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GENERAL INTRODUCTION  1.1 1  XXX GENERAL XXX 1.2 KEY QUESTIONS XXX  This thesis is breathing new life into the location choice models of establishments. The need for methodological advances in order to model more realistically the complexity of establishments' decision-making processes, such as their optimal location choices is the key motivation of this thesis. A clear distinction between an establishment and a firm should be made in the first place. An establishment is defined as a distinct economic unit that produces goods or services at a single physical location. In contrast, a firm is a legal entity that consists of one or more establishments or plants under common ownership and control (van Wissen, 2000).  A number of key questions will be addressed along this thesis. First, location choice models use geo-referenced data, for which choice sets have an explicit spatial component. It is thus critical to understand how to represent spatial aspect in location choice models. Second, what makes these discrete choices particularly interesting and challenging to analyze is that decisions of a particular establishment are interrelated with choices of the others. These thorny problems posed by the interdependence of decisions generally cannot be assumed away, without altering the realism of the model of establishment decision making. The conventional approaches to location selection fail by providing only a set of systematic steps for problem-solving without considering strategic interactions between the establishments in the market. One of the goals is to explore how to correctly adapt location choice models to study establishments' discrete choices when they are interrelated. Third, a firm can open a number of units and serves the market from multiple locations. Once again traditional theory and methods may not be suitable to situations wherein individual establishments instead of locating independently from each other, form a whole large organization following common strategy and control. 1.1 KEY QUESTIONS  A number of key questions will be addressed along this thesis. First, location choice models use geo-referenced data, for which choice sets have an explicit spatial component. It is thus critical to understand how to represent spatial aspect in location choice models. Second, what makes these discrete choices particularly interesting and challenging to analyze is that decisions of a particular establishment are interrelated with choices of the others. These thorny problems posed by the interdependence of decisions generally cannot be assumed away, without altering the realism of the model of establishment decision making. The conventional approaches to location selection fail by providing only a set of systematic steps for problem-solving without considering strategic interactions between the establishments in the market. One of the goals is to explore how to correctly adapt location choice models to study establishments' discrete choices when they are interrelated. Third, a firm can open a number of units and serves the market from multiple locations. Once again traditional theory and methods may not be suitable to situations wherein individual establishments instead of locating independently from each other, form a whole large organization, such as a chain facing in addition a fierce competition from other chains. Illustrative questions that can be answered are: How fast do the firm profit and the market power decline when the number of firms and their outlets in the market increases or when the distance to their rivals decreases? How does a firm perceive a rival store located in a very close neighborhood and how if it is located far away? What is the nature and degree of competition for each of the analyzed chain?  An intensified research effort along the lines of location choices is still desirable to bring the answers to many questions of this type.  1.2.1) 1.2  WHY SHOULD WE BOTHER? 1.2.1)  In this thesis, we concentrate our research on the Paris region, a vibrant and innovative region with over 5,6 million jobs, 37 percent of French executives, and 40 percent of national workforce in research and development. It is the first R&D hub in Europe and the third worldwide. 11.7 million people or over 19 percent of the country's population reside in the area which occupies only 2.2 percent of the surface of France. The Paris region is the third World touristic destination (in 2013) (Global Destination Cities Index 2015) with 16 millions of visitors from abroad. The GDP of the region amounts to 29 percent of total French GDP (IAU IdF, 2014) %31 percent of total French GDP (http://www.grand-paris.jll.fr/fr/paris/chiffres-cles/) that is 612 milliards euros (2012). It is 1st European city considering the number of firms classified in Fortune 500 (July 2014).   Yet, the Paris region’s economy is spatially unbalanced (Combes et al., 2011). The region is highly heterogeneous, especially regarding economic activity. While few municipalities host a large number of new establishments, others struggle to be chosen by any, and a large group of municipalities is left with no new creation. When the observed data display a higher fraction of zeros than would be typically explained by the standard count data models, two types of models can be suggested: the hurdle model (Mullahy, 1986) or the zero-inflated model (Lambert, 1992). The hurdle model reflects a two-part decision making process. It relaxes the assumption that the zero observations and the positive observations come from the same data generating process.