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Debt repayment problems: short-term and long-term implications for spending

Abstract

The paper investigates the economic consequences of financial difficulties. A unique quarterly panel dataset from 2004–2011 from Estonia, a euro area country, makes it possible to estimate the quarterly spending response to debt repayment problems on top of the effect of income and indebtedness. The results imply that problems lead to a substantial short-term drop in spending. Although spending recovers after the debt repayment problems are resolved, the increase is smaller than the original decline and spending remains at a lower level than before the problems emerged. An important finding is that the longer the problems last, the more severe the decline in spending is. The results suggest that the experience of debt repayment problems has severe long-term economic implications, a cost that should be taken into account when the consequences of indebtedness are assessed.

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Notes

  1. The statistics are available from Eurostat http://ec.europa.eu/eurostat/data/database (ilc_mdes06).

  2. Eq. (1) combines two Euler equations, a one-period Euler equation and a two-period one.

  3. The total number of private customers was 580,000 in 2004 which is 44 per cent of the total population of Estonia (http://www.seb.ee/files/aruanded/aastaraamat_2004_eng.pdf). However, a substantial share of the customers are not active customers of the financial institution; the sample for the analysis is selected from the active customers.

  4. The statistics on loans overdue 60 days are available from the Bank of Estonia, https://www.eestipank.ee/en/statistics, Table 3.3.11.

  5. The obligation to make regular debt repayments is linked to one sight account of an individual and if the balance of this particular sight account is zero at the time of repayment, the financial institution cannot debit the payments but instead creates a flag for the problem. The other accounts of the individual are neither debited nor flagged for temporary repayment delays. More action, such as revising credit conditions, rescheduling loans, or reporting to a credit bureau, is taken once the problems persist.

  6. The differences between the dataset and the EU-SILC emerge from several sources. First, the EU-SILC provides data for households, while individual level data are used in the dataset, and the prevalence of arrears among individuals is lower than it is among households. Second, in the EU-SILC households report arrears which occurred in the last 12 months, while the dataset records the current status of debt, suggesting that the prevalence of arrears is higher in the EU-SILC than in the dataset. Finally, it is not possible to distinguish between debt repayment problems for mortgages and those for other types of loans in the dataset, while the EU-SILC reports arrears on mortgages or rent, meaning the content of the arrears is different and is not comparable one-to-one.

  7. The dataset contains individuals who are considered to be regular bank clients, which means they have income transferred to their sight account regularly. The definition of a regular bank client has been provided by the financial institution.

  8. Although Nickell (1981) finds that the fixed effects estimations are biased when a dynamic model is used, Monte Carlo simulations show that the bias of the estimated AR coefficient is marginal and the bias of the estimated coefficients for other explanatory variables is almost non-existent when the autoregressive coefficient is below 0.2 (Judson and Owen 1999). We considered estimating the model without the lagged dependent variable as the inclusion of the variable does not affect the results, but as we control for lags of other explanatory variables, the lagged dependent variable has been included for consistency.

  9. They use the Estonian Household Budget Survey to investigate the consumption response to income shocks of different persistence and find that total consumption reacts to income shocks by 0.3–0.4 depending on the persistence of the income shock.

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Acknowledgements

The author would like to thank Lennart Kitt for his help with the database, Tansel Yilmazer, the co-editor of REHO, and two anonymous referees, Karsten Staehr, Jens Hölscher, Liina Malk, Anastasia Litina, Nhung Luu and Tairi Rõõm, SAEe 2015, EMS 2016, and SMYE2016 conference participants, BCB, Bundesbank PHF and 4th Lu-HFC Workshop participants, for their useful comments. Support from Base Financing grant no. B45/2015 and B57/2016 is acknowledged.

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Correspondence to Merike Kukk.

Appendix

Appendix

Figure 9 and Tables 27

Fig. 9
figure 9

The prevalence of arrears in the total population in the EU-SILC and the dataset from 2004 to 2011

Table 2 Definitions of all the variables used in the empirical model with summary statistics
Table 3 Spending model with dependent variable log cit. Parameter estimates for the dummy of debt repayment problems
Table 4 Estimations for all the explanatory variables in the baseline model. Dependent variable: log cit
Table 5 Robustness test of the estimated coefficient of debt repayment problems in the baseline model. Dependent variable: log cit
Table 6 The estimated coefficient of debt repayment problems in the baseline model for different sub-samples
Table 7 Parameter estimates for the dummy of falling into debt repayment problems. Dependent variable:log cit

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Kukk, M. Debt repayment problems: short-term and long-term implications for spending. Rev Econ Household 17, 715–740 (2019). https://doi.org/10.1007/s11150-018-9424-2

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  • DOI: https://doi.org/10.1007/s11150-018-9424-2

Keywords

  • Debt repayment problems
  • Spending
  • Indebtedness
  • Borrowing constraints
  • Duration of arrears

JEL codes

  • D12
  • D14
  • E21
  • G21