Journal of African Business
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Intellectual Capital Efficiency and Risk-Taking
Behaviour of Insurance Companies in Ghana
Saint Kuttu, Lord Mensah & Daniel Attah-Kyei
To cite this article: Saint Kuttu, Lord Mensah & Daniel Attah-Kyei (2023): Intellectual Capital
Efficiency and Risk-Taking Behaviour of Insurance Companies in Ghana, Journal of African
Business, DOI: 10.1080/15228916.2023.2230396
To link to this article:  https://doi.org/10.1080/15228916.2023.2230396
Published online: 02 Jul 2023.
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JOURNAL OF AFRICAN BUSINESS                      
https://doi.org/10.1080/15228916.2023.2230396
Intellectual Capital Efficiency and Risk-Taking Behaviour of 
Insurance Companies in Ghana
Saint Kuttu , Lord Mensah and Daniel Attah-Kyei
Department of Finance, University of Ghana Business School, University of Ghana, Accra, Ghana
ABSTRACT KEYWORDS 
Our study focused on the link between intellectual capital efficiency Intellectual capital; 
(ICE) and its individual components and underwriting risk. It exam- underwriting risk; human 
ined the effect of ICE, and structural (SCE) and human capital (HCE) capital efficiency; structural 
efficiency on the underwriting risk of Ghanaian life and non-life capital efficiency
insurers. It used panel data of 31 insurance firms in Ghana, of which 
14 and 17 were life and non-life insurers, respectively, from 2008 to 
2019. A generalized least squares estimation technique was used to 
examine the relationship between ICE and underwriting risk in life, 
non-life, and the entire insurance sector. The results suggest that 
there is a significant negative relationship between HCE and under-
writing risk in the Ghanaian insurance sector. However, there was 
no relationship between ICE and underwriting risk and between 
SCE and underwriting risk. As the first to examine ICE and risk- 
taking behavior in any African country, our study is significant in 
managerial decision-making and insurance policy formulation to 
reduce risk in the insurance industry.
1. Introduction
Insurance firms play a significant role in risk management (Ivanovna et al., 2018). A core 
part of this role involves risk-taking. Akotey and Abor (2013) posited that underwriting 
risk is major and faced by all insurers in Ghana. Underwriting risk is the potential loss to 
an insurer emanating from faulty underwriting. This may affect the solvency and profit-
ability of an insurer adversely (Adams & Buckle, 2003). Therefore, the criticality of this 
risk and its high likelihood of causing failure indicates its relevance in determining 
a firm’s profitability and productivity. Therefore, insurers are effective and successful 
when they can maintain their risk exposure within proper and acceptable parameters. 
There is growing interest in intangibles, including intellectual capital efficiency (ICE) and 
its components, namely human (HCE) and structural (SCE) capital efficiency, in the 
business world. ICE is the aggregate of all HCE and SCE. Thus, employee expertise, 
organizational processes, and other intangibles contribute to a company’s profitability.
Ulrich (1998) found two important reasons for the importance of ICE in the service 
economy. These are the increasing demand for knowledge workers (HCE) and the 
growing importance of maintaining proper structures and procedures (SCE). Insurance 
CONTACT Lord Mensah Lmensah@ug.edu.gh Department of Finance, University of Ghana Business School, 
University of Ghana, P. O. Box LG 78, Legon, Accra, Ghana
© 2023 Taylor & Francis Group, LLC 
2 S. KUTTU ET AL.
firms whose managers have the requisite knowhow in the insurance environment can 
apply professional judgment to make better decisions and ensure that the firm is profit-
able, efficient, and has minimum exposure to risks (Alhassan & Asare, 2016).
Kusi et al. (2019) highlighted that an insurer with good management can easily 
identify, measure, monitor, and mitigate underwriting risk. They noted that well- 
established systems help insurers identify the sources of underwriting risk to minimize 
losses. These are indications of the central role that ICE plays in minimizing an insurer’s 
exposure to risks and the associated effects of firm failure.
Empirical evidence supports the view that high exposure to risks is a major cause for 
financial firms’ insolvency and failures (Njanike, 2009). The cruciality of risk and its high 
likelihood of causing firm failure has necessitated the examination of factors that affect 
risks. The factors identified include macroeconomic factors such as inflation, unemploy-
ment, exchange rate, economic growth, and productivity (Ali & Daly, 2010; Castro, 2013; 
Louzis et al., 2012; Waemustafa & Sukri, 2015). Few studies have identified firm-specific 
determinants of risks in financial institutions. These include efficiency, firm size, capital 
adequacy, profitability, and ICE (Louzis et al., 2012).
Studies on the impact of ICE and its components have focused on the sustainable 
development of healthcare (Dalwai et al., 2023), fraud and money laundering (Salehi 
et al., 2022), firm performance and bankruptcy risk (Dalwai & Salehi, 2021), annual 
report readability (Dalwai, Mohammadi, et al., 2021), fraud in financial statements (Lotfi 
et al., 2021), corporate value-creation and growth (Salehi & Zimon, 2021), shariah 
governance (Nawaz et al., 2021), and related party transactions on contractual costs 
(Salehi, Ahmadzadeh, et al., 2020). Evidence on the role of ICE in the financial sector 
have focused on firm performance (Asare et al., 2017; Firer & Mitchell Williams, 2003, 
Uadiale & Uwuigbe, 2011; Kweh et al., 2014; Nourani et al., 2018; Olarewaju & Msomi,  
2021; Oppong et al., 2019; Zakery & Afrazeh, 2015).
However, in the banking industry, Nguyen et al. (2021), Dalwai, Singh, et al. (2021), 
Azmi and Kurniawan (2021), Zheng et al. (2018), Ghosh and Maji (2014), and Nawaz 
(2019) investigated the influence of ICE on risk. They discovered a substantial positive 
connection between ICE and risk. Mwangi and Iraya (2015), Angima, Mwangi, Kaijage, 
and Ogutu (2017), Makau and Okeyo (2021), and Deyganto and Alemu (2019) found 
a negative link between underwriting risk and company performance. Relatedly, Abbas 
et al. (2021) found a negative link between capital ratio and risk. Al-Maliki et al. (2022) 
found a negative relationship between COVID-19 and risk-taking behavior. Salehi et al. 
(2021) found a negative association between managerial characteristics and risk. Salehi, 
Naeini, et al. (2020) discovered a positive relationship between managers’ narcissism and 
risk.
Given the significance of ICE and its implications for risk, little research has been 
conducted to examine its impact on insurer risk. Yu et al. (2008) conducted the only 
study in the literature that investigated how incentives to safeguard intangible assets 
affect the risk-taking behavior of property and liability insurers in the property and 
liability insurance markets of industrialized countries. Thus, we looked into the impact of 
ICE and its components on the risk-taking behavior of insurers in Ghana.
Most African countries’ steady economic growth, combined with a largely under-
developed insurance industry, has propelled the continent to the second fastest-growing 
insurance market after Latin America. Before COVID-19, the African insurance market 
JOURNAL OF AFRICAN BUSINESS 3
was predicted to grow at a compound annual growth rate of 7% between 2020 and 2025, 
about twice as quickly as North America, more than thrice that of Europe, and faster than 
Asia’s 6% (McKinsey & Company, 2020). Thus, growth will undoubtedly be supported by 
the risk-taking behavior of insurance firms across the continent, and, to a lesser extent, 
the sum of employee expertise, organizational processes, and other intangibles that add 
up to ICE, and how these variables contribute to an insurance company’s profitability. 
We aim to fill the gap in the literature by investigating the impact of ICE, HCE, and SCE 
on risk-taking behavior in Ghanaian insurance firms.
Recent key market drivers such as economic growth, rising government spending, 
technological innovations, and increased consumer awareness of insurance products in 
Ghana have reinforced the need to investigate the relationship between ICE (and its 
individual components) and risk-taking behavior in Ghanaian insurance firms. The 
growing number of digital distribution channels makes it easier for insurers to get 
insurance coverage. Insurtech, messaging platforms, and online sales channels all con-
tribute to Ghana’s insurance scene and carry risk (Ghana Insurance Market Report,  
2019).
We add to the literature by investigating the influence of ICE and its components, 
specifically HCE and SCE, on the underwriting risk of Ghanaian insurers. We investi-
gated the following issues. What is the relationship between ICE and the underwriting 
risk of Ghanaian insurers? How do HCE and SCE affect their underwriting risk?
We focused on Ghana because insurance growth is catching up to GDP growth. The 
Ghanaian life insurance market and bancassurance premium share have nearly doubled 
from 7% in 2015 to 13% in 2019 (McKinsey & Company, 2020). The expansion of 
Ghana’s insurance sector has underlined the need for employee and managerial creativity 
and value-creation skills. Investment in HCE, SCE, and ICE is critical for the insurance 
industry to support long-term growth (Joshi et al., 2013). As the insurance industry 
necessitates the intensive application of human knowledge and abilities and capital 
structures, it was considered acceptable to set this study in the insurance industry. 
Knowledge of the impact of ICE and its components on risk-taking behavior will help 
insurance industry participants in making managerial decisions. Our conclusions will 
support the regulator in policy formulation.
Our work adds to the literature in two ways. First, it is the only study to investigate the 
influence of ICE on underwriting risk in Ghana’s life and non-life insurance markets, and 
to a lesser extent, in any other African country. Second, to the best of our knowledge, it is 
the only study to break down ICE into HCE and SCE and investigate their impact on the 
underwriting risk of insurance firms in general.
We assessed the influence of ICE and its components on the underwriting risk of 
Ghanaian insurance companies using a generalized least squares estimator. We used data 
from the National Insurance Commission (NIC) on 31 insurance businesses, of which 14 
and 17 were life and non-life insurers, respectively. According to the NIC (2020) annual 
report, the 14 life businesses chosen constituted 77% of Ghana’s total life insurance 
market, whereas the 17 non-life companies chosen represented 75% of Ghana’s total 
non-life insurance market. We focused on data from 2008 to 2019 owing to availability.
The findings suggest a significant negative relationship between HCE and under-
writing risk for the Ghanaian insurance industry. However, there was no relationship 
between ICE and SCE on the one hand, and underwriting risk on the other. At a minor 
4 S. KUTTU ET AL.
level, our results show that other factors that influence underwriting risk include firm 
size, profitability, type of insurer, and age. The findings highlight the need for managers 
of insurance firms to improve their HCE as it has a negative impact on their exposure to 
underwriting risk.
This study is crucial because it may provide insights on prospective policy improve-
ment routes. As using HCE to minimize underwriting risk is critical, policymakers can 
affect the trajectory of the Ghanaian insurance business environment by making adjust-
ments to existing policies to foster the development of HCE in the insurance industry. 
This will have a favorable impact on the insurance industry’s contribution to Ghana’s 
economic growth. The rest of this paper is organized as follows: Section 2 reviews the 
literature, Section 3 presents the methodology, Section 4 presents the data, Section 5 
presents the findings, and Section 6 concludes the paper.
2. Literature review and hypothesis development
2.1. The Resource-Based View (RBV)
The RBV of a company, as formulated by Penrose (1959), explains why certain organiza-
tions perform better than others. According to the RBV, a firm with specific resources 
can obtain a competitive edge over another firm (Wernerfelt, 1984). Assets are either 
tangible or intangible resources that a company employs in order to develop products or 
services that generate value. According to Barney (1991), organizations whose strategies 
are based on leveraging intangible assets outperform those based on tangible ones. 
However, as other organizations may quickly reproduce tangible assets, the competitive 
advantage acquired from them is not sustainable for lengthy periods of time. Intangible 
assets are scarce and difficult to replicate, and thus provide a long-term advantage. The 
preceding arguments emphasize that organizations do well when they have, properly 
manage, and use intangible resources (mainly managerial skills, knowledge, experience, 
and competence). Thornhill and Amit (2003) argued that the RBV is appropriate in 
corporate bankruptcies. They contended that managerial shortcomings can lead to 
insolvency and corporate failure. Given that underwriting risk is a leading cause for 
insurer failure, it stands to reason that managerial skill may influence insurers’ exposure 
to underwriting risks.
2.1.1. Intellectual capital efficiency
Pulic (1998, 2000) created a value-added intellectual coefficient (VAIC) model to 
quantify the value added by tangible and intangible assets. HCE, SCE, and capital 
employed efficiency are components of the VAIC model. This model is widely 
considered the standard measurement approach for comparing industries and 
nations. Given the ease with which information from financial accounts can be 
applied, this model has been widely employed in various empirical studies 
(Mohammadi & Taherkhani, 2017; Xu & Wang, 2018). Pulic (1998, 2000) decom-
posed ICE into HCE and SCE using the VAIC. Employees’ expertise, abilities, 
experience, and training constitute HCE, whereas organizational resources such as 
management measures, techniques, strategies, and databases constitute SCE. Several 
studies have examined the influence of ICE on financial performance in the 
JOURNAL OF AFRICAN BUSINESS 5
insurance industry. Oppong et al. (2019), for example, argued that there is 
a considerable positive association between ICE and the productivity of the 
Ghanaian insurance market from 2008 to 2016. They also discovered that HCE 
significantly impacts insurance company productivity. Chen et al. (2014) discovered 
that ICE, HCE, and SCE had a favorable impact on general insurance firm pro-
ductivity in Malaysia from 2008 to 2011. Alipour (2012) discovered that value- 
added ICE, HCE, and SCE have a considerable beneficial effect on insurance 
company profitability in a study of 39 Iranian insurance firms. When Zakery and 
Afrazeh (2015) evaluated the impact of ICE on the Iranian insurance industry, they 
corroborated this outcome. They discovered that HCE has a considerable favorable 
impact on insurers’ efficiency. However, SCE has a negative effect on the efficiency 
of insurers. In their analysis of Indian insurance companies, Smriti and Das (2018) 
confirmed the considerable positive relationship between ICE and financial 
performance.
Another body of scholarship has concentrated on the impact of underwriting risk on 
the performance of insurance companies. Adams and Buckle (2003) discovered 
a negative relationship between underwriting risk and performance in their analysis of 
the Bermudan insurance market. This finding was supported by Wani and Dar (2015), 
who studied the Indian insurance market and Zainudin, Mahdzan, and Leong (2018), 
who examined Asian insurance firms. Kusi et al. (2019) found a negative relationship 
between underwriting risk and performance in Ghana’s insurance business. From 1999 
to 2012, Ghosh and Maji (2014) investigated the influence of ICE, HCE, and SCE on the 
risks of Indian commercial banks. They discovered that ICE was inversely related to risk 
and that among the components, HCE was adversely related to risk. Lin, Yu et al. (2008) 
discovered similar results when they analyzed corporations in the US property and 
liability insurance markets. The literature shows that, on average, ICE and its compo-
nents affect performance positively, whereas underwriting risk does so negatively. 
However, there has been little discussion on the relationship between ICE, HCE, and 
SCE and underwriting risk in the literature. Nuryaman (2015) contended that ICE can 
offer value to a company. Thus, the ICE of a firm will boost investor trust, which may 
increase the company’s worth. However, research has proven that risk has a negative 
impact on company value and that risk management has a beneficial impact on firm 
value (Bohnert et al., 2017; Faisal et al., 2021; Krause & Tse, 2016). As a result, we 
hypothesize as follows:
Hypothesis 1. ICE has a negative effect on underwriting risk in insurance companies in 
Ghana.
HCE comprises a firm’s intangible assets, which include its employees’ intellectual 
aptitude, creativity, and invention. Zheng et al. (2018) and Hasnaoui et al. (2021) 
found that HCE reduced risk in firms. Thus, we hypothesize as follows:
Hypothesis 2. HCE has a negative effect on underwriting risk in insurance companies 
in Ghana.
6 S. KUTTU ET AL.
Hardware, software, trademarks, patents, licenses, and other things that can improve 
employee productivity are examples of SCE. They are the infrastructure that supports 
employee performance. No study has linked SCE to risk. Popoola et al. (2019) and 
Pedraza Melo and Gala Velásquez (2022) discovered a link between SCE and company 
performance. As Nuryaman (2015) argued that SCE is a component that adds value to 
the firm, we propose the following:
Hypothesis 3. SCE has a negative effect on underwriting risk in insurance companies in 
Ghana.
3. Methodology
This section is divided into three subsections. The first sub-section discusses underwriting 
risk. Over the study period, underwriting risk was utilized as a proxy for risk-taking behavior 
in insurance firms in Ghana. Pulic’s (1998) VAIC model was used to explain the independent 
variables in the second sub-section. The control variables are described in the third sub- 
section. Table 1 presents detailed information on all factors included in this study. The last 
sub-section describes the regression model that was used to investigate the link between 
underwriting risk and ICE and its components.
3.1. Dependent variables
3.1.1. Underwriting risk
This work builds on prior research (Adams & Buckle, 2003; Akotey et al., 2013; 
Alhassan et al., 2015; Kusi et al., 2019) by measuring underwriting risk as a ratio of 
losses incurred to premiums earned (loss ratio). This metric quantifies the risk that 
premiums received will not be sufficient to cover losses sustained under the policies 
covered (Charumathi, 2012). Sound underwriting concepts and criteria, according to 
Adams and Buckle (2003), are critical to the financial performance of insurance 
companies. They are affected by insurance companies’ risk appetite. Insurers that 
insure risky businesses (e.g. catastrophe coverage) must ensure that appropriate man-
agement practices are followed to reduce their exposure to underwriting losses and 
maximize returns on invested assets.
3.2. Independent variables
3.2.1. Ice
The VAIC coefficient was proposed by Pulic (1998, 2000) as a quantitative instrument for 
predicting future capacities. We used financial data from the annual reports of insurance 
businesses as submitted to the NIC in Ghana during the study period to calculate the 
efficiency of their IC and asset values. Following Pulic (1998, 2000), we computed ICE in 
the following stages using the VIAC model:
JOURNAL OF AFRICAN BUSINESS 7
Table 1. Summary of variables and measures.
Variable 
Number Variable name Codes Measurement Method References Data Source
1 Dependent Underwriting Urisk Ratio of incurred losses to earned premiums (loss ratio) Adams and Buckle (2003), Akotey National Insurance 
Risk et al. (2013), Kusi et al. (2019), Commission 
Alhassan et al. (2015) annual reports
2 Independent Human HCE VA/HC, where VA is valued added, and HC is employees’ annual salaries and Olarewaju and Msomi (2021), National Insurance 
Capital wages. VA = Depreciation (D) + Amortisation (A) + Operating Profit (OP) +  Oppong et al. (2019), Nourani Commission 
Efficiency Employees Cost (EC) et al. (2018) annual reports
3 Independent Structural SCE SC/VA where SC includes hardware, software, trademarks, patents, licenses, Olarewaju and Msomi (2021), National Insurance 
Capital and all other factors that can enhance employee productivity. VA =  Oppong et al. (2019), Nourani Commission 
Efficiency Depreciation (D) + Amortisation (A) + Operating Profit (OP) + Employees et al. (2018). annual reports
Cost (EC)
4 Independent Intellectual ICE HCE+SCE Olarewaju and Msomi (2021), National Insurance 
Capital Oppong et al. (2019), Nourani Commission 
Efficiency et al. (2018). annual reports
5 Control Firm size Size Natural logarithm of total assets Salas and Saurina (2002) National Insurance 
Commission 
annual reports
6 Control Profitability ROA Log of earnings before interest and tax/total assets Ahmed, Ahmed and Usman (2011), National Insurance 
Connelly and Limpaphayom Commission 
(2004) annual reports
7 Control Leverage Lev Ratio of total liability and total asset Alhassan et al. (2015), Biener et al. National Insurance 
(2016) Commission 
Aslam and Haron (2020) annual reports
8 Control Age Age Age is measured as the time from the insurer’s inception to the current year Biener et al. (2016) National Insurance 
Commission 
annual reports
9 Control Type of Type Dummy Biener et al. (2016) Self
Insurer
VA and HC denote value added (total revenue minus total expenses) and human capital (total staff cost), respectively. HCE, SCE, and ICE denote human, structural, and intellectual capital 
efficiency, respectively.
8 S. KUTTU ET AL.
(1) Value added (VA) was first computed in order to determining ICIE. Thus, VA =  
Depreciation (D) + Amortisation (A) + Operating Profit (OP) + Employees Cost (EC)
(2) HCE = VA/HC, where VA is valued added, and HC is employees’ annual salaries 
and wages. This model determines the contribution of human resources to the 
creation of additional value.
(3) SCE = SC/VA, where SC includes hardware, software, trademarks, patents, 
licenses, and all other factors that can enhance employee productivity structural 
capital as derived from subtracting HC from VA. Thus, SC includes everything, 
except for human resources, that creates firm value.
(4) ICE is the sum of HCE and SCE. Thus, ICE = HCE+SCE
(5) VAIC is the sum of HCE, SCE, and CEE. Thus: VAIC = HCE + SCE + CEE. This 
implies that, VAIC = ICE + CEE.
(6) CEE = VA/CE, where CE denotes capital employed, which is measured by deduct-
ing the intangible assets from total assets.
3.3. Control variables
Control variables are included to account for the influence of other factors that may 
explain insurance underwriting risk. These variables are provided to avoid model mis-
specification. Firm size, proxied as the natural logarithm of total assets, is used to control 
for the impact of firm size on the insurer’s underwriting risk (Salas & Saurina, 2002). 
Profitability is used to control for the impact of profitability on underwriting risk, as 
assessed by return on asset (the ratio of profit before taxes to total assets; Ahmed, Ahmed 
& Usman, 2011; Connelly & Limpaphayom, 2004). A dummy is used to distinguish 
between different types of insurers: 1 if the company is engaged in life insurance, and 0 if 
it is engaged in other domains of insurance. Leverage is calculated as the ratio of total 
liabilities to total assets (Alhassan et al., 2015). This is used to mitigate the impact of 
leverage on underwriting risk. Age is measured as the time from the insurer’s inception to 
the current year, which is employed as a control variable (Biener et al., 2016).
3.4. Model specification
The relationship between ICE and insurance underwriting risks was investigated using 
a generalized least squares regression model. The first and second equations estimate the 
impact of HCE and SCE, and ICE on underwriting risk, respectively. Thus, 
URiskit ¼ β0 þ β1HCEit þ β2SCEit þ β3Sizeit þ β4ROAit þ β5Typeit þ β6Levit þ β7Ageit þ εit (1) 
URiskit ¼ β0 þ β1ICEit þ β2Sizeit þ β3ROAit þ β4Typeit þ β5Levit þ β6Ageit þ εit; (2) 
where, URisk, ICE, HCE, SCE, Size, ROA, Type, Lev, and Age denote underwriting risk, 
ICE, HCE, SCE, firm size, profitability, insurer type, leverage, and insurer age, respec-
tively. Owing to the presence of multicollinearity between ICE and its components (HCE 
and SCE), their impact on underwriting risk is not analyzed in a single equation. We 
anticipated a positive link between business size and underwriting risk. We contend that 
as firms grow, managers may find it difficult to personally supervise underwritten 
policies. As organizations grow, they may fail to invest in managerial capacity to control 
JOURNAL OF AFRICAN BUSINESS 9
costs and boost profitability. When organizations invest in managerial capacity, there is 
always a lag between growth and investment. Altuntas and Rauch (2017) supported this 
claim.
We anticipated a negative link between profitability and underwriting risk. When 
insurance risk rises, so do the costs associated with underwriting, thus reducing profit-
ability. We predicted leverage and underwriting risk to be negatively related as leverage is 
associated with borrowing to invest in a firm. We expected firm size to affect under-
writing risk negatively. This effect was achieved by leveraging the firm’s asset size. Finally, 
we anticipated that age will have a negative impact on underwriting risk. This is a result 
of experience honed over the years in underwriting policies properly.
4. Data
As seen in Table 2, 31 insurance companies out of the 44 life and non-life insurance 
companies currently operating in Ghana were surveyed over a 12-year period (2008 to 
2019), as several firms lacked yearly financial data during the study period. Some insurers 
engaged in mergers or acquisitions, and some ceased operations during the study period. 
Data from the yearly reports given by insurance firms in Ghana to the NIC were used, as 
NIC is the insurance regulatory organization, and all insurers must submit annual 
reports to it. Most research on insurance in Ghana have used NIC data (Akotey & 
Abor, 2013; Alhassan & Asare, 2016, Owusu-Ansah et al., 2010; Ansah-Adu et al.,  
2011; Kusi et al., 2019).
4.1. Descriptive statistics on variables used
Table 3 presents the descriptive statistics for the variables used. Underwriting risk had 
an average of 41.6% for the cross-section of 31 insurance firms in Ghana evaluated 
between 2008 and 2019, implying that 41.6% of net premiums were subject to 
underwriting risk. This amount is slightly higher than the figure published by Kusi 
et al. (2019) for the same market in a previous era (40.68%). Alhassan et al. (2015) 
estimated a somewhat lower value of 36 and 18.1% for the Ghanaian life and non-life 
insurance markets, respectively. The industry saw a low and high of 2.2 and 223.3%, 
respectively. However, underwriting risk appears to be high in the industry on 
average. The HCE of Ghanaian insurers was 1.689 on average, with a standard 
deviation of 2.147. This means that for every cedi spent on human resources, the 
insurer generated a value of GH 1.689. HCE calculated the amount of value created 
per Ghana cedi spent on workers. During the study period, the SCE of Ghanaian 
insurers averaged 0.46. This means that Ghanaian insurers generated a value of GH 
0.46 for every cedi spent on structural capital. ICE has an overall mean of 2.146. This 
means that for every cedi spent on intellectual capital, Ghanaian insurers generated 
GH 2.146. These values were slightly lower than those published in Oppong et al. 
(2019), where the average HCE and SCE were 2.127 and 0.524, respectively. There 
was substantial evidence that insurers’ ICE depended more on HCE than did SCE. 
Size was calculated as a natural log of insurers’ total assets and indicated an average 
growth rate of 17.298%. Kusi et al. (2019) found an average size of 16.88%, which is 
comparable to our findings. The average return on assets, which represents 
10 S. KUTTU ET AL.
Table 2. List of insurance operators in Ghana.
Type of Insurance Operators Total Number (2021) Number Sampled
Life 17 14
Non-life 27 17
Source: NIC (2020).
Table 3. Descriptive statistics.
Variable Obs. Mean Std. Dev. Min Max
Urisk 372 0.416 0.259 0.022 2.233
HCE 372 1.686 2.147 −6.291 28.651
SCE 372 0.46 4.345 −43.366 64.778
ICE 372 2.146 4.831 −43.344 64.762
Size 372 17.298 1.326 12.597 20.542
ROA 372 0.036 0.155 −1.035 1.411
Age 372 28.887 0.508 1 95
Type 372 0.452 0.498 0 1
Leverage 372 0.647 0.508 .015 7.128
Urisk, HCE, SCE, ICE, Size, ROA, Type, and Leverage denote underwriting risk, human, structural, and 
intellectual capital efficiency, Firm size, profitability, insurer type, and leverage, respectively.
stakeholder returns, was 3.6%, whereas Kusi et al. (2019) and Akomea-Frimpong, 
Andoh, and Ofosu-Hene (2016) found average returns on assets of 1.19 and 1.12%, 
respectively. This distinction was also because of the industry’s expansion. The 
period’s smallest and largest return on assets was 103.5 and 141.1%, respectively. 
The average leverage for insurance firms in Ghana was 0.647. This is slightly lower 
than the average leverage discovered by Alhassan et al. (2015). According to them, 
while non-life insurers reported an average of 0.765, life insurers reported an average 
of 0.67. For the study period, the average age of enterprises was 28.89 years, with 
minimum and maximum ages of 1 and 95 years, respectively.
Figure 1 depicts the underwriting risk trend in the insurance business. Underwriting 
risk peaked in 2018, at 53.53%. This tendency could be the result of faulty policy 
underwriting or the underwriting of riskier businesses throughout the era.
4.2. Preliminary statistical analysis
When the independent variables used in the regression analysis are highly linearly 
correlated, multicollinearity exists. The Pearson correlation and Variance Inflation 
Factor (VIF) are used to test for multicollinearity. According to Kennedy (2008), the 
criterion for multicollinearity is 0.7. According to Tables 4 and 5, the absolute values of 
all correlation coefficients are less than 0.7, indicating that none of the variables were 
multicollinear. The VIF verifies the eligibility of all variables used.
We used the Wooldridge (2002) test for autocorrelation in panel data from Table 6 
and found strong first-order serial correlation in the series. The Breusch-Pagan-Godfrey 
Lagrange multiplier test revealed heteroscedasticity in the series.
JOURNAL OF AFRICAN BUSINESS 11
Underwriting Risk
0.6
0.5
0.4
0.3
0.2
0.1
0
2006 2008 2010 2012 2014 2016 2018 2020
Figure 1. The trend of underwriting risk from 2008 to 2019.
Table 4. Correlation matrix for variables in equation (1).
Variables Risk HCE SCE Size ROA Type Leverage Age VIF
URisk 1.000
HCE 0.005 1.000 1.396
SCE −0.038 −0.008 1.000 1.008
Size 0.300 0.242 0.043 1.000 1.321
ROA −0.158 0.472 −0.012 0.164 1.000 1.458
Type 0.188 −0.026 0.010 0.069 −0.136 1.000 1.620
Leverage −0.020 −0.092 −0.049 −0.040 0.152 .105 1.000 1.100
Age 0.050 0.152 −0.010 0.449 0.246 −.035 −0.047 1.000 1.337
Urisk, HCE, SCE, Size, ROA, Type, Leverage, and Age denote underwriting risk, human and structural capital efficiency, Firm 
size, profitability, insurer type, leverage, and age, respectively.
Table 5. Correlation matrix for variables in equation (2).
Variables Risk ICE Size ROA Type Leverage Age VIF
URisk 1.000
ICE −0.032 1.000 1.078
Size 0.300 0.147 1.000 1.292
ROA −0.158 0.199 0.164 1.000 1.181
Type 0.188 −0.003 0.069 −0.136 1.000 1.620
Leverage −0.020 −0.085 −0.040 0.152 .105 1.000 1.072
Age 0.050 0.058 0.449 0.246 −.035 −0.047 1.000 1.334
Urisk, ICE, Size, ROA, Type, Leverage, and Age denote underwriting risk, intellectual capital efficiency, Firm size, profit-
ability, insurer type, leverage, and age, respectively.
Table 6. Tests for autocorrelation and heteroscedasticity.
Equation (1) Equation (2)
Test P-value Test P-value
Breusch-Pagan test for heteroskedasticity .000 Breusch-Pagan test for heteroskedasticity .000
Test for first-order autocorrelation .006 Test for first-order autocorrelation .006
12 S. KUTTU ET AL.
5. Empirical results and analysis
5.1. Regression results: ICE and underwriting risk
We used the generalized least squares (GLS) estimation methodology to determine the 
influence of ICE on underwriting risk. It corrected the serial correlation and hetero-
scedasticity in the early statistical examination of our data. It is acceptable as it addressed 
heteroskedasticity and autocorrelation. The GLS estimation methodology is effective in 
decreasing estimation errors.
Tables 7 and 8 show how ICE and its components affect underwriting risk. For the 
combined data on life and non-life insurance, individual life insurance, and non-life 
insurance companies, our results in Table 7 reveal a significant negative relationship 
between HCE and underwriting risk. This suggests that increasing HCE in insurance 
firms reduces underwriting risk. From Table 8, it is clear that there is no significant 
relationship between ICE and underwriting risk, and between SCE and underwriting 
risk. The analysis found a substantial positive relationship between underwriting risk and 
company size for the control variables. Profitability and age were negatively related to 
underwriting risk. Insurance type was discovered to have a significant positive effect on 
underwriting risk.
6. Discussion
Given the scarcity of research on ICE and its components on risk-taking behavior in 
the insurance industry, we analyzed our findings in the context of the finance 
industry. According to Ghosh and Maji (2014) and Dalwai, Singh, et al. (2021), 
Table 7. GLS regression results.
All Insurers Non-Life Insurers Life Insurers
Coefficient Coefficient Coefficient 
URisk (Standard Error) (Standard Error) (Standard Error)
HCE −0.003** −0.007** −0.029** 
(0.007) (0.005) (0.017)
SCE −0.003 0.004 −0.003 
(0.003) (0.009) (0.004)
Size 0.067** 0.049** 0.066** 
(0.011) (0.011) (0.018)
ROA −0.327** −0.368** −0.362 
(0.096) (0.079) (0.242)
Type 0.071** 
(0.025)
Leverage 0.003 0.009 0.022 
(0.025) (0.021) (0.064)
Age −0.001 0.001** −0.002** 
(0.001) (0.001) (0.001)
Constant −0.755** −0.483** −0.674 
(0.176) (0.187) (0.298)
Number of Obs. 372 204 168
Number of groups 31 17 14
Prob>chi2 0.000 0.000 0.000
Log-likelihood 7.761 −35.435 89.787
Mean dep. var 0.416 0.370 0.470
SD dependent var 0.259 0.179 0.322
**denotes 5% significance levels. Urisk, HCE, SCE, Size, ROA, Type, Leverage, and Age denote under-
writing risk, human, structural, and intellectual capital efficiency, firm size, profitability, type of insurer, 
leverage, and age, respectively.
JOURNAL OF AFRICAN BUSINESS 13
Table 8. GLS regression results.
All Insurers Non-Life Insurers Life Insurers
Coefficient Coefficient Coefficient 
URisk (Standard Error) (Standard Error) (Standard Error)
ICE −0.002 −0.004 −0.003 
(0.003) (0.004) (0.004)
Size 0.069** 0.051** 0.077** 
(0.011) (0.011) (0.017)
ROA −0.289** −0.391** −0.07 
(0.086) (0.076) (0.191)
Type 0.072** 
(0.025)
Leverage 0.001 0.009 −0.035 
(0.025) (0.021) (0.057)
Age −0.001 0.001 −0.002** 
(0.001) (0.001) (0.001)
Constant −0.769 −0.501 −0.777** 
(0.176) (0.187) (0.297)
Number of Obs. 372 204 168
Number of groups 31 17 14
Prob>chi2 0.000 0.000 0.000
Log-likelihood 7.351 −37.283 89.233
Mean dep. var 0.416 0.370 0.470
SD dependent var 0.259 0.179 0.322
**denotes 5% significance levels. Urisk, ICE, Size, ROA, Type, Leverage and Age denote underwriting risk, 
human, structural, and intellectual capital efficiency, firm size, profitability, type of insurer, leverage, 
and age, respectively.
who investigated the impact of HCE on risk in the banking industry, HCE has 
a negative effect on the underwriting risk of insurance companies in Ghana. The 
findings support Thornhill and Amit’s (2003) case for a risk-based approach to 
company bankruptcy. They contended that managerial deficiencies can lead to cor-
porate collapse. Given that risk is a major reason for insurer failure, it stands to 
reason that managerial competence may influence insurers’ exposure to underwriting 
risks.
The significant inverse relationship between HCE and underwriting risk for Ghanaian 
insurance (including life and non-life insurers) means that as insurance firm employee 
knowledge, skills, and competence improve, so too will their underwriting risk. This 
supports the conclusions of Ghosh and Maji (2014) and Nawaz (2019), who suggested 
that financial institutions must rely on their knowledge, professional experience, and 
skillsets to achieve efficiency in their business operations. These would allow the firm’s 
workers to appropriately recognize and analyze potential signals of default throughout 
the proposal and screening processes, when the decision to underwrite a business is 
made. They emphasize that financial institutions rely on the knowledge of their staff to 
cope with problems that may surface in the underwriting process.
This finding shows that insurers must be cautious about the quality of the 
personnel they hire and guarantee the efficient utilization of staff talents and 
capabilities. This can have a significant impact on their underwriting risk manage-
ment and exposure. As the insurance industry is knowledge-intensive, consider-
able investments in HCE are essential for insurers to survive (Kweh et al., 2014). 
Dalwai, Singh, et al. (2021) studied banks in 12 rising Asian nations and found 
that ICE had an insignificant negative effect on underwriting risk. They stated 
14 S. KUTTU ET AL.
that ICE had no relationship with bank risk-taking or stability. Our study lends 
credence to the resource-based approach, which contends that tangible and intan-
gible assets drive corporate performance. Azmi and Kurniawan (2021) demon-
strated that ICE has a detrimental impact on the risk of the banking sector. 
However, Zheng et al. (2018) found a positive relationship between risk-taking 
behavior and ICE in Bangladesh’s banking sector.
SCE has a negative but minor effect on underwriting risk. It has the least impact 
on insurance underwriting risk. Chen et al. (2014) discovered that SCE leads to 
knowledge generation in a firm. Insurance companies in Ghana are evidently not 
making the best use of their organizational resources to ensure proper policy 
underwriting. The results could be attributed to the fact that the impact of SCE 
on underwriting risk may not be realized immediately owing to delays (Sydler et al.,  
2014).
This study found a significant positive relationship between the firm size of all insurers 
and the sub-divisions (life and non-life insurers) and underwriting risk. This aligns with 
Altuntas and Rauch (2017), who asserted that large insurers are more likely exposed to 
the risk than are their small counterparts. As insurers become large, it becomes difficult 
for managers to directly monitor individual policies underwritten, which may increase 
their underwriting risks.
Profitability, measured by return on assets, was found to have a significant negative 
connection with underwriting risk. Our findings support Ahmed, Ahmed, and Usman 
(2011), who demonstrated that underwriting risk influences insurance firms’ return on 
assets. Connelly and Limpaphayom (2004) discovered a negative association between 
underwriting risk and insurance profitability.
The type of insurer had a considerable positive association with underwriting risk. 
Thus, assuming that all other things remained constant, the insurer’s underwriting risk 
was projected to be 0.071 and 0.072 units for a life and non-life insurer, respectively. This 
could be related to the higher claims paid by life insurers relative to non-life insurers 
throughout the time. Akotey et al. (2013) confirmed that life insurers experience high 
underwriting losses owing to overtrading and price undercutting. Age, defined as the 
time the insurer was founded to the current year, had a considerable negative impact on 
the insurer’s underwriting risk for life and non-life insurers. This is expected, as older 
insurers have greater expertise in adequately underwriting their products. This greatly 
reduces their underwriting risk.
In line with Kweh et al. (2014) and Oppong et al. (2019), we found that underwriting 
risk negatively impacts profitability. Our findings align with those of Kusi et al. (2019), 
Wani and Dar (2015), Adams and Buckle (2003), and Zainudin, Mahdzan, and Leong 
(2019). Independent of managerial skill enhancement, an incorrect underwriting of 
insurance policies may reduce profitability. However, research from the banking sector 
indicates that advancements in ICE and its components minimize the risk of these 
financial organizations (Ghosh & Maji, 2014; Nawaz, 2019; Nguyen et al., 2021). The 
significance of the findings cannot be understated. Given that HCE is negative and 
statistically significant, managers and policymakers in Ghana must devise strategies 
and policies to raise HCE over the life of non-life insurance enterprises. Alhassan and 
Asare (2016) discovered that investing in staff training can boost productivity and 
creativity.
JOURNAL OF AFRICAN BUSINESS 15
7. Conclusion
The link between underwriting risk, ICE, and its components is absent in the literature 
on the insurance sector, despite the numerous studies in the banking sector examining 
them. Our study fills this gap in the literature. We examined the impact of ICE, HCE, and 
SCE on the underwriting risk of insurers in Ghana. The GLS estimation methodology 
was applied to panel data from 31 insurance companies (14 and 17 life and non-life 
insurers, respectively) from 2008 to 2019. This was after our preliminary statistical 
analysis indicated that the GLS estimation methodology was appropriate. We discovered 
a substantial negative connection between HCE and underwiring risk. Therefore, we 
recommend that insurers be cautious about who they hire because this can affect their 
exposure to underwriting risks. All insurers should ensure that their policy underwriting 
team is highly qualified, competent, and knowledgeable. Highly skilled personnel can 
appropriately recognize and analyze possible symptoms of default in the first phase when 
the choice to underwrite a policy is taken and then deal with concerns in the policy term. 
The analysis demonstrates that an insurer’s size positively affects its underwriting risk. As 
insurers grow, the quantity and quality of staff should be increased to provide for direct 
control of underwriting activities. Insurance company executives must ensure that their 
underwriting efforts are adequately managed and drastically minimized. Periodic staff 
training should be encouraged to improve job efficiency. The regulator may implement 
incentives that promote insurers’ HCE in Ghana. This can be accomplished by mandat-
ing a minimum set of academic and professional qualifications and certain years of 
relevant experience for core roles.
Acknowledgments
Insightful comments from anonymous referees are highly appreciated.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Saint Kuttu http://orcid.org/0000-0001-7165-2223
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