Skip to main content
SpringerLink
Log in

The Index of Household Financial Condition, Combining Subjective and Objective Indicators: An Appraisal of Italian Households

  • Published:
Social Indicators Research Aims and scope Submit manuscript

An Erratum to this article was published on 31 August 2013

Abstract

With data from the Italian Survey of Household Income and Wealth, we present an Index of Household Financial Condition and quantify with it the position of households between 2004 and 2010. The Index of Household Financial Condition is composed of subjective and objective indicators, which enable to capture differently the existing uncertainty concerning the future development of a household’s financial situation. We show with a measurement model based on multi-group confirmatory factor analysis (MGCFA) that the proposed Index is two-dimensional and comprises financial position and financial prudence. Through application of the MGCFA, we show that the interrelations between the indicators had not changed at four measurement occasions (2004–2010), and thus the proposed set comprises a coherent and time-invariant framework for measuring two dimensions of the latent concept: financial condition. Established measurement invariance in the MGCFA framework allows an assessment of trend in financial position and financial prudence of Italian households. We show that the financial position of Italian households improved in the period 2004–2006 and later declined. Improvement of the financial prudence was observed, however, till 2008. Finally, we incorporate a set of socioeconomic features of Italian households into a structural equation model. With the provided set of indicators, we find positive relation between age and both financial position and prudence, but also we show the positive impact of white-collar jobs on scores in each of the dimensions of the financial condition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Bank of Italy, Survey on Household Income and Wealth. http://www.eui.eu/Research/Library/ResearchGuides/Economics/Statistics/DataPortal/SHIW.aspx; http://www.bancaditalia.it/statistiche/indcamp/bilfait.

  2. Bank of Italy, Survey on Household Income and Wealth.

  3. It must be noted that the interviews for the sample survey in 2004 were conducted between February and July 2005, the interviews for the sample survey in 2006 were conducted between March and October 2007, the interviews of the sample survey in 2008 were conducted between January and September 2009 and the interviews of the sample survey in 2010 were conducted between January and September 2011.

  4. Taking the logarithm of both income and wealth is motivated both theoretically and pragmatically. From a theoretical perspective, in life-cycle models, it is common to model the utility of households using a constant relative risk aversion type of function and present empirical implications with one of its representatives—the logarithmic function (for some recent applications see e.g. Carroll and Toche 2009; Onori 2012; Zhang 2012). On this basis, utility becomes proportional to the logarithm of consumption (income) (Osberg 1991). The same logic applies to wealth. From a pragmatic perspective, the variances of non-transformed income and wealth are very high and their distributions are highly skewed. The distributions of income and wealth often resemble log-normal distributions, which further justifies the logarithmic transformation. Strong support for logarithmic transformation of incomes is also provided by Kahneman and Deaton (Kahneman and Deaton 2010) who claim that “The logarithmic transformation represents a basic fact of perception known as Weber’s Law, which applies generally to quantitative dimensions of perception and judgment (e.g., the intensity of sounds and lights)”. The procedure of log-transformation is also required to linearize income and wealth (Bostic et al. 2009).

  5. The sample includes households reporting negative incomes (about 0.01–0.05 % of households per wave) and/or wealth (2.30–2.93 % of households per wave). Although it is not always clear what a negative income implies and whether it is connected with change in the value of household assets we assumed that it corresponds to real though very bad economic situation of a household. The same reasoning applied to negative wealth. Second, in order to apply logarithmic transformation, negative incomes and negative wealth need to be treated but they cannot be simply excluded from the sample. Therefore, the following outlier treatment method is used. Each of the negative values of income or wealth is replaced with value 1. The same approach was employed by Huber et al. (2002) and Osborne (2010), among others. Furthermore Osborne (2010) showed that it is the most efficient among all Box-Cox transformations. Then the logarithmic transformation of each of the two variables is conducted. The transformed income data are used to calculate four ratio indicators (inc_suff, c_total, c_food, sav).

  6. Exactly the same method is adopted in Saisana (2010). Frigge et al. (1989) provide substantial arguments for using 1.5 as the multiplier for the interquartile range, while application of winsorization as a tool for dealing with outliers in constructing indices can be found for example in Environmental Performance Index prepared jointly by Yale University and Columbia University (Esty et al. 2008).

References

  • Anderloni, L., Bacchiocchi, E., & Vandone, D. (2012). Household financial vulnerability: An empirical analysis. Research in Economics, 66(3), 284–296. doi:10.1016/j.rie.2012.03.001.

    Article  Google Scholar 

  • Anderloni, L., & Vandone, D. (2010). Risk of overindebtedness and behavioural factors. Working papers, Vol. 21501, pp. 1–19.

  • Atkinson, A., McKay, S., Kempson, E., & Collard, S. (2006). Levels of financial capability in the UK: Results of a baseline survey. Consumer Research, 46, 1–150. Retrieved from http://www.fsa.gov.uk/pubs/consumer-research/crpr47.pdf.

  • Bagozzi, R. P. (2007). On the meaning of formative measurement and how it differs from reflective measurement: Comment on Howell, Breivik, and Wilcox (2007). Psychological Methods, 12(2), 229–237. doi:10.1037/1082-989X.12.2.229.

    Article  Google Scholar 

  • Banca d’Italia. (2011). Supplements to the statistical Bulletin. Monetary and Financial Indicators. Household Wealth in Italy 2010. New series year XXII—14 December 2011, Vol. 64, pp. 1–30.

  • Banca d’Italia. (2012). Supplements to the statistical Bulletin. Sample Survey. Household Income and Wealth in 2010. New series: Year XXII—25 January 2012, Vol. 6, pp. 1–144.

  • Banco de España. (2011). Survey of household finances (EFF) 2008: Methods, results and changes since 2005. Economic Bulletin, 91–123.

  • Betti, G., Dourmashkin, N., Rossi, M., Verma, V., & Yin, Y. (2001). Study of the problem of consumer indebtedness: Statistical aspects final report. London: ORC Macro.

    Google Scholar 

  • Bollen, K. A. (2007). Interpretational confounding is due to misspecification, not to type of indicator: comment on Howell, Breivik, and Wilcox (2007). Psychological Methods, 12(2), 219–228. doi:10.1037/1082-989X.12.2.219.

    Article  Google Scholar 

  • Bostic, R., Gabriel, S., & Painter, G. (2009). Housing wealth, financial wealth, and consumption: New evidence from micro data. Regional Science and Urban Economics, 39(1), 79–89. doi:10.1016/j.regsciurbeco.2008.06.002.

    Article  Google Scholar 

  • Brown, S., & Taylor, K. (2008). Household debt and financial assets: Evidence from Germany, Great Britain and the USA. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(3), 615–643. doi:10.1111/j.1467-985X.2007.00531.x.

    Article  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21, 230–258.

    Article  Google Scholar 

  • Brunetti, M., Giarda, E., & Torricelli, C. (2012). Is financial fragility a matter of illiquidity? An appraisal for Italian households. CEFIN working papers, Vol. 32, pp. 1–28.

  • Byrne, B. M. (2008). Testing for multigroup equivalence of a measuring instrument: a walk through the process. Psicothema, 20(4), 872–882. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18940097.

  • Carroll, C. D., & Toche, P. (2009). A tractable model of buffer stock saving. NBER working paper no. 15265.

  • Christelis, D., Jappelli, T., Paccagnella, O., & Weber, G. (2009). Income, wealth and financial fragility in Europe. Journal of European Social Policy, 19(4), 359–376. doi:10.1177/1350506809341516.

    Article  Google Scholar 

  • Cobb-Clark, D. A., & Ribar, D. C. (2009). Financial stress, family conflict, and youths’ successful transition to adult roles. IZA discussion paper, Vol. 4618, pp. 1–42.

  • Davidov, E. (2008). A cross-country and cross-time comparison of the human values measurements with the Second Round of the European Social Survey. Survey Research Methods, 2(1), 33–46.

    Google Scholar 

  • Davidov, E., Meuleman, B., Billiet, J., & Schmidt, P. (2008). Values and support for immigration: A cross-country comparison. European Sociological Review, 1–17. doi:10.1093/esr/jcn020.

  • De Jong, M. G., Steenkamp, J.-B. E. M., & Fox, J.-P. (2007). Relaxing measurement invariance in cross- national consumer research using a hierarchical IRT model. Journal of Consumer Research, 34(August), 260–278.

    Article  Google Scholar 

  • Dey, S., Djoudad, R., & Terajima, Y. (2008). A tool for assessing financial vulnerabilities in the household sector. Bank of Canada Review, (Summer 2008), 45–54.

  • Disney, R., Bridges, S., & Gathergood, J. (2008). Drivers of over-indebtedness. Report to the Department for Business, Enterprise and Regulatory Reform, (October), pp. 1–87.

  • Esty, D. C., Levy, M. A., Kim, C., De Sherbinin, A., Srebotnjak, T., & Mara, V. (2008). Environmental performance index. New Haven.

  • Frigge, M., Hoaglin, D. C., & Iglewicz, B. (1989). Some Implementations of the Boxplot. The American Statistician, 43(1), 50–54.

    Google Scholar 

  • Fuenzalida, M., & Ruiz-Tagle, J. (2010). Household financial vulnerability. In R. A. Alfaro (Ed.), Financial stability, monetary policy, and central banking (pp. 299–326). Central Bank of Chile.

  • Georgarakos, D., Lojschova, A., & Ward-Warmedinger, M. (2010). Mortgage, indebtedness and household financial distress. Working paper series, Vol. 1156, pp. 1–59.

  • Giarda, E. (2010). Persistency of financial distress amongst Italian households: Evidence from dynamic probit models.

  • Gomez-Salvador, R., Lojschova, A., & Westermann, T. (2011). Household sector borrowing in the Euro Area. A microdata perspective. Occasional paper series, pp. 1–41.

  • Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework. Medical Care, 44(11), 78–94.

    Article  Google Scholar 

  • Horn, J. L., & Mcardle, J. J. (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research: An International Journal Devoted to the Scientific Study of the Aging Process, 18(3), 117–144.

    Article  Google Scholar 

  • Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12(2), 205–218. doi:10.1037/1082-989X.12.2.205.

    Article  Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

    Article  Google Scholar 

  • Huber, W., Von Heydebreck, A., Sultmann, H., Poustka, A., & Vingron, M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics, 18(Suppl. 1), S96–S104.

    Article  Google Scholar 

  • Jansen, R. (2011). Using differential item functioning approach to investigate measurement invariance. In E. Davidov, P. Schmidt, & J. Billiet (Eds.), Cross-cultural analysis: Methods and applications (pp. 415–432). New York, NY: Routledge, Taylor & Francis Group.

    Google Scholar 

  • Jappelli, T., & Padula, M. (2007). Households’ saving and debt in Italy.

  • Jappelli, T., Pagano, M., & Di Maggio, M. (2008). Households’ indebtedness and financial fragility. 9th Jacques Polak annual research conference (pp. 1–43). Washington, DC.

  • Kahneman, D., & Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences of the United States of America, 107(38), 16489–16493. doi:10.1073/pnas.1011492107.

    Article  Google Scholar 

  • Katona, G. (1946). Psychological analysis of business decisions and expectations. American Economic Review, 36(1), 44–62.

    Google Scholar 

  • Katona, G. (1947). Contribution of psychological data to economic analysis. Journal of the American Statistical Association, 42(239), 449–459.

    Article  Google Scholar 

  • Keese, M. (2012). Who feels constrained by high debt burdens? Subjective vs. objective measures of household debt. Journal of Economic Psychology, 33(1), 125–141. doi:10.1016/j.joep.2011.08.002.

    Article  Google Scholar 

  • Lusardi, A., Schneider, D., & Tufano, P. (2011). Financially fragile households: Evidence and implications. Brookings papers on economic activity (Vol. 42, pp. 1–48). doi:10.1353/eca.2011.0002.

  • May, O., & Tudela, M. (2005). When is mortgage indebtedness a financial burden to British households? A dynamic probit approach. The Bank of England working paper, Vol. 277, pp. 1–42.

  • McCarthy, Y. (2011). Behavioural characteristics and financial distress. Working paper series, Vol. 1303, pp. 1–37.

  • Meredith, W. (1993). MI, factor analysis and factorial invariance. Psychometrika, 58, 525–543.

    Article  Google Scholar 

  • Meredith, William., & Teresi, J. A. (2006). An essay on measurement and factorial invariance. Medical Care, 44(11 Suppl 3), 69–77. doi:10.1097/01.mlr.0000245438.73837.89.

    Article  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (2012). Mplus user’s guide (7th ed., pp. 1–856). Los Angeles, CA: Muthen&Muthen.

  • Onori, D. (2012). Welfare, competition, specialization and growth. Research in Economics, 66(4), 355–370. doi:10.1016/j.rie.2012.07.002.

    Article  Google Scholar 

  • Osberg, L. (Ed.). (1991). Economic inequality and poverty: international perspectives. Armonk, New York: M.E. Sharpe Publishers.

    Google Scholar 

  • Osborne, J. W. (2010). Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research & Evaluation, 15(12).

  • Saisana, M. (2010). ELLI-Index: A sound measure for lifelong learning in the EU. doi:10.2788/145.

  • Van de Vijver, F. J. R. (2011). Capturing bias in structural equation modeling. In E. Davidov, P. Schmidt, & J. Billet (Eds.), Cross-cultural analysis. Methods and applications (pp. 3–34). New York, London: Routledge, Taylor&Francis Group.

  • Worthington, A. C. (2006). Debt as a source of financial stress in Australian households. International Journal of Consumer Studies, 30(1), 2–15.

    Article  Google Scholar 

  • Wu, A. D., Li, Z., & Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factor analysis: A demonstration with TIMSS data. Practical Assessment Research & Evaluation, 12(3), 1–26. Retrieved from http://pareonline.net/pdf/v12n3.pdf.

  • Zhang, L. (2012). Saving and retirement behavior under quasi-hyperbolic discounting. Journal of Economics, 109(1), 57–71. doi:10.1007/s00712-012-0302-8.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Statistics and Demography, Warsaw School of Economics, Ul. Madalinskiego 6/8, 02-513, Warsaw, Poland

    Piotr Bialowolski

  2. Department of Economics, Management and Quantitative Methods, University of Milan, Milan, Italy

    Piotr Bialowolski

  3. Econometrics and Applied Statistics Unit, Institute for the Protection and Security of the Citizen, Joint Research Centre, European Commission, Via E. Fermi 2749, TP 361, 21027, Ispra, VA, Italy

    Dorota Weziak-Bialowolska

Authors
  1. Piotr Bialowolski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dorota Weziak-Bialowolska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dorota Weziak-Bialowolska.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bialowolski, P., Weziak-Bialowolska, D. The Index of Household Financial Condition, Combining Subjective and Objective Indicators: An Appraisal of Italian Households. Soc Indic Res 118, 365–385 (2014). https://doi.org/10.1007/s11205-013-0401-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-013-0401-0

Keywords

Search

Navigation