Performance determinants of non-life insurance firms: a systematic review of the literature

Zinyoro, Tafadzwanashe; Aziakpono, Meshach Jesse Article Performance determinants of non-life insurance firms: a systematic review of the literature Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Zinyoro, Tafadzwanashe; Aziakpono, Meshach Jesse (2024) : Performance determinants of non-life insurance firms: a systematic review of the literature, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-38, https://doi.org/10.1080/23311975.2024.2345045 This Version is available at: https://hdl.handle.net/10419/326254 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Performance determinants of non-life insurance firms: a systematic review of the literature Tafadzwanashe Zinyoro & Meshach Jesse Aziakpono To cite this article: Tafadzwanashe Zinyoro & Meshach Jesse Aziakpono (2024) Performance determinants of non-life insurance firms: a systematic review of the literature, Cogent Business & Management, 11:1, 2345045, DOI: 10.1080/23311975.2024.2345045 To link to this article: https://doi.org/10.1080/23311975.2024.2345045 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 06 May 2024. Submit your article to this journal Article views: 3130 View related articles View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Banking & Finance | Review aRticle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2345045 Performance determinants of non-life insurance firms: a systematic review of the literature tafadzwanashe Zinyoroa and Meshach Jesse aziakponob astellenbosch university Business school, Cape town, south africa; bRhodes university, Makhanda, south africa ABSTRACT the performance of non-life insurers is essential to the economy because of their role in mitigating the risks firms and households face. this study provides a comprehensive overview of studies examining factors affecting non-life insurers’ performance. Based on 235 studies published between 1990 and 2021, the review demonstrates that firm-level factors such as size, organisational form, diversification, capital structure, risk, reinsurance, corporate governance, distribution system, and group affiliation, and external factors such as market structure, macroeconomic, financial, and institutional development are the major determinants of non-life insurers’ performance. although the empirical evidence on the effect of these factors is generally mixed, firm size, capitalisation, risk, macroeconomic conditions, and, to some extent, corporate governance and market structure issues show a clear relationship with insurer performance. One of the implications of this study is that there may be a need for increased solvency surveillance, especially for smaller insurers, which appear to have a higher risk of insolvency than their larger counterparts. IMPACT STATEMENT the performance of non-life insurers is important to the economy because of the role they play in mitigating the risks firms and households face. this study provides a comprehensive overview of studies published between 1990 and 2021 that examined factors affecting the performance of non-life insurers. Based on 235 studies the review demonstrates that firm-level factors such as size, organisational form, diversification, capital structure, risk, reinsurance, corporate governance, distribution system, and group affiliation and external factors such as market structure, macroeconomic, financial, and institutional development are the major determinants of non-life insurers’ performance. One of the implications of this survey is that there may be need for increased solvency surveillance, especially of smaller insurers which appear to have a higher risk of insolvency than their larger counterparts. Proper risk management could play a significant role in the operations of non-life insurers. 1. Introduction a robust insurance industry plays a significant role in the economy of any nation. through insurance, economic agents can efficiently manage insurable risks threatening their viability. insurance firms help reduce the cost of goods and services by indemnifying the insured. apart from that, they facilitate trade and commerce, substitute and complement government security programs, encourage loss mitigation, and promote investment in critical sectors of the economy. However, the extent to which insurance companies perform these functions could depend on their productivity, efficiency, solvency, and profitability. Hence, it is vital to understand the factors that influence the performance of insurance firms. Many empirical studies have examined these aspects, including economies of scale and scope, regulatory change, market structure, mergers and acquisitions, organisational form, distribution systems, corporate governance, macroeconomic conditions, and risk. it is worth noting that one stream of the literature © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Meshach Jesse aziakpono [email protected] Rhodes university, Makhanda, south africa https://doi.org/10.1080/23311975.2024.2345045 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 16 July 2023 Revised 4 april 2024 accepted 9 april 2024 KEYWORDS Determinants; performance; efficiency; non-life insurance; systematic literature review REVIEWING EDITOR David McMillan, University of Stirling, United kingdom SUBJECTS economics; Finance; Business, Management and accounting 2 t. ZinYORO anD M. J. aZiakPOnO that has experienced phenomenal growth focused on the application of frontier efficiency methodologies. For instance, cummins and weiss (2000) analysed 21 studies published between 1983 and 1999 that utilised frontier efficiency methodologies. their study revealed six aspects (i.e., economies of scale and scope, organisation form, distribution systems, mergers and acquisitions, regulatory change, and management strategies) influencing insurer efficiency. cummins and weiss (2013) examined 53 additional studies published from 2000 to 2011 and identified two extra factors, market structure and corporate governance. amel etal., (2004) focused mainly on studies that analysed the effects of mergers and acquisitions on the performance of firms in the financial sector in industrialised countries, including insurance companies. in addition to discussing most aspects identified in cummins and weiss (2000, 2013), eling and luhnen (2010b) analysed three more factors (i.e., financial intermediation, risk management, and capital utilisation) explored in the literature on the efficiency of the insurance firms. their review was based on 95 studies spanning 1993–2008. kaffash et al., (2020) surveyed 132 studies published between 1993 to 2018. their review focused exclusively on insurance studies that applied data envelopment analysis (Dea). they identified seven additional factors: business environment, failure, capacity, contingent commissions, customer loyalty, innovation, and intellectual capital. Zinyoro and aziakpono (2023) conducted a comprehensive systematic analysis, examining 129 studies published between 1991 and 2021 to investigate the drivers of life insurance performance. notably, among these studies, 71 utilised frontier efficiency methodologies. their analysis identified seven key firm-level factors (i.e., size, organizational structure, capital structure, diversification, distribution systems, risk management, and reinsurance strategies) and three external factors (i.e., deregulation, competition, and macroeconomic conditions) that influence the performance of life insurers. this review synthesizes studies exploring factors that may explain the variation in performance across non-life insurers and markets. to our knowledge, this study represents the first and most comprehensive systematization of the literature on drivers of performance of non-life insurers using five main categories of performance measures: efficiency, accounting, market, insolvency prediction, and rating). Previous reviews exclusively focused on studies that utilized frontier efficiency methodologies, except Zinyoro and aziakpono (2023), who focused on performance drivers for the life insurance segment. Other measures of performance that indicate, for example, an insurer’s solvency, claims-paying ability, and shareholder value are equally important because of the fiduciary nature of the relations in insurance markets (Brockett etal., 1998, Brockett etal., 2004). the present review aligns with Zinyoro and aziakpono (2023) study in scope but distinguishes itself significantly. while Zinyoro and aziakpono focused on the performance of life insurance firms, our study concentrates on non-life insurance firms, thus contributing to a comprehensive understanding of the broader insurance industry. Our study covers the period from 1990 to 2021 and, thus, provides the most recent empirical evidence on factors that influence non-life insurer performance. the study also discusses additional firm-level factors (i.e., capital structure, reinsurance, risk, and group affiliation) and country-level factors (i.e., macroeconomic conditions, financial sector, regulatory and institutional development indicators) that have been identified in the literature as essential drivers of non-life insurer performance. the rest of the study is organised as follows. Section 2 describes our data collection method, data sources, and the studies we reviewed. Section 3 discusses the determinants of the performance of non-life insurers frequently examined in the literature. Section 4 summarizes the findings, conclusions, policy implications, and areas for future research. 2. Methodology and data description we conducted a systematic literature review to assess the determinants of non-life insurer performance following the Preferred Reporting items for Systematic Reviews and Meta-analyses (PRiSMa) guidelines.1 Figure 1 shows the main steps to identify the articles for inclusion in the final sample. 2.1. Search strategies the first step in our search strategy was to define a list of relevant keywords based on Barniv and McDonald (1992), cummins and weiss (2000, 2013), eling and luhnen (2010a, 2010b), and kaffash et al. (2020). we read the literature on organisational performance (particularly Richard etal., 2009) to capture cOgent BUSineSS & ManageMent 3 the different performance dimensions. we ultimately used three groups of keywords. the first group comprised performance-related keywords: productivity, efficiency, profitability, cash holdings, solvency, insolvency (failure/financial distress), financial stability, rating, and performance. the second group comprised insurers, property-liability (casualty)/non-life/general,2 and insurance (all related to the insurance industry). the last group was related to specific aspects influencing insurer performance, as identified in previous reviews. examples included organizational form, consolidation, (de)regulation, diversification, market structure, and corporate governance. we used these keywords to search databases such as the web of Science, eBScO, Science Direct Scopus, and google Scholar. additional articles were identified through the reference sections of relevant Figure 1. search methods, strategies and sample selection. 4 t. ZinYORO anD M. J. aZiakPOnO articles and other related reviews. Finally, specific insurance, economics, finance, and management-related journals were manually searched to identify more articles. 2.2. Inclusion and exclusion criteria For this study, we considered only published papers in peer-reviewed journals, working papers, and book chapters between 1 January 1990 and 31 December 2021 for two main reasons. First, research applying frontier efficiency methodologies in the insurance industry started in the early 1990s (Berger etal., 1993), and have since witnessed a surge in number after that (see cummins & weiss, 2000, 2013). Second, most developments in the non-life insurance industry, including regulatory changes, advances in computing and communications, and consolidations, happened during this period (cummins & Xie, 2008; eling & luhnen, 2010b). we scanned the title, abstract, introduction, and, in some instances, the entire article to determine the eligibility of an article.3 Only studies that examined the factors or aspects influencing the performance outcomes of property-liability insurers were considered. we further excluded studies not written in english and those published in poor language (these papers are available from the authors upon request). Of the 348 articles identified through database search, 137 were eligible for inclusion in the initial sample. we also identified 55 studies by reviewing the reference sections of existing surveys and other relevant articles. Finally, 43 more studies were added through manual searches of specific journals, bringing the total number of studies in the final sample to 235. 2.3. Bibliometric analysis this section presents a bibliometric analysis of the studies included in this review. 2.3.1. Distribution of studies by publication source table 1 lists the 17 sources with the highest number of studies that explored the drivers of the performance of non-life insurers. Most of the studies (202, 86.0%) in our dataset were published in peer-reviewed journals, while 13 studies (5.5%) were published in working papers and four studies (1.7%) as book chapters. the most common journals include the Journal of Risk and Insurance (42 studies, 20.8%), The Geneva Papers on Risk and Insurance-Issues and Practice (19 studies, 9.4%), Journal of Banking and Finance (11 studies, 5.4%), Journal of Insurance Issues (11 studies, 5.4%), European Journal of Operational Research (6 studies, 3.0%), Risk Management and Insurance Review (5 studies, 2.5%) and Journal of Productivity Analysis (5 studies, 2.5%).4 Table 1. top 17 study sources. study source no. of studies Journal of Risk and insurance 42 the geneva Papers on Risk and insurance-issues and Practice 18 Working Papers 13 Journal of Banking and Finance 11 Journal of insurance issues 11 european Journal of operational Research 6 Risk Management and insurance Review 5 Journal of insurance Regulation 4 Journal of Productivity analysis 4 Journal of Risk Finance 4 Journal of Financial services Research 3 Managerial Finance 3 Book chapters 3 international Journal of emerging Markets 2 international Journal of Financial studies 2 Journal of Risk and uncertainty 2 north american actuarial Journal 2 Total 136 Source: authors’ compilation. cOgent BUSineSS & ManageMent 5 2.3.2. Distribution of studies by year Figure 2 presents the Distribution of studies by year. as evident from the figure, the number of studies exhibited significant fluctuations throughout the study period. 1991, 1994, 2000, and 2006 marked the lowest count of studies (1) recorded, while the highest count occurred in 2021. notably, the fluctuation in the number of studies between 2008 and 2021 was less pronounced than in the earlier period (1990– 2007), yet it consistently remained above 6. 2.3.3. Distribution of studies by number of authors Figure 3 illustrates the Distribution of studies according to the number of authors. Four hundred twelve distinct authors were identified, with an average of approximately two authors per publication. Single authors wrote approximately 16% of the studies, while 43% were collaborative efforts of two authors. Publications attributed to three authors constituted 29%, whereas those involving four authors accounted for 9%. a minority, roughly 4%, were contributed by five authors, representing the lowest proportion. 2.3.4. Distribution of studies by country and performance measure table 2 shows the Distribution of studies by country and performance measure. as can be observed, most country-specific studies (161, 78.5%) concentrated on developed insurance markets, mainly the US, Figure 2. Distribution of studies by year. Figure 3. Distribution of number of studies by authors. 6 t. ZinYORO anD M. J. aZiakPOnO which accounts for 55.6% (114 studies). the 30 studies in our sample (both regional and global) that explored insurer performance determinants across countries are skewed in favour of european (13 studies, 43.3%) and global insurance (12 studies, 40.0%) markets (see table 3). Regarding the performance measures, studies in our database utilised five classes of indicators: accounting-, efficiency-, market-, insolvency prediction- and rating-based measures. accounting-based measures comprise traditional financial ratios such as ROa, ROe, profit margin, loss ratios, combined ratios, and expense ratios. these indicators (dominated by ROa, ROe, and profit margin) were our reviewed studies’ most frequently used performance measures (40.4%, 95 studies). However, traditional financial ratios have been criticised for providing only a partial assessment of performance (Doumpos et al., 2012). the major problem with partial measures is that a firm may perform well based on one indicator but badly based on another, thereby giving an inconclusive picture of a firm’s overall performance. efficiency measures were the second most popular performance indicators in our sample. these measures dominate traditional performance measures, particularly those that rely on book values Table 2. Distribution of studies by country and performance measure. Country Performance measure Developed efficiency accounting Market insolvency prediction Rating number of studies australia 1 1 2 austria 1 1 Belgium 1 1 Canada 2 1 3 France 3 3 germany 5 4 9 italy 1 1 Japan 3 2 1 6 netherlands 1 1 1 3 Portugal 1 1 south Korea 1 1 1 spain 5 5 sweden 1 1 switzerland 1 1 2 uK 7 1 8 us 29 50 14 14 7 114 Sub-total 48 69 15 23 7 161 Developing argentina 1 1 Bangladesh 2 2 China 4 2 6 ecuador 1 1 ghana 3 3 india 9 9 iran 22 Malaysia 2 1 3 Pakistan 2 2 south africa 2 1 3 taiwan 4 4 8 thailand 2 2 turkey 1 1 2 Sub-total 27 17 44 Total 75 86 15 23 7 205 Source: authors’ compilation. Table 3. Distribution of multi-country studies by performance measure. Focus Performance measures no. of studiesefficiency accounting Market insolvency Rating europe 9 2 1 1 13 eastern europe 11 asia 1 1 2 saDC 1 1 BRiC 1 1 global 2 8 1 1 12 Total 13 13 1 1 2 30 Notes: saDC = southern africa Development Community. BRiC = Brazil, Russia, india and China. Source: authors’ compilation. cOgent BUSineSS & ManageMent 7 instead of market values, such as accounting ratios (cummins & weiss, 2013). efficiency indicators were utilised in 88 studies (37.4%) in our dataset. in this class, technical, scale, allocative, and cost efficiency were the common efficiency measures. table a1 (appendix 1) shows our sample’s main frontier efficiency estimation techniques, including sample sizes, inputs, outputs, efficiency types, and average yearly efficiency. among the two main approaches (Dea and SFa) for measuring efficiency5, most frontier efficiency studies (61, 69.3%) applied Dea. Dea dominates in the literature because it possesses several desirable characteristics (cummins & weiss, 2013). First, Dea is non-parametric, meaning it avoids misspecification of the functional form (such as cost, revenue, or profit) or distributional assumptions of the error terms. Second, it is firm-specific, allowing for the decomposition of efficiency for each firm. third, it enables the decomposition of cost and revenue efficiency into pure technical, scale, and allocative components. Fourth, the Dea can operate with a small number of firms. Fifth, it corresponds to maximum likelihood estimation. Sixth, its estimators are consistent and converge much faster than other frontier techniques’ estimators. Seventh, its estimators are also unbiased. Finally, Dea provides reliable estimates of the effect of contextual factors in a two-stage framework. what is also noticeable is that 82.0% (50) of the Dea-based studies utilized classic models, i.e., the ccR (charnes et al., 1978) and Bcc (Banker et al., 1984) models. non-classic models include range adjusted measure (RaM) Dea (Brockett et al., 1998, 2004, 2005; Jeng & lai, 2005), robust Dea (naini & nouralizadeh, 2012), SBM Dea (kweh et al., 2014), network Dea (Hwang & kao, 2006; kao & Hwang, 2008; Sinha, 2021), dynamic network Dea (kuo et al., 2017), multi-stage metafrontier SBM (Shieh et al., 2020) and bootstrapped Dea combined with metafrontier analysis (Doumpos et al., 2018). SFa was applied in 23 studies (26.1%), varian’s weak axiom of Profit Maximisation (vwaPM) in two studies (emm, 2014; garven & grace, 2001), and DFa (Berger etal., 1997) and tFa (Bikker & gorter, 2011) in one study. as can be seen from table a1 in appendix 1, there appears to be agreement in frontier efficiency studies as to what constitutes inputs and outputs for insurance firms (see cummins & weiss, 2013). labour, business services, debt, and equity capital, represent the commonly used inputs, while losses (claims) incurred and invested assets represent the outputs frequently utilized in our sample. However, due to data limitations, some studies combined labour and business services (e.g., altuntas et al., 2019; luhnen, 2009; wende et al., 2008). Others (especially those outside the US) utilised losses incurred (e.g., Huang et al., 2011; Park & Park, 2015) instead of the present value of losses incurred (e.g., cummins & Xie, 2008; weiss & choi, 2008; Xie, 2010). Using actual values of inputs and outputs in the studies reviewed is also standard practice. Sixteen (6.8%) studies used market-based measures. Stock returns and tobin’s Q were the most often utilised in this category. Market-based measures are forward-looking and less prone to manipulation than performance measures that rely on accounting data (book values). nonetheless, market-based indicators can only be used for publicly traded insurers. For this reason, only a few studies have applied these measures in the insurance industry since most insurers, especially in developing countries, are private companies. twenty-four studies (10.2%) in our sample used insolvency prediction performance indicators. this stream of the literature relies on samples of insolvent and solvent insurers. the main drawback of this class of performance measures is that data on insolvent insurers is generally unavailable in most jurisdictions. among the five classes of performance measures, rating-based measures were the least popular; they constituted only 3.0% (7) of the studies we reviewed since ratings are primarily standard among large insurers. all studies based on this class concentrated on the US non-life insurance market. 3. Determinants of non-life insurer performance the objective of this section is to discuss the performance determinants of non-life insurers. it discusses nine firm-level (i.e., size, organisational form, diversification, capital structure, risk, reinsurance, corporate governance, distribution system, and group affiliation) and three external determinants of non-life insurer performance (such as market structure, macroeconomic and financial sector, and institutional development) that have been commonly investigated in the literature (see table a2). 14 t. ZinYORO anD M. J. aZiakPOnO & Jia, 2018; Jeng & Yang, 2014) effect. Results from studies that investigated the relationship between inflation and non-life insurer performance indicated largely a negative (e.g., adams et al., 2019; altuntas & Rauch, 2017; chang & tsai, 2014; Doumpos etal., 2012, 2018; Shiu, 2004) or insignificant (e.g., altuntas & Rauch, 2017; Browne & Hoyt, 1995; eling & Jia, 2018) effect. Similarly, studies that analysed the effect of interest rates on non-life insurer performance found either a negative (see Bajtelsmit & Bouzouita, 1998a, 1998b; gius, 1998; Haley, 1993; Shiu, 2004) or an insignificant (e.g., altuntas & Rauch, 2017; Browne & Hoyt, 1995; Fields et al., 2012) effect. Overall, the studies above agree that low economic growth, high inflation, and interest rates are associated with a decline in non-life insurer performance. 3.2.3. Financial sector and institutional development and non-life insurer performance a few studies that explored the relationship between the financial sector and institutional development and non-life insurer performance are mainly cross-country studies. among these studies, Doumpos etal. (2012), Hsieh etal. (2015), and Moro and anderloni (2014) found that stock market capitalization is positively related to insurer performance. concerning the effect of insurance sector development (measured by the penetration ratio minus gross written premiums divided by gDP) on non-life insurer performance, Ma et al. (2013) reported a negative effect in the asian insurance markets, while Bahloul and Bouri (2016), eling and Jia (2018), Moro and anderloni (2014) and Doumpos et al. (2012) reported an insignificant impact in the eU and global insurance markets, respectively. Doumpos etal. (2012) also considered the effect of banking sector development (measured as bank credit to gDP) on non-life insurer performance. their results indicate that banking sector development is not an essential driver of the performance of the non-life insurance industry. Fields et al. (2012) found limited evidence that better investment protection (proxied by anti-director, anti-self-dealing, disclosure index, liability standard index, and creditor rights), higher quality government (measured by the rule of law, anti-corruption, common law, and regulation quality), and greater contract enforcement (proxied using judicial independence and business environmental risk intelligence) collectively affect the underwriting performance of non-life insurers. they concluded that these factors lead to less risk-taking by insurers, preventing managers from expropriating wealth from policyholders and outside stockholders. Boubakri etal. (2008) reported that mergers and acquisitions of firms in countries with weaker investor protection positively correlate with performance. Similarly, elango and wieland (2015) concluded that the quality of governance negatively influences insurers’ profitability. Oetzel and Banerjee (2008) confirmed that insurers operating in emerging countries with better regulatory environments outperform insurers operating in environments with poor regulatory quality in terms of profitability (measured by ROa and return on premium). Zanghieri (2009) supported these findings regarding cost and profit efficiency. Davutyan and klumpes (2008) showed that regulatory scrutiny (control of corruption) in the eU insurance industry has a positive effect on managerial efficiency, a negative effect on scale efficiency, and an insignificant effect on overall technical efficiency (Ote). Doumpos et al. (2012) revealed that the institutional environment (measured by the institutional development index, enforcement index, financial freedom index, and economic financial freedom index) has no significant effect on non-life insurer performance (measured by a multi-criteria score). to sum up, the above empirical evidence indicated that the effect of financial sector and institutional development on non-life insurer performance varies with the independent variable proxy, performance measure, sample size, geographical coverage, and methods employed. it is also evident that the studies that explored the effect of these variables on non-life insurer performance are few, which makes it challenging to conclude their impact. 4. Summary and conclusion the determinants of the performance of non-life insurers have received considerable interest in the insurance literature. However, a comprehensive study is yet to be carried out to synthesise this stream of research. therefore, this study was to fill this research gap by systematically reviewing 235 studies cOgent BUSineSS & ManageMent 15 published between 1990 and 2021. the most popular journals in our dataset were the Journal of Risk and Insurance, The Geneva Papers on Risk and Insurance-Issues and Practice, the Journal of Banking and Finance, the Journal of Insurance Issues and Risk Management, and the Insurance Review. about country-specific studies, 57% focused on the US insurance industry, and cross-country studies are skewed in favour of developed insurance markets. we identified five classes of performance measures accounting- (traditional financial ratios), efficiency-, market-, insolvency prediction- and ratings-based (financial strength) measures. traditional financial ratios (99 studies, 42.1%) have been the most frequently used performance measures in the literature, followed by efficiency-based measures with 88 studies (37.4%). Market-, insolvency prediction- and rating-based performance measures were utilized in 15 (6.8%), 24 (10.2%), and 9 (3.8%) studies, respectively. we identified nine firm-level factors (size, organisational form, diversification, capital structure, risk, reinsurance, corporate governance, distribution system, and group affiliation) and three external factors (market structure, macroeconomic conditions, and financial sector and institutional development) that have been frequently investigated in the literature as the most critical drivers of non-life insurer performance. insurer size showed a non-linear effect on efficiency, a positive effect on profitability and financial strength performance, and a negative effect on the insolvency of non-life insurers. the findings on the effect of organizational form, reinsurance, leverage, distribution system, group affiliation, and market share on insurer performance are mainly mixed. the mixed results could be due to different performance measures, methodologies, sample sizes, jurisdictions, and proxies. there is a consensus in the literature that capitalisation positively affects performance. also, insurers that assume higher investment and underwriting risks underperform those with lower risk. the presence of monitoring mechanisms, particularly boards and their committees, improves non-life insurer performance. although country-specific studies indicate mixed evidence on the effect of concentration on non-life insurer performance, most multi-country studies showed that concentration positively influences non-life insurer performance. the literature also provided some evidence that low economic growth, high inflation, and interest rates are associated with a decline in non-life insurer performance. the effect of financial sector and institutional development on non-life insurers varies across studies. Several research gaps emerged from this survey. First, country-specific and multi-country studies concentrated on developed insurance markets. Future research could focus on emerging and developing countries. Second, more recent Dea methodologies, such as dynamic Dea, need to be applied, which provide room for their adoption. third, in exploring the effect of diversification on non-life insurer performance, it may be essential to consider the conditions under which diversification can result in a discount or premium, for example, organizational form, leverage, reinsurance, and risk. Fourth, some performance determinants, such as corporate governance, financial sector, and institutional development, have received less attention in the literature. Hence, there is a need for more studies to investigate their effect on non-life insurer performance. One specific topic that needs investigation is the nexus between corporate governance and cash holdings. Finally, additional studies could utilise non-structural competition measures, such as the Boone indicator.7, to analyse the competition-performance relationship. One of the implications of this survey is that there may be a need for increased solvency surveillance, especially of smaller insurers, which appear to have a higher risk of insolvency than their larger counterparts. Proper risk management could play a significant role in the operations of non-life insurers. adopting and enforcing a risk-based supervisory framework may be worthwhile to ensure that policyholders are protected against excessive risk-taking by non-life insurers. insurance supervisors may also need to ensure that insurance firms have well-functioning boards and committees. Notes 1. PRiSMa is a set of guidelines and a checklist to improve the reporting quality of systematic reviews and meta-analyses. its purpose is to enhance transparency and accuracy by providing a structured framework for researchers to follow in conducting and reporting these types of research studies (Sarkis-Onofre et al., 2021). 2. we interchangeably use property-liability, property-casualty, general, and non-life because terminology differs from region to region. 16 t. ZinYORO anD M. J. aZiakPOnO 3. For inclusion in the final sample. Full articles were examined, mainly if it needed to be made apparent that the study focused on the property-liability industry. 4. Other journal sources include academia economic Papers, acta Universitatis Danubius: Oeconomica, aestimatio, african Journal of Business and economic Research, annals of Operations Research, applied economics, applied economics letters, applied Financial economics, asia-Pacific Journal of Risk and insurance, asian economic and Financial Review, Benchmarking, an international Journal Benchmarking: an international Journal, British accounting Review, British actuarial Journal, British Journal of Management, china economic Review, computers & Operations Research, De economist, economic Modelling, economic Research-ekonomska istraživanja, empirical economics, eurasian economic Review, european Journal of Finance, Financial History Review, Fuzzy economic Review, indian economic Review, insurance and Risk Management, insurance Markets and companies, insurance: Mathematics and economics, intelligent Systems in accounting, Finance and Management, international Business Review, international Journal of economic Sciences, international Journal of industrial Organisation, international Journal of information Systems in the Service Sector, international Journal of Management, international Journal of Marketing, Financial Services and Management Research, international Journal of Systems Science, international Journal of the economics of Business, international Review of accounting, Banking and Finance, international Review of applied economics, international Review of Financial analysis, international Review of law and economics, investment Management and Financial innovations, islamic economic Studies, iUP Journal of Risk and insurance, Jing Ji lun wen cong kan, Journal of accounting and Public Policy, Journal of applied Statistics, Journal of asian Business and economic Studies, Journal of Business, Journal of Business and economic Studies, Journal of Business Finance and accounting, Journal of centRUM cathedra, the Business and economics Research Journal, Journal of centrum cathedra: Business and economics Research Journal, Journal of Developing areas, Journal of economic and administrative Sciences, Journal of economic Studies, Journal of economics and Management, Journal of Financial economics, Journal of Financial intermediation, knowledge Management, Journal of Money, credit and Banking, Journal of Regulatory economics, Journal of Risk and Financial Management, Journal of Service Management, Journal of Sustainable Finance and investment, Journal of the Operational Research Society, Management Decision, Management Science, Mathematical Problems in engineering, Omega, Pakistan Business Review, Service industries Journal, South african Journal of economics, Spanish Journal of Finance and accounting, Strategic Management Journal, taiwan economic Review, the accounting Review, the annals of the University of Oradea, the British accounting Review, the european Journal of Finance, the international Journal of Business and Finance Research, the international Journal of Digital accounting Research, tijdschrift voor economie en Management, total Quality Management and Business excellence, Zeitschrift für die gesamte versicherungswissenschaft.Journal of Financial Stability, Journal of intellectual capital, Journal of international Business Studies, Journal of international Management. 5. See cummins and weiss (2013) for a detailed discussion of the frontier efficiency approaches, including their advantages and disadvantages. 6. these measures do not rely on information on the market structure, such as size and number of firms, but instead directly measure competition (Bikker & van leuvensteijn, 2008). 7. it is an indirect measure of competition, posing that firms with diminished marginal costs are more efficient and consequently acquire larger market shares or higher profits (abel & Marire, 2021). it combines the lerner index, which measures a company’s market power, with the Herfindahl-Hirschman index (HHi), which measures market concentration. the Boone indicator is denoted by: Boone indicator = (1 - lerner index) * (1 / HHi). a positive Boone indicator indicates that a firm has some market power and can set prices above marginal costs. at the same time, a negative indicator suggests that firms are pricing closer to their marginal costs due to competitive pressures. Disclosure statement no potential conflict of interest was reported by the author(s). About the authors Tafadzwanashe Zinyoro (PhD) holds in Development Finance at Stellenbosch University Business School. His thesis is titled ‘insurer Performance and its Determinants: evidence from Selected african countries.’ His research interests encompass the efficiency and productivity of insurers and other financial service providers, the regulation of insurance markets, inclusive insurance, as well as climate and disaster risk financing and insurance. cOgent BUSineSS & ManageMent 17 Prof. Meshach J. Aziakpono is a Professor of economics in the Department of economics and economic History at Rhodes University, South africa. Before joining Rhodes University, he was a Professor of Development Finance and the Head of Development Finance programmes at Stellenbosch Business School. He holds PhD degree in economics from the University of the Free State, Bloemfontein in South africa. His PhD thesis titled: ‘the Depth of Financial integration and its effects on Financial Development and economic Performance of the Southern african customs Union countries’ won the Founders’ Medal for the best PhD dissertation in economics in South africa. He has published over 50 papers in international peer-reviewed journals and chapters of books. ORCID tafadzwanashe Zinyoro http://orcid.org/0000-0002-8550-6275 Meshach Jesse aziakpono http://orcid.org/0000-0002-5290-3311 References abel, S., & Marire, J. (2021). competition in the insurance sector—an application of Boone indicator. Cogent Economics & Finance, 9(1), 1–36. https://doi.org/10.1080/23322039.2021.1974154 adams, M., & Jiang, w. (2016). Do outside directors influence the financial performance of risk-trading firms? evidence from the United kingdom (Uk) insurance industry. Journal of Banking & Finance, 64, 36–51. https://doi.org/10.1016/j. jbankfin.2015.11.018 adams, M., & Jiang, w. (2017). 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(2004) China 1995–2002 14 Dea Labour, business services and materials, debt capital, equity capital Losses incurred, invested assets te Pte se 0.866 0.902 0.957 Hwang and Kao (2006) taiwan 2001–2002 24 network Dea stage 1: Business and administrative expenses, commissions and acquisition expenses; stage 2: Direct written premiums, reinsurance premiums received stage 1: Direct written premiums, reinsurance premiums received; stage 2: net underwriting income, net investment income Marketing efficiency Profitability efficiency te 0,849 0,596 0,561 Wang etal. (2007) taiwan 2000–2002 16 Dea Labour, business services, debt capital, equity capital Losses incurred, invested assets te ae Ce 0.871 0.789 0.716 Kasman and turgutlu (2009) turkey 2000–2005 22 Malmquist index Labour, business services, equity capital total losses paid tFP eC tC PeC seC 1.061 0.745 1.423 0.957 0779 Hsu and Petchsakulwong (2010) thailand 2000–2007 17 Dea Labour, materials and business services, equity capital Losses incurred, invested assets te ae Ce Re 0.941 0.626 0.593 0.852 singh and Kumar (2011) india 1993–2008 4 Dea operating expenses, equity capital net written premiums, incurred claims, investment income te Pte se 0.769 0.861 0.893 naini and nouralizadeh (2012) iran 2003–2010 17 Robust Dea Labour, general and administrative expenses, equity capital Losses incurred, Roe overall efficiency (CRs) 0.728 Chen et al. (2014) Malaysia 2008–2011 16 Dea, oLs Real labour and business services, debt capital, equity capital incurred claims + additions to reserves, invested assets tFP teC tC 1.212 0.992 1.220 Kweh et al. (2014) China 2006–2010 32 Dynamic sBM-Dea input1 = operating expenses Carry-over1 = debt capital Carry-over2 = equity capital incurred losses + additions to reserves, investment profits operating efficiency 0.839 Yaisawarng etal. (2014) thailand 2000–2007 46 sFa Labour, business services, debt capital, equity capital no. of policies, invested assets Ce (Model a) Ce (Model B) scale eco, Model a, B 0.793 0. 742 0.922, 0.916 alhassan and Biekpe (2015) south africa 2007–2012 70 Dea, Labour and business services, Debt capital, equity capital net premium earned, Claims incurred, investment income te (Model 1, Model 2) Pte (Model 1, Model 2) se (Model 1, Model 2) 0.522, 0.573 0.593, 0.672 0.878, 0.873 Table A1. Continued. (Continued) cOgent BUSineSS & ManageMent 31 study Country sample Period no. of insurers efficiency estimation inputs outputs efficiency type average efficiency Per Year Zakery and afrazeh (2015)iran 2006–2012 17 Dea 1st stage (intellectual capital creation) owned (financial) resources, no. of employees, general and administrative expenses 2nd stage (intellectual capital application) staff education, no. of agencies, product portfolio diversity 1st stage (intellectual capital creation) staff education, no. of agencies, product portfolio diversity 2nd stage (intellectual capital application) Roe, losses incurred te 0.769 alhassan and Biekpe (2016) south africa 2007–2012 66 sFa Labour and business services, Debt capital, equity capital Claims incurred, investment income Ce Pe 0.801 0.457 Kuo etal. (2017) Malaysia 2008–2013 16 Dynamic network sBM-Dea input1 = Labour (staff costs) input2 = Business services intermediate1 = incurred claims + additions to reserves intermediate2 = investment assets Carry-over1 = total liabilities Carry-over2 = equity capital net income, operating cash flow Marketing efficiency (stage 1) Profitability efficiency (stage 2) overall operating efficiency 0.854 0.932 0.848 sinha (2017) india 2013–2014 16 Dea operating expenses, net premium income operating income, assets under management, benefit paid te (input orientation) te (output orientation) 0.9001 0.898 Ferro and León (2018) argentina 2009–2014 69 sFa Direct labour costs, commissions to brokers, financial capital (debt and equity) total losses te 0,423 ilyas and Rajasekaran (2019) india 2005–2016 14 Dea Labour and business services, debt capital, equity capital Losses incurred, invested assets te Pte se ae Ce tFP tC teC PteC seC 0.792 0.895 0.884 0.817 0.818 1.321 1.268 1.042 1.218 0.855 ilyas and Rajasekaran (2020) india 2005–2016 15 Dea, Fare-Primont Pi Labour and business services, debt capital, equity capital Losses incurred, total investments tFP 1,124 ilyas and Rajasekaran (2021) india 2005–2016 15 Dea Labour and business services (operating expenses), debt capital, equity capital incurred claims, total investments tFP 1,007 Li et al. (2021) China 2011–2017 47 Dea total capital, fixed assets, management expenses Premium written, investment income, incurred claims te tFP 0,905 1,055 Table A1. Continued. (Continued) 32 t. ZinYORO anD M. J. aZiakPOnO study Country sample Period no. of insurers efficiency estimation inputs outputs efficiency type average efficiency Per Year Zhao et al. (2021) China 2013–2017 53 Dea Compensation expense, tax and surcharges, service charge and commission fee, operation and administrative expense Premium earned Pe ae te 0,626 0,746 0,827 sinha (2021) india 2010–2018 15 network Dea stage 1: number of branches, operating expenses, equity capital; stage 2: net premium income stage 1: net premium income; stage 2: assets under management; claims paid 1st stage 2nd stage overall te 0,955 0,784 0,870 Multi-country studies Diacon (2001)Multi-country, 6 eu 1999 431 Dea total operating expenses, total capital, total technical reserves, total borrowings from creditors net earned premiums, total investment income te 0.71 Davutyan and Klumpes (2008) Multi-country, 7 eu 1996–2002 284 Dea Labour, business services, equity capital PV of losses incurred, net earned premiums, average invested assets te Pte se 0.267 0.352 0.759 Fenn et al. (2008)Multi-country, 14 eu 1995–2001 562 sFa Labour, debt capital, equity capital net incurred claims Ce 0.930 Zanghieri (2009)Multi-country, 15 eu 1997–2006 not given sFa Labour, debt capital, equity capital Claims paid Ce Pe — — eling and Luhnen (2010b)Multi-country, 36 developed and developing incl. sa 2002–2006 3,566 sFa, Dea Labour and business services, debt capital, equity capital Claims + additions to reserves, total investments te Ce 0.810 0.740 Kasman and turgutlu (2011) Multi-country, 19 eu, switzerland, turkey, norway 1995–2005 472 sFa Debt capital, equity capital incurred claims Ce 0.882 Bahloul et al. (2013)Multi-country, 7 eu 2002–2008 125 sFa Labour and business services, debt capital, equity capital Losses incurred, reinsurance reserves, reserves for primary insurance contracts, total investments Ce FtP 0.690, 0.678 1.017 Huang and eling (2013)Multi-country, BRiC 2000–2008 819 Dea Labour, debt capital, equity capital net written premiums, invested assets te Pte se tFP teC tC 0.413 0.492 0.850 0.990 0.970 1.020 Bahloul and Bouri (2016)Multi-country, 7 eu 2002–2008 125 sFa, Fe Labour and business services, debt capital, equity capital Losses incurred, reinsurance reserves, reserves for primary insurance contracts, total investments Ce (Model 1) Ce (Model 2) 0.691 0.679 Doumpos et al. (2018)Multi-country, 8 eu 2000–2012 458 Metafrontier Re not given not given te 0.595 Note: Dea = data envelopment analysis, sFa = stochastic frontier analysis, tFa = thick frontier approach, DFa = distribution free approach, RaM = range adjusted measure, sBM = slacks-based measure, te = technical efficiency, Pte = pure technical efficiency, se = scale efficiency, ae = allocative efficiency, Ce = cost efficiency, Pe = profit efficiency, Re = revenue efficiency, Cse = cost scale efficiency, Rse = revenue scale efficiency, tFP = total factor productivity, eC = efficiency change, tC = technical change, PteC = pure technical efficiency change, PtC = pure technical change, seC = scale efficiency change, PV = present value. Source: authors’ compilation Table A1. Continued. cOgent BUSineSS & ManageMent 33 Table A2. summary of studies that examined drivers of non-life insurer performance. authors Countries Period no. of insurers Measure(s) of performance Methodology Determinants Country-specific studies-Developed countries Fields etal. (1990) us 1990 & 1988 36 stock return/value Cross-sectional regression sZ Fecher etal. (1991) France 1984–1989 164 te Comparison using ratios sZ, oF, RK, Re Barniv and McDonald (1992) us 1974–1988 294 insolvency MDa, LPM, exponential generalised beta distribution sZ shelor et al. (1992) us 1989 79 stock price/value gLs, Modified weighted least squares, Cross-sectional regression RK Barrese and nelson (1992) us 1978–1990 46 expenses Regression-error components Ds Carroll (1993) us 1980–1987 — Profit Margin 2sLs sZ, Cs, RK, Ms, Me Cummins and Weiss (1993) us 1980–1988 261 Ce sFa sZ Fecher etal. (1993) France 1984–1989 164 te Comparison using ratios sZ, oF, RK, Re Haley (1993) us 1930–1989 56 underwriting margin Cointegration techniques-VeCM, impulse response Me Brockett etal. (1994) us 1991–1992 243 insolvency Logistic regression, neural network approach Cs Cummins etal. (1995) us 1989–1991 1,596 insolvency Logistic regression sZ, oF, Cs Delhausse et al. (1995) Belgium 1984–1988 191 te, se Variance analysis oF, RK, Re, PM Kim et al. (1995) us 1984–1990 125 insolvency event history analysis DV, Re, ag, gW staking and Babbel (1995) us 1981–1987 25 tobin’s Q Regression analysis Cs, RK Kazenski et al. (1995) us 1985–1991 not given insolvency oLs RK, Ms Browne and Hoyt (1995) us 1970–1990 3,676 insolvency (solvency) Logistic regression Ms, Me grace and Hotchkiss (1995) us 1974–1990 not given Combined ratio VeCM, variance decomposition, impulse response Me gron (1995) us 1972–1983 44 Market share Multiple regression-weighted least squares Me Haley (1993) us 1949–1992 42 underwriting profit Cointegration techniques-VeCM Me Cagle et al. (1996) us 1968–1991 27 Various ratios e.g. direct written premiums, nWP, expense ratio, Roa, net underwriting income Ratio analysis oF Lee and urrutia (1996) us 1980–1991 164 insolvency Logit & hazard model Cs, gW, PM Kramer (1996) netherlands 1992 195 Financial solidity-subjective measures (strong, moderate, weak) ordered logit model Ms Berger et al. (1997) us 1981–1990 472 Ce, Pe Regression sZ, og, Ds, PM Brockett etal. (1997) us 1987–1990 44 insolvency Logistic regression, neural network approach Cs Chidambaran et al. (1997) us 1984–1993 18 economic loss ratio 2sgLs RK, Ds, Ms Fok etal. (1997) us 1991 400 income to surplus ratio Factor & regression analysis RK, Re, gW, Ms tennyson (1997) us 1992 64 expense ratio, market share Regression analysis gW, Me Bouzouita and Young (1998) us 1989–1992 not given Rating category ordered probit model, oLs sZ, oF, Cs, gW, PM Fields etal. (1998) us 1993 46 stock price/value event study, cross-sectional regression sZ, Re Brockett etal. (1998) us 1989 1524 efficiency Mann-Whitney statistic oF, Ds gius (1998) us 1976–1990 not given Premiums Re DV, RK, Ds, Me Bajtelsmit and Bouzouita (1998b) us 1984–1992 not given Profit margin Re Ds, Ms, Me Bajtelsmit, and Bouzouita (1998a) us 1984–1992 not given Profit margin Regression-instrumental variable approach Ms, Me Colquitt etal. (1999) us 1993–1995 1,400 Cash holdings oLs, Rne sZ, oF, Cs, RK, ga Cummins etal. (1999) us 1990–1992 268 insolvency Logistic regression sZ, oF (Continued) Appendix 2. Characteristics of studies that examined drivers of non-life insurer performance 34 t. ZinYORO anD M. J. aZiakPOnO authors Countries Period no. of insurers Measure(s) of performance Methodology Determinants Pottier and sommer (1999) us 1996 1,678 Rating category ordered probit regression sZ, DV, Cs, RK, Re, gW, PM Cummins etal. (1999) us 1981–1990 417 te, Ce Dea, Cross-frontier oF Born (2001) us 1984–1991 1,440 Roe Quantile regression, oLs sZ, oF, DV, Cs, RK, ag, PM, Ms, Me garven and grace (2001) us 1986–1996 500 efficiency score tobit sZ, oF, ga, Ds, PM Cummins and nini (2002) us 1993–1998 645 te, ae, Ce, Re, Roe oLs sZ, oF, DV, Cs, RK, Re, gW, PM Pottier and sommer (2002) us 1996–1998 1,779 insolvency Logistic regression sZ, oF Worthington and Hurley (2002) australia 1998 46 Pte, se, Ce, ae tobit sZ, oF, DV Lai and Limpaphayom (2003) Japan 1983–1994 24 Roa, expense ratio, commissions, loss ratio, investment income, free cash flow Pooled regression, 2sLs sZ, oF, Cs, RK Li and greenhood (2004) Canada 1993–1998 137 Roa Re sZ, DV, RK, gW, Ms shiu (2004) uK 1986–1999 137 investment yield, %ge change in shareholders’ funds, return on shareholders’ funds oLs, Rne, Fe sZ, Cs, Re, gW, Me Brockett etal. (2004) us 1989 1,524 efficiency Mann-Whitney statistic oF, Ds salcedo-sanz et al. (2004) spain 1983–1994 72 insolvency support Vector Machines-based methods Cs segovia-Vargas etal. (2004) spain 1983–1994 72 insolvency support Vector Machines, genetic algorithm & a simulated annealing Cs gaver and Pottier (2005) us 1997 80 Rating categories ordered probit regression sZ, Cs, RK Brockett etal. (2005) us 1989 1,524 efficiency Mann-Whitney statistic oF, Ds Choi and Weiss (2005) us 1992–1998 4,777 underwriting profit-margin gMM, Heteroskedastic 2sLs oF, Re, ga, gW, Ds, PM, Ms Jeng and Lai (2005) Japan 1985–1994 19 te, Ce RaM-Dea, Cross-frontier oF Díaz-Martínez et al. (2005) spain 1983–1994 72 insolvency non-parametric machine learning techniques, see5 & Rough set Cs Hwang and Kao (2006) taiwan 2001–2002 24 Marketing & Profitability eff network Dea sZ, oF, Ms Dionne et al. (2007) us 1995–2003 369 Ce suR sZ, oF, DV, Cs, RK, Re, ga, Ds, PM sharpe and stadnik (2007) australia 1999–2001 68 Financial distress (1 & 2) Logit regression sZ, Re, gW, PM scordis and Barrese (2007) us 1994–2004 41 Holding period return, market value, cash dividends Re, Fe Cs, RK, Re Cummins and Xie (2008) us 1994–2003 1,550 tFP, Pte, ae, se, Ce, Re oLs sZ, oF, DV, Cs, ga, PM elango et al. (2008) us 1994–2002 787 Roe, Roa, RaRoe, RaRoa Fe sZ, oF, DV, Cs, RK, ga, Ds, PM Liebenberg and sommer (2008) us 1995–2004 629 Roa, Roe, tobin’s Q oLs, 2sLs, Heckman sZ, oF, DV, Cs, RK, ga, PM, Ms Ma and elango (2008) us 1992–2000 41 RaRoa Fe sZ, DV Weiss and Choi (2008) us 1992–1998 1,119 CXe, RXe, Cse, underwriting profit margin 2sLs, two-stage tobit sZ, oF, DV, Re, ga, gW, Ds, PM, Ms, Me Boubakri et al. (2008) us 1995–2000 177 Market returns Regression oF, Cg, FsiD Ma and elango (2008) us 1992–2000 41 RaRoa Fe oF, RK, Re, ga, Ds elango (2009) us 2000–2004 847 Roa Logistic regression sZ, oF, DV, RK, ga, ag Kleffner and Lee (2009) Canada 1980–2004 159 insolvency Logistic regression sZ, oF, Cs, RK, ga, gW Luhnen (2009) germany 1995–2006 229 te, Ce truncated regression sZ, oF, DV, Cs, gW, Ds Park etal. (2009) us 1990–2001 370 Ce, Re Pooled oLs, two-way Fe, two-way Re sZ, oF, Cs, ga, Ds, PM Table A2. Continued. (Continued) cOgent BUSineSS & ManageMent 35 authors Countries Period no. of insurers Measure(s) of performance Methodology Determinants Barth and eckles (2009) us 1998–2005 1,545 Loss ratio Fe gW Chen et al. (2010) us 1985–2008 960 Roa, Roe, Combined ratio (CR), economic loss ratio (eLR) Panel regression sZ, oF, DV, Cs, Re, ga, Ms Cummins etal. (2010) us 1993–2006 718 te, se, Ce, ae, Re, Pe Multiple regression sZ, oF, DV, Cs, RK, Re, Ds, PM Lei and schmit (2010) us 1995–2006 102 RaRoa, Roe Re, Fe sZ, oF, DV, Cs, ga Parente et al. (2010) us 1992–2000 not given Market share growth, profitability oLs sZ, DV, Ds, PM Yin (2010) Japan 1989–1995 & 2004–2006 21 expense ratio Fe, Rne sZ, oF, Re, ag, PM Choi (2010) us 1992–2001 823 growth rate Heckman’s 2 stage regression oF, DV, Re, ga, ag, Ds Xie (2010) us 1994–2005 312 te, ae, se, Ce, Re, Roa, loss ratio, expense ratio, premium growth, other financials Fe oF Wang (2010) us 1995–2006 3,088 Rating grades Least squares regressions gW, Me Choi and elyasiani (2011) us 1992–1998 953 CXe, Cse, RXe, Rse, Roa, inverse of loss ratio, gMM sZ, oF, DV, Cs, Re, ga, Ds, PM eckles and Pottier (2011) us 1996–2000 1,158 Rating grades ordered logit model sZ, oF, Cs He et al. (2011) us 1995–2006 557 Ce, Re, tFP, tC, teC Multivariate regression sZ, oF, DV, Cs, Cg, PM Huang et al. (2011) us 2000–2007 28 te, Ce tobit sZ, Cg shi and Zhang (2011) us 2001–2006 1,527 Ce sFa-Copula regression sZ, oF shim (2011a) us 1990–2004 190 Pte, se, ae, Ce, Re Fe sZ, oF, DV, Cs, ga, Ds, PM shim (2011b) us 1989–2004 190 Roe, Roa, RaRoa, RaRoe, Z-score Regression lagged structure model sZ, oF, DV, Cs, RK, ga, Ds, PM Bikker and gorter (2011) netherlands 1995–2005 195 Ce, se oLs oF, DV Chen et al. (2011) us 1990–2001 540 te, ae, Ce, tFP oLs oF Lin et al. (2011) us 2002–2004 1,657 Ce sFa Cs, Re, gW Cheng and Weiss (2012) us 1994–2005 1,845 insolvency Logistic pooled regression sZ, oF, RK, Ms, Me Cummins etal. (2012) us 1993–2009 1,260 Roa, Roe, Ce, Re, Pe 2 way oLs Fe sZ, oF, DV, Cs, RK, Re, ga, PM Huang et al. (2012) Japan 1992–2005 22 te, ae, Ce truncated regression sZ, oF, DV Leverty and grace (2012) us 1989–2000 1,018 Ce, Re, insolvency, cost of insolvency Heckman 2-stage regression, logistic regression, tobit sZ, oF, Cg Lin et al. (2012) us 2000–2007 63 tobin’s Q, Roa underwriting Roa treatment-effect model sZ, DV, Cs, RK, Re, gW, PM andersson et al. (2013) sweden 1903–1939 102 investment returns gMM sZ, oF, Cs, RK Choi et al. (2013) us 1998–2007 1,717 Liquidity creation Regression sZ, oF, DV, Cs, Re, ga, PM Cummins and Xie (2013) us 1993–2009 781 Pte change, se change, Ce change, Re change, tFP change, Rts Fe, multinomial logit sZ, oF, DV, Cs, ga, Ds, PM Ma et al. (2013a) us 1993–2008 1,116 Ce, Re, Roe, Roa Regression sZ, oF, DV, Cs, Ds, PM Kelly et al. (2013) Canada 1991–2008 6 Loss ratio (LR) 3sLs PM, Ms Chang and tsai (2014) us 2006–2010 1,423 Liquidity oLs, Quantile regression sZ, oF, DV, Cs, RK, Re, ga, gW, Me Chen et al. (2014) us 2000–2011 1,479 RaRoa, RaRoe, CR Fe sZ, oF, DV, Cs, ga, PM emm (2014) us 1988–2001 630 Ce tobit sZ, oF, DV, Ds Park and Xie (2014) us 2003–2009 1,041 Rating, rating downgrade, stock price/return ordered Probit regression, 2sLs, Panel Regression sZ, oF, Cs, RK, Re, ga, ag, PM Hsu et al. (2015) us 1997–2002 242 Cash holdings Fe sZ, Cs, RK, Cg, ag Park and Park (2015)south Korea 2006–2011 17 te, Pte, Roa oLs sZ, oF, DV, Cs, ga, Ds, PM, Ms Table A2. Continued. (Continued) 36 t. ZinYORO anD M. J. aZiakPOnO authors Countries Period no. of insurers Measure(s) of performance Methodology Determinants Rauch and Wende (2015) germany 2004–2011 108 solvency ratio oLs, Logistic regression sZ, oF, DV, Cs, RK, gW Zhang and nielson (2015) us 1996–2006 1,422 insolvency Logistic regression DV, Cs, RK, ga, Me adams and Jiang (2016) uK 1999–2012 77 PM, Roa, Roe, solvency, CR 2sLs sZ, oF, DV, Re, Cg, ag ai et al. (2016) us 2006–2013 58 Roa, tobin’s Q 2sLs, Pooled oLs sZ, oF, DV, Cs, RK, ga, PM altuntas and gößmann (2016) germany 1999–2011 32 Roa oLs sZ, oF, DV, Cs, RK, Re, ag, PM altunta et al. (2016) germany 2004–2012 92 Roa, Roe, RaRoa, RaRoe oLs sZ, oF, DV, Cs, RK, ga Biener et al. (2016) switzerland 1997–2013 51 te, Ce, Re truncated regression sZ, oF, DV, Cs, ag, gW Chang and Jeng (2016) us 1994–2006 2,180 Liquidity 2sLs sZ, oF, DV, Cs, Re, ga, gW Cummins and Xie (2016) us 1993–2011 768 Ce, Re, Pe, tFP two-way Fe, one-way Fe sZ, oF, DV, Cs, Ds, PM Liu et al. (2016) uK 1994–2011 127 Liquidity 2sLs sZ, oF, DV, Cs, Re, PM adams and Jiang (2017) uK 1999–2012 77 PM oLs sZ, oF, Re, Cg, ag Caporale etal. (2017) uK 1985–2014 167 Default probabilities Reduced form regression sZ, oF, DV, Cs, Re, gW, Me Che et al. (2017) us 1997–2013 639 investment return oLs, Heckman model & 2sLs sZ, oF, DV, Cs, Re, ga, PM, Ms Lambalk and de graaf (2017) netherlands 2008–2012 25 Profit, CR, LR, oe, iR Regression oF, DV shim (2017a) us 1992–2010 738 Z-score oLs, 2sLs sZ, oF, DV, Cs, RK, Re, ga, gW, PM, Ms, Me shim (2017b) us 1996–2010 1,003 RaRoa Quantile regression sZ, oF, DV, Cs, RK, Re, ga, ag, gW, Ds, Ms ames et al. (2018) us 2007–2013 396 Rating category, Roe ordered logistic regression sZ, Cs, Cg adams et al. (2019) uK 1985–2010 145 CR, LR, eLR, Roa gLs sZ,DV, Cs, Re, PM, Me Lei (2019) us 1993–2008 1,505 RaRoe two-way Fe regression sZ, oF, DV, Cs, RK, Re, ga, PM Liebenberg and Lin (2019) us 2004–2013 747 Roa oLs, 2sLs, Heckman’s model sZ, oF, DV, Cs, RK, ga, Ms Maichel-guggemoos and Wagner (2019) germany 2001–2016 91 Profitability, growth, safety Regression analysis sZ, oF, gW, Ms Zhang et al. (2019) us 1992–2011 28 sharpe ratio, treynor ratio oLs, time-fixed oLs sZ, Cs, gW shiu (2020) uK 1994–2011 129 Roa three-equation structural model sZ, Cs, Re, RK, DV eling et al. (2020) germany 1954–2016 95 tFP Multiple regression sZ, oF, Ms, Me, DV adams and Baker (2021) uK 1999–2013 73 Roe, solvency Re, Fe sZ, oF, Re, Cg griffith and Liebenberg (2021) us 2002–2015 1,557 Roa, Roe, loss ratio, expense ratio oLs Re Park etal. (2021) us 2001–2009 1,321 Roa, Roe, investment return on equity (iRe); underwriting return on equity (uRe) oLs, 2sLs sZ, oF, Cs, Re, DV, ga Country-specific studies-Emerging countries Leverty et al. (2004) China 1995–2002 14 te, Pte, se, productivity Weighted tobit, WLs regression sZ, oF, Ms Wang etal. (2007) taiwan 2000–2002 16 te, ae, Ce, Roa Regression sZ, DV, Re, Cg, PM Kasman and turgutlu (2009) turkey 2000–2005 22 tFP tFP, eC, tC, PteC seC oF Hsu and Petchsakulwong (2010) thailand 2000–2007 17 te, ae, Ce, Re truncated regression sZ, Cg Table A2. Continued. (Continued) cOgent BUSineSS & ManageMent 37 authors Countries Period no. of insurers Measure(s) of performance Methodology Determinants shiu (2010) taiwan 2001–2003 24 solvency ratio Heckman 2 stage regression, oLs, Re sZ, DV, Cs, RK, Re, PM Foong and idris (2012) Malaysia 2006–2009 24 Roe Regression sZ, DV, Cs naini and nouralizadeh (2012) iran 2003–2010 17 overall efficiency generalised estimating equations (gee) sZ, oF, Cs, Ms Lee and Lee (2012) taiwan 1999–2009 15 Roa two-equation simultaneous model, 2sLs sZ, oF, DV, Cs, RK, Re, ga, gW, Ms Chen et al. (2014) Malaysia 2008–2011 16 Malmquist Productivity index oLs sZ, oF, Cs, Ms Jeng and Yang (2014) China 2000–2006 13 Roa, growth two-equation simultaneous model, gMM sZ, oF, DV, Cs, Re, ag, gW, Ms, Me Lee (2014) taiwan 1999–2009 15 Roa, operating ratio Rne sZ, DV, Cs, RK, Re, ga, gW, Ms, Me Kweh etal. (2014) China 2006–2010 32 operating efficiency oLs sZ, Cs iqbal and Rehman (2014) Pakistan 2002–2011 22 Loss ratio, expense ratio Pooled oLs, Fe, Rne Re Yaisawarng etal. (2014) thailand 2000–2007 46 Ce, tFP sFa PM alhassan and Biekpe (2015) south africa 2007–2012 70 te, Pte, Rts truncated, Logistic regression sZ, DV, Cs, Re, ag alhassan et al. (2015) ghana 2007–2011 22 Roa PCse, Fe sZ, Cs, RK, Ms, Me sandada et al. (2015) Zimbabwe n/a n/a non-financial measures Regression Cg Öner Kaya (2015) turkey 2006–2013 24 technical profitability ratio, sales profitability ratio Fe sZ, Cs, RK, Re, ag, gW, PM alhassan and Biekpe (2016b) south africa 2007–2012 66 Ce, Pe Fe sZ, DV, Cs, RK, Re, ag, Ms asare etal. (2017) ghana 2007–2011 22 Roa, uPM oLs-PCse sZ, Cs, RK Lee (2017) taiwan 1999–2010 15 RaRoa, RaRoe Fe sZ, DV, Cs, Re, ga, gW, PM, Ms sinha (2017) india 2013–2014 16 te truncated regression oF, Cs Kuo et al. (2017) Malaysia 2008–2013 16 Marketing efficiency, profitability efficiency truncated regression Cg ishtiaq (2017) Pakistan 2009–2013 40 Roa Multiple regression Me Ferro and León (2018) argentina 2009–2014 69 te average scores sZ alhassan and Biekpe (2018) south africa 2007–2012 37 Z-score isuR, oLs-PCse, QR, gMM sZ, oF, DV, Cs, Re, Ms Barua et al. (2018) Bangladesh 2000–2014 16 Roa, Roe Fe, Rne, Pooled oLs, PMg sZ, Cs, RK, ag, gW Hasan et al. (2018) Bangladesh 2009–2015 32 Roa, Roe Fe, Rne sZ, Cs, RK, ag, Me ilyas and Rajasekaran (2019) india 2005–2016 14 te, Pte, se, Ce, ae truncated regression, Mann-Whitney u test, Wilcoxon rank-sum tests sZ, oF, DV, Cs, RK, Re, ag Camino-Mogro and Bermúdez-Barrezueta (2019) ecuador 2001–2017 25 Roa, investment income, profit after tax PCse oF, DV, Cs, RK, Ms, Me, FsiD ilyas and Rajasekaran (2020) india 2005–2016 15 tFP Mann-Whitney U test oF Li et al. (2021) China 2011–2017 47 te, tFP Dea, Fuzzy set qualitative comparative analysis oF, Cs, Re, RK Wu and Li (2021) China 2009–2015 53 solvency step-wise regression Re andoh and Yamoah (2021) ghana 2008–2018 20 Roa Re sZ, Re, Me Zhao et al. (2021) China 2013–2017 53 Pe, ae, te Dea, tobit sZ, DV ilyas and Rajasekaran (2021) india 2005–2016 15 tFP Dea, Bootstrap truncated regression sZ, DV, Re sinha (2021) india 2010–2018 15 1st stage, 2nd stage, overall te network Dea, censored & probit regression oF Multi-country studies Diacon (2001)Multi-country, 6 eu 1999 431 te tobit sZ, oF, Cs, Re, PM, Ms Florez-Lopez (2007)Multi-country, 14 eu 1997–1999 257 Rating grades Multivariate models-MDa, logit, C4.5, CaRt gini sZ Davutyan and Klumpes (2008) Multi-country, 7 eu 1996–2002 284 te, Pte, se Fe sZ, oF, DV, Cs, ga, Me, FsiD Table A2. Continued. (Continued) 38 t. ZinYORO anD M. J. aZiakPOnO authors Countries Period no. of insurers Measure(s) of performance Methodology Determinants Fenn etal. (2008)Multi-country, 14 eu 1995–2001 562 Ce sFa sZ, Ms oetzel and Banerjee (2008) Multi-country, 31 emerging 1998–2003 307 Roe, Roa, Return on sales gLs Re sZ, oF, DV, ga, Me, FsiD Pope and Ma (2008)Multi-country, 23 developed & developing 1996–2003 15 uPM 2-way Fe PM, Ms, Me Zanghieri (2009)Multi-country, 15 eu 1997–2006 not given Ce, Pe sFa (translog cost function) sZ, DV, PM, Ms, FsiD eling and Luhnen (2010b) Multi-country, 36 developed & developing 2002–2006 3,566 te, Ce Conditional Mean approach sZ, oF, Cs Kasman and turgutlu (2011) Multi-country, 19 eu, switzerland, turkey, norway 1995–2005 472 Ce sFa (translog cost function) sZ Berry-stolzle et al. (2011)Multi-country, 12 countries 2003–2007 319 Profit margin oLs, 2sLs oF, Re, ga, gW, PM, Ms njegomir and stojić (2011) Multi-country, 11 eastern europe 2004–2008 not given Profit margin Fe PM, Ms, Me Doumpos et al. (2012)Multi-country, 91 developed & developing 2005–2009 1,836 Multicriteria score Fe sZ, Re, Me, FsiD Fields etal. (2012)Multi-country, 66 developed & developing 1992–2006 312 uPM oLs sZ, Cs, RK, Re, gW, Me, FsiD Huang and eling (2013) Multi-country, BRiC 2000–2008 819 te, Pte, se truncated regression sZ, Cs, RK, Cg Ma et al. (2013)Multi-country, 4 asian 2003–2008 36 uPM gMM sZ, DV, Re, ag, Ms, FsiD Moro and anderloni (2014) Multi-country, 9 eu 2004–2012 198 Roa, Roe Re sZ, oF, DV, Cs, RK, Re, gW, FsiD Hsieh et al. (2015)Multi-country, 62 developed & developing 1995–2009 43 Roa, Roe, tobin’s Q, Z-score Dynamic Panel gMM sZ, DV, Cs, Re, gW, Me, FsiD elango and Wieland (2015) Multi-country, 32 countries 2001–2007 2,293 Roa Hierarchical linear modelling techniques (HLM) Me, FsiD Biener et al. (2016) Multi-country, various 2003–2013 283 RaRoa, RaRoe, Ce LsDV, tobit, truncated regression sZ, DV, Cs, Re, gW, Me, FsiD Bahloul and Bouri (2016)Multi-country, 7 eu 2002–2008 125 Ce Fe Cg, Ms, Me, FsiD altuntas and Rauch (2017) Multi-country, 29 developed & emerging 2004–2012 1,600 Z-score, RaRoe oLs sZ, oF, Cs, RK, Re, ga, Ms, Me Doumpos et al. (2018)Multi-country, 8 eu 2000–2012 458 Metafrontier efficiency Re sZ, oF, DV, Re, Ms, Me, FsiD eling and Jia (2018)Multi-country, 16 eu 2006–2013 770 Probability of failure Logistic regression sZ, oF, Cs, RK, ga, gW, Me, FsiD olarewaju and Msomi (2021) saDC 2008–2019 58 Roa 2sLs,Fe, Re, 2-step system gMM sZ, Cs, RK Notes: te = technical efficiency, Pte = pure technical efficiency, se = scale efficiency, ae = allocative efficiency, Re = revenue efficiency, Pe = profit efficiency, tFP = total factor productivity, tC = technical change, teC = technical efficiency change, PteC = pure technical efficiency change, CXe = cost X-efficiency, RXe = revenue X-efficiency, Cse = cost scale efficiency, Rse = revenue scale efficiency, Rts = returns to scale, Roa = return on assets, Roe = return on equity, RaRoa = risk adjusted return on assets, RaRoe = risk adjusted return on equity, CR = combined ratio, LR = loss ratio, eLR = economic loss ratio, uPM = underwriting profit margin, oe = operating expenses, iR = investment return, oLs = ordinary least squares, Fe = fixed effects, Rne = random effects, PM = profit margin, gMM = generalised method of moment, gLs = generalised least squares, PCse = panel corrected standard errors, 2sLs = two-stage least squares, LsDV = least squares dummy variable, sFa = stochastic frontier approach, MDa = multiple discriminant analysis, QR = quantile regression, LPM = linear probability model, nPDM = non-parametric discriminant model, MPg = pooled mean group, sZ = size, oF = organisational form, DV = diversification, Cs = capital structure, RK = risk, Re = reinsurance, gW = growth, ga = group affiliation, Ds = distribution system, Cg = corporate governance, Ms = market structure, Me = macroeconomic, FsiD = financial sector and institutional development. Source: authors’ compilation. Table A2. Continued.