CHAPTER 6: Conclusion and policy inferences
6.3 Limitations of the study
Firstly, and above all, the main limitation stems from the fact that it is not possible to assess what would have happened in the absence of the EMU. Secondly, it is not clear whether market concentration is the cause of high profits; as high levels of market concentration and profits may be the result of superior performance by a few efficient and effective firms.185 As stated by Phillips (1976, p. 248), “…better theory, better data, and above all, better econometrics are needed before
policy can be based on anything other than in-depth institutional studies of particular markets”.
185 This is discussed at length in survey of empirical literature undertaken by Phillips (1986); where the superior skill, foresight and industry of a firm
APPENDIX A1: Data sources and variable construction
A1.1 Country, zone and industry
In order to assess the impact of the introduction of the euro on participating countries, three geographic zones have been created as follows: euro, continental, and the UK. The countries that are included in each of the zones are listed in Table A1-1; which also summarises the year of entry into the EU and the EMU. The euro-zone includes eleven countries that were participants in the launch of the euro in 1999 [i.e. founding members]. Furthermore, there are sixteen countries that are classified under the continental-zone as they are a member of the EU and not the EMU; or they joined the currency union after 1999.186 Finally, the UK has been separated into the UK-zone; given the economic maturity of the country in contrast to those classified in the continental-zone.
Table A1-1: Summary of countries
Year of entry Year of entry
Country EU EMU Zone Country EU EMU Zone
Austria 1995 1999 Euro Latvia 2004 2014 Continental Belgium 1952 1999 Euro Lithuania 2004 Continental Bulgaria 2007 Continental Luxembourg 1952 1999 Euro Cyprus 2004 2008 Continental Malta 2004 2008 Continental Czech Republic 2004 Continental Netherlands 1952 1999 Euro Denmark 1973 Continental Poland 2004 Continental Estonia 2004 2011 Continental Portugalb 1986 1999 Euro Finland 1995 1999 Euro Romania 2007 Continental Francea 1952 1999 Euro Slovakia 2004 2009 Continental Germanya 1952 1999 Euro Slovenia 2004 2007 Continental
Greeceb 1981 2001 Continental Spainb 1986 1999 Euro Hungary 2004 Continental Sweden 1995 Continental Irelandb 1973 1999 Euro Switzerland Continental Italyab 1952 1999 Euro United Kingdoma 1973 UK
a
Germany, France, Italy and the UK are commonly referred to as the Big 4 b
Portugal, Ireland, Italy, Greece and Spain are typically referred to as the PIIGS nations Source: the author
The PIIGS nations are an acronym used to refer to five nations, which were considered weaker economically following the financial crisis: Portugal, Italy, Ireland, Greece and Spain – as notated in Table A1-1.187 Germany, France, Italy and the UK are often referred to as the ‘Big 4’ due to the size of their respective economies.
186
It is important to note that Cyprus, Malta, Slovakia, Slovenia and Estonia joined the euro after 2007. Furthermore, Switzerland has been included given the close geographical proximity to those countries participating in the currency union.
187 This is due to the PIIGS nations being unable to employ independent monetary policy; in order assist in minimising the economic downturn that
All firms in the OSIRIS dataset are allocated a SIC code. A SIC code is a method used to categorise industries and services. The code is a number that corresponds to a defined industry type. The SIC code can be used to find specific business types and retrieve industry and firm statistics. In the late 1930’s, the Central Statistical Board of the United States created the SIC code structure; which was last reviewed and revised in 1987.
The SIC code system groups all forms of industry and services into ten broad divisions (i.e. level one) as shown in Table A1-2. Each of these divisions is subdivided into major groups, industry groups and specific industry types. For example, ‘silver ores’ is categorised under the industry group ‘gold and silver ores’, which falls under the ‘metal mining’ major group, which in turn is classified under the ‘mining’ division.
Table A1-2: Summary of standard industrial classification (SIC) codes
Two-digit SIC Three-digit SIC Four digit SIC
Major group (Level 2) Industry group (Level 3) Industry type (Level 4) D iv is ion ( Lev el 1) : O ne di gi t S IC
A Agriculture, Forestry and Fishing 5 20 27
B Mining 4 20 28
C Construction 3 13 21
D Manufacturing 20 132 381 E Transportation, Communications,
Electric, Gas and Sanitary Services 10 34 58 F Wholesale Trade 2 18 63
G Retail Trade 8 34 53
H Finance, Insurance and Real Estate 7 28 41
I Services 14 53 115
J Public Administration 5 6 8
Total 78 358 795
Source: the author
The SIC code consists of a four-digit number that is assigned to each unique industry or service type. First, the SIC system assigns a two-digit number (01 to 99) to each major industry group (i.e. level two). The system then assigns two additional digits to each industry group and type [i.e. level three]. For example, ‘silver ores’ is classified under SIC code 1044. The major group determines the first two digits (10), the ‘gold and silver ores’ industry group determines the third digit (4) and the specific industry type determines the last digit (4).
A1.2 Market definition
The market is defined by the geographical location and industry classification of each firm. The country in which a firm is located defines the geographical location. There are two geographical locations allocated to each firm. The first is the country where the firm is located; the second is the zone [that is assigned to each country], as defined by Table A1-1. There are four industry codes [or sectors] assigned to each firm. The industry sectors are defined by the SIC code allocated to a firm, as summarised by Table A1-2 in Appendix A1.1. Hence, the SIC code assigned to each firm are a division (one-digit SIC), major group (two-digit SIC), industry group (three-digit SIC), and industry type (four-digit SIC); and is listed in Table A1-3 [in Appendix A1.3].
To summarise, this means that there are two geographical sectors (locations) and four industry sectors (classifications) that are assigned to each firm. Hence, by combining each geographical and industry sector, there are eight markets that have been defined as follows:
1) Country (by division): one-digit SIC 2) Country (by major group): two-digit SIC 3) Country (by industry group): three-digit SIC 4) Country (by industry type): four-digit SIC 5) Zone (by division): one-digit SIC
6) Zone (by major group): two-digit SIC 7) Zone (by industry group): three-digit SIC 8) Zone (by industry type): four-digit SIC
A1.3 Time period and data sources
The variables to be included in the models estimated in Chapters 4 and 6 are summarised under Table A1-3. The variables have been separated into three groups as follows: firm (i), industry (j), and country (k). The time frame encompasses twenty-one years of annualised data from 1987 to 2007. Furthermore, the dataset has been classified into three time-periods as follows:
Pre-1992: 1987 to 1991 (five years);
Single-market: 1992 to 1998 (seven years); and Euro-era: 1999 to 2007 (nine years).
Table A1-3: Summary of variables
Variable name Variable description Measure Data source
FIRM (i) VARIABLES
Standard Industry Classification (SIC)
SIC_DIVISION Division (Level 1): one -digit SIC Market definition OSIRIS SIC_MAJOR_GRP Major Group (Level 2): two-digit SIC Market definition OSIRIS SIC_INDUSTRY_GRP Industry Group (Level 3): three-digit SIC Market definition OSIRIS SIC_INDUSTRY_TYPE Industry Type (Level 4): four-digit SIC Market definition OSIRIS
Income Statement and Balance Sheet
SALES Operating revenue/turnover Variable input OSIRIS
PROFIT (Absolute) profit Profitability Constructed variable
ROS Return on sales [i.e. profit margin] Profitability OSIRIS ROE Return on ordinary shareholders’ equity Profitability OSIRIS ROA Return on total assets Profitability OSIRIS ASSETS (Absolute) assets Initial Capital
Requirements
Constructed variable
ASSET_SALES_RATIO Assets-to-sales ratio Initial Capital Requirements
Constructed variable
CURRENT_RATIO Current ratio Liquidity OSIRIS SOLVENCY_RATIO Solvency ratio Solvency OSIRIS
INDUSTRY (j) VARIABLES Herfindahl-Hirschman index
HHI_1DIGIT_SIC_COUNTRY HHI by country (by division) Market structure Constructed variable
HHI_2DIGIT_SIC_COUNTRY HHI by country (by major group) Market structure Constructed variable
HHI_3DIGIT_SIC_COUNTRY HHI by country (by industry group) Market structure Constructed variable
HHI_4DIGIT_SIC_COUNTRY HHI by country (by industry type) Market structure Constructed variable
HHI_1DIGIT_SIC_ZONE HHI by zone (by division) Market structure Constructed variable
HHI_2DIGIT_SIC_ ZONE HHI by zone (by major group) Market structure Constructed variable
HHI_3DIGIT_SIC_ ZONE HHI by zone (by industry group) Market structure Constructed variable
HHI_4DIGIT_SIC_ ZONE HHI by zone (by industry type) Market structure Constructed variable Market share
MSHARE_1DIGIT_SIC_COUNTRY Market share by country (by division) Market structure Constructed variable
MSHARE_2DIGIT_SIC_COUNTRY Market share by country (by major group)
Market structure Constructed variable
MSHARE_3DIGIT_ SIC_COUNTRY Market share by country (by industry group)
Market structure Constructed variable
MSHARE_4DIGIT_ SIC_COUNTRY Market share by country (by industry type)
Market structure Constructed variable
MSHARE_1DIGIT_ SIC_ZONE Market share by zone (by division) Market structure Constructed variable
MSHARE_2DIGIT_ SIC_ZONE Market share by zone (by major group) Market structure Constructed variable
MSHARE_3DIGIT_ SIC_ZONE Market share by zone (by industry group)
Market structure Constructed variable
MSHARE_4DIGIT_ SIC_ZONE Market share by zone (by industry type) Market structure Constructed variable Industry Growth
GROWTH_4DIGIT_SIC_COUNTRY Industry growth by country (by industry type)
Market demand Constructed variable
Table A1-3: Summary of variables
Variable name Variable description Measure Data source
MES_50_PERCENTILE MES at the 50th percentile Economies of scale Constructed variable
MES_75_PERCENTILE MES at the 75th percentile Economies of scale Constructed variable COUNTRY (k) VARIABLES
EU_MEMBER Member of the European Union (EU) Dummy variable Yes = 1, No = 0 EURO_MEMBER Member of the European Monetary
Union (EMU)
Dummy variable Yes = 1, No = 0
GEOGRAPHIC_ZONE Continental, Euro or UK Market definition Constructed variable
BOND_YIELD Government bond yield Sustainability Datastream CPI Consumer price index Stability Datastream FDI Foreign direct investment (FDI) Sustainability World bank GDP Gross domestic product (GDP) Size and Wealth World bank GDP_PER_CAPITA GDP per capita Wealth (of the wallet) World bank GDS Gross domestic savings Confidence World bank GFCF Gross fixed capital formation (GFCF) Sustainability World bank HFCE Household final consumption
expenditure (HFCE)
Confidence World bank
MARKET_CAP Market capitalisation Sustainability World bank PORTFOLIO_EQUITY Portfolio equity Sustainability World bank PORTFOLIO_INVEST Portfolio investment Sustainability World bank POPULATION Population Size World bank TAX_REVENUE Tax revenue Wealth World bank TRADE Trade Protectionism World bank UNEMPLOYMENT Unemployment rate Health (general) World bank
Source: the author
A1.4 Definition of variables
The total operating revenue/turnover (SALES variable) of a firm is the sum of net sales and other revenues; and has been extracted by firm (and by year) from OSIRIS. The Herfindahl-Hirschman index variables are constructed using the SALES variable as per Equation (3.4–A) – refer to Section 4.3 for a detailed definition.
The SALES variable is also used to calculate the market share and industry growth assigned to each firm. The MARKET SHARE variable is defined as the sales of the firm divided by the total sales of the specified market as per Equation (3.3–A). The specified market is grouped into each of the eight market definitions used to calculate the HHI. Whereas the INDUSTRY GROWTH variable is only calculated at the four-digit SIC at the country level using the SALES variable as follows: [current year sales] less [previous year sales], and then divided by [previous year sales].
The minimum efficient scale variables are constructed using the MARKET_SHARE variable calculated for each firm. The 50th percentile of market share is then estimated for each four-digit SIC code within each country; and allocated to each firm (MES_50_PERCENTILE variable). The 75th percentile of market share is also estimated for each four-digit SIC code within each country; and allocated to each firm (MES_75_PERCENTILE variable). This is consistent with the empirical literature discussed in Section 4.2.
There are three measures of profitability that have been extracted by firm (and by year) from OSIRIS as follows: return on sales, return on total assets and return on ordinary shareholders’ equity. Dividing the profit before tax by the operating revenue, and then multiplying by one hundred calculates the profit margin (ROS variable). The return on total assets (ROA variable) is calculated by the profit or loss of a firm for a specified year divided by the firm’s total assets in the same year; and then multiplying it by one hundred. The return on ordinary shareholders’ equity (ROE variable) is calculated by dividing the profit or loss of a firm for a specified year divided by the firm’s shareholders funds in the same year; then multiplying it by one hundred. The fourth profitability measure: Absolute profit (PROFIT variable) has been constructed by multiplying the
ROS by the SALES variable.
As shown in Table A1-4, ROA is highly positively correlated with both ROE and ROS – which is consistent with Gale and Branch (1982). The reason for this is because in order to calculate the
ROA, ROE and ROS; the profit of a firm is included in the numerator of all three equations as
defined in Section 5.2.1. However, interestingly ROE and ROS are less correlated than: (1) ROE and ROA, and (2) ROA and ROS.
Initial capital requirements (i.e. high start-up costs) will deter firms from initial market entry. Most of these costs are sunk costs, which cannot be recovered if a firm exits a market.188 Initial start-up costs are commonly proxied using the asset-to-sales ratio of a firm (for example, see Chou 1988). The asset-to-sales ratio is calculated by dividing total assets by sales revenues. The asset-to-sales ratio is often used to compare the value of a firms assets relative to the amount of revenues a firm can generate using their assets. The numerator of the assets to sales formula, total assets, is averaged over the time period that is being evaluated; and can be located on a firm’s balance sheet. The denominator, sales revenues, is located on a firms’ income statement. It is important to note
188
that the asset-to-sales ratio does not look at a firm's net income (or profit). It only looks at sales, which may or may not relate to a firm's actual profit.
Absolute assets (ASSETS variable) have been constructed by dividing return on assets (ROA) by the absolute profit (PROFIT) variable. Furthermore, the asset-to-sales ratio (ASSET_TO_SALES
variable) has been constructed by dividing absolute assets (ASSETS) by the SALES variable. This is
consistent with the methodology discussed in Chapters 4 and 6.189
The current ratio (CURRENT_RATIO variable) is a measure of liquidity and has been extracted by firm (and by year) from OSIRIS.190 The current ratio is calculated by dividing the current assets by the current liabilities. An increase in the current ratio of a firm is likely to have an inverse relationship with competition, given that extraordinary cash is being retained on the balance sheet; rather than passing this cash onto consumers as a cost saving. This may infer that anti-competitive behaviour exists, as firms do not feel compelled to reduce their profit margin due to a lack of competition; discussed extensively in Chapter 5.
The solvency ratio (SOLVENCY_RATIO variable) is a measure of long-term liquidity; and has been extracted by firm (and by year) from OSIRIS.191 Dividing the shareholder funds by the total assets, and multiplying by one hundred calculates the solvency ratio. Much like the current ratio of a firm, the solvency ratio is likely to have an inverse relationship with competition. Hence, the owners may be retaining extraordinary cash rather than reducing the price of the goods or services offered. Ultimately this suggests the presence of anti-competitive behaviour.
On the other hand, if a firm becomes insolvent, this removes the firm from the market as a competitor; which means that there is a larger piece of the pie to be shared amongst the remaining market participants. Therefore the remaining competitors may potentially increase prices in the market given that there are less cost pressures to keep product costs down. Ultimately, this anti- competitive behaviour will reduce competition and increase market concentration.
Two dummy variables have been created which captures a country’s membership in the EU (EU_MEMBER) and the EMU (EURO_MEMBER). The dummy variables are used as a proxy to
189
(1969) and (2009) use ‘absolute’ assets as a proxy for initial capital requirements when modelling market concentration. Alternatively, (1999) uses capital intensity as an explanatory variable (i.e. assets divided by the number of employees).
190 Liquidity is the ability of a firm to meet its immediate financial obligations 191
identify whether any costs or benefits are currency union specific or alternatively, market related. Hence, each firm is assigned these two dummy variables based on the country in which the firm is geographically located and grouped under the country (j) variables. Therefore, if the country in which the firm is geographically located (for a specified year) is a member of the EU, the
EU_MEMBER variable is assigned a value of one; alternatively a value of zero is allocated.
Furthermore, if the country in which the firm is geographically located (for a specified year) is a member of the EMU, the EURO_MEMBER variable is assigned a value of one; alternatively a value zero is allocated.
The consumer price index (CPI variable) dataset has been extracted from DataStream for all countries defined in Table A1-1. The dataset has been manually adjusted to ensure that the index is equal to 100 in the year 2000 for all countries. The consumer price index (CPI) is a measure that examines the weighted average of prices of a basket of goods and services (i.e. transportation, food and medical care) that households acquire, use or pay for consumption.192 The CPI is considered a social and economic indicator; that is constructed to measure changes over time in the general level of consumer prices. A core responsibility of the European Central Bank (ECB) is price stability. According to the ECB, this means that annual price increases measured by the harmonised index of consumer prices (HICP), of less than [but close to] 2 per cent is required over the medium term.
Foreign direct investment (FDI variable) is the net inflows (new investment inflows minus disinvestment) in the reporting economy from foreign investors; which is consequently divided by GDP. The net inflows are the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital: as shown in the balance of payments. Furthermore, the FDI variable captures the net inflows of investment required to acquire a lasting management interest: 10 percent or more of voting stock in an enterprise operating in an economy (other than that of the investor). Gross domestic product (GDP) is typically used to measure the size and wealth of an economy.193 GDP is a measure of economic activity for a country as a whole; and refers to the market value of all final goods and services produced within a country in a given time period. Furthermore, GDP is considered the most comprehensive measure of the economic health of a nation.
Gross domestic product (GDP variable) at purchaser's prices is the sum of gross value added by all resident producers in the economy; plus any product taxes, and minus any subsidies not included in
192 The primary purpose of the CPI is to measure: (a) changes in the purchasing power of money incomes, (b) changes in living standards (i.e. the cost
of living), and (c) price inflation experienced by households.
193
the value of the products. GDP is calculated without making deductions for depreciation of fabricated assets (or for depletion and degradation of natural resources). The data extracted from the World Bank is in constant 2000 US dollars, which has been converted from domestic currencies using 2000 official exchange rates. Furthermore, GDP has been adjusted for CPI.
GDP per capita measures the wealth of the wallet and therefore is also a proxy for country (economies of scale) size. GDP per capita (GDP_PER_CAPITA variable) is gross domestic product divided by mid-year population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. Furthermore, GDP is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. The data extracted from the World Bank is in constant 2000 US dollars. GDP per capita is not a measurement of the standard of