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URL:
https://www.elibrary.imf.org/view/journals/087/2023/001/article-A001-en.xml?cid=nl-com-nn-nn202302-headline
Submission: On May 31 via api (May 31st 2023, 5:56:25 pm UTC) from US — Scanned from DE

Form analysis
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* Previous Article

Accelerating Innovation and Digitalization in Asia to Boost Productivity
Author:
Ms. Era Dabla-Norris
Ms. Era Dabla-Norris
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Mr. Tidiane Kinda
Mr. Tidiane Kinda
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Kaustubh Chahande
Kaustubh Chahande
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Hua Chai
Hua Chai
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Yadian Chen
Yadian Chen
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Alessia De Stefani
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Yosuke Kido
Yosuke Kido
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Fan Qi
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Alexandre Sollaci
Alexandre Sollaci 0000000404811396 https://isni.org/isni/0000000404811396
International Monetary Fund

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Publication Date: 09 Jan 2023 eISBN: 9798400224034 Language: English Keywords:
laggard firm; productivity growth; B. firm heterogeneity; firm level;
productivity distribution
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* Abstract
* Full Text
* Related Publications

COVID-19 hit on the back of weakening productivity growth in many advanced and
emerging Asian countries, a trend that could be exacerbated by the pandemic.
Interestingly, productivity growth in the region was slowing even amid increased
innovation effort, as proxied by spending on research and development (R&D) and
number of patents. A key element underpinning this disconnect is the growing
dispersion in productivity growth, innovation effort, and digitalization across
and within sectors. Asia has risen to become an innovation powerhouse,
contributing to more than half of world patents. The rise of Asia as an
innovation hub has been driven by a few frontier countries that have experienced
a sharp increase in digital and computer-related patents, supported by solid R&D
spending and a large share of researchers in the labor force. Within countries,
R&D has become more concentrated in a smaller share of firms in frontier Asia.
Empirical evidence using firm-level data highlight that the high concentration
in R&D is associated with large dispersion in productivity. External exposure to
competition and innovation, including through trade, supports innovation and
help close productivity gaps for firms closer to the frontier. Non-frontier
Asian developing countries have benefited from technology diffusion through a
higher share of imported high-technology goods and by granting more patents to
non-residents, supported by improvements in human capital and digital
infrastructure. For these countries, further integration to the international
economy, including global value chains, greater entrepreneurship, and expanding
innovative labour supply could support productivity by encouraging innovation,
including process innovation which is associated with larger productivity at the
firm-level. Policies to foster innovation, reduce productivity gaps, and
ultimately boost aggregate productivity can be grouped into two buckets. For
countries close to the technological frontier, R&D tax credits and grants,
business-university R&D collaboration, and lower trade barriers would support
broader-based innovation and help close productivity gaps. For countries farther
from the frontier, further improvements in digital infrastructure, skilled labor
force, openness to trade and FDI, and patent protection, could promote resource
reallocation to the most productive firms and enhance incentives for
technological adoption, supporting diffusion and higher productivity.

ABSTRACT

1. INTRODUCTION

Productivity growth in Asia was slowing before the COVID-19 pandemic.
Productivity—whether measured in terms of labor productivity (output per worker)
or as total factor productivity (TFP, a measure of economic efficiency)—has been
on a downward trend worldwide, including in Asia. The slowdown, which started in
the aftermath of the global financial crisis, has been particularly pronounced
since 2015, impacting both advanced economies and developing countries alike in
the Asia and Pacific region (Figure 1). Before the global financial crisis,
productivity growth in emerging and developing Asia rose above that of advanced
economies, leading to some catch-up effects. However, since the global financial
crisis, productivity growth in emerging and developing countries in the region
has significantly slowed toward advanced economies’ levels. As a consequence,
productivity levels in many Asian countries remain below the global productivity
frontier (proxied by the productivity level of the United States).

View Full Size
Figure 1.

Average Annual TFP Growth by Region

(Percent change, year-over-year)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: University of Groningen; Penn World Tables; and IMF staff calculations.
* Download Figure
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Figure 1.

Average Annual TFP Growth by Region

(Percent change, year-over-year)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: University of Groningen; Penn World Tables; and IMF staff calculations.
* Download Figure
* Download figure as PowerPoint slide

The productivity slowdown seems puzzling at first glance as it occurred
concomitantly with noticeable advances in digital technologies and innovation in
the region. Digital technologies allow firms to access new tools and ways to
design, produce, and sell goods and services.1 Advances in various areas such as
artificial intelligence, robotics, computing power, and big data in the past
decades have triggered a new wave of innovations and a rapid rise of
digitalization across a range of sectors in recent years, from e-commerce,
digital financial technology (fintech), ridesharing, and mobile app-enabled
service.2 Yet this surge in digital technologies and innovation has failed to
offset the slowdown in aggregate productivity in many countries in Asia. A
leading explanation of the inability of digital technologies to counter the
slowdown in aggregate productivity to date lies in the sizeable dispersion in
access to digital technologies across and within countries, and insufficient
investment in enabling and complementarity factors such as organizational
capital and management skills, human capital, and Information and Communications
Technology-related (ICT-related) skills, and access to digital infrastructure
(Brynjolfsson, Rock, and Syverson 2018; OECD 2021).

The pandemic has accelerated digitalization in the region, presenting a
potential upside for productivity growth. The need to reduce in-person
interactions and enhance social distancing experiences during the pandemic has
put a premium on digitalization and accelerated its adoption. People and
businesses turned to online platforms to make online purchases and pursue
communication, education, and work. Digital solutions, including software and
platforms, have surged to facilitate remote work, online platform activities,
e-commerce, and online access to public services during lockdowns and to support
safe distancing measures afterwards. For instance, spending on e-commerce rose
by over 30 percent year-on-year in some countries in Asia.3 Some consumer-based
surveys have highlighted that technology adoption could remain strong in the
near term and post-pandemic (Kinda 2021). If maintained, the recent boost in
digitalization, and associated increase in investment in intangibles to fuel it
could boost aggregate productivity.4

However, the pandemic could also present challenges for aggregate productivity
growth. The slowdown in productivity growth could be exacerbated by the ensuing
economic scarring as the health crisis has resulted in unprecedented output
losses (Figure 2). Evidence suggests that previous epidemics (including SARS,
Mers, Ebola, and Zika) had significant and persistent negative impacts on labor
productivity (OECD 2020). While some sectors, in particular export-oriented
sectors, have recovered from the health crisis, domestic-oriented sectors are
still impacted, posing risks of hysteresis. In addition, the uneven diffusion of
digital technologies, the concentration of digital investments and major
innovations in a few large firms, and the resilience of highly digitalized firms
during the pandemic could raise their market power, widen productivity
divergence, and weigh on aggregate productivity over the longer run. The
pandemic has also led to an erosion of human capital caused by the disruption of
work, school, and university education as well as weaker investment that could
delay broad-based digitalization and weigh on aggregate productivity growth. In
addition, some of the policies implemented to cushion the economic fallout from
the pandemic have reduced business exit and increased the survival likelihood of
low performing firms (Barrero, Bloom, and Davis 2020).

View Full Size
Figure 2.

Asia and Pacific Region: Comparison of Pre-Pandemic and Latest Real GDP
Projections

(Index, 2019=100)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: IMF, World Economic Outlook; and IMF staff calculations.
* Download Figure
* Download figure as PowerPoint slide

Figure 2.

Asia and Pacific Region: Comparison of Pre-Pandemic and Latest Real GDP
Projections

(Index, 2019=100)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: IMF, World Economic Outlook; and IMF staff calculations.
* Download Figure
* Download figure as PowerPoint slide

The recovery offers the opportunity to redesign policies to durably accelerate a
broad-based digital transformation and innovation that can lift aggregate
productivity. While the pandemic and some of the policies implemented to dampen
its impact on firms can exacerbate the uneven digital transformation and worsen
firms’ dynamism, it offers an opportunity to redesign policies to accelerate
broad-based innovation and digitalization. This paper proposes a multipronged
approach to durably accelerate the production and diffusion of digital
technologies and foster innovation-led growth.

The innovation imperative across the region will require a differentiated
response across countries, sectors, and firms. Innovation activity leads to
technological progress in two distinct ways. Purposeful research and development
(R&D) can result in the invention of completely new products and processes. This
kind of innovative activity moves the global technological frontier and mainly
occurs in developed Asian countries and China. But innovation also consists of
the adoption and adaptation of existing technology, which closes the gap between
countries converging towards the global technological frontier and those on the
leading edge. As such, for emerging and developing countries in Asia with widely
varying institutional, technological, and firm-level capacities, innovation
entails not only the invention of new products and processes but also the
diffusion and adoption of existing technologies or practices.

For all countries, the narrowing of productivity and digital/technological gaps
across sectors and firms will be critical as this can have big payoffs in the
aggregate. The productivity growth of countries is determined by the performance
of individual firms in a country and by the reallocation of resources between
the firms in that country. The latter results from business dynamism, that is,
the growth of some (ideally the most productive and innovative) firms and the
decline of other (ideally the least productive) firms. Firms in many advanced
and frontier Asian economies, however, are well behind the technological
frontier and some indicators suggest this gap is widening as firm-level
productivity dispersion has increased. Firm-level evidence from OECD countries
suggest that the economic impact of reducing this dispersion can be
significant.5 In emerging and developing Asia, such dispersion can be even
larger across regions, sectors, and firms. In fact, the low average productivity
in emerging and developing countries is mostly driven by a thick left tail of
small and unproductive firms, while relatively productive firms exist even in
the poorest countries (Hsieh and Klenow, 2009; Hsieh and Olken 2014).

Against this backdrop, the paper is structured as follows: Chapter 2 examines
the landscape of innovation and digitalization in Asian countries before and
during the pandemic and the extent of technology diffusion. Chapter 3 uses
firm-level data for both advanced and developing economies in the region to
investigate the role of innovation and digitalization for productivity growth
and dispersion across firms and identify factors that impede faster innovation
(for countries closer to the technological frontier) and broader technological
diffusion (for countries farther from the frontier). Chapter 4 provides a
detailed mapping of the policies and mechanisms, depending on where countries
and firms stand, to foster broader-based innovation and boost aggregate
productivity and longer-term growth prospects.

2. THE LANDSCAPE OF INNOVATION AND PRODUCTIVITY IN ASIA

This paper adopts a broad view of innovation as the accumulation of knowledge
and implementation of new ideas. It classifies innovation into four categories,
based on the difference between product and process innovations, as well as
innovation by discovery and innovation by diffusion.

* Product innovation leads to the introduction of new or improved goods and
services. This type of innovation is usually easier to measure, as some of
its outputs are observable (for example, patents or trademarks). In
developing economies, product innovation often refers to the adoption of new
or improved goods and services that differ from the firm’s previously
produced goods or services.

* Process innovation leads to novel or improved managerial practices or
business operations that differ from the firm’s existing business processes.
This type of innovation typically increases the productivity of a firm by
fine tuning the coordination between production processes or changing the way
the firm operates (instead of through the adoption of new machinery or
technology).

* Innovation by discovery concerns the invention of new ideas and is produced
through R&D or other creative activities that push the technological
frontier. The paper includes both basic and applied research into this
category. This type of innovation is more prominent in advanced economies and
in emerging economies such as China, where firms on the technology frontier
typically have more incentive and resources to invest in R&D.

* Innovation by diffusion includes direct technology transfers, knowledge
spillovers, or the adoption of existing business practices that were
previously not used by a company. Most firms in emerging and developing
economies are constrained to, or reap higher benefits from, this type of
innovation.

Advances in the digitalization of production involve all of the categories
above. Invention of new digital technologies, typically through R&D, pushes the
technology frontier. Growing adoption of digital technologies lead to new
business processes and products. Digitalization also increases ease of diffusion
of ideas, technologies, and practices. Higher degree of digitalization can be
understood as both an output and an input of innovation. It is an output because
new technologies tend to produce goods and services with a higher digital
content. It is considered as an input because the digitalization or automation
of production processes can increase the productivity of firms (process
innovation) and are increasingly required to conduct the R&D that leads to the
creating of new/improved goods and services.

In what follows, the paper presents a landscape of innovation across Asia by
examining both outputs of and inputs into innovative activities. Outputs include
patents based on both basic and applied research, while inputs into innovation
including R&D spending and human capital, among others. Following IMF (2021b),
we distinguish between basic research (undirected, theoretical, or experimental
research), and applied research, which is directed and for practical purposes,
such as bringing goods to markets. For this paper, we refer to all high-income
countries in Asia and China as “frontier Asia,” and to other countries as
developing or “non-frontier” Asia. In the remainder of this chapter, we first
provide an overview of progress achieved in innovation in recent decades in
frontier and non-frontier Asia respectively, and then identify shifting trends
of innovation toward accelerated digitalization since the onset of the COVID-19
pandemic. For non-frontier Asia, we present indicators that capture the
diffusion of technology and innovation elsewhere. We conclude this chapter by
discussing challenges in further advancing innovation and digitalization in
Asia.

A. ASIA AS INNOVATION POWERHOUSE

Asia has become a powerhouse when it comes to applied research as measured by
patents. Data on the spatial density of patents filed under the Patent
Cooperation Treaty (PCT) of the World Intellectual Property Organization
(indicate that less than 40 percent of world patents originated from Asia at the
beginning of the century. In less than a decade, Asia’s contribution to world
patents increased to about 50 percent. By 2019, this share has reached 54
percent (Figure 3, panel 1). Asia is ahead of Europe and the Americas in terms
of patent outputs, although in per capita terms it still trails Europe. The
lion’s share of patents in Asia are accounted for by a few countries, most
notably China, Japan, and Korea, with China’s rise being particularly striking
in the past decade (Figure 3, panel 2). Other high-income countries in the
region, such as New Zealand and Singapore produce significantly fewer patents
due to their smaller scale but are nevertheless innovative relative to their
size.

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Figure 3.

Outputs of Innovation: Patents in Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: World Intellectual Property Rights Organization.
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Figure 3.

Outputs of Innovation: Patents in Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: World Intellectual Property Rights Organization.
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Asia’s focus on basic research—undirected, theoretical, or experimental work—is
close to the most innovative economies worldwide. Basic research, as distinct
from applied research, plays an especially important role in innovation. In
frontier economies, between 10 to 25 percent of total R&D spending is devoted to
basic research (except China), which is close to world leading innovators
(Figure 4, panel 1). New Zealand and Singapore are among the countries that
spend the most in basic research in percent of GDP. A higher share of frontier
Asia’s patents is related to or contribute to basic scientific research,
compared with leading innovators in the world, with New Zealand and Singapore
taking the top spots worldwide (Figure 4, panel 2).6 Globally, while the United
States remains the main source of cited works, citations to Chinese science have
grown strongly since 2005 (albeit from a low base), as have citations across
Asian countries (IMF 2021b).

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Figure 4.

Inputs into Innovation in Asia and Selected Countries

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: OECD; PATSTAT Global 2020; Reliance on Science in Patenting; UNESCO;
and IMF staff calculations.
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Figure 4.

Inputs into Innovation in Asia and Selected Countries

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: OECD; PATSTAT Global 2020; Reliance on Science in Patenting; UNESCO;
and IMF staff calculations.
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Frontier Asia has devoted large amounts of financial and human capital to R&D.
Frontier Asia spends close to the most innovative economies elsewhere in R&D,
with Korea being a world leader in R&D spending at 4.6 percent of GDP in 2019.
Most other innovative economies spend between 2 to 3.5 percent of GDP in R&D
(Figure 4, panel 3). The share of researchers in the labor force is also close
to peers (except in China), with Korea again taking the leading spot at least in
2018 (Figure 4, panel 4).

Non-frontier Asia, while not engaging intensively in R&D activities, benefits
significantly from international technology diffusion, supported by improvements
in human capital and digital infrastructure. High-tech imports in most
low-and-middle-income countries in Asia, particularly Bangladesh, India,
Malaysia, Nepal, the Philippines, Sri Lanka, Thailand, and Vietnam are higher as
a share of total imports than the world median (Figure 5, panel 1). Although
many of these countries’ participation in the trade of high-value added goods
began with less-sophisticated components and assembly, these measures reflect
the increased adoption of global technologies and production processes over time
through FDI, creation of joint ventures, and participation in trade and global
value chains (GVCs).7 In addition, foreign ideas started to diffuse more
profusely since 2013, as non-frontier Asia accounted for an increasing share of
patents granted from Asia to nonresidents (Figure 5, panel 2). At the same time,
human capital improved significantly, especially in India, Malaysia, and
Vietnam, where tertiary education enrollment rate has increased by more than 10
percentage points in the last two decades, enhancing firms’ capacity for
technology adoption and innovation, particular product innovation (Figure 5,
panel 3).8

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Figure 5.

Indicators of Technology Diffusion in Emerging and Developing Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Global Innovation Index 2021; World Bank, World Development Indicators;
World Intellectual Property Organization; and IMF staff calculations.
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Figure 5.

Indicators of Technology Diffusion in Emerging and Developing Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Digital infrastructure has also been significantly enhanced in non-frontier
Asia. For example, the number of secure internet servers has seen a more than
200-fold increase, contributing to a much-reduced gap with high income countries
(Figure 5, panel 4). India, in particular, has become a global information
technology services powerhouse and a pioneer of “digital stacks” that bring
together digital payments and identification services, among others, and upon
which innovators can build additional services and applications (World Bank
2021b).

B. THE PANDEMIC AND INNOVATION IN ASIA: A BOOST TO DIGITALIZATION

Innovation in digital/ICT technologies was advancing rapidly in Asia even prior
to the pandemic. While the growth in patents in frontier Asia was broad-based,
the increase was particularly prominent in digital and ICT technologies. Asia
started to account for a higher share of world patents in these technologies
than the rest of the world combined since 2017, representing about 60 percent of
world total patents in digital and computer technologies by 2020 (Figure 6).
Asia dominates all of the digital/ICT technology sub-categories in terms of the
number of patents, including telecommunications, digital communication, basic
communication processes, computer technology, and semiconductors (Figure 7). Not
surprisingly, the ICT sector in Asia is among the world’s largest. The sector
accounted for more than 12 and 7 percent of total value added in Korea and
India, respectively (Dabla-Norris and others 2021), comparable in size to most
other OECD countries.9 China’s ICT sector is estimated to be about 6 percent of
GDP (Herrero and Xu 2018).

View Full Size
Figure 6.

Patent Grants for Digital Communication and Computer Technology

(Percent share of total patent grants in digital communication and computer
technology)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: World Intellectual Property Organization; and IMF staff calculations.
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Figure 6.

Patent Grants for Digital Communication and Computer Technology

(Percent share of total patent grants in digital communication and computer
technology)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: World Intellectual Property Organization; and IMF staff calculations.
* Download Figure
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View Full Size
Figure 7.

Patent Publications per Field of Technology by Region, 2020

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: World Intellectual Property Organization; and IMF staff calculations.
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Figure 7.

Patent Publications per Field of Technology by Region, 2020

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: World Intellectual Property Organization; and IMF staff calculations.
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Many Asian economies were also at the frontier in terms of adoption of digital
technologies, including robotics and e-commerce. In keeping with Asia’s moniker
of “manufacturing powerhouse,” about two-thirds of the world’s industrial robots
are employed in the region. China alone is the single biggest user of robots
(accounting for some 30 percent of the market), and China, Japan, and Korea each
employed more robots than the United States on the eve of the pandemic. The
rising trend of industrial robot use has been relatively broad-based in the
region (Figure 8, panels 1 and 2). Online sales are also more common in some
Asian economies than in other regions, including e-commerce exports, a trend
that is expected to accelerate in the wake of the pandemic (Figure 8, panels 3
and 4). Business-to-Consumer (B2C) e-commerce in China and Korea is larger than
in the United States. Cross-border e-commerce is also substantial, with B2C
e-commerce exports from China exceeding that of advanced economies (Dabla-Norris
and others 2021).

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Figure 8.

Widespread Use of Robots and E-Commerce in Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: International Federation of Robotics; Statista Digital Market Outlook;
IMF, World Economic Outlook Oct 2021; and IMF staff calculations.
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Figure 8.

Widespread Use of Robots and E-Commerce in Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Asia stands out from other regions in having large home-grown tech giants. China
has several of the largest e-commerce companies in the world, both measured in
terms of market share and total sales. For instance, China’s Alibaba Group and
JD.com have nearly 40 percent of global e-commerce market share by merchandise
volume (Dabla-Norris and others 2021), although the total value of Alibaba’s
transactions is smaller than that of Amazon.10 Japan’s Rakuten and Singapore’s
Sea Group (trading as subsidiary Shopee) are other major players in e-commerce
as are Korea’s Coupang and Indonesia’s Go-Jek. These local firms generate
similar levels of revenue in Asia to large firms in the United States, including
Amazon, Walmart, and their local subsidiaries. Asia is also home to some of the
world’s largest providers of digital services other than e-commerce, such as
China’s Tencent (operating the WeChat communications, social media and payment
platform) and Baidu (China’s largest internet search engine).

The pandemic has changed innovation trends and accelerated digitalization and
automation. As remote working has become more prevalent in many countries in the
region, demand for digital solutions for work and life, including communication
and shopping, have risen significantly and boosted innovation in digital
technologies (Figure 9, panel 1). Patent application data suggest that the
proportion of patent applications for remote work and e-commerce technologies
have also increased substantially compared to pre-COVID times (Figure 9, panel
2), including by Asian countries (Asian Development Bank 2021). The use of
e-commerce has accelerated during the pandemic, with Asia now accounting for
nearly 60 percent of the world’s online retail sales. For instance, e-commerce
revenues grew by 30–50 percent in many Asian economies in 2020, outpacing most
countries in the world (Figure 9, panel 3). This rapid increase was driven by an
increased reliance on e-commerce spurred by the ongoing trend away from cash
payments and further development of new payment methods, particularly for
e-wallets and prepaid cards.11 Despite the pandemic’s impact on global economic
activities, robot installation in Asia increased in 2020 relative to other
regions. In China, for instance, the installation of robots in electronics
increased sharply, reflecting high demand for digital investment, including for
5G (IFR 2021). Going forward, the expected strong demand for electronics,
digital infrastructure, and automation technologies could boost robot
installation in Asia and further support digital commerce (Figure 9, panel 4).

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Figure 9.

Remote Work and E-Sales Growth in the Wake of the Pandemic

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: McKinsey Global Business Ececutive Survey, July 2020; Singapore
Department of Statistics; Statista; USPTO; and IMF staff calculations.Note: In
panel 2, non-provisional utility and plant patent applications only. Based on
methodology in Bloom and others (2021).
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Figure 9.

Remote Work and E-Sales Growth in the Wake of the Pandemic

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Many countries in Asia actively promoted digitalization and innovation in the
wake of the pandemic. In addition to leveraging technology resources for disease
prevention and control, several countries in the region launched multi-faced
initiatives to promote the digital economy as part of their stimulus packages.

Tax incentives, public spending, and R&D loan programs have been used to support
innovation and digitalization in the private sector. Countries in the region
have also accelerated the deployment of fintech, digital public services, and
provided support to SMEs for the adoption of digital technologies, including
e-commerce platforms (Box 1).12

Box 1.

Digitalization and Innovation Policies during the COVID-19 Pandemic

Many Asian countries have accelerated innovation and digitalization policies in
the wake of the pandemic. These include policies to improve digital skills among
SMEs, scale up digital infrastructure, promote cashless payment, and promote the
digitalization of public services. Some countries formulated digitalization and
innovation strategies to promote their post-COVID recovery.

Initiatives launched to promote digitalization. Korea unveiled a Digital New
Deal as part of the Korean New Deal, with the aim to build a digital economy and
promote growth in promising industries that rely less heavily on human contact.
Malaysia announced the Twelfth Malaysia Plan, which aims to boost the digital
economy and enhance broad-based productivity drivers. The government launched
the Malaysia Digital Economic Blueprint (MyDIGITAL) to enable greater digital
inclusiveness and promote growth of the digital economy. Vietnam announced the
National Digital Transformation strategy to strengthen the online public
services, accelerate non-cash payments, and e-commerce, and improve shared
database for state management. India accelerated digitalization, including
through increased digital payments, contactless payments, digital education.

Fiscal and financial support for digitalization and innovation. Japan introduced
tax incentives for digital investments as part of the 2021 tax reform package.
Vietnam has scaled up public investments in innovation and digitalization in the
context of its Program for Recovery and Development. New Zealand introduced a
one-off R&D loan scheme to support R&D investment of firms that have been
severely affected by the pandemic.

Fintech. Cambodia introduced Bakong, a new payment system operated by the
National Bank of Cambodia, using blockchain technology and providing
real-time-gross settlement, to promote digitalization, cashless payment, and
financial inclusion. The system provides e-wallets, mobile payments, online
banking, and financial applications in a single interface.

Public service. Japan established the Digital Agency to promote digitalization
of the central and local governments, while supporting uptake of national ID
cards (My Number). Philippines has digitalized revenue collection and has
launched its digital ID system, which will support public service delivery such
as social protection.

SMEs. Singapore’s SMEs Go Digital program has supported SMEs’ adoption and use
of digital technologies through various channels. China and Singapore have
actively supported SMEs in accessing e-commerce platforms with regional or
global reach, to help them reduce costs or sell overseas through digital means.
New Zealand introduced Digital Boost for SMEs to improve their digital skills
and promote the take-up of digital technologies. Japan designed a business
continuity subsidy to help firms diversify and expand their sales channels.
Korea encouraged brick- and-mortar shops to open their business online through a
dedicated support program.

C. CHALLENGES IN ADVANCING INNOVATION AND DIGITALIZATION

Despite these successes, Asian countries still face important challenges in
fostering an innovation-led growth, with significant heterogeneity in
performance across countries, sectors, and firms that weigh on aggregate
performance. Inventions and new technologies offer the possibility for large
increases in productivity in frontier economies, but this alone is not
sufficient. What matters for a country’s growth and productivity performance is
how rapidly technology and innovation diffuse across countries as well as across
sectors and firms within a country.13 Many countries in the region appear to
underperform on several standard indicators of innovation for both diffusion and
discovery. Further, limited spillovers from sectors that perform well relative
to the rest of the economy constrain the contribution of innovation to overall
growth. Within sectors, the large productivity and technological divides between
the leading and lagging firms drives down aggregate productivity growth (see
next chapter).

The quality and impact of R&D in Frontier Asia leave significant room for
improvement. Despite the rapidly increasing number of patents generated in
Frontier Asia, patent citations—a measure of the quality and impact of
innovation—has been stagnant as a share of worldwide citations, reflecting the
relative rarity of groundbreaking innovations originating from Asia (Figure 10).
This could be related to weaknesses in basic research in the region. Basic
scientific research in many frontier economies in Asia is underfunded, with
significant heterogeneity across countries. For instance, the three countries
with the most patent output in Asia, namely China, Japan, and Korea, are near
the lower end in both spending in basic research and contribution to basic
research in comparison with world leaders such as The Netherlands and
Switzerland (Figure 4, panel 1). In addition, patents per researcher, a proxy
for the productivity of R&D, has been stagnant or declining in recent years in
some frontier countries in Asia (Figure 11).

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Figure 10.

Share of Total Patent Citations by Applicant Regions

(Percent share)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: The Lens (lens.org); and IMF staff calculations.
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Figure 10.

Share of Total Patent Citations by Applicant Regions

(Percent share)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: The Lens (lens.org); and IMF staff calculations.
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View Full Size
Figure 11.

Labor Productivity in R&D

(Patent per researcher)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: World Intellectual Property Organization; UNESCO; and IMF staff
calculations.
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Figure 11.

Labor Productivity in R&D

(Patent per researcher)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: World Intellectual Property Organization; UNESCO; and IMF staff
calculations.
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Innovation in Asia is increasingly concentrated in a handful of firms. While R&D
in frontier economies has increased in recent years, it has become more
concentrated in a smaller set of firms since the global financial crisis. R&D
spending per worker fell off the cliff in firms in Asia around 2009 but has
since gradually recovered (Figure 12). However, the share of firms engaging in
R&D, which has experienced a similar drop in 2009, has remained low, implying
that a larger share of R&D activities is undertaken by a much smaller set of
firms. A similar concentration of innovation in a minority of firms is seen in
emerging and developing Asia. For instance, less than 30 percent of firms in
developing Asia surveyed in the World Bank Enterprise Surveys (WBES) report
having introduced any innovation over the previous three years. The
concentration of R&D activity is likely to be a major drawback for the region’s
capacity to introduce breakthrough technology. Importantly, this concentration
could result in divergence of productivity growth across firms and sectors, and
ultimately weigh on aggregate productivity.14

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Figure 12.

R&D Expenditure Concentration in Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and IMF staff calculations.
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Figure 12.

R&D Expenditure Concentration in Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and IMF staff calculations.
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Access to cutting-edge technologies, particularly digital technologies, is also
highly uneven across and within countries and across firms. Firm-level measures
of innovation based on WBES data reveal significant heterogeneity in technology
adoption in the region. For example, while 20 percent of Chinese firms license
foreign technology, in Myanmar and Thailand only 5 percent of firms have any
technology licensed from foreign companies (World Bank 2021b). In particular,
SMEs face significant barriers related to access and use of digital
technologies, preventing them from reaping the full rewards of participating in
the new economy and reaching their full potential.15 Low levels of
digitalization and difficulties in accessing and adopting new technologies made
it particularly difficult for those firms to change existing work processes, by
introducing teleworking or an e-commerce sales channel.16 Within sectors, the
productivity and technological divide between the leading and lagging firms in
both frontier and non-frontier Asia reflects the slow diffusion of technology.
Insufficient investment in enabling and complementarity factors such as
organizational capital and management skills, human capital, and ICT-related
skills, hampers access to digital infrastructure as discussed below.

Diffusion of innovation remains a challenge. In developing Asia, despite notable
achievements in accelerating innovation through the acquisition of technologies
embedded in imports and FDI, this has not induced broad diffusion of new
technologies and processes beyond export-linked firms. Even in the more advanced
and frontier economies in the region, there is limited diffusion of innovation
by the more frontier firms to other firms in the same country. Technology
adoption and diffusion are determined by a range of factors, including access to
finance, firm-level capabilities, and availability of skills, among others.17

* Access to Finance. Investing in new capabilities, such as skills, innovation,
digital technologies, or machinery and equipment requires access to finance.
Theory and empirical evidence suggest that the level of productivity and the
likelihood of innovation, through invention or adoption, depend on the
availability of financing (Hall and Lerner 2010). When asked explicitly about
factors holding back business operations, about 20 percent of firms in
emerging and developing Asia report financing constraints as the main
obstacle in the WBES (Figure 13). By comparison, only 7 percent of firms in
the non-Asia sample report credit constraints as the main obstacle. This is
true for firms regardless of whether they innovate or not. Nearly half of
SMEs and roughly one-third of large firms in emerging and developing Asia
report difficulty in obtaining financing as a major barrier to technology
adoption.18 While purely descriptive, this evidence suggests that financing
constraints may indeed be one of the factors holding back the diffusion of
innovation in developing Asia.

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Figure 13.

Major Reported Obstacles by Firms in Developing Countries

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: WBES, 2006–20.Note: Percentage of firms reporting any given option as
the main obstacle to their business operations.
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Figure 13.

Major Reported Obstacles by Firms in Developing Countries

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: WBES, 2006–20.Note: Percentage of firms reporting any given option as
the main obstacle to their business operations.
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* Management capabilities. Adoption of new technologies and implementation of
organizational changes require strong quality of management (Bloom and Van
Reenen 2007). Results from the World Management Survey, however, highlight
large variation in management quality across Asian countries, with some
countries lagging behind peers at similar income levels (Figure 14) or those
at the global frontier. Significant dispersion in management practices also
exists within countries in both advanced and developing Asia, although weak
management performance is more prevalent for smaller firms in developing
countries (Figure 15). Large dispersion in management quality within Asian
countries reflects underlying structural issues and firm specific
characteristics.19 This gap in management capabilities likely contributes to
the innovation gaps between the region and the global frontier.

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Figure 14.

Management Scores

(Management scores in Y axis, logarithm GDP per capita in PPP in X axis)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: IMF, World Management Survey.
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Figure 14.

Management Scores

(Management scores in Y axis, logarithm GDP per capita in PPP in X axis)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: IMF, World Management Survey.
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View Full Size
Figure 15.

Firm-Level Overall Management Scores in Asian Countries, by Firm Size

(Scale 0 to 5, 5 is best)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: World Management Survey; and IMF staff estimates.Note: The panels
show distribution of firm-level management score from World Management Survey
(2004-15). Lines represents kernel density estimation. Asia AE includes,
Australia, Japan, New Zealand, and Singapore. Asia EM includes China, India,
Myanmar, and Vietnam. Larger firms are firms that employ 500+ workers.
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Figure 15.

Firm-Level Overall Management Scores in Asian Countries, by Firm Size

(Scale 0 to 5, 5 is best)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

* Adequacy of skills. A range of advanced skills are important in enabling
innovation at the firm and country levels, with such skills becoming
increasingly important as firms move from diffusion and technology adoption
toward the technological frontier. However, firms in the region consistently
report skills gaps as serious impediments to their operations, as also
reflected in variation in PISA scores in the region (Figure 16). More than 50
percent of innovating firms in ASEAN+3 countries cite a lack of managerial
and leadership skills as a challenge when hiring new workers (World Bank
2021a). And more than half of all innovative firms in many of these countries
cite the scarcity of interpersonal and communication, ICT, or technical
skills as critical challenges when it comes to hiring. Educational
achievement in developing Asian economies also tend to lag behind that of
advanced economies.

View Full Size
Figure 16.

PISA Scores

(PISA scores in y axis, Log values in x axis)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: OEDC; and IMF, World Economic Outlook.
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Figure 16.

PISA Scores

(PISA scores in y axis, Log values in x axis)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: OEDC; and IMF, World Economic Outlook.
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* Access to external knowledge and information. Access to external knowledge—by
using knowledge information services, tapping knowledge created in
universities, or learning from other firms via trade flows or connections
through global value chains—is an important driver of technology adoption.
Flows of specialized information are particularly important for small
businesses. Although firms in developing Asia can learn and improve their
technological know-how through these different sources and have incentives to
do so, access to information is oftentimes inadequate, particularly for small
business, which tend to be less informed about the latest technologies
available in the market. Filling this information gap is important to
minimize entrepreneurs’ uncertainty about technology adoption. As access to
technology needs to be followed by its adoption to have the expected effects,
facilitating information flows and reducing the perceived and actual cost of
technological adoption, including through public policy, is key. Weaknesses
in the legal environment in some developing countries, including lack of
adequate legislations on data protection and cybercrime and ineffective
enforcement mechanisms, hinder information sharing and confidence for
technological adoption.

3. HOW CAN INNOVATION AND DIGITALIZATION HELP CLOSE PRODUCTIVITY GAPS?

Aggregate TFP in an economy depends on not only the efficiency of individual
firms or industries but also how inputs are allocated across them. Economic
theory suggests that more productive firms should be more innovative and use
more resources (capital and labor) than less efficient firms. Over time, less
productive firms either become more efficient, or are replaced by more
productive entrants. This process brings about capital and labor reallocation,
which impacts measured TFP and output. Misallocation of resources, however, can
arise if impediments exist to the movement of factors between heterogeneous
firms (particularly young firms). This can give rise to persistent differences
in the rates of return across firms and sectors, undermining aggregate TFP
growth.

In this chapter, we focus on firm-level data, diving deeper into the
determinants of productivity levels and innovation capacity across Asian firms
prior to the pandemic. This can help shed light on longstanding structural
challenges that have dragged down aggregate productivity growth and provide a
roadmap of policies to address gaps. We begin by examining the relationship
between innovation and productivity at the firm level in Asia and the rest of
the world. We then turn to the drivers of productivity growth, discussing which
characteristics lead some firms to be leaders in their sectors, and others to be
laggards. In the third subsection, we zoom in on the drivers of firm-level
innovation to identify which types of firms are more likely to push the
technological frontier by introducing new products or processes. We conclude
this chapter by reviewing its main takeaways.

To address these issues, we exploit different firm-level datasets, covering both
frontier and non-frontier Asia. For advanced and emerging Asia, we rely on the
Orbis database, covering firms in 16 different countries, distinguishing between
Asia and rest of the world (see Appendix 1 for data sample). To capture the
relationship between productivity and international trade (for example, due to
imports of new technology or exposure to global competition, Keller 2004;
Aghion, Bergeaud, and Van Reenen 2021), we merge the Orbis database with Zephyr
to obtain information on FDI and mergers at the firm level. While allowing us
access to detailed information, these data are skewed toward firms in more
developed Asian economies. We complement this information by leveraging the
latest waves of the WBES, which shed light on the link between innovation and
productivity in emerging and developing Asia.20

A. INNOVATION AND DIGITALIZATION AS ENGINES FOR PRODUCTIVITY GROWTH

FIRM-LEVEL EVIDENCE FOCUSING ON ADVANCED AND LARGE EMERGING MARKET ECONOMIES

Innovation and digitalization are important drivers of firm-level productivity
in Asia and elsewhere. The link between productivity and innovation intensity
(measured as research and development expenses per worker) at the firm-level is
well understood in the economic literature: higher R&D intensity leads to
technological advances, which in turn increase TFP. Our results from a linear
regression model (Figure 17, Annex Table 1.1) confirm this relationship. We also
find that digitalization (proxied by the ratio of intangible to tangible
capital21) is a key driver of TFP, particularly for Asian countries. The
digitalization of production processes can increase the efficiency of specific
tasks, leading to gains in overall productivity. For example, Gal and others
(2019) estimate that a 10 percentage point increase in the sector-wide adoption
rate of cloud computing is associated with a 3.5 percent productivity increase
for the average European firms after five years. Furthermore, complementary
investment in skills and factors such as software and data, important parts of
many firms’ intangible capital, may be necessary to reap the benefits of
digitalization (for example, van Ark 2016, Brynjolfsson and McAfee 2011). In
contrast to physical capital, intangibles can be scaled-up easily at low costs
and allow firms to grow rapidly. Studies from other regions have shown that
firms that spend the most on intangible assets have the strongest productivity
growth (see for example, Crouzet and Eberly 2018), and intangibles support the
translation of technology into improved productivity (Mohnen, Polder, and van
Leeuwen 2018).

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Figure 17.

Elasticity of Productivity (TFP) with Respect to Firm Characteristics

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; Zephyr; and authors’ calculations.Note: ihs(x)=ln(x+√1+x2)
indicates the inverse hyperbolic sine function. Because it quickly converges to
ln(2x), the coeffcients can be interpreted as elasticities. Regressions include
a firm and country-by-year fixed effects. Standard errors are clustered at the
country-sector level and *, ** and *** indicate that coeffcients are
statistically significant at the 10%, 5%, and 1% levels, respectively (see Annex
Table 1.1 for details).
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Figure 17.

Elasticity of Productivity (TFP) with Respect to Firm Characteristics

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Participation in international trade is positively associated with firm-level
productivity, but this relationship is stronger for countries outside of Asia.
This result confirms a positive correlation between international exposure (that
is, firms that export their production or have received FDI) and higher
productivity for a sample of firms in non-Asian countries, while the results are
statistically insignificant for the sample of firms in Asia. There are several
channels through which exposure to international trade can affect productivity,
including self-selection (only productive firms choose to participate in
international markets, since they possess the capacity to produce at larger
scale); competition (unproductive firms entering a competitive market are
eventually driven out); or learning (firms learn from foreign companies in the
same market).22 The smaller coefficient for Asian countries could be due to
weaker spillovers from international participation. Another possibility is that
firms compete in a different institutional environment, whereby the selection of
firms that export their production is less related to productivity. Yet a third
explanation could be that Asian companies are more (or less) likely enter and
exit international markets, depending on the costs and benefits of doing so.23

Firm-level evidence for emerging and developing Asia also shows that innovative
firms tend to be more productive than other firms. This evidence is based on
regression analyses using firm-level data from the WBES, covering more than
8,000 firms in 19 emerging market economies and developing countries over 14
years.24 The outcome variable is firm-level productivity, regressed on a
variable indicating whether the firm has innovated over the previous three
years.25 Innovation here is defined broadly as the introduction of new
production processes or product lines, so that it includes firms adopting
existing technology. In general, product and process innovation, including both
new-to-market and new-to-firm innovation, is associated with both higher labor
productivity and higher revenue TFP, controlling for firm-level and market
characteristics (Figure 18 and Annex Table 2.1). The findings for this sample
confirm earlier results using the WBES, focusing on a different subset of
countries (Dabla-Norris and others 2012).

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Figure 18.

Innovation and Productivity

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: WBES, 2006–2020.Note: Charts represent OLS regressions, with
productivity as an outcome variable and innovation as a dependent variable. Firm
age, size, location, R&D expenditure, share of high-skilled workers,
imports/exports as a share of sales are included as controls, as well as country
and year fixed effects. Each dot represents 50 data points.
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Figure 18.

Innovation and Productivity

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

In developing Asia, the association between innovation and productivity level is
stronger for process innovation than for product innovation (Figure 19, Annex
Table 2.1). Product innovation is defined as the introduction of new products,
new to the firm or even to the reference market, over the previous three years.
Process innovation, by contrast, is the introduction of new means of production:
the adoption of new technologies, machinery, ways of organizing business,
managerial capabilities. Importantly, process innovation includes digitalization
processes, for example the adoption of IT or e-commerce practices. E-commerce,
in particular, has been shown to be a key driver of productivity growth in Asia
(Kinda 2019). This result suggests firms in developing Asia do not need to be at
the cutting-edge of innovative processes or produce innovation by discovery to
benefit from innovation. The adoption of existing technologies and processes can
lift many firms up the productivity ladder.

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Figure 19.

Productivity and Different Types of Innovation

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

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Figure 19.

Productivity and Different Types of Innovation

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

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Productivity also depends on the share of workers with higher educational
attainment and on the degree of R&D expenditure at the firm level (Annex Table
2.1). These variables can be considered as proxies for the likelihood of
introducing non-imitative, cutting-edge innovation, or innovation by discovery.
A range of advanced skills are important in enabling innovation at the firm and
country levels. While R&D activities tend to be concentrated at the very top of
the productivity distribution, the availability of a skilled workforce has the
potential to create gains across a broader spectrum of firms, including by
raising managerial competence and the firms’ capacity to absorb positive
spillovers from innovative and higher-performing firms.

B. FIRM HETEROGENEITY AND AGGREGATE PRODUCTIVITY GROWTH IN ASIA

Aggregate productivity growth depends on both expanding the technology frontier
and closing productivity gaps across firms. The previous section shed light on
the characteristics that differentiate between high- and low-productivity firms,
with a focus on innovation. However, a country’s productivity growth performance
depends on not only the TFP growth of firms at the technological frontier, but
also how rapidly technology and innovation diffuse across firms within a
country. Indeed, the productivity and technological divide between the leading
and lagging firms in Asia is likely the consequence of slow diffusion within
countries. In this context, assessing the extent of productivity dispersion and
understanding its driving factors, including over time, are important policy
issues. In what follows, we first examine the determinants of productivity
dispersion within sectors, and the characteristics of laggard firms in Asian
countries.

Large dispersion in productivity exists within narrowly defined industries,
particularly in high-tech sectors and services. TFP in the most productive firms
can be up to seven (≈ exp(2); see Figure 20) times bigger than in the median
firm, even within narrowly defined sectors.26 In addition, productivity
dispersion in high-tech sectors and in services is considerably larger than in
manufacturing (Figure 20).27 This dispersion is not unique to Asian countries
but could be an important contributor to the relatively low aggregate TFP growth
observed in recent years. For instance, Andrews, Criscuolo, and Gal (2016) show
that the aggregate productivity slowdown in many OECD countries reflects weaker
productivity growth of firms outside of the top 5 to 10 percent of companies
with the highest productivity. By contrast, productivity growth of top firms has
been strong across many OECD economies, suggesting weaker technology diffusion
from the “best to the rest.”

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Figure 20.

TFP Dispersions across Sectors

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.
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Figure 20.

TFP Dispersions across Sectors

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.
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Productivity dispersion tends to be higher in more digitalized sectors, and in
sectors less exposed to international markets. To analyze the determinants of
productivity dispersion in more detail, we look at the ratio between the 90th
and 10th percentiles of the TFP distribution within 4-digit sectors, country,
and year (90/10 TFP ratio). Consistent with the results above, productivity
dispersion is considerably higher in high-tech sectors, followed by services and
manufacturing. TFP dispersion tends to be larger in sectors where firms have a
higher intangible-to-tangible capital ratio, and are less exposed to
international competition, with both effects stronger in Asia than in the rest
of the world (Table 1). One potential explanation for those results is that
higher digitalization provides larger benefits for firms that are already highly
productive, leading to an increase in TFP dispersion. In contrast, exposure to
international markets might force unproductive firms out of the market,
decreasing the TFP dispersion. In both cases, however, we would see an increase
in average productivity, as predicted by our results in the previous section.

Table 1.

Regression of 90/10 TFP Ratio (by country-sector-year) on Sector Characteristics

Sources: Orbis; Zephyr; and authors’ calculations. Note: All specifications
include country and year fixed effects. Standard errors are shown in parenthesis
and clustered at the country-sector (4-digit) level. *, ** and *** indicate that
coefficients are statistically different from 0 at the 10%, 5%, and 1% levels,
respectively. ihs represents the inverse hyperbolic sine function,
ihs(x)=ln(x+√1+x2). A&P = Asia and Pacific; RoW = rest of the world. View Table
Table 1.

Regression of 90/10 TFP Ratio (by country-sector-year) on Sector Characteristics

(1)

A&P (2)

A&P (3)

RoW (4)

RoW Services 0.5552***

(0.0958) 0.5298***

(0.0959) 0.0024

(0.0902) 0.0769

(0.0953) Manufacture –0.5424***

(0.0719) –0.5545***

(0.0730) –1.1494***

(0.0765) –0.9813***

(0.0842) High-tech 0.9193***

(0.1274) 0.8176***

(0.1291) 1.5476***

(0.1326) 1.6216***

(0.1342) Invests R&D –0.1181

(0.3289) 0.0428

(0.4990) Digitalization

(ihs[Intangible/Tangible K]) 0.1246***

(0.0321) –0.0694**

(0.0295) International Exposure –1.4188

(1.2702) –0.8006***

(0.1785) Observations 25,919 25,875 53,480 53,480 Within R2 0.1846 0.1897 0.1795
0.1839

Productivity dispersion has increased over time. Plotting the 90/10 TFP ratio
over time highlights the fact that productivity dispersion in Asia, despite
being lower than in our sample of advanced economies, has increased in recent
years (Figure 21). In addition, this increase has been much more pronounced in
high-tech sectors, compounding on its already higher levels of dispersion
(Figure 22). Given that large dispersion in firm-level productivity can hold
back aggregate productivity, it is important to understand what characteristics
are associated with the firms at the bottom of the TFP distribution. We discuss
this in the next chapter.

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Figure 21.

TFP Dispersion over Time

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.
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Figure 21.

TFP Dispersion over Time

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.
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Figure 22.

TFP Dispersion by Sector

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.Note: Includes only Asia and Pacific
countries.
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Figure 22.

TFP Dispersion by Sector

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.Note: Includes only Asia and Pacific
countries.
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WHO ARE THE LAGGARD FIRMS HOLDING BACK AGGREGATE PRODUCTIVITY?

Understanding the characteristics of “laggard” firms can help shed light on the
impediments to firm growth and productivity in frontier and emerging Asia.
Following OECD (2020), this paper defines laggard firms as those in the bottom
40 percent of the productivity distribution within each country-year-sector. To
understand the characteristics that are most associated with laggard firms, we
estimate a linear probability model, where the dependent variable is a
firm-level indicator for whether each firm is classified as a laggard in any
given year. This allows us to determine the extent to which different features
affect the likelihood that a firm is classified as a laggard. We discuss our
findings below.

Laggard firms tend to be smaller and older. Our empirical results highlight that
laggard firms tend to be small and old, both in Asia and in the rest of the
world (Table 2). Plotting the average size (number of employees) and age of
firms in each percentile of the relative productivity distribution shows that
productivity and size are closely linked, but productivity and age have a
nonlinear relationship (Figure 23). Very young firms tend to be financially
constrained and often fail, as they are unable to realize productivity growth
(Haltiwanger and others 2017). However, the relationship between age and
productivity quickly peaks, and we see a negative correlation between the two
variables outside of the bottom quintile of the TFP distribution: as firms age,
they can become less innovative, which can lead to them eventually being
replaced by younger competitors (Akcigit and Kerr 2018).

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Figure 23.

Firm Size and Age by Relative Productivity

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.
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Figure 23.

Firm Size and Age by Relative Productivity

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: Orbis; and authors’ calculations.
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C. CLOSING PRODUCTIVITY GAPS

WHAT DRIVES INNOVATION (BY DISCOVERY) AT THE FRONTIER?

Innovation by discovery is a matter of selected few. Only a small share of firms
registers any R&D expenses across all years. In fact, only about 1 percent of
firms have positive R&D expenses in the sample of countries.28 Given the
relevance of R&D to productivity growth, it is worthwhile investigating which
firms invest in R&D and the drivers of such investments. We follow a similar
empirical approach as above, estimating a linear probability model in which the
dependent variable is a firm-level indicator that equals one if a firm has
registered positive R&D expenses in at least one year during our sample.

Table 2.

Regression of Laggard Indicator on Firm Characteristics

Source: Orbis; Zephyr; and authors’ calculations. Note: All specifications
include a sector fixed effect and a country-by-year fixed effect. Standard
errors are shown in parenthesis and clustered at the country-sector (4-digit)
level. *, ** and *** indicate that coefficients are statistically different from
0 at the 10%, 5%, and 1% levels, respectively. ihs represents the inverse
hyperbolic sine function, ihs(x)=ln(x+√1+x2). A&P = Asia and Pacific; RoW = rest
of world. View Table
Table 2.

Regression of Laggard Indicator on Firm Characteristics

(1)

A&P (2)

A&P (3)

RoW (4)

RoW ln(Employment) –0.0212***

(0.0047) –0.0112**

(0.0053) –0.0209***

(0.0035) –0.0168***

(0.0035) Age 0.0042***

(0.0005) 0.0050***

(0.0006) 0.0015***

(0.0003) 0.0012***

(0.0003) ln(Employment) X Age –0.0004***

(0.0001) –0.0005***

(0.0001) –0.0003***

(0.0001) –0.0002***

(0.0001) International Exposure –0.0334***

(0.0106) –0.0481***

(0.0053) R&D investment

(ihs[R&D Expense/L]) –0.0168***

(0.0012) –0.0084***

(0.0009) Digitalization

(ihs[Intangible/Tangible K]) –0.0221***

(0.0015) –0.0095***

(0.0006) Number of Observations 7,245,791 6,595,033 12,212,401 12,157,864 Within
R2 0.0077 0.0184 0.0039 0.0080

Firms that invest in R&D tend to be larger and pay higher wages. The estimates
show a positive correlation between the probability of investing in R&D and
employment as well as wages in a firm (Appendix Table 1.3). This suggests that
firms with a large and qualified workforce are more likely to invest in R&D,
which is not surprising. It also suggests that one important bottleneck for
firms to invest in innovation is the ability to attract qualified workers into
their ranks, as suggested by Van Reenen (2021). We also note that this
relationship is robust to the inclusion of gross profits, equity, and debt (as
proxies for financial or cash constraints) for each firm in the regression, as
well as the inclusion of our direct measure of TFP.

R&D-intensive firms also tend to have higher capital intensity, be more
digitalized, and are more likely to operate in international markets. This
result corroborates the fact that R&D tend to be more prevalent in high-tech
sectors, highlighting the close association between innovation and
digitalization. R&D-intensive firms are more likely to be exposed to
international markets, either through exports or through FDI. This association
could happen through many channels. These include: (1) selection, as high
productivity firms self-select into expanding their market to other countries;
(2) learning/technology transfer from other firms, for example through FDI,
partnerships, or by participating in a larger production chain; and (3)
competition from foreign companies, which might push firms to innovate in order
to move ahead of their competitors (escape competition).29

Tax incentives and macroeconomic stability can encourage innovative investment.
The literature points to the effectiveness of government support measures in
stimulating private innovative investment. In their survey of literature, Hall
and Van Reenen (2000) and Becker (2015) find that R&D tax credits have a
positive and significant effect on R&D expenditure.30 Akcigit and others (2018)
argue that such policies in the United States were an effective response to
foreign competition, leading to much higher welfare gains than the introduction
of tariffs would. In addition, R&D tax credits can be used as an incentive for
inventors and firms to locate in the same places, benefitting from agglomeration
spillovers and increasing aggregate innovation (Sollaci 2022). An empirical
analysis using Australian firm-level data finds heterogenous effects of tax
incentives across firm groups (Box 2).31 Specifically, tax incentives tend to
have higher stimulative effects on innovative investment for smaller firms and
those in the manufacturing sector. Recent studies have also highlighted that
having well-designed R&D tax incentives is important to benefit from their
positive effects (Guceri and Liu 2019, Chen and others 2020).32 In contrast,
macroeconomic uncertainty tends to weigh on innovative investment, particularly
for fast-growing companies.

Box 2.

Firm-Level Determinants of Intangible Investment: Evidence from Australia

This box uses Australian firm-level data to shed light on the heterogenous
impact of uncertainty and government tax incentives on intangible investment of
different firm groups. The Australian government has offered R&D tax incentives
since 1985, with a major change of the scheme in 2011 (Bakhtiari and Breunig
2018). A number of changes in the R&D tax incentives were also introduced in
2021, which include the increase in R&D expenditure ceilings. In this box, we
employ an R&D investment model similar to Bloom (2007) and augment it with the
R&D tax incentive, interacted with firm characteristics. The model can be
written as follows:

ITAi,t = αi + αt + β1ΔSalesi,t + β2σi,t + β3σi,t * ΔSalesi,t + β4ITAi,t-1 +
β5σi,t * ITAi,t-1

+ β6ExternalFinancei,t * Incentivett-1 + β7Manufacturingi,t * Incentivet-1 +
β8Smalli,t

* Incentivet-1 + β9High Future Growthi,t * Incentivet-1 + εi,t(X)

where ITAi,t denotes the growth rate of intangible capital for firm i at time t,
ΔSalesi,t denotes the growth rate of sales, σi,t denotes firm-level uncertainty
proxied by the volatility in weekly stock returns of the firm (annualized). In
addition, the model incorporates lagged government tax incentives as a share of
GDP Incentivet-1, interacted with various dummy variables capturing firm
characteristics. ExternalFinancei,t dummy takes value 1 if firms have higher
external finance dependence (above median), and Manufacturingi,t and Smalli,t
are dummy variables for the manufacturing sector and smaller firms (asset size
below 25th percentile of the sample). High future growth firms (High Future
Growthi,t) are proxied with firms with higher-than-median Tobin’s Q. We employ
annual Australian firm level data obtained from IMF Corporate Vulnerability Unit
Database, which is based on the Thomson Reuters Worldscope database. Data are
from 2001 to 2018 and include the nonfinancial sector.

The results point to positive impacts of tax incentives, with some heterogeneity
across firm groups. The firm-level regression suggests that the effects of tax
incentives depend on firm size, sectors, financing structures, and viability
(Box Table 2.1). In particular, when aggregate tax incentives are higher, these
tend to benefit smaller firms who increase intangible capital by a larger
amount. This result is consistent with the existing literature, such as Lach
(2002), OECD (2020), and Bakhtiari (2021), which finds that subsidies for small
firms have a strong stimulative effect after the first year of subsidies. Hall,
Lotti, and Mairesse (2009) argue that SMEs that have not conducted R&D before
are

Box Table 2.1.

Determinants of Firm-Level Intangible Investments in Australia

Source: IMF staff estimates. Note: Data are from IMF CVU firm database. Reports
results for estimates of the equation described in the box and its variants for
Australian firms. R&D tax incentives are in percent of GDP. High External
Finance Dependence is a dummy variable for firms with higher external finance
dependence (measured as Rajan-Zingales finance dependence index), Manufacturing
is a dummy variable for manufacturing firms, Small is a dummy variable for
smaller firms (sales size below 25 percentile of the sample), and High Expected
Growth is a dummy variable for firms with higher expectations for future growth
(Tobin’s Q above median of the samples). The regression controls for the lagged
dependent variable. Some outliers of dependent variables and independent
variables are excluded. Standard errors are clustered at firm level. *, **, and
*** indicate significance at the 10, 5 and 1 percent level, respectively. View
Table
Box Table 2.1.

Determinants of Firm-Level Intangible Investments in Australia

(1) (2) (3) (4) (5) Dependent Variable: Growth Rate of Intangible Capital Sales
Growth .2464***

(.0609) .2486***

(.0610) .2510***

(.0612) .2509***

(.0611) .2497***

(.0612) Uncertainty –0.0251

(.0516) –0.0333

(.05067) –0.0230

(.05072) –0.0360

(.05094) –0.0186

(.0514) Sales Growth* Uncertainty –.3318***

(.1071) –.3347***

(.1071) –.3354***

(.1076) –.3332***

(.1075) –.3358***

(.1076) Uncertainty* Lagged Dependent Variable 1.5171***

(.0655) 1.5174***

(.0653) 1.5183***

(.0653) 1.5212***

(.0654) 1.5173***

(.0653) High Ext. Finance Dep.* RD tax incentives (–1) .3279***

(.1094) .3267***

(.1097) Manufacturing* RD tax incentives (–1) 1.1048*

(.6241) 1.1420*

(.6479) Small* RD tax incentives (–1) 1.0199***

(.4211) 1.1134***

(.4310) High Exp. Growth* RD tax incentives (–1) 0.2529***

(.1221) 0.2823***

(.1245) Firm Fixed Effects Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes
Yes Yes R2 0.7597 0.7614 0.7588 0.7606 0.7623 Sample Period 2001–18 2001–18
2001–18 2001–18 2001–18 Number of Observations 4,006 4,006 4,006 4,006 4,006

more likely to start investing in R&D if they receive a subsidy. Quantitatively,
our results suggest that the positive impact of increasing tax incentives by 0.1
percentage point of GDP (nearly doubling) on the growth of intangible capital
next year is about 10.2 percentage points stronger for SMEs. The results also
suggest that industry type and financing structures play a role, with the
manufacturing sector and firms more dependent on external financing seeing a
larger increase in intangible capital when aggregate incentives increase. In
addition, firms with higher expectations for growth (proxied by higher Tobin’s
Q) tend to increase intangible investment more in response to government tax
incentives than less viable firms.

In addition, the results highlight some effects of uncertainty on intangible
investment. As Bloom (2007) suggests, uncertainty tends to make intangible
investment less responsive to changes in business situations and makes firms
reluctant to change their investment plans, leading to more persistent
intangible investment.

WHAT DRIVES ADOPTION (INNOVATION BY DIFFUSION) IN DEVELOPING AND LOW-INCOME
ASIA?

As in advanced and emerging Asia, R&D investment is a strong predictor of the
likelihood of innovating in developing Asia (Figure 24, Appendix Table 2.2).
While R&D aims at the development of new products, or innovation by discovery,
innovation can also occur via the adoption of existing processes or
technologies. This distinction is particularly relevant for developing Asia,
where not all firms may have the capital, adequate access to financing or skills
to introduce products which are new to their reference markets. In fact,
technological diffusion via the adoption of existing technology (or licensing
from foreign firms) may be a more cost-effective path to the improvement of
productivity levels, especially for financially constrained SMEs (World Bank
2021b).

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Figure 24.

Characteristics of Innovators in Emerging Market Economies and Developing
Countries

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: WBES, 2006–2020.Note: Regressions results based on a linear probability
model, with innovation as a dependent variable. Additional controls include
country and year fixed effects.*p<.05, ** p<.01, *** p<.001.
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Figure 24.

Characteristics of Innovators in Emerging Market Economies and Developing
Countries

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Both product and process innovation are more likely to occur in larger firms,
particularly those located in capital cities (Figure 25; Appendix Table 2.2).
Geographic concentration of innovative activity is a feature of developing Asia,
with high degrees of spatial clustering of startups and venture capital
investment.33 Firms located in cities are also more likely to benefit from
agglomeration effects, including positive spillovers such as technological
diffusion by proximity and imitation. This implies that despite a level of
technological achievement in major cities that might rival that of higher-income
countries, low levels of technological advancement in lagging areas mean that,
in aggregate, emerging market economies and developing countries in Asia are not
as technologically advanced as high-income economies in Asia or elsewhere in the
world.

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Figure 25.

Share of High-Productivity Firms

(Proportion of firms in the region)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: WBES, 2006–20.Note: High-productivity firms are defined as those in the
top decile of the country-year productivity distribution.
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Figure 25.

Share of High-Productivity Firms

(Proportion of firms in the region)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: WBES, 2006–20.Note: High-productivity firms are defined as those in the
top decile of the country-year productivity distribution.
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Innovation in emerging and developing Asia is associated with higher trade
integration and participation in GVCs (Figure 24, Appendix Table 2.2). Firms
more integrated in GVCs, proxied by the value of imports and exports as a share
of annual sales, are also more likely introduce product and process innovation.
As in advanced economies, greater exposure to competition from abroad and
dynamics of agglomeration and diffusion originating domestically may be
important drivers of productivity growth in emerging and developing Asia (Amiti
and Konings 2007; Goldberg and others 2010; Aghion and others 2018, 2019; Coelli
and others 2022).

Inadequate access to financing opportunities, lack of a skilled workforce, and
competition from the informal sector are frequently cited as the strongest
impediments to growth by firms that do not innovate (Figure 26). Many firms also
report that high tax rates are an obstacle to their business operations.34 While
the survey questions do not elaborate on the channels underlying these
obstacles, it is possible to infer that a lack of financing opportunity and high
tax rates may reduce resources available for exploring opportunities to grow and
innovate. Self-reported constraints, or firms’ subjective perceptions of
impediments to growth are a good meter to identify areas of potential policy
intervention.

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Figure 26.

Innovation: Obstacles in Developing Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Sources: WBES, 2006–20.Note: Regressions results based on linear probability
model; innovation as a dependent variable. Additional controls include country
and year fixed effects, firm age, sector, size, R&D expenditure, ownership
status, and GVC participation.*p<.05, ** p<.01, *** p<.001
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Figure 26.

Innovation: Obstacles in Developing Asia

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

CLOSING PRODUCTIVITY GAPS: WHICH FACTORS MATTER WITHIN COUNTRIES AND SECTORS?

Assessing the factors that impact TFP growth at different points of the firm
productivity distribution can help identify policies for closing productivity
gaps. As illustrated above, productivity dispersion across firms within our
sample is large, suggesting potential resource misallocation (Hsieh and Klenow
2009). As a result, we examine how firm-specific characteristics, including a
firm’s distance to the technology frontier (Aghion and Howitt 2006; Acemoglu,
Aghion, and Zilibotti 2006), can affect its productivity growth each year. The
key intuition is that firms that are farther away from the global technological
frontier tend to grow mainly through technology adoption and imitation, whereas
firms closer to the frontier rely more on innovation. Therefore, the set of
policies aimed at sustaining productivity growth across firms, industries, and
countries could vary depending on their locations vis-à-vis their technological
frontiers. To capture those heterogeneous effects, we split firms into groups
based on their position in their country-sector-year-specific distribution of
TFP and allow all coefficients to vary by group. Frontier firms are defined as
those in the top decile of the TFP distribution of their sector, while
non-frontier firms are split into 3 subgroups: top (90th–60th percentiles),
middle (60th–30th percentiles), and bottom (below the 30th percentile).35

Firms tend to benefit from productivity spillovers from their peers at the
frontier, and there is some evidence of convergence. TFP growth across firms is
spurred by developments at the technological frontier (captured by the positive
coefficient of TFP growth at the frontier), suggestive of significant
productivity-enhancing knowledge spillovers from the technological leaders
(Table 3).36 Spillovers seem to be strongest for the top (non-frontier) firms,
indicating that these firms are better positioned to take advantage of
innovation and growth at the frontier. Furthermore, we find evidence that
productivity growth across firms is driven by a catching-up process associated
with the gradual adoption of newer technologies. In particular, the pace of
convergence of non-frontier firms increases with the distance to the
technological frontier (measured by the positive coefficient of the TFP gap).
However, this effect is nonlinear: as firms grow farther from the technological
frontier, they also become more likely to lack the capacity to effectively adopt
new technologies created by firms in the frontier. At this point, increasing the
TFP also decreases productivity growth, as captured by the negative coefficient
on the TFP gap squared. Indeed, this is how the data can simultaneously support
increasing TFP dispersion (Figure 21) and catching-up of non-frontier firms.
Note that this non-linearity is particularly pronounced in non-Asian countries.

Digitalization and international competition foster TFP growth, but largely for
non-frontier firms at the top group. Our results show that an increase in
digitalization (intangible capital) is associated with higher productivity
growth, but the impact is largest for non-frontier firms that are closer the
technology frontier (that is, top group, followed by middle group, and finally
no discernible effect for the bottom group; see Table 3).37 Looking at how
exposure to international markets affects productivity growth, we find a
positive effect for non-frontier firms in the top group, but negative effects
for firms in the middle and bottom groups of the country-specific firm
productivity distribution. This result again corroborates the idea that firms in
the bottom of the productivity distribution are somehow worse at learning from
more productive firms or adapting to tougher competition, compared to firms at
the top. It also strongly suggests that policies that foster greater
international integration might have radically different effects on firms
belonging to different groups.

Table 3.

Distance to the Frontier and Firm Productivity Distribution

Sources: Orbis; Zephyr; and authors’ calculations. Note: *, ** and *** indicate
that coeicients are statistically different from 0 at the 10%, 5%, and 1%
levels, respectively. All regressions include a firm fixed effect and a
country-by-year fixed effect. Standard errors are clustered the country-sector
(4-digit) level. View Table
Table 3.

Distance to the Frontier and Firm Productivity Distribution

Variable Firm Group Asia and Pacific Rest of World (1) (2) (3) (1) (2) (3) TFP
growth rate at frontier Top *** *** *** *** *** *** Middle *** *** *** *** ***
*** Bottom *** *** *** *** *** *** Gap relative to frontier Top *** *** *** ***
*** *** Middle *** *** *** *** *** *** Bottom *** *** *** *** *** *** Gap
relative to frontier - squared Top *** *** *** Middle *** *** ** Bottom *** ** *
International exposure Top *** *** *** *** Middle *** *** *** *** Bottom *** ***
*** *** Intangible/tangible capital ratio Top *** *** *** *** Middle *** *** ***
Bottom *** ** Sectoral std. deviation: log-TFP Top *** *** Middle *** *** Bottom
*** *** Number of observations 7,556,396 6,939,968 6,900,854
14,448,480 14,407,254 14,401,055 R2 0.2 0.1992 0.2418 0.2 0.2005 0.2351
Positive Zero Negative
Not included in specification

Lower resource misallocation is beneficial for firms of all types. Exploring how
productivity growth at the firm level might be affected by the sectoral
productivity dispersion yields stark results: increasing the standard-deviation
of log-productivity by 0.01 (equivalent to a 3.7 percent increase in
log-standard deviation for the median sector38) would reduce the average firm’s
TFP growth by 1.5 to 2.1 percentage points (see Appendix Table 1.3). Following
Hsieh and Klenow (2009), we interpret this productivity dispersion as a measure
of resource misallocation.39 As such, this result suggests that firms in sectors
with higher resource misallocation grow considerably more slowly, potentially
because capital and/or labor are locked up in unproductive firms. In addition,
note that this effect is more pronounced for non-frontier firms in the middle
and bottom groups, suggesting that small (and possibly young) firms are less
able to adapt to the market distortions that generate resource misallocation.
Finally, we caveat our findings by noting that there are other potential sources
of productivity dispersion, such as firm-specific shocks and varying degrees of
market power across firms. However, the patterns we see in the data are
decidedly consistent with the misallocation interpretation.

D. CONCLUSIONS AND KEY TAKEAWAYS

Using firm-level data from a broad spectrum of Asian economies, the paper has
identified some of the mechanisms linking innovation, digitalization, and
productivity in the region. The following are their key findings:

* Asian firms that are more innovative tend to be more productive: this result
holds at all levels of development, and sectors and controlling for various
firm characteristics. Preliminary evidence indicates, however, that firms do
not need to be at the technological frontier to benefit from innovation: the
adoption of existing technologies is often sufficient to increase
productivity levels, at least for firms operating in emerging and developing
Asia.

* The productivity distribution within countries in Asia is often bimodal, with
a few top performers coexisting with a much larger share of laggard firms.
Laggard firms tend to share a few characteristics: they are smaller, older,
and less likely to participate in international trade. They are also less
likely to invest in research and development and to digitalize their
activities.

* Innovation is highly concentrated in a narrow subset of firms: across all
levels of economic development, innovation tends to be a prerogative of
larger and more capital-intensive firms, which invest in R&D and have strong
links with foreign markets through international trade. A number of key areas
for policy intervention are identified. Improving access to financing
opportunities and increasing educational attainment emerge as keys to foster
innovation in the region. To make the most of existing and emerging
technologies, it will be important for firms in emerging and developing Asia
to continue strengthening their innovation capabilities—first by upgrading
their processes using digital technologies, and then by adopting more
sophisticated technologies. The dividends from doing so, in terms of
productivity gains, can be large.

* Significant productivity-enhancing spillovers accrue from the technological
leaders (frontier firms) in Asia and benefit most firms that are positioned
at the next level (top non-frontier firms). In the same vein, the impact of
digitalization on productivity growth is higher for non-frontier firms closer
to the technology frontier. However, laggard firms in Asia appear to be
falling further behind. This highlights the importance of participation in
international trade, including GVCs, and strengthening supplier linkages to
facilitate technology diffusion. In addition, policies should focus on
facilitating greater firm entry and exit, capitalizing on the potential of
new firms while reducing the presence of stagnant and unproductive (“zombie”)
firms in the economy.

4. SUPPORTING PRODUCTIVITY GROWTH WITH INNOVATION AND DIGITALIZATION

Innovation and digitalization will be paramount to promote post-pandemic durable
growth in Asia. Building on the findings above, this chapter provides a
comprehensive policy toolkit to help policymakers achieve this goal, taking into
consideration country circumstances and firms’ positions in the innovation and
productivity distribution. Many countries in Asia have already introduced policy
measures to revive productivity growth and avoid scarring from the pandemic, but
more can be done.

Post-pandemic recovery offers an opportunity to boost productivity. Although
Asian frontier economies have become a global powerhouse of innovation, there is
scope to further push the technology frontier and improve the quality of
innovation. In non-frontier economies, innovation is still constrained by
institutional and infrastructure bottlenecks. Furthermore, cross-border and
within-country innovation spillovers have slowed, calling for increased efforts
to speed-up technology diffusion. Policy actions are also needed to accelerate
digitalization and address digital inequalities in the wake of the pandemic.
Countries and firms at different stages of innovation ladder require customized
policies to foster innovation and promote resource reallocation (Figure 27).
This chapter aims to provide a comprehensive toolkit for policy makers.

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Figure 27.

Policy Priorities to Promote Innovation and Digitalization

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: ???.
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Figure 27.

Policy Priorities to Promote Innovation and Digitalization

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: ???.
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A. OVERARCHING POLICY PRIORITIES

Reforms should center on regulatory reforms that promote competition,
innovation, and needed digitalization after the pandemic. A large body of
literature suggest that product market deregulation would promote innovation and
boost overall growth potential of the economy (for example, IMF 2016 and
references therein). It would promote competition and more efficient allocation
of resources, thereby reducing misallocation in Asian economies identified in
the previous chapters. Product market regulations in many Asian economies are
more restrictive compared to international best practices (Figure 28),
particularly in the area of state involvement and barriers to entry, suggesting
scope for improvement. In addition, regulations in the upstream network sectors
tend to be restrictive, including in e-communications, which could be an
impediment for further digitalization in Asia (Figure 29). Reducing restrictions
in the upstream sectors could boost productivity in highly dependent downstream
sectors.

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Figure 28.

Product Market Regulation

(0 to 5, 1 is most restrictive)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 28.

Product Market Regulation

(0 to 5, 1 is most restrictive)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 29.

Network Sector Regulations

(0 to 5, 1 is most restrictive)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 29.

Network Sector Regulations

(0 to 5, 1 is most restrictive)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Post-pandemic recovery offers a tremendous opportunity for further
digitalization. As highlighted in Chapter 2, the COVID-19 pandemic has provided
a unique opportunity for digital innovation and many policymakers in Asia are
taking actions to accelerate digitalization (Box 1). To fully reap the benefits
of digitalization, policymakers need to facilitate firms’ adoption of digital
technology by reducing regulation, modifying supervision in line with the
evolving digital industry, and facilitating digital trade (Figure 30). Private
sector digitalization should be matched by a similar drive in the public sector,
where Asia still lags behind OECD countries in GovTech.40

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Figure 30.

Digital Service Trade Restrictiveness Index

(0 to 1, 1 is most restrictive)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 30.

Digital Service Trade Restrictiveness Index

(0 to 1, 1 is most restrictive)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Closing large infrastructure gaps in Asian developing countries will be
paramount to support digitalization over the long term. In developing Asia,
large infrastructure gaps remain in areas such as energy, transport, and
telecommunications, and additional spending on digital infrastructure would be
required to accelerate digitalization. Filling the infrastructure gaps,
particularly in digital infrastructure, would also enhance information flows and
facilitate technology diffusion and adoption by the private sector. Every 10
percent increase in broadband penetration increases GDP in developing countries
by 1.4 percent and doubling broadband speed leads to 0.3 percent increase in per
capita GDP growth (AIIB 2020).

B. PUSHING THE FRONTIER OF INNOVATION AND DIGITALIZATION

POLICIES TO FOSTER PRODUCTION OF INNOVATION

Tax incentives and well-targeted grants can stimulate innovative investment in
the post-COVID recovery. In Asia, R&D spending declined since the onset of the
pandemic and has not recovered to the pre-pandemic trend in many countries.
Fiscal incentives targeted at R&D activities, such as R&D tax credits and
allowances, could be used to stimulate innovation by increasing the return to
R&D. Numerous studies have confirmed the effectiveness of R&D tax incentives in
boosting R&D investment and its qualities (Bloom, Van Reenen, and Williams 2019;
Akcigit and others 2018; Sollaci 2022). Asian economies could consider
increasing the generosity of tax credits to boost innovative investment by firms
in the post-pandemic recovery phase, in an effort to limit scarring. In doing
so, the targeting and design of schemes will be critical in maximizing the
effectiveness of tax credits, and careful cost-benefit analysis is warranted.
Well-balanced intellectual property rights (neither too restrictive nor too
loose), that reward disruptive innovations more than incremental improvements
would also support cutting-edge innovation at the frontier.

Government investment would also promote innovation, including in basic
research. Recent literature suggests that government R&D would stimulate, rather
than crowd out, private R&D (Becker 2015). Government can also play a pivotal
role in basic research given its positive externality and longer development
cycle. Recent studies point to a declining trend of public funding for basic
research in advanced economies, which may have contributed to the global
productivity growth slowdown (IMF 2021b). Asian economies, though becoming
increasingly important as a source of basic knowledge, could still improve
compared to top performers. To push the technology frontier, government spending
for research institutes could be scaled up, together with grants and subsidies
targeting basic research and firm-academia cooperation (Figures 31 and 32).

POLICIES TO FACILITATE EXPERIMENTATION AND BRING INNOVATION TO MARKETS

Access to finance by new, small, and digital firms needs to improve. Small and
young firms can play a pivotal role in innovation as the literature shows that
large firms tend to focus more on improving existing innovations, while small
firms tend to contribute to more radical innovations (Akcigit and Kerr 2018).
Cross-country data suggest that loan interest rate spreads between SMEs and
large firms in Asian countries are relatively wide compared to other countries
(Figure 33). Alleviating financing constraints faced by SMEs and young
innovative firms can help productive firms grow and adopt new technologies.
Measures aimed at improving matching between businesses and investors and
enhancing financial literacy among SMEs through training can help in this regard
(OECD 2018). To facilitate market-based financing, developing a deep and
diversified capital market to provide various financial instruments to SMEs and
new entrants is key. Government R&D loan or credit guarantee schemes, adopted in
some Asian countries like Indonesia, Malaysia, and New Zealand, could alleviate
financing constraints by addressing the lack of collateral.

Venture capital (VC) could become an important funding source for startups and
innovative firms. VC is specialized in addressing the asymmetric information
issue for new and intangible-intensive firms. Evidence from the United States
suggest that the overall efficiency of VC-backed firms is higher than
non-VC-backed firms and the difference arises from both screening and monitoring
effects that VC can bring (Chemmanur, Krishnan, and Nandy 2011). Corrado and
others (2021) suggest that early-stage VC helps lower productivity dispersion by
facilitating knowledge diffusion and helping new firms to catch up. Scope
remains for Asian countries to further deepen VC markets, both for early and
later stage investment (Figure 34). VC market can be expended, for example, by
introducing government-sponsored funds or co-investment funds and removing
potential barriers to investment, which could improve young firms’ access to
finance while promoting productivity growth.

POLICIES TO FACILITATE TECHNOLOGY DIFFUSION

Participation in international trade and the global value chain accelerates
technology diffusion and adoption. There is room to promote participation in
global trade, including GVC, for some Asian countries (Figure 35, panel 1).
Policy options include reducing tariff and nontariff trade barriers,
facilitating access to trade finance, and investing in international logistics
infrastructure.41 These policies could also help promote cross border
e-commerce, further boosting technology diffusion and adoption. At the same
time, a proper regulatory framework should be implemented to avoid excessive
market power to large domestic and foreign corporations. This includes enforcing
merger controls in product markets, as well as curtailing market power in labor
markets. In addition, given the fast pace of the digital economy, authorities
should consider making use of interim measures (imposed before a final decision
is reached) and developing specific expertise by building digital economy units
(IMF 2021a).

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Figure 31.

Implied R&D Tax Subsidy Rates

(Percent)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 31.

Implied R&D Tax Subsidy Rates

(Percent)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 32.

R&D Tax Support

(Percent of business enterprises R&D)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 32.

R&D Tax Support

(Percent of business enterprises R&D)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 33.

Loan Interest Rate Spread between Large and SMEs

(Percentage points)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 33.

Loan Interest Rate Spread between Large and SMEs

(Percentage points)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 34.

Venture Capital Investment

(Venture capital as percent of GDP, 2019 or latest available year)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 34.

Venture Capital Investment

(Venture capital as percent of GDP, 2019 or latest available year)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 35.

GVC Participation and FDI Flows in Asia and Select Economies

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: Asia Development Bank.Sources: OECD; UNCTAD; and IMF staff
calculations.Note: In panel 2, the aggregate values presented are simple
averages.
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Figure 35.

GVC Participation and FDI Flows in Asia and Select Economies

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Figure 35.

GVC Participation and FDI Flows in Asia and Select Economies

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Streamlining FDI-related regulations could also support entry of foreign firms
and enhance knowledge transfers and productivity growth, including in services.
The literature finds that liberalizing FDI-related restrictions would boost FDI,
bringing positive spillover effects and promoting competition (Javorcik 2004;
Haskel, Pereira, and Slaughter 2007; and Mistura and Roulet 2019). Relative to
their economic size, FDI inflows in many developing Asian countries are smaller
than peers (Figure 35, panel 2). For these countries, regulatory barriers to FDI
tend to be high, with relatively stringent restrictions on services, suggesting
scope for further deregulation (Figure 35, panels 3 and 4). In particular,
greater FDI in the service sectors would offer opportunities for laggard firms
in Asia to catch up with industry leaders (Fernandes and Paunov 2012).
Facilitating cooperation between foreign and local firms, for instance by
developing a network of providers, would also support knowledge transfer.

POLICIES TO DEVELOP FIRMS’ ABSORPTIVE CAPACITY

Strengthened collaboration among firms, academia, and government could help
reduce the costs of searching for external technology. Bloom and others (2011)
argue that informational barriers are the primary factor explaining the lack of
technology adoption. An open and collaborative innovation network, consisting of
firms, academia, and relevant government agencies, could help firms in the
middle and bottom quickly obtain information of new technologies and adopt them.
Such a collaboration network could take various forms, including
industry-academia collaborative projects, government consulting services to
small and new businesses, national or international product expositions, and
digital platforms. Economies of agglomeration could also be explored,
particularly in the knowledge intensive high-tech industries, to facilitate
knowledge diffusion among firms in the same industry and generate synergy
effects. Enhancing the legal environment, including legislations on data
protection and cybercrime together with effective enforcement mechanisms, will
also help lower barriers to information sharing and support technological
adoption.

Broadening and deepening the skill base will allow better exploitation of new
technology. The literature has long demonstrated the importance of a
well-educated workforce for firms to absorb new technology (Van Reenen 2021).
The paper’s analysis finds that the education level of the workforce is
positively related with firms’ productivity. Without adequate supply of
qualified human capital, firms would be unable to exploit new technologies.
However, many firms in developing Asia are reporting difficulties in hiring
workers with adequate skills, especially foreign language, managerial, and IT
skills (World Bank 2021a). Policymakers should assess the skill sets most needed
for their countries to boost innovation and digitalization in the post-pandemic
phase and formulate a holistic human capital development strategy accordingly.

Improving management practices and digital skills of laggard firms can play a
pivotal role in promoting long-term growth. As discussed in Chapter 2, a large
portion of Asian firms, especially in developing countries, have weaker
management qualities. There is significant room for improving management
practices of less efficient firms, for example by providing training, thereby
boosting overall productivity performance. Promoting uptake of new digital
technologies by less productive firms, including firms in services, and
supporting training of digital skills would help improve their productivity.

C. FACILITATING REALLOCATION OF RESOURCES AND PREPARING THE NEXT GENERATION

POLICIES TO ENCOURAGE EFFICIENT REALLOCATION OF RESOURCES

Healthy competition and strong firm dynamism could facilitate needed resource
reallocation after the pandemic and limit scarring effects of the pandemic.
Chapter 3 identified a large dispersion of productivity among Asian firms and
identifies laggard firms, which are often small and old. Strong firm dynamism
helps resource allocation through creative destruction, allowing inefficient
firms to exit and young innovative firms to enter (Aghion and Howitt 1992). In
Asian countries, market concentration, as measured by markups, appears to have
been increasing in recent years (Figure 36), which could be an impediment for
growth as it may reflect barriers to entry, lower investment, and weaker
innovation. In addition, despite the pandemic’s large economic impacts, firm
exit remains low (Figure 37), in part due to lifeline measures deployed at the
onset of the pandemic (Vandenberg 2021). This could have adverse effects on
productivity by preserving less productive and zombie firms. Supporting exit of
such firms, for example by simplifying the insolvency framework, would reduce
misallocation by freeing up resources to be used by more productive firms.

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Figure 36.

Markup in Asia and World

(Markup)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: De Loecker and Eeckhout (2021).Note: Asia EM includes China, India,
Indonesia, Malaysia, Philippines, and Thailand. Asia AE includes Australia, Hong
Kong SAR, Japan, Korea, New Zealand, Singapore, and Taiwan Province of China.
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Figure 36.

Markup in Asia and World

(Markup)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

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Figure 37.

Exit of Firms

(Exit of firms, index, 100 in 2007)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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Figure 37.

Exit of Firms

(Exit of firms, index, 100 in 2007)

Citation: Departmental Papers 2023, 001; 10.5089/9798400224034.087.A001

Source: OECD.
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ANNEX 1. ORBIS AND ZEPHYR

DATA

The paper uses the Orbis and Zephyr databases (both maintained by Bureau van
Dijk) for extensive coverage of firm-level data. Orbis contains detailed data on
each firm’s accounting data, while Zephyr provides information on mergers &
acquisitions and FDI deals (which we use, along with export revenues, to
construct our measure of exposure to foreign markets). We closely follow Diez
and others (2021) for the data cleaning and firm-level TFP calculations in
Orbis. In the Zephyr database, we only keep completed, cross-border deals with a
single acquiror (both single and multiple target deals). When companies have
multiple deals in a single year, all of their values are summed to obtain the
total amount invested. We merge Orbis and Zephyr using unique firm identifiers,
along with each firm’s country of origin and the year of the observation.

The paper’s final data set contains more than 34 million observations on 6.4
million individual firms, between 1995 and 2018, and across 16 countries. We
split countries into two comparison groups: Asia and Pacific (A&P) and Rest of
World (RoW). Asia and Pacific countries can also be further classified into
frontier and non-frontier, based on their levels of development and production
of innovation. The table accompanying Annex Figure 1.1 details the countries
included in each category, and Annex Figure 1.1 shows our data coverage by firm
origin.

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Annex Figure 1.1.

Data Coverage

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Source: ???.
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Annex Figure 1.1.

Data Coverage

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Source: ???.
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MEASURING PRODUCTIVITY

The paper estimates productivity at the firm-level following the control
function approach proposed by Ackerberg, Caves, and Frazer (2015) but using
turnover revenue as the output measure and the cost of goods sold as the measure
of variable inputs. Specifically, we assume the following production function:

yit = β0 + βvvit + βkkit + ωit + εit

where yit it is turnover revenue, vit are variable inputs (measured by the cost
of goods sold), kit is the value of physical capital used in production (in US
dollars), and ωit it is TFP (all variables measured in logs). We assume that TFP
is an increasing function of both variable costs and capital, ωit = h(vit,kit),
so the production function becomes

yit = ϕt(vit, kit) + εit

We estimate ϕt non-parametrically in the first stage. Assuming that TFP follows
a AR(1) process ωit = ρωit-1 + ξit it, we can write our second stage equation

yit=β0+βvvit+βkKit+ρ(ˆϕt−1−βvvit−1−βkKit−1)+ξit+εit

Plugging in the first-stage estimate ϕt-1, we can estimate the parameters in
this equation using the moment condition E[εit + ξit|It-1 ] = 0 where It-1 is
the information set in year t-1. Each elasticity—and therefore TFP—is estimated
at the country-industry level, and country-industry pairs that contain less than
300 observations are dropped from the data to increase precision of the
estimates.

Note that we do not include intangible capital as an input into the production
function of firms. This is mainly driven by two features of the data. First, by
its definition, intangible capital includes brand value, some forms of
innovation (patents, trademarks), marketing and managerial expenses, among
others. All of those can influence the level and growth of productivity but are
not usually thought of as direct inputs. Second, many firms in the data have no
intangible capital at all, suggesting that the role of intangible capital in the
production process of a firm has a different nature than that of labor or
physical capital. Having said that, the decision to not include intangible
capital directly into the production function is not innocuous: if some
components of intangible capital are indeed better described as inputs to the
production function, and at the same time are correlated with the firm’s
productivity, then our estimated TFP could be overstated.

COMPARING TFP MEASURES

Multiple observations of the same firm across time are not always available in
datasets, including the World Bank Enterprise Survey (WBES), which we also
explore in this report due its better coverage of developing economies. When
using WBES data, we construct a different measure of productivity we regress
log-sales for the year on firm-level labor (log-head count), log-capital, and
fixed effects for the firm’s country, sector, and year. TFP is defined as the
residual of this regression.

This method raises concerns related to biased elasticity estimates and whether
this is an accurate representation of TFP at the firm level. We test whether
this concern might drive any of our results by computing this measure in the
Orbis data set and comparing the results with the TFP measured using the method
above. We find that, except for the extremes of the residual-TFP measure, it
correlates very strongly with the control function TFP measure, yielding more
credibility to our results concerning productivity in the WBES.

DEALING WITH ZEROES

Due to the large number of firms that do not record any expenditures on R&D, or
have no stock of intangible capital, the paper uses the inverse hyperbolic sine
function to obtain the elasticities between those variables and the outcomes of
interest. This function is defined as ihs(x)=ln(x+√1+x2) which has equals zero
when x = 0, but quickly converges to ln(2x) as x increases.

Since R&D intensity (R&D expenses/employment) tends to be on the hundreds or
thousands for most firms that invest in R&D, the difference between the ihs and
ln functions is very small. However, the intangible-to-tangible capital ratio is
frequently smaller than 1, even after conditioning on the firms have some
intangible capital. As a result, we adjust the scale of the intangible capital
ratio by a factor of k, defining a slightly modified function
ihs(x;k)=ln(kx+√1+(kx)2)−ln(k), where
wherek=mean(R&Dintensity|R&Dintensity>0)mean(intangible/tangibleK|intangible/tangibleK>0)

Annex Table 1.1.

Regression of ln(TFP) on Firm Characteristics

Sources: Orbis; Zephyr; and authors’ calculations. Note: All specifications
control for capital intensity (K/L) and average wages paid by the firm (as a
measure of human capital in the labor force). The paper also includes a firm
fixed effect and a country-by-year fixed effect. Standard errors are shown in
parenthesis and clustered at the country-sector (4-digit) level. *, ** and ***
indicate that coefficients are statistically different from 0 at the 10%, 5%,
and 1% levels, respectively. ihs represents the inverse hyperbolic sine
function, ihs(x)=ln(x+√1+x2). A&P = Asia and Pacific; RoW = rest of world. View
Table
Annex Table 1.1.

Regression of ln(TFP) on Firm Characteristics

(1)

Full Sample (2)

A&P (3)

Rest of World ihs(R&D Expense/L) 0.0033***

(0.0004) 0.0035***

(0.0003) 0.0016*

(0.0008) ihs(Intangible/Tangible K) 0.0040***

(0.0003) 0.0051***

(0.0008) 0.0033***

(0.0002) International Exposure 0.0022**

(0.0010) –0.0001

(0.0021) 0.0029***

(0.0010) Number of Observations 15,322,552 3,776,025 11,546,527 Within R2 0.0167
0.0556 0.0401

Regression of I(R&D expenses > 0) on Firm Characteristics

Sources: Orbis; Zephyr; and authors’ calculations. Note: All specifications
control for firm age, debt and equity (both measures of financial access), and
include country-by-sector fixed effects. Standard errors are shown in
parenthesis and clustered at the country-sector (4 digit) level. *, ** and ***
indicate that coefficients are statis tically different from 0 at the 10%, 5%,
and 1% levels, respectively. ihs represents the inverse hyperbolic sine
function, ihs(x)=ln(x+√1+x2). A&P = Asia and Pacific; RoW = rest of world. View
Table
Annex Table 1.2.

Regression of I(R&D expenses > 0) on Firm Characteristics

(1)

Full Sample (2)

A&P (3)

RoW International Exposure 0.0143***

(0.0020) 0.1543***

(0.0079) 0.0079***

(0.0014) ihs(Intangible/Tangible K) 0.0013***

(0.0002) 0.0067***

(0.0010) 0.0005***

(0.0001) ln(K/L) 0.0018***

(0.0002) 0.0041***

(0.0007) 0.0009***

(0.0001) ln(Wages) 0.0034***

(0.0004) 0.0171***

(0.0019) 0.0015***

(0.0002) ln(Employment) 0.0065***

(0.0006) 0.0232***

(0.0028) 0.0026***

(0.0002) Number of Observations 2,800,409 643,697 2,156,712 Within R2 0.0114
0.0505 0.0076

The empirical evidence on the distance to frontier regressions relies on the
following baseline equation:

Δln(TFPisct)=Σg∈Gl(i∈g){βg1Δln(TFPfsct)+βg2gapisct+βg3gap2isct+βg4Xisct}+δi+δct+εisct

where Δln(TFPisct) is the change in log-productivity for firm i (in sector s and
country c) between years t and t + 1; Δln(TFPsct) represents the average change
in log-productivity for firms in the frontier (defined as the firms in the top
10 percent of each sector-country-year); gapisctis the productivity gap for firm
ln(TFPsct) – ln(TFPsct); Xisct; is a collection of firm-level characteristics;
and δi and δct are firm and country-year fixed effects.

We allow for each of those variables to have a different effect on TFP growth
depending on each firm’s group g: top firms (which are between the 60th and 90th
percentiles of the sector-country-year productivity distribution), middle firms
(between the 30th and 60th percentiles), and bottom firms (below the 30th
percentile). Firm-level characteristics included in the vector Xisct are
international exposure, intangible capital ratio, the mean intangible capital
ratio in the firm’s sector, and the standard deviation of ln(TFP) in the firm’s
sector.

Annex Table 1.3.

Regression of Change ln(TFP) on Policy and Firms Characteristics

Sources: Orbis; Zephyr; and authors’ calculations. Note: All specifications
include a firm fixed effect and a country-by-year fixed effect. Standard errors
are shown in parenthesis and clustered at the country-sector (4-digit) level. *,
** and *** indicate that coefficients are statistically different from 0 at the
10%, 5%, and 1% levels, respectively. ihs represents the inverse hyperbolic sine
function, ihs(x)=ln(x+√1+x2). A&P = Asia and Pacific; RoW = rest of world. View
Table
Annex Table 1.3.

Regression of Change ln(TFP) on Policy and Firms Characteristics

Variable Group (1)

A&P (2)

A&P (3)

A&P (4)

RoW (5)

RoW (6)

RoW Δ Frontier ln(TFP) Top 0.2651***

(0.0077) 0.2687***

(0.0078) 0.1064***

(0.0051) 0.2503***

(0.0074) 0.2510***

(0.0074) 0.0959***

(0.0050) Middle 0.2386***

(0.0073) 0.2439***

(0.0078) 0.0774***

(0.0048) 0.2359***

(0.0072) 0.2355***

(0.0072) 0.0812***

(0.0044) Bottom 0.2401***

(0.0089) 0.2462***

(0.0095) 0.0761***

(0.0059) 0.2437***

(0.0078) 0.2428***

(0.0078) 0.0906***

(0.0045) ln(TFP) Gap Top 0.4185***

(0.0210) 0.4289***

(0.0213) 0.6549***

(0.0121) 0.3378***

(0.0181) 0.3416***

(0.0179) 0.5042***

(0.0102) Middle 0.5013***

(0.0176) 0.4831***

(0.0184) 0.6410***

(0.0126) 0.4112***

(0.0164) 0.4081***

(0.0166) 0.5155***

(0.0148) Bottom 0.5418***

(0.0169) 0.5130***

(0.0197) 0.7968***

(0.0178) 0.4530***

(0.0157) 0.4463***

(0.0160) 0.6987***

(0.0173) [ln(TFP) Gap]2 Top –0.0029

(0.0107) –0.0082

(0.0107) –0.0058

(0.0052) 0.0275***

(0.0103) 0.0259**

(0.0103) 0.0170***

(0.0045) Middle –0.0329***

(0.0084) –0.0247***

(0.0085) –0.0071

(0.0048) 0.0004

(0.0084) 0.0013

(0.0085) 0.0112**

(0.0047) Bottom –0.0318***

(0.0084) –0.0206**

(0.0093) –0.0100*

(0.0058) –0.0049

(0.0073) –0.0032

(0.0073) 0.0035

(0.0046) International Exposure Top 0.0098***

(0.0014) 0.0086***

(0.0013) 0.0130***

(0.0014) 0.0110***

(0.0013) Middle –0.0036***

(0.0011) –0.0038***

(0.0009) –0.0025***

(0.0006) –0.0018***

(0.0005) Bottom –0.0161***

(0.0016) –0.0154***

(0.0015) –0.0119***

(0.0010) –0.0101***

(0.0009) ihs(intangible K ratio) Top 0.0021***

(0.0002) 0.0020***

(0.0002) 0.0021***

(0.0002) 0.0022***

(0.0001) Middle 0.0001

(0.0002) 0.0007***

(0.0001) 0.0006***

(0.0001) 0.0009***

(0.0001) Bottom –0.0009***

(0.0003) 0.0002

(0.0002) –0.0003**

(0.0002) 0.0001

(0.0001) Std Dev[ln(TFP)] Top –1.7397***

(0.0429) –1.4706***

(0.0445) Middle –1.7050***

(0.0393) –1.4663***

(0.0465) Bottom –2.1339***

(0.0428) –1.9496***

(0.0442) Number of Observations 7,556,396 6,939,968 6,900,854 14,448,480
14,407,254 14,401,055 Within R2 0.2000 0.1992 0.2419 0.2000 0.2005 0.2351

ANNEX 2. WORLD BANK ENTERPRISE SURVEY

Innovation is defined as a dummy, which takes value 1 if the firm has introduced
any new products or processes over the previous three years; 0 otherwise. The
paper uses two measures of productivity levels: Annual sales divided by the
number of workers, and a residual (TFP) based on a Cobb-Douglas with log
sales/worker as output, log capital and labor as controls (plus year and country
FE). log(Salesicst) = α + β1log(Ki) + β2log(labori) + θs + φc + θt + εicst. We
obtain predicted values based on this regression log(ˆSalesicst) and then
compute residuals TFP=log(ˆSalesicst)−log(ˆSalesicst)

Annex Table 2.1.

Drivers of Productivity

Source: WBES, 2006–20. Note: OLS regression. The dependent variable in columns
1, 3, and 4 is firm-level TFP. In column 2, the dependent variable is sales per
worker in nominal NCU. These variables are regressed on a set of firm-level
characteristics : firm age, sector, size, R&D expenditure, ownership status, GVC
participation, proxied by the ratio of imports of imports and exports to annual
sales. Columns 1 and 2 include also controls for firm-level innovation, measured
as the introduction of new processes or products over the previous three years
columns 3 and 4 instead include an indicator variable for product and process
innovation, respectively. Country and year fixed effects are included. The
sample includes Cambodia, China, Fiji, India, Indonesia, Lao P.D.R., Micronesia,
Mongolia, Myanmar, Nepal, Papua New Guinea, Philippines, Samoa, Solomon Islands,
Sri Lanka, Thailand, Timor-Leste, Vanuatu, Vietnam. View Table
Annex Table 2.1.

Drivers of Productivity

(1)

TFP (2)

Sales/Worker (3)

TFP (4)

TFP Innovation 0.095***

(0.034) 0.096***

(0.024) Size: Medium

(20–99) –0.006

(0.032) 0.057**

(0.023) 0.002

(0.032) –0.009

(0.032) Size: Large

(100 And over) 0.107**

(0.043) 0.282***

(0.030) 0.122***

(0.043) 0.098**

(0.043) Manufacturing –0.463***

(0.050) –0.024

(0.096) –0.453***

(0.055) –0.460***

(0.050) Services –0.647***

(0.187) 0.016

(0.096) –0.639***

(0.188) –0.629***

(0.189) High–tech sector 0.055

(0.038) 0.244***

(0.029) 0.056

(0.038) 0.058

(0.038) Firm age 0.000

(0.000) –0.000

(0.000) 0.000

(0.000) –0.000

(0.000) GVC participation 0.002***

(0.000) 0.001***

(0.000) 0.002***

(0.000) Foreign ownership 0.000

(0.001) 0.001*

(0.001) 0.000

(0.001) Education workforce 0.012***

(0.003) 0.010***

(0.003) 0.011***

(0.003) 0.012***

(0.003) Credit constrained –0.081***

(0.030) –0.127***

(0.021) –0.080***

(0.030) –0.083***

(0.030) Capital city 0.230***

(0.048) 0.237***

(0.033) 0.231***

(0.048) 0.229***

(0.049) R&D expenditure 0.048

(0.038) 0.180***

(0.026) 0.093**

(0.039) 0.032

(0.038) Product innovation –0.019

(0.032) Process innovation 0.130***

(0.034) Year FE Yes Yes Yes Yes Country FE Yes Yes Yes Yes Number of
Observations 8,431 18,721 8,422 8,411 R2 0.144 0.593 0.143 0.145

Drivers of Innovation

Source: WBES, 2006–20. Note: Linear probability model. The dependent variables
are indicator variables taking value 1 if the firm has introduced any innovation
over the previous 3 years (column 1), 0 otherwise; columns 2 and 3 split between
product and process innovation. These indicators of innovative activity are
regressed over firm level characteristics: firm age, sector, size, R&D
expenditure, ownership status, GVC participation proxied by the ratio of imports
of imports and exports to annual sales. Controls include year and country fixed
effects. The sample includes Cambodia, China, Fiji, India, Indonesia, Lao
P.D.R., Micronesia, Mongolia, Myanmar, Nepal, Papua New Guinea, Philippines,
Samoa, Solomon Islands, Sri Lanka, Thailand, Timor-Leste, Vanuatu, Vietnam.
Standard errors in parentheses are robust to heteroskedasticity *** p <0.01, **
p <0.05, * p <0.1 View Table
Annex Table 2.2.

Drivers of Innovation

(1)

Innovation (2)

Product (3)

Process Size 0.081***

(0.007) 0.066***

(0.008) 0.087***

(0.007) Manufacturing –0.012

(0.010) –0.032***

(0.010) –0.010

(0.010) High tech sector 0.006

(0.009) 0.035***

(0.010) –0.007

(0.009) Firm age 0.000

(0.000) –0.000

(0.000) 0.000

(0.000) GVC 0.060***

(0.010) 0.031***

(0.010) 0.046***

(0.010) Foreign ownership –0.000

(0.000) –0.000

(0.000) –0.000*

(0.000) Education workforce –0.001*

(0.001) 0.001

(0.001) –0.001*

(0.001) Credit constrained 0.019***

(0.007) 0.011*

(0.007) 0.017**

(0.007) Capital city 0.036***

(0.009) 0.068***

(0.009) 0.021**

(0.009) R&D expenditure 0.385***

(0.007) 0.388***

(0.008) 0.402***

(0.007) Country FE Yes Yes Yes Year FE Yes Yes Yes Number of Observations 19,701
19,681 19,648 R2 0.255 0.195 0.263

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Some studies have argued that while digital technologies offer a vast potential
to boost productivity growth (Acemoglu and Restrepo 2020; Cette, Devillard, and
Spiezia 2021; Mosiashvili and Pareliussen 2020), their full potential is yet to
be realized.

For instance, China accounted for less than 1 percent of global e-commerce
retail transaction value about a decade ago, but that share has grown to more
than 40 percent on the eve of the pandemic, and the penetration of e-commerce
(as a share of total retail sales) stood at 15 percent, compared to 10 percent
in the United States. A similar picture emerges for many other Asian countries,
where e-commerce and fintech have grown rapidly (Dabla-Norris and others, 2021).

Spending on e-commerce (in percent of total retail sales) also rose
significantly in many countries in the region. For instance, in Vietnam small
firms relied to a greater extent on e-commerce during the pandemic for business
continuity (Dabla-Norris, Nguyen, and Zhang, forthcoming).

Intangible capital includes all intangible assets such as formation expenses,
research expenses, goodwill, development expenses, and all other expenses with a
long-term effect.

For instance, firm-level evidence suggests that lifting the productivity of
firms in the bottom 40 percent of the productivity distribution to median
productivity level in OECD countries would have a sizeable macroeconomic impact
by boosting aggregate output by up to 6 percent (Berlingieri and others 2020).

Calculated as the share of patents that cite scientific literature in total
patents. Patents are counted equally for each country of origin of each of the
patent applicants.

For example, between 2000 and 2008, the share of the domestic content of exports
in electronics grew significantly in Malaysia and Thailand, as well as in
industrial machinery in Indonesia and the Philippines (World Bank 2021).

See ADB (2020) for a study on the role of human capital in innovation in Asia.

Although fully comparable data are not available, McKinsey Global Institute
(2019) estimate that India’s ICT sector alone accounted for about 7 percent of
GDP in 2017–18, mainly reflecting IT and digital communications services.

Alibaba operates China’s most visited online marketplaces, Taobao (Consumer to
Consumer (C2C)) and TMall (Business to Consumer (B2C)), while JD.com’s
marketplace has a large in-house delivery network. OECD (2020) notes that the
move of JD.com, now one of the largest online retailers in the world, from
brick-and-mortar to online sales in 2004 was a direct response to the SARS
crisis. The same crisis also provided the consumer base for Alibaba’s
business-to-consumer (B2C) branch Taobab, which was launched in 2003.

In many Asian countries, the expansion in e-commerce involved customers and
firms that traditionally did not shop online. For instance, OECD (2020) notes
that the increase in the share of online purchases in credit card transactions
was highest for users in their 60s (from 15.4 percent in January to 21.9 percent
in March 2020) and those in their 70s (from 10.9 percent to 16.4 percent). On
the supply side, evidence from Vietnam shows that many operators of
brick-and-mortar stores, including SMEs, that often were forced to completely
shut down their physical business, adopted e-commerce ((Dabla-Norris, Nguyen,
and Zhang, forthcoming).

While platforms can magnify the benefits of e-commerce, they can raise
competition issues. Exclusive access to information of platforms can pose
anti-competitive concerns, particularly when these platforms become large.
Network effects also make it challenging for retailers and vendors to switch
platforms, reinforcing platforms’ market power, and exacerbating risks of
anticompetitive practices (Kinda 2019).

IMF (2021b) finds that access to foreign research has a larger estimated effect
on innovation in emerging markets and developing economies than in advanced
economies, pointing to important international spillover effects.

This concentration of R&D projects carried out by a handful of frontier firms is
also found in other regions. For example, calculations using data from the
European Union (EU) Industrial R&D Investment Scoreboard suggest that the top 10
public companies account for slightly less than 20 percent of aggregate private
sector R&D spending in the United States (Hernández and others 2020).

As small firms tend to be more women-owned, uneven access to technologies could
also affect women relatively more, worsening gender inequality.

In 2017, the participation rate for SMEs in e-commerce was less than half the
rate for large firms in a majority of OECD countries (OECD 2020). These gaps
were exacerbated by the pandemic.

Khera and Xu (2022) also show that factors such as (1) the degree of
digitalization in the public sector, (2) user trust in digital technologies and
consumer data protection, and (3) lack of digital literacy due to aging could
play a role in the adoption of digitalization.

Costs of government regulations and lack of adequate infrastructure (such as
electricity or internet) are also cited by firms in developing Asia as barriers
to technology adoption, albeit to a lesser extent.

For instance, firms exporting goods and services are exposed to global
competition and tend to have better management practices compared to
non-exporting firms. By contrast, family-owned and government-owned firms tend
to be managed poorly.

The measures of TFP used in the paper are different across the two data sets due
to differences in data availability. While Orbis allows for a better measure of
firm-level TFP (as it tracks firms over time), WBES has a broader coverage
across emerging market and developing economies. See Appendix 1 for more details
on the data sets and a discussion on the different productivity measures used in
the paper.

We recognize that digitalization and the adoption of intangible capital are not
necessarily the same. Digitalization encompasses the use of digital
processes—including software and the adoption of new technologies—to increase
the efficiency of production. Intangible capital is a broader concept that also
includes brand value, some forms of innovation, marketing and managerial skills,
and others. However, it remains the best proxy for digitalization as no direct
evidence on the adoption of digital processes is currently available.

See also Melitz (2003) and De Loecker and Warzynski (2012).

Note that the presence of firm fixed effects and country-specific time effects
imply that the coefficients shown here are identified using within-firm
variation only. As a result, the impact of international exposure cannot be
estimated for firms are either always or never exposed to international
competition.

The sample includes the following Asian countries, surveyed in different years
between 2006 and 2020: Cambodia, China, Fiji, India, Indonesia, Lao P.D.R.,
Micronesia, Mongolia, Myanmar, Nepal, Papua New Guinea, Philippines, Samoa,
Solomon Islands, Sri Lanka, Thailand, Timor-Leste, Vanuatu, Vietnam.

See Appendix 1 for details on the construction of the TFP measure using the
WBES.

The relative ln(TFP) for each firm in our sample is defined by the difference
between the firm’s log productivity and the median log productivity in the
firms’ (4-digit) sector each year: ln(TFPi)−ln(TFPmeds(i)). All firms are then
sorted according to their relative ln(TFP) and binned into percentiles, which
are illustrated Figure 20.

High-tech sector is defined as ICT; professional, scientific, and technical
services; manufacturing of computers and electrical/ electronic products;
manufacturing of chemicals; and manufacturing of pharmaceuticals.

This share increases to about 1.5 percent in Asian countries.

Competition could also have the opposite effect on innovation, as it increases
the likelihood that firms are replaced by competitors, which decreases the
expected gains from developing a new product (Aghion and others 2005, Akcigit
and Melitz 2021).

Estimates of the effect R&D tax incentives on welfare require a cost-benefit
analysis. While many studies show a net positive impact of R&D tax credit (for
example, Foreman-Peck 2013 and Russo 2004) some studies point to limited or
potentially negative effects of R&D tax incentives, depending on assumptions
(Parsons and Phillips 2007). This suggests the need for careful design of these
schemes and continuous cost-benefit analyses.

Using cross-country data on the manufacturing sector of nine OECD countries for
1979–97, Bloom, Griffith, and Van Reenen (2002) estimate a long-term elasticity
of R&D with respect to its user cost and find that R&D tax incentives are
generally effective. Using European firm-level data, Hussinger (2008) and
Cerulli and Potì (2012) find positive effects of government-funded R&D on
private R&D investment. Other strands of literature point to adverse effects of
uncertainty on R&D investments, including Bloom (2007).

Tax incentives should also be assessed against their costs. For instance, they
may not be the most effective instruments in developing countries with limited
fiscal space and facing structural issues such as weak infrastructure or low
human capital.

Prud’homme and Zhang (2019) discuss spatial concentration of innovation in
China.

The obstacles variables are categorical variables, taking value 1 if the firm
reports a specific item (such as high tax rates) as a main obstacle to business
operations; 0 otherwise.

The details of the analysis, as well as the different regression specifications,
are discussed in Appendix 1 (see also Appendix Table 1.3).

Our regressions include both firm fixed effects and country-by-year fixed
effects, which control for permanent firm-specific differences and
country-specific trends in TFP growth. However, we cannot rule out that our
results are contaminated by country- and-sector specific trends, as their
measure of productivity growth at the frontier varies at this same level. In
other words, we are unable to distinguish between the effects of spillovers from
frontier firms and unobserved country-by-sector trends that might impact both
frontier and non-frontier firms at once.

These productivity gains are more than doubled for high productivity firms in
comparison to low productivity firms.

This number is obtained by calculating, for each country-sector-year, the value
of a constant k such that kc,s,t sd(ln(TFPc,s,t)) = sd(ln(TFPc,s,t)) + 0.01.
After obtaining the distribution of such constants, we find that the median
value of k is approximately 1.037.

Intuitively, the argument proposed by Hsieh and Klenow (2009) is as follows: if
all firms are operating in a frictionless market, then the marginal product of
all factors should be equated across firms. However, market distortions (for
example lack of access to capital) will drive a wedge between those marginal
products, as some firms will face higher input costs than others. Importantly,
those distortions generate a higher dispersion in firm-level productivity, which
is what allows us the use TFP dispersion as an indicator of resource
misallocation. Mathematically, it can be shown that a firm’s measured TFP is
proportional to the wedges Wi (representing the market distortions) that it
faces. Thus, the standard deviation of log-TPF is in fact a measure of the
extent to which resources are misallocated across firms: sd(ln(TFPi)) = sd(ln(C
× Wi)) = sd(ln(Wi)).

See the World Bank’s 2020 GovTech Maturity Index and database.

See IMF (2021b) for the impact of reduction in nontariff barriers on trade, GVC
participations, and productivity.

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