1. No category

Report on main directions of research towards better assessment of competitiveness.
By
Maria Bas, Philippe Martin and Thierry Mayer
Work Package 5 / Deliverable 5.3
Sciences Po was the partner responsible for the preparation of this
report.
20February 2014
1 Mapcompete is a project designed to provide an assessment of data
opportunities and requirements for the comparative analysis of
competitiveness in European countries
The project leader is László Halpern for the Hungarian Academy of Sciences. The leaders of
the six teams are: Carlo Altomonte, Bocconi University, for Bruegel; Giorgio Barba
Navaretti, University of Milan, for Ld’A; GáborBékés for CERSHAS; KatjaNeugebauer, for
IAW; Lionel Fontagné, PSE, University of Paris I, for PSE; Philippe Martin, for Science Po.
Delphine Michel, Bruegel, is the project manager.
Mapcompete Supporting Institutions: National Bank of Belgium, Banque de France, Banco de
España, Deutsche Bundesbank, Bancad’Italia, Magyar Nemzeti Bank, The Italian National
Institute of Statistics (ISTAT).
LEGAL NOTICE: The searching leading to this report has received funding from the
European Union’s Seventh Framework Programme (FP7/2007-2013) under grant number
320197. The views expressed in this publication are the authors’ alone, and do not necessarily
reflect the views of the European Commission.
2 Executive summary
Non price competitiveness is an important issue for European firms given that they
produce in a high wage environment. However, non price competitiveness is difficult
to measure, more anyway than price or cost competitiveness. This is true at the firm
or at a more aggregate such as sector or country level. Whereas price
competitiveness implies low prices, non price competitiveness implies higher demand
for a given price. This can come from higher quality, differentiation, better design,
brand image, associated services…This report proposes to use a quality measure
which has the advantage of being simple and intuitive. Itmeasures the characteristics
of the goods that increase its demand for a given price or similarly that explain why
consumers do not decrease their demand even when they have to pay a higher
price. This quality measure is based on trade data and can be applied to different
sectors and different countries of the EU across time. Our indicator of quality
competitiveness seems to be a good proxy of product quality since it is positively
correlated with the level of economic development, human capital, physical capital
and innovation intensity. Our preliminary results on quality changes across European
countries suggest for example that the competitiveness gains of Germany during the
2000-2009 period are, at least in the high-technology (machinery and electronics)
sectors we analyze, very much linked to improvement in the quality of goods
produced.
3 1. Introduction
This report, which is the third deliverables of WP51, aims to shed light on the main
directions of research towards better assessment of competitiveness. This report
proposes a new measure of quality adjusted prices based on the recent literature that
has been reviewed in the WP D.5.2. It also describes the future research agenda
highlighting the main questions on quality adjusted competitiveness in terms of policy
implications that are still open.
Different measures of competitiveness at the micro and macro level have been
developed in the literature. Two different groups of competitiveness indicators have
been emphasized. The first one measures price / cost competitiveness: the lower the
unit cost of production the higher the efficiency and the more competitive the
country/industry/firm. Within this framework lower marginal costs imply lower final
goods prices and higher revenues. The second group of competitiveness indicators
focused on non-price competitiveness. These measures of competitiveness are
related to innovation (R&D investments, product and process innovation measures),
the skilled-intensity, capital intensity and product quality. Note that in the first group of
indicators of price/cost competitiveness the lower the prices the higher the efficiency
gains, while the second group of indicators might imply that high prices are
associated with higher competitiveness since prices are correlated with product
quality.
Having a precise measure of quality competitiveness is important for several reasons
as emphasized by the European Competitiveness Report 2013. First, European firms
1
It corresponds to the deliverable D.5.3. The others deliverables are D5.1, which is a “Theoretical and
policy aspects of competitiveness at different aggregation levels” which will be delivered by PSE and
D.5.2 which is a report on “Indicators of quality adjusted price competitiveness.” carried out by
Sciences Po.
4 have a comparative advantage to produce more sophisticated products since they
are specialized in high-quality segments. This report shows that by EU manufacturing
industries were able to keep their competitive position in 2009 relative to 1995 thanks
to quality upgrading by increasing the complexity of their products. Second, quality
competitiveness is also an important factor driving economic growth in the long run
as compare to price competitiveness. Third, enhancing non-price competitiveness is
also required for EU firms to be able to compete with firms from emerging countries
mainly the BRICs (Brazil, Russia, India and China) that have gradually increased
their presence not only in low-technology but also in high-technology-intensive
segments in the recent years (European Competitiveness Report 2010 and 2013).
Even though the European Commission recognizes the importance of quality as a
key competitiveness factor for European firms it also acknowledges that it is difficult
to measure due to the difficulty of observing the product quality and the lack of
information on the share of value added produced domestically.
In this report we propose a quality measure which has the advantage of being
simple and intuitive. It is intuitive and directly related to the views of the European
Commission on quality that defines it in the following manner "Upgrading the
products may also make customers willing to pay a premium for them if the products
are perceived to be of high enough quality" (European Competitiveness Report
2013). Our quality measure is exactly based on this view as it measures the
characteristics of the goods that increase its demand for a given price or similarly that
explain why consumers do not decrease their demand even when they have to pay a
higher price. This quality measure is based on trade data and can be applied to
different sectors and different countries of the EU across time. Our indicator of quality
competitiveness seems to be a good proxy of product quality since it is positively
5 correlated with the level of economic development, human capital, physical capital
and innovation intensity. Our preliminary results on quality changes across European
countries suggest for example that the competitiveness gains of Germany during the
2000-2009 period are, at least in the high-technology (machinery and electronics)
sectors we analyze, very much linked to improvement in the quality of goods
produced.
Several empirical works in international trade studying the determinants of product
quality across products and countries rely on unit values to capture differences in
quality at the product level. This literature argues that prices (unit values) can be
used as a proxy of product quality since higher prices act as a signal for higher
product quality in imperfect market conditions following the idea of Akerlof's (1970)
market for lemons. On the supply side, export prices are correlated with exporters'
income per capita and relative physical and human capital endowments (Schott,
2004). Cross-country comparisons reveal that capital and skill abundant countries
export goods at higher unit values. Countries that increase their skill and capital
deepening over time experienced greater growth of unit values. Hummels and
Klenow (2005) rely on the quantity exported and proxies for the number of varieties
to explain that differences in quality are necessary to describe the observed
differences in unit values. On the demand side, Hallak (2006) combines unit values
at the product-country of origin level with information on countries income per capita.
He also interprets the results in terms of product-quality: richer countries import
relatively more from countries producing high-quality goods.
Other works relying on cross-country and product international trade data that use
unit values to measure quality are Schott (2008), Fontagne et al (2008) and Berthou
6 and Emlinger (2010) among others. They construct a relative price that compares for
each market, the unit value of each individual trade flow to the average trade price of
Recent firm level analyses also focus on prices (unit values) to capture differences
across firms-products in terms of quality. They find that exporting firms charge higher
prices in more distant markets and to high-income countries (Bastos and Silva 2010,
Gorg, Halpern and Murakovy 2010, Manova and Zhang, 2012; Martin, 2012). Bigger,
more productive and skilled intensive firms charge higher prices and pay higher input
prices (Harrigan, Ma and Shlychkov, 2012, Kugler and Verhoogen, 2012, Hallak and
Sivadasan 2013). Firms exporting to high-income countries buy more expensive
inputs (Bastos, Silva and Verhoogen, 2013).
However, prices are imperfect measures of product quality. Variations in prices
across markets may indeed reflect differences on market structure or supply shocks.
Changes in unit values across firms-products might be associated to variations in
markups (pricing-to-market) or marginal costs rather than quality. In this work, we use
an alternative methodology to estimate quality of exported goods at the productcountry of origin level. We rely on the methodology developed by Khandelwal, Schott
and Wei (2013). They estimate quality and quality-adjusted prices at the firm-productcountry of destination relying on a demand function estimation and firm level data
from the textile sector in China. They show that assuming a CES utility function
where product quality acts as a demand shifter, the quality of each product can be
estimated using information on quantities, unit values and the elasticity of substitution
across products.
We adapt this methodology to estimate the quality at the product level by country
of origin of exports using detailed data of bilateral trade flows at the hs6 product level
7 for the period 2000-2009 from BACI-COMTRADE. This dataset allows us to estimate
a measure of quality for different countries at the product level and to predict a
ranking of product-quality across countries by sector. We focus on a group of
selected industries (textile, chemical, machinery and electronics) to provide a
comparison of product quality across countries of origin of exports and over time. In a
second step, we focus on European countries within the euro zone in the period
2000-2009 that have the same currency. We re-estimate the quality indicators for the
subsample of intra-Euro zone trade flows to eliminate nominal exchange rate
movements that generate large relative cost shocks.
Other alternative empirical methodologies disentangle price and quality using trade
values and quantities to obtain a quality-adjusted measure of unit values. Quality
differentiation across products is associated to product characteristics that are valued
by consumers. Hallak and Schott (2011) develop a methodology that decomposes
countries observed export prices into quality and quality-adjusted components using
information of their trade balances from the demand side. For constant observed
export prices, they infer that countries with trade surpluses produce high-quality
products relative to countries with trade deficits. Khandelwal (2010) proposes a
measure of quality that accounts for both product prices and market shares. He uses
unit value and quantity information for imports of the US to infer quality from the
estimation of a nested logit demand system. This method requires instrumental
variables to identify the parameters. Khandelwal (2010) relies on variety-specific unit
transportation cost, country level exchange rates and the interaction of distance to
the US with oil prices to instrument prices. This methodology provides quality
estimates at the product level in which imported products with higher market shares
are assigned higher quality after controlling for price differences and country size.
8 The next section of this work describes the empirical methodology. The third
section presents the dataset used in the empirical analysis. The fourth section
describes the correlation of the quality indicators and other performance measures.
The fifth section presents the results. The final section concludes.
2. Methodology
We have adapted the methodology developed by Khandelwal, Schott and Wei
(2013) to estimate quality at the product and country of origin of exports level using
bilateral trade data. This quality measure is simple and intuitive: it measures the
characteristics of the goods that increase its demand for a given price.
Our quality estimate is consistent with a demand function that depends negatively
on prices with a constant elasticity of substitution and positively on the quality of the
product. Hence, product quality is interpreted as a demand shifter and does not
depend on price elasticity. The quality for each hs6 product, country of origin and
destination of exports can be estimated using information on quantities and unit
values. We estimate quality as a demand shifter that corresponds to the residual of
an OLS estimation of the following regression:
xkint+σpkint= βPopit+λDin+ αknt+εkint(1)
9 wherexkintand pkintdenote the natural logs of the quantity and price (unit value) of
product k exported by country i to destination n in year t. The product-destination
country-time fixed effect αkntcontrols for differences across products and also for
differences in price index and income at destination. We modify the methodology of
Khandelwal, Schott and Wei (2013) to take into account the observable bilateral
determinants of trade flows across countries. We include a set of dyadic terms that
determine bilateral trade flows as distance, common language, common border
(contiguity) and colonial links (Din).
We also include in the estimation of equation (1) origin country's population as a
proxy for unobserved varieties within a product as suggested by Khandelwal (2010).
This variable allows us to control for the fact that larger countries export more
products independently of their quality. We also used as an alternative measure of
unobserved varieties the number of establishments within each 2 digit Isic industry in
the origin country from UNIDO dataset. This latter variable has the advantage of
being a more precise proxy for the amount of varieties produced by a country but the
disadvantage of being available for the period for a restricted group of countriesindustries.
The estimated logarithm of quality, qkint, depends on the residual εkintand the
elasticity of substitution σ between products:
qkint=εkint/(σ-1)
10 Conditional on price, a variety with a higher quantity (demand) is assigned higher
quality. In this framework, quality is any unobserved product characteristic that
increases demand other than price so it acts as a demand shifter. Identification of
equation (1) is based on variation across exporting countries in export quantities for
countries selling the same product in the same destination market and year.
Estimation of equation (1) requires an assumption on the elasticity of substitution
across products. We rely on the elasticity of substitution for the US at the 5 digit SITC
rev3 from Broda and Weinstein (2006).
Box 1 – Methodology to estimate quality adjusted competitiveness measures at
the product level
Alternative methodologies have been developed aiming at disentangling price from
quality indicators. The idea behind these methodologies is very simple and intuitive: a
higher quality is measured as all characteristics of a good that increase demand for a
given price.
The indicator of quality adjusted price competitiveness derived from this methodology
is closely related to the views of the European Commission on quality that defines it
in the following manner "Upgrading the products may also make customers willing to
pay a premium for them if the products are perceived to be of high enough quality"
(European Competitiveness Report 2013).
11 3. Data
The main dataset used in this report relies on BACI dataset from CEPII. The
original data are provided by the United Nations Statistical Division (COMTRADE
database). This dataset provides bilateral values and quantities of exports at the HS
6-digit product level, for more than 200 countries.
We rely on the period 2000-2009 for which information on prices and quantities are
more exhaustive. Our analysis focuses on the 50 exporters and importers countries
with highest trade flows. The list of countries used in the full sample is available in
Table 1 (Appendix).
Information on bilateral determinants of international trade comes from the
GeoDist, the CEPII distance dataset.The CEPII distance dataset contains dyadic
variables that determine trade costs. It includes variables valid for pairs of countries
like distance, common language, colonial links and common border (contiguity). We
use these variables in the estimation of equation (1) to take into account the
observable bilateral determinants of trade flows between pair of countries.
Two alternative control variables for the number of unobserved varieties in the
country of origin are used in the estimation. The first one is the total population by
exporting country. This variable comes from the Penn World tables as well as capital
per worker at the country level that it is used to measure our quality adjusted
competitiveness measure. The second proxy that we use is the number of
establishments within each 2 digit Isic industry in the origin country from UNIDO
dataset. We rely on concordance tables from WITS dataset (World Bank) between
the 2 digit ISIC rev3 classification and the HS6 product level classification. These
12 concordance
tables
are
available
at
the
following
web
site:
http://wits.worldbank.org/wits/productconcordance.html.
This latter variable is only available for a restricted sample of countries for all
industries during the period 2000-2009.
Finally, we use the elasticity of substitution across products for the US at the 5 digit
SITC rev3 estimated by Broda and Weinstein (2006). We rely on concordance tables
from WITS dataset (World Bank) between the 5 digit SITC rev3 classification and the
HS6 product level classification. These concordance tables are available at the
following web site: http://wits.worldbank.org
We use alternative measures of innovation at the country level to test the correlation
of our quality estimates with innovation. These measures come from OECD. The
main indicators of innovation that we use are R&D over GDP, Government
Government-financed R&D over GDP, private sector financed R&D over GDP, the
number of patents and the number of researchers at the country level. The measure
of human capital endowment is the percentage of the population aged over 25 with at
least secondary education in 1999 drawn from Barro and Lee (2001).
Box 2 – Data requirements to compute the quality indicator at the product level
While it is very difficult to have access to this information for domestic production,
most of the studies rely on trade flows data that provide information on values and
quantities at the product level for exports and imports.
13 The data requirements to apply this methodology at the product level are:
1) Bilateral trade flows data at the HS6 product level comes from BACI dataset
(CEPII) build on data provided by the United Nations Statistical Division
(COMTRADE database).For more information see
http://www.cepii.fr/CEPII/fr/bddmodele/presentation.asp?id=1 2)Bilateral determinants of international tradearepairs of countries like distance,
common language, colonial links and common borderfrom the GeoDist, the CEPII
distance dataset. For detailed information on GeoDist dataset see:
http://www.cepii.fr/CEPII/fr/bddmodele/presentation.asp?id=6 3)The elasticity of substitution across products for the US at the 5 digit SITC rev3
comes from the estimations by Broda and Weinstein (2006). For detailed information
on these elasticities at different levels see:
http://www.columbia.edu/~dew35/TradeElasticities/TradeElasticities.html 4. Aggregate quality indicator and country performance
This section presents evidence that shows that our quality indicators are good
proxy of quality measures. As highlighted by the European Competitiveness Report
2010 and 2013, quality competitiveness reflects the ability of firms to produce
complex and more differentiated products. The production of high-quality products
14 requires Research and Development (R&D) investments, human and physical
capital. The degree of product sophistication and quality should be related with the
degree of economic development, skilled, capital and innovation intensity of the
country.
We look at the correlation of our quality indicator at the country of origin level and
GDP per capita, human capital, capital intensity, two indicator of innovation intensity
(R&D over GDP and the number of patents), high-technology exports, market shares
and export unit values. GDP per capita and high-technology exports come from the
World Bank. The log of capital stock per worker is taken from the Penn World Tables
and the measure of human capital endowment is the percentage of the population
aged over 25 with at least secondary education in 1999 drawn from Barro and Lee
(2001). The innovation indicators are from OECD. We aggregate the quality
indicators at the country level taking the median of the quality estimates across all
products for each country of origin and year. All variables are expressed in logarithm.
Figure 1 presents the correlation between the quality measures by country and GDP
per capita in the year 2000. As can be seen there is a positive correlation between
the level of economic development of a country and the quality of their products.
High-income countries export high-quality goods. This evidence is in line with
previous findings in the literature (Schott, 2004).
Figure 2shows that our quality estimate is positive correlated with export prices
(unit values). Countries exporting high-priced goods tend to export also high-quality
products. This evidence confirms previous results in the literature.
15 Figure 1: Correlation between the quality indicator and GDP per capita in 2000
Note: authors calculations based on BACI dataset.
Figure 2: Correlation between the quality indicator and unit values in 2000
Note: authors calculations based on BACI dataset.
16 4.1. Aggregate quality indicator and factor endowments
In order to produce high quality goods firms rely on specific inputs such as skilled
labor and capital investments as emphasized by Verhooghen (2008). Hence, we
expect that our quality measure to be correlated with those inputs. We look at the
relationship between our quality indicator measure and the level of human capital
endowments and capital stock in figures 3 and 4. These figures indicate that
countries with larger endowments of human and physical capital produce high-quality
products. More skilled and capital intensive countries have the human resources and
technology required to produce products with a higher level of quality. This evidence
confirms the findings of Schott, 2004.
Figure 3: Correlation between the quality indicator and Human Capital in 2000
Note: authors calculations based on BACI dataset.
17 Figure 4: Correlation between the quality indicator and Capital intensity in 2000
Note: authors calculations based on BACI dataset.
4.2. Aggregate quality indicator and innovation patterns
Producing high-quality products requires also technological investments on
product innovation. We explore in this section the relationship between our quality
measure and several indicators of innovation activity at the country level. We use as
a proxy of innovation R&D over GDP, Government Government-financed R&D over
GDP, private sector financed R&D over GDP, the number of patents and the number
of researchers at the country level. These measures of innovation come from the
OECD.
Figures 5 to 7 show a positive correlation between R&D investments as a
percentage of GDP (total, private and public sector) and the product quality estimate
indicating that countries that countries that are more intensive in innovation activities
produce goods with a higher quality.
18 Figure 5: Correlation between the quality indicator and R&D intensity in 2000
Note: authors calculations based on BACI dataset.
Figure 6: Correlation between the quality indicator and private sector financed
R&D as percentage of GDP in 2000
Note: authors calculations based on BACI dataset.
19 Figure 7: Correlation between the quality indicator and Government-financed
R&D as percentage of GDP in 2000
Note: authors calculations based on BACI dataset.
Next we explore the relationship between our quality indicator and the total number
of patents and researchers by country. As can be seen in figures 8 and 9, countries
that have greater amount of patents and researchers produce goods with a highquality content.
Finally, we also look at the relationship between our quality indicator and a
measure of the technological content of exports from the World Bank. As can be
noticed in Figure 10 countries that produce and export high quality goods also export
high-technology products.
20 Figure 8: Correlation between the quality indicator and patents in 2000
Note: authors calculations based on BACI dataset.
Figure 9: Correlation between the quality indicator and the number of
researchers in 2000
Note: authors calculations based on BACI dataset.
21 Figure 10: Correlation between the quality indicator and High-technology
exports in 2000
Note: authors calculations based on BACI dataset.
Box 3 – Main results
As highlighted by the European Competitiveness Report 2010 and 2013, quality
competitiveness reflects the ability of firms to produce complex and more
differentiated products. We have shown that our indicators of quality products are
positively correlated with economic development and innovation performance
required to produce those high-quality goods.
The average quality indicators at the country level are positively correlated
with:
- Economic development measured by GDP per capita;
- Unit values measured by average prices (value over quantity);
- Human and physical capital.
22 -Innovation patterns measured by total, industry-financed and government financed
Research and Development (R&D) investments over GDP, number of patents and
researchers.
- High-technology exports
4.3. Quality indicators and market shares
Our measure captures all the attributes of a product that increase its demand for a
given price. Conditional on prices, products with larger market shares have a greater
quality. First we show that at the aggregate level our quality indicator is positively
correlated with market shares in 2000 (Figure 11).
Figure 11: Correlation between the quality indicator and export market shares
in 2000
Note: authors calculations based on BACI dataset.
23 In the same line, countries that upgrade the quality of their products during the
period should have experienced a greater increased in their market shares. We
explore this relationship relying on the quality estimates for the euro-zone countries
at the 2-digit industry level. Export market shares in this case are calculated for each
industry as the ratio of exports of country over the world exports of that industry.
Figure 12 shows how the percentage change in product quality relates to the
percentage change in market shares between 2000 and 2009. This evidence
indicates that European countries that have improved their product quality between
2000 and 2009 were able to gain export market shares.
Figure 12: Correlation between the percentage change in the quality indicator
and in export market shares (2000-2009) for euro-zone countries at the industry
Note: authors calculations based on BACI dataset.
24 5. Ranking of quality indicators for selected industries
Estimation of equation (1) allows us to have a measure of quality at the hs6
product level, country of origin of exports, destination and year. This indicator of
quality competitiveness is in line with the views of the European Commission that
highlights in the European Competitiveness Report 2010 and 2013 that EU firms are
able to increase their market shares through product differentiation and quality
upgrading in the recent years. These reports also emphasized that product quality is
related to consumers’ willingness to pay a premium for those products that have
attributes that they considered of a higher quality.
Our indicator of quality captures exactly this point raised by the European
Commission since it measures any unobserved product characteristic different from
the price that increases demand for that product. In that sense, quality is any
unobserved attribute of the product that acts as a demand shifter conditional on
prices. This indicator of quality is computed using the residual of that estimation and
the elasticity of substitution among varieties as shown in equation (2).
In order to have a comparable measure of quality, we aggregate the quality
estimates at the exporting country and 2-digit industry level across hs6 products and
destination countries. For each 2-digit industry, exporting country and year, we take
the median value of the quality indicator across all destination countries and hs6
products within the 2-digit manufacturing industries. Next we rank the aggregate
quality indicator in levels by year and 2-digit industry across all exporting countries.
We exclude the outlier values of unit values and quality estimates (values higher than
the 99th percentile and lower than the 1st percentile of the distribution of these
variables).
25 Figures 13 to 16show the results of this ranking of quality indicators for the top 10
origin countries (among the 50 countries) within the 2-digit industry for four selected
industries and for the initial year, 2000, and the last year, 2009. This quality
estimates are computed from estimation of equation (1) relying on the full sample of
50 countries and using population in the origin of export country as a proxy of country
size and unobserved number of varieties. The four selected industries are textile,
chemical, machinery and electronics industry.
As can be seen, for the textile industry (figure 13) among the ten exporting
countries with highest quality we found in 2000 the following countries in the order of
the ranking from the top 1 to the top 10: Italy, Japan, France, UK, US, Germany,
Korea, Taiwan and Spain. Comparing this ranking of quality in the textile sector with
the ranking for 2009 reveals that Germany and Switzerland have increased their
quality. Germany passed from the top 6th country in 2000 to the 2th highest quality
exporting country in the textile sector with an increase of 40 percent in their quality
between 2000 and 2009 for a given price. Switzerland was in the 10th position in
2000 and appears in the 4th position in 2009. Other countries have maintained the
same level of quality among both years like Italy, Japan and France.
Looking at the Chemical sector ranking of quality reveals a similar pattern in quality
between 2000 and 2009 (figure 14). The countries that increased the most their
quality of chemical products are Switzerland and Germany. Switzerland jumps from
the 8th position in 2000 to the 4th position in 2009 with an increase in their quality of
37 percent. For a given price, Germany increased its quality of chemical products of
40 percent between 2000 and 2009.
26 Figure 13: Ranking of quality indicators in the textile sector 2000-2009
Note: authors calculations based on BACI dataset.
Figure 14: Ranking of quality indicators in the chemical sector 2000-2009
Note: authors calculations based on BACI dataset.
27 Figure 15: Ranking of quality indicators in the machinery sector 2000-2009
Note: authors calculations based on BACI dataset.
Figure 16: Ranking of quality indicators in the electronics sector 2000-2009
Note: authors calculations based on BACI dataset.
28 Figures (15) and (16) present the ranking of quality across exporting countries for the
machinery and the electronics sectors. In both sectors, Germany is the country that
has experienced the highest quality upgrading between 2000 and 2009. For a
constant price, Germany has increased the quality of the machinery goods of almost
50 percent and it has triplicated the quality of the electronic products between 2000
and 2009.
As a robustness tests, we present in the Appendix similar figures (21 to 24) for the
ranking of quality for these four industries based on the measures of quality from
estimation of equation (1) using as a proxy of unobserved varieties in the exporting
country the number of establishments by 2-digit industry from UNIDO dataset. As
previously highlighted this is a reduced sample since the number of establishments is
only available for a restricted sample of countries for all industries during the period
2000-2009. This reduced sample corresponds to 43% of observations in the full
sample.
Box 4 – Industry level results
1) Textile industry: Germany passed from the top 6th country in 2000 to the 2th
highest quality exporting country in the textile sector with an increase of 40 percent in
their quality between 2000 and 2009 for a given price. Switzerland was in the 10th
position in 2000 and appears in the 4th position in 2009. Other countries have
maintained the same level of quality among both years like Italy, Japan and France.
2) Machinery and electronics industries: The competitiveness gains of Germany
during the 2000-2009 period are, at least in the high-technology sectors, very much
linked to improvement in the quality of goods produced.
29 3) Chemical industries: The countries that increased the most their quality of
chemical products are Switzerland and Germany. Switzerland jumps from the 8th
position in 2000 to the 4th position in 2009 with an increase in their quality of 37
percent. For a given price, Germany increased its quality of chemical products of 40
percent between 2000 and 2009.
5.1. Ranking of quality indicators for intra-Eurozone trade
In this section we restrict the analysis the European Union countries within the
euro zone in the period 2000-2009 that have the same currency. This enables to
eliminate nominal exchange rate movements that generate large relative cost
shocks. We re-estimate equation (1) for the restricted sample of intra-euro zone trade
flows and so we re-calculate the quality indicator for those countries.
Figures 17 to 20 show the results of this ranking of quality indicators for the top 10
European countries for 2000 and 2009 for the four selected industries. As can be
seen, differences in quality are lower since we are comparing a similar group of
countries. When equation (1) is estimated within trade flows for intra-euro zone
countries, the identification of the quality indicator is based on variation across similar
EU exporting countries in export quantities for countries selling the same product in
the same destination market and year.
In the textile sector, Italy is still the leader in product quality for the year 2000 but it
passes to the 2nd rank in 2009 due to the increase in the quality of Portugal. In the
chemical sector, Ireland is the leader in 2000 but it passes second in 2009 after
Belgium that was ranking number four in 2000. The outstanding result is that
Germany is the country with the highest quality in the high-technology sectors,
30 machinery and electronics, in both years. Moreover, Germany is the country with the
highest quality upgrading in the electronics industry between 2000 and 2009,
confirming the previous results.
Figure 17: Ranking of quality indicators in the textile sector 2000-2009
Eurozone
Note: authors calculations based on BACI dataset.
31 Figure 18: Ranking of quality indicators in the chemical sector 2000-2009
Eurozone
Note: authors calculations based on BACI dataset.
Figure 19: Ranking of quality indicators in the machinery sector 2000-2009
Eurozone
Note: authors calculations based on BACI dataset.
32 Figure 20: Ranking of quality indicators in the electronics 2000-2009 Eurozone
Note: authors calculations based on BACI dataset.
Figures in the Appendix (25 to 28) show the ranking of quality for the intra-euro
zone countries for these four industries based on the measures of quality from the
reduced sample using the number of establishments to control for the unobserved
varieties in the exporting country.
33 6. Research agenda
There are still several questions for which there are partial answers or they are still
open concerning the policy implications for firm non-price competitiveness. In this
section, we review and discuss four key questions that could be incorporated the
future research agenda on the determinants of firms quality adjusted price
competitiveness.
1) Does quality upgrading insulate firms from cost competitiveness
shocks? Do firms react differently to macro shocks depending on their
quality?
This first group of questions is still open and future research is required. One
recent work that tries to answer these questions is the work of Chen and Juvenal
(2013). They extend the analysis of Berman, Martin and Mayer (2012) and of
Crozet, Head and Mayer (2012) to explore the relationship between real
exchange rate pass-through and firms’ product quality. They rely on experts wine
ratings as a measure of product quality. Their findings suggest that high-quality
firms react to depreciation by raising more their markups and by increasing less
their export volume.
2) How do management practices affect firm competitiveness?
Another important question is if differences in management practices across
firms can explain differences in their competitiveness. Bloom et al. (2013)
investigate this question by relying on a management field experiment on large
Indian textile firms. Their methodology provided free consulting on management
practices to randomly chosen treatment plants and compared their performance
to a set of control plants. Their findings suggest that those firms that have
adopted these management practices raised productivity by 17% in the first year
34 through improved quality and efficiency and reduced inventory, and within three
years led to the opening of more production plants.
Bloom, Sadun and Van Reenen (1012) have collected data on management
practices on over 8,000 firms in 20 countries in the Americas, Europe and Asia.
They have shown that management practices account for up to half of the TFP
gap between the US and other countries. Concerning the macroeconomic policies
that shape the relationship between management practices and firm performance
they find that the lower trade barriers and weaker labor regulation the higher the
correlation between firms’ managerial practices and their size (and growth).
3) Which
are
the
public
policies
that
improve
firm
non-price
competitiveness?
There are several study cases that have highlighted the role of different public
policies on enhancing firm competitiveness. A first set of works focuses on the
impact of trade liberalization on firm performance. Two main channels through
which trade reform affects firm competitiveness have been highlighted. The
first one is related to reductions on tariff affecting final consumption goods that
increases foreign competition creating incentives for firms to upgrade their
quality, innovate, adopt new technologies and improve their productivity in
order to compete with foreign goods (Amiti and Khandelwal, 2013, Fernandes
and Paunov, 2011, Amiti and Konings, 2007, among others). The second
channel is related to the access to high quality/more efficient inputs from
abroad allows firms to upgrade the quality of their final products (Goldberg et
al, 2012, Amiti and Konings, 2007, Bas and Strauss-Kahn, 2013).
A second set of works focuses on industrial policies affecting firm
competitiveness.
Micro-level empirical studies investigate the effects of
industrial policy through clusters on firm-level performance (Criscuolo et al.
2012, Martin et al. 2011a and b, 2013 and see Nathan and Overman, 2013,
for a complete survey). These studies find mixed results. The main
econometric issue that these studies face is to find a rigorous identification
35 strategy that allows a micro-econometric evaluation of the causal effects of
industrial policy on firm competitiveness.
4) If a policy objective is to increase aggregate exports, should public
policy target small or big firms?
This is question that raises the public debate and has important policy
implications. From the industrial cluster analysis we have learned that small
firms are more responsive to cluster and R&D policies but most of the microlevel empirical trade studies highlight that at the aggregate level small firms do
not account for much of the aggregate export patterns (See Mayer and
Ottaviano, 2007, for European firms and Bernard, Redding and Jensen, 2007,
for the US).
Box 5 – Future research agenda
There are still other questions for which there are partial answers or they are still
open.
1)
Do
different
countries
and
sectors
in
Europe
react
differently
to
macroeconomic shocks like exchange rate shocks?
2)
What is the scope for internal competitiveness adjustment among the euro
zone countries?
3)
Do public policies targeting improvements of firm competitiveness affect
differently firms depending on their characteristics?
The answer of these questions requires on measures of quality beyond unit values
and prices. Direct measures of quality derived from consumers’ behavior and tastes
for specific products could help to shed light on the determinants of firms’ quality
upgrading and the policy implications that are at play.
36 7. Conclusions
In this work we have constructed a new set of measures of product quality at the
micro level for several countries. We have adapted the methodology developed by
Khandelwal, Schott and Wei (2013) to estimate product quality for exporting
countries at the HS6 product level using bilateral flow data on quantities and values.
The quality indicators are then estimated as a residual from a demand function
estimation using information on quantities, unit values and the elasticity of
substitution across products.
Our quality indicators are positively correlated with origin country GDP per capita,
human capital, capital intensity and patenting indicating that they are a good proxy of
product quality. More developed countries with high income export high-quality
products. In order to compare the product quality across origin exporting countries,
we aggregate the quality indicators at the 2-digit industry level by country of origin of
exports. Next, we compute a ranking across countries for a selected set of industries
(textile, chemical, machinery and electronics) on the quality of their products for the
period 2000-2009.
37 7. Appendix
38 7.1. Ranking of quality indicator by industry. Reduced sample
Figure 21: Ranking of quality indicators in the textile sector 2000-2009
Note: authors calculations based on BACI dataset.
Figure 22: Ranking of quality indicators in the chemical sector 2000-2009
Note: authors calculations based on BACI dataset.
39 Figure 23:Ranking of quality indicators in the machinery sector 2000-2009
Note: authors calculations based on BACI dataset.
Figure 24: Ranking of quality indicators in the electronics sector 2000-2009
Note: authors calculations based on BACI dataset.
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