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“If you wait to do everything until you’re sure it’s right, you’ll probably never do much of anything.” – Win Borden  Paraphrasing the quote of Winston Churchill, courage of  writing thesis is going from failure to failure without losing enthusiasm (Original quote: “Courage is going from failure to failure without losing enthusiasm.” – Winston Churchill)
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We concentrate our research on the Paris region, called as Ile-de-France - 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. 1st R&D hub in Europe and the third worldwide. 11.7 million people - over 19 percent of the country’s population reside in the area which occupies 2.2 percent of the surface of France. 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). 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 Paris 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. The Grand Paris is a development project for the whole of the Paris metropolitan area. It is designed to improve residents’ quality of life, address regional inequalities and build a sustainable city (https://www.societedugrandparis.fr/english). It will cost around 32.4 billion euros (up to 2030).   GENERAL INTRODUCTION  This thesis is breathing new life into the location choice models of establishments. Location choice models use geo-referenced data, for which choice sets have an explicit spatial component.   It is critical to understand and represent spatial aspect in location choice models.   We concentrate our research on the Paris region, called as Ile-de-France - 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 a 1st 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. The Grand Paris is a development project for the whole of the Paris metropolitan area. It is designed to improve residents’ quality of life, address regional inequalities and build a sustainable city (https://www.societedugrandparis.fr/english). It will cost around 32.4 billion euros (up to 2030).  Depending on the analyzed sector, the percentage of municipalities left with no new establishment  creation ranges from 34 percent up to 69 percent. 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, also called the two-part 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. The two-step decision-making process is reflected through the hurdle model interpretation. We respond to the complaint voiced by Liviano-Solis and Arauzo-Carod (2013) and Bhat et al. (2014) who notice that heretofore the hurdle model technique has not been fully explored when analyzing location phenomena.  Much work has been done in the domain of location choice models, however, several issues arise when analyzing involved phenomena, which scholars have yet to fully explore: 1) addressing the excess of zeros problem in the location choice model in highly heterogeneous geographic areas and 2) determining an appropriate way to accommodate spatial effects for location decisions.   These are the first challenges that we take in the first chapter.   1b)   %When selecting the appropriate location in which to set up in the market, an establishment may consider not only the characteristics of a particular area, but also the characteristics of neighboring zones.   When accounting for the linkage between neighboring observations since the final decision of an establishment should be related to the surrounding economic landscape, we need to decide on the spatial weight matrix specification. Yet, since there exist no solitary claim on the concept of space, the form of the weight matrix is largely debated. One of the problems hides in the definition of distance usually based on the straight-line segment connecting two locations. Extending the research presented in the first chapter of this thesis, where Euclidean distance was used to account for spatial spillovers in location choice model, other alternative distance metrics are to be proposed when building the spatial distance weight matrices.  Geographic factors such as terrain, land cover, infrastructure, and traffic congestion may cause agents not to follow pure Euclidean relations. Euclidean distance is believed to be only one simplistic possibility out of an infinite number of shortest path relations. The Euclidean distance might thus not always be the most relevant one depending on the problem considered. Interest in this question dates at least to the 1960s and research on network models in geography (Haggett 1967). There are insights to be gained by mindfully reconsidering and measuring distance depending on a given problem.  The second chapter investigates establishments location decisions in the Paris region where high congestion, speed limits, or physical uncrossable barriers, such as rivers or industrial corridors can diminish or totally eliminate the linkage between neighboring areas. Rather than imposing a restrictive structure of the weight matrix, this research proposes a flexible toolkit to point which distance metric is more appropriate to correctly account for the surrounding economic landscape. A probabilistic mixture of two ”mono-distance” hurdle-Poisson models was developed. Each model’s latent class uses a different distance representation to incorporate spillover effects in location choices of establishments from several activity sectors. Seven distance metrics were considered: Euclidean distance, two road distances (with and without congestion), public transit distance, and the corresponding travel times. This methodology allows to capture the diversity of agents’ behavior, i.e., to distinguish establishments which are more time- or more distance-oriented given location.   2)   %In spite of recognizing the importance of incorporating spatial effects in establishments location decision processes, the literature is still scarce on previous attempts.   enhancing and extending the existing literature in a  number of municipalities left ways:   We incorporate strategic interactions among establishments.   What makes these discrete choices particularly interesting and challenging to analyze is that decisions of a particular establishment are interrelated  with zero newly created choices of the others because an establishment accounts for the actions of other agents when making its own decisions (Draganska et al., 2008). 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 (Berry and Reiss, 2007). The conventional approaches to location selection, i.e., traditional theory and methods, fail (Thill, 1997) by providing only a set of systematic steps for problem-solving without considering strategic interactions between the  establishments in the industry sector equals to 734, in construction 439, commerce 440, transport 837, financial activities 799, real estate activities 738, hotels and restaurants 792, information and communication 771, special, scientific and technical activities 569, education 890, health and social activities 794 out market. Being non-strategic would mean that an establishment ignores other players’ decisions (Toivanen and Waterson, 2005). A properly specified model of simultaneous entry or location decisions needs to recognize this interdependence of  profits (Berry and Reiss, 2007).  There is a need for more realistic studies  of 1300 possible municipalities. complex establishment’s decision-making processes. Even though the computational burden imposed by these models considering strategic interactions is relatively high, it seems that the costs imposed are more than offset by the benefits that accumulate/accrue (Draganska et al., 2008).  3) We   This thesis investigates establishments location decisions in the Paris region where high congestion, speed limits, or physical uncrossable barriers, such as rivers or industrial corridors can diminish or totally eliminate the linkage between neighboring areas.  Chapter I  1. Location choices of newly created establishments: Spatial patterns at the aggregate level  Much work has been done in this the  domain, however, several issues arise when analyzing involved phenomena, which scholars have yet to fully explore: 1) addressing the excess of zeros problem in the location choice model in highly heterogeneous geographic areas and 2) determining an appropriate way to accommodate spatial effects for location decisions. When the observed data display a higher fraction of zeros than would be typically explained by the standard count data models, the zero inflated or hurdle models can be suggested. In this paper, we respond to the complaint voiced by Liviano-Solis and Arauzo-Carod (2013) and Bhat et al. (2014) who notice that heretofore scholars have not fully explored the hurdle model technique when analyzing location phenomena.  Spatial effects can be incorporated in location choice models when modeling the observable explanatory variables and the unobservable components. However, most often, these spatial effects are either not properly treated or are completely ignored in the analysis of the establishment’s location.   in spite of recognizing the importance of incorporating spatial interactions in location choice models (and establishment location choice models in particular), research is insufficient on this topic   Furthermore, even if spatial effects are present they are not incorporated in traditional discrete choice models.  
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The paper finds that the models tested with the distance matrix indicate that the incorporation of spatial spillovers leads to an enhancement in the models’ performance.  we make use of the distance matrix to characterize spatial patterns12. patterns.  Currently, there are two basic categories that define neighbors: contiguity (shared borders) and distance. Contiguity-based weights matrices include rook and queen matrices. Distance-based weights matrices include distance bands and k nearest neighbors. Agglomeration etc.: 
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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 our present paper. We shed light on strategic interactions, fundamental in establishments’ location choices, yet largely unheeded in the empirical literature. If establishments acted in isolation, it would be a relatively simple matter to adapt existing discrete-choice models. Yet, being non-strategic means that a firm ignores other players’ de- cisions. Less is known about how to correctly adapt location choice models to study establishments’ discrete choices when they are interrelated. In very sparse empirical applications, when locational choice models are developed for several activity sectors, each of the model is typically run independently.  Many key strategic decisions establishments make, such as where to set up in the market, involve discrete choices (Draganska et al., 2008). These decisions are fairly complex, yet, yet  particularly important since, unlike other marketing mix elements, they are less adjustable in the short-run without incurring significant costs (Zhu and Singh, 2009). In the literature on location choices of establishments, usually only one activity sector, typically an industrial or a re- tail sector, is considered at a time. In very sparse empirical applications, when locational choice models are developed for several activity sectors, each of the 10 model is typically run independently (see, e.g., Chatman et al., 2016; Bucz- kowska and Lapparent, 2014). 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 because an establishment accounts for the actions of other agents when making its own decisions (Draganska et al., 2008).