Expansion and consolidation of bancassurance in the 21st century

Int. Fin. Markets, Inst. and Money 19 (2009) 633–644

Contents lists available at ScienceDirect

Journal of International FinancialMarkets, Institutions & Money

journal homepage: www.elsevier.com/locate/ intf in

Expansion and consolidation of bancassurance in the 21stcentury�

Zhian Chena, Donghui Lia,∗, Li Liaob, Fariborz Moshiriana, Csaba Szablocsa

a School of Banking and Finance, University of New South Wales, Sydney, Australiab School of Management and Economics, Tsinghua University, Beijing, China

a r t i c l e i n f o

Article history:Received 15 October 2007Accepted 19 October 2008Available online 30 October 2008

JEL classification:G15

Keywords:BancassuranceFinancial servicesFinancial deregulation

a b s t r a c t

Bancassurance has received much attention from both researchersand policymakers, as it is a major step towards the creation of uni-versal financial markets in the 21st century which are no longersegregated based on industry operations. This study is the firstcomprehensive study to identify and measure the determinantsof bancassurance using a sample of firms from 28 developedand developing countries. Our results complement the existingliterature on bancassurance demand, insurance demand, and inter-national insurance services, while also providing additional insightin key areas. The empirical results, based on panel analysis, indicatethat reduction in company risk, the size of the company, reductionsin company costs and increases in company revenues, the size ofthe national banking industry, the level of financial deregulationwithin a country, and the national inflation rate all play significantroles as determinants of bancassurance.

© 2008 Elsevier B.V. All rights reserved.

1. Introduction

Conglomeration, defined as the combination of bank and non-bank activities for financial institu-tions by De Nicolo et al. (2004), has recently been attracting the attention of academics and policymakers. For example, De Nicolo and Kwast (2002), De Nicolo et al. (2004) and Elyasiani et al. (2007)

� The authors wish to thank an anonymous referee and the Editor (Ike Mathur) for their helpful comments. Donghui Li andLi Liao also wish to thank Xianrun Luo, from Tsinghua University, for his excellent research assistance on the earlier version ofthis paper. All errors remain ours.

∗ Corresponding author. Tel.: +61 2 93855873; fax: +61 2 93856347.E-mail address: [email protected] (D. Li).

1042-4431/$ – see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.intfin.2008.10.002

http://www.sciencedirect.com/science/journal/10424431

http://www.elsevier.com/locate/intfin

mailto:[email protected]

dx.doi.org/10.1016/j.intfin.2008.10.002

634 Z. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 633–644

have examined consolidation and the convergence of the risk-return inter-relationships betweenbanks and other financial service activities. The bancassurance phenomenon,1 which is part of theconglomeration process, has been growing at an accelerated rate ever since its conception duringthe late 1970s and early 1980s in Europe. The combination of inadequate national welfare initia-tives, the increasing demands of consumers for more variety in their insurance products, and thebenefits that participating companies receive have seen the continuing success of bancassurancemarkets. A large number of previous studies on the blossoming bancassurance markets have onlybeen descriptive in nature, providing broad insight into reasons believed to be behind the successof this venture, and potential benefits and disadvantages for all associated parties. However, onlya few researchers, such as Carow (2001a,b), Estrella (2001) and Cowan et al. (2002) have providedquantitative findings, and most of these studies focused mainly on the potential risk diversifica-tion benefits associated with bank expansion into the insurance industry. It can be argued that thelack of quantitative studies results from a lack of available data regarding bancassurance operations.Improved disclosure requirements applying to companies, and improved collection of national statis-tics, now provide the means for increased research into this rapidly growing area of the financialmarkets, knowledge of which is highly sought after by such parties as businesses, policymakers andregulators.

This study is the first comprehensive study of bancassurance that is able to fill a gap in the existingliterature in this area, as it overcomes some of the shortage of data for bancassurance operations andhence discusses and measures the determinants of bancassurance by using a sample of firms fromboth developed and developing countries. In doing so, this study makes significant contributions tothe existing literature. This paper reconciles existing literature on bancassurance with studies in thearea of life insurance, non-life insurance and banking, and provides a more complete picture of themain factors that determine the overall success and popularity of bancassurance operations around theworld in both developed and developing countries. To date, no quantitative study has examined such alarge number of economic, institutional and demographic determinants on the global bancassurancemarket as our study does. This study also makes methodological improvements on previous studies inthe general field of insurance (including bancassurance) by using the advanced estimation techniqueknown as GMM, which enables us to exploit the time series nature of the data and overcome potentialdata limitations.

The results obtained from this study indicate that reduction in company risk, the size of the com-pany, reductions in company costs and increases in company revenues, the size of the national bankingindustry, the level of financial deregulation within a country, and the national inflation rate, all playsignificant roles as determinants of bancassurance. The results of this study are important to policy-makers and regulators in particular, since it indicates that they should focus their attention on furtherfinancial deregulation to encourage greater market accessibility since it is found that targeting this areahas potential benefits to the bancassurance sector. For bank managers, this study indicates that theyshould seriously consider expansion into bancassurance operations because of the benefits providedthrough diversification of risk, reduction in company fixed costs, increase in fee-based revenues, andimprovement in overall company competitiveness.

The remainder of this study is structured as follows: Section 2 discusses the determinants ofbancassurance and offers a model to measure these determinants. Section 3 presents the data andmethodology used in this study. Section 4 provides the empirical results for the differing test sam-ples and also discusses the empirical findings. Finally, Section 5 presents the conclusions of thestudy.

1 According to Swiss Re (2002), a general definition of bancassurance is that it involves the manufacture and/or distributionof insurance products by banks. Distribution in this sense can mean either directly through the branches of the bank or wherethe bank acts as an agent for the insurer and promotes the insurer’s products to its clients, even though the bank itself doesnot provide the product directly. There are a number of ways that bancassurance can arise. The weakest form of union wouldinvolve a distributional agreement through which the bank would distribute stand-alone insurance products with little or nosharing of the customer base. A higher form of integration would be a strategic alliance, which would see a greater sharing ofthe banks customer base and also greater investments required by both parties.

Z. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 633–644 635

2. The determinants of bancassurance services: empirical model and hypotheses

In this study, the model that is used incorporates variables from the literature on bancassurance andlife and non-life insurance. While it is unfortunate that we had to exclude certain explanatory variablesdue to data constraints (i.e. social security expenditure, education and life expectancy), we believe thatthe eight variables included in our study form a major part of explaining the success of bancassuranceoperations, and hence can be classified as key determinants of bancassurance. These variables comprisecompany risk, company size, reduction in costs; increase in revenues, size of national banking industry;level of deregulation within a country; changes in national income; and level of inflation.

The following empirical model for the determinants of bancassurance is proposed

BA = f [?

RISK,+VESIZE,

+VEEXP,

+VEREV,

+VESIZE(N),

+VEREGL,

+VEGNI,

?INFL] (1)

where RISK, company risk proxy; SIZE, company size/customer base proxy; EXP, cost decrease proxy;REV, revenue increase proxy; SIZE(N), size of national banking sector; REGL, level of national dereg-ulation; GNI, proxy for the level of insurance demand within a Country; INFL, proxy for inflationarychanges within a Country.

Linear form of our model:

BA = ˛0 + ˛1(RISK) + ˛2(SIZE) + ˛3(EXP) + ˛4(REV) + ˛5(SIZE(N))

+˛6(REGL) + ˛7(GNI) + ˛8(INFL) + ε (2)

Box–Cox revealed that only the company size independent variable required transformation, whereasthe rest, including the dependent, may remain unchanged. As such, the model we shall use for testingpurposes becomes

BA = ˛0 + ˛1(RISK) + ˛2(Log(SIZE)) + ˛3(EXP) + ˛4(REV) + ˛5(SIZE(N))

+˛6(REGL) + ˛7(GNI) + ˛8(INFL) + ε (3)

Once we have determined the functional form of our model we firstly employ the OLS method oftesting, which is a quick way to obtain unbiased and consistent estimates.2 The main drawback ofthis procedure is that a number of assumptions have to be adhered to. In order to test that the mainassumptions required for this method of testing are adhered to we incorporate diagnostic tests tocheck for heteroscedasticity, normality of residuals and multicollinearity. The second testing procedureutilised is known as GMM and it allows for consistent and efficient estimates to be derived, even ifsome of the assumptions required for OLS do not hold.

The remainder of this section discusses these eight factors as the possible determinants of bancas-surance services.

2.1. Risk proxy [RISK]: company risk may be either a positive or negative determinant of bancassurance

Past researchers’ findings in relation to the effect of non-traditional banking activities on the overallriskiness of the bank are mixed. Boyd et al. (1993), Boyd and Graham (1988), and Estrella (2001) findrisk reduction benefits associated with non-traditional banking activities. Boyd and Graham (1988)find mergers with life insurance companies reduce overall risk, while mergers with securities or realestate firms increase risk. Boyd et al. (1993) find similar results to Boyd and Graham (1988) in that

2 In line with the anonymous referee’s suggestion, we employ a two-equation system of simultaneous equations using GMMto estimate the following system. Due to page limits, we only report the results of our main concern, which is given in Eq. (3).

BA = ˛0 + ˛1(RISK) + ˛2(log(SIZE)) + ˛3(EXP) + ˛4(REV) + ˛5(SIZE(N)) + ˛6(REGL) + ˛7(GNI) + ˛8(INFL) + ε (4)

RISK = ˇ0 + ˇ1(BA) + ˇ2(log(SIZE)) + ˇ3(EXP) + ˛4(REV) + ˇ5(SIZE(N)) + ˇ6(REGL) + ˇ7(GNI) + ˇ8(INFL) + ε (5)


mergers of BHCs with life or P&C insurance companies may reduce risks, while those with securitiesor real estate firms would be likely to increase risk. Estrella (2001) finds diversification gains fromall combinations of banks with insurers. On the other hand, Allen and Jagtiani (2000), De Young andRoland (2001), De Young and Rice (2004), and Stiroh (2004) find that diversification into non-bankingactivities increases the riskiness of banks.

If a negative relationship exists, then it means diversification benefits are predominant, i.e. riskreductions are synonymous with growth of bancassurance operations. If a positive relationship exists,then it means diversification is outweighed by other risks brought about by the implementation ofa bancassurance operation. The proxy to be used to measure this hypothesis is standard deviation ofdaily stock returns over a given year.

2.2. Customer base/size proxy [SIZE]: bank size, which is indicative of customer base, is a positivedeterminant of bancassurance

Theoretically, a larger bank can take advantage of a greater customer base, in which it can promotenew products. Larger banks would then have a greater chance of selling more insurance products,while spending proportionately less on promotions and advertisements.3 Canals (1998) argues thatthe size of the bank is also important when gaining new customers since it indicates maturity andsolvency. Chen and Wong (2004) agree, arguing that the size of total assets is indicative of the healthof an organisation, since regulators are less likely to liquidate larger companies. However, at the sametime, it may mean that managing changes within the organisation is more difficult. For example, Amelet al. (2004) oppose this view, arguing that consolidation in the financial sector, especially amongcommercial banks and insurance companies, is only beneficial up to a certain (relatively small) size inorder to reap economies of scale.

We believe that larger banks can undertake a more successful bancassurance operation, due largelyto the greater experience they have within the market of managing different operations, and of effec-tively handling competition from other insurers, bancassurers and boutique companies. The bestmeasure of customer base or company size is the total assets of a company. We initially use a grossfigure here, but as shown earlier in this section, we use the log of this figure for calculation purposes.Thus the proxy to be used to measure this hypothesis is total banking assets (company figure) as agross amount.

2.3. Costs savings proxy [EXP]: cost savings proxy is positively related to the demand for bancassurance

As mentioned earlier, one of the major benefits of starting a bancassurance operation is theresulting reduction in company expenses. The multitude of researchers such as Cowan et al. (2002)agree that there would be a positive relationship between this proxy and our dependent, meaningthat as the bancassurance operation grows, the expenses generated by each new output4 shouldbe proportionately less. Canals (1998) argues that one of the advantages of universal banks is theeconomies of scope they create by allowing costs to be shared amongst different business units. Asa measure we use the ratio of the total bank expenses to its total revenues, since this will clearlyshow whether expenses have risen or fallen in regard to new operations. Thus the proxy to beused to measure this hypothesis is annual percentage change in (total bank expenses)/(total bankrevenue).5

3 Assuming an advertising campaign, such as a TV advertisement, costs the same to both a small and a large bank, the profitthat the larger bank receives would be considerably more than the smaller bank’s profit, since it is reaching a larger customerbase. So if a similar proportion of the customer base of the larger and smaller banks takes up the offer, the larger bank will profitmore from the bancassurance operation. This assumes customers behave similarly in all banks.

4 Output = Insurance Product. Here we refer to all costs associated with the product from conception to final sale to theconsumer.

5 Total bank revenue includes banking revenue from all sources including bancassurance.


2.4. Revenue increase proxy [REV]: there is a positive relationship between increases in bank revenueand the commencement of bancassurance operations

As with the cost savings proxy, the general consensus amongst researchers is that one of the majorbenefits of bancassurance is the increase in revenues that it will bring to the bank. Klein (2001) andAgrawal (2002) both argue that one of the major advantages of bancassurance is the potential for asubstantial increase in bank revenue. Once again opponents exist, such as Berger et al. (1996) who findno revenue economies of scope in their study. We measure this variable by observing the differenceover years in the ratio of a bank’s total revenue (from all operations) to its size. Thus the proxy tobe used to measure this hypothesis is annual percentage change in (total bank revenues)/(total bankassets).

2.5. Nation’s banking sector proxy [SIZE(N)]: there is a positive relationship between the size of acountry’s banking sector and the success of bancassurance operations

The size of the banking sector within a country is symbolic of the ability of the member banks torun efficiently and productively in all the business operations they are involved in. Banks within acountry with a larger and more established banking sector would undoubtedly have more opportu-nities to begin bancassurance operations, finding it easier to compete against any negative sentimenttowards such a move that may come from powerful bodies such as the insurance lobby.6 The size ofthe banking sector is also indicative of the faith and loyalty that customers place in banks within thatcountry, something that will prove to be extremely important for the commencement of a successfulbancassurance operation. On the other hand, having a large banking sector means that there is littleroom for error. Banks will begin bancassurance operations if they are convinced by its potential suc-cess, since the consequences of failure for that one particular operation could prove disastrous for therest of the bank’s operations.

We hypothesize that due to the benefits that banks gain from operating in a country with a wellestablished banking system, the likelihood of starting bancassurance operations is going to be higher,and these operations will also be more successful since banks will only begin such a course of action ifthey are certain of success. For this measure we use the total banking assets of a country, as indicatedon its respective national bank website, divided by GDP. We use this ratio since we believe it serves as abetter comparative measure between countries than simple gross figures. Thus the proxy to be used tomeasure this hypothesis is annual percentage change in the (total banking assets of a country)/(GDP).7

2.6. Level of deregulation (within a Country) [REGL]: the greater the level of deregulation within acountry, the more successful the bancassurance industry should be within that country. Hence thesuccess and size of individual banks’ insurance operations should, on average, also be morepronounced and profitable

Regulation has been one of the major impediments to the growth of bancassurance across the globe.As previously discussed, countries such as the US and Japan, while being global banking superpowers,have only recently begun allowing banks to expand into the field of bancassurance following regula-tory changes that removed constraints to possible expansion across industries.8 This is by no means

6 Banks in the US are finally overcoming restrictive pressures applied by the insurance lobby when it comes to expansion intobancassurance.

7 According to De Nicolo et al. (2004), the bank consolidation pattern is uneven across the world—“. . .clustering of con-solidation in particular regions and/or countries, rather than global convergence in banking system structures” (pp. 4–5). Wehypothesize that it is the “growth” of the relative importance of banking sector in a particular nation, not the “absolute” levelof the importance of the banking sector, which contributes to the relative significance of bancassurance.

8 In the case of the US (as well as the UK) even though it is considered a deregulated economy, the presence of powerfulunions and insurance lobbies has presented significant hurdles impeding the successful implementation of bancassurance(White (1990)).


limited to these two countries, but is a common occurrence in other areas of the world.9 Wheneverderegulation is discussed by researchers, it is always found that benefits exist for the domestic ban-cassurance markets, whether from increased competition that forces incumbent players out of themarket, or through added product innovations that are brought to the marketplace. Carow (2001a)finds benefits from the removal of regulatory barriers to all parties involved (banks, insurers and con-sumers). Another benefit of deregulation is the ability of established foreign firms to gain access tolocal markets where they can employ their expertise in the field of bancassurance to facilitate growthwithin the industry by enabling local firms to apply tried and tested methods from abroad. In this con-text, deregulation is not limited to the financial services industry, but relates to the whole economy.The proxy to be used to measure this hypothesis is a variable symbolising the overall competitivenessof a country.

2.7. Demand for insurance proxy – gross national income per capita [GNI]: as GNI per capita increasespremium income levels obtained by banks should also increase as more consumers purchase insuranceproducts

The overall demand for insurance products is obviously going to affect how successful the bancas-surance industry is within a country and hence how profitable each bancassurer is as well. Browneand Kim (1993), Outreville (1996), Li et al. (2007) have found a positive correlation between nationalincome and the overall demand for life insurance within a country. Outreville (1990) also found apositive relationship between the property-liability insurance market development and income.

Both life and general insurance can be characterised as luxury items. They are not items that theconsumer places at the top of their list; instead, insurance products are only purchased if the consumerhas enough disposable income. GNI per capita represents how much income each individual withina country has. As GNI per capita increases, the consumers are more likely to spend their disposableincome on insurance products. For example, life insurance products become more affordable as incomerises and as there is a greater need to protect dependants from unexpected future income lossesresulting from the wage earner’s premature death. The proxy to be used to measure this hypothesis isthe change of gross national income per capita.

2.8. Level of inflation [INFL]: inflation may affect the bancassurance industry, however the directioncould be either positive or negative

Inflation is a difficult aspect to test for, since it affects every part of the economy and not alwaysin the same predictable way. Two issues must be taken into account when analysing the impact ofinflation on the bancassurance industry. First we must analyse the impact it has on the companythat offers the bancassurance products. Cummins (1991) found that inflation increases an insurancecompany’s premium income as well as reducing the real value of insurance liabilities. This influencewould similarly be found in companies that offer bancassurance. However, the degree to which thiswould be true would depend on how established the operation is within the company.10

Second we must focus on how inflation impacts the consumer of the bancassurance product and inturn how it affects demand. A similar argument can be used as in the previous section, where we studiedGNI per capita. As inflation increases, the overall cost of a basket of ‘necessities’ increases, however arise in inflation does not necessarily mean that the income of the consumer will also increase. This,in turn, would mean that consumers would have less disposable income, and hence their demand forbancassurance products would undoubtedly diminish. Browne and Kim (1993), Outreville (1996), andLi et al. (2007) found that inflation would be detrimental to savings through insurance products as it

9 China, India and South Korea are three other nations that have begun the slow progression towards deregulation of thefinancial services industry.

10 Those banks that have only recently started up a bancassurance operation could not possibly gain as much benefit frominflations positive effect on premium income and liabilities unlike those banks where insurance now comprises a key componentof everyday operations.


Table 1Breakdown of sample companies by country.

Argentina 1 Australia 2 Belgium 2 Canada 3Czech Republic 2 Denmark 1 Estonia 1 France 4Germany 3 Greece 1 Hong Kong 3 Ireland 1Italy 1 Japan 1 Luxembourg 1 Malaysia 3Netherlands 3 Norway 1 Peru 1 Philippines 1Portugal 1 South Africa 4 Sweden 2 Switzerland 1Taiwan 1 Turkey 2 UK 9 USA 15Total 71

The sample consists of the 71 banks engaged in bancassurance operations from a total of 28 countries. The breakdown detailsof these banks by country are presented.

erodes the value of the product. (Any final claim payment that one receives in a year of high inflationwould be drastically decreased.)

If the influence is positive, it means that the positive impact it has on the company’s premiumincome and liabilities outweighs the negative impact it has on consumer demand. If the influence isnegative, it means that consumer demand decreases to such an extent that the company is adverselyinfluenced regardless of any positive effects it may achieve for an increase in inflation. We use theactual average inflation rate per annum for each country akin to Chen and Wong (2004).

3. Data and methodology

This study employs a time-series and cross-sectional data for the 5-year period from 1999 to 2003comprising 71 banks engaged in bancassurance operations from a total of 28 countries, with thebreakdown details of those banks by country presented in Table 1.

Data for premium income generated from bank insurance operations was obtained from a combi-nation of Compustat and bank annual reports. Bank specific expenses, revenues and total assets arealso obtained from a combination of Compustat and bank annual reports. The competitiveness figuresfor each country were obtained from the International Institute for Management Developments (IMD)World Competitiveness Yearbook. The daily stock price is from DataStream. For inflationary figures, weused the data from IMFs World Economic Outlook, while for GNI and GDP figures, we used data fromthe World Bank. Table 2 presents the summary statistics for the regression variables used.

Table 2Descriptive summary statisticsa.

Variable Mean S.D. Minimum Maximum

Total premium income (BA) 0.1258 0.1572 0.0002 0.8165Risk proxy (RISK) 2.2797 0.9054 0.8141 7.9866Customer base/size proxy (SIZE) 10.7818 1.9922 5.3463 14.0498Cost savings proxy (EXP) 0.0220 0.3064 −0.8891 3.5071Revenue increase proxy (REV) 1.1200 21.5669 −0.7059 406.2901Nations banking sector proxy (SIZE(N)) 2.4067 3.7192 0.2748 32.7621Level of deregulation (REGL) 16.9662 14.4527 1.0000 60.0000Demand for insurance proxy (GNI) 0.0374 0.0971 −0.4147 0.4999Level of inflation (INFL) 3.5341 8.3825 −4.0000 64.9000

The sample consists of the 71 banks engaged in bancassurance operations from a total of 28 countries. The sample period isbetween 1999 and 2003. Means, standard deviations, minimums, and maximums of total premium income (BA), risk proxy(RISK), customer base/size proxy (SIZE), cost savings proxy (EXP), revenue increase proxy (REV), Nations banking sector proxy(SIZE(N)), level of deregulation (REGL), demand for insurance proxy (GNI), and Level of inflation (INFL) are presented in Table 2.Total premium income represents the ratio between total premium income for the bank generated by insurance operations andthe total revenue for the bank generated from all operations. All figures are taken as average for the period between years 1999and 2003.

a Appendix A provides data sources for each variable used in this study.


Table 3Whole sample and whole sample (less the US) regression results – dependent variables, BA.

Variables Whole sample Whole sample (less the US)

Estimate t-stat Estimate t-stat

Intercept 0.1229 1.54 0.1884 1.83*

RISK −0.1100 −8.8*** −0.0979 −12.3***

SIZE 0.0133 1.9* 0.0093 1.17EXP 0.0926 3.71*** 0.0881 3.54***

REV 0.0002 2.77*** 0.0001 1.18SIZE(N) 0.0185 4.62*** 0.0154 4.17***

REGL 0.0036 4.77*** 0.0020 1.61GNI −0.0050 −0.05 0.0000 0INFL 0.0011 1.93* 0.0014 2.13**

Eqs. (4) and (5) are estimated simultaneously. Table 3 presents the GMM results for both the whole sample of 71 banks engagedin bancassurance operations from 28 countries and the whole sample less the US observations (56 companies in total from 27countries) over the 5-year period of 1999–2003. An additional 1 year lag is also incorporated for each variable. BA, bancassurance;RISK, company risk proxy; SIZE, company size/customer base proxy; EXP, cost decrease proxy; REV, revenue increase proxy;SIZE(N), size of national banking sector; REGL, level of national deregulation; GNI, proxy for the level of insurance demandwithin a Country; INFL, proxy for inflationary changes within a Country.

* 10 percent significant level.** 5 percent significant level.

*** 1 percent significant level.

4. Empirical results

4.1. The determinants of bancassurance

Table 3 provides the results by employing bancassurance as the dependent variable for the wholesample and the sample without the US. As can be seen from Table 3, first the risk proxy is a consis-tently significant and negative determinant of bancassurance for the sample with and without theUS. This means that as the company risk decreases, the level of bancassurance operations increaseswithin the company. This supports the findings of researchers such as Boyd et al. (1993) and Boyd andGraham (1988) in regard to insurance operations acting as a stabiliser for bank earnings by providingdiversification benefits.

Second, for the whole sample, the size of the company and its customer base, as proxied by its totalassets, proves significant and positively related to level of bancassurance operations within the com-pany, as was expected. This is indicative of larger banks having a larger customer base to which they canmarket new products, as is supported by Canals (1998). However for the sample without the US, thisvariable is not significant, though it is positive. Third, the cost savings proxy is significant and positivelyrelated to the size of the bancassurance operation. This indicates that cost benefits are a key determi-nant of bancassurance, in line with the results of Canals (1998), and Cowan et al. (2002). By utilisingbancassurance operations, banks can share existing costs with the new operation while also being ableto produce and sell cost-effective products. Fourth, the revenue increase proxy is positively significant.This is to be expected and is in conjunction with the findings of researchers such as Agrawal (2002), andKlein (2001). This shows that the subsequent increases in fee-based revenues are a key determinant ofthe growing popularity of bancassurance operations. Fifth, as expected, the size of a nations bankingsector is significant and positive for the whole sample. This is consistent with the belief that banks findit easier to compete with negative sentiment towards bancassurance, and are able to make use of strongcustomer loyalties when beginning bancassurance operations in countries with established bankingsectors. Sixth, the level of deregulation has a positive impact on bancassurance, although once theUS sample is removed, the significance disappears. The overall results suggest that a more successfulbancassurance industry within a country is associated with a greater level of deregulation within thatcountry. US sample banks biased up the results due to recent removals of restrictions to the formation oflarge bancassurance operations, and it takes time for such removals to take effect. Seventh, the resultsindicate that GNI per capita as a proxy for the income of the populace is insignificant. This finding is not


Table 4Whole sample and whole sample (less the US) regression results – dependent variables, ln BA/1 − BA.

Variables Whole sample Whole sample (less the US)

Estimate t-stat Estimate t-stat

Intercept −2.4321 −2.55** −1.2405 −1.08RISK −1.4476 −6.64*** −1.3428 −7.87***

SIZE 0.1631 2.12** 0.0861 1EXP 0.8753 3.43*** 0.8311 3.3***

REV 0.0017 2.12** 0.0006 0.56SIZE(N) 0.1826 5.78*** 0.1501 5.05***

REGL 0.0426 4.69*** 0.0222 1.66*

GNI −0.5872 −0.47 −0.4578 −0.37INFL 0.0160 1.86* 0.0205 2.05**

Eqs. (6) and (7) are estimated simultaneously. Table 4 presents the GMM results for both the whole sample of 71 banks engagedin bancassurance operations from 28 countries and the whole sample less the US observations (56 companies in total from 27countries) over the 5-year period of 1999–2003. An additional 1 year lag is also incorporated for each variable. BA, bancassurance;RISK, company risk proxy; SIZE, company size/customer base proxy; EXP, cost decrease proxy; REV, revenue increase proxy;SIZE(N), size of national banking sector; REGL, level of national deregulation; GNI, proxy for the level of insurance demandwithin a Country; INFL, proxy for inflationary changes within a Country.



consistent with the results of Browne and Kim (1993), and Outreville (1990, 1996), who examine theinsurance industry as a whole. This indicates that the insurance product provided by bancassurance isdifferent from that provided by the insurance industry. Bank customers may not need insurance prod-ucts sold by banks when shopping in banks regardless of their total income level. Finally, the positivesignificance of the inflation proxy supports the idea that the negative impact an increase in inflationhas on the consumer is less than the positive impact it has on the company in terms of increasing pre-mium income values, and further discounting the insurance liabilities. This is consistent with Cummins(1991), while in contrast with researchers such as Browne and Kim (1993) and Outreville (1996).

Table 4 reports the regression results for the whole sample and the sample without the US by usinglnBA/1 − BA as the dependent variable, other variables being the same as Table 3.11 Table 4 measures theprobability of the bancassurance for a particular bank that is influenced by these factors. The results arevery similar to those in Table 3. This indicates that the factors influencing the bancassurance proportionalso influence the likelihood that a particular bank is involved in bancassurance activity with the samedirection and a similar statistical significance.12

4.2. Bancassurance and average abnormal returns (AAR)

To further test the benefits/costs of bancassurance for a particular listed bank worldwide, we esti-mate the abnormal returns for our sample firms from the beginning of 2004 to the end of 2007. Thereare 11 firms with significantly negative long-term abnormal returns, and six of them with signifi-cantly positive long-term abnormal returns. However, the overall long-term abnormal return duringthis period is negative but insignificant. In order to examine the possible relationship between thelong-term abnormal return and bancassurance activities, the long-term abnormal returns are also

11 In line with the anonymous referee’s suggestion, we employ a two-equation system of simultaneous equations using GMMto estimate the following system. Due to page limits, we only report the results of our main concern, which is given in Eq. (3).

lnBA

1 − BA= ˛0 + ˛1(RISK) + ˛2(log(SIZE)) + ˛3(EXP) + ˛4(REV) + ˛5(SIZE(N)) + ˛6(REGL) + ˛7(GNI) + ˛8(INFL) + ε (6)

RISK = ˇ0 + ˇ1(BA) + ˇ2(log(SIZE)) + ˇ3(EXP) + ˇ4(REV) + ˇ5(SIZE(N)) + ˇ6(REGL) + ˇ7(GNI) + ˇ8(INFL) + ε (7)

12 Thanks to the anonymous referee for pointing out this logistic model.


Table 5Long-term regression analysis – dependent variable, AAR.

Variables Estimate t-stat Estimate t-stat

Intercept 0.0017 2.49** 0.0022 2.76***

BA 0.0015 2.75***

ln (BA/1 − BA) 0.0001 2.58**

RISK −0.0002 −0.97 −0.0002 −0.89SIZE −0.0001 −2.53** −0.0001 −2.49**

EXP 0.0029 2.29** 0.0030 2.49**

REV 0.0000 −8.09*** 0.0000 −8***

SIZE(N) 0.0000 0.8 0.0000 0.93REGL 0.0000 1.83* 0.0000 1.83*

GNI −0.0101 −2.24** −0.0095 −2.23**

INF 0.0000 0.9 0.0000 0.84

Table 5 presents the regression results of sample firms’ daily average long-term abnormal returns over the four-year period of2004–2007 on the specific variables as follows. BA, bancassurance; RISK, company risk proxy; SIZE, company size/customer baseproxy; EXP, cost decrease proxy; REV, revenue increase proxy; SIZE(N), size of national banking sector; REGL, level of nationalderegulation; GNI, proxy for the level of insurance demand within a Country; INFL, proxy for inflationary changes within aCountry.



regressed by using the mean of BA (or ln BA/1 − BA), RISK, SIZE, EXP, REV, SIZE(N), REGL, GNI, INF, thosevariables we have used previously.13 The empirical results are presented in Table 5.Table 5 shows thatthe banks with a higher proportion of bancassurance will exhibit higher wealth effects measured byaverage abnormal returns, which arguably indicates that the market reacts positively to banks withbancassurance operations.

5. Conclusion

This study aims to fill a gap in existing literature by investigating a number of key determinantsof bancassurance using a sample of banks from both developed and developing countries. The studyalso aims to provide an in-depth analysis of previous literature, both quantitatively and descriptively,in order to provide a thorough review of the bancassurance market to date.

The empirical results find that the following factors have played significant roles in the expansionand consolidation of bancassurance: reduction in company risk, the size of the company, reductions incompany costs and increases in company revenues, the size of the national banking industry, the levelof financial deregulation within a country, and the national inflation rate. The study thus complementsthe previous literature on demand for bancassurance, insurance and international insurance services.A future avenue of research along the lines suggested by this paper could be an extension to include allbanks with or without bancassurance as a component of their total income. This will pave the way toinvestigating factors influencing the probability of banks offering bancassurance products. The mainissue examined by this paper can also be extended by including more macroeconomic and firm-levelfactors as explanatory variables, if the sample size could be enlarged.

There are some policy implications. Promoting further deregulation of the financial services indus-tries will enhance the development and growth of both the banking and insurance industries throughallowing the augmentation of distributional channels such as bancassurance, which incorporate theknowledge and expertise of both industries. In addition, consolidation and internationalisation of largefinancial institutions become more feasible if conglomeration, including bancassurance is encouragedby government policy.

13 Due to the fact that there are only 61 firms have price data till the end of 2007, the daily average abnormal returns are usedas our dependent variable in order to ensure consistency across overall sample firms.


Appendix A. Data sources and descriptions

Variable Source Description

BA Compustat + Company Annual Reports Bank operated insurance premium income as apercentage of total bank income for the period.a

RISK DataStream Standard deviation of the banks daily share pricemeasured on an annual basis.

SIZE Compustat + Company Annual Reports The log of the banks total assets.EXP Compustat + Company Annual Reports Measured as the annual percentage change of the

ratio of the banks total expenses over the bankstotal revenues.

REV Compustat + Company Annual Reports Measured as the annual percentage change of theratio of the banks revenues over the banks totalassets.

SIZE(N) Each countries respective National Bank website Annual percentage change in the total bankingassets of a country.

REGL World Competitiveness Yearbook (IMD) Ranking from 1 to 60 symbolizing the overallcompetitiveness of a country based on a number ofpre-specified factors.b

GNI World Bank Annual percentage change in a country’s GNI percapita based on the Atlas Method.c

INFL World Economic Outlook (IMF) Annual average inflation rate for a country.

The Atlas conversion factor for any year is the average of a country’s exchange rate (or alternative conversion factor) for thatyear and its exchange rates for the two preceding years, adjusted for the difference between the rate of inflation in the countryand that in the Group of Five (G-5) countries (France, Germany, Japan, the United Kingdom, and the United States). A country’sinflation rate is measured by the change in its GNP deflator.

a Premium income does not include any form of commissions received from insurance operations.b For more information on the calculation of rankings please visit the IMD website.c In calculating GNP, GNI and respective per capita figures in US dollars for certain operational purposes, the World Bank uses

a synthetic exchange rate commonly called the Atlas conversion factor. The purpose of the Atlas conversion factor is to reducethe impact of exchange rate fluctuations in the cross-country comparison of national incomes.

References

Agrawal, M., 2002. Distributing insurance in India: the Tata AIG experience in India. Tata AIG Insurance.Allen, L., Jagtiani, J., 2000. The risk effects of combining banking, securities, and insurance activities. Journal of Economics and

Business 52, 485–497.Amel, D., Barnes, C., Panetta, F., Salleo, C., 2004. Consolidation and efficiency in the financial sector: A review of the international

evidence. Journal of Banking and Finance 28, 2493–2519.Berger, A.N., Humphrey, D.B., Pulley, L.B., 1996. Do consumers pay for one-stop banking? Evidence from an alternative revenue

function. Journal of Banking and Finance 20, 1601–1621.Boyd, J.H., Graham, S.L., 1988. The profitability and risk effects of allowing bank holding companies to merge with other financial

firms: a simulation study. Quarterly Review, Federal Reserve Bank of Minneapolis 12, 3–20.Boyd, J.H., Graham, S.L., Hewitt, R.S., 1993. Bank holding company mergers with nonbank financial firms: Effects on the risk of

failure. Journal of Banking and Finance 17, 43–63.Browne, M.J., Kim, K., 1993. An international analysis of life insurance demand. Journal of Risk and Insurance 60, 616–634.Canals, J., 1998. Universal banks: the need for corporate renewal. European Management Journal 16, 623–634.Carow, K.A., 2001a. Citicorp-Travelers Group merger: challenging barriers between banking and insurance. Journal of Banking

and Finance 25, 1553–1571.Carow, K.A., 2001b. The wealth effects of allowing bank entry into the insurance industry. Journal of Risk and Insurance 68,

129–150.Chen, R., Wong, K.A., 2004. The determinants of financial health of Asian insurance companies. Journal of Risk and Insurance 71,

469–499.Cowan, A.R., Howell, J.C., Power, M.L., 2002. Wealth effects of banks’ rights to market and originate annuities. Quarterly Review

of Economics and Finance 42, 487–503.Cummins, J.D., 1991. Static and financial models of insurance pricing and the insurance firm. Journal of Risk and Insurance 58,

261–302.De Nicolo, G., Bartholomew, P., Zaman, J., Zephirin, M., 2004. Bank consolidation, internationalization, and conglomeration:

trends and implications for financial risk. Financial Markets, Institutions and Instruments 13, 173–217.De Nicolo, G., Kwast, M., 2002. Systemic risk and financial consolidation: are they related? Journal of Banking and Finance 26,

861–880.De Young, R., Rice, T., 2004. Noninterest income and financial performance at US commercial banks. Financial Review 39, 101–127.De Young, R., Roland, K.P., 2001. Product mix and earnings volatility at commercial banks: evidence from a degree of total

leverage model. Journal of Financial Intermediation 10, 54–84.


Elyasiani, E., Mansur, I., Pagano, M.S., 2007. Convergence and risk-return linkages across financial service firms. Journal of Bankingand Finance 31, 1167–1190.

Estrella, A., 2001. Mixing and matching: prospective financial sector mergers and market valuation. Journal of Banking andFinance 25, 2367–2392.

Klein, R.A., 2001. Bancassurance in practice, Muich Re. Group.Li, D., Moshirian, F., Nguyen, P., Wee, T., 2007. The demand for life insurance in OECD countries. Journal of Risk and Insurance

74, 637–652.Outreville, J.F., 1990. The economic significance of insurance markets in developing countries. Journal of Risk and Insurance 57,

487–498.Outreville, J.F., 1996. Life insurance markets in developing countries. Journal of Risk and Insurance 63, 263–278.Stiroh, K.J., 2004. Diversification in banking: is non-interest income the answer? Journal of Money Credit and Banking 36,

853–882.Swiss Re., 2002. Bancassurance developments in Asia—Shifting into a higher gear. Sigma Research Article, No.7.White, M.D., 1990. US bancassurance in the US and abroad. In: Proceedings of the 2nd International Life Insurance Conference,

Life Insurance International, London, England.

Documents

Expansion and consolidation of bancassurance in the 21st century