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Systemic importance of financial services and insurance sectors: a world input–output network analysis

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Abstract

This study examines the systemic importance of the financial services sector in the global input–output network between 2000 and 2014. To measure the systemic importance of the financial services and insurance sectors of 13 major economies with the Financial Stability Board identified Globally Systemically Important Banks and Globally Systemically Important Insurers, we construct a local and global shock contagion index using the partial extraction technique in input–output analysis. We demonstrate how local supply reductions in the insurance or other financial services sectors can have a global spillover effect on GDP. We also find that the systemic importance of the financial services and insurance sectors varies over time depending on the size and structure of the economy in different economies (such as the U.S. and China). Additionally, we investigate the impact of simultaneous supply shocks that have originated in the insurance and other financial services (e.g., banking) sectors of different economies. Empirical evidence suggests that simultaneous supply shocks in the financial services sectors in certain economies could contribute to, at least a large portion of, economic downturns, as observed during the 2008 Global Financial Crisis.

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Notes

  1. Note that the FSB has identified G-SIBs since 2011 in consultation with the Basel Committee on Banking Supervision (BCBS) and national authorities. The list of G-SIBs is updated on an annual basis and is based on the methodology published by the BCBS. For the insurance sector, the FSB began to identify G-SIIs in 2013 in consultation with the International Association of Insurance Supervisors (IAIS) and national authorities. The list of G-SIIs was updated on an annual basis until 2016. No new lists of G-SIIs were published in 2017 and 2018 because the IAIS was developing a holistic framework for the assessment and mitigation of systemic risk in the insurance sector from 2017. Eventually, the IAIS published its holistic assessment framework (IAIS 2019a) in November 2019. The holistic framework includes supervisory policy measures, a global monitoring exercise, and implementation assessment activities. In the holistic framework, the notion of G-SII was partially replaced by the concept of an internationally active insurance group (IAIG). The FSB suspended the identification of G-SIIs at the beginning of 2020 due to the adoption of the holistic framework. The FSB will further review the need to either discontinue or re-establish an annual identification of G-SIIs in November 2022. The lists of G-SIBs and G-SIIs can be found at the FSB website (https://www.fsb.org/work-of-the-fsb/market-and-institutional-resilience/post-2008-financial-crisis-reforms/ending-too-big-to-fail/global-systemically important-financial-institutions-g-sifis/). The FSB (2021) summarizes the regulatory framework reform for the banking and insurance sectors over the past 10 years.

  2. The adoption of the GHE model in this study is a tradeoff between plausible model assumptions and model complexity. We assume that, in the short-run, the intermediate supply of the target industry will be reduced by a fixed percentage, suggesting limited technical substitutability for the target industry’s output. This assumption can be reasonable in the short-run (Oosterhaven, 2017) and is less restrictive than that of the HE method or other IO models which assume zero substitutability. Moreover, the GHE model may be more advantageous than the HE method criticized by Oosterhaven (2017) and the non-linear programming approach (Oosterhaven and Bouwmeester 2016) because it preserves the IO structure and only reduces the linkage strength. We thank one anonymous referee for pointing out this issue.

  3. We have adopted the terms “insurance” and “other financial services” to avoid potential confusion with the original WIOD sector names. This does not mean that the other financial services sector is less important. For the U.S., the “financial services” industry, defined in the WIOD 2016 release, corresponds to “Federal Reserve banks, credit intermediation, and related activities” in the Bureau of Economic Analysis input–output table.

  4. We use the FSB-identified lists of G-SIBs and G-SIIs up to 2016 when the WIOD 2016 release was published. Moreover, the G-SIIS list has not been updated since 2016. Furthermore, although the FSB has continuously updated the G-SIB list since 2016, these changes in G-SIBs lists have little impact on our empirical results because the current coverage of G-SIBs’ domicile countries are not greatly affected by later changes. For example, there are 30 banks in the 2021 list, which is the same as the one in 2020. These 30 G-SIBs are from 12 jurisdictions, which are covered by the 13 G-SIB economies in this study (except for Italy). The lists of G-SIBs and G-SIIs can be found at the FSB website (https://www.fsb.org/work-of-the-fsb/market-and-institutional-resilience/post-2008-financial-crisis-reforms/ending-too-big-to-fail/global-systemically important-financial-institutions-g-sifis/).

  5. Under the common framework for supervision (IAIS 2019b), IAIS began to register IAIGs based on their international activity and size in 2020. In 2021, there were 48 registered IAIGs from 16 jurisdictions (IAIS 2021). The coverage of the countries of the 2021 IAIGs list (16 countries) is bigger than the G-SIIs list (six countries) used in this study. The 2021 IAIGs list replaced ‘China’ with ‘China, Hong Kong’ and contained nine countries that are not covered in the G-SIIs list (i.e., Belgium, France, Spain, Japan, Switzerland, Canada, Singapore, Austria, and South Africa). In general, the results for the insurance sector in countries with G-SIIs will not be affected by the recognition of IAIGs. We thank one anonymous referee for pointing out this issue.

  6. Counties other than the 43 economies are combined as one economy, termed as the Rest of the World.

  7. WIOD Release 2016 includes five categories of final demand for each economy: (1) final consumption expenditure by household, (2) final consumption expenditure by non-profit organization serving households, (3) final consumption expenditure by government, (4) gross fixed capital formation, and (5) changes in inventories and valuables. We aggregate these categories to get a vector of final demand for each country–industry pair/sector.

  8. Dietzenbacher and Lahr (2013) propose two ways to handle the final demand. One way is to assume that the final demand for sector i does not change, even if its intermediate outputs were reduced. The other way is to assume the final demand for sector i facing capacity constraints also decreases by the same percentage. We have chosen the second approach to handle the final demand. The reason being is that households also provide savings and premiums to the insurance sector and other financial services sector. The disruptive events (e.g., financial crises) that cause supply reductions in the two key sectors under investigation may also lead to a reduced demand for such services. We thank one anonymous referee for pointing out this issue.

  9. In prior IO literature, several indices that examine the effects of inter-industrial propagation have been proposed. For example, Miyazawa (1966) proposes internal and external matrix multipliers to examine the propagation activities within and between industry groups. Our proposed indices are not directly comparable to these indices in Miyazawa (1966) for at least two major reasons. First, the internal and external matrix multipliers in Miyazawa (1966) focus on the industry groups consisting of more than one industry. However, our indices focus on one industry. The internal propagation within industry groups, which is captured by the internal matrix multiplier in Miyazawa (1966), is less of a concern in our study. Second, the internal and external matrix multipliers in Miyazawa (1966) provide a partition of the Leontief inverse, so that one can separate the propagations within and between industry groups. In contrast, our proposed indices aim to capture the systemic importance/relevance of an industry by the potential recursive economic loss that could be triggered by its supply reduction. We thank one anonymous referee for pointing out this issue.

  10. The 13 countries include Belgium, Canada, Switzerland, China, Germany, Spain, France, the U.K., Italy, Japan, the Netherlands, Sweden, and the U.S. See Table 1 for the complete list of G-SIBs in each country.

  11. Another important domicile for global reinsurers is Bermuda, which is absorbed in the rest of world and not shown as a single economy.

  12. Note that we include the ‘rest of the world’ as the last row in the heatmaps in Fig. 8A and B as well. As it is a highly aggregated account, the losses for its aggregated industries could also incur large losses (see Fig. 8).

  13. Note that the U.S. insurance sector is a highly aggregated account that might also include life, property–casualty, health insurance, and reinsurance.

  14. Although the extraction parameter is larger than empirically observed for the U.S. financial services industry, we would argue that our simulation results still provide a lower bound for the potential economic impacts. The reason being that in Eq. (8), we only reduce the final demand for the target sectors (i.e., financial services sectors) and leave the final demand for other sectors unchanged. The simulated losses would be much larger if the final demand for the goods and services of other sectors also reduced after economic contraction (e.g., the U.S. 2007–2008 financial crisis).

References

  • Acemoglu, D., V.M. Carvalho, A. Ozdaglar, and A. Tahbaz-Salehi. 2012. The Network Origin of Aggregate Fluctuations. Econometrica 80 (5): 1977–2016.

    Article  Google Scholar 

  • Acemoglu, D., A. Ozdaglar, and A. Tahbaz-Salehi. 2017. Microeconomic origins of macroeconomic tail risks. The American Economic Review 107 (1): 54–108.

    Article  Google Scholar 

  • Atalay, E. 2017. How important are sectoral shocks? American Economic Journal: Macroeconomics 9 (4): 254–280.

    Google Scholar 

  • Baluch, F., S. Mutenga, and C. Parsons. 2011. Insurance, systemic risk and the financial crisis. Geneva Papers on Risk and Insurance -Issues and Practices 36: 126–163.

    Article  Google Scholar 

  • Baqaee, D.R. 2016. Cascading failure in production networks. Working Paper. SRRN. https://ssrn.com/abstract=2909868.

  • Baqaee, D.R., and E. Farhi. 2017. The macroeconomic impact of microeconomic shocks: Beyond Hulten’s theorem. Working Paper No. 22212, National Bureau of Economic Research.

  • Benoit, S., J. Colliard, C. Hurlin, and C. Perignon. 2016. Where the risks lie: A survey on systemic risk. Review of Finance 21 (1): 109–152.

    Article  Google Scholar 

  • Bigio, S., and J. La’O. 2016. Financial frictions in production networks. Working Paper No. 22212, National Bureau of Economic Research.

  • Botzen, W.J., O. Deschenes, and H. Sanders. 2019. The economic impacts of natural disasters: A review of models and empirical studies. Review of Environmental Economics and Policy 13 (2): 167–188.

    Article  Google Scholar 

  • Boyd, J.H., and A. Heitz. 2016. The social costs and benefits of too-big-too-fail banks: A ‘bounding’ exercise. Journal of Banking and Finance 68: 251–265.

    Article  Google Scholar 

  • Boyd, J.H., S. Kwak, and B. Smith. 2005. The real output losses associated with modern banking crises. Journal of Money, Credit and Banking 37 (6): 977–999.

    Article  Google Scholar 

  • Caliendo, L., F. Parro, and A. Tsyvinski. 2017. Distortions and the structure of the world economy. Working Paper No. 23332, National Bureau of Economic Research.

  • Carvalho, V.M. 2010. Aggregate fluctuations and the network structure of intersectoral trade. Economics Working Papers 1206, Department of Economics and Business, Universitat Pompeu Fabra.

  • Carvalho, V.M., M. Nirei, and Y. Saito. 2014. Supply chain disruption: Evidence from the Great East Japan Earthquake. Technical Report 35, RIETI.

  • Cerina, F., Z. Zhu, A. Chessa, and M. Riccaboni. 2015. World Input–output network. PLoS ONE 10 (7): e0134025. https://doi.org/10.1371/journal.pone.0134025.

    Article  Google Scholar 

  • Chen, H., and T. Sun. 2020. Tail risk networks of insurers around the globe: An empirical examination of systemic risk for G-SIIs vs Non-G-SIIs. Journal of Risk and Insurance 87: 285–318.

    Article  Google Scholar 

  • Contreas, M.G.A., and G. Fagiolo. 2014. Propagation of economic shocks in input–output networks: A cross-country analysis. Physical Review E 90 (6): 062812. https://doi.org/10.1103/PhysRevE.90.062812.

    Article  Google Scholar 

  • Del Rio-Chanona, R.M., J. Grujic, and H.J. Jensen. 2017. Trends of the world input and output network of global trade. PLoS ONE 12 (1): e0170817. https://doi.org/10.1371/journal.pone.0170817.

    Article  Google Scholar 

  • Dietzenbacher, E., and M.L. Lahr. 2013. Expanding extractions. Economic Systems Research 25 (3): 341–360.

    Article  Google Scholar 

  • Dietzenbacher, E., and R. Miller. 2015. Reflections on the inoperability input–output model. Economic Systems Research 27: 478–486.

    Article  Google Scholar 

  • Domar, E.E. 1961. On the measurement of technological change. The Economic Journal 71: 709–729.

    Article  Google Scholar 

  • Financial Stability Board. 2021. 2021 Resolution report: Glass half-full or still half-empty? https://www.fsb.org/wp-content/uploads/P071221.pdf.

  • Gabaix, X. 2011. The granular origins of aggregate fluctuations. Econometrica 79 (3): 733–772.

    Article  Google Scholar 

  • Hulten, C.R. 1978. Growth accounting with intermediate inputs. The Review of Economic Studies 45 (3): 511–518.

    Article  Google Scholar 

  • International Association of Insurance Supervisors (IAIS). 2019a. Holistic framework for systemic risk in the insurance sector. https://www.iaisweb.org/page/supervisory-material/financial-stability/file/87109/holistic-framework-for-systemic-risk.

  • IAIS. 2019b. Insurance core principles and common framework for the supervision of internationally active insurance groups. https://www.iaisweb.org/page/supervisory-material/insurance-core-principles-and-comframe/file/91154/iais-icps-and-comframe-adopted-in-november-2019.

  • IAIS. 2021. Register of internationally active insurance groups based on information publicly disclosed by group-wide supervisors. https://www.iaisweb.org/page/supervisory-material/insurance-core-principles-and-comframe/file/101896/register-of-internationally-active-insurance-groups-iaigs.

  • IMF, BIS, FSB. 2009. Guidance to assess the systemic importance of financial institutions, markets, and instruments: Initial considerations. https://www.imf.org/external/np/g20/pdf/100109.pdf.

  • Jobst, A.A. 2014. Systemic risk in the insurance sector: A review of current assessment approaches. Geneva Papers on Risk and Insurance—Issues and Practice 39: 440–470.

    Article  Google Scholar 

  • Jonkeren, O., and G. Giannopoulos. 2014. Analysing critical infrastructure failure with a resilience inoperability input–output model. Economic Systems Research 26: 39–59.

    Article  Google Scholar 

  • Kim, J. 2017. Inter-industry analysis in the Korean flow-of-funds accounts. Journal of Economic Structures 6: 25. https://doi.org/10.1186/s400008-017-0084-9.

    Article  Google Scholar 

  • Klein, L.R. 2003. Some potential linkages for input–output analysis with flow-of-funds. Economic Systems Research 15: 269–277.

    Article  Google Scholar 

  • Koch, C.T. 2014. Risky adjustments or adjustments to risks: decomposing bank leverage. Journal of Banking and Finance 45: 242–254.

    Article  Google Scholar 

  • Koks, E.E., L. Carrera, O. Jonkeren, J.C.J.H. Aerts, T.G. Husby, M. Thissen, G. Standardi, and J. Mysiak. 2015. Regional disaster impact analysis: Comparing input–output and computable general equilibrium models. Natural Hazards and Earth System Sciences 16: 1911–1924.

    Article  Google Scholar 

  • McGuire, P., and G. von Peter. 2016. The resilience of bank’s international operations. Bank of International Settlements Quarterly Review, Bank for International Settlements, March 2016.

  • Miller, R.E., and P.D. Blair. 2009. Input–output analysis: Foundations and extensions, 2nd ed. Cambridge University Press.

    Book  Google Scholar 

  • Miller, R.E., and M.L. Lahr. 2001. A taxonomy of extractions. In Regional science perspective in economic analysis, ed. M.L. Lahr and R.R. Miller. Amsterdam: Elsevier Science.

    Google Scholar 

  • Miyazawa, K. 1966. Internal and external matrix multipliers in the input–output model. Hitotsubashi Journal of Economics 7: 38–55.

    Google Scholar 

  • Neveu, A.R. 2018. A survey of network-based analysis and systemic risk measurement. Journal of Economic Interaction and Coordination 13: 241–281.

    Article  Google Scholar 

  • Okuyama, Y., and J.R. Santos. 2014. Disaster impact and input–output analysis. Economic System Research 26: 1–12.

    Article  Google Scholar 

  • Oosterhaven, J. 2017. On the limited usability of the inoperability IO model. Economic Systems Research 29: 452–461.

    Article  Google Scholar 

  • Oosterhaven, J., and M.C. Bouwmeester. 2016. A new approach to modeling the impact of disruptive event. Journal of Regional Science 56: 583–595.

    Article  Google Scholar 

  • Orsi, M.J., and J. Santos. 2010. Probabilistic modeling of workforce-based disruptions and input–output analysis of interdependent ripple effects. Economic Systems Research 22: 3–18.

    Article  Google Scholar 

  • Santos, J.R. 2006. Inoperability input–output modelling of disruptions to interdependent economic systems. Systems Engineering 9 (1): 20–34.

    Article  Google Scholar 

  • Santos, J.R., and Y.Y. Haimes. 2004. Modeling the demand reduction input–output (I-O) inoperability due to terrorism of interconnected infrastructures. Risk Analysis 24: 1437–1451.

    Article  Google Scholar 

  • Sato, K., and N. Shrestha. 2014. Global and regional shock transmission—an Asian perspective. Center for Economic and Social Studies in Asia (CESSA) Working Paper 2014-04. Yokohama National University.

  • Thimann, C. 2015. Systemic features of insurance and banking, and the role of leverage, capital and loss absorption. Geneva Papers on Risk and Insurance – Issues and Practices 40: 359–384.

    Article  Google Scholar 

  • Timmer, M.P., E. Dietzenbacher, B. Los, R. Stehrer, and G.J. de Vries. 2015. An illustrated user guide to the world input–output database: The case of global automotive production. Review of International Economics 23 (3): 575–605.

    Article  Google Scholar 

  • Timmer, M.P., B. Los, R. Stehrer, and G.J. de Vries. 2016. An anatomy of the global trade slowdown based on the WIOD 2016 release. GGDC Research Memorandum Number 162. University of Groningen.

  • Tsuchiya, S., H. Tatano, and N. Okada. 2007. Economic loss assessment due to railroad and highway disruptions. Economic Systems Research 19: 147–162.

    Article  Google Scholar 

  • United States Government Accountability Office. 2013. Financial regulatory reform: Financial crisis losses and potential impacts of the Dodd-Frank Act. GAO-13-180. https://www.gao.gov/products/GAO-13-180.

  • Valencia, F., and L. Laeven. 2013. Systemic banking crises database. IMF Economic Review 61: 225–270.

    Article  Google Scholar 

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Acknowledgements

This research is partially funded by the Lingnan University Direct Grant (Grant Number: DR20B2).

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  1. Department of Finance and Insurance, Lingnan University, 8 Castle Peak Road, Tuen Mun, Hong Kong SAR, China

    Tao Sun

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Appendix 1

Appendix 1

See Tables 4 and 5.

Table 4 WIOD release 2016 covered economies
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Table 5 WIOD release 2016 covered industries
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Sun, T. Systemic importance of financial services and insurance sectors: a world input–output network analysis. Geneva Pap Risk Insur Issues Pract 49, 63–96 (2024). https://doi.org/10.1057/s41288-022-00277-3

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