IDEAS home Printed from https://ideas.repec.org/p/bdi/wptemi/td_870_12.html

To misreport or not to report? The measurement of household financial wealth

Author

Listed:
  • Andrea Neri

    (Bank of Italy)

  • Maria Giovanna Ranalli

    (Department of Economics, Finance and Statistics, University of Perugia)

Abstract

The objective of the paper is to adjust for the bias due to unit non-response and measurement error in survey estimates of total household financial wealth. Sample surveys are a useful source of information on household wealth. Yet, survey estimates are affected by non-sampling errors. In particular, in the case of household wealth, unit non-response and measurement error can severely bias the estimates. Using the Italian Survey on Household Income and Wealth (SHIW), we exploit the available auxiliary information in order to assess the magnitude of this bias. We find evidence that for this kind of survey, non-sampling errors are a major issue, possibly more serious than sampling errors. Moreover, in the case of SHIW the potential bias due to measurement error seems to outweigh that induced by non-response.

Suggested Citation

  • Andrea Neri & Maria Giovanna Ranalli, 2012. "To misreport or not to report? The measurement of household financial wealth," Temi di discussione (Economic working papers) 870, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_870_12
    as

    Download full text from publisher

    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2012/2012-0870/en_tema_870.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, August.
    2. Claudia Biancotti & Giovanni D'Alessio & Andrea Neri, 2008. "Measurement Error In The Bank Of Italy'S Survey Of Household Income And Wealth," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 54(3), pages 466-493, September.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, August.
    4. Giovanni D'Alessio & Ivan Faiella, 2002. "Non-response behaviour in the Bank of Italy�s Survey of Household Income and Wealth," Temi di discussione (Economic working papers) 462, Bank of Italy, Economic Research and International Relations Area.
    5. Emilia Bonaccorsi di Patti & Enrico Sette, 2012. "Bank balance sheets and the transmission of financial shocks to borrowers: evidence from the 2007-2008 crisis," Temi di discussione (Economic working papers) 848, Bank of Italy, Economic Research and International Relations Area.
    6. Leandro D�Aurizio & Ivan Faiella & Stefano Iezzi & Andrea Neri, 2006. "The under-reporting of financial wealth in the Survey on Household income and Wealth," Temi di discussione (Economic working papers) 610, Bank of Italy, Economic Research and International Relations Area.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Philip Vermeulen, 2016. "Estimating the Top Tail of the Wealth Distribution," American Economic Review, American Economic Association, vol. 106(5), pages 646-650, May.
    2. Arthur B. Kennickell & Peter Lindner & Martin Schürz, 2022. "A new instrument to measure wealth inequality: distributional wealth accounts," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/21.
    3. Giovanni D'Alessio & Andrea Neri, 2015. "Income and wealth sample estimates consistent with macro aggregates: some experiments," Questioni di Economia e Finanza (Occasional Papers) 272, Bank of Italy, Economic Research and International Relations Area.
    4. Antonietta di Salvatore & Mirko Moscatelli, 2024. "Improving survey information on household debt using granular credit databases," Questioni di Economia e Finanza (Occasional Papers) 839, Bank of Italy, Economic Research and International Relations Area.
    5. Engel, Janina & Riera, Pau Gayà & Grilli, Joseph & Sola, Pierre, 2022. "Developing reconciled quarterly distributional national wealth – insight into inequality and wealth structures," Working Paper Series 2687, European Central Bank.
    6. Vincenzo Alfano, 2020. "Anatomy of social security contribution evasion in Italy," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2020(2), pages 7-37.
    7. Steiner, Viktor & Zhu, Junyi, 2021. "A joint top income and wealth distribution," Discussion Papers 2021/3, Free University Berlin, School of Business & Economics.
    8. Jaanika Meriküll & Tairi Rõõm, 2022. "Are survey data underestimating the inequality of wealth?," Empirical Economics, Springer, vol. 62(2), pages 339-374, February.

    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. Andrea Neri & Roberta Zizza, 2010. "Income reporting behaviour in sample surveys," Temi di discussione (Economic working papers) 777, Bank of Italy, Economic Research and International Relations Area.
    2. Wu, Ximing & Sickles, Robin, 2018. "Semiparametric estimation under shape constraints," Econometrics and Statistics, Elsevier, vol. 6(C), pages 74-89.
    3. Zanin, Luca & Marra, Giampiero, 2012. "Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach," Economic Modelling, Elsevier, vol. 29(4), pages 1328-1337.
    4. Julie Vercelloni & M Julian Caley & Mohsen Kayal & Samantha Low-Choy & Kerrie Mengersen, 2014. "Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    5. Ni, Xiao & Zhang, Hao Helen & Zhang, Daowen, 2009. "Automatic model selection for partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2100-2111, October.
    6. Proietti, Tommaso, 2010. "Trend Estimation," MPRA Paper 21607, University Library of Munich, Germany.
    7. Samiran Sinha & Bani K. Mallick & Victor Kipnis & Raymond J. Carroll, 2010. "Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error," Biometrics, The International Biometric Society, vol. 66(2), pages 444-454, June.
    8. Peter Hall & Joel L. Horowitz, 2013. "A simple bootstrap method for constructing nonparametric confidence bands for functions," CeMMAP working papers CWP29/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    10. Javier Parada Gómez Urquiza & Alejandro López-Feldman, 2013. "Poverty dynamics in rural Mexico: What does the future hold?," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 55-74, November.
    11. Bethany Everett & David Rehkopf & Richard Rogers, 2013. "The Nonlinear Relationship Between Education and Mortality: An Examination of Cohort, Race/Ethnic, and Gender Differences," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 32(6), pages 893-917, December.
    12. Göran Kauermann & Renate Meyer, 2014. "Penalized marginal likelihood estimation of finite mixtures of Archimedean copulas," Computational Statistics, Springer, vol. 29(1), pages 283-306, February.
    13. O. Gimenez & C. Crainiceanu & C. Barbraud & S. Jenouvrier & B. J. T. Morgan, 2006. "Semiparametric Regression in Capture–Recapture Modeling," Biometrics, The International Biometric Society, vol. 62(3), pages 691-698, September.
    14. Becker, William, 2020. "Metafunctions for benchmarking in sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    15. Tatiyana V. Apanasovich & David Ruppert & Joanne R. Lupton & Natasa Popovic & Nancy D. Turner & Robert S. Chapkin & Raymond J. Carroll, 2008. "Aberrant Crypt Foci and Semiparametric Modeling of Correlated Binary Data," Biometrics, The International Biometric Society, vol. 64(2), pages 490-500, June.
    16. Eduardo L. Montoya & Wendy Meiring, 2016. "An F-type test for detecting departure from monotonicity in a functional linear model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 322-337, June.
    17. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    18. Beran, Rudolf, 2014. "Hypercube estimators: Penalized least squares, submodel selection, and numerical stability," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 654-666.
    19. Katrien Antonio & Jan Beirlant, 2008. "Issues in Claims Reserving and Credibility: A Semiparametric Approach With Mixed Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 643-676, September.
    20. Umlauf, Nikolaus & Adler, Daniel & Kneib, Thomas & Lang, Stefan & Zeileis, Achim, 2015. "Structured Additive Regression Models: An R Interface to BayesX," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i21).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:bdi:wptemi:td_870_12. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.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.