A stochastic approach for quantifying immigrant integration: the Spanish test case

Abstract

We apply stochastic process theory to the analysis of immigrant integration. Using a unique and detailed data set from Spain, we study the relationship between local immigrant density and two social and two economic immigration quantifiers for the period 1999–2010. As opposed to the classic time-series approach, by letting immigrant density play the role of 'time' and the quantifier the role of 'space,' it becomes possible to analyse the behavior of the quantifiers by means of continuous time random walks. Two classes of results are then obtained. First, we show that social integration quantifiers evolve following diffusion law, while the evolution of economic quantifiers exhibits ballistic dynamics. Second, we make predictions of best- and worst-case scenarios taking into account large local fluctuations. Our stochastic process approach to integration lends itself to interesting forecasting scenarios which, in the hands of policy makers, have the potential to improve political responses to integration problems. For instance, estimating the standard first-passage time and maximum-span walk reveals local differences in integration performance for different immigration scenarios. Thus, by recognizing the importance of local fluctuations around national means, this research constitutes an important tool to assess the impact of immigration phenomena on municipal budgets and to set up solid multi-ethnic plans at the municipal level as immigration pressures build ; This work is supported by the FIRB grants RBFR08EKEV and RBFR10N90W. EA and AB acknowledge also partial financial support by GNFM-(INdAM) via Progetto Giovani 2013 (AB) and 2014 (EA). RS is grateful to the project Competition, Adaptation and Labour-Market Attainment of International Migrants in Europe (CALMA)? granted by the VI National Plan for Scientific Research, Spanish Ministry of Economy and Competitiveness (CSO2012-38521), for partial financial support.