Artificial intelligence capital stock in Europe, UK, USA and Japan 1995 - 2020
Description
Following (Rimmington et al., 2018), we target the EUKLEMS & INTANProd database data on capital stocks, categorized by SIC code and country, including software, databases, computer hardware, and machinery. From their definition of AI, we measure four distinctive AI capital stock categories: 1. Data and equipment: computing equipment, communication equipment and compute software and databases, chained linked volumes (2015), millions of national currencies. 2. Research and development: chained linked volumes (2015), millions of national currency. 3. Intangible assets: organizational capital, brand, industrial design, chained linked volumes (2015), millions of national currency. 4. Skills: training, labor compensation for NACE_R2 Computer programming, consultancy, and information service activities and NACE_R2 Education, chained linked volumes (2015), millions of national currency. Building on our methodology, we get a closer proxy for AI stock using the data from (Bontandini et al., 2023) and (Rimmington et al., 2018) approach. We improve them to get more reliable and exact AI stock measurements using four AI dimensional coefficients. The AI dimensional coefficients approach we develop here builds on AI intensify coefficient applied to calculate AI investments (Evas et al., 2022). We derive four AI dimensional coefficients following (Evas et al., 2022) analysis of AI investments in the EU. These coefficients will enable us to calculate the AI net stock per country accurately. 1. AI dimensional coefficient 1 (AI1): number of AI economic players / GDP in billion € (ratio). For details, see G2: AI player intensity (Righi et al., 2021), 2. AI dimensional coefficient 2 (AI2): number of AI players in AI R&D / GDP in billion € (ratio). For details, see R1: AI players in AI R&D (Righi et al., 2021), 3. AI dimensional coefficient 3(AI): University places with AI content in the EU(Bachelor and master level) / GDP in millions € (ratio). For details, see S6: University places with AI content in the EU (Righi et al., 2021). Data for the USA, UK, and Japan for AI dimensional coefficients AI3 were derived using (OECD.AI, 2023), 4. AI dimensional coefficient 4 (AI4): AI investments in the EU in million € / GDP in billion €, (ratio). For details, see G6: AI investments in the EU (Righi et al., 2022). Data for the USA, UK, and Japan for AI dimensional coefficients AI4 were derived using - venture capital investments in AI start-ups (OECD.AI, 2023). We have developed and implemented AI dimensional coefficients (AI1 - AI4) to improve the accuracy of data analysis. Our revised data is presented as follows: Total revised AI net capital stock = AI data and equipment net capital stock x (AI1) + AI research and development net capital stock x (AI2) + AI skills stock net x (AI3) + AI net intangible assets stock x (AI4).
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Steps to reproduce
Step 1: Obtain access to the EUKLEMS & INTANProd database by contacting the authors of the study by Bontandini et al. (2023). Request permission to use the database for your research purposes. Step 2: Once you have obtained access to the database, navigate to the relevant sections that provide information on productivity, capital stock, and intangible assets across industries and countries. Identify the variables of interest, including output, intermediate inputs, gross value added, employment, employee compensation, and investment in tangible and intangible assets. Step 3: Extract the data from the database for the desired time period, which spans from 1995 to 2020. Ensure that the data covers the 27 EU Member States, the United States of America, Japan, and the United Kingdom, as well as the 40 industries and 23 industry aggregates. Step 4: Create a new AI stock database by defining the parameters that constitute AI based on the definition established in Rimmington et al. (2018). Include computer systems capable of perceiving their surroundings, engaging in cognitive processes, and acquiring knowledge to achieve their goals. This definition encompasses various AI applications such as digital assistants, question-and-answer systems, and machine vision. Step 5: Refer to the EUKLEMS & INTANProd database and identify the relevant data categories for measuring AI capital stock, which include data and equipment, research and development, intangible assets, and skills. These categories should be classified by SIC code and country, covering software, databases, computer hardware, machinery, and other related aspects. Step 6: Apply the four AI dimensional coefficients (AI1 - AI4) derived from Evas et al. (2022) to refine the measurement of AI net capital stock. Calculate each coefficient based on the specific variables and ratios described in the previous description. For example, AI1 represents the ratio of the number of AI economic players to GDP in billion €, AI2 represents the ratio of the number of AI players in AI R&D to GDP in billion €, and so on. Step 7: Utilize the AI dimensional coefficients to modify and enhance the existing data from the EUKLEMS & INTANProd database. Multiply the relevant AI capital stock categories (data and equipment, research and development, intangible assets, and skills) by their corresponding AI dimensional coefficients to obtain more accurate figures for AI net capital stock. Step 8: Aggregate the revised data based on the calculated AI net capital stock. Combine the AI data and equipment net capital stock, AI research and development net capital stock, AI skills stock net, and AI net intangible assets stock using the provided formula: Total revised AI net capital stock = AI data and equipment net capital stock x (AI1) + AI research and development net capital stock x (AI2) + AI skills stock net x (AI3) + AI net intangible assets stock x (AI4).