WORKS IN PROGRESS:
Optimal allocation of corporate subsidy
Size-dependent policy and firm growth
Firm growth and supply chain network structure
Using supply chain network information to characterize industries
CEO markets in Japan
Declaration of corporate growth strategy
Work from home and office capacity
Reallocation of tangibles: Evidence from post-leasing contracts transactions
Rent and innovation: Policy perspective
Impacts of industrial policy: Granular data approach
Measuring elasticity of substitution: Granular data approach
Forgiveness versus Financing: The determinants and impact of SME debt forbearance in Japan
SELECTED DISCUSSION PAPERS / WORKING PAPERS:
Maehashi, K., D. Miyakawa, and T. Sasaki (2026) “Heterogeneous Views and Currency Swing Prediction: Evidence from Trade Repository Data,” Bank of Japan Working Paper Series No.26-E-10.
Maehashi, K., D. Miyakawa, and K. Sasamoto (2024) “Pricing Implication of Centrality in an OTC derivative Market: An Empirical Analysis Using Transaction-Level CDS Data,” Bank of Japan Working Paper Series No.24-E-11.
Benes, N., Garton, B., Miyakawa, D. and J. Yamanoi (2024) “Using High-dimensional Corporate Governance Variables to Predict Firm Performance,” RIETI Discussion Paper Series 24-E-030.
Arata, Y., D. Miyakawa, and K. Mori (2023) “The U-Shaped Law of High Growth Firms,” NTC Joint Research Discussion Paper Series 230200–01HJ.
Fukunaga, K. and D. Miyakawa (2022) “Credit Supply and Supply Chain Network,” IMES Discussion Paper Series 2022-E-8, Bank of Japan.
Miyakawa, D., T. Hayakawa, D. Kato, R. Koyano, and M. Nakayama (2022) “Task Allocation between Machine and Human for Investments in Financial Markets,” JSDA Capital Markets Forum Working Paper (In Japanese).
Miyakawa, D. and M. Takizawa (2022) “Productivity and Business Dynamism in Japan: Comparison with the EU countries using firm-level data,” RIETI Policy Discussion Paper Series 22-P-005 (In Japanese).
Kondo, S., D. Miyakawa, K. Shiraki, M Suga, and T. Usuki (2019) “Using Machine Learning to Detect and Predict Accounting Fraud,” RIETI Discussion Paper Series 19-E-103.
Koujaku, S. and D. Miyakawa (2019) “Communities of Investors and Start-up Companies: An Analysis Using Bipartite Stochastic Block Model,” RIETI Discussion Paper Series 19-E-101.
Miyakawa, D. (2019) “Shocks to Supply Chain Networks and Firm Dynamics: An Application of Double Machine Learning,” RIETI Discussion Paper Series 19-E-100.
Komai, H., R. Koyano, and D. Miyakawa (2017) “Contrarian Trades and Disposition Effect: Evidence from Online Trade Data,” SSRN Working paper.
Miyakawa, D. and K. Ohashi (2016), “Multiple Lenders, Temporary Debt Restructuring, and Firm Performance: Evidence from Contract-Level Data,” RIETI Discussion Paper Series 16-E-030.
Uchida, H., D. Miyakawa, K. Hosono, A. Ono, T. Uchino, and I. Uesugi (2014), “Natural Disaster and Natural Selection,” RIETI Discussion Paper Series 14-E-055.
OTHER DISCUSSION PAPERS / WORKING PAPERS:
Furuya, T., D. Miyakawa, and M. Takizawa “Using Corporate Tax Filing Data to Measure Business Dynamism.” NTC Joint Research Discussion Paper Series 240100-01HJ.
Shinozaki, S., D. Miyakawa, and R. Arahan (2024) “Factors Affecting Micro, Small, and Medium-Sized Enterprise Development in Developing Asia: Findings from a Probabilistic Principal Component Analysis,” ADB Economics Working Paper Series No.715.
Inaba, K. M. Komatsu, and D. Miyakawa (2023) “A First Foray into the Phenomenon of Equity Crowdfunding in Japan: What Do Private Fundraising Records Tell?” SSRN Working Paper.
Kato, R., D. Miyakawa, Y. Yanaoka, and S. Yukimoto (2024) 「滞在人口データを用いた取引先企業のリース需要予測」 “Using Supply Chain Network Information and High-Frequency Mobility Data to Forecast Firm Dynamics,” 人工知能学会全国大会論文集, 2024年度人工知能学会全国大会（第38回）.
Arata, Y. and D. Miyakawa “The Size of Micro-originated Aggregate Fluctuations: An analysis of firm-level input-output linkages in Japan,” (former title: An Empirical Analysis for the Micro Origin of Aggregate Fluctuations) RIETI Discussion Paper Series 21-E-066.
Usuki, T., S. Kondo, K. Shiraki, D. Miyakawa, Y, Yanaoka (2021)「機械学習手法を用いた不正会計予測：非上場企業データを用いた検討」 “Using Machine-Learning Technique to Predict Accounting Fraud” HUB FS Working Paper Series, FS-2021-J-001 (In Japanese).
Usuki, T., S. Kondo, K. Shiraki, T. Sanada, K. Suzaki, and D. Miyakawa (2021)「機械学習手法を用いた勘定科目レベルの異常検知」 “Using Machine-Learning Technique to Detect Anomaly of Accounting Items” RIETI Discussion Paper Series 21-J-049 (In Japanese).
Miyakawa, D. and M. Takizawa (2020) 「日本企業の労働生産性：財務データを用いた計測と分布に基づく議論」 “Labor Productivity of Japanese Firms”, mimeo (In Japanese).
Miyakawa, D. and M. Takizawa (2019) 「ICT投資が雇用と生産性に与える因果効果：税制ショックを用いた実証分析」 “How Does ICT investment Affect Employment and Productivity: An Natural Experiment Approach Using Tax Shock”, RIETI Discussion Paper Series 19-J-068.
Usuki, T., S. Kondo, K. Shiraki, M. Suga, and D. Miyakawa (2019) 「機械学習手法を用いた不正会計の検知と予測」 “Using Machine Learning to Detect and Predict Accounting Fraud,” RIETI Discussion Paper Series 19-J-039 (In Japanese).
Hosono, K., M. Hotei, and D. Miyakawa (2019) “Size-dependent VAT, Compliance Costs, and Firm Growth,” RIETI Discussion Paper Series 19-E-041.
Miyakawa, D., and M. Takizawa (2018) 「産業別労働生産性の国際比較：水準とダイナミクス」“International Comparisons of Labor Productivity: Level and dynamics,” RIETI Policy Discussion Paper Series 18-P-007 (In Japanese).
Koujaku, S. and D. Miyakawa (2017) “Venture Capital Networks: An Analysis using Exponential Random Graph Model,” RIETI Discussion Paper Series 17-E-084.
Hosono, K., D. Miyakawa, and M. Takizawa (2017) “Do Overseas Subsidiaries Benefit from Parent Firms’ Intangibles?” RIETI Discussion Paper Series 17-E-073.
Hosono, K., D. Miyakawa, and M. Takizawa (2017) “Intangible Assets and Firms’ Liquidity Holdings: Evidence from Japan,” RIETI Discussion Paper Series 17-E-053.
Miyakawa, D. and M. Takizawa (2015), “Capital Supply Channel through Venture Capitals: Evidence from Matched Data,” RIETI Discussion Paper Series 15-E-141.
Hosono, K., D. Miyakawa, and M. Takizawa (2015), “Firms’ Overseas Activities and Firm Performance: A Role of Transaction Partners,” RIETI Discussion Paper Series 15-E-053.
Miyakawa, D. and M. Takizawa (2015),「ベンチャーキャピタル投資のメンバー構成とその含意：投資ラウンド明細データを用いた分析」 (Exploring the Patterns of Venture Capital Syndication), RIETI Discussion Paper Series 15-J-009. (In Japanese).
Hosono, K., Hotei, M., and D. Miyakawa (2015), 「研究開発税額控除は研究開発投資を促進するか？―資本コストと内部資金を通じた効果の検証―」“R&D Tax Policy and Firms’ R&D Investments,” RIETI Discussion Paper Series 15-J-030 (In Japanese).
Ito, K., T. Inui, and D. Miyakawa (2014), “Lender Banks’ Provision of Overseas Market Information: Evidence from Japanese small and medium-sized enterprises’ export dynamics,” RIETI Discussion Paper Series 14-E-064.
Hosono, K. and D. Miyakawa (2014), “Business Cycles, Monetary Policy, and Bank Lending: Identifying the Bank Balance Sheet Channel with Firm-Bank Match-Level Loan Data,” RIETI Discussion Paper Series 14-E-026.
Miyakawa, D. and M. Takizawa (2014), “Time to IPO: The Role of Venture Capital Firm Heterogeneity,” mimeo. Older version is published as RIETI Discussion Paper Series 13-E-022. Submitted.
Miyakawa, D. (2013), 「銀行ローンシェア構造の決定要因：企業-銀行マッチレベルデータからの含意」 (Bank Loan Share Structure: Empirical Examination), Economics Today vol.33 No.1 (In Japanese).
Miyakawa, D. and M. Takizawa (2013), “Performance of Newly Listed Firms: Evidence from Japanese firm and venture capital data,” RIETI Discussion Paper Series 13-E-019.
Inui, T., Miyakawa, D., and K. Shoji (2012), “Bank Efficiency and Client Firms' Export Behavior: Evidence from Firm-Bank Match-Level Data,” RIETI Discussion Paper Series 12-E-018.
Miyakawa, D., T. Inui, and K. Shoji (2011) “Bank Efficiency and Client Firms' Productivity,” ESRI Discussion Paper Series No.273.
Miyakawa, D., T. Inui, and K. Shoji (2011) “Productivity of Banks and its Impact on the Capital Investments of Client Firms,” RIETI Discussion Paper Series 11-E-016.
Miyakawa, D. (2010), “How Does the Stability of Loan Relations Depend on its Duration? Evidence from Firm- and Bank-Level Data,” Economics Today vol.31 No.3, pp.1-34. (In Japanese)