Skip to main content
SpringerLink
Log in

Enterprise risk management and economies of scale and scope: evidence from the German insurance industry

  • S.I.: Recent Developments in Financial Modeling and Risk Management
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Enterprise risk management (ERM) is the approach of managing all risks faced by an enterprise in an integrated, holistic fashion. This research investigates whether the utilization of the ERM approach helps firms achieve economies of scale and scope. We use detailed survey data of German property-liability insurance companies that allows us to construct continuous measures of ERM quality. We find that ERM quality positively moderates the size-scale efficiency relationship, and we find that ERM positively moderates the diversification-revenue scope efficiency relationship, indicating that ERM facilitates economies of scale and economies of scope with respect to revenue complementarities. We do not find any evidence of economies of scope with respect to cost complementarities. Our results suggest that ERM’s impact on economies of scale and scope is one answer to the question of how ERM can create value.

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

Similar content being viewed by others

Notes

  1. One exception is Berry-Stölzle and Xu (2018) who examine whether the ERM approach reduces firms’ cost of external financing. They document that ERM adoption significantly decreases the cost of equity capital for U.S. publicly-traded insurance companies.

  2. We use the Central Limit Theorem to make a conceptual argument. We also highlight that distributions in practice can be highly skewed resulting in aggregate profit distributions that may approach normality very slowly. Therefore, actual capital requirements for firms may be substantially higher than the capital requirements predicted by the normal distribution.

  3. The KIVI GmbH analyzes financial statements of German insurance companies (see http://www.kivi-online.de) and publishes firm-level performance measures in an annual report (see, e.g., KIVI GmbH 2018).

  4. An empirical research study should use a dataset that allows a sound test of the research question. To answer our research question whether ERM helps firms achieve economies of scale and scope, we examine the time period during which the first half of the property-liability insurance companies in Germany adopt an ERM program (1999–2009). We argue that this time period is well suited for our analysis.

  5. The telephone interviews were conducted with the Chief Risk Officer, his assistant, or an individual named by the CFO or his assistant. The survey was sent to the company in advance and they were asked to collect the information prior to the interview. Surprisingly, in many cases, the Chief Risk Officer himself spent about 1 h on the phone discussing the firm’s risk management approach. If the interviewer had the impression that a question was misunderstood or if the participant could not answer a question based on the information he had collected prior to the interview, there was either a follow-up phone call or email conversation to clarify the open points. This process ensured that the survey reflects the combined institutional knowledge of the firms rather than the memory of one individual. Participants received a summary report including descriptive statistics of the survey results in exchange for their time. The interviewer had the impression that participants were very interested in the topic and how the industry as a whole is implementing ERM, creating incentives to participate in the survey.

  6. The survey design was based on the components of the risk management process. Representatives of the German insurance industry participated in informal discussions during the design stage of the survey, and questions on additional organizational details were included when relevant. The original German questionnaire as well as an English translation is available upon request.

  7. The complete set of descriptive statistics as well as a univariate analysis of survey responses can be found in Altuntas et al. (2011).

  8. We also eliminate companies with zero equity capital, reducing the sample by only two observations.

  9. To minimize the impact of outliers, we winsorize outputs, inputs, and prices used in the Data Envelopment Analysis (DEA) at the 1st and 99th percentiles; two exceptions are the labor cost index for insurance business, and the two- to three-year German treasury bill rate.

  10. We adopt the DEA approach because it has attractive statistical properties for our project. First, as shown in Banker (1993), DEA is equivalent to a maximum likelihood estimation. Second, DEA estimators are consistent and converge faster than estimators from other frontier methods (Grosskopf 1996). Third, DEA estimators are also unbiased if we assume that there is no underlying model or reference technology. If one believes in an underlying model, then the problem of bias in DEA estimates arises, but this bias decreases with sample size (Kittelsen 1999). Fourth, Banker and Natarajan (2008) show that DEA is a non-parametric stochastic frontier estimation methodology that performs better than parametric procedures in the estimation of individual decision making unit productivity. Finally, Banker and Natarajan (2008) also show that the two-stage approach utilized here (DEA followed by regressions) is statistically consistent in a composed error framework, i.e., that DEA (like SFA) incorporates random errors.

  11. The efficiency scores vary between 0 and 1, with efficiencies equal to 1 for fully efficient firms and efficiencies between 0 and 1 for inefficient firms.

  12. Note that cost frontiers are derived by minimizing inputs whereas revenue frontiers are derived by maximizing outputs. Scale efficiency can be calculated with both an input- and an output-orientation. We use the standard input-oriented scale efficiency scores in this paper with one exception: When purging revenue efficiency of its scale component, we divide by output-oriented scale efficiency scores.

  13. Actual or optimal profits are not used as the denominator because actual profits can be negative and optimal profits can be zero. The use of the sum of costs and revenues as the denominator is somewhat arbitrary but has become the standard approach in the DEA profit efficiency literature (Cummins and Weiss 2013).

  14. The German accounting standard does not require insurers to report losses incurred net of reinsurance by line of business. German insurers only report gross losses incurred by business line. We, hence, approximate losses incurred net of reinsurance using the following steps: First, we add gross losses incurred from liability insurance and auto liability to get gross losses incurred in long-tail lines. Second, we add gross losses incurred from all other lines to get gross losses incurred from short-tail lines. Third, we multiply gross losses incurred in short-tail and long-tail lines with the ratio of total losses incurred net of reinsurance to total gross losses incurred. Fourth, we compute the present value of the approximated losses incurred net of reinsurance. Loss payouts for each line are calculated with data from the German Federal Financial Supervisory Authority (BaFin) using the chain-ladder method. Losses are discounted using German Treasury yield curves from the Deutsche Bundesbank. This four step approximation is accurate if the fraction of reinsured losses is the same for long-tail lines, short-tail lines and the total insurance portfolio. We examine this assumption with aggregate industry-level data and find that the fraction of reinsured losses for long-tail and short-tail lines is close to the fraction of total reinsured losses for all sample years.

  15. In our study, we follow the business lines classification suggested by the German Insurers Association (Gesamtverband der Deutschen Versicherungswirtschaft or GDV) in its publication GDV (2005). Long-tail lines include liability insurance and auto liability. We cannot distinguish personal lines from commercial lines because the German accounting standard does not require insurance companies to make this distinction.

  16. The rate of return on the realized investment income is calculated by dividing the realized investment income for the year by the average of beginning and end-of-year total invested assets.

  17. We calculate the expected return on equity as the predicted value of the ratio of net income before taxes to book value surplus. The prediction is based on a pooled cross-sectional time-series regression of the return on equity variable on the following independent variables capturing insurer characteristics: The percentage of stocks in the investment portfolio, the percentage of bonds in the investment portfolio, the insurance output quantities, the premiums-to-surplus ratio, the intermediate-term government bond yield, and year dummies.

  18. Technical provisions net of reinsurance is the terminology used in Germany for insurance reserve liabilities.

  19. We follow Cummins et al. (2004) and Cummins and Rubio-Misas (2006) and use treasury bill rates as the price of the debt capital input. The price should be an annualized interest rate with maturity equal to the effective duration of the insurers’ liabilities and reflecting the insurers’ credit quality (Cummins et al. 2009). We view the two- to three-year German Treasury Bill rates as reasonable proxies of the price of debt capital in the context of our analysis.

  20. We use the entropy measure of diversification for conceptual reasons. At the core of ERM is the idea that a risk management program is only as strong as its weakest part. Therefore, ERM takes a holistic, enterprise-wide approach to risk management. The entropy measure is capturing this basic concept well as the following example highlights: Firm A and B both have three ERM components. The quality of the three components for Firm A is 0.2, 0.4 and 0.4, respectively. The quality of Firm B’s ERM components is 0.3, 0.3 and 0.3, respectively. Firm B’s weakest component has a higher quality than Firm A’s weakest component (0.3 > 0.2). The entropy measure we use in this study assigns Firm B the value 1.099 which is higher than the 1.055 assigned to Firm A. However, the average ERM quality index leads to the opposite result, assigning Firm A a higher value than Firm B (0.333 > 0.300).

  21. The lines of business included in the calculation are: personal accident, personal liability, total auto, legal expenses, fire, homeowners’ personal property, residential and commercial building damage, transportation, credit and other miscellaneous business.

  22. The lines of business included are: personal accident, personal liability, total auto, legal expenses, fire, homeowners’ personal property, residential and commercial building damage, and transportation. The omitted category is the aggregate of credit insurance and other miscellaneous business.

  23. A detailed description of the approach can be found in Berry-Stölzle et al. (2012, p. 390ff.). The main steps of the approach are as follows: (1) Take pairs of business lines and count the number of insurance companies writing both lines. (2) Adjust the raw counts for random combinations. (3) Adjust the counts for economic importance, using the fraction of premiums written as weights. (4) Solve a shortest path algorithm to fill in relatedness scores for combinations of business lines that have not been observed. (5) Transform the scores to percentile ranks. (6) Perform the previous steps for each year in the dataset separately and calculate average scores across years. The outcomes are stable relatedness scores between pairs of business lines.

  24. Firms with an unrelated diversification score close to 1 exhibit relatively high levels of unrelated diversification; whereas firms with an unrelated diversification score close to 0 only diversify in related business lines or do hardly diversify at all. Undiversified firms, by definition, have an unrelated diversification score of 0.

  25. We also estimate Eq. (9) with an alternative diversification measure. This alternative measure is an indicator variable which takes on the value 1 if an insurance company writes business in more than three lines, and 0 otherwise. Our results are robust to this alternative model specification.

References

  • Ai, J., Bajtelsmit, V. L., & Wang, T. (2018). The Combined Effect of Enterprise Risk Management and Diversification on Property and Casualty Insurer Performance. Journal of Risk and Insurance, 85(2), 513–543.

    Google Scholar 

  • Ai, J., Chen, H., & Zhao, Y. (2014). Does ERM improve firm value? Evidence from listed Chinese nonfinancial SOEs. Fox School of Business Research Paper No. 15-068, Temple University, Philadelphia, PA.

  • Altuntas, M., Berry-Stölzle, T. R., & Hoyt, R. E. (2011). Implementation of enterprise risk management: Evidence from the German Property-Liability Insurance Industry. The Geneva Papers on Risk and Insurance—Issues and Practice, 36, 414–439.

    Google Scholar 

  • Altuntas, M., Berry-Stölzle, T. R., & Hoyt, R. E. (2015). Dynamic determinants of enterprise risk management adoption: Evidence from the Property-Liability Insurance Industry. Working paper, University of Iowa.

  • Banker, R. D. (1993). Maximum likelihood, consistency, and data envelopment analysis: A statistical foundation. Management Science, 39, 1265–1273.

    Google Scholar 

  • Banker, R. D., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56, 48–58.

    Google Scholar 

  • Barney, J. B. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120.

    Google Scholar 

  • Beasley, M. S., Clune, R., & Hermanson, D. R. (2005). Enterprise risk management: An empirical analysis of factors associated with the extent of implementation. Journal of Accounting and Public Policy, 24, 521–531.

    Google Scholar 

  • Berger, A. N., Cummins, J. D., Weiss, M. A., & Zi, H. (2000). Conglomeration versus strategic focus: Evidence from the insurance industry. Journal of Financial Intermediation, 9, 323–362.

    Google Scholar 

  • Berger, P. G., & Ofek, E. (1995). Diversifications’ effect on firm value. Journal of Financial Economics, 37, 39–65.

    Google Scholar 

  • Berry-Stölzle, T. R., Liebenberg, A. P., Ruhland, J. S., & Sommer, D. W. (2012). Determinants of corporate diversification: Evidence from the Property-Liability Insurance Industry. Journal of Risk and Insurance, 79(2), 381–413.

    Google Scholar 

  • Berry-Stölzle, T. R., & Xu, J. (2018). Enterprise risk management and the cost of capital. Journal of Risk and Insurance, 85(1), 159–201.

    Google Scholar 

  • Bryce, D. J., & Winter, S. G. (2009). A general interindustry relatedness index. Management Science, 55(9), 1570–1585.

    Google Scholar 

  • Colquitt, L. L., Hoyt, R. E., & Lee, R. B. (1999). Integrated risk management and the role of the risk manager. Risk Management and Insurance Review, 2, 43–61.

    Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive test with models, applications, references, and DEA-Solver Software. Boston: Kluwer.

    Google Scholar 

  • COSO (Committee of Sponsoring Organizations of the Treadway Commission). (2004). COSO Enterprise Risk Management—Integrated Framework. New York: American Institute of Certified Public Accountants.

    Google Scholar 

  • COSO (Committee of Sponsoring Organizations of the Treadway Commission). (2017). Enterprise risk management—Integrating with strategy and performance. New York: American Institute of Certified Public Accountants.

    Google Scholar 

  • Cummins, J. D., Dionne, G., Gagné, R., & Nouira, A. H. (2009). Efficiency of insurance firms with endogenous risk management and financial intermediation activities. Journal of Productivity Analysis, 32(2), 145–159.

    Google Scholar 

  • Cummins, J. D., & Rubio-Misas, M. (2006). Deregulation, consolidation, and efficiency: Evidence from the Spanish Insurance Industry. Journal of Money, Credit, and Banking, 38(2), 323–355.

    Google Scholar 

  • Cummins, J. D., Rubio-Misas, M., & Zi, H. (2004). The effect of organizational structure on efficiency: Evidence from the Spanish Insurance Industry. Journal of Banking & Finance, 28(12), 3113–3150.

    Google Scholar 

  • Cummins, J. D., & Weiss, M. A. (2013). Analyzing firm performance in the insurance industry using frontier efficiency and productivity methods. In G. Dionne (Ed.), Handbook of insurance (2nd ed.). New York: Springer.

    Google Scholar 

  • Cummins, J. D., Weiss, M. A., Xie, X., & Zi, H. (2010). Economies of scope in financial services: A DEA efficiency analysis of the US Insurance Industry. Journal of Banking & Finance, 34(7), 1525–1539.

    Google Scholar 

  • Cummins, J. D., Weiss, M. A., & Zi, H. (1999). Organizational form and efficiency: The coexistence of stock and mutual property-liability insurers. Management Science, 45, 1254–1269.

    Google Scholar 

  • Daud, W. N. W., Haron, H., & Ibrahim, D. N. (2011). The role of quality board of directors in enterprise risk management (ERM) practices: Evidence from binary logistic regression. International Journal of Business and Management, 6(12), 205–211.

    Google Scholar 

  • Doumpos, M., Guyot, A., Galariotis, E., & Zopounidis, C. (2018). Assessing the quality of life in French municipalities: A multidimensional approach. Annals of Operations Research. https://doi.org/10.1007/s10479-018-3068-8.

    Article  Google Scholar 

  • Eckles, D. L., Hoyt, R. E., & Miller, S. M. (2014). The impact of enterprise risk management on the marginal cost of reducing risk: Evidence from the Insurance Industry. Journal of Banking & Finance, 43, 247–261.

    Google Scholar 

  • Emrouznejad, A. (2014). Advances in data envelopment analysis. Annals of Operations Research, 241(1), 1–4.

    Google Scholar 

  • Farrell, M., & Gallagher, R. (2015). The valuation implications of enterprise risk management maturity. Journal of Risk and Insurance, 82, 625–657.

    Google Scholar 

  • GDV (The German Insurers). (2005). Discussion paper on a standard modeling approach within the Solvency II framework (Pillar I)—Model description (version 1.0), Berlin.

  • Gordon, L., Loeb, M., & Tseng, C. (2009). Enterprise risk management and firm performance: A contingency perspective. Journal of Accounting and Public Policy, 28(4), 301–327.

    Google Scholar 

  • Grace, M. F., Leverty, J. T., Phillips, R. D., & Shimpi, P. (2015). The value of investing in enterprise risk management. Journal of Risk and Insurance, 82, 289–316.

    Google Scholar 

  • Grosskopf, S. (1996). Statistical inference and nonparametric efficiency: A selective survey. Journal of Productivity Analysis, 7, 161–176.

    Google Scholar 

  • Harrington, S. E., & Niehaus, G. R. (2003). Risk management and insurance (2nd ed.). New York: McGraw Hill.

    Google Scholar 

  • Hill, C. W. L., Hitt, M. A., & Hoskisson, R. E. (1992). Cooperative versus competitive structures in related and unrelated diversified firms. Organization Science, 3(4), 501–521.

    Google Scholar 

  • Hou, C., Lu, W., & Hung, S. (2017). Does CSR matter? Influence of corporate social responsibility on corporate performance in the creative industry. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2626-9.

    Article  Google Scholar 

  • Hoyt, R. E., & Liebenberg, A. P. (2011). The value of enterprise risk management. Journal of Risk and Insurance, 78, 795–822.

    Google Scholar 

  • Kaffash, S., & Marra, M. (2017). Data envelopment analysis in financial services: A citation network analysis of banks, insurance companies and money market funds. Annals of Operations Research, 253(1), 307–344.

    Google Scholar 

  • Kittelsen, S. (1999). Monte Carlo simulations of DEA efficiency measures and hypothesis tests, memorandum 09/99, Department of Economics, University of Oslo, Oslo, Norway.

  • KIVI GmbH. (2018). Zahlenband 2017 Schadenversicherung, electronic publication. http://www.kivi-online.de/prod_zbd_standard.html.

  • Kleffner, A. E., Lee, R. B., & McGannon, B. (2003). The effect of corporate governance on the use of enterprise risk management: Evidence from Canada. Risk Management and Insurance Review, 6, 53–73.

    Google Scholar 

  • Liebenberg, A. P., & Hoyt, R. E. (2003). Determinants of enterprise risk management: Evidence from the appointment of Chief Risk Officers. Risk Management and Insurance Review, 6, 37–52.

    Google Scholar 

  • Liu, X., Sun, J., Yang, F., & Wu, J. (2018). How ownership structure affects bank deposit and loan efficiencies: An empirical analysis of chinese commercial banks. Annals of Operations Research. https://doi.org/10.1007/s10479-018-3106-6.

    Article  Google Scholar 

  • Milliman, (2012). Enterprise risk management: How are companies gaining value from their ERM strategies?. Chicago, IL: Milliman.

    Google Scholar 

  • Ouenniche, J., & Carrales, S. (2018). Assessing efficiency profiles of UK Commercial Banks: A DEA analysis with regression-based feedback. Annals of Operations Research, 266(1–2), 551–587.

    Google Scholar 

  • Pagach, D., & Warr, R. (2011). The characteristics of firms that Hire Chief Risk Officers. Journal of Risk and Insurance, 78, 185–211.

    Google Scholar 

  • Palepu, K. (1985). Diversification strategy, profit performance, and the entropy measure. Strategic Management Journal, 6, 239–255.

    Google Scholar 

  • Panzar, J. C., & Willig, R. D. (1977). Economies of scale in multi-output production. Quarterly Journal of Economics, 91, 481–493.

    Google Scholar 

  • Panzar, J. C., & Willig, R. D. (1981). Economies of scope. American Economic Review, 71, 268–272.

    Google Scholar 

  • Seiford, L. M. (1997). A bibliography for data envelopment analysis (1978–1996). Annals of Operations Research, 73, 393–438.

    Google Scholar 

  • Stein, J. C. (1997). Internal capital markets and the competition for corporate resources. Journal of Finance, 52(1), 111–133.

    Google Scholar 

  • Teece, D. J. (1980). Economies of scope and the scope of the enterprise. Journal of Economic Behavior & Organization, 1, 223–247.

    Google Scholar 

  • Tziogkidis, P., Matthews, K., & Philippas, D. (2018). The effects of sector reforms on the productivity of Greek Banks: A step-by-step analysis of the pre-Euro Era. Annals of Operations Research, 266(1–2), 531–549.

    Google Scholar 

  • Vidal-García, J., Vidal, M., Boubaker, S., & Hassan, M. (2018). The efficiency of mutual funds. Annals of Operations Research, 267(1–2), 555–584.

    Google Scholar 

  • Wernerfelt, B. (1984). A Resource-Based View of the Firm. Strategic Management Journal, 5(2), 171–180.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Risk Management and Insurance, University of Cologne, Albertus-Magnus-Platz, 50923, Cologne, Germany

    Muhammed Altuntas

  2. Henry B. Tippie College of Business, University of Iowa, 108 John Pappajohn Business Bldg, Iowa City, IA, 52242-1994, USA

    Thomas R. Berry-Stölzle

  3. Temple University, 617 Alter Hall, 1801 Liacouras Walk, Philadelphia, PA, 19122, USA

    J. David Cummins

Authors
  1. Muhammed Altuntas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas R. Berry-Stölzle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. David Cummins
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas R. Berry-Stölzle.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: ERM components from survey

Appendix: ERM components from survey

The average ERM quality index and the ERM entropy measure are based on the following seven components of an ERM system:

  1. 1.

    Identification of firm’s risk appetite = (targetrating + riskstrategy)/2

    • targetrating = indicator variable equal to 1 if company determines how much risk they are willing to assume (e.g., in the form of an acceptable ruin probability or a target rating), 0 otherwise.

    • riskstrategy = indicator variable equal to 1 if the company has a risk strategy (in the context of the overall corporate strategy), which defines how risks should be handled, 0 otherwise.

  2. 2.

    Risk aggregation = (investrisk + liquidrisk + underwrisk + concentrisk + catastrisk + operatrisk + stratrisk + reputrisk + corrmodel + opulas)/10

    • investrisk = indicator variable equal to 1 if the company’s aggregate risk model includes investment risk, 0 otherwise.

    • liquidrisk = indicator variable equal to 1 if the company’s aggregate risk model includes liquidity risk, 0 otherwise.

    • underwrisk = indicator variable equal to 1 if the company’s aggregate risk model includes underwriting risks in different business lines, 0 otherwise.

    • concentrisk = indicator variable equal to 1 if the company’s aggregate risk model includes concentration risk, 0 otherwise.

    • catastrisk = indicator variable equal to 1 if the company’s aggregate risk model includes catastrophe claim risk, 0 otherwise.

    • operatrisk = indicator variable equal to 1 if the company’s aggregate risk model includes operational risk, 0 otherwise.

    • stratrisk = indicator variable equal to 1 if the company’s aggregate risk model includes strategic risk, 0 otherwise.

    • reputrisk = indicator variable equal to 1 if the company’s aggregate risk model includes reputation risk, 0 otherwise.

    • corrmodel = indicator variable equal to 1 if company models correlations between individual risks in the risk aggregation process, 0 otherwise.

    • copulas = indicator variable equal to 1 if company uses copulas (non-linear correlations) to model dependencies in the risk aggregation process, 0 otherwise.

  3. 3.

    Risk capital allocation = (units + risktypes + regions + products + investrisk + liquidrisk + underwrisk + concentrisk + catastrisk + operatrisk + stratrisk + reputrisk + diversification)/13

    • units = indicator variable equal to 1 if company allocates risk capital to more than one business unit/department, 0 otherwise.

    • risktypes = indicator variable equal to 1 if company allocates risk capital to more than one type of risk, 0 otherwise.

    • regions = indicator variable equal to 1 if company allocates risk capital to more than one region, 0 otherwise.

    • products = indicator variable equal to 1 if company allocates risk capital to more than one product, 0 otherwise.

    • investrisk = indicator variable equal to 1 if company considers investment risk in the capital allocation process, 0 otherwise.

    • liquidrisk = indicator variable equal to 1 if company considers liquidity risk in the capital allocation process, 0 otherwise.

    • underwrisk = indicator variable equal to 1 if company considers underwriting risks in different business lines in the capital allocation process, 0 otherwise.

    • concentrisk = indicator variable equal to 1 if company considers concentration risk in the capital allocation process, 0 otherwise.

    • catastrisk = indicator variable equal to 1 if company considers catastrophe claim risk in the capital allocation process, 0 otherwise.

    • operatrisk = indicator variable equal to 1 if company considers operational risk in the capital allocation process, 0 otherwise.

    • stratrisk = indicator variable equal to 1 if company considers strategic risk in the capital allocation process, 0 otherwise.

    • reputrisk = indicator variable equal to 1 if company considers reputation risk in the capital allocation process, 0 otherwise.

    • diversification = indicator variable equal to 1 if company’s capital allocation procedure takes portfolio or diversification effects into account, 0 otherwise.

  4. 4.

    Performance measurement = (measure + divisions + perfimpact)/3

    • measure = indicator variable equal to 1 if company measures business success with one or several (performance-)measure(s), 0 otherwise.

    • divisions = indicator variable equal to 1 if company uses performance measurement for divisions/departments within the company, 0 otherwise.

    • perfimpact measures to what degree assumed risks influence measured performance in the company. The original survey response is on a seven-point Likert scale (from 1 = no influence to 7 = very strong influence); the perfimpact variable is standardized to be 1 for the scale value 7.

  5. 5.

    Incentive contracts = (compensation + compimpact)/2

    • compensation = indicator variable equal to 1 if manager compensation depends on performance measures, 0 otherwise.

    • compimpact measures to what degree assumed risks have an impact on manager compensation. The original survey response is on a seven-point Likert scale (from 1 = no impact to 7 = very strong impact); the compimpact variable is standardized to be 1 for the scale value 7.

  6. 6.

    Risk management culture = (employees + training + intranet + system + suggestion + decision + strategy)/7

    • employees measures to what degree “employees are familiar with the risk management concept.” The original survey response is on a seven-point Likert scale (from 1 = no impact to 7 = very strong impact); the employees variable is standardized to be 1 for the scale value 7.

    • training measures to what degree “in-house training addresses risk management.” The original survey response is on a seven-point Likert scale (from 1 = no impact to 7 = very strong impact); the training variable is standardized to be 1 for the scale value 7.

    • intranet measures to what degree an intranet platform is used to support risk management. The original survey response is on a seven-point Likert scale (from 1 = no impact to 7 = very strong impact); the intranet variable is standardized to be 1 for the scale value 7.

    • system measures to what degree “there is an employee suggestion system on risk management.” The original survey response is on a seven-point Likert scale (from 1 = no impact to 7 = very strong impact); the system variable is standardized to be 1 for the scale value 7.

    • suggestion measures to what degree “employees’ suggestions related to risk management are considered.” The original survey response is on a seven-point Likert scale (from 1 = no impact to 7 = very strong impact); the suggestion variable is standardized to be 1 for the scale value 7.

    • decision measures to what degree “employees consider risks in their decisions.” The original survey response is on a seven-point Likert scale (from 1 = no impact to 7 = very strong impact); the decision variable is standardized to be 1 for the scale value 7.

    • strategy = indicator variable equal to 1 if company has a strategy for risk management culture, 0 otherwise.

  7. 7.

    Audit = (independent + right + legal + corporate + quality + data + efficiency)/7

    • independent = indicator variable equal to 1 if company’s risk management process is evaluated by an independent department (e.g. internal audit), 0 otherwise.

    • right = indicator variable equal to 1 if company’s internal audit function has the unrestricted right to conduct evaluations, 0 otherwise.

    • legal = indicator variable equal to 1 if company evaluates the compliance with legal and regulatory requirements, 0 otherwise.

    • corporate = indicator variable equal to 1 if company evaluates the compliance with corporate guidelines and policies, 0 otherwise.

    • quality = indicator variable equal to 1 if company evaluates the quality of the risk management process, 0 otherwise.

    • data = indicator variable equal to 1 if company evaluates the relevance and quality of the data used in the risk management process, 0 otherwise.

    • efficiency = indicator variable equal to 1 if company evaluates the efficiency of the risk management process, 0 otherwise (Table 9).

      Table 9 Relatedness scores
      Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altuntas, M., Berry-Stölzle, T.R. & Cummins, J.D. Enterprise risk management and economies of scale and scope: evidence from the German insurance industry. Ann Oper Res 299, 811–845 (2021). https://doi.org/10.1007/s10479-019-03393-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-019-03393-x

Keywords

Search

Navigation