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The working papers are produced by the Bradford University School of Management and are to be circulated for
discussion purposes only. Their contents should be considered to be preliminary. The papers are expected to be
published in due course, in a revised form and should not be quoted without the authors permission.
Working Paper SeriesCost Efficiency and Total Factor Productivity in the European LifeInsurance Industry: The Development of the German Life InsuranceIndustry Over the Years 1991-2002
Stephanie HusselsDr Damian Ward
Working Paper No 04/05
February 2004
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COST EFFICIENCY AND TOTAL FACTOR
PRODUCTIVITY IN THE EUROPEAN LIFE
INSURANCE INDUSTRY: THE DEVELOPMENT
OF THE GERMAN LIFE INSURANCE
INDUSTRY OVER THE YEARS 1991-2002
Stephanie Hussels
Dr Damian Ward
Address for correspondence:
University of Bradford School of Management
Emm Lane
Bradford, West-Yorkshire
BD9 4JL
UK
Tel: +44 (0) 1274 233194Fax: +44 (0) 1274 234355;
Email: [email protected] and
www.brad.ac.uk/acad/management/index.html
ABSTRACT
In 1994, the single European insurance market
became a legal reality with the adoption of the
Third Generation Insurance Directive, which
deregulated the entire industry. To explore how
the German life insurance industry has dealt with
the changes brought about by the deregulation
this paper estimates efficiency and productivity
over the years 1991 and 2002 by adopting data
envelopment analysis. Empirical evidence
suggests that the Germany life insurance industry
encounters an average growth in productivity of
2.6 percent. Moreover, the decomposition of cost
efficiency highlights that by insurance companies
adopting the most eff icient available technology
and choosing the cost-minimising combination of
inputs, they can achieve the largest improvementsin cost efficiency. Finally, the Tobit model for
censored data highlights that over the years 1995
to 2002 the age, company size, organisational
form, and the composition of the investment
portfolio partly explain inter-company differences
in efficiency.
JEL: D24, G22
Keywords: European Single Market, total factor
productivity, efficiency, data envelopment
analysis, Germany life insurance industry,
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W O R KI N G P AP E R S E R IE S
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1. INTRODUCTION
Since the founding of the European Community in
1957, the European Community has attempted to
create an integrated economic market among its
member states. Although the European
Community initially hoped to create the single
market rapidly, economic recessions and
continuing protectionism among member states
have impeded the progress towards European
integration. In order to remove technical, fiscal,
and physical barriers to allow free trade among
the member states, a series of directives have
been introduced to abolish these barriers and to
establish a regulatory framework for achieving the
Single European Market. As a result, the single
insurance market became a legal reality in 1994
with the adoption of the Third GenerationInsurance Directive, which has deregulated the
European insurance industry.
The creation of a single insurance market has
changed the landscape of the entire industry and
is expected to enhance market efficiency and
productivity, change distribution channels, as well
as the relationship between insurers and other
financial service providers, increase cross-boarder
activity, and increase consumer choice through
greater competition. This is due to insurancecompanies new ability to operate freely
throughout the community, either by establishing
a distribution network in other member states or
offering services across borders (Hogan, 1995).
The impact of these developments are however
likely to vary between the different European
markets, as the European insurance industry
formerly consisted of several separate markets
with each market being based on different
regulatory approaches which uniquely affected
pricing, contractual provision, establishment of
branches, and solvency standards in each country.The new environment is likely to put pressure on
less efficient firms to improve their efficiency and
productivity in order to survive the anticipated
increased competition.
In order to assess the financial consequences of
the various changes and developments in the
European life insurance industry, one might
expect an extensive amount of papers on the
relative efficiency and productivity. However, only
Cummins and Rubio-Misas (2001) and Mahlbergand Url (2000) have analysed the effects of
deregulation and consolidation by examining the
entire, life and non-life, Spanish and German
insurance industry, respectively1. Therefore, as an
initial step in understanding how the European
life insurance markets have coped with the
creation of the European single insurance market,
this paper examines cost efficiency and total
factor productivity (TFP) in the German life
insurance industry.
The choice of the German life insurance industry
is not ad hoc, but based on the enormous
challenges and massive cultural changes the
German life insurance companies had to face as a
result of the changes in regulatory policy brought
about by the deregulation of the industry
(Grenham et al., 2000). The German life
insurance market had to move from a maximal-
regulation policy, which placed emphasis onmaintaining insurer solvency; and included control
of insurance rates and policy conditions, to a
lighter European regulatory approach (Rees and
Kessner, 1998). The former regulatory scheme
provided stability and transparency of the market,
but at the cost of low levels of competition and
limited product portfolios, as life insurance
companies could only differentiate themselves by
the quality of services, rather than the content or
price of insurance products (Sigma, 1996,
Ennsfellner and Dorfman, 1998). Moreover, whendiverging from the cost minimising or profit
maximising strategies, German life insurance
companies were enjoying the protection offered
by national authorities.
The purpose of this paper is to partially fill the
gap in the existing literature by analysing the
German life insurance industry over the twelve-
year period of 1991 to 2002, which spans the
implementation of the Third Generation Insurance
Directive. It explicitly attempts to provide an
initial understanding of how the German lifeinsurance industry has dealt with the changes
brought about by the deregulation of the
European life insurance industry in terms of
relative efficiency and TFP, and outline potential
reasons for the development. Moreover, key
characteristics of insurance companies such as
scale of operations, age, and organisational form,
are introduced to test whether these have a
significant effect on the estimated degree of
efficiency. The study contributes to the European
insurance efficiency literature by conducting amore extensive analysis of the German market
using a twelve-year sample period and
decomposing the cost efficiency measures into its
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W O R KI N G P AP E R S E R IE S
1 A detailed overview of the remaining efficiency studies covering the European insurance industry are given in the two survey
papers by Cummins and Weiss (1999) and Berger and Humphrey (1997).
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components. Additionally, in contrast to previous
studies, which jointly analyse life and non-life
markets, this paper concentrates solely on the life
insurance industry, thereby avoiding the distortion
of the efficiency estimates of each segment.
To measure efficiency, the best practice
production and cost frontiers are estimated for
each year of the sample period using the non-
parametric data envelopment analysis (DEA). This
technique does not only allow an assessment of
the cost efficiency of individual firms with regard
a set of best practice firms in the industry, but
also enables a decomposition of cost efficiency
into its components, thereby giving a first
indication of the reasons behind the changes in
efficiency over time. The effects of the singleinsurance market on the German life insurance
industry are however hidden in the annual
efficiency scores, since they only represent a
relative measure at a point in time. The simple
measurement of efficiency development over time
provides no information about the associated
productivity changes. To measure productivity
before and after the establishment of the single
European insurance market in 1994, the DEA-
based Malmquist index approach is adopted.
Moreover, to partly explain inter-companyefficiency differences, the Tobit model for
censored data is applied.
The reminder of this paper is organised as follows.
In section two, the frontier efficiency concept, as
well as the associated DEA and the Malmquist
methodologies, are described in detail. Section
three outlines and discusses the sample, the input
and output measures, and the choice of
environmental variables. The empirical results are
summarised and discussed in section four. The
final section offers concluding remarks and makessuggestions for future research.
2. METHODOLOGY
2.1 Frontier efficiency concept
The primary focus of research concerning the
measurement of eff iciency and TFP is to establish
a benchmark, which enables a comparison
between companies performance. Traditionally,
to estimate the degree of efficiency, researchers
used conventional financial ratios such as the
return on equity or return on assets, whichsummarised a firms performance in a single
statistic. The departure from the traditional
approach was achieved by Farrell (1957) who was
largely inspired by the work of Debreu (1959) and
Koopmans (1957), in defining a simple measure
of a firms efficiency which could account for
multiple inputs. For this purpose, Farrell (1957)
introduced the concept of the efficiency frontier,
measuring the efficiency of a firm relative to an
empirical production frontier, which represents the
technological limits of what a firm can achieve
with a given level of inputs.
Farrell (1957) introduced an efficiency measure
that consists of two components, technical
efficiency and allocative efficiency. Technical
efficiency is defined as the ratio of the input
usage of a fully efficient firm to the input usage
of the firm under consideration producing the
same output vector. Allocative efficiency reflectsthe ability of a firm to use the inputs in optimal
proportions given their respective prices. The
product of these two measures provides a
measure of economic or overall efficiency2. These
concepts are illustrated in Figure 1 with reference
to a single output firm utilising two input factors.
FIGURE 1: CONCEPT OF TECHNICAL AND
ALLOCATIVE EFFICIENCY
Figure 1 shows an isoquant QQ for a firm, which
represents the various combinations of the twoinputs (x1, x2) required to produce a fixed amount
of the single output by using state of the art
production technology. Firms which are operating
on the isoquant are therefore considered
technically efficient. For illustrative purposes, the
production technology is assumed to have
constant returns to scale, which allows the
technology to be represented using the unit
isoquant. The approach can, however, be
extended to accommodate multiple inputs and
outputs and non-constant returns to scale.Moreover, the production technology can be
computed assuming either an input oriented
frontier, thereby addressing the question By how
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W O R KI N G P AP E R S E R IE S
2 The concept of economic and overall ef ficiency is often regarded as being identical to the meaning of X-eff iciency introduced by
Leibenstein (1966).
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much can input quantities be proportionally
reduced without changing the output quantities
produced? or by an output oriented model
answering the question By how much can output
quantities be proportionally expanded without
altering the input quantities used?. As an input-
oriented model is chosen for the analysis (see
section 2.2), the efficiency concepts are outlined
by assuming an input-orientation.
The cost minimising aspect needed to illustrate
allocative efficiency is incorporated in Figure 1
through the isocost curve represented as a slope
equal to w2/w1, with the vertical and horizontal
intercepts C0/w2 and C0/w1. These intercepts
correspond to the quantities of each input factor
that could be purchased if all costs were spent onone input. One could specify more isocost curves,
each corresponding to a different level of total
costs, but each line would have the same slope.
In Figure 1, the specific isocost line has been
chosen which happens to be tangent to the
isoquant QQ. A firm is referred to as fully cost
efficient if it is operating at the point where the
isoquant and the isocost line touch. Point D* =
(x1D, x2
D) in Figure 1 therefore represents a fully
cost efficient firm with no technical or allocative
inefficiencies.
Inefficient firms by definition intersect an isocost
line, which lies to the right of the tangent isocost
line and hence involve a higher cost than C. A
firm operating at, for example, point A = (x1A, x2
A)
therefore exhibits both technical and allocative
inefficiency. It is not technically efficient because
it is not operating on the best technology
isoquant, which can be represented by A/B, and
not allocatively efficient, because it is not using
its inputs in the correct proportions. More
specifically, firm A is using too much of input 2and too little of input 1, which is represented by
the ratio C/B. Technical efficiency can be further
decomposed into pure technical efficiency and
scale efficiency. In order to illustrate and explain
the two concepts, two production frontiers based
on the single-input-output case are used.
The Vc frontier represents a constant returns to
scale frontier and Vv a variable return to scale
frontier. A variable returns to scale frontier has
regions that are characterised by increasing,decreasing, and constant returns to scale
represented, by a rising, falling, and horizontal
frontier shape, respectively. As it is socially and
economically optimal for firms to operate at
constant returns to scale, it is interesting to
investigate what makes firms deviate from the Vc
(Coelli et al., 1998). This can be achieved by
separating technical efficiency into pure technical
and scale efficiency. Pure technical efficiency
represents the proportion by which a firm can
reduce its input usage by adopting the best
technology represented by the variable returns to
scale frontier. Pure technical efficiency is
measured relative to the variable returns to scale
frontier and in the case of firm (xi, yi) pure
technical inefficiency is therefore equal to B/C. A
firm that is also operating on the variable returnsto scale frontier is however also scale inefficient,
because it is not operating on the constant
returns to scale frontier. In the case of firm (xi, yi),
scale inefficiency is represented by the ratio A/B.
However, the efficiency measures assume that the
production and cost function of the fully efficient
firm is known. In practice, these efficiency frontiers
are however unknown and need to be estimated
from observations. Two types of approaches have
been developed in the past: the parametric and
non-parametric approaches. These approaches
differ in the assumptions they make regarding the
shape of the efficient frontier and the treatment of
the error term3. Despite intense research efforts,
there is no consensus on the best approach for
estimating efficiency frontiers in the literature, with
some researchers arguing for the parametric and
some for the non-parametric approaches. Berger
and Humphrey (1997) report an approximately
even split between the applications, of non-
parametric (69 applications) and parametric
techniques (60 applications), using financialinstitutions data. Since the true level of efficiency
is unknown, it is therfore not possible to determine
which of the two methods dominates the other.
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W O R KI N G P AP E R S E R IE S
FIGURE 2: CONCEPT OF PURE TECHNICAL AND
SCALE EFFICIENCY
3 A more detailed overview of the parametric and non-parametric techniques with regards to the insurance industry is presented
in Cummins and Weiss (1999).
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In this paper, the best practice production and
cost frontiers for the German life insurance
industry are estimated by applying the non-
parametric DEA. The choice of DEA is due to four
reasons. First, the parametric models necessitate
a larger sample size to make reliable estimations
than DEA, which is less data demanding. Second,
DEA provides a very convenient method for
decomposing cost efficiency into the efficiency
components outlined before. Third, the DEA-
based Malmquist approach has become the most
frequent used methodology for estimating the
evolution of productivity over time, thereby
allowing the application of the same methodology
throughout the whole paper. Fourth, as the
existing studies that analysed the German
insurance industry and the impact of insurancemarket deregulation have applied DEA, it will be
beneficial to use DEA for comparative reasons. In
the following subsections, the non-parametric
DEA associated with the estimation of the frontier
functions is outlined, before briefly describing the
Malmquist methodology.
2.2 Estimation methodology
The approach to frontier estimation proposed by
Farrell was not given much empirical attention
until Charnes et al. (1978) introduced DEA, whichis the most frequently used implementation of the
non-parametric deterministic frontiers. Since this
seminal paper, numerous papers in the literature
have extended and employed DEA4.
In the context of DEA, the organisation under
study is called the decision-making unit. A
decision-making unit is regarded as an entity
responsible for converting inputs into outputs
whose performance is to be evaluated. DEA is a
non-parametric approach, which applies
mathematical programming to form the bestpractice frontier with the set of best practice
input-output observations. The DEA efficient
frontier is therefore composed of undominated
firms and represents the convex combination of
firms in the industry that dominate the others
yielding in convex production possibility set.
These firms are self-efficient because no
combination of the other firms in the sample can
produce their output vector by using a smaller
amount of inputs. Decision-making units that lie
on the frontier are therefore deemed self-efficientin DEA, whereas the firms that do not lie on the
frontier are termed inefficient.
Charnes et al. (1978, 1981, 1979) regard their
efficiency frontier as an envelope developed from
the observational data of all decision-making
units. The envelope is often also referred to as a
given firms reference set, and is therefore used to
estimate the degree of efficiency of a specific firm
in the sample by comparing the firm vis--vis
other firms in the industry. The DEA efficiency
score is hence not defined by an absolute
standard but is defined relative to other decision-
making units in the specific data set under
consideration. If the reference set merely consists
of the firm itself, it is considered self-efficient and
has an efficiency score of 1.0. If the dominating
set consists of other firms, the firms efficiency is
less than 1.0. Efficiency estimates can again be
decomposed into various elements as described insection 2.1.
The original DEA approach by Charnes et al.
(1978) assumed constant returns to scale. This
assumption is however only appropriate when all
decision-making units operate at an optimal scale.
Factors such as imperfect competition or limited
financial resources may cause decision-making
units not to be operating at an optimal scale
(Coelli et al., 1998). Consequently, the use of the
constant returns to scale specification might resultin measures of technical efficiency which are
confounded by scale efficiencies. Later studies
have therefore considered alternative sets of
assumptions, such as Banker et al. (1984), who
first introduced the assumption of variable returns
to scale. To avoid these potential drawbacks, the
study applies DEA assuming both constant
returns to scale and variable returns to scale.
Furthermore, an input-oriented model is chosen,
as the emphasis is on cost control. The variable
linear programming model used can be defined
as follows:
(1)
The value of represents the value of the
efficiency score for the ith decision-making unit.
is the vector of constants. The linear
programming has to be solved N times, once for
each decision-making unit in the sample. The
convexity constraint (N1=1) ensures that aninefficient firm is only compared against firms of
similar size, and therefore provides the basis for
measuring economies of scale within the DEA
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W O R KI N G P AP E R S E R IE S
4 For a comprehensive bibliography of the existing studies following the seminal paper by Charnes et al. (1978) see Seiford
(1990).
minq,
yi+Y0 x
iX>0
N1 - 1 0
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concept. The convexity constraint determines how
closely the production frontier envelops the
observed input-output combinations and is not
imposed in the constant returns to scale case.
The variable returns to scale technique therefore
forms a convex hull which envelops the data more
tightly than the constant returns to scale, and
thus provides efficiency scores that are greater
than or equal to those obtained from the constant
returns to scale model.
Besides measuring cost efficiency, it is interesting
to assess the evolution of TFP and efficiency over
time in order to assess whether a change in
efficiency has occurred before and after
deregulation. The concept of TFP is very closely
related to the concept of efficiency. TFP growth isdefined as the change in output due to technical
change and technical efficiency change over time,
whereby technical change is represented by a
shift in the production frontier of period t and
period t+1, whereas efficiency change is
represented by the movement of a firm closer to
or further from the present and past frontiers.
Technical change and technical efficiency change
cannot be measured accurately using trends in
annual average efficiency scores because the
average scores are based on separate frontiersestimated for each year of the sample period.
The productivity indices that have been most
frequently used in the literature are those by
Fisher (1922), Toernqvist (1936), and Malmquist
(1953). The benefit of the Fisher and Toernqvist
indices is that they do not require the estimation
of the technology of the insurance companies, but
only require outputs, inputs, and the respective
prices. The Malmquist productivity index, which
requires the specification of the production
frontier, does, however, allow the decompositioninto technical change and efficiency change.
The Malmquist productivity index uses the idea of
the distance function so that a preceding
estimation of the corresponding frontier is
required. The estimations are carried out using
DEA, as it not only permits a consistent approach
within the paper, but also uses the same
methodology as the majority of existing papers
(Cummins and Weiss, 1999). This paper makes
use of an input-oriented Malmquist index becauseit provides the best measure for potential savings
by cutting out the excessive use of inputs. The
computations are conducted using Formula (2)
below.
(2)
The Malmquist index represents the productivity
of the production point (Xi,t,Yi,t) relative to the
production point (Xi,t+1,Yi,t+1). Each measure takes
any positive value, which is in contrast to the
efficiency measures where each measure has a
value less than or equal to 1.0. A value greater
than 1.0 indicates an increase in TFP from period
t to period t+1, whereas a value below 1.0
represents a decline in TFP. The above outlinedMalmquist index is the geometric mean of two
input-based Malmquist TFP indices to avoid an
arbitrary choice of base years. The Malmquist
productivity index can be decomposed into
measures of technical efficiency change and
technical change by factoring, as follows:
(3)
The first ratio in Formula (3) represents technical
efficiency change, the relative distance from the
input-output combination from the frontier in
period t and t+1. Both the numerator and
denominator of this ratio must be greater or equal
to 1.0. Thus, if technical efficiency is higher in
period t+1 than in period t, the value of this ratio
is greater than 1.0, while if the efficiency declinesbetween the two periods, the value of the ratio will
be smaller than 1.0. The second factor in Formula
(3) is a geometric mean representing technical
change between periods t and t+1. Values greater
than 1.0 imply technical progress whereas values
smaller than 1.0 imply technical regress. The
second factor in brackets represents the distance
between the period t and t+1 frontiers.
To sum up, the paper estimates efficiency
estimates and Malmquist productivity indices forthe German life insurance industry by using the
non-parametric DEA. All DEA-based efficiency
and productivity estimations are conducted with
the software DEAP Version 2.1 developed by
Coelli (1996).
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W O R KI N G P AP E R S E R IE S
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3. DATA AND CHOICE OF VARIABLES
3.1 Data
The production and cost functions are estimated
using detailed annual accounting data of German
life insurance companies over the period of 1991
to 2002. The German data were collected
manually from the respective annual company
accounts. After eliminating inactive firms and
firms where data were only available for part of
the sample period, thirty-one life insurance
companies, which together account for
approximately 65 percent of total premiums, were
randomly chosen among those firms that existed
over the entire sample period. It is therefore
possible that only the more efficient companies
are included, as the others have ceased to exist
due to mergers and acquisitions. This is likely tocause survivorship bias in the estimates, which
needs to be acknowledged. To overcome the
changes in the company accounts from 1994 to
1995, the data have been made roughly
comparable by adjusting the annual accounts
before 1995.
3.2 Output and input measures
To estimate the best practice frontiers, input and
output factors, as well as the respective prices,
need to be identified
5
. Since insurance output ismostly intangible, it is necessary to find suitable
approximations for the volume of services
provided by insurers.
Three principal approaches have been applied in
the literature to measure outputs in the financial
services sector, the asset or intermediation
approach; the user-cost approach and the value-
added approach. This paper adopts the value-
added approach, as it has been proved to be the
most appropriate method for studying insurance
efficiency (Cummins and Weiss, 1999). The value-added approach identifies all those activities
having some output characteristics that create
significant value-added as judged by using
operating cost allocations. Within the life
insurance industry, these are primarily the risk
bearing/risk pooling and intermediation services.
Life insurers collect premiums and annuity
considerations from customers and redistribute
most of the funds to those policyholders who
sustain losses, thereby offering risk pooling and
risk bearing services to their customers. Moreover,life insurance companies collect funds in advance
of paying benefits that are held in reserves until
claims are paid. The process of working with the
funds during the time lag is referred to as the
intermediation service.
In the literature, there has been a long-standing
debate among researchers between using
premiums or claims, sometimes referred to as
benefit payments, to approximate the risk bearing
and risk pooling service of life insurance
companies. Net premiums written represent the
values that free willing consumers attribute to the
insurance service they are seeking, and therefore
directly concern the technical activity of an
insurance company. The insurance premium is the
price which makes an individual just indifferent
between retaining and insuring a risk. However,
net premiums written do not reflect a firms
financial activity. In other words, the ability toinvest the necessary reserves in an appropriate
way hence does not measure the intermediation
services of life insurance companies. Cummins et
al. (1996, 1998, 1999) and Ward (2002)
therefore state that a satisfactory approximation
for the two main services offered by the insurance
industry is incurred benefits plus addition to
reserves. Incurred benefits, payments received by
policyholders in the current year, are a good
approximation for the risk-pooling and risk-
bearing functions, as they measure the amount offunds pooled by insurers and redistributed as
benefits. Additions to reserves represent the
funds collected in advance of paying benefits and
held in reserves until claims are paid, thereby
approximating the intermediation service of
insurance companies.
Diacon et al. (2002) argue that it is difficult to
understand why management would seek to
maximise the value of insurance claims. Besides,
the time lag in the payment of claims means that
accounting entries for insurance losses accruedinvolve a substantial element of estimation and
year on year adjustment. Funds that are not
needed for benefit payments and expenses are
added to the policyholders reserve. Additions to
reserves are hence highly correlated with the
intermediation output.
The paper will follow the efficiency studies by
Fecher et al. (1993), Rai (1996), Hardwick (1997)
and Diacon et al. (2002) which use total net
premiums written as their output measure toapproximate the risk-pooling and risk-bearing
service to customers. Total premiums, premiums
earned from all lines of the business including
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W O R KI N G P AP E R S E R IE S
5 The survey article by Berger and Humphrey (1997) provides a detailed discussion of the issues involved in choosing the inputs
and outputs to be used to evaluate insurance companies performance.
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both individual and group premiums, are included
instead of premiums or claims per product line, as
these could not yet be clearly identified for the
entire sampling period. Furthermore, additions to
reserves are used as an output variable to
approximate the intermediation service of
insurance companies.
In contrast to the discussions associated with
choosing the best approximation of insurance
outputs, there seems to be a high degree of
uniformity in the research community regarding
the choice of input measures. Two input
measures are included for estimating the frontier
functions, namely labour and cost of capital,
which is in line with the existing literature
(Cummins and Weiss, 1999). To capture theeffects of labour and capital over the twelve years
of the study, the annual average number of
employees per company and the total
shareholders equity at the end of each financial
year are used. As in Hardwick (1997) and Ward
(2002), the price of labour is measured by the
average gross weekly earnings of workers in the
insurance sector, as published by the German
Statistischen Bundesamt. A measurement of the
cost of capital is more complicated by the
incidence of mutual organisations within theindustry. The price of cost of capital for each year
is estimated by utilising the traditional capital
asset pricing model6. In order to correct for
inflation, total premiums, additions to reserves,
and the average wage rates were deflated to
1995 prices using the gross domestic product
deflator for the particular years as issued by the
Statistisches Bundesamt (2002). Moreover, all
variables were converted into US$ for comparative
reasons, using the exchange rate published by the
International Monetary Fund (2003).
Table 1 shows that the sample is characterised by
huge differences in company size. These
differences are significant if one considers that
the majority of German firms in the sample tend
to be positioned within the top twenty German
life insurance companies according to premium
income over the entire sampling period.
3.3 Environmental variables
In order to give further insight into the variation
of efficiency scores among individual companies,
a second-stage analysis is conducted, whereby theestimated efficiency scores from the DEA are
taken as the independent variable and regressed
against the environmental variables using a Tobit
model. The outlined environmental variables do
not form an exhaustive list, but merely serve as a
starting point to identify inter-company efficiency
differences within the German life insurance
industry. Due to the changes in the accounts
from 1994 to 1995 as a result of the deregulation
of the European life insurance industry, the
second stage analysis is only conducted for theeight-year time period 1995 to 2002.
The German life insurance industry is characterised
by differences in companies size. Meador et al.
(2000) state that firms that serve a large
percentage of the market tend to have market
power and therefore tend to be more efficient.
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W O R KI N G P AP E R S E R IE S
6 The cost of capital was estimated by using the CAPM formula (k=rf +(rm-rf)). The risk free rate (rf) for each year was
approximated by using short term government bonds as published by the International Monetary Fund (2003) and the market
premium (rm) were measured by a benchmark market index. The beta, the measure of the systematic and non-diversifiable risk,
was approximated for each year by taking the industry betas published in Kielholz (2000) and estimations by the authors.
TABLE 1: DESCRIPTIVE STATISTICS OF OUTPUT AND INPUT VARIABLES OF THE GERMAN SAMPLE.
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Moreover, it can be assumed that larger firms will
be able to benefit from economies of scale in
underwriting and investment. The scale of
operations is expected to be positive related to
efficiency, and larger companies are hence likely to
encounter higher degrees of efficiency. On the
other hand, Jensen and Meckling (1976) and
Zahra and Pearce (1989) argue that as firms
increase in scale and their operations become
more complex, information asymmetries between
the various contracting groups worsen.
Opportunistic behaviour by managers is going to
increase, which subsequently is likely to have a
negative impact on the degree of efficiency. It will
therefore be interesting to see whether the scale of
operations partly explains inter-company efficiency
variations. The sizes of the German life insurancefirms are estimated by taking the log of the total
assets for each year of the sample period to reduce
the correlation between inputs and outputs, and
decrease the risk that the results may be
confounded by extreme values in the data set.
Life insurance companies exhibit a rich variation in
their ownership structure including stock
companies, and mutual companies. Both mutual
and stock companies differ fundamentally in the
way they combine the three key stakeholdergroups (managers, owners and customers) in an
insurance company, thereby creating different
incentives for the various contracting parties. The
variations in costs for controlling the resulting
incentive problems suggest that different
organisational forms are likely to vary in terms of
efficiency. Two leading hypotheses, the expense
preference hypothesis (Mester, 1989), and the
managerial discretion hypothesis (Mayers and
Smith Jr., 1988), have been developed over the
years. These non-mutually exclusive hypotheses
are based on the agency theoretic observation thatthe mutual organisational form provides weaker
mechanisms for owners to control managers than
the stock organisational form. It will therefore be
interesting to test whether there is a difference in
terms of efficiency and productivity between stock
and non-stock organisational forms. To test the
impact of the choice of the organisational form on
efficiency a dummy variable (0 and 1) is used to
mark the type of organisational form with and
without stock.
Another variable that might have an impact on the
efficiency of insurance companies is the age of the
firm. Since there are high entry costs during the
build-up phase of the risk pool, older companies are
likely to face lower average costs. Older insurance
companies might also be able to build on their
expertise to improve efficiency and productivity.
However, the age of companies could also have a
negative impact, as age could hinder the
introduction of new, less rigid organisational
structures and innovative products, such as new
distribution channels. The age of the respective life
insurance companies in each year under
consideration is estimated by considering the point
of the initial entry in the trade register. Moreover,
in order to test the potential link between total
administrative, acquisition as well as the investment
costs, on the degree of efficiency, the cost items are
estimated as a percentage of total premiums.
Investments are a crucial factor for the life
insurance business, which is based on a promise
and capital guarantees that future funds will be
available in the case that some contractually
specified event occurs where insurance losses
might exceed premiums. Life insurance companies
must disburse claims from insurance policies and
associated business expenses with their own
capital funds plus the funds from the insurance
premiums. There are important time lags between
the raising of capital, collection of premiums, andpayment of losses and expenses. Insurers use
these time lags and invest both their investors
capital and insurance premiums until claim and
expense payments are required. However, the
compositions of investment portfolios varies
between life insurance companies. In order to test
whether the composition of the investment
portfolio has an impact on efficiency, six
subcategories of investments as a per cent of total
assets are analysed.
4. EMPIRICAL RESULTS AND DISCUSSION
4.1. Efficiency and productivity scores
In order to assess the effects of deregulation on
the German life insurance market, the empirical
analysis starts by estimating the degree of cost
efficiency for each of the twelve years of the
sample period. The annual efficiency estimates
are weighted by companies share of total
shareholders equity to take into account the
different company sizes when computing the
industry efficiency levels7. In doing so, the
efficiency scores of larger companies are given agreater weighting in the calculation of the
industry average. The cost efficiency results are
presented in Table 2.
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7 Total premiums per company have also been used as a weighting measure but no significant difference could be found to using
shareholders equity. Moreover, the calculations have been conducted with and without Allianz Life Insurance Company, which is
the largest life insurance company in the German market.
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The efficiency estimates reveal that over the
twelve-year time period, cost efficiency under
constant returns to scale is on average 35.6
percent, implying that, on average, German life
insurance companies are off the best practice
frontier by 64.4 percent. In the case of the
variable returns to scale production technology,
an average cost efficiency of 56.0 percent can be
observed, implying that firms are on average 44.0
percent inefficient. The difference in the degreeof cost efficiency under both constant returns to
scale and variable returns to scale is surprising, as
the variable returns to scale technique forms a
convex hull which envelops the data more tightly
than the constant returns to scale, and thus
provides efficiency scores that are greater than or
equal to those obtained from the constant returns
to scale model. So far, only Cummins and Rubio-
Misas (2001) have computed cost efficiencies for
European insurance firms when analysing the
Spanish life and non-life insurance market. The
average cost efficiencies levels in the Spanish
insurance companies during the years 1989 to
1998 are considerably lower, with an average
degree of cost efficiency of 17.4 percent.
When looking at the annual average cost
efficiency estimates, no clear pattern in the
development of cost efficiency over the twelve-
year period can be observed. In 1991, constant
returns to scale cost efficiency estimates are 30.8
percent, whereas in 2002, cost efficiency
estimates increase slightly to 32.6 percent. Thisdevelopment is not surprising, as it can be
expected that the new environment should put
increasing pressure on the German life insurance
companies to improve their efficiency in order to
survive the anticipated European competition.
Due to the anticipated challenges and cultural
changes the German life insurance companies had
to face as a result of the changes in regulation,
one could have expected more profound changes
in cost efficiency over the twelve-year sample
period. Moreover, it is interesting that cost
efficiency drops during the years 2000 and 2001,
which coincides with the strong slump of the
worldwide stock markets in the same years.
To provide a more detailed understanding of the
cost efficiency estimates for the German life
insurance industry, the technical, allocative and
scale efficiency findings are outlined. These
results are summarised in Tables 3 and 4.
Under the assumption of constant returns to
scale, the mean technical efficiency estimate is
equal to 54.3 percent, whereas the variable
returns to scale estimate is again much higher,
with a degree of technical efficiency of 76.4
percent. These results imply that German life
insurance companies can produce their products
and services, on average, with about 45.7 and
23.6 percent less inputs if they operate on the
best practice production frontier. In other words,
the high degree of average technical inefficiency
is attributable to the fact that some German life
insurance companies do not seem to be using the
most efficient technology available to transform
the inputs into outputs.
The estimated technical efficiency estimates are
much higher than the ones found by Mahlberg
and Url (2000) who provided a first attempt to
assess the degree of technical efficiency in the
German life and non-life insurance industry from
1992 to 1996. They highlight that under variablereturns to scale, technical efficiency is
approximately fifty percentage points, indicating
that an enormous potential for cost cutting is
evident in the German insurance industry.
Moreover, the presented technical efficiency
estimates shown are higher than the DEA
estimates presented by Fecher et al. (1993) when
analysing the French life insurance industry over
the period 1984 to 1989. Depending on the
output measures used in their analysis, Fecher et
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TABLE 2: COST EFFICIENCY ESTIMATES FOR THE YEARS 1991 TO 2002 UNDER CRS AND VRS
TABLE 3: TECHNICAL EFFICIENCY ESTIMATES FOR THE YEARS 1991 TO 2002 UNDER CRS AND VRS
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al. (1993) show an average technical efficiency
for the French non-life insurance industry of
0.504 to 0.537. Cummins and Rubio-Misas
(2001), furthermore, estimate an average
technical efficiency in the Spanish life and non-
life insurance industry between 1989 and 1998
of 53.6 percent.
A direct comparison of the efficiency results may
however be misleading, due to three reasons.
First, the studies do not merely analyse the life
insurance segment of the respective markets but
combine life and non-life observations. As both
segments are likely to have different best practice
frontiers, due to the differences in products and
services offered, a misrepresentation of the
individual efficiency results may occur. Second, inthe different European insurance markets,
insurance outputs may consist of different services
or of similar services provided in different
proportions. Third, insurers in one sample could
be using a technology that dominates the
technology used by firms in another sample. It is
hence not surprising that the technical efficiency
results are not all within the ranges found by
previous researchers.
In accordance with the study by Mahlberg (1999),
who analysed the technical efficiency in the
German insurance market over the time period of
1992 to 1996, it becomes evident that the
technical efficiency tends to slightly decrease over
the twelve year time period, with variable returns
to scale technical efficiency ranging between
0.821 in 1991 and 0.753 in 2002. The variable
returns to scale technical efficiency show a strong
improvement in 2002, with an overall degree of
technical efficiency of 59.8 percent. German life
insurance companies nevertheless still have
considerable scope to improve their ability toproduce the maximum output possible from the
inputs they employ.
The average constant returns to scale-based
allocative efficiency estimate is equal to 61.9
percent, and the average variable returns to scale-
based allocative efficiency is equal to 69.1
percent, suggesting that German life insurance
companies are on average not doing their best job
in choosing the cost-minimising combination of
inputs. This phenomenon might be due be to the
fact that the German life insurance industry has
been sheltered from competition for a long time,
due to the tight former regulatory approach, and
has still maintained a pre-1994 business
perspective. This is reinforced by the only slight
improvement in the annual allocative efficiency
estimates since 1991. In line with these results,
Cummins and Rubio-Misas (2001) highlight a low
degree of allocative efficiency during the years
1989 and 1998 in the Spanish insurance industry,
which was formerly also characterised by having a
tight regulatory approach. The low allocativeefficiency scores may be due to insurance
companies inability to adjust to the new
environment or high non-recurring costs in order
to adjust to the deregulation of the European
insurance industry.
Given the industry-wide phenomenon in the
European insurance industry of mergers and
acquisitions, it is interesting to examine whether
German life insurance companies can improve
their efficiency by increasing their size. The DEA
results show that the German life insurance
companies are not operating at an optimal scale,
with an average scale efficiency of merely 71.3
percent. This implies that German life insurance
companies are able to improve their efficiency on
average by nearly 30 percent by adjusting to an
optimal size.
Additionally, DEA allows assessing whether a firm
lies in the range of increasing, constant, and
decreasing returns to scale. If a market contains
firms operating with increasing and decreasingreturns to scale, market efficiency can be
increased if more firms attain constant returns to
scale, because fewer resources are wasted due to
firms being either too small or too large. The
measurement of economies of scale therefore also
helps to assess at the same time whether further
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TABLE 4: ALLOCATIVE AND SCALE EFFICIENCY ESTIMATES FOR THE YEARS 1991 TO 2002
UNDER CRS AND VRS
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consolidation, and hence higher market
concentration, should be encouraged to improve
efficiency. In the following Table 5, the number
of German life insurance companies in each group
is presented.
The results show that on average 20 out of the 31
companies within the sample encounterdecreasing returns to scale, highlighting that the
majority of the firms in the sample are too big,
whereas, on average, only four companies
encounter constant and seven increasing returns
to scale. This phenomenon might be partially due
to the fact that the sample includes the majority
of the top twenty German life insurance
companies according to premiums. Fields (1988),
moreover, states that insurance markets might not
have taken advantage of the available cost
savings due to absent price transparency plus alack of policy conditions and premiums on the
part of the consumers, which in turn have allowed
scale inefficient firms to survive. The scale
efficiency estimates therefore clearly indicate that
further mergers and acquisitions with their usual
subsequent increase in company size are not likely
to improve overall efficiency. Moreover, policy
makers who are concerned with the efficient
regulation of the industry should take this into
account that by fostering mergers and
acquisitions they will not be able to improveefficiency and withhold the competitive pressures
of other European life insurance companies.
To provide a more detailed picture of the financial
consequences of the European single insurance
market on the German life insurance industry,
Malmquist indices are estimated. Table 6shows
the Malmquist productivity indices and the
decomposition into indices of technical efficiency
change and technical change over the sample
period. Unlike the efficiency estimates outlinedbefore, which are based on the frontier of an
indicated year, the Malmquist indices and its
components compare changes across two years.
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TABLE 5: NUMBER OF INCREASING, CONSTANT, AND DECREASING RETURNS TO SCALE DURING THE
YEARS 1991-2002
TABLE 6: TFP DEVELOPMENT IN THE GERMAN LIFE INSURANCE INDUSTRY FROM 1991-2001
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The Malmquist indices highlight improvements in
productivity in seven out of eleven two-year
comparisons and productivity regress in merely four
out of eleven years. The Malmquist estimates show
that during the time period, German life insurance
companies experienced an average overall TFP
increase of 2.6 percent. During the same time
period the average annual growth rate of the
German gross domestic product was merely 1.5
percent, emphasising the extent of the average TFP
rate of the German life insurance industry (The
World Bank Group, 2002). Most of this increase in
productivity over the sampling period is
attributable to technical change, with an average
technical improvement of 2.2 percent. Moreover,
the technical change results show technological
improvements in six out of eleven two-yearcomparisons, with the most pronounced
improvement between the years 1993 to 1995.
Furthermore, the estimates show that stock
companies in the sample encountered on average
slightly higher TFP estimates over the entire sample
period than non-stock companies, with an average
TFP score of 3.0 and 2.0 percent, respectively.
Furthermore, the Malmquist computations
indicate that Germany experienced exceptional
high TFP growth rates between the years 1991 to1993 which is line with Mahlberg and Url (2000).
These high TFP figures are likely to be the result
of the reunification of the two former German
states. In the former DDR planned-economy, only
two government-owned insurance companies, the
Staatliche Versicherung der DDR and Auslands
und Rueckversicherungs AG der Deutschen
Demokratischen Republik existed, which offered a
limited product portfolio (Klein, 1991). Private
health and life insurance products were not
offered to the general public in the former DDR.
The unification of the two states therefore notonly resulted in a substantially larger market, with
approximately 16 million new potential
customers, but also in a dissolution of the former
state monopoly, offering a lucrative business
opportunity for the existing insurance companies
(Wagner, 1991).
More specifically, between 1991 and 1992, the
improvement in productivity could be mainly
attributed to an improvement in efficiency, whereas
between 1992 and 1994, TFP improvement wasmainly due to technological change. The periods
1995 to 1996, as well as 1997 to 1998, and finally,
the years 2000 to 2002, can be characterised as
periods of stagnation, with the first two periods due
to an average drop in the efficiency change, and the
latter due to a decline in technological change. The
period between 1994 and 1995, along with the
period 1999 and 2000, shows a further
improvement of the average firm in terms of
technology and efficiency change, respectively,
accompanied by an impressive improvement in
general productivity. Over the whole sample period,
one can observe a catching up of the average life
insurance firm towards best practice. When
considering the DEA efficiency results, it is not
surprising that the German life insurance industry
has a positive TFP score. Being more productive is
likely to achieve major cost advantages and thereby
a higher degree of cost efficiency.
When looking at the descriptive statistics, it needs
to be acknowledged that the high standard
deviations of both the efficiency change as wellas TFP over the years 1991 to 1993 is due to the
entry of one new life insurance company into the
market in 1991. In the following years, the
company has developed in line with the other
companies in the sample. It is interesting that the
technical change between the years 1995 and
1996 has a standard deviation of only 0.068 and
0.066, highlighting that there is little variation in
technological change among the German life
insurance companies.
The TFP measures for Germany are considerably
lower than the ones found by Mahlberg and Url
(2000) who analysed both life and non-life
insurance companies over the years 1992 to
1996. They estimate a significant increase in
productivity, with the average company
encountering productivity gains of approximately
13 percent. However, this increase was unequally
distributed around companies, including both life
and non-life companies. Moreover, the German
average productivity gains over the twelve year
sample period are identical to those found in theSpanish insurance industry over the time period of
1989 to 1998, where an equal increase in
productivity of 2.6 percent was observed
(Cummins and Rubio-Misas, 2001). The Italian
insurance industry encountered a significant
decrease in productivity over the years 1985 to
1993, with an average productivity decline of
24.78 percent (Cummins et al., 1996). These
studies have however all looked at both life and
non-life insurance companies, which makes a
direct comparison of the estimates difficult, dueto the different nature of the services and
products offered.
In contrast to the European Commission (1992),
who argue that ... the new competitive pressures
brought about by the completion of the internal
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market can be expected to produce appreciable
gains in internal efficiency...., the above findings
indicate that the financial consequences of the
creation of the European life insurance industry
seem less positive than anticipated. This is in line
with the findings by Berger (forthcoming), who
suggests that the creation of a single market for
the European financial service industry is not
likely to bring about strong efficiency gains, and
the view of Grenham et al. (2000), who state that
German life insurance companies have maintained
a pre-1994 mentality, as only a slight overall
improvement in eff iciency and productivity can be
observed during the sample period.
4.2. Inter-company efficiency differences
In order to test the impact of the variousindependent environmental variables, the paper
utilises the Tobit model for censored data in order
to allow for the restricted range of Farrells
efficiency scores. Thereby, the environmental
factors are regressed against the cost, technical,
allocative, and scale efficiency estimates. Due to
the high numbers of iterations needed to run the
regressions, the random effects model is used
instead of the fixed effects model. The sample
includes all thirty-one German life insurance
companies in a pooled data set over the years1995 to 2002. The results of the calculations
and the descriptive statistics of the selected
variables are provided in detail inAppendices 1
and 2.
Cost and technical efficiency estimates are
strongly associated with the size of operations.
This is in line with Fecher et al. (1993) who show,
when analysing the degree of cost efficiency in
the French insurance industry over the period
1984 to 1989, that size of a company is a vital
factor to explain ef ficiency, as these companiesmight for example be able to take advantage of
finance or investment opportunities. On the other
hand, the opportunistic behaviour by managers as
a result of increase in scale and more complex
operations does not seem to be a vital factor to
explain the impact of company size on efficiency.
In five out of six estimations, stock companies are
slightly more efficient than non-stock companies,
which is in line with Fecher et al. (1993) who find
that stock life insurers have higher average
efficiency scores than mutuals. It needs howeverto be acknowledged that the coefficients indicate
merely a minuscule difference. This is again in
accordance with the work by Cummins et al.
(1997), Hardwick (1997), and Fukuyama (1997)
who observe that mutual companies are, on
average, only slightly less economically inefficient
than stock companies, and hence have
approximately equal efficiency scores.
Moreover, the regression results reveal that, in
three cases, the age of the life insurance
companies seems to have a significant but
negative effect on explaining the degree of
efficiency over the eight-year sample period. The
results therefore confirm that younger companies
seem to benefit from being more innovative and
able take advantage of more efficient processes
and technologies. The reputation and the lower
average costs of more mature firms do not seem
to be a key factor.
When examining the results analysing the
composition of the investment portfolio of theGerman life insurance companies, it becomes
obvious that investments in property have a
positive significant effect on overall cost efficiency,
with a coefficient of 2.76 when assuming constant
returns to scale, and 3.12 when assuming variable
returns to scale. These findings look sensible, as
over the eight-year time period, the world economy
had to face slumps in the stock market, making
investments in properties more attractive and
worth while. With exceptions to cost efficiency
and allocative efficiency under variable returns toscale, investments in shares do not seem to have a
significant impact on the various efficiency
degrees. However, despite its significance of
shares at the 1 percent level, cost efficiency under
constant returns to scale and variable returns to
scale encounter opposite signs of the coefficient,
allowing no clear indication of its impact.
Registered debentures are highly positively
significant, which could be due to due to the fixed
interest characteristics of the products. Deposits
and cash are only negative significant for cost
efficiency under variable returns to scale,highlighting that as more cash is lying idle
efficiency declines. The results emphasise that the
different types of investments are partly associated
with a higher degree of efficiency, but further
analysis needs to be conducted.
Acquisition costs show a positive significant impact
when regressing both against the constant returns
to scale and variable returns to scale based
allocative efficiency estimates. This could be due
to the fact that companies with higheradministrative expenses are more capable in
identifying cost-minimising inputs. All other
variables do not play a significant role in explaining
the degree of efficiency. The authors wish to apply
a broader range of variables in the next version of
the paper in order to provide more insight into
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what influences efficiency and productivity of the
German life insurance companies.
5. CONCLUSION AND FUTURE RESEARCH
In line with the creation of the European Single
Market, the German life insurance industry had to
face deregulation and profound transformation,
which resulted in many challenges and a
complete change in business culture for the
companies affected. The paper attempts to
extend the established literature on insurance
efficiency by analysing how the German life
insurance industry has coped with these
challenges in terms of efficiency and TFP.
The sample used includes data on thirty-one
German life insurance companies over the twelveyears 1991 to 2002. The non-parametric DEA is
adapted to measure the various efficiency
estimates using annual best practice frontiers.
This allows assessment of the cost efficiency of
individual firms in relation to a set of best
practice or benchmark firms. Cost efficiency is
decomposed into technical and allocative and
scale efficiency for each firm in the sample in
each year. Besides, DEA-based Malmquist indices
are generated to estimate the level of productivity
in the German life insurance market.
The findings indicate that the financial
consequences in terms of efficiency and
productivity of the creation of the European life
insurance industry seem to be more positive than
anticipated, with an average overall increase in
efficiency and TFP. The decomposition of the
concept of cost efficiency highlights that there
remains a high potential for efficiency
improvement in the German life insurance. The
estimated cost efficiency scores are predominately
attributable to the low level of technical efficiencyduring the sample period, as German life
insurance companies on average seem to be not
using the most efficient technology, thereby
preventing the most efficient transformation of
inputs into outputs due to a general overuse, or
wasting of inputs. Moreover, the results show that
the German life insurance companies are not
operating at an optimal scale, nor are they
choosing the cost-minimising combination of
inputs. The Tobit regression results highlight that
over the years 1995 to 2002, the age, size andorganisational form of the German life insurance
companies partly explain the inter-company
differences in efficiency. The different types of
investments are partly associated with a higher
degree of efficiency, but further analysis needs to
be conducted.
This paper serves as a starting point of research
that attempts to link the current literature on
efficiency in financial services with the growing
literature that is concerned with the role of
financial services in the process of economic
growth (see the work of Outreville (1990), Levine
and Zervos (1998), Levine (1999, 1998), Levine et
al. (2000), and Ward and Zurbruegg (2000)). It is
anticipated to extend the research by conducting
efficiency and productivity comparisons among
the European life insurance markets by using a
global best practice frontier. So far, only three
comparative studies of efficiency, covering a
whole range of insurance industries, by Rai
(1996), Donni and Fecher (1997), and Diacon et
al. (2002), have been undertaken in the European
insurance market. These studies all broadly showthat international differences in the degree of
efficiency are apparent, but do not give explicit
reasons for this. By identifying, the factors that
promote efficiency in the European life insurance
industry, it will be possible to reveal some of the
factors that promote economic development.
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APPENDICES
1. DESCRIPTIVE STATISTICS: ENVIRONMENTAL VARIABLES FOR TOBIT REGRESSION, N=2288, 1995-2002
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W O R KI N G P AP E R S E R IE S
2.RESULTSOFTOBITREGRESSIO
NI
NE
FFICIENCY
SCORES
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LIST OF WORKING PAPER TITLES
2004
04/05 Stephanie Hussels & Damian Ward
Cost Efficiency and Total Factor Productivity in the European Life
Insurance Industry: The Development of the German Life Insurance
Industry Over the Years 1991-2002
04/04 Axle Giroud & Hafiz MirzaIntra-firm Technology Transfer: The Case of Japanese Manufacturing
Firms in Asia
04/03 David Spicer
The Impact of Approaches to Learning and Cognition on Academic
Performance in Business and Management
04/02 Hafiz Mirza & Axle Giroud
Regionalisation, Foreign Direct Investment and Poverty Reduction:
The Case of ASEAN
04/01 Gretchen Larsen & Veronica George
The Social Construction of Destination Image A New Zealand Film
Example
2003
03/35 Alexander T Mohr & Jonas F Puck
Asymmetries in Partner Firms Perception of Key Variables and thePerformance of International Jo int Ventures
03/34 Hafiz Mirza & Axle Giroud
The Impact of Foreign Direct Investment on the Economic Development
of ASEAN Economies: A Preliminary Analysis
03/33 Raissa Rossiter
Networks, Collaboration and the Internationalisation of Small and
Medium-Sized Enterprises: An Interdisciplinary Perspective on the
Network Approach Part 1
03/32 Stephanie Hussels, Damian Ward & Ralf Zurbruegg
How Do You Stimulate Demand For Insurance?
03/31 Donal Flynn & Zahid I Hussain
A Qualitative Approach to Investigating the Behavioural Definitions of
the Four-Paradigm Theory of Information Systems Development
03/30 Alexander T Mohr & Simone Klein
Adjustment V. Satisfaction An Analysis of American ExpatriateSpouses in Germany
03/29 David Spicer & Eugene Sadler-Smith
Organisational Learning in Smaller Manufacturing Firms
03/28 Alex Mohr & Markus Kittler
Foreign Partner Assignment Policy & Trust in IJVs
03/27 Avinandan Mukherjee & Rahul Roy
Dynamics of Brand Value Management of Entertainment Products
the Case of a Television Game Show
03/26 Professor Andrew Taylor
Computer-Mediated Knowledge Sharing and Individual User Difference:
An Exploratory Study
03/25 Dr Axle Giroud
TNCs Intra- and Inter-firms' Networks: The Case of the ASEAN Region
03/24 Alexander T Mohr & Jonas F Puck
Exploring the Determinants of the Trust-Control-Relationship in
International Joint Ventures
03/23 Scott R Colwell & Sandra Hogarth-Scott
The Effect of Consumer Perception of Service Provider Opportunism
on Relationship Continuance Behaviour: An Empirical Study in
Financial Services
03/22 Kathryn Watson & Sandra Hogarth-Scott
Understanding the Influence of Constraints to International
Entrepreneurship in Small and Medium-Sized Export Companie
03/21 Dr A M Ahmed & Professor M Zairi
The AEQL Framework Implementation: American Express Case Study
03/20 Dr K J Bomtaia, Professor M Zairi & Dr A M Ahmed
Pennsylvania State University Case Study:
A Benchmarking Exercise in Higher Education
03/19 Alexander T Mohr & Jonas F PuckInter-Sender Role Conflicts, General Manager Satisfaction and Joint
Venture Performance in Indian-German Joint Ventures
03/18 Mike Tayles & Colin Drury
Profiting from Profitability Analysis in UK Companies?
03/17 Dr Naser Al-Omaim, Professor Mohamed Zairi & Dr Abdel
Moneim Ahmed
Generic Framework for TQM Implementation with Saudi Context:
An Empirical Study
03/16 AM Al-Saud, Dr AM Ahmed & Professor KE Woodward
Global Benchmarking of the Thrid Generation Telecommunication
System: Lessons Learned from Sweden Case Study
03/15 Shelley L MacDougall & Richard Pike
Consider Your Options: Changes to Stratetic Value During
Implementation of Advanced Manufacturing Technology
03/14 Myfanwy Trueman & Richard PikeBuilding Product Value by Design. How Strong Accountants/Design
Relationships Can Provide a Long-Term Competitive
03/13 Jiang Liu, Ke Peng & Shiyan Wang
Time Varying Prediction of UK Asset Returns
03/12 A M Ahmed, Professor M Zairi & S A Alwabel
Global Benchmarking for Internet & E-Commerce Applications
03/11 A M Ahmed, Professor M Zairi & Yong Hou
Swot Analysis for Air China Performance and Its Experience with Quality
03/10 Kyoko Fukukawa & Jeremy Moon
A Japanese Model of Corporate Social Responsibility?:
A study of online reporting
03/09 Waleed Al-Shaqha and Mohamed Zairi
The Critical Factors Requested to Implement Pharmaceutical Care in
Saudit Arabian Hospitals: A Qualitative Study
03/08 Shelly MacDougall & Richard Pike
The Elusive Return on Small Business Investment in AMT: Economic
Evaluation During Implementation
03/07 Alexander T Mohr
The Relationship between Inter-firm Adjustment and Performance in
IJVs the Case of German-Chinese Joint Ventures
03/06 Belinda Dewsnap & David Jobber
Re-thinking Marketing Structures in the Fast Moving Consumer Goods
Sector: An Exploratory Study of UK Firms
03/05 Mohamed Zairi & Samir Baidoun
Understanding the Essentials of Total Quality Management:
A Best Practice Approach Part 2
03/04 Deli Yang & Derek Bosworth
Manchester United Versus China: The Red Devils Trademark Problems
in China
03/03 Mohamed Zairi & Samir Baidoun
Understanding the Essentials of Total Quality Management:
A Best Practice Approach Part 1
03/02 Alexander T Mohr
The Relationship Between Trust and Control in International Joint Ventures
(IJVs) An Emprical Analysis of Sino-German Equity Joint Ventures
03/01 Mike Tayles & Colin Drury
Explicating the Design of Cost Systems
2002
02/34 Alexander T Mohr
Exploring the Performance of IJVs A Qualitative and Quantitative
Analysis of the Performance of German-Chinese Joint Ventures in the
Peoples Republic of China
02/33 John M T Balmer & Edmund Gray
Comprehending Corporate Brands
02/32 John M T Balmer
Mixed Up Over Identities
02/31 Zo J Douglas & Zoe J Radnor
Internal Regulatory Practices: Understanding the Cyclical Effects within
the Organisation
02/30 Barbara Myloni, Dr Anne-Wil Harzing & Professor Hafiz Mirza
A Comparative Analysis of HRM Practices in Subsidiaries of MNCs and
Local Companies in Greece
02/29 Igor Filatotchev
Going Public with Good Governance: Board Selection and Share
Ownership in UK IPO Firms
02/28 Axele Giroud
MNEs in Emerging Economies: What Explains Knowledge Transfer to
Local Suppliers
02/27 Niron Hashai
Industry Competitiveness The Role of Regional Sharing of Distance-
Sensitive Inputs (The Israeli Arab Case)
02/26 Niron Hashai
Towards a Theory of MNEs from Small Open Economics Static and
Dynamic Perspectives
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02/25 Christopher Pass
Corporate Governance and The Role of Non-Executive Directors in Large
UK Companies: An Empirical Study
02/24 Deli Yang
The Development of the Intellectual Property in China
02/23 Roger Beach
Operational Factors that Influence the Successful Adoption of InternetTechnology in Manufacturing
02/22 Niron Hashai & Tamar Almor
Small and Medium Sized Multinationals: The Internationalization
Process of Born Global Companies
02/21 M Webster & D M Sugden
A Proposal for a Measurement Scale for Manufacturing Virtuality
02/20 Mary S Klemm & Sarah J Kelsey
Catering for a Minority? Ethnic Groups and the British Travel Industry
02/19 Craig Johnson & David Philip Spicer
The Actio