Abstract:

In this new era of knowledge-based systems, financial institutions tend to improve as per performance standards using tangible and intangible resources. Intellectual capital (IC) gained much attention and in the recent past has encouraged the researchers to shed light on the connection of IC. The insurance companies plays a vital role in the financial system. This study investigates the impact of IC on the insurance sector's performance, i.e. sustainable growth (SGR), earnings and profitability, using value-added intellectual coefficient (VAIC) and modified value-added intellectual coefficient (MVAIC) methods. In addition, among all the IC elements, the study finds physical capital/capital employed (CE) and human capital (HC) most contributing factors in IC performance, whereas structural capital (SC) needs more focus to enhance the performance. Furthermore, the results suggest more attention towards relational capital (RC) as the study finds it's a positive impact on the performance, but it continues to remain insignificant. This study will be prospectively helpful for academics, policymakers, economists and managers. This study enlightens the IC’s role in achieving sustainable growth

Key Words:

Sustainable Growth, Earnings, Profitability, Intellectual Capital, Insurance Companies, Pakistan

Introduction

A knowledge-based management of intangible mostly influences sustainable performance compared to tangible resources (Reed, Lubatkin, & Srinivasan, 2006). There is a consensus among the academic community that IC is the firm’s primary intangible asset and a critical source of competitive advantage (Bontis, 1998; Stewart, 2010).  However, most empirical studies have focused on Anglophonic (e.g., Canada, UK) or Scandinavian (e.g., Sweden) research settings (Serenko & Bontis, 2017). 

There is very little research on whether IC impacts performance in developing nations like Pakistan.  However, Pakistan is a relatively large country by population (over 212 million inhabitants) but with a relatively low level of individual wealth (USD 5,872 GDP per capita).  Nevertheless, it has a burgeoning financial services sector and the potential to become a significant economy in the long term.  

This study perform within the Pakistani insurance sector using the VAIC and MVAIC. VAIC was first developed by Pulic (1998), and impact of (CEE), human capital efficiency (Makhloufi, Laghouag, Ali Sahli, & Belaid), on financial performance. There has not been any such previous study conducted related to IC that applied SGR and the other two determinants, earnings, and profitability as dependent variables over the insurance sector of Pakistan.

Literature Review Definition and Measurement

Many previous studies have defined and provided measurement approaches for intellectual capital. (Edvinsson & Malone, 1997) proposed IC as a collection of resources that delivered a competitive advantage to an organization. Soon after, several seminal studies classified IC into physical capital, (Bontis, 1998; Stewart, 2010). 

Pulic (1998) originally developed the VAIC approach an empirical method of measuring IC when most research studies examined IC from a qualitative and case study perspective. Although VAIC had some initial limitations, Pulic (2004) & Pulic (2000) continued to refine their approach and soon added a measure for relational capital efficiency to their existing model.

Several academic studies have since included relational capital in their measurement approach (i.e., by adding RCE to the previous components of CEE, HCE and SCE), which is now considered a valid modification of the original VAIC approach (MVAIC) (Chan, 2009; Vidyarthi, 2019; Xu & Wang, 2019; Yao, Haris, Tariq, Javaid, & Khan, 2019). 

IC Performance

A rich literature on IC and specific financial performance in single and multiple economies is available. However, for dynamic panel data analysis, GMM has been applied in some recent studies (Adesina, 2019; Haris, Yao, Tariq, Malik, & Javaid, 2019; Yao et al., 2019). GMM estimator was first used by (Arellano & Bond, 1991) and developed lately for vibrant data sets by (Arellano and Bover, 1995). The system-GMM estimator reflects the determination of profits that affect the performance, which is difficult to measure or identify in a single equation (Yao, Haris, & Tariq, 2018). The IC performance (Ashraf, Li, & Mehmood, 2017; H. Li et al., 2021), Insurance Sector using a GMM two-step system estimator in Pakistan.

However, among existing IC studies, Ahmad and Ahmed (2016) applied linear regression over 2008–2013 on a sample of 78 Pakistan financial institutions. They found that among all the VAIC components, CEE has significant importance to raise profitability. Another study conducted by (Haris, Yao, Tariq, Javaid, and Malik (2018); Mangenda Tshiaba, Wang, Ashraf, Nazir, & Syed, 2021) and 20 banks were engaged in 2007–2016. They applied multiple regression and reported a higher contribution of HCE among all VAIC components. 

Hypothesis Development

IC and Performance

Intangible resources are particularly important because they help achieve a competitive advantage and improve performance by sustaining it (Haris et al., 2019; Wernerfelt, 1984). A rich literature has supported between IC and the different financial institutions (Ahmad & Ahmed, 2016; F.-C. Chen, Liu, & Kweh, 2014; Haris et al., 2018; Mondal & Ghosh, 2012; Tasawar & Roszaini, 2017; Yalama, 2013).

Hypothesis 1(H1)

IC (VAIC and MVAIC) has a positive impact on the performance of Insurance companies in Pakistan.

CEE and Performance

VAIC, CEE is related to the measurement of the efficiency of physical capital invested in the company. In addition, however, some previous literature found and CEE performance. A few studies found no impact of CEE on performance (Firer & Williams, 2003; Joshi, Cahill, & Sidhu, 2010; Poh, Kilicman, & Ibrahim, 2018; Smriti & Das, 2018). Therefore, we propose our second hypothesis:

Hypothesis 2 (H2)

CEE has a positive relationship with the performance of Insurance companies in Pakistan.

HCE and Performance

Human capital consists of intangible resources such as knowledge, expertise, talents, ideas, experience, capabilities, and creative skills. Bontis (1998) suggested that, in a knowledge-based economy, the organization can utilize HC to achieve strategic goals and even get an innovative competitive advantage. HC is being evaluated by HCE. Many researchers studied HCE and positive and negative with performance. However, some studies (Ahmad & Ahmed, 2016; Haris et al., 2018; Yao et al., 2019), positive relationship impact of HCE on performance.

Hypothesis 3 (H3)

HCE has a positive relationship with the performance of Insurance companies in Pakistan.

SCE and Performance

SC is described as the organizational system and structure consisting of a database, corporate approaches, management processes, and organizational strategies. Moreover, some studies found positive relationship between SCE and profitability

(M. A. K. Al-Musali & Ismail, 2014; F.-C. Chen et al., 2014; Y. Li & Zhao, 2018; Yao et al., 2019). Some studies found an insignificant impact on profitability (Alhassan & Asare, 2016; Kehelwalatenna & Premaratne, 2014; Smriti & Das, 2018; Tasawar & Roszaini, 2017; Tran & Vo, 2018).

Hypothesis 4 (H4)

SCE has a positive impact on the performance of Insurance companies in Pakistan

RCE and Performance

RC is related to the sustainable and long-term relationships with external factors, including vendors, customers, creditors, and even competitors. Some studies found a negative impact of RCE on performance and profitability

(M. C. Chen, Cheng, & Hwang, 2005; Vidyarthi, 2019). Some studies (Ashraf, Li, Butt, Naz, & Zafar, 2019; Sardo & Serrasqueiro, 2017; Xu & Wang, 2018, 2019), reported a positive RCE and performance. Nimtrakoon (2015) and Soetanto and Liem (2019) found no interaction between performance and RCE. Yao et al. (2019) reported an insignificant intraction of RCE and performance of 111 institutions of Pakistan. 

Hypothesis 5 (H5)

There is a positive relationship between RCE and the performance of Pakistani insurance companies.

Sample and Methodology

Sample and Data

In the Pakistani insurance sector, at present, 30 non-life/general insurance companies, 7 life insurance, 1 re-insurance company, and 1 Islamic Takaful company are operating in Pakistan. This study utilized a sample of 31 insurance companies from 2007–2016, in which 25 non-life insurance and 6 life insurance companies are included. 1 foreign, 4 life insurance, 1 non-life insurance, 1 re-insurance and 1 Islamic Takaful company excluded due to the unavailability of the required financial data. Sindh Insurance was established in 2014, so the study has taken the data from 2014–2016. For this study, the required financial data is acquired from both audited consolidated and unconsolidated financial statements maintained by each company and also from IAP (Insurance Association of Pakistan), which maintains the database for all the insurance companies (world bank) in the country and the data related to macro-economic variables. All the information utilized and attain the current research are relevant, authentic, and reliable to perform realistic research.  




Literature Review Definition and Measurement

Many previous studies have defined and provided measurement approaches for intellectual capital. (Edvinsson & Malone, 1997) proposed IC as a collection of resources that delivered a competitive advantage to an organization. Soon after, several seminal studies classified IC into physical capital, (Bontis, 1998; Stewart, 2010). 

Pulic (1998) originally developed the VAIC approach an empirical method of measuring IC when most research studies examined IC from a qualitative and case study perspective. Although VAIC had some initial limitations, Pulic (2004) & Pulic (2000) continued to refine their approach and soon added a measure for relational capital efficiency to their existing model.

Several academic studies have since included relational capital in their measurement approach (i.e., by adding RCE to the previous components of CEE, HCE and SCE), which is now considered a valid modification of the original VAIC approach (MVAIC) (Chan, 2009; Vidyarthi, 2019; Xu & Wang, 2019; Yao, Haris, Tariq, Javaid, & Khan, 2019). 

IC Performance

A rich literature on IC and specific financial performance in single and multiple economies is available. However, for dynamic panel data analysis, GMM has been applied in some recent studies (Adesina, 2019; Haris, Yao, Tariq, Malik, & Javaid, 2019; Yao et al., 2019). GMM estimator was first used by (Arellano & Bond, 1991) and developed lately for vibrant data sets by (Arellano and Bover, 1995). The system-GMM estimator reflects the determination of profits that affect the performance, which is difficult to measure or identify in a single equation (Yao, Haris, & Tariq, 2018). The IC performance (Ashraf, Li, & Mehmood, 2017; H. Li et al., 2021), Insurance Sector using a GMM two-step system estimator in Pakistan.

However, among existing IC studies, Ahmad and Ahmed (2016) applied linear regression over 2008–2013 on a sample of 78 Pakistan financial institutions. They found that among all the VAIC components, CEE has significant importance to raise profitability. Another study conducted by (Haris, Yao, Tariq, Javaid, and Malik (2018); Mangenda Tshiaba, Wang, Ashraf, Nazir, & Syed, 2021) and 20 banks were engaged in 2007–2016. They applied multiple regression and reported a higher contribution of HCE among all VAIC components. 

Hypothesis Development

IC and Performance

Intangible resources are particularly important because they help achieve a competitive advantage and improve performance by sustaining it (Haris et al., 2019; Wernerfelt, 1984). A rich literature has supported between IC and the different financial institutions (Ahmad & Ahmed, 2016; F.-C. Chen, Liu, & Kweh, 2014; Haris et al., 2018; Mondal & Ghosh, 2012; Tasawar & Roszaini, 2017; Yalama, 2013).

Hypothesis 1(H1)

IC (VAIC and MVAIC) has a positive impact on the performance of Insurance companies in Pakistan.

CEE and Performance

VAIC, CEE is related to the measurement of the efficiency of physical capital invested in the company. In addition, however, some previous literature found and CEE performance. A few studies found no impact of CEE on performance (Firer & Williams, 2003; Joshi, Cahill, & Sidhu, 2010; Poh, Kilicman, & Ibrahim, 2018; Smriti & Das, 2018). Therefore, we propose our second hypothesis:

Hypothesis 2 (H2)

CEE has a positive relationship with the performance of Insurance companies in Pakistan.

HCE and Performance

Human capital consists of intangible resources such as knowledge, expertise, talents, ideas, experience, capabilities, and creative skills. Bontis (1998) suggested that, in a knowledge-based economy, the organization can utilize HC to achieve strategic goals and even get an innovative competitive advantage. HC is being evaluated by HCE. Many researchers studied HCE and positive and negative with performance. However, some studies (Ahmad & Ahmed, 2016; Haris et al., 2018; Yao et al., 2019), positive relationship impact of HCE on performance.

Hypothesis 3 (H3)

HCE has a positive relationship with the performance of Insurance companies in Pakistan.

SCE and Performance

SC is described as the organizational system and structure consisting of a database, corporate approaches, management processes, and organizational strategies. Moreover, some studies found positive relationship between SCE and profitability

(M. A. K. Al-Musali & Ismail, 2014; F.-C. Chen et al., 2014; Y. Li & Zhao, 2018; Yao et al., 2019). Some studies found an insignificant impact on profitability (Alhassan & Asare, 2016; Kehelwalatenna & Premaratne, 2014; Smriti & Das, 2018; Tasawar & Roszaini, 2017; Tran & Vo, 2018).

Hypothesis 4 (H4)

SCE has a positive impact on the performance of Insurance companies in Pakistan

RCE and Performance

RC is related to the sustainable and long-term relationships with external factors, including vendors, customers, creditors, and even competitors. Some studies found a negative impact of RCE on performance and profitability

(M. C. Chen, Cheng, & Hwang, 2005; Vidyarthi, 2019). Some studies (Ashraf, Li, Butt, Naz, & Zafar, 2019; Sardo & Serrasqueiro, 2017; Xu & Wang, 2018, 2019), reported a positive RCE and performance. Nimtrakoon (2015) and Soetanto and Liem (2019) found no interaction between performance and RCE. Yao et al. (2019) reported an insignificant intraction of RCE and performance of 111 institutions of Pakistan. 

Hypothesis 5 (H5)

There is a positive relationship between RCE and the performance of Pakistani insurance companies.

Sample and Methodology

Sample and Data

In the Pakistani insurance sector, at present, 30 non-life/general insurance companies, 7 life insurance, 1 re-insurance company, and 1 Islamic Takaful company are operating in Pakistan. This study utilized a sample of 31 insurance companies from 2007–2016, in which 25 non-life insurance and 6 life insurance companies are included. 1 foreign, 4 life insurance, 1 non-life insurance, 1 re-insurance and 1 Islamic Takaful company excluded due to the unavailability of the required financial data. Sindh Insurance was established in 2014, so the study has taken the data from 2014–2016. For this study, the required financial data is acquired from both audited consolidated and unconsolidated financial statements maintained by each company and also from IAP (Insurance Association of Pakistan), which maintains the database for all the insurance companies (world bank) in the country and the data related to macro-economic variables. All the information utilized and attain the current research are relevant, authentic, and reliable to perform realistic research.  




Table 1. Presents a List of Companies Analysed in this Study (C. Li et al., 2020).

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S. No width="186"> Name width="54"> Abb. width="108"> Year Of Establishment width="96"> Assets (PKR’000) width="56"> Share%

1 width="186"> Adamjee Insurance Company Ltd. width="54"> ADI width="108"> 1960 width="96"> 38,579,911 width="56"> 3.70%

2 width="186"> Alfalah Insurance Company Ltd. width="54"> ALIC width="108"> 2007 width="96"> 2,808,426 width="56"> 0.27%

3 width="186"> Alpha Insurance Company Ltd. width="54"> APIC width="108"> 1951 width="96"> 1,105,534 width="56"> 0.11%

4 width="186"> Asia Insurance Company Ltd. width="54"> ASIC width="108"> 1980 width="96"> 1,054,652 width="56"> 0.10%

5 width="186"> Askari General Insurance Company  Ltd. width="54"> ASKC width="108"> 1995 width="96"> 3,726,578 width="56"> 0.36%

6 width="186"> Atlas Insurance Company Ltd. width="54"> AT:LC width="108"> 1934 width="96"> 4,277,603 width="56"> 0.41%

7 width="186"> Century Insurance Company Ltd. width="54"> CIC width="108"> 1989 width="96"> 2,660,683 width="56"> 0.26%

8 width="186"> Cooperative Insurance Company Ltd. width="54"> COIC width="108"> 1949 width="96"> 2,222,045 width="56"> 0.21%

9 width="186"> Crescent Star Insurance Company Ltd. width="54"> CSIC width="108"> 1957 width="96"> 1,009,123 width="56"> 0.10%

10 width="186"> EFU General Insurance Company Ltd. width="54"> EFUC width="108"> 1932 width="96"> 36,204,203 width="56"> 3.48%

11 width="186"> East West Insurance Company Ltd. width="54"> EWIC width="108"> 1983 width="96"> 2,335,785 width="56"> 0.22%

12 width="186"> Habib Insurance Company Ltd. width="54"> HBIC width="108"> 1942 width="96"> 2,759,878 width="56"> 0.27%

13 width="186"> New Jubilee Insurance Company Ltd. width="54"> NJIC width="108"> 1953 width="96"> 17,226,095 width="56"> 1.65%

14 width="186"> PICIC Insurance Company Ltd. width="54"> PICIC width="108"> 2004 width="96"> 335,902 width="56"> 0.03%

15 width="186"> Pakistan General Insurance Company Ltd. width="54"> PGIC width="108"> 1947 width="96"> 960,234 width="56"> 0.09%

16 width="186"> Premier Insurance Company Ltd. width="54"> PRIC width="108"> 1952 width="96"> 3,745,154 width="56"> 0.36%

17 width="186"> Reliance Insurance Company Ltd. width="54"> REIC width="108"> 1982 width="96"> 1,811,478 width="56"> 0.17%

18 width="186"> Saudi-Pak Insurance Company Ltd. width="54"> SPIC width="108"> 2005 width="96"> 1,033,260 width="56"> 0.10%

19 width="186"> Security General Insurance Company Ltd. width="54"> SGIC width="108"> 1996 width="96"> 12,588,143 width="56"> 1.21%

20 width="186"> Shaheen Insurance Company Ltd. width="54"> SHIC width="108"> 1996 width="96"> 770,634 width="56"> 0.07%

21 width="186"> Sindh Insurance Company  Ltd. width="54"> SIC width="108"> 2014 width="96"> 2,985,812 width="56"> 0.29%

22 width="186"> TPL Direct Insurance Company Ltd. width="54"> TPC width="108"> 2005 width="96"> 2,277,971 width="56"> 0.22%

23 width="186"> UBL Insurance Company Ltd. width="54"> UBLC width="108"> 2007 width="96"> 3,531,828 width="56"> 0.34%

24 width="186"> United Insurance Company Ltd. width="54"> UNIC width="108"> 1959 width="96"> 5,446,580 width="56"> 0.52%

25 width="186"> Universal Insurance Company Ltd. width="54"> UNVC width="108"> 1958 width="96"> 803,566 width="56"> 0.08%

26 width="186"> East West Life Insurance Company Ltd. width="54"> EWLC width="108"> 1992 width="96"> 476,272 width="56"> 0.05%

27 width="186"> EFU Life Insurance Company Ltd. width="54"> EFULC width="108"> 1991 width="96"> 106,301,531 width="56"> 10.21%

28 width="186"> IGI Life Insurance Company Ltd. width="54"> IGILC width="108"> 1994 width="96"> 19,232,731 width="56"> 1.85%

29 width="186"> Jubilee Life Insurance Company Ltd. width="54"> JLIC width="108"> 1994 width="96"> 102,796,766 width="56"> 9.87%

30 width="186"> State Life Insurance Company Ltd. width="54"> SLFC width="108"> 1972 width="96"> 659,811,390 width="56"> 63.56%

31 width="186"> TPL Life Insurance Company Ltd. width="54"> TPLC width="108"> 2008 width="96"> 433,002 width="56"> 0.04%

width="186"> Total Assets width="54">   width="108">   width="96"> 1,041,312,770 width="56">

Variable Selection Dependent Variables

This study uses three performance indicators, i.e., sustainable growth, earnings, and profitability, used in previous studies. In previous studies that applied sustainable growth (SGR) and the other two factors as dependent variables over the insurance sector of Pakistan. The profitability (ROE) is calculated by the ratio and average equity, that defines capability of shareholders to enhance profits through their investments, obtained from (Haris et al. (2019). SGR is the degree of the uses its monetary funds to avoid external loans to achieve growth (Xu & Wang, 2018; ZHANG & YU, 2008). The calculation of SGR is given as follows in Eq. 1

SGR=Net profit ratio*Asset turnover ratio

*Retention rate*Equity Mulitplier Eq.1

Independent Variables

IC Determinants

This study follows the previous studies to measure the VAIC, MVAIC, and their components (Haris et al., 2018; Haris et al., 2019; Pulic, 1998, 2000; Rehman, Ilyas, & Rehman, 2011), per equation 1, mentioned below, VA is considered as the difference between output and input. 

VAit=PRit+PCit+DPit+Ait Eq.2

In Eq. 2, VA is the value-added, PR represents operating profits, PC represents the personal cost such as salaries and wages, DP is the depreciation. A represents amortization, followed by (Haris et al., 2019).

Further, followed by the previous literature (Haris et al., 2018; Pulic, 1998), VAIC calculation is summarized as mentioned below:

 CEEit=VAit/CEit                Eq.3     

HCEit=VAit / HCit               Eq.4    

SCEit=SCit / VAit                  Eq.5     

SCit=VAit-HCit                  Eq.6     

VAICit=CEEit+HCE+SCEit      Eq.7     

Followed by previous studies (Yao et al., 2019) MVAIC is formed by four components RCE, SCE, HCE, CEE. 

Calculation of MVAIC is given below in Eq. 8 and Eq. 9.

RCit=RCit/VAit        Eq.8

 MVAICit=CEEit+HCE+SCEit+RCEit   Eq.9  

Where RC represents the relational capital, it can be measured by the sum of marketing, advertising, and selling expenses.

Other Variables

Furthermore, this study uses company-specific and macro-economic variables. Company size (SIZE), Capitalization (Casado-Belmonte et al.), and Operational Efficiency (OEF) have been used as company-specific indicators in the study, followed by (Haris et al., 2019; Tan, 2016; Xu & Wang, 2019; Yao et al., 2019)). To calculate the SIZE, the study used a proxy of company size. Capitalization CAP is measured by the ratio of shareholder’s equity and total assets, the ratio among operating expenses and average assets, is used to calculate the operational efficiency (OEF). Macro-economic indicators, which have been examined in this study, are crisis (CRISIS), economic growth (EGR) and Inflation, followed by the previous literature (Haris et al., 2019; Oppong & Pattanayak, 2019; Tan & Floros, 2012; Vidyarthi, 2019; Yao et al., 2018; Yao et al., 2019). Moreover, to measure the CRISIS author allocated value 1 for the financial crisis period of 2008-2009, and value 0 is assigned. 

Econometric Methodology

Following the previous studies, this study used GMM, developed by Arellano and Bond (1991). Arellano and Bover (1995) have improvised the efficiency of GMM; they introduced more instruments by designing two equation systems, level equation the first-difference equation. GMM does not use any unnecessary information or data but is confined in the moment settings, so its estimators are known to be consistent, efficient, and normal (Hansen, Heaton, & Yaron, 1996). In this study, a two-step GMM system estimator is used for efficiency.

This study employs a sample of 31 companies using 2007–2016. The study uses unbalanced panel data to avoid errors and biased results; this study applies Windmeijer (2005) correction to get more robust and accurate results. Following are the econometric models for this study mentioned below:

P_it  =?_0+?P_(it-1)+ ?_a ?VAIC?_it+?_b ?INSSIZE?_it+?_c ?CAP?_it+?_d ?OEF?_it+?_e ?CRISES?_t+?_f ?EGR?_t+?_g ?INF?_t+?_h ?TD?_t+v_it+?_it       Eq.(a)  

P_it  =?_0+?P_(it-1)+ ?_a ?CEE?_it+?_b ?HCE?_it+?_c ?SCE?_it+?_d ?INSSIZE?_it+?_e ?CAP?_it+?_f ?OEF?_it+?_g ?CRISES?_t+?_h ?EGR?_t+?_i ?INF?_t+?_j ?TD?_s+v_it+?_it                 Eq.(b)                

P_it  =?_0+?P_(it-1)+ ?_a M?VAIC?_it+?_b ?INSSIZE?_it+?_c ?CAP?_it+?_d ?OEF?_it+?_e ?CRISES?_t+?_f ?EGR?_t+?_g ?INF?_t+?_h ?TD?_t+v_it+?_it        Eq.(c)             

                        

P_it  =?_0+?P_(it-1)+ ?_a ?CEE?_it+?_b ?HCE?_it+?_c ?SCE?_it+?_d ?RCE?_it+?_e ?INSSIZE?_it+?_f ?CAP?_it+?_g ?OEF?_it+?_h ?CRISES?_t+?_i ?EGR?_t+?_j ?INF?_t+?_k ?TD?_s+v_it+?_it    Eq.(d)

In the following equations, P expresses the performance, i.e., SGR, EBITDA, and ROE. Pit–1 one-year lag of performance, ? is the constant term, ? is the ? is the determined profitability which ranges from 0 to 1, vit represents the unobserved company individual effect, whereas. Uit is residual, TD represents time dummies used to control the year effect. Further, for the detail of variables, see Table 1.

Findings

Descriptive Statistics

Descriptive statistics of the Insurance industry are presented in table 3. The results show that the insurance sector in Pakistan reports a 0.278 mean value of SG, 0.089 mean value of ROE, and 11.839 mean value of EBITDA, 2007-2016. The mean value of VAIC is 4.051, which is higher value of VAIC 3.015 of Pakistani Banks and lower than the mean value of VAIC 15.25 of Malaysian general insurance firms (Chen et al., 2014).

Moreover, this study reports that the HCE (2.515) is higher of CCE (0.370) and SCE (1.165). The average mean value of RCE is 0.007, which suggests the RCE has a low contribution to the IC performance of Pakistani Insurance companies. A study also reported RCE at a low level of 0.017 in IC efficiency of Indian banks (Vidyarthi, 2019). 




Table 3. Summary Statistics

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Variable width="114"> Obs. width="134"> Mean width="145"> Std. Dev.

SG width="114"> 250 width="134"> 0.278 width="145"> 1.121

ROE width="114"> 250 width="134"> 0.089 width="145"> 0.256

EBITDA width="114"> 250 width="134"> 11.839 width="145"> 1.842

VAIC width="114"> 250 width="134"> 4.051 width="145"> 10.265

MVAIC width="114"> 250 width="134"> 4.057 width="145"> 10.027

CEE width="114"> 250 width="134"> 0.370 width="145"> 0.380

HCE width="114"> 250 width="134"> 2.515 width="145"> 2.530

SCE width="114"> 250 width="134"> 1.165 width="145"> 10.095

RCE width="114"> 250 width="134"> 0.007 width="145"> 0.271

SIZE width="114"> 250 width="134"> 14.735 width="145"> 1.592

CAP width="114"> 250 width="134"> 0.388 width="145"> 0.202

OEF width="114"> 250 width="134"> 0.443 width="145"> 1.184

CRISIS width="114"> 250 width="134"> 0.064 width="145"> 0.245

EGR width="114"> 250 width="134"> 0.132 width="145"> 0.052

INF width="114"> 250 width="134"> 120.075 width="145"> 26.437

Diagnostic Test

In this study, two pre-estimation tests are applied to ensure that unbalanced panel data is valid. At first, an (ADF) fisher test is applied to examine the unit root. Table 4 presents the results of the ADF test, according to which each variable with a significant p-value demonstrates a rejection of unit root in the data and provides an indication that all variables are stationary. Secondly, correlation is applied to data to examine multi-collinearity between all independent variables. Table 5 presents the correlation matrix. As per table 5, the study finds no higher collinearity among the variables, also finds that the coefficient of correlation among independent variables (Haris et al., 2019).

Furthermore, we applied a Variance Inflationary Test (VIF) to check the multi-collinearity and PV is 000**. Table 6 presents the VIF values. The VIF cut-off values at 10, indicate the absence of multi-collinearity among independent variables.

Table 4. Unit Root Test (Augmented Dickey-Fuller (ADF)

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width="240"> Level width="176" colspan="2"> First Difference

width="240"> Coef. width="174"> Coef. >

SG width="240"> 271.132 width="174"> 468.587 >

ROE width="240"> 453.038 width="174"> 655.461 >

EBITDA width="240"> 97.346 width="174"> 253.579 >

VAIC width="240"> 310.854 width="174"> 648.395 >

MVAIC width="240"> 162.390 width="174"> 515.246 >

CEE width="240"> 269.312 width="174"> 633.072 >

HCE width="240"> 279.530 width="174"> 619.672 >

SCE width="240"> 141.835 width="174"> 537.888 >

RCE width="240"> 395.580 width="174"> 447.982 >

SIZE width="240"> 287.858 width="174"> 389.019 >

CAP width="240"> 143.032 width="174"> 195.496 >

OEF width="240"> 119.735 width="174"> 244.779 >

CRISIS width="240"> 93.952 width="174"> 829.560 >

EGR width="240"> 125.945 width="174"> 968.870 >

INF width="240"> 630.798 width="174"> 1178.348 >

Table 5. Correlation Matrix

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width="55" valign="top"> SG width="61" valign="top"> ROE width="61" valign="top"> EBITDA width="61" valign="top"> VAIC width="55" valign="top"> MVAIC width="55" valign="top"> CEE width="55" valign="top"> HCE width="55" valign="top"> SCE width="42" valign="top"> RCE width="52" valign="top"> SIZE width="43" valign="top"> CAP width="39" valign="top"> OEF width="48" valign="top"> CRISIS width="39" valign="top"> EGR width="34" valign="top"> INF

SG width="55"> 1.000 width="61">   width="61">   width="61">   width="55">   width="55">   width="55">   width="55">   width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

ROE width="55"> 0.500*** width="61"> 1.000 width="61">   width="61">   width="55">   width="55">   width="55">   width="55">   width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

EBITDA width="55"> 0.368*** width="61"> 0.618*** width="61"> 1.000 width="61">   width="55">   width="55">   width="55">   width="55">   width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

VAIC width="55"> 0.157*** width="61"> 0.093 width="61"> 0.139** width="61"> 1.000 width="55">   width="55">   width="55">   width="55">   width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

MVAIC width="55"> 0.121 width="61"> 0.126 width="61"> 0.099 width="61"> 0.022 width="55"> 1.000 width="55">   width="55">   width="55">   width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

CEE width="55"> 0.080 width="61"> 0.483*** width="61"> 0.301*** width="61"> 0.006 width="55"> 0.102 width="55"> 1.000 width="55">   width="55">   width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

HCE width="55"> 0.515*** width="61"> 0.545*** width="61"> 0.613*** width="61"> 0.198** width="55"> 0.083 width="55"> 0.172*** width="55"> 1.000 width="55">   width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

SCE width="55"> 0.028 width="61"> -0.060 width="61"> -0.024 width="61"> 0.767*** width="55"> -0.003 width="55"> -0.075 width="55"> -0.056 width="55"> 1.000 width="42">   width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

RCE width="55"> -0.029 width="61"> 0.092 width="61"> 0.042 width="61"> -0.781*** width="55"> 0.017 width="55"> 0.109 width="55"> 0.082 width="55"> -0.620*** width="42"> 1.000 width="52">   width="43">   width="39">   width="48">   width="39">   width="34">

SIZE width="55"> 0.288*** width="61"> 0.353*** width="61"> 0.503*** width="61"> 0.104 width="55"> 0.127* width="55"> 0.500*** width="55"> 0.325*** width="55"> 0.006 width="42"> 0.032 width="52"> 1.000 width="43">   width="39">   width="48">   width="39">   width="34">

CAP width="55"> 0.255*** width="61"> 0.061 width="61"> -0.038 width="61"> 0.077 width="55"> -0.075 width="55"> -0.461*** width="55"> 0.280*** width="55"> 0.025 width="42"> -0.063 width="52"> -0.363*** width="43"> 1.000 width="39">   width="48">   width="39">   width="34">

OEF width="55"> 0.014 width="61"> 0.098 width="61"> -0.064 width="61"> 0.012 width="55"> -0.049 width="55"> -0.105* width="55"> 0.080 width="55"> -0.004 width="42"> -0.004 width="52"> -0.097 width="43"> 0.115** width="39"> 1.000 width="48">   width="39">   width="34">

CRISIS width="55"> 0.000 width="61"> -0.226*** width="61"> -0.150** width="61"> -0.054 width="55"> 0.014 width="55"> -0.178*** width="55"> -0.194*** width="55"> 0.000 width="42"> -0.018 width="52"> -0.064 width="43"> -0.014 width="39"> -0.033 width="48"> 1.000 width="39">   width="34">

EGR width="55"> -0.015 width="61"> -0.068 width="61"> -0.127 width="61"> -0.050 width="55"> -0.247 width="55"> -0.100 width="55"> -0.096 width="55"> -0.023 width="42"> 0.046 width="52"> -0.151 width="43"> 0.014 width="39"> -0.024 width="48"> 0.093 width="39"> 1.000 width="34">

INF width="55"> -0.003 width="61"> -0.217 width="61"> -0.106 width="61"> -0.052 width="55"> 0.010 width="55"> -0.189 width="55"> -0.155 width="55"> -0.007 width="42"> 0.003 width="52"> -0.035 width="43"> -0.004 width="39"> -0.029 width="48"> 0.631*** width="39"> -0.044 width="34"> 1.000

Notes: Level of significance at 1 %, 5 % and 10 % are represented by the *, **, and ***, respectively

Table 6. VIF

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width="114" colspan="2"> Eq. (1) width="105" colspan="2"> Eq. (2) width="105" colspan="2"> Eq. (3) width="115" colspan="2"> Eq. (4)

VAIC width="53"> 1.03 width="62"> 01.03 width="53">   width="53">   width="53">   width="53">   width="53">   width="62">

MVAIC width="53">   width="62">   width="53">   width="53">   width="53"> 1.02 width="53"> 1.02 width="53">   width="62">

CEE width="53">   width="62">   width="53"> 1.59 width="53"> 1.66 width="53">   width="53">   width="53"> 1.59 width="62"> 1.66

HCE width="53">   width="62">   width="53"> 1.48 width="53"> 1.52 width="53">   width="53">   width="53"> 1.50 width="62"> 1.53

SCE width="53">   width="62">   width="53"> 1.01 width="53"> 1.02 width="53">   width="53">   width="53"> 6.60 width="62"> 6.64

RCE width="53">   width="62">   width="53">   width="53">   width="53">   width="53">   width="53"> 6.67 width="62"> 6.73

SIZE width="53"> 1.18 width="62"> 1.21 width="53"> 1.64 width="53"> 1.67 width="53"> 1.17 width="53"> 1.20 width="53"> 1.64 width="62"> 1.67

CAP width="53"> 1.18 width="62"> 1.18 width="53"> 1.71 width="53"> 1.72 width="53"> 1.16 width="53"> 1.16 width="53"> 1.73 width="62"> 1.73

OEF width="53"> 1.02 width="62"> 1.02 width="53"> 1.03 width="53"> 1.03 width="53"> 1.02 width="53"> 1.02 width="53"> 1.03 width="62"> 1.03

CRISIS width="53">   width="62"> 3.43 width="53">   width="53"> 3.46 width="53">   width="53"> 3.43 width="53">   width="62"> 3.46

EGR width="53">   width="62"> 1.09 width="53">   width="53"> 1.09 width="53">   width="53"> 1.09 width="53">   width="62"> 1.10

INF width="53">   width="62"> 3.40 width="53">   width="53"> 3.45 width="53">   width="53"> 3.40 width="53">   width="62"> 3.45

Mean-VIF width="53"> 1.10 width="62"> 1.77 width="53"> 1.41 width="53"> 1.85 width="53"> 1.09 width="53"> 1.76 width="53"> 2.96 width="62"> 2.90

Empirical Findings

IC and SGR, Earnings, and Profitability, of insurance companies. The empirical results of the study are presented in Tables 7–11. Table 7 presents the relationship between VAIC, its components, and SGR. Table 8 provides the impact of MVAIC and its components on SGR. Further, In our study, Table 10 and Table 11 are added for the additional robust checks. Table 10 provides earnings and IC, using VAIC, MVAIC, and their components. Table 11 provides the results of the relationship between profitability and IC, using VAIC, MVAIC, and their components. In the study’s analysis (see Tables 7–11), F-statistics report that all regression models are jointly significant. Results report the insignificant p-values of AR(1) and AR(2), which indicate the absence of autocorrelation (Yao et al., 2018). Impact of IC on SGR

Table 7 presents the impact of VAIC on SGR in equations 1 and 2. In Table 7, coefficients of VAIC are positively significant in models 1 and 2 of equation 1, which indicates a positive impact of IC on SGR, consistent with (Haris et al. (2018), Haris et al. (2019) and Xu and Wang (2018). Equation 2 impact of VAIC, i.e., CEE, HCE and SCE, on SGR.  Thus, this finding supports H1. Results show the positive significant coefficients of CEE  in model 1 and in model 2, which is consistent with some previous studies (M. A. Al-Musali & Ismail, 2016; Haris et al., 2019). Thus, this finding supports H2.  Results find that the coefficients of HCE are positively significant in Models 1 and 2 of Equation-2 (Ozkan, Cakan, & Kayacan, 2016; Ting & Lean, 2009; Xu & Wang, 2018), this supports the H3.

The results show that SCE (? = 0.078, p > 10%) is positive but not significant in model 1 of Equation-2, consistent with (Tasawar & Roszaini, 2017), results also find the positive significant coefficient of SCE (? = 0.145, p < 5%) in model 2 of Equation-2, this result is consistent with (Soetanto & Liem, 2019). Thus, this finding supports the H4. Moreover, results show that each component of VAIC is positively related to SGR. To offer robustness, the study also inspects the impact of company-specific and macroeconomic variables on SGR. Among company-specific variables, the results found a positive impact on company SIZE, CAP, and OEF on the SGR. However, among macro-economic variables, the study finds that an increase in the economic growth (EGR) increases the SGR, while an increase in inflation decreases the SGR of insurance companies.  

Table 7. Impact of VAIC on SGR

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width="219" colspan="2"> Equation 1 width="210" colspan="2"> Equation 2

width="104"> Model (1) width="115"> Model (2) width="104"> Model (1) width="107"> Model (2)

Lag-SGR width="104"> 0.118** align="center">(0.064) width="115"> 0.183*** align="center">(0.089) width="104"> 0.202** align="center">(0.108) width="107"> 1.005*** align="center">(0.412)

VAIC width="104"> 0.154*** align="center">(0.076) width="115"> 0.108*** align="center">(0.052) width="104">   width="107">

CEE width="104">   width="115">   width="104"> 0.338*** align="center">(0.134) width="107"> 2.596** align="center">(1.383)

HCE width="104">   width="115">   width="104"> 0.236*** align="center">(0.085) width="107"> 0.510** align="center">(0.340)

SCE width="104">   width="115">   width="104"> 0.078 align="center">(0.085) width="107"> 0.145** align="center">(0.103)

SIZE width="104"> 0.167** align="center">(0.093) width="115"> 0.139** align="center">(0.078) width="104"> 0.003 align="center">(0.153) width="107"> -0.446 align="center">(0.281)

CAP width="104"> 2.160** align="center">(1.087) width="115"> 2.427*** align="center">(1.035) width="104"> 0.894 align="center">(1.351) width="107"> 2.443 align="center">(1.477)

OEF width="104"> 0.502* align="center">(0.314) width="115"> 0.977*** align="center">(0.984) width="104"> 0.654*** align="center">(0.243) width="107"> -0.829 align="center">(0.206)

CRISIS width="104">   width="115"> -3.275*** align="center">(0.984) width="104">   width="107"> -5.604** align="center">(3.018))

EGR width="104">   width="115"> 3.092*** align="center">(2.185) width="104">   width="107"> 3.454* align="center">(2.260)

INF width="104">   width="115"> -2.025*** align="center">(0.701) width="104">   width="107"> -4.054*** align="center">(1.982)

Const. width="104"> -3.831*** align="center">(1.838) width="115"> 2.905** align="center">(1.492) width="104"> -1.251 align="center">(2.571) width="107"> 6.251 align="center">(4.464)

Obs. width="104"> 219 width="115"> 219 width="104"> 219 width="107"> 219

Insurance Companies width="104"> 31 width="115"> 31 width="104"> 31 width="107"> 31

Instrument width="104"> 22 width="115"> 22 width="104"> 22 width="107"> 22

F-Statistics width="104"> 4.00*** width="115"> 15.73*** width="104"> 4.15*** width="107"> 1.90**

AR-1 (P-value) width="104"> -0.99 align="center">(0.232) width="115"> -1.37 align="center">(0.170) width="104"> -0.75 align="center">(0.451) width="107"> -1.36 align="center">(0.173)

AR-2(P-value) width="104"> 0.83 align="center">(0.409) width="115"> 0.88 align="center">(0.381) width="104"> 0.43 align="center">(0.667) width="107"> 1.32 align="center">(0.188)

Hansen-(P-value) width="104"> 16.10 align="center">(0.446) width="115"> 6.84 align="center">(0.910) width="104"> 13.18 align="center">(0.512) width="107"> 4.46 align="center">(0.954)

Additional Robust Checks Impact of IC on Earning

Table 10 presents the impact of IC on Earnings (EBITDA). Equation 1 represents the positive coefficients of VAIC, which are significant as well in Models 1 and 2, respectively, which affirm a positive relationship of IC with EBITDA, thus supports the acceptance of H1. This indicates that higher VAIC affects higher earnings positively.  Equation 2 consists of components of VAIC, where CEE is positively significant, which support the H2. HCE is positively significant and support the H3. Moreover, SCE is also positively significant in Models 1 and 2, respectively, which supports the H4. Amongst all IC components, HCE is a higher positively significant, which means HC is a more important IC variable concerned with earnings.

Table 10. Impact of IC on Earning

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width="114" colspan="2"> Equation 1 width="117" colspan="2"> Equation 2 width="117" colspan="2"> Equation 3 width="120" colspan="2"> Equation 4

width="58" valign="top"> Model (1) width="56" valign="top"> Model (2) width="61" valign="top"> Model (1) width="56" valign="top"> Model (2) width="56" valign="top"> Model (1) width="61" valign="top"> Model (2) width="61" valign="top"> Model (1) width="60" valign="top"> Model (2)

Lag-EBITDA width="58"> 0.003>(0.001) width="56"> 0.009>(0.001) width="61"> 0.007***>(0.001) width="56"> 0.007***>(0.001) width="56"> 0.008>(0.001) width="61"> 0.009***>(0.000) width="61"> 0.002**>(0.000) width="60"> 0.001**>(0.000)

VAIC width="58"> 0.548***>(0.119) width="56"> 0.513***>(0.167) width="61">   width="56">   width="56">   width="61">   width="61">   width="60">

MVAIC width="58">   width="56">   width="61">   width="56">   width="56"> 0.073*** >(0.036) width="61"> 0.162*** >(0.046) width="61">   width="60">

CEE width="58">   width="56">   width="61"> 0.632***>(0.173) width="56"> 0.532***>(0.161) width="56">   width="61">   width="61"> 0.627***>(0.191) width="60"> 0.555***>(0.144)

HCE width="58">   width="56">   width="61"> 0.335***>(0.106) width="56"> 0.300***>(0.146) width="56">   width="61">   width="61"> 0.388***>(0.117) width="60"> 0.300**>(0.167)

SCE width="58">   width="56">   width="61"> 6.235***> (0.774) width="56"> 5.923***> (1.221) width="56">   width="61">   width="61"> 6.241***>(0.858) width="60"> 6.075***>(1.333)

RCE width="58">   width="56">   width="61">   width="56">   width="56">   width="61">   width="61"> 1.181>(1.846) width="60"> -0.893>(2.567)

SIZE width="58"> 0.548***>(0.117) width="56"> 0.475**>(0.275) width="61"> 0.645***>(0.914) width="56"> 0.638***>(0.178) width="56"> 0.955***>(0.187) width="61"> 0.747***>(0.046) width="61"> 0.646***>(0.084) width="60"> 0.609***>(0.161)

CAP width="58"> 0.218*>(0.984) width="56"> 0.312>(0.544) width="61"> 1.611***>(0.450) width="56"> 1.530***>(0.751) width="56"> 3.246***>(0.827) width="61"> 3.295***>(0.682) width="61"> 1.615***>(0.593) width="60"> 1.306> (0.872)

OFF width="58"> -0.970*** >(0.413) width="56"> -1.116*** >(0.423) width="61"> -0.480> (0.317) width="56"> -0.574***> (0.339) width="56"> -0.854*>(0.605) width="61"> -1.949*** >(0.492) width="61"> -0.527>(0.313) width="60"> -0.567**>(0.368)

CRISIS width="58">   width="56"> -2.955>(3.818)) width="61">   width="56"> -2.718>(2.363) width="56">   width="61"> -0.695*>(1.171) width="61">   width="60"> -0.323>(0.243)

EGR width="58">   width="56"> 5.247>(5.013) width="61">   width="56"> 1.957>(8.250) width="56">   width="61"> 14.828***>(3.725) width="61">   width="60"> 0.960>(9.348)

INF width="58">   width="56"> -0.013>(0.208) width="61">   width="56"> -0.006>(0.208) width="56">   width="61"> -0.012>(0.010) width="61">   width="60"> -0.002>(0.022)

Const. width="58"> 0.861>(1.820) width="56"> 5.997>(6.790) width="61"> -0.869>(1.432) width="56"> 0.519>(6.392) width="56"> -3.497>(2.837) width="61"> 3.332>(2.488) width="61"> -0.851>(1.451) width="60"> 0.013>(6.506)

Obs. width="58"> 186 width="56"> 186 width="61"> 186 width="56"> 186 width="56"> 186 width="61"> 186 width="61"> 186 width="60"> 186

Insurance Companies width="58"> 30 width="56"> 30 width="61"> 30 width="56"> 30 width="56"> 30 width="61"> 30 width="61"> 30 width="60"> 30

Instrument width="58"> 22 width="56"> 22 width="61"> 23 width="56"> 23 width="56"> 22 width="61"> 22 width="61"> 23 width="60"> 23

F-Statistics width="58"> 37.88*** width="56"> 19.47*** width="61"> 134.83*** width="56"> 69.45*** width="56"> 15.13*** width="61"> 25.75*** width="61"> 105.90*** width="60"> 75.67***

AR-1 (P-value) width="58"> -1.57>(0.116) width="56"> -1.62>(0.106) width="61"> -1.53>(-0.125) width="56"> -0.90>(-0.366) width="56"> -1.14>(0.254) width="61"> -1.14>(0.254) width="61"> -1.01>(0.313) width="60"> -0.56>(0.575)

AR-2(P-value) width="58"> -0.80>(0.425) width="56"> -0.86>(0.392) width="61"> 0.08>(0.934) width="56"> 0.02>(0.983) width="56"> -0.6>(0.949) width="61"> 1.07>(0.286) width="61"> 0.08>(0.934) width="60"> 0.05>(0.960)

Hansen-(P-value) width="58"> 18.19>(0.313) width="56"> 17.60>(0.173) width="61"> 16.85>(0.328) width="56"> 15.16>(0.233) width="56"> 24.48>(0.178) width="61"> 12.84>(0.460) width="61"> 16.16>(0.304) width="60"> 13.27>(0.276)

Notes: Level of significance at 1 %, 5 % and 10 % are represented by the *, **, *** respectively. Lag-EBITDA is the one-year lag of the dependent variable. 

IC and Profitability

Furthermore, role of IC on profitability as presented in Table 11. According to Models 1-2 in Equation 1, the coefficients of VAIC are positive with H1. Equation 2 reports the of VAIC on ROE, where coefficient value of CEE are positively significant. H2 is supported. The coefficient values of HCE are positively significant. Thus, H3, is supported (Haris et al., 2019). However, the coefficient values of SCE are negatively insignificant. Thus, this finding supports the H4, which is followed by (Xu & Wang, 2018).

Table 11. Impact of IC on Profitability

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width="120" colspan="2"> Equation 1 width="126" colspan="3"> Equation 2 width="114" colspan="3"> Equation 3 width="120" colspan="2"> Equation 4

width="60"> Model (1) width="60"> Model (2) width="60"> Model (1) width="66" colspan="2"> Model (2) width="49" colspan="2"> Model (1) width="66"> Model (2) width="60"> Model (1) width="60"> Model (2)

Lag-ROE width="60"> 0.230*** align="center">(0.109) width="60"> 0.373*** align="center">(0.125) width="60"> 0.103* align="center">(0.068) width="54"> 0.016* align="center">(0.216) width="60" colspan="2"> 0.251*** align="center">(0.084) width="66" colspan="2"> 0.431** align="center">(0.234) width="60"> 0.129 align="center">(0.080) width="60"> 0.272 align="center">(0.330)

VAIC width="60"> 0.040** align="center">(0.022) width="60"> 0.029*** align="center">(0.011) width="60">   width="54">   width="60" colspan="2">   width="66" colspan="2">   width="60">   width="60">

MVAIC width="60">   width="60">   width="60">   width="54">   width="60" colspan="2"> 0.048*** align="center">(0.023) width="66" colspan="2"> 0.007*** align="center">(0.003) width="60">   width="60">

CEE width="60">   width="60">   width="60"> 0.303** align="center">(0.158) width="54"> 0.238** align="center">(0.128) width="60" colspan="2">   width="66" colspan="2">   width="60"> 0.304*** align="center">(0.146) width="60"> 0.235* align="center">(0.157)

HCE width="60">   width="60">   width="60"> 0.063*** align="center">(0.244) width="54"> 0.055** align="center">(0.321) width="60" colspan="2">   width="66" colspan="2">   width="60"> 0.045** align="center">(0.025) width="60"> 0.058*** align="center">(0.247)

SCE width="60">   width="60">   width="60"> -0.041 align="center">(0.050) width="54"> -0.022 align="center">(0.044) width="60" colspan="2">   width="66" colspan="2">   width="60"> 0.012 align="center">(0.054) width="60"> 0.049 align="center">(0.047)

RCE width="60">   width="60">   width="60">   width="54">   width="60" colspan="2">   width="66" colspan="2">   width="60"> 1.400 align="center">(1.264) width="60"> 2.511 align="center">(2.414)

SIZE width="60"> 0.035 align="center">(0.035) width="60"> 0.022 align="center">(0.022) width="60"> -0.130 align="center">(0.014) width="54"> -0.014 align="center">(0.013) width="60" colspan="2"> 0.055*** align="center">(0.023) width="66" colspan="2"> 0.010 align="center">(0.10) width="60"> -0.107 align="center">(0.170) width="60"> -0.023 align="center">(0.016)

CAP width="60"> 0.590*** align="center">(0.175) width="60"> -0.023 align="center">(0.221) width="60"> 0.158 align="center">(0.280) width="54"> -0.069 align="center">(0.223) width="60" colspan="2"> 0.498** align="center">(0.271) width="66" colspan="2"> -0.035 align="center">(0.119) width="60"> 0.265 align="center">(0.254) width="60"> 0.027 align="center">(0.403)

OFF width="60"> -0.402** align="center">(0.518) width="60"> 0.041 align="center">(0.049) width="60"> -0.052 align="center">(0.107) width="54"> 0.056 align="center">(0.075) width="60" colspan="2"> 0.030 align="center">(0.029) width="66" colspan="2"> 0.051 align="center">(0.096) width="60"> 0.062 align="center">(0.095) width="60"> -0.062 align="center">(0.058)

CRISIS width="60">   width="60"> -1.413*** align="center">(0.430) width="60">   width="54"> -0.719 align="center">(0.640) width="60" colspan="2">   width="66" colspan="2"> -1.007*** align="center">(0.190) width="60">   width="60"> -0.275 align="center">(0.406)

EGR width="60">   width="60"> 1.930 align="center">(2.327) width="60">   width="54"> 3.211 align="center">(3.752) width="60" colspan="2">   width="66" colspan="2"> 1.480*** align="center">(0.470) width="60">   width="60"> 5.689 align="center">(6.55)

INF width="60">   width="60"> -0.009 align="center">(0.005) width="60">   width="54"> -0.008 align="center">(0.007) width="60" colspan="2">   width="66" colspan="2"> -0.081 align="center">(0.053) width="60">   width="60"> -0.011 align="center">(0.012)

Const. width="60"> -0.560 align="center">(0.540) width="60"> 1.101 align="center">(0.990) width="60"> 0.038 align="center">0.318) width="54"> 1.670 align="center">(1.531) width="60" colspan="2"> -1.120 align="center">(0.405) width="66" colspan="2"> -0.267 align="center">(0.225) width="60"> -0.098 align="center">(0.366) width="60"> 2.143 align="center">(2.330)

Obs. width="60"> 219 width="60"> 219 width="60"> 219 width="54"> 219 width="60" colspan="2"> 219 width="66" colspan="2"> 219 width="60"> 219 width="60"> 219

Insurance Companies width="60"> 31 width="60"> 31 width="60"> 31 width="54"> 31 width="60" colspan="2"> 31 width="66" colspan="2"> 31 width="60"> 31 width="60"> 31

Instrument width="60"> 22 width="60"> 22 width="60"> 22 width="54"> 22 width="60" colspan="2"> 24 width="66" colspan="2"> 24 width="60"> 22 width="60"> 22

F-Statistics width="60"> 11.97*** width="60"> 21.92*** width="60"> 12.53*** width="54"> 3.45*** width="60" colspan="2"> 6.47*** width="66" colspan="2"> 29.44*** width="60"> 12.97*** width="60"> 38.21***

AR-1 (P-value) width="60"> -1.04 align="center">(0.296) width="60"> -1.54 align="center">(0.123) width="60"> -0.77 align="center">(0.440) width="54"> -0.74 align="center">(0.461) width="60" colspan="2"> -0.99 align="center">(0.321) width="66" colspan="2"> -1.02 align="center">(0.430) width="60"> -1.02 align="center">(0.309) width="60"> -1.04 align="center">(0.300)

AR-2(P-value) width="60"> -0.75 align="center">(0.451) width="60"> -0.77 align="center">(0.440) width="60"> -0.41 align="center">(0.679) width="54"> 0.37 align="center">(0.713) width="60" colspan="2"> 0.41 align="center">(0.685) width="66" colspan="2"> 0.77 align="center">(0.440) width="60"> 0.40 align="center">(0.688) width="60"> 0.91 align="center">(0.363)

Hansen-(P-value) width="60"> 18.01 align="center">(0.324) width="60"> 9.46 align="center">(0.737) width="60"> 14.56 align="center">(0.409) width="54"> 9.24 align="center">(0.600) width="60" colspan="2"> 19.75 align="center">(0.347) width="66" colspan="2"> 7.45 align="center">(0.944) width="60"> 12.62 align="center">(0.478) width="60"> 5.83 align="center">(0.830)

Conclusion and Limitations

Furthermore, results report that among components of VAIC and MVAIC, CEE and also HCE has the SCE on the performance, while finds a negative RCE and performance. In Pakistan, insurance companies are registered, regulated with (IAP) and (SECP). Overall, the growth rate of the finance and insurance sector in the year 2018-19 is 5.14%. The sectorial share of financial institutions, including insurance companies, in GDP, is 3.5% in 2018–2019 (PES, 2018-2019).

The Pakistani insurance sector has shown a significant level regarding its IC performance. The ability to perform better is highly dependent on the HC in the insurance sector. This study is conducted to IC. i.e., VAIC and MVAIC, i.e., sustainable growth, and earnings. This study used a sample of 31 insurance companies operating in Pakistan from 2007–2016. Furthermore, dependent variables, i.e., SGR (sustainable growth), EBITDA (earning indicator), and ROE (profitability indicator), are used.

On the other hand, independent variables are segregated into intellectual capital, i.e., VAIC, MVAIC, CEE, HCE, SCE, and RCE, company-specific, i.e., SIZE, CAP, and OEF, and macro-economic variables, i.e., CRISIS, EGR, and INF.

Furthermore, Results report that operational efficiency in the insurance sector positively influences performance. Financial Crisis caused a slump in the insurance sector during the study period. On the other hand, EGR is positively related to the performance of the insurance sector. Moreover, the insurance companies with better utilization of their resources can achieve a competitive advantage; thus, guaranteeing their sustainable growth in the financial system.  

This research is limited to the Pakistan insurance industry that could unlock opportunities for further research as the researchers may extend to do a comparative and reasonable analysis of services and manufacturing sectors. It is suggested that researchers may add another component of social capital to examine if it may have any effect. Variables set of other emerging economies; it would be an interesting comparative analysis. The researcher can further include other financial institutions, such as development, asset management companies, currency exchanges, micro-loan organizations. The methodologies with the same dependent and independent variables that might be attention-grabbing for researchers.

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