Massimiliano Gambardella
email: massimiliano.gambardella@gmail.com

Erin Koval

Inna Lyubareva

Keywords: Open Production, Open Diffusion, Creative Commons, Open Licenses, Extrinsic, Intrinsic, Monetary, Non-Monetary, Motivations, Institutional Analysis and Development Framework, Common Goods, Digital Goods, For-Profit, Non-Profit. JEL Numbers: D20, L82

This paper aims to explore the impact of ex-ante legal status of creator on ex-post open

Introduction

For more than 150 years the dominant model to produce cultural contents was the so-called industrial model. It was structured around high costs and high volumes of physical goods and diffusion through mechanical instruments such as telegraph, radio, television, cable, cinema and satellite systems.

Due to the technological shock induced by new technologies and the Internet, inexpensive instruments, such as computers, digital videos and audio systems, are currently able to perform most physical capital functions without substantial investments. Therefore users nowadays are able to overcome the economic barriers and create new and innovative contents.

The economic literature suggests that users start to invest skills, money and time to produce new contents in order to satisfy their needs, when physical capital cost for fixation and communication is low and widely distributed and then possible information is treated as a common good [START_REF] Benkler | Coase's penguin, or, linux and the nature of the firm[END_REF][START_REF] Von Hippel | The sources of innovation[END_REF][START_REF] Von Hippel | Democratizing innovation[END_REF]. Under these conditions new production and diffusion models start to emerge and allow new creators, with different needs and motivations, to create and diffuse contents with characteristics similar to common goods. Indeed digital contents are technically non-rivals and non-exludables.

When using Open Licenses (like General Public Licence for software and Creative Commons licences for cultural contents) digital contents became legally similar to commons goods, indeed it became possible to share and modify the content.

The use of Open Licenses is usually related to the use of an "Open Model" to produce and diffuse the contents. The most known and studied " Open Model" is the Free/Libre Open Source Software (FLOSS). The main characteristic of "Open Model" is the unpaid volunteering cooperation and funding to the project.

Having unpaid volunteers, sponsors and for-profit firms involved to create a common good freely available is a big puzzle for economists. [START_REF] Fershtman | Open source software: Motivation and restrictive licensing[END_REF] collect several possible explanations concerning the incentives that drive developers to invest time and effort in a FLOSS project: to acquire reputation in the job market [START_REF] Lerner | Some simple economics of open source[END_REF], to benefit from sharing innovation [START_REF] Harhoff | Profiting from voluntary information spillovers: how users benefit by freely revealing their innovations[END_REF], the use of the final product [START_REF] Johnson | Open source software: Private provision of a public good[END_REF] and intrinsic motivations [START_REF] Lakhani | Why hackers do what they do: Understanding motivation and effort in free/open source software projects[END_REF].

The common cultural, legal and digital environment among FLOSS and cultural contents (video, music, texts, etc) under Creative Commons (CC) licenses suggest that the motivation to participate in a project under CC license can be similar in both "Open Models".

The aim of this paper is to investigate the determinants of openness licenses choice to create a digital "common" good. Particularly I want to investigate the determinants of those licenses from the point of view of both production and diffusion.

The Creative Commons licenses

Research Framework

The paper focuses on the link among the formalized institutions, the attributes of the community and the creators of digital goods similar to public goods.

To do that the paper adopts the Institutional Analysis and Development (IAD) framework [START_REF] Ostrom | Community management and common property of coastal fisheries in Asia and the Pacific: concepts, methods and experiences[END_REF] originally created for analyses of commonpool resources. IAD framework is already used to analyze the various dynamics of collective-action initiative to create digital "common" goods under Creative Commons and Open Source licenses [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF][START_REF] Schweik | Free/open source software as a framework for scientific collaboration[END_REF].

In Open Cultural Contents (OCC) production and diffusion, Creative Commons licenses represent the formal institution. I argue that as in case of FLOSS production and distribution, also in case of OCC, creators that use these formal institutions could not only cooperate, but also encourage and reinforce cooperative behaviour [START_REF] Lerner | The scope of open source licensing[END_REF][START_REF] Lyubareva | Cooperation in network as an alternative for the knowledge creation. example of free and open source software[END_REF] and obtain ex-ante and/or ex-post production funding and contributions from the related community [START_REF] Stewart | Impacts of license choice and organizational sponsorship on success in open source software development projects[END_REF][START_REF] Belleflamme | Crowdfunding: Tapping the right crowd[END_REF].

Figure 1 shows the IAD applied to the production of videos under Creative Commons and stored on Internet Archive. As [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF] suggest there are three ways to enter the framework (from the middle, from the righthand side or from the left-hand side). According to [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF], I enter the analysis with the technical and institutional characteristics by discussing the left-hand side, because it is more appropriate when you decide to focus on knowledge common and the creation of a new form of commons such as a digital repository video under Creative Commons licenses. Moreover the Internet and new technologies allow new actors to cross the barriers and produce new digital goods (online videos in these case) according to their business model, goals and knowledge that they want to share (see figure 1).

According with [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF] ideas are the intangible content contained in artifacts. In case of Open Video (video under Open License) production and distribution there are two types of artifacts. I define internal artifact the video as it is and external artifact the storage and diffusion archives (like Facebook, YouTube and Internet Archive).

Attributes of the Community

I distinguish five different actors that define the attributes of the Open Video Community within the Internet Archive (see figure 1):

1. Users: The users are those who watch/use the video 2. Creators: The creators are those who create and publish the video 3. Sponsors: The sponsors are those who finance ex-ante and/or ex-post (i.e. by buying DVDs) the production (involving the crowdfunding).

Participants:

The participants are those who contribute to the creation as directly (i.e. they contribute with music, writing part of the scenario or recording scenes) as indirectly (i.e. they contribute with feedback within the forum).

Policymakers:

• Formal: States, Internet Archive administrator and the Creative Commons Foundation, make Formal Policymakers.

• Informal: the different actors within the Open Community, like Users, Creators, Sponsors and Participants, make Informal Policymakers.

Rules-in-Use

According with [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF] In this paper the rules-in-use are the Intellectual Property Rights and Creative Commons licenses, which are national and international formal rules regarding the creation of Open Video repository (see figure 1).

To illustrate the characteristic of property rights in Open Cultural Content production and diffusion I use the literature on management of commons in natural environment [START_REF] Ostrom | Governing the commons -the evolution of institutions for collective action[END_REF][START_REF] Ostrom | Sustainability -revisiting the commons: Local lessons, global challenges[END_REF][START_REF] Schlager | Property-rights regimes and natural resources: A conceptual analysis[END_REF] applied to digital environment [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF]. This representation was already used in FLOSS environment [START_REF] Lyubareva | Cooperation in network as an alternative for the knowledge creation. example of free and open source software[END_REF] and Creative Commons licenses [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF]. [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF] identify seven major types of property rights, which are particularly relevant in the case of digital knowledge commons. These are:

1. Access: The right to enter a defined physical area and enjoy nonsubtractive benefits

Action Arena

The Action Arena (see figure 1) is made by the interacting actors who take decisions in situations that are affected by the thecnical, community and institutional characteristics [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF].

Action Situation and Actors

The Action Situation (see figure 1) focuses on how people cooperate or do not cooperate with each other in various circumstances [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF].

To build a repository of Open Video there are different levels of actions and decisions. To analyze the action situation it is important to understand the different incentives of interacting actors.

The standard property right theorist approach claims that only the regime of private property rights provide sufficient motivation for creators to produce contents and to innovate. This implies that the attenuation of property rights causes economic inefficiency. An alternative approach considers "that creators' property rights can be well protected in the absence of intellectual property, and that the latter does not increase either innovation or creation. They are an unnecessary evil" [START_REF] Boldrin | Against intellectual monopoly[END_REF]. Some authors consider the existence of alternative incentives to extrinsic monetary motivations. Indeed, in case of attenuation of property rights and of extrinsic motivations, extrinsic non-monetary and intrinsic motivations incentivise people to participate to the creation of contents [START_REF] Lerner | Some simple economics of open source[END_REF][START_REF] Lakhani | Why hackers do what they do: Understanding motivation and effort in free/open source software projects[END_REF][START_REF] Valentinov | The property rights approach to nonprofit organization: The role of intrinsic motivation[END_REF]. According to this approach it is possible to organise the motivations in three main groups:

1. Extrinsic Monetary Motivations

• administrative commands [START_REF] Valentinov | The property rights approach to nonprofit organization: The role of intrinsic motivation[END_REF]: the orders of the management of a firm

• monetary [START_REF] Valentinov | The property rights approach to nonprofit organization: The role of intrinsic motivation[END_REF]: to earn money

Extrinsic Non-Monetary Motivations

• reputation [START_REF] Lerner | Some simple economics of open source[END_REF]: to show one's capability so that others can admire you

• career concerning [START_REF] Lerner | Some simple economics of open source[END_REF]: to show one's capability to firms, hoping in a future job

• peer recognition [START_REF] Lerner | Some simple economics of open source[END_REF]: to show one's capability or interest, to be accepted in a group

Intrinsic Motivations

• activity itself [START_REF] Valentinov | The property rights approach to nonprofit organization: The role of intrinsic motivation[END_REF]: the mere enjoyment of an activity

• ego gratification [START_REF] Lerner | Some simple economics of open source[END_REF]: personal achievement

• need [START_REF] Von Hippel | The sources of innovation[END_REF][START_REF] Von Hippel | Democratizing innovation[END_REF]: users create solutions to their particular needs For the purpose of this analysis I group creators in four different categories, according to their organizational status, by checking the declared legal status.

1. For-Profit: a for-profit creator is a creator that operates primarily to earn money. He/she is more able to benefit from the extrinsic monetary motivations.

2. Non-Profit: In contrast, a non-profit creator is a creator who focuses primarily on social, cultural, or political goals rather than on making profits. He/she is more able to benefits from the extrinsic non-monetary and intrinsic motivations. The extrinsic non-monetary and intrinsic motivation compensate for a lower salary and incentivise volunteering and donations. In the case of For-Profit the main motivational instruments are the extrinsic monetary motivations [START_REF] Valentinov | The property rights approach to nonprofit organization: The role of intrinsic motivation[END_REF]. By contrast, they are not able to benefit from extrinsic nonmonetary and intrinsic motivation of the community members.

Patterns of Interaction

The technical characteristics, the incentives, the actions and the actors, all contribute to the patterns of interaction (see figure 1) [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF]. To be able to adopt the OP or OD model, OCC creators use a particular set of licenses, the so-called Creative Commons licenses, that allow them to define the degree of openness in both production and diffusion processes.

By using

Hence it is possible to distinguish two different models:

1. Open Production (OP): the creator renounces contribution, management/ participation, exclusion and extraction rights. The creator grants the possibility to re-use his/her contents as input to create new contents.

The creator could decide to grant or not to grant the Alienation right.

He/she could decide to grant the previously mentioned rights only for non-commercial purposes by other users.

2. Open Diffusion (OD): the creator renounces access, alienation and exclusion rights. In other words the creator grants the diffusion of the content to other users also for commercial purposes. Table 2 shows the degree of openness as well as in production as in diffusion. The goal of this study is to explore the impact of the legal status of creators on the license choice. The paper argues that the license choice affects production and diffusion process.

Hypotheses

The FLOSS literature shows the importance of organizational structure and license choice to entice developers and users [START_REF] Lerner | The scope of open source licensing[END_REF][START_REF] Stewart | Impacts of license choice and organizational sponsorship on success in open source software development projects[END_REF][START_REF] Singh | Determinants of open source software license choice: A social influence perspective[END_REF][START_REF] Colazo | Impact of license choice on open source software development activity[END_REF].

Creators that show limited monetary incentives attract more easily contributions by users, then Non-Profit actors tend to be more successful in attracting community interest and contribution, such as in the case of crowdfunding, crowdsourcing, volunteering, etc [START_REF] Belleflamme | Crowdfunding: Tapping the right crowd[END_REF][START_REF] Lambert | An empirical analysis of crowdfunding[END_REF][START_REF] Stewart | Impacts of license choice and organizational sponsorship on success in open source software development projects[END_REF].

Moreover, a high degree of openness in license tends to be more successful in attracting community interest and contribution. Therefore it is not surprising that Non-Profit status combined with a high degree of openness in license tends to be even more successful in attracting community interest and contribution [START_REF] Stewart | Impacts of license choice and organizational sponsorship on success in open source software development projects[END_REF].

It follows that, to show limited monetary incentives, and attracting community, For-Profit actors need to use a high degree of openness in license and then increase the attractiveness to benefit from crowdfunding, crowdsourcing, volunteering, etc.

My hypotheses are:

• Creators that use open in production model (OP) will compensate by using less open diffusion model (OD) and vice-versa (H1).

• To entice community and benefit from them, the creator with For-Profit legal status will use a more high degree of openness in Creative Commons license in both aspects, production (H2a) and diffusion (H2b).

Then I focus on the impact of the different ex-ante organizational structures, described by the legal status, on the ex-post choice of the different degree of openness in both an Open Production and an Open Diffusion models.

The objective is to hypothesise the existence of both Open Production and Open Diffusion models (H1) and to test whether the legal status of the creator affects the choice of openness of Creative Commons license and the Open Production and Open Diffusion models. Indeed I argue that For Profit creators use the highest degree of openness in both the OP and OD models to attract interest and contributions from community (H2a and H2b).

Data Collection

To explore the impact of ex-ante organizational structure on ex-post choice of This selection has resulted in a sample of 999 observations.

Variables

The contents in the sample were published under some CC license or under Open Production. This variable indicates if the creator allows others to modify his product or not. This variable indicates whether there is the noderivative-work clause or not. I distinguish between licenses that allow modification (PD, CC by, CC by-sa, CC by-nc and CC by-nc-sa) and licenses that do not allow modification (CC by-nc-nd, CC by-nd). This dummy variable takes the value "1" if the users can modify and re-use the contents as input and "0" otherwise.

Open Diffusion. This variable indicates if the creator allows others to sell his product or not. This variable indicates whether there is the noncommercial clause or not. I distinguish between licenses that allow commercial purposes (PD, CC by, CC by-sa and CC by-nd) and licenses that do not allow commercial purposes ( CC by-nc, CC by-nc-nd and CC bync-sa). This dummy variable takes the value "1" if users can re-use and diffuse the contents for commercial purposes.

OpenProdDeg. This variable represents the degree of openness of the license from the point of view of the production process. I distinguish among three degrees of openness. Table 3 shows the percentage of different Status among the contents under restrictive (0) or permissive (1) both OP and OD models: Because Public Administrations are usually obliged by law to share the pro-perty rights with citizens5 , it is not surprising that the majority of the contents produced by the Public Administration was published under permissive CC licenses or PD using open OP (97.83%) and open OD (99.07%) models.

Table 4 shows the percentage of different legal status among the different degree of openness in production. Table 5 shows the percentage of different legal status among the different degree of openness in diffusion. Table 6 shows the correlation analysis of our variables. It is important to remark that the openness in production (OpenProduction)

and the openness in diffusion (OpenDiffusion) are negatively correlated (-0.171).

Therefore who uses more OD is more likely to use less OP and vice-versa. This supports the hypotheses H1.

The degree of openness in production (OpenProdDeg) is positively correlated (0.680) with the degree of openness in diffusion (OpenDiffDeg). This is not surprising because when creator increases the degree of openness in production he/she will automatically increase the degree of openness in diffusion.

Both OpenProduction and OpenDiffusion are positively correlated with the openness in production (OpenProdDeg) and diffusion (OpenDiffDeg). This can be explained by the fact that when creator decides to adopt openness in production (or in diffusion) he/she will automatically increases the degree of openness in diffusion (or in production).

OpenProduction is positively correlated with Public (0.268) and For Profit (0.092), and is negatively correlated with Informal (-0.184) and Non Profit (-0.236). This supports the hypothesis H2a.

The correlation between OpenDiffusion and For Profit is not significant. The correlation between OpenDiffusion and Non Profit is not significant either.

The correlation between the degree of openness in production OpenProd-Deg and For Profit is not significant, but the degree of openness in production

OpenProdDeg is negatively correlated with Non Profit (-0.153) and Informal (-0.153) and it is positively correlated with Public (0.247). This is coherent with hypotheses H2a.

The correlation between the degree of openness in diffusion OpenDiffDeg and For Profit is not significant. The correlation between OpenDiffDeg and Non Profit is not significant either.

Regression Results

Open Production and Open Diffusion

To further explore the impact of openness in production on openness in distribution, I estimated two ordered logistic regressions.

Table 7 shows the logistic regression and marginal effect results of the impact of OpenDiffusion on OpenProduction and table 8 shows the logistic regression and marginal effect results of the impact of OpenProduction on OpenDiffusion. Table 9 shows the results of the regression and of the marginal effect. The values of Wald Chi-Square and the p-value indicate that our model is statistically significant. I also used the Brant test that has confirmed that our model is statistically significant.

Except for Medium degree of openness of Non Profit all other results are statistically significant (p < 0.001).

It is not surprising that all independent variables (Informal, Non Profit and For Profit) have a negative impact on the dependent variable (OpenProdDeg).

The negative effect of the included variables can be explained by the fact that the variable Public (the most likely to adopt the higher degree of openness) has been dropped. All results are statistically significant (p < 0.001). Like in previously regression, also in this case it is not surprising that all independent variables (Informal, Non Profit and For Profit) have a negative impact on dependent variable (OpenDiffDeg), because the fact that the variable Public has been dropped.

For Profit (-0.166) is more likely to adopt a high degree of openness of license than Non Profit (-0.174) and Informal (-0.259).

Informal (0.0693) and For Profit (0.0548) are more likely to adopt a medium degree of openness than Non Profit (0.0441).

Both Non Profit (0.130) and Informal (0.190) are more likely to adopt a lower openness degree in production than For Profit (0.111).

These results confirm hypothesis H2b. Also in the case of diffusion I argue that For Profit actors needed to adopt a higher degree of openness than Non Profit actors to benefit from crowdfunding, crowdsoucing and to attract interest of community. Openness in diffusion is also helpful to increase the diffusion of the goods. The paper leaves a number of issues open. Therefore future investigations are necessary. Particularly it will be interesting to investigate the different business strategies of creators and the evaluative criteria (see figure 1), to explore and to assess current results as well as under alternative actions or institutional arrangements [START_REF] Hess | A framework for analyzing the knowledge commons: a chapter from understanding knowledge as a commons: from theory to practice[END_REF].

  According to the Creative Commons website 1 , Creative Commons licenses are several copyright licenses released on December 16, 2002 by Creative Commons Corporation, a U.S. non-profit corporation founded in 2001 by Lawrence Lessig. Creative Commons Corporation is headquartered in San Francisco, California, United States and is devoted to expanding the range of creative works available for others to build upon legally and to share. Particularly by using Creative Commons licences the creator can use different combinations of the following clauses to declare which rights he/she wants to grant to users: 1. Attribution (by): Users may copy, distribute, display and perform the work and make derivative works based on it only if they give the creator the credits in the manner specified by these. 2. Non-Commercial (nc): Users may copy, distribute, display, and perform the work and make derivative works based on it only for non-commercial purposes. 3. No Derivative Works (nd): Users may copy, distribute, display and perform only verbatim copies of the work, not derivative works based on it. 4. Share Alike (sa): Users may distribute derivative works only under a license identical to the license that governs the original work. I notice that not all the combinations are allowed, indeed the "nd" and "sa" clauses are mutually exclusive. By using the different combinations of clauses of Creative Commons license, the creator could decide to grant to users the right to copy, to modify and to make money. Following the six most used Creative Commons licenses: 1. Creative Commons Attribution alone (CC-by): This license allows users to redistribute, modify, using as input and contribute to the content. This license does not oblige derivative works under the same license, but forces a declaration of the original author. 2. Creative Commons Attribution + Non-Commercial (CC by-nc): This license allows users to redistribute, modify, using as input and contribute to the content, but only for non-commercial purposes. This license does not oblige derivative works under the same license, but forces a declaration of the original author. 3. Creative Commons Attribution + No-Derivative-works (CC by-nd): This license allows users to redistribute the content, but does not allow modification, using as input and contribute to the content. 4. Creative Commons Attribution + Share-Alike (CC by-sa): This license allows users to redistribute, modify using as input and contribute to the content. This license obliges derivative works under the same license and force a declaration of the original author. 5. Creative Commons Attribution + Non-Commercial +No-Derivative-works (CC by-nd): This license allows users to redistribute the content, but only for non-commercial purposes. This license does not allow users to modify, using as input and contribute to the content. 6. Creative Commons Attribution + Non-Commercial + Share-Alike (CC by-nc-sa): This license allows users to redistribute, modify, using as input and contribute to the content, but only for non-commercial purposes. This license obliges derivative works under the same license and forces a declaration of the original author. Using the Public Domain tool called CC0, available on Creative Commons website 2 , copyright owners are able to dedicate their works to the worldwide Public Domain. Public Domain allows users to redistribute, modify, using as input and contribute to the content.

Figure

  Figure 1: Institutional Analysis and Development

  Rules-in-Use are shared normative instructions written in administrative procedures, legislation, contracts and licenses. They define what the interacting actors should, should not or could do in different situations. The rules-in-use are enforced and known by the interacting actors. They generate opportunities and constraints.

2.

  Contribution: The right to contribute to the content 3. Extraction: The right to obtain resource units or products of a resource system 4. Removal: The right to remove one's artifacts from the resource system 5. Management/Participation: The right to regulate internal use patterns and transform the resource by making improvements 6. Exclusion: The right to determine who will have access, contribution, extraction, and removal rights and how those rights may be transferred 7. Alienation: The right to sell or lease management/participation and exclusion rights By licensing cultural contents under a Creative Commons license, the creator can decide the part of copyright that he wants to grant to users.

  3. Informal : They do not declare any legal status. They cannot collect money from the government or community or stay in the market. 4. Public Administrations: They are usually forced by law to use Public Domain or high degree of openness in licenses.

  Open licenses like Creative Commons licenses, creators decide to keep different degrees of property rights. Therefore two different new propertyrights regimes/models seem to emerge and co-exist: Open Production and Open Diffusion. With Open Production (OP) I identify the characteristic of the content to be produced in a cooperative model, independently from the control of the initial creator. This means that, concerning the production of the content, the creator decide to reduce or completely lose the contribution, management/participation, exclusion and extraction rights. With Open Diffusion (OD) I identify the characteristic of the contents to be freely diffused. The creator decides to reduce or completely lose the access, alienation and exclusion rights in the diffusion content. Moreover, it is possible to have different degrees of restrictiveness of both Open Production and Open Diffusion.

CC

  Attribution + Non-commercial + NoDerivs -OP ; -OD CC Attribution + Non-commercial + ShareAlike +OP ; -OD According with to the degree of openness I ordered the CC licenses (plus PD) in three groups from the point of view of the production and of the distribution. "CC by" and PD do not impose any restriction. I consider them as the Maximum level of openness in both production and diffusion. From the point of view of the production, I consider that the use of No-Derivative-works clause indicates the Minimum level of openness. The clauses Non-Commercial and/or Share-Alike, reduce the level of openness, but less than the clause of No-Derivative-works. I label it Medium level of openness in production. On the other hand, from the point of view of the diffusion, the using of Non-Commercial clause indicates the Minimum level of openness. The clauses Non-Derivative-works and/or Share-Alike, reduce the level of openness, but less than the clause Non-Commercial. I label it Medium level of openness in diffusion.

  Public Domain by different creators with different legal status. I distinguish among four groups of creators, the For-Profit creators (1), that tend to attract interest of contributors more influenced by extrinsic monetary motivations; the Non-Profit creators (2), that tend to attract interest of contributors more influenced by intrinsic and extrinsic non-monetary motivations; the Informal creators (3) that are not organized to benefit from contributions of extrinsic monetary motivated actors; and the Public Administrations (4), that tend to attract interest of contributors more influenced by intrinsic and extrinsic non-monetary motivations. I checked the legal status of each creator. I also checked the different clauses of CC licenses. The variables are:

7. 2 . 2

 22 Impact of Legal Status on Openness in Production To investigate the impact of the legal Status of creator on the degree of openness of license chosen, from the point of view of the production (OpenProdDeg), I estimated another ordered logistic regression. To avoid the multicollinearity problem I dropped one variable concerning the legal status of the creators. I decide to drop the Public variable because I know that Public Administrations are more likely to adopt a high degree of openness.

8

  Conclusions and future researchIn this paper I have analyzed the impact of the organizational structure on openness in production and distribution, by assessing the impact of the ex-ante legal status of creator on the ex-post choice of the emergent Creative Commons licenses. I hypothesise the existence of two different emergent open models: the Open Diffusion and the Open Production model.According to the standard property right approach, only the regime of private property rights provide sufficient motivation for creators to produce contents and to innovate. As a consequence For-Profit actors should be more likely to use restrictive licenses in both production and diffusion models. My results suggest the opposite. I show that when For-Profit actors decide to use the Creative Commons licenses, they are more likely to use a high degree of openness in license in both production and distribution point of view. This can be explained as the result of the intent of For-Profit creators to be more attractive to users, participants and sponsors in order to obtain advantages from community and people who are motivated by extrinsic non-monetary and intrinsic motivations.

Table 1

 1 shows the six commonly used licenses plus the Public Domain. It shows also if they allow openness (+) or not (-) in OP and/or OD:

Table 1 :

 1 Open Models

	License	Model
	Public Domain	+OP ; +OD
	CC Attribution alone	+OP ; +OD
	CC Attribution + Non-commercial	+OP ; -OD
	CC Attribution + NoDerivs	-OP ; +OD
	CC Attribution + ShareAlike	+OP ; +OD

Table 2 :

 2 Degree of openness

	Degree of openness Production Diffussion
	Maximum	PD	PD
		CC by	CC by
	Medium	CC by-sa	CC by-sa
		CC by-nc	CC by-nd
		CC by-nc-sa
	Minimum	CC by-nd	CC by-nc
		CC by-nd-nc CC by-nc-sa
			CC by-nc-nd

  openness in Open Production and Open Diffusion models, I have assembled an original database of videos under Public Domain and CC licenses, using the subsection "Community Video" 3 hosted on Internet Archive.

The Internet Archive (IA) is a non-profit digital library, founded in 1996, operating in the United States with the stated mission: "universal access to all knowledge". It offers permanent storage and access to collections of digitized materials, including websites, music, videos, and books. IA is a member of the American Library Association and is officially recognized by the State of California as a library. The IA began to archive the World Wide Web from 1996, but it did not make this collection available until 2001. To use IA as storage is also suggested by CC tools and website, particularly when using CC license tools.

IA collects more than 100.000 videos under the subsection "Community Video", but only 27.939 provide detailed information of the CC license 4 . Some observations were dropped for the purpose of this study, as they do not provide detailed information on creator, publisher or year of creation or year of publication. Because the first set of CC licenses were created in December 2002, I dropped the observations about videos created and/or published before 2003.

Table 3 :

 3 Percentage of different Legal Status

			OpenProduction		OpenDiffusion
	Status	(0)	%	(1)	%	(0)	%	(1)	%	Total	%
	Informal	78	27.46 %	206 72.54 %	89	31.34 %	195 68.66 %	284	28.43 %
	For Profit	27	10.71 %	225 89.29 %	41	16.27 %	211 83.73 %	252	25.23 %
	Non Profit	54	38.30 %	87	61.70 %	25	17.73 %	116 82.27 %	141	14.11 %
	Public	7	2.17 %	315 97.83 %	3	0.93 %	319 99.07 %	322	32.23 %
										999	100 %

Table 4 :

 4 Percentage of different legal Status on degree of openness in produc-

	tion
			OpenProdDeg
	Status	Minimum Medium Maximus	Total
	Informal	78	115	91	284
		27.46%	40.49%	32.04%	100%
		46.99%	25.61%	23.70%	28.43%
	ForProfit	27	138	87	252
		10.71%	54.76%	34.52%	100%
		16.27%	30.73%	22.66%	25.23%
	Non Profit	54	40	47	141
		38.30%	28.37%	33.33%	100%
		32.53%	8.91%	12.24%	14.11%
	Public	7	156	159	322
		2.17%	48.45%	49.38%	100%
		4.22%	34.74%	41.41%	32.23%
	Total	166	449	384	999
	16.62%	44.94%	38.44%	100%
	100%	100%	100%	100%

Table 5 :

 5 Percentage of different legal Status on degree of openness in produc-

	tion
			OpenDiffDeg
	Status	Minimum Medium Maximus	Total
	Informal	89	104	91	284
		31.34%	36.62%	32.04%	100%
		56.33%	22.76%	23.70%	28.43%
	For Profit	41	124	87	252
		16.27%	49.21%	34.52%	100%
		25.95%	27.13%	22.66%	25.23%
	Non Profit	25	69	47	141
		17.73%	48.94%	33.33%	100%
		15.82%	15.10%	12.24%	14.11%
	Public	3	160	159	322
		0.93%	49.69%	49.38%	100%
		1.90%	35.01%	41.41%	32.23%
	Total	158	457	384	999
		15.82%	45.75%	38.44%	100%
		100%	100%	100%	100%

Table 6

 6

			: Correlation
	Variables	OpenProduction OpenDiffusion OpenProdDeg OpenDiffDeg
	OpenProduction	1.000
	OpenDiffusion	-0.171	1.000
		(0.000)
	OpenProdDeg	0.767	0.145	1.000
		(0.000)	(0.000)
	OpenDiffDeg	0.156	0.758	0.680	1.000
		(0.000)	(0.000)	(0.000)
	Informal	-0.184	-0.268	-0.153	-0.197
		(0.000)	(0.000)	(0.000)	(0.000)
	For Profit	0.092	-0.007	0.016	-0.036
				Continued on next page...

Table 7

 7

	: Logistic Regression and Marginal Effect Results
		(Dep. Var.)	(MarginalEffects)
		OpenProduction	mfx
	OpenDiffusion (d)	-2.519 * * *	-0.175 * * *
		(0.590)	(0.0174)
	N	999	999
	Log lik.	-428.4	-428.4
	Chi-squared	18.25	18.25
	p	0.0000193	0.0000193
	Marginal effects; Standard errors in parentheses
	(d) for discrete change of dummy variable from 0 to 1
	* p < 0.05, * * p < 0.01, * * * p < 0.001

Table 8

 8

	: Logistic Regression and Marginal Effect Results
		(Dep. Var)	(Marginal Effects)
		OpenDiffusion	mfx
	OpenProduction (d)	-2.519 * * *	-0.168 * * *
		(0.590)	(0.0170)
	N	999	999
	Log lik.	-415.3	-415.3
	Chi-squared	18.25	18.25
	p	0.0000193	0.0000193
	Marginal effects; Standard errors in parentheses
	(d) for discrete change of dummy variable from 0 to 1
	* p < 0.05, * * p < 0.01, * * * p < 0.001
	OpenDiffusion has a negative impact (-0.175) on OpenProduction and Open-

Table 9

 9

	: Ordered Logistic Regression and Marginal Effect Results
		(Dep. Var.)	(Marginal Effects)
		OpenProdDeg Maximum Medium Minimum
	Informal (d)	-1.085 * * *	-0.231 * * *	0.0611 * * *	0.170 * * *
		(0.158)	(0.0292)	(0.0110)	(0.0305)
	For Profit (d)	-0.609 * * *	-0.135 * * *	0.0443 * * * 0.0904 * * *
		(0.138)	(0.0289)	(0.00884)	(0.0230)
	Non Profit (d)	-1.370 * * *	-0.262 * * *	0.0159	0.246 * * *
		(0.241)	(0.0334)	(0.0259)	(0.0551)
	N	999	999	999	999
	Log lik.	-989.3	-989.3	-989.3	-989.3
	Chi-squared	67.28	67.28	67.28	67.28
	p	1.64e-14	1.64e-14	1.64e-14	1.64e-14
	Marginal effects; Standard errors in parentheses
	(d) for discrete change of dummy variable from 0 to 1

* p < 0.05, * * p < 0.01, * * * p < 0.001

  Public is dropped. Indeed Public Administration is more likely to use a high degree of openness. The results of the regression and of the marginal effects are showed in the table 10.

	For Profit (-0.135) is more likely to adopt a high degree of openness of license
	than Informal (-0.231) and Non Profit (-0.262).
	Both Non Profit (0.246) and Informal (0.170) are more likely to adopt a
	lower openness degree in production than For Profit (0.0904).
	The values of Wald Chi-Square and the p-value indicate that our model is
	statistically significant. I also used the Brant test that has confirmed that our
	model is statistically significant.
	To avoid the multicollinearity problem I drop one variable concerning the
	legal status of the creators. Like in the previously regression I decide to drop
	the Public variable because I know that Public Administration are more likely
	to adopt the higher degree of openness.

These results confirm hypothesis H2a. Indeed concerning the production of a digital goods with characteristics similar to a public good (a video under Creative Commons license in this case), I argue that For Profit actors, in order to attract interest of users, participants and sponsors (crowdfunding and crowdsourcing), need to use a high degree of openness than Non Profit actors that are able to attract community interest and contributions because of its legal status.

7.2.3 Impact of Legal Status on Openness in Diffusion

To investigate the impact of the legal Status of creator on the degree of openness of license chosen, from the point of view of the diffusion(OpenDiffDeg), I estimated another ordered logistic regression. To avoid the multicollinearity problem the dummy variable

Table 10

 10

	: Ordered Logistic Regression and Marginal Effect Results
		(Dep. Var.)	(Marginal Effects)
		OpenDiffDeg Maximum Medium Minimum
	Informal (d)	-1.233 * * *	-0.259 * * *	0.0693 * * *	0.190 * * *
		(0.166)	(0.0292)	(0.0126)	(0.0329)
	For Profit (d)	-0.760 * * *	-0.166 * * *	0.0548 * * *	0.111 * * *
		(0.147)	(0.0296)	(0.00907)	(0.0256)
	Non Profit (d)	-0.830 * * *	-0.174 * * *	0.0441 * * *	0.130 * * *
		(0.183)	(0.0334)	(0.00909)	(0.0350)
	N	999	999	999	999
	Log lik.	-983.5	-983.5	-983.5	-983.5
	Chi-squared	68.76	68.76	68.76	68.76
	p	7.88e-15	7.88e-15	7.88e-15	7.88e-15
	Standard Error	Robust	Robust	Robust	Robust

Marginal effects; Standard errors in parentheses (d) for discrete change of dummy variable from 0 to 1 * p < 0.05, * * p < 0.01, * * * p < 0.001

http://creativecommons.org/

https://creativecommons.org/publicdomain/

http://www.archive.org/details/opensource movies

last check on February 2010

i.e. in U.S.A. according to Federal Copyright Act contents produced by the Federal Government can not be copyrighted

1. The first category indicates the Maximum degree of openness (PD and CC-by).

2. The second category indicates the Medium degree of openness (CC by-sa, CC by-nc and CC by-nc-sa).

The third category indicates the

Minimum degree of openness (CC by-nd, CC by-nd-nc). OpenDiffDeg. This variable represents the degree of openness of the license from the point of view of the diffusion process. I distinguish among three degrees of openness. 1. The first category indicates the Maximum degree of openness (PD and CC-by). 2. The second category indicates the Medium degree of openness (CC by-sa and CC by-nd). 3. The third category indicates the Minimum degree of openness (CC by-nc, CC by-nc-sa and CC by-nc-nd).

Informal. This dummy variable takes the value of "1" in the absence of any legal status.

For-profit. This dummy variable takes the value of "1" in the presence of the for-profit legal status (i.e. firms) Non-profit. This dummy variable takes the value of "1" in the presence of the non-profit legal status (i.e. foundations) Public. This dummy variable takes the value of "1" in the presence of a Public Administration.

The objective of this study is to show that the ex-ante organizational structure, has a significant impact on the ex-post choice of the degree of openness of license, in both production and diffusion.


Marc Joëts
email: marc.joets@u-paris10.fr

Valérie Mignon
email: valerie.mignon@u-paris10.fr.

On the link between forward energy prices: A nonlinear panel cointegration approach *

Keywords: JEL Classification: C33, Q40 forward energy prices, speculation, panel cointegration, nonlinear model, PSTR

This paper investigates the relationship between forward prices of oil, gas, coal, and electricity using a nonlinear panel cointegration framework. To this end, we consider a panel of 35 maturities and control for the economic and financial environment using equity futures prices. Estimating the cointegrating relationship, we find that oil, gas and coal forward prices are positively linked, while the negative link between oil and electricity prices is consistent with a substitution effect between the two energy sources on the long run. Estimating panel smooth transition regression (PSTR) models, we show that the forward oil price adjustment process toward its equilibrium value is nonlinear and asymmetric, putting forward the key role played by self-sustaining dynamics and speculation phenomena.

Introduction

Investigating the interactions between energy markets if of crucial importance to correctly apprehend and understand their price dynamics. Indeed, energy prices are obviously connected through the production process, and economic theory suggests that a relationship should exist between input and output prices. Besides, oil-due to its physical properties and the importance of its market-is often viewed as an economic "driver" influencing the other energy prices, such as coal, gas, and electricity.

However, energy markets recently experienced significant developments that are likely to modify the potential interactions between energy prices. European gas and electricity markets have freshly known a liberalization process allowing the emergence of new contracts making prices more likely to be influenced by market participants rather than regulators [START_REF] Mjelde | Market integration among electricity markets and their major fuel source markets[END_REF]. While various studies1 have investigated the links between energy prices on spot markets, they generally do not consider the long-run financial dimension of energy markets. Moreover, by relying on spot data, previous literature does not account for potential heterogeneity in the energy prices relationships across different maturities. This may be viewed as an important limitation since considering prices at various maturities allows accounting for arbitrage investors' behavior over the long run-the contracts being not only traded by agents who need physical energy delivery, but also by speculators with purely financial motivation.

In this paper, we account for the term structure of energy prices through an analysis of forward prices. More specifically, we consider forward prices of oil, coal, gas and electricity at 35 maturities, and aim at modelling the relationship between these four energy sources for all maturities in a panel data cointegration setting. This approach allows us to study the links within and between heterogeneous maturities accounting for long-term arbitrage behaviors, and to overcome the low power of traditional time series unit root and cointegration tests against the near stationary alternative.

It is worth mentioning that, given the various factors that may influence energy markets, the dynamics of energy prices is likely to be characterized by nonlinearities. Such nonlinearities may come from both fundamental factors or speculative forces. Regarding the first point, the recent huge increase in the Chinese oil demand coupled with an unexpected halt of non-OPEC production, as well as the loss of OPEC spare capacity since 2004 [START_REF] Kaufmann | The role of market fundamentals and speculation in recent price changes for crude oil[END_REF] are examples of fundamental shocks that may generate nonlinearities and regime-switching. Turning to the speculative factors, investors may hold long-run positions to force energy prices rising, and switch between investment strategies (Ellen and Zwinkels, 2010). These elements play in favor of nonlinear, regime-switching models. To this end, we rely on panel smooth transition regression (PSTR) models, allowing us to model the nonlinear behavior of the forward energy prices adjustment process to the equilibrum value.

To sum up, and given the key role played by oil in energy markets, the aim of this paper is to investigate the nonlinear adjustment process of the forward oil price toward its equilibrium value given by the estimated long-term relationship between oil, gas, coal and electricity forward prices. To our best knowledge, our contribution is the first to account for interactions between energy prices at various maturities in a nonlinear panel data framework.

The rest of the paper is organized as follows. Section 2 presents some stylized facts and reviews the literature on the links between energy prices. Section 3 describes the econometric methodology. Data and results of panel unit root and cointegartion tests are displayed in Section 4. Section 5 reports the PSTR estimation results, and Section 6 concludes the article.

2 Relationships between energy prices: stylized facts and literature review

The natural gas market is often considered to be potentially linked to other primary energy sources by different ways. Technically, gas is extracted from the soils either alone ("dry gas"), or associated with the oil exploration ("associated gas"). Consequently, natural gas and oil have the same extraction/exploration process, and oil producers are often gas producers too, creating an implicit link between prices. Usually, the natural gas is used for domestic needs and as an input to the electricity production process. However, it has no captive use and is in constant competition with other energy sources (with domestic and heavy fuel oil in domestic needs, and with coal in the power production). These characteristics explain the existence of (i) an input-output relationship between natural gas and electricity, and (ii) competitive relation between oil, gas, and coal. Despite the close relationship between energy markets, the entry of the gas market in the liberalization process is likely to exacerbate short-run decorrelation between oil and gas prices. Indeed, long-term gas contracts are no longer indexed to oil contracts, but to spot and futures prices, rending prices more sensitive to the behavior of market participants.

Turning to coal, due to its apparent abundance,2 it is dominant in two specific sectors: manufacture of cement and steel, and electricity generation (IEA, 2010a andBritish Petroleum, 2010). It is the main input to the electricity production, making energy prices potentially connected through an input-output relationship. However, coal being extremely pollutant, it is in competition with gas and oil in the power production, likely to create substitution effects. Besides, due to its solid state and its inert nature, coal transportation is very expensive3 because it requires seaborne trade. Then, coal prices strongly depend on the variability of the freight rates, which are significantly variable since the 1950s [START_REF] Lundgren | Bulk trade and maritime transport costs: The evolution of global markets[END_REF]. Consequently, oil and coal prices may be indirectly related each other through the fluctuations of the transport fuel derived from oil.

Unlike oil, natural gas and coal, electricity is not a fossil energy. It can be produced either as primary energy from natural sources (like hydro, wind, solar, ...), or as secondary energy from the heat of nuclear fission, the geothermal and solar thermal heat, or by the combustion of fossil fuels (coal, natural gas, and oil) (IEA, 2010b). Accordingly, and as previously mentioned, electricity prices may be related to its products through an input-output relationship. Moreover, power energy is used for most human activities (heating, lighting, computers, powering machines, transport, ...) and in several sectors (transformation and energy sectors, transmission and distribution of electricity sectors, and final consumption4 ). Thus, in addition to an obvious relation through the production process, electricity, gas, oil and coal prices may be interrelated by competition and substitution links.

On the whole, various factors may explain the interactions between energy prices. Turning to the empirical literature, [START_REF] Serletis | Testing for common features in North American energy markets[END_REF] investigate the strength of shared dynamics between North American daily spot Henry Hub gas and WTI crude oil prices over the period after the deregulation, from January 1991 to April 2001. They found that while the US market deregulation has 'decoupled' the prices' relationship, North American natural gas prices are largely defined by the US Henry Hub prices trends. Focusing on the UK, [START_REF] Panagiotidis | Oil and gas markets in the UK: Evidence from a cointegrating approach[END_REF] examine whether oil and gas prices 'decoupled' during the post market deregulation period (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003). Using cointegration techniques, they show that a cointegrating relationship is present throughout the sample period, especially between 1999 and 2000. Relying on daily ICE futures prices of gas and Brent for five contracts, [START_REF] Westgaard | Co-integration of ICE gas oil and crude oil futures[END_REF] find that a long-term relationship exists between prices depending on the length of the contracts.

Considering more energy sources, [START_REF] Bachmeier | Testing for market integration crude oil, coal, and natural gas[END_REF] investigate the degree of integration between crude oil, coal, and natural gas markets. Using data from January 1990 to August 2003, and relying on the estimation of bivariate error correction models, they find a weak degree of integration between energy markets. Investigating the relationship between weekly spot prices among US electricity and its major fuel inputs (natural gas, uranium, coal and crude oil) over the period from June 6, 2001 to April 23, 2008, [START_REF] Mjelde | Market integration among electricity markets and their major fuel source markets[END_REF] put forward that electricity prices influence natural gas prices, which in turn affect crude oil prices. Finally, one can mention the study by [START_REF] Ma | The integration of major fuel sources markets in China: Evidence from panel cointegration tests[END_REF] concerning energy prices comovements in China from January 1995 to December 2005, showing that coal and electricity prices have comoved since 1997.

To sum up, the previous literature globally puts forward some links between energy markets, depending on the market location and the type of energy considered. However, most of them deal with spot prices. Consequently, they do not investigate the potential relationships at various maturities, a fact that is of considerable importance when one wishes to account for the financial dimension of energy markets. Furthermore, relying on spot models based on long-term price relationships often require knowledge of the convenience yield for risk-neutral valuation. However, the later is not observable and difficult to deduce, whereas this is not the case for forward prices models [START_REF] Eydeland | Energy power risk management -New developments in modeling, pricing, and hedging[END_REF]. For these resaons, and to account for the financial dimension of energy markets, it seems particularly relevant to focus on forward prices data, which is the aim of the present contribution.

Methodology

As previously mentioned, our aim is to investigate the nonlinear behavior of the forward oil price adjustment process toward its equilibrium value by estimating a panel smooth transition regression (PSTR) model. To this end, the first step consists in estimating the equilibrium value of forward oil price, given the values of the other forward energy prices and the economic and financial environment, the later being proxied by an European equity futures index (see below, Section 4). More specifically, we estimate the following long-term relationship:

p oil i,t = a i + b 1 p elec i,t + b 2 p gas i,t + b 3 p coal i,t + b 4 p Stoxx i,t + i,t (1)
where i = 1, ..., 35 denotes the maturity, and t = 1, ..., T the time. p oil i,t , p elec i,t , p gas i,t and p coal i,t

respectively denote the forward prices of oil, electricity, gas and coal. p Stoxx i,t stands for the equity futures price index. All price series are expressed in logarithmic terms.

The estimation of Equation (1) using efficient panel cointegration techniques (see below, Section 5) gives the forward oil price equilibrium value, denoted as poil i,t . The difference between the observed and the equilibrium value of the oil price defines the misalignment for each maturity i:

z i,t = p oil i,t -poil i,t (2)
The corresponding error correction model is specified in a nonlinear form to account for the potential nonlinear adjustment of the forward oil price toward its equilibrium value. To this end, we rely on the PSTR model introduced by [START_REF] González | Panel Smooth Transition Regression Models[END_REF]:

y i,t = µ i + β 1 x i,t + β 2 x i,t g (s i,t ; γ, c) + ε i,t (3)
where g (s i,t ; γ, c) is the transition function, normalized and bounded between 0 and 1. s i,t denotes the transition variable-which may be an exogenous variable or a combination of the lagged endogenous one-, γ the speed of transition from one regime to the other and c the threshold parameter. As it is clear from Equation (3), the observations in the panel are divided into two regimes depending on whether the transition variable is lower or larger than c. The logistic specification can be used for the transition function to account for a smooth and gradual change from one regime to the other:

g (s i,t ; γ, c) = 1 + exp -γ m l=1 (s i,t -c l ) -1 (4)
with γ > 0 and c 1 ≤ c 2 ≤ ... ≤ c m . Turning to empirical considerations, it is sufficient to consider only the cases of m = 1 (logistic PSTR) or m = 2 (logistic quadratic PSTR) to capture the nonlinearities due to regime switching (see [START_REF] González | Panel Smooth Transition Regression Models[END_REF].

Following the methodology used in the time series context, [START_REF] González | Panel Smooth Transition Regression Models[END_REF] propose a three-step strategy to apply PSTR models: (i) the identification step aiming at testing for homogeneity against the PSTR alternative and selecting both the transition variable and the order m, (ii) the estimation step based on nonlinear least squares, and (iii) the evaluation step that consists in applying misspecification tests to check the validity of the estimated PSTR model.

On the whole, the model that will be estimated is given by:

∆p oil i,t = µ i + (λ 1 z i,t-1 + B 1 X i,t ) + (λ 2 z i,t-1 + B 2 X i,t )g(s i,t ; γ, c) + ε i,t (5)
where X i,t represents the vector of contemporaneous and lagged first-differenced forward oil price determinants, namely ∆p elec i,t , ∆p gas i,t , ∆p coal i,t , and ∆p Stoxx i,t

. Depending on the value of the transition variable, the link between ∆p oil i,t and its determinants evolves between B 1 and λ 1 in Regime 1 (corresponding to g(.) = 0) and B 1 + B 2 and λ 1 + λ 2 in Regime 2 (corresponding to g(.) = 1). Three transition variables will be considered: the oil price misalignment, the oil price variation, and the economic and financial environment proxied by the equity futures price returns.

Data, unit root and cointegration tests

We consider daily data over the January 3, 2005 to December 31, 2010 period. We rely on European forward prices of oil, gas, coal, and electricity for 35 maturities. 5 Using such a large sample of maturities allows us to account for possible heterogeneity in the relationship between energy prices, 6 as well as long-run arbitrage behavior of market participants. Energy price data are extracted from the Platt's Information Energy Agency. To control for the economic and financial environment that may impact all energy price series, we rely on a European equity futures index-which has the advantage of being available at a daily frequency. This variable also allows considering oil as a financial asset and controls for the recent financial turmoil. Our retained equity variable is the Dow Jones Euro Stoxx 50, the European leading stock index for futures contracts, extracted from Datastream. All price series are in logarithms.

Before estimating Equation (1), panel unit root and cointegration tests have to be applied. To obtain robust results, we rely on various panel unit root tests. We first consider firstgeneration tests that are based on the assumption of cross-sectional independence among the panel members, i.e. among the various maturities. 7 Given that cross-sectional independence may be viewed as a restrictive hypothesis, 8 we also apply second-generation panel unit root tests that relaxe this assumption.

Table 4 in Appendix B reports the results of four first-generation tests. Levin andLin (1992, 1993)-LL-and [START_REF] Hadri | Testing unit roots in heterogeneous panel data[END_REF] tests are based on a common unit root process. Given that this hypothesis is a rather restrictive assumption on the dynamics of the series under the alternative hypothesis, we also consider two other tests. The IPS [START_REF] Im | Testing for unit roots in heterogeneous panels[END_REF] and [START_REF] Maddala | A comparative study of unit root tests with panel data and a new simple test[END_REF]-MW-tests allow for heterogeneity in the value of the autoregressive coefficient under the alternative hypothesis. LL, IPS and MW tests are based on the unit root null hypothesis, while the Hadri's test considers the null of no unit root. Results in Table 4 show that all series are I(1) when relaxing the common unit root hypothesis under the alternative.

These conclusions are supported by the results reported in Table 6 (Appendix B) relating to second-generation panel unit root tests. The [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF] CIPS test is based on Dickey-Fuller-type regressions augmented with the cross-section averages of lagged levels and first 5 As an example, Figure 1 in Appendix A depicts the one-month forward energy prices (in logs). 6 See Joëts (2010). 7 See [START_REF] Hurlin | Second generation panel unit root tests[END_REF] and [START_REF] Hurlin | What would Nelson and Plosser find had they used panel unit root tests?[END_REF] for a detailed presentation of panel unit root tests. 8 Cross-section dependence can arise for several reasons, such as spatial spillovers, financial contagion, socioeconomic interactions, and common factors [START_REF] Pesaran | General diagnostic tests for cross-section dependence in panels[END_REF]. In the presence of cross-section correlations in the panel, first-generation tests suffer from size distortions. Regarding our panel, the application of the CD test developed by [START_REF] Pesaran | General diagnostic tests for cross-section dependence in panels[END_REF]-based on the average of pair-wise correlation coefficients of OLS residuals from the individual regressions-shows that such correlations exist in our sample (see Table 5 in Appendix B).

differences of the individual series. Regarding the [START_REF] Moon | Testing for a unit root in panels with dynamic factors[END_REF] test, it is constructed on de-factored observations-deviations from the common components-and the factor loadings are estimated by principal component analysis. The [START_REF] Choi | Combination unit root tests for cross-sectionally correlated panels[END_REF] test relies on an error-components panel model and removes the cross-section dependence by eliminating (i) individual effects using the [START_REF] Elliott | Efficient tests for an autoregressive unit root[END_REF] methodology (ERS), and (ii) the time trend effect by centering on the individual mean. As shown in Table 6, all tests conclude in favor of the unit root hypothesis meaning that all forward energy price series entering in Equation (1) are I(1).

Turning now to the cointegration case, we first rely on first-generation tests, namely the seven tests proposed by [START_REF] Pedroni | Critical values for cointegration tests in heterogenous panels with multiple regressors[END_REF][START_REF] Pedroni | Panel cointegration asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis[END_REF] as well as the [START_REF] Kao | Spurious regression and residual-based tests for cointegration in panel data[END_REF] test which are all based on the null hypothesis of no cointegration. Among the 7 Pedroni's tests, 4 are based on the within dimension (panel cointegration tests) and 3 on the between dimension (group-mean panel cointegration tests). Group-mean panel cointegration statistics are more general in the sense that they allow for heterogeneous coefficients under the alternative hypothesis. As reported in Table 1, all tests conclude that forward oil prices and the four considered fundamentals are cointegrated. As for the unit root case, we also apply second-generation cointegration tests accounting for cross-sectional dependence. The four panel error correction-based tests proposed by [START_REF] Westerlund | Testing for error correction in panel data[END_REF] rely on structural dynamics and are a panel extension of the [START_REF] Banerjee | Error-correction mechanism tests for cointegration in a single-equation framework[END_REF] tests developped in the time series context. Among the four Westerlund's tests, two consider an homogeneous cointegrating relation under the alternative, while the two others allow for an heterogeneous long-term relationship. Results reported in Table 2 show that our energy forward prices are cointegrated. Finally, given that our sample covers a quite turbulent period, we implement the [START_REF] Westerlund | Simple tests for cointegration in dependent panels with structural breaks[END_REF] secondgeneration panel cointegration test that is robust to unknown heterogeneous breaks in both the intercept and slope of the cointegrating regression. Our findings reported in Table 2 confirm that energy prices are cointegrated. The number of common factors is determined by IC 1 criterion (see [START_REF] Bai | A PANIC attack on unit roots and cointegration[END_REF]) with a maximum factor number of 5. (2) For the Westerlund and Edgerton's test: (a) 5 Panel smooth transition regressions: estimation results

Estimation of the cointegrating relationship

Our considered series being I(1) and cointegrated, we first proceed to the estimation of the cointegrating relationship (1). Given that the distributions of the OLS estimates corresponding to Equation ( 1) are biased and dependent on nuisance parameters associated with the serial correlation properties of the data, it is necessary to use an efficient estimation procedure.

We rely here on the panel Dynamic OLS (DOLS) procedure developed by [START_REF] Kao | On the estimation and inference of a cointegrated regression in panel data[END_REF] and [START_REF] Mark | Cointegration vector estimation by panel DOLS, and long-run money demand[END_REF], which consists in augmenting the cointegrating relationship with lead and lagged differences of the regressors to control for the endogenous feedback effect.9

The estimated cointegrating relationship is given by:

poil i,t = âi -0.126p elec i,t + 0.149p gas i,t + 0.610p coal i,t + 0.428p Stoxx i,t (6)
This estimated relationship between oil, gas and coal forward prices is positive, while the link between oil and electricity forward prices is negative. The relationship between gas and oil prices has the expected sign given that the gas extraction process is very similar to that of oil. As a consequence, there exists a strong link across the two energies. Turning to coal, which is mainly used for electricity production, an increase in its price leads to a rise in oil price on the long run, due to an increasing demand for electricity and heating. Regarding electricity, two facts have to be highlighted. First, there exists an input-output relationship between this energy and oil. Second, electricity is used in various activities, mainly for final consumption. In Europe, specifically, electricity is intensively used for heating purposes and is thus in competition with oil. The negative link between oil and electricity forward prices on the long run may be interpreted in terms of a substitution effect, rather than in terms of an input-output effect. Finally, given that equity prices may be viewed as a proxy for the economic and financial environment, the positive relationship between oil and equity prices may be interpreted as follows: a rise in equity futures prices refers to a period of growing economic activity, leading to an increase in oil consumption and, consequently, in oil price.

Estimation of the nonlinear oil price dynamics

Following the three-step strategy proposed by [START_REF] González | Panel Smooth Transition Regression Models[END_REF], we start by applying linearity tests. We test the null hypothesis of linearity in Equation ( 5) using three transition variables: equity futures returns, the forward oil price misalignment, and the forward oil price variation. For all three variables, the null of linearity is strongly rejected in favor of the PSTR alternative.10 Results corresponding to the estimation of the PSTR models are reported in Table 3. Let us first consider the model with equity futures returns as the transition variable. In this case, forward oil price tends to reverts to its equilibrium value whatever the considered regime. This mean-reverting behavior is slower in the first state corresponding to a stock market which is decreasing or weakly increasing (until a threshold equal to 7%). Assuming that the stock market is a proxy for economic activity, this result is logical in the sense that reversion to the equilibrium is harder and takes a longer time in a depressing period than in an expansion state. Moreover, the other forward energy price returns positively affect oil price returns in both regimes, with a stronger impact in Regime 2. This result shows that when financial markets are booming, forward energy prices tend to augment. This may be explained by two reasons: (i) the need for more energy in periods of intense economic activity, and (ii) speculation purposes. Speculation on energy products goes along with speculation on financial assets. Expecting that the growing trend will continue, traders tend to take long positions on long-term contracts, selling them at higher prices before the expiry date and re-investing in new ones; a behavior that produces self-sustaining dynamics [START_REF] Cifarelli | Oil price dynamics and speculation: A multivariate financial approach[END_REF].

When the oil price misalignment acts as the transition variable, the estimated threshold is equal to -36%, corresponding to a 36% undervaluation of the forward oil price compared to its equilibrium value given by the cointegrating relationship. When oil price is strongly undervalued, a mean-reversion dynamics takes place (Regime 1). The more the reduction of the misalignment, the weaker the mean-reverting speed. In other words, corrections of disequilibria appear when oil price tends to strongly decrease, while it is not the case when oil price strongly augments compared to its fundamentals. This illustrates an asymmetric phenomenon: the adjustment process is at play only for high undervaluations, not for overvaluations. Regarding forward energy price returns, they vary in the same way as oil in case of strong undervaluations, with a decreasing influence when the magnitude of undervaluation tends to diminish. In the later case, the variable which has the strongest impact is the equity returns, again putting forward the importance of the speculation phenomenon: when oil price rises, the links across markets tend to be stronger, encouraging speculation. From a speculative viewpoint, it is reasonable to think that when oil prices are highly undervalued, the market is dominated by irrational speculators (chartists), who base their expectations on past prices fluctuations and believe trends to continue in the same direction. These speculators have a destabilizing effect, making prices deviate from their long-run fundamental equilibrium. However, when the threshold of -36% is reached, chartists no longer believe on the undervaluation and rational speculators (fundamentalists)-who base their expectations on economic fundamentals-become more prevalent. Fundamentalists believe that energy prices will revert to the intrinsic long-run equilibrium and therefore have a stabilizing effect. Consequently, chartists change their expectations and become followers of the fundamentalists. When prices tend to be overvalued, an asymmetric phenomenon occurs, that may be explained by the loss aversion behavior. Indeed, investors react differently when they are facing potential losses and profits. According to the prospect theory, agents are more hesitant to sell during overvaluation than to buy during undervaluation [START_REF] Kahneman | Prospect Theory: An analysis of decision under risk[END_REF].

Consider now the third case, with the forward oil price variations as the transition variable. The mean-reverting behavior is observed only in Regime 1, characterized by an oil price growth rate lower than 3%. This means that there exists a floor price under which oil producers decide to not produce due to profitability considerations. On the contrary, in periods of oil price boom, there is no mean-reverting behavior: the growing price tends to go away from its fundamental value, leading to self-sustaining behaviors. The other forward energy price returns are positively linked to oil price returns, except in Regime 2 for electricity. Again, this can be interpreted in terms of a substitution effect between electricity and oil when the later reaches very high values. Finally, note the positive relationship between oil and stock returns, a fact that is consistent with speculating dynamics.

Conclusion

This paper investigates the relationship between daily forward prices of oil, gas, coal and electricity. Relying on a panel of 35 maturities and controling for the economic and financial environment using equity futures prices, we test whether energy prices evolve toward a common long-run relationship. Using panel cointegration techniques, we show that all forward price series are cointegrated. More specifically, while oil, gas and coal forward prices are positively linked, oil and electricity display a negative relationship, consistent with a substitution effect between the two energy sources on long horizons. Paying a particular attention to the financial dimension of energy markets, we account for potential nonlinearities notably induced by market participants' behavior. To this end, we estimate panel smooth transition regression models, and show that the forward oil price adjustment process toward its equilibrium value is nonlinear and asymmetric. More precisely, our findings put forward the key role played by speculative factors and self-sustaining dynamics in phases of booming oil prices and growing economic activity. [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF] are -2.769, -2.653, and -2.589, at 1%, 5%, and 10% significance levels respectively. (b) For the Moon and Perron's tests, the long-run variance used in the construction of t * α and t * β is computed using the Andrews and Monahan (1992)'s estimator; the maximum number of common factors selected using AIC is 8 (see [START_REF] Bai | Determining the number of factors in approximate factor models[END_REF]; all statistics are computed with individual effects and time trends; the Moon-Perron statistics are standard Normal for large T under the unit root hypothesis. (c) For the Choi's test, the optimal lag orders in the individual ERS statistics [START_REF] Elliott | Efficient tests for an autoregressive unit root[END_REF] for each series are determined with p max = 12; all tests are computed with individual effects and time trends specifications; under the unit root hypothesis the Choi's statistics are standard Normal when T and N converge jointly to infinity.

  For the Westerlund's test: (a) between parentheses: p-values with cross-section dependence based on bootstrapped distribution (100 bootstrap replications). (b) Tests are computed with individual effects and time trends. (c) The Bartlett kernel is used for the semiparametric corrections. (d) The leads and lags in the error correction test are chosen using Akaike criterion. (e)

  between parentheses: p-values. (b) All tests statistics are limiting Normal distributions free of nuisance parameters under the null hypothesis. (c) The tests are implemented using the Campbell and Perron (1991) automatic procedure to select the lag length. (d) We use three breaks, which are determined by grid search.

Table 1 :

 1 First-generation panel cointegration tests Between parentheses: p-values. For Pedroni's tests, all statistics are computed with individual effects and time trends. Kao's test statistic is computed with individual effects only.Statistics are standard Normal.

		Pedroni	Kao
	Within dimension	Between dimension
	Panel v	12.89 (0)	Group rho	-11.85 (0) -13.06 (0)
	Panel rho	-11.59 (0)	Group P P	-9.85 (0)
	Panel P P	-9.09 (0) Group ADF	-8.85 (0)
	Panel ADF	-8.40 (0)
	Notes:

Table 2 :

 2 Second-generation panel cointegration tests

		Westerlund		Westerlund & Edgerton
	Group-mean statistics	Panel statistics	Model	τ N	φ N
	G τ	G α	P τ	P α	No break	-20.85 (0) -43.54 (0)
	32.462	1.7e+03	-192.407 1.7e+03	Level break	-18.59 (0) -40.

Table 3 :

 3 Estimation of PSTR models

	s i,t	∆p Stoxx i,t-1	z i,t-1	∆p oil i,t-1
		Regime 1 Regime 2 Regime 1 Regime 2 Regime 1 Regime 2
	z i,t-1	-0.0040	-0.0479	-0.0111	0.0097	-0.0074	0.0174
		(-4.80)	(-1.64)	(-2.37)	(1.67)	(-6.07)	(3.45)
	∆p oil i,t-1	0.0060	-2.33	-0.1303	0.1639	0.0288	-0.1232
		(1.16)	(-8.63)	(-3.23)	(3.36)	(3.75)	(-6.26)
	∆p elec i,t-1	0.0163	1.9036	0.0484	-0.0288	0.0526	-0.2360
		(5.43)	(14.43)	(1.57)	(-0.78)	(9.71)	(-8.28)
	∆p gas i,t-1	0.0134	0.1585	0.0569	-0.0561	0.0105	0.0068
		(5.27)	(1.11)	(1.82)	(-1.51)	(2.57)	(0.33)
	∆p coal i,t-1	0.0506	0.3725	0.5266	-0.5606	0.0174	0.3197
		(7.28)	(3.44)	(6.63)	(-5.96)	(1.89)	(8.90)
	∆p Stoxx i,t-1	-0.0855	0.0315	-0.7727	0.8407	-0.1254	0.2058
		(-12.40)	(0.44)	(-9.84)	(9.05)	(-11.70)	(4.94)
	γ	106.8853		4.7058		60.1668
	ĉ	0.0736		-0.3623		0.0358
				Between parentheses: t-statistics.

Table 6 :

 6 Second-generation panel unit root tests -2.480 (0.25) -1.805 (0.03) -0.700 (0.24) -3.829 (0.99) 3.393 (0.99) 3.059 (0.99) -3.162 (0.01) -1.287 (0.09) -0.783 (0.21) -5.308 (1.00) 7.894 (1.00) 7.557 (1.00) Between parentheses: p-values. (a) For the CIPS test, all statistics are based on univariate AR(p) specifications with p ≤ 8 including individual effects and time trends; the critical values tabulated in

		CIPS	Moon-Perron		Choi
			t * α	t * β	P m	Z	L *
	p oil i,t
	p coal i,t	-2.078 (0.95) 0.047 (0.51)	0.016 (0.50) -3.358 (0.99) 2.299 (0.98) 2.041 (0.97)
	p elec i,t p gas i,t	-2.707 (0.02)	1.355(0.91)	0.543 (0.70) -2.152 (0.98) 1.090 (0.98) 0.993 (0.83)
	Notes:

See references in Section

The reserves/production ratio is equal to 145 years (British Petroleum, 2010), and reserves are also well distributed (no cartel exists).

This is one of the reasons explaining the fact that the coal market was initially regional.

The final consumption sector represents the main sector for electricity consumption.

As a robustness check, we also estimate Equation (1) using the Fully-Modified OLS (FM-OLS) method proposed by[START_REF] Phillips | Statistical inference in instrumental variables regression with I(1) processes[END_REF]. The results were very similar to those obtained with the DOLS procedure and are available upon request to the authors.

Detailed results are available upon request to the authors.

Appendix A