-
Growing like Spain: 1995-2007
Manuel Garca-Santana
Universite Libre de Bruxelles (ECARES)
Enrique Moral-Benito
Banco de Espana
Josep Pijoan-Mas
CEMFI and CEPR
Roberto Ramos
Banco de Espana
April 9, 2015
Abstract
Spanish GDP grew at an average rate of 3.5% per year during the
expansion of 1995-2007,
above the EU average of 2.2%. However, this growth was based on
factor accumulation rather
than productivity gains. In particular, TFP fell at an annual
rate of 0.7%, while it increased
at 0.4% in the EU and 0.7% in the US. Why did Spain fail to
benefit from the growth of the
technological frontier? We argue that deterioration in the
allocative efficiency of productive
factors across firms is at the root of the low TFP growth in
Spain. Using administrative data of
firms we show that within-sector misallocation of production
factors increased substantially over
the period in all industries, with most of the effects coming
from inefficient capital and labor
mix rather than inefficient size. We find that absent such
deterioration, average TFP growth
would have been around 0.8% per year, in line with the growth of
the technological frontier.
Finally, we provide empirical evidence that differences in the
influence of the public sector
across industries is a potential source of this deterioration.
In contrast, sectoral differences in
skill intensity, innovative content, or financial dependence are
unrelated to changes in allocative
efficiency. We also document that young and small firms were the
most affected.
JEL Codes: D24, O11, O47.
Keywords: TFP, Misallocation, Spain.
We thank John Fernald for sharing the financial intensity data
with us, and Eric Bartelsman for helpful discussion.We also thank
seminar participants at Banco de Espana for useful comments.
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1 Introduction
The 1994-2007 expansion was the longest in Spanish history (see
Berge and Jorda (2013)). GDP grew
at an average 3.5% per year, which compares very favourably to
the EU average of 2.2% over the same
period.1 However, Spanish growth during this expansion was based
on factor accumulation rather
than productivity gains. In particular, annual TFP growth was
-0.7%, which is low in comparison to
other developed economies such as the US or EU. Such a dismal
performance of productivity growth
is surprising for a country that is so well integrated in a
trade and monetary union with some of the
World technology leaders. Did Spain fail to keep up with the
technological frontier?
In this paper, we argue that the source of negative TFP growth
has been the increase in the
within-sector misallocation of production factors across firms.
We use a large administrative data
set of Spanish firms to compute several measures of allocative
efficiency for every year between 1995
and 2007. In particular, we compute the potential TFP gains due
to factor reallocation as Hsieh and
Klenow (2009) and the Olley and Pakes (1996) covariances. All
measures show a severe deterioration
of allocative efficiency over the period. Furthermore, we find
the phenomenon to be present in all
sectors of activity, which casts doubt on the widespread view
that specialization in low productivity
sectors such as construction was the main force behind Spanish
low TFP growth. We thus argue
that allocative efficiency of resources across firms is at the
root of the low rates of TFP growth
observed in Spain. Our results are very stark: had the level of
within-sector allocative efficiency
remained constant, TFP growth would have been around 0.8% per
year. Therefore, our conclusion is
that Spain did not fail to keep up with the technological
frontier. Aggregate productivity stagnated
because the economy increasingly allocated capital and labor in
the wrong place across firms within
each industry.2
The deterioration of factor allocation across firms during an
economic expansion is arguably a
singular experience in Spain. Bartelsman, Haltiwanger, and
Scarpetta (2013) find that misalloca-
tion remained roughly constant over the nineties and early 2000s
in several developed countries
such as US, UK, Germany or the Netherlands, while it clearly
fell for the transitional economies
of Central and Eastern Europe. Lewrick, Mohler, and Weder (2014)
find that improvements in the
within-industry allocation of resources is one of the main
drivers of aggregate TFP growth in Swiss
manufacturing. Using the Hsieh and Klenow (2009) framework,
Bellone and Mallen-Pisano (2013)
find that misallocation remained constant between 1998 and 2005
in France, while Dias, Robalo, and
1EU average refers to the EU15 group, which includes Austria,
Belgium, Denmark, Finland, France, Germany,Greece, Ireland, Italy,
Luxembourg, the Netherlands, Portugal, Spain, Sweden and the United
Kingdom. We takethis reference group of developed countries similar
to Spain because we have comparable growth accounting data
fromEU-KLEMS.
2The prominent role of within industry misallocation as a
hindrance to TFP growth has relevant policy implica-tions. For
instance, reallocation of workers across industries is generally
more costly than reallocation within industries(see e.g. Shin
(1997)).
1
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Richmond (2014) show that misallocation increased in Portugal
between 1996 and 2011, but this
was a period of stagnation in Portugal. Finally, Chen and
Irarrazabal (forthcoming) find that the
Chilean economy experienced a substantial decline in
misallocation over its 1983-1996 expansion.
In order to shed some light on the potential sources of this
phenomenon in Spain, we evaluate the
relationship between several sector-specific characteristics and
the changes in allocative efficiency. In
particular, we find that industries in which the influence of
the public sector is larger (e.g. through
licensing or regulations) experienced significantly larger
increases in misallocation. In contrast, other
sectoral characteristics such as skill intensity, innovative
content or financial dependence are unrelated
to changes in allocative efficiency. Turning to firm-specific
distortions, we find that small and young
firms in Spain might have faced higher market distortions than
large and mature firms. As a result,
these firms grew less than optimal and operated with
capital-labor ratios smaller than optimal (i.e.
those observed in the same industries in the US). Finally, we
show that the increase in misallocation
across firms is present in all Spanish regions, and that
regional differences in average wage growth
are uncorrelated with the increase in distortions.
It remains to be understood why the Spanish economy accumulated
capital and labor at such
a fast pace despite the negative increase in aggregate
productivity. Our view is that this was due
to exogenous supply factors. The convergence process leading to
the entry to the EMU reduced
interest rates dramatically and created an unprecedented credit
boom. As shown by Fernandez-
Villaverde, Garicano, and Santos (2013), the total credit to GDP
ratio tripled between 1994 to
2007.3 Along these lines, Gopinath, Kalemli-Ozcan,
Karabarbounis, and Villegas-Sanchez (2015)
rationalize low rates of TFP growth in South Europe by
developing a model of heterogeneous firms
that can generate misallocation of capital across firms as a
result of financial frictions and investment
adjustment costs. An alternative view of this supply side
explanation is given by Daz and Franjo
(2014). These authors show that the large increase in capital
accumulation over the period was
largely due to capital structures, which they interpret as the
result of government subsidies. The
process of capital accumulation triggered by cheap credit could
by itself increase the employment
rate. However, there were also clear labor supply factors at
play: the working-age population ratio
increased over the period and females of new cohorts
participated in the labor market at a much
larger rate than females of the older cohorts.
The rest of the article is organized as follows. Section 2
briefly shows the growth accounting
results for Spain as well as some micro-based evidence
motivating the paper. Section 3 describes the
data. Then, Section 4 presents the main results regarding the
increase in misallocation and Section 5
explores the potential sources of misallocation. Some concluding
remarks are provided in Section 6.
3These authors also argue that the credit boom might be the very
reason behind an increase in misallocationof productive factors
across firms. Banks face a signal-extraction problem to identify
good firms. In bubble timesthe signal becomes more noisy and hence
credit may be allocated less efficiently. This would be consistent
with ourfinding that the increase in distortions was larger among
young firms. However, we should also expect the increase
inmisallocation to be larger in sectors with higher financial
dependence, a pattern that is absent in the firm-level data.
2
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Finally, Appendix A reviews the theoretical framework of Hsieh
and Klenow (2009) and Appendix
B contains additional results.
2 The 1995-2007 growth experience
The Spanish economy grew by around 3.5% per year between 1995
and 2007. This expansion, the
longest in the twentieth century, helped Spanish income per
capita surpass the EU average in the
early 2000s. Growth accounting exercises show that the boom was
driven by factor accumulation
(labor and capital) rather than increases in productivity. Using
data from EU-KLEMS, Figure 1
clearly illustrates this pattern.
Figure 1: The Spanish growth experience Macro evidence
100
120
140
160
180
1995 1998 2001 2004 2007
Production Labor Capital TFP
The labor contribution to output (total hours worked) expanded
3.8 percent a year in 1995-2007.
This was the result of three main factors: a fast growing
working age population, mainly due to
migration flows, and an increasing labor force participation
rate, mainly reflecting the incorporation
of women into the labor market, and a decline of the
unemployment rate since the high values
achieved in 1993. The capital stock also grew at an
unprecedented pace of 5.2 percent a year. The
rise of the construction sector together with easy borrowing
conditions played an important role
in the expansion of the capital stock in Spain. Since both labor
and capital grew more than final
production, total factor productivity (TFP) was reduced by 0.7%
per year.
3
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These Spanish figures are in sharp contrast to other developed
economies. In the average EU
country, output growth was 2.2% per year with growth rates of
1.1% and 3.3% for labor and capital
respectively. As a result, TFP growth in the EU was on average
0.4% per year in contrast to the
Spanish annual rate of -0.7%. This difference is even more
pronounced with respect to the US
economy, which experienced TFP growth rates around 0.7% per year
over the 1995-2007 period.4
Figure 2: The Spanish growth experience Micro evidence
0Ch
ange
in sh
are 20
012
007
0Relative TFP in 2001
Manufacture of butter
0Ch
ange
in sh
are 20
012
007
0Relative TFP in 2001
Manufacture of toys
0Ch
ange
in sh
are 20
012
007
0Relative TFP in 2001
Joinery installation
0Ch
ange
in sh
are 20
012
007
0Relative TFP in 2001
Sale of textiles
0Ch
ange
in sh
are 20
012
007
0Relative TFP in 2001
Retail sale of bread
0Ch
ange
in sh
are 20
012
007
0Relative TFP in 2001
Retail sale of telecom
1.96 1.96
0.05
.1
.15
30 20 10 0 10
tstatistics for industryspecific regressions
Notes: Relative TFP refers to the logarithm of firm-specific TFP
relative to the industry average, ln(TFPi/TFP).Change in share
refers to the difference in firm-specific market share measured in
terms of sales.
Turning to the micro-based evidence, a first glimpse at the data
cleary illustrates the deterioration
in the within-industry allocation of resources across firms. In
the upper panel of Figure 2 we plot
the change in market shares over the 2001-2007 period against
the level of TFP in 2001 for six
selected 4-digit industries.5 In all the six cases the
relationship is negative, which means that firms
with initial TFP below the industry average gained market share
at the expense of firms with larger
4See EU-KLEMS dataset at www.euklems.net.5For illustrative
purposes we focus on the 2001-2007 period to maximize the number of
observations in the scatter
plots. This is so because we use a balanced version of our panel
dataset in order to compute changes in shares.
4
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TFP. This negative relationship is negative and statistically
significant for 80 per cent of the 356
industries considered as we can see in the bottom panel of
Figure 2, which plots the distribution of
the t-statistics resulting from the 356 sector-specific
regressions. We interpret this evidence as an
intuitive illustration of how more productive firms lost market
share at the expense of less productive
firms, which, in our view, is a clear indication of a
deterioration in the allocation of resources across
firms within each industry. However, in the remainder of the
paper we focus on well-known indicators
of allocative efficiency that facilitate the mapping between the
micro and the macro evidence.
3 Data
We use a firm-level dataset containing information of a
representative sample of non-financial com-
panies in Spain from 1995 to 2007. The sample contains an
average number of 497,782 firms per
year. This database is named Central Balance Sheet Data (Central
de Balances) and is provided by
the Banco de Espana.
The database is comprised of two complementary datasets. The
first one is based on a stan-
dardized voluntary survey handled to companies at the time of
requesting compulsory accounting
information. Each year around 9,000 companies fill this survey.
The information gathered is very de-
tailed, but the sample size is low and big firms are over
represented. The second dataset contains the
balance sheets of a much larger number of companies. It
originates from the firms legal obligation
to deposit their balance sheets on the Mercantile Registry.
Therefore, coverage is much wider.
The Bank of Spain Central Balance Sheet Office is in charge of
collecting and cleaning these
datasets. All of the variables contained in the second database
are also included in the first one. For
each firm, we observe its value added, total wage bill,
employment, book value of the capital stock
(both physical and intangible) and sector of activity at the
4-digit level (according to NACE rev. 2
classification). Since most of the variables are recorded in
nominal terms, we employ sector-specific
deflators for capital and value added, to compute real values
with 2000 as base year.6
Panel A of Table 1 illustrates the size distribution of firms in
our raw sample for the year 2001.
The table also compares this distribution with that obtained
from the Central Business Register
available from the National Statistics Institute. There are two
important aspects to highlight. First,
the coverage of our raw sample is remarkably large in terms of
both the number of firms (56% of
the operating firms in Spain) and the level of employment (54%
of total employment). Second, our
sample provides an excellent representation of the firm size
distribution in Spain. In particular, small
firms (less than 10 employees) account for 83.90% of the total
number of firms and 20.47% of the
employment in our sample versus 83.07% and 20.23% in the
population. At the other extreme, large
firms (more than 200 employees) represent less than 0.5% of the
total number of firms both in our
6The capital deflator is collected from Mas, Perez, and Uriel
(2013) and the value added deflator is taken fromthe National
Accounts. Both deflators are constructed at the 2-digit NACE
classification.
5
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sample and in the population, while they account for 33.47% of
the employment in our sample and
32.13% in the population.
Table 1: Size distribution of firms in our sample and in the
census.
Central Balance Sheet Dataset Central Business Register
Firms Labor Firms Labor
Number of employees Total (#) Share (%) Total (#) Share (%)
Total (#) Share (%) Total (#) Share (%)
PANEL A: Raw Sample
0-9 406,924 83.90 941,897 20.47 715,795 83.07 1,718,600
20.2310-19 41,664 8.59 583,312 12.68 77,372 8.98 1,050,038
12.3620-49 27,125 5.59 828,714 18.01 46,683 5.42 1,400,422
16.4950-199 8,064 1.66 707,535 15.38 17,781 2.06 1,596,481
18.79+200 1,245 0.26 1,540,260 33.47 4,082 0.47 2,728,958 32.13All
485,022 100.00 4,601,718 100.00 861,713 100.00 8,494,499 100.00
PANEL B: Final Sample
1-9 249,770 76.34 907,098 20.00 531,399 78.46 1,718,600
20.2310-19 41,272 12.62 577,844 12.74 77,372 11.42 1,050,038
12.3620-49 26,919 8.23 822,699 18.14 46,683 6.89 1,400,422
16.4950-199 7,984 2.44 700,565 15.44 17,781 2.63 1,596,481
18.79+200 1,219 0.37 1,528,178 33.69 4,082 0.60 2,728,958 32.13All
327,164 100.00 4,536,384 100.00 677,317 100.00 8,494,499 100.00
Notes: Figures refer to the year 2001. Self-employed persons are
not included.
From this original sample we drop observations with missing or
non-positive values for the number
of employees, value added, or capital stock. We also eliminate
observations at the top and bottom 1%
of these variables. Since our misallocation measures are
computed within each 4-digit industry, we
also drop firms belonging to industries with less than 10 firms
per year. Therefore, we are left with
around 350,000 firms per year distributed in 518 4-digit
industries. Turning to this final sample in
Panel B of Table 1, our screening strategy slightly over-samples
larger firms because small firms with
less than 10 employees are more prone to misreport their
information in the Mercantile Registries.
Note also that our final sample does not include firms with 0
employees because these firms represent
mostly firms with no production, created merely for tax
purposes. In any event, since those firms
account for a small fraction of employment, the
representativeness of our final sample in terms of
employment remains noticeably good.7 It is our view that the
availability of information on small
firms is crucial for measuring within industry misallocation at
the 4-digit level as opposed to typical
7The Amadeus database, commercialised by Bureau Van Dyck, also
provides firm-level information extracted fromfirms balance-sheets
on a set of variables for all European OECD members including
Spain. For instance, Hsieh andKlenow (2014) exploited this dataset.
However, Amadeus presents some well-known drawbacks. First,
informationon employment (typically a non-mandatory item in balance
sheets) is only available for 40-50% of the firms in thesample
(this implies that although listed in terms of identifier in the
Amadeus data, 50-60% of the firms do not provideinformation about
employment). Second, the large attrition bias generates a the lack
of representativeness in termsof size, European Central Bank
(2014). Third, the readily usable version of the Amadeus data
currently starts in theyear 2004.
6
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datasets used in the literature that are restricted to samples
of larger firms (e.g. with more than 10 or
20 employees). Indeed, using only large firms in our sample, the
estimated increase in misallocation
is two times smaller than that obtained from our full
sample.
4 Misallocation and productivity in the Spanish boom
Our main finding is illustrated in Figure 3. Applying the
methodology of Hsieh and Klenow (2009)
to our sample of Spanish firms, we find that potential TFP gains
from reallocation steadily increased
over the 1995-2007 period. While TFP could have been around 24%
higher in 1995, this figure doubled
by 2007.8 Between 1995 and 2007, the allocative efficiency
decreased by 20 percent (1.49/1.24 - 1),
or a reduction of about 1.7 percent per year.
These hypothetical increases in the level of aggregate TFP would
result from fully equalizing
TFPR across firms in each 4-digit sector, i.e., from
reallocating resources from firms with low physical
TFP towards firms with high TFP. As acknowledged by Hsieh and
Klenow (2009), these counterfac-
tuals do not allow for measurement error or model
misspecification, which may cast doubt on the
usefulness of these numbers without a reference point to
compare. However, we do not focus on the
level but on the change of potential TFP gains relative to the
year 1995. Our implicit assumption is
that neither measurement error nor model misspecification have
changed over time.
In any event, changes in dispersion of TFPR might arise from
other frictions apart from id-
iosyncratic distortions of the type embedded in the Hsieh and
Klenow (2009) theoretical framework.
For instance, overhead labor or quasi-fixed capital (see
Bartelsman, Haltiwanger, and Scarpetta
(2013)). Based on Olley and Pakes (1996), we thus explore two
covariances as alternative measures
of misallocation. First, we compute a covariance term between
firm-specific labor shares and labor
productivity; and second we compute the covariance between
firm-specific production shares and
total factor productivity. Under an efficient allocation of
resources, more productive firms should
produce more and hire more workers.9
Table 2 summarizes the different measures of misallocation for
two sub-periods, namely, 1995-
2000 and 2001-2007. In particular, we use the Hsieh and Klenow
(2009) measure of potential TFP
gains (labeled as HK) together with the standard deviation of
log TFP (labeled as STD), which
8Crespo and Segura-Cayuela (2014) consider a sample of French,
Italian, German, and Spaninsh firms in orderto compare TFP gains
resulting from the HK methodology in selected years. According to
their results, TFP gainsin Spain are larger than those in France
but smaller than those in Germany and Italy. Moreover, they also
find anincrease in Spanish TFP gains between the years 2002 and
2008.
9To be more precise, for industry j and year t, the covariance
statistic for labor productivity (LPR) is given by:
OPj,t =i
(ij,t j,t)(ij,t j,t)
where i is the firm index, ij,t refers to the firm-specific
labor share, ij,t is the firm-specific labor productivity, and
j,tand j,t are the unweighted averages of industry j. The same
covariance can be computed for TFP using firm-specificproduction
shares as originally considered by Olley and Pakes (1996).
7
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Figure 3: Potential TFP gains from reallocation
.25
.3
.35
.4
.45
.5
1995 1997 1999 2001 2003 2005 2007
measures the dispersion of (log) TFPR at the firm level as an
alternative measure of misallocation in
the HK framework.10 We also report the two covariance terms as
used by Bartelsman, Haltiwanger,
and Scarpetta (2013) labeled as OP, one based on labor
productivity (LPR) and labor shares,
and the other based on total factor productivity (TFP) and
production shares.
All the statistics in Panel A of Table 2 clearly point to an
increase in the degree of misallocation
in the Spanish economy over the last expansion as documented in
Figure 3. While the dispersion
in TFP increased from 0.42 to 0.47, the OP covariance of LPR and
labor shares was reduced from
0.30 to 0.21, and the covariance between TFP and production
shares moved from 1.59 to 1.35. This
finding is present not only for the aggregate economy but also
for the main four the sectors of the
economy as shown in Panel B of Table 2.11 Depending on the
misallocation measure considered, the
sector with the largest misallocation deterioration is either
construction or services. However, the
four measures of misallocation point to a deterioration in
allocative efficiency in the four sectors.
Moreover, Table A1 in Appendix B reports the corresponding
results for disaggregated sectors at
NACE rev 2 - 2 digits showing that this deterioration is
prevalent among virtually all of the 58 2-digit
sectors considered.
This fall in the allocative efficiency of production factors is
distinctive of the Spanish growth
experience. In particular, Bartelsman, Haltiwanger, and
Scarpetta (2013) find that misallocation
10Under joint log normality of Asi, 1 Ysi , and 1 + Ksi , both
measures are equivalent.11Note that the sector-specific results are
based on misallocation within 4-digit industries aggregated using
industry
weights.
8
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remained roughly constant over the nineties and early 2000s in
several developed countries such as
US, UK, Germany or the Netherlands, while it clearly fell for
the transitional economies of Central
and Eastern Europe.
Table 2: Misallocation in Spain over the period 1995-2007.
PANEL A: Total Economy
HK STD TFP OP LPR OP TFP
1995-2000 0.29 0.42 0.30 1.592001-2007 0.43 0.47 0.21 1.35
PANEL B: By sector
HK STD TFP OP LPR OP TFP
1995-2000 Manufacturing 0.23 0.42 0.32 1.432001-2007 0.32 0.45
0.27 1.13
1995-2000 Construction 0.36 0.38 0.15 1.612001-2007 0.62 0.42
0.10 1.28
1995-2000 Trade 0.38 0.43 0.31 1.732001-2007 0.48 0.48 0.25
1.39
1995-2000 Services 0.40 0.44 0.37 1.722001-2007 0.54 0.50 0.19
1.58
Notes: HK refers to the potential TFP gains if resources were
allocated efficiently as proposedby Hsieh and Klenow (2009). OP
refers to the Olley and Pakes (1996) covariance term. STDrefers to
standard deviation as a measure of dispersion. LPR refers to log
labor productivityand TFP to log total factor productivity.
We argue that the stark increase in within-sector misallocation
over the Spanish boom is at the
root of the bad performance of aggregate TFP. We next compute
potential TFP growth under the
assumption that the level of misallocation remains constant at
its 1995 level. This counterfactual
exercise provides the aggregate TFP that we would have observed
during the expansion without
increases in misallocation. To be more precise, we simply
multiply the observed aggregate TFP by
the year-specific percentage of TFP gains given by the HK
exercise above (see Figure 3). Then, we
plot the resulting potential TFP growth rates together with the
observed ones in Figure 4. Annual
growth rates of potential TFP growth would have been between
0.6% and 1.1% with an average
of 0.8% under the assumption of constant within-sector
misallocation. In contrast, observed TFP
growth was -0.7% on average, ranging from -0.8% to -0.5%. We can
also use the OP methodology to
compute the potential TFP growth. Had the OP covariance term
remained constant for the period,
TFP would have grown at a 1.1% annual rate12 which is similar to
the counterfactual TFP growth
computed using HK methodology.
12This number can be computed by calculating the percentage
change in the OP covariance term between 1995-2000and 2001-2007 as
reported in Panel A of table 2, which is 24%, and obtaining the
corresponding average annual rateover the 12-year span.
9
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Finally, we also compute an alternative counterfactual TFP
growth based on aggregating sector-
specific TFP growth rates with weights given by the sector
shares in 1995. This exercise aims to
illustrate the role of between sector misallocation in the
evolution of aggregate TFP. While we see
that this counterfactual TFP growth is higher than the observed
one, it is substantially smaller than
the counterfactual based on constant within-sector
misallocation. More specifically, it ranges from
-0.8% to -0.1% with an average annual growth of -0.4%
All in all, these counterfactual exercises speak in favor of the
crucial role of within-sector misallo-
cation in the evolution of aggregate TFP in Spain over the last
expansion. This finding casts doubt
on the traditional view that between-sector misallocation (i.e.
specialization in low productivity
sectors such as construction) was behind low TFP growth in
Spain.
Figure 4: Potential TFP growth under 1995 misallocation
level
1
.5
0.5
1
1995 1997 1999 2001 2003 2005 2007
Observed TFP growthPotential TFP growth constant
misallocationPotential TFP growth constant sector shares
4.1 Robustness analysis
In this Section we perform three robustness exercises related to
the level of industry disaggregation,
to the distinction between intensive and extensive margin only,
and to the elasticity of substitution.
4.1.1 Industry classification
Our baseline results are based on misallocation within 4-digit
industries because the HK theoretical
framework relies on the assumption that each industry represents
a monopolistic competitive market
10
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in which firms produce different varieties of the same
intermediate good. Therefore, the greater
the level of disaggregation the more plausible this assumption
is when taken to the data. However,
since the 4-digit level of disaggregation requires very large
samples of firms to obtain meaningful
figures for more than five hundred industries, we investigate if
the deterioration in allocative efficiency
documented for Spain at the 4-digit level is also present when
considering 2- and 3-digit classifications.
Table 3 shows the evolution of allocative inefficiency in Spain
in terms of potential TFP gains
from reallocation to an efficient allocation of resources across
firms within each 4-, 3-, and 2-digit
sectors in columns (1), (2) and (3). The increase in TFP gains,
or the deterioration in allocative
efficiency, is prevalent among all the three industry
classifications. Moreover, the increases over the
whole period are of the same magnitude, around 20% or 1.7% per
year, in all the cases. In particular,
the average increases are 1.7, 1.6, and 1.8 percent per year for
the exercises at 4-, 3-, and 2-digit
industries, respectively.
4.1.2 Balanced versus unbalanced panel
Our baseline sample is an unbalanced panel including firms that
might enter or exit at any time. The
extensive margin may also play a role in shaping the evolution
of allocative efficiency depicted above.
However, the potential sources of misallocation might be
different depending on the importance of
this extensive margin relative to the intensive margin of
misallocation of resources within established
firms. In order to quantify the importance of the extensive
margin in terms of efficient TFP and the
evolution of allocative efficiency over time, we consider a
balanced panel restricted to firms that were
in the sample for the whole period (1995-2007). In the balanced
version of the panel we have only
5,419 firms per year, which precludes us from considering
misallocation within 4-digit industries.
Column (4) in Table 3 shows the resulting TFP gains from the
balanced panel under the 2-digit
disaggregation. We find that the deterioration in allocative
efficiency over time still holds, although
smaller in size: while the increase in misallocation is around
20% (1.49/1.24 - 1) or 1.7% per year
when considering the unbalanced panel, the corresponding figures
are 7% (1.28/1.20 - 1) and 0.6%
under the balanced panel. These numbers suggest that about two
thirds of the deterioration in
allocative efficiency is due to the extensive margin.
4.1.3 Elasticity of substitution
As as final robustness test we repeat the exercise with a higher
elasticity of substitution: =5. This
figure is also used by Dias, Robalo, and Richmond (2014) for
Portugal and comes from the estimates
for the Eurozone in Christopoulou and Vermeulen (2012). In
column (5) of Table 3 we report the
results. As expected, TFP gains increase for all years when =5.
Moreover, the magnitude of the
increase in misallocation over the 1995-2007 period is similar
to that of the case =3, a decrease of
21% (1.69/1.39 - 1) or 1.8 percent per year.
11
-
4.1.4 Measurement error
Our estimated increases in TFP gains from reallocation might be
driven by an increase in measure-
ment error in our data as a result of the year-to-year increases
in our sample size. While this concern
is partially addressed in the balanced panel exercise, we also
consider an alternative robustness check
based on recording errors created by extreme outliers. In
particular, following Hsieh and Klenow
(2009) we trim the 2% tails of TFPR and TFPQ in order to avoid
the potentially increasing influence
of outliers in our sample. Column (6) of Table 3 shows the
resulting TFP gains, which clearly point
to a large deterioration in allocative efficiency of around 14%
(1.42/1.25 - 1) or 1.1 percent per year.
4.1.5 Sample of large firms
We now check the sensitivity of our findings to the size
distribution of firms in our sample. In
particular, we computed TFP gains resulting from removing
idiosyncratic distortions in a subsample
of large firms (more than 50 employees). We report the results
in Column (7) of Table 3. While the
deterioration in allocative efficiency still arises, its
magnitude is substantially smaller, 0.6% percent
per year against the baseline of 1.7% per year. Moreover, the
levels of TFP gains are substantially
smaller than those of the full sample in the baseline case. Our
interpretation of this result is that
datasets of large firms typically used in the literature might
under-estimate the magnitude of within-
industry misallocation.
12
-
Table 3: Misallocation in Spain over the period 1995-2007
Robustness analysis
TFP gain from reallocation
Baseline 3-digit 2-digit Balanced = 5 M. error Large firms(1)
(2) (3) (4) (5) (6) (7)
1995 0.24 0.27 0.33 0.20 0.39 0.25 0.141996 0.26 0.28 0.37 0.20
0.45 0.26 0.141997 0.27 0.31 0.38 0.22 0.42 0.27 0.161998 0.28 0.32
0.41 0.20 0.45 0.29 0.161999 0.34 0.39 0.45 0.23 0.52 0.32 0.182000
0.36 0.39 0.46 0.23 0.52 0.32 0.172001 0.38 0.40 0.46 0.23 0.53
0.33 0.212002 0.40 0.42 0.48 0.23 0.53 0.35 0.232003 0.41 0.44 0.51
0.24 0.54 0.36 0.202004 0.44 0.46 0.54 0.25 0.61 0.38 0.202005 0.45
0.48 0.58 0.27 0.61 0.39 0.232006 0.47 0.51 0.62 0.29 0.72 0.40
0.212007 0.49 0.52 0.62 0.28 0.69 0.42 0.22
Notes: Baseline in column (1) refers to our benchmark results
based on misallocation within 4-digitindustries, =3, and the
unbalanced panel. Columns (2) and (3) report the results when
consideringindutries at 3- and 2-digit classifications (NACE 2 rev.
2). Column (4) is based on the balanced versionof our panel. Column
(5) reports the TFP gains when considering =5 instead of =3. Column
(6) refersto the trimming of the 2% tails of TFPR and TFPQ in order
to alleviate the influence of measurementerror. Finally, column (7)
is based on a sample of large firms (more than 50 employees).
5 Sources of misallocations evolution
In the previous sections we have uncovered a large decrease in
within-industry allocative efficiency,
which we have shown to be the main source of low TFP growth
observed over the 1995-2007 period in
Spain. Given this finding, the challenge is to identify the
economic forces that led to the increase in
the misallocation of production factors across firms. In this
section, we make a first step by providing
some descriptive evidence together with some tentative
interpretations regarding the potential sources
of the Spanish increase in misallocation over the last
expansion. In particular, we estimate the
relationship between different sector- and firm-specific
characteristics with the observed sector and
firm-specific changes in allocative efficiency.
5.1 Size versus capital distortions
In this section we explore the firm-specific measures of
distortions implied by the Hsieh and Klenow
(2009) exercise of Appendix A. In particular, we have two
measures of distortions, one labeled as
K that distorts the capital-labor ratio, and one labeled as Y
that distorts the size of the firm.
Intuitively, a firm faces a high distortion in the capital-labor
ratio (i.e. K is high) when the ratio of
13
-
labor to capital compensation is high relative to what one would
expect in the absence of distortions
(i.e. when there is no within-industry variation on the ratio of
labor to capital compensation). On
the other hand, a firm faces a high distortion in its size (i.e.
a high Y ) when it is smaller than it
should be, in other words, when the labor compensation of the
firm is low compared to what one
would expect given the industry elasticity of output with
respect to labor.
Potential TFP gains increased from around 0.25 in 1995 to around
0.50 in 2007 (see Figure 3).
Figure 5 plots the resulting TFP gains when eliminating
variation in one distortion at a time. By
switching down the capital-labor distortion, i.e., fixing Ki = 0
i, TFP gains increased from around0.01 in 1995 to around 0.03 in
2007. Therefore the level of TFP gains due to size distortions is
very
low. In contrast, the level of TFP gains remain relatively high
with an increase from 0.11 in 1995 to
0.20 in 2007 when the size distortion is switched off (i.e. Yi =
0 i) and the K distortion is present.All in all, the K distortion
to the capital-labor ratio seems to be the most important
distortion in
explaining the evolution of aggregate misallocation in Figure 3.
Moreover, the interaction between
both distortions also explains a significant part of the
misallocation level and increase. A possible
rationale for this finding is that firms operating with bad
input mixes (large capital distortion) also
tend to be larger than optimal (large size distortion),
worsening the misallocation problem. For
instance, Smagghue (2015) shows that size-dependent regulations
might also generate an inefficient
reallocation of resources from capital intensive to labor
intensive firms. Indeed, the within industry-
year correlation between both distortions is 0.40 in levels and
0.24 in growth rates.
Figure 5: Potential TFP gains from reallocation by type of
firm-specific distortion
0.1
.2
.3
.4
.5
1995 1997 1999 2001 2003 2005 2007
Overall TFP Gain Due to interactionDue to capital distortion Due
to size distortion
14
-
5.2 Sector-level analysis
We first analyze the type of sectors in which the deterioration
in allocative efficiency between 1995
and 2007 was more pronounced. We have information on several
characteristics of 58 sectors at
the 2-digit NACE rev. 2 classification (see Table B1 in the
Appendix). In order to exploit this
information we consider here changes in allocative efficiency at
the 2-digit level.13 Figure 6 plots
the level of potential TFP gain from reallocation in 1995
against its change between 1995 and 2007.
Most of the sectors (51 out of 58) experienced increases in such
TFP gains, i.e. deterioration in
allocative efficiency, which explains the overall deterioration
in Figure 3. In particular, the industries
Warehousing and support activities for transportation,
Electricity, gas, steam and air conditioning
supply, and Activities of head offices; management consultancy
activities worsened the most while
Manufacture of furniture, Manufacture of beverages, and Motion
picture, video and television
programme production, sound recording experienced slight
improvements in allocative efficiency.
Moreover, there is no relationship between the initial level of
allocative efficiency and the change
over the 1995-2007 period. Therefore, we next investigate to
what extent observable sector-specific
characteristics can generate such differences in allocative
efficiency between sectors. In particular,
we consider four different dimensions that might be related to
the evolution of allocative efficiency.
Figure 6: TFP gain and its change by 2-digit sector
10
11
1314
15
16
171820
212223
24
25
26
27
28
29
3031
32
33
35
37
38
39
41
42
43
45
4647
49
50
52
53
555658
59
6061
62
63
6869
70
71
72
7374
757778
79
8081 82
.5
0.5
1C
hang
e in
TFP
gai
n (1
995
2007
)
0 .5 1 1.5TFP gain in 1995 by sector (NACE 2digits)
First, we explore the role of skill intensity differences across
sectors. There are several reasons
13Note that the results are based on misallocation within
4-digit sectors that is aggregated to the 2-digit level
usingproduction-based weights as oppossed to computing
misallocation within each 2-digit sector.
15
-
why this may matter. For instance, firing costs have been long
blamed as a possible source of
misallocation of workers across firms (see Dolado, Ortigueira,
and Stucchi (2011)). Firing costs on
open ended contracts are high in Spain. However, the share of
employment under flexible fixed-
term contracts was large and stable over the period.14 It has
been argued that fixed-term contracts,
because they preclude human capital accumulation on the job, are
more prevalent among low skilled
occupations. Hence, if firing costs are a source of
misallocation, we should expect a larger increase
in misallocation in high-skill industries. Skill intensity in US
sectors is taken as our baseline proxy
because it is expected to be exogenous to the evolution of
allocative efficiency in Spanish sectors of
activity. As a robustness check, we also consider the share of
skilled workers in Spain taken from
PITEC (Panel de Innovacion Tecnologica), which is based on a
survey of innovative firms conducted
by the National Statistics Institute.15
Second, differences in external financial dependence across
sectors may affect the resource alloca-
tion process. The sharp expansion in bank lending during the
period 1995-2007 originated a stock of
loans from credit institutions to non-financial corporations of
90% of GDP in 2007 while it was 38%
in 1995. The increasing abundance of new credit to firms
together with a loose screening process by
banks can generate a deterioration in allocative efficiency if
bad firms are able to survive hampering
the reallocation process towards better firms. In order to check
this potential channel, we consider
a sector-specific finance intensity variable constructed by
Fernald (2014) for the US. Exploiting I-O
tables, this finance intensity variable is given by nominal
purchases of intermediate financial services
as a share of industry gross output. Again, using US sector
characteristics ensures exogeneity with
respect to the evolution of allocative efficiency in Spanish
industries. As an alternative measure of
sector-specific financial dependence, we consider the ratio of
sectors total liabilities as a percentage
of its total assets computed using firm-level data from the
Central Balance Sheet Data.
Third, more dynamic industries can be expected to produce better
allocations of resources. For
instance, more innovative sectors have usually larger shares of
innovative and young firms that
can easily adapt to shifts in demand or actions taken by
competitors. Cecchetti and Kharroubi
(2012) argue that credit booms (such as the one witnessed in
Spain over 1995-2007) undermine R&D
intensive sectors, which might be related to the deterioration
in TFP growth. Along these lines, we
consider Fernald (2014) IT intensity variable at the sector
level in the US, which consists on the
payments for IT as a share of income (taken from the Bureau of
Labor Statistics). As an alternative
measure of sector-specific IT content, we exploit the Spanish
PITEC to construct shares of R&D
investment over total investment.
Finally, some sectors may be less competitive because business
success is related to state licensing
or regulation. If this is the case, we could expect some firms
in such sectors to operate with size or
14The share of fixed-term contract was around 35% of employment
in those years, but it witnessed a large increasebefore 1995.
15See www.icono.fecyt.es/PITEC for more details.
16
-
input mix far from optimal and still survive. To explore this
hypothesis, we define a dummy variable
taking value 1 for those sectors considered to be crony sectors,
which we define as those sectors
susceptible to monopoly or requiring licensing or highly
dependent on government regulation.16
As an alternative measure, we also consider the Bribe Payers
Index constructed by Transparency
International with information at the sector level. The 2011
Bribe Payers Survey, on which the index
is based, asked business executives how common bribery was in
the sectors with which they have
business relations.17
Table 4 shows some correlations between the sector
characteristics just described and the changes
in allocative efficiency. In particular, we regress the change
in sector-specific potential TFP gains on
the different characteristics measured as the average over the
1995-2007 period. Columns (1)-(5) are
based on linear regressions with different covariates. Column
(6) is based on weighted-average least
squares (WALS), a model averaging approach that provides
standard errors incorporating not only
parameter uncertainty but also model uncertainty.18
16These sectors are, casinos, coal, palm oil and timber,
defense, deposit-taking banking and investment
banking,infrastructure and pipelines, ports, airports, real estate
and construction, steel, other metals, mining and
commodities,utilities and telecoms services. In our dataset, we
label as crony the following 2-digit sectors: 24, 35, 37, 38, 39,
41,42, 50, 51, 61, and 68 (see Table B1 in the Appendix).
17The survey asked how often three different types of bribery
were perceived to occur in each sector: firstly, briberyof
low-ranking public officials; secondly, improper contributions to
high-ranking politicians to achieve influence; andthirdly, bribery
between private companies. Answers were given on a 5-point scale.
This was then converted to a10-point scale where 0 indicates that
companies in that sector are perceived to always pay bribes and 10
to never paybribes.
18Model uncertainty results from the lack of theoretical
guidance on the particular regressors to include in theempirical
model. When model uncertainty is present, traditional standard
errors would under-estimate the realuncertainty associated to the
estimate of interest because variation across models is ignored. In
order to account forboth levels of uncertainty, model averaging
techniques (e.g. WALS) estimate all possible combinations of
regressorsand constructs a single estimate by averaging all
model-specific estimates (see Moral-Benito (2015) for an
in-depthanalysis of model averaging).
17
-
Table 4: Misallocation and sector-specific characteristics.
Dep. Variable: TFP Gain
(1) (2) (3) (4) (5) (6)OLS OLS OLS OLS OLS WALS
High-skill intensity (US share) 0.064 -0.008 -0.008(0.219)
(0.271) (0.210)
Innovative content (US IT intensity) 0.284 0.333 0.188(0.445)
(0.501) (0.408)
Financial dependence (US financial intensity) 0.044 0.033
0.025(0.029) (0.032) (0.027)
Public sector influence (crony dummy) 0.226*** 0.209**
0.150**(0.081) (0.086) (0.077)
Constant 0.219*** 0.216*** 0.148** 0.197*** 0.112 0.149**(0.069)
(0.046) (0.066) (0.034) (0.078) (0.068)
Observations 58 58 58 58 58 58R-squared 0.00 0.01 0.04 0.12 0.15
-
Notes: TFP Gain refers to the change over the 1995-2007 period
in the ratio of optimal TFP in the absenceof misallocation to
observed TFP.
We fail to find any statistically significant relationships
between skill intensity, innovative content,
or financial dependence with the change in allocative efficiency
(see Column (1)-(3) in Table 4).
Furhtermore, the R-squared indicates that variation in these
characteristics can only account for less
than 0.5% of the variation in misallocation changes. In
contrast, Column (4) in Table 4 indicates that
the deterioration in allocative efficiency was 22.6 points
larger in crony than in non-crony sectors.
This statistically significant difference implies that the
eleven industries in which success in business
depends more on relationships between firm managers and public
sector officials were the industries
experiencing the largest increases in misallocation over the
1995-2007 period. In addition, the crony
dummy is able to account for 11% of this variation. When all the
variables are jointly included in
the regression in column (5), the magnitude and significance of
the crony dummy remains virtually
unaltered; however, partial correlations of skill intensity,
innovative content, and financial dependence
are statistically indistinguishable from zero. Finally, column
(6) reports WALS estimates confirming
the conclusion from column (5) even when we also account model
uncertainty.
18
-
Table 5: Misallocation and sector-specific characteristics
Alternative proxy regressors.
Dep. Variable: TFP Gain
(1) (2) (3) (4) (5) (6)OLS OLS OLS OLS OLS WALS
High-skill intensity (Spain share) 0.163 0.149 0.083(0.155)
(0.157) (0.131)
Innovative content (Spain R&D share) -0.303 -0.475*
-0.339(0.249) (0.249) (0.215)
Financial dependence (Spain debt burden) 0.021 -0.025
-0.022(0.097) (0.092) (0.086)
Public sector influence (BPI index) -0.264*** -0.266***
-0.194**(0.086) (0.090) (0.084)
Constant 0.187*** 0.257*** 0.228*** 2.015*** 2.033***
1.553***(0.053) (0.040) (0.051) (0.058) (0.623) (0.585)
Observations 58 58 58 58 58 58R-squared 0.02 0.03 0.00 0.14 0.21
-
Notes: See notes to Table 4.
Table 5 shows the same set of results but considering
alternative proxy variables for each sector-
specific characteristic. For the influence of public sector we
consider the BPI index described above
and for the other three dimensions we evaluate the Spanish
counterparts of the US indicators. Again,
public sector influence is significantly related to changes in
misallocation; in particular, sectors in
which the incidence of bribery is larger experienced large
increases in misallocation over the 1995-
2007 period (note that the lower the BPI index the higher the
bribery incidence). Turning to skill
intensity, innovative content, and financial dependence in
columns (1), (2), and (3), respectively, we
again fail to find statistically significant correlations in all
the three cases. Using these alternative
proxies, columns (5) and (6) of Table 5 also confirm the results
in the corresponding columns of
Table 4.
5.3 Firm-level analysis
We now turn to exploit firm-level data on distortions. Formally,
the two measures of firm-specific
distortions provided by the theoretical model, Ki and Yi , are
given by equations (17) and (18),
respectively. We aim to investigate the characteristics of firms
behind the increases in misallocation
over the period; hence, we define the firm-specific growth rates
of Ki and Yi to facilitate the
interpretation of our estimates.19 We then regress the
firm-specific changes in distortions on two
firm characteristics (size and age) including a set of 4-digit
industry dummy variables as well as a
set of year dummy variables to ensure that identification is
based on across firms variation within
each industry-year.
19More specifically, ln(1+ Ki,t) = ln(1+ Ki,t) ln(1+ Ki,t1) and
ln(1 Yi,t) = ln(1 Yi,t) ln(1 Yi,t1).We do not use the firm-specific
growth rate over the entire period because it would drastically
reduce the number ofobservations available for estimation.
19
-
We focus on size and age of the firm because these are natural
candidate characteristics to explain
the within industry-year variation in firm-specific distortions.
There are some size-dependant policies
in Spain favoring smaller firms (e.g. by reducing the cost of
labor through labor regulations or less
strict enforcement activity of tax collection for smaller
firms), but other economic mechanisms such
as access to credit may be hindering the growth of smaller
firms. The credit channel may as well
hamper the access to credit of young firms without credit
reputation while other firm-age contingent
policies favor younger firms (e.g. special lines of credit or
labor regulations for start-ups). Given
our interest in changes in distortions, our hypothesis is that
these channels may have been amplified
during the 1995-2007 period; for instance, the increasing
abundance of public revenues and bank
credit due to the housing boom allowed the proliferation of
public subsidies and loose bank lending
policies that may have evolved differently for different types
of firms.
Table 6 reports some estimation results. Regarding distortions
to the capital-labor ratio, columns
(1) and (2) show that smaller firms faced on average larger
increases in their capital distortions. In
particular, the average yearly growth rate of the capital
distortion was 8.5 log points larger for
firms with less than 50 employees according to column (2). Also,
a size increase of 100 employees
is associated to an average reduction of 0.7 log points in the
capital distortion each year according
to column (1). One possible interpretation of this finding is
that larger firms enjoyed increasing
subsidies to capital, for instance through cheaper access to
credit.20 Columns (3) and (4) indicate
that younger firms experienced higher capital distortions than
older firms. For instance, firms with
less than 10 years old experienced an average capital distortion
growth 1.7 log points larger than
mature firms (10 years and above).21 Moreover, according to
column (3) a 10-year increase in firm
age is associated to a yearly reduction of 1.3 log points in the
capital distortion. This result would
confirm the hypothesis that younger firms faced more
difficulties in the access to credit relative to
mature firms, and that this has worsened over the 1995-2007
period.
20Note that the dependent variable can be written as ln(1 +
Ki,t) = ln(wLi,t) ln(RKi,t) under theassumption that s does not
vary over time. Thefore, this result implies that, in small firms
the difference betweenlabor compensation growth and capital
compensation growth was larger than in large firms within each
industry.
21We take the definition of young firms from Haltiwanger,
Jarmin, and Miranda (2013). However, this result isrobust to
alternative thresholds.
20
-
Table 6: Changes in firm-level distortions and firm size and
age
Dep. Variable: ln(1 + Ki,t) Dep. Variable: ln(1 Yi,t)(1) (2) (3)
(4) (5) (6) (7) (8)
OLS OLS OLS OLS OLS OLS OLS OLS
Size -0.00007*** 0.00009***(0.00002) (0.00002)
Small dummy 0.085*** -0.113***(0.004) (0.004)
Age -0.0013*** 0.0017***(0.0001) (0.0001)
Young dummy 0.017*** -0.025***(0.003) (0.001)
Productivity 0.072*** 0.074*** 0.083*** 0.083*** -0.082***
-0.085*** -0.097*** -0.097***(0.002) (0.002) (0.003) (0.003)
(0.002) (0.002) (0.003) (0.003)
Size dummies NO NO YES YES NO NO YES YESAge dummies YES YES NO
NO YES YES NO NOIndustry dummies YES YES YES YES YES YES YES
YESTime dummies YES YES YES YES YES YES YES YESR-squared 0.02 0.02
0.02 0.02 0.05 0.05 0.06 0.06Observations 1,682,056 1,682,056
1,682,056 1,682,056 1,682,056 1,682,056 1,682,056 1,682,056
Notes: Standard errors are clustered at NACE rev. 2 4-digit
level. Firms with less than 50 employees are labeledas small. Young
firms are less than 10 years old, see Haltiwanger, Jarmin, and
Miranda (2013). Four groups areconsidered for the size dummies,
1-10 employees, 10-50 employees, 50-250 employees, and more than
250 employees.Age dummies are based on age groups divided by
year-specific quartiles. Estimation sample covers the
period1995-2007.
Turning to the size distortion in columns (5)-(8), we find that
smaller and younger firms faced
larger increases in size distortions.22 To be more precise, our
results could imply that the growth
of small and young firms over the 1995-2007 period was hampered
by increasing distortions faced
by those firms. For instance, column (5) implies that a size
increase of 100 employees is associated
to an average increase of 0.9 log points in the size distortion
each year, while according to column
(7) a 10-year increase in firm age is associated to an annual
increase of 1.7 log points in the size
distortion.23 Finally, note that the estimates in Table 6 are
robust to the inclusion of industry-year
dummy variables, region dummy variables, and region-year dummy
variables, as well as to outliers
(i.e. excluding the top and bottom 1% of the dependent
variables).
5.4 Regional misallocation
Spanish regions (Comunidades Autonomas) have the political power
to enact laws and establish reg-
ulations. Indeed, Marcos, Santalo, and Sanchez-Graells (2010)
document the existence of substantial
22Note that the dependent variable is defined as the change in
ln(1 Yi,t). According to equation (18), smallervalues of 1 Yi,t
correspond to firms smaller than optimal given s, , and their
production.
23Under the assumption of constant industry elasticities, the
growth of the size distortion can be decomposed as ln(1 Yi,t) =
ln(wLi,t) ln(Pi,tYi,t). Thus, our results imply that in small and
young firms the differencebetween labor compensation growth and
sales growth was smaller than in large firms within each industry,
i.e., giventheir sales those firms should have hired more
workers.
21
-
heterogeneity in region-specific regulations. In addition, the
Spanish labor market is characterized
by large regional differences in employment and wages, see
Bentolila and Jimeno (1998). Under these
circumstances, a natural concern is whether the overall
deterioration in allocative efficiency across
firms might be just reflecting heterogeneity in the change of
the relative cost of capital and labor
in different regions. We argue that this does not seem to be the
case. First, Figure 7 shows that
the increase in misallocation was present in all the seventeen
Spanish regions.24 Second, using data
from the Encuesta de Estructura Salarial for the years 2002 and
2006,25 we regress the region-specific
average wage growth on the change in missallocation. The
estimated coefficient renders this relation-
ship statistically insignificant, and has a point estimate of
0.047 (t-stat = 0.79). We thus conclude
that the deterioration in allocative efficiency uncovered in
this paper is caused by nationwide forces.
Figure 7: Evolution of TFP gains in Spanish regions
100
150
200
2001 2003 2005 2007
ANDALUCIA
100
150
200
2001 2003 2005 2007
ARAGON10
015
020
0
2001 2003 2005 2007
ASTURIAS
100
150
200
2001 2003 2005 2007
BALEARES
100
150
200
2001 2003 2005 2007
CANARIAS
100
150
200
2001 2003 2005 2007
CANTABRIA
100
150
200
2001 2003 2005 2007
CASTILLA LA MANCHA
100
150
200
2001 2003 2005 2007
CASTILLA Y LEON
100
150
200
2001 2003 2005 2007
CATALUNYA
100
150
200
2001 2003 2005 2007
COMUNIDAD VALENCIANA
100
150
200
2001 2003 2005 2007
EXTREMADURA10
015
020
0
2001 2003 2005 2007
GALICIA
100
150
200
2001 2003 2005 2007
COMUNIDAD DE MADRID
100
150
200
2001 2003 2005 2007
COMUNIDAD MURCIANA
100
150
200
2001 2003 2005 2007
NAVARRA
100
150
200
2001 2003 2005 2007
PAIS VASCO
100
150
200
2001 2003 2005 2007
LA RIOJA
24We compute potential TFP gains from within-industry
reallocation for each region-year pair over the 2001-2007period.
The number of firms steadily increased over the sample period in
Spain so that for certain small regions thereare not enough firms
in each 4-digit sector in the first years to estimate meaningful
TFP gains. Focusing on 2-digitsectors we can compute those measures
and the increases are also generalized for these intial years.
25Available at http://goo.gl/tbYiOp.
22
-
6 Concluding Remarks
Spanish growth during the 1994-2007 expansion was based on
factor accumulation rather than pro-
ductivity gains. In particular, annual TFP growth was -0.7%,
which is low in comparison to other
developed economies such as the US or EU. In this paper, we
argue that the source of negative TFP
growth has been the increase in the within-sector misallocation
of production factors across firms.
Furthermore, we find the phenomenon to be present in all sectors
of activity, which casts doubt on
the widespread view that specialization in low productivity
sectors such as construction was the main
force behind Spanish low TFP growth.
In order to shed some light on the potential sources of this
phenomenon in Spain, we find that
industries in which the influence of the public sector is larger
(e.g. through licensing or regulations)
experienced significantly larger increases in misallocation. In
contrast, other characteristics such
as skill intensity, innovative content or financial dependence
are unrelated to changes in allocative
efficiency. Turning to firm-specific distortions, we find that
small and young firms in Spain might
have faced higher market distortions than large and mature
firms.
In light of these findings, the next challenge is to develop a
framework for understanding the
major forces and policies behind these patterns of allocative
efficiency and firm-specific distortions.
For instance, Garcia-Santana, Moral-Benito, Pijoan-Mas, and
Ramos (2015) explore the role of public
procurements on the allocation of resources in the private
sector.
23
-
A Theoretical framework
This section presents the model of monopolistic competition with
firm heterogeneity a` la Melitz
(2003) introduced by Hsieh and Klenow (2009) (HK) to measure
within industry misallocation as a
source of differences in aggregate TFP. The crucial
characteristic of this model is that firms differ
not only in their efficiency levels but also in the capital and
output distortions they may face when
taking their production decisions.
The HK model is characterized by a closed economy with two
primary inputs (capital and labor)
and S industries producing differentiated intermediate goods
that are combined by a pure assem-
bly sector to produce an homogeneous final good. Firms producing
the intermediate differentiated
goods operate under monopolistic competition and sell their
products to the final good producers. In
the absence of distortions, the allocation of resources across
firms producing the intermediate goods
depends only on physical levels of firm-specific TFP, which
yields to the optimal level of aggregate
TFP. However, the model features firm-specific distortions that
preclude firms from optimally choos-
ing their levels of output and capital-labor mix. This implies
within industry misallocation, which
deviates aggregate measured TFP from its optimal level.
HK assume that there are S different industries in the economy.
The output of each of the
industries s S is the outcome of aggregating Ms differentiated
intermediate goods:
Ys =
(Msi=1
Y1
si
) 1
(1)
where is the elasticity of substitution between goods. Each of
these goods is produced by a firm
that operates in a monopolistic competitive market and has
access to a Cobb-Douglas production
function that combines labor and capital:
Ysi = AsiKssi L
1ssi (2)
Firms choose labor and capital to maximize profits:
pisi = maxLsi,Ksi
{(1 Ysi)PsiYsi wLsi (1 + Ksi)RKsi} (3)
where Ysi and Ksi are firm-specific distortions. Notice that Ysi
distorts the size of the firm, whereas
Ksi distorts the optimal capital-labor ratio decission. This
problem yields the following first order
conditions:
24
-
(1 Ysi)PsiAsi(1 )Lsi Ksi( 1
)W = 0 (4)
PsiAsiL1si K
1si
( 1
)R(1 + Ksi) = 0 (5)
These two first order conditions imply that the price of firms
output equals a mark-up over the
marginal cost:
Psi =
1(R
s
)s ( W1 s
)1s 1Asi
(1 + Ksi)s
1 ysi(6)
where 1 is the mark-up charged by the firm and
(Rs
)s (W
1s
)1s1Asi
(1Ksi )s(1Ysi )
is its marginal
cost. This optimal pricing rule yields labor demand and output
that are proportional to the firms
physical TFP and the idiosyncratic distortions:
Lsi A1si (1 ysi)
(1 Ksi)s(1)
Ysi Asi(1 ysi)
(1 Ksi)s
and a capital-labor ratio that depends only on the firms
idiosyncratic distortions and relative prices:
KsiLsi
=s
1 sw
R
1
1 + Ksi(7)
In the absence of distortions, the allocation of resources
across firms depends only on physical levels
of firms TFP, yielding to a equalization of capital-labor ratios
and marginal revenue products of
labor and capital. In the presence of distortions, both
capital-labor ratios and total outputs become
distorted, generating variation on the marginal revenue products
and hence misallocation.
A.1 Within-industry Misallocation
Total factor productivity revenue of firm i is defined as:
TFPRsi PsiAsi (8)
Therefore, substituting equation (6) into equation (8):
TFPRsi =
1(R
s
)s ( W1 s
)1s (1 + Ksi)s1 ysi
(9)
25
-
Note that, in the absence of idiosyncratic distortions the
TFPRsi would equalize across firms oper-
ating in the same industry. Suppose, for example, that there is
a firm with a relatively high level
of physical TFP (Asi). This firm would want to attract labor and
capital until reaching the point
where its lower price makes its TFPRsi the same as the one of
less productive firms. In this situation,
revenue marginal products of labor and capital are equalized
across firms and the first best allocation
is achieved.
Observed TFP in a given industry is defined as:
TFPs =
[Msi=1
(Asi
TFPRsTFPRsi
)1] 11(10)
where TFPRsi is the total factor productivity revenue of firm i
in industry s defined and TFPRs is the
weighted average total factor productivity revenue in industry
s. Equation (10) clearly suggests that,
conditional on the distribution of firms physical productivity
Asi, the industry TFPs is maximized
when there is no variation in TFPRsi across firms. Then, the
higher the variation in the firms
idiosyncratic distortions, the higher the variation in the
within-industry TFPRsi, and hence the
higher the amount of misallocation.
A.2 Aggregate TFP
In the model, there is a single final consumption good produced
by a representative firm in a perfectly
competitive final good market. This firm combines intermediate
goods Ys produced in a finite number
of different industries s S. These intermediates are aggregated
to produce the final good using aCobb-Douglas technology:
Y =Ss=1
Y ss (11)
whereS
s=1 s = 1. The optimization problem of the representative firm
implies:
PsYs = sY (12)
where Ps refers to the price of industry output Ys. The price
index P S
s=1
(Pss
)sis set equal to 1.
It is important to emphasize that, due to the Cobb-Douglas
assumption, the only source of inefficiency
in this model is the within-industry misallocation: the increase
in an industrys productivity is fully
compensated by a the decrease in its price index, so firms
idiosyncratic distortions do not affect the
sectoral composition of the economy. GDP can be expressed as a
function of industries amounts of
labor, capital, and TFPs:
26
-
Y =Ss=1
(TFPsKss L
ss )
s (13)
Then, by using equations (10) and (13) the aggregate observed
TFP becomes:
TFP =Ss=1
TFPss =Ss=1
( Msi=1
(Asi
TFPRsTFPRsi
)1) 11s (14)This expression clearly shows how within-industry
misallocation of labor and capital yields a lower
measured aggregate TFP. To understand how costly are the
idiosyncratic distortions one can define
the optimal level of TFP (i.e. the TFP level in the absence of
firm-specific distortions):
TFP =Ss=1
TFPs
s =Ss=1
( Msi=1
(Asi)1) 1
1s (15)
The ratio of optimal TFP to observed TFP (i.e. TFP
TFP 1) is the potential TFP gain from
reallocation that we will use throughout the paper. In
particular, we analyze its evolution over time
as an indication of the relevance of changes in within sector
misallocation to explain the evolution
of aggregate TFP growth in Spain.
A.3 Identification of firm-specific distortions
Using the firms optimality conditions we can infer the level of
idiosyncratic distortions by picking
the values of Ksi and Ysi that, through the lens of the model,
rationalize the combinations of labor,
capital, and production that we observe in the data.
Aggregate parameters: we follow Hsieh and Klenow (2009) by
setting R to 10% (5% interest
rate and 5% depreciation rate) and the elasticity of
substitution to 3.26 The industry-specific
capital shares s are set to 1 minus the labor share in industry
s in the US.
Pinning-down firms physical TFP: For every firm in the data we
infer its physical TFP
using the expression:
Asi = s(PsiYsi)
1
Kssi L1ssi
(16)
26Note that the gains from reallocation increase in , and this
is a conservative value given that industries aredefined at the
4-digit level. Moreover, we later conduct some robustness checks
evaluating the importance of thisassumption.
27
-
where s =w1s(PsYs)
11
Psis a industry-specific constant. Since it does not affect
relative productiv-
ities within industry, we set s = 1 for all industries. Note
that we do not observe firms real output
Ysi but rather its total revenue PsiYsi. We hence use revenue
data and the elasticity of substitution
to infer real output.
Pinning-down capital accumulation distortions: Equation (7)
pins-down the distortion
associated to capital accumulation:
1 + Ksi =s
1 swLsiRKsi
(17)
The model identifies a high Ksi when the ratio of labor to
capital compensation is high, relative to
what one would expect in the absence of distortions. In a
situation in which no firm faces distortions
on capital accumulation, we should observe that there is no
within-industry variation on the ratio
of labor to capital compensation. Through the lens of the model,
a relatively high level of labor
to capital compensation is associated to the firm facing an
idiosyncratic tax distorting its optimal
capital-labor ratio. For instance, labor market regulations that
result in a high cost of labor only for
some firms would be reflected in low Ksi for those firms.
Conversely, financial markets frictions that
raise financial costs for some firms would be reflected in high
Ksi for those firms.
Pinning-down size distortions: After some straightforward
manipulation, we can express
equation (4) as:
(1 Ysi) =
1wLsi
(1 s)PsiYsi (18)
This equation pins-down a high Ysi when the labor compensation
of the firm is low compared to what
one would expect given the industry elasticity of output with
respect to labor (adjusted for mark-
ups). In the presence of distortions, the before-taxes marginal
revenue products are not equalized
across firms, and hence misallocation arises. Any policy that
penalizes firms growth would appear
in the form of a high inferred Ysi s.
28
-
B Misallocation Results for All Sectors NACE rev.2
Table A1: Misallocation in Spain over the period 1995-2007.
Sectors 10-23.
Sector HK STD TFP OP LPR OP TFP
1995 - 2000 10 0.24 0.45 0.33 1.262001 - 2007 0.32 0.48 0.26
1.11
1995 - 2000 11 0.32 0.53 0.57 1.702001 - 2007 0.32 0.52 0.50
1.39
1995 - 2000 12 0.16 0.62 0.43 1.422001 - 2007 0.16 0.64 1.17
1.99
1995 - 2000 13 0.27 0.39 0.14 1.172001 - 2007 0.28 0.42 0.13
0.90
1995 - 2000 14 0.29 0.38 0.18 1.152001 - 2007 0.36 0.45 0.17
1.09
1995 - 2000 15 0.40 0.46 0.05 0.822001 - 2007 0.53 0.53 0.04
0.72
1995 - 2000 16 0.25 0.34 0.19 1.152001 - 2007 0.27 0.39 0.14
0.89
1995 - 2000 17 0.24 0.39 0.44 1.382001 - 2007 0.32 0.48 0.41
1.11
1995 - 2000 18 0.21 0.36 0.26 1.422001 - 2007 0.24 0.39 0.22
1.13
1995 - 2000 20 0.29 0.50 0.57 1.612001 - 2007 0.44 0.58 0.49
1.26
1995 - 2000 21 0.35 0.54 0.31 1.292001 - 2007 0.26 0.59 0.42
1.42
1995 - 2000 22 0.17 0.35 0.42 1.412001 - 2007 0.21 0.38 0.31
1.15
1995 - 2000 23 0.19 0.38 0.38 1.282001 - 2007 0.26 0.44 0.31
1.04
29
-
Table A2: Misallocation in Spain over the period 1995-2007.
Sectors 24-39.
Sector HK STD TFP OP LPR OP TFP
1995 - 2000 24 0.21 0.37 0.60 2.012001 - 2007 0.32 0.44 0.58
2.05
1995 - 2000 25 0.27 0.41 0.16 0.972001 - 2007 0.35 0.44 0.11
0.83
1995 - 2000 26 0.31 0.53 0.54 1.742001 - 2007 0.54 0.58 0.42
1.19
1995 - 2000 27 0.17 0.36 0.34 1.742001 - 2007 0.28 0.38 0.40
1.23
1995 - 2000 28 0.16 0.29 0.26 1.082001 - 2007 0.19 0.33 0.20
0.94
1995 - 2000 29 0.13 0.41 0.38 2.022001 - 2007 0.27 0.48 0.46
2.17
1995 - 2000 30 0.10 0.25 -0.08 1.092001 - 2007 0.12 0.33 0.18
1.03
1995 - 2000 31 0.16 0.36 0.20 1.002001 - 2007 0.25 0.40 0.15
0.92
1995 - 2000 32 0.21 0.36 0.23 1.162001 - 2007 0.31 0.44 0.19
0.95
1995 - 2000 33 0.20 0.30 0.30 0.992001 - 2007 0.24 0.36 0.12
0.92
1995 - 2000 35 0.24 0.59 0.69 1.842001 - 2007 0.36 0.70 1.11
1.98
1995 - 2000 37 0.43 0.63 -0.02 0.872001 - 2007 0.79 0.62 0.15
0.57
1995 - 2000 38 0.57 0.61 0.13 1.012001 - 2007 0.60 0.63 0.12
1.12
1995 - 2000 39 0.64 0.69 -0.06 1.822001 - 2007 0.83 0.67 -0.09
1.18
30
-
Table A3: Misallocation in Spain over the period 1995-2007.
Sectors 41-58.
Sector HK STD TFP OP LPR OP TFP
1995 - 2000 41 0.32 0.37 0.20 1.662001 - 2007 0.82 0.44 0.10
1.43
1995 - 2000 42 0.17 0.31 0.12 1.612001 - 2007 0.20 0.34 0.28
1.37
1995 - 2000 43 0.22 0.35 0.11 1.552001 - 2007 0.25 0.35 0.07
1.10
1995 - 2000 45 0.55 0.56 0.21 1.202001 - 2007 0.65 0.62 0.23
1.15
1995 - 2000 46 0.41 0.45 0.17 1.432001 - 2007 0.50 0.48 0.13
1.28
1995 - 2000 47 0.31 0.42 0.45 2.572001 - 2007 0.33 0.43 0.35
1.86
1995 - 2000 49 0.20 0.36 0.14 1.412001 - 2007 0.38 0.43 0.13
1.10
1995 - 2000 50 0.36 0.50 0.44 1.322001 - 2007 0.43 0.49 0.52
1.10
1995 - 2000 51 0.12 0.46 0.43 2.042001 - 2007 0.16 0.51 0.52
2.66
1995 - 2000 52 0.68 0.56 0.54 1.602001 - 2007 1.01 0.60 0.43
1.30
1995 - 2000 53 0.75 0.64 -0.03 1.042001 - 2007 1.12 0.71 0.13
0.88
1995 - 2000 55 0.22 0.41 0.21 1.742001 - 2007 0.38 0.49 0.19
1.56
1995 - 2000 56 0.23 0.37 0.08 1.222001 - 2007 0.44 0.46 0.01
1.00
1995 - 2000 58 0.28 0.41 0.58 1.982001 - 2007 0.41 0.46 0.55
1.82
31
-
Table A4: Misallocation in Spain over the period 1995-2007.
Sectors 59-77.
Sector HK STD TFP OP LPR OP TFP
1995 - 2000 59 0.57 0.52 0.16 1.402001 - 2007 0.69 0.56 0.09
1.30
1995 - 2000 60 0.33 0.50 1.29 2.002001 - 2007 0.79 0.54 0.90
1.63
1995 - 2000 61 1.22 0.68 1.86 2.052001 - 2007 1.12 0.79 2.03
2.75
1995 - 2000 62 0.15 0.38 0.33 2.162001 - 2007 0.20 0.41 0.43
2.29
1995 - 2000 63 0.05 0.25 0.43 1.912001 - 2007 0.23 0.42 0.37
1.97
1995 - 2000 68 1.67 0.90 -0.15 1.312001 - 2007 2.02 0.96 -0.14
1.32
1995 - 2000 69 0.57 0.49 0.21 1.612001 - 2007 0.64 0.47 0.09
1.29
1995 - 2000 70 0.38 0.39 0.36 1.572001 - 2007 0.62 0.53 0.06
1.43
1995 - 2000 71 0.19 0.42 0.21 2.212001 - 2007 0.49 0.51 0.07
1.63
1995 - 2000 72 0.25 0.51 0.18 1.122001 - 2007 0.15 0.50 0.18
1.26
1995 - 2000 73 0.37 0.46 -0.20 1.692001 - 2007 0.57 0.49 -0.24
1.55
1995 - 2000 74 0.30 0.41 -0.11 1.532001 - 2007 0.35 0.45 -0.15
1.44
1995 - 2000 75 0.19 0.35 0.10 0.942001 - 2007 0.26 0.38 0.15
0.86
1995 - 2000 77 0.42 0.50 0.09 1.272001 - 2007 0.55 0.58 0.10
1.28
32
-
Table A5: Misallocation in Spain over the period 1995-2007.
Sectors 78-82.
Sector HK STD TFP OP LPR OP TFP
1995 - 2000 78 0.19 0.33 -0.34 1.682001 - 2007 0.26 0.35 -0.33
1.69
1995 - 2000 79 0.36 0.47 0.25 1.702001 - 2007 0.47 0.52 0.29
1.43
1995 - 2000 80 0.17 0.31 0.15 1.802001 - 2007 0.19 0.35 0.17
1.90
1995 - 2000 81 0.20 0.31 0.01 1.482001 - 2007 0.27 0.38 -0.04
1.44
1995 - 2000 82 0.31 0.41 -0.10 1.402001 - 2007 0.33 0.43 -0.15
1.49
Notes: See notes to Table 2.
33
-
C Two Digit NACE rev.2 Classification
Table B1: Description of sectors
Code Description
10 Manufacture of food products11 Manufacture of beverages12
Manufacture of tobacco products13 Manufacture of textiles14
Manufacture of wearing apparel15 Manufacture of leather and related
products16 Manufacture of wood and of products of wood and cork,
except furniture17 Manufacture of paper and paper products18
Printing and reproduction of recorded media20 Manufacture of
chemicals and chemical products21 Manufacture of basic
pharmaceutical products and pharmaceutical preparations22
Manufacture of rubber and plastic products23 Manufacture of other
non-metallic mineral products24 Manufacture of basic metals25
Manufacture of fabricated metal products, except machinery and
equipment26 Manufacture of computer, electronic and optical
products27 Manufacture of electrical equipment28 Manufacture of
machinery and equipment n.e.c.29 Manufacture of motor vehicles,
trailers and semi-trailers30 Manufacture of other transport
equipment31 Manufacture of furniture32 Other manufacturing33 Repair
and installation of machinery and equipment35 Electricity, gas,
steam and air conditioning supply37 Sewerage38 Waste collection,
treatment and disposal activities; materials recovery39 Remediation
activities and other waste management services41 Construction of
buildings42 Civil engineering43 Specialised construction
activities
34
-
Table B2: Description of sectors (cont.)
Code Description
45 Wholesale and retail trade and repair of motor vehicles and
motorcycles46 Wholesale trade, except of motor vehicles and
motorcycles47 Retail trade, except of motor vehicles and
motorcycles49 Land transport and transport via pipelines50 Water
transport51 Air transport52 Warehousing and support activities for
transportation53 Postal and courier activities55 Accommodation56
Food and beverage service activities58 Publishing activities59
Motion picture, video and television programme production, sound
recording60 Programming and broadcasting activities61
Telecommunications62 Computer programming, consultancy and related
activities63 Information service activities68 Real estate
activities69 Legal and accounting activities70 Activities of head
offices; management consultancy activities71 Architectural and
engineering activities; technical testing and analysis72 Scientific
research and development73 Advertising and market research74 Other
professional, scientific and technical activities75 Veterinary
activities77 Rental and leasing activities78 Employment
activities79 Travel agency, tour operator reservation service and
related activities80 Security and investigation activities81
Services to buildings and landscape activities82 Office
administrative, office support and other business support
activities
35
-
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37
IntroductionThe 1995-2007 growth experienceDataMisallocation and
productivity in the Spanish boomRobustness analysisIndustry
classificationBalanced versus unbalanced panelElasticity of
substitutionMeasurement errorSample of large firms
Sources of misallocation's evolutionSize versus capital
distortionsSector-level analysisFirm-level analysisRegional
misallocation
Concluding RemarksTheoretical frameworkWithin-industry
MisallocationAggregate TFPIdentification of firm-specific
distortions
Misallocation Results for All Sectors NACE rev.2Two Digit NACE
rev.2 Classification