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Despite the vast research about discrimination, there is little evidence about how space interacts with it. Our main hypothesis is that a discriminated group could have incentives to stay together, even if the location is less dynamic—avoiding areas where firms do not usually hire workers of their group. A virtuous and/or vicious circle emerges for each group, even in the long run. Using USA as an example, this article introduces a theoretical economic model to explain the incentives of ethnic groups in terms of location. We extend the well-known model of discrimination with imperfect information to a spatial framework. The results seem to indicate that the initial population distribution and the barriers to agglomerate activity (transport costs), as well as the behavior of employees, are key elements to determine the equilibrium. As a general conclusion, discrimination processes could clearly modify the location pattern of the population. Hence, the discriminated group could suffer from lower incentives to move from non-agglomerated areas to dynamic areas. These processes could help to explain why some populations prefer to maintain their traditional location in poor areas even if there are places with higher wages or better quality of life. [ABSTRACT FROM AUTHOR]

Difference in terms of cost-of-living between rural and urban areas is a frequent theoretical analysis in Regional Economics. Lack of routine measures in rural areas does not usually allow to observe changes in rural costs. We adapt the Big Mac index, typically used to measure purchasing power parity between countries, as a potential quick and inexpensive indicator of short-term local price variations. With a national random sample of McDonald's stores repeated in time, we find prices grew slightly faster in rural areas than in urban areas. Spatial transmission of prices seems to be limited to very localized effects, meaning that rural price increases are not due to urban spillover effects. [ABSTRACT FROM AUTHOR]

The extensive literature on economic convergence has explored a wide variety of ways of measuring convergence in addition to finely tuning and improving the applicable econometric techniques. However, very few contributions analyze the relevance of the spatial level of analysis. Our hypothesis is that studying the convergence at the level of large regions (states) could conceal intraregional heterogeneity. This hypothesis is consistent with the New Economic Geography framework, which highlighted core-periphery patterns at the local level. However, this polarization mechanism may become difficult to identify with aggregated data or neoclassical dynamics operating at the same time. This paper proposes a multilevel approach to study this question. It allows the identification of possible heterogeneous local patterns of behavior within regions. It is applied to the US economy in a hierarchy of two levels: states and counties. The results show high intraclass correlation, indicating significant variance within states. An overall pattern of convergence is observed in line with previous results, although some states present internal patterns of divergence or significant changes in the rate of convergence. [ABSTRACT FROM AUTHOR]