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SCIENCE & ENGINEERING INDICATORS

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PRODUCTION AND TRADE OF KNOWLEDGE- AND TECHNOLOGY-INTENSIVE INDUSTRIES

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2022
April 2022

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* Executive Summary
* Introduction
* Production Patterns and Trends of Knowledge- and Technology-Intensive
Industries Expand collapse
* KTI Industries in the United States
* KTI Industries in the Global Economy
* Global Trade in Knowledge- and Technology-Intensive Output Expand collapse
* Gross Flows of Trade in KTI Output
* Value-Added Trade in KTI Output
* Enabling Technologies Expand collapse
* Artificial Intelligence (AI)
* Biotechnology
* Conclusion
* Glossary Expand collapse
* Definitions
* Key to Acronyms and Abbreviations
* References
* Notes
* Acknowledgments and Citation Expand collapse
* Acknowledgments
* Citation
* Supplemental Materials Expand collapse
* Supplemental Tables
* Technical Appendix
* General Methodology
* Data Expand collapse
* Tables
* Figures
* Data Sources
* Downloads
* Contact Us Expand collapse
* Report Author
* NCSES

* Executive Summary
* Introduction
* Production Patterns and Trends of Knowledge- and Technology-Intensive
Industries Expand collapse
* KTI Industries in the United States
* KTI Industries in the Global Economy
* Global Trade in Knowledge- and Technology-Intensive Output Expand collapse
* Gross Flows of Trade in KTI Output
* Value-Added Trade in KTI Output
* Enabling Technologies Expand collapse
* Artificial Intelligence (AI)
* Biotechnology
* Conclusion
* Glossary Expand collapse
* Definitions
* Key to Acronyms and Abbreviations
* References
* Notes
* Acknowledgments and Citation Expand collapse
* Acknowledgments
* Citation
* Supplemental Materials Expand collapse
* Supplemental Tables
* Technical Appendix
* General Methodology
* Data Expand collapse
* Tables
* Figures
* Data Sources
* Downloads
* Contact Us Expand collapse
* Report Author
* NCSES

TECHNICAL APPENDIX

INDUSTRY DATA, METHODOLOGY, AND TERMINOLOGY

This thematic report uses a variety of data sources, including U.S. data on
industry value added from the U.S. Bureau of Economic Analysis (BEA), U.S.
employment and occupation data from the U.S. Census Bureau’s American Community
Survey (ACS), international industry production data from IHS Markit, trade data
from the Organisation for Economic Co-operation and Development (OECD), and
various data sources on artificial intelligence (AI) and biotechnology. This
appendix provides information on the classification of industries and the main
data sources utilized in this report. Internationally comparable data are
generally compiled from multiple national sources and are prone to varying
issues of quality and reliability that need caution when making international
comparisons. In addition, most data used in this report are periodically revised
to reflect new information and methodology improvements.

CLASSIFICATION OF INDUSTRIES BASED ON RESEARCH AND DEVELOPMENT INTENSITY

This report defines KTI industries using an OECD taxonomy of economic activities
based on research and development (R&D) intensity developed by Galindo-Rueda and
Verger (2016). The OECD taxonomy clusters industries into five R&D intensity
groups—high, medium-high, medium, medium-low, and low—based on a measure of R&D
performance intensity computed as the ratio of each industry’s business R&D
expenditures to the industry’s value-added output. Value added is a net measure
of output; it is the difference between the value of goods and services produced
by an industry (gross output) and the total cost of inputs that were used in
production, including energy, materials, and services purchased from other
businesses. Value added is used in the computation of R&D intensity instead of
gross output because it avoids double counting of intermediate production. KTI
industries include industries in the high and medium-high R&D intensity groups.
The full classification of industries is presented in Table SAKTI-1.

Table SAKTI-1
OECD classification of industries, by R&D intensity
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OECD CLASSIFICATION OF INDUSTRIES, BY R&D INTENSITY

(List of industries and percent)

R&D intensity categoryManufacturingNonmanufacturingISIC, Rev.4, industry
codeNameR&D intensity (%)ISIC, Rev.4, industry codeNameR&D intensity (%)High R&D
intensity303Air and spacecraft and related machinery31.6972Scientific research
and development30.3921Pharmaceuticals27.98582Software publishing28.9426Computer,
electronic, and optical products24.05 empty empty emptyMedium-high R&D
intensity252Weapons and ammunition18.8762 - 63IT and other information
services5.9229Motor vehicles, trailers, and
semi-trailers15.36 empty empty empty325Medical and dental
instruments9.29 empty empty empty28Machinery and equipment
nec7.89 empty empty empty20Chemicals and chemical
products6.52 empty empty empty27Electrical
equipment6.22 empty empty empty30XRailroad, military vehicles, and transport nec
(ISIC 302, 304, and 309)5.72 empty empty emptyMedium R&D intensity22Rubber and
plastic products3.58 empty empty empty301Building of ships and
boats2.99 empty empty empty32XOther manufacturing except medical and dental
instruments2.85 empty empty empty23Other non-metallic mineral
products2.24 empty empty empty24Basic metals2.07 empty empty empty33Repair and
installation of machinery and equipment1.93 empty empty emptyMedium-low R&D
intensity13Textiles1.7369 - 75XProfessional, scientific and technical activities
except scientific R&D services (ISIC 69 - 75 less 72)1.7615Leather and related
products1.6561Telecommunications1.4517Paper and paper products1.5805 - 09Mining
and quarrying0.810 - 12Food products, beverages and tobacco1.44581Publishing of
books and periodicals0.5714Wearing apparel1.40 empty empty empty25XFabricated
metal products except weapons and ammunition (ISIC 25 less
252)1.19 empty empty empty19Coke and refined petroleum
products1.17 empty empty empty31Furniture1.17 empty empty empty16Wood and
products of wood and cork0.70 empty empty empty18Printing and reproductions of
recorded media0.67 empty empty emptyLow R&D intensity empty empty empty64 -
66Financial and insurance activities0.38 empty empty empty35 - 39Electricity,
gas, and water supply; waste management; and remediation0.35 empty empty empty59
- 60Audiovisual and broadcasting activities0.32 empty empty empty45 -
47Wholesale and retail trade0.28 empty empty empty01 - 03Agriculture, forestry,
and fishing0.27 empty empty empty41 - 43Construction0.21 empty empty empty77 -
82Administrative and support services0.18 empty empty empty90 - 99Arts,
entertainment, repair of household goods, and other
services0.11 empty empty empty49 - 53Transportation and
storage0.08 empty empty empty55 - 56Accommodation and food service
activities0.02 empty empty empty68Real estate activities0.01

ISIC, Rev.4 = International Standard Industrial Classification, Revision 4; IT =
information technology; nec = not elsewhere classified; OECD = Organisation for
Economic Co-operation and Development.

NOTE(S):

R&D intensity is measured as the ratio of global R&D expenditures to global
value added output of industry. The global R&D and value added excludes several
economies, including China and India, due to incomplete or missing industry
value added and R&D data. Industries are classified according to ISIC, Rev.4.

SOURCE(S):

Galindo-Rueda F, Verger F, OECD Taxonomy of Economic Activities Based on R&D
Intensity, OECD Science, Technology and Industry Working Papers, 2016/04, OECD
Publishing, Paris (2016).

Science and Engineering Indicators

Industries are classified according to the United Nations’ International
Standard Industrial Classification of All Economic Activities, Revision 4 (ISIC,
Rev.4). This classification delineates the economic activities of industries
based on similarities in inputs and factors of production, the process and
technology of production, and characteristics and use of outputs. For economic
units that engage in several types of independent activities, considerable
proportions of the activities of the unit may be included in more than one class
of ISIC. If the unit cannot be split into separate statistical units based on
these activities, it is classified based on the principal activity, the activity
that contributes most to the value added of the unit. More information on ISIC,
Rev.4, is available at
https://unstats.un.org/unsd/classifications/Econ/Structure.

The R&D-intensity measure captures the R&D directly performed by industries but
does not capture any R&D embedded in an industry’s purchases of intermediate
inputs and capital goods. For any given industry, the R&D intensity is computed
as a weighted average of corresponding industry R&D intensities for a core
sample of countries using value added in purchasing power parities as weights.
The countries are 27 OECD countries and 2 partner countries or economies
(Singapore and Taiwan). The OECD countries are Australia, Austria, Belgium,
Canada, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary,
Ireland, Italy, Japan, South Korea, Mexico, the Netherlands, Norway, Poland,
Portugal, Slovakia, Slovenia, Spain, Sweden, the United Kingdom, and the United
States. Because this taxonomy is based on average R&D intensities across
countries, the R&D intensities for industries in individual countries are likely
to vary from the average.

The OECD taxonomy is sensitive to the choice of the group of countries. The OECD
analysis underlying this classification does not fully capture global production
and R&D because of the exclusion of several economies that have incomplete data,
including Brazil, India, and China. Because these economies may have large
global production shares and R&D intensities that may be substantially different
from the average of the core sample of economies included in the OECD analysis,
their exclusion may result in differences in classification of R&D intensity.
The study reports that including partial data from China decreases the average
R&D intensity in more than two-thirds of the industries. This is consistent with
China producing a large share of global output for these industries while
investing a below-sample-average share in R&D. This is particularly the case for
computer, electronic, and optical products; transport equipment; and chemicals
and pharmaceuticals. The inclusion of China would downgrade, for instance, the
computer, electronic, and optical products industry from the high R&D intensive
group to the medium-high R&D intensive group. For the impact on other
industries, see Galindo-Rueda and Verger (2016:25).

The R&D intensities are also sensitive to the national accounting conventions
used to compile and report the output data. The output data are compiled and
reported under the 1993 System of National Accounts (SNA) prior to the
capitalization of R&D expenditures with the 2008 SNA. The treatment of R&D
expenditures as an investment directly increases the measure of value added and
decreases the R&D intensities; the more intensive the industries, the higher the
downward impact in the R&D intensities. In a series of robustness checks, the
authors estimate that with the 2008 SNA standard, the R&D intensities drop by
20% in the highest group, 6% in the medium-high group, 3% in the medium and
medium-low groups, and 1% in the low group. The classification of industries in
these groups, however, remains stable. See Galindo-Rueda and Verger (2016) for a
full discussion of robustness checks.

U.S. VALUE ADDED, BY INDUSTRY AND STATE

The U.S. industry production data come from the Industry and Regional Economic
Accounts of BEA, which publishes annual value added by industry statistics for
over 130 industries and annual gross output by industry statistics for over 400
industries. These industries are based on the North American Industry
Classification System (NAICS)—specifically, the 2012 revision of NAICS. The
value-added data discussed in this report are presented in nominal values; the
value of production is measured at current market prices, and it is not adjusted
for inflation.

For KTI industries, the U.S. data need to be converted from the 2012 NAICS basis
to the ISIC Rev.4 basis. The concordance used is presented in Table SAKTI-2. For
most industries, this adjustment of value added is straightforward. In some
cases, however, the value added of a detailed industry, not available in the
published data, needs to be estimated. In this case, value added for detailed
industries was estimated from corresponding gross output ratios as follows:

Value added for the detailed industry equals gross output for the detailed
industry over gross output for the aggregate industry times value added for the
aggregate industry.

This approach assumes that the ratio of intermediate consumption relative to
industry output for the detailed industry is the same as the ratio of the
intermediate consumption to the industry output for the aggregate industry.

Table SAKTI-2
2012 NAICS to ISIC, Rev.4, concordance for U.S. value added, by industry data
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2012 NAICS TO ISIC, REV.4, CONCORDANCE FOR U.S. VALUE ADDED, BY INDUSTRY DATA

(List of industries)

R&D intensity categoryValue added, by industryRelated 2012 NAICSISIC,
Rev.4Industry titleIndustry codeIndustry titleIndustry codeIndustry titleHigh
R&D intensityPharmaceutical and medicine manufacturing3254Pharmaceutical and
medicine manufacturing21Manufacture of pharmaceuticals, medicinal chemical, and
botanical products Computer and electronic products334 Computer and electronic
product manufacturing26Manufacture of computer, electronic, and optical
productsOptical instrument and lens manufacturinga333314Optical instrument and
lens manufacturingAerospace product and parts manufacturing3364Aerospace product
and parts manufacturing303Manufacture of air and spacecraft and related
machinerySoftware publishers5112Software publishers582Software
publishingScientific research and development services5417Scientific research
and development services72Scientific research and development
servicesMedium-high R&D intensityBasic chemical manufacturing3251Basic chemical
manufacturing20Manufacture of chemicals and chemical productsResin, rubber, and
artificial fibers manufacturing3252Resin, synthetic rubber, and artificial
synthetic fibers and filaments manufacturingOther chemical manufacturing3253
Pesticide, fertilizer, and other agricultural chemical manufacturing3255Paint,
coating, and adhesive manufacturing3256Soap, cleaning compound, and toilet
preparation manufacturing3259Other chemical product and preparation
manufacturingElectrical equipment, appliances, and components335Electrical
equipment, appliance, and component manufacturing27Manufacture of electrical
equipment Agricultural implement manufacturing3331 Agriculture, construction,
and mining machinery manufacturing28Manufacture of machinery and equipment
necConstruction machinery manufacturingMining and oil and gas field machinery
manufacturingOther machineryb3332 Industrial machinery
manufacturing333316Photographic and photocopying equipment
manufacturing333318Other commercial and service industry machinery
manufacturing3334Ventilation, heating, air-conditioning, and commercial
refrigeration equipment manufacturing333517Machine tool
manufacturing333514Special tool, die, jig, and fixture
manufacturing333515Cutting tool and machine tool accessory
manufacturing333519Rolling mill and other metalworking machinery
manufacturing3336Engine, turbine, and power transmission equipment
manufacturing3339Other general purpose machinery manufacturingPlumbing fixture
fitting and trim manufacturinga332913Plumbing fixture fitting and trim
manufacturingBall and roller bearing manufacturinga332991Ball and roller bearing
manufacturingMotor vehicle manufacturing3361Motor vehicle
manufacturing29Manufacture of motor vehicles, trailers and semi-trailersLight
truck and utility vehicle manufacturingHeavy duty truck manufacturingMotor
vehicle body, trailer, and parts manufacturingc3362Motor vehicle body and
trailer manufacturing33631Motor vehicle gasoline engine and engine parts
manufacturing33632Motor vehicle electrical and electronic equipment
manufacturing33633Motor vehicle steering and suspension component (except
spring) manufacturing33634Motor vehicle brake system manufacturing33635Motor
vehicle transmission and power train parts manufacturing33636Motor vehicle
seating and interior trim manufacturing33639Other motor vehicle parts
manufacturingAll other transportation equipment manufacturingd3365Railroad
rolling stock manufacturing30XManufacture of railroad, military vehicles and
transport nec (ISIC 302, 304, and 309)3369Other transportation equipment
manufacturingMedical equipment and supplies manufacturing3391Medical equipment
and supplies manufacturing325Manufacture of medical and dental instruments and
supplies Computer systems design and related services5415Computer systems design
and related servicesJ62 - J63IT and other information servicesData processing,
internet publishing, and other information services518Data processing, hosting,
and related services519Other information services

ISIC, Rev.4 = International Standard Industrial Classification, Revision 4; IT =
information technology; NAICS = North American Industry Classification System;
nec = not elsewhere classified.

a The value added for these detailed industries has been estimated using gross
output ratios. See Technical Appendix for more details.

b Other machinery includes Optical instrument and lens manufacturing and
Industrial mold manufacturing industries. In the ISIC, Rev.4, classification,
Optical lens manufacturing is under the Computer, electronic, and optical
products industry, whereas the Industrial mold manufacturing is under Fabricated
metal products, except machinery and equipment. Value added for these detailed
industries has been estimated using gross output ratios. See Technical Appendix
for more details.

c Motor vehicle body, trailer, and parts manufacturing incudes Motor vehicle
metal stamping. In the ISIC, Rev.4, classification, Motor vehicle metal stamping
is under Fabricated metal products, except machinery and equipment. Value added
for Motor vehicle metal stamping has been estimated using gross output ratios.
See Technical Appendix for more details.

d All other transportation equipment manufacturing includes Ship building and
repairing and Boat building. These industries have been excluded from the
Organisation for Economic Co-operation and Development classification of the
medium-high R&D intensive industry Manufacture of railroad, military vehicles,
and transport nec (ISIC 302, 304, and 309). Value added for these detailed
industries has been estimated using gross output ratios. See Technical Appendix
for more details.

SOURCE(S):

This concordance was developed using the Census Bureau 2012 NAICS to ISIC,
Rev.4, concordance available at: https://www.census.gov/naics/. More information
on 2012 NAICS classification is available at: https://www.census.gov/naics/.
More information on ISIC, Rev.4, is available at:
https://unstats.un.org/unsd/classifications/Econ/Structure.

Science and Engineering Indicators

EMPLOYMENT IN U.S. KTI INDUSTRIES

This report uses the 2019 ACS 1-year Public Use Microdata Sample (PUMS) to
estimate employment in U.S. KTI industries. The PUMS is a subsample of
respondents in the ACS and contains modifications to protect respondent
confidentiality in the microdata. These modifications can include, for example,
the “top-coding” of continuous variables with outlying values (such as income or
transportation time) and the reduction of category availability for variables
with hundreds of categories (such as ancestry or birthplace). These data were
pulled from the U.S. Census Bureau’s File Transfer Protocol site on 20 December
2020. See https://www2.census.gov/programs-surveys/acs/data/pums/2019/1-Year/.
This report uses information on the respondent’s occupation, industry of
employment, sex, race or ethnicity, nativity, and citizenship status.

Occupations are grouped into science, technology, engineering, and mathematics
(STEM) and non-STEM based on Census occupational codes. (See Indicators 2022
report “The STEM Labor Force of Today: Scientists, Engineers, and Skilled
Technical Workers” for details on how specific Census occupational codes feed
into STEM and non-STEM.) STEM workers are further divided into two groups based
on educational attainment: STEM workers with a bachelor’s degree or above, and
STEM workers without a bachelor’s degree. This latter group is also known as the
skilled technical workforce. The current analysis includes workers in all
occupations aged 16–75 years old, except for those in military occupations
(Census occupational codes 9800–9830) or currently attending grade school.

Industry employment in the PUMS is reported by a modified version of the NAICS.
NAICS codes are all numeric. In PUMS, some NAICS codes are combined into
modified NAICS codes, which are identified as being both numeric and alpha. (See
how PUMS combines NAICS codes in its modified version of NAICS in “2019 ACS
1-year PUMS Code Lists” at
https://www.census.gov/programs-surveys/acs/microdata/documentation.html.) To
identify workers employed in KTI industries in the PUMS, the modified NAICS
codes are assigned to ISIC industries. Excluding computer, electronic, and
optical products and machinery and equipment, all KTI-related ISIC industries
directly align with the modified NAICS in the PUMS. For example, the ISIC
industry aircraft aligns with the modified NAICS industries 33641M1 and 33641M2
(Table SAKTI-3).

Table SAKTI-3
Concordance for KTI employment data
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CONCORDANCE FOR KTI EMPLOYMENT DATA

(List of industries and rate of partial employment)

ISIC, Rev.4Industry titleNAICS codeaModified NAICS codebRate of partial
employment in NAICScC303Aircraft336433641M1, 33641M2naC26Computer, electronic,
and optical products334, 3333143341, 3345, 334M1, 334M2, pt
3333na emptyCommercial and service industry machinery333314pt
33330.204821544C21Pharmaceuticals32543254naM72Scientific research and
development54175417naJ582Software51125112naC20Chemicals excluding
pharmaceuticals3251, 3252, 3253, 3255, 3256, 3259325M, 3252, 3253, 3255,
3256naC27Electrical equipments335335M, 3352naJ62 - J63IT services5415, 518,
5195415, 5182, 51912, 51913, 5191ZMnaC28Machinery and equipment3331, 3332,
333316, 333318, 3334, 333514, 333515, 333517, 333519, 3336, 3339, 332913,
33299133311, pt 3333, 333MS, pt 3335, 3336, pt 332MZna emptyCommercial and
service industry machinery333316, 333318pt 33330.795178456 emptyMetal working
machinery nec333514, 333515, 333517, 333519pt 33350.760893075 emptyMiscellaneous
fabricated metal products332913, 332991pt 332MZ0.119286701C29Motor vehicles3361,
3362, 33631, 33632, 33633, 33634, 33635, 33636, 33639336MnaC30XRailroad and
other transportation3365, 33693365, 3369naC325Medical and dental
equipment33913391na

na = not applicable.

ISIC, Rev.4 = International Standard Industrial Classification, Revision 4; IT =
information technology; KTI = knowledge and technology intensive; NAICS = North
American Industry Classification System; nec = not elsewhere classified; pt =
partial assignment.

a The description of NAICS codes in the modified NAICS codes in the Public Use
Microdata Sample (PUMS) can be found here:
https://www.census.gov/programs-surveys/acs/microdata/documentation.html.

b Modified NAICS codes are used in the PUMS. Theses codes are generally the
4-digit NAICS code, but some industries combine multiple 3- or 4-digit NAICS
codes, and these modified codes are both alpha and numeric.

c The rate of partial employment is calculated from detailed 6-digit NAICS
employment estimates from the 2017 Economic Census.

SOURCE(S):

Science and Engineering Indicators

Two ISIC industries align to multiple modified NAICS industries because some of
the modified NAICS industries are too coarse to allow for direct matches.

* Computer, electronic, and optical products, ISIC industry (C26). This ISIC
industry concords with the modified NAICS industries 3341, 3345, 334M1, and
334M2 and with part of 3333. Part of the modified NAICS industry 3333,
commercial and service industry machinery manufacturing, is assigned to ISIC
industry C26, and another part of it is assigned to C28. (More details about
this ISIC industry appear below.) The NAICS 4-digit industry 3333 includes
NAICS 6-digit industries 333314 (optical instrument and lens manufacturing),
333316 (photographic and photocopying equipment manufacturing) and 333318
(other commercial and service industry machinery manufacturing). The NAICS
industry 333314 is part of ISIC C26, and the other NAICS industries are part
of C28. This partial assignment is denoted with a “pt” designation in the
modified NAICS code column and the NAICS column in Table SAKTI-3.
* Machinery and equipment. This ISIC industry concords with modified NAICS
industries 331, 3332, 333MS, and 3336 and with parts of NAICS 3333, 3335, and
332MZ. Table SAKTI-3 shows the partial assignments of modified NAICS
industries 3333, 3335, and 332MZ.

Employment information from the 2017 Economic Census was used to prorate
estimated employment based on the PUMS for modified NAICS industries partially
assigned to multiple ISIC industries (i.e., those denoted with “pt” in Table
SAKTI-3). For example, the 2017 Economic Census reported that about 20% of
employment in NAICS industry 3333 (commercial and service industry machinery
manufacturing) was 6-digit NAICS industry 333314 (optical instrument and lens
manufacturing). About 80% of NAICS industry 3333 was the other 6-digit NAICS
industries (333316 and 333318). Hence, 20% of estimated employment for modified
NAICS industry 3333 based on the PUMS is assigned to ISIC industry C26, and 80%
to ISIC industry C28. Similar calculations were applied to estimated employment
based on PUMS for modified NAICS industries 3335 and 332MZ.

IHS MARKIT COMPARATIVE INDUSTRY SERVICE

The international industry production data are drawn from a proprietary database
compiled by IHS Markit. The IHS Comparative Industry Service (CIS) Forecast
Database provides consistent coverage for over 70 countries and over 100
industrial sectors for several macroeconomic indicators, including the
value-added data presented in this report. The primary data sources on industry
output are the National Income Accounts from the countries’ national statistical
agencies and also the cross-national organizations, including the Industrial
Structure Statistics from the OECD Structural Analysis (STAN) database, the
International Yearbook of Industrial Statistics from the United Nations
Industrial Development Organization (UNIDO), and the National Accounts
Statistics from the United Nations System of National Accounts (UNSNA).

IHS Markit compiles the data in the CIS database using a tiered approach, where
data from OECD, UNSNA, and UNIDO form the foundation for all sectors and most
countries. These are harmonized data that provide consistency and comparability
across countries and across time. The OECD STAN database provides data for the
OECD member countries. For countries not included in the OECD database, and for
industries whose coverage in STAN is not sufficiently detailed, IHS Markit
combines the OECD data with the UNSNA and UNIDO databases. The UNSNA contains
national accounts data for most countries in the world and facilitates
macroeconomic comparisons among national economies at the broad industry level.
Data availability, however, varies across countries and fiscal years because not
all UN member countries are able to provide a complete set of data. The UNIDO
data set provides highly disaggregated data for the manufacturing sector. Both
the UN and OECD data are further supplemented by other international sources
such as the International Labor Organization and Eurostat and by individual
country sources.

For some countries or economies, IHS Markit collects more granular and timely
data directly from their national statistical agencies. These countries or
economies include the United States (BEA), Brazil (Statistics Brazil), China
(China National Bureau of Statistics), and Taiwan (Taiwan Directorate-General of
Budget, Accounting and Statistics; National Statistics). Finally, IHS Markit
brings the collected data forward in time as needed using data from individual
country sources, global trade associations, and other sources to provide timely
measures of industry-level business activity.

The value-added data discussed in this report are presented in current dollars.
For countries outside the United States, value added is recorded in the local
currency and converted at the prevailing nominal market exchange rate. This
choice comes with some limitations, particularly when an economy’s currency
exchange rates are not market determined.

TRADE IN VALUE-ADDED DATABASE

The trade in value-added indicators come from the 2021 OECD Trade in Value Added
(TiVA) database. The OECD TiVA statistics are derived from OECD’s Inter-Country
Input-Output (ICIO) tables, which provide a globally balanced view of
inter-country, inter-industry flows of intermediate and final goods and
services. The OECD TiVA database is available at
https://www.oecd.org/industry/ind/measuring-trade-in-value-added.htm. The ICIO
tables are available at https://oe.cd/icio. The ICIO tables are constructed
using national annual Supply and Use Tables (SUTs) or, if necessary, benchmark
Input-Output Tables (IOTs), harmonized to a common format and common industry
list, linked with balanced bilateral trade in goods and services, and
constrained under countries’ latest SNA main aggregates and industry output and
value-added time series. The SUTs and IOTs are sourced from national statistical
agencies and harmonized by OECD. They show production of output by all sectors
and the allocation of domestic output among intermediate and final uses,
including exports. The linkage of input-output data with trade data captures the
bilateral exchanges of intermediate goods and services.

For countries in the TiVA database, the overall trade balance is consistent with
official national accounts figures. However, the bilateral trade estimates may
differ from those reported by national statistical agencies. By necessity,
asymmetries in reported bilateral trade (exports of a product reported by
country A to country B may differ from country B’s reported imports from country
A) need to be balanced. There are many reasons for asymmetries, including
differences across countries in treatment of various aspects of trade such as
re-exports and transit trade. The international statistics community continues
its work to improve consistency in measuring international trade flows,
particularly in services, where there are substantial differences across
national statistics. For a more detailed description of concepts and
methodologies related to the TiVA statistics and a discussion of ongoing
projects and initiatives, see OECD–World Trade Organization (n.d.).

The most recent update (2021) of the TiVA database covers 66 economies,
including the OECD countries, the European Union (EU) and the G20 countries, and
several East and Southeast Asian economies and South American countries for the
years 1995–2018. The G20 is a group of 19 individual countries and the EU. The
countries are Argentina, Australia, Brazil, Canada, China, France, Germany,
India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa,
South Korea, Turkey, United Kingdom, and United States. The input-output tables
from which the TiVA indicators are derived are based on the 2008 SNA. Indicators
are available for 45 industries within a hierarchy based on ISIC, Rev.4.

Along with its advantages, trade in value added presents some measurement
challenges. The value added of companies with diversified businesses may be
assigned to the single industry that accounts for the largest share of the
company’s business. A company classified as manufacturing may include services
(and vice versa). Disentangling the domestic and foreign content in global value
chains is further complicated by fragmentation of production within
multinational enterprises and trade in inputs that are further traded as more
processed inputs. Quality and availability of data (e.g., SUTs, IOTs, and SNA)
in many countries, and inconsistencies in national statistics within countries,
are major challenges when constructing global IOTs.

PITCHBOOK VENTURE CAPITAL DATA

The venture capital data shown in the report are from PitchBook Data, Inc., a
private-sector financial services company that collects financial and business
data on the Web and provides subscription-based data (https://pitchbook.com/).
PitchBook classifies companies by industry and industry vertical: an industry is
a “broad group of companies that operate in the same general space,” whereas “an
industry vertical is more specific and describes a group of companies that focus
on a shared niche or specialized market spanning multiple industries” (PitchBook
2021). AI is defined as an industry vertical, and biotechnology is defined as an
industry. Companies are classified in one primary industry but can also be
classified in secondary industries. Industry verticals are listed in Table
SINV-100 in the Indicators 2022 report “Invention, Knowledge Transfer, and
Innovation”—where the data are used more extensively than in this report.

Within PitchBook’s proprietary industry classification system, biotechnology is
an industry within the health care sector and the pharmaceuticals and
biotechnology industry group. It is the broad area of biology, involving living
systems and organisms (e.g., bacteria, mammalian cells, T cells) to develop or
make products. Companies in this category are researching and using biological
systems to develop new drugs and therapies for medical patients. Pharmaceutical
companies, which are within the same industry group, differ from biotechnology
companies in that these companies are primarily involved in manufacturing and
distributing drugs, generally from chemical and synthetic processes. Drug
discovery is another industry within this industry group, and these companies
research and develop new drugs. Many of the companies classified in the
pharmaceutical and drug discovery primary industries are also classified in the
biotechnology secondary industry. Therefore, this analysis includes companies
that are listed in the biotechnology primary industry and secondary industry.

AI is defined for PitchBook investment reporting as companies developing
technologies that enable computers to autonomously learn, deduce, and act
through utilization of large data sets. The technology enables development of
systems that collect and store massive amounts of data and analyze that content
to make decisions based on probability and statistical analysis. Applications
for AI and machine learning include speech recognition, computer vision, robotic
control, and accelerating processes in the empirical sciences where large data
sets are essential, such as gene sequencing in life sciences (PitchBook/NVCA
2019).

For the biotechnology industry and AI industry vertical, the analysis includes
all completed venture capital deals at all stages (i.e.,
pre/accelerator/incubator, angel, seed, early stage venture capital, later-stage
venture capital, other stages), all rounds, and all series (i.e., seed, series
A–series D, and later series). All investor types are included—except government
and sovereign wealth fund because these are not considered private investors.

AI AND BIOTECHNOLOGY PATENTS

AI and biotechnology patent data are from OECD Science, Technology and Patents
(2022), United States Patent and Trademark Office (USPTO) (2022), and 2022
Indicators report “Invention, Knowledge Transfer, and Innovation.”

OECD collects data on patent families for the five largest patent offices in the
world (IP5 patent families)—European Patent Office, Japan Patent Office, Korean
Intellectual Property Office, State Intellectual Property Office of the People’s
Republic of China, and USPTO. IP5 patent families refer to patents that have
been filed in at least two IP offices worldwide, one of which is among the IP5.
This avoids double counting of patents filed in multiple jurisdictions. (For
more details, see Indicators 2020 report “Invention, Knowledge Transfer, and
Innovation.”) This report uses OECD’s definitions of AI-related patents (OECD
2017) and biotechnology patents (OECD 2021).

This report also presents data on patents granted by USPTO. Data on AI patents
granted by USPTO are reported in Toole et al. (2020). USPTO patents on
biotechnology are reported in Table SINV-59 in the Indicators 2022 report
“Invention, Knowledge Transfer, and Innovation.” These data use the World
Intellectual Property Organization definition of biotechnology (Schmoch 2008),
which differs from the OECD definition.

EMSI BURNING GLASS JOB POSTINGS DATA

The proprietary Emsi Burning Glass (Emsi) data set includes over 30,000 skills
from millions of online sources, including job posting websites and resumes.
Emsi retrieves information from over 100,000 websites, including company career
sites, national and local job boards, and job posting aggregators. More
information on Emsi’s methodology for job postings can be found on Emsi’s
website
(https://kb.emsidata.com/methodology/job-posting-analytics-documentation/).

For this analysis, skills related to biotechnology and AI were entered into the
Emsi searchable database to extract job postings that featured one of the
highlighted skills. The specific skills used for both biotechnology and AI will
be highlighted in the methodology below.

It is important to note that the number of job postings do not reflect the
actual number of hires. Job postings, regardless of industry, might not yield an
actual person being hired. It is also possible that one job posting may be used
to fill several vacancies at a company. Further, some websites may post
duplicate postings of a job. To reduce duplicate postings for this analysis,
Emsi data were filtered to remove postings that originated from staffing
companies. This thematic report uses analysis data from October 2016 to
September 2021.

Using the Emsi Application Programming Interface, the top 25 skills were
extracted for both AI and biotechnology. These skills groupings are based on a
review of job postings collected by Emsi, and they only include skills
categorized by Emsi as a hard skill. More information on Emsi’s skill clustering
methodology can be found on Emsi’s website
(https://skills.emsidata.com/faqs#how-are-skills-selected).

The top 25 AI-related skills identified by Emsi are algorithms, Apache Spark,
artificial neural networks, big data, blockchain, computer vision, data science,
deep learning, distribute computing, Internet of Things, machine learning,
machine learning algorithms, mathematical modeling, natural language processing,
Pandas (Python package), predictive analytics, predictive modeling, R
(programming language), robotic process automation, Scala (programming
language), speech recognition, statistical modeling, TensorFlow, time series,
and unstructured data.

The top 25 biotechnology-related skills identified by Emsi are
biopharmaceuticals, biostatistics, case report forms, clinical pharmacy,
clinical research, clinical study design, clinical trial management systems,
clinical trials, drug development, drug discovery, electronic data capture, good
clinical practices, International Council for Harmonisation of Technical
Requirements for Pharmaceuticals for Human Use Guidelines, key opinion leader
development, life sciences, medical affairs, medical devices, medical
guidelines, nondisclosure agreements (intellectual property law),
pharmaceuticals, pharmacovigilance, pre-clinical development, regulatory
filings, scientific literature, and Title 21 of the Code of Federal Regulations.

REFERENCES

Galindo-Rueda F, Verger F. 2016. OECD Taxonomy of Economic Activities Based on
R&D Intensity. OECD Science, Technology and Industry Working Papers No. 2016/04.
Paris: OECD Publishing. Available at https://doi.org/10.1787/5jlv73sqqp8r-en.
Accessed 18 April 2019.

Organisation for Economic Co-operation and Development (OECD). 2017. OECD
Science, Technology and Industry Scoreboard 2017: The Digital Transformation.
Paris: OECD Publishing. Available at
https://www.oecd.org/sti/oecd-science-technology-and-industry-scoreboard-20725345.htm.
Accessed 11 November 2022.

Organisation for Economic Co-operation and Development (OECD). 2021.
Methodological Work and Publications—Patents in Selected Fields: Biotechnology.
Available at
https://www.oecd.org/sti/inno/intellectual-property-statistics-and-analysis.htm#method.
Accessed 18 November 2021.

Organisation for Economic Co-operation and Development, World Trade Organization
(OECD/WTO). N.d. Trade in Value-Added: Concepts, Methodologies, and Challenges.
Joint OECD-WTO Note. Available at https://www.oecd.org/sti/ind/49894138.pdf.
Accessed 27 June 2019.

PitchBook, National Venture Capital Association (PitchBook/NVCA). 2019. Venture
Monitor 4Q 2018. Seattle, WA: PitchBook Data, Inc. Available at
https://pitchbook.com/news/reports/4q-2018-pitchbook-nvca-venture-monitor.
Accessed 14 March 2019.

PitchBook. 2021. “What Are Industry Verticals?” Available at
https://pitchbook.com/what-are-industry-verticals. Accessed 29 September 2021.

Schmoch U. 2008. Concept of a Technology Classification for Country Comparisons:
Final Report to the World Intellectual Property Organisation (WIPO). Available
at
https://www.wipo.int/export/sites/www/ipstats/en/statistics/patents/pdf/wipo_ipc_technology.pdf.
Accessed 18 November 2021.

Toole A, Pairolero N, Giczy A, Forman J, Pulliam C, Such M, Chaki K, Orange D,
Homescu A, Frumkin J, Chen Y, Gonzales V, Hannon, C, Melnick S, Nilsson E,
Rifkin B. 2020. Inventing AI: Tracing the Diffusion of Artificial Intelligence
with U.S. Patents. Alexandria, VA: U.S. Patent and Trademark Office. Available
at
https://www.uspto.gov/about-us/news-updates/new-benchmark-uspto-study-finds-artificial-intelligence-us-patents-rose-more.
Accessed 15 October 2021.

NOTES

1. 1Value added is a net measure of output; it is the difference between the
value of goods and services produced by an industry (gross output) and the
total cost of inputs that were used in production, including energy,
materials, and services purchased from other businesses. Value added is
used in the computation of R&D intensity instead of gross output because it
avoids double counting of intermediate production.
2. 2More information on ISIC, Rev.4, is available at
https://unstats.un.org/unsd/classifications/Econ/Structure.
3. 3The countries are 27 OECD countries and 2 partner countries or economies
(Singapore and Taiwan). The OECD countries are Australia, Austria, Belgium,
Canada, Czechia, Denmark, Estonia, Finland, France, Germany, Greece,
Hungary, Ireland, Italy, Japan, South Korea, Mexico, the Netherlands,
Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, the United
Kingdom, and the United States.
4. 4The study reports that including partial data from China decreases the
average R&D intensity in more than two-thirds of the industries. This is
consistent with China producing a large share of global output for these
industries while investing a below-sample-average share in R&D. This is
particularly the case for computer, electronic, and optical products;
transport equipment; and chemicals and pharmaceuticals. The inclusion of
China would downgrade, for instance, the computer, electronic, and optical
products industry from the high R&D intensive group to the medium-high R&D
intensive group. For the impact on other industries, see Galindo-Rueda and
Verger (2016:25).
5. 5See Galindo-Rueda and Verger (2016) for a full discussion of robustness
checks.
6. 6These modifications can include, for example, the “top-coding” of
continuous variables with outlying values (such as income or transportation
time) and the reduction of category availability for variables with
hundreds of categories (such as ancestry or birthplace).
7. 7See https://www2.census.gov/programs-surveys/acs/data/pums/2019/1-Year/.
8. 8NAICS codes are all numeric. In PUMS, some NAICS codes are combined into
modified NAICS codes, which are identified as being both numeric and alpha.
(See how PUMS combines NAICS codes in its modified version of NAICS in
“2019 ACS 1-year PUMS Code Lists” at
https://www.census.gov/programs-surveys/acs/microdata/documentation.html.)
9. 9The OECD TiVA database is available at
https://www.oecd.org/industry/ind/measuring-trade-in-value-added.htm. The
ICIO tables are available at https://oe.cd/icio.
10. 10For a more detailed description of concepts and methodologies related to
the TiVA statistics and a discussion of ongoing projects and initiatives,
see OECD–World Trade Organization (n.d.).
11. 11The G20 is a group of 19 individual countries and the EU. The countries
are Argentina, Australia, Brazil, Canada, China, France, Germany, India,
Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa, South
Korea, Turkey, United Kingdom, and United States.

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