## aidabpea ## Source details **Canonical URL:** [aidabpea](https://www.imf.org/-/media/files/publications/dp/2023/english/aidabpea.pdf) ## Other formats - [Markdown version](/-/media/files/publications/dp/2023/english/aidabpea.pdf.md) - [Structured JSON version](/-/media/files/publications/dp/2023/english/aidabpea.pdf.json) --- ### Executive Summary - Productivity—measured as labor productivity or total factor productivity (TFP)—has been on a downward trend worldwide, including in Asia, with the slowdown particularly pronounced since 2015. - The productivity slowdown occurred despite advances in digital technologies and innovation across the region. - Digitalization can mitigate scarring during downturns (for example, by facilitating virtual education, remote work, and contactless sales) and improve productivity and innovation during expansions. - The pandemic accelerated digitalization in Asia, creating potential upside for productivity growth but also risks of increased scarring, market-power concentration, and erosion of human capital. - Strategic implication: recovery from the pandemic is an opportunity to redesign policies to durably accelerate broad-based digital transformation and innovation-led growth. ### Key empirical observations about innovation and digitalization in Asia - Patent and technology presence: - Asia’s share of world patents: less than 40 percent at the beginning of the century; about 50 percent within a decade; 54 percent by 2019. - Asia’s share of world total patents in digital and computer technologies: about 60 percent by 2020. - About two-thirds of the world’s industrial robots are employed in Asia. - China accounts for some 30 percent of the global robot market. - Asia accounts for nearly 60 percent of the world’s online retail sales. - E-commerce and robots: - E-commerce revenues grew by 30–50 percent in many Asian economies in 2020. - Panel data (figures): robot installations shown for years 2011–24 (IFR projection for 2021–24); China robot installations by industry (Electronics, Automotive, Metal and machinery, Others) displayed in (1,000 units). - E-Commerce Revenue panels measured in "Percent of GDP" for years 2017–25 with axis ticks 0.0 to 4.0 and 0 to 9 respectively. - Frontier vs non-frontier classification: - All high-income countries in Asia and China are labeled “frontier Asia.” - Other countries are labeled “non-frontier Asia.” - Human capital and infrastructure: - R&D spending, 2019: Korea at 4.6 percent of GDP; most other innovative economies spend between 2 to 3.5 percent of GDP in R&D. - Frontier economies: between 10 to 25 percent of total R&D spending is devoted to basic research (except China). - Secure internet servers in non-frontier Asia increased more than 200-fold (timeframe shown in source figures). - Tertiary education enrollment rates increased by more than 10 percentage points in the last two decades in India, Malaysia, and Vietnam. ### Main frictions limiting innovation-led productivity gains - Concentration and dispersion: - Innovation and access to cutting-edge technologies are increasingly concentrated in a handful of firms; innovation quality can be improved. - Growing dispersion in innovation and digitalization within and across sectors limits aggregate productivity gains. - Limited diffusion channels: - Constraints in access to finance. - Insufficient management capabilities. - Skill gaps in information and communications technologies (ICT). - Digital divides and unequal access to digital technologies. - Pandemic-related risks to aggregate productivity: - Uneven diffusion of digital technologies. - Concentration of digital investments and innovations in large firms, potentially raising market power and widening productivity divergence. - Erosion of human capital from disruptions to work, school, and university education. - Policies that reduced business exit and increased survival likelihood of low-performing firms. ### Firm-level evidence on innovation, digitalization, and productivity - Positive associations: - Higher R&D intensity (measured as research and development expenses per worker) is positively associated with firm-level productivity (TFP). - Digitalization, proxied by the ratio of intangible to tangible capital, is a key driver of TFP, particularly for Asian countries. - Annex Table 1.1 coefficients (Full Sample): - ihs(R&D Expense/L): 0.0033*** (0.0004) - ihs(Intangible/Tangible K): 0.0040*** (0.0003) - International Exposure: 0.0022** (0.0010) - Number of Observations: 15,322,552 - Within R^2: 0.0167 - Heterogeneity and dispersion: - TFP in the most productive firms can be up to seven (≈ exp(2)) times bigger than in the median firm. - Productivity dispersion tends to be higher in more digitalized sectors and in sectors less exposed to international markets. - Firms benefit from productivity spillovers from frontier peers, but benefits accrue disproportionately to top firms; firms at the bottom are being left behind. - Laggard firms (bottom 40 percent within country-year-sector) characteristics: - Smaller and older. - Selected Table 2 coefficients (exact figures preserved): - ln(Employment): −0.0212*** (A&P, col 1); −0.0112** (A&P, col 2); −0.0209*** (RoW, col 3); −0.0168*** (RoW, col 4). - Age: 0.0042*** (A&P, col 1); 0.0050*** (A&P, col 2); 0.0015*** (RoW, col 3); 0.0012*** (RoW, col 4). - ln(Employment) X Age: −0.0004*** (A&P, col 1); −0.0005*** (A&P, col 2); −0.0003*** (RoW, col 3); −0.0002*** (RoW, col 4). - International Exposure: −0.0334*** (A&P, col 2); −0.0481*** (RoW, col 4). - R&D investment (ihs[R&D Expense/L]): −0.0168*** (A&P, col 2); −0.0084*** (RoW, col 4). - Digitalization (ihs[Intangible/Tangible K]): −0.0221*** (A&P, col 2); −0.0095*** (RoW, col 4). - Sample sizes reported in Table 2: Number of Observations: 7,245,79; 16,595,033; 12,212,401; 12,157,864 (as printed in source). ### Quantitative findings and indicators (selected exact figures) - Patents and R&D: - Asia’s share of world patents: less than 40 percent at the beginning of the century; about 50 percent within a decade; 54 percent by 2019. - Asia’s share of world total patents in digital and computer technologies: about 60 percent by 2020. - Korea: R&D spending at 4.6 percent of GDP in 2019. - Frontier economies: 10 to 25 percent of total R&D spending devoted to basic research (except China). - E-commerce and robots: - Asia accounted for nearly 60 percent of the world’s online retail sales. - E-commerce revenues grew by 30–50 percent in many Asian economies in 2020. - Two-thirds of the world’s industrial robots are employed in Asia; China accounts for some 30 percent of the market. - Panel and figure notes: - Panel 1 (Annual Installations of Industrial Robots): units in (1,000 units); axis ticks 0, 50, 100, 150, 200, 250, 300, 350, 400 for years 2011–24; IFR projection for 2021–24. - Panel 3 (E-Commerce Revenue): measured in "Percent of GDP" with axis ticks 0.0 to 4.0 for years 2017–25. - Panel 4 (E-Commerce Revenue: Top Asian and Pacific Economics): measured in "Percent of GDP" with axis ticks 0 to 9 for years 2017–25. ### Policy priorities and recommended reform buckets - Three reform buckets: 1. Foster innovation at the technological frontier: - Well-designed and targeted R&D tax credits and grants. - Higher public spending on basic research. - Measures to facilitate experimentation and commercialization, including improving small and medium enterprises’ access to finance and digital technologies. 2. Facilitate technology diffusion and unlock potential: - Lower trade barriers to foster greater integration with the international economy. - Streamline foreign direct investment regulations to encourage foreign firm entry, particularly in services. - Facilitate information and knowledge sharing between foreign and local firms. - Enhance business-university R&D collaboration. - Improve digital infrastructure and the ICT skill base. - Strengthen the legal environment for data protection and cybercrime. 3. Enable healthy competition and firm dynamism: - Simplify the insolvency framework to support resource reallocation post pandemic through the exit of less productive firms and the entry of new innovative firms. - Overarching recommendations (selected exact measures and empirical links): - Every 10 percent increase in broadband penetration increases GDP in developing countries by 1.4 percent. - Doubling broadband speed leads to 0.3 percent increase in per capita GDP growth (AIIB 2020). - Policy instruments to consider: R&D tax credits and allowances, targeted grants, government R&D scaling, venture capital development, competition policy enforcement, FDI deregulation (especially in services), digital infrastructure spending, GovTech improvements, insolvency reform. ### Data, measurement, and methodological notes - Main firm-level databases: Orbis and Zephyr (Bureau van Dijk). - Final sample: more than 34 million observations on 6.4 million individual firms between 1995 and 2018 across 16 countries. - Productivity (TFP) estimation: - Production function: y_it = b_0 + b_v v_it + b_k k_it + ω_it + ε_it (logs), estimated using control-function approach with Φ_t(v_it, k_it) and AR(1) for ω_it. - Country-industry pairs with less than 300 observations are dropped. - Intangible capital: - Not included as an input in the production function; ihs (inverse hyperbolic sine) transformations used to handle zeros. - Alternative TFP for WBES: - Residual-TFP from regression of log-sales on log-head count and log-capital with sector, country, year fixed effects; validated against Orbis control-function TFP. - Key Annex regression results (exact coefficients preserved where reported): - Annex Table 1.1 Full Sample: - ihs(R&D Expense/L): 0.0033*** (0.0004) - ihs(Intangible/Tangible K): 0.0040*** (0.0003) - International Exposure: 0.0022** (0.0010) - Number of Observations: 15,322,552 - Within R^2: 0.0167 - Annex Table 1.2 A&P: - International Exposure: 0.1543*** (0.0079) - ihs(Intangible/Tangible K): 0.0067*** (0.0010) - ln(Employment): 0.0232*** (0.0028) - Number of Observations: 643,697 - Within R^2: 0.0505 - Annex Table 1.3 Δ ln(TFP) — selected coefficients: - Δ Frontier ln(TFP) A&P Top: 0.2651*** (0.0077) - ln(TFP) Gap A&P Bottom: 0.5418*** (0.0169) - International Exposure A&P Bottom: −0.0161*** (0.0016) - ihs(intangible K ratio) A&P Top: 0.0021*** (0.0002) - Std Dev[ln(TFP)] A&P Bottom: −2.1339*** (0.0428) - Number of Observations Column (1) A&P: 7,556,396 ### Implementation challenges and targeted policy implications - Address financing constraints: - "About 20 percent of firms in emerging and developing Asia report financing constraints as the main obstacle" in WBES; only 7 percent in the non-Asia sample report credit constraints as the main obstacle. - 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. - Improve management and skills: - Large variation in management quality across Asian countries (World Management Survey). - More than 50 percent of innovating firms in ASEAN+3 countries cite a lack of managerial and leadership skills when hiring new workers. - More than half of all innovative firms in many of these countries cite scarcity of interpersonal and communication, ICT, or technical skills as critical hiring challenges. - Facilitate diffusion: - Promote trade integration and GVC participation; evidence: GVC participation and imports/exports as share of sales linked to higher likelihood of product and process innovation. - Strengthen legal frameworks on data protection and cybercrime to lower barriers to information sharing. - Reallocation and competition: - Simplify insolvency frameworks and reinforce competition policy to reduce resource misallocation and counter rising markups and market concentration. *Source: IMF Departmental Paper "Accelerating Innovation and Digitalization in Asia to Boost Productivity" (Executive Summary and extracted chapters and annexes).* ### Executive Summary ................................................................................................v ### Executive Summary ### Productivity context and the role of digitalization and innovation - Productivity—measured as labor productivity or total factor productivity (TFP)—has been on a downward trend worldwide, including in Asia, with the slowdown particularly pronounced since 2015. - The productivity slowdown occurred despite advances in digital technologies and innovation across the region. - Digitalization can mitigate scarring during downturns (for example, by facilitating virtual education, remote work, and contactless sales) and improve productivity and innovation during expansions. - The pandemic accelerated digitalization in Asia, creating a potential upside for productivity growth but also risks of increased scarring, market-power concentration, and erosion of human capital. ### Key empirical observations about innovation and digitalization in Asia - Asia contributed to more than half of world’s patents before the pandemic, including 60 percent of patents in digital and computer technologies. - About two-thirds of the world’s industrial robots are employed in Asia. - Asia accounts for nearly 60 percent of the world’s online retail sales. - Patent applications for remote work, e-commerce sales, and the use of industrial robots have risen sharply since the onset of the pandemic. - Spending on e-commerce rose by over 30 percent year-on-year in some countries in Asia. - Asian developing countries have benefited from technology diffusion via a higher share of imported high-technology goods and by granting more patents to nonresidents, supported by improvements in human capital and digital infrastructure. ### Main frictions limiting innovation-led productivity gains - Growing dispersion in innovation and digitalization within and across sectors limits aggregate productivity gains. - Innovation and access to cutting-edge technologies are increasingly concentrated in a handful of firms; innovation quality can be improved. - Diffusion of innovation from high-performing firms to other firms is limited due to: - Constraints in access to finance. - Insufficient management capabilities. - Skill gaps in information and communications technologies (ICT). - Digital divides and unequal access to digital technologies preventing many firms and workers from fully participating in the new economy. - The pandemic heightened risks to aggregate productivity through: - Uneven diffusion of digital technologies. - Concentration of digital investments and innovations in large firms, potentially raising market power and widening productivity divergence. - Erosion of human capital from disruptions to work, school, and university education. - Policies that reduced business exit and increased survival likelihood of low-performing firms. ### Firm-level evidence on innovation, digitalization, and productivity - Asian firms that are more innovative and digitalized tend to be more productive. - High concentration of innovation in large, capital-intensive firms is associated with large dispersion in productivity within countries and sectors, weighing on aggregate productivity. - Productivity dispersion tends to be higher in more digitalized sectors and in sectors less exposed to international markets. - Firms benefit from productivity spillovers from frontier peers, but benefits accrue disproportionately to top firms; firms at the bottom are being left behind. - External exposure to competition and innovation—through trade and greater digitalization—supports innovation and helps close productivity gaps for firms closer to the frontier. ### Policy priorities and recommended reform buckets - Decisive, proactive policies are needed to achieve broad-based innovation and digitalization across countries, sectors, and firms. Policies can be grouped into three buckets: - Foster innovation at the technological frontier: - Well-designed and targeted R&D tax credits and grants. - Higher public spending on basic research. - Measures to facilitate experimentation and commercialization, including improving small and medium enterprises’ access to finance and digital technologies. - Facilitate technology diffusion and unlock potential: - Lower trade barriers to foster greater integration with the international economy. - Streamline foreign direct investment regulations to encourage foreign firm entry, particularly in services. - Facilitate information and knowledge sharing between foreign and local firms (for example, by developing a network of providers). - Enhance business-university R&D collaboration to reduce the cost of technology adoption. - Improve digital infrastructure and the ICT skill base to facilitate adoption of new technologies. - Strengthen the legal environment for data protection and cybercrime to lower barriers to information sharing. - Enable healthy competition and firm dynamism: - Simplify the insolvency framework to support resource reallocation post pandemic through the exit of less productive firms and the entry of new innovative firms. ### Strategic implications - Countries do not need to be at the technological frontier to benefit: adoption and adaptation of existing technologies can close gaps for countries converging toward the frontier. - Narrowing productivity and digital/technological gaps across sectors and firms is critical for large aggregate payoffs; reallocation dynamics and firm-level performance determine country-level productivity growth. - The recovery from the pandemic is an opportunity to redesign policies to durably accelerate broad-based digital transformation and innovation-led growth. *Source: Executive Summary of IMF Departmental Paper "Accelerating Innovation and Digitalization in Asia to Boost Productivity"* ### Chapter 3 uses firm-level data for both advanced and developing economies in the region to investigate the ### aidabpea - Chapter 3 uses firm-level data for both advanced and developing economies in the region to investigate the ### The Landscape of Innovation and Productivity in Asia - Innovation is defined broadly as the accumulation of knowledge and implementation of new ideas and classified into four categories: - Product innovation: introduction of new or improved goods and services; often observable via patents or trademarks. - Process innovation: novel or improved managerial practices or business operations that increase firm productivity. - Innovation by discovery: invention of new ideas produced through R&D (includes both basic and applied research); more prominent in advanced economies and emerging economies on the technology frontier. - Innovation by diffusion: direct technology transfers, knowledge spillovers, or adoption of existing business practices; most firms in emerging and developing economies are constrained to, or reap higher benefits from, this type. - Digitalization interacts with these categories as both an output and an input of innovation: - Output: new technologies produce goods and services with higher digital content. - Input: digitalization and automation of production processes increase firm productivity and facilitate R&D. - For the paper’s analysis: - All high-income countries in Asia and China are labeled “frontier Asia.” - Other countries are labeled “non-frontier Asia.” - Chapter 3 uses firm-level evidence to investigate: - Role of innovation and digitalization for productivity growth and dispersion across firms. - Factors that impede faster innovation in countries closer to the technological frontier and factors that impede broader technological diffusion in countries farther from the frontier. - Policy mapping to foster broader-based innovation and boost aggregate productivity and longer-term growth prospects is provided in Chapter 4. ### A. Asia as Innovation Powerhouse — Outputs and Inputs - Patent outputs: - Less than 40 percent of world patents originated from Asia at the beginning of the century; within a decade Asia’s contribution rose to about 50 percent; by 2019 this share reached 54 percent. - A few countries account for the lion’s share of patents in Asia, most notably China, Japan, and Korea; China’s rise is particularly striking in the past decade. - Basic research and R&D inputs: - In frontier economies, between 10 to 25 percent of total R&D spending is devoted to basic research (except China). - 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 are related to or contribute to basic scientific research, with New Zealand and Singapore taking top spots worldwide. - R&D spending, 2019: Korea at 4.6 percent of GDP; most other innovative economies spend between 2 to 3.5 percent of GDP in R&D. - Researchers (full-time equivalents), 2018: Korea leads in researchers per thousand labor force (exact values shown in Figure 4 in source). - Non-frontier Asia and technology diffusion: - High-technology imports in many low-and-middle-income Asian countries (Bangladesh, India, Malaysia, Nepal, the Philippines, Sri Lanka, Thailand, Vietnam) are higher as a share of total imports than the world median. - Since 2013, non-frontier Asia accounted for an increasing share of patents granted from Asia to nonresidents, indicating more diffuse foreign idea flows. - Tertiary education enrollment rates increased by more than 10 percentage points in the last two decades in India, Malaysia, and Vietnam. - Digital infrastructure improvements: number of secure internet servers increased more than 200-fold in non-frontier Asia, reducing the gap with high-income countries. - India is highlighted as a global information technology services powerhouse and a pioneer of “digital stacks” that facilitate digital payments and identification services. ### B. The Pandemic and Innovation in Asia: A Boost to Digitalization - Pre-pandemic trends: - Growth in patents in frontier Asia was broad-based, particularly prominent in digital and ICT technologies. - Since 2017 Asia accounted for a higher share of world patents in digital and computer technologies than the rest of the world combined; by 2020 representing about 60 percent of world total patents in digital and computer technologies. - Asia dominates digital/ICT sub-categories in patent counts: telecommunications, digital communication, basic communication processes, computer technology, and semiconductors. - ICT sector size examples: - Korea: ICT sector accounted for more than 12 percent of total value added. - India: ICT sector accounted for more than 7 percent of total value added (McKinsey Global Institute estimate cited). - China’s ICT sector estimated at about 6 percent of GDP (source citation in text). - Robot deployment and e-commerce adoption were already high: - About two-thirds of the world’s industrial robots are employed in Asia. - China accounts for some 30 percent of the global robot market; China, Japan, and Korea each employed more robots than the United States on the eve of the pandemic. - Business-to-Consumer (B2C) e-commerce in China and Korea is larger than in the United States. - Pandemic effects and acceleration: - Remote working increased demand for digital solutions and boosted innovation in digital technologies; patent applications for remote work and e-commerce technologies increased substantially compared to pre-COVID times. - Asia’s share of world online retail sales rose to nearly 60 percent. - E-commerce revenues grew by 30–50 percent in many Asian economies in 2020, outpacing most countries globally. - Robot installation in Asia increased in 2020 relative to other regions; in China robot installation in electronics increased sharply, reflecting high demand for digital investment including for 5G. - Expected strong demand for electronics, digital infrastructure, and automation technologies could further boost robot installation and support digital commerce. - Home-grown tech giants and platforms: - China: Alibaba Group and JD.com have nearly 40 percent of global e-commerce market share by merchandise volume. - Other notable regional firms: Japan’s Rakuten; Singapore’s Sea Group (Shopee); Korea’s Coupang; Indonesia’s Go-Jek; China’s Tencent and Baidu. - These firms generate revenue levels in Asia comparable to large firms in the United States and are major providers of digital services beyond e-commerce. - Policy responses: - Many Asian countries actively promoted digitalization and innovation as part of pandemic stimulus initiatives, leveraging technology for disease prevention, control, and digital economy promotion. ### Key Quantitative Findings and Indicators (as reported) - Patent shares and growth: - Asia’s share of world patents: less than 40 percent at the beginning of the century; about 50 percent within a decade; 54 percent by 2019. - Asia’s share of world total patents in digital and computer technologies: about 60 percent by 2020. - R&D and researchers: - Korea: R&D spending at 4.6 percent of GDP in 2019. - Frontier economies: 10 to 25 percent of total R&D spending devoted to basic research (except China). - E-commerce and digital adoption: - Asia accounted for nearly 60 percent of the world’s online retail sales (post-pandemic observation). - E-commerce revenues grew by 30–50 percent in many Asian economies in 2020. - Digital infrastructure: - Secure internet servers in non-frontier Asia increased more than 200-fold (timeframe shown in source figures). - Robot deployment: - Two-thirds of the world’s industrial robots are employed in Asia; China accounts for some 30 percent of the market. *IMF Departmental Papers — Accelerating Innovation and Digitalization in Asia to Boost Productivity, Chapter 3 (excerpt provided)* ### 1. Annual Installations of Industrial Robots ### 1. Annual Installations of Industrial Robots ### Key data and projections - Panel 1 (Annual Installations of Industrial Robots): units shown in (1,000 units) with axis ticks 0, 50, 100, 150, 200, 250, 300, 350, 400 and years 2011–24. Note: "In panel 1, for 2021–24, projection by IFR is shown." - Panel 2 (China: Installations of Robots by Industry): units shown in (1,000 units) with axis ticks 0, 10, 20, 30, 40, 50, 60, 70 and industries listed as Electronics, Automotive, Metal and machinery, Others. - Panel 3 (E-Commerce Revenue): measured in "Percent of GDP" with axis ticks 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0 and years 2017–25. Note: "In panel 3, Asia frontier economies are Asian AEs and China. Rest of Asia economies are classified as non-frontier." - Panel 4 (E-Commerce Revenue: Top Asian and Pacific Economics): measured in "Percent of GDP" with axis ticks 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 and years 2017–25. - Data sources: International Federation of Robotics; Statista Digital Market Outlook; IMF, World Economic Outlook Oct 2021; and IMF staff calculations. ### Policy support and digitalization measures during the COVID-19 pandemic - Tax, fiscal, and financial measures: - Japan introduced tax incentives for digital investments as part of the 2021 tax reform package. - Vietnam scaled up public investments in innovation and digitalization in its Program for Recovery and Development. - New Zealand introduced a one-off R&D loan scheme to support R&D investment of firms affected by the pandemic. - National digital strategies and initiatives: - Korea: Digital New Deal (part of the Korean New Deal) to build a digital economy. - Malaysia: Twelfth Malaysia Plan and Malaysia Digital Economic Blueprint (MyDIGITAL). - Vietnam: National Digital Transformation strategy to strengthen online public services, accelerate non-cash payments and e-commerce, and improve shared database for state management. - India: accelerated digitalization including increased digital payments, contactless payments, digital education. - Fintech and payments: - Cambodia introduced Bakong, a payment system using blockchain technology providing real-time-gross settlement, e-wallets, mobile payments, online banking, and financial applications. - Public service digitalization: - Japan established the Digital Agency and supported uptake of national ID cards (My Number). - Philippines digitalized revenue collection and launched a digital ID system to support public service delivery such as social protection. - SME support: - Singapore’s SMEs Go Digital program supports SME adoption and use of digital technologies. - China and Singapore supported SMEs accessing e-commerce platforms with regional or global reach. - New Zealand introduced Digital Boost for SMEs. - Japan designed a business continuity subsidy to help firms diversify and expand sales channels. - Korea encouraged brick-and-mortar shops to open businesses online through a dedicated support program. ### Challenges in advancing innovation and digitalization - Diffusion vs. discovery: Rapid invention and frontier technologies are insufficient alone; the speed of diffusion across countries, sectors, and firms determines growth and productivity impact. - Patent quality and basic research: - Patent citations from Frontier Asia have been "stagnant as a share of worldwide citations," indicating rarity of groundbreaking innovations originating from Asia. - Basic scientific research in many frontier economies in Asia is underfunded; China, Japan, and Korea—three countries with the most patent output in Asia—are "near the lower end in both spending in basic research and contribution to basic research" compared with world leaders. - "Patents per researcher" has been stagnant or declining in recent years in some frontier countries in Asia. - Concentration of innovation: - R&D in frontier economies has increased but 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." - "The share of firms engaging in R&D ... has remained low," implying R&D is undertaken by a much smaller set of firms. - "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." ### Firm-level constraints and barriers to diffusion - Access to finance: - "About 20 percent of firms in emerging and developing Asia report financing constraints as the main obstacle" in the WBES. - "Only 7 percent of firms in the non-Asia sample report credit constraints as the main obstacle." - "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." - Management capabilities: - World Management Survey shows "large variation in management quality across Asian countries," with some countries lagging peers at similar income levels. - "Significant dispersion in management practices also exists within countries" and "weak management performance is more prevalent for smaller firms in developing countries." - Skills and hiring: - Firms in the region "consistently report skills gaps" and variation in PISA scores reflects this. - "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." - "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." - Access to external knowledge and information: - Small businesses often face inadequate access to specialized information, hampering technology adoption. - Weaknesses in the legal environment in some developing countries—such as lack of adequate legislation on data protection and cybercrime and ineffective enforcement—"hinder information sharing and confidence for technological adoption." - Diffusion outcomes: - In developing Asia, acquisition of technologies through imports and FDI "has not induced broad diffusion of new technologies and processes beyond export-linked firms." - Even in frontier economies, "there is limited diffusion of innovation by the more frontier firms to other firms in the same country." ### Implications and policy considerations - Policies to support both discovery and diffusion are necessary: funding basic research, improving quality of R&D, and widening participation in R&D across more firms. - Address financing constraints to enable investment in digital technologies and organizational changes—targeted support may be needed for SMEs, given "nearly half" report financing difficulties. - Strengthen management quality and firm-level capabilities to facilitate adoption of new technologies. - Improve skills supply—managerial, interpersonal, ICT, and technical—to reduce hiring constraints cited by innovating firms. - Enhance access to external knowledge and information flows and strengthen legal frameworks on data protection and cybercrime to build trust and lower adoption costs. *Source: IMF DEPARTMENTAL PAPERS • Accelerating Innovation and Digitalization in Asia to Boost Productivity (figures and text from the chapter "1. Annual Installations of Industrial Robots" and related panels).* ### 3. How Can Innovation and Digitalization ### 3. How Can Innovation and Digitalization Help Close Productivity Gaps? ### A. Role of Innovation and Digitalization in Firm-Level Productivity - Aggregate TFP depends on firm efficiency and allocation of inputs across firms; misallocation from impediments to factor movement can undermine aggregate TFP growth. - Higher R&D intensity (measured as research and development expenses per worker) is positively associated with firm-level productivity (TFP). - Digitalization, proxied by the ratio of intangible to tangible capital, is a key driver of TFP, particularly for Asian countries. - Evidence/example: 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. - Intangible assets (software, data, managerial skills, brand value, marketing) can be scaled up easily at low costs and support rapid firm growth and stronger productivity growth. - Complementary investments in skills and factors such as software and data are important to reap digitalization benefits. ### B. International Exposure and Productivity - Participation in international trade (exports or receiving FDI) is positively associated with firm-level productivity for non-Asian countries in the sample, but results are statistically insignificant for the sample of firms in Asia. - Possible explanations for smaller coefficient for Asian countries: - Weaker spillovers from international participation. - Different institutional environment affecting selection into exporting. - Differences in entry and exit behavior in international markets. - Identification note: presence of firm fixed effects and country-specific time effects implies coefficients are identified using within-firm variation only; impact of international exposure cannot be estimated for firms always or never exposed to international competition. ### C. Evidence from Emerging and Developing Asia (WBES) - Sample: more than 8,000 firms in 19 emerging market economies and developing countries over 14 years. - Innovation is defined broadly as the introduction of new production processes or product lines over the previous three years; includes adoption of existing technology (new-to-firm and new-to-market). - Findings: - Innovative firms tend to be more productive than other firms (higher labor productivity and higher revenue TFP), controlling for firm-level and market characteristics. - In developing Asia, the association between innovation and productivity level is stronger for process innovation than for product innovation. - Process innovation includes adoption of IT or e-commerce practices; e-commerce has been shown to be a key driver of productivity growth in Asia. - Adoption of existing technologies and processes can lift many firms up the productivity ladder without being at the cutting-edge of discovery. - Productivity also depends on: - Share of workers with higher educational attainment. - Degree of R&D expenditure at the firm level. - These variables are proxies for likelihood of introducing non-imitative, cutting-edge innovation (innovation by discovery). ### D. Data and Methodological Notes - Data sources used: - Orbis database covering firms in 16 different countries for advanced and emerging Asia; merged with Zephyr for firm-level FDI and merger information. - WBES (World Bank Enterprise Surveys) for emerging and developing Asia. - Caveats: - Orbis and Zephyr data skew toward firms in more developed Asian economies. - Measures of TFP differ across the two data sets due to data availability; Orbis allows for a better firm-level TFP measure as it tracks firms over time, while WBES has broader coverage across emerging market and developing economies. *Source: IMF Departmental Paper — chapter "3. How Can Innovation and Digitalization Help Close Productivity Gaps?"* ### 2. Process ### 2. Process ### Process innovation - Charts represent OLS regressions, with productivity as an outcome variable and innovation as a dependent variable. Controls: firm age, size, location, R&D expenditure, share of high-skilled workers, imports/exports as a share of sales, country and year fixed effects. Each dot represents 50 data points. (Source: WBES, 2006–2020.) - Skilled workforce availability can create gains across a broader spectrum of firms by raising managerial competence and firms’ capacity to absorb positive spillovers from innovative and higher-performing firms. ### Firm heterogeneity and aggregate productivity growth in Asia - Aggregate productivity growth depends on both expanding the technology frontier and closing productivity gaps across firms; slow within-country diffusion likely contributes to the productivity and technological divide between leading and lagging firms in Asia. - Large within-industry productivity dispersion: - TFP in the most productive firms can be up to seven (≈ exp(2)) times bigger than in the median firm (see Figure 20). - Productivity dispersion is considerably larger in high-tech sectors and in services than in manufacturing. - Determinants of productivity dispersion (90/10 TFP ratio within 4-digit sectors, country, and year): - Dispersion is higher in high-tech sectors, followed by services and manufacturing. - TFP dispersion tends to be larger in sectors with a higher intangible-to-tangible capital ratio and in sectors less exposed to international competition. - Both effects (intangible intensity and lower international exposure) are stronger in Asia and Pacific (A&P) than in the rest of the world (RoW). - One interpretation: higher digitalization provides larger benefits to already highly productive firms, increasing dispersion; international exposure can force unproductive firms out, decreasing dispersion. - Time trends: - Productivity dispersion has increased over time in Asia, with a much more pronounced increase in high-tech sectors (Figures 21 and 22). - Spatial concentration: - High-tech and services show higher dispersion and are more likely to be geographically concentrated (agglomeration and spillovers matter). ### Who are the laggard firms holding back aggregate productivity? - Definition: Laggard firms = firms in the bottom 40 percent of the productivity distribution within each country-year-sector (following OECD 2020). - Empirical approach: Linear probability model where dependent variable equals one if a firm is classified as a laggard in a given year. - Key characteristics of laggard firms (Table 2; results shown separately for A&P and RoW): - Size and age: - Laggard firms tend to be smaller and older. - Relationship detail: productivity and size are closely linked; productivity and age have a nonlinear relationship—very young firms tend to be financially constrained and often fail, while beyond the bottom quintile of TFP the correlation between age and productivity becomes negative as firms age and become less innovative. - Selected Table 2 coefficients (preserve exact figures): - ln(Employment): −0.0212*** (A&P, column 1); −0.0112** (A&P, column 2); −0.0209*** (RoW, column 3); −0.0168*** (RoW, column 4). - Age: 0.0042*** (A&P, col 1); 0.0050*** (A&P, col 2); 0.0015*** (RoW, col 3); 0.0012*** (RoW, col 4). - ln(Employment) X Age: −0.0004*** (A&P, col 1); −0.0005*** (A&P, col 2); −0.0003*** (RoW, col 3); −0.0002*** (RoW, col 4). - International Exposure: −0.0334*** (A&P, col 2); −0.0481*** (RoW, col 4). - R&D investment (ihs[R&D Expense/L]): −0.0168*** (A&P, col 2); −0.0084*** (RoW, col 4). - Digitalization (ihs[Intangible/Tangible K]): −0.0221*** (A&P, col 2); −0.0095*** (RoW, col 4). - Sample sizes reported in Table 2: - Number of Observations: 7,245,79 (column formatting in source); 16,595,033; 12,212,401; 12,157,864 (as printed in source). - Note: All specifications include a sector fixed effect and a country-by-year fixed effect. ihs represents the inverse hyperbolic sine function. A&P = Asia and Pacific; RoW = rest of world. ### Closing productivity gaps — Innovation by discovery (frontier) - R&D is highly concentrated: - Only about 1 percent of firms have positive R&D expenses in the sample of countries; this share increases to about 1.5 percent in Asian countries. - Predictors of R&D investment: - Firms that invest in R&D tend to be larger and pay higher wages (Appendix Table 1.3). - Positive correlation between probability of investing in R&D and employment and wages; robust to inclusion of gross profits, equity, debt, and direct measure of TFP. - R&D-intensive firms also tend to have higher capital intensity, be more digitalized, and be more likely to operate in international markets (exports or FDI). - Channels linking international exposure and R&D: selection (high-productivity firms self-select into export/FDI), learning/technology transfer, and competition (escape competition). - Policy influences on innovative investment: - Tax incentives and macroeconomic stability can encourage innovative investment. - Literature surveyed: Hall and Van Reenen (2000) and Becker (2015) find R&D tax credits have a positive and significant effect on R&D expenditure. - Heterogeneity and design matters: - R&D tax incentives often have larger stimulative effects on smaller firms and those in manufacturing (Box 2 evidence). - Well-designed R&D tax incentives are important to realize positive effects (Guceri and Liu 2019; Chen and others 2020). - Cost-benefit caveats: - Estimates of welfare effects require cost-benefit analysis; some studies show net positive impacts while others point to limited or potentially negative effects depending on assumptions (e.g., Parsons and Phillips 2007). - Tax incentives may not be the most effective instruments in developing countries with limited fiscal space and structural issues (weak infrastructure, low human capital). - Selected regression results from Table 1 (90/10 TFP Ratio on Sector Characteristics; coefficients preserved exactly): - Services: 0.5552*** (A&P, col 1); 0.5298*** (A&P, col 2); 0.0024 (RoW, col 3); 0.0769 (RoW, col 4). - Manufacture: −0.5424*** (A&P, col 1); −0.5545*** (A&P, col 2); −1.1494*** (RoW, col 3); −0.9813*** (RoW, col 4). - High-tech: 0.9193*** (A&P, col 1); 0.8176*** (A&P, col 2); 1.5476*** (RoW, col 3); 1.6216*** (RoW, col 4). - Digitalization (ihs[Intangible/Tangible K]): 0.1246*** (A&P, col 2); −0.0694** (RoW, col 4). - International Exposure: −1.4188 (A&P, col 1) [note: large SE shown]; −0.8006*** (RoW, col 4). - Observations: 25,919 (col 1); 25,875 (col 2); 53,480 (col 3); 53,480 (col 4). - Within R2: 0.184 (col 1); 0.1897 (col 2); 0.1795 (col 3); 0.1839 (col 4). - Box 2 — Evidence from Australia (firm-level determinants of intangible investment): - Context: Australian R&D tax incentives since 1985, major change in 2011; further changes in 2021. - Empirical model: Growth rate of intangible capital (ITA) regressed on sales growth, firm-level uncertainty (annualized volatility of weekly stock returns), lagged intangible, interactions with lagged R&D tax incentives (share of GDP), and firm characteristics (ExternalFinance, Manufacturing, Small, High Future Growth). - Key Box Table 2.1 coefficients (preserve exact figures where shown): - Sales Growth: .2464*** (col 1); .2486*** (col 2); .2510*** (col 3); .2509*** (col 4); .2497*** (col 5). - Uncertainty: −0.0251 (col 1); −0.0333 (col 2); −0.0230 (col 3); −0.0360 (col 4); −0.0186 (col 5). - Sales Growth* Uncertainty: −.3318*** (col 1); −.3347*** (col 2); −.3354*** (col 3); −.3332*** (col 4); −.3358*** (col 5). - Uncertainty* Lagged Dependent Variable: 1.5171*** (col 1); 1.5174*** (col 2); 1.5183*** (col 3); 1.5212*** (col 4); 1.5173*** (col 5). - High Ext. Finance Dep.* RD tax incentives (−1): .3279*** (cols shown). - Manufacturing* RD tax incentives (−1): 1.1048* (one column), 1.1420* (another column). - Small* RD tax incentives (−1): 1.0199*** (one column), 1.1134*** (another column). - High Exp. Growth* RD tax incentives (−1): 0.2529***; 0.2823*** in alternative specification. - Interpretation: Tax incentives have positive impacts on intangible investment with heterogeneity—smaller firms, manufacturing firms, firms with high external finance dependence, and high expected growth firms respond differently to incentives. Sample period: 2001–18. Number of observations: 4,006. R2 ≈ 0.7597–0.7623 across specifications. ### What drives adoption (innovation by diffusion) in developing and low-income Asia? - R&D investment is a strong predictor of the likelihood of innovating in developing Asia (Figure 24; Appendix Table 2.2). - Adoption (diffusion) matters: - Innovation via adoption of existing processes/technologies (licensing, imitation) may be more cost-effective in developing Asia, especially for financially constrained SMEs. - Firm-level correlates of adoption and innovation (Figure 25; Appendix Table 2.2): - Both product and process innovation are more likely in larger firms and in firms located in capital cities. - Geographic concentration of innovative activity in developing Asia implies cities capture agglomeration benefits and positive spillovers (technological diffusion by proximity and imitation), but this can leave lagging regions/firms behind. *IMF DEPARTMENTAL PAPERS • Accelerating Innovation and Digitalization in Asia to Boost Productivity — Chapter 2 content (extracted from source PDF).* ### Box 2. Firm-Level Determinants of Intangible Investment (continued) ### Box 2. Firm-Level Determinants of Intangible Investment (continued) ### Effects of tax incentives and firm characteristics on intangible investment - Firms are more likely to start investing in R&D if they receive a subsidy. - Quantitative estimate: increasing tax incentives by 0.1 percentage point of GDP (nearly doubling) raises the growth of intangible capital next year about 10.2 percentage points stronger for SMEs. - Industry and financing structure heterogeneity: - Manufacturing sector firms exhibit larger increases in intangible capital when aggregate incentives increase. - Firms more dependent on external financing show larger increases in intangible capital when aggregate incentives increase. - Firm growth expectations matter: - Firms with higher expectations for growth (proxied by higher Tobin’s Q) increase intangible investment more in response to government tax incentives than less viable firms. ### Role of uncertainty - Following Bloom (2007), uncertainty makes intangible investment less responsive to changes in business conditions. - Uncertainty leads firms to be reluctant to change investment plans, producing more persistent intangible investment behavior. ### Innovation, trade integration, and GVC participation - Innovation in emerging and developing Asia is associated with higher trade integration and participation in GVCs. - Firms more integrated in GVCs (proxied by the value of imports and exports as a share of annual sales) are more likely to introduce product and process innovation. - Greater exposure to competition from abroad and domestic agglomeration and diffusion dynamics may be important drivers of productivity growth. ### Obstacles to innovation reported by non-innovating firms - Frequently cited strongest impediments: - Inadequate access to financing opportunities - Lack of a skilled workforce - Competition from the informal sector - Many firms also report that high tax rates are an obstacle to business operations. - Interpretation: lack of financing opportunity and high tax rates may reduce resources available for exploring opportunities to grow and innovate. - Self-reported constraints serve as indicators for potential policy intervention areas. ### Closing productivity gaps: distance to the technology frontier and heterogeneous effects - Firm groups by country-sector-year TFP distribution: - Frontier firms: top decile. - Non-frontier firms split into three subgroups: top (90th–60th percentiles), middle (60th–30th percentiles), bottom (below the 30th percentile). - Spillovers from frontier firms: - TFP growth across firms is spurred by developments at the technological frontier (positive coefficient of TFP growth at the frontier). - Spillovers strongest for top non-frontier firms, indicating these firms are better positioned to benefit from frontier innovation. - Convergence and nonlinearity: - Productivity growth across firms is driven by catching-up associated with adoption of newer technologies (positive coefficient of the TFP gap). - Effect is nonlinear: as firms grow farther from the frontier, they may lack capacity to adopt new technologies, producing a negative coefficient on the TFP gap squared. - Nonlinearity is particularly pronounced in non-Asian countries. - Digitalization and international exposure: - Increase in digitalization (intangible capital) is associated with higher productivity growth, largest for non-frontier firms closer to the frontier (top group), followed by middle group, and no discernible effect for the bottom group. - International exposure has a positive effect for non-frontier top-group firms, but negative effects for middle- and bottom-group firms—suggesting bottom firms are worse at learning from or adapting to tougher competition. - Resource misallocation and sectoral dispersion: - Increasing the standard-deviation of log-productivity by 0.01 (equivalent to a 3.7 percent increase in log-standard deviation for the median sector) would reduce the average firm’s TFP growth by 1.5 to 2.1 percentage points. - Interpretation: higher sectoral productivity dispersion indicates greater resource misallocation (following Hsieh and Klenow 2009), which slows firm-level growth. - Effect is more pronounced for non-frontier firms in middle and bottom groups, implying smaller or younger firms are less able to adapt to distortions that generate misallocation. - Caveat: other potential sources of productivity dispersion exist (firm-specific shocks, varying degrees of market power), but observed patterns are consistent with a misallocation interpretation. ### Conclusions and key takeaways - Innovative firms are more productive across development levels and sectors; adoption of existing technologies can be sufficient to raise productivity for firms in emerging and developing Asia. - Productivity distributions within Asian countries are often bimodal: a few top performers coexist with a larger share of laggard firms. Laggards tend to be smaller, older, less trade-participating, less likely to invest in R&D, and less likely to digitalize. - Innovation is concentrated in larger, more capital-intensive firms with strong foreign-market links. Key policy levers: - Improve access to financing opportunities. - Increase educational attainment / workforce skills. - Strengthen firm innovation capabilities by first upgrading processes using digital technologies, then adopting more sophisticated technologies. - Spillovers from frontier firms are productivity-enhancing and benefit mainly top non-frontier firms; digitalization’s productivity impact is larger for firms closer to the frontier. Laggard firms risk falling further behind. - Policy implications: - Promote participation in international trade, including GVCs, and strengthen supplier linkages to facilitate technology diffusion. - Facilitate greater firm entry and exit to capitalize on new firms’ potential and reduce the presence of stagnant and unproductive (“zombie”) firms. *Source: WBES, 2006–2020; Box text and figures from the IMF Departmental Paper excerpt.* ### 4. Supporting Productivity Growth ### 4. Supporting Productivity Growth ### A. Overarching Policy Priorities - Objective: Promote competition, innovation, and digitalization after the pandemic through regulatory reforms that reduce misallocation and boost growth potential. - Key policy directions: - Streamline product market regulations, reducing barriers to entry and state involvement (product market regulations in many Asian economies are more restrictive compared to international best practices). - Reduce restrictions in upstream network sectors (including e-communications, energy, transport) to remove impediments to digitalization and boost productivity in downstream sectors. - Facilitate firms’ adoption of digital technology by: - Reducing regulation and modifying supervision in line with the evolving digital industry. - Facilitating digital trade. - Matching private sector digitalization with public sector GovTech improvements (Asia lags OECD on GovTech). - Close infrastructure gaps in developing Asia, including energy, transport, and telecommunications; additional spending on digital infrastructure is required to accelerate digitalization. - Quantified linkage between connectivity and growth: - Every 10 percent increase in broadband penetration increases GDP in developing countries by 1.4 percent. - 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: - Use fiscal incentives (R&D tax credits and allowances) and well-targeted grants to stimulate innovative investment; careful targeting and cost-benefit analysis are critical. - Maintain well-balanced intellectual property rights that reward disruptive innovations more than incremental improvements. - Scale up government R&D spending, including basic research and firm-academia cooperation; government R&D can stimulate private R&D. - Policies to facilitate experimentation and bring innovation to markets: - Improve access to finance for new, small, and digital firms to address financing constraints that hinder radical innovation. - Evidence: Loan interest rate spreads between SMEs and large firms in Asian countries are relatively wide compared to other countries. - Deepen capital markets to offer a variety of financial instruments for SMEs and new entrants. - Consider government R&D loan or credit guarantee schemes (examples noted in some Asian countries). - Develop venture capital (VC) markets for early- and later-stage investment: - VC can address asymmetric information and improve screening and monitoring; early-stage VC helps lower productivity dispersion and aids catch-up. - Policy tools include government-sponsored funds or co-investment funds and removing barriers to investment. - Policies to facilitate technology diffusion: - Promote participation in international trade and global value chains (GVC) to accelerate technology diffusion and adoption. - Policy options: reduce tariff and nontariff trade barriers, facilitate access to trade finance, invest in international logistics infrastructure, and promote cross-border e-commerce. - Enforce competition policy and merger controls to avoid excessive market power by large domestic and foreign corporations; consider interim measures and build digital economy expertise (digital economy units). - Streamline FDI-related regulations to attract FDI and support knowledge transfer: - Many developing Asian countries have FDI inflows smaller relative to peers and face relatively stringent restrictions on services, indicating scope for deregulation. - Greater FDI in services can help laggard firms catch up. - Policies to develop firms’ absorptive capacity: - Strengthen collaboration networks among firms, academia, and government (industry-academia projects, government consulting, expositions, digital platforms) to reduce search costs for external technology. - Explore agglomeration in knowledge-intensive high-tech industries to facilitate knowledge diffusion. - Enhance legal environment: data protection and cybercrime legislation and effective enforcement to lower barriers to information sharing. - Broaden and deepen skill base: - Education and workforce skills (foreign language, managerial, IT) are positively related with firms’ productivity; many firms in developing Asia report hiring difficulties for these skills. - Policymakers should assess skills needed post-pandemic and formulate holistic human capital strategies. - Improve management practices and digital skills of laggard firms through training and digital uptake support. ### C. Facilitating Reallocation of Resources and Preparing the Next Generation - Policies to encourage efficient reallocation of resources: - Promote healthy competition and firm dynamism to enable creative destruction: allow inefficient firms to exit and innovative young firms to enter. - Observations and concerns: - Large dispersion of productivity among Asian firms; many laggard firms are small and old. - Market concentration, as measured by markups, appears to have been increasing in recent years, which could impede growth. - Firm exit remained low despite the pandemic; lifeline measures contributed to preserved firms, including potentially less productive or zombie firms. - Policy actions: - Support exit of non-viable firms by simplifying the insolvency framework to reduce misallocation and free resources for more productive firms. - Reinforce competition policy to counter rising markups and barriers to entry. *Source: aidabpea - 4. Supporting Productivity Growth.* ### Annex 1. Orbis and Zephyr ### Annex 1. Orbis and Zephyr ### Data - Data sources: Orbis and Zephyr (both maintained by Bureau van Dijk). - Use of databases: - Orbis: detailed firm accounting data. - Zephyr: information on mergers & acquisitions and FDI deals; used, along with export revenues, to construct measure of exposure to foreign markets. - Zephyr filtering rules: - Keep only completed, cross-border deals with a single acquiror (both single and multiple target deals). - When companies have multiple deals in a single year, all values are summed to obtain the total amount invested. - Merge key: unique firm identifiers, firm’s country of origin, and year of observation. - Final sample: - More than 34 million observations on 6.4 million individual firms. - Period: between 1995 and 2018. - Coverage: across 16 countries. - Countries split into two groups: Asia and Pacific (A&P) and Rest of World (RoW). A&P further classified into frontier and non-frontier by development and innovation production. - Data coverage visualization: Annex Figure 1.1 shows number of firms (thousands) by country: Australia, China, Japan, Korea, New Zealand, Malaysia, Philippines, Thailand, Vietnam, France, Germany, Great Britain (UK), Italy, Norway, Sweden, United States. ### Measuring Productivity (TFP estimation) - Production function specification (logs): - y_it = b_0 + b_v v_it + b_k k_it + ω_it + ε_it - y_it: turnover revenue (log). - v_it: variable inputs measured by cost of goods sold (log). - k_it: value of physical capital used in production (in US dollars, log). - ω_it: TFP (log). - Assumption: ω_it = h(v_it, k_it), thus production function written as y_it = Φ_t(v_it, k_it) + ε_it. - Estimation approach: - First stage: estimate Φ_t non-parametrically. - Assume TFP follows AR(1): ω_it = ρ ω_it−1 + ξ_it. - Second-stage equation: - y_it = b_0 + b_v v_it + b_k k_it + ρ(Φ̂_{t−1} − b_v v_{it−1} − b_k k_{it−1}) + ξ_it + ε_it. - Identification uses moment condition E[(ε_it + ξ_it) | I_{t−1}] = 0. - Elasticities and TFP estimated at the country-industry level. - Country-industry pairs with less than 300 observations are dropped to increase precision. - Intangible capital: - Not included as an input in the production function. - Rationale: intangible capital definition includes brand value, patents, trademarks, marketing, managerial expenses; many firms record zero intangible capital. - Acknowledged risk: if some intangible components are true production inputs correlated with productivity, estimated TFP could be overstated. ### Comparing TFP Measures - Alternative TFP measure for datasets without panel observations (e.g., WBES): - Regress log-sales on log-head count, log-capital, and fixed effects for country, sector, and year. - Define TFP as the residual of this regression. - Validation: - The residual-TFP measure computed on Orbis correlates very strongly with the control-function TFP measure, except at the extremes of the residual-TFP distribution. - Conclusion: residual-TFP provides more credibility to WBES-based productivity results. ### Dealing with Zeroes (ihs transformations) - Use inverse hyperbolic sine (ihs) to handle zeros: - ihs(x) = ln(x + sqrt(1 + x^2)), equals zero when x = 0, converges to ln(2x) for large x. - Application: - R&D intensity (R&D expenses/employment) often in the hundreds or thousands for R&D-investing firms — ihs ≈ ln has negligible difference. - Intangible-to-tangible capital ratio often < 1; adjust scale by factor k: - ihs(x; k) = ln(kx + sqrt(1 + (kx)^2)) − 2 ln(k), - where k = mean(R&D intensity | R&D intensity > 0) / mean(intangible/tangible K | intangible/tangible K > 0). ### Distance-to-frontier empirical specification - Baseline equation for TFP growth: - Δ ln(TFP_isct) = sum_g [ I(i ∈ g) { β_{1g} Δ ln(TFP_sct^f) + β_{2g} gap_isct + β_{3g} gap_isct^2 + β_{4g} X_isct } ] + δ_i + δ_ct + ε_isct - Δ ln(TFP_isct): change in log-productivity for firm i (sector s, country c) between t and t+1. - Δ ln(TFP_sct^f): average change in log-productivity for frontier firms (top 10 percent of each sector-country-year). - gap_isct: ln(TFP_sct^f) − ln(TFP_sct) (firm productivity gap). - X_isct: firm-level characteristics (international exposure, intangible capital ratio, mean intangible ratio in sector, standard deviation of ln(TFP) in sector). - δ_i and δ_ct: firm and country-year fixed effects. - Group g: top firms (60th–90th percentiles), middle firms (30th–60th), bottom firms (below 30th). - Estimation excludes a firm-group if insufficient observations apply (as per earlier 300-observation rule for country-industry pairs). ### Key regression findings and coefficients (exact values preserved) - Annex Table 1.1 — Regression of ln(TFP) on Firm Characteristics (controls include K/L and wages; firm FE and country-by-year FE) - Full Sample (1): - ihs(R&D Expense/L): 0.0033*** (0.0004) - ihs(Intangible/Tangible K): 0.0040*** (0.0003) - International Exposure: 0.0022** (0.0010) - Number of Observations: 15,322,552 - Within R^2: 0.0167 - A&P (2): - ihs(R&D Expense/L): 0.0035*** (0.0003) - ihs(Intangible/Tangible K): 0.0051*** (0.0008) - International Exposure: −0.0001 (0.0021) - Number of Observations: 3,776,025 - Within R^2: 0.0556 - Rest of World (3): - ihs(R&D Expense/L): 0.0 016* (0.0008) - ihs(Intangible/Tangible K): 0.0033*** (0.0002) - International Exposure: 0.0029*** (0.0010) - Number of Observations: 11,546,527 - Within R^2: 0.0401 - Annex Table 1.2 — Regression of I(R&D expenses > 0) on Firm Characteristics (controls include firm age, debt, equity; country-by-sector FE) - Full Sample (1): - International Exposure: 0.0143*** (0.0020) - ihs(Intangible/Tangible K): 0.0013*** (0.0002) - ln(K /L): 0.0018*** (0.0002) - ln(Wages): 0.0034*** (0.0004) - ln(Employment): 0.0065*** (0.0006) - Number of Observations: 2,800,409 - Within R^2: 0.0114 - A&P (2): - International Exposure: 0.1543*** (0.0079) - ihs(Intangible/Tangible K): 0.0067*** (0.0010) - ln(K /L): 0.0041*** (0.0007) - ln(Wages): 0.0171*** (0.0019) - ln(Employment): 0.0232*** (0.0028) - Number of Observations: 643,697 - Within R^2: 0.0505 - RoW (3): - International Exposure: 0.0079*** (0.0014) - ihs(Intangible/Tangible K): 0.0005*** (0.0001) - ln(K /L): 0.0009*** (0.0001) - ln(Wages): 0.0015*** (0.0002) - ln(Employment): 0.0026*** (0.0002) - Number of Observations: 2,156,712 - Within R^2: 0.0076 - Annex Table 1.3 — Regression of Δ ln(TFP) on Policy and Firm Characteristics (separate columns for A&P and RoW and for Top/Middle/Bottom groups) - Key coefficients (selected, exact values preserved): - Δ Frontier ln(TFP): - A&P Top: 0.2651*** (0.0077) - A&P Middle: 0.2386*** (0.0073) - A&P Bottom: 0.2401*** (0.0089) - RoW Top: 0.2503*** (0.0074) - RoW Middle: 0.2359*** (0.0072) - RoW Bottom: 0.2437*** (0.0078) - ln(TFP) Gap: - A&P Top: 0.4185*** (0.0210) - A&P Middle: 0.5013*** (0.0176) - A&P Bottom: 0.5418*** (0.0169) - RoW Top: 0.3378*** (0.0181) - RoW Middle: 0.4112*** (0.0164) - RoW Bottom: 0.4530*** (0.0157) - [ln(TFP) Gap]^2 (selected): - RoW Top: 0.0275*** (0.0103) - RoW Middle: 0.0004 (0.0084) - RoW Bottom: −0.0049 (0.0073) - International Exposure (selected): - A&P Top: 0.0098*** (0.0014) - A&P Middle: −0.0036*** (0.0011) - A&P Bottom: −0.0161*** (0.0016) - RoW Top: 0.0110*** (0.0013) - RoW Middle: −0.0018*** (0.0005) - RoW Bottom: −0.0101*** (0.0009) - ihs(intangible K ratio) (selected): - A&P Top: 0.0021*** (0.0002) - A&P Middle: 0.0001 (0.0002) - A&P Bottom: −0.0009*** (0.0003) - RoW Top: 0.0021*** (0.0002) - RoW Middle: 0.0006*** (0.0001) - RoW Bottom: −0.0003** (0.0002) - Std Dev[ln(TFP)] (selected A&P): - Top: −1.7397*** (0.0429) - Middle: −1.7050*** (0.0393) - Bottom: −2.1339*** (0.0428) - Number of Observations (selected): - Column (1) A&P: 7,556,396 - Column (4) RoW: 14,448,480 - Within R^2 examples: - Column (1) A&P: 0.2000 - Column (4) RoW: 0.2000 ### Findings from World Bank Enterprise Survey (WBES) annexes - Definition: - Innovation defined as dummy = 1 if firm introduced any new products or processes over previous three years; 0 otherwise. - TFP measures in WBES: - Sales per worker (annual sales / number of workers). - Residual TFP from Cobb-Douglas regression: log(Sales_icst) = a + b1 log(K_i) + b2 log(labor_i) + sector FE + country FE + year FE + ε_icst; TFP = log(Sales_icst) − log(Ŝales_icst). - Annex Table 2.1 — Drivers of Productivity (WBES, 2006–20; OLS; country and year FE) - Innovation: 0.095*** (0.034) in TFP regression (column 1). - Size: - Large (100 and over): 0.107** (0.043) in TFP (column 1); 0.282*** (0.030) in Sales/Worker (column 2). - Manufacturing: −0.463*** (0.050) in TFP (column 1). - GVC participation: 0.002*** (0.000) in TFP (column 1) and 0.001*** (0.000) in Sales/Worker (column 2). - Education workforce: 0.012*** (0.003) in TFP (column 1). - Credit constrained: −0.081*** (0.030) in TFP (column 1). - Capital city location: 0.230*** (0.048) in TFP (column 1). - R&D expenditure: 0.048 (0.038) in TFP (column 1); 0.180*** (0.026) in Sales/Worker (column 2). - Number of Observations: - Column 1 (TFP): 8,431 - Column 2 (Sales/Worker): 18,721 - R^2 examples: - Column 1: 0.144 - Column 2: 0.593 - Sample countries listed in note include: 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. - Annex Table 2.2 — Drivers of Innovation (WBES; Linear probability model) - Size: 0.081*** (0.007) for Innovation (column 1). - GVC: 0.060*** (0.010) for Innovation. - R&D expenditure: 0.385*** (0.007) for Innovation. - Capital city: 0.036*** (0.009) for Innovation. - Education workforce: −0.001* (0.001) for Innovation. - Number of Observations: 19,701 (column 1). - R^2 examples: - Column 1: 0.255 - Column 3 (Process): 0.263 ### Implications and interpretation highlighted in the annex - R&D intensity and intangible capital are positively associated with higher firm-level TFP (coefficients small but statistically significant in large samples). - International exposure: - Positively associated with likelihood of R&D (I(R&D expenses > 0)) in the full sample and particularly in A&P (0.1543***), but heterogeneous effects on subsequent TFP growth across firm groups (positive for top firms; negative for middle and bottom firms in A&P). - Distance-to-frontier dynamics: - Frontier productivity growth (Δ Frontier ln(TFP)) positively associated with firm TFP growth across top, middle, bottom groups in both A&P and RoW. - Larger initial gaps (ln(TFP) Gap) are strongly positively associated with subsequent TFP growth, but squared gap terms and heterogeneity across regions/groups indicate nonlinear convergence dynamics. - Sectoral and firm characteristics: - Larger firms, GVC participation, educated workforce, and R&D spending are correlated with higher productivity and higher probability of innovation. - Credit constraints are negatively associated with both productivity and innovation outcomes. *Source: Annex 1. Orbis and Zephyr, aidabpea - Annex 1. 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