The status of immigrants on Italian labour market in the context of economic decline: Evidence from survey, macroeconomic and big data

Abstract

Considering the impact of economic crisis on Italian labour market, the aim of this paper is to analyse the status of immigrants from Italy in the labour field using more sources of data: survey data from the research “Case study of Labour Force Survey”, macroeconomic data from official statistics of Eurostat and big data associated to Internet queries on Google. Survey analysis for the 20 regions in Italy confirmed the foreign women advantage of finding a job better than foreign men. The decline in the GDP at regional level affected the immigrants, but their searches for jobs on the Internet were significant in explaining the status of immigrants on labour market. The data at national level used in estimating Bayesian generalized ridge regression suggested that the number of unemployed immigrants from Italy since 2008 is explained by changes in risk of poverty or social exclusion, exports of goods and services, housing cost overburden rate, inflation and tax rate on low wage earners and adult participation in learning.

Sinirlarin Ötesi̇ndeki̇ Di̇ji̇tal İzler: Göç Kri̇zi̇ Üzeri̇ne Si̇stemati̇k Bi̇r Derleme

“…Choi et al (2020) presented the psychological resilience and coping mechanisms of immigrants and emphasized the importance of developing policies that aim to strengthen the mental health of immigrants. Simionescu (2021) used microeconomic and macroeconomic big data and revealed the possibilities of immigrants to be employed with high wages. Ahmed et al (2020) shared the geographical structure of immigrant camp sites and drew attention to the risks in these areas and improvements that should be made.…”

“…Wilson et al (2020) reported that these groups had lower health expenditures compared to individuals born in the US where they immigrated, it was estimated that approximately half of unregistered immigrants worked without insurance, and it was determined that this rate was significantly higher compared to the rates of registered immigrants and individuals born in the US (Wilson et al, 2020). Using regression models, Simionescu (2021) determined that immigrants with education degrees higher than high school had lower chances of being employed compared to those with education degrees of high school or below, and those who came from households with higher income preferred to wait until they found a betterpaying job. Ruhnke et al (2012) showed that using machine learning, especially the Random Forest Algorithm, to determine the legal status of refugees provided more accurate results in identifying undocumented refugees and minimized prejudice.…”