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Abstract and poster of paper 0286 presented at the Digital Humanities Conference 2019 (DH2019), Utrecht , the Netherlands 9-12 July, 2019.

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Historical ownership data of AET by Orbis Investment Management U S L P

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Historical ownership data of RVBD by Orbis Investment Management U S L P

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This database links innovation data to Compustat firms. When using the data, please cite "Knowledge Spillovers and Corporate Investment in Scientific Research" (Arora, Belenzon and Sheer), NBER WP 23187. A special thanks and appreciation go to Bernardo Dionisi , Honggi Lee, Dror Shvadron and JK Suh for their diligent work and dedication to this effort over the past several years.

This project introduces major data extension and improvement to the historical NBER patent dataset, which should be valuable for all researchers working with patent data linked to firms. In updating the data to match between Compustat and patents to 2015, we address two major challenges: name changes and ownership changes. These challenges are central to how patents are assigned to firms over time. To be consistent over the sample period, we reconstruct the complete historical data covered in the NBER data files.

About 30% of the Compustat firms in our sample change their name at least once. Accounting for name changes improves the accuracy and scope of matches to patents (and other assets), ownership structure, and dynamic reassignments of GVKEY codes to companies. Dynamic reassignment means that, for instance, if a sample firm merges with another firm, the patents of the merged firm are included in the stock of patents linked to the Compustat record from that point onward, but not before.

For ownership and subsidiary data we rely on a wide range of M&A data, including SDC, historical snapshots of ORBIS files for 2002-2015, 10-K SEC filings, and NBER2006 as well as perform extensive manual checks that help us uncover firms’ structure and ownership changes before proceeding to the patent match. Thus, we have extended and improved the NBER patent data. In the enclosed "Data Appendix", we document our data construction work, present several examples (“case studies”), and outline the improvements we made to existing NBER historical patent data.

Historical ownership data of AMGN by Orbis Investment Management U S L P

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Historical ownership data of AAPL by Orbis Investment Management U S L P

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Historical ownership data of MU by Orbis Investment Management U S L P

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Orbis-Systems.

Source: ICE Data Services, Orbis (Bureau Van Dijk), Network for Greening the Financial System (NGFS); IMF staff calculations.Category: Transition to a Low-Carbon EconomyData series: Revenues at Risk in Disclosing FirmsAssets at Risk in Disclosing FirmsMetadata:The data from the private data vendor ICE Data Services are used for firm level emissions. ICE Data Services provides company-level data on emissions disclosures, estimated emissions, emission targets, and model-based emission projections up to 2050. The Revenues/Assets at Risk in Disclosing Firms indicators leverage on firm-level emissions disclosures and projections of these firms for the period 20252050. The sample is restricted to firms whose emissions disclosures are deemed complete. Data for a firm are considered “Complete” when at least 95% of a company’s worldwide Scope 1 and 2 emissions within an appropriately chosen reporting boundary are covered by their reporting. Firm identifiers (ISIN or LEI codes) from ICE are mapped to Orbis dataset for the compilation of the indicators. Orbis is a dataset compiled and maintained by Bureau Van Dijk and owned by Moody’s Analytics. The Revenues/Assets at Risk in Disclosing Firms indicators are calculated using the information on company revenues, assets, country of incorporation, and ISIC core sector provided by ICE.The Network for Greening the Financial System (NGFS) produces model-based simulation of the joint climate-economic systems under different scenarios. The Revenues and Assets at Risk in Disclosing Firms indicators are calculated using NGFS Phase 3 output from the simulation of the model REMIND-MAgPIE 3.0-4.4 downloaded by IMF staff in December 2023.Methodology:The Revenues/Assets at Risk in Disclosing Firms indicators are calculated at the level of ISIC Section (letter code), ISIC Division (2 digits), and WEO country grouping, but not crossing the sectoral and geographic dimensions. These indicators are only calculated for Scope 1 emissions. Specifically, for each of the sectors or geographies, the indicator is defined as the share of total revenues or assets in each sector or geography in firms at risk. In turn, firms at risk as defined as those disclosing firms for which, in at least one of the projection years 2025-2050, the total emissions in each scenario/year times the corresponding carbon price in that scenario/year from the NGFS phase 3 scenarios exceeds 10% of the revenues for that firm. The underlying assumption when calculating these indicators based on Scope 1 emissions is that companies will be directly and fully taxed at the posted carbon tax rate under the assumed scenario. As estimates of future revenues are not available, firms at risk are identified in comparison to the 2021 revenues to scale the carbon tax burden on the firms. However, current revenues may not fully reflect future revenues. To address this, a second version of the estimates is calculated by relaxing the constant revenues and assets assumption. For this set of indicators, firms’ revenues and assets are adjusted by a growth factor by considering past growth trends (based on five years sector-country average growth rate pre-covid) and WEO growth assumptions. To guarantee the preservation of a reasonable degree of confidentiality/ representativeness of the underlying company-level information, results are not calculated in those instances in which either the number of disclosing firms in each sector/geography is strictly less than 10 (9 or lower), or the share of either assets or revenues (whichever is applicable) of the top 2 firms exceeds 85%.

Historical ownership data of LBTYK by Orbis Investment Management U S L P

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Table of correlation coefficient matrix of enterprise patent code.