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on Economic Geography |
| By: | Michael Amior |
| Abstract: | The US suffers from large regional disparities in employment rates which have persisted for many decades. It has been argued that foreign migration offers a remedy: it "greases the wheels" of the labor market by accelerating the adjustment of local population. Remarkably, I find that new migrants account for 30 to 60 percent of the average population response to local demand shocks since 1960. However, population growth is not significantly more responsive in locations better supplied by new migrants: the larger foreign contribution is almost entirely offset by a reduced contribution from internal mobility. This is fundamentally a story of "crowding out": I estimate that new foreign migrants to a commuting zone crowd out existing US residents one-for-one. The magnitude of this effect is puzzling, and it may be somewhat overstated by undercoverage of migrants in the census. Nevertheless, it appears to conflict with much of the existing literature, and I attempt to explain why. Methodologically, I offer tools to identify the local impact of immigration in the context of local dynamics. |
| Keywords: | migration, geographical mobility, local labor markets, employment |
| JEL: | J61 J64 R23 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1582&r=geo |
| By: | ANDREOLI Francesco |
| Abstract: | The neighborhood inequality (NI) index measures aspects of spatial inequality in the distribution of incomes within the city. The NI index is defi ned as a population average of the normalized income gap between each individual's income (observed at a given location in the city) and the incomes of the neighbors, living within a certain distance range from that individual. This paper provides minimum bounds for the NI index standard error and shows that unbiased estimators can be identifi ed under fairly common hypothesis in spatial statistics. These estimators are shown to depend exclusively on the variogram, a measure of spatial dependence in the data. Rich income data are then used to infer about trends of neighborhood inequality in Chicago, IL over the last 35 years. Results from a Monte Carlo study support the relevance of the standard error approximations. |
| Keywords: | income inequality; individual neighborhood; geostatistics; variogram; census; ACS; ratio measures; variance approximation; Chicago; Monte Carlo |
| JEL: | C12 C46 D63 R23 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:irs:cepswp:2018-19&r=geo |
| By: | Modai-Snir, Tal (Delft University of Technology); van Ham, Maarten (Delft University of Technology) |
| Abstract: | The socio-economic mosaic of urban neighbourhoods changes under the influence of three distinctive distributional processes: reordering of the socio-economic position of urban neighbourhoods; changing levels of inequality between neighbourhoods; and an overall growth or decline in income levels which affects all neighbourhoods of an urban area. With the common practices in analysing neighbourhood change, the roles of these underlying processes are unclear. This paper builds on a decomposition method to analyse the roles of the three components of change in four largest Dutch city-regions. The results points to substantial variations in components of change in the four city-regions. |
| Keywords: | neighbourhood change, socioeconomic change, income inequality, spatial polarisation, socio-spatial structure |
| JEL: | O18 P25 R23 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp11883&r=geo |
| By: | Edmark, Karin (Swedish Institute for Social Research, Stockholm University) |
| Abstract: | This paper studies the location decisions of Swedish start-up independent schools. It makes use of the great expansion of independent schools following a reform implemented in 1992 to test what local market characteristics are correlated with independent school entry. The results suggest that independent schools are more likely to choose locations with a higher share of students with high-educated parents; a higher student population density; and a lower share of students with Swedish-born parents. There is also some evidence that independent schools are less likely to locate in municipalities with a left-wing political majority. These results are robust to various alternative and flexible definitions of local school markets, which were employed in order to alleviate the Modifiable Areal Unit Problem. For some of the included variables, the definition of the local market however had a large impact on the results, suggesting that the issue of how to define regions in spatial analyses can be important. |
| Keywords: | Private provision; Mixed markets; Education sector; Modifiable Areal Unit Problem |
| JEL: | H44 I28 L19 R32 |
| Date: | 2018–11–14 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:sofiwp:2018_011&r=geo |
| By: | Marco Modica (Gran Sasso Science Insitute); Aura Reggiani (Department of Economics, University of Bologna); Peter Nijkamp (Tinbergen Institute, Gustav Mahlerlaan 117, 1082 MS Amsterdam) |
| Abstract: | The economic recession which followed the 2008 financial crisis has raised important issues concerning the asymmetry of the shocks - at both the regional and the community level, especially in the European Union Member States. The asymmetry of the shock might be due to the different levels of vulnerability and exposure. These differences can arise because of dissimilarities in the intrinsic characteristics of regions or communities (e.g. the pre-crisis economic characteristics of regions, ageing, household income, and so on). While a great deal of attention has been paid, in the scientific literature, to the concept of resilience (e.g. the capacity to bounce back or to resist a given shock) and vulnerability (e.g. the inherent characteristics that create the potential for harm), less attention has been devoted to the full set of measures of socio-economic exposure (e.g. the things affected by a shock), as well as both to the relationship between vulnerability, exposure and resilience and to the losses which ensue as a result of different external shocks and exposure. The objective of this paper is the exploration of the above-mentioned links, since these interrelations might produce different outputs. To this purpose, we first review the existing literature on vulnerability, exposure and resilience, in order to understand the connections between these concepts, with reference not only to economic shocks but also to other catastrophic events, such as natural disasters, man-made disasters, and so on. We then provide evidence of the impact mechanism of the 2008 financial crisis at the regional and the age-cohort level for the German labour markets, by highlighting how a shock turns into different losses according to the vulnerability and exposure of the objects (the regions and the age cohorts) under analysis. |
| Keywords: | Resilience; Vulnerability, Exposure; Economic shock; German districts |
| JEL: | R11 R23 Q54 Q56 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:srt:wpaper:1318&r=geo |
| By: | Schintler, Laurie A.; Fischer, Manfred M. |
| Abstract: | Big Data on cities and regions bring new opportunities and challenges to data analysts and city planners. On the one side, they hold great promise to combine increasingly detailed data for each citizen with critical infrastructures to plan, govern and manage cities and regions, improve their sustainability, optimize processes and maximize the provision of public and private services. On the other side, the massive sample size and high-dimensionality of Big Data and their geo-temporal character introduce unique computational and statistical challenges. This chapter provides overviews on the salient characteristics of Big Data and how these features impact on paradigm change of data management and analysis, and also on the computing environment. |
| Keywords: | massive sample size, high-dimensional data, heterogeneity and incompleteness, data storage, scalability, parallel data processing, visualization, statistical methods |
| Date: | 2018–10–28 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus046:6637&r=geo |