IDEAS home Printed from https://ideas.repec.org/a/isa/journl/v10y2008i1p59-72.html
   My bibliography  Save this article

Estimation of Poverty Rates for the Italian Population classified by Household Type and Administrative Region

Author

Listed:
  • Claudio Ceccarelli
  • Enrico Fabrizi
  • Maria Rosaria Ferrante
  • Silvia Pacei

    (Italian National Institute of Statistics
    DISES, Facoltà di Economia, Università Cattolica, Piacenza
    Università di Bologna
    Università di Bologna)

Abstract

The aim of the work is to provide estimates of some poverty rates for domains defined by cross-classifying the Italian population by household typology and administrative region, on the basis of data collected for Italy by the new “European Union – Statistics on Income and Living Conditions” survey (EU-SILC). This survey is designed to provide reliable estimates for large areas within countries much bigger than the sub-populations of our interest. To solve this problem, we suggest small area estimators derived from multivariate area level models, that improve the reliability of estimates “borrowing strength” over areas and by exploiting the correlation between the considered indicators. The unemployment rate calculated by household typology within administrative regions is used as auxiliary information to improve the precision of model based estimators. As estimation method we use a Hierarchical Bayesian approach implemented by means of MCMC computation methods. Among the different models being compared, the Multivariate Normal-Logistic model is found out to be the best performer.

Suggested Citation

  • Claudio Ceccarelli & Enrico Fabrizi & Maria Rosaria Ferrante & Silvia Pacei, 2008. "Estimation of Poverty Rates for the Italian Population classified by Household Type and Administrative Region," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 10(1), pages 59-72, October.
    • Handle: RePEc:isa:journl:v:10:y:2008:i:1:p:59-72
    as

    Download full text from publisher

    File URL: http://www.istat.it/it/files/2011/05/1_20081.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Enrico Fabrizi & Maria Rosaria Ferrante & Silvia Pacei, 2008. "Measuring Sub‐National Income Poverty By Using A Small Area Multivariate Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 54(4), pages 597-615, December.
    2. Jiang, Jiming & Lahiri, P., 2006. "Estimation of Finite Population Domain Means: A Model-Assisted Empirical Best Prediction Approach," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 301-311, March.
    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. Benedetti, Ilaria & Crescenzi, Federico, 2023. "The role of income poverty and inequality indicators at regional level: An evaluation for Italy and Germany," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

    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. Marisa Bottiroli Civardi & Renata Targetti Lenti, 2008. "Multiplier Decomposition, Inequality and Poverty in a SAM Framework," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 10(1), pages 31-57, October.
    2. Roberto Gismondi & Andrea Carone, 2008. "Statistical Criteria to Manage Non-respondents’ Intensive Follow Up in Surveys Repeated along Time," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 10(1), pages 5-29, October.
    3. Enrico Fabrizi & Maria Rosaria Ferrante & Silvia Pacei, 2014. "A Micro-Econometric Analysis of the Antipoverty Effect of Social Cash Transfers in Italy," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(2), pages 323-348, June.
    4. Karlberg Forough, 2015. "Small Area Estimation for Skewed Data in the Presence of Zeroes," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 541-562, December.
    5. Wang, Jianqiang C., 2012. "Sample distribution function based goodness-of-fit test for complex surveys," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 664-679.
    6. Guadarrama, María & Molina, Isabel & Rao, J.N.K., 2018. "Small area estimation of general parameters under complex sampling designs," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 20-40.
    7. Enrico Fabrizi & Chiara Mussida, 2020. "Assessing poverty persistence in households with children," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(4), pages 551-569, December.
    8. Enrico Fabrizi & Chiara Mussida, 2018. "Assessing poverty persistence in households with dependent children: the role of poverty measurement," DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali dises1839, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    9. Forough Karlberg, 2015. "Small Area Estimation For Skewed Data In The Presence Of Zeroes," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 541-562, December.
    10. Maria Rosaria Ferrante & Silvia Pacei, 2017. "Small domain estimation of business statistics by using multivariate skew normal models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1057-1088, October.
    11. Jerry J. Maples, 2017. "Improving small area estimates of disability: combining the American Community Survey with the Survey of Income and Program Participation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1211-1227, October.
    12. Maryna Prus & Hans-Peter Piepho, 2021. "Optimizing the Allocation of Trials to Sub-regions in Multi-environment Crop Variety Testing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 267-288, June.
    13. Alberto Díaz Dapena & Esteban Fernández Vázquez & Fernando Rubiera Morollón & Ana Viñuela, 2021. "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 63-81, February.
    14. Sanjay Chaudhuri & Malay Ghosh, 2011. "Empirical likelihood for small area estimation," Biometrika, Biometrika Trust, vol. 98(2), pages 473-480.
    15. U C Sud & Hukum Chandra & HVL Bathla, 2010. "Small Area Estimation Under a Mixture Model," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 11(3), pages 503-516, December.
    16. Xin Wang & Emily Berg & Zhengyuan Zhu & Dongchu Sun & Gabriel Demuth, 2018. "Small Area Estimation of Proportions with Constraint for National Resources Inventory Survey," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 509-528, December.
    17. Forough Karlberg, 2015. "Small area estimation for skewed data in the presence of zeroes," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 541-562, December.
    18. Guadarrama, María & Molina, Isabel & Rao, J.N.K., 2016. "Small area estimation of general parameters under complex sampling designs," DES - Working Papers. Statistics and Econometrics. WS 22731, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Isabel Molina & Ewa Strzalkowska‐Kominiak, 2020. "Estimation of proportions in small areas: application to the labour force using the Swiss Census Structural Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 281-310, January.
    20. Ganesh, N., 2009. "Simultaneous credible intervals for small area estimation problems," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1610-1621, September.

    More about this item

    Keywords

    Financial Poverty Measures; European Union - Statistics on Income and Living Conditions; Multivariate Hierarchical Bayes Model;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

    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:isa:journl:v:10:y:2008:i:1:p:59-72. 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: Stefania Rossetti (email available below). General contact details of provider: https://edirc.repec.org/data/istgvit.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.