IDEAS home Printed from https://ideas.repec.org/a/ite/iteeco/190403.html
   My bibliography  Save this article

Gross income projection in Labour Force Survey Data

Author

Listed:
  • Esposito Laura
  • Fioroni Livia
  • Guandalini Alessio

Abstract

No abstract is available for this item.

Suggested Citation

  • Esposito Laura & Fioroni Livia & Guandalini Alessio, 2019. "Gross income projection in Labour Force Survey Data," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 73(4), pages 41-52, October-D.
  • Handle: RePEc:ite:iteeco:190403
    as

    Download full text from publisher

    File URL: http://www.sieds.it/listing/RePEc/journl/2019734P04_036_Esposito_Fioroni_Guandalini.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriella Donatiello & Marcello D’Orazio & Doriana Frattarola & Antony Rizzi & Mauro Scanu & Mattia Spaziani, 2014. "Statistical Matching of Income and Consumption Expenditures," International Journal of Economic Sciences, Prague University of Economics and Business, vol. 2014(3), pages 50-65.
    2. Gabriella Donatiello & Marcello D'Orazio & Doriana Frattarola & Antony Rizzi & Mauro Scanu & Mattia Spaziani, 2014. "Statistical matching of income and consumption expenditures," Proceedings of International Academic Conferences 0100965, International Institute of Social and Economic Sciences.
    3. Gianni Betti & Gabriella Donatiello & Vijay Verma, 2011. "The siena microsimulation model (sm2) for net-gross conversion of eu-silc income variables," International Journal of Microsimulation, International Microsimulation Association, vol. 4(1), pages 35-53.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. D'Alberto, Riccardo & Zavalloni, Matteo & Raggi, Meri & Viaggi, Davide, 2021. "A Statistical Matching approach to reproduce the heterogeneity of willingness to pay in benefit transfer," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    2. Marcello D’Orazio, 2015. "Integration and imputation of survey data in R: the StatMatch package," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 57-68, June.
    3. Cristina Cirillo & Lucia Imperioli & Marco Manzo, 2021. "The Value Added Tax Simulation Model: VATSIM-DF (II)," Working Papers wp2021-12, Ministry of Economy and Finance, Department of Finance.
    4. Andrea Cutillo & Mauro Scanu, 2020. "A Mixed Approach for Data Fusion of HBS and SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(2), pages 411-437, July.
    5. Baris Ucar & Gianni Betti, 2016. "Longitudinal statistical matching: transferring consumption expenditure from HBS to SILC panel survey," Department of Economics University of Siena 739, Department of Economics, University of Siena.
    6. Menyhért, Bálint, 2024. "Energy poverty in the European Union. The art of kaleidoscopic measurement," Energy Policy, Elsevier, vol. 190(C).
    7. Luca Gandullia & Lucia Leporatti, 2019. "Distributional effects of gambling taxes: empirical evidence from Italy," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(4), pages 565-590, December.
    8. Junyi Zhu, 2014. "Bracket Creep Revisited - with and without r > g: Evidence from Germany," Journal of Income Distribution, Ad libros publications inc., vol. 23(3), pages 106-158, November.
    9. Lidia CERIANI & Carlo V. FIORIO & Chiara GHIGLIARANO, 2013. "The importance of choosing the data set for tax-benefit analysis," Departmental Working Papers 2013-05, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    10. Lidia Ceriani & Carlo V. Fiorio & Chiara Gigliarano, 2013. "The importance of choosing the data set for tax-benefit analysis," International Journal of Microsimulation, International Microsimulation Association, vol. 1(6), pages 86-121.
    11. Andrea Albarea & Michele Bernasconi & Cinzia Di Novi & Anna Marenzi & Dino Rizzi & Francesca Zantomio, 2015. "Accounting for Tax Evasion Profiles and Tax Expenditures in Microsimulation Modelling. The BETAMOD Model for Personal Income Taxes in Italy," International Journal of Microsimulation, International Microsimulation Association, vol. 8(3), pages 99-136.
    12. Gabriella Donatiello & Gianni Betti & Paolo Consolini, 2012. "The Construction of Gross Income Variables of Eusilc (Eu Statistics on Income and Living Conditions) in Italy: A Mixed Strategy Using Microsimulation and Administrative Data," Department of Economics University of Siena 652, Department of Economics, University of Siena.
    13. Maria Cozzolino & Marco Di Marco, 2015. "Micromodelling Italian Taxes and Social Policies," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 17(2), pages 17-26.
    14. Stefano Boscolo, 2019. "The Contribution of Proportional Taxes and Tax-Free Cash Benefits to Income Redistribution over the Period 2005-2018: Evidence from Italy," Department of Economics 0152, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    15. Boscolo, Stefano, 2019. "The contribution of proportional taxes and tax-free cash benefits to income redistribution over the period 2005-2018: Evidence from Italy," EUROMOD Working Papers EM18/19, EUROMOD at the Institute for Social and Economic Research.
    16. Ana Kreter & Gianni Betti & Renata Del-Vecchio & Jefferson Staduto, 2015. "The Siena Micro-Simulation Model (SM2): a contribution for informality studies in Brazil," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2251-2268, November.
    17. Flores Unzaga, Ismael Martin & Zhu, Junyi, 2014. "Bracket Creep Revisited: Progressivity and a Solution by Adjusting the Rich Tax in Germany," MPRA Paper 57664, University Library of Munich, Germany.
    18. Paolo Consolini & Gabriella Donatiello, 2015. "Multi-source data collection strategy and microsimulation techniques for the Italian EU-SILC," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 17(2), pages 77-96.
    19. Fernando Di Nicola & Giorgio Mongelli & Simone Pellegrino, 2015. "The static microsimulation model of the Italian Department of Finance: Structure and first results regarding income and housing taxation," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2015(2), pages 125-157.
    20. Figari, Francesco & Paulus, Alari & Sutherland, Holly, 2014. "Microsimulation and policy analysis," ISER Working Paper Series 2014-23, Institute for Social and Economic Research.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ite:iteeco:190403. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Claudio Ceccarelli (email available below). General contact details of provider: https://edirc.repec.org/data/siedsea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.