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Comparable Tendencies

The following sector has it's focus on the ways how the two industries are comparable.

The reason why we chose to compare the telecommunications and insurance sector, were due to similar characteristics regarding; product necessity, consumer behaviour, threat of competitive entries, number of large competitors, threat of substitute products and low supplier power.

In terms of product necessity both products are considered a necessity by consumers. This is clearly shown in table (X), showing that for each danish citizen we have about 1,5 sim cards with including subscriptions (Energistyrelsen, 2015). Furthermore, insurance is in several instances obliged by law, such as fire insurance for mortgaged property, casco insurance for mortgaged vehicles, liability insurance for all vehicles etc., all of which are non-life insurances (IF Skadeforsikring, 2015).

Consumer behaviour:

The consumer loyalty is significantly low. This is why in the non-life insurance sector consumers are usually willing to choose the cheapest deal. This goes the same for the telecom sector. Since there aren't any fees or constrictions to prevent the consumers to switch from one company to another. With the increased use of the Internet people have the possibility to contrast and compare. This is one of the reasons why the tendency to switch in both sectors is rather high.

Since there aren't any substitutes for insurance and some policies are usually legal requirement, some consumers might start practicing ‘self-insurance'. This means that the consumer has a certain amount of money, which is set aside in order to compensate for a loss that might occur in the future. However, in order to obtain a mortgage, having an insurance is mandatory.

As for the telecom sector, there aren't any substitutes for telecom.

Buyer power, for the insurance sector on overall, is assessed as moderate. The reason why it is moderate is because the consumer is constrained by legal frameworks. The telecom sector has a significantly high buyer power.

The consumers in both markets seem to have quite a lot of leverage

Low supplier power: Losing leverage to the consumer - 1,4 million?(Nicklas) change their company every year - wanting to change because of price. Power as in leverage, not physical supply (oranges example).

Both industries shows very price sensitive tendencies. Illustrated through both sectors easy and costless transferring procedures i.e, switching cost, as well as a low unique value effect and high transparency. The cost of switching in both insurance and telco is free of charge, and can be done online or through direct contact with the provider. Online platformication has further increased the price sensitivity, since they have full view of the entire market and price ranges of both industries.

Within every year 1,4 million Danish customers change telco provider, displaying the willingness to change, primarily due to price - whereas in the insurance industry 68% of the market uses price a the main catalyst for choosing insurance provider.

Both markets experience equally high threat of entries and general competitive pressure, with low price companies accessing the market with the intention of causing market disturbance. Examples of this could be telco companies such as Callme, where Henrik Larsen, part of the executive management is quoted saying;

“Every monday morning we would clock in, check our competitors prices, and discuss how we should act accordingly? If Oister dropped in price, should we also go down in price, or add more to our packages…?” (Christiansen et al., 2014)

Likewise companies such as GNFF in the insurance sector, also entered with a business model focused on competitive disturbance. Its short term goal was acquiring 40% market share with an extremely price driven business plan (Nyholm & Sixhøj, 2013). This is in combination with a market already fairly saturated with 3 to 4 very large competitors , differing from 10% to 40% in total market share within their respective industries, equals a very high competitive pressure (source).  

Problem formulation

Write the research question!

“Can the insurance sector experience the same destructive price sensitivity as seen in telco, and could increased competitive disturbance catalyze this tendency?

Furthermore, how would the competitive pressure in the insurance sector affect innovation? “

Methodology

Critically assessing

In the following section, there is outline on how the problem formulation is handled and work with the intension of answering it. In the project, there lies a basic idea that is to compare the two sectors. The basic idea is build upon the new threats in the insurance sector that maybe can interrupt the, market as seen in the telecom where this disturbance has started a price war. The wish is to analyses the sectors and look is the indicators can indicate the same indicators seen in telecom. There will accure a ranges of methods.

Theory

Introduction to the theories

Habermas

The inverted-U

Value theory

How we apply them, and to what gain?

Why those, and why 3 not 2 (WIP)

In what areas of the analysis are these theories relevant?

How are they different, and how do they complement each other

Criticism of the theories

Analysis

Product value and price war

Value of things (WIF)

In both insurance and Telecom, we are dealing with homogenous products. A service, which is only differentiated, by the level of service. The theories of value are divided into the intrinsic theory of value and the subjective theory of value.

The intrinsic theory of value is the theory of objective value, which advocates that the values of the goods or services are an objective judgment. You find the intrinsic value by taking into account the production process of the item and costs required and related to the production of the product.

In the subjective theory of value, an object will only have value when it can satisfy human wants and is limited in supply. The marginalist theory of value is based on this, which advocates that the price of an object is based on the needs and wants of human beings and not of the intrinsic value of the object. Therefore, that means the object is considered to have value only when it is useful to members of society. Its value depends on the ability to the wants of an individual. The perceived value from insurance payments comes from the buyer placing a subjective value on the dollar amount of the insurance benefits. This subjective value of payment is associated with the decision maker's attitude towards financially supporting his/her property. The subjective value varies with the size of the household, wealth and income.

The value of spectrum[1] products, is determined by the service and partly the physical part of the product i.e. antennas and wiring. This is something telecommunications and insurance share, a market in which the product is infinite and homogenous. This means that the transparency level in the market is high, which creates a difficulty in creating value in order to differentiate their service offerings, and in order to attract and keep customers. Marketing leaders are moving towards a dynamic exchange relationship, which involves exchanging skills and services in order to co-create value with the customer. (Prahalad and Ramaswamy, 2004)

Previously value was regarded as the ratio between cost and quality, in this new perspective it is instead the value that is realized when the customer consumes a service. This means that the belief is that how customers experience activities is crucial to their perception of value, rather than it being embedded in goods and services, in sectors like insurance and telecommunications, the service employee has the power to influence the value-creating experience by interacting with the customer. Customer expectations are in many cases not met in relation to creating customer value. One reason for this might be, that managers are not sure what creates value, or what brings value.

In this project, we are not going to concentrate on the traditional perspective of Zeithaml (give for what you get). The reason for this is that it does not take into account the part that the consumers play in the co-creation of value.

Another way to look at the situation is by using the goods-dominant logic. However, it fails to explain the case with YouTube. How a fairly young company has a financial position which is greater than a long time established giants? The answer for this is that it allows the consumers to co-create value.

What Vargo and Lusch propose is that a business hardwired to value creation is adaptive to consumers\' dynamic and individual needs and they do this by learning from and collaborating with consumers. What the company can do is it can offer value propositions. This means that the consumer must participate in creating and determining value. The way he can do this is by means of the consumption process. This means that value will be gained only after the consumer has made his part in making use of the activities that create the service.

The value we add to insurance and telecommunications

The competition and consumer authority committee of Denmark wants to make it easier for consumers to switch their telecommunications and insurance companies. This is done in the belief that competition is inherently good, as it will force interchangeability in the two sectors, and force the providers to offer a better product in order to reduce the loss of customers. This committee performed a study, examining the various reasons for consumers switching service providers, how it affects competition and consumer conditions. Throughout the study the mobile market, represent a market with high mobility, while the insurance represents a market with lower mobility.

Consumers switch mobile and insurance providers in order to save money, annually every third switch their telecommunications provider and nearly a 1/10th switch their insurance company. The wish for a cheaper telecom/insurance solution is the reason to change provider, as of the study conducted by the committee which showed that 44% of Telekom customers, and 68% of insurance customers were willing to switch due to price.

Price advertising in most cases inspire people to switch mobile subscriptions; in the mobile market, the effect of advertising is grandiose, especially customers look for a plan to fits them specifically. In contrast, insurance company advertising much more image-based rather than price advertising, as we might expect from the insurance sector. Consumers are lead to change insurance company due to new life-situation, new car or buying of a house, and a major issue in insurance, is the consumers low involvement in the forming of the insurance. A mobile phone is an integrated part of the consumer's everyday life, whereas insurance does not go into interest or account of your everyday life.  Leading insurance companies also believe the low involvement, to be part of reason to the low mobility in the insurance sector.

Lack of transparency, is probably also one of the main challenges to change provider in other markets. Ex. A consumer compares two insurance companies, but is unable to find the significant characteristics and different in product. Therefore, it is expected that the consumer will find issues in interchangeability.

 

Price Sensitivity

When shortly glancing over the two industries and their economic transformation, e.g., the telco revenue drop (source) combined with a seemingly highly price focused consumer behavior in insurance, where upwards of 68% choose insurance due to price (source). It is likely to assume that both industries experience high levels of price sensitivity. Making it suitable to measure, because of its importance towards describing the price focused consumer behavior.

In the following segment we will cover how price sensitive the two industries actually are, and how price sensitivity can be measured. Furthermore, to what extend these measures follows the tendencies seen in the different sectors. Price sensitivity will help uncover to what extend the price of product, affects price focused consumers' behavior, and how insurance can accommodate said behavior.

Description and Criticism

To determine the price sensitivity, and in turn how price volatile the insurance sector is, several factors needs to be considered (Nagle and Holden, 1995). The tool used for evaluating the price sensitivity is Nagle and Holden's ten factors of price sensitivity, introduced in their book “The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making”. These ten factors affect the industries in different ways, therefore, only those relevant will be highlighted e.g., unique value effect, threat of substitutes, transparency etc. Although these parameters of assessment are considered to be illustrative to the amount of price sensitivity present within the product and industry (Nagle and Holden, 1995: 95-99), they only provide a partial view.

In that relation a calculation of the price elasticity of demand will also be conducted and set in relation to the earlier findings, of which increased validity should be acquired. The price elasticity of demand helps define the relationship between the quantity of demand and is following change in price, i.e., the responsiveness of demand to changes in price. However, there are several drawbacks to the price elasticity of demand formula, e.g., the dataset. Some of these issues are also present in this analysis, and will be covered in section 8.2.4.

Calculations

The data foundation needed to calculate the price elasticity of demand consists of the old price from the start of the timeframe and the new price from the end of the timeframe, in order to calculate the difference between them. Furthermore, figures of the old and the new demand are also needed. Since the latter dataset isn't widely used, we had considerable difficulties with finding them. This resulted in using data in the insurance calculation, which was not entirely representative.

For the demand side, we used units sold, i.e., numbers of insurances sold within that year. However, within the insurance sector this is not available for all their services, most importantly not the entire sectors non-life insurance. Instead we had to use health insurance as a benchmark for our calculations. This data does not cover the entire industry, but shows similar tendencies, as the remaining sector (source).

The formula chosen to calculate the elasticity was the Formula for Price Elasticity of Demand, and consist of P (Price) Q (Demand), categorized as P1 (new price), P2 (old price), Q1 (new demand) and Q2 (old demand) and EP referring to the price elasticity of demand.

E_p=∆Q/∆P  ((P_2+P_1)/2)/((Q_2+Q_1)/2)=(Q_2-Q_1)/(P_2-P_1 )  (P_2+P_1)/(Q_2+Q_1 )

When calculating the Price elasticity of demand for the two industries these were the results:

Telecommunications = 0,95%

Insurance = 3,04%

Telecommunications (WIP)

The telecommunications sector has experienced a very high competitive strain within the last decade, with several large competitors making their entry, and causing competitive disturbance. Nevertheless, the competitiveness present in the telco sector has also resulted in a very preferable situation for the consumers. Since the Telco industry haven't been prone to drastic to radical product innovation, no company has really been able to escape the competition – therefore, remaining fairly neck and neck. Instead turning to price cuts in order to gain the competitive advantage and market shares. This price drop is covered in earlier sections, see section 2.2. fig.1.

With continuous lack of innovation, the price sensitivity for the Telco sector worsened, and is now at a point of almost complete commodification. But what factors contribute to the Telco sectors seemingly high price volatility?

The product itself, whether it being cell coverage for the smartphone or Wifi at home, is a very homogenous product. The service doesn't change performance standards depending on the company, and they all provide similar services with minor adjustment, but with different price tags. The four main competitors, in this industry being Telenor, Telia, TDC and Hi3G, all have their own network of celltowers, differing in size and capacity (depending on the spectrum bought ), but all of them applying similar technology – the same product. Thus, when the products are this homogenous, and not being proactively differentiated by the companies, they will lose the unique value effect, and hence their consumers loyalty i.e., increased price sensitivity.

Further adding to an increase in the industry's price sensitivity is the very low switching cost, combined with an equally high market transparency.

When Danish consumers wish to switch telco carriers, they are not required to pay any fees or premiums like similar markets in other countries e.g., the US (FCC, 2016). However, the industry provides the transfer with all the current information, only using the consumer's social security number and address with Wifi related products. Additionally, the increase in online platformification seen within the later years has heavily increased the market's product transparency. The internet and its following digital platform is becoming continuously more important, as argued in the article “Platforms: Something to Stand On” from the Economist:

“proliferating digital platforms will be at the heart of tomorrow's economy, and even government” (The Economist, 2014)

This development of digital platforming and online economics has had both beneficial as well as negative results for the industry, however primarily positive for the consumer. The telco industry is very aware of the need for digital platforming, and the possibility of using it as a sales channel. Therefore, they add as much information about their offers, discounts, add-ons etc. This has the beneficial tendency of adding revenue through increased sales or by allocating revenue to a more cost lean sales channel . However, also grating the consumer almost today transparency. All the necessary information about products, services, coverage etc., when changing carriers, becomes completely accessible. Even going as far as having websites and services collecting it all to one specific search engine. Thereby, assembling the entire market and its offers only a few clicks away from the consumer. Although, some offers such as churn minimizing products and loyalty programs is still kept away from the public eye, in attempts of maintaining profitability.

Even though several aspects of the Telco sector have price sensitive tendencies, there are also profound aspects helping the industry maintain a stable price sensitiveness. Nagle and Holdens 10 factors contributing to price sensitiveness also argues that aspects such as end-benefit, how much do we gain from buying the product, and the expenditure effect, covering how big an expense the product is for the household income.

Since the Telco industry fought, and to some extend still is fighting, a price war, the prices of their products dropped rapidly in price. Naturally leading to very low prices. Initially in our research period (1996-2015), the prices for Telco products averaged at about 600 (ask Nicklas, not sure), which out of the 15.833,5 kr. Average monthly disposable income  (Disponibel indkomst – familie, dst [bogmærke]), is only 1,82% (287,44/15.833,5 * 100 = 1,82). This has only decreased, whereas the price has now dropped to averaging at 105 kr. out of a monthly disposable income of 28.737,25 kr., thus only demanding 0,35% of the monthly household income per subscription. Combined with the data showing we on average have 1,45 simcards per person (Energistyrelsen, 2015) and an average size of 2,15 people per household (Statistikbanken, 2012), this shows that the total expenditure to Telco within Danish households on average is 327,34kr.,  (100 * 1,45 = 152,25 152,25kr. * 2,15 [antal personer pr husstand] = 311,75 kr.) or 1,14%. So, in turn the expenditure effect, since it consists of such small a percentage, does by no means have a negative correlation to the telco sectors price sensitivity. Furthermore, as covered in the study “Forbrugernes skift af mobil- og forsikringsudbyder (Consumers chaning mobile and insurance provider)” by the Danish Competetition and Consumer Authority it shows that the consumers have a more personal relationship with the product end benefit. The smartphone and its connected services have become a necessary product in our everyday life, therefore, strengthening the value we gain from the End Product and decreasing the price sensitivity.

MORE:

Unique value effect, switching cost, transparency (difficult comparison effect)

LESS:

Expenditure effect, End Benefit

Insurance

Like the Telco industry, the insurance sector has also got several factors adding to a high price sensitivity. However, they haven't reached the same degree of product commodification as Telco. Although, there are considerable indicators, that they potentially could end up in a similar state. Most profoundly is when looking through the Consumer Analysis “Forbrugernes skift af mobil- og forsikringsudbyder (Consumers chaning mobile and insurance provider)” by the Danish Competetition and Consumer Authority 68% of the respondents showed that they chose price as the main catalyst for changing insurance provider.

But how come an industry that serves the purpose of safeguarding our most precious possessions has issues with price oriented customers?

First of there is the expenditure effect. Contradictory to the Telco sector, where the expense of the products in relation to the household income was minimal, the expense to insurance is quite high. This is one of the reasons there is an exceedingly low rate of insurance customers within the young age group of 21-24, where 35% instead turn to “self-insuring” – the cost doesn't outweigh the benefits (forsikring og pension – unge og forsikring [bogmærke]).

Where the average percentage of the households' disposable income for the Telco industry was 1,14% the average expenditure of insurance is 2,84% - almost 3 times greater. This high amount gives the households an increased incentive to find cheaper alternatives within the substitute products, hence resulting in an increase in price sensitivity. Especially when considering the products homogenous factors, as argued for in previous sections (see section 8.1.1.).

Besides the expenditure percentage in relation to household income, the factors affecting the price sensitivity within insurance are similar to the Telco industry. Like Telco the insurance sector has also invested their focus in making online platforms more accessible, for instance by offering discounts when buying online, and give the customer a possibility apply for offers via webpages, and with free transferring also only using the social security number (exactly like Telco). As with Telco this gives the customer almost total transparency of the market's offers and, therefore, full opportunity to choose the cheapest alternative, in turn increasing the products price sensitivity. Although, when comparing with the Telco industry, the insurance sector has a major factor in this regard that differs the sensitivity – the products complexity. Even though the customer has full access to offers from almost the entire market, an average consumer doesn't have the knowledge to fully assess the legal measures within their policy. Combined with the fact that the product act as a safeguard for our belongings, this safety high End-Benefit is an accident struck, this could act as a deterrent when changing providers. This

MORE:

Expenditure effect, switching cost, transparency, substitute

LESS:

End benefit, Difficulty comparing effect (transparency), følelser I forsikring (sikkerhed)

Results

As seen in the earlier paragraphs both sectors have factors increasing their price sensitivity e.g., Expenditure effect, low switching cost, high focus on online platforming (increased product transparency) etc. However, combined with the calculated Price Elasticity of Demand, the picture changes dramatically. Here the insurance sector is over 3 times as elastic as the Telco industry. This itself is quite contradictory to our initial beliefs, that the telco industry is more price elastic than insurance industry. The reason behinds this, is difficult to assess with complete certainty and will (on top of this section) be further analyzed with the view of Jürgen Habermas' theory of communicative in later segments (see 8.4.)

Even though the Price Elasticity of Demand is lower for Telco, the commodification and its consequences has been obvious. Instead the industry has employed measures in order to accommodate these problems. Primarily using innovation.  

Increased service, with the intense competition (transfer cost, transparency, add-ons) telco

Used innovation to value add, that's why they aren't as harshly affected by elasticity

Results from price elasticity formula – why is telco so low (innovation) and why is insurance so high (expenditure effect, system verden [driven by capital and power])

Look into commodification by John Quelch, Theodore Levitt, Peter Drucker

Competitive disturbance

Argue with departure in the above, how this could be the point of no return for insurance (WIP)

Daniel's timeline of both sectors (WIP)

Habarmas View

Use Habermas to explain how the Insurance Sector can avoid becoming “commodified”

The transfer from the life to system world (innovation)

The model of the Inverted U

In this chapter we are trying to investigate the true relationship between product market competition(PMC) and innovation. Philippe Aghion, Nick Bloom, Richard Blundell, Rachel Griffith and Peter Howitt have found evidence that the relationship between competition and innovation has an inverted U shape. To understand and explain what drives this U-shape we used the theoretical literature on innovation by the same authors and data from Denmark statistics, (MADS KILDE), and the four largest companies in the telecom sector annual report to the market share. They produced a model that delivers an inverted U-turn prediction in which competition might increase the incremental profit from innovating, labeled the ”escape-competition effect” but competition may also reduce the possibility for innovation for the companies that are underperforming, labeled the ”schumpeterian effect”. The balance between these two effects changes between low and high levels of competition (nonmonotonic-flow) generating an inverted U relationship, in addition this new theory provides 2 new predictions: first the equilibrium degree of neck-and-neckness among firms should decrease PMC and second, the higher the average degree of neck-and-neckness in an industry the steeper the inverted U-relationship between PMC and innovation.

http://www.ucl.ac.uk/~uctp39a/ABBGH_QJE_2005.pdf

In addition to the Inverted-U, it is mentionable that the inverted U in relation to the project, only is used as a basic model and not the main method which we based all the calculations. The inverted-U´s basic idea that innovation and competition is related to each other, is a multiple opinion in the industry, and is therefore also our basis.  

Goal for calculation

As the following chapters are describing more detailed, the measurement of the competition situation and the innovation are both hard to do. Both has several ways of determine it. Thereof the inverted-U´s evidence is taking as a basic idea of approach to the data and furthermore taking the data and the inverted-U´s basic idea as a judiciously discussion about the innovation and competition level in both industries.

The inverted U´s goal, is to predicts for both the telecom industry and the insurance to see the development in competition, and to assessment if competition is too intense so the innovation in the industries are declining. We urged to see if competition, when too high, are decreasing the innovation and to find out if our consumption about lack of innovation in telecom is real. Furthermore, to see if this is current in the insurance.

Method for calculation

Innovation

The measurement for innovation is complex and there is a lot of literature for measuring the innovation. However, the most common are the patenting activity and the R&D expenditure (703).

The patenting activity are not taking in to an account, because the Insurance and the Telecom industries are service industries, therefore the patenting activity is not relevant.

Instead we chose the Innovation data from Denmark statistic, as they also account the R&D expensive and the implementation of the innovation. The statistics gives a pinpoint of how many business in the industry in percent that there are innovative, and in what specific innovation type.

The data from the statistics focus is to see the innovation in the Danish business. Innovation in this data is express as implementing of new product, productions and working process and marketing and organization. The innovation in the Danish business is an annual calculation of used reassures to research and development, calculated in mio.kr. and as a share of the innovated companies.

Competition

When wanting to measuring the competition situation in a market and with the intention of describing if the competition is not existing or too much several indicators are needed. There isn´t one way of measuring the competition situation in each market. (Department of Competition, 1999).

“It is also hard to get a sufficient comprehensive dataset and it is therefore necessary to supplement the empirical data with a more general knowledge about the market- and competition relationships when the competition situation shall be described.” (Department of competition: 1999 p.8)

When the general though was to use the inverted U, were the x-axis is competition measured in no competition and too much competition, the quoted above describes the struggle of determine the competition situation in the sectors. Thereof, the measurement for the competition situation is going to be with the following indexes: (HHI – index, market concentration, price mobility, plus some second hand qualitative interview (Prize war, suicide idea, the data with the price dropping and price rising plus the revenue drop).   

The interlacing of the measuring of competition thereof shaped more as a debate than as an inset on the inverted U, x-axis

HHI Index

The Herfindahl-Hirschman index (HHI) is a measure of market concentration. It is calculated by squaring the market share of each firm competing in a market, and then summing the resulting numbers, and can range from close to zero to 10,000. The HHI, is therefore calculated by market share of each firm squared by 2 and plus next firm, and so on.

The closer a market is to being a monopoly, the higher the market\'s concentration (and the lower its competition) where there is a firm that has 100% market share, the HHI would equal 10,000 indicating a monopoly situation, and if there were thousands of companies competing , the HHI would be close to zero, indicating nearly perfect competition. We have chosen the HHI index as it provides a more complete picture of industry concentration than does for example the concentration ratio. The HHI uses the market shares of all the firms in the industry, and these market shares are squared in the calculation to place more weight on the larger firms.

The department of justice in US “fields” that a HHI under 1500 is a competitive marketplace here there are no dominant competitors in the market, and it is unconcentrated. HHI from 1500 to 2500 is a moderately concentrated market.

HHI over 2500 is a highly concentrated market, here there is one or more dominant competitors in this market.

(https://www.justice.gov/atr/horizontal-merger-guidelines-08192010#5c 5.3)

Term of concentration is a pinpoint of the market, is it high, then the market can be seen as a monopoly or oligopol. Is the concentration low, then the market sees to be competitive.

http://www.investopedia.com/exam-guide/cfa-level-1/microeconomics/markets-concentration-measures.asp

http://www.investopedia.com/terms/h/hhi.asp

(http://www.dst.dk/da/Statistik/dokumentation/statistikdokumentation/innovationsstatistik)

The four-firm concentration ratio:

The ratio is an indicator of the four largest firms market share of the whole industry. The results are defining the market between have the concentration are in the market. From 0% to 50% considered a low concentration of the market and this can indicate that there is perfectly competition in the industry. Between 50% to 80 % there is medium concentration and can indicate that the industry is an oligopoly. the ranges between 80% to 100% considers a high concentration in the market and the level indicates a oligopoly.

http://www.investopedia.com/terms/c/concentrationratio.asp

Price-change :

The last indicator to look at when wanting to mesurer competition, price change is first, relevant as we see a price war in telecom, furthermore price change can be seen as an indicator if the firms are in competition or not. If the is frequence of a change in prices over time this could be a indicator for competition. If the prize is constant then it could be a indicator for lack of competition. (Department of competition: 1999)

Assessment of data

The data can furthermore be criticized, the market share is only being calculated from 4 firms, where HHI-index recommends 50 firms. Here there shall be taking in mind that in the telecom business there is only missing data from 8 remained % of the total market, in insurance there is only calculated with 50 % of the market.

In addition to the data for the price the telecom sector has an restricting from the government, that it needs to be the cheapest. (Kilde: tidligere, ang lovgivning.

Furthermore the innovation is still very debatable how to measure, and there is many factors of seeing how a business is innovative, and how many money they use on it and if it is effective.

Results

Competition.

HHI, price change in the given year,

Years Telecom - HHI Telecom – Price change in relation to last year (%) Telecom - price change in internet in relation to last year (%) Telecom -

Concentration ratio Insurance - HHI Insurance – Price change in relation to last year (%) Insurance – concentration ratio

2009 4529 -1,72% * * 625 7,19% *

2010 3605 -2,58% -11,28% * 902 5,16% *

2011 3594 -0,63% -6,79% 94% 1422 4,06% 64%

2012 4695 -4,8% -12,53% 98% 836 5,93% 55,1%

2013 3278 -2,18% -11,43% 96% 941 3,54% 59,4%

2014 2857 -0,83% -1,89% 85% 714 0,55% 51,1%

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