Ulrich Harmes-Liedtke & Juan José Oteiza Di Matteo
MESOPARTNER | ANALYTICAR
Measurement and performance of
A PROPOSAL FOR A GLOBAL QUALITY INFRASTRUCTURE INDEX
This is a draft document and the information contained herein is sub-
ject to change as the document is currently undergoing review
Title: Measurement and Performance of Quality Infrastructure - A proposal for a Global
Quality Infrastructure Index
Author: Ulrich Harmes-Liedtke and Juan José Oteiza Di Matteo
Version: Buenos Aires and Duisburg, Version 1, 5 December 2019
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Table of Contents
1. Introduction ...................................................................................................................... 4
2. Methodology ..................................................................................................................... 5
2.1 The research subject .............................................................................................................. 5
2.2 The formula ........................................................................................................................... 5
2.3 The scores .............................................................................................................................. 7
2.4 The sample ............................................................................................................................. 8
2.5 The data ................................................................................................................................. 9
3. Empirical Results ............................................................................................................. 11
4. Performance .................................................................................................................... 13
5. Conclusions and further research .................................................................................... 16
List of acronyms
Bureau International des Poids et Mesures
[International Bureau of Weights and Measures]
Brazil, Russia, India, China and South Africa
Conformity Assessment Body
International Committee for Weights and Measures
Calibration and Measurement Capabilities
Development Assistance Committee
Economic Complexity Index
Forest Stewardship Council
Global Competitiveness Index
Global Quality Infrastructure Index
Inter-American Accreditation Council
International Accreditation Forum
International Electrotechnical Commission
International Laboratory Accreditation Cooperation
International Organization for Standardization
International Telecommunication Union
Key and Supplementary Comparisons
Key Comparison Data Base
Massachusetts Institute of Technology
Mutual Recognition Arrangement (term used by BIPM and ILAC)
Multilateral Recognition Arrangement (term used by IAF)
National Accreditation Body
National Metrology Institute
National Quality Infrastructure
Official Development Aid
Organization for Economic Co-operation and Development
International Organization of Legal Metrology
Physikalisch-Technische Bundesanstalt [German Metrology Institute]
World Trade Organization
In 2011 we conducted a global study to measure the level of development and performance
of the National Quality Infrastructure (NQI) (Harmes-Liedtke & Oteiza Di Matteo, 2011). The
study was published as Discussion Paper 5/2011 by the International Technical Cooperation
of the National Metrology Institute (NMI) of the Federal Republic of Germany (Physikalisch-
Technische Bundesanstalt, PTB). The purpose was to present "...a methodological proposal
for measuring the Quality Infrastructure (QI) of countries and promote discussion on this lit-
tle-explored topic." (Harmes-Liedtke & Oteiza Di Matteo, 2011, 5). Our initial hypothesis pro-
poses that a country with a well-developed QI is, at the same time, economically prosper-
ous; and conversely, a country lacking in the development of its QI is also economically less
favoured (Harmes-Liedtke & Oteiza Di Matteo, 2011,6).
In the following years, more than a thousand researchers and Quality Infrastructure experts
have downloaded and quoted (Mavroeidis & Tarnawska, 2015; Blind, 2015; Wolters, 2019;
Sun et al., 2019) the document. In the most recent publication of the World Bank Group and
the PTB on Quality Infrastructure, the research is presented as one of the few quantitative
studies that demonstrate a positive correlation between QI and economic performance
(Kellermann, 2019, 24 ff.).
Our 2011 publication is still a unique study that systematically evaluates publicly available
data on metrology, standardisation, accreditation and certification. The composite index is
also so far the only quantitative instrument with which to compare the development status
and performance of QI of different countries. This singularity of the index and the interest of
the professional public have prompted us to write a new edition of the study. We have used
the same databases and mostly retained the formula of the index, which we now call the
Global Quality Infrastructure Index (GQII). At the same time, we have increased the number
of countries covered from fifty-three (53) to seventy (70). The index thus covers all full mem-
bers of the International Accreditation Forum (IAF), i.e. the signatories of the Multilateral
Recognition Arrangement (MLA) (up to 1 October 2019).
International, regional and national organisations of metrology, standardisation and accredi-
tation were consulted to verify data. The need for data verification applies in particular to
the accreditation bodies, whose data are, in principle, publicly accessible on their websites.
However, the presentation does not make it easily accessible for statistical purposes. In this
respect, our database currently presents the best information available on QI globally. Some
errors may have occurred in the data collection process, and we would be grateful for any
information in this respect. At the end of this study we recommend ways in which the qual-
ity of the data could be further improved.
2.1 The research subject
The GQII measures various aspects of the areas of metrology, accreditation, standardisation
and certification of products and services, both on the supply side (the international QI sys-
tem and its services) and on the demand side (companies and other users of QI services). Ac-
cording to the open data approach, we collect information available on websites of interna-
tional bodies as well as national and international organisations related to QI. This measure-
ment aims to establish country-level comparisons of all countries included in the sample (see
section 2.4). Therefore, these measurements are made for a specific set of metrics and
within a selected group of countries. Due to their characteristics, this set of countries and
metrics allows valid comparisons to be established. Some metrics are affected by the size of
the country, specifically its population or GDP, so they have been relativized by dividing by
the total number of inhabitants in each case.
A summary of the components included in the
global index is presented below.
Table 1: GQII components in population and absolute terms
Source: Own elaboration based on the Key Comparison Database of the BIPM
, the ISO Survey 2018
and MLA signatories on the website of the IAF.
2.2 The formula
We have considered using the business population (number of enterprises) as a denominator instead of the
number of inhabitants. This denominator would make sense as it is mainly companies that use QI services. Due
to the lack of reliable data, however, we have refrained from doing so.
https://www.bipm.org/kcdb/ [Access date 04/12/19]
https://www.iso.org/the-iso-survey.html [Access date 04/12/19]
https://www.iaf.nu//articles/IAF_MEMBERS_SIGNATORIES/4 [Access date 04/12/19]
The formula refers to three components of the Quality Infrastructure (accreditation, stand-
ardisation and metrology) and the last component represents the membership of interna-
tional organisations of the QI.
The selection of the components also depends on the availability and quality of the statisti-
cal data. Concerning accreditation schemes and standards, we have selected the data sets
that are important for international comparison.
The components of the formula mostly match those of the 2011 version.
For metrology, we take into account the two central metrological competency metrics pub-
lished by the BIPM. Firstly, the Calibration and Measurement Capabilities (CMC) with which
the National Metrology Institutes (NMI) demonstrate their measurement capabilities; and
secondly, the Key and Supplementary Comparisons (K&SC) with which NMIs show in com-
parative measurements that they are "getting the right answer" (Henson, 2015) and ade-
quately estimating the uncertainties of their results.
In the area of standardisation and certification, we refer to the participation of countries in
ISO standards committees and the number of certifications of quality management systems.
In this edition, we have modified the calculation in the measurement of the accreditation.
The accreditation sub-components are now counted separately, whereas in our previous
publication of 2011 we counted the sum of all accredited Conformity Assessment Bodies
(CABs). The reason for this modification is that the number of CABs ISO/IEC 17025 – Testing
Laboratories is usually much higher than the number of CABs ISO/IEC 17021 – ISO 9001
(Quality Management Systems).
By counting separately, we give equal value to the accredi-
tation of (testing) laboratories and certification bodies.
A national system of QI builds on the membership of international organisations. There are
usually levels of technical competence. The classification considers only the internationally
recognised services of full members and signatories to the Multilateral Recognition Arrange-
ment (MLA). In total, there is membership in eight international organisations, which relate
to the components of QI accreditation (IAF and ILAC), standardisation (IEC, ISO and ITU) and
metrology (BIPM/CIPM and OIML), as well as membership of the World Trade Organization
The components of the indicator are calculated in relative or absolute terms. They are then
normalised by dividing by the maximum value observed in the sample, denoted as the
. This procedure allows the sub-indicators to be summed up and the averaged fi-
nal score to be obtained. As the normalisation is done in this way, in the future we may ob-
serve ascents and descents in the rankings. By doing it in this way, changes in the sample
Regarding accreditation data, we have selected the two most common accreditations for quality management
systems CABs ISO/IEC 17021 - ISO 9001 and testing laboratories CABs ISO/IEC 17025. We have deliberately re-
frained from considering other data on accredited certification bodies and testing facilities. Only these accredi-
tation data fall entirely under IAF MLA or ILAC MRA. Also, there is a significant correlation between the selected
and non-considered indicators.
composition will not affect the scores of each country. If a country leaves the sample in the
next edition, the rest of the countries will not change their scores, although in relative terms
the countries that remain in the base could gain or lose their position.
The general formula of GQII makes it possible to give different weights to the seven compo-
nents. Assuming that all the areas of QI are equally relevant, the coefficients
of the for-
mula are all equal to each other and equal to 1. In this way we give equal importance to
each area. Under this criterion, higher values will be given to countries whose QI has been
developed in a comprehensive manner – it is recognised that efforts to advance in this area
take time, and require a variety of achievements and the participation of many actors.
Above all, the QIs will have a more significant impact on the countries’ economies if they are
developed as a system.
The general formula of the GQII reads as follows:
123(40"..'!5-6,20,70(8'"&09(6:;-/0)<= )>= )?= )@= )A= )B= )C= D
6 = E,'2-4F<G0 E,'2-4F>G H E,'2-4FI
a(E;$ J,!!$ /02'!L(40,70a(E;26E"&0J,!!6--((.b05"4-6E65"-6,2.0"EE,4362:0-,0MTU0
2.3 The scores
The maximum GQII value of the current sample is defined as 100 points. A country with a
score of 100 points would have reached the maximum possible value for each item and
would be first in the ranking of each of the seven sub-components. In the future, we can
refresh the indicator database, incorporating sources of variation explained by the new val-
ues that each country offers. But we continue normalising by the maximum value obtained
in this sample so that values of even greater than 100 points could be observed without this
presenting a problem. The GQII is dimensionless, so in the end it serves to rank the countries
and be able to establish comparisons of the current status of their QI. It will also allow us to
decompose the factors that explain their position in the ranking. We can analyse for each
economy in which components they have relative advantages over other countries and in
which components they are lacking. Thus, a country distinguished by its area of metrology
and accreditation could lose its position if it is not fully integrated into the international QI
system or if it is not strong in the adoption of quality standards.
2.4 The sample
In this edition of the GQII, we have included all but one
full member countries of the IAF.
We consider only signatories to the IAF Multilateral Recognition Arrangement (MLA) up to 1
In addition, we have included Bangladesh, Jamaica and Nigeria; although
they are not full members of the IAF, they are seen as "emerging countries" in QI in different
regions of the world. The inclusion extends the list to a total of 70 countries.
The selection of this group has been discussed in depth by Harmes-Liedtke & Oteiza Di
Matteo (2011, Chap. 2.1.4). In summary, all countries participate in the international QI sys-
tem and are recognised for their technical competence by their peers. Moreover, the devel-
opment and performance of the QI of each country must be measured based on comparable
data of the seven components of the GQII. The full list of the 70 countries have been
grouped according to their status as aid recipients or aid donors published each year by the
OECD Development Assistance Committee.
Taiwan was not considered due to its uncertain political status. Not all international Quality Infrastructure or-
ganisations recognise the country. Although the country is a founding member of ISO, its membership has been
suspended since 1974. https://www.taiwannews.com.tw/en/news/3812381
of-ODA-Recipients-for-reporting-2020-flows.pdf [Access date 04/12/19]
Table 2: Countries according to their role in Development Assistance
Source: OCDE, ODA recipients effective for reporting on aid in 2018 and 2019
2.5 The data
When searching for data on publicly accessible websites of the QI organisations, we noticed
various shortcomings (e.g. non-functioning links and websites, differently prepared data and
inconvenient search functions). A particular problem with data collection is time. Different
organisations have different practices and – except the ISO Survey – lack historical time se-
Metrology data are published by BIPM on the Key Comparison Data Base (KCDB) website
and updated monthly.
On 29 October 2019 the KCDB website (KCDB 2.0) came online with
expanded search options. The GQII data are based on information from KCDB 2.0. KCDB data
are published monthly, and only the current value is displayed. The CIPM are currently work-
ing on a statistic function on their website.
Standards data are available on the ISO website. The development of international ISO
standards is carried out by the Technical Committees (TC). At the time of data collection, the
ISO website listed 249 TCs.
By participating in the TCs, member countries receive im-
portant information on future trade rules and can influence the development of standards.
On its website
ISO indicates the number of TCs in each member country. This list is up-
dated periodically, and no historical data are reported.
https://www.bipm.org/kcdb/ [Access date 09/12/19]
https://www.iso.org/technical-committees.html [Access date 04/12/19]
https://www.iso.org/members.html [Access date 04/12/19]
“The ISO Survey is an annual survey of valid certifications to ISO management system stand-
ards issued by accredited certification bodies worldwide. It is the most comprehensive over-
view of certifications to these standards currently available.”
The ISO Survey provides data
on the certification, or more precisely, the number of valid management standards certifi-
cates per country. Data on management standards certifications are published annually at
the end of August of the following year. Historical data dating back to 1993 can be found on
the ISO Survey website.
With the adjustments made by some of the data providers in relation to the number of cer-
tificates, locations and sectors, a comparison of the figures of the number of valid certifi-
cates with those of the previous survey would not lead to precise conclusions. For ISO the
level of certificates valid in 2018 better reflects the market situation. As a result, country
data are not fully comparable in historical time series.
Accreditation data are not accessible in a consolidated form for the selected countries. At
first, the lack of accreditation data is surprising, as the National Accreditation Body (NAB) re-
quired by ISO/IEC 17011:2017 to make information regarding accredited Conformity Assess-
ment Bodies (CABs) publicly available. The data can only be found on the websites of the Na-
tional Accreditation Bodies (NABs) but are not prepared for statistical use.
The NABs regu-
larly update the information regarding accredited CABs, although the practice may vary sig-
nificantly from one organisation to another. The International Accreditation Forum (IAF) and
the Inter-American Accreditation Council (IAAC) publish historical data on accreditation, but
only in aggregated form, so these data cannot be used for the study.
Counting accredited CABs is the most collecting data for the component because a manual
count must be performed by going to each national accrediting authority's website. Thus,
the websites of the authorities identified by the IAF and the International Laboratory Accred-
itation Cooperation (ILAC) as locally referenced bodies are consulted, which generally pro-
vide access to a directory of accredited members in the different areas. Since the infor-
mation is not presented consistently in each country, it has been necessary to restrict the
count to only two areas of application, thus relinquishing the use of data that would contrib-
ute a priori to improving the measurement of the QI. In this sense, we have focused on
counting accredited members for Management System Certification according to ISO/IEC
17021-3 (Quality Management System ISO 9001) and Testing Laboratories (ISO/IEC 17025).
Data on accredited Conformity Assessment Bodies (CABs/CSTs) were collected in July and
The ISO survey of Management System Standard Certifications – 2018 – Explanatory Note, September 2019.
https://www.iso.org/the-iso-survey.html [Access date 04/12/19]
IAF Multilateral Recognition Arrangement (MLA) Annual Report 2018 https://www.iaf.nu/up-
Files/IAF%20MLA%20-%20Annual%20Report%202018%20v12.pdf [Access date 05/12/19]
3. EMPIRICAL RESULTS
The table shows the QI ranking of the 70 selected countries.
Table 3: GQII ranking and subcomponent rankings
European countries have a strong presence and occupy the highest positions in the ranking.
The Czech Republic (1st place), Germany (2nd) and Belgium (3rd) are in the lead. Among the
top 23, there are only four non-European countries: South Korea (11th), Japan (12th), United
States of America (22nd) and Singapore (23rd). In the mid-range (24 to 46), all world regions
are present. Among them are also three BRICS countries, China (29th), India (33rd) and
South Africa (34th), and at the bottom of this ranking in South American countries with Uru-
guay (40th), Argentina (43rd) and Chile (45th). Also, at the end of the ranking all continents –
except Australia – are represented. Ecuador (67th), Nigeria (69th) and Bangladesh (70th) oc-
cupy the last positions. However, counting the 164 members of the World Trade Organiza-
it becomes clear that all 70 countries have reached a significant level of QI de-
velopment compared to non-included countries.
The absolute ranking of a country in the GQII correlates with the individual ranking in the
sub-indexes. It is noteworthy that countries in the upper GQII range occupy higher ranks in
the sub-indexes (highlighted in orange in Table 3). The leader, the Czech Republic, ranks sec-
ond in ISO, fifth in CABs 9001 and sixth in CMCs. The country is the weakest in the K&SC
ranking in 19th place. Conversely, the lowest ranked countries of the GQII tend to be at the
bottom of the scale in other sub-indexes (highlighted in blue in Table 3).
In some countries we observe a substantial deviation of individual sub-indexes. Japan,
ranked 12th in the GQII, only ranks 57th in the number of accredited testing laboratories. In
contrast, Kazakhstan ranks 52nd in the GQII, but 8th in the sub-index of accredited testing
laboratories. This could indicate errors in data collection, although in these cases we have
returned to the websites of the accreditation bodies. Other reasons could be the different
average size of laboratories in different countries or differences in cross-border accredita-
However, despite these differences, the table shows a high degree of consistency in the de-
velopment of different QI components.
Since 29 July 2016 the WTO has 164 members – see https://www.wto.org.
A significant gap in the GQII index is the Russian Federation, whose accreditation bodies are not yet signatories
to the IAF MRL.
The numbers of accreditations refer to CABs which have been accredited by a NAB in the respective category.
This includes both CABs operating in the country of the accreditation body and CABs from other countries.
Figure 1: GQII-index map
The map shows the state of development of QI from a geographical perspective. European
countries have generally well-developed QI. In the Americas, the United States is the QI
leader, but many countries in South America and the Caribbean also have a considerable
level of QI. In Asia, Japan, New Zealand and South Korea lead, followed by Australia and
China in a mid-range. The countries of South and Southeast Asia are part of the GQII ranking
and have an essential QI. In Africa, the QI is hardly developed, with the notable exceptions of
South Africa, Egypt and Kenya.
Table 3 compares the position in the GQII ranking with the positions taken by countries in
the Global Competitiveness Ranking (Schwab, 2019) and GDP per capita.
ships are essential for analysing the contribution of QI to a country's competitiveness and
economic development. Our starting hypothesis is that a country with a well-developed QI is
economically prosperous; and conversely, a country lacking in the development of its QI is
economically less favoured (Harmes-Liedtke & Oteiza Di Matteo, 2011, 6).
Table 3 shows that high positions in the GQII ranking (highlighted in orange) correlate with
high or medium performances in Competitiveness and GDP per capita. The opposite occurs
We use World Bank Open, https://data.worldbank.org/indicator/NY.GDP.PCAP.CD [Access date 04/12/19]
in the lower part of the ranking. In the central positions of the table, there is a transition
area where countries show a moderate behaviour and combine heterogeneous perfor-
mances in the three positions GQII, GCI and GDP per capita. The relationship is neither linear
nor perfect between a country’s QI performance and its competitiveness and wealth. How-
ever, it indicates a positive correlation as expected.
The following graph shows the positive correlation between GQII and GCI. The linear correla-
tion (Pearson) (r) coefficient is considerable (r = 0.698; p = 0.0001)
, and the Spearman
range correlation coefficient (s) is, as in 2011, medium-high and positive (s = 0.722; p =
0.0001). These coefficients support the observation in the graph regarding the joint perfor-
mance in QI and competitiveness. OECD DAC countries occupy most of the leading (upper-
right) quadrant, while the least competitive countries with a relative lack in the development
of their QI are fundamentally the recipients of that aid.
Figure 2: Correlation between GQII and GCI
p represents the probability of being making a mistake by having rejected the null hypothesis of absence of
correlation. In every correlation, Linear or Spearman, model gave us p-values equal or lower than 0,0001.
The Spearman correlation coefficient (s) measures the correlation between the ranking positions of individu-
als in two variables using a monotonic function. To calculate (r) the data are sorted and replaced by their re-
spective positions in the ranking. Then Pearson's linear correlation coefficient is calculated. The maximum
value of r is 1 in the case of a positive and perfect correlation, or -1 in the case of a perfect negative correlation.
Comparing country’s QI performance with its wealth (measured as GDP per capita), we ob-
serve a slightly higher dispersion. The linear correlation is weaker but still positive (r = 0.532;
p = 0.0001), while if we see how the positions in both rankings are correlated, the relation-
ship is narrower (s = 0.682; p = 0.0001). These results are almost identical to those observed
in 2011. Significant outliers are the two city-states of Hong Kong and Singapore, as well as
the small states of the United Arab Emirates and Luxemburg. DAC countries here again oc-
cupy the upper-right quadrant, while ODA countries are located mainly in the opposite
Figure 3: Correlation between GQII and GDP per capita
In this edition, we include a new metric to evaluate the performance of the GQII. The Eco-
nomic Complexity Index (ECI) measures the intensity of an economy in terms of the
knowledge it incorporates in the products it exports. This indicator predicts economic
growth (Hausmann et al., 2013) and explains international variations in income inequality
(Hartmann & Hidalgo, 2017). The linear correlation between GQII and ECI is once again posi-
tive and moderate (r = 0.622; p = 0.0001). This finding supports the well-known relationship
between a country's diversity of its export basket and its QI.
Figure 4: Correlation between GQII and ECI
5. CONCLUSIONS AND FURTHER RESEARCH
The analysis of the 2019 data confirms the key findings of our previous study. The new data
validate a positive correlation between the development of the QI and the competitiveness,
economic performance and complexity of a country. There is also a strong coherence be-
tween the development of the individual components of the QI. A country with high values
in the field of metrology is usually also advanced in accreditation, standardisation and certifi-
cation. The opposite also applies in principle.
For this study, we used only publicly available data. The major problem was that the data of
the NABs are processed in very different forms from country to country and are only compa-
rable to a limited extent. Here we see the regional and international accreditation organisa-
tions such as the IAF and the ILAC having a duty to ensure higher data transparency.
The cross-border activities of the CABs are so far an unexplored area. The relationship is
characterised by competition and cooperation between domestic and foreign organisations
and is correspondingly complex. NAB data, in particular, could more clearly identify the
home country of the accredited CAB.
The data on metrology (BIPM/CIPM) and standardisation/certification (ISO Survey) are well
structured and easily accessible. Nevertheless, the data collections of BIPM, ISO and
IAF/ILAC need to be harmonised. The first step should be to harmonise the names of
countries using the ISO 3166 (Country Codes) standard. Annual data should be consolidated
and published so that the development of QI and its components becomes traceable. The
publication of statistical data should follow the principles of open data, i.e. data should be
complete, primary, timely available, accessible, machine-processable, non-discriminatory,
non-proprietary and licence-free.
Currently the ISO Survey is limited to the recording of management standards. It should be
considered to extend the contents of the ISO Survey to product standards as well. The latter
could be collected at least indirectly via the accreditations of the product certification bodies
(ISO/IEC 17025). Blind spots are private sustainability standards (in particular GlobalGap and
the Forest Stewardship Council (FSC)). The data of private standard bodies could be com-
pared with the information of NABs.
In determining the GQII, we weighted the various components of the QI equally. We can
only make minimal statements – such as the correlation described in the ranking – about the
interaction of the components. For a better understanding of the interaction of the compo-
nents, country studies are needed. A study of the relationship between standards and eco-
nomic growth in China (Zhang et al., 2019) is a good example that can easily be extended to
other components of QI. As the example of China shows, additional data are often available
at the national level, which makes a more in-depth analysis possible. The analysis of the data
series can also be used to analyse the evolution of NQIs over time.
As with our 2011 paper, we see this study as a contribution to a better understanding of QI
and its contribution to economic development. Additionally, the GQII would be a great mon-
itoring tool for international development projects working on QI and their overarching um-
brella programs, e.g. on sustainable economic development. For this, however, the GQII
needs to be prepared annually or at least every second year.
The 8 Open Government Data Principles, which 30 Open Data advocates drew up in Sebastopol, California, in
2007, provide a good orientation: https://public.resource.org/8_principles.html. [Access Date 05/12/19]
Table 4: GQII – absolute numbers by QI component and population by country
BLIND, K. 2015. From standards to quality infrastructure – Review of impact studies and an
outlook. In: DELIMATSIS, P. (ed.) The Law, Economics and Politics of International
Standardisation. Cambridge/ UK.
HARMES-LIEDTKE, U. & OTEIZA DI MATTEO, J. J. 2011. Measurement of Quality
Infrastructure. Discusion Paper. Braunschweig: Physikalisch-Technische
HARTMANN, D. & HIDALGO, C. 2017. Economic complexity, institutions and income
inequality. In: LOVE, P. & STOCKDALE OTÁROLA, J. (eds.) Debate the issues:
Complexity and policy making. Paris.
HAUSMANN, R., HIDALGO, C. A., BUSTOS, S., COSCIA, M., SIMOES, A. & YILDIRIM, M. A.
2013. The Atlas of Economic Complexity. Mapping paths to prosperity. Cambridge,
Massachusetts, The MIT Press.
HENSON, A. 2015. The CIPM MRA: Past, present and future. CIPM MRA Review – background
paper. Sevres/ France: BIPM.
KELLERMANN, M. 2019. The Importance of QI Reform and Demand Assessment. Washington
DC: The World Bank Group and PTB.
MAVROEIDIS, V. & TARNAWSKA, K. Macro-level efficiency of the EU National Quality
Infrastructure by data development anlaysis assessment. 11th International
Conference of ASECU “Openess, innovation, efficency and democratization as
precondition for economic development”. 2015. Cracow, Poland, 74.
SCHWAB, K. 2019. The Global Competitiveness Report. Cologny/ Geneva: World Economic
SUN, Y., GAO, Z. & WANG, Z. Research on Quality Infrastructure Evaluation of Mechanical
Manufacturing Enterprise Based on ANP-Fuzzy. The 4th International Conference on
Economy, Judicature, Administration and Humanitarian Projects (JAHP 2019). 2019.
WOLTERS, J. 2019. Unmanned cargo aircraft: the structured development of a deployment
area assessment instrument. University of Twente.
ZHANG, H., JIANG, J., ZHENG, L. & LI, X. 2019. The interaction between standards
development and economic growth of China. International Journal of Quality
Innovation, 5, 9.