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Persistency of financial distress amongst Italian households: Evidence from dynamic models for binary panel data


  • Giarda, Elena


This paper builds on existing studies on households’ financial distress and provides new evidence on the determinants of financial hardship in Italy and its persistence over time. It suggests a quantitative definition of financial distress based on the distribution of net wealth, and tests whether the probability of experiencing financial difficulty is persistent over time, using (random and fixed effects) dynamic models for binary panel data. The analysis exploits the longitudinal component of the Bank of Italy Survey on Household Income and Wealth for the period 1998–2006. Its results show that, after accounting for unobserved heterogeneity, past values of the outcome variable play a large part in explaining the probability of experiencing financial distress. In addition, the probability of financial vulnerability decreases with income and greater sophistication of the household portfolio and, at least in one of the model specifications, increases in areas with higher unemployment rates.

Suggested Citation

  • Giarda, Elena, 2013. "Persistency of financial distress amongst Italian households: Evidence from dynamic models for binary panel data," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3425-3434.
  • Handle: RePEc:eee:jbfina:v:37:y:2013:i:9:p:3425-3434 DOI: 10.1016/j.jbankfin.2013.05.005

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    References listed on IDEAS

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    Cited by:

    1. repec:jss:jstsof:v:079:i08 is not listed on IDEAS
    2. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.
    3. Pigini, Claudia & Presbitero, Andrea F. & Zazzaro, Alberto, 2016. "State dependence in access to credit," Journal of Financial Stability, Elsevier, vol. 27(C), pages 17-34.
    4. repec:eee:wdevel:v:98:y:2017:i:c:p:338-350 is not listed on IDEAS
    5. Declan French, 2016. "Financial Strain in the United Kingdom," CHaRMS Working Papers 16-02, Centre for HeAlth Research at the Management School (CHaRMS).
    6. Chichaibelu, Bezawit & Waibel, Hermann, 2015. "The Interrelated Dynamics of Multiple Borrowing and Over-indebtedness among Rural Households in Thailand and Vietnam," 2015 Conference, August 9-14, 2015, Milan, Italy 211463, International Association of Agricultural Economists.
    7. Luca Zanin, 2016. "On Italian Households’ Economic Inadequacy Using Quali-Quantitative Measures," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(1), pages 59-88, August.
    8. K. Sudhir & Nathan Yang, 2014. "Exploiting the Choice-Consumption Mismatch: A New Approach to Disentangle State Dependence and Heterogeneity," Cowles Foundation Discussion Papers 1941, Cowles Foundation for Research in Economics, Yale University.
    9. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    10. Francesco Bartolucci & Claudia Pigini, 2017. "Granger causality in dynamic binary short panel data models," Working Papers 421, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    11. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "The existence and persistence of household financial hardship: A Bayesian multivariate dynamic logit framework," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 285-298.

    More about this item


    Household financial distress; Net wealth; Dynamic models for binary panel data; SHIW;

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities


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