## 2. Industrial Policies by Sector, 2023 ## Source details **Canonical URL:** [2. Industrial Policies by Sector, 2023](https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024176-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2024/english/wpiea2024176-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2024/english/wpiea2024176-print-pdf.pdf.json) --- ### Purpose and conceptual framework - Framework to assess when sector-specific fiscal support for innovation (“industrial policy”) is preferable to sector-neutral support (“horizontal policy”). - Builds on a model of endogenous innovation with a sectoral network of knowledge spillovers (Liu and Ma, 2023). - Key mechanism: - Sector-specific support can direct innovation to sectors generating higher knowledge spillovers to other domestic sectors (measured by cross-sector patent citations), raising economy-wide innovation, productivity growth, and welfare. - Extensions: - Implementation frictions: random policy mistakes or political capture that misallocate innovation inputs. - Alternative government goals: supporting green innovation. ### Model structure and calibration highlights - Representative economies calibrated: Germany, Japan, Korea, Mainland China, the Netherlands, Taiwan Province of China (P.O.C.), and the United States. - Sectors: 117 sectors at the 3-digit International Patent Classification (IPC) level. - Calibrated parameters (selected): - 휆 Innovation step size: 0.5 - 휌 Discount rate: 0.5 - Data sources and mappings: - 훽 vector matches share of value added using World Input-Output Database (WIOD). - Spillover elasticities 휔 calibrated using PatStats patent citation shares. - Share of domestic spillovers 푥 calibrated with PatStats (average 2015-2019). - Political connectedness 휙 proxied by sector markups (Díez, Fan, and Villegas-Sánchez, 2021) and alternatively by share of lobbying expenditures (LobbyView, Kim, 2018). - Share of green patents from IPC Green Inventory (average 2010-2020). - Note on horizontal policy: sector-neutral subsidies do not change the share of scientists across sectors; only distribution matters, not level. ### Quantified welfare impacts (no implementation frictions) - United States (large, advanced economy): optimally targeting support to sectors with larger knowledge spillovers can increase welfare by almost 3 percent compared to an equivalent amount of sector-neutral support. - Including green innovation goals and redirecting support to sectors with a higher share of green patents can raise welfare gains to up to 6 percent (under the 2-percentage-point calibration described below). - Green-goal calibration: - 훼 vector proportional to share of green patents, scaled so welfare gains from green industrial policy are 2 percentage points larger than gains from “regular” industrial policy (baseline calibration), matching meta-analysis by Tol (2024). - Alternative calibration considered with gain reduced to 1 percentage point. ### Implementation frictions and sensitivity - Frictions modeled: - Political capture: government favors politically connected sectors (weights 휙 and parameter 휃). - Random mistakes: government makes random implementation errors (shocks 휓). - Key quantitative thresholds and findings: - As weight on political influence 휃 increases, welfare gains diminish; when 휃 reaches about 0.3 (30 percent of the weight given to the sector consumption elasticities), welfare gains from industrial policy turn into losses. - Mistakes (random distortions) are just as harmful to welfare as political capture in simulations; in some calibrations mistakes can be slightly worse. - Sensitivity example: benchmark 휃 = 0.5 leads to a misallocation of resources equivalent to 10 percent of the overall misallocation gap between the United States and large emerging market economies (Hsieh and Klenow 2009). ### Economy openness and international spillovers - Domestic industrial policy influence limited in small or open economies because larger shares of knowledge flows come from abroad. - Simulated potential welfare gains (no frictions) vary across the seven economies; the United States has the largest potential gains due to about 70 percent of its patent citations being domestic. - Distributional concentration of domestic citations matters: economies with concentrated domestic citations in sectors of comparative advantage (e.g., Taiwan P.O.C.) can obtain similar gains to larger-economy counterparts despite lower average domestic citation shares. - Geoeconomic fragmentation risk: less open advanced economies with higher domestic spillovers have greater incentives to implement industrial policies that may discriminate against foreign firms, potentially triggering costly retaliation. ### Green goals vs. other objectives - Incorporating green innovation goals increases potential welfare gains of industrial policy (up to 6 percent absent implementation frictions under the 2-percentage-point calibration). - Net effect depends on correlation across sectors between green intensity (share of green patents) and knowledge spillovers; if correlation is sufficiently negative, adding green goals can reduce welfare. - Including green goals slightly attenuates negative effects of political capture (flatter welfare curves) due to a small positive correlation between market power and green intensity in the calibration. ### Allocation across sectors: green intensity and AI exposure - Under optimal policy with green goals, greener sectors generally receive more support but not in a one-to-one relationship: sectoral spillover centrality is decisive. - Sectors projected to be more exposed to AI do not necessarily warrant higher fiscal support: AI exposure is not strongly correlated with either green intensity or cross-sector spillovers in available data. ### Assessment of existing policies (model vs. observed inventor distribution) - Method: compare model-implied optimal distribution of scientists (with green goals, no frictions) to actual distribution of inventors (patent authors) from PatStats. - Concentration measured by Herfindahl-Hirschman Index (HHI) at 3-digit IPC sector level. - Table of observed and optimal HHI, HHI Ratio (Optimal/Observed), and correlation (Optimal, Observed): - China - Observed HHI: 557 - Optimal HHI: 278 - HHI Ratio (Optimal/Observed): 0.50 - Correlation (Optimal, Observed): 0.31 - United States - Observed HHI: 528 - Optimal HHI: 264 - HHI Ratio (Optimal/Observed): 0.50 - Correlation (Optimal, Observed): 0.56 - Taiwan, P.O.C. - Observed HHI: 497 - Optimal HHI: 388 - HHI Ratio (Optimal/Observed): 0.78 - Correlation (Optimal, Observed): 0.70 - Japan - Observed HHI: 367 - Optimal HHI: 320 - HHI Ratio (Optimal/Observed): 0.87 - Correlation (Optimal, Observed): 0.56 - Korea - Observed HHI: 372 - Optimal HHI: 270 - HHI Ratio (Optimal/Observed): 0.73 - Correlation (Optimal, Observed): 0.71 - Netherlands - Observed HHI: 343 - Optimal HHI: 355 - HHI Ratio (Optimal/Observed): 1.03 - Correlation (Optimal, Observed): 0.35 - Germany - Observed HHI: 302 - Optimal HHI: 325 - HHI Ratio (Optimal/Observed): 1.07 - Correlation (Optimal, Observed): 0.60 - Interpretation: - United States and China: model suggests sectoral concentration would fall by half under the optimal policy—current policies appear excessively concentrated in a few sectors. - Korea and Taiwan P.O.C.: high correlation between observed and optimal distributions, reflecting targeted support to sectors with comparative advantage. - European economies (Germany, Netherlands): observed concentration similar to or less than model optimal. ### Policy implications and recommendations - Industrial policy for innovation can be beneficial but only under restrictive conditions: - Externalities must be correctly identified and precisely measured (e.g., carbon emissions). - Domestic knowledge spillovers from targeted sectors must be strong. - Government capacity must be high enough to prevent misallocation (e.g., political capture). - Where industrial policy is pursued, governments should: - Strengthen technical capacity to vet subsidized projects. - Establish clear benchmarks and objectives ex ante (including tradeoffs across goals such as green innovation and spillover maximization). - Conduct exhaustive assessment of fiscal costs and risks. - Recalibrate support as conditions change. - Foster competition to limit rent-seeking and capture. - Coordination across economies (e.g., regional programs) can increase effectiveness for smaller, more open economies where domestic spillovers are limited. ### Main caveats and avenues for future work - Analysis focuses on long-run impacts on distribution of innovation resources (not levels), under balanced growth path and without switching costs between sectors. - Framework abstracts from strategic international interactions; geoeconomic strategic considerations can lead to departures from welfare-maximizing policies. - Further research needed on: - Concrete sectoral policies and instruments other than innovation subsidies (e.g., production subsidies, tax incentives, credit allocation, trade restrictions). - Policies distinguishing between firms within sectors (e.g., national champions). ### Annex highlights - Annex A. Decentralized Equilibrium and Implementation of the Optimal Policy: - Each variety produced by a distinct monopolist; current highest-quality firm charges a markup (1+λ). - Variety production, cost, pricing, and profit conditions and sectoral cost minimization FOCs reproduced as in source. - Innovation and R&D subsidies: entrant FOC s_{iν} = V_{iν} (1−σ_i) / w_s and allocation formula s_{iν} = β_i/(1−σ_i) / ∑_{j} β_j/(1−σ_j). - Government can implement any scientist allocation {ŝ_{i}} by choosing {σ_i} to satisfy β_i/(1−σ_i) / ∑_{j} β_j/(1−σ_j) = ŝ_{i}. - Government budget constraint: ∑_{i} σ_i w_s s_i = T. - Annex B. Welfare Costs of Ignoring Green Goals: - Setup: government fixes objective giving green innovation a 2 percentage point welfare advantage; question is welfare cost of implementing baseline industrial policy instead. - Key findings: - When θ = 0, the optimal policy increases welfare relative to the baseline industrial policy by 2 percent (by construction). - The baseline industrial policy increases welfare (relative to a horizontal policy) by about 4 percent—higher than its base effect from Figure 6. - Mechanism and non-monotonicity: welfare impact of baseline policy is not monotonic in weight on political capture because green sectors have slightly more market power; low levels of political capture may move allocation toward greener sectors, but beyond a threshold political capture decreases welfare. - Figure B.1: compares welfare responses to political capture for policies that account for green goals (blue curve) versus those that do not (red dashed line); implementation friction measured by political favoritism toward sectors with higher average markups. *Source: IMF Working Paper — Industrial Policies for Innovation: A Cost-Benefit Framework (Chapter 2: Industrial Policies by Sector, 2023).* ### REFERENCES _____________________________________________________________________________________ ### IMF WORKING PAPERS Industrial Policies for Innovation: A Cost-Benefit Framework ### Introduction - Recent strategic push for industrial policies in large economies has raised whether and under what conditions governments should direct fiscal support toward innovation in specific sectors or technologies. - Examples of recent industrial policy initiatives cited: - CHIPS Act (United States) - Inflation Reduction Act (United States) - Green Deal Industrial Plan (European Union) - New Direction on Economy and Industrial Policy (Japan) - K-Chips Act (Korea) - Longstanding policies in emerging market economies like China - Common features of these packages: - Strong emphasis on innovation in specific sectors - Fiscal incentives for innovation in green and advanced technology sectors (such as AI and semiconductors) - Heavy reliance on costly subsidies - Reference to Figure 1: "Increasing Use of Industrial Policies for Innovation" with panel breakdowns: - Panel 1: "Share of Industrial Policies (Percent of total trade policies)" - Panel 2: emphasis on green and advanced technology sectors ### Figures and Visual Evidence - Figures listed in the content unit: - Figure 1. Increasing Use of Industrial Policies for Innovation - Figure 2. Sectoral Citation Network, Select Sectors, United States - Figure 3. Domestic Knowledge Spillovers, Select Economies - Figure 4. Evolution of the Average Share of Domestic Citations - Figure 5. Simulated Welfare Impact of Industrial Policy, United States - Figure 6. Simulated Welfare Impact of Industrial Policy, Select Economies - Figure 7. Welfare Gains of Industrial Policy with Green Goals - Figure 8. Optimal R&D Support by Sector’s Share of Green Patents and AI exposure - Figure B.1. Welfare Gains from Alternative Policies ### Tables and Calibration - Tables listed in the content unit: - Table 1. Calibrated Parameters - Table 2. Optimality of Existing Industrial Policies for Innovation, Select Economies ### Annex and Analytical Components - Annex sections enumerated: - A. Decentralized Equilibrium and Implementation of the Optimal Policy - B. Welfare Costs of Ignoring Green Goals - Annex page references: - ANNEX begins at page 31 - A. Decentralized Equilibrium and Implementation of the Optimal Policy starts on page 31 - B. Welfare Costs of Ignoring Green Goals referenced (page 33) - Figure B.1 on Welfare Gains from Alternative Policies referenced (page 34) ### Key Structural Elements and Page Markers - References section header present - Page markers in header/footer area indicate: - "28" near REFERENCES - "31" near ANNEX - Document header shows "INTERNATIONAL MONETARY FUND" and page number "4" in the sample page excerpt *Source: wpiea2024176-print-pdf - REFERENCES _____________________________________________________________________________________* ### 2. Industrial Policies by Sector, 2023 ### 2. Industrial Policies by Sector, 2023 ### Purpose and conceptual framework - Paper develops a framework to assess when sector-specific fiscal support for innovation (“industrial policy”) is preferable to sector-neutral support (“horizontal policy”). - Framework builds on a model of endogenous innovation with a sectoral network of knowledge spillovers (Liu and Ma, 2023). - Key mechanism: sector-specific support can direct innovation to sectors generating higher knowledge spillovers to other domestic sectors (measured by cross-sector patent citations), raising economy-wide innovation, productivity growth, and welfare. - Extensions introduced: - Implementation frictions: random policy mistakes or political capture that misallocate innovation inputs. - Alternative government goals: supporting green innovation. ### Model structure and calibration highlights - Representative economies calibrated: Germany, Japan, Korea, Mainland China, the Netherlands, Taiwan Province of China (P.O.C.), and the United States. - Sectors: defined at the 3-digit International Patent Classification (IPC) level, total of 117 sectors. - Calibrated parameters (selected): - 휆 Innovation step size: 0.5 - 휌 Discount rate: 0.5 - Data sources and mappings: - 훽 vector matches share of value added using World Input-Output Database (WIOD). - Spillover elasticities 휔 calibrated using PatStats patent citation shares. - Share of domestic spillovers 푥 calibrated with PatStats (average 2015-2019). - Political connectedness 휙 proxied by sector markups (Díez, Fan, and Villegas-Sánchez, 2021) and alternatively by share of lobbying expenditures (LobbyView, Kim, 2018). - Share of green patents from IPC Green Inventory (average 2010-2020). - Note on horizontal policy: sector-neutral subsidies do not change the share of scientists across sectors; only distribution matters, not level. ### Quantified welfare impacts (no implementation frictions) - Large, advanced economy example (United States): optimally targeting support to sectors with larger knowledge spillovers can increase welfare by almost 3 percent compared to an equivalent amount of sector-neutral support. - When the government includes green innovation goals and redirects support to sectors with a higher share of green patents, welfare gains can rise to up to 6 percent (under the 2-percentage-point calibration described below). - Green-goal calibration: - 훼 vector proportional to share of green patents, scaled so welfare gains from green industrial policy are 2 percentage points larger than gains from “regular” industrial policy (baseline calibration), matching meta-analysis by Tol (2024). - Alternative calibration considered with gain reduced to 1 percentage point. ### Implementation frictions and sensitivity - Two types of frictions modeled: - Political capture: government favors politically connected sectors (weights 휙 and parameter 휃). - Random mistakes: government makes random implementation errors (shocks 휓). - Key quantitative thresholds and findings: - As the weight on political influence 휃 increases, welfare gains diminish; when 휃 reaches about 0.3 (30 percent of the weight given to the sector consumption elasticities), welfare gains from industrial policy turn into losses. - Mistakes (random distortions) are shown to be just as harmful to welfare as political capture in simulations; in some calibrations mistakes can be slightly worse. - The welfare impact is sensitive to the size of 휃: e.g., a benchmark 휃=0.5 leads to a misallocation of resources equivalent to 10 percent of the overall misallocation gap between the United States and large emerging market economies (Hsieh and Klenow 2009). ### Economy openness and international spillovers - Ability of domestic industrial policy to influence knowledge spillovers limited in small or open economies because larger shares of knowledge flows come from abroad. - Simulated potential welfare gains (no frictions) vary across the seven economies; the United States has the largest potential gains due to about 70 percent of its patent citations being domestic. - Distributional concentration of domestic citations matters: economies with concentrated domestic citations in sectors of comparative advantage (e.g., Taiwan P.O.C.) can obtain similar gains to larger-economy counterparts despite lower average domestic citation shares. - Geoeconomic fragmentation risk: less open advanced economies with higher domestic spillovers have greater incentives to implement industrial policies that may discriminate against foreign firms, potentially triggering costly retaliation. ### Green goals vs. other objectives - Incorporating green innovation goals increases potential welfare gains of industrial policy (up to 6 percent absent implementation frictions under the 2-percentage-point calibration). - The net effect depends on correlation across sectors between green intensity (share of green patents) and knowledge spillovers; if correlation is sufficiently negative, adding green goals can reduce welfare. - Including green goals slightly attenuates negative effects of political capture (flatter welfare curves) due to a small positive correlation between market power and green intensity in the calibration. ### Allocation across sectors: green intensity and AI exposure - Under optimal policy with green goals, greener sectors generally receive more support but not in a one-to-one relationship: the degree to which sector innovation benefits other sectors (spillover centrality) is decisive. - Sectors projected to be more exposed to AI do not necessarily warrant higher fiscal support: AI exposure is not strongly correlated with either green intensity or cross-sector spillovers in the available data. ### Assessment of existing policies (model vs. observed inventor distribution) - Method: compare model-implied optimal distribution of scientists (with green goals, no frictions) to actual distribution of inventors (patent authors) from PatStats. - Concentration measured by Herfindahl-Hirschman Index (HHI) at 3-digit IPC sector level. - Table of observed and optimal HHI, HHI Ratio (Optimal/Observed), and correlation (Optimal, Observed): - China - Observed HHI: 557 - Optimal HHI: 278 - HHI Ratio (Optimal/Observed): 0.50 - Correlation (Optimal, Observed): 0.31 - United States - Observed HHI: 528 - Optimal HHI: 264 - HHI Ratio (Optimal/Observed): 0.50 - Correlation (Optimal, Observed): 0.56 - Taiwan, P.O.C. - Observed HHI: 497 - Optimal HHI: 388 - HHI Ratio (Optimal/Observed): 0.78 - Correlation (Optimal, Observed): 0.70 - Japan - Observed HHI: 367 - Optimal HHI: 320 - HHI Ratio (Optimal/Observed): 0.87 - Correlation (Optimal, Observed): 0.56 - Korea - Observed HHI: 372 - Optimal HHI: 270 - HHI Ratio (Optimal/Observed): 0.73 - Correlation (Optimal, Observed): 0.71 - Netherlands - Observed HHI: 343 - Optimal HHI: 355 - HHI Ratio (Optimal/Observed): 1.03 - Correlation (Optimal, Observed): 0.35 - Germany - Observed HHI: 302 - Optimal HHI: 325 - HHI Ratio (Optimal/Observed): 1.07 - Correlation (Optimal, Observed): 0.60 - Interpretation: - United States and China: model suggests sectoral concentration would fall by half under the optimal policy—current policies appear excessively concentrated in a few sectors. - Korea and Taiwan P.O.C.: high correlation between observed and optimal distributions, reflecting targeted support to sectors with comparative advantage. - European economies (Germany, Netherlands): observed concentration similar to or less than model optimal. ### Policy implications and recommendations - Industrial policy for innovation can be beneficial but only under restrictive conditions: - Externalities must be correctly identified and precisely measured (e.g., carbon emissions). - Domestic knowledge spillovers from targeted sectors must be strong. - Government capacity must be high enough to prevent misallocation (e.g., political capture). - Where industrial policy is pursued, governments should: - Strengthen technical capacity to vet subsidized projects. - Establish clear benchmarks and objectives ex ante (including tradeoffs across goals such as green innovation and spillover maximization). - Conduct exhaustive assessment of fiscal costs and risks. - Recalibrate support as conditions change. - Foster competition to limit rent-seeking and capture. - Coordination across economies (e.g., regional programs) can increase effectiveness for smaller, more open economies where domestic spillovers are limited. ### Main caveats and avenues for future work - Analysis focuses on long-run impacts on distribution of innovation resources (not levels), under balanced growth path and without switching costs between sectors. - Framework abstracts from strategic international interactions; geoeconomic strategic considerations can lead to departures from welfare-maximizing policies. - More granular research needed on concrete sectoral policies and instruments other than innovation subsidies (e.g., production subsidies, tax incentives, credit allocation, trade restrictions), and on policies distinguishing between firms within sectors (e.g., national champions). *Source: IMF Working Paper — Industrial Policies for Innovation: A Cost-Benefit Framework (Chapter 2: Industrial Policies by Sector, 2023).* ### References ### References ### Bibliographic references - Acemoglu, Daron, Simon Johnson, Amir Kermani, James Kwak, and Todd Mitton. 2016. “The Value of Connections in Turbulent Times: Evidence from the United States.” Journal of Financial Economics 121 (2): 368–391. - Aghion, Philippe, Nick Bloom, Richard Blundell, Rachel Griffith, and Peter Howitt. 2005. “Competition and Innovation: An Inverted-U Relationship.” The Quarterly Journal of Economics 120: 701–728. - Akcigit, Ufuk, Salomé Baslandze, and Francesca Lotti. 2023. “Connecting to Power: Political Connections, Innovation, and Firm Dynamics.” Econometrica 91 (2): 529–564. - Baqaee, David Rezza, and Emmanuel Farhi. 2020. “Productivity and Misallocation in General Equilibrium.” The Quarterly Journal of Economics 135 (1):105–163. - Barwick, Panle Jia, Hyuk-soo Kwon, Shanjun Li, and Nahim Bin Zahur. 2024. “Driving Down the Cost: Learning By Doing and Government Policy in the Electric Vehicle Battery Industry.” Working Paper, 2024. - Barwick, Panel Jia, Myrto Kalouptsidi, and Nahim Bin Zahur. 2023. “Industrial Policy Implementation: Empirical Evidence from China’s Shipbuilding Industry.” NBER Working Paper 26075. - Bilal, Adrien and Diego Känzig. 2024. “The Macroeconomic Impact of Climate Change: Global vs. Local Temperature.” NBER Working Paper 32450. - Bils, Mark, Peter J. Klenow, and Cian Ruane. 2021. “Misallocation or Mismeasurement?” Journal of Monetary Economics 124S:S39-S56. - Bloom, Nicholas, Mark Schankerman, and John Van Reenen. 2013. “Identifying Technology Spillovers and Product Market Rivalry.” Econometrica 81 (4):1347-1393. - Bloom, Nicholas, John Van Reenen, and Heidi Williams. 2019. “A Toolkit of Policies to Promote Innovation.” Journal of Economic Perspectives 33 (3):163–84. - Cherif, Reda, and Fuad Hasanov. 2019. “The Leap of the Tiger: Escaping the Middle-Income Trap to the Technological Frontier.” Global Policy 10 (4): 497–511. - Choi, Joonkyu, Veronika Penciakova, and Felipe Saffie. 2021. “Political Connections, Allocation of Stimulus Spending, and the Jobs Multiplier.” FEDS Working Paper No. 2021-005R1. - David, Joel M., and Venky Venkateswaran. 2019. “The Sources of Capital Misallocation.” American Economic Review, 109 (7): 2531–67. - Díez, Federico, Jiayue Fan, and Carolina Villegas-Sánchez. 2021. “Global declining competition?” Journal of International Economics (132). - Evenett, Simon, Adam Jakubik, Fernando Martín, and Michele Ruta. 2024. “The Return of Industrial Policy in Data”. IMF Working Paper No. 2024/001. - Felten, Edward, Manav Raj, and Robert Seamans. 2021. “Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and its Potential Uses.” Strategic Management Journal 42 (12): 2195–2217. - Garcia-Macia, Daniel. 2017. “The Financing of Ideas and the Great Deviation.” IMF Working Paper 2017/176. - Garcia-Macia, Daniel, and Rishi Goyal. 2020. “Technological and Economic Decoupling in the Cyber Era.” IMF Working Paper 20/257. - Hsieh, Chang-Tai, and Peter J. Klenow. 2009. “Misallocation and Manufacturing TFP in China and India.” The Quarterly Journal of Economics 124 (4):1403–1448. - Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow. 2019. “The Allocation of Talent and U.S. Economic Growth.” Econometrica 87(5):1439-1474. - Hopenhayn, Hugo, and Francesco Squintani. 2021. “On the Direction of Innovation.” Journal of Political Economy 129 (7). - Kim, In Song. 2018. “LobbyView: Firm-level Lobbying & Congressional Bills Database.” Working Paper. - Liu, Ernest. 2019. “Industrial Policies in Production Networks.” The Quarterly Journal of Economics 134 (4):1883–1948. - Liu, Ernest, and Song Ma. 2023. “Innovation Networks and R&D Allocation.” NBER Working Paper 29607. - IMF. 2024a. “Expanding Frontiers: Fiscal Policies for Innovation and Technology Diffusion”. April 2024 Fiscal Monitor, Chapter 2. - IMF. 2024b. “Industrial Policy Coverage in IMF Surveillance—Broad Considerations”, IMF Policy Paper No. 2024/008. - Juhász, Réka, Nathan Lane, Emily Oehlsen, and Verónica C. Pérez. 2022. “The Who, What, When, and How of Industrial Policy: A Text-Based Approach.” Working Paper. - Juhász, Réka, Nathan J. Lane, and Dani Rodrik. 2023. “The New Economics of Industrial Policy.” NBER Working Paper 31538. - Lybbert, Travis, and Nikolas Zolas. 2019. “Getting Patents and Economic Data to Speak to Each Other: An ‘Algorithmic Links with Probabilities’ Approach for Joint Analyses of Patenting and Economic Activity.” Research Policy, 43.3 (2014): 530–542. - Maslej, Nestor, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Vanessa Parli, Yoav Shoham, Russell Wald, Jack Clark, and Raymond Perrault. 2023. “The AI Index 2023 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA. - Peters, Michael. 2020. “Heterogeneous Markups, Growth, and Endogenous Misallocation.” Econometrica 8(5):2037-2073. - Restuccia, Diego, and Richard Rogerson. 2008. “Policy Distortions and Aggregate Productivity with Heterogeneous Establishments.” Review of Economic Dynamics 11(4). - Sollaci, Alexandre. 2022. “Agglomeration, Innovation, and Spatial Reallocation: The Aggregate Effects of R&D Tax Credits.” IMF Working Paper 2022/131. - Timmer, Marcel., Erik Dietzenbacher, Bart Los, Robert Stehrer, and Gaaitzen de Vries. 2015. “An Illustrated User Guide to the World Input–Output Database: The Case of Global Automotive Production.” Review of International Economics, 23: 575–605. - Tol, Richard. 2024. “A Meta-Analysis of the Total Economic Impact of Climate Change.” Energy Policy 185. ### Annex A. Decentralized Equilibrium and Implementation of the Optimal Policy - Assumptions: - Each variety is produced by a distinct monopolist; the current highest-quality firm charges a markup (1+λ) over marginal cost. - Firm size limited to one variety; entrants invest in innovation to “steal” a variety; entrant becomes monopolist and incumbent exits. - Continuum (measure 1) of potential entrants per variety who hire scientists; a successful innovation improves a randomly drawn variety/sector. - Representative household supplies scientists and production labor and receives wage income and profits. - Cost minimization and production: - Expenditure share in each sector equals its elasticity β_i: p_{iν} y_{iν} / y_i = β_i. - Variety production: y_{iν}(ν) = q_{iν}(ν)^{ν} ℓ_{iν}(ν), marginal cost = w_l ℓ_{iν}(ν) / q_{iν}(ν)^{ν}. - Monopolist price: p_{iν}(ν) = (1+λ) w_l ℓ_{iν}(ν) / q_{iν}(ν)^{ν}. - Firm profit per variety: = λ w_l ℓ_{iν}(ν). - Sectoral cost minimization problem (as in source): - min_{ℓ(·)} ∫_{ν∈{a,b}} (1+λ) w_l ℓ_{iν}(ν) dν s.t. ln y_i = ∫_{ν} ln y_{iν}(ν) dν, leading to FOC (1+λ) w_l = μ_{iν} and ℓ_{iν}(ν) ≡ ℓ_i constant across firms within a sector. - Perfect competition on sectoral good implies ℓ_{iν} = β_i y_i (1+λ) w_l. With total production workers ∑_{i,ν} ℓ_{iν} = ℓ̄, one gets ℓ_{iν} = β_i ℓ̄ for all sectors. - Profits: p_{iν}(ν) y_{iν}(ν) − w_l ℓ_{iν}(ν) = λ/(1+λ) β_i y_i ≡ π_{iν}. - Innovation and R&D subsidies: - Let r be interest rate and δ be (constant) rate of innovation in balanced growth path. - Monopolist value: V_{iν} = ∫_{s≥0} e^{−rs} π_{i0} ds = λ/(1+λ) β_i ∫_{s≥0} e^{−rs} y_0 ds. - Entrant problem (with sector-specific R&D subsidy σ_i): V_{iν}^e = max_{s_{iν}≥0} { (1−σ_i) w_{s} s_{iν} + ln[s_{iν} η_{i} χ_{iν} q_{iν}] V_{iν} } (as in source notation). - FOC: s_{iν} = V_{iν} (1−σ_i) / w_s, implying s_{iν} / s_{jν} = β_i/(1−σ_i) ÷ β_j/(1−σ_j). - With ∑_{i} s_{iν} = 1, s_{iν} = β_i/(1−σ_i) / ∑_{j} β_j/(1−σ_j). - Implementation of optimal allocation: - In fully decentralized equilibrium (σ_i = 0 ∀ i), s_{iν} = β_i for all sectors. - Government can implement any scientist allocation {ŝ_{i}} by choosing {σ_i} such that β_i/(1−σ_i) / ∑_{j} β_j/(1−σ_j) = ŝ_{i}. - Optimal allocation ŝ_i^* is implemented by sector-specific subsidies/taxes given by σ_i = 1 − [β_i/ŝ_i^* (∑_{j} β_j/(1−σ_j))]^{1/5} (expression as presented in the source). - Government budget constraint: - ∑_{i} σ_i w_s s_i = T, where T denotes a lump-sum transfer or tax. ### Annex B. Welfare Costs of Ignoring Green Goals - Setup and question: - Government fixes objective function assuming green innovation increases welfare by 2 percentage points relative to the baseline industrial policy; the question: welfare consequences of implementing baseline industrial policy anyway (including political capture). - Key quantitative findings: - When there is no political capture (θ = 0), the optimal policy increases welfare relative to the baseline industrial policy by 2 percent (by construction). - The baseline industrial policy increases welfare (relative to a horizontal policy) by about 4 percent—higher than its base effect from Figure 6. - Mechanism and non-monotonicity: - Having green goals in the objective moves the optimal allocation farther from the no-industrial-policy equilibrium (s_i = β_i). - Welfare impact of the baseline industrial policy is not monotonic in the weight on political capture because green sectors happen to have slightly more market power (measure of political connectedness) than brown sectors; increasing the weight on political capture initially moves allocation toward greener sectors (the “correct” direction) for low levels of political capture, but beyond a certain level political capture decreases welfare. - Figure B.1 (as described in the source): - Shows welfare impact of different policies under the same welfare function favoring greener-sector innovation. - Blue curve: welfare response to political capture under a policy that accounts for green goals. - Red dashed line: welfare evolution under a policy that does not take green goals into account (the “wrong” subsidies). - Implementation friction measured by political favoritism toward sectors with higher average markups, as estimated by Díez, Fan, and Villegas-Sánchez (2021). *Industrial Policies for Innovation: A Cost-Benefit Framework. Working Paper No. WP/2024/176* --- _Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024176-print-pdf.pdf_