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Introduction  1. Tezy i cel pracy - 1 strona, dlaczego to jest wazne i warte zachodu.  2. Analiza literaturowa (wez z papierow lub raportow + ksiazki wazne)  3. Opisac pokrotce co bedzie w rozdzialach (2 strony)  4. Rozdzialy  1. Location choices of newly created establishments: Spatial patterns at the aggregate level  2. Euclidean versus network distance in business location: A probabilistic mixture of hurdle-Poisson models  3. Location choices under strategic interactions: Interdependence of establishment types  4. Locational strategies of multi-store firms.  Chapter I  1. Location choices of newly created establishments: Spatial patterns at the aggregate level  Much work has been done in this 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.   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.   Traditional discrete choice modeling methods are often based on the assumption of independence among choice alternatives, which according to Sener et al. (2011) is not appropriate. Furthermore, Sener et al. (2011) claim that the estimations of the parameters of the standard logit models are biased and inconsistent.  We wish to combine the methods used to produce location choice models with spatial econometric techniques by examinig the role of space in in these models. This is a new and challenging field of research.  As seen in the review by Arauzo-Carod et al. (2010) and the literature review, the focus has been, up to this point, on the manufacturing/industrial and high- tech or R&D sectors. We here consider several other sectors, such as construction, hotels and restaurants, and real estate activities. Thus, the existing literature can be treated as a guide to motivate factors that may be studied in the econometric models.   Def. of establishment and firm:   We define an establishment 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).  Paris region:   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. 2.2 percent of the surface of France, 11.7 million, that is over 19 percent of the country’s population reside in this area. The GDP of the region amounts to 29 percent of total French GDP (IAU IdF, 2014).   The Paris region’s economy is spatially unbalanced (Combes et al., 2011).   The Grand Paris Project, up to 2030 and will cost around 32.4 billion euros.  The Paris region is highly heterogeneous16, 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 entries.   Liviano-Sol ́ıs and Arauzo-Carod (2014) based on the analysis of the Catalan data. The authors state that the distribution of entries is heavily skewed: a small group of municipalities meet the largest number of entries, while more than a half receive no entries at all. Municipalities range from small isolated villages in rural areas to huge and densely populated cities.  Depending on the analyzed sector, the percentage of municipalities left with no new creation ranges from 34 percent up to 69 percent. The number of municipalities left with zero newly created 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 of 1300 possible municipalities.  --> In addition, 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-stage decision-making process is reflected through the hurdle model interpretation. A zero-inflated model assumes that there are two sources of zero counts (not just one as in the hurdle model).   We concluded that an establishment does not act in isolation during its decision-making processes and that it is likely to be influenced by other establishments located nearby.   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. 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.:  Rocha (2008) and Liviano-Soĺıs and Arauzo-Carod (2011) concur, finding that local employment density attracts new entrants in related sectors and has a positive impact on the establishment’s productivity. However, when this density is too high, the effect becomes negative due to congestion costs, including for example, high land prices and costly commuting.   Maoh and Kanaroglou (2005, 2007) state that agglomeration effects tend to be more significant in particular activity sectors, such as retail and services.  Chapter II  2. Euclidean versus network distance in business location: A probabilistic mixture of hurdle-Poisson models  As noticed by McMillen and McDonald (2004) and emphasized by Rincke (2010), Bill ́e and Arbia (2013), and Vega and Elhorst (2013), the use of an arbitrary matrix is often the starting point to specify the linkage between neighboring observations followed by the sensitivity analysis based on models estimations using alternative, equally arbitrary, matrices. However, this arbitrary choice has a disadvantage of imposing a restrictive structure that can bias results when inappropriate.   several studies have considered alternative distance measures that are not purely based on topography (e.g., Conley and Ligon 2002; Slade 2005), including network distances and transport costs. However, there is little comparison with the geographical distance (Euclidean or great circle depending on the spatial scale), and when there is, it is based on the relative performances of two models, one based on the alternative distance and the other on the geographical distance.  This research proposes a new, flexible approach, where several distance measures may coexist and be combined instead of being systematically opposed. The methodology is based on a mixture of ”mono- distance models” which allows us to capture the diversity of agents’ behavior, and provides a more direct and integrated way of comparing various distance measures with each other. We address the criticism of Rincke (2010), Vega and Elhorst (2013), and other authors that the choice of the spatial weight matrix is usually quite arbitrary, while it refers to the choice of the distance measure.  Yet, whenever the distance measure was used in the weight matrix to implement the spatial dependencies or spatial spillovers in location choice models, no discussion was provided on the choice of the distance measure itself and the Euclidean distance was utilized.   However, whenever a distance measure was used in the weight matrix to implement the spatial effects in the location choice model, no discussion was provided on the choice of the distance measure itself.  they do not concentrate on the distance definition.   Hence, we find it necessary to open up a discussion on distance definition to be used in the location  choice models.  Definition of Euclidean distance:   Euclidean distance is the ”ordinary” distance between two points that one would measure with a ruler (Dattorro 2015).   The use of the Euclidean distance is widespread in economics (e.g., Duranton and Overman 2005; Partridge et al. 2008). This metric is known to all and experienced by all in everyday life, hence a prime candidate in economics. It is easily available to boot. Combes and Lafourcade (2003, 2005) claim that any Euclidean distance can only be regarded as a proxy for the actual physical distance. The curvature of the Earth is the first source of systematic error. The second source of systematic error comes from the fact that in practice, people (or goods) move along a transport network. For instance, car users may only drive on the existing road network. They rarely go from point A to point B along the straight line as assumed in the Fetter’s ”Law of Markets” (1924).   Interest in this question dates at least to the 1960s and research on network models in geography (Haggett 1967). According to Guy (1983), the use of air-line distance to represent a travel function is unsatisfactory, although it simplifies computation. In most cities transportation is along a network of roads and Euclidean metric is not appropriate for the study of intra-urban location (Eaton and Lipsey 1980; Perreur and Thisse 1974).  Actual driving distances over a road network and their corresponding travel times are perceived also by Boscoe et al. (2012) to be superior and substantially more precise than the straight-line distance.   In addition, heavy street use, road and parking congestion, speed limits, one-way roads, interstate highways with limited crossings, river with insufficient bridges, parks, and cemeteries may cause drivers to make detours in order to reduce their travel time, meaning that the shortest path may not be the fastest one.   Based on these considerations, several authors advocate the use of ”real” distance measures based on a transport network over geographical distance measures, Euclidean and great circle alike (Combes and Lafourcade 2005; Graham 2007; Duran-Fernandez and Santos 2014; Weisbrod 2008; Faber 2014; Kwon 2002). This point is especially cogent when it comes to the location choice of economic establishments, for which the role of a transport infrastructure is now well-known  Locations separated by rivers, lakes, mountains, steep hills, parks, cemeteries, golf courses, landmarks, highways, rail roads, train routes, industrial corridors often mark neighboring boundaries and have higher-than-expected travel times (see Fig. 3 and 4). Physical barriers are also formed by major single-purpose zones and major transport infrastructures that can only be crossed at the cost of substantial effort and tend to reduce the mobility of population living nearby (H́eran 2011).  The barriers tend to be difficult to cross and the whole zone is marked by the severance effect. The mobility number of journeys of residents declines in intensity. One can observe a reduction in neighborhood relations. Barriers require to make detours, expend additional energy. Access to employment and population becomes highly restricted (Motte-Baumvol et al. 2015). Jacobs (1961) finds that barriers usually make destructive neighbors by limiting interactions. Barriers mitigate neighbor externalities (Noo- nan 2005). Barriers that mitigate spatial externalities are expected to have important differential effects on neighborhood and land use patterns (Noonan 2005). Chakravorty (1996) mentions that physical features may imply a total non-contiguity.  When choosing an appropriate place in which to set up on the market, an establishment can take into account not only the characteristics of a particular area but also those of its surroundings. The degree of spatial correlations is expected to be greater among choice alternatives that are close to one another. Despite the existence of these spatial effects, they are most often completely ignored in the analysis of the unit location. There is little mention in the literature of previous attempts to incorporate spatial effects in establishment or firm location decision-making processes (Bhat et al. 2014; Buczkowska and Lapparent 2014; Liviano-Sol ́ıs and Arauzo-Carod 2013; Liesenfeld et al. 2015; Lambert et al. 2010; Klier and McMillen 2008).  In this paper, we respond to the complaint voiced by Liviano-Sol ́ıs 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. Consequently, the empirical evidence (for comparisons purposes) is still scarce. We will try to fill this gap in the business location modeling literature limited to two recent papers 1) of Liviano-Soĺıs and Arauzo-Carod (2013) and 2) of Buczkowska and Lapparent (2014).  ((Liviano-Sol ́ıs and Arauzo-Carod (2013) find that the hurdle approach fits their industrial sector loca- tion data better than the zero-inflated approach. The authors compare several models: Poisson, negative binomial, zero-inflated versions of these models, hurdle-Poisson (HP) and hurdle negative-binomial (HNB). They show that the hurdle models (HP and HNB) are the models whose expected number of zero counts match the observed zero counts, and that the distribution of the HNB model is the one that best fits the data under study. They account for the excess of zeros problem and the overdisperssion (the excess of conditional variance over the conditional mean). They conclude that the use of a HNB clearly improves the explanatory power of the econometric estimations, and they suggest that the analysis of firm location behavior should consider the following factors: 1) the existence of a threshold that allows a site to be chosen by at least one firm and 2) the number of times that this site is chosen by the total population of plants during the analyzed period.))  ((Buczkowska and Lapparent (2014) test various count data models: Poisson, zero-inflated Poisson, zero-inflated (tau) Poisson, negative binomial, zero-inflated negative binomial, and hurdle-Poisson models. Hav- ing estimated 84 nested and non-nested count data models for various activity sectors, the authors demon- strate that the hurdle models are preferable for taking into account the presence of excess zeros. Hurdle models offer greater flexibility in modeling zero outcomes than the zero-inflated models and relax the as- sumption that the zero observations and the positive observations come from the same data generating process.))  We modify the modeling framework of Buczkowska and Lapparent (2014) in order to consider alternative ”transport distances” in addition to the Euclidean distance, namely: two road distances (with or without congestion), the public transit distance, and the corresponding travel times.   Chapter III  3. Location choices under strategic interactions: Interdependence of establishment types  Citing other papers is easy. Voilà: \cite{2012} or \cite{Holstein_2009}. Click on the \verb|cite| button in the toolbar to search articles and cite them. Authorea also comes with a powerful commenting system. Don't agree that $E = mc^{3}$?!? Highlight the text you want to discuss or click the comment button. 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