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Business environment, political risk, governance, Shariah compliance and efficiency in insurance companies in the MENA region

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Abstract

Widespread political and economic changes over the past several decades have influenced MENA insurance firms’ business environment fundamentally. This study investigates the influence of business environment, political risk, governance and Shariah compliance on efficiency in the insurance market from 2007 to 2017 using unbalanced panel data from 125 listed insurance firms across MENA countries. The study emphasises that a less favourable business environment exerts pressure on insurance firms and also identified an improvement in efficiency over the sample period, which may be attributed to the role played by challenging business conditions in increasing efficiency. Further, the results showed that variability in general economic conditions and influence of governance indicators explain efficiency, while solvency has a multifaceted effect on efficiency. Finally, the study found that Islamic insurance’s relative failure to enhance efficiency may be attributable to its failure to establish a distinct identity or draw a clear line between its mode of operation and that of conventional insurance, as well as the issue of customer confidence. Overall, the study emphasises the importance of controlling business environmental conditions in cross-country efficiency studies.

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Data availability

The datasets used and/or analysed during the study can be provided upon reasonable request.

Notes

  1. Middle East and North Africa.

  2. Between exporting oil-rich countries such as the UAE, Saudi Arabia, Kuwait, etc. and low-income countries such as Egypt, Jordan, Morocco, etc.

  3. Such as Gibraltar, Suez, Bab-el-Mandeb and Hormuz.

  4. Southeast East Asian.

  5. Also see Eling and Schaper (2017) for developed countries.

  6. Brazil, Russia, India and China.

  7. As an illustration, Vaaler et al. (2005), Henisz and Zelner (2010) and Julio and Yook (2012) adopted electoral uncertainty as a measure of political risk, while Li and Resnick (2003) used social unrest. Wei (2000), Habib and Zurawicki (2002) and Uhlenbruck et al. (2006) relied on corruption, and Loree and Guisinger (1995), Sethi et al. (2003) and Brogaard et al. (2014) applied political stability. In this work, the political risk measure is consistent with the case of the MENA region, following Globerman and Shapiro (2003) and Darendeli and Hill (2016).

  8. According to the Middle East Institute, see Backfire in the Arab Spring.

  9. See Chapter 2 in The Future of the Arab Spring: Civic Entrepreneurship in Political, Art, and Technology Startups.

  10. They applied it to ethnic groups and politically discriminated populations, while this study applied it to the conflict risk level.

  11. Corporate governance involves many variables, but this study includes only four: board size, ownership, gender diversity and location, which follows Basu et al. (2007). Further, there is a lack of data for other corporate governance dimensions in MENA.

  12. These aspects have not been examined yet in the context of insurance firms in the MENA region. Thus, the banking literature may help us understand their influence on insurance companies’ efficiency (e.g. for Shariah compliance, see Hosen and Muhari 2013; for political risk, see Ghosh 2016 and Bitar et al. 2017; and for oil price, see Hesse and Poghosyan 2016 and Al-Khazali and Mirzaei 2017).

  13. Standard DEA is referred to as standard output-oriented DEA, using a return-to-scale technique (see Emrouznejad et al. 2010a, b).

  14. Q10, Q25, Q50, Q75 and Q90.

  15. These explanatory variables are described in Table 1.

  16. The quantiles, \(\tau\), are the tenth, twenty-fifth, fiftieth, seventy-fifth, and ninetieth.

  17. See https://data.worldbank.org/country/ZQ

  18. See https://www.bvdinfo.com/en-gb/our-products/data

  19. See Satloff (1995).

  20. The standard deviation of oil price is used in the empirical model.

  21. The details are not presented in this paper to save space, but are available from the author upon request.

  22. Depends on the median.

  23. Only approximately 5 per cent of GDP.

  24. The average age of board directors is approximately 60 years, with approximately 100 years as the maximum (see Table 2).

  25. This phenomenon requires further study.

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  1. Department of Finance, King Abdulaziz University, P.O. Box : 80200, Jeddah, 21589, Saudi Arabia

    Ali Shaddady

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  1. Ali Shaddady
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Appendices

Appendix

Appendix A: Levels of conflict across countries

Country

Situation

Rank in risk level

Algeria

Major protests

2

Bahrain

Protests and governmental changes

3

Egypt

Government overthrown more than once

6

Iraq

Major protests

2

Israel

No protest

0

Jordan

Protests and governmental changes

3

Kuwait

Protests and governmental changes

3

Malta

No protest

0

Morocco

Protests and governmental changes

3

Oman

Protests and governmental changes

3

Palestinian Territories

Minor protests

1

Qatar

No protest

0

Saudi Arabia

Minor protests

1

Syrian Arab Republic

Civil war in some parts but no governmental changes

4

Tunisia

Government overthrown

5

United Arab Emirates

No protest

0

  1. Index from 0 to 6 where 0 = no protests (stable); 1 = minor protests; 2 = major protests; 3 = protests and governmental changes; 4 = civil war in some parts but government does not change; 5 = government overthrown; and 6 = government overthrown more than once (unstable). See Table 1

Appendix B: Slack regression results (SFA model)

Environmental variable

Slack1

Slack2

Slack3

Underwriting expenses

Equity capital

Net claims incurred

Interest rate

 − 0.679*

 − 0.029**

 − 0.306**

 

(0.967)

(0.920)

(0.300)

Inflation

 − 0.191

 − 0.519***

 − 0.453***

 

(0.083)

(0.695)

(0.344)

Unemployment

0.133**

0.114

0.764*

 

(0.289)

(0.688)

(0.303)

Economic growth

 − 0.466***

0.376**

0.194

 

(0.957)

(0.980)

(0.595)

Effective exchange rate

 − 0.931**

 − 0.642

0.501***

 

(0.402)

(0.925)

(0.959)

Oil price

 − 0.249**

0.101**

0.394

 

(0.108)

(0.218)

(0.386)

Political risk

 − 0.040**

 − 0.240**

0.071

 

(0.071)

(0.881)

(0.036)

Quality of regulation

0.446***

 − 0.325

 − 0.957**

 

(0.684)

(0.569)

(0.342)

Financial freedom

0.005

 − 0.888*

 − 0.312

 

(0.055)

(0.446)

(0.286)

Insurance assets

0.029***

0.082**

0.704*

 

(0.762)

(0.837)

(0.736)

Insurance premium

 − 0.400**

0.904

0.720**

 

(0.404)

(0.101)

(0.080)

Log likelihood function

 − 2565.37

4853.34

4778.68

Observations

1349

1349

1349

Number of insurance firms

125

125

125

  1. This table presents slack regression across inputs: underwriting expenses, equity and net claims incurred, and is based upon the SFA slack regression model that followed Huang and Eling (2013) and Eling and Schaper (2017)
  2. ***, ** and * represent significance at 1%, 5% and 10%, respectively
  3. The SFA regression is only for the second-stage regression

Appendix C: Term efficiencies of dynamic DEA model (over time)

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Shaddady, A. Business environment, political risk, governance, Shariah compliance and efficiency in insurance companies in the MENA region. Geneva Pap Risk Insur Issues Pract 47, 861–904 (2022). https://doi.org/10.1057/s41288-021-00232-8

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