analyses, we use the measure Global

Compact Member, which captures a firm’s strategic decision to engage in CSR signaling. As an alternate

measure, we use Global Compact Level, which is a categorical measure of firm level CSR signaling.

Compiled from the United Nations Global Compact Initiative database, Global Compact Level records the

level of CSR engagement that a firm self-reports and promises to continue each year. The categories for

20

this variable are as follows: 0 = Non-member, 1 = GC General Member, 2 = GC Learner, 3 = GC Active,

4 = GC Advanced. Each level indicates a different, and sequentially increasing, level of adherence to and

self-reporting of the Ten Principles of Sustainable Development as outlined by the Initiative that cover the

different aspects of sustainability and responsible practices from environmental concerns, to labor rights,

to transparency. It is also important to note that as with the main variable of interest, this alternative

measure also captures whether or not a company was expelled from the Initiative in a given year and

whether it rejoined in a later year. In order to account for this categorical variable, we run a time-series

ordinal logit model. The results support our original findings.

Alternate Independent Variables. Additionally, we use an alternative measure for the

independent variable of interest, global integration, which captures the country’s trade network centrality.

The measure we use in the main analyses, Trade Network Weight, is a measure of valued centrality that

seeks to capture how globally integrated a country is through trade networks and political unions. As an

alternate measure, we use Trade Network Ties. Compiled from the World Trade Organization’s Regional

Trade Agreement database, Trade Network Ties is a binary centrality measure that indicates the number

of trade network ties a country has in a given year. It serves to answer: how many trade ties to other

countries (i.e., nodes) does the focal country have in any given year? *Note that a "tie" represents one

agreement with a country. Country X in year XXXX could have 1, 2, 3, etc. ties to Country Y and each is

represented by one positive integer in this variable measurement. Further it is important to note that this

measure accounts for whether and when a country ends an agreement. To examine this alternative

measure, we use a time-series random effects logit model. This model supports our original results.

Alternate Control Variables. Moreover, to ensure the validity of our results, we use alternative

measures of the control variables at both the firm and country level.

First, in our main model we use employees to capture self-reported firm size. The variable

captures the number of employees operating within a firm at any given year as reported on the annual

Global Compact Initiative membership reports (needed to avoid expulsion). We use a categorical

variable, firm type, as an alternative measure of employees as an indicator of the size of a firm. Recorded

21

from the Initiative’s database, the values are as follows: 1 = SME (Small or Medium Enterprise); 2 =

Company; and 3 = FT (Financial Times) 500 Company. As such, this categorical variable captures

whether the firm is one of the largest in the world (i.e., FT 500), a small/medium company (i.e., SME) or

somewhere in between (i.e., Company), thus providing a rough measure of firm size. By utilizing the

same model from the main hypothesis, our findings are reinforced.

Second, in order to control for the regulatory environment of a given country, our main model

uses regulatory quality, a variable recorded from the World Bank Country Indicators database. The

purpose of this variable is to assess the regulatory strength of a nation. As an alternative measure, we use

government effectiveness. Also recorded from the World Bank’s Country Indicators database, government

effectiveness captures perceptions of the quality of public services, the quality of the civil service, and the

degree of its independence from political pressures, the quality of policy formulation and implementation,

and the credibility of the government's commitment to such policies. The estimate provides the country's

score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from

approximately -2.5 to 2.5. Our original findings using this measure are upheld.

Third, as another alternative control of the regulatory environment of a nation we run the same

models using a categorical variable business environment collected from the World Bank Country

Indicators database. This variable is recorded on a sequentially increasing rank scale from 1 to 6, with 1

representing a low score (i.e., weak level of business regulatory environment) and 6 representing a high

score (i.e., strong level of business regulatory environment). Our findings are also verified with this

measure.

Finally, to account for the possibility that the large number of control variables included in the

analyses could be affecting the results, we reran the analyses without any of the control variables. The

results provide equivalent support for the hypotheses.

22

DISCUSSION AND CONCLUSION

The focus of this article, the effects of global integration on CSR signaling, represents an important

debate on the outcomes of globalization. This particular research study extends institutional theory by

combining it with complex interdependence theory through the suggestion that global organizations

develop global (or supranational) institutions that create rules and guidelines for firms to follow in a

globalizing world. Further it asserts that these global institutions have a different positive impact on state-

firms that seek political accountability and reputation in the same way that states do. The results of the

analyses, using different methods and measures, provide robust support for the arguments. Thus, through

this examination we gain a deeper understanding of how state and private firms operate and add a new

dimension to the accountability structure that different categorizations of firms operate within.

This article contributes to two main literatures within international business. First, it contributes

to institutional theory by establishing a new source of creation of institutions and the pressures they create

by utilizing complex interdependence theory from international relations. Traditionally, the international

business literature focuses on national-level governments and government agencies as the main creators

of formal institutions for businesses to follow. We argue that in light of globalization, there is something

beyond nation-level institutions that firms must follow: global institutions created by supranational

organizations and actors. The international relations’ literature has discussed how these institutions

impact national-level factors, but limited attention has been given to how these institutions impact firms.

Furthermore, this is one of the first articles to bring complex interdependence theory to the international

business literature. Therefore by combining complex interdependence theory from international relations

with institutional theory, we examine these organizations and actors and link them accordingly to the

research on globalization and firms. These arguments are important for policy-makers as they face

decisions regarding global integration, as well as for managers and shareholders responsible for making

CSR strategic decisions in a globalizing world.

Second, it contributes to the literature on state-ownership through the lens of social network

theory. The international business literature focuses on accountability and the different pressures that

23

firms face. Private firms are held accountable to national-level governments and national-level

institutions. State-firms, however, often make strategic decisions for political gains. We argue that as the

world continues to globalize and as states become more accountable to each other, states must follow

global institutions to maintain positive political gains. We extend this logic to the firm level to propose

that state firms, much like states themselves, are accountable to global institutions in order to advance

political gains. This extension is critical for politicians who serve as decision makers for both states and

state-firms as the world globalizes.

Despite the contributions of this article there are several limitations that can be addressed in

future research. First, we study CSR signaling as one concept through the United Nations Global

Initiative Compact, but there are several different mechanisms within this signaling. Although our study

uses different measures to provide robust results, future studies may include even more measures to assess

the different unique dimensions involved with this signaling. Second, our study looks at both developing

and developed countries without direct differentiation for the unique pressures they face. Although our

research accounts for country-level controls and regional differentiation, the literature suggests that

emerging markets face different pressures than advanced markets. Future studies would benefit from

examining either emerging markets or advanced markets specifically to understand the different pressures

they face. Third, the purpose of this research is to focus on state-firms vs. non-state firms. This allows us

to highlight the impact of global integration on the different accountability structures of state-firms.

Future research, however, may examine how this global integration impacts different categorizations of

firms beyond state-firms vs. non-state firms. Finally, we examine the effects of global integration only on

CSR signaling. Although we propose that this integration has a positive impact on firm-level signaling, it

may also impact other economic, political, and firm strategic facets that may be addressed in future

research.

Amidst the negative and positive consequences of globalization, interdependence among nations

and governments continues to rise (Baden, et al., 2014, Singh, 2012). Commodities, labor, and capital are

increasingly flowing across state borders, but so too are common interests and goals in light of the global

24

nature of problems in today’s world (Kwon, 2012; Stohl, 2004). Global organizations are ever increasing

in scope and size (Nye & Keohane, 1971; Pegram, 2010) and in the process, a network of supranational

institutions and rules to hold states and their firms accountable have been developing. With states

continuing to join international organizations and trade networks, it is imperative for scholars and

practitioners to keep a careful eye on the impacts that states, and their domestic actors such as firms,

experience in order to better understand how to operate and function in a globalizing world.

25

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Table 1. Measures and Data Sources (including measures used in robustness tests)

Measure Description Value Source

Firm Unique indicator of the firm 1 to 11,990 World Bank Development Indicators

Country Categorical indicator of the country (see Table 2) 1 to 153 World Bank Development Indicators

Region Categorical indicator of the geographic region (see Table 2) 1 to 7 World Bank Development Indicators

Year Categorical indicator of the year of analysis 2000 to

2014

World Bank Development Indicators

Industry Categorical indicator of the industry of a firm using the primary

SIC classification code

1 to 11 United Nations' Global Compact and

SIC Classification Codes

Global Compact

Member

Dummy indicator for whether or not the firm is a member of the Global Compact Initiative

0 or 1 United Nations' Global Compact

Global

Compact Level

Categorical indicator of the level of membership of a firm within

the Global Compact Initiative (used in robustness test section)

0 to 4 United Nations' Global Compact

Firm Size Indicator of the firm's number of employees Continuous United Nations' Global Compact

Firm Type Categorical indicator of the firm size with three categories: SME,

Company, and FT500 (used in robustness test section)

1 to 3 United Nations' Global Compact

Publicly

Traded

Dummy indicator for whether or not the firm is publicly traded 0 or 1 United Nations' Global Compact

State Firm Dummy indicator for whether or not the firm's majority owner is

the state

0 or 1 United Nations' Global Compact

Trade Network

Weight

Valued centrality measure for how integrated a country is in

global trade networks, aggregated by volume and measured to

account for type of network (see text for detailed description)

Continuous World Trade Organization Regional

Trade Agreements Database

Trade Network

Ties

Binary centrality measure of the number of countries (nodes) a

particular nation has trade networks with. Each agreement represents one tie. (Used in robustness test section)

Continuous World Trade Organization Regional

Trade Agreements Database

Economic

Development

Gross domestic product in thousands of U.S. dollars divided by

the total population

Positive World Bank Development Indicators

Business Cycle Difference in gross domestic product for the year and the previous year divided by gross domestic product in the previous year

Continuous World Bank Development Indicators

Regulatory Quality

Regulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and

regulations that permit and promote private sector development.

Estimate gives the country's score on the aggregate indicator


Business

Environment

Categorical indicator of the business regulatory environment that

assesses the extent to which the legal, regulatory, and policy environments help or hinder private businesses in investing,

creating jobs, and becoming more productive (used in robustness

test section)

1 to 6 World Bank Development Indicators

Government

Effectiveness

Government Effectiveness captures perceptions of the quality of

public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy

formulation and implementation, and the credibility of the

government's commitment to such policies. Estimate gives the country's score on the aggregate indicator, in units of a standard

normal distribution (used in robustness test section)


32

Table 2. List of Countries by Geographic Region (Source: World Bank Development Indicators)

North America

o Bermuda

o Canada

o Mexico

o United States of America

Latin America and the Caribbean

o Antigua And Barbuda

o Argentina

o Barbados

o Bolivia, Plurinational State of

o Brazil

o Chile

o Costa Rica

o Dominica

o Dominican Republic

o Ecuador

o El Salvador

o Guatemala

o Guyana

o Haiti

o Honduras

o Jamaica

o Nicaragua

o Panama

o Paraguay

o Peru

o Trinidad And Tobago

o Uruguay

o Venezuela, Bolivarian Rep. of

East Asia and the Pacific

o Australia

o Cambodia

o China

o Indonesia

o Japan

o Korea, Republic Of

o Malaysia

o Mongolia

o Myanmar

o New Zealand

o Philippines

o Singapore

o Thailand

o Viet Nam

South Asia

o Afghanistan

o Bangladesh

o India

o Maldives

o Nepal

o Pakistan

o Sri Lanka

Europe and Central Asia

o Albania

o Andorra

o Armenia

o Austria

o Azerbaijan

o Belarus

o Belgium

o Bosnia and Herzegovina

o Bulgaria

o Croatia

o Cyprus

o Czech Republic

o Denmark

o Estonia

o Finland

o France

o Georgia

o Germany

o Greece

o Hungary

o Iceland

o Ireland

o Italy

o Kazakhstan

o Latvia

o Liechtenstein

o Lithuania

o Luxembourg

o Macedonia, the FYR of

o Moldova, Republic of

o Monaco

o Montenegro

o Netherlands

o Norway

o Poland

o Portugal

o Romania

o Russian Federation

o Serbia

o Slovak Republic

o Slovenia

o Spain

o Sweden

o Switzerland

o Turkey

o Ukraine

o United Kingdom

o UNMIK/Kosovo

o Uzbekistan

Middle East and North Africa

o Algeria

o Bahrain, Kingdom of

o Egypt

o Iran

o Iraq

o Israel

o Jordan

o Kuwait, the State of

o Lebanese Republic

o Libya

o Morocco

o Oman

o Qatar

o Saudi Arabia, Kingdom of

o Syrian Arab Republic

o Tunisia

o United Arab Emirates

o Yemen

Sub-Saharan Africa

o Angola

o Benin

o Cabo Verde

o Cameroon

o Congo, Democratic Rep. of the

o Côte d'Ivoire

o Ethiopia

o Gabon

o Guinea

o Kenya

o Liberia, Republic of

o Madagascar

o Malawi

o Mali

o Mauritius

o Mozambique

o Namibia

o Nigeria

o Rwanda

o Sao Tome and Principe

o Senegal

o Seychelles, Republic of

o Sierra Leone

o Somalia

o South Africa

o South Sudan

o Sudan

o Tanzania

o Togo

o Uganda

o Zambia

o Zimbabwe

Table 3. Descriptive Statistics and Correlations

Variable Mean s.d. 1 2 3 4 5 6 7

1. Global Compact Member 0.33 0.47

2. Global Integration 41.33 28.92 0.19

3. State Firm 0.02 0.15 0.00 -0.02

4. Publicly Traded 0.11 0.31 0.12 -0.01 -0.05

5. Firm Size 0.02 1.07 0.10 0.03 0.10 0.27

6. Economic Development 0.04 1.01 0.08 0.65 -0.01 0.09 0.06

7. Business Cycle 0.02 0.98 -0.07 -0.47 0.03 -0.02 0.00 -0.43

8. Regulatory Quality 0.07 0.92 -0.02 0.64 -0.02 0.08 0.06 0.80 -0.43

Correlations with an absolute value greater than or equal to 0.01 are significant at 0.05 level (two-tailed)

Descriptive statistics and correlations for the 153 countries, 7 regions, 11 sectors, and 15 years are not

included for the sake of parsimony

N=152,241

Table 4. Results of the Analyses of the Time Series Random Effects Logit Models for the Effect of

Global Trade Integration on CSR Signaling

Variables Model 1 Model 2 Model 3 Model 4

Intercept -4.67 ** (1.65) -4.67 ** (1.65) -3.70 * (1.66) -3.75 * (1.66)

Publicly Traded 1.05 *** (0.05) 1.05 *** (0.05) 1.06 *** (0.05) 1.05 *** (0.05)

Firm Size 0.25 *** (0.01) 0.25 *** (0.01) 0.26 *** (0.02) 0.26 *** (0.02)

Economic Development 0.43 *** (0.04) 0.43 *** (0.04) 0.28 *** (0.04) 0.27 *** (0.04)

Business Cycle 0.01

(0.01) 0.01

(0.01) 0.02 * (0.01) 0.03 * (0.01)

Regulatory Quality 0.33 *** (0.06) 0.33 *** (0.06) 0.48 *** (0.06) 0.47 *** (0.06)

Country Control Included Included Included Included

Region Control Included Included Included Included

Year Control Included Included Included Included

Industry Control Included Included Included Included

State Firm

-0.01

(0.09) -0.01

(0.09) -0.08

(0.10)

Global Integration

0.52 *** (0.03) 0.51 *** (0.03)

Global Integration x State Firm

0.93 *** (0.09)

Wald Chi Squared 21865.01 *** 21864.98 *** 21783.61 *** 21754.90 ***

Log Likelihood -70853.91 -70853.91 -70701.54 -70642.53

Firms 11,990 11,990 11,990 11,990

Observations (n) 152,241 152,241 152,241 152,241

Indicators for each country (153), region (7), industry (11), and year (15) are included in the models, but their coefficients are not reported

for the sake of brevity

* p<0.05

** p<0.01

*** p<0.001

Figure 1. Levels of Economic Integration (based on Hill, 2013; World Trade Organization, 2016)

PoliticalUnion

EconomicUnion

CommonMarket

CustomsUnion

FreeTradeArea

PreferentialTrade

Agreement

0.5

1.0

1.5

2.0

2.5

3.0

Trade Network Weight

Figure 2. Example Network Showing Ties and Weights (i.e., Values) of Those Ties for Each Level of

Economic Integration

CountryX

PreferentialTrade

Agreement

FreeTradeArea

CustomsUnion

CommonMarket

EconomicUnion

Political

Union

0.5

1.0

1.5

2.0

2.5

3.0

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