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Performance of the Life Insurance Industry Under Pressure: Efficiency, Competition and Consolidation

TL;DR: In this paper, the authors investigated efficiency and competition in the Dutch life insurance market by estimating unused scale economies and measuring efficiency market share dynamics during 1995-2010, concluding that further consolidation would reduce costs.
Abstract: This article investigates efficiency and competition in the Dutch life insurance market by estimating unused scale economies and measuring efficiency‐market share dynamics during 1995–2010. Large unused scale economies exist for small‐ and medium‐sized life insurers, indicating that further consolidation would reduce costs. Over time average scale economies decrease but substantial differences between small and large insurers remain. A direct measure of competition confirms that competitive pressure is lower than in other markets. We do not observe any impact of increased competition from banks, the so‐called investment policy crisis or the credit crisis, apart from lower returns in 2008.

Summary (5 min read)

1. Introduction

  • This paper investigates efficiency and competitive behaviour on the Dutch life insurance market.
  • Another advantage of the PCS indicator is that it requires only a small number of data series.
  • The authors combine the two measures of efficiency and competition, scale economy and PCS indicator, to find out whether they match or differ.
  • The authors data sets allow the subdivision of insurance policies into collective and individual contracts and, for each submarket, a split into unit-linked policies (where investment results are for the 4 risk of policyholders) and policies guaranteeing benefit payouts in euro.

2. The production of life insurances

  • Life insurance covers deviations in the timing and size of predetermined cash flows due to (non-) accidental death or disability.
  • Alternatively, the insurance benefits may be linked to capital market investments, e.g. a basket of shares, so that the insurance firm bears no investment risk at all.
  • Such policies are usually referred to as unit-linked funds.
  • A major feature of life insurance is its long-term character, often continuing for decades.
  • Significant expenditures include sales expenses, whether in the form of direct sales costs or agency, administrative costs, asset management and product development.

3. Literature on performance in the life insurance industry

  • In the literature, direct measurements of competition on the life insurance market are virtually absent.
  • All three studies present higher scale inefficiency for small insurers, lower inefficiency for medium-sized and larger firms, while the largest companies show again more scale inefficiency, pointing to a certain optimal scale.
  • Remarkable is the huge spread in cost (and profit) inefficiencies, due to the variation across countries and sample periods, but also to the different parametric and non-parametric measurement approaches of inefficiency and varying definitions (e.g. allocative versus technical inefficiency).
  • Cummins, Rubio-Misas and Zi (2004) do not find support for the EPH.
  • Stock insurers and mutuals have different production functions, so that comparative cost advantages determine the dominance of each type on the various submarkets, in line with the efficient structure hypothesis.

4. The measurement of inefficiency and competition

  • At the same time the authors interpret the existence of unused scale economies as lack of competitive pressure to push down costs.
  • Furthermore, the authors will measure competition directly using the PCS indicator.

4.1. Economies of scale

  • The intuition behind using economies of scale to measure inefficiency is that a highly competitive market is expected to force life insurance companies to improve their efficiency in order to be able to survive and gain sustainable profit.
  • The translog cost function (TCF), introduced by Christensen, Jorgenson and Lau (1973), has long been used extensively to measure economies of scale and is regarded as one of the most effective models.
  • The same TCF will also be used to estimate the marginal costs that will be incorporated as a proxy of efficiency in the PCS model of competition.
  • All output terms (in logarithms) are expressed as deviations from their averages (in logarithms),10 calculated over all insurer-year 7.
  • Hence, scale economies can be derived from a proportional increase in total costs resulting from a proportional increase in the output level, that is, the elasticity of total costs with respect to output level.

4.1.1. Definition of output

  • In the life insurance sector, output is intangible.
  • Additions to provisions (Provisionss,t+1 – Provisionsst) plus ‘incurred benefits’ are equal to: Net premiumsst – Costsst – Profit BTst + Eq. (5) shows that premiums have been corrected for the profit and cost margins, so that potentially distorting systematic differences in costs and profits across large and small firms are excluded.
  • Alternatively, the authors have also applied the TCF model of Eq. (1) to each of these four categories separately.
  • Note that when an insurer, with a given output, has more or, on average, smaller policies, this implies providing more client services, which go hand in had with higher operating costs.

4.1.2. The translog cost model

  • Besides output terms, the translog cost model (1) contains control variables, which have impact on operational costs and help to refine scale economies easurement.
  • A prominent hypothesis in this context is the ‘expense preference hypothesis of organizational form’, which is derived from agency theory.
  • The share of unit-linked premiums (ULPst/GPst) may also affect the cost level.
  • The real wage level is an input price (Wage).

4.2. The PCS model of competition

  • The PCS model assumes that more efficient firms (that is, firms with lower marginal costs) will gain higher market shares or profits, and that this effect will be stronger with competition.
  • This insurer maximizes profits πs = (ps – mcs) qs by choosing the optimal output level qs.
  • Hence in equilibrium, an insurer enters the insurance industry if, and only if πs ≥ ε. Note that Eq. (9) provides a relationship between output and marginal costs.
  • Competition in this market increases as the (portfolios of) services produced by the various insurers become closer substitutes, that is, s d increases (with d kept below b).

4.2.1. The empirical PCS model

  • The parameter of interest, βt, is expected to have a negative sign, because relatively efficient insurers will gain higher market shares.
  • The PCS indicator requires data on fairly homogeneous products.
  • Marginal costs are not observed but can be derived, using the translog cost function, from Eq. (3), as the authors will do.
  • In all these cases market shares in Eq. (10) can be replaced by profit, which is the product of pr fit margin and market share.

5. The structure of the Dutch life insurance industry

  • The authors explain the structure of the Dutch life insurance industry using the key data presented in Table 5.1.
  • The latter ind cates a decline in the sale of new life policies.
  • The authors do not analyse investment costs in the same way as operating costs: where operating costs are a pure overhead, investment costs may lead to higher expected returns.
  • In the bottom part of Table 5.1, the authors split the life policy market into subdivisions (collective versus individual policies, lump-sum versus periodical payments, unit-linked policies versus fixed benefits in euro policies, and endowment policies versus annuities), based on their share in premiums and insurance provisions; they do not observe substantial changes over time.

5.1 The life submarkets

  • Table 5.2 presents key data on two submarkets, policies in uro (or fixed-benefit policies) and unitlinked policies, each split further into individual and collective policies, for two subperiods: 1995- 2002 and 2003-2010.
  • While the profit margin on unit-linked policies is very minor, the profit margin on fixedbenefit policies is relatively high, during the first period especially on collective policies, and duringthe second subperiod particularly on individual policies Operating cost margins are much lower for collective policies than for individual ones, reflecting another element of scale economies.
  • Large contracts often concern pension benefits for employees of a company and are negotiated between experts at both ends of the table, in sharp contrast to individual contracts with uninformed private persons.
  • 17 Fig. 5.1 presents a weighed HHI of individual insurers’ market shares on submarkets which describes the focus of insurers on specialization over time (as in Bikker and Gorter, 2011).
  • The graph shows a decline in specialization over time, particularly in the earlier years and most recently.

6. Estimation results on scale inefficiencies

  • The authors estimate Eq. (6) using data over 1995-2010 to obtain a measure of the economies of scale (EoS) present in the Dutch life insurance industry.
  • The first row of Table 6.1 presents results for insurance output (I.O.) as output size measure.
  • Stock firms have significantly higher cost than mutual firms.
  • The estimates make clear that the average scale economies level – one minus the sum of the two linear output coefficients, see Eq. (2) – is at 0.107 somewhat smaller than in the case of the single-output measure, but still significantly different from CRS, see the test in the last row of the table.
  • Where the annual premium amount per policy is relatively high – as holds for collective contracts, lump-sum policies and, likely, unit-linked contracts – the respective coefficients shift downwards when controlled for the number of policies: the relatively lower costs are now also attributed to a relatively lower number of policies.

6.1. Estimation results over time

  • Table 6.3 presents cost elasticity effects over time.
  • Apparently, the first effect, concentration, dominated possible optimal scale shifting developments, so that unused scale economies fell over time.
  • The authors attribute the latter to the very small and declining number of mutual firms (to 5, on average, against 12 in the first subperiod) in this period, 22 The critical value of the CRS test statistic for the last column in Table 6.3, F(2, 211), at the 1% significance level, is 4.71.
  • As a robustness analysis the authors repeat the estimates of Table 6.3 for the 75% censoring case and the alternative model which also includes the number of policies as measure of output.
  • CRS is rejected significantly in all cases.

6.2. Estimation results per product type

  • The authors estimate Eq. (6) also for the four product types: the split of all polices either into unit-linked policies versus policies expressed in euro (left-hand panel of Table 6.4), or into individual versus collective policies (right-hand side).
  • In line with expectations, the unused scale economies are by far the smallest for collective policies (11%) where con entration is higher than elsewhere and the policies themselves are, of course, already on a larger scale.
  • Other characteristics are similar as before, whether or not the authors apply censoring.
  • When the authors split the policies further (first into unit-linked policies and policies expressed in euro and each group further into individual and collective policies), they find the smallest EoS of 10% for Euro-Collective against 20% for the other 3 combinations.

7. Estimates of the PCS indicator of competition

  • The more strongly market shares or profits of life insurance firms are determined by their marginal costs, the sronger competition on that market is.
  • Evidently, an insurer’s market share increases with the number of submarkets where the firm is active.
  • Fig. 7.1 shows the development of the PCS indicator ove time, using the FE market share model, but estimating beta for each year separately, that is, dropping the βt = β restriction.
  • The authors take market shares as dependent variable, marginal costs as explanatory variables, and include the lagged endogenous variable.
  • The PCS model does not indicate any competitive pressure on this market.

8. Conclusions

  • Efficiency and competition on the life insurance sector are important for companies and households to keep prices low and innovation and quality high.
  • This paper investigates efficiency and competition on this market with – given the large unfavourable changes in economic, financial and institutional conditions for life insurers – special attention to developments over time.
  • When the authors split the sample into two subperiods, they observe less unused economies of scale in more recent years (13% versus 18%).
  • In this light it is remarkable that the operating costs as a percentage of gross premiums remained the same: cost efficiency did not improve.
  • But this competitive pressure is weak, compared to the indicator values in other industrial sectors including banks and non-life insurers.

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Tjalling C. Koopmans Research Institute
Discussion Paper Series nr: 12-19
Performance of the life insurance
industry under pressure: efficiency,
competition and consolidation
Jacob A. Bikker

Tjalling C. Koopmans Research Institute
Utrecht School of Economics
Utrecht University
Kriekenpitplein 21-22
3584 EC Utrecht
The Netherlands
telephone +31 30 253 9800
fax +31 30 253 7373
website www.koopmansinstitute.uu.nl
The Tjalling C. Koopmans Institute is the research institute
and research school of Utrecht School of Economics.
It was founded in 2003, and named after Professor Tjalling C.
Koopmans, Dutch-born Nobel Prize laureate in economics of
1975.
In the discussion papers series the Koopmans Institute
publishes results of ongoing research for early dissemination
of research results, and to enhance discussion with colleagues.
Please send any comments and suggestions on the Koopmans
institute, or this series to J.M.vanDort@uu.nl
çåíïÉêé=îççêÄä~ÇW=tofh=ríêÉÅÜí
How to reach the author
Jacob A. Bikker
De Nederlandsche Bank (DNB)
Supervisory Policy Division
Strategy Department
P.O. Box 98
NL-1000 AB Amsterdam
The Netherlands
E-mail: j.a.bikker@dnb.nl
Utrecht University
Utrecht School of Economics
Kriekenpitplein 21-22
3584 TC Utrecht
The Netherlands.
This paper can be downloaded at: http://
www.uu.nl/rebo/economie/discussionpapers

Utrecht School of Economics
Tjalling C. Koopmans Research Institute
Discussion Paper Series 12-19
Performance of the life insurance industry
under pressure: efficiency,
competition and consolidation
Jacob A. Bikker
De Nederlandsche Bank (DNB)
Utrecht School of Economics
Utrecht University
November 2012
Abstract
A well-performing life insurance industry benefits consumers, producers and
insurance firm stockholders alike. Unfavourable market conditions stress the need
for life insurers to perform well in order to remain solvent. Using a unique
supervisory data set, this paper investigates competition and efficiency in the Dutch
life insurance market by estimating unused scale economies and measuring
efficiency-market share dynamics during 1995-2010. Large unused scale economies
exist for small and medium-sized life insurers, indicating that further consolidation
would reduce costs. Over time average scale economies decrease but substantial
differences between small and large insurers remain. A direct measure of
competition confirms that competitive pressures are at a lower level than in other
markets. We do not observe any impact of increased competition from banks, the
so-called investment policy crisis or the credit crisis, apart from lower returns in
2008. Investigation of product submarkets reveals that competition is higher on the
collective policy market, while the opposite is true for the unit-linked market, where
the role of intermediary agents is largest.
Keywords: Life insurance, competition, efficiency, Performance-Conduct-Structure
model, Boone indicator, concentration, economies of scale.
JEL classification: G22; L1.
Acknowledgements
The author is grateful to Paul Cavelaars and Janko Gorter for useful comment and Jack
Bekooij for excellent research assistance.

2
1. Introduction
This paper investigates efficiency and competitive behaviour on the Dutch life insurance market.
2
In
the Netherlands, the life insurance sector is important with a business volume of € 22 billion in terms
of annual premiums paid, invested assets of € 337 billion and insured capital of € 990 billion at end-
2010. This market provides important financial products such as endowment insurance, annuities, term
insurance and burial funds, of frequently sizeable value to consumers. Financial planning of many
households depends on the proper functioning of this market. The complexity of the products and
dependency on future investment returns make many life insurance products rather opaque. Therefore,
competition and efficiency in this sector are important for consumers (Bikker and Spierdijk, 2010).
Most life insurance policies have long life spans, which makes consumers sensitive to the reliability of
the respective firms. Life insurance firms need to remain in a financially sound condition over decades
in order to be able to pay out the promised benefits.
In recent years, the life insurance sector has been confronted with several major challenges. First, the
ongoing long-lasting decline in interest rates, world-wide but particularly in the euro area, has reduced
insurers’ income which is – among other things – needed to cover future benefits to policyholders.
Second, the credit and government debt crises have lowered the value of stocks and PIIGS countries
bonds, which have impaired insurance firms’ buffers. In the Netherlands, two additional problems for
life insurers have emerged. In order to increase competition between banks and insurers, tax privileges
for insurance products, such as old-age savings and redemption plans for mortgage loans, have also
been made available for comparable banking products. The impact of this tax reform has been huge:
more than half of the new production on the respective insurance markets has been gained by the
banks. Finally, the insurers face the so-called investment policy crisis. Around 2006, public awareness
increased that various types of unit-linked saving policies (such as annuities and mortgage redemption
saving plans), which were based on capital market investment at the risk of policyholders, carried high
operational costs and relatively high premiums on included life risk policies, eating 50-60% of the
invested premiums. Under public pressure, insurers agreed to pay compensation to policy holders for
incurred and future costs on the respective policies, estimated at € 2.5 to 4.5 billion,
3
while potential
claims may come to a multiple of that amount. One of the consequences of this crisis is that consumer
trust in insurance firms and the volume of new production have decreased
Competition and efficiency in financial markets is difficult to measure, particularly due to the
unavailability of data with respect to costs and prices of individual financial products (Bikker, 2010).
2
Different from most other countries, in the Netherlands, health, disability and accident insurance is not included
in the life sector, but in ‘non-life’.
3
http://www.verzekeraars.nl/sitewide/general/nieuws.aspx?action=view&nieuwsid=880.

3
The solution in the literature has been to assume a single insurance (or banking) product and to use
balance sheet and profit and loss data of entire financial institutions. As a first measure, this paper
estimates unused scale economies, which is a proxy of inefficiency, but at the same time an indirect
measure of competition: where competition is high, insurers are forced to reduce their cost level
wherever possible. Further, we apply a measure of competition which has to date been rarely used in
the literature, namely a Performance-Conduct-Structure (PCS) model, also known as the Boone
indicator, developed by Hay and Liu (1997) and Boone (2000, 2008). Where the well-known
Structure-Conduct-Performance (SCP) paradigm of Mason (1939) and Bain (1951) explains
performance via conduct from market structure, this alternative model explains market structure via
conduct or competition from performance, as in the so-called efficiency hypotheses (Smirlock, 1985;
Goldberg and Rai, 1996). This approach is based on the notion that competition rewards efficiency
and punishes inefficiency. In competitive markets, efficient firms perform better – in terms of market
share and hence profit – than inefficient firms. The PCS indicator measures the extent to which
efficiency differences between firms are translated into performance differences. The more
competitive the market is, the stronger is the relationship between efficiency and performance. The
PCS indicator is usually measured over time, giving a picture of the development of competition.
Other measures of competition, such as the Lerner index and the Panzar and Rosse model, are less
suitable because the required data (output prices, cost price, profit margin) are lacking, while the
Concentration index and the SCP model have serious shortcomings, see Bikker and Bos (2008).
Another advantage of the PCS indicator is that it requires only a small number of data series. We
combine the two measures of efficiency and competition, scale economy and PCS indicator, to find
out whether they match or differ.
Earlier research on the life insurance market has revealed that the efficiency of life insurers tends to to
be poor (Cummins and Weiss, 2012) and that competition between insurers is not strong (Bikker and
Van Leuvensteijn, 2008). Unused scale economies point to weak competition: stronger pressure on
cost efficiency would lead to further consolidation. The four abovementioned problems facing the
Dutch life insurance sector all have in common that cost efficiency would help (i) to maintain market
shares in the competition struggle against banks, and (ii) either to restore profitability and impaired
buffers, or to reduce the hidden costs in unit-linked products (or both).
Life insurance firms sell several different products through various distribution channels, thereby
creating several submarkets. The degree of efficiency and competition varies across these submarkets.
For instance, the submarket where parties negotiate collective contracts (mainly employer-provided
pension schemes) is expected to be more competitive than the submarkets for individual policy
holders. Our data sets allow the subdivision of insurance policies into collective and individual
contracts and, for each submarket, a split into unit-linked policies (where investment results are for the

Citations
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01 Jan 2013
TL;DR: In this article, the authors examined the financial performance of Indian life insurers on the basis of various parameters such as liquidity, solvency, profitability and leverage of the insurance players and found that profitability of life insurers is positively influenced by liquidity and size and negatively related with capital.
Abstract: The performance of the company plays a leading role towards the growth of the industry which ultimately leads to the overall success of the economy. The present study attempts to examine the financial performance of Indian life insurers on the basis of various parameters. For measuring it, various financial ratios have been calculated taking into consideration liquidity, solvency, profitability and leverage of the insurance players. Generally, performance can be estimated by measuring the profitability of firm and insurers. In order to accomplish the aim, the study determines the impact of liquidity, solvency, leverage, size and equity capital on the profitability of life insurers in India. The sample for this study includes 18 Indian life insurers (including 1 public and 17 private) and it analyses the data of 5 years from 2007-08 to 2011-12. The study uses multiple linear regression model to measure the extent to which these determinants exert impact on life insurers profitability. The results of the study reveal that profitability of life insurers is positively influenced by liquidity and size and negatively related with capital. Profitability does not show any relationship with solvency and insurance leverage.

31 citations

Journal ArticleDOI
TL;DR: In this article, the determinants of pricing power in the South African non-life insurance market were investigated. And the effect of insurer level characteristics on pricing power was found to be heterogeneous across different quantiles of the pricing power.
Abstract: In less competitive markets, firms with market power are likely to exercise pricing power by setting output prices above their marginal cost, inducing welfare losses from resource misallocation, managerial inefficiency and market instability. In order to address such market imperfections, it is important for regulatory authorities to identify the sources of pricing power and devise policies to address their adverse effects. In this context, the purpose of this paper is to undertake an empirical analysis to identify the determinants of pricing power in the South African non-life insurance market.,The authors estimate the Lerner competitive index as the proxy for pricing power using annual data on 79 firms from 2007 to 2012. In the second stage, the paper employs panel regression techniques in the ordinary least squares, random effects and generalised method of moment’s estimations to examine the effect of insurer level characteristics on pricing power.,The authors find the market to be characterised by firms with high pricing power. Domestic-owned insurers are found to exercise high pricing power compared with foreign-owned insurers. The authors also identify size, cost efficiency, product line diversification, market concentration, leverage and reinsurance contracts as the significant predictors of pricing power in the market. Finally, through a quantile regression analysis, the authors find the effect of cost efficiency, business line diversification and reinsurance to be heterogeneous across different quantiles of pricing power.,The findings provide regulatory authorities with useful indicators in addressing anti-competitive behaviour in high pricing power to enhance the stability of the insurance market and improve consumer welfare and economic development.,To the best of the authors’ knowledge, this is first paper to examine the determinants of pricing power and competitive behaviour in an insurance market.

6 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examine the history that paved the way to these reforms, and evaluate their impacts for consumers and insurers, and suggest that the application of unfair contract terms protections to insurance contracts has potential to address consumer harm without resulting in prohibitive costs for the insurance industry.
Abstract: As part of the Australian Consumer Law reforms of 2010, unfair contract terms protections were implemented nationwide across most sectors that use standard form contracts in their dealings with consumers, including financial services. However, until recently, these protections did not apply to general insurance contracts covered by the Insurance Contracts Act 1984 (Cth). Consumer groups, as well as a series of government and independent inquiries and reviews, have long called for reforms to bring insurance within the ambit of the unfair contract terms protections contained in the Australian Securities and Investments Commission Act 2001 (Cth). In February 2020, legislation was passed to remedy the situation. In this article, we examine the history that paved the way to these reforms, and evaluate their impacts for consumers and insurers. We argue that this legislation indicates a move away from the view of insurance contracts as having a ‘unique character’ that renders them ‘unsuited’ to the consumer protections that apply to other financial products and services. We suggest that the application of unfair contract terms protections to insurance contracts has potential to address consumer harm without resulting in prohibitive costs for the insurance industry.

6 citations

Journal ArticleDOI
TL;DR: In this paper, the efficiency of a life insurer company is influenced by whether it is a mutual or a stock company, and the efficiency can determine how well said companies perform in the future.
Abstract: Research background: Mutual companies are a major component of the life insurance industry worldwide and moreover are growing in importance. Efficiency, potentially affected by whether a life insurer company is mutual or stock, can determine how well said companies perform.

3 citations

Journal ArticleDOI
09 Mar 2020
TL;DR: In this article, the authors overview the types of insurance operating in Nepal, product delivered by them and status of insurance market in Nepalese economic development and conclude that with little improvisation based on market research and consumer awareness can lead insurance companies & the concept to a peak level in Nepal.
Abstract: The study aims to overview the types of insurance operating in Nepal, product delivered by them and status of insurance market in Nepalese economic development. The paper is based on secondary data and literature reviews. Insurance can be acknowledged as tool that shares risk, offers financial protection, minimizes the financial distress and accelerates the pace of economic growth. Insurance encourages saving in the society and collects the scattered fund in term of premium and invest for maximization. Presently 40 insurance companies (19 life insurance, 20 non-life insurance and 1 reinsurance) are operating in Nepal providing diversified range of services. Recently agriculture insurance on crop and livestock sector and health insurance policy is being offered through many governmental and private insurance companies of Nepal. Insurance Board statistics of 2017 revealed total premium of 46.97 billion rupees and 2.03% contribution in total gross domestic product. We cannot deny the fact that insurance market of Nepal is witnessing major obstacles in terms of new product innovation, service issues related to consumers and time lapse of long-term policy. The study concludes that with little improvisation based on market research and consumer awareness can lead insurance companies & the concept to a peak level in Nepal.

2 citations


Cites background from "Performance of the Life Insurance I..."

  • ...Bikker, J. A. (2012). Performance of the life insurance industry under pressure: efficiency, competition and consolidation....

    [...]

  • ...`A wellperforming and financially sound life insurance industry benefits consumers, producers and stockholders (Bikker, 2012)....

    [...]

  • ... `A wellperforming and financially sound life insurance industry benefits consumers, producers and  stockholders (Bikker, 2012)....

    [...]

References
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Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the effects of deregulation and consolidation in financial services markets by analyzing the Spanish insurance industry and found that many small, inefficient and financially underperforming firms were eliminated from the market due to insolvency or liquidation and that acquirers in the mergers and acquisitions market prefer relatively efficient target firms.
Abstract: This paper provides new information on the effects of deregulation and consolidation in financial services markets by analyzing the Spanish insurance industry. The sample period 1989-1998 spans the introduction of the European Union?s Third Generation Insurance Directives, which deregulated the EU insurance market. Deregulation has led to dramatic changes in the Spanish insurance market; the number of firms declined by 35 percent and average firm size increased by 275 percent. We analyze the causes and effects of consolidation using modern frontier efficiency analysis to estimate cost, technical, and allocative efficiency, as well as using Malmquist analysis to measure total factor productivity change. The results show that many small, inefficient, and financially under-performing firms were eliminated from the market due to insolvency or liquidation and that acquirers in the mergers and acquisitions market prefer relatively efficient target firms. As a result, the market experienced significant growth in total factor productivity over the sample period. Consolidation reduced the number of firms operating with increasing returns to scale but also increased the number operating with decreasing returns to scale. Hence, many large firms should focus on improving efficiency rather than on further growth.

209 citations

Book ChapterDOI
TL;DR: A review of frontier efficiency and productivity methodologies that have been developed to analyze firm performance, emphasizing applications to the insurance industry, is presented in this article, where the focus is on the two most prominent methodologies: stochastic frontier analysis using econometrics and non-parametric frontier analyses using mathematical programming.
Abstract: This chapter reviews the modern frontier efficiency and productivity methodologies that have been developed to analyze firm performance, emphasizing applications to the insurance industry. The focus is on the two most prominent methodologies—stochastic frontier analysis using econometrics and non-parametric frontier analysis using mathematical programming. The chapter considers the underlying theory of the methodologies as well as estimation techniques and the definition of inputs, outputs, and prices. Seventy-four insurance efficiency studies are identified from 1983 to 2011, and 37 chapters published in upper tier journals from 2000 to 2011 are reviewed in detail. Of the 74 total studies, 59.5% utilize data envelopment analysis as the primary methodology. There is growing consensus among researches on the definitions of inputs, outputs, and prices.

200 citations

Posted Content
TL;DR: In this article, the authors investigated the economic impact of diversification in the US insurance industry over the period 1993-2006 and concluded that strategic focus is superior to conglomeration in the insurance industry.
Abstract: This paper investigates economies of scope in the US insurance industry over the period 1993-2006. We test the conglomeration hypothesis, which holds that firms can optimize by operating a diversity of businesses, versus the strategic focus hypothesis, which holds that firms can best add value by focusing on core competencies. We analyze whether it is advantageous for firms to diversify by offering both life-health and property-liability insurance or to specialize in one major industry segment. We estimate technical, cost, revenue, and profit efficiency utilizing data envelopment analysis (DEA) and test for scope economies by regressing efficiency scores on an indicator variable for strategic focus and control variables. Property-liability insurers realize cost scope economies, but they are more than offset by revenue scope diseconomies. Life-health insurers realize both cost and revenue scope diseconomies. Hence, strategic focus is superior to conglomeration in the insurance industry.

174 citations

Book
25 Jul 2008
TL;DR: In this article, the authors provide an all-embracing framework for the various existing theories in this area and illustrate these theories with practical applications, and provide an overview of the current major trends in banking and relate them to the assumptions of each model.
Abstract: Economic literature pays a great deal of attention to the performance of banks, expressed in terms of competition, concentration, efficiency, productivity and profitability. This book provides an all-embracing framework for the various existing theories in this area and illustrates these theories with practical applications. Evaluating a broad field of research, the book describes a profit maximizing bank and demonstrates how several widely-used models can be fitted into this framework. The authors also present an overview of the current major trends in banking and relate them to the assumptions of each model, thereby shedding light on the relevance, timeliness and shelf life of the various models. The results include a set of recommendations for a future research agenda. Offering a comprehensive analysis of bank performance, this book is useful for all of those undertaking research, or are interested, in areas such as banking, competition, supervision, monetary policy and financial stability.

105 citations


"Performance of the Life Insurance I..." refers methods in this paper

  • ...Further, we apply a measure of competition which has to date been rarely used in the literature, namely a Performance-Conduct-Structure (PCS) model, also known as the Boone indicator, developed by Hay and Liu (1997) and Boone (2000, 2008)....

    [...]

  • ...Other measures of competition, such as the Lerner index and the Panzar and Rosse model, are less suitable because the required data (output prices, cost price, profit margin) are lacking, while the Concentration index and the SCP model have serious shortcomings, see Bikker and Bos (2008)....

    [...]

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TL;DR: In this paper, a new measure of competition, the Boone indicator, is applied to the banking industry, which is able to measure competition of bank market segments such as the loan market, whereas many well-known measures of competition can consider the entire banking market only.
Abstract: This paper is the first that applies a new measure of competition, the Boone indicator, to the banking industry. This approach is able to measure competition of bank market segments, such as the loan market, whereas many well - known measures of competition can consider the entire banking market only. A caveat of the Boone - indicator may be that it assumes that banks generally pass on at least part of their efficiency gains to their clients. Like most other model - based measures, this approach ignores differences in bank product quality and design, as well as the attractiveness of innovations. We measure competition on the lending markets in the five major euro countries as well as, for comparison, the UK, the US and Japan. Bearing the mentioned caveats in mind, our findings indicate that over the period 1994 - 2004 the US had the most competitive loan market, whereas overall loan markets in Germany and Spain were among the best competitive in the EU. The Netherlands occupied a more intermediate position, whereas in Italy competition declined significantly over time. The French, Japanese and UK loan markets were generally less competitive. Turning to competition among specific types of banks, commercial banks tend to be more competitive, particularly in Germany and the US, than savings and cooperative banks.

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Q1. What contributions have the authors mentioned in the paper "Performance of the life insurance industry under pressure: efficiency, competition and consolidation" ?

Using a unique supervisory data set, this paper investigates competition and efficiency in the Dutch life insurance market by estimating unused scale economies and measuring efficiency-market share dynamics during 1995-2010. Large unused scale economies exist for small and medium-sized life insurers, indicating that further consolidation would reduce costs.