Cost efficiency

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The working papers are produced by the Bradford University School of Management and are to be circulated for

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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

[email protected]

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,

3

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

4


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

5


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.

6


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

7


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).

8



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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

9


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


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.

10


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.

11


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.


11/26

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

12


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


12/26

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

13


TABLE 4: ALLOCATIVE AND SCALE EFFICIENCY ESTIMATES FOR THE YEARS 1991 TO 2002

UNDER CRS AND VRS


13/26

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.

14


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


14/26

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


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

15



15/26

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

16



16/26

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.

17



17/2618


APPENDICES

1. DESCRIPTIVE STATISTICS: ENVIRONMENTAL VARIABLES FOR TOBIT REGRESSION, N=2288, 1995-2002


18/2619


2.RESULTSOFTOBITREGRESSIO

NI

NE

FFICIENCY

SCORES


19/26

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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


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


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

23



23/26

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

Documents

Cost efficiency