Abstract
This article deals with the issue of economic integration of immigrants in Italy. Ethnic inequality and economic stratification are analysed based both on the European Survey on Income and Living Conditions (EU-SILC) and the ad-hoc survey on households with foreign people conducted by the National Institute of Statistics in 2009. With reference to the native group and the groups identified by the largest ethnic communities living in Italy, through the Analysis of Gini decomposition technique, the article highlights that the between-group inequality accounts for a barely four percent of the overall inequality, the largest share being due to the inequalities within the groups. The policy prescription suggested is the adoption of “peculiar” policies on specific subgroups of immigrants aimed at reducing the disparities within the subgroups.
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Notes
The term community can be used to describe a variety of relationships as well as geographical space. For the purpose of this paper the term refers to the community based on ethnic identity that is measured through the country of origin.
In this context, along the economic dimension, the assimilation can be defined as the decline of differences between groups by following Alba and Nee (1997) who describe assimilation as “the decline, and at its endpoint the disappearance, of an ethnic/racial distinction and the cultural and social differences that express it”.
Generally the issue of “social disadvantage” of immigrants includes many dimensions (culture, language, equal opportunities, to list a few) but we concentrate the attention only on the economic dimension.
In order to respond to this need, in 2009 the Italian National Institute of Statistics (ISTAT) conducted the Survey on Income and Living Conditions of Household with Foreign People using both methodology and definitions harmonized with the European Statistics of Income and Living Conditions survey (EU-SILC). Therefore, the two surveys can be used for a comparative analysis between Italian and foreign households (ISTAT 2011).
This set includes: all EU-15 and EFTA countries, some of the OECD countries, some of the countries that are in the top 45 positions of the 2011 ranking based on the Human Development Indicator and finally Kuwait, United Arab Emirates, Oman and Saudi Arabia. The remaining countries are classified as countries with SMP.
The decision to define the household citizenship on the basis of the citizenship of the household head (the highest income earner) is justified since households composed of members with different citizenships constitute <2 %. Furthermore, the percentage of immigrant households composed of members who live together with no family ties is negligibly small (<1 %).
Several alternatives computational methods exist for deriving the Gini coefficient. They include the relative mean difference approach, the geometric approach and the covariance approach. Based on the covariance approach, the Gini coefficient can be expressed as \(G = \frac{{2cov\left( {y,F\left( y \right)} \right)}}{{\mu_{y} }}\) where y is the income, F(y) is its distribution function and μ y its mean value.
Since the intra-group component results from a weighted sum of the Gini coefficients of each group, where the weights are given by the income shares of the groups, it is highly influenced by the inequality of the groups that hold the higher income shares.
The bootstrap confidence intervals are not shown here. They are available on request from the Authors.
We split further the immigrants with respect to their citizenship in order to have a sufficient number of observations for the regression analysis. However, we grouped citizenship at area level when the number of households corresponding to a particular citizenship was less than 3. Details of the classification used are available upon request.
Each explanatory variable is computed as difference with respect to the mean level observed for the Italian community, except the coefficient of variation of number of years spent in Italy and the dummy for MDC.
The high negative and significant correlation observed (−0.698, p value < 0.0001) between low and university educational level drives the choice to include only the share of the lowest educational level in the regression analysis.
We ran the model on 59 observations and checked for robustness in order to see if the results were being driven by a few special cases. We performed Robust Regression, and four countries of origin (Spain, Netherlands, Japan and Ivory Coast) have been deleted as they were depicted as outliers. Results are available on request from the Authors.
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D’Agostino, A., Regoli, A., Cornelio, G. et al. Studying Income Inequality of Immigrant Communities in Italy. Soc Indic Res 127, 83–100 (2016). https://doi.org/10.1007/s11205-015-0954-1
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DOI: https://doi.org/10.1007/s11205-015-0954-1