The progressive flexibility of labour relations has not always been perceived by workers as an opportunity, but as an ever-growing sense of precariousness. This appears all the more true in a country like Italy, where the labour market has long been characterized by some rigidity. Health is a fundamental domain of well-being and, although the relationship between health and job insecurity has been clearly highlighted, the correct causal effect along with differences at the territorial level have not always been brought into focus. The aim of this paper is to provide insights on health status differentials across Italian territories, in relation to individual working histories. To analyze the potential causal effects of career on self-reported health (SRH) a propensity score approach, based on the inverse probability of treatment weighing, has been used. Available data shows great differences at territorial level: in Northern Italy, the economic conditions seem to increase the probability of having good health, while in the other regions of Italy key factors seem to be age, children or the sector and size of the company where an individual works. Our findings have also shown the presence of a causal effect on the work history patterns of SRH in northern Italy, and not elsewhere. This may imply that in an area with high occupational levels having only a temporary employment is perceived as the merest chance, and could generate stress both at a physical and mental level. In other areas, high unemployment levels seem to arouse low expectations and the only fact of having a job—even if temporary—can be perceived as a positive aspect.
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What Istat experienced with the introduction of the BES was particularly significant also in relation to the possibility of using these indicators in order to affect the country’s economic policy. In fact, starting from 2016, BES became part of the objectives included in the so-called Economic and Financial Document (DEF), which is one of the main public finance instruments of the Italian Government. Starting from 2018 the list—which first had included 4 indicators—was extended to a total of 12 indicators, which are monitored with a view to guaranteeing a fair and sustainable growth of the Italian economy.
We have also performed a sensitivity analysis and we have excluded issues related to non-ignorable or Informative Drop-out (ID) based on the observed individual features. Focusing on the universe of respondents to IT-SILC, for respondents we do not dispose of the tax code we have established whether this exclusion affects the representativeness of our sample, as well as to verify to what extent the excluded observations differ from the included ones with respect to the observed covariates. We have fitted a logistic regression model for the drop-out process to evaluate the probability of informative drop-out, depending on observed covariates, after a cross-validation process. Parameters in the model are estimated by maximum likelihood and inferences drawn through conventional likelihood procedures.
At least 7 out of 10 years.
The latter category is represented by the so called "partita iva", i.e. individuals employed with a taxpayer identification number that formally are non-subordinate workers, but, in most of the cases, they work not as employer, but as employees, with reduced social and welfare guarantees.
For a deep explanation of the underlined methodology see Lunceford and Davidian (2004).
The models have been fit using R and package ipw (Van der Wal and Geskus 2011) with default settings.
Different studies suggest lower risk factors (smoking, drinking, diet, exercise, use of illegal drugs, household safety, use of preventive medical care, and care for hypertension and diabetes) associated with the better educated.
Agresti, A. (2018). Statistical methods for the social sciences: 5th edition (pp. 1–618). Harlow: Pearson.
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov (Ed.), Proceedings of the second international symposium on information theory. Budapest: Akademiai Kiado.
Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research,46(3), 399–424.
Bandura, R. (2008). A survey of composite indices measuring country performance: 2008 update. New York: United Nations Development Programme, Office of Development Studies.
Bartley, M. (1994). Unemployment and ill health: Understanding the relationship. Journal of epidemiology and community health,48(4), 333–337.
Bartley, M., Sacker, A., & Clarke, P. (2004). Employment status, employment conditions, and limiting illness: Prospective evidence from the British household panel survey 1991–2001. Journal of Epidemiological Community Health,5, 501–506.
Bleys, B. (2012). Beyond GDP: Classifying alternative measures for progress. Social Indicators Research,109(3), 355–376.
Binder, M., & Coad, A. (2015). Heterogeneity in the relationship between unemployment and subjective well-being: A quantile approach. Economica,82, 865–891.
Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Survey,22(1), 31–72.
Ciccarelli, A. (2006). L’articolazione della competitività a livello territoriale. In E. Del Colle (Ed.), Tecnopoli. L’articolazione territoriale della competitività in Italia (pp. 29–64). Milano: FrancoAngeli.
Ciccarelli, A. (2008). Il procedimento statistico di individuazione dei Sistemi Economici e Ambientali Locali S.E.A.L. e la misurazione della relativa qualità della vita. In E. Del Colle (Ed.), Come si vive in Abruzzo. Misura della qualità della vita dei sistemi economico-ambientali locali (pp. 61–97). Roma: Aracne Editrice.
Ciccarelli, A. (2012). Il Welfare nelle regioni dell’Unione Europea: aspetti distintivi e disuguaglianze. In E. Del Colle (Ed.), Il welfare territoriale Le regioni italiane nel confronto interno e internazionale (pp. 65–110). Milano: FrancoAngeli.
Cole, S. R., & Hernàn, M. A. (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology,168(6), 656–664.
Ciommi, M., Gigliarano, C., Chelli, F.M. & Gallegati, M. (2013). Behind, beside and beyond GDP: alternative to GDP and to macro-indicators. [Electronic version]. Retrieved October 29, 2019, from: https://www.eframeproject.eu/fileadmin/Deliverables/Deliverable3.1.pdf
Chelli, F., Ciommi, M., Emili, A., Gigliarano, C., & Taralli, S. (2017). A new class of composite indicators for measuring well-being at the local level: An application to the Equitable and Sustainable Well-being (BES) of the Italian Provinces. Ecological Indicators,76, 281–296.
Ciommi, M., Gentili, A., Ermini, B., Gigliarano, C., Chelli, F., & Gallegati, M. (2017). Have your cake and eat it too: The well-being of the Italians (1861–2011). Social Indicators Research,134(2), 473–509.
Conceição, P., & Bandura, R. (2008). Measuring subjective wellbeing: A summary review of the literature. New York: Office of Development Studies, UNDP.
Devillanova, C., Raitano, M., & Struffolino, E. (2019). Longitudinal employment trajectories and health in middle life: Insights from linked administrative and survey data. Demographic Research.,40, 1375–1412.
D’Acci, L. (2011). Mesauring well-being and progress. Social Indicators Research,104(1), 47–65.
Drydakis, N. (2015). The effect of unemployment on self-reported health and mental health in Greece from 2008 to 2013: A longitudinal study before and during the financial crisis. Social Science and Medicine,128, 43–51.
Ferrara, A. R., & Nisticò, R. (2014). Measuring well-being in a multidimensional perspective: A multivariate statistical application to Italian regions. Working Paper, 6. Rende: Dipartimento di Economia, Statistica e Finanza, Università della Calabria.
Ferré, F., de Belvis, A. G., Valerio, L., Longhi, S., Lazzari, A., Fattore, G., et al. (2014). Italy: Health system review. Health Systems in Transition, 16(4), 1–168.
Giltay, E. J., Vollaard, A. M., & Kromhout, D. (2012). Self-rated health and physician-rated health as independent predictors of mortality in elderly men. Age and Ageing,41(2), 165–171.
Giraudo, M., Bena, A., Leombruni, R., & Costa, G. (2016). Occupational injuries in times of labour market flexibility: the different stories of employment-secure and precarious workers. BMC Public Health.,16, 150.
Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology,10, 37–48.
Hernàn, M. A., Brumback, B. A., & Robins, J. M. (2000). Marginal structural models to estimate the causal effect of Zidovudine on the survival of HIV-positive men. Epidemiology,11(5), 561–570.
Idler, E., & Kasl, S. (1991). Health perceptions and survival: Do global evaluations of health status really predict mortality? Journal of Gerontology.,46(2), S55–S65.
ISTAT. (2018). Rapporto Bes 2018. Il benessere equo e sostenibile in Italia. [Electronic version]. Retrieved October 14, 2019, from: https://www.istat.it/it/archivio/224669.
ISTAT. (2019). Le differenze territoriali del benessere. Una lettura a livello provinciale. [Electronic version]. Retrieved October 16, 2019, from: https://www.istat.it/it/archivio/233243.
Landsbergis, P. A., Grzywacz, J. G., & LaMontagne, A. D. (2014). Work organization, job insecurity, and occupational health disparities. American Journal of Industrial Medicine,57(5), 495–515.
László, K. D., Hynek, P., Kopp, M. S., Bobak, M., Pajak, A., Malyutina, S., et al. (2010). Job insecurity and health: A study of 16 European countries. Social Science and Medicine,70(6), 867–874.
Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine,23, 2937–2960.
Joffe, M. M., Ten Have, T. R., Feldman, H. I., & Kimmel, S. E. (2004). Model selection, confounder control, and marginal structural models: Review and new applications. The American Statistician,58(4), 272–279.
Joumard, I., André, C. Nicq, C. & Chatal, O. (2010) Health Status Determinants: Lifestyle, Environment, Health Care Resources and Efficiency. OECD Economics Department Working Paper No. 627. Paris: OECD Publishing.
Kiuila, O., & Mieszkowski, P. (2007). The effects of income, education and age on health. Health Economics.,16(8), 781–798.
Kim, M. H. K., Kim, C. Y., Park, J. K., & Kawachi, I. (2008). Is precarious employment damaging to self-rated health? Results of propensity score matching methods, using longitudinal data in South Korea. Social Science and Medicine,67, 1982–1994.
Kuznets, S. (1962). How to judge quality. The New Republic, [Electronic version]. Retrieved October 14, 2019, from: https://static1.squarespace.com/static/5536fbc7e4b0d3e8a9803aad/t/554d19f6e4b0005c69696961/1431116278720/Kuznets_How+to+judge+Quality_1962.pdf.
Mazziotta, M., & Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Research,127(3), 983–1003.
Oakes, J. M., & Johnson, P. J. (2006). Propensity score matching for social epidemiology. In J. M. Oakes & J. S. Kaufman (Eds.), Methods in social epidemiology (pp. 370–392). San Francisco: Jossey-Bass.
OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD publishing.
OECD. (2011). How’s life? Measuring well-being. Paris: OECD Publishing.
OECD. (2013). Guidelines on measuring subjective well-being. Paris: OECD Publishing.
OECD. (2018). Beyond GDP. Measuring what counts for economic and social performance. Paris: OECD Publishing.
ONU. (2018). Human development report. Technical notes. [Electronic version]. Retrieved October 10, 2019, from: https://hdr.undp.org/en/content/human-development-indicators-and-indices-2018-statistical-update-readers-guide
Robins, J.M. (1998). Marginal Structural Models. In Paper presented at the 1997 Proceedings of the American Statistical Association, Section on Bayesian Statistical Science. [Electronic version]. Retrieved October 14, 2019, from: https://cdn1.sph.harvard.edu/wp-content/uploads/sites/343/2013/03/msm-web.pdf.
Robins, J. M., Hernàn, M. A., & Brumback, B. A. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology,11, 550–560.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika,70, 41–55.
Rosenbaum, P. R. (1987). Model-based direct adjustment. Journal of the American Statistical Association,82(398), 387–394.
Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology,66, 688–701.
Sarti, S., & Zella, S. (2016). Changes in the labour market and health inequalities during the years of the recent economic downturn in Italy. Social Science Research.,57, 116–132.
Stiglitz, J.E., Sen, A. & Fitoussi, J.P. (2009). Report by the Commission on the Measurement of economic performance and social progress. [Electronic version]. Retrieved October 14, 2019, from: https://ec.europa.eu/eurostat/documents/118025/118123/Fitoussi+Commission+report.
Stringhini, S., Carmeli, C., Jokela, M., Avendaño, M., Muennig, P., et al. (2017). Socioeconomic status and the 25×25 risk factors as determinants of premature mortality: A multicohort study and metaanalysis of 1.7 million men and women. The Lancet,389(10075), 1229–1237.
Starfield, B. (1992). Effects of poverty on health status. Bulletin of the New York Academy of Medicine,68(1), 17–24.
Stenholm, S., Kivimaki, M., Jylha, M., Kawachi, I., Westerlund, H., Pentti, J., et al. (2016). Trajectories of self-rated health in the last 15 years of life by cause of death. European Journal of Epidemiology.,31(2), 177–185.
Strully, K. W. (2009). Job loss and health in the US labour market. Demography,46(2), 221–246.
Sverke, M., Hellgren, J., & Naswall, K., (2002). No security: A meta-analysis and review of job insecurity and its consequences. Journal of Occupational Health Psychology,7, 242–264.
Ta, V. M., Holck, P., & Gee, G. C. (2010). Generational status and family cohesion effects on the receipt of mental health services among Asian Americans: Findings from the National Latino and Asian American Study. American Journal of Public Health,100(1), 115–121.
Thomas, P. A., Liu, H., & Umberson, D. (2017). Family relationships and well-being. Innovation in Aging,1(3), 1–11.
Tsuji, I., Minami, Y., Keyl, P. M., Hisamichi, S., Asano, H., et al. (1994). The predictive power of self-rated health, activities of daily living, and ambulatory activity for cause-specific mortality among the elderly: a three-year follow-up in urban Japan. Journal of American Geriatric Society.,42(2), 153–156.
Veenhoven, R. (2004). Subjective measures of well-being. WIDER research paper 2004/7. Helsinki: United Nations University.
van der Wal, W. M., & Geskus, R. B. (2011). ipw: an R package for inverse probability weighting. [Electronic version]. Journal of Statistical Software,43(13), 1–23.
Virtanen, M., Kivimäki, M., Joensuu, M., Virtanen, P., Elovainio, M., & Vahtera, J. (2005). Temporary employment and health: A review. International Journal of Epidemiology.,34, 610–622.
Waenerlund, A. K., Gustafsson, P. E., Hammarström, A., & Virtanen, P. (2014). History of labour market attachment as a determinant of health status: A 12-year follow-up of the Northern Swedish cohort. British Medical Journal Open,4(2), e004053.
Wanberg, C. R. (2012). The individual experience of unemployment. Annual review of psychology,63, 369–396.
We are very grateful to the Ministry of the Economy and Finance for the use of AD-SILC dataset. AD-SILC dataset can be required, for research proposal, to the Ministry of the Economy and Finance. We are also grateful to two anonymous reviewers for the valuable suggestions provided.
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Ciccarelli, A., Fabrizi, E., Romano, E. et al. Health, Well-Being and Work History Patterns: Insight on Territorial Differences. Soc Indic Res (2020). https://doi.org/10.1007/s11205-020-02393-w
- Work history patterns
- Causal effect
- Territorial gap