Abstract
What mechanisms govern the mobility of employees who voluntarily switch employers for better opportunities, given a temporary contract (TC)? We attempt to answer this question by exploring this issue in Southern and Central European countries. We use cross-sectional data from the European Union Statistics on Income and Living Conditions survey for the 2005–2016 period. We estimate a flexible simultaneous equation model for binary responses by assuming the presence of an age-varying association between voluntary mobility and having a TC. After accounting for several socio-demographic and economic variables, we find a nonlinear decreasing relation between age and the outcomes, while we detect heterogeneous nonlinear patterns in the association between voluntary mobility and having a TC across countries. These insights can support policy-makers aiming to promote initiatives that facilitate the professional mobility of employees given a TC for an efficient allocation of human capital in the production system.
Acknowledgements
We thank the two anonymous reviewers for their useful suggestions, which helped improve the clarity and quality of the article. The opinions expressed herein are those of the authors and do not reflect those of the institutions of affiliation.
Compliance with Ethical Standards
Funding: This research has not received funding.
Conflict of Interest: The authors declare that they have no conflict of interest.
Appendix
Kendall’s
| Country | Kendall’s | Confidence intervals |
|---|---|---|
| France | 0.0812 | (-0.096,0.247) |
| Italy | 0.0346 | (-0.167,0.257) |
| Spain | 0.0411 | (-0.153,0.208) |
| Austria | 0.0205 | (-0.163,0.268) |
| Germany | 0.0944 | (-0.160,0.364) |
| Czech Republic | 0.0333 | (-0.111,0.222) |
| Poland | -0.1680 | (-0.337,0.031) |
The Akaike information criterion obtained after estimation of the system of two binary equations without including a third equation to model the copula association parameter as a function of employee age. We have estimated 15 models for each country (3 combinations of link functions
| Southern European countries | Central European countries | ||||||
|---|---|---|---|---|---|---|---|
| Link function and copula | France | Italy | Spain | Austria | Germany | Czech Republic | Poland |
| Eq.1:Probit; Eq.2:Probit | |||||||
| Gaussian | 49,330 | 85,338 | 77,263 | 25,417 | 55,914 | 54,956 | 99,236 |
| Clayton | 49,311 | 85,318 | 77,275 | 25,411 | 55,901 | 54,941 | 99,218 |
| Gumbel | 49,337 | 85,348 | 77,273 | 25,420 | 55,925 | 54,965 | 99,254 |
| Joe | 49,366 | 85,430 | 77,383 | 25,442 | 56,011 | 55,028 | 99,415 |
| Frank | 49,353 | 85,394 | 77,316 | 25,436 | 55,973 | 55,010 | 99,306 |
| Eq.1:Probit; Eq.2:Cloglog | |||||||
| Gaussian | 49,349 | 85,340 | 77,256 | 25,428 | 55,947 | 54,957 | 99,243 |
| Clayton | 49,331 | 85,319 | 77,270 | 25,422 | 55,934 | 54,943 | 99,225 |
| Gumbel | 49,356 | 85,350 | 77,265 | 25,431 | 55,958 | 54,966 | 99,262 |
| Joe | 49,386 | 85,432 | 77,373 | 25,452 | 56,045 | 55,029 | 99,424 |
| Frank | 49,372 | 85,396 | 77,307 | 25,446 | 56,007 | 55,011 | 99,314 |
| Eq.1:Cloglog; Eq.2:Cloglog | |||||||
| Gaussian | 49,487 | 85,658 | 77,836 | 25,378 | 55,850 | 54,983 | 99,603 |
| Clayton | 49,468 | 85,636 | 77,847 | 25,372 | 55,837 | 54,968 | 99,579 |
| Gumbel | 49,493 | 85,669 | 77,845 | 25,381 | 55,861 | 54,992 | 99,622 |
| Joe | 49,522 | 85,754 | 77,954 | 25,403 | 55,952 | 55,055 | 99,787 |
| Frank | 49,508 | 85,713 | 77,884 | 25,396 | 55,907 | 55,036 | 99,672 |
| Sample period | 2005– | 2005– | 2005– | 2005– | 2007– | 2006– | 2006– |
| 2016 | 2015 | 2016 | 2016 | 2016 | 2016 | 2016 | |
The Akaike information criterion obtained after estimation of the system of two binary equations and including a third equation to model the copula association parameter as a function of employee age (see Section 3). We have estimated 15 models for each country (3 combinations of link functions
| Southern European countries | Central European countries | ||||||
|---|---|---|---|---|---|---|---|
| Link function and copula | France | Italy | Spain | Austria | Germany | Czech Republic | Poland |
| Eq.1:Probit; Eq.2:Probit | |||||||
| Gaussian | 49,290 | 85,340 | 77,245 | 25,404 | 55,871 | 54,937 | 99,189 |
| Clayton | 49,297 | 85,311 | 77,253 | 25,407 | 55,882 | 54,931 | 99,191 |
| Gumbel | 49,299 | 85,349 | 77,249 | 25,410 | 55,876 | 54,941 | 99,193 |
| Joe | 49,309 | 85,421 | 77,314 | 25,418 | 55,909 | 54,966 | 99,252 |
| Frank | 49,296 | 85,390 | 77,275 | 25,409 | 55,892 | 54,961 | 99,219 |
| Eq.1:Probit; Eq.2:Cloglog | |||||||
| Gaussian | 49,309 | 85,660 | 77,238 | 25,415 | 55,903 | 54,939 | 99,198 |
| Clayton | 49,316 | 85,631 | 77,248 | 25,418 | 55,914 | 54,933 | 99,199 |
| Gumbel | 49,318 | 85,669 | 77,241 | 25,420 | 55,908 | 54,942 | 99,203 |
| Joe | 49,328 | 85,744 | 77,305 | 25,429 | 55,942 | 54,967 | 99,262 |
| Frank | 49,315 | 85,709 | 77,267 | 25,420 | 55,924 | 54,962 | 99,229 |
| Eq.1:Cloglog; Eq.2:Cloglog | |||||||
| Gaussian | 49,445 | 85,660 | 77,817 | 25,365 | 55,809 | 54,964 | 99,557 |
| Clayton | 49,451 | 85,631 | 77,828 | 25,367 | 55,820 | 54,959 | 99,554 |
| Gumbel | 49,454 | 85,669 | 77,820 | 25,370 | 55,814 | 54,968 | 99,561 |
| Joe | 49,463 | 85,744 | 77,882 | 25,380 | 55,848 | 54,995 | 99,620 |
| Frank | 49,450 | 85,709 | 77,842 | 25,370 | 55,829 | 54,987 | 99,586 |
| Sample period | 2005– | 2005– | 2005– | 2005– | 2007– | 2006– | 2006– |
| 2016 | 2015 | 2016 | 2016 | 2016 | 2016 | 2016 | |
References
Addabbo, T., R. García-Fernández, C. Llorca-Rodríguez. and A. Maccagnan. 2012. Poverty and Unemployment: The Cases of Italy and Spain, AIEL Series in Labour Economics,in: Parodi, G. and Sciulli, D. (ed. s), Social Exclusion. Short and Long Term Causes and Consequences, chapter 10: 199–219.Search in Google Scholar
Amabile T., and Kramer S. 2011.” The Progress Principle: Using Small Wins to Ignite Joy, Engagement, and Creativity at Work. Boston, Massachusetts: Harvard Business Review Press.Search in Google Scholar
Baranowska, A., M. Gebel, and I. E. Kotowska. 2011. “The Role of Fixed-Term Contracts at Labour Market Entry in Poland: Stepping Stones, Screening Devices, Traps or Search Subsidies?” Work, Employment and Society 25: 777–93.10.1177/0950017011419705Search in Google Scholar
Bentolila, S., P. Cahuc, J. J. Dolado, and T. Le Barbanchon. 2012. “Two Tier Labour Markets in the Great Recession: France Versus Spain.” Economic Journal, Royal Economic Society 122: F155–87.Search in Google Scholar
Berger, M., and S. Schaffner. 2015. “A Note on How to Realize the Full Potential of the EU-SILC data.” Discussion Paper No. 15-005, Centre for European Economic Research, ZEW.10.2139/ssrn.2567255Search in Google Scholar
Berton, F., F. Devicienti, and L. Pacelli. 2011. “Are Temporary Jobs a Port of Entry into Permanent Employment? Evidence from Matched Employer-Employee Data.” International Journal of Manpower 32 (8): 879–99.10.1108/01437721111181651Search in Google Scholar
Bjelland, M., B. Fallick, J. Haltiwanger, and E. McEntarfer. 2012. Employer-to-Employer Flows in the United States: Estimates Using Linked Employer-Employee Data.” Journal of Business & Economic Statistics 29: 493–505.10.3386/w13867Search in Google Scholar
Baslevent, C., and H. Kirmanoglu. 2014. “The Impact of Deviations from Desired Hours of Work on the Life Satisfaction of Employees.” Social Indicators Research 118: 433–43.10.1007/s11205-013-0421-9Search in Google Scholar
Bertola, G. and P. Garibaldi. 2003. “The Structure and History of Italian Unemployment.” CESifo Working Paper Series 907, CESifo Group Munich.10.2139/ssrn.395420Search in Google Scholar
Brechmann, E. C., and U. Schepsmeier. 2013. “Modeling Dependence with c- and d-vine Copulas: The R package CDVine.” Journal of Statistical Software 52: 1–27.10.18637/jss.v052.i03Search in Google Scholar
Boeri, T. and P. Garibaldi. 2007. “Two Tier Reforms of Employment Protection: a Honeymoon Effect?” The Economic Journal 117 (521): 357–85.10.1111/j.1468-0297.2007.02060.xSearch in Google Scholar
Boeri, T. 2011. “Institutional Reforms and Dualism in European Labor Markets.” in Handbook of Labor Economics, chapter 13, edited O. Ashenfelter and D. Card, 1173–236. Elsevier.10.1016/S0169-7218(11)02411-7Search in Google Scholar
Booth, A., M. Francesconi, and J. Frank. 2002. “Temporary jobs: stepping stones or dead ends.” The Economic Journal 112 (480): 189–213.10.1111/1468-0297.00043Search in Google Scholar
Bosler, C. and N. Petrosky-Nadeau. 2016. Job-to-Job Transitions in an Evolving Labor Market, FRBSF Economic Letter, 34, Federal Reserve Bank of San Francisco.Search in Google Scholar
Cahuc, P., and F. Postel-Vinay. 2002. “Temporary Jobs, Employment Protection and Labor Market Performance.” Labour Economics 9 (1): 63–91.10.1016/S0927-5371(01)00051-3Search in Google Scholar
Comi, S., and M. Grasseni. 2012. “Are Temporary Workers Discriminated Against? Evidence from Europe.” The Manchester School 80: 28–50.10.1111/j.1467-9957.2011.02231.xSearch in Google Scholar
Christensen, B. J., R. Lentz, D. T. Mortensen, G. R. Neuman, and A. Werwatz. 2005. “On-the-Job Search and the Wage Distribution.” Journal of Labor Economics 23: 31–58.10.1086/425432Search in Google Scholar
Cracolici, M.F, M. Cuffaro, and P. Nijkamp. 2010. “The Measurement of Economic, Social and Environmental Performance of Countries: A Novel Approach.” Social Indicators Research 95: 339–56.10.1007/s11205-009-9464-3Search in Google Scholar
De Pater, I. E., A. E. M. Van Vianen, and M. N. Bechtoldt. 2010. “Gender Differences in Job Challenge: A Matter of Task Allocation.” Gender, Work and Organization 17 (4): 433–53.Search in Google Scholar
Dolton, P. J., and M. P. Kidd. 1998. “Job Changes, Occupational Mobility and Human Capital Acquisition: An Empirical Analysis.” Bulletin of Economic Research 50: 265–95.10.1111/1467-8586.00065Search in Google Scholar
Eurofound Austria: Latest working life developments Q4. 2017. Available at: https://www.eurofound.europa.eu/printpdf/observatories/eurwork/articles/austria-latest-working-life-developments-q4-2017.Search in Google Scholar
European Commission. 2014. “Eurostat. Employment and Unemployment (LFS).” Available at: http://ec.europa.eu/eurostat.Search in Google Scholar
Eurostat. 2010. “Description of Target Variables: Cross-Sectional and Longitudinal.” EU-SILC 065/2010.Search in Google Scholar
Farber, S. H. 1999. “Mobility and Stability: The Dynamics of Job Change in Labor Market.” in Handbook of Labor Economics, vol. 3, edited by O. Ashenfler and D. Card, 2439-83. Amsterdam: Elsevier Science.10.1016/S1573-4463(99)30023-7Search in Google Scholar
Farber, S. H. 2017. “Employment, Hours, and Earnings Consequences of Job Loss: US Evidence from the Displaced Workers Survey.” Journal of Labor Economics 35 (S1): S235–S272.10.1086/692353Search in Google Scholar
Foster-McGregor, N. and J. Poschl. 2016. “Productivity Effects of Knowledge Transfers Through Labour Mobility.” Journal of Productivity Analysis 46: 169–84.10.1007/s11123-016-0478-ySearch in Google Scholar
Gielen, A. C., and K. Tatsiramos. 2012. “Quit Behavior and the role of job protection.” Labour Economics 19: 624–32.10.1016/j.labeco.2012.05.002Search in Google Scholar
Gielen, A. C., and J. C. van Our. 2006. “Age-Specific Cyclical Effects in Job Reallocation and Labor Mobility.” Labour Economics 13: 493–504.10.1016/j.labeco.2006.02.006Search in Google Scholar
Givord, P. and W. Lionel. 2015. “When Does the Stepping‐Stone Work? Fixed‐Term Contracts Versus Temporary Agency Work in Changing Economic Conditions.” Journal of Applied Econometrics 30 (5): 787–805.10.1002/jae.2394Search in Google Scholar
Gervais M, N. Jaimovich, HE Siu, and Y. Yedid-Levi. 2016. “What Should I Be When I Grow Up? Occupations and Unemployment over the Life-Cycle.” Journal of Monetary Economics 83: 54–70.10.1016/j.jmoneco.2016.08.003Search in Google Scholar
Greene, W. H. 2012. Econometric Analysis. New York: Prentice Hall.Search in Google Scholar
Guell, M. and B. Petrongolo. 2007. “How binding are legal limits? Transitions from temporary to permanent work in Spain.” Labour Economics 14 (2): 153–83.10.1016/j.labeco.2005.09.001Search in Google Scholar
Gunkel, M., E. J. Lusk, B. Wolff, and F. Li. 2007. “Gender-Specific Effects at Work: An Empirical Study of Four Countries.” Gender, Work and Organization 14 (1): 56–79.10.1111/j.1468-0432.2007.00332.xSearch in Google Scholar
Hahn, J. K., H. R. Hyatt, H. P. Janicki, and S. R. Tibbets. 2017. “Job-to-Job Flows and Earnings Growth.” American Economic Review 107: 358–63.10.1257/aer.p20171077Search in Google Scholar
Hyatt, H. R., and E. McEntarfer. 2012. “Job-to-Job Flows in the Great Recession.” American Economic Review 102: 580–83.10.1257/aer.102.3.580Search in Google Scholar
Hyatt, H. R. 2015. “The Decline in Job-to-Job Flows.” IZA World of Labor 175: 1–10.10.15185/izawol.175Search in Google Scholar
Ingham, H. and M. Ingham. 2014. “Towards Eurosclerosis: Will Poland Escape?” The World Economy 37: 290–310.10.1111/twec.12106Search in Google Scholar
Izquierdo, M., J. F. Jimeno, T. Kosma, A. Lamo, S. Millard, T. Room, E. Viviano. 2017. “Labour Market Adjustment in Europe During the Crisis: Microeconomic Evidence from the Wage Dynamics Network Survey.” The Occasional Paper Series. BANCO DE ESPANA.10.2139/ssrn.2993279Search in Google Scholar
Jovanovic B. 1979. Job Matching and the Theory of Turnover. Journal of Political Economy 87 (5): 972–90.10.1086/260808Search in Google Scholar
Judge, T. A. and J. D. Kammeyer-Mueller. 2012. “Job Attitudes.” Annual Review of Psychology 63: 341–67.10.1146/annurev-psych-120710-100511Search in Google Scholar
Jurajda, S., K. Terrel. 2007. “Regional Unemployment and Human Capital in Transition Economies.” CEPR Discussion Paper No. 6569.10.2139/ssrn.1113855Search in Google Scholar
Kahn, L. 2012. “Labour Market Policy: A Comparative View on the Costs and Benefits of Labour Market Flexibility.” Journal of Policy Analysis and Management 31 (1): 94–110.10.1002/pam.20602Search in Google Scholar
Karahan, F., R. Michaels, B. Pugsley, A. Åžahin, and R. Schuh. 2017. “Do job-to-job transitions drive wage fluctuations over the business cycle?” American Economic Review 107: 353–57.10.1257/aer.p20171076Search in Google Scholar
King, G. and L. Zeng. 2001. “Logistic Regression in Rare Events Data.” Political Analysis 9: 137–63.10.1093/oxfordjournals.pan.a004868Search in Google Scholar
Kirchmeyer, C. 2006. “The Different Effects of Family on Objective Career Success Across Gender: A Test of Alternative Explanations.” Journal of Vocational Behavior 68 (2): 323–46.10.1016/j.jvb.2005.05.002Search in Google Scholar
Lasi, H., P. Fettke, H. G. Kemper, T. Feld, M. Hoffmann. 2014. “Industry 4.0.” Business & Information Systems Engineering 6: 239–42.10.1007/s12599-014-0334-4Search in Google Scholar
Levitt, S. D. 2016. “Heads or Tails: The Impact of a Coin Toss on Major Life Decisions and Subsequent Happiness.” NBER Working Paper no. 22487. Cambridge: National Bureau of Economic Research.10.3386/w22487Search in Google Scholar
Li, Jin. 2013. “Job Mobility, Wage Dispersion, and Technological Changes: An Asymmetric Informal Perspective.” European Economic Review 60: 105–26.10.1016/j.euroecorev.2013.01.008Search in Google Scholar
Lilla, M. and S. Staffolani. 2012. “Young Entrants, Temporary Jobs and Career Opportunities: Short-Term Perspectives of Young Italian Workers.” Rivista di Statistica Ufficiale 1: 49–60.Search in Google Scholar
Longhi, S. and M. Taylor. 2014. “Employed and Unemployed Job Seekers and the Business Cycle.” Oxford Bulletin of Economics and Statistics 76 (4): 463–83.10.1111/obes.12029Search in Google Scholar
McGovern, P., D. Smeaton, and S. Hill. 2004. “Bad Jobs in Britain: Nonstandard Employment and Job Quality.” Work and Occupations 31: 225–249.10.1177/0730888404263900Search in Google Scholar
Marino, M., P. Parrotta, and D. Pozzoli. 2016. “Educational Diversity and Knowledge Transfers via Inter-Firm Labor Mobility.” Journal of Economic Behavior & Organization 123: 168–83.10.1016/j.jebo.2015.10.019Search in Google Scholar
Marra, G., R. Radice, T. Barnighausen, S. N. Wood, M. E. McGovern. 2017. “A Simultaneous Equation Approach to Estimating HIV Prevalence with Non Ignorable Missing Responses.” Journal of the American Statistical Association 112: 484–96.10.1080/01621459.2016.1224713Search in Google Scholar
Marra, G., and R. Radice. 2017. “A Joint Regression Modeling Framework for Analyzing Bivariate Binary Data in R.” Dependence Modeling 5: 268–94.10.1515/demo-2017-0016Search in Google Scholar
Marra, G., and R. Radice. 2018. GJRM: Generalised Joint Regression Modelling. R package version 0.1-4.Search in Google Scholar
Mukoyama, T. 2014. “The Cyclicality of Job-to-Job Transitions and its Implications for Aggregate Productivity.” Journal of Economic Dynamics and Control 39: 1–17.10.1016/j.jedc.2013.12.004Search in Google Scholar
Montt, G. 2017. “Field-of-Study Mismatch and Overqualification: Labour Market Correlates and Their Wage Penalty.” IZA Journal of Labor Economics 6: 2.10.1186/s40172-016-0052-xSearch in Google Scholar
Mosthaf, A., Schnabel, C., and Stephani, J. 2011. “Low-Wage Careers: Are There Dead-End Firms and Dead-End Jobs?” Journal for Labour Market Research 43: 231–49.10.1007/s12651-010-0036-4Search in Google Scholar
Mussida, C., and L. Zanin. 2019. “I Found a Better Job Opportunity! Voluntary Job Mobility of Employees and Temporary Contracts Before and After the Great Recession in France, Italy and Spain.” Empirical Economics. https://doi.org/10.1007/s00181-019-01622-7.Search in Google Scholar
Neffke, F. M. H., A. Otto, and A. Weyh. 2017. “Inter-Industry Labor Flows.” Journal of Economic Behavior & Organization 142: 275–292.10.1016/j.jebo.2017.07.003Search in Google Scholar
Newell, A. 2006. “Skill Mismatch and Regional Unemployment in Poland.” In The European Labour Market Regional Dimensions. AIEL Series in Labour Economics, chapter 8, edited by F. E. Caroleo, and S. Destefanis, 187–201. Heidelberg: Physica-Verlag.10.1007/3-7908-1680-9_9Search in Google Scholar
Nieuwenhuis, R., and L. C. Maldonado. 2018. The Triple Bind of Single-Parent Families: Resources, Employment and Policies to Improve Wellbeing. Bristol, UK: Policy Press, University of Bristol.10.2307/j.ctt2204rvqSearch in Google Scholar
Pavlopoulos, D. 2009. “Starting Your Career with a Temporary Job: Stepping Stone or “Dead-End”?” SOEP papers on Multidisciplinary Panel Data Research, No 228, DIW Berlin, The German Socio-Economic Panel.10.2139/ssrn.1498543Search in Google Scholar
Pellizzari, M., and A. Fichen. 2017. “A New Measure of Skill Mismatch: Theory and Evidence from PIAAC.” IZA Journal of Labor Economics 6: 1.10.1186/s40172-016-0051-ySearch in Google Scholar
Perez, G. J. I., and Y. R. Sanz. 2005. “Wage Changes Through Job Mobility in Europe: A Multinomial Endogenous Switching Approach.” Labour Economics 12: 531–55.10.1016/j.labeco.2005.05.005Search in Google Scholar
Picchio, M. 2008. “Temporary Contracts and Transitions to Stable Jobs in Italy.” LABOUR 22 (s1): 147–74.10.1111/j.1467-9914.2008.00415.xSearch in Google Scholar
Pilc, M. 2017. “The Temporary Employed in Poland: Beneficiaries or Victims of the Liberal Labour Market?” Economic and Industrial Democracy 38 (3): 400–24.10.1177/0143831X15574113Search in Google Scholar
R Core Team. 2018. “R: A language and environment for statistical computing.” R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org.Search in Google Scholar
Radice, R., G. Marra, and M. Wojtys. 2016. “Copula Regression Spline Models for Binary Outcomes.” Statistics and Computing 26: 981–99.10.1007/s11222-015-9581-6Search in Google Scholar
Schnabel, C. 2016. “United, Yet Apart? A Note on Persistent Labour Market Differences between Western and Eastern Germany.” Journal of Economics and Statistics 236 (2): 157–79.10.1515/jbnst-2015-1012Search in Google Scholar
Silva, A. D., and A. Turrini. 2015. “Precarious and less well-paid? Wage differences between permanent and fixed-term contracts across the EU countries.” Economic Papers 544, European Commission - Directorate-General for Economic and Financial Affairs.Search in Google Scholar
Van Lancker, W. 2012. “The European World of Temporary Employment: Gendered and Poor?” European Societies 14 (1): 83–111.10.1080/14616696.2011.638082Search in Google Scholar
von Wachter, T, and S. Bender. 2006. “In the Right Place at the Wrong Time: The Role of Firms and Luck in Young Workers’ Careers.” American Economic Review 96: 1679–705.10.1257/aer.96.5.1679Search in Google Scholar
Zanin, L., and G. Marra. 2012. “A Comparative Study of the use of Generalized Additive Models and Generalized Linear Models in Tourism Research.” International Journal of Tourism Research 14: 451–68.10.1002/jtr.872Search in Google Scholar
Zanin L., R. Radice, and G. Marra. 2014. “A comparison of approaches for estimating the effect of women’s education on the probability of using modern contraceptive methods in Malawi.” The Social Science Journal 51 (3): 361–67.10.1016/j.soscij.2013.12.008Search in Google Scholar
Zanin, L. 2015. “Determinants of Risk Attitudes Using Sample Surveys: The Implications of a High Rate of Nonresponse.” Journal of Behavioral and Experimental Finance 8: 44–53.10.1016/j.jbef.2015.08.001Search in Google Scholar
Zanin, L., and R. Calabrese. 2017. “Interaction Effects of Region-Level GDP Per Capita and Age on Labour Market Transition Rates in Italy.” IZA Journal of Labor Economics 6: 4.10.1186/s40172-017-0054-3Search in Google Scholar
Zanin, L. 2018. “Private Monetary Transfers Between Households: Who is Helped and by Whom?” Journal of Behavioral and Experimental Finance 17: 76–82.10.1016/j.jbef.2017.12.010Search in Google Scholar
Wood, S. N. 2017.” Generalized Additive Models: An Introduction with R, 2nd ed., London: Chapman & Hall/CRC.10.1201/9781315370279Search in Google Scholar
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