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Competition and Risk-Taking Behaviour in the Non-Life Insurance Market in South Africa

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This study examines the non-linear effect of competition on risk-taking behaviour in an emerging insurance market. The paper utilises annual firm-level data on 79 non-life insurance firms to estimate the Lerner index as the proxy for competition, while the z-score and standard deviation of loss ratio are employed as proxies for risk-taking behaviour. Using the seemingly unrelated regression, quantile regression and system generalised method of moments techniques, we find evidence in support of the Martinez-Miera and Repullo hypothesis which suggests a non-linear inverted U-shaped relationship between competition and insurance solvency. We also document evidence to suggest that the competition-fragility effect is stronger for weaker insurers, indicating that competition has a greater destabilising effect on weaker insurers. Other significant predictors of solvency in the insurance market are identified as firm size, capitalisation, reinsurance, business line diversification and foreign ownership. Policy recommendations for improving industry stability are discussed.

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  1. Keeley (1990).

  2. Boyd and De Nicolo (2005).

  3. Martinez-Miera and Repullo (2010).

  4. A fourth theory also suggests a dual relationship between competition and stability (Refer to Schaeck et al., 2009, Turk-Ariss, 2010). For the purposes of this study, we restrict our arguments to the first three views.

  5. Ren and Schmit (2006) refer to this as an attractive risk-shifting strategy since it accords insurers the opportunity to generate income before the settlement of claims in the future.

  6. Ren and Schmit (2006).

  7. See studies by Salas and Saurina (2003), Beck et al. (2006), De Nicolo et al. (2006), Yeyati and Micco (2007), Berger et al. (2009), Jimenez et al. (2013), Liu and Wilson (2013), Fungacova and Weill (2013), Kouki and Al-Nasser (2017) and Schaeck and Cihák (2014).

  8. The South African non-life market accounted for about 50 per cent of gross written premiums in Africa in 2012.

  9. Under the regulation of the Financial Services Board, South African non-life insurance comprises 92 direct insurers and 8 reinsurers as of December 2012. The market is regulated by the Short-Term Insurance Act 53 (1998) which covers the underwriting of insurance policies in eight different business classifications. The market accounts for over 50 per cent of non-life premiums in Africa (Swiss Re, 2011).

  10. Cheng et al. (2011), Ng et al. (2013), Pasiouras and Gaganis (2013), Cummins et al. (2017), Shim (2015).

  11. In this paper, (in)solvency and stability are used interchangeably to denote risk-taking behaviour.

  12. Panzar and Rosse (1987). Also refer to studies by Murat et al. (2002), Kasman and Turgutlu (2008), Coccorese (2010, 2012) and Jeng (2015) on the application of the Panzar and Rosse H-statistics to estimate insurance market competition.

  13. Boone (2008). Refer to studies by Bikker and van Leuvensteijn (2008), Bikker (2012), Bikker and Popescu (2014) and Cummins et al. (2017) for applications of the Boone indicator to measure the competitiveness of insurance markets.

  14. This refers to two equations with the same set of independent variables but different dependent variables.

  15. Zellner (1962).

  16. Arellano and Bover (1995).

  17. The two papers on the linear relationship between competition and stability are by Cummins et al. (2017) and Shim (2015).

  18. Petersen and Rajan (1995).

  19. This refers to the insurer–policyholder relationship. In competitive insurance markets, policyholders tend to move between different insurers after a contract elapses.

  20. Since policyholders may not stick with an insurer for a longer period, insurers are unable to monitor the attitude of policyholders to risk and adapt their pricing accordingly.

  21. Allen and Gale (2004).

  22. See Demsetz et al. (1996), Saunders and Wilson (2001), Salas and Saurina (2003), Beck et al. (2006), De Nicolo et al. (2006), Yeyati and Micco (2007), Berger et al. (2009), Jimenez et al. (2013), Liu and Wilson (2013) and Kouki and Al-Nasser (2017).

  23. Cummins et al. (2017).

  24. Shim (2015).

  25. Baranoff and Sager (2003).

  26. Chen and Wong (2004).

  27. Shiu (2005).

  28. Cole et al. (2011).

  29. Cheng et al. (2011).

  30. Ho et al. (2013).

  31. Cheng and Weiss (2013).

  32. Pasiouras and Gaganis (2013).

  33. Marek and Eling (2014).

  34. Ng et al. (2013).

  35. Hu and Yu (2014).

  36. Ansah-Adu et al. (2012) and Alhassan et al. (2015), Alhassan and Biekpe (2015).

  37. Alhassan and Biekpe (2016, 2017).

  38. Laeven and Levine (2009).

  39. See Adams and Buckle (2003), Fields et al. (2012), Ng et al. (2013), Pasiouras and Gaganis (2013), Cummins et al. (2017) and Shim (2015).

  40. Fields et al. (2012), Pasiouras and Gaganis (2013), Cummins et al. (2017) and Shim (2015).

  41. Due to the shorter time series, this paper was unable to employ a rolling time window approach for computing the standard deviation of return on assets. Hence, this estimation of the z-score did not allow for variation of the denominator. However, since both numerators vary over firms and time, the estimated z-score also varies over firms and time.

  42. This represents the deviation of return on assets from the average return on assets from 2007 to 2012.

  43. To account for the negative values in the z-score, a constant (1) and absolute minimum value are added before the logarithmic transformation.

  44. Adams and Buckle (2003).

  45. This measure of underwriting risk is consistent with studies on insurance markets by authors such as Lamm-Tennant and Starks (1993), Ho et al. (2013) and Shim (2015). This approach was adopted on the advice of two anonymous referees. An earlier draft of this paper only used the loss ratio as the proxy for underwriting risk.

  46. Bain (1951).

  47. Demsetz (1973).

  48. Lerner (1934).

  49. H-statistics of Panzar and Rosse (1987), the conjectural variation of Bresnahan (1982) and Lau (1982) and the Boone (2008) indicator.

  50. Refer to studies by de Guevara et al. (2005), Berger et al. (2009), Turk-Ariss (2010) among others.

  51. Refer to Huang et al. (2007), Alhassan and Biekpe (2015), Alhassan and Biekpe (2016).

  52. See Alhassan and Biekpe (2016).

  53. Cummins et al. (1999).

  54. Hardwick et al. (2011).

  55. Jimenez et al. (2013).

  56. In the estimation of the inflection points, this paper adopts the formula of Lind and Mehlum (2010) which is specified as \( x^{min} = - {{\widehat{{\theta _{1} }}} \mathord{\left/ {\vphantom {{\widehat{{\theta _{1} }}} {2\widehat{{\theta _{2} }}}}} \right. \kern-\nulldelimiterspace} {2\widehat{{\theta _{2} }}}} \).

  57. Hardwick (1997).

  58. Cummins and Sommer (1996) and Baranoff and Sager (2003).

  59. Koehn and Santomero (1980).

  60. Berger et al. (2009).

  61. Claessens et al. (1998).

  62. To account for the high correlation coefficient between Lerner and \( Lerner^{2} \) of above 0.7 (0.773 as observed from the correlation matrix in Appendix A), the variable employed in the regression models is centred. This is achieved by taking the mean value of Lerner from all the index values. The square of the centred Lerner becomes the quadratic term. The correlation coefficient falls to 0.321 after centring.

  63. Similarly, the results are consistent with the competition–stability of Boyd and De Nicolo (2005) that intense competition is associated with low solvency and operational risks.

  64. Baranoff and Sager (2003).

  65. Cummins and Sommer (1996).

  66. Garven and Lamm-Tennant (2003).

  67. As a robustness analysis, the model is re-estimated by including the two macroeconomic variables in inflation and economic growth in line with Cheng and Weiss (2012). The points of inflection are all found to have a maximum value to confirm the non-linear effect of competition on risk-taking in all estimations (ISURE, OLS and GMM) using the z-score as the proxy for risk-taking. The results of the control variables were found to be qualitatively similar to the baseline models estimated in the paper. With regard to the macroeconomic variables, the coefficients of both inflation and economic growth were positively related to risk-taking behaviour. The results are attached as Appendix B.

  68. The QR estimation is undertaken with 1,000 bootstrapping replications to generate heteroscedasticity-consistent coefficients. The test of coefficient equality across quantiles is performed under the null hypothesis of equal coefficients across the quantiles. The rejection of the null hypothesis validates the use of the QR. From Table 6, the null hypothesis is rejected for the coefficients of the Lerner index, size and business line diversification. As observed from the summary statistics, solvency risk, underwriting risk and Lerner index exhibit extreme outliers. The use of the QR technique is premised on these observations. To conserve space, only the QR estimates using the solvency ratio as the dependent is reported.

  69. Koenker and Bassett (1978).

  70. The null hypothesis that the coefficient of the Lerner index is equal across all quantiles of the z-score is rejected at a level of 5 per cent as indicated by the F-test in Table 6. This validates the use of QR in examining the competition–stability relationship.

  71. Hansen (1982).

  72. Arellano and Bond (1991).

  73. Roodman (2009).


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Correspondence to Abdul Latif Alhassan.


Appendix A

Table A Correlation matrix

Appendix B

Table B Estimations with macroeconomic variables

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Alhassan, A.L., Biekpe, N. Competition and Risk-Taking Behaviour in the Non-Life Insurance Market in South Africa. Geneva Pap Risk Insur Issues Pract 43, 492–519 (2018).

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