Analysis of Takaful vs. conventional insurance firms' efficiency: Two-stage DEA of Saudi Arabia's insurance market

Almulhim, Tarifa Article Analysis of Takaful vs. conventional insurance firms' efficiency: Two-stage DEA of Saudi Arabia's insurance market Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Almulhim, Tarifa (2019) : Analysis of Takaful vs. conventional insurance firms' efficiency: Two-stage DEA of Saudi Arabia's insurance market, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 6, pp. 1-18, https://doi.org/10.1080/23311975.2019.1633807 This Version is available at: https://hdl.handle.net/10419/206202 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/ BANKING & FINANCE | RESEARCH ARTICLE Analysis of Takaful vs. Conventional insurance firms’efficiency: Two-stage DEA of Saudi Arabia’s insurance market Tarifa Almulhim 1 * Abstract: Despite the remarkable growth in the insurance industry over the past two decades, few studies evaluate the performance of Takaful vs. conventional insurance firms with focus on the standard structure of production as a two-stage process, that is, operations and profitability. Thus, this research examines the performance of Saudi Arabia’s insurance market using a two-stage data envelopment analysis to assess the efficiency of the two production stages and accordingly, define the leader stage. The empirical results obtained using data for 26 conventional and seven Takaful insurance firms for 2014–2017 indicate declining average efficiency scores for both firm types. In other words, Saudi Arabia’s insurance market warrants new consolidation and foreign participation regulations to assist firms in becoming dynamic and strong. This study makes a significant contribution given the dearth of an exclusive analysis on the two-stage efficiency of Saudi Arabia’s Takaful and conventional insurance firms. Further, it offers key implications for decision makers, regulators, and managers associated with the insurance industry in Saudi Arabia and other emerging insurance markets. Subjects: Insurance; Operational Research / Management Science; Operations Management ABOUT THE AUTHOR Tarifa Almulhim received a bachelor’s degree in mathematics from King Faisal University, Alhassa, Saudi Arabia, in 2005; a master’sin science degree in operational research and applied statistics from the University of Salford, Salford, United Kingdom, in 2010; and a PhD in business and management (operational research) from the University of Manchester, Manchester, United Kingdom, in December 2014. She is, at present, the vice dean at the College of Business Administration and an assistant professor of quantitative methods at King Faisal University. She has published four research papers related to operational research, risk and insurance, decision theory, and fuzzy logic systems in international journals. Her current research interests include operational research, operations management, risk and insurance, multiple criteria decision analysis under uncertainties, decision theory, and fuzzy logic systems. PUBLIC INTEREST STATEMENT This study aims to evaluate the performance of Saudi Arabia’s insurance industry, particularly Takaful and conventional insurance firms, during 2014–2017 using a two-stage data envelopment analysis (DEA) method. The efficiency scores are based on data from 33 insurance firms listed on the Saudi stock exchange (Tadawul), of which seven are Takaful firms and 26 are conventional firms. In addition, this research investigates whether the operations and profitability stage is the optimal or leader stage in insurance firms. The key findings highlight that the performance in terms of average efficiency has monotonically decreased for both Takaful and conventional insurance firms. This study can assist regulators and managers attempting to develop Saudi Arabia’s insurance industry and improve its performance. Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Received: 06 February 2019 Accepted: 14 June 2019 First Published: 20 June 2019 *Corresponding author: Tarifa Almulhim, Quantitative Methods Department, School of Business, King Faisal University, 31982, Al-Ahsa, Saudi Arabia E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, Stirling, United Kingdom Additional information is available at the end of the article Page 1 of 18 Keywords: Takaful firms; Saudi Arabian insurance market; two-stage data envelopment analysis; efficiency 1. Introduction The development of the insurance industry has been commonly acknowledged as a significant catalyst for sustainable economic growth across the world (Ward & Zurbruegg, 2000). The insurance industry has witnessed substantial and accelerated growth over the past two decades (Arena, 2008). Numerous studies have been devoted to understanding insurance market activities and measuring insurance efficiency in both industrialized and developed countries. While there is extensive research on the insurance markets of developed countries, the coverage of the insurance industry in developing countries, particularly the emerging markets of Asia and more specifically, Saudi Arabia, remains narrow. This study focuses on Saudi Arabia’s insurance industry becauseit is the largest and oldest insurance market among the Cooperation Council for the Arab States of the Gulf, also known as the Gulf Cooperation Council (GCC), 1 countries (Samargandi, Fidrmuc, & Ghosh, 2014). In addition, Saudi Arabia is the largest economy in the Arab region. As per OPEC, it has the second largest (after Venezuela) proven oil reserves in the world, and it is a member of the Group of Twenty (G-20). The 2017 national institution statistics for GCC countries reports that Saudi Arabia’s population accounts for 65% of the GCC’s population. Saudi Arabia’s insurance industry has been significantly growing over the past few years owing to the application of actuarial pricing in 2013 and the increasing demand through economic development projects consistent with the 2030 vision. According to Albilad Capital (2015) report, Saudi Arabia’s insurance industry is the second largest in the Gulf region: it is valued at US $9.5 billion with a 16% growth rate driven by the increase in medical and motor coverage, which contributed to 81% of the insurance market during 2015. Insurance activities are still relatively new to Saudi Arabia, where the Law on Supervision of Cooperative Insurance Companies was enacted at the end of 2003 and its regulations were implemented in 2004. The Saudi Arabian Monetary Authority (SAMA) is responsible for regulating the Saudi insurance sector, licensing insurance firms, and analysing the market. In addition to SAMA, Saudi Arabia follows a cooperative insurance model called Islamic insurance (Takaful), which is an Islamic or Shari’ah-compliant alternative to conventional insurance. The term Takaful originated from the Arabic word “kafl”denoting assurance or responsibility (Jamil & Akhter, 2016). Currently, there are two forms of insurance operated and licensed in Saudi Arabia, conventional and Takaful insurance. Like any insurance sector in the world, Saudi Arabia’s insurance industry has a standard structure, that is, a production process comprising two stages, operations and profitability. While some studies have evaluated the overall performance of Saudi Arabia’s insurance companies, their efficiency in the two-stage production process remains insufficiently explored. These concerns can be summarized into the following questions. How efficient are conventional and Takaful insurance companies in the two production stages of operations and profitability? How do companies’decision makers ascertain which is the dominant stage in their overall performance? To the best of the author’s knowledge, no research has investigated the efficiency of Saudi Arabia’s Takaful and conventional insurance firms in the two-stage production process and their order of priority on the basis of the lead–follow concept. This study aims to bridge this gap by applying the two-stage data envelopment analysis (DEA) (Despotis, Sotiros, & Koronakos, 2016) to evaluate the efficiency of 26 conventional and seven Takaful insurance firms in the Saudi market in the context of operations and profitability. In addition, it adopts Li, Chen, Cook, Zhang, and Zhu (2018) extended method to determine the relationship between the firms’performance and the two substages during 2014–2017. Further, this study identifies which among the two sub-stages is the leader stage to improve our understanding on determining overall efficiency in the two types of insurance firms. It offers insight into the performance of Takaful operators by comparing their profit-sharing root operations with those of conventional insurance firms. Finally, this research offers recommendations for both researchers and regulators in the insurance sector. Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 2 of 18 The remainder of this paper is structured as follows. Section 2provides an overview of theoretical papers on efficiency and the empirical literature on Saudi Arabia’s insurance industry. Section 3describes the data and research methodologies utilized in this study. Section 4presents the empirical analysis and discusses its major findings. Section 5concludes the paper with a summary of our key findings. 2. Literature review The concept of efficiency evaluation has been predominant in the insurance literature. Data envelopment analysis (DEA), introduced by Charnes, Cooper, and Rhodes (1978), is a non-parametric method to assess the efficiency of decision-making units (DMUs) in a single stage that uses multiple inputs to yield multiple outputs. The DEA technique has been widely used to evaluate the performance and efficiency of the insurance industry. This section reviews pertinent efficiency studies in the context of insurance and particularly, research on Saudi Arabia’s insurance market. A majority of research on the efficiency of conventional insurance industries focuses on the United States and other developed countries. Cummins and Zi (1998) perform a DEA and mathematical programming to examine the efficiency of US insurance companies from 1988 to 1992 and deduce that the DEA is a better approach to evaluate insurance industry efficiency. Diacon, Starkey, and Obrien (2002) assess the pure technical and scale efficiencies of 450 insurance firms across 15 European countries and conclude the average technical efficiency declined during 1996–1999. Eling and Luhnen (2010) use DEA to perform a comprehensive efficiency assessment of the global insurance industry. Kaffash and Marra (2017) examine 620 papers published in journals indexed in the Web of Science database during 1985–2016 and employ DEA approaches with focus on financial services (e.g. insurance). While large numbers of studies evaluate the efficiency of conventional insurance, few pay attention to the efficiency of Takaful insurance. Saad, Majid, Yusof, Duasa, and Rahman (2006) measure the efficiency of Malaysia’s life insurance market using data for Takaful and conventional insurance firms. Their findings indicate that conventional firms perform better than Takaful firms and Takaful companies should grow to their optimal size to improve their efficiency score. Kader, Adams, and Hardwick (2009) conduct a DEA to analyse the cost efficiency of 26 Takaful insurance firms in 10 Islamic countries from 2004 to 2006 and indicate that the size of a firm and its board as well as product specialization positively impact Takaful insurance firms’cost efficiency. Ismail, Alhabshi, and Bacha (2011) examine the efficiency of Takaful and conventional insurance firms in Malaysia from 2004 to 2009 and conclude that the efficiency score of Tankful firms is low. Accordingly, they recommend that Takaful companies should decrease their organizational and management expenses to improve their efficiency scores. Al-Amri, Gattoufi, and Al-Muharrami (2012) analyse the performance of the insurance sector in GCC countries and present a comparative analysis of its different units between 2005 and 2007. Their results reveal that the efficiency of GCC’s insurance industry was moderate and there is scope for improvement. However, Al-Amri et al.’s study is limited to four insurance companies and thus, cannot be considered representative of Saudi Arabia’s insurance sector. Akhtar (2018) examines the performance of Saudi Arabia’s Takaful and conventional insurance companies during 2010–2015 by conducting a DEA and recommend that Takaful and large conventional insurance firms must follow the industry’s best practices to improve their efficiency and productivity levels. However, Akhtar considers the production process as a single stage and ignores the intermediate stage, which poses limitations when identifying the sources of inefficiency. Further, Akhtar’s(2018) study ignores the fact that the production process of Saudi Arabia’s insurance industry, like any insurance sector in the world, is a standard structure comprising two stages, operations and profitability. Traditional DEA considers the behaviour of decision-making units to be a black box and disregards the intermediate stages, thus detrimentally impacting high overall efficiency scores (Kao, 2009). To overcome this issue, the insurance literature has proposed several DEA frameworks with network systems including internal processes. Kao and Hwang (2008) argue an intermediate stage combining inputs with outputs must be considered in performance analyses to derive a precise overview of insurance Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 3 of 18 companies’performance. Using data on non-life insurance companies from Taiwan, they propose a novel relational DEA approach to evaluate decomposition efficiency in the two-stage production process by considering output variables in the first stage as input variables in the second stage. Cummins, Weiss, Xie, and Zi (2010) employ a two-stage DEA to examine the efficiency scores of insurance firms providing life health and property liability products during 1993–2006. Huang and Martin (2013) evaluate the efficiency of non-life insurance firms in four of the world’s fastest growing industries (Brazil, Russia, India, and China) during 2000–2008 using a multi-stage DEA approach. Their study captures inefficiencies attributable to external environmental circumstances, thus implying that country-specific environmental circumstances have a strong effect on the insurance industry. Despotis et al. (2016)presentanovel network DEA method to assess multi-stage efficiencies. Their proposed approach overcomes the lack of generality in existing multi-stage DEA approaches and offers unique and unbiased efficiency scores for the two-stage production process while treating each stage equally (Despotis et al., 2016). However, the two-stage DEA approach has been criticized for failing to define a leader stage. Liang, Yang, Cook, and Zhu (2008) argue the need to understand the relationship among the two stages and conclude that identifying the inefficient stage in a system is important to increase its efficiency by excluding inputs that are actually pending outputs from the efficient stage. Consequently, the authors develop an approach to determine the leader stage between the two stages; more specifically, they extend Despotis et al.’s(2016) study to develop a network DEA with a Pareto solution to examine for the dominant stage. A survey of the extant literature highlights that research measuring efficiency in multiple stages and identifying the leader stage have neglected the insurance markets, particularly Saudi Arabian insurance market with its two insurance types (conventional and Takaful). Thus, using Despotis et al.’s(2016) approach to estimate two-stage efficiencies, this study evaluates the efficiency of conventional and Takaful insurance companies in Saudi Arabia. In addition, it applies Li et al.’s (2018) proposed approach to determine the leader among the two stages. 3. Methodology and data description 3.1. Methodology Despotis et al. (2016) propose their composition approach using a bi-objective program model. They use typical DEA scores to determine the ideal efficiency point for each stage and the biobjective program model to locate a point on the Pareto front by minimizing the maximum weighted deviation from the ideal point in the objective function space (Despotis et al., 2016;Li et al., 2018). Unlike existing DEA methods, Despotis et al.’s(2016) approach offers unique efficiency scores and treats each stage equivalently. Figure 1illustrates the division of the two-stage configuration system into two subsystems of a series, in which the first stage will be the inputs of the second stage. Consider the following basic notations (Despotis et al., 2016; Li et al., 2018): j2J¼1;2;...nis the index of nDMUs; jo2Jis the evaluated DMU; Xj¼ðxij;i¼1;2;...m) is the vector of stage-one inputs utilized by DMUj.; Zj¼ðzpj;p¼1;2;...q) is the vector of intermediate variables for DMUj.; Yj¼ðyrj;r¼1;2;...s) is the vector of inputs in the second stage utilized by DMUj;η¼η1;η2;...;ηm ðÞ is the vector of weights for stage-one inputs in the fractional model; v¼v1;v2;...;vm ðÞis the weights vector for stage-one inputs in the linear model; φ¼φ1;φ2;...;φq  is the weights vector for the intermediate variables in the fractional model; w¼w1;w2;...;wq  is the weights vector for the Stage 1 Stage 2 X Z Y Figure 1. Series of two-stage processes. Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 4 of 18 intermediate variables in the linear model; ω¼ω1;ω2;...;ωs ðÞis the weights vector for stage-two outputs in the fractional model; u¼u1;u2;...;us ðÞis the weights vector for stage-two outputs in the linear model; eo jis the overall efficiency of DMUj,ek jis the efficiency of stage kfor DMUj,k¼1;2; Ek jis the independent efficiency score of stage kfor DMUj,k¼1;2; ek;L jis the lower bound of stage k’s efficiency for DMUj,k¼1;2; and ek;U jis the upper bound of stage k’s efficiency for DMUj,k¼1;2. Consider a basic constant-returns-to-scale–DEA (CRS–DEA) model that evaluates the efficiency of the first and second stages to independently assess DMU jo(Despotis et al., 2016; Kao & Hwang, 2008):, E1 j0¼max φZjo ηXjo ; s:t: φZjηXj0;j¼1;...;n; ωYjφZj0;j¼1;...;n; φε;ηε;ωε:(1) E2 j0¼max ωYjo φZjo s:t: φZjηXj0;j¼1;...;n; ωYjφZj0;j¼1;...;n; φε;ηε;ωε;(2) where εis a non-Archimedean constant (see Amin & Toloo, 2004). Despotis et al. (2016) propose the following bi-objective program model to evaluate the efficiencies of the two stages:, max wZjo max uYjo wZjo s:t: vXjo¼1 wZjvXj0;j¼1;...;n; uYjwZj0;j¼1;...;n vε;wε;uε:(3) Vector ðE1 j0;E2 j0Þestablishes the ideal point of the bi-objective program (3) in the objective functions space. The optimal solution for model (3) can be achieved using a two-phase procedure, which is equivalent to utilizing lexicographically L∞and L1 norms (Despotis et al., 2016). In the first phase, Despotis et al. (2016) assume no specific evidence prioritizing one of the two stages and then employ the unweighted Tchebycheff norm to their assessments as follows:, Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 5 of 18 minδ; s:t: E1 j0wZjoδ; ðE2 j0δÞwZjouYjo0; vXjo¼1 wZjvXj0;j¼1;...;n; uYjwZj0;j¼1;...;n vε;wε;uε;δ0:(4) While model (4) is non-linear, it can be easily solved using a bisection search (Despotis, 1996). Let δ ;v ;w ;u ðÞbe an optimal solution to model (4) and e1 j0¼wZjo vXjo ¼wZjo;e2 j0¼uYjo wZjo : In the second phase, Despotis et al. (2016) apply an equivalent to utilize the lexicographically L1 norm for the optimal solutions set of model (4) to determine a Pareto optimal solution for Equation (3) as follows:, maxs1þs2; s:t: E1 j0wZjoþs1¼δ ; ðE2 j0δÞwZjouYjoþs2wZjo¼0; vXjo¼1 wZjvXj0;j¼1;...;n; uYjwZj0;j¼1;...;n vε;wε;u0; δs10;δs20:(5) In model (5), δis the optimal value of the objective function in model (4) and wZjois the optimal virtual intermediate measure derived by model (4). The optimal solution ^ v;^ w;^ uðÞfor model (5), which is the Pareto optimal solution from model (3); unit jo’s efficiency scores in the first and second stages; and the overall system efficiency is, respectively, as follows: ^ e1 j0¼^ wZjo ^ vXjo ¼^ wZjo;^ e2 j0¼^ uYjo ^ wZjo ;^ e0 j0¼^ uYjo ^ vXjo ¼^ uYjo Since ^ e0 j0¼^ e1 j0:^ e2 j0,^ e2 j0¼^ e0 j0 ^ e1 j0 . Li et al. (2018) extend Despotis et al.’s(2016) study to produce a Pareto solution and define the dominant stage in a two-stage DEA. They show that the global optimal solution can be identified by comparing differences in the efficiency scores between the upper and lower bounds of the two Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 6 of 18 stages (Li et al., 2018). If e1;U je1;L j  >e2;U je2;L j  , then the first stage will be the leader; however, if e1;U je1;L j  <e2;U je2;L j  , then the second stage is the leader (Li et al., 2018). The ideal point in Despotis et al. (2016)is E1 j;E2 j  and in Li et al.’s(2018) extended model, it is ðe1;U j;e2;U jÞ. Li et al. (2018) employ the augmented weighted Tchebycheff metric to solve model (3) to determine e1;L j;e2;L j  . For a detailed review of the extended model, see Li et al. (2018). 3.2. Data description This study analyses data for 33 insurance firms listed on Saudi’s stock market, Tadawul (www. tadawul.com.sa), of which seven are Takaful firms and 26 are conventional insurance firms. The data are obtained from annual financial reports published by the insurance firms during 2014–2017. For the list of the companies included in this study, see Appendix I. Drawing on Kao and Hwang (2008) and Akhtar (2018), this study includes the following set of variables: equity (X1), net claims incurred (X2) and general and administrative expenses (X3) are input variables; net premium earned (Y1) and investment and management fee income (Y2) are final output variables; and direct written premium (Z1) and reinsurance premium (Z2) are intermediate variables. The first stage is defined as the operational (or premium) stage, and the second stage is the profitability/investment stage. 4. Empirical results and discussion In Tables 1and 2, the second and third columns present the stage-one efficiency scores, ^ e1 j;j¼1;2...33, and its rank; the fourth and fifth columns show the stage-two efficiency scores, ^ e2 j;j¼1;2...33;and its rank; and the last two columns list the overall efficiency scores, ^ e0 j;j¼1;2;...33, and its rank. A comparison of the efficiency ranking derived for the two stages (operational and profitability) and the overall efficiency scores for the production process highlights four companies with the overall efficiency of one in both stages during 2014–2016. The results benchmark two Takaful companies, SABB Takaful (No. 31) and Aljazira Takaful Taawuni Co. (No. 29), for 2014 and 2016. Further, Tables 1and 2indicate that Bupa Arabia for Cooperative Insurance (No. 10) reports consistent efficiency in both substages and overall efficiency in 2015. In 2017, The Company for Cooperative Insurance (Tawuniya) (No. 21) is benchmarked in the operational stage (first stage) and has high overall efficiency scores during the entire production process. According to SAMA (2017), Bupa Arabia for Cooperative Insurance and Tawuniya are the largest insurance firms (in assets) in Saudi Arabia and operate in diversified business areas (e.g. vehicle, marine, and health insurance as well as protection and savings). Among the 33 studied firms, Takaful companies are among the top 10 companies in terms of overall efficiency scores and yet, none of them perform efficiently in both sub-processes (see Tables 1and 2). Al-Rajhi Company for Cooperative Insurance (No. 27), Aljazira Takaful Taawuni Company (No. 29), SABB Takaful (No. 31), and Salama Cooperative Insurance Company (No. 32) are among the top 10 companies for 2014. Al-Rajhi Company for Cooperative Insurance (No. 27), Aljazira Takaful Taawuni Company (No. 29), and SABB Takaful (No. 31) rank among the top 10 companies for 2015 and 2016. Al-Rajhi Company for Cooperative Insurance (No. 27) and Salama Cooperative Insurance Company (No. 32) rank fifth and eight in 2017. Al-Rajhi Company for Cooperative Insurance (No. 27) is among the top five in 2014, 2016, and 2017. SABB Takaful (No. 31) ranks among the top five in 2014 and 2016. Evidently, some firms report significant differences in their performance ranking between the two sub-processes. For example, in 2017, AXA Cooperative Insurance Company (No. 9) and SABB Takaful (No. 31) are among the top 10 firms but perform unsatisfactorily in the second stage compared with the first stage. While the overall efficiency assists decision makers in identifying the sub-stage contributing to inefficiencies, it is important to determine the dominant stage. Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 7 of 18 Table 3shows that the average efficiency scores for Saudi Arabia’s Takaful and conventional insurance companies during 2014–2017 is 0.44, which is less than the score (0.61) estimated by Al-Amri et al. (2012) for GCC firms. Similarly, it is less than the average efficiency score (0.83) presented by Akhtar (2018) for both firm types from 2010 to 2014. Thus, exploring production process efficiency in terms of operations and profitability can offer deeper insight, particularly from a managerial perspective. Table 3also shows that Saudi Arabia’s insurance market is characterized by vast irregularities during the study period, with average efficiency scores for Takaful and conventional firms ranging between 0.05 and 1.00. Determining production process efficiency on the basis of operational and Table 1. Efficiency measures and their ranks for 33 insurance companies in Saudi Arabia, 2014–2015 2014 2015 DMU ^ e1Rank ^ e2^ e0Rank ^ e1Rank ^ e2Rank ^ e0Rank 1 0.8976 15 0.4664 27 0.4186 22 0.8914 11 0.6202 18 0.5528 12 2 0.3683 30 0.7292 17 0.2685 30 0.6653 23 0.5555 22 0.3696 23 3 0.3806 29 0.9798 5 0.3729 25 0.507 30 0.8413 9 0.4265 21 4 0.5765 27 0.3292 33 0.1898 32 0.7828 16 0.0966 33 0.0756 33 5 1 1 0.5921 23 0.5921 11 1 1 0.5185 25 0.5185 15 6 0.4229 28 1 1 0.4229 21 0.6267 25 0.5425 23 0.34 25 7 1 1 0.625 21 0.625 10 0.711 20 0.6042 20 0.4296 20 8 0.8332 17 0.5209 25 0.4341 20 0.8048 14 0.8947 7 0.7201 3 9 0.5884 25 0.8381 10 0.4932 14 1 1 0.8836 8 0.8836 2 10 1 1 0.8637 9 0.8637 2 1 1 1 1 1 1 11 0.699 20 0.7215 18 0.5043 13 0.4667 31 0.7412 11 0.3459 24 12 0.9578 12 0.3678 31 0.3523 26 0.7545 17 0.4288 28 0.3235 26 13 0.7603 19 0.4523 28 0.3439 27 0.5879 29 0.4718 26 0.2773 28 14 0.6746 21 0.5575 24 0.3761 24 0.6927 22 0.3599 30 0.2493 31 15 0.6349 23 0.7322 15 0.4649 17 0.7034 21 0.9556 3 0.6721 6 16 1 1 0.381 30 0.381 23 1 1 0.2567 31 0.2567 29 17 1 1 0.6758 19 0.6758 6 1 1 0.7038 14 0.7038 4 18 0.1846 33 0.9983 4 0.1843 33 0.6278 24 0.4006 29 0.2515 30 19 0.9966 10 0.6367 20 0.6345 9 0.8657 13 0.5335 24 0.4619 17 20 0.3006 31 0.9497 6 0.2855 29 0.6096 26 0.6675 15 0.4069 22 21 0.5839 26 0.7912 13 0.462 18 0.5942 28 0.8062 10 0.4791 16 22 0.9032 14 0.7316 16 0.6608 7 0.7515 18 0.908 6 0.6823 5 23 1 1 0.8312 11 0.8312 3 0.8839 12 0.6168 19 0.5452 13 24 0.8598 16 0.6232 22 0.5359 12 0.9017 10 0.5852 21 0.5277 14 25 1 1 0.483 26 0.483 15 1 1 0.6349 16 0.6349 8 26 0.9785 11 0.4488 29 0.4391 19 1 1 0.4341 27 0.4341 18 27 0.8141 18 0.9126 8 0.743 5 0.6065 27 1 1 0.6065 10 28 1 1 0.3373 32 0.3373 28 0.9879 9 0.2399 32 0.237 32 29 0.6559 22 1 1 0.6559 8 0.7161 19 0.9286 4 0.665 7 30 0.6342 24 0.738 14 0.468 16 0.7881 15 0.74 12 0.5832 11 31 1 1 1 1 1 1 1 1 0.6223 17 0.6223 9 32 0.9327 13 0.8041 12 0.7499 4 0.4645 32 0.9257 5 0.43 19 33 0.272 32 0.9395 7 0.2555 31 0.3947 33 0.7284 13 0.2875 27 Note: The rows highlighted in grey refer to Takaful firms. Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 8 of 18 Table 5. (Continued) 2016 2017 DMU e1;Ue1;Le2;Le2;ULeader Stage e1;Ue1;Le2;Le2;ULeader Stage 24 0.9547 0 0.6931 0.2216 1 0.6012 0 0.4054 0.2216 1 25 1 0.6237 0.9395 0.6674 1 0.4133 0.4838 0.3873 0.6481 2 26 1 0.3383 1 0.3383 1 1 0.2296 0.5685 0.3383 1 27 0.8518 0.8784 0.6313 1 2 0.8932 0.8608 0.8262 1 2 28 1 0.2393 0.4508 0.2549 1 0.3258 0.2504 0.0767 0.3229 2 29 111110.0509 1 0.0509 1 2 30 0.812 0.5525 0.6256 0.7642 1 0.6231 0.5525 0.5517 0.7996 2 31 1 0.7451 1 0.7451 1 0.1294 0.5419 0.1286 0.6422 2 32 0.6876 0.6057 0.3448 0.8684 2 0.6742 0.6057 0.6119 0.8736 2 33 0.2225 0.7952 0.1692 1 2 0.1026 0.7952 0.0985 1 2 Note: The rows highlighted in grey refer to Takaful firms. Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 15 of 18 Funding The author received no direct funding for this research. Author details Tarifa Almulhim 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-0228-1462 1 Quantitative Methods Department, School of Business, King Faisal University, Al-Ahsa, 31982, Saudi Arabia. Citation information Cite this article as: Analysis of Takaful vs. Conventional insurance firms’efficiency: Two-stage DEA of Saudi Arabia’s insurance market, Tarifa Almulhim, Cogent Business & Management (2019), 6: 1633807. Note 1. The Gulf Cooperation Council (GCC) member states include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. Disclosure statement The author has no conflict of interest to declare. References Akhtar, M. H. (2018). Performance analysis of Takaful and conventional insurance companies in Saudi Arabia. Benchmarking: An International Journal,25(2), 677–695. doi:10.1108/BIJ-01-2017-0018 Al-Amri,K.,Gattoufi,S.,&Al-Muharrami,S.(2012). Analyzing the technical efficiency of insurance companies in GCC. 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Journal of Risk and Insurance,67, 489–506. doi:10.2307/253847 Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 16 of 18 Appendix I Table A1. List of Saudi Arabia’s Takaful and conventional insurance Companies in the sample Number of DMUs Company Name Conventional Insurance Companies 1 Al Alamiya for Cooperative Insurance Company 2 Al Sagr Co-operative Insurance Cooperative 3 Al-Ahlia Insurance Company 4 Alinma Tokio Marine Company 5 Allianz Saudi Fransi Cooperative Insurance Company 6 Amana Cooperative Insurance Company 7 Arabia Insurance Cooperative Company 8 Arabian Shield Cooperative Insurance Company 9 AXA Cooperative Insurance Company 10 Bupa Arabia for Cooperative Insurance 11 Buruj Cooperative Insurance Company 12 Chubb Arabia Cooperative Insurance Company (Chubb) 13 Gulf General Cooperative Insurance Company 14 Gulf Union Cooperative Insurance Company 15 Malath Cooperative Insurance and Reinsurance Company 16 MetLife, American International Group and Arab National Bank Cooperative Insurance Company. 17 Saudi Arabian Cooperative Insurance Company 18 Saudi Enaya Cooperative Insurance Company 19 Saudi Indian Company for Cooperative Insurance 20 Saudi Re for Cooperative Reinsurance Company 21 Company for Cooperative Insurance (Tawuniya) 22 Mediterranean and Gulf Cooperative Insurance and Reinsurance Company 23 Trade Union Cooperative Insurance Company 24 United Cooperative Assurance Company 25 Walaa Cooperative Insurance Company 26 Wataniya Insurance Company Takaful Insurance Companies 27 Al-Rajhi Company for Cooperative Insurance 28 AlAhli Takaful Company 29 Aljazira Takaful Taawuni Company 30 Allied Cooperative Insurance Group 31 SABB Takaful Company 32 Salama Cooperative Insurance Company 33 Solidarity Saudi Takaful Company Almulhim, Cogent Business & Management (2019), 6: 1633807 https://doi.org/10.1080/23311975.2019.1633807 Page 17 of 18 © 2019 The Author(s). 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