OECD Technical Workshop
on Trade Barrier Assessment Methodology
Paris, 12 December 2008
How large are competitive pressures in
services markets? – Estimation of mark-ups
for selected OECD countries
by
Margit Molnar and Novella Bottini
OECD Trade and Agriculture Directorate
TABLE OF CONTENTS
Executive summary .........................................................................................................................................4
Introduction .....................................................................................................................................................6
New estimates using firm-level data ...............................................................................................................6
How are the estimates obtained? ..............................................................................................................7
Data ..........................................................................................................................................................8
Choices of estimation and specification techniques ...............................................................................10
How large are the estimates and how do they compare across countries and sectors? ..........................11
How do mark-ups relate to regulations? ........................................................................................................15
What have we learnt and what else could be done? ......................................................................................17
References .....................................................................................................................................................18
Tables
Table 1. Estimated mark-ups for services industries in Austria, 1993-2006 ................................................ 19
Table 2. Estimated mark-ups for services industries in Belgium, 1993-2006 .............................................. 20
Table 3. Estimated mark-ups for services industries in Switzerland, 1993-2006......................................... 21
Table 4. Estimated mark-ups for services industries in the Czech Republic, 1993-2006 ............................ 22
Table 5. Estimated mark-ups for services industries in Germany, 1993-2006 ............................................. 23
Table 6. Estimated mark-ups for services industries in Denmark, 1993-2006 ............................................. 24
Table 7. Estimated mark-ups for services industries in Spain, 1993-2006................................................... 25
Table 8. Estimated mark-ups for services industries in Finland, 1993-2006 ............................................... 26
Table 9. Estimated mark-ups for services industries in France, 1993-2006 ................................................. 27
Table 10. Estimated mark-ups for services industries in the United Kingdom, 1993-2006 ......................... 28
Table 11. Estimated mark-ups for services industries in Greece, 1993-2006 .............................................. 29
Table 12. Estimated mark-ups for services industries in Hungary, 1993-2006 ............................................ 30
Table 13. Estimated mark-ups for services industries in Iceland, 1993-2006 .............................................. 31
Table 14. Estimated mark-ups for services industries in Italy, 1993-2006 .................................................. 32
Table 15. Estimated mark-ups for services industries in the Netherlands, 1993-2006 ................................ 33
Table 16. Estimated mark-ups for services industries in Norway, 1993-2006 ............................................. 34
Table 17. Estimated mark-ups for services industries in Poland, 1993-2006............................................... 35
Table 18. Estimated mark-ups for services industries in Portugal, 1993-2006 ............................................ 36
Table 19. Estimated mark-ups for services industries in the Slovak Republic, 1993-2006 ......................... 37
Table 20. Estimated mark-ups for services industries in Sweden, 1993-2006 ............................................. 38
Figures
Figure 1. Mark-ups in computer services are relatively low in most countries ............................................ 12
Figure 2. Mark-ups in the construction sector are relatively low ................................................................. 13
Figure 3. Mark-ups in architecture and engineering services are higher ...................................................... 14
Figure 4. Newly estimated mark-ups are comparable to earlier estimates ................................................... 15
2
Figure 5. In some countries with strict restriction on FDI, mark-ups are higher in construction services,
2006............................................................................................................................................... 15
Figure 6. Countries with higher mark-ups tend to be more restrictive towards FDI in telecommunications
services, 2006 ................................................................................................................................ 16
Figure 7. Higher mark-ups are associated with higher PMR in the architectural and engineering services
sector, 2007 ................................................................................................................................... 16
3
HOW LARGE ARE COMPETITIVE PRESSURES IN SERVICES MARKETS? – ESTIMATION
OF MARK-UPS FOR SELECTED OECD COUNTRIES
Margit Molnar and Novella Bottini**
Executive summary
One way to infer the relative restrictiveness of economic regulation in different markets is to compare
competitive pressures prevailing in those markets. Mark-ups can provide valuable information on
competitive pressures in various sectors of the economy, reflecting pressures stemming from rules of
conduct imposed by regulators as well as those arising from such factors as increasing consumer demands
in terms of price and quality. Trade and FDI are also sources of such pressure. In particular, FDI can be
important is very often it is the only source of competitive pressure. Although mark-ups are only an
imperfect measure of competition, the information they provide on market conditions can be useful for
inferring trade restrictiveness as they can at a later stage be decomposed into the major sources driving
mark-ups in various sectors.
Developments in the theoretical and empirical literature over the past decades suggest a departure
from the standard assumptions of neoclassical production theory with respect to perfect competition and
constant returns to scale. This departure allows for the assumption of monopolistic firms charging markups over marginal costs and opens up new avenues for the estimation of such mark-ups. Following Roeger
(1995), the difference between the primal and the dual Solow residuals is explained as a result of imperfect
competition and by subtracting the two residuals from each other, the unobservable productivity term
cancels out. This way, the price-cost margins can be estimated consistently by standard econometric
techniques, though the results obtained are mark-ups over average cost and not marginal cost. The
assumption of constant returns to scale may, however, be too restrictive as not all sectors in all countries
may exhibit constant returns to scale. Following Dobrinsky et al. (2004), this assumption is relaxed and
mark-ups are re-estimated for the sectors and countries for which the hypothesis of constant returns to
scale was rejected.
The novelty of this paper is that it (i) allows for non-constant returns to scale when estimating markups for a number of OECD countries, (ii) jointly estimates mark-ups for all sectors and in all countries, (iii)
estimates mark-ups for services sectors at a conveniently detailed level of disaggregation and (iv) uses
different estimates of input coefficients of the production function that feed into the estimation function of
mark-ups to check for robustness of results. The estimation is done for the period 1993-2006 and is
confined to European Members of the OECD because the underlying database for the analysis (Amadeus)
only covers Europe.
In general, the estimated mark-ups are higher for professional services, real estate, renting and
utilities, while they tended to be substantially lower for construction, computer services, retail and
wholesale trade and catering. From the limited number of estimates for the telecommunications sector it
can be inferred that mark-ups in this sector are somewhere in between these two ends of the spectrum.
There is also large variation across countries in terms of the sizes of the estimated mark-ups. Competitive
pressures according to these mark-ups should be large in the United Kingdom and most Scandinavian
countries, and relatively small in Central European countries, Sweden and Italy. Spain, Germany and
France are in between these two groups.

Corresponding author, Senior Economist, Trade and Agriculture Directorate, OECD. T: +33 1 4524 8949. Email: [email protected].
**
Consultant, Trade and Agriculture Directorate, OECD at the time of writing the paper.
4
To allow for comparison across a larger number of OECD countries, the analyses could be extended
to non-European Members using the Orbis database. The methodology applied could also be further
refined, for example, by using the Levinsohn-Petrin method to obtain the input coefficients of the
production function. With such improvements, these mark-up estimates could better serve as inputs for use
in a top-down approach to assessment of service trade restrictiveness. Also, in future analysis, competitive
pressures stemming from regulation could be delineated from other pressures stemming from other sources
using the newly estimated mark-ups.
5
Introduction
One way to infer the relative restrictiveness of economic regulation in different markets is to compare
competitive pressures prevailing in those markets. Mark-ups can provide valuable information on
competitive pressures in various sectors of the economy, reflecting pressures stemming from rules of
conduct imposed by regulators as well as those arising from such factors as increasing consumer demands
in terms of price and quality. Trade and FDI are also sources of such pressure. In particular, FDI can be
important is very often it is the only source of competitive pressure. Although mark-ups are only an
imperfect measure of competition, the information they provide on market conditions can be useful for
inferring trade restrictiveness as they can at a later stage be decomposed into the major sources driving
mark-ups in various sectors.
Mark-ups have some analytical advantages over approaches to assessment of competitive pressures
that rely on concentration indices such as the Herfindahl-Hirsch Index. These indices may be misleading in
that higher market shares are not necessarily associated with lower competitive pressure; by the same
token, in fragmented markets with numerous small players there is not necessarily higher competitive
pressure. Mark-ups, in turn, assess the effect of a number of sources of competitive pressure that may not
be related to market structures.
Mark-up estimation belongs to the top-down approaches for assessment of services trade
restrictiveness. The mark-up approach provides information on the overall competitive pressure in a
market, but does not allow for the identification of particular regulatory measures that drive these
competitive pressures. Therefore, it may be appropriate to use mark-ups in combination with regulatory
indices to get a fuller picture of the impact of regulation on services trade.
In lack of available recent estimates of mark-ups in services industries for OECD countries, this paper
proposes new estimates based on micro-data. This allows for aggregation at a detailed level so that
estimates for, for instance, different professional services can be obtained separately. An estimation at a
disaggregated level also allows for testing (i) whether mark-ups are higher in services where customerspecific products prevail and markets are segmented such as legal services, (ii) whether mark-ups are lower
in services that are more traded such as computer services or business consultancy (iii) whether mark-ups
are higher in services that require more human capital input such as engineering and (iv) whether mark-ups
are higher in network industries owing to the large sunk and fixed costs these industries have to assume.
The paper is structured as follows: after setting the scene by highlighting the need to estimate markups using firm-level data, the estimation method is described along with the data used; then, the estimates
are compared across countries and sectors and finally their relationship with regulatory indicators is
examined. A summary of the findings and possible further steps conclude the paper.
New estimates using firm-level data
There is an abundant literature on estimating mark-ups, but most studies are constrained to a
particular country and to manufacturing industries. Very few studies cover several countries, let alone
focus on OECD countries and include services industries. The study by Høj et al. (2007) is one of the few
exceptions, estimating mark-ups using industry-level data for 19 OECD countries1 over 1975-2002. The
coverage of services in that study, however, is limited to just 6 industries2 and it does not include
1.
Austria, Belgium, Canada, Germany, Denmark, Spain, Finland, France, UK, Italy, Japan, Korea, Luxembourg,
Netherlands, Norway, Sweden and the United States.
2.
Electricity, gas and water supply; wholesale and retail trade, repairs; transport and storage; post and
telecommunications, financial intermediation and business services.
6
construction. In addition, the authors lump telecommunications with posts and provide only a single
estimate for all business services.3
The use of firm-level data in the analysis that follows allows for aggregation of sectors depending on
the purpose of analysis and also for the exploitation of information in a manner that is not possible in the
case of industry-level data. Also, using firm level data avoids measurement problems related to input and
output measures at the sectoral level. At the same time, there are also drawbacks to using firm-level data,
in particular the limited length of available time series. Also, due to constraints in the data drawn from the
Amadeus database (the main data source for the present study), mark-ups can only be estimated for
countries and sectors where there are a sufficient number of firms. In addition, dealing with micro-data
implies much higher work intensity in the estimation of mark-ups and constrains the choices of estimation
methods and software used.
How are the estimates obtained?
Developments in the theoretical and empirical literature over the past decades suggest a departure
from the standard assumptions of the neoclassical production theory in terms of perfect competition and
constant returns to scale. This departure allows for the assumption of monopolistic firms charging markups over marginal costs and opens up new avenues for the estimation of such mark-ups. Here the Roeger
(1995) method is applied, which has recently been widely used owing to its simplicity.
Roeger (1995) explains the difference between the primal and the dual Solow residuals as a result of
imperfect competition and his method has the beauty of simplicity relative to the method pioneered by Hall
(1988), overcoming the need to use variables in volumes and to apply instrumental variables technique.
Roeger (1995) also exploits the cancelling out of the unobservable productivity term (present in both
residuals) when subtracting the two Solow residuals from each other. This way, the price-cost margins can
be estimated consistently by standard econometric techniques. A drawback of this method is that the
results obtained this way are mark-ups over average cost and not marginal cost.
The production technology is assumed to be defined by the neoclassical production function:
(1)
where Y is output, A is multifactor productivity growth, there are three inputs: N is labour, M is
intermediate inputs and K is capital and F(.) is a homogenous function of degree lambda (the degree of
returns to scale). The firm and year subscripts are subtracted for the sake of simplicity. After logdifferentiation4 and re-arranging:
(2)
where
is the primal Solow residual, the lower case indicates log-differentiation,
revenue share of factor i and B is the Lerner index, which is closely related to the mark-up µ:
is the
3.
Due to the limited availability of data, Høj et al. (2007) only estimate mark-ups for all the 6 services sectors
for 8 countries: Austria, Germany, Denmark, Finland, France, Netherlands, Norway and the United States. No
services mark-ups are available from the study for Spain and the number of services sectors for which markups are estimated varies between 1 and 5 for the remaining countries. Owing to insufficient length of time
series, mark-ups are not estimated for any Central European country.
4.
Through differentiation, the growth rate of output can be related to the growth rates of inputs, i.e. capital,
labour and materials.
7
(3)
The dual, or priced-based Solow residual is derived by using the cost-function associated with the
production function in equation (1).
(4)
where w is the growth rate of wages,
is of material prices, r is of the rental price of capital and p is
of output. By subtracting (4) from (2) and adding an error term, B can be estimated as Roeger (1995)
showed. As the unobservable productivity term, a cancels out with this subtraction, this equation is
relatively easy to estimate.
Oliveira Martins et al. (1996) show that the equation to estimate the mark-up can also be derived from
the direct definition of the mark-up over average cost:
(5)
where AC is average cost, P, W,
and R are the prices of output, labour, intermediate inputs and capital,
respectively, whereas λ is an index of returns to scale (i.e. average costs over marginal costs) and µ is the
mark-up.
After differentiation and under the assumption of constant returns to scale (λ=1) the equation to
estimate (after adding an error term) is obtained:
(6)
where the first term in the left-hand side is nominal output, the second is wage cost multiplied by the
estimated coefficient on labour
from the production function, the third is material costs multiplied by
the estimated coefficient on material inputs
and the fourth is the rental price of capital multiplied by the
estimated coefficient on capital (1- ), all in differences. The totality of the left-hand side is the
Solow residual with variables measured in nominal terms. In the right hand-side, B is the Lerner index
((Price-Average Cost)/Price) to estimate, which can be used to compute the mark-ups according to:
Data
Most of the data are obtained from a subset of the Amadeus database comprising nearly 1.5 million
European companies.5 The subset was obtained by including any company that meets one of the following
criteria, depending on the reporting country:
A. France, Germany, Italy and the United Kingdom
(i) operating revenue equal to at least EUR 1.5 million,
(ii) total assets equal to at least EUR 3 million, or
5.
Unlike the case of sectoral data, adjustment for differences in tax systems across countries is not necessary as
no taxes affect only one side of either inputs or outputs.
8
(iii) the number of employees equals at least 20.
B. Austria, Belgium, Czech Republic, Denmark, Finland, Greece, Hungary, Iceland, Ireland,
Netherlands, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden and Switzerland
(i) operating revenue equal to at least EUR 1 million,
(ii) total assets equal to at least EUR 2 million or
(iii) the number of employees equals at least 20.
The dataset contains firms’ balance sheets and profit and loss accounts and information on stocks,
shareholders, subsidiaries and activities.
Company-specific nominal data needed for the estimation of the production function and the markups are directly obtained from the Amadeus database. For the output variable, operating revenue or sales is
used; for labour the number of employees is used and for capital, fixed assets. For material – in most
countries – the variable called “material costs” is used, but for Denmark, Greece, Iceland, Ireland,
Netherlands and the UK, cost of goods sold is used. For wages, there is a straightforward variable to use,
except in the case of Greece, where due to the lack of the wage expenditure variable (or company-specific
information on the wage rate), mark-ups were estimated using the industry-specific wage expenditure from
the STAN database.
The rental price of capital is calculated from the following equation:
(7)
where i is the long term interest rate,
is expected inflation and is the depreciation ratio and pk is the
fixed asset investment deflator. For the calculation of the rental price of capital, the yield on benchmark
government 10 year bonds (or similar maturity) are used from the OECD Economic Outlook database;6
expected inflation is proxied by the Hodrick-Prescott-filtered GDP deflator, which is also extracted from
the OECD Economic Outlook database. The depreciation ratio is country-specific and is calculated from
the depreciation costs available in the Amadeus database. For the fixed-asset investment deflator sectorspecific deflators are used with the exception of Greece, Iceland, Poland, Portugal, Sweden, Switzerland
and the United Kingdom, where, in lack of such data, the economy-wide deflator for business investment
from the OECD Economic Outlook database is applied.
The Amadeus database classifies firms into 4-digit and 2-digit NACE categories. Due to the limited
number of companies in several countries at the 4-digit level and to a slightly different selection of sectors
of interest for the present study, the industries were reclassified into a mix of 2- and 4-digit NACE
categories, avoiding overlap. This way, mark-ups were estimated for 29 services sectors, including
electricity, gas, steam and hot water supply, collection, purification and distribution of water, construction,
car sale and gasoline retail, wholesale trade, retail trade, hotels and restaurants, land transport, water
transport, air transport, other transport activities, post, telecommunications, financial intermediation,7
activities related to financial intermediation, real estate, renting of machinery and equipment, computer and
6.
For Hungary, data for the yield on benchmark government 10-year bonds were obtained from the National
Bank of Hungary and for Greece from Eurostat. In lack of such data for the Czech Republic and Poland,
long-term lending rates are used.
7.
Except insurance and pension funds which are missing from the database.
9
related activities, research and development, legal services, accounting services, advertising services,
engineering and architecture services, education and other community, social and personal services.
Given the coverage of the Amadeus database with 21 countries and the selection of 29 services
sectors, ideally there should be 609 mark-up estimates. In reality, however, given the limited number of
observations for some countries and several sectors, the number of estimated mark-ups is substantially
lower.
Choices of estimation and specification techniques
Mark-ups are estimated using two alternative techniques that differ at the first step of estimating the
production function; factor shares were determined by: (i) using an ordinary least squares (OLS) method
with fixed effects or (ii) computing the shares. The OLS fixed-effects method assumes that productivity
that influences firms’ choice is a time-invariant firm-specific attribute and corrects for it by including firm
fixed effects.8 The second method relies on computation of the factor shares instead of econometric
estimation. The OLS fixed effects method can produce country and sector estimates, while computed
factor shares are obtained at the firm level. The application of the two methods allows for the exploitation
of the richness of information related to heterogeneity at the country, sector and firm level (and across
time).
The second step, which is the estimation of the Lerner indices, is identical in the two cases. The
Lerner indices were estimated by OLS with and without fixed effects, year dummies and time trend. The
Lerner indices were jointly estimated for all countries and all sectors in the sample. The mark-ups were
then retrieved from equation (3) and the corresponding standard errors were computed by the delta method.
Both estimation methods were applied for two specifications: (i) assuming constant returns to scale
and (ii) relaxing the constraint of constant returns to scale. The validity of the assumption of constant
returns is checked by econometric tests. Where constant returns were rejected, this constraint was relaxed
and the mark-up was estimated under non-constant returns.
From the assumption of common input coefficients to sector- and country-specific ones
The easiest way to estimate the mark-ups would be to assume that the production functions are
identical across countries and industries. A simple F-test, however, shows that even for the 21 European
countries covered by the database we cannot assume that firms in the same sector behave the same way
across countries. By the same token, firms in the same countries but in different sectors exhibit different
characteristics. Therefore, country- and industry-specific input coefficients are estimated simultaneously to
be used as inputs for the second step where mark-ups are derived. The second approach, where the factor
shares are computed and not estimated, implies firm-specific shares, i.e. the input coefficients are
computed at the firm level.
Relaxing the assumption of constant returns to scale
Constant returns to scale may not hold for all industries, therefore its validity needs to be tested. The
hypothesis of constant returns to scale was rejected for several groups; therefore the mark-ups were reestimated under the relaxation of the assumption of constant returns. Following Dobrinsky et al. (2004), to
estimate the mark-ups under non-constant returns to scale, the returns to scale index, λ was estimated, and
the new mark-up computed from the Lerner index obtained in the case of constant returns to scale:
8.
Resources permitting, superior techniques such as the Levinsohn-Petrin method could also be employed to
estimate the input coefficients of the production function.
10
(8)
How large are the estimates and how do they compare across countries and sectors?
The estimated mark-ups confirm large heterogeneity across sectors and countries. This is not
surprising given that sector-specific characteristics affect the mark-ups companies can charge over average
costs and that the forces driving competition – and hence reducing mark-ups – vary across sectors and
countries. These differences may, to a considerable extent, be related to different domestic regulatory
regimes.
Notwithstanding this heterogeneity in the estimated mark-ups, some general trends can be identified.
With regards to the pilot sectors (i.e. those covered in the current phase of the OECD services trade
restrictiveness work), in general, mark-ups are high in professional services, lower in telecom services and
lowest in the construction industry and computer services. Not surprisingly, in professional services, such
as legal or consultancy services, where human capital is a major input, where most products are customerspecific and where information asymmetry is a major issue, mark-ups are higher than in most other
services industries. Some of these services, such as legal services, are less tradable than other professional
services, implying even higher mark-ups. Network industries, in general, exhibit higher mark-ups than
inherently competitive non-network services such as retail trading or construction. Among network
services, mark-ups in telecommunications are relatively lower, probably implying higher competitive
pressures in telecommunications markets and increasingly demanding customers with respect to services
quality and prices.
When looking also at other sectors, mark-ups appear high in financial services, real estate and renting,
while trade-related activities (sale of motor vehicles, wholesale and retail trade) are among sectors with the
lowest mark-ups. Transport-related sectors are placed between these two extremes. Real estate and renting
are characterised by high level of asymmetric information between suppliers and customers giving rise to
pricing powers by suppliers.
Across countries, the Central European members appear to experience the highest mark-ups alongside
Italy, Portugal and Sweden. At the other end, the United Kingdom and some Scandinavian countries seem
to have the lowest mark-ups. The large continental countries of France and Germany as well as Spain are
in-between these two groups.
The following sector-specific discussion of some of the findings will give some insights into the
differences in mark-ups across sectors and countries. Estimates obtained by both methods (i.e. with input
coefficients estimated by fixed-effect OLS and computed input shares) are presented to show the
robustness of results. The relative sizes of the estimates somewhat differ, with the computed method
having an upward bias. While the estimated-coefficient method is economically more solid, many
observations are lost owing to the estimation procedure. Therefore, where the purpose is to compare as
many countries/sectors as possible, the computed-coefficient method may be more useful.
Competitive pressure in computer services implies small mark-ups in most countries
The computer services market is unregulated in the countries in the sample and computer services are
generally tradable. As expected under such circumstances, the analysis points to relatively low mark-ups in
most countries (Figure 1). Exceptions are the Central European countries, and to a lesser extent Italy,
Sweden and Austria. Most computer services sectors are characterised by non-constant returns to scale
except in Finland, where the constant returns to scale hypothesis was not rejected.
11
Figure 8. Mark-ups in computer services are relatively low in most countries
Panel A. Mark-ups in the computer services sector obtained by estimating input coefficients
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
PL
IT
SE
DE
BE
FR
NO
ES
FI
GB
Panel B. Mark-ups in the computer services sector obtained by computing input coefficients
4
3.5
3
2.5
2
1.5
1
0.5
0
SK
IT
SE
AT
FI
PT
CH
FR
BE
NO
ES
GB
DK
Note: The two series of mark-ups were obtained using two different estimation methods; therefore their absolute
magnitudes are not comparable.
Source: Authors’ estimation.
Mark-ups in the construction sector are relatively low in general with elevated levels in some countries
Generally, the construction sector also appears to exhibit relatively low mark-ups (Figure 2). This
wedge is very small in Denmark, relatively small in the UK, Spain, Hungary, Finland, Belgium, Norway
and Greece, but relatively high for other Central European countries, Sweden, Portugal and Italy. Given the
large number of players in the sector in most countries, mark-ups in construction were obtained using the
fixed-effect method for the estimation of the input coefficients for as many as 15 countries. With the
computed input coefficients, the number of estimates is even larger (18), though this method tends to
overestimate mark-ups. The sector exhibits non-constant returns to scale in all countries in the sample.
12
Figure 9. Mark-ups in the construction sector are relatively low
Panel A. Mark-ups in the construction sector obtained by estimating input coefficients
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
PL
IT
SE
PT
FR
GR
BE
ES
NO
FI
GB
Panel B. Mark-ups in the construction sector obtained by computing input coefficients
3
2.5
2
1.5
1
0.5
0
CZ PL SK PT IT
SE FR DE NL CH GR NO BE
FI HU ES GB DK
Note: The two series of mark-ups were obtained using two different estimation methods; therefore their absolute
magnitudes are not comparable.
Source: Authors’ estimation.
Mark-ups are high in professional services
In general, mark-ups are high in professional services sectors in most countries. In architectural and
engineering services, Finland, Greece, Norway, Spain and the UK appear to have relatively high
competitive pressure and therefore low mark-ups (Figure 3). Most other countries have high mark-ups in
this sub-sector, e.g. Italy, Poland, Sweden or the Czech Republic. In accounting, the number of countries
where data availability allowed for the estimation of mark-ups is fewer (given the small number of large
and large number of small players in this market and the limited sample to medium- to large-size firms). In
countries for which mark-ups were estimated, they are generally high with the exception of Finland,
France, Greece, Iceland, Norway and Poland. Given the small size of legal services firms, in general and
the exclusion of such firms from the sample, the mark-ups in this sector could only be estimated for a
handful of countries. Finland and Greece again appear to charge smaller mark-ups over average costs,
while in most other countries for which it could be estimated, the mark-ups are relatively high, e.g. in Italy,
Norway, Poland and Sweden.
13
Figure 10.
Mark-ups in architecture and engineering services are higher
Panel A. Mark-ups in the architecture and engineering services sector obtained by estimating input
coefficients
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
PL
IT
CZ
SE
FR
ES
FI
BE
NO
GB
Panel A. Mark-ups in the architecture and engineering services sector obtained by computing input
coefficients
4
3.5
3
2.5
2
1.5
1
0.5
0
CZ
SE
IT
FR
DE
IS
ES
FI
NO
GR
DK
GB
Note: The two series of mark-ups were obtained using two different estimation methods; therefore their absolute
magnitudes are not comparable.
Source: Authors’ estimation.
Professional services are inherently competitive services and the exceptionally high mark-ups in this
sector may indicate inadequate competitive pressure. Unnecessary regulation and the lack of recognition of
qualifications may give a particular boost to firms’ pricing power in this sector.
Comparing the newly obtained mark-ups with previous OECD estimates
Comparability with previous OECD estimates is limited, but the relative magnitudes of mark-ups
across countries are mostly similar. Oliveira Martins et al. (1996) provided the first comprehensive markups for OECD countries, though their estimation of mark-ups was limited to manufacturing industries. As
noted above, Høj et al. (2007) extended the coverage to include up to 6 services industries (depending on
the country) and estimated the mark-ups for 19 countries over 1975-2002. In trade, where owing to the
large number of observations, mark-ups were estimated for a number of countries, the new estimates are
comparable to the ones obtained in Høj et al. (2007) especially when taking into consideration the different
time periods (1993-2006 and 1975-2002) for which the mark-ups were estimated (Figure 4).
14
Figure 11.
Newly estimated mark-ups are comparable to earlier estimates
The average of the new mark-ups in the wholesale and retail sector 1992-2006 and earlier estimates in the wholesale
and retail trade and repairs 1975-2002
6
5
4
3
2
1
0
CZ PT SK SE HU PL GR IT DE FR GB BE NO FI NL AT CH ES DK
Average of wholesale and retail
Høj et al. (2007)
Source: Authors’ estimation and Høj et al. (2007).
How do mark-ups relate to regulations?
While mark-ups reflect competitive pressures in markets, such competitive pressures can partly stem
from conduct rules imposed by regulatory bodies, but also from other sources such as increasingly
demanding consumers, international trade or foreign direct investment in the country. The proper
delineation of sources of competitive pressure remains a topic for future work, but preliminarily, simple
charts can already provide some indication of the relationship between mark-ups and regulations.
In construction services, those countries with the highest mark-ups such as Poland, Slovak Republic,
Sweden and the Czech Republic also have restrictive foreign direct investment (FDI) policies (Figure 5).
However, Finland’s score on restrictiveness towards inward FDI is even more restrictive but it has low
mark-ups in the construction sector. FDI, of course, is not the only source of competitive pressure and even
in the absence of foreign investors, there may develop a competitive domestic market. Nevertheless, the
positive association between FDI restrictiveness and mark-ups indicate that foreign competitors may
indeed constitute an important source of competitive pressure.
In some countries with strict restriction on FDI, mark-ups are higher in construction services,
2006
.12
Figure 12.
Finland
.08
Slovak Rep
Poland
Sweden
.06
Norway
.02
.04
FDI restr
.1
Czech Rep.
BEL
Italy
DenmarkESP GRC Germany
UK
0
.5
1
Mark-up in construction services
Source: OECD FDI restrictiveness indices and authors’ estimation.
15
1.5
In the telecommunications sector, similarly to other industries, foreign competition can be an
important source of competitive pressure. In the countries where mark-ups are high in telecommunications
services, restrictiveness towards inward FDI in the sector also tends to be high (Figure 6). Iceland is a case
in point, but also other countries, such as France and Sweden, fit this description.
Figure 13.
Countries with higher mark-ups tend to be more restrictive towards FDI in telecommunications
services, 2006
1
ISL
FDI Restrictiveness Index - Telecoms
0.9
0.8
0.7
0.6
0.5
0.4
0.3
ESP
FRA
0.2
SWE
0.1
NOR
DNK
ITA
GBR
0
0
1
2
3
4
5
6
Markup in telecoms
Source: OECD FDI restrictiveness indices and authors’ estimation.
Professional services, for instance architectural and engineering services, are not an exception to the
observed positive association between the size of mark-ups and the restrictiveness of regulations. Higher
regulation in architectural and engineering services including entry, qualification requirements and
regulations related to conduct, are associated with higher mark-ups in the sector (Figure 7).
Figure 14.
Higher mark-ups are associated with higher PMR in the architectural and engineering services
sector, 2007
4.0
3.5
Atchitecture PMR 2007
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0
0.5
1
1.5
2
2.5
3
3.5
4
Mark-up in the architectural and engineering services sector
Note: The country names are not shown as the 2007 vintage of the PMR indicators is not yet published and these indicators are
preliminary. Mark-ups are estimated for 1993-2006.
Source: OECD FDI restrictiveness indices and authors’ estimation.
16
What have we learnt and what else could be done?
Mark-ups provide a comparative perspective on competitive pressures in markets in different
countries and industries. In general, the estimated mark-ups were higher for professional services, real
estate, renting and utilities, while they tended to be substantially lower for construction, computer services,
retail and wholesale trade and catering. From the limited number of estimates for the telecommunications
sector it can be inferred that mark-ups in this sector are somewhere in between these two ends of the
spectrum. There is also large variation across countries in terms of the sizes of the estimated mark-ups.
Competitive pressures according to these mark-ups should be large in the United Kingdom and most
Scandinavian countries, and relatively small in Central European countries, Sweden and Italy. Spain,
Germany and France are in between these two groups.
To allow for comparison across a larger number of OECD countries, the analyses could be extended
to non-European Members using the Orbis database. The methodology applied could also be further
refined, for example, by using the Levinsohn-Petrin method to obtain the input coefficients of the
production function. With such improvements, these mark-up estimates could better serve as inputs for use
in a top-down approach to assessment of service trade restrictiveness.
Also, in future analyses, competitive pressures stemming from regulation could be delineated from
other pressures stemming from other sources using the newly estimated mark-ups.
17
REFERENCES
Dobrinsky, R., G. Körösi, N. Markov and L. Halpern (2004), “Firms’ price mark-ups and returns to scale
in imperfect markets: Bulgaria and Hungary”, William Davidson Institute Working Paper No. 710.
Hall, R. E. (1988) “The relation between price and marginal cost in U.S. industry”, Journal of Political
Economy 96Høj, J., M. Jimenez, M. Maher, G. Nicoletti and M. Wise (2007), “Product market competition in the
OECD countries: Taking stock and moving forward”, OECD Economics Department Working
Papers No. 575.
Oliveira Martins, J., S. Scarpetta and D. Pilat (1996), “Mark-up ratios in manufacturing industries:
Estimates for 14 OECD countries”, OECD Economics Department Working Papers No. 162.
Roeger, W. (1995), “Can imperfect competition explain the differences between primal and dual
productivity measures? Estimates for U.S. manufacturing”, The Journal of Political Economy
Vol. 103-2.
18
Table 1. Estimated mark-ups for services industries in Austria, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
se
OLS_yd
se
Computation
CRS
yd
se
trend
se
CRS
1.01***
1.42***
1.91***
1.82***
1.76***
3.69***
2.9***
0.13
0.05
0.11
0.07
0.00
0.13
0.07
1.01***
1.42***
1.9***
1.81***
1.67***
3.7***
2.87***
0.14
0.05
0.11
0.07
0.00
0.20
0.08
0
0
0
0
0
0
0
1.45***
0.06
1.48***
0.08
2.29***
2.11***
1.77***
0.00
0.10
0.04
2.27***
2.11***
1.83***
0.00
0.10
0.01
0
0
0
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
19
Table 2. Estimated mark-ups for services industries in Belgium, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
0.89***
4100 Water collection, purification etc.
4500 Construction
1.48***
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
1.34***
5200 Retail trade
1.24***
5500 Hotels and restaurants
1.37***
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 1.62***
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
1.89***
6700 Activ. auxiliary to fin. int.
7000 Real estate
2.99***
7100 Renting
2.19***
7200 Computer activities
1.75***
7300 Research and development
7400 Other business activ.
1.69***
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ. 1.48***
7415 Man. activ. of holding companies 2.14***
7420 Architectural, engineering etc
1.66***
8000 Education
7599 Other
1.87***
Computation
se
OLS_yd
se
CRS
0.13
0.93***
0.12
1
yd
se
trend
se
CRS
0.04
1.48***
0.03
0
2.6***
1.3***
0.18
0.02
2.58***
1.3***
0.17
0.02
0
0
0.01
0.03
0.05
1.34***
1.23***
1.37***
0.01
0.03
0.05
0
0
1
1.26***
0.01
1.27***
0.01
0
2.63***
1.71***
0.26
0.06
2.63***
1.71***
0.25
0.06
0
0
0.08
1.6***
0.05
0
1.5***
0.04
1.51***
0.04
0
0.09
1.87***
0.07
0
1.88***
0.20
1.89***
0.20
0
0.12
0.13
0.07
2.77***
2.16***
1.82***
0.09
0.14
0.07
0
0
0
1.89***
1.55***
1.74***
0.13
0.04
0.06
1.89***
1.55***
1.75***
0.13
0.04
0.06
0
0
0
0.05
1.67***
0.04
0
1.5***
0.05
1.5***
0.05
0
1.75***
0.05
1.74***
0.04
0
1.5***
1.97***
0.03
0.09
0***
1.97***
0.03
0.09
0
0
0.07
0.27
0.2
1.44***
2***
1.63***
0.07
0.15
0.16
0
0
1
0.1
1.85***
0.07
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
20
Table 3. Estimated mark-ups for services industries in Switzerland, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
1.09***
se
0.05
OLS_yd
se
1.1***
0.06
Computation
CRS
1
yd
se
trend
se
CRS
1.49***
0.93***
0.01
0
0***
1.04***
0.01
0
0
0
1.2***
1.76***
2.31***
1.03***
1.87***
1.59***
0.05
0.13
0.17
0.00
0.12
0.00
1.21***
1.78***
2.32***
1.03***
1.88***
1.59***
0.05
0.12
0.18
0.00
0.14
0.00
0
0
0
1
0
0
1.8***
0.17
1.77***
0.17
0
1.22***
0.89***
1.47***
0
0
0.04
1.23***
0.88***
1.48***
0
0
0.05
0
1
0
1.5***
1.45***
0
0.06
1.47***
1.46***
0
0.06
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
21
Table 4. Estimated mark-ups for services industries in the Czech Republic, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
2.29***
5000 Sale, maintenance, etc. of vehicles 3.88***
5100 Wholesale trade, etc.
4.12***
5200 Retail trade
2.4***
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 2.7***
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
4.58***
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
2.87***
8000 Education
2.41***
7599 Other
Computation
se
OLS_yd
se
CRS
yd
se
trend
se
CRS
0.12
0.13
0.08
0.09
2.2***
3.85***
4.06***
2.32***
0.1
0.1
0.06
0.05
0
0
0
0
2.98***
13.52***
19.18***
12.35***
0.13
0.23
0.11
0.14
2.98***
13.5***
19.16***
12.34***
0.13
0.23
0.11
0.14
0
0
0
0
3.23***
0.20
3.22***
0.20
0
4.04***
0.00
4.03***
0.00
0
2.39***
0.11
2.37***
0.10
0
-8.78***
0.5
-8.55***
0.45
0
3.66***
0.19
3.66***
0.19
3.01***
0.13
3***
0.13
0
0
0
0.2
2.81***
0.15
0
0.75
3.81***
0.31
0
0.19
0.1
2.92***
2.39***
0.11
0.06
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
22
Table 5. Estimated mark-ups for services industries in Germany, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 5.22***
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
1.82***
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies 2.83***
7420 Architectural, engineering etc
8000 Education
7599 Other
2.39***
se
OLS_yd
se
Computation
CRS
yd
se
trend
se
CRS
1.79***
1.76***
0.05
0.05
1.79***
1.76***
0.05
0.05
0
0
1.29***
0.04
1.29***
0.04
0
2.04***
0.13
2.04***
0.13
0
2.99***
0.15
2.98***
0.15
0
0.24
5.02***
0.12
0
0.06
1.78***
0.06
0
0.23
2.69***
0.12
0
2.03***
1.99***
1.97***
0.12
0.17
0.13
2.03***
1.99***
1.97***
0.12
0.17
0.13
0
0
0
0.14
2.3***
0.08
0
2.36***
0.11
2.35***
0.11
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
23
Table 6. Estimated mark-ups for services industries in Denmark, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
1.32***
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
1.27***
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 1.26***
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
1.48***
6700 Activ. auxiliary to fin. int.
2***
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies 1.5***
7420 Architectural, engineering etc
8000 Education
7599 Other
1.64***
Computation
se
OLS_yd
se
CRS
yd
se
trend
se
CRS
0.07
1.35***
0.07
1
1.16***
0.09
1.16***
0.09
0
1.05***
1.09***
1.13***
1.11***
1.23***
0.99***
0.02
0.02
0.01
0.01
0.07
0.01
1.05***
1.09***
1.13***
1.11***
1.23***
0.99***
0.02
0.02
0.01
0.01
0.07
0.01
0
0
0
0
0
0
1.09***
0.02
1.08***
0.02
0
1.47*
1.42***
0.18
0.07
1.47*
1.42***
0.19
0.07
1
0
1.16***
1.16***
1.09**
0.04
0.00
0.04
1.09**
0.04
0.36
0
0
1
1
1.64*
0.23
1.64*
0.23
1
1.39***
1.22***
1.43***
1.21***
0.11
0.06
0.08
0.06
1.39***
1.22***
1.42***
1.21***
0.11
0.06
0.08
0.06
0
1
0
1
0.05
1.27***
0.05
0
0.03
1.29***
0.04
0
0.08
0.76
1.53***
1.55***
0.1
0.35
0
1
0.08
1.46***
0.05
0
0.06
1.61***
0.05
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
24
Table 7. Estimated mark-ups for services industries in Spain, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
1.89***
4100 Water collection, purification etc.
4500 Construction
1.35***
5000 Sale, maintenance, etc. of vehicles 1.22***
5100 Wholesale trade, etc.
1.27***
5200 Retail trade
1.21***
5500 Hotels and restaurants
1.53***
6000 Land transport
1.82***
6100 Water transport
2.4***
6200 Air transport
6300 Transport supporting activities, etc. 1.79***
6410 Post and courier activ.
1.78***
6420 Telecommunications
1.43***
6500 Financial intermediation
1.68***
6700 Activ. auxiliary to fin. int.
2.16***
7000 Real estate
2***
7100 Renting
1.62***
7200 Computer activities
1.44***
7300 Research and development
2.09***
7400 Other business activ.
1.67***
7411 Legal activ.
1.46***
7412 Accounting, etc.
2.08***
7413 Market research, etc.
1.83***
7414 Bus. and man. consultancy activ. 2.25***
7415 Man. activ. of holding companies 2.13***
7420 Architectural, engineering etc
1.72***
8000 Education
2.05***
7599 Other
1.75***
Computation
se
OLS_yd
se
CRS
yd
se
trend
se
CRS
0.1
1.92***
0.12
0
0.01
0.01
0
0.01
0.02
0.02
0.06
1.35***
1.22***
1.26***
1.2***
1.52***
1.83***
2.43***
0
0
0
0.01
0.01
0.02
0.07
0
0
0
0
0
0
0
1.88***
1.58***
1.25***
1.13***
1.17***
1.21***
1.71***
1.51***
0.11
0.06
0.01
0.01
0.00
0.01
0.03
0.02
1.89***
1.58***
1.25***
1.14***
1.18***
1.21***
1.72***
1.52***
0.11
0.06
0.01
0.01
0.00
0.01
0.03
0.02
0
0
0
0
0
0
0
0
0.04
0.02
0.05
0.33
0.11
0.02
0.03
0.03
0.14
0.02
0.18
0.13
0.07
0.12
0.14
0.07
0.06
0.02
1.77***
1.8***
1.44***
1.69***
2.08***
1.94***
1.61***
1.45***
2.02***
1.66***
1.56***
1.97***
1.87***
2.22***
2.24***
1.69***
2.01***
1.75***
0.03
0.02
0.05
0.22
0.08
0.01
0.03
0.03
0.11
0.01
0.18
0.08
0.07
0.08
0.15
0.04
0.04
0.02
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1.36***
1.27***
1.5***
0.03
0.01
0.04
1.36***
1.27***
1.5***
0.03
0.02
0.04
0
0
0
1.98***
1.79***
1.69***
1.43***
2***
1.35***
0.11
0.02
0.02
0.02
0.16
0.01
1.98***
1.79***
1.69***
1.43***
2***
1.35***
0.11
0.02
0.02
0.02
0.16
0.01
0
0
0
0
0
0
2.09***
1.57***
1.83***
2.02***
1.55***
1.74***
1.81***
0.14
0.05
0.09
0.15
0.04
0.05
0.02
2.09***
1.57***
1.83***
2.02***
1.55***
1.74***
1.82***
0.14
0.05
0.1
0.15
0.04
0.05
0.02
0
0
0
0
0
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
25
Table 8. Estimated mark-ups for services industries in Finland, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
1.93***
4100 Water collection, purification etc.
4500 Construction
1.33***
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
1.32***
5200 Retail trade
5500 Hotels and restaurants
1.47***
6000 Land transport
1.77***
6100 Water transport
2.01***
6200 Air transport
6300 Transport supporting activities, etc. 1.55***
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
1.68***
6700 Activ. auxiliary to fin. int.
1.62***
7000 Real estate
1.79***
7100 Renting
1.77***
7200 Computer activities
1.43***
7300 Research and development
7400 Other business activ.
1.73***
7411 Legal activ.
1.98***
7412 Accounting, etc.
1.69***
7413 Market research, etc.
7414 Bus. and man. consultancy activ. 1.47***
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
1.67***
8000 Education
2.57***
7599 Other
1.83***
Computation
se
OLS_yd
se
CRS
0.07
1.93***
0.07
0
0.02
1.33***
0.01
0
0.01
1.32***
0.01
0
0.03
0.02
0.17
1.46***
1.78***
1.95***
0.03
0.03
0.11
0
0
0
0.06
1.55***
0.04
0
0.1
0.26
0.04
0.11
0.12
1.66***
1.48***
1.77***
1.77***
1.38***
0.07
0.19
0.03
0.11
0.08
0
1
0
0
1
0.04
0.28
0.12
1.72***
1.97***
1.66***
0.03
0.17
0.08
0
0
0
0.07
1.46***
0.06
0
0.05
0.22
0.09
1.65***
2.59***
1.81***
0.04
0.22
0.06
0
0
0
yd
se
trend
se
CRS
1.26***
1.15***
1.25***
1.26***
1.66***
1.39***
0.02
0.01
0.01
0.02
0.03
0.03
1.26***
1.15***
1.25***
1.26***
1.66***
1.39***
0.02
0.01
0.01
0.02
0.03
0.03
0
0
0
0
0
0
1.53***
0.05
1.54***
0.05
0
2.01***
0.16
2.01***
0.16
0
1.49***
0.05
1.49***
0.05
0
1.88***
0.03
1.88***
0.03
0
1.51***
0.04
1.51***
0.04
0
1.82***
0.09
1.81***
0.09
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
26
Table 9. Estimated mark-ups for services industries in France, 19932006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
2.84***
4100 Water collection, purification etc. 3.14***
4500 Construction
1.6***
5000 Sale, maintenance, etc. of vehicles 1.27***
5100 Wholesale trade, etc.
1.38***
5200 Retail trade
1.28***
5500 Hotels and restaurants
1.78***
6000 Land transport
1.62***
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.2.03***
6410 Post and courier activ.
6420 Telecommunications
1.84***
6500 Financial intermediation
2.24***
6700 Activ. auxiliary to fin. int.
1.74***
7000 Real estate
2.67***
7100 Renting
1.85***
7200 Computer activities
1.69***
7300 Research and development
2.95***
7400 Other business activ.
1.93***
7411 Legal activ.
7412 Accounting, etc.
1.72***
7413 Market research, etc.
5.9**
7414 Bus. and man. consultancy activ. 2.16***
7415 Man. activ. of holding companies 2.26***
7420 Architectural, engineering etc
2.1***
8000 Education
2.82***
7599 Other
2.08***
Computation
se
OLS_yd
se
CRS
yd
se
0.16
0.05
0.01
0.01
0.01
0.01
0.04
0.01
2.7***
3.17***
1.59***
1.26***
1.38***
1.27***
1.75***
1.62***
0.12
0.05
0
0.01
0.01
0.01
0.03
0.01
0
trend
se
CRS
1.82***
0
1.82***
0
0
1.26***
1.27***
2.12***
2.07***
3.23***
3.29***
2.76***
0.01
0.01
0.04
0.01
0.09
0.07
0.03
1.26***
1.28***
2.13***
2.08***
3.24***
3.32***
2.77***
0.01
0.01
0.04
0.01
0.09
0.07
0.03
0
0
0
0
0
0
0
0.04
2.03***
0.04
0
0.2
0.25
0.13
0.06
0.12
0.02
0.33
0.03
1.88***
2.17***
1.6***
2.56***
1.8***
1.69***
2.88***
1.93***
0.16
0.19
0.09
0.04
0.07
0.01
0.28
0.02
1
1
1
0
0
0
0
0
2.54***
0.12
2.55***
0.12
0
2.12***
2.14***
2.4***
1.76***
3.15***
1.79***
0.12
0.05
0.11
0.02
0.28
0.02
2.12***
2.14***
2.39***
1.76***
3.14***
1.79***
0.12
0.05
0.11
0.02
0.28
0.02
0
0
0
0
0
0
0.03
2.52
0.1
0.09
0.05
0.06
0.04
1.74***
5.06***
2.13***
2.15***
2.07***
2.85***
2.06***
0.02
1.27
0.06
0.05
0.04
0.07
0.02
0
0
0
0
0
0
0
1.66***
0.02
1.67***
0.02
0
2.16***
2.08***
2.09***
2.05***
2.27***
0.09
0.09
0.03
0.03
0.04
2.16***
2.09***
2.1***
2.06***
2.28***
0.09
0.09
0.03
0.03
0.04
0
0
0
0
0
0
0
0
0
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
27
Table 10. Estimated mark-ups for services industries in the United Kingdom, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
1.27***
5000 Sale, maintenance, etc. of vehicles 1.15***
5100 Wholesale trade, etc.
1.19***
5200 Retail trade
1.17***
5500 Hotels and restaurants
1.24***
6000 Land transport
1.19***
6100 Water transport
1.53***
6200 Air transport
1.11***
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
1.32***
6700 Activ. auxiliary to fin. int.
7000 Real estate
2.02***
7100 Renting
1.2***
7200 Computer activities
1.32***
7300 Research and development
7400 Other business activ.
1.28***
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
1.44***
7414 Bus. and man. consultancy activ. 1.34***
7415 Man. activ. of holding companies 1.31***
7420 Architectural, engineering etc
1.39***
8000 Education
1.34***
7599 Other
1.34***
se
OLS_yd
se
Computation
CRS
yd
se
trend
se
CRS
1.43***
1.09*
1.16***
1.12***
1.2***
1.32***
1.41***
0.97***
1.44***
1.1*
1.16***
1.12***
1.21***
1.32***
1.42***
0.97***
1.15**
1.17***
0.11
0.05
0.03
0.02
0.01
0.02
0.03
0.01
0.00
0.06
0.03
1.15**
1.18***
0.11
0.05
0.03
0.02
0.01
0.02
0.03
0.01
0.00
0.06
0.03
0
1
0
0
0
0
0
0
1
1
0
0.09
0.06
0.04
0.07
0.04
0.03
0.20
0.02
1.44***
1.54***
1.38***
1.83***
1.12***
1.41***
1.59***
1.19***
0.09
0.06
0.04
0.07
0.03
0.03
0.20
0.02
0
0
0
0
0
0
0
0
0.08
0.02
0.05
0.06
0.02
0.04
0.04
1.28***
1.2***
1.34***
1.01***
1.05***
1.26***
0.08
0.02
0.05
0.06
0.02
0.04
0.04
1
0
0
0
0
0
0
0.02
0.02
0.01
0.02
0.02
0.01
0.06
0.04
1.26***
1.15***
1.19***
1.17***
1.24***
1.18***
1.59***
1.1***
0.01
0.01
0.01
0.02
0.01
0.01
0.04
0.03
0
0
0
0
0
0
0
1
0.05
1.35***
0.04
0
0.07
0.02
0.02
1.92***
1.2***
1.33***
0.04
0.01
0.02
0
0
0
0.01
1.29***
0.01
0
1.44***
1.54***
1.38***
1.83***
1.12***
1.41***
1.58***
1.19***
0.03
0.03
0.04
0.03
0.03
0.02
1.44***
1.32***
1.3***
1.39***
1.34***
1.34***
0.03
0.03
0.03
0.02
0.03
0.02
0
0
0
0
0
0
1.27***
1.2***
1.34***
1.01***
1.05***
1.26***
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
28
Table 11. Estimated mark-ups for services industries in Greece, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
1.5***
5000 Sale, maintenance, etc. of vehicles 1.25***
5100 Wholesale trade, etc.
1.2***
5200 Retail trade
1.22***
5500 Hotels and restaurants
2.11***
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 1.34***
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
1.38***
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ. 1.55***
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
2.02***
Computation
se
OLS_yd
se
CRS
0.03
0.03
0.01
0.01
0.04
1.5***
1.25***
1.2***
1.23***
2.09***
0.02
0.02
0
0.02
0.03
0
0
0
0
0
0.02
1.34***
0.02
0.08
0.05
0.14
1.34***
0.06
1.5***
0.04
2.01***
0.11
yd
se
trend
se
CRS
0
1.15***
1.39***
1.29***
1.29***
1.45***
1.59***
1.15***
1.24***
1***
1.22***
0.07
0.03
0.03
0.01
0.03
0.04
0.02
0.05
0.06
0.02
1.14***
1.39***
1.29***
1.29***
1.45***
1.57***
1.15***
1.23***
1***
1.22***
0.05
0.03
0.03
0.01
0.03
0.04
0.02
0.06
0.06
0.02
0
0
0
0
0
0
0
1
0
0
1
3.13***
2.42***
1.13***
0.22
0.30
0.10
3.08***
2.41***
1.12***
0.22
0.30
0.10
0
0
0
1.38***
1.29***
0.06
0
1.38***
1.29***
0.06
0
0
0
0
1.47***
1.44***
0.06
0.05
1.46***
1.44***
0.06
0.05
0
0
0
1.36***
0.91***
1.56***
0.06
0.05
0.15
1.36***
0.9***
1.55***
0.06
0.05
0.15
0
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
29
Table 12. Estimated mark-ups for services industries in Hungary, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
se
OLS_yd
se
Computation
CRS
yd
se
trend
se
CRS
1.78***
0.2
1.73***
0.19
0
1.26***
0.03
1.26***
0.03
0
1.75***
1.65***
0.04
0.12
1.73***
1.64***
0.04
0.11
0
0
1.47***
0.12
1.46***
0.12
0
1.69***
0.11
1.68***
0.11
0
1.21***
0.98***
0.54***
0
0.1
0
1.2***
0.99***
0.55***
0
0.1
0
1
0
0
1.8***
0.18
1.79***
0.18
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
30
Table 13. Estimated mark-ups for services industries in Iceland, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
se
OLS_yd
se
Computation
CRS
yd
se
trend
se
CRS
0.43***
0
0.43***
0
0
2.06***
0.09
2.06***
0.09
0
4.79***
0.00
4.74***
0.00
0
1.35***
5.4***
0.00
0.03
1.33***
5.16***
0.00
0.01
0
1
2.34***
0.01
2.37***
0.03
1
2.27***
0.04
2.18***
0.03
0
1.41***
0
1.43***
0
0
3.52***
0.01
3.38***
0
0
1.85***
0.08
1.82***
0.08
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
31
Table 14. Estimated mark-ups for services industries in Italy, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
1.78***
4100 Water collection, purification etc. 2.07***
4500 Construction
2.43***
5000 Sale, maintenance, etc. of vehicles 1.39***
5100 Wholesale trade, etc.
1.49***
5200 Retail trade
1.45***
5500 Hotels and restaurants
2.64***
6000 Land transport
3.24***
6100 Water transport
3.36***
6200 Air transport
6300 Transport supporting activities, etc. 3.05***
6410 Post and courier activ.
6420 Telecommunications
2.75***
6500 Financial intermediation
2.82***
6700 Activ. auxiliary to fin. int.
1.84***
7000 Real estate
3.01***
7100 Renting
2.21***
7200 Computer activities
2.65***
7300 Research and development
2.57***
7400 Other business activ.
2.8***
7411 Legal activ.
7412 Accounting, etc.
2.69***
7413 Market research, etc.
2.98***
7414 Bus. and man. consultancy activ. 2.87***
7415 Man. activ. of holding companies 1.71***
7420 Architectural, engineering etc
2.99***
8000 Education
3.24***
7599 Other
3.04***
Computation
se
OLS_yd
se
CRS
yd
se
trend
se
CRS
0.07
0.13
0.03
0.05
0.01
0.01
0.13
0.09
0.34
1.84***
2.07***
2.39***
1.37***
1.49***
1.45***
2.58***
3.21***
3.3***
0.05
0.08
0.02
0.03
0.01
0.01
0.07
0.06
0.24
0
0
0
0
0
0
0
0
0
2.06***
0.1
2.07***
0.1
0
2.35***
1.27***
1.33***
1.3***
2.22***
2.73***
3.88***
0.03
0.04
0.01
0.02
0.12
0.07
0.29
2.35***
1.27***
1.33***
1.3***
2.23***
2.73***
3.9***
0.03
0.04
0.01
0.02
0.12
0.07
0.29
0
0
0
0
0
0
0
0.05
3.05***
0.03
0
3.11***
0.04
3.12***
0.04
0
0.35
0.25
0.08
0.1
0.07
0.05
0.16
0.05
2.81***
2.62***
1.9***
3.01***
2.28***
2.6***
2.53***
2.83***
0.21
0.18
0.07
0.07
0.06
0.03
0.12
0.03
0
0
0
0
0
0
0
0
2.79***
2.91***
3.74***
2.71***
2.22***
2.3***
3.08***
2.23***
0.23
0.28
0.07
0.09
0.07
0.04
0.13
0.04
2.78***
2.92***
3.76***
2.71***
2.22***
2.3***
3.08***
2.23***
0.23
0.28
0.06
0.09
0.07
0.04
0.13
0.04
0
0
0
0
0
0
0
0
0.09
0.09
0.1
0.13
0.06
0.07
0.1
2.69***
2.93***
2.77***
1.85***
3.05***
3.37***
2.98***
0.07
0.06
0.06
0.12
0.05
0.09
0.06
0
0
0
0
0
0
0
2.41***
2.39***
2.73***
2.07***
2.65***
3.3***
2.76***
0.12
0.06
0.09
0.16
0.05
0.06
0.09
2.41***
2.4***
2.73***
2.07***
2.65***
3.28***
2.77***
0.12
0.06
0.09
0.16
0.05
0.06
0.09
0
0
0
0
0
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
32
Table 15. Estimated mark-ups for services industries in the Netherlands, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
se
OLS_yd
se
Computation
CRS
yd
se
trend
se
CRS
4.02***
1.5***
0.1
0.02
4.26***
1.49***
0.11
0.02
0
0
1.24***
0.01
1.24***
0.01
0
1.77***
1.57***
0.08
0.06
1.86***
1.69***
0.11
0.08
0
0
1.51***
0.00
1.45***
0.00
0
1.59***
0.00
1.63***
0.00
0
2.42***
0.01
2.48***
0.01
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
33
Table 16. Estimated mark-ups for services industries in Norway, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
2.68***
4100 Water collection, purification etc.
4500 Construction
1.35***
5000 Sale, maintenance, etc. of vehicles 1.26***
5100 Wholesale trade, etc.
1.26***
5200 Retail trade
1.16***
5500 Hotels and restaurants
1.41***
6000 Land transport
2.02***
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 1.57***
6410 Post and courier activ.
6420 Telecommunications
1.48***
6500 Financial intermediation
1.13***
6700 Activ. auxiliary to fin. int.
7000 Real estate
2.43***
7100 Renting
1.58***
7200 Computer activities
1.62***
7300 Research and development
3.17***
7400 Other business activ.
1.73***
7411 Legal activ.
7412 Accounting, etc.
1.52***
7413 Market research, etc.
7414 Bus. and man. consultancy activ. 1.67***
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
1.63***
8000 Education
2.21***
7599 Other
1.87***
Computation
se
OLS_yd
se
CRS
yd
se
trend
se
CRS
0.21
2.61***
0.19
0
2.29***
0.14
2.29***
0.14
0
0.01
0.01
0.01
0.01
0.03
0.02
1.34***
1.26***
1.26***
1.16***
1.39***
2***
0.01
0
0.01
0.01
0.02
0.02
0
0
0
0
0
0
1.32***
0.01
1.32***
0.01
0
1.28***
1.24***
1.68***
0.01
0.01
0.03
1.28***
1.24***
1.68***
0.01
0.01
0.03
0
0
0
0.02
1.58***
0.02
0
0.05
0.05
1.62***
1.23***
0.06
0.14
0
1
1.37***
0.05
1.37***
0.05
0
0.07
0.04
0.05
0.4
0.07
2.39***
1.57***
1.6***
3.05***
1.7***
0.05
0.04
0.04
0.27
0.04
0
0
0
0
0
1.99***
1.81***
1.65***
1.62***
0.12
0.08
0.04
0.04
2***
1.82***
1.65***
1.62***
0.12
0.08
0.04
0.04
0
0
0
0
1.57***
0.07
1.57***
0.07
0
0.12
1.59***
0.17
0
0.05
1.67***
0.04
0
1.48***
0.02
1.48***
0.02
0
0.04
0.04
0.03
1.6***
2.23***
1.85***
0.03
0.02
0.02
0
0
0
1.49***
0.03
1.49***
0.03
0
1.83***
0.04
1.83***
0.04
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
34
Table 17. Estimated mark-ups for services industries in Poland, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
6.99***
4100 Water collection, purification etc. 12.85***
4500 Construction
4.83***
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5.66***
5200 Retail trade
4.03***
5500 Hotels and restaurants
6000 Land transport
4.82***
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 4.1***
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
3.46***
6700 Activ. auxiliary to fin. int.
7000 Real estate
3.07***
7100 Renting
7200 Computer activities
4.66***
7300 Research and development
9.3***
7400 Other business activ.
2.78***
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
4.27***
8000 Education
7599 Other
Computation
se
OLS_yd
se
CRS
yd
se
trend
se
CRS
0.13
0.08
0.21
6.85***
12.92***
4.63***
0.17
0.06
0.11
0
0
0
0.05
0.27
5.64***
4.1***
0.04
0.19
0
0
1.68***
1.93***
2.72***
1.73***
1.6***
1.77***
0.1
0.1
0.17
0.12
0.04
0.16
1.7***
1.95***
2.74***
1.74***
1.61***
1.78***
0.1
0.11
0.17
0.12
0.04
0.16
0
0
0
0
0
0
0.15
4.84***
0.1
0
2.05***
0.08
2.06***
0.08
0
0.11
4.27***
0.11
0
2.97***
0.11
2.99***
0.11
0
0.45
3.55***
0.36
0
0.12
3.08***
0.08
0
2.43***
0.21
2.44***
0.21
0
0.4
0.19
0.12
4.47***
9.13***
2.65***
0.28
0.16
0.07
0
0
0
1.94***
2.4***
0.13
0.12
1.95***
2.41***
0.13
0.12
0
0
2.32***
0.11
2.32***
0.11
0
1.69***
2.21***
0.09
0.19
1.71***
2.22***
0.09
0.19
0
0
0.36
3.98***
0.22
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
35
Table 18. Estimated mark-ups for services industries in Portugal, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
2***
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
se
0.05
OLS_yd
se
1.96***
0.04
Computation
CRS
0
yd
se
trend
se
CRS
1.98***
0.09
2***
0.1
0
2.43***
7.91***
5.13***
5.67***
2.54***
1.88***
5.76***
0.04
0.09
0.10
0.08
0.18
0.05
0.14
2.43***
7.91***
5.15***
5.71***
2.57***
1.91***
5.74***
0.04
0.09
0.10
0.08
0.19
0.05
0.14
0
0
0
0
0
0
0
2.13***
0.00
2.09***
0.00
0
2.85***
0.21
2.86***
0.21
0
1.9***
0.03
1.92***
0.04
0
2.01***
0
2.01***
0
0
3.09***
0.17
3.16***
0.17
0
2.8***
0.14
2.83***
0.15
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
36
Table 19. Estimated mark-ups for services industries in the Slovak Republic, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
5000 Sale, maintenance, etc. of vehicles
5100 Wholesale trade, etc.
3.89***
5200 Retail trade
5500 Hotels and restaurants
6000 Land transport
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc.
6410 Post and courier activ.
6420 Telecommunications
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
7100 Renting
7200 Computer activities
7300 Research and development
7400 Other business activ.
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ.
7415 Man. activ. of holding companies
7420 Architectural, engineering etc
8000 Education
7599 Other
5.6***
se
0.1
0.29
OLS_yd
se
4***
0.06
6.1***
0.28
Computation
CRS
0
yd
se
trend
se
CRS
1.33***
0.04
1.33***
0.04
0
2.59***
8.02***
5.81***
0.08
0.22
0.10
2.59***
7.99***
5.81***
0.08
0.21
0.10
0
0
0
1.77***
2.17***
0.05
0.02
1.77***
2.17***
0.05
0.02
0
0
2.85***
4.57***
3.68***
0.11
0.18
0.14
2.85***
4.57***
3.68***
0.11
0.18
0.14
0
0
0
2.52***
0.07
2.52***
0.07
0
2.31***
0.06
0***
0.06
0
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
37
Table 20. Estimated mark-ups for services industries in Sweden, 1993-2006
Ordinary Least Squares
NACE Sectors
FE_yd
4000 Electricity, gas, etc.
4100 Water collection, purification etc.
4500 Construction
2.07***
5000 Sale, maintenance, etc. of vehicles 2***
5100 Wholesale trade, etc.
2.24***
5200 Retail trade
1.87***
5500 Hotels and restaurants
1.78***
6000 Land transport
2.24***
6100 Water transport
6200 Air transport
6300 Transport supporting activities, etc. 3.13***
6410 Post and courier activ.
6420 Telecommunications
1.71***
6500 Financial intermediation
6700 Activ. auxiliary to fin. int.
7000 Real estate
2.84***
7100 Renting
1.98***
7200 Computer activities
2.15***
7300 Research and development
7400 Other business activ.
2.25***
7411 Legal activ.
7412 Accounting, etc.
7413 Market research, etc.
7414 Bus. and man. consultancy activ. 2.06***
7415 Man. activ. of holding companies 2.68***
7420 Architectural, engineering etc
2.53***
8000 Education
7599 Other
2.32***
se
OLS_yd
se
Computation
CRS
yd
se
trend
se
CRS
2.71***
0.27
0***
0.3
0
0.05
0.05
0.04
0.04
0.04
0.08
1.99***
1.95***
2.17***
1.82***
1.7***
2.28***
0.03
0.04
0.03
0.03
0.03
0.07
0
0
0
0
0
0
2.03***
1.72***
1.91***
1.73***
2.44***
2.3***
0.02
0.05
0.03
0.03
0.03
0.06
2.06***
1.73***
1.93***
1.75***
2.49***
2.38***
0.02
0.06
0.03
0.03
0.03
0.06
0
0
0
0
0
0
0.26
3.01***
0.2
0
1.7***
2.55***
0.09
0.17
1.71***
2.61***
0.09
0.18
0
0
0.2
1.72***
0.16
1
2.72***
0.25
2.74***
0.25
0
0.1
0.12
0.11
2.71***
1.91***
2.04***
0.07
0.09
0.07
0
0
0
1.81***
2.39***
2.46***
2.21***
0.12
0.08
0.08
0.06
1.81***
2.42***
2.53***
2.24***
0.13
0.08
0.09
0.06
0
0
0
0
0.12
2.17***
0.08
0
2.21***
0.05
2.26***
0.06
0
2.01***
0.15
0***
0.15
0
2.01***
2.47***
2.72***
2.28***
2.49***
0.06
0.11
0.17
0.12
0.07
2.03***
2.52***
2.77***
2.33***
2.55***
0.06
0.11
0.17
0.13
0.07
0
0
0
0
0
0.09
0.15
0.25
2.04***
2.42***
2.3***
0.07
0.11
0.15
0
0
0
0.13
2.19***
0.09
0
Note: In the left-hand side of the panel, the input coefficients of the production function were estimated by ordinary
least squares and the mark-ups estimated using fixed effects and year dummies and without fixed effects but with year
dummies, respectively. In the right-hand side of the panel, the input coefficients were computed and the mark-ups
estimated with year dummies and with a time trend, respectively. Standard errors (se) were computed by the delta
method. CRS indicates whether there are constant returns to scale in the sector (1=yes, 0=no).
Source: Authors’ estimation.
38