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Introduction

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  • Daniel Vaughan-Whitehead

Abstract

La presente sezione monografica si prefigge lo scopo di affrontare il tema della disuguaglianza di reddito sotto due punti di vista: innanzitutto, individuando quali elementi e tendenze nel mondo del lavoro possono aver contribuito alla disparità di reddito; in secondo luogo, analizzando le diseguaglianze non solo partendo dai due estremi - il livello più alto e quello più basso della distribuzione del reddito - ma anche nell'ottica delle fasce a medio reddito o di quella che genericamente viene definita "classe media". Disuguaglianze di reddito sempre più forti e una maggiore polarizzazione stanno comportando l'erosione della classe media? Quali sono le principali trasformazioni nel mondo del lavoro dell'ultimo ventennio che hanno interessato la classe media? E in che modo la crisi economica e finanziaria ha colpito la classe media? Alla luce di tutto ciò, su quali fattori determinanti nel mondo del lavoro dovremmo far leva al fine di limitare o financo ridurre le disuguaglianze di reddito negli anni a venire?

Suggested Citation

  • Daniel Vaughan-Whitehead, 2016. "Introduction," Economia & lavoro, Carocci editore, issue 2, pages 7-12.
    • Handle: RePEc:caq:j950ix:doi:10.7384/84398:y:2016:i:2:p:7-12
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    1. Gideon Simon Ghajiga & Dikeledi Jacobeth Warlimont, 2020. "The Use of Blockchain Technology in Identity Storage and Management," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 71-77, August.
    2. Eliseo Reategui & Alause Pires & Michel Carniato & Sergio Roberto Kieling Franco, 2020. "Evaluation of Brazilian research output in education: confronting international and national contexts," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 427-444, October.
    3. Daniel Béland & Gregory P. Marchildon & Michael J. Prince, 2020. "Understanding Universality within a Liberal Welfare Regime: The Case of Universal Social Programs in Canada," Social Inclusion, Cogitatio Press, vol. 8(1), pages 124-132.
    4. Paul R. Dewick & Shuangzhe Liu, 2022. "Copula Modelling to Analyse Financial Data," JRFM, MDPI, vol. 15(3), pages 1-11, February.
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    6. Roy Gernhardt & Bjorn Persson, 2020. "On the Equivalence of Neural and Production Networks," Papers 2005.00510, arXiv.org, revised Mar 2021.
    7. Dackehag, Margareta & Ellegård, Lina Maria & Gerdtham, Ulf-G & Nilsson, Therese, 2018. "Social Assistance and Mental Health: Evidence from Longitudinal Data on Pharmaceutical Consumption," Working Papers 2018:2, Lund University, Department of Economics.
    8. Berggren, Niclas & Ljunge, Martin, 2017. "Does Religion Make You Sick? Evidence of a Negative Relationship between Religious Background and Health," Working Paper Series 1173, Research Institute of Industrial Economics.
    9. William Ascher, 2021. "Coping with intelligence deficits in poverty-alleviation policies in low-income countries," Policy Sciences, Springer;Society of Policy Sciences, vol. 54(2), pages 345-370, June.
    10. Loh, Wen Wei & Ren, Dongning, 2021. "Data-driven Covariate Selection for Confounding Adjustment by Focusing on the Stability of the Effect Estimator," OSF Preprints yve6u, Center for Open Science.
    11. Schneidewind, Uwe & Singer-Brodowski, Mandy & Augenstein, Karoline & Stelzer, Franziska, 2016. "Pledge for a transformative science: A conceptual framework," Wuppertal Papers 191, Wuppertal Institute for Climate, Environment and Energy.

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