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Generational accounts for Belgium: fiscal sustainability at a glance

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Abstract

This paper uses generational accounts to analyse the long term sustainability of Belgian public finances. We derive age-profiles of detailed tax and expenditure categories from micro data and microsimulation models, and plug them into a long run demographic projection. We assess fiscal long term sustainability under current fiscal and budgetary policy for the base year 2010, and perform simulations of counterfactuals to determine the relative contribution of the most important factors of the long run unsustainability. This update of the generational accounts for Belgium shows that, not unexpectedly, the budgetary situation in Belgium violates the intertemporal budget constraint and hence is unsustainable in the long run. The current level of explicit debt, however, only plays a minor role in explaining this sustainability problem. Ageing and the related increase in age related expenditures are the main drivers of the long run fiscal imbalance and the high level of implicit debt. We disentangle the Belgian generational accounts into their regional components and show that the major explanation for regional differences in generational accounts is not divergent demographic projections, but the wide differences in socio-economic situations, as revealed by the region specific age-profiles.

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Notes

  1. They analyse the long term fiscal sustainability of Spain, Switzerland, Austria, Norway, Germany, France, United Kingdom and the United States.

  2. Cattoir and Docquier (2004) also calculate generational accounts for Belgium for the year 1999. However, they use the methodology for a very different purpose since they try to find a new debt-sharing rule between seceding regions and do not consider the issue of sustainability of public finances.

  3. There are two main differences. First, the projection of non-age related expenditures differs substantially from the generational accounting method and secondly, the OECD method works with a finite time horizon. For a detailed comparison of the OECD method and the Generational accounting methodology, see Benz and Fetzer (2006).

  4. Productivity gains affect both the absolute level of future age-specific tax payments and transfer receipts. However, the purpose of generational accounting is not to forecast these productivity effects. The literature assumes that both age-specific taxes and transfers grow with a constant and time invariant productivity growth rate x.

  5. Note that each specific age profile grows over time according to the constant productivity growth rate x.

  6. In some papers, e.g. Raffelhüschen (1999a, b), these intertemporal public liabilities are presented relative to current GDP. However, we prefer to present liabilities relative to the present value of all future GDP’s, as is done in e.g. studies of the Congressional Budget Office (CBO). This way of representing the sustainability problem allows us to compare sustainability gaps between different countries with other population evolutions. Different population projections influence the future ability of countries to face the sustainability problem. Working with the present value of all future GDP’s takes this caveat into account.

  7. See Decoster et al. (2011) for details on the link between \( FU_{t}^{*} \) and the \( GA_{t,k}^{*} \)’s.

  8. We calculated these growth rates for the following age-groups: 0–11, 12–18, 19–25, 26–45, 46–55 year and 65+.

  9. As a sensitivity analysis, we have checked whether the results change significantly when using a different time interval than 2055–2060. The results remained relatively constant.

  10. The non cyclically adjusted primary deficit was 1 % of GDP in 2010.

  11. This is a simplification, the cyclical component being obviously different for indirect taxes, corporate taxes, or social security contributions. Sensitivity analysis regarding this assumption showed that it had no significant impact on the results.

  12. MIMOSIS stands for Microsimulation Model for Belgian Social Insurance Systems, a microsimulation model developed at the University of Leuven (CES), University of Liège (CREPP) and the University of Antwerp (CSB) in partnership with, and funded by FPS Social Security and Federal Science Policy. The model is based on administrative data for more than 300,000 individuals and 100,000 households from 2001 and is now in use and maintained by FPS Social Security. For more information, see Decoster et al. (2007) or http://www.socialsecurity.fgov.be/.

  13. See Decoster et al. (2009), Decoster et al. (2010), and Sutherland et al. (2009) for details of this extension of EUROMOD.

  14. Using our population projections and assumed real growth, we calculated the average percentage point adjustment for health care expenditure, needed to reproduce the projected evolution of health care cost as a share of GDP by the Ageing Commission. For another study that includes an increased health care cost per capita growth rate, see Hagist et al. (2005).

  15. To avoid health care costs exceeding the level of GDP, we also impose that from 2050 onwards, health care costs again grow in line with productivity growth.

  16. Tables with the decomposition are available from the authors upon request.

  17. Average health-care costs are higher for women around the age of 25, which is explained by pregnancy related expenses. But also higher life expectancy of women translates into more health-care costs than for men.

  18. This is analogous to the arbitrariness of considering 100 % of GDP as an alarming level of debt. When choosing quarterly levels of GDP, the same nominal debt level is expressed as 400 %, and when choosing a two-year period, the debt level will be 50 %. The only value of linking a stock variable to the flow variable GDP, is that it enhances cross country and temporal comparisons with varying GDP’s.

  19. Another, slightly different, indicator consists in the calculation of the immediate and permanent increase in the primary balance in order to return to sustainability. Our results (not displayed in Table 2) show that to return to long term sustainability, the primary balance should be improved permanently with 11.5 percentage points. A similar indicator is frequently used by the European Commission, under the concept of S2-indicator which equals 5.3 for 2009 (European Commission 2009). However, as stated in Benz and Fetzer (2006), the OECD-method that is used by the European Commission differs from the generational accounting framework. This discrepancy might serve as a partial explanation why the S2-indicator differs from the result in our work.

  20. Our results are in line with the ones obtained by Hagist et al. (2009a) who analyse the fiscal stance of Austria in 2005. The generational account of a newborn equals €-130,000, and the future generational account needed to restore sustainability equals €239,900, leading to an intergenerational gap of €369,900.

  21. MEFISTO is a tax benefit calculator which calculates for each household the net disposable income, corresponding to gross income components under given tax and benefit rules. It is an expanded version of the Belgian module of EUROMOD, as developed in the SBO-project “FLEMOSI: A tool for ex ante evaluation of socio-economic policies in Flanders”, funded by IWT Flanders. For more details on the FLEMOSI-project, see www.flemosi.be. For more information on the original EUROMOD-model, see Sutherland (2001) and https://www.iser.essex.ac.uk/euromod/.

  22. EU-SILC is based on a two-step sampling procedure and representative for the Belgian population in private households (persons living in collective households and in institutions are excluded from the target population). The data used here were collected in the second half of 2006 and contain information on income received in 2005. The sample consists of 5,860 households or 14,329 individuals.

  23. In order to assign these people gross wages, an hourly gross wage is essential. However, the EU-SILC does not report an hourly wage for people who are not working at the time of the survey. We therefore estimated and imputed an hourly wage by means of a Heckman selection model (see Decoster and Vanleenhove 2012).

  24. We could produce region specific age profiles for personal income taxes, social security contributions, sickness-disability benefits, unemployment benefits and pensions.

  25. The regional decomposition of expenditures and revenues, as well as the regionalized age profiles can be obtained from the authors upon request.

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Acknowledgments

For useful comments on earlier versions of this paper, we are grateful to Christian Hagist, Stefan Moog, Lionel Artige, Sergio Perelman, Pierre Pestieau, Erik Schokkaert, Guy Van Camp (for the MIMOSIS data), Dirk Verwerft and participants of the EUROMOD workshop at the Baltic International Centre for Economic Policy Studies (BICEPS) in Riga, Latvia October 12–14 2011. We also thank two anonymous referees for their useful remarks. The usual disclaimer applies. Besides financial support from IWT Flanders in the SBO-project “FLEMOSI: A tool for ex ante evaluation of socio-economic policies in Flanders”, we also benefited from funding by Federal Public Service for Science Policy (BELSPO) under contract no. TA/00/39 (BELDEBT).

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Authors and Affiliations

  1. Department of Economics, KULeuven, Naamsestraat 69, 3000, Leuven, Belgium

    André Decoster & Pieter Vanleenhove

  2. CREPP-HEC Université de Liège, Bat. B31 Economie publique appliquée, Boulevard du Rectorat 7, 4000, Liège, Belgium

    Xavier Flawinne

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  1. André Decoster
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  2. Xavier Flawinne
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Correspondence to Pieter Vanleenhove.

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Appendix

Appendix

See Fig. 2; Table 8

Fig. 2
figure 2

Age profiles for Belgium: average benefit, tax or contribution in € per year by age group

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Table 8 Sensitivity results for the Belgian generational accounts in baseline of 2010
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Decoster, A., Flawinne, X. & Vanleenhove, P. Generational accounts for Belgium: fiscal sustainability at a glance. Empirica 41, 663–686 (2014). https://doi.org/10.1007/s10663-013-9223-1

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