Abstract
This paper uses an innovative survey instrument on employment and earnings quantitative expectations to measure the amount of job instability, insecurity, and earnings risk that jobless young skilled perceive. The survey was fielded in Italy, as one of the EU countries that suffered the highest increase in youth unemployment, and the data were merged with administrative records on local labor market conditions. The results show that Italian jobless young skilled perceive substantial job instability, insecurity and earnings risk, which correlate with several important choices and behaviors, and depend on individual characteristics rather than on local labor market conditions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Evidence from the Italian Labour Force Survey: http://dati.istat.it/Index.aspx.
The job crisis hit Italian University graduates despite Italy having one of the lowest shares of University graduates in Europe: in 2014, the share of Italian University graduates in the age group 30–34 was 22% compared to an EU average of 37% (http://ec.europa.eu/eurostat).
Consistently with this employment crisis, unemployment in Italy is perceived as top priority problem: according to the Eurobarometer 2015, 46% of Italians think that unemployment is among the top two most pressing issues the country is facing, and 44% think that life for the future generations will be harder.
VE questions are common in many large surveys such as the World Values Surveys, the European Values Survey, the European Elections Survey, the American and British National Election Studies, the Eurobarometer Series, and the International Social Survey Programme.
This VE question on job security is included in several major surveys, such as the European Community Household Panel, which has been extensively used to study job security (for example: Böckerman et al. 2011).
Two examples of surveys that include QE questions on both expected earnings and the expected probability to find a job are the US Health and Retirement Study and Survey of Economic Expectations, and the Italian Survey of Household Income and Wealth.
Using a weighting scheme has some disadvantages such as assuming that a very high probability of finding a short-term job is equal to a very low probability of finding a long-term job. However, at the aggregate level, when averaging across all individuals, it satisfies the fundamental property that perceived stability increases when either the probability of finding a job in the next 12 months increases, or when the expected duration of this hypothesized job increases. An alternative method to elicit accurate perceptions of future job stability would be to elicit a series of questions on the probability of finding a job of a given expected duration.
Eliciting expected earnings requires an explanation of the probability concept, which increases the length of the questions but it is necessary for respondents to be willing and able to provide meaningful answers (Manski 2004, 2017). Dominitz and Manski (1996) is the seminal paper that discusses the survey methodology of using the quantitative subjective elicitation of future expectations. They provide evidence that quantitative expectation data are informative and reliable by considering the internal consistency of the answers, the prevalence of response patterns, and the comments made by respondents in a debriefing session.
At the time of the survey, the AlmaLaurea consortium was representative of 76% of all Italian graduates.
Gender, type of undergraduate degree, field of study, and area of residence have, respectively, 2, 4, 16 and 6 categories. Age group has 4 categories each representing 25% of the population. The combination of all the different values that each stratification variable could take resulted into 1,597 strata. AlmaLaurea used these strata to randomly proportionally select a sample of 17,784 graduates who were asked whether they were not currently employed and available to take part in a study on employment and earnings expectations. Of the graduates that stated to be jobless, 1462 agreed to participate to the study.
The IYES asks respondents to report their area of study using the AlmaLaurea classification of Italian University degrees into four main macro-areas: “Medicine”, which includes Medicine and Nursing degrees; “Hard Sciences”, which includes Math, Physics, Computer Science, Architecture, Engineering, Chemistry, Pharmacy, Industrial Design, Agrarian and Veterinarian studies, Biology, Sports, Defense and Strategic Studies, Cultural Heritage, Art, Geology, and Geography; “Social Sciences”, which includes Economics, Statistics, Law, Political Science, Communication Sciences, Psychology, International Relations, Tourism, and Sociology; “Humanities and Teaching”, which includes Italian Literature, Anthropology, Art, Religion, Language, Translation, Education, History, Philosophy, Pedagogy, and Art History. There is also a residual category “other-specify”, which no IYES respondent in the sample chose.
In Italy there are three main types of undergraduate degrees: "laurea triennale di primo livello", "laurea di 4 o piu’anni" (also called "laurea magistrale a ciclo unico"), and "laurea specialistica/magistrale biennale". "Laurea triennale di primo livello" is a 3-year undergraduate degree, which is comparable to a UK and American bachelor degree. "Laurea di 4 o piu’ anni" is a 4 to 6-year undergraduate degree that is specific to some fields of studies such as medicine and engineering. "Laurea specialistica/magistrale biennale" is a two-year advanced undergraduate degree that can be earned only after having completed either a 3-year or 4 to 6-year undergraduate degree.
This result is consistent with the finding that 70% of all Italian graduates have parents without a university degree (AlmaLaurea 2015).
The IYES does not collect detailed information on previous job experience, such as the type and number of previous job opportunities, including internships. This is a limitation of the IYES, which is mainly due to the length of the survey. Since the survey already includes 71 questions with complex QE questions, and because job experience is not prevalent among Italian University graduates, job history questions were not included. A future round of data collection could correct this limitation by piloting a more extensive survey that includes a job history section.
As discussed by Ding et al. (2010), these two measures correlate with experimental measures of risk aversion by generating valid indicators of choices under risk in an experimental setting where real money is at stake.
The reservation price question delivers results that are consistent with the self-assessment question, and are all available upon request.
Using data for 16 countries in 2008 from the European Social Survey, Cardoso et al. (2016) construct a similar measure of unemployment misperceptions, and also find that people significantly overestimate the unemployment rate in their country of residence.
The INPS data were collected in Rome (Italy) during the summer of 2016 as part of a research project funded by the VisitINPS Scholars Program ‘Expectations of Job Instability, Job Insecurity and Earnings Risk of the Italian Skilled Unemployed: Patterns and Impact on Behavior’.
Unfortunately, the administrative INPS data do not report the information on the individual’s level of education; therefore, I consider the universe of the 25–34 years old unconditional on their level of completed education.
By denoting as “low” a subjective probability that is either lower or equal to 50%, a low subjective probability perceived by a given respondent denotes high job instability and job insecurity.
By giving more weight to the data points around the midpoint of the distribution, the triangular distribution provides a more realistic description of the probability distribution of earnings with respect to alternative assumptions of the distribution of f(y). Another common assumption in the literature is the uniform distribution. Reassuringly and consistently with the previous literature (e.g. Attanasio and Kaufmann 2013), the results under the uniform distribution’ assumption are very similar (and all available upon request). An alternative elicitation method of future expected earnings is to use a battery of probability questions for different quantiles of the expected earnings' distribution. Asking a battery of questions avoids making distributional assumptions on the density function of future expected earnings. Piloting this alternative elicitation method and comparing the results with the method used in the IYES is left for future work.
Guiso et al. (2002) use data before the 2008 financial crisis; therefore, we would expect the estimated amount of earnings risk to be lower than the one estimated using data in the post-crisis period. However, the three-fold size difference suggests that only part of the difference can be attributed to the financial crisis.
In the IYES the response rate to each subjective probability question was above 90%. Similar high response rates have been documented in comparable surveys eliciting future expectations such as Dominitiz and Manski (1997a).
For each individual the IYES survey provides detailed information on the city and province where the individual is currently living. The region of residence has been constructed by mapping each individual city and province with the corresponding region and by locating the region either in the north, center, south or in the islands (Sardinia and Sicily) of Italy.
This happens because the average proportions of 10 and 5% answers are small, at, respectively, 7 and 2%.
In a recent paper, Giustinelli et al. (2018) develop a framework that interprets each numerical response given by a respondent as an interval and propose a two-stage algorithm to systematically account for rounding.
For example, one could follow up with these questions: Q1. When you said [X%] just now, did you mean this as an exact number or were you rounding or approximating? If a person answers “rounding or approximating,” one might then ask: Q2. What number or range of numbers did you have in mind when you said [X%]? The elicited probability is taken at face value when a person responds to Q1 stating that the response was intended as an exact number; otherwise, the exact or range response to Q2 is used.
The two-level hierarchical linear model (HLM) is explicitly designed to account for clustering. In particular, I assume that expectations are affected by local labor market conditions in the province of residence, and I model this dependency directly by specifying a HLM model where the clusters are the provinces. HLM estimation provides cluster-robust standard errors without requiring an explicit model for within-cluster error correlation. However, this technique does require the additional assumption that the number of clusters, and not only the number of observations, is sufficiently high. In our case, this assumption is satisfied since Italy has 110 provinces.
In Italian high schools and universities, students take a final exam. Both the mark of the final high school and the mark of the final university exam account for the average marks that each student has across all the studied subjects in the final year, and for the score obtained in the final exam itself.
The inclusion of the full set of indicators that I discuss in Sect. 3.2 does not improve the model’s performance and leaves the main results unchanged.
Results are robust to using both robust standard errors and bootstrapping.
The estimation results of a system of equations that allows job stability and security to be correlated are substantially the same, and are available upon request.
Results are robust to using both robust standard errors and bootstrapping.
References
Aaronson, D., & Sullivan, D. (1998). The decline in job security in the 1990s: Displacement, anxiety, and their effect on wage growth. Economic Perspectives, 22(1), 17–43.
AlmaLaurea. (2014). XVI Indagine Condizione Occupazionale dei Laureati. http://www.almalaurea.it/universita/occupazione/occupazione12.
AlmaLaurea. (2015). XVII Indagine Condizione Occupazionale dei Laureati. http://www.almalaurea.it/universita/occupazione/occupazione13.
AlmaLaurea. (2017). XIX Indagine Condizione Occupazionale dei Laureati. http://www.almalaurea.it/universita/occupazione/occupazione15.
Arulampalam, W. (2001). Is unemployment really scarring? Effects of unemployment experiences on wages. Economic Journal, 111(475), F585–F606.
Attanasio, O. P., & Kaufman, K. (2013). Education choices and returns to schooling: Mothers’ and youths’ subjective expectations and their role by gender. Journal of Development Economics, 109C, 203–216.
Becker, S. O., Bentolila, S., Fernandes, A., & Ichino, A. (2010). Youth emancipation and perceived job insecurity of parents and children. Journal of Population Economics, 23(3), 1175–1199.
Blanchflower, D. G., & Bell, D. N. F. (2011). Young people and the great recession. Oxford Review of Economic Policy, 27(2), 241–267.
Böckerman, P. (2004). Perception of job instability in Europe. Social Indicators Research, 67, 283.
Böckerman, P., Ilmakunnas, P., & Johansson, E. (2011). Job security and employee well-being: Evidence from matched survey and register data. Labor Economics, 18, 547–554.
Bonin, H., Dohmen, T., Falk, A., Huffman, D., & Sunde, U. (2007). Cross-sectional earnings risk and occupational sorting: The role of risk attitudes. Labour Economics, 14(6), 926–937.
Brunello, G. (2002). Absolute risk aversion and the returns to education. Economics of Education Review, 21(6), 635–640.
Camerer, C., & Webber, M. (1992). Recent developments in modeling preferences: Uncertainty and ambiguity. Journal of Risk and Uncertainty, 5, 325–370.
Cardoso, A. R., Loviglio, A., & Piemontese, L. (2016). Misperceptions of unemployment and individual labor market outcomes. IZA Journal of Labor Policy, 5, 13.
Chung, H., Bekker, S., & Houwing, H. (2012). “Young people and the post-recession labour market in the context of Europe 2020. Transfer European Review of Labour and Research, 18(3), 299–315.
Clark, A., & Postel-Vinay, F. (2009). Job security and job protection. Oxford Economic Papers, 61(2), 207–239.
Davoine, L., Erhel, C., & Guergoat-Larivìere, M. (2008). A taxonomy of European labour markets using quality indicators. Final report for the European Com-mission. Rapport de recherche du Centre d’Etudes de l’Emploi n 45.
De Weerdt, J. (2005). Measuring risk perceptions: Why and how. World Bank Social Protection Discussion Paper No. 0533
Delavande, A., Gine, X., & McKenzie, D. (2011). Measuring subjective expectations in developing countries: A critical review and new evidence. Journal of Development Economics, 94, 151–163.
Delavande, A., & Rohwedder, S. (2008). Eliciting subjective probabilities in internet surveys. Public Opinion Quarterly, 72(5), 866–891.
Ding, X., Joop, H., & Yuze, S. (2010). Can we measure individual risk attitudes in a survey? IZA Discussion Papers 4807, Institute for the Study of Labor (IZA).
Dolado, J. J. (Ed.). (2015). No Country for young people? Youth labour market problems in Europe. London: CEPR Press.
Dominitz, J., & Manski, C. F. (1996). Eliciting student expectations of the returns to schooling. Journal of Human Resources. https://doi.org/10.2307/146041.
Dominitz, J., & Manski, C. F. (1997a). Using expectations data to study subjective income expectations. Journal of the American Statistical Association, 92, 855–867.
Dominitz, J., & Manski, C. F. (1997b). Perceptions of economic insecurity: Evidence from the survey of economic expectations. Public Opinion Quarterly, 61, 261–287.
European Commission. (2016). Employment and social developments in Europe. Annual Review 2016. European Commission Directorate-General for Employment, Social Affairs and Inclusion.
Fairlie, R. W., & Kletzer, L. G. (2003). The long-term costs of job displacement among young workers. Industrial and Labor Relations Review, 56(4), 682–698.
Giustinelli, P, Manski, C. F., & Molinari, F. (2018). Tail and center rounding of probabilistic expectations in the health and retirement study. NBER Working Paper 24559.
Goldsmith, A. H., Veum, J. R., & Darity, W. (1996). The impact of labor force history on self-esteem and its component parts, anxiety, alienation and depression. Journal of Economic Psychology, 17(2), 183–220.
Gottschalk, P., & Moffitt, R. (1999). Changes in job instability and insecurity using monthly survey data. Journal of Labor Economics, 17(S4), S91–S126.
Gregg, P. A., & Tominey, E. (2005). The wage scar from male youth unemployment. Labour Economics, 12, 487–509.
Guiso, L., Jappelli, T., & Pistaferri, L. (2002). An empirical analysis of earnings and employment risk. Journal of Business and Economic Statistics, 20(2), 241–253.
Guiso, L., & Paiella, M. (2005). The role of risk aversion in predicting individual behaviour. Bank of Italy Economic Working Paper No. 546.
Hammarström, A., & Janlert, U. (1997). Nervous and depressive symptoms in a longitudinal study of youth unemployment: Selection or exposure? Journal of Adolescence, 20(3), 293–305.
International Labour Organization. (2014). Global employment trends 2014: Risk of a jobless recovery?. Geneva: ILO.
Istituto per lo Sviluppo della Formazione Professionale dei Lavoratori (ISFOL). (2014). Rapporto di Monitoraggio del Mercato del Lavoro 2014.
Italian National Institute of Statistics. (2013). Rapporto annuale 2013–La situazione del Paese. Rome: Istat.
Kim, T., & von dem Knesebeck, O. (2015). Is an insecure job better for health than having no job at all? A systematic review of studies investigating the health-related risks of both job insecurity and unemployment. BMC Public Health, 15, 985.
Lillard, L. A., & Willis, R. J. (2002). Cognition and wealth: The importance of probabilistic thinking. Michigan Retirement Research Center Research Paper Number 2001-007
Lippman, S., & McCall, J. (1976). The economics of job search: A survey. Economic Inquiry, 14, 155–189.
Loveless, M., & Binelli, C. (2018). Economic expectations and satisfaction with democracy: Evidence from Italy. Forthcoming Government and Opposition. https://doi.org/10.1017/gov.2018.31692.
Manski, C. F. (2004). Measuring expectations. Econometrica, 72(5), 1329–1376.
Manski, C. F. (2017). Survey measurement of probabilistic macroeconomic expectations: Progress and promise. National Bureau of Economic Research Working Paper 23418
Manski, C. F., & Molinari, F. (2010). Rounding probabilistic expectations in surveys. Journal of Business and Economic Statistics, 28(2), 219–231. https://doi.org/10.1198/jbes.2009.08098.
Manski, C. F., & Straub, J. D. (2000). Worker perceptions of job insecurity in the mid-1990s: Evidence from the survey of economic expectations. Journal of Human Resources, 35(3), 447–479.
Martin, J. P., Scarpetta, S. (2011). Setting it right: Employment protection, labour reallocation and productivity, Bonn. IZA Policy Paper No. 25
Mortensen, D. (1986). Job search and labor market analysis. In O. Ashenfelter & R. Layard (Eds.), Handbook of labor economics (pp. 849–919). Amsterdam: North-Holland.
Origo, F., & Pagani, L. (2009). Flexicurity and job satisfaction in Europe: The importance of perceived and actual job stability for well-being at work. Labour Economics, 16, 547–555.
Pellegrino, B., & Zingales, L. (2017). Diagnosing the Italian disease. NBER Working Paper No. 23964.
Pissarides, C. A. (1974). Risk, job search, and income distribution. Journal of Political Economy, 82(6), 1255–1267.
Schmid, G., & Wagner, J. (2017). Managing social risks of non-standard employment in Europe. Conditions of Work and Employment Series No. 91, International Labour Office, Geneva.
Sverke, M., Hellgren, J., & Näswall, K. (2006). Job insecurity: A literature review. Report No. 1:2006, SALTSA, Stockholm, Sweden.
Theodossiou, I., & Vasileiou, E. (2007). Making the risk of job loss a way of life: Does it affect job satisfaction? Research in Economics, 61(2), 71–83.
Acknowledgements
I thank Matthew Loveless for many helpful comments and discussions, and Annalisa Loviglio, Tito Boeri and the participants to the Trento Economics Festival 2015, to the Bocconi Food for Thought seminar, and to the INPS seminar series for useful comments and suggestions. Three anonymous reviewers provided several useful feedbacks. The financial support of the University of Southampton through a Strategic Research Development Fund, and the financial support and data access provided by the Italian Istituto Nazionale della Previdenza Sociale (INPS) through the VisitINPS Scholars Program are gratefully acknowledged. A previous version of the paper circulated under the title “Expectations of Job Instability, Job Insecurity, and Earnings Risk: A Pilot Study of Jobless Young Skilled in Italy”. All errors are mine.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Binelli, C. Employment and Earnings Expectations of Jobless Young Skilled: Evidence from Italy. Soc Indic Res 145, 201–231 (2019). https://doi.org/10.1007/s11205-019-02106-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-019-02106-y