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PRICE-COST MARGINS AND SHARES
OF FIXED FACTORS
Jozef Konings, Werner Roeger
and Liqiu Zhao
PRICE-COST MARGINS AND SHARES OF FIXED
Jozef Konings, Katholieke Universiteit Leuven and CEPR
Werner Roeger, European Commission
Liqiu Zhao, Katholieke Universiteit Leuven
Discussion Paper No. 8290
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Copyright: Jozef Konings, Werner Roeger and Liqiu Zhao
CEPR Discussion Paper No. 8290
Price-Cost Margins and Shares of Fixed Factors*
Reduced form approaches to estimate price-cost markups typically exploit
variation in observed input and output. However, these approaches ignore the
presence of fixed input factors, which may result in an overestimation of the
price-cost margins. We first propose a new methodology to simultaneously
estimate price-cost margins and the shares of fixed inputs. We then use
Belgian firm level data for manufacturing and service sectors to show that
markups are lower when taking into account fixed input factors. We find that
the average price-cost margin of manufacturing firms is 0.041, compared to
0.090 when we do not control for fixed costs of production. We also show that
price-cost margins increase with the share of fixed costs in turnover. Our
findings provide new insights about observed high price-cost margins in
service industries. In particular, we show that once fixed costs are taken into
account, price-cost margins in service industries are comparable to those in
JEL Classification: L11, L13 and L60
Keywords: fixed input costs, price-cost margins and Solow residual
Faculty of Economics and Applied
Katholieke Universiteit Leuven
For further Discussion Papers by this author see:
For further Discussion Papers by this author see:
Faculty of Business and Economics
Catholic University Leuven
For further Discussion Papers by this author see:
* We would like to thank Jan De Loecker, Klaus Desmet, Damiaan Persyn,
Patrick Van Cayseele, Stijn Vanormelingen, Frank Verboven, Frederic
Warzynski and seminar participants at Louvain La Neuve (Doctoral
Workshop), for useful comments and suggestions. Jozef Konings and Liqiu
Zhao gratefully acknowledge the financial support from the research council
(LICOS excellence center).
Submitted 04 March 2011
The economic implications of institutional change, competitive pressure and anti-trust policy on
market power have been widely conjectured and researched1. One prominent concern has been
how to estimate market power. When detailed data on product characteristics, supply and demand
information, price and input prices for narrowly defined sectors are available, structural models as
introduced by Rosse (1970), Just and Chern (1980) and Bresnahan (1981, 1982) have proven to
be a useful tool, in particular for merger simulation (see Bresnahan, 1989; Budzinski and Ruhmer,
2010, for a review)2. The estimates of price-cost margins obtained from these structural approaches,
however, seem rather high, for example, 0.504 for food processing industry (Lopez, 1984), 0.40 for
railroads industry (Porter, 1983) and 0.16 for luxury car industry (Verboven, 1996). Boyer (1996),
for instance, argues that structural models implicitly assume immediate adjustment of inputs to
changes in costs and hence they fail to capture the diversity of oligopoly with respect to the fixity of
capital, capital adjustment costs, etc.. With less detailed data on product characteristics, alternative
approaches have been adopted making use of the observed variation in output and input factors,
following the methodology of Hall (1986, 1988, 1990) and Roeger (1995). The markup estimate of
these reduced form approaches is less sensitive to specification bias, but is sensitive to the choice
of input factors included in the model specification.Largely overlooked in these reduced form
approaches, however, has been the issue of whether fixed costs in production condition how price-
cost margins are affected. This is not surprising as fixed costs of production are usually not observed
to the econometrician.
Recently, reduced form approaches have commonly been used for estimating markups in the
literature, but, like in the structural approaches, the estimates of price-cost margins usually are
rather high (between 0.15 and 0.25). As suggested by Roeger and Warzynski (2009), one explanation
for these high estimates is that typically fixed costs of production are not taken into account, often
because fixed costs are usually not observable in firm level data. For example, using a production
function specification in which capital is held quasi-fixed, Klette (1999) obtains a very low markup
estimate. Alternatively, de Loecker and Warzynski (2009) propose a new estimation approach, which
does not require measuring the cost of capital or assuming constant returns to scale. They start from
Hall and apply the control function approach of Olley and Pakes (1996) to control for unobserved
productivity. But their approach may be subject to the omitted price variable bias by using deflated
sales as a proxy for physical quantity. The treatment of capital cost, as either fixed, variable or
both, seems important for estimating markups. Our estimation methodology allows for the flexible
1For example, Domowitz, Hubbard, and Peterson (1988) in the context of cyclical fluctuations, Levinsohn (1993)
and Harrison (1994) in the context of trade liberalization, Konings, Cayseele, and Warzynski (2005) analyze the effect
of privatization, Konings and Vandenbussche (2005) evaluate the effect of trade policy on markups of firms. Kee and
Hoekman (2007) study the impact of competition law, import exposure and the number of domestic firms on markups.
2The main disadvantage of the structural approach is that the results depend critically on a variety of assumptions
treatment of capital and allows to solve the endogeneity problems concerning productivity.
To this end, we start from the approaches introduced by Hall (1988) which estimates markups
from primal Solow residual and suffers from endogeneity problems and Roeger (1995), which exploits
the variation in the primal and dual Solow residual to derive a consistent estimate of the markups.
But instead of assuming that all input factors in production are fully flexible we introduce a distinc-
tion between variable and fixed capital as well as variable and fixed labor input. This will allow us
to simultaneously estimate the fixed costs in the production process and the price-cost margins. The
method that we introduce, has the advantage that we can both estimate the markups and the fixed
shares of input factors in a consistent manner, without having to worry about potential correlations
between the unobserved productivity shocks and the input factors of production. An additional
advantage of this approach is that it also relaxes the constant returns to scale assumption which is
required in Roeger (1995), instead, the constant returns to scale on the variable factors is required
in the model.
We then apply our methodology to micro data of Belgian firms operating in manufacturing sectors
and in service sectors for the period 1999-2008. We find that our estimates of price-cost margins
are lower when controlling for the existence of fixed capital and labor in manufacturing sectors, and
the estimated price-cost margins increase with the share of fixed costs in turnover. The markups in
service sectors seem rather high in earlier studies explained by the nature of services exchange and
pervasive regulations in services. This has inspired the European Union to implement some policies
to strengthen competition in services since the 1990s. Nevertheless, recent empirical studies find it
somewhat puzzling that so little progress has been made in reducing the markups in service sectors.
Badinger (2007) even finds a small increase in service markups in the EU, despite efforts by the
EU Commission (European Commission 2002) to implement the so-called “Service Directive” which
aims at reducing administrative entry barriers and increase cross border service flows3. Applying
our methodology to firms in service sectors allows us to analyze whether the higher markups that
are often observed in service sectors (e.g. Siotis, 2003; Christopoulou and Vermeulen, 2008; Martins,
Scarpetta, and Pilat, 1996) are potentially driven by the presence of fixed costs. We find that in
knowledge-intensive service sectors fixed costs are important and can explain why in earlier studies
service sectors had high markups. Our analysis can potentially shed some lights on the importance
of technological factors for explaining persistent markup differences across sectors.
The remainder of the paper is organized as follows. The next section summarizes Hall’s and
Roeger’s approaches and then extends it to allow for fixed input factors. In section 3 we describe the
firm level data that used and section 4 provides the results and discussions. In section 5, we apply
our approach to the service industry. Section 6 gives some concluding remarks and discussions.
3The “Services Directive” was adopted in 2006 aiming to further the “Single Market” for services by reducing
the barriers to cross-border trade, principally by doing away with the service industry regulations of individual EU
Consider a production function Q = F(K,L,M)Θ, where K, L and M are capital, labor and
material inputs, respectively, Θ is an index of technical progress. Under the assumptions of perfect
competition and constant returns to scale, the Solow residual is given by SRQR≡ ∆q −WL
PQ∆m − (1 −WL
1957), where ∆q, ∆l, ∆m and ∆k are the growth rates of output, labor, material and capital inputs,
PQ)∆k and captures the growth rate of total factor productivity (Solow,
respectively. By relaxing the condition that price equals marginal cost, Hall (1988) shows that the
Solow residual can be decomposed into a markup and a productivity factor:
SRQR= B(∆q − ∆k) + (1 − B)∆θ(1)
where B is the price-cost margin defined as B ≡P−MC
via µ = 1/(1 − B).
The problem in estimating equation (1) is that unobserved productivity shocks may be positively
, which is directly related to the markups
correlated with output growth. Thus instrumental variables are required to estimate B. However, it
is difficult to find instruments that are correlated with output growth but are neither a consequence
nor a cause of technological innovations. Then the estimated markup has an upward bias.
To deal with the potential endogeneity problem, Roeger (1995) obtains a dual price-based Solow
residual by solving the dual cost minimization problem, and applies it to cancel out the productivity
shocks factor. The dual Solow residual is:
= −B(∆p − ∆r) + (1 − B)∆θ
∆pM+ (1 −WL
)∆r − ∆p
where ∆p, ∆w, ∆pMand ∆r are the growth rates of product price, wage, material price and the
rental price of capital, respectively.
Subtracting equation (2) from (1), the term capturing productivity shocks is eliminated. And the
price-cost margins B can be consistently estimated by equation (3) if factors of production can be
adjusted instantaneously (that is, they are costless to adjust) and variables in (3) can be measured
SRQR− SRPR= B[(∆q + ∆p) − (∆k + ∆r)] + ?(3)
However, the assumption that factors of production can be adjusted instantaneously may not be
satisfied and may be more problematic for capital, because it goes against the evidence of considerable
adjustment costs in capital. We next introduce fixity of inputs and by taking difference-in-differences
we are able to estimate both markups and the shares of fixed input factors consistently.
2.3.1Primal and Dual Solow Residuals with Revenue-based Shares
We start from a standard production function with constant returns to scale on the variable factors:
Q = F(K − Kf,L − Lf,M)Θ(4)
where output Q is produced with variable capital K − Kf, variable labor L − Lfand material
M4. Θ is the productivity term. Kf(Lf) is the type of capital (labor) which is not adjusted
within a period to current demand and cost changes. While Kv(Lv) is the fraction of total capital
(labor) which is adjusted to current demand and cost changes without friction. In equation (4), the
short-run fixity of capital and labor inputs is allowed for5. Data availability precludes an explicit
distinction between the two types of capital (labor). Let svkand svldenote the share of variable
capital Kv/(Kv+ Kf) and share of variable labor input Lv/(Lv+ Lf), respectively, which capture
the production technology that firms apply but are unobservable for economists. As will be shown
below, the shares can be estimated in the model simultaneously.
Under imperfect competition, the first order condition and Euler’s law imply that the output
growth is determined by a weighted sum of the input growth and the growth rate of productivity.
Input weights are given by the corresponding shares of variable costs in revenue adjusted by markups.
are shares of variable capital cost, variable labor cost and material
cost in turnover, respectively. Constant returns to scale implies that the total variable cost is
Cv= MC · Q = svkRK + svlWL + PMM.
As in Hall (1988) the primal Solow residual with revenue-based shares is defined as
SRQR≡ ∆q −WL
∆m − (1 −WL
Substituting equation (5) into equation (6), we get the primal Solow residual with revenue-based
SRQR=B(∆q − ∆k) +(1 − svl)WL
∆k +(svl− 1)WL
(∆kv− ∆k) +svlWL
(∆lv− ∆l) + (1 − B)∆θ
Following Roeger (1995), we apply the dual price-based Solow residual to eliminate the growth
rate of productivity in equation (7). The dual cost minimization problem gives Cv=
which corresponds to the production function (4). So the marginal cost is MC =
4We assume that fixed capital and fixed labor input do not directly enter into production function and they can
be treated as overhead costs. All material is variable input.
5For instance, because of hiring and firing costs or the presence of trade union, labor cannot be adjusted freely.
Logarithmic differentiation of marginal cost and using Shepard’s lemma gives:
Substituting equation (8) into the dual Solow residual with revenue-based shares defined as
equation (9), we obtain equation (10).
∆pM+ (1 −WL
)∆r − ∆p(9)
SRPR= −B(∆p − ∆r) +(svl− 1)WL
∆r +(1 − svl)WL
∆w + (1 − B)∆θ(10)
By subtracting (10) from equation (7), the growth rate of productivity is eliminated. The differ-
ence of the primal and dual Solow residual with revenue-based shares is,
SRQR− SRPR=B[(∆q + ∆p) − (∆k + ∆r)] +(1 − svl)WL
(∆k + ∆r)+
(∆w + ∆l) + svkRK
PQ(∆kv− ∆k) + svlWL
The difference of the Solow residual and the price-based dual Solow residual is explained by
capital (labor) fixity and imperfect competition6. In contrast to equation (3), four extra terms
appear in equation (11) which make the prediction of the direction of the estimation bias of Roeger
(1995) impossible. The omission of terms
PQ(∆k + ∆r) and
PQ(∆w + ∆l) leads to a downward
bias, while the omission ofRK
PQ(∆lv−∆l) leads to an upward bias. In addition,
equation (11) cannot be used to estimate B, the average share of fixed capital sfk≡ 1 − svkand
the average share of fixed labor input sfl≡ 1 − svleither, as the growth rate of variable inputs
∆kvand ∆lvare unobservable in the firm level data. And ∆kvand ∆lvare positively correlated
with the growth rate of output, which may lead to an upward bias in the estimate of the price-cost
margins using equation (11). In the next section, we try to construct a similar difference of primal
and dual Solow residual with cost-weighted shares to eliminate the unobservable terms.
2.3.2Primal and Dual Solow Residuals with Cost-based Shares
Hall (1990) proposes a cost-weighted TFP measures as a way of avoiding the bias caused by imperfect
competition. However, in the presence of fixed inputs, the cost-weighted Solow residual captures
not only productivity growth but also the fixity of inputs. In this section, we derive cost-weighted
primal and dual Solow residuals allowing for the presence of fixed inputs.
Similarly, the growth rate of output can be written as a cost-weighted average of the growth rate
of variable inputs plus the growth rate of productivity.
∆m + ∆θ(12)
6Shapiro (1987) focus on capital fixity to explain why the Solow residual is poorly correlated to the dual Solow
residual, while Roeger (1995) stresses imperfect competition in explaining the difference between Solow residual and
dual Solow residual.
The primal Solow residual with cost-based shares SRQCis defined as follows:
SRQC≡ ∆q −WL
Substituting equation (12) into equation (13), we have
SRQC=(1 − svk)RK
(∆q − ∆k) + (1 − svl)WL
(∆kv− ∆k) + svlWL
(∆q − ∆l)+
(∆lv− ∆l) +Cv
The dual cost minimization problem implies that the growth rate of price can be written as a
cost-weighted average of the growth rate of inputs’ prices minus the growth rate of productivity.
∆pM− ∆θ (15)
The dual Solow residual with cost-based shares is then
= −(1 − svk)RK
(∆p − ∆r) − (1 − svl)WL
(∆p − ∆w) +Cv
By subtracting (16) from equation (14), the growth rate of productivity is eliminated. The
difference of the primal and dual Solow residual with cost-based shares is,
SRQC− SRPC=(1 − svk)RK
[(∆q + ∆p) − (∆k + ∆r)] + (1 − svl)WL
(∆kv− ∆k) + svlWL
[(∆q + ∆p) − (∆l + ∆w)]+
We find that in equation (11) and (17) the unobservable parts are similar except for the denominator.
Multiplying both sides of equation (11) by PQ and multiplying both sides of equation (17) by C
(SRQR− SRPR)PQ =B · PQ[(∆q + ∆p) − (∆k + ∆r)] + (1 − svl)WL(∆k + ∆r)+
(svl− 1)WL(∆w + ∆l) + svkRK(∆kv− ∆k) + svlWL(∆lv− ∆l)
(SRQC− SRPC)C =(1 − svk)RK[(∆q + ∆p) − (∆k + ∆r)] + (1 − svl)WL[(∆q + ∆p) − (∆l + ∆w)]+
svkRK(∆kv− ∆k) + svlWL(∆lv− ∆l)
By subtracting equation (18) from equation (19), the unobserved parts svkRK(∆kv− ∆k) and
svlWL(∆lv− ∆l) can be cancelled out.
(SRQC− SRPC)C − (SRQR− SRPR)PQ =
− B · PQ[(∆q + ∆p) − (∆k + ∆r)] + sfkRK[(∆q + ∆p) − (∆k + ∆r)] + sflWL[(∆q + ∆p) − (∆k + ∆r)]
In equation (20), all variables are observable, so equation (20) can easily be estimated with firm
level data to obtain the estimates of price-cost margins B, share of fixed capital sfkand share of
fixed labor input sfl. The parentheses terms on the right side of equation (20) all refer to growth
rates of nominal values of output and input factors. Since the left-hand side of equation (20) is
the difference of the difference of primal and dual Solow residual with cost-based shares and the
difference of primal and dual Solow residual with revenue-based shares, we call it “difference-in-
difference” (DID) approach.
3Data and Variables
The data used in this paper are drawn from the Belfirst database collected by Bureau van Dijk.
The database includes the full income statements of every Belgian firm that has to report to the
tax authorities. We have data of firms active in manufacturing and firms that are active in services
and have a panel with observations running from 1999 through 2008. The variables used for the
analysis are turnover, number of employees (in full time equivalents), wage bill of full-time equivalents
employees, material costs (raw materials, consumables and services) and tangible fixed assets.
Our final sample consists of an unbalanced panel of 9,103 firms operating in manufacturing sectors
with a total of 44,253 observations and 61,117 firms operating in service sectors with a total of 239,116
observations (See Appendix A for a detailed description of the dataset and cleaning process). Table
1 provides some summary statistics of the main variables. The median manufacturing firm has 15
employees, 0.404 million Euro tangible fixed assets, earns a revenue of 3.29 million Euro and faces
staff cost of 0.58 million Euro per year. Firms in service industries seem to be smaller and have a
larger variation. The median service firm has 3 employees, 98 thousand Euro tangible fixed assets,
earns a revenue of 0.668 million Euro and faces staff cost of 85 thousand Euro per year.
[Table 1 about here.]
3.2 Capital Cost
The measure of capital cost commonly used in literature is based on Hall and Jorgenson (1967). For
capital, we use the book value of the tangible fixed assets. The rental price of capital is based on
the standard Hall and Jorgenson (1967) formula: R = PI(r − π + δ), where PIdenotes the index of
investment goods prices, r stands for the nominal interest rate, π is the inflation rate, and δ is the
depreciation rate on fixed assets which we assume to be 20% for every sector.
We start by estimating the average price-cost margins for the manufacturing industry as a whole
without controlling for fixed inputs using the traditional Roeger approach. The results shown in
column (1) and (2) of Table 2 indicate that the average price-cost margin is 0.090, suggesting the
average markup is 1.10 for manufacturing firms in Belgium. In column (3) and (4), we apply our
new methodology to estimate the average price-cost margins and the average shares of fixed inputs.
The fixed effect model gives similar results as OLS. The average price-cost margin is 0.041 which
is much lower than the estimate using the traditional Roeger approach. The average share of fixed
capital is 28% and the average share of fixed labor input is 7.3%.
[Table 2 about here.]
4.2 Do Profits Cover Fixed Costs?
The estimated price-cost margin using DID approach is much lower than that from Roeger (1995),
which gives rise to one concern: is it high enough to cover fixed costs? To address this question,
we compare the calculated profit and fixed cost based on the coefficients estimated. The estimated
profit is ˆ π = (P − MC)Q =ˆB × PQ, and the estimated fixed cost isˆF =
define the excess profit margin as (ˆ π −ˆF)/PQ.
Figure 1 shows the kernel probability density estimates of the excess profit margin7. The average
sfkRK +ˆ sflWL. We
excess profit margin is 0.0061 in the sample which is consistent with the free entry condition in the
market. While around 75% firms make positive excess profits.
[Figure 1 about here.]
4.3Price-cost Margins, Fixed Costs and Estimation Bias
In this section, we investigate the relation between price-cost margins, fixed costs and estimation
bias through estimating equation (20) by sectors. Table 3 shows the estimates of price-cost margins
and shares of fixed inputs for each NACE 2-digit manufacturing sector in Belgium. Column (1)
reports the price-cost margins estimated by the traditional Roeger approach. Column (2) to (4)
show the results estimated by our approach. Here we only focus on the sectors with at least 500
observations in order to obtain statistically reliable results.
[Table 3 about here.]
7In order to focus on the shape of distribution, we exclude the smallest 1st percentile and largest 99th percentile
observations in the kernel density estimates.
We find that the average price-cost margins vary across sectors. On average, price-cost margins
estimated by Roeger (1995) are larger than that estimated by “difference-in-difference” approach
for all sectors except three – pulp, paper and paper products; other nonmetallic mineral products;
radio, TV, and communication equipment – with significantly higher shares of fixed labor input.
The PCM estimated by DID is positively correlated with that by Roeger (1995) with the correlation
coefficient of 0.36. The results in column (2) also show that other nonmetallic mineral products,
machinery and equipment n.e.c., radio, TV, and communication equipment are among the highest
Regarding the share of fixed capital, the results shown in Table 3 are in line with our expectations,
as the share is larger in sectors where we would expect fixed costs to be high. Basic metals (0.86)
has the highest share of fixed capital, followed by motor vehicles, trailers, and semi-trailers (0.75),
wood, straw, and plaiting materials (0.63), chemicals and chemical products (0.64), and machinery
and equipment n.e.c.(0.58). Turning to the share of fixed labor input, 9 out of 18 sectors have
significantly positive share of fixed labor input. Radio, TV, and communication equipment has the
highest share of fixed labor input (0.34).
As expected, firms with higher fixed costs (sfkRK + sflWL) would have higher price-cost
margins, i.e., price-cost margins increase with the share of fixed costs in turnover. Figure 2 provides
a strong evidence for it. Sectors with higher share of fixed costs in turnover are likely to charge
[Figure 2 about here.]
In addition, we are also interested in the relation between the estimation bias and the shares
of fixed inputs. Since all estimates vary across sectors, we are able to have rough pictures of them
by looking into the estimates by sectors. As discussed in section 2, equation (11) suggests that the
bias introduced by ignoring fixed costs is negatively correlated with the share of fixed labor input8.
However, the relation between the estimation bias and the share of fixed capital is more complex.
There should exist an inverted U-shaped relationship between the estimation bias and the share of
fixed capital: when the share of fixed capital is 0, svk RK
OQ(∆kv− ∆k) in equation (11) equals to 0,
OQ(∆kv− ∆k) also equals to 0, but if the
OQ(∆kv−∆k) is positive which leads to an upward
when the share of fixed capital is increased to be 1, svk RK
share of fixed capital falls in the range (0,1), svk RK
bias. The scatterplot of the bias and the share of fixed capital (share of fixed labor input) is shown
in Figure 3. The bias is strongly negatively correlated with the share of fixed labor input, which is
in line with our expectation. However, the relation between the bias and the share of fixed capital
is weak and not clear.
[Figure 3 about here.]
8In equation (11), sflis the coefficient of both downward bias terms.
4.4Different Production Technology: High-tech Manufacturing Sectors
To examine whether different production technology matters in the estimation of markups, manufac-
turing firms are split into high-technology and low-technology sectors following Eurostat’s definition
of high-technology, medium high-technology, medium low-technology and low-technology according
to technological intensity (see Appendix C for details). Table 4 reports the results for high-tech and
low-tech sectors separately. The average price-cost margin for high-tech sectors is 0.047, the average
share of fixed capital and the average share of fixed labor input are 43% and 10%, respectively. The
results indicate that high-tech manufacturing sectors have higher shares of fixed capital and labor
input, and charge higher markups, which are in line with theories.
[Table 4 about here.]
5 An Application: Manufacturing Vs. Service Industries
5.1A Debate over Service Industries
Since the mid 1990s, the productivity gap between Europe and the United States has increased
dramatically: GDP per hour worked in the EU has decreased from 98.3 percent of the U.S. level in
1995 to 90.0 percent in 2006. van Ark, O’Mahony, and Timmer (2008) show that the productivity
slowdown in European countries is largely the result of slower productivity growth in service sectors,
particularly in trade, finance, and business services9. They further argue that the lack of flexibility
and competitiveness in labor and product markets in service sectors in the EU is one of the causes of
the trend10. Desmet and Parente (2010) also suggest that the European service sectors can benefit
(productivity gains) from the increase in the competition and spatial concentration in the service
sectors. In particular, the network utilities, such as post and telecommunications, air transport,
are still highly regulated in Europe. For example, incumbent operators are largely protected from
competition in most EU countries through their monopolies or other regulations. So increasing the
flexibility in labor market and strengthening the competition in service product markets within and
across countries are claimed to be important to improve productivity growth in European service
Unlike manufacturing, services do not suffer much from international competition because of
the nature of services exchange and the restrictiveness of services trade policies, suggesting that
there is less competitive pressure in service sector. A number of empirical studies find that service
industries have higher markups than manufacturing industry (e.g. Siotis, 2003; Christopoulou and
Vermeulen, 2008; Martins, Scarpetta, and Pilat, 1996).Nevertheless, since the 1990s, EU has
9That is, productivity levels in manufacturing are relatively similar across countries compared to intermediate
10The productivity in service sectors is important for the whole economy. First, services account for 70% of GDP
and employment in most EU Member States. Second, competition in the Service Sector is a major determinant of
the performance of manufacturing firms Francois and Hoekman (2010).
implemented a series of policies to encourage competition between European service producers so as Download full-text
to strengthen competition and foster efficiency in service sectors, for example, the “Single Market”
and “Services Directive”. However, despite the great efforts made by the EU Commission, Badinger
(2007) shows that the markups in most service industries have increased since the early 1990s in EU
countries. Hence, if the competition is as low as shown by the estimated markups in service sectors,
liberalization and deregulation of services are likely to have a pro-competitive effect. However,
this crucially relies on the reliability of the estimation of markups. We therefore apply our new
methodology to investigate whether service industries charge high markups after controlling for the
fixed factors of production, i.e., the shares of fixed inputs.
5.2 Price-cost margins in Manufacturing and Service Industries
There is large variation in service industries in terms of sales, employment, tangible fixed assets
etc.(see Table 1). As shown in Figure 4, the price-cost margins calculated following Collins and Pre-
ston (1969) are higher in service sectors, especially the knowledge-intensive services (KIS hereafter),
than manufacturing sectors (See Appendix D for the definition of KIS and LKIS). As the fixity of
capital input may matter more in KIS, we focus on this sector.
[Figure 4 about here.]
The first two columns in Table 5 show the results using the Roeger (1995) approach, suggesting
that the estimated price-cost margin in KIS industries is 0.15, which is almost the double of that
in manufacturing industry (0.09). The last two columns in Table 5 report the results using our
new approach. The estimated price-cost margin is 0.049, slightly higher than that in manufacturing
industry (0.041) but similar as that in high-tech manufacturing sector (0.047). The average share
of fixed capital is 0.49, which is much higher than that in manufacturing industry (0.28), but the
coefficient of share of fixed labor input is insignificant.
The results in Table 5 imply that the price-cost margins of KIS are overestimated by the tradi-
tional approach not taking into account of the fixity of capital, which is high in KIS industries. After
controlling for the fixity of inputs, the price-cost margin in KIS industries is only slightly higher than
that in manufacturing industry.
[Table 5 about here.]
The KIS sectors have relatively higher markups but also a higher share of fixed capital. We
therefore check whether KIS sectors have higher excess profit margins compared to manufacturing
sectors. As in Figure 1, we depict the Kernel probability density estimates of the excess profit margin
for service sector in Figure 5. The average excess profit margin is -0.0058 in the sample and the left
hand tail is longer comparing to the distribution for manufacturing industry. While around 70% of