Andreas M Hein

Marija Jankovic

Romain Farel

A CONCEPTUAL FRAMEWORK FOR ECO-INDUSTRIAL PARKS

An eco-industrial park is a set of businesses that share resources in order to increase profitability and reduce environmental impact. The implementation of eco-industrial parks may significantly contribute to the creation of a sustainable economy. Despite this prospect, the actual development of eco-industrial parks is challenging, as a variety of factors must be considered. Not only technical, economic, and environmental factors are relevant but numerous stakeholder relationships as well, such as between firms, governmental bodies, and local communities. This paper presents a conceptual framework that is used to capture these diverse aspects and the relationships between them. The Unified Modeling Language (UML) is used for modeling its concepts and relationships. First, based on a literature survey, relevant concepts of eco-industrial parks are identified. One central concept is "industrial symbiosis". A novel value-based interpretation of industrial symbiosis is presented. Second, the park's economic, local and regional development context, as well as its internal technical components and their relationships are modeled. Finally, the framework is used for modeling a concrete ecoindustrial park, in this case part of the Kalundborg eco-industrial park.

INTRODUCTION

. Industrial ecology basically attempts to create industrial economies that are sustainable. Natural ecologies serve as a model or metaphor [3; p.1], [4]-[6].

BACKGROUND ECO-INDUSTRIAL PARKS

. Some of these challenges are pertinent to large-scale infrastructural projects in general, such as geographic attractiveness and managing complex stakeholder relationships [14], [15; pp.25-28]. Some challenges are specific to EIPs such as the implementation of by-product exchange relationships between EIP participants, called industrial symbiosis. [16; pp.987-988], [14; p.1685]. Current research suggests that initiatives by governmental actors alone, without the support of firms, do not lead to successful EIP development [16]-[19].

Research on EIP development has just recently started. Romero & Ruiz [START_REF] Romero | Framework for applying a complex adaptive system approach to model the operation of eco-industrial parks[END_REF] use adaptive complex systems theory for modeling and simulationg EIP operations, integrating economic, environmental, and social aspacts. Romero & Ruiz [START_REF] Romero | Proposal of an agent-based analytical model to convert industrial areas in industrial eco-systems[END_REF] explore how system dynamics and agent-based modeling can be used for modeling and simulating the evolution of EIPs. Rosa & Beloborodko [START_REF] Rosa | A decision support method for development of industrial synergies: case studies of Latvian brewery and wood-processing industries[END_REF] propose a qualitative decision-support tool for evaluating existing industrial symbiosis. A scoring system is used, which takes geographic proximity, economic, and ecological performance into account. Cao et al. [START_REF] Cao | Applying agent-based modeling to the evolution of ecoindustrial systems[END_REF] have demonstrated the use of agent-based modeling for simulating the dynamic behavior of a hypothetical EIP. Coté et al. [START_REF] Côté | Designing eco-industrial parks: a synthesis of some experiences[END_REF] and Chertow [START_REF] Chertow | Industrial symbiosis: Literature and taxonomy[END_REF] describe existing and possible approaches to design eco-industrial parks such as guidelines, input-output matching, stakeholder processes, and material budgeting. Haskins [START_REF] Haskins | Multidisciplinary investigation of eco-industrial parks[END_REF], [START_REF] Haskins | A systems engineering framework for eco-industrial park formation[END_REF] and Maya Sopha et al. [START_REF] Sopha | Using systems engineering to create a framework for evaluating industrial symbiosis options[END_REF] identify disciplines that may contribute to the understanding of EIPs and ultimately how to develop them. They identify disciplines such as supply chain management, stakeholder analysis, and systems engineering. The variety of stakeholders involved in EIP development calls for a sociological analysis of EIPs. Eilering & Vermeulen [START_REF] Eilering | Eco-industrial parks: toward industrial symbiosis and utility sharing in practice[END_REF] identify a range of social enabling factors for the successful development of EIPs. Spekkink [START_REF] Spekkink | Institutional capacity building for industrial symbiosis in the Canal Zone of Zeeland in the Netherlands: A process analysis[END_REF], [START_REF] Spekkink | Building capacity for sustainable regional industrial systems: an event sequence analysis of developments in the Sloe Area and Canal Zone[END_REF] use event sequence graphs for mapping out the key events that lead to the foundation as well as evolution of an EIP. Ashton [START_REF] Ashton | Understanding the organization of industrial ecosystems[END_REF] uses social network analysis to analyse the network of actors involved in the evolution of EIPs. Zeng et al. [START_REF] Zeng | Vulnerability analysis of symbiosis networks of industrial ecology parksa[END_REF] use social network analysis to analyse the vulnerability of EIPs. Furthermore, a number of optimization approaches for EIPs have been presented in recent years [START_REF] Kim | Economic and environmental optimization of a multi-site utility network for an industrial complex[END_REF]- [START_REF] Monteiro | Pareto optimization of an industrial ecosystem: Sustainability maximization[END_REF]. Schulze [37, pp.68-75] provides a comprehensive overview of existing EIP modeling and optimization approaches. The optimization objective is to find by-product exchange networks in EIPs that maximize economic and environmental performance.

To summarize, the existing literature on EIP development mainly deals with their evolution and idenfiticaion of optimal configuration of exchange relationships. However, a number of aspects have not wet been addressed. For example, there is currently no approach for assessing EIP performance across different economic and regulatory contexts. Another important aspect that is not yet addressed by existing research is how different potential EIP configurations deliver value to key stakeholders. [START_REF] Monteiro | Pareto optimization of an industrial ecosystem: Sustainability maximization[END_REF] Simulation / Optimization Agent-based simulation of a hypothetical EIP; mixed-integer programming for generating EIP configurations Coté et al. [START_REF] Côté | Designing eco-industrial parks: a synthesis of some experiences[END_REF], Chertow [START_REF] Chertow | Industrial symbiosis: Literature and taxonomy[END_REF] Environmental sciences Guidelines for developing EIPs Desrochers [START_REF] Desrochers | Industrial ecology and the rediscovery of inter-firm recycling linkages: historical evidence and policy implications[END_REF]- [START_REF] Desrochers | Industrial symbiosis: The case for market coordination[END_REF] History Centralized planning without stakeholder involvement will probably fail. Ashton [START_REF] Ashton | Understanding the organization of industrial ecosystems[END_REF] Organizational sociology, economic geography Exploration of relationships between EIP participants Sun et al. [START_REF] Sun | Measuring interdependency in industrial symbiosis network with financial data[END_REF] Economics Uncovering symbiosis by financial dependencies between EIP participants This paper provides the basis for addressing these gaps by presenting an initial conceptual framework for EIPs. The framework provides a conceptual basis for later tradespace exploration and optimization of EIPs.

CONTEXT AND OBJECTIVES

A number of French companies are currently interested in the concept of EIP due to its potential economic and ecological benefits. One of the potential sites for such a future EIP is in the port city of Le Havre. Numerous companies are located in the city's industrial zone. However, for assessing the viability of a potential EIP, they need to answer the following questions: a) Which aspects need to be taken into account in order to make a decision for investing into the development of an EIP? b) What are the costs, benefits, opportunities, and risks of an EIP throughout its lifecycle? c) Who are the key stakeholders that need to be considered? d) How do different EIP options deliver value to key stakeholders? e) Which development strategies are suitable for a specific EIP? f) How can system properties such as flexibility and robustness built into an EIP for providing value to stakeholders in changing contexts?

Most of these questions have only be partly addressed by the existing literature. In order to answer these questions, we use a systems architecting approach, as illustrated in Fig. 1 [40], [START_REF] Crawley | The Influence of Architecture in Engineering Systems[END_REF]. Systems architecting deals with the initial stages of systems development where stakeholder needs are translated into requirements and a first high-level design of the system, often called "system architecture" [42; p.22]. Recently, systems architecting has been introduced to developing urban areas and large-scale infrastructures [START_REF] Alfaris | Hierarchical Decomposition and Multidomain Formulation for the Design of Complex Sustainable Systems[END_REF], [START_REF] Adepetu | Complex Urban Systems ICT Infrastructure Modeling: A Sustainable City Case Study[END_REF]. systems architecting methods are for example tradespace exploration, where a large number of system architectures is automatically generated and evaluated with respect to classical criteria such as cost and performance [START_REF] Ross | Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design[END_REF], [START_REF] Ross | The tradespace exploration paradigm[END_REF]. Non-traditional criteria such as flexibility and robustness in changing contexts have also been considered [START_REF] Mcmanus | A Framework for Incorporating 'ilities' in Tradespace Studies[END_REF]. Another method is stakeholder analysis, where actors that have a stake in the system are identified and prioritized [START_REF] Feng | Strategic Management for Large Engineering Projects: The Stakeholder Value Network Approach[END_REF], [START_REF] Freeman | A Stakeholder Approach to Strategic Management[END_REF], [START_REF] Hein | Project Icarus: Stakeholder Scenarios for an Interstellar Exploration Program[END_REF]. Stakeholder analysis is crucial for large-scale engineering projects, as they often fail due to the negligence of key stakeholders [START_REF] Feng | Strategic Management for Large Engineering Projects: The Stakeholder Value Network Approach[END_REF]. Hence, systems architecting is the natural choice for answering questions a) -f). This paper will deal with the conceptual framework from point 1 which lays the groundwork for point 2.

CONCEPTUAL FRAMEWORK

Developing a conceptual framework or conceptual model is a common step in modeling [49; p.9], [50; p.547]. The framework defines the entities and their relationships that need to be modeled for achieving the modeling objectives. This step precedes the development of a quantitative model. For example, a conceptual model for a pendulum may consist of a string and a weight attached to the string. The quantitative model then goes on to define parameters such as the mass of the weight and string as well as differential equations. The quantitative model is used for analyzing the motion of the pendulum. Hence, the conceptual framework serves as a basis for building later quantitative models. For constructing the framework, mainly data from available literature is used. It is validated by EIP and industrial engineering experts.

The conceptual framework is divided into a context model defining the EIP and its context and an internal model defining the EIP's internal elements. The separate modeling of a system's context and its internal structure is a standard practice in systems engineering [START_REF] Karban | Cookbook for MBSE with SysML[END_REF], [START_REF] Haberfellner | Systems Engineering: Grundlagen und Anwendung[END_REF]. It helps to distinguish between what is part of the system and what is considered outside of the system boundary. We model the conceptual framework by using the Unified Modeling Language (UML) [START_REF] Group | UML 2.0 Superstructure Specification[END_REF]. As UML originated in object-oriented software engineering, it distinguishes between "class" and "instance". A class can be interpreted as a template for instances. For example, the class "car" may have the attributes "maximum speed" and "fuel consumption". An instance of a car has concrete values associated with its attributes. For example, the "Ford Model T" is an instance of a car with "maximum speed: 72 km/h" and "fuel consumption: 0.17 l/km". UML class diagrams are used for defining classes and their relationships. It is used here for defining EIP model elements, taxonomies of model elements, attributes, and relationships. EIP instances are represented by the Systems Modeling Language (SysML) internal block diagram. SysML is a derivative of UML for systems engineering [START_REF] Friedenthal | A practical guide to SysML: The systems modeling language[END_REF]. The internal block diagram can be interpreted as a "box-and-arrow" diagram. The "boxes" are instances and arrows are flows between instances. Flows enter and exist instances via interfaces. These interfaces are called "ports".

MODELING INDUSTRIAL SYMBIOSIS

One key concept pertaining to EIPs is "industrial symbiosis". Therefore, a conceptual framework for EIPs needs to include this concept. Industrial symbiosis describes a by-product exchange relationship in an industrial economy [55; p.2]. An extensive body of literature on industrial symbiosis has been published during the last decade and a half [START_REF] Yu | Understanding the Evolution of Industrial Symbiosis Research[END_REF]. Two widely cited definitions of "industrial symbiosis" are from [START_REF] Chertow | Industrial symbiosis: Literature and taxonomy[END_REF] and [START_REF] Chertow | Uncovering' Industrial Symbiosis[END_REF]. Chertow [25;p.314] elaborates that "industrial symbiosis engages traditionally separate industries in a collective approach to competitive advantage involving physical exchange of materials, energy, water, and by-products" [25; p.314]. In order to put an emphasis on more complex exchange relationships than simple one-way exchanges, Chertow [START_REF] Chertow | Uncovering' Industrial Symbiosis[END_REF] introduced the "3-2 heuristic". The heuristic requires that "at least three different entities must be involved in exchanging at least two different resources to be counted as a basic type of industrial symbiosis." [START_REF] Chertow | Uncovering' Industrial Symbiosis[END_REF] Industrial symbiosis is usually attempted for [57; p.13]:

-Increasing economic efficiency.

-Ensuring long-term availability of critical resources such as water, raw materials, and energy.

-Reacting to regulatory pressure such as taxes on emissions and waste production.

The existing definitions of "industrial symbiosis" mostly focus on the physical aspect of the by-product exchange such as matter and energy. In this section, we will propose a wider perspective on industrial symbiosis that integrates the creation and delivery of different forms of value to stakeholders. A value-centric interpretation of industrial symbiosis is crucial for applying different systems architecting methods, as these strongly rely on the notion of "value" for evaluating systems architectures. Paquin et al. [START_REF] Paquin | Facilitating regional industrial symbiosis: Network growth in the UK's National Industrial Symbiosis Programme[END_REF] mention the connection between industrial symbiosis and value. We recognize that the notion of "industrial symbiosis" has its origin in the biological notion of symbiosis. Therefore, we will demonstrate that a value-centric interpretation of symbiosis can be traced back to the literature on biological symbiosis, where it is expressed in terms of "cost" and "benefit" [START_REF] Matsuda | Cost-benefit model for the evolution of symbiosis[END_REF]- [START_REF] Connor | The benefits of mutualism: a conceptual framework[END_REF].

In biology, "symbiosis" was formerly associated with the notion of "mutualism", where both species benefit from a prolonged interaction. For example, flowering plants that provide nutrition to butterflies and butterflies that disseminate the pollen of these plants. "Benefit" is defined as an increase in fitness. Fitness in the context of evolutionary biology is linked to a species' ability to survive and reproduce [65; pp. [START_REF] Lowe | Eco-industrial park handbook for Asian developing countries[END_REF][START_REF] Frosch | Strategies for manufacturing[END_REF][START_REF] Erkman | Industrial ecology: an historical view[END_REF][START_REF] Hess | The Ecosystem: Model or Metaphor?[END_REF][START_REF] Mcmanus | A Framework for Incorporating 'ilities' in Tradespace Studies[END_REF][START_REF] Ayres | On the life cycle metaphor: where ecology and economics diverge[END_REF][START_REF] Chertow | Industrial symbiosis[END_REF][START_REF] Shi | Developing country experience with eco-industrial parks: a case study of the Tianjin Economic-Technological Development Area in China[END_REF][START_REF] Jacobsen | Industrial Symbiosis in Kalundborg, Denmark: A Quantitative Assessment of Economic and Environmental Aspects[END_REF][START_REF] Korhonen | Analysing the evolution of industrial ecosystems: Concepts and application[END_REF][START_REF] Sokak | Local systems, global impacts : Using life cycle assessment to analyse the potential and constraints of industrial symbioses[END_REF][START_REF] Chertow | Quantifying economic and environmental benefits of colocated firms[END_REF][START_REF] Hodge | Quantifying potential profit from material recycling: A case study in brick manufacturing[END_REF][START_REF] Gibbs | Reflections on implementing industrial ecology through ecoindustrial park development[END_REF][START_REF] Feng | Strategic Management for Large Engineering Projects: The Stakeholder Value Network Approach[END_REF][START_REF] Heeres | Eco-industrial park initiatives in the USA and the Netherlands: First lessons[END_REF][START_REF] Desrochers | Industrial ecology and the rediscovery of inter-firm recycling linkages: historical evidence and policy implications[END_REF][START_REF] Desrochers | Cities and industrial symbiosis: Some historical perspectives and policy implications[END_REF][START_REF] Desrochers | Industrial symbiosis: The case for market coordination[END_REF]. A flowering plant that provides nutrition to butterflies is more likely to reproduce, as the butterflies distribute its pollen. Influential definitions of industrial symbiosis such as Chertow [START_REF] Chertow | Industrial symbiosis: Literature and taxonomy[END_REF] 1 explicitly refer to biological mutualism as a source of inspiration [25; p.314]. The benefits from a mutualistic relationship often come at a cost. "Cost" is defined as a form of investment [START_REF] Connor | The benefits of mutualism: a conceptual framework[END_REF]. A flowering plant that provides nutrition for butterflies invests in the production of nutrition. The relevant types of mutualism from Connor [START_REF] Connor | The benefits of mutualism: a conceptual framework[END_REF] are listed in Table 1. It is worth mentioning that mutualism is only one form of symbiosis. A wide range of other persistent interactions between distinct species fall under the term symbiosis, such as parasitism [START_REF] Bradford | Current Usage of Symbiosis and Associated Terminology[END_REF].

The cost and benefit aspects of symbiosis are particularly relevant when it comes to industrial symbiosis. Although the biological notion of symbiosis serves as an inspiration, it is clear that it cannot be directly applied to industrial economies.

Table 2 provides an overview of the commonalities and differences between biological and industrial symbiosis with respect to key characteristics. However, a direct transfer of biological "cost" and "benefit" to industrial symbiosis would be flawed. As Ayres [START_REF] Ayres | On the life cycle metaphor: where ecology and economics diverge[END_REF] has pointed out; biological "cost" and "benefit" need to be understood in a purely evolutionary context. "Cost" and "benefit" in industrial symbiosis is related to economics with its market mechanisms as well as other social concepts such as political influence. For using the economic and social notion of "benefit" in industrial symbiosis, we use the more common term of "value". "Value" is a measure for the extent to which an actor receives a benefit at a cost in an exchange, such as buying a product in exchange for money. Value is not limited to economic exchanges but can be extended to other forms of social exchanges [START_REF] Cropanzano | Social Exchange Theory: An Interdisciplinary Review[END_REF]- [START_REF] Emerson | Social exchange theory[END_REF].

Using the notion of "value", a value-based definition for industrial symbiosis can be formulated:

An industrial symbiosis can be defined as an exchange relationship where byproducts and utilities of industrial plants are used as resources in other, distinct industrial plants, thereby creating economic, environmental, and societal value for stakeholders.

Fig. 2: Industrial symbiosis model

For an exchange relationship to be an industrial symbiosis, it needs to at least deliver economic and environmental value. Fig. 2 shows how the value industrial symbiosis generates is delivered to EIP stakeholders. Another important aspect is that an industrial symbiosis is an exchange relationship. A by-product that would otherwise be disposed provides value to the symbiosis partner. In exchange, the partner provides some form of compensation, for example informational, financial, and political. Hence, similar to the symbiosis taxonomy from Connor [START_REF] Connor | The benefits of mutualism: a conceptual framework[END_REF], such an exchange can be termed "reciprocal symbiosis", as the exchange of value is or is expected to be bidirectional. Similar to biological symbiosis, creating industrial symbiosis incurs cost, mostly for the construction of the exchange infrastructure but also its operation. Contrary to biological symbiosis, the infrastructure for industrial symbiosis can be funded by sophisticated project financing methods, such as debt and equity financing or by establishing public-private partnerships [70; Chapter 8], [START_REF] Lessard | House of Project Complexity -Understanding Complexity in Large Infrastructure Projects[END_REF], [START_REF] Esty | Why Study Large Projects? An Introduction to Research on Project Finance[END_REF].

The advantage of a value-centric definition of industrial symbiosis is that it merges the spheres of material / energy exchange, relevant in industrial ecology with economic and social exchange relationships from mainstream economics and sociology.

EIP CONTEXT

Fig. 3 shows the EIP and its context. An EIP is characterized by its economic, environmental, and social effects. These effects may lead to the creation of value for EIP stakeholders. EIP stakeholders have an interest in the EIP and can also affect the EIP. A specific type of EIP stakeholder is an EIP company. An EIP company is an owner of an EIP element such as a power plant. The EIP can have a governing body that oversees the EIP's development and operation. The governing body usually consists of EIP stakeholders. In general, EIP stakeholders can take part in relationships with other EIP stakeholders. Stakeholders can be competing, cooperating, or indifferent. For example, collaboration between companies is a prerequisite for forming an EIP [START_REF] Chertow | Industrial symbiosis: Literature and taxonomy[END_REF]. Relationships can also be defined by value exchanges between stakeholders. Types of value exchanges are financial, political, goods and services [START_REF] Feng | Strategic Management for Large Engineering Projects: The Stakeholder Value Network Approach[END_REF]. Geography also plays a crucial role in EIP development [START_REF] Gibbs | Reflections on implementing industrial ecology through ecoindustrial park development[END_REF]. The topology of the land influences the cost of constructing plants and exchange infrastructure. For example, terrain features such as rivers and hills increase the cost of constructing a pipeline [START_REF] Dey | An integrated assessment model for cross-country pipelines[END_REF], [74; p.72]. Preexisting industry may provide a supply of highly qualified personnel as well as an established transportation infrastructure. Furthermore, ownership of land influences its availability and the cost of buying or leasing it. Finally, an EIP must be financed. Project financing deals with funding large engineering projects [70; Chapter 8], [START_REF] Lessard | House of Project Complexity -Understanding Complexity in Large Infrastructure Projects[END_REF], [START_REF] Esty | Why Study Large Projects? An Introduction to Research on Project Finance[END_REF]. The financial mechanisms for financing an EIP can consist of various debt and equity financing schemes [70; Chapter 8]. Allocating risks to investors is a key criteria for choosing a financing scheme [START_REF] Brealey | Using project finance to fund infrastructure investments[END_REF].

After the EIP context has been modeled, the internal elements of an EIP are defined.

EIP INTERNAL ELEMENTS

Fig. 4 shows the internal elements of an EIP. An EIP is composed of EIP elements. The arrow with the diamond represents a "whole-part" relationship. The whole is the EIP and the EIP elements are parts of the EIP. An EIP element can be either an industrial plant (factory, power plant, etc.), a flow transformation device (heat exchanger, condenser, etc.) or a connection (pipeline, electrical circuit etc.). What is the difference between a flow transformation device and a connection? A flow transformation device is deliberately designed for transforming the state of a flow. For example, a condenser transforms vapor into water. A connection's primary purpose is to transport a flow. A connection usually changes the state of the flow. For example, a pipeline induces a pressure drop into the transported liquid or gas. The main difference is that the change in state is undesired.

A pipeline with less pressure drop is considered better than a pipeline with a larger pressure drop. An EIP element can be owned by an EIP stakeholder. A stakeholder that owns an EIP element is assigned the role "owner". An EIP element can have one or more owners. A stakeholder may own an arbitrary number of EIP elements.

The internal EIP model further introduces the concept of "interface". Interface compatibility is defined at the level of the connector and the flow entering or leaving the interface [START_REF] Friedenthal | A practical guide to SysML: The systems modeling language[END_REF], [76; p.176]. A connector is for example a power plug and socket. A power plug is compatible with a power socket on a connector level if the two can be connected. In this case the connection is mechanical. For software interfaces the connection might be logical. However, compatibility on a connector level is not a sufficient condition for allowing interactions between two components. For example, the required voltage of the "female" socket might be 100V and the provided voltage of the "male" socket 50V. In such a case, there is a mismatch between the required and provided flow. Hence, the interfaces are incompatible. An example from an EIP would be a mismatch of required and provided vapor pressure and temperature between two plants.

How does the initially defined industrial symbiosis fit into the internal EIP model? An industrial symbiosis assigns certain roles to EIP elements. An EIP element taking part in a symbiosis is called "symbiosis element". An industrial plant taking part in a symbiosis is called "technological symbiosis partner". In some cases an industrial plant only serves as an intermediary between two symbiosis partners. Such a plant is called "intermediary". A connector taking part in a symbiosis is assigned the role "connector symbiosis infrastructure". A flow transformation device taking part in a symbiosis is assigned the role "transformation symbiosis infrastructure". According to our definition, industrial symbiosis is not only an exchange relationship on a technological or physical level but also involves stakeholders that receive value from the exchange. Such stakeholders are called "organizational symbiosis partners".

EIP INSTANCE

The conceptual framework is used for modeling an EIP instance. A part of the Kalundborg EIP is modeled, as it is the most widely known instance of an EIP [START_REF] Ehrenfeld | Industrial Ecology in Practice: The Evolution of Interdependence at Kalundborg[END_REF]. The instance is created by using the Systems Modeling Language (SysML) internal block diagram. The internal block diagram is used for modeling the exchange of flows between instances. Fig. 5 shows the exchange of deionized water between the Dong Energy Annaes Power Station and the Statoil plant via pipeline. Drain water is exchanged via channels and an intermediate reuse basin. Fly ash from the power plant is transferred to the cement industry. The framework model elements are represented as UML / SysML stereotypes, which appear in "<< >>" brackets at the top of the instances. The roles assigned by the industrial symbiosis is represented by the names in front of the ":" of the instances. For example, the Statoil plant has the role "symbiosis partner 2" assigned to it. What is not depicted in Fig. 5 are the exchange relationships taking place at an organizational level. These exchanges would be modeled in a separate internal block diagram, showing the flows between the organizations.

DISCUSSION

One of the challenges of creating a conceptual framework in the field of industrial ecology is the immaturity of the domain. Immature means that there is no consensus on the definition of key concepts within the domain. Without a consensus on what industrial symbiosis is, any conceptual framework can only represent a certain perspective on industrial symbiosis. This means that our interpretation of industrial symbiosis may be inconsistent with other definitions. Nevertheless, we think that our definition may open up fruitful new avenues of research by bringing different forms of exchanges together, such as material / energy exchanges between plants and exchanges on an organizational level. These are all interpreted as exchanges of value. "Value" is essentially an economic term. Using an economic term in the context of sustainability has its challenges. For example, there is ongoing debate on how "environmental value" can be defined and measured [START_REF] Brouwer | Environmental value transfer: state of the art and future prospects[END_REF]. Ecological economics and environmental economics try to develop approaches for bridging the gap between mainstream economics and ecology [START_REF] Ayres | On the life cycle metaphor: where ecology and economics diverge[END_REF], [START_REF] Gómez-Baggethun | The history of ecosystem services in economic theory and practice: From early notions to markets and payment schemes[END_REF]. Therefore, progress in these domains may also help to advance research on industrial symbiosis.

CONCLUSIONS

This paper presents an initial conceptual framework for architecting EIPs. The conceptual framework includes industrial symbiosis, which is pertinent to EIPs. A novel value-centric definition of industrial symbiosis extends the existing industrial symbiosis perspective on industrial symbiosis. This perspective may open up the possibility to use approaches from mainstream economics and sociology to analyze industrial symbiosis. Furthermore, an EIP's internal and contextual elements are defined.

The conceptual framework is a first step towards the application of system architecting methods such as tradespace exploration and stakeholder analysis to EIP development. For the application of tradespace exploration, the current qualitative model needs to be quantified by adding values for key attributes for plant inputs and outputs as well as basic relationships that describe infrastructure cost and changes in flow parameters. A further interesting avenue of research is the use of system architecting methods to conduct an integrated technological, organizational, as well as project finance analysis to EIP development.

Fig. 1 :

 1 Fig. 1: Using systems architecting for addressing key EIP decision-making dimensions The following two steps are proposed for answering questions a) -f): 1. Develop a conceptual framework for EIPs that specifies its elements and relationships between these elements. It furthermore specifies the various views on EIPs such as economic and environmental. The conceptual framework serves as a qualitative basis for quantitative models. It answers question a) and c). 2. Based on the framework, a quantitative techno-economic model is derived, which is used for exploring different EIP architectures by tradespace exploration. The resulting architectures are evaluated with respect to costs, benefits, risks, and, opportunities. The techno-economic model intends to provide an answer to question b), d), e), f).

Fig. 3 :Fig. 4 :

 34 Fig. 3: EIP context model

Fig. 5 :

 5 Fig. 5: Part of the Kalundborg EIP as an instance of the conceptual framework

Table 1 :

 1 Overview of EIP development publications

	Publications	Domain / methods used Contribution
	Haskins [26],	Systems	engineering,	Identification	of	systems	engineering
	[27],	Maya	complexity science	approaches for developing EIPs
	Sopha [28]
	Eilering	&	Sociology		Identification of social enabling factors for
	Vermeulen [29]			EIPs
	Cao et al. [23],
	Kim et al. [34],
	Gu et al. [35],
	Maillé & Fraillet
	[36], Schulze,
	[37], Monteiro
	et al.,

Table 2 :

 2 Mutualism types according to Connor[START_REF] Connor | The benefits of mutualism: a conceptual framework[END_REF]

	Mutualism type	Characteristics
	Byproduct exchange	Species A provides a
		byproduct for species B
		from which B benefits. A
		is not investing in the
		byproduct.
	Reciprocity	The same as byproduct
		exchange but A expects
		a	compensation.
		However, B has the
		possibility to withhold
		compensation.
	Pseudo-reciprocity	Same as reciprocity
		without a possibility to
		withhold compensation.

Table 3 :

 3 Comparison of biological and industrial symbiosis

	Characteristic Biological		Industrial
		symbiosis		symbiosis
	Unit of analysis Species and their	Industries and
		relationships	their exchange
				relationships
	Symbiosis	Evolutionary	Evolutionary or
	development			deliberate
				planning
	Exchange	Nutrition (larva -	Physical objects
	objects	ants), protection	(CO2,	vapor,
		(ants	protect	biomass,
		larva), locomotion	gypsum),
		(bees -pollen),	financial


Rafael Cezar
email: rafael.cezar@dauphine.fr

Octavio R Escobar
email: oescobar@esg.fr

Institutional Distance and Foreign Direct Investment

Keywords: Foreign Direct Investment, institutions, heterogeneous firms, gravity model. JEL: F12, F23, H80, K20

This paper studies the link between Foreign Direct Investment (FDI) and institutional distance. Using a heterogeneous firms framework, we develop a theoretical model to explain how institutional distance influences FDI and it is shown that institutional distance reduces both the likelihood that a firm will invest in a foreign country and the volume of investment it will undertake. We test our model, using inward and outward FDI data on OECD countries. The empirical results confirm the theory and indicate that FDI activity declines with institutional distance. In addition, we find that firms from developed economies adapt more easily to institutional distance than firms from developing economies.

Introduction

Developing and transition economies increasingly attract Foreign Direct Investment (FDI) flows (UNCTAD, 2012). Three important patterns, observable in UNCTAD (2012) data, are helpful in understanding this trend. First, most FDI outflows are from developed economies, but their share in total FDI outflows is decreasing over time. Since 2003, the share of FDI outflows of developing and transition economies has consistently increased, reducing the share of FDI from developed economies to approximately 75% in 2011. Second, firms primarily invest in countries with similar levels of development as their own. More than 70% of outward FDI from developing and transition economies goes to other developing and transition economies, and approximately 50% of this outward FDI goes to economies located in the source economy's region. Developed economies also locate most of their operations in other developed economies. Third, developed economies have a greater capacity to diversify their operations than developing and transition economies. Approximately 45% of outward FDI from developed economies goes to developing and transition economies. Moreover, developed economies are the main source of FDI in developing and transition economies.

Differences in the quality of institutions across countries are the main determinant of differences in economic development [START_REF] Acemoglu | The Role of Institutions in Growth and Development[END_REF]; we thus expect that institutional distance is an important determinant of FDI, and helps to explain recent FDI patterns. We develop a theoretical model, using a heterogeneous firms framework, to explain how institutional distance influences decisions to invest in a country and the volume of investment undertaken. According to the heterogeneous firms literature [START_REF] Helpman | Estimation trade flows: trading partners and trade volumes[END_REF][START_REF] Yeaple | Firm heterogeneity and the structure of U.S. multinational activity[END_REF], a productivity threshold must be overcome to make FDI profitable. Thus, only the most productive firms, mainly from developed countries, can invest abroad.

When entering foreign markets, multinational enterprises (MNEs) must adapt their strategies to the requirements of local institutions, which may differ from the institutions of their home countries. Our model suggests that MNEs face an adaptation cost in adjusting to the institutional environments of host countries. As adaptation costs increase with institutional distance, institutional distance determines the productivity threshold at which FDI is more profitable than exporting as a means of accessing foreign markets. Thus, increasing institutional distance reduces the number of firms that undertake FDI. In addition, adaptation costs reduce firm profits and the profitability of FDI.

Accordingly, firms' FDI declines with institutional distance.

Firms perform better in foreign markets similar to their home markets than in markets that are dissimilar, as similarities are easier for firms to manage [START_REF] Johanson | The internationalization process of the firm -a model of knowledge development and increasing foreign market commitments[END_REF]. In addition to institutional similarities, development, geographical and cultural proximity are important determinants of FDI [START_REF] Head | FDI as an outcome of the market for corporate control: Theory and evidence[END_REF][START_REF] Helpman | Estimation trade flows: trading partners and trade volumes[END_REF][START_REF] Guiso | Cultural Biases in Economic Exchange[END_REF]. To control for differences in all of these factors across countries, when studying the determinants of FDI, the empirical literature uses gravity equations. Studies show that institutional distance matters for bilateral FDI. Differences between host and source countries in terms of corruption [START_REF] Habib | Corruption and Foreign Direct Investment[END_REF], legal rules [START_REF] Guiso | Cultural Biases in Economic Exchange[END_REF], credit market regulations, legal constraints in recruiting and firing, and decentralisation of wage bargaining [START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF] reduce bilateral FDI flows. However, the costs of institutional distance may differ for firms from developed and developing countries, owing to firm heterogeneity. Firms from developed countries may have more experience and better networks, which reduce the cost of institutional distance, than firms from developing countries [START_REF] Johanson | The internationalization process of the firm -a model of knowledge development and increasing foreign market commitments[END_REF].

We proceed to an empirical validation of our model. Using alternative indicators of institutional distance, the results suggest that FDI activity declines as institutional distance increases. When investing in countries with weak institutions, firms from countries with weak institutions face lower costs than firms from countries with strong institutions. The results also suggest that institutional distance more strongly influences firms' decisions to invest in developing than in developed economies. Once investment decision is made, institutional distance equally affects the amount of investment from developed and developing economies. This paper is organised as follows. Section 2 presents the theoretical model. Section 3 describes the empirical specification of the model and the estimation strategy. Section 4 describes the data and the measures of institutional distance used. The empirical results are presented in Section 5. Section 6 concludes.

The model

The model, based on [START_REF] Melitz | The impact of trade on intra-industry reallocations and aggregate industry productivity[END_REF], [START_REF] Helpman | Export Versus FDI with Heterogeneous Firms[END_REF] and [START_REF] Yeaple | Firm heterogeneity and the structure of U.S. multinational activity[END_REF], focuses on firms' arbitrage between exporting and producing abroad. The world economy features countries with firms in each. Each firm produces a single variety of good and is monopolistic in this variety.

Consumer preferences, which are identical across countries, are represented by the following constant elasticity of substitution (CES) utility function:

, where is the elasticity of substitution between goods and denotes the set of varieties available in country . Maximisation of the utility function entails the following demand function for variety :

, ( 1
)
where ! is the price of variety , " ! is the ideal price index, and is total income. The market is characterised by monopolistic competition, and the price of variety is a mark-up over marginal cost:

! # $, (2)
where % is a parameter that measures the inverse of the degree of differentiation between goods so that # indicates firms' unit margin, and $ is marginal cost.

Labour is the only input, and firms are heterogeneous in their firm-specific productivity levels, denoted by &. This parameter indicates the quantity of goods produced with one unit of labour, and we Labour costs are country specific, with the wage in country denoted by -. The marginal cost of output -.& is decreasing in productivity, constant at a given productivity level, and specific to each country.

Costs of internationalisation

The model focuses on firms' internationalisation decisions and only firms already operating in the domestic market are considered in the analysis. Firms wishing to sell their products abroad face a tradeoff between two internationalisation strategies: exporting or FDI that enables it to produce within the economy of its trading partner.

To export, firms face two additional costs: fixed and variable trade costs. Fixed trade costs relate to marketing, certifications, regulations, etc. These costs are bilateral and denoted by / 0 for the pair ) 1.

Firms also face variable costs in selling their products abroad, modelled as iceberg transportation costs. Specifically, the delivery of one unit of a good in country 1 requires that 2 0 units be shipped and the marginal cost of exporting from country to country 1 is 2 0 -& . These costs are country-pair specific. The total cost of exporting units by a firm with productivity & is 34 0 5 6 7 8 9 : ; / 0 .

Firms can also open a subsidiary to produce directly in the destination country. This alternative allows a firm to economise on transportation costs but incurs management costs and high fixed costs related to the construction of new facilities and adaptation to the host country. The management and communication costs affect technology transfers and affect firms' productivities and marginal costs.

These costs are modelled as iceberg costs with < 0 = . Therefore, the variable cost of FDI faced by a firm from country with productivity & and that produces in country 1 is -0 >< 0 &? . The fixed costs associated with this activity are @ 0 . The total cost of producing units in country 1 is

3/ 0 8 7 A 7 9
; @ 0 .

Firms' arbitrage

To focus on the tradeoff between exports and FDI, firms are assumed to only use FDI to access the host country's market. It is used neither as an export platform nor for outsourcing production.

Transportation costs are assumed to be relatively high compared to the difference between labour costs and technology transfer costs (i.e., 2 0 -< 0 -0 for all country-pairs), and exporting fixed costs are always lower than FDI fixed costs (i.e., @ 0 / 0 ).

From The marginal cost of firms that export from country to 1 is 2 0 -& . The marginal cost of firms from country 1 that open a subsidiary in country is >< 0 -0 ? & . As 2 0 -< 0 -0 and ε > 1, the variable income for MNEs -and their marginal profit -is always higher than the marginal income of exporting firms, as

PF M 9 P9 PF J 9 P9
. However, as fixed FDI costs are higher than fixed export costs, the productivity threshold above which firms will export is typically lower than the productivity threshold for FDI.

Figure 1 As productivity levels are not observable, firms' income function is used as a proxy for the FDI threshold. Income is a good proxy for &, as it is an increasing function of productivity. From Equation 3, the income level above which firms from country invest (and produce) in country 1 is

B>& H 0 R ? >N 7 L 7 ? S T 7 U V 7 U 7 W . ( 4
)
All firms from country with income levels higher than or equal to B>& H 0 R ? engage in FDI in country 1.

As Equation 4 indicates that & H 0 R depends on the difference between the fixed costs of FDI and the fixed costs of exporting and on the ratio between the marginal costs of FDI and the marginal costs of exporting. An increase in fixed FDI costs raises the marginal income required to delocalise production and reduces the number of firms able to produce abroad. A reduction in fixed trade costs produces the same result. Specifically, an increase in the difference between @ 0 and / 0 makes exports relatively more attractive than FDI.

As the ratio between marginal costs is always greater than one,

6 7 8
A 7 8 7

is less than unity. Therefore, & H 0 R is larger when the distance between 2 0and < 0 -0 is small, while an increase in variable trade costs -such as customs barriers, poor infrastructure or distance -reduces the income threshold required to engage in FDI and raises FDI. Communication problems with the subsidiary or increasing labour costs in the host country have the opposite effect, reducing FDI.

Institutional distance and fixed FDI costs

Firms face two types of fixed FDI costs: i) construction of new facilities, and ii) adaptation costscosts required to produce in the institutional, political and economic environment of the host country.

Equation 1 indicates that the demand of goods rises as price falls, and price -according to Equation 2-decreases as firm productivity rises. Therefore, MNEs' demand depends on productivity, and the most productive firms face the highest demand, selling more goods in the host country. Subsequently, 1 Both exporting firms and MNEs also produce in the source country for the domestic market.

the size of the facilities that multinationals construct in the host country is proportional to their productivity level, with the most productive firms building the largest facilities.

Therefore, the cost of investment in new facilities is assumed to be a function of expected profits in the host market2 . For simplicity, the investment cost function is assumed to be monotone and linear.

The cost for firms of country of opening a subsidiary in country 1 is -0 [F 0 & , where the parameter  is positive and strictly less than one, and F 0 is defined as in Equation 3. Thus, the fixed cost of investing in new facilities depends on firm productivity.

The second fixed FDI cost is the adaptation investment in the new institutional environment. To produce in the host country, firms must adapt to its legal system, tax laws, political and governmental framework, conditions of access to credit, and regulations. Such adaptation costs depend on the institutional framework of the host country. Countries with weak institutional environments have high adaptation costs, while improvements in the institutional environment lower these costs [START_REF] Daude | The quality of institutions and foreign direct investment[END_REF]. On the other hand, firms are already accustomed to the institutional environments of their domestic markets and have experience in coping with them. Such experience can reduce adaptation costs, especially when the institutional environments of the country-pair are similar [START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF][START_REF] Guiso | Cultural Biases in Economic Exchange[END_REF][START_REF] Habib | Corruption and Foreign Direct Investment[END_REF]. Thus, institutional proximity between source and host countries reduces adaptation costs and facilitates FDI. A firm accustomed to a weak institutional environment will find it easier to invest in a country with similar characteristics, while the same firm will need to invest more to adapt to a country with an efficient institutional system.

Let us denote by \ the level of institutional development of country . This parameter measures the overall institutional quality of a country, including regulations, property rights, access to information, financial constraints, level of corruption, and political stability, as well as the formalities involved in opening a business, executing a contract, and registering a property. If \ and \ 0 represent the overall level of institutional development in the source and host countries, respectively, the cost of institutional adaptation is an increasing function of the distance between \ and \ 0 . When this distance is large, firms face strong institutional barriers, and adaptation costs are high. However, when institutional environments are similar, that is, when \ and \ 0 are close, firms in country are familiar with the institutional environment in country 1 and adaptation costs are low.

The adaptation cost associated with the institutional environment of country 1 for a firm from country is denoted -]>\ 0 Y\ ?, where the adaptation cost is measured in labour units of the source-country, as firms pay this cost in their own country before investing in the host country. The adaptation cost function is specific to the country-pair, monotone, strictly positive and increasing in institutional distance, such as

P^>_ 7 _ ? P>_ 7 _ ?
,.

The fixed cost of engaging in FDI in country 1 for firms from country is

@ 0 -0 [F 0 & ; -]>\ 0 Y\ ?. (5)

Institutional distance and FDI margins

From Equations 4 and 5, we can rewrite the income threshold above which firms from country invest in country 1 as

B>& H 0 R ? 8 7 `I 7 9 a8 ^>_ 7 _ ? L 7 S T 7 U V 7 U 7 W . ( 6
)
Equation 6 indicates a direct and negative relationship between institutional distance and the marginal variation in the proportion of firms that engage in FDI (extensive margin). On the one hand, an increase in institutional distance raises the income threshold and reduces the set of firms that are able to invest abroad. On the other hand, a decrease in institutional distance reduces adaptation costs and increases the number of firms that engage in FDI. This relationship is shown by

P9 M 7 R P>_ 7 _ ? b PD59 M 7 R : P>_ 7 _ ? ,, (7)
where & H 0 R >\ 0 Y \ ? is the elasticity of the FDI productivity threshold with respect to institutional distance. This elasticity is positive, and so, a positive change in >\ 0 Y \ ? increases & H 0 R . An increase in the FDI productivity threshold reduces the proportion of MNEs (extensive margin) because & is fixed and identical in all countries. This relationship is summarised in the following proposition:

Proposition 1: There is a direct and negative relationship between the institutional distance between countries and the marginal variation in the extensive margin, such as an increase in this distance raises the productivity threshold above which FDI is profitable and reduces the proportion of multinational firms in country that invest in country 1.

To examine how institutional distance affects the volume of FDI between two countries (intensive margin), we define in the equation below c 0 as the average productivity of firms from country that invest in country 1:

c 0 d & & / & H 0 R e & * 9 f 9 M 7 R , / & H 0 R & * (8)
If & H 0 R & * , c 0 , because the productivity threshold is higher than the productivity of the most productive firm, and no firm from country is sufficiently productive to invest in country 1.

When & H 0 R e & * , at least one firm has productivity sufficient to invest abroad, and c 0 ,. This variable is country-pair specific, with c 0 g c 0 , which allows for asymmetric FDI flows.

The intensive margin is the sum of all investments made by each firm (for which & = & H 0 R ) in the construction of new facilities3 . From Equations 5 and 8, total FDI from country to country 1 is

@hi 0 -0 [F 0 & c 0 S > 8 7 `?8 7 ` >8 7 `?j W E 0 >< 0 -0 ? & Y -]>\ 0 Y\ ? c 0 (9)
Equation 9 indicates that the cost of institutional adaptation has a negative effect on the value of FDI, and a positive change in the first variable reduces FDI from country to country 1. ]>\ 0 Y \ ? also affects the number of investing firms via the productivity threshold, which is included in c 0 . Thus, institutional distance affects the intensive margin of FDI through these two distinct channels. Based on these observations, the second theoretical proposition is presented as follows:

Proposition 2: Institutional distance negatively affects the intensive margin of FDI, such as an increase in institutional distance reduces FDI.

Empirical specification

The gravity equation is commonly used to study the determinants of FDI as they can be derived from various theoretical models [START_REF] Head | FDI as an outcome of the market for corporate control: Theory and evidence[END_REF][START_REF] Kleinert | Gravity for FDI[END_REF]. We develop a gravity equation to test the propositions of our model. First, our model suggests that institutional distance influences decisions to invest abroad (the extensive margin). Second, our model suggests that institutional distance influences the profitability of foreign investment and the volume of investment (the intensive margin). Because the volume of investment depends on the extensive margin, we develop, following [START_REF] Helpman | Estimation trade flows: trading partners and trade volumes[END_REF], a two-stage gravity equation to estimate the extensive and intensive margins. In the first stage, or selection equation, firms choose whether to invest (extensive margin); in the second stage, or primary equation, firms that invest decide how much to invest (intensive margin).

Empirical specification of the selection equation

From Equation 8, the decision to invest depends on firms' productivity and on the productivity threshold. We define the variable k 0 as the ratio of the productivity of the most productive firm (& * )

to the productivity threshold (& H 0 R ). If k 0 , then firms from country i invest in country j. We assume that the productivity of the most productive firm (& * ) in country i is given; thus, variations in k 0 are caused by changes in the threshold at which FDI is more profitable than exports. Therefore, the estimation of k 0 allows us to estimate the impact of institutional distance on the productivity threshold

(& H 0 R
) and the decisions of firms to invest abroad:

k 0 9 f 9 M 7 R l 5mn 7 :
o 7 p5>A 7 8 7 ? >6 7 8 ? :

N 7 L 7 & * . ( 10
)
We assume that differences in fixed and variable costs between exporting and FDI are stochastic.

More precisely, we suppose that >< 0 -0 ? Y >2 0 -? q r4! s -; s t -0 ; s u v ; s w x 0 ; s y z 0 ; X 0 , where v is a measure of the costs of exporting, such as customs procedures and regulations in country i, and is independent from the export destination; x 0 is a measure of trade barriers, such as customs procedures and regulations, imposed by the importing country j on all exporters; z 0 measures country-pair characteristics, such as bilateral distance and ease of communication, that influence both trade costs >2 0 ? and coordination costs >< 0 ?; and X 0 { ,) | t is an error term. With respect to differences in fixed costs, we assume that @ 0 Y / 0 q r4! } -; } t -0 ; } u ]>\ 0 Y \ ? ; ~ 0 , where ~ 0 { ,) | • t is an error term. We can express Equation 10 in log form as

€ 0 • ' ; • ; • 0 ; • 0 ; ƒ 0 , (11)
where

€ 0 "… k 0 ; • ' Y "… % Y "… is a constant; • Y "… & * ; s ; } -;
s u v represents the characteristics of the source country ; • 0 = > Y "… " 0 ; "… 0 ? ; s t ; } t -0 ; s w x 0 represents the characteristics of the host country 1 ; • 0 s y z 0 ; } u ]>\ 0 Y \ ?

represents the characteristics of the country-pair ) 1 ; and ƒ 0 X 0 ; † 0 { ,) | t ; | • t is an independent and identically distributed (iid) error term.

We cannot measure € 0 because neither firms' productivity levels nor the productivity threshold are observable. However, the presence of firms from country in country 1 implies that € 0 , . A selection indicator ‡ 0 is generated, using a latent variable such as ‡ 0 if firms from country invest in country 1 and ‡ 0 , otherwise. Let ˆ 0 be the probability that country invests in country

ˆ 0 "B> ‡ 0 ‹OE•ŽrB•r ••B ••"rŽ? '>• ' ; • ; • 0 ; • 0 ; ƒ 0 ? ) (12)
where ' ' is the cumulative distribution function of the unit-normal distribution. We then estimate the predicted value of € 0 as € " 0 ' " 0 . It is important to note that the selection equation (Equation 12), is derived from a decision at the firm level. It shows how changes in the characteristics of the countries affect FDI decisions. More specifically, marginal changes in the characteristics of country or 1 modify the productivity threshold and affect the choice between exporting or performing FDI.

Equation 12 then provides information on the marginal variation in the proportion of firms from country investing in country 1.

Empirical specification of the primary equation

FDI flows from country to country 1 , given by Equation 9, can be expressed in log form as "…>@hi 0 ? [ ; "…>-0 ? ; "…>F 0 ? ; "…>c 0 ? ; "… . Profits (F 0 ) depend on demand as well as on production and implantation costs. Thus, we estimate the following equation "…>@hi 0 ? " ; " ; " 0 ; " 0 ; • 0 ; • 0 ,

where " , " 0 , and " 0 are the characteristics of country , country 1, and country-pair ) 1, respectively;

• 0 "… c 0 is the logarithm of the average productivity level of firms from country that invest in country 1; and • 0 { ,) | - t is an error term.

From Equations 4 and 7, following [START_REF] Helpman | Estimation trade flows: trading partners and trade volumes[END_REF], we specify • 0 q † € 0 as an arbitrary increasing function of € 0 . More precisely, we control for -'c 0 ˜O ) ‡ 0 +, using †>€ " 0 ?, which we approximate with a cubic polynomial in € " 0 .

Estimation strategy

Equations 12 and 13 include common exogenous variables specific to the FDI source country , host country 1, and country-pair ) 1. GDP per capita proxies for wage levels, geographical and cultural distance proxy for trade and coordination costs, and country size or GDP levels proxy for demand.

These proxies enable us to construct a gravity equation for both the selection and primary equations.

The literature suggests that under general equilibrium, bilateral FDI depends on the same exogenous determinants as bilateral trade flows [START_REF] Bergstrand | A knowledge-and-physical-capital model of international trade flows, foreign direct investment, and multinational enterprises[END_REF]Egger, 2010).

Although the exogenous variables included in the selection and primary equations may be identical, an additional variable not included in the primary equation is also required in the selection equation [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. In addition, incorporating panel data estimates from the selection equation into the primary equation entails potential autocorrelation bias. We follow [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF], who proposes estimating the selection equation for each year t and using the resulting estimates to compute € " 0 . This procedure is similar to the two-stage estimators of Heckman (1979); however, we only control for firms' heterogeneity, not for selection bias. To address zero flow observations, we employ the Santos-Silva and Tenreyro (2010) Poisson pseudo-maximum-likelihood (PPML) estimator.

Data

We construct a panel database of the bilateral relationships between 31 OECD countries and 125 countries in different stages of economic development. Data are available for the 2004-2009 period.

The dependent variable is the ratio of the bilateral stock of foreign direct investment -inward into and outward from OECD countries -to the GDP of the FDI receiving country 4 . Data come from the International Direct Investment Statistics database, available from the OECD, and from the World Development Indicators (WDI) of the World Bank. WDI also provides data on GDP per capita.

Geographic and cultural variables are available from the Center for Studies, Prospective and International Information (CEPII).

Institutional distance index

Different institutional frameworks and their impact on economic activity have received substantial attention in the recent literature on FDI [START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF][START_REF] Guiso | Cultural Biases in Economic Exchange[END_REF]. Generally, the term "institution" refers to structures that affect economic relations. [START_REF] North | Economic Performance Through Time[END_REF] defines institutions as the constraints built by men and designed to organise social relations. Formal constraints include regulations, property rights, the financial system and contract enforcement [START_REF] Levchenko | Institutional Quality and International Trade[END_REF]; while informal constraints include levels of social trust [START_REF] Algan | Inherited Trust and Growth[END_REF] and corruption [START_REF] Habib | Corruption and Foreign Direct Investment[END_REF].

A measure of the distance between institutional environments must therefore be sensitive to various aspects of the institutional structures of each country. Thus, a wide range of indicators of various formal and informal constraints is used to construct a single composite index for each country in the sample, using principal component analysis (PCA). The institutional indexes of countries and 1 are denoted by \ and \ 0 . The institutional distance between two countries is then calculated from the composite index and equals the absolute value of the difference between two countries' indices:

h Ž™ 0 ‹\ Y \ 0 ‹.

Principal component analysis

Principal component analysis is used to construct the composite institutional index. This method allows for a set of multivariate observations to be described by a linear combination of these observations so as to maximise the variance explained by the new variable. Specifically, the original provide a more accurate measure than flow data; (ii) Stocks are much less volatile than flows; and (iii) Stock data greatly reduce the number of zero observations in the sample.

variables š ) › ) š are transformed into a new variable oe, such that the variance of oe in the total sample is maximal. In other words, the weighting coefficients of the first component maximise the variance and minimise the loss of information from the original sample5 .

We estimate the institutional index using the first component of the analysis. This component alone explains approximately 68% of the total variance in the sample constructed6 , a high level of explanatory power that fully justifies the use of this component to summarise the information contained in the various selected variables.

Institutional data

We use 13 indicators of the efficiency of structures that affect economic relations to measure the functioning of the economic and institutional environment. Two different composite indicators are constructed from the combination of these variables, and one distance measure is constructed for each indicator (see Table 1).

The first institutional index we construct is composed of six indicators. Among these, four indicators measure the quality of governments and their policies: corruption index, government effectiveness, political stability, and regulatory quality. The first captures the extent to which public power is exercised for private gain. The second is sensitive to the quality of public services, their independence from political pressure and the credibility of government commitments. Political stability indicates views about the probability that a government could be destabilised or overthrown by unconstitutional or violent means. The final indicator measures the ability of government to formulate and implement policies and regulations that promote development of the private sector. These indicators are made available by the World Governance Indicators project of the World Bank. Two additional variables, which also measure the quality of regulatory policy and the institutional environment faced by firms and their access to information, are employed in the construction of this first index. Credit rights measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus the functioning of the credit market. The Information index measures rules affecting the scope, accessibility, and quality of information available through either public or private credit registries. These two variables are made available by the Doing Business project of the World Bank.

The second institutional index is composed of the six indicators presented below and seven others7 .

Three of the indicators concern bureaucratic practices and laws imposed on businesses: the costs of executing a contract, of registering a property and of starting a business. Two other indicators measure trade institutions: cost to export and cost to import, both measured as cost per container in U.S.

dollars. These five indicators are provided by the World Bank's Doing Business project. An additional indicator is employed to measure governments' protectionist policies: the simple mean applied tariff rate, as a percentage of price, for all traded goods. This indicator is calculated in the Global Development Indicators, using data from the Trade Analysis and Information System of the United Nations and the Integrated Data Base of the World Trade Organization. The seventh measure used is the private credit to GDP ratio, which indicates the financial resources provided to the private sector through loans, purchases of non-equity securities, and trade credits. This variable indicates financial constraints in the economy and is provided by the International Monetary Fund.8

Results

Our model suggests that institutional distance reduces both the extensive and intensive margins of FDI. This section presents empirical results that verify the propositions of the model, using two alternative datasets. The first uses data on OECD countries' outward FDI, while the second uses data on the OECD countries' inward FDI. We find differences between the determinants of outward and inward FDI, differences that help explain the contrasting patterns of FDI outflows between developing and developed countries.

Determinants of OECD countries' outward FDI

First, we nominated an exogenous variable correlated with the selection indicator ‡ 0 but not with FDI stocks. Column 1 of Table 2 shows that the contiguity dummy variable and the colonial tie dummy variable are good candidates for such a variable, as they are not significantly correlated with FDI. The estimates of the probit model indicate that the colonial tie dummy variable is significantly correlated with the selection indicator, but the contiguity dummy is not. Thus, we use the colonial tie dummy variable as an exogenous variable in the selection equation and not in the primary equation.

Column 2 presents the probit estimates of the selection equation, or the extensive margin. The results indicate that GDP per capita in the source country, similarity in size between the countries, common language, and colonial ties increase the number of firms from country investing in country 1, but geographical and institutional distance reduce this number. This result is robust to estimates based on the second index of institutional distance (column 4).

The probit estimates provide information regarding the main determinants of the extensive margin.

However, using these estimates can lead to serious bias when estimating the primary equation, or the intensive margin [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. We estimate the parameter for the number of MNEs from country in country 1 (€ " 0 ), using different probit estimates for each year t. We then add € " 0 to the primary equation to estimate the determinants of the amount of FDI, or the intensive margin. Column 3 presents the results for the primary equation, using the first index of institutional distance. On the one hand, bilateral FDI increases in the number of investing firms (€ " 0 ), GDP per capita of both the source and host countries, similarity in size and common language. On the other hand, bilateral FDI declines with both geographical and institutional distance. Like the probit estimates, the PPML estimates are quite similar for the second index of institutional distance presented in column 5.

The results show similar determinants of the intensive margin (selection equation) and the extensive margin (primary equation). However, the extensive margin is more sensitive to similarities in GDP level, common language, and institutional distance, but less sensitive to geographical distance. GDP per capita of the host country is not significant for the intensive margin but is significant for the extensive margin. However, similar GDP levels are more important than the host country's GDP per capita.

Determinants of OECD countries' inward FDI

We proceed to estimate the results using inward FDI instead of outward FDI. The results are presented in Table 3. First, we identify an exogenous variable correlated with ‡ 0 and not with @hi 0 . Column 1

indicates that the colonial ties dummy is not correlated with FDI. Thus, we use the colonial ties variable as an exogenous variable in the selection equation, but we exclude it from the primary equation.

Columns 2 and 4 present the probit estimates of the selection equation. The likelihood that a firm from country invests in country 1 increases with similarities in size and culture, but this likelihood decreases with geographical and institutional distance. The results are robust to changing the institutional index. Institutional distance reduces the number of firms that engage in FDI, as suggested by the model.

We estimate € " 0 using different probit estimates for each year ™; we then add € " 0 to the primary equation to estimate the determinants of FDI volume. Columns 3 and 5 indicate that FDI volume also increases in the number of MNEs, GDP per capita of the host country, similarities in size between the source and host countries and common language but declines in geographical and institutional distance.

Among these variables, only GDP per capita is not correlated with the selection indicator. More importantly, the extensive margin is more sensitive than the intensive margin to GDP and language similarities but less sensitive to geographical distance. The difference in sensitivity to institutional distance between the two margins is, however, small.

Institutional distance and FDI patterns

The results, presented in Tables 2 and3, highlight similarities and differences in the patterns of outward and inward FDI. GDP per capita of the host economy increases the volume of FDI but does not affect the likelihood that new firms will engage in both outward and inward FDI. Average income in the host economy appears to increase the profitability of investment, which encourages firms to undertake the largest investments in the most developed countries.

GDP per capita of the source country is very important in outward FDI of OECD countries but insignificant in inward FDI. The wealthiest OECD countries are present in more countries and invest larger amounts than less developed OECD countries. OECD countries also attract FDI from various locations but not necessarily from the most developed countries.

Similarities in economic size and culture increase both the number of investing firms and the volume of FDI. Similarity of GDP levels is, however, a stronger determinant of inward FDI than of outward FDI. As OECD countries are among the largest economies in the world, this result suggests that among non-OECD countries, differences in the investment capacities between small and large countries are larger than differences in the abilities of small and large countries to attract FDI from OECD countries.

As our model suggests, institutional distance influences which firms will engage in FDI, or the extensive margin. However, the costs of institutional distance for developed and developing countries are asymmetric. OECD inward FDI is more sensitive to institutional distance than OECD outward FDI. According to our model, institutional distance is a cost that increases the productivity threshold above which FDI is profitable. As firms from non-OECD countries are on average less productive than firms from OECD countries, the probability that the productivity of such firms will exceed the productivity threshold is lower than for firms from OECD countries.

The theoretical model proposes that institutional distance also reduces the profitability of investment and the volume of FDI undertaken. The empirical results validate this proposition. In addition, our results show that the sensitivity of FDI volume to institutional distance is similar for OECD and non-OECD firms. The effect of institutional distance on the profitability of investment is thus similar for OECD and non-OECD countries.

Conclusion

We propose a theoretical model to explain the impact of institutional distance on FDI. We assume that institutional distance imposes a cost on investors. Precisely, investors must interact with foreign institutions to obtain credit and complete administrative, bureaucratic, and legal procedures when investing abroad; institutional proximity increases the expertise available to cope with these procedures. The model suggests that institutional distance influences both the decision to invest abroad (extensive margin) and the volume of investment a firm undertakes (intensive margin). As adaptation costs increase with institutional distance between source and host countries, institutional distance determines the productivity threshold at which FDI is more profitable than exporting as a means of entering a foreign market. Increases in institutional distance raise this threshold and the number of firms that undertake FDI decreases. In addition, institutional distance also affects the total volume of FDI undertaken by the source country in the host economy by affecting the extensive margin and firms' profitability.

We conduct an empirical investigation to validate our model, using data on FDI of OECD countries.

Using alternative indicators of institutional distance, the results suggest that both the extensive and intensive margins of FDI decrease as institutional distance increases. Institutional distance plays an important role in FDI. First, institutional distance reduces the number of firms for which FDI is sufficiently profitable that they prefer exporting over this mode of implantation. Second, institutional distance reduces the profitability of FDI such as the amount of firms' FDI decreases with this distance.

Institutional distance has differing effects on OECD countries' outward and inward FDI. In particular, institutional distance is more important for OECD countries' inward FDI than for their outward FDI.

This indicates that there is an asymmetry in bilateral FDI flows between developed and developing countries. Perceived institutional distance from a developed country to a developing country and from the same developing to the developed country are not equal. Firms from developed countries can more easily cope with the costs of institutional distance, which explains why FDI flows from developed countries are larger than FDI flows from developing countries.

In addition to institutional distance, differences in economic size and in geographical and cultural distance discourage FDI. So, to date, proximity has been a major determinant of bilateral FDI. As cultural and economic proximity cannot be modified in the short run, authorities in developing countries should focus on improving institutions to improve FDI performance.

Figure 1

 1 Figure 1 divides firms into three distinct groups: i) firms with productivity below & G that produce only for the domestic market; ii) exporting firms with productivity between & G and & H R ; and iii) MNEs with productivity of at least equal to & H R that produce in the destination country through FDI 1 .

Figure 1 .

 1 Figure 1. Productivity thresholds and profits from exporting and FDI.

  assume that a cumulative distribution function (cdf) & , with support '& ( ) & * +, describes the distribution of & across firms, where & * & ( ,, and & ( and & * indicate the productivity levels of the least and most productive firms, respectively. The cdf is assumed to be identical across countries.

  , indicates the productivity threshold above which firms can pay fixed FDI costs. These two thresholds are defined as F & G 0 , and F & H 0 ,, respectively. Firms with productivity levels equal to or greater than & G export, and all multinational firms have productivity at least equal to & H .

	& H
		Equations 1 and 2, income from this activity can be represented as a function of firms' marginal
	costs. Specifically, B 0 &	>%" 0 ?	0 $ &	denotes income from sales to country 1 by a firm in
	country with productivity &, where $ & is the firm's marginal cost. The variable income of this project can also be represented as a function of marginal costs: C 0 & D 7 E 0 $ & , where
	E 0	># 7 ?	0 is specific to country 1 and measures demand adjusted for the elasticity of substitution.
	Let us define F G & 0 as export profits and F H & 0 as FDI profits. They are, respectively:
					I J 9 7 KE 7 >6 7 8 ?	9	L 7 )	(3)
					I M 9 7 KE 7 >A 7 8 7 ?	9	N 7 O
	Marginal costs are decreasing in &; thus, C 0 & and profits are increasing in productivity, that is,
	PQ 9 P9	, and	PF 9 P9	, , for both internationalisation strategies. Because firms only sell their
	products abroad if their profits are at least zero -that is, if variable income at least equals fixed costs -
	two productivity thresholds can be defined. The first, denoted & G , indicates the productivity level
	above which firms generate sufficient variable income to pay fixed export costs. The second, denoted

  in the appendix illustrates the productivity thresholds and profits that determine whether firms export or engage in FDI. & H indicates productivity above which FDI is profitable. However, at

	this productivity level, exporting is more profitable than FDI. Production is moved abroad only if
	profits tereby generated exceed those of exporting, that is, if F H & = F G & . This threshold is represented in Figure 1 by & H R . Specifically, & H R is such as F H & H R F G & and represents the productivity of the marginal multinational firm. This productivity threshold is country-pair specific,
	and all firms in country with productivity above & H R produce directly in country 1.

Table 1 .

 1 Institutional indexes

	π
	φ X
	f ij	φ I	φ I *	φ
	F ij
	Variables		Index 1	Index 2
	Corruption		Yes	Yes
	Government effectiveness		Yes	Yes
	Political stability		Yes	Yes
	Regulatory quality		Yes	Yes
	Credit rights		Yes	Yes
	Information index		Yes	Yes
	Cost to execute a contract			Yes
	Cost to register a property			Yes
	Cost to start a business			Yes
	Cost to export			Yes
	Cost to import			Yes
	Protectionist policy			Yes
	Private credit			Yes

Table 2 .

 2 Extensive and intensive margins of outward FDI : * Significant at 5%, ** significant at 1%. Standard errors robust to country-pair clustering recorded in parentheses. Each regression includes a constant. Time, source-country and host-country dummies not reported. Reported R-squared values for probit regressions correspond to pseudo R-squared values.

	Estimation method	PPML	Probit	PPML	Probit	PPML
	Dependent variable	š• (1)	ž š• (2)	š• (3)	ž š• (4)	š• (5)
	Source GDP per capita	2.283 **	2.347 **	2.662 **	2.347 **	2.608 **
		(0.867)	(0.493)	(0.983)	(0.498)	(0.962)
	Host GDP per capita	1.145 **	-0.131	1.173 **	-0.069	1.118 **
		(0.245)	(0.253)	(0.264)	(0.258)	(0.255)
	GDP Similarity	0.172 **	0.152 **	0.215 **	0.151 **	0.234 **
		(0.055)	(0.037)	(0.051)	(0.037)	(0.049)
	Geographical distance	-0.479 **	-0.717 **	-0.394 **	-0.718 **	-0.402 **
		(0.063)	(0.045)	(0.052)	(0.046)	(0.053)
	Contiguity dummy	-0.022	-0.157	0.039	-0.142	0.027
		(0.151)	(0.239)	(0.132)	(0.242)	(0.129)
	Common language dummy	0.676 **	0.387 **	0.716 **	0.383 **	0.667 **
		(0.133)	(0.096)	(0.115)	(0.096)	(0.110)
	Colonial tie dummy	0.239	1.161 **		1.164 **
		(0.138)	(0.148)		(0.149)
	Institutional distance		-0.053 *	-0.099 **
	Index 1		(0.025)	(0.027)
	Institutional distance				-0.056 *	-0.093 **
	Index 2				(0.027)	(0.026)
	Ÿ š•			1.144 ** (0.147)		1.148 ** (0.151)
	t Ÿ š•			-0.432 ** (0.102)		-0.435 ** (0.104)
	u Ÿ š•			0.056 ** (0.019)		0.057 ** (0.019)
	Observations	23064	22755	22755	22476	22476
	R 2	0.87	0.54	0.89	0.54	0.89

Notes

Table 3 .

 3 Extensive and intensive margins of inward FDI : * Significant at 5%, ** significant at 1%. Standard errors robust to country-pair clustering recorded in parentheses. Each regression includes a constant. Time, source-country and host-country dummies not reported. Reported R-squared values for probit regressions correspond to pseudo R-squared values.

	Estimation method	PPML	Probit	PPML	Probit	PPML
	Dependent variable	š• (1)	ž š• (2)	š• (3)	ž š• (4)	š• (5)
	Source GDP per capita	0.775	-0.346	0.907	-0.341	0.942
		(0.726)	(0.259)	(0.734)	(0.263)	(0.712)
	Host GDP per capita	2.288 **	0.641	2.256 **	0.744	2.201 **
		(0.575)	(0.494)	(0.603)	(0.498)	(0.571)
	GDP Similarity	0.332 **	0.241 **	0.366 **	0.242 **	0.382 **
		(0.064)	(0.039)	(0.067)	(0.039)	(0.064)
	Geographical distance	-0.473 **	-0.616 **	-0.440 **	-0.615 **	-0.464 **
		(0.057)	(0.044)	(0.046)	(0.044)	(0.047)
	Contiguity dummy	0.222	-0.079	0.234	-0.088	0.172
		(0.129)	(0.200)	(0.121)	(0.202)	(0.123)
	Common language dummy	0.525 **	0.476 **	0.564 **	0.475 **	0.537 **
		(0.131)	(0.103)	(0.128)	(0.103)	(0.108)
	Colonial tie dummy	0.159	0.723 **		0.733 **
		(0.116)	(0.146)		(0.146)
	Institutional distance		-0.080 **	-0.075 *
	Index 1		(0.025)	(0.029)
	Institutional distance				-0.106 **	-0.109 **
	Index 2				(0.027)	(0.028)
	Ÿ š•			1.153 ** (0.207)		1.136 ** (0.211)
	t Ÿ š•			-0.605 ** (0.134)		-0.597 ** (0.134)
	u Ÿ š•			0.091 ** (0.024)		0.089 ** (0.024)
	Observations	23064	22755	22755	22476	22476
	R 2	0.90	0.57	0.90	0.57	0.91

Notes

An alternative explanation is that the return on capital is calculated based on the sum of actualised expected profits.

As adaptation cost is incurred in the source country before investments are complete, FDI exclusively concerns investments in new facilities.

FDI stock data are widely used in the literature. The most frequent arguments used to justify the use of FDI stock data are as follows: (i) FDI is also financed by markets in the host country, and therefore, stock data

As the growth of these coefficients increases the variance indefinitely, the sum of the squared coefficients is constrained to equal unity. Furthermore, to address the different scales and units of the variables, the initial sample is centred-reduced, such that the mean is equal to zero and the standard deviation is equal to one.

For index 1, 68%; 40% in index 2.

The main criterion used in selecting variables and in their division into the two composite indexes is the availability of data.

An increase in all 13 indicators employed in both indexes indicates development of the institutional environment, such that an increase in the two composite indexes proposed in this section indicates an improvement in institutional quality. Nevertheless, this is not the objective of this exercise, which is mainly to measure the difference in institutional environments across countries.