Insurance Development,Global Economic Policy Uncertainty and Geopolitical Risk: The Asymmetric Relationship Evidence from ASEAN-5

Abstract

Academia and governments have shown interest in studying the influence of the global economic policy uncertainty index (GEPU) and the geopolitical risk index (GPR) on insurance development. Several empirical studies have been conducted to examine the influence of GEPU and GPR on insurance development. Nevertheless, the empirical results failed to achieve consensus. This study aims to study the asymmetric impacts of the GEPU and the GPR on insurance sector development in ASEAN from 1990 to 2020. We used the linear panel autoregressive distributed lag (PARDL) approach and the asymmetric panel autoregressive distributed lag (PNARDL) approach. The results from the linear panel ARDL showed that GEPU and GPR had an impact on non-life insurance (NI), but both variables did not have an impact on life insurance (LI). Furthermore, the asymmetric panel ARDL showed no significant impact of positive shocks from GEPU had a positive impact on LI and NI, but the negative shocks from GEPU only had a positive impact on NI. Positive and negative shocks from GEPU lead to an increase in the NI. Interestingly, we found that the positive shocks of GPR led to an increase in NI, but positive or negative shocks of GPR did not have an impact on LI. Policymakers in ASEAN should closely look at the GEPU and the GPR for insurance policy development.

JEL Codes: E6, G0

Keywords

Asymmetry insurance sector development global economic policy uncertainty index (GEPU)geopolitical risk index (GPR) and ASEAN

Introduction

Insurance has evolved in line with other key financial sectors to minimise economic uncertainty (Low & Fekete-Farkas, 2021) and plays a crucial function in controlling global and local investment levels, as well as individual and business risks. In recent years, researchers globally have focused on the correlation between insurance and related uncertainties and risks. Multiple studies have investigated the relationship between economic policy uncertainty and insurance (Balcilar et al., 2020; Canh et al., 2021; Gupta et al., 2019; Jeris et al., 2023). In addition, other studies examine the geopolitical risk associated with insurance premiums (Balcilar et al., 2019; Hemrit & Nakhli, 2021; Lee & Lee, 2020; Olasehinde-Williams & Balcilar, 2022; Shahbaz et al., 2018). Furthermore, few studies have specifically examined the impact of both economic policy uncertainty and geopolitical risk on insurance premiums.

Economic policy uncertainty refers to the degree of uncertainty or unpredictability that arises from government policies related to areas such as taxes, trade, monetary policy and regulation (Jeris et al., 2023). In addition, the existence of economic policy uncertainty is a specific type of financial risk that has an impact on insurance demand. Therefore, it is likely to influence insurance prices (Lee et al., 2013). Moreover, Gupta et al. (2019) showed that economic policy uncertainties impose a certain degree of pressure on economic activity, and it is reasonable to expect that they will also impact insurance purchasing behaviour. The presence of economic policy uncertainty might potentially affect the functioning of the insurance market, as the insurance industry plays a crucial role in protecting individuals and businesses from various risks. With the increasing uncertainty, it is reasonable to expect an increase in the demand for insurance products (Balcilar et al., 2020).

Hemrit and Nakhli (2021) indicate that insurance companies may raise premiums in response to rising uncertainty and risk caused by uncertain economic policies. This could result in a decline in the market for insurance products, as customers may seek more extensive coverage from other sources. Increased volatility in the financial market during periods of uncertainty can lead to increased costs for insurance companies, reducing their capacity for risk and perhaps reducing the demand for insurance products, so impacting the insurance business. The agency theory suggests a relationship between insurance premiums and economic policy uncertainty, suggesting that insurers may engage in riskier behaviour in periods of unclear economic policy. Agency theory highlights the disagreements that arise between the insured and the insurance company.

Geopolitical risk encompasses several types of geopolitical tensions, including terrorist attacks and war hazards, which disturb the standard and peaceful progress of international relations (Caldara & Iacoviello, 2022). According to Hemrit and Nakhli (2021), geopolitical risk is increasingly becoming a significant determinant of consumer decisions and risk-taking behaviour. It encompasses both political dangers, such as terrorism, and routine events that take place in certain countries, such as elections.

Geopolitical risk can significantly influence insurance premiums. First, geopolitical instability can restrict insurance firms from implementing and enforcing additional investment and coverage operations, as well as creating new insurance policies (Hemrit &Nakhli, 2021). Second, by expanding the impact of the geopolitical risk’s negative effects, public and government projects can be cancelled or postponed, licences may be suspended and foreign companies encounter the risk of having their assets expropriated, implying that insurance premiums will begin to fall (Hemrit & Nakhli, 2021). Third, capital constraints and reduced capital inflows into the economy as a result of geopolitical tensions might be viewed as the primary causes of soft insurance markets (Cummins & Danzon, 1997). Insurance premiums may then fluctuate in response to an adverse shock that diminishes the insurer’s capital. Fourth, when geopolitical risks increase, insurers become more hesitant to approve insurance for individuals due to greater information asymmetry, as well as difficulties such as moral hazard and adverse selection (Olasehinde-Williams & Balcilar, 2022). Consequently, geopolitical risk could potentially have a negative impact on insurance premiums.

There is a lack of research examining the combined impact of economic policy uncertainty and geopolitical risk on the insurance market (Hemrit & Nakhli, 2021; Xiang et al., 2023). The asymmetric relationship between insurance, geopolitical risk and economic policy uncertainty is ignored in those studies. So, this study aims to investigate the impact of economic policy uncertainty and geopolitical risk on insurance market development by comparing linear and non-linear panel ARDL models.

The main contribution of this article is to use the latest non-linear panel autoregressive distributed lags (NARDL) model, as proposed by Shin et al. (2014), in order to investigate the relationship between global economic policy uncertainty, geopolitical risk and insurance premiums (total, life and non-life). Second, previous studies have focused on the relationship between economic policy uncertainty and the insurance market (Balcilar et al., 2020; Canh et al., 2021; Gupta et al., 2019; Jeris et al., 2023), as well as geopolitical risk and the insurance market (Hemrit & Nakhli, 2021; Lee & Lee, 2020; Olasehinde-Williams & Balcilar, 2022; Shahbaz et al., 2018). However, these studies have primarily examined developed and industrialised economies, ignoring emerging countries, particularly ASEAN countries. Therefore, this article aims to be the first to investigate the impact of global economic uncertainty and the geopolitical risk index (GPR) on insurance premiums in ASEAN countries. Third, this research aims to compare the findings of both linear and non-linear models in order to provide deeper and clearer insights.

The remainder of this work is structured into four sections. The second section provides literature reviews on economic policy uncertainty, geopolitical risk and the development of insurance markets. The third section provides a thorough overview of the data and methodologies used in this study. The empirical results are presented in the fourth section. The fifth section concludes the study.

Literature Review

There is an enormous amount of research on the relationship between economic growth and the development of finance, which also includes the insurance industry (Chang et al., 2014; Hatemi-J, 2012; Lee et al., 2013; Liu et al., 2022; Si et al., 2018; Vadlamannati, 2008; Ward & Zurbruegg, 2000). However, there is a lack of research examining the relationship between insurance development and its related uncertainties and risks (Tables 1 and 2). Hemrit (2022) and Xiang et al. (2023) consider that both economic policy uncertainty and geopolitical risk influence insurance development. Hemrit (2022) examines the possible impact of economic policy uncertainty, geopolitical risk, non-oil output, inflation and corporate governance characteristics on insurance companies in Saudi Arabia. This study discovered that there are negative short-term effects due to geopolitical risk and uncertainty regarding government economic policy on the demand for insurance. Xiang et al. (2023) analyses the asymmetric and non-linear impacts of economic policy uncertainty, climate policy uncertainty and geopolitical risk on the premiums of life insurance in China. According to Xiang et al. (2023), economic policy uncertainty generally has a negative effect on life insurance premiums, except for the lowest quantiles. On the other hand, geopolitical risk tends to have a negative influence on the higher quantiles. Certain studies only examine the impact of economic policy uncertainty on the development of insurance (Balcilar et al., 2020; Canh et al., 2021; Gupta et al., 2019; Jeris et al., 2023). Balcilar et al. (2020) examine the influence of economic policy uncertainty on insurance premiums and discovered a significant correlation between the two variables. The study found that economic policy uncertainty leads to higher insurance premiums, though the impact is less significant in the short run compared to the long run. Moreover, economic policy uncertainty has a greater impact on non-life insurance compared to life insurance. Canh et al. (2021) investigate the impact of economic policy uncertainty on the liveliness of local insurance markets. The results revealed a negative correlation between economic policy uncertainty and the development of life insurance in a country. However, it was found that economic policy uncertainty does not have a significant effect on the development of non-life insurance. Gupta et al. (2019) explore the asymmetric and non-linear transmission of economic policy uncertainty to insurance premiums. They discover that while both total and non-life insurance show a positive relation with economic policy uncertainty, life insurance shows a negative relation. Jeris et al. (2023) examine the relationship between economic policy uncertainty and insurance rates. They discovered that economic policy uncertainty has a more significant impact on insurance premiums in the long term compared to the short term. Additionally, they observed that economic policy uncertainty plays a more prominent role in influencing life insurance premiums compared to non-life insurance premiums.

Table 1.

Summary of Empirical Studies Between Economic Policy Uncertainty and Insurance Development.

Author(s)	Country	Data	Method	Finding
Balcilar et al. (2020)	15 countries	1998 to 2016, yearly	PMG, MG and DFE	EPU => (+)TI EPU => (+)LI EPU => (+)NI
Canh et al. (2021)	16 OECD countries	1998 to 2017, yearly	PCSE, robust POLS, POLS and FGLS	EPU => (−)LI EPU =/=> NI
Gupta et al. (2019)	USA	1980 to 2014	NARDL	EPU => (+)TI EPU⁺ => (−)LI EPU⁻ => (+) LI EPU⁺ => (+)NI EPU⁻ =/=> NI
Hemrit (2022)	Saudi Arabia	2013Q1 to 2019Q4, quarterly	PMG	EPU => (−)ID
Jeris et al. (2023)	22 countries	1996 to 2020	PMG, FMOLS and DOLS	EPU => (+)LI EPU => (+)NI
Xiang et al. (2023)	China	January 2000 to August 2022, monthly	QARDL	EPU => (−)LI in medium and high quantile, except low quantile

Table 2.

Summary of Empirical Studies Between Geopolitical Risk and Insurance Development.

Author(s)	Country	Data	Method	Finding
Hemrit (2022)	Saudi Arabia	2013Q1 to 2019Q4, quarterly	PMG	GPR =/=> ID
Hemrit and Nakhli (2021)	14 countries	2000 to 2019, yearly	NARDL	GPR⁺ => (−) TI in China, South Africa and Venezuela GPR⁺ => (+) TI for Brazil, Hong Kong and Russia GPR⁻ => (−) TI for Mexico, South Africa, Turkey, Ukraine and Venezuela GPR⁻ => (+) TI in Brazil
Lee and Lee (2020)	BRICS	1985 to 2017, yearly	Quantile Granger Causality	GPR => TI in different quantiles for Brazil, Russia, India, China and Russia
Olasehinde-Williams and Balcilar (2022)	18 countries	1985 to 2016, yearly	MG	GPR => (+) TI GPR => (+) LI GPR => (+) NI
Shahbaz et al. (2018)	18 countries	1985 to 2016, yearly	MG	GPR => (+) TI GPR => (+) LI GPR => (+) NI
Xiang et al. (2023)	China	January 2000 to August 2022, monthly	QARDL	GPR => (−) LI in high quantile.

While certain studies focus only on geopolitical risk in insurance development (Hemrit & Nakhli, 2021; Lee & Lee, 2020; Olasehinde-Williams & Balcilar, 2022; Shahbaz et al., 2018), Hemrit and Nakhli (2021) analyse short- and long-run asymmetric responses of insurance premiums through positive and negative partial sum decompositions of changes in the GPR. According to Hemrit and Nakhli (2021), the geopolitical risk affects insurance premiums in an asymmetric and non-linear manner, and the impact is considerably greater in the long run than in the short run in most emerging countries. Lee and Lee (2020) reveal that unidirectional causality that runs from real output and geopolitical risk to insurance activities in Brazil and South Africa and bidirectional lower-tail causality among real output, insurance premiums and geopolitical risk in Russia. Olasehinde-Williams and Balcilar (2022) found strong evidence of a positive impact on insurance premiums by geopolitical risks and specifically found that the impact of geopolitical risks on non-life insurance premium is higher than the impact on life insurance premium.

Most of the studies apply linear panel models, which ignore the asymmetric relationship of economic policy uncertainty and geopolitical risk on insurance development, except Gupta et al. (2019) and Hemrit and Nakhli (2021), who employ time series analysis by using the NARDL model.

A large number of previous studies focused on developed and industrialised economies (Tables 1 and 2). For instance, Hemrit (2022), Xiang et al. (2023) and Gupta et al. (2019) focus on individual countries, such as Saudi Arabia, China and the United States. Furthermore, Balcilar et al. (2020), Canh et al. (2021), Jeris et al. (2023), Hemrit and Nakhli (2021), Lee and Lee (2020), Olasehinde-Williams and Balcilar, (2022) and Shahbaz et al. (2018) concentrate on group economies in different countries and regions.

This study aims to fill the existing knowledge gap by specifically examining the growing economies among the countries of ASEAN. It will also take into account the asymmetric relationships between economic policy uncertainty and geopolitical risk in relation to the development of the insurance market.

Data and Methodology

Data

This study has employed annual balance panel data from 1997 to 2022. The decision to use this range of data is driven by the available data, which provided 26 observations for each country. The ASEAN economies, including Indonesia, Malaysia, the Philippines, Singapore and Thailand, were selected for their relevance to the study. Real gross domestic product per capita is selected to represent the economic growth, and we have employed various proxies to measure insurance development, including life insurance premiums per capita, non-life insurance premiums per capita and total insurance premiums per capita.¹ This comprehensive approach has allowed us to represent each dimension of insurance development. The economic growth and insurance development indicators are measured in constant 2010 US$.

The economic policy uncertainty index used in our study follows the Baker et al. (2016) historical measure of uncertainty. The index is constructed from monthly newspaper searches for economic and policy uncertainty-related issues. The index can be downloaded from https://www.policyuncertainty.com/.

For the GPR, we follow Caldara and Iacoviello (2022), who calculate this parameter by counting the number of articles related to GPR in each newspaper (as a share of the total number of news articles) for each month. The data are available at https://www.matteoiacoviello.com/gpr.htm. Due to the global policy uncertainty and GPR being available in monthly data, we then average 12 months’ average to convert into annual values. The data definition and source for each variable are shown in Table 3.

Table 3.

Variable Definition.

Variable	Definition	Unit	Source
TI	Total insurance premium per capita	Constant 2010 US$	Swiss Re
LI	Life insurance premium per capita	Constant 2010 US$	Swiss Re
NI	Non-life insurance premium per capita	Constant 2010 US$	Swiss Re
GDP	Gross domestic product per capita	Constant 2010 US$	WDI
GEPU	Global economic policy uncertainty index	Index	Baker et al. (2016)
GPR	Geopolitical risk index	Index	Caldara and Iacoviello (2022)

Note: The constant term of each insurance indicator is calculated by the authors.

Methodology

The model of this study is developed based on the studies of Xiang et al. (2023) and Hemrit (2021), the following equation explains the insurance development speciation:

L n I D_{i t} = α_{0 i} + α_{1 i} L n G D P_{i t} + α_{2 i} L n G E P U_{i t} + μ_{i t},

(1)

L n I D_{i t} = β_{0 i} + β_{1 i} L n G D P_{i t} + β_{2 i} L n G P R_{i t} + μ_{i t},

(2)

where ${L n I D}_{i t}$ is the natural logarithm of insurance development proxies, including total, life and non-life insurance premiums per capita, ${L n G D P}_{i t}$ is the natural logarithm of GDP per capita, ${L n G E P U}_{i t}$ denotes the natural logarithm of economic policy uncertainty index, and ${L n G P R}_{i t}$ represents the natural logarithm of the GPR. The index $i$ represents the country, and $t$ is the number of times.

Linear Panel ARDL

We start with the linear panel ARDL by following the work of Pesaran et al. (1996, 2001). The insurance development specification in Equations (1) and (2) can be formulated in the form of panel linear ARDL in Equations (3) and (4) as follows:

Δ L n I D_{i t} = η_{0} + γ_{1 i} L n I D_{i, t - 1} + γ_{2 i} L n G D P_{i, t - 1} + γ_{3 i} L n G E P U_{i, t - 1} + \sum_{j = 1}^{p_{1}} δ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} δ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{p_{3}} δ_{3 i j} Δ L n G E P U_{i, t - j} + ε_{1 i t},

(3)

Δ L n I D_{i t} = φ_{0} + π_{1 i} L n I D_{i, t - 1} + π_{2 i} L n G D P_{i, t - 1} + π_{3 i} L n G P R_{i, t - 1} + \sum_{j = 1}^{p_{1}} ρ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} ρ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{p_{3}} ρ_{3 i j} Δ L n G P R_{i, t - j} + ε_{2 i t},

(4)

where $Δ$ is the first difference operator, $η_{0}$ and $φ_{0}$ are constant, $δ_{s i j (s = 1,2, 3)}$ and $ρ_{s i j (s = 1,2, 3)}$ are short-run coefficients, $γ_{m i (m = 1,2, 3)}$ and $π_{m i (m = 1,2, 3)}$ are long-run coefficients, and $ε_{1 i t}$ and $ε_{2 i t}$ are error terms. The optimum lag is selected based on the Schwarz information criterion. The error correction term can be included in the equation by reformulating Equations (3) and (4) as follows:

Δ L n I D_{i t} = ζ_{0} + \sum_{j = 1}^{p_{1}} δ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} δ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{p_{3}} δ_{3 i j} Δ L n G E P U_{i, t - j} + ϕ_{1} E C T_{i, t - 1} + ε_{1 i t},

(5)

Δ L n I D_{i t} = θ_{0} + \sum_{j = 1}^{p_{1}} ρ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} ρ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{p_{3}} ρ_{3 i j} Δ L n G P R_{i, t - j} + ϕ_{2} E C T_{i, t - 1} + ε_{2 i t},

(6)

where ${E C T}_{t - 1}$ denotes error correction term, and $ϕ_{1}$ and $ϕ_{2}$ represent the speed of adjustment to the long-run equilibrium.

Non-linear Panel ARDL

The non-linear panel ARDL is an extended version of the traditional linear panel ARDL (Shin et al., 2014), which allows us to develop asymmetric shocks of global economic policy uncertainty (GEPU) and the GPR. The asymmetric shocks can be decomposed into a positive partial sum $∆ {L n G E P U}_{i, t}^{+}$ and negative shocks $∆ {L n G E P U}_{i, t}^{-}$ , which can capture positive and negative changes. By following the work of Shin et al. (2014), the decomposition of GEPU and the GPR can be obtained as follows:

L n G E P U_{i, t}^{+} = \sum_{j = 1}^{t} Δ L n G E P U_{i, t}^{+} = \sum_{j = 1}^{t} \max (Δ L n G E P U_{i, t}^{+}, 0)

(7)

and

L n G E P U_{i, t}^{-} = \sum_{j = 1}^{t} Δ L n G E P U_{i, t}^{-} = \sum_{j = 1}^{t} \min (Δ L n G E P U_{i, t}^{-}, 0),

(8)

L n G P R_{i, t}^{+} = \sum_{j = 1}^{t} Δ L n G P R_{i, t}^{+} = \sum_{j = 1}^{t} \max (Δ L n G P R_{i, t^{'}}^{+}, 0)

(9)

and

L n G P R_{i, t}^{-} = \sum_{j = 1}^{t} Δ L n G P R_{i, t}^{-} = \sum_{j = 1}^{t} \min (Δ L n G P R_{i, t^{'}}^{-}, 0) .

(10)

We then incorporate Equations (7), (8), (9) and (10) into the following equations to obtain the asymmetric version of panel ARDL equations:

Δ L n I D_{i t} = τ_{0} + γ_{1 i} LnID D_{i, t - 1} + γ_{2 i} Ln G D P_{i, t - 1} + γ_{3 i}^{+} {LnGEPU}_{i, t}^{+} + γ_{3 i}^{-} Ln G E P U_{i, t}^{-} + \sum_{j = 1}^{p_{1}} δ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} δ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{p_{3}} δ_{3 i j}^{+} Δ L n G E P U_{i, t - j}^{+} + \sum_{j = 1}^{p_{4}} δ_{3 i j}^{-} Δ L n G E P U_{i, t - j}^{-} + ε_{1 i t}

(11)

Δ L n I D_{i t} = ϑ_{0} + π_{1 i} LnID D_{i, t - 1} + π_{2 i} Ln G D P_{i, t - 1} + π_{3 i}^{+} Ln G P R_{i, t}^{+} + π_{3 i}^{-} L n G P R_{i, t}^{-} + \sum_{j = 1}^{p_{1}} ρ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} ρ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{3} ρ_{3 i j}^{+} Δ L n G P R_{i, t - j}^{+} + \sum_{j = 1}^{p_{4}} ρ_{3 i j}^{-} Δ L n G P R_{i, t - j}^{-} + ε_{2 i t},

(12)

The short-run asymmetric shocks of GEPU and the GPR to insurance development can be captured from the coefficients $δ_{3 i j}^{+}$ , $δ_{3 i j}^{-}$ , $ρ_{3 i j}^{+}$ , $ρ_{3 i j}^{-}$ and $δ_{3 i j}^{-}$ . Hence, $γ_{5 i}^{+}$ , $γ_{5 i}^{-}$ , $π_{3 i}^{+}$ , and $π_{3 i}^{-}$ are the coefficients of long-run positive and negative shocks. Equations (11) and (12) can be rewritten to obtain the error correction from the asymmetric version of equations as follows:

Δ L n I D_{i t} = λ_{0} + \sum_{j = 1}^{p_{1}} δ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} δ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{p_{3}} δ_{3 i j}^{+} Δ G E P U_{i, t - j}^{+} + \sum_{j = 1}^{p_{4}} δ_{3 i j}^{-} Δ L n G E P U_{i, t - j}^{-} + Φ_{1} E C T_{i, t - 1}^{'} + ε_{1 i t,}

(13)

Δ L n I D_{i t} = μ_{0} + \sum_{j = 1}^{p_{1}} ρ_{1 i j} Δ L n I D_{i, t - j} + \sum_{j = 1}^{p_{2}} ρ_{2 i j} Δ L n G D P_{i, t - j} + \sum_{j = 1}^{p_{3}} ρ_{5 i j}^{+} Δ G P R_{i, t - j}^{+} + \sum_{j = 1}^{p_{4}} ρ_{5 i j}^{-} Δ L n G P R_{i, t - j}^{-} + Φ_{2} E C T_{i, t - 1}^{'} + ε_{2 i t} .

(14)

${E C T}_{i, t - 1}^{'}$ depicts the asymmetric error correction term. $Φ_{1}$ and $Φ_{2}$ capture the speed of adjustment to the long-run equilibrium after asymmetric responses.

Empirical Results

The descriptive statistics in Table 4 show that the highest average is real GDP per capita (GDP), which accounts to US$ 12,857.96. The insurance sector development is detected in ASEAN through total insurance (US$ 786.566 per capita), composing US$ 599.734 for life insurance and US$ 186.875 for non-life insurance. Two indices, namely, the global economic policy uncertainty index (GEPU) and the GPR show an average of about US$ 137.643 and US$ 100.096, respectively.

Table 4.

Descriptive Statistics.

	TI	LI	NI	GDP	GEPU	GPR
Mean	786.566	599.734	186.857	12,857.960	137.643	100.096
Median	190.624	109.019	76.375	4,896.372	116.986	92.342
Maximum	6,167.945	4,953.603	1,214.342	67,533.110	327.531	176.302
Minimum	14.967	6.418	7.844	1,350.660	64.786	50.915
Std. Dev.	1,328.441	1,061.828	271.688	17,239.280	71.462	32.027
Skewness	2.050	2.106	1.861	1.667	1.210	1.025
Kurtosis	6.458	6.724	5.543	4.329	3.597	3.305
Jarque–Bera	155.820	171.246	110.069	69.768	33.653	23.256
Probability	0.000	0.000	0.000	0.000	0.000	0.000
Observations	130	130	130	130	130	130

Note: Bold value indicates rejection of the null hypothesis of normal distribution.

Furthermore, the standard deviation shows that real GDP per capita is the most volatile among the series, followed by three insurance development proxies, include total insurance per capita, life insurance per capita, non-life insurance per capita, GEPU index and GPR. The skewness depicts that all series are positive, implying that the longer tail of distribution on the right-hand side. In addition, the kurtosis shows the value is greater than three among in all series, indicating the Leptokurtic characteristics and heavy tail. Moreover, the Jarque–Bera statistic confirms that all series reject the null hypothesis of normal distribution, implying the series does not follow the normal distribution.

We next check the order of integration of each variable to confirm that none of the variables is the second order of integration using both common (LLC and Breitung) and individual (IPS, ADF and PP) unit-root process, and the results are reported in Table 5. The finding depicts that LnNI and LnGEPU are found to be stationary at level in both cases common and individual processes, while LnLI and LnGPR are found to be stationary in the case of the PP unit-root test only. In addition, at first difference, all variables indicate the rejection of the null hypothesis of non-stationary in all unit-root processes, implying the first order of integration or I(1). In conclusion, all series are the mixed order of integration between I(0) and I(1), and it is confirmed that panel ARDL and NARDL are appropriate in estimation.

Table 5.

Results of the Panel Unit-root Test.

Variable	Common Unit Root		Individual Unit Root
Variable	LLC	Breitung	IPS	ADF	PP
Level
LnTI	1.701	1.312	0.764	7.605	7.064
LnLI	1.476	1.648	0.713	10.449	18.406**
LnNI	0.002	−1.522*	−1.624*	19.081**	16.506*
LnGDP	1.271	−0.330	0.181	7.537	12.835
LnGEPU	−4.385***	−2.081**	−1.867**	17.348*	12.285
LnGPR	2.067	0.441	−0.857	11.392	17.644*
First difference
∆LnTI	−4.575***	−2.709***	−5.225***	43.355***	73.325***
∆LnLI	−3.675***	−2.521***	−4.384***	36.125***	55.983***
∆LnNI	−2.397***	−1.592*	−5.167***	42.947***	102.068***
∆LnGDP	−6.020***	−1.447*	−6.444***	53.342***	92.160***
∆LnGEPU	−5.759***	−6.403***	−6.215***	51.143***	130.792***
∆LnGPR	1.949	1.736	−1.922**	17.777*	57.684***

Note: ***, ** and * indicate statistical significance at 1%, 5% and 10%, respectively.

The panel cointegration test is applied using both the Pedroni and the Kao cointegration tests, and the results are reported in Table 6. The findings from Table 6 show that, for Pedroni cointegration, there is evidence of the cointegration in all models with LnGEPU and LnGPR in panel ADF-statistic, group ADF-statistic, panel v-statistic and panel PP-statistic. We confirm this cointegration by using the Kao test, and the results show that all models with LnGEPU and LnGPR can reject the null hypothesis of no cointegration, indicating that there are cointegrations among the series.

Table 6.

Results of the Panel Cointegration Test.

Dependent Variable	Model with LnGEPU			Model with LnGPR
Dependent Variable	LnTI	LnLI	LnNI	LnTI	LnLI	LnNI
Pedroni cointegration
Panel v-statistic	1.534*	1.792**	0.566	1.689**	2.050**	0.480
Panel rho-statistic	−0.305	−0.461	−0.699	−0.334	−0.388	−0.312
Panel PP-statistic	−0.745	−0.963	−1.503*	−0.612	−0.520	−1.292*
Panel ADF-statistic	−1.570*	−2.150**	−1.751**	−2.052**	−2.071**	−1.555*
Group rho-statistic	0.520	0.429	0.040	0.645	0.485	0.282
Group PP-statistic	−0.576	−0.946	−1.197	−1.088	−0.491	−1.082
Group ADF-statistic	−1.349*	−2.022**	−2.266**	−2.365***	−2.145**	−2.124**
Kao cointegration
ADF	−2.218**	−2.186**	−2.525***	−2.025**	−2.125**	−2.113**

Note: ***, ** and * indicate statistical significance at 1%, 5% and 10%, respectively.

Table 7 reports the findings of the linear panel ARDL. The finding indicates that there is no impact of the GEPU index and the GPR on insurance development in the short run, and positive relationships between GDP and each insurance development proxy are found in all cases, except the case of non-life insurance is the dependent variable in model with the GPR. The error correction has a negative sign and statistical significance at a 1% level in all cases, confirming that there is a speed of adjustment to the long-run equilibrium.

Table 7.

Results of PMG Estimation.

Dependent Variable:
Independent Variables	LnTI	LnLI	LnNI	LnTI	LnLI	LnNI
Long run
LnGDP	0.680**	0.782**	0.442*	0.997***	1.102***	0.735***
LnGEPU	0.184	0.190	0.619***
LnGPR				0.169*	0.071	0.233***
Short run
Error correction	−0.157***	−0.184***	−0.168***	−0.194***	−0.200***	−0.267*
∆LnGDP	0.715***	0.735***	0.726***	0.662***	0.717***	0.377
∆LnGEPU	−0.012	−0.056	−0.018
∆LnGPR				0.040	0.039	−0.001
Constant	−0.139***	−0.407***	−0.475***	−0.753***	−0.952***	−0.993

Note: ***, ** and * indicate statistical significance at 1%, 5% and 10%, respectively.

The global policy uncertainty index has a positive statistically significant impact on non-life insurance in the long run at a 1% level, indicating a 1% increase in the global policy uncertainty index leads to an increase of 0.619% in non-life insurance. Furthermore, the results show that a 1% increase in the GPR leads to an increase in total insurance and non-life insurance by 0.169% and 0.233%, respectively.

The asymmetric impact of the global economic uncertainty index and the GPR is reported in Table 8. In the short run, the only negative shock of the global economic uncertainty index is found to have a positive effect on total insurance with a 5% significant level. In addition, error correction in all cases has adverse signs, confirming the speed of adjustment to long-run equilibrium.

In the long run, the findings indicate that a 1% increase in the positive shock of the GEPU index can raise the total, life and non-life insurance by 0.182%, 0.183% and 0.613%, respectively. Interestingly, there is evidence of a positive relationship between the negative shock of the GEPU index and non-life insurance. In addition, the effect of the positive shock of the GPR is found only in the case of non-life insurance as a dependent variable, which shows that a 1% increase in the shock of the GPR leads to an increase in non-life insurance by 0.222% with a 1% significance level. However, there are no significant impacts from the negative shock of the GPR to each insurance development proxy found in the long run (Table 8).

Table 8.

Results of Asymmetric PMG Estimation.

Dependent Variable:
Independent Variables	LnTI	LnLI	LnNI	LnTI	LnLI	LnNI
Long run
LnGDP	0.084	−0.066	0.089	0.636*	1.108**	0.525***
LnGEPU⁺	0.182**	0.183*	0.613***
LnGEPU⁻	−0.155	−0.341	0.496*
LnGPR⁺				0.003	−0.114	0.222***
LnGPR⁻				−0.189	−0.120	−0.010
Short run
Error correction	−0.212***	−0.237***	−0.184***	−0.210***	−0.244***	−0.289*
LnGDP	0.434**	0.411**	0.509**	0.440**	0.407*	0.106
LnGEPU⁺	−0.053	−0.124	−0.051
LnGEPU⁻	0.135**	0.178	0.048
LnGPR⁺				0.097	0.100	0.018
LnGPR⁻				−0.011	−0.035	−0.006
Constant	1.145**	1.446**	0.540**	0.024	−1.084***	−0.356

Note: ***, ** and * indicate statistical significance at 1%, 5% and 10%, respectively.

Conclusion

This study aimed to study the asymmetric impacts of the GEPU index and the GPR on insurance sector development in ASEAN from 1990 to 2020. We have used the linear panel autoregressive distributed lag (ARDL) approach (Pesaran et al., 1996, 2001) and the asymmetric panel autoregressive distributed lag (NARDL) approach (Shin et al., 2014). The results from the linear panel ARDL show that GEPU and GPR had an impact on non-life insurance (NI), but both variables did not have an impact on life insurance (LI).

Furthermore, the asymmetric panel ARDL shows no significant impact of positive shocks from GEPU having a positive impact on LI and NI, but the negative shocks from GEPU only had a positive impact on NI. Positive and negative shocks from GEPU lead to an increase in the NI. More interestingly, we have found that the positive shocks of GPR led to an increase in NI, but positive or negative shocks of GPR did not have an impact on LI. Policymakers in ASEAN should closely look at the GEPU and the GPR for insurance policy development.

Footnotes

Acknowledgments

The authors are grateful to one anonymous referee for useful comments.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Note

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