The economic and fiscal benefits of guarantee banks in Germany

Abstract

State-backed credit guarantee schemes aim to close the gap in the financing of small enterprises or start-ups caused by lacking collateral and high information asymmetry. The present study discusses the effectiveness of German guarantee banks compared to credit guarantee schemes in other countries and quantifies their economic and fiscal net benefits in the new federal states of Germany, where economic development is still lacking behind. Using data of five guarantee banks and from enterprise and bank surveys, we measure finance and project additionality of loan and equity guarantees provided over the period 1991–2015. Cost-benefit analyses show that the economic benefits of the guarantee banks are considerable because of increased production and employment, while the economic costs are negligible. The real GDP increases by about 1.2 euro per euro guarantee each year. For the years 2008–2014, we find net fiscal gains of several hundred million euros in each federal state.

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Notes

  1. 1.

    Situated in the federal states Berlin, Brandenburg, Mecklenburg Western Pomerania, Saxony-Anhalt, and Thuringia.

  2. 2.

    The target group was extended from sound firms to firms in difficulties, the maximum coverage ratio was increased from 80 to 90%, the guarantee ratio covered by the state was increased from 65 to 80% in the “old” federal states and from 80 to 90% in the “new” federal states, the guarantee volume was increased from 1 million euro to 2 million euro, and the decision paths were shortened.

  3. 3.

    According to the European Commission, a microenterprise, respectively small enterprise is defined as an enterprise which employs fewer than 10 persons, respectively 50 persons and whose annual turnover or balance sheet total does not exceed EUR 2 million, respectively EUR 10 million (Commission of the European Communities 2003, p. 39).

  4. 4.

    Since the survey was conducted by the guarantee bank, possible selection effects cannot be excluded.

  5. 5.

    In our study, we do not differentiate between various forms of credits and guarantees. We assume that they, directly or indirectly, equally stimulate investments (see also Brautzsch et al. 2015, p. 58).

  6. 6.

    Note: For the costs and benefits analysis, we use averages of the survey over all federal states because the sample sizes for the individual states are too small.

  7. 7.

    The results of our firm survey indicate considerable positive effects on the turnover of the firms that received a guarantee credit. Of all responding companies, 88.9% replied that their revenues increased after they invested. Seventy-one percent of the companies link that directly or at least partially to the investment financed by the guaranteed credit. On average, their turnover increased by a factor of 1.5 in the first year after the investment and by a factor of 2 in the last year.

  8. 8.

    Brautzsch et al. (2015) use two structural, dynamic, macroeconometric simultaneous equation models (D*-Model and IWH-Model). Hearts of these models are a combination of a demand side based on the final expenditures compilation of the national accounts and a neoclassical supply side based on a Cobb-Douglas type production function with technological progress. The long-term solution of the model is a neoclassical growth equilibrium (steady state) that is entirely determined by the growth of production factors and technological advancement. However, in the short-run, their production and price levels can diverge from the growth path because of changes in macroeconomic demand or supply owing to delayed price reactions. In the long-run, changes of relative prices ensure a return to the steady state growth path. The steady-state growth path (also known as the production potential) is entirely exogenous in the IWH-Model. In the D*-Modell, it is partially endogenous because the capital stock is incremented with the net-investments (Brautzsch et al. (2015), p. 15–16). The INFORGE model of Schmidt and Elkan (2006) belongs to the class of macroeconomic input-output models. INFORGE’s main difference to the other models is that it explicitly models 59 interdependent sectors of the Germany economy while the D*-Model and IWH-Model only models the aggregated German economy. Furthermore, the INFORGE-Model does not have an exogenous long-term steady-state solution. Its long-term solution is the endogenous result of updating and aggregating its sectorial and macroeconomic values (Schmidt and Elkan (2006), p. 109–111).

  9. 9.

    Using multiplier effects that were estimated for Germany as a whole for some of Germany’s federal states makes the implicit assumption that their economies react in the same way to stimulated investments as the overall Germany economy. Thus, any potential specifics of the economies of these states are neglected.

  10. 10.

    In the D*-Model and INFORGE Model, the additional investments lead to a temporarily higher capital stock and, hence, a temporarily higher production potential. In contrast, the production potential of the IWH-Model is fully exogenous.

  11. 11.

    This approach assumes a constant guarantee coverage ratio and a constant share of windfall effects according to Table 8. Data of the Association of German guarantee banks’ annual reports show that the coverage ratio fluctuates only marginally between 2008 and 2014.

  12. 12.

    This approach assumes that the additional production is equally labor-intensive as the overall GDP and that the additionally employed persons have the same productivity as an average employed person.

  13. 13.

    The default ratio is defined as the guarantee volume honored in a certain year due to credit defaults in relation to the overall outstanding guarantee volume in the same year in percent. It can also be interpreted as the average yearly risk that a guarantee needs to be honored due to a credit default. Assuming an equal distribution of the risk, that risk occurs every year until the credit matures.

  14. 14.

    That assumes a fixed tax-to-GDP ratio. As not all taxes increase proportionally with GDP, that estimate is rather crude.

  15. 15.

    That assumes that the additional employment exhibits the same share of self-employed persons as in the whole population and that the additional employees are paid on average as much as an average employee in the respective federal state.

  16. 16.

    Additional tables are available on request.

  17. 17.

    Data from the German Federal Employment Agency show that the probability of a long-term unemployed person (unemployment of more than 1 year) to start a new job or self-employment within the next month is 1.5%. By contrast, the probability of a short-term unemployed person is considerably higher with 9.9% (Bundesagentur für Arbeit 2015).

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Acknowledgements

Provision of data and funding by the guarantee banks of Berlin, Brandenburg, Mecklenburg-Western Pomerania, Saxony-Anhalt, and Thuringia is gratefully acknowledged.

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Correspondence to Doris Neuberger.

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Hennecke, P., Neuberger, D. & Ulbricht, D. The economic and fiscal benefits of guarantee banks in Germany. Small Bus Econ 53, 771–794 (2019). https://doi.org/10.1007/s11187-018-0069-6

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Keywords

  • Small business finance
  • Loan guarantee schemes
  • Collateral
  • Credit rationing
  • Public guarantees
  • Cost-benefit analysis

JEL classification

  • D61
  • E17
  • G21
  • G28
  • G38
  • H81
  • O16
  • L26