into the local economy in the form of increased income. A description of the analytical framework used to express these mechanisms can be found in Appendix 10.1 which goes into the details of the main analytic relationships but section 4.1 has already introduced these in their most basic form.

NEDUM-2D differs from the standard model developed in Fujita (1989) for three main reasons. First, it can be used dynamically, which enables to take into account the effect of a global change in income or population. As explained in Gusdorf et al. (2008), urban economics has mostly been used to explore the characteristics of long run equilibria. However, the existence of urban stationary equilibria is questionable: when population, transport prices, or income vary (sometimes in unpredictable ways), housing infrastructure cannot adapt instantaneously to changing conditions. NEDUM-2D explicitly takes these dynamics into account and describes cities as non-equilibrium systems. The model can also be run at the equilibrium for a specific year if data for a larger time period are not available.

Second, the theoretical model described by Fujita (1989) represents spatial differences solely as a function of the distance to the city center. As the name suggests NEDUM-2D is two dimensional meaning that it is an urban model that represents urbanization on a map rather than on a single axis. NEDUM-2D uses a grid with cells of variable size (classically 1km2). It can therefore account for spatial differences in land use and accessibility at a much finer scale. The model can represent differences between two cells situated at the same distance from the city center such as the amount of land that can be built upon or the proximity to public transport.

Third, the classic urban economy model only represents one means of transport. In NEDUM-2D there are three main transport options: private cars, public transport and walking. For each location in the urban area, citizens choose between walking, public transport and private vehicles, or a combination of these as a means for commuting. The competition between these modes is organized on the basis of their _generalized costs_ (i.e. the total cost including both the cost of time and the monetary costs incurred during the trip to the city center). It is assumed in this study that modal switch does not affect congestion levels and therefore leaves commuting times unchanged. In order to reflect the heterogeneity of citizens’ preferences in terms of transport modes we employ a discrete choice model (De Palma and Thisse 1987). We follow a common approach in transportation economics by using a multinomial logit model which assigns usage probabilities to each transport mode (see Salon 2009; Washbrook, Haider, and Jaccard 2006). This method ensures that even when one mode is much cheaper than the others, it will not be used by all residents in a location for all trips.

In the present study, we use this model with a certain number of simplifying hypotheses. First, we assume that all households commute every day to the center of the _Ciudad Autónoma de Buenos Aires_ (CABA), where jobs are assumed to be located. This mono-centric hypothesis is a clear simplification but one that is widely used in the literature and that finds some support in the data for the specific case of Buenos Aires. Indeed, the Buenos Aires region displays a strong mono-centric structure. For instance, in 2012 more than 40% of all jobs in the Buenos Aires region are localized in CABA even though CABA only represents 5.3% of _Region Metropolitana_ ’s area (see Appendix 10.1.5 for a more in-depth discussion about the validity of the mono-centric assumption for the Buenos Aires urban area)<sup>5</sup> .

Second, NEDUM-2D also relies on the hypothesis that all households within the urban area earn the same average income. It does not currently account for multiple household types and thus ignores income

> 5 Projections for 2012 based on Censo Nacional Economic 04/05 (CNE 04/05) and Encuesta Permanente de Hogares, 2011 (EPH, 2011).

15