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.

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

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

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

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

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

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

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

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

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

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

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