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Precautionary saving in Spain during the great recession: evidence from a panel of uncertainty indicators

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Abstract

The aim of this paper is to empirically study the effect of uncertainty on private consumption using a sample of Spanish households, and therefore, to test the existence of a precautionary motive for saving. Using data provided by the Spanish Survey of Household Finances and the Labour Force Survey we construct several uncertainty measures that are commonly used in the literature and an additional indicator based on job insecurity data, and we consequently estimate different econometric models under the life-cycle/permanent income hypothesis, including these measures of uncertainty. Our results are twofold: first, we find evidence in favour of the precautionary saving hypothesis. Secondly, we find that, unlike other variables related to the performance of the labour market (such as the unemployment rate) the job insecurity indicator is an appropriate variable to approximate income uncertainty in any macroeconomic context.

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Notes

  1. A detailed review of the existing evidence on precautionary savings, as well as the different econometric approaches and uncertainty proxies can be found in Lugilde et al. (2017).

  2. Usually, the Euler equation includes also the income growth to capture the existence of liquidity constraints or myopia effects of the consumers which consume all their income.

  3. Among papers using macro data we highlight the contributions of, among others, Hahm (1999), Hahm and Steigerwald (1999), Lyhagen (2001), Menegatti (2007, 2010), Mody et al. (2012) or Bande and Riveiro (2013). In the group of papers using micro data good examples are the contributions of Hall and Mishkin (1982), Skinner (1988), Attanasio and Weber (1989), Zeldes (1989a, b), Guiso et al. (1992, 1996), Dynan (1993), Lusardi (1993, 1997, 1998), Carroll (1994), Carroll and Samwick (1997), Kazarosian (1997), Miles (1997), Banks et al. (2001), Guariglia (2001), Guariglia and Kim (2003), Benito (2006), Deidda (2013) and Mastrogiacomo and Alessie (2014).

  4. Table 6, in the Appendix, contains the list of variables used in the model and their description while Table 7 provides a descriptive table of the main characteristics of households in the sample.

  5. Due to the size of our sample, obtaining estimates of permanent income is not entirely correct, ruling out this approach to the subject matter.

  6. The specific question is: “Do you think that in the future your income will be higher, lower or the same as at present?”

  7. See Appendix for definitions of uncertainty measures.

  8. Following Guiso et al. (1992) and Lusardi (1997, 1998) we justify this procedure by the underlying assumption that the variance of household income can be reasonably approximated by the variance of the income of the household reference person.

  9. In particular, the question is: “At present there are people who lose their job due to termination of work contract, dismissal or other reasons. On a scale of 0 to 100, what do you think is the probability that you will lose your job in the next twelve months?”

  10. The variable labour income is constructed from the income data for the reference person in the current year provided by the survey.

  11. The Bank of Spain has collected information about the province of birth and region of residence but this is not reported for confidentiality issues.

  12. Note, however, that to avoid multicollinearity this forces us to drop from the group of control variables the age of the reference person. Also, note the unemployment rate is clustered in a fixed number of groups, which must be taken into account in the estimations to avoid the Moulton or group bias, which can lead to lower standard errors. We therefore use cluster standard errors using a robust covariance matrix.

  13. The EFF also allows for the computation of total wealth, net worth and net financial worth, and therefore we could also opt for the estimation of a wealth equation, adding an uncertainty term. However, this analysis would be out of the scope of the present paper, and is left for future research.

  14. We take the variables in logarithms to eliminate the effect of the different units of measure in which they are expressed.

  15. All variables related to income, wealth, debt and expenditures are expressed in 2011 euros using the Consumer Price Index (CPI) as deflator. To adjust assets and debts to 2011 euros, the data from the EFF2008 have been multiplied by 1.0741. To adjust the household income for the calendar year prior to the survey to 2011 euros, factors were 1.0780 for 2008 and 1.0238 for 2011 (Banco de España 2014).

  16. Although we are only considering employees, the income variable comprises all incomes they declare that have earned in the previous year and not just salary or extra payments received.

  17. These time invariant effects could also affect wealth accumulation, either real of financial, which in turn could introduce potential endogeneity problems with these wealth variables. Thus, the panel estimation with fixed effects also accounts for this potential endogeneity problem. We acknowledge one of the reviewers for this important insight.

  18. The credit constraint variable is a dummy equal to one when the household reports he/she has been denied a loan or has been granted a loan for an amount less than that he/she requested for during the last two years, or that he/she has not applied for a loan on the belief that the application would be turned down.

  19. According to the Bank of Spain, compared to the first quarter of 2009, in the first quarter of 2011 the percentage of Spanish households with any type of financial asset was greater (and the increase in this percentage was higher in the lower half of the wealth distribution). For families with some kind of financial asset, the median value of these assets increased by 23.1%. See Banco de España (2014).

  20. This dummy variable takes value one when households report they are not willing to take on financial risk when they make an investment, zero otherwise.

  21. These results are not reported for brevity but are available from authors upon request.

  22. We also run the regressions for the whole sample, pooling observations of both waves, including interaction terms between time and uncertainty indicators. Results do not change for the uncertainty variables: the significant uncertainty proxies are the objective variables. These results are not reported for brevity, are available from authors upon request.

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Acknowledgements

Comments from other members of the GAME research group, participants at seminars at the University of Naples and at the University of Santiago de Compostela, as well as from Tullio Jappelli, are gratefully acknowledged. Remaining errors are our sole responsibility.

Funding

This research has been partially funded by Xunta de Galicia through grant ED431C 2017/44. Alba Lugilde acknowledges financial support from the Spanish Ministry of Education through the FPU grant FPU13/01940.

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Authors and Affiliations

  1. GAME-IDEGA, Universidade de Santiago de Compostela, IDEGA, Avenida das Ciencias, Chalet n° 1. Campus Vida Santiago de Compostela, 15782, A Coruna, Spain

    Alba Lugilde

  2. Department of Economics, Faculty of Economics and Business Administration, GAME-IDEGA, Universidade de Santiago de Compostela, Avenida do Burgo s/n Santiago de Compostela, 15782, A Coruna, Spain

    Roberto Bande & Dolores Riveiro

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  1. Alba Lugilde
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  2. Roberto Bande
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Correspondence to Alba Lugilde.

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Appendix

Appendix

Tables 6 and 7

Table 6 List of variables used in the model and their description
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Table 7 Sample characteristics
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Lugilde, A., Bande, R. & Riveiro, D. Precautionary saving in Spain during the great recession: evidence from a panel of uncertainty indicators. Rev Econ Household 16, 1151–1179 (2018). https://doi.org/10.1007/s11150-018-9412-6

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