Youth unemployment in Italy and Russia: Aggregate trends and individual determinants

Abstract

Youth unemployment rates in most countries are considerably higher than total unemployment rates and increased significantly in many countries following the global financial crisis. Young people in long-term unemployment risk becoming a ‘lost generation’. We investigate individual and family characteristics predicting young people’s vulnerability to the scarring effects of long-term unemployment. After overviewing aggregate youth unemployment trends in several European countries, we focus on Russia and Italy – countries with contrasting structural and institutional conditions and exhibiting different macroeconomic trends – in order to determine whether, despite these differences, there were similar patterns in the relationship between individual and family characteristics and the of risk of unemployment and its adverse impacts. We use a Heckman probit model to estimate the unemployment risk of young people – compared to adults – during the period 2004–2011, before and after the global financial crisis. Despite many differences between the two countries, most of the explanatory variables acted in the same direction in each and so we compare the relative size of such effects. The policy significance of the findings is that personal and family characteristics are more amenable to modification than macroeconomic variables. Specific school-to-work interventions are needed to avoid creating a ‘lost generation’.

Impact of labour attributes and probability of employability in Indian labour market: A sectoral analysis

“…While using the multinomial logit model, enough care has been taken to address the caution of independence of irrelevant alternatives-which says that choices of independent variables must be considerable different from each other (Gujarati, 2011). The other studies with similar kind of logistic model are done by Hout and Rosen (1999), Kingdon (1998), Kong (2011), Marelli and Vakulenko (2016), and Schmidt and Strauss (1975) who used "standard logistic regression model," "binary probit model," "multinomial probit model," "binary choice model," and "multiple logit model," respectively. Among all these, the models of Marelli and Vakulenko (2016) and Schmidt and Strauss (1975) are considered as the base logistic model for the present study.…”

“…However, this labour discrimination/favouritism tendency differs from region to region and then from industry to industry where the social, political, economic, and personal conditions of individuals, families, societies, and regions play a vital role in employment attainments (Schioppa & Lupi, ). With respect to the regional variations among labour attributes, the family attributes of youths of Italy and Russia are found dominating over the personal labour attributes for entering and surviving in the labour market (Marelli & Vakulenko, ). These abatements of entering and surviving in the job market imbalances the labour market equilibrium by diverting some particular jobs towards some particular sections of people.…”

“…The analysis of economic sectors with regard to labour attributes is essential for the developing nations like India, which are highly diversified in terms of society, religion, region, language, culture, climate, education, and gender. This higher level of diversities promotes discrimination, nepotism, and favouritism in the labour market, where the intensity of this unequal distribution of labour varies over regions, castes, genders, religions, languages, race, marriage, education, health, family background, and so forth (Aggarwal, Johnes, Freguglia, & Spricigo, ; Baah‐Boateng, ; Banerjee, ; Marelli & Vakulenko, ; Schioppa & Lupi, ). The increasing level of diversity on the basis of mentioned labour attributes further disturbs the balance of employment distribution in the labour market.…”

Investigating Changing Work and Economic Cultures Through the Lens of Youth Employment: A Case Study from a Psychosocial Perspective in Italy

“…Italy was one of the countries in Europe most adversely affected by the great recession, in terms of productivity and employment. In 2013, the youth unemployment rate was 3.3 times as high as the total unemployment rate, making it one of the worst YUR/TUR ratios in Europe (Marelli & Vakulenko, 2016). Italy is also one of the countries that has most embraced making the job market flexible as the main strategy to help restart and energize the national economy (Fana, Guarascio, & Cirillo, 2017).…”

Forecasting the Composition of Spain’s Unemployed Population by Genetic Algorithms

“…According to Li and Zhang (2010), the Chinese job-market prefers male graduates over their female peers with similar qualifications. The relationship between age and unemployment rate is dealt with in Caliendo and Schmidl (2016) or Marelli and Vakulenko (2016). According to Klein (2015), higher education reduces the incidence of unemployment.…”