Tracking the Rise of Robots: The IFR Database

We present and analyze the dataset on the international distribution of industrial robots by country, industry, and application provided by the International Federation of Robotics (IFR) since 1993. After describing the IFR we point out specificities and limitations of its dataset. We explain the process of data collection, develop a correspondence table between the IFR industry classification and the ISIC rev. 4 industry classification, and clarify the applied compliance rules. We further compute average implicit depreciation rates inherent to the robot stocks in the IFR dataset in the range of 4–7% per year between 1993 and 2019. We also find that the share of industrial robots that are not classified to any industry or application has sharply declined since 2005.

Industry Concentration and Profitability in Europe: The Case of Norway

Several recent studies show that market concentration in the US has increased over time, with firm profits increasing in the same period. The consistency of findings from the US is contrasted by more varying results from studies of the development of market concentration in Europe. In this study, we utilise the completeness of Norwegian microdata to investigate how methodological choices and data limitations impact results with respect to the market concentration and its relationship with profitability. First, we find that concentration in Norway has decreased slightly over the last two decades. Over the same period, profitability has increased slightly for two profitability measures and been stable for the other two. Despite a difference in overall trends, at the industry level, we find a positive and statistically significant relationship between concentration and profitability for three out of four profitability measures, in line with the market power hypothesis. Investigating the effect of methodological choices and data limitations, we find that concentration trends are quite robust to exclusion of smaller companies, the incorporation of ownership structures in concentration measures and the choice of industry classification. However, the positive relationship between concentration and profitability is almost non-existent when using readily available industry classification instead of more product market-oriented industry classifications and disappears completely when we do not exclude export-oriented industries. Our study is relevant for future research, as well as for policymakers, as our results indicate that one should be careful when interpreting results from studies of market concentration that fail to handle these methodological challenges.

Markups for Consumers

A central motivating factor for studying price markups is their effect on consumer welfare. However, reported estimates of (firm-level) price markups in the literature often focus on industry or cross-country comparisons. These treat different industries equally rather than based on how relevant they are for consumers. We propose markup measures in which firm-level price markups are weighted according to consumption expenditures in the respective industries. Using a concordance table between consumption categories (otherwise used for the calculation of consumer price indices) and a firm’s industry classification, we report results for Germany for the years 2002 through 2016. We find that consumption-weighted price markups are higher and have increased faster than the conventionally reported revenue-weighted markups. We further show that consumption-weighted markups are highest for low-income households, highlighting the potential role of price markups as a contributing factor to changes in inequality in society.

Chinese environmentally extended input-output database for 2017 and 2018

Environmental footprint analyses for China have gained sustained attention in the literature, which rely on quality EEIO databases based on benchmark input-output (IO) tables. The Chinese environmentally extended input-output (CEEIO) database series provide publically available EEIO databases for China for 1992, 1997, 2002, 2007, and 2012 with consistent and transparent data sources and database structure. Based on the latest benchmark IO tables for China for 2017 and 2018, here we develop the corresponding 2017 and 2018 CEEIO databases following the same method used to develop previous CEEIO databases. The 2017 and 2018 CEEIO databases cover 44 and 28 types of environmental pressures, respectively, and consider multiple sector classifications including ones consistent with previous CEEIO databases and ones following the 2017 China’s national economy industry classification standard. A notable improvement in the 2017 and 2018 CEEIO databases is the comprehensive inclusion of CO2 emissions from additional industrial processes. This work provides a consistent update of the CEEIO database and enables a wide range of timely environmental footprint analyses related to China.

Constructing economic taxonomy reflecting firm relationships based on news reports

PurposeThis paper aims to propose a novel approach to constructing an economic taxonomy that demonstrates the complex relationships between firms, which are not fully revealed by traditional industry classification systems such as the NAICS or ICB.Design/methodology/approachBased on narrative economic theory, data from CNBC news reports between 01/01/2019 and 03/27/2019 regarding four selected firms, namely, Walmart, Amazon, Netflix and Boeing, were analyzed and coded as the basis to guide the construction of a firm-to-firm relationship taxonomy.FindingsThe relationships between firms are more complex than the simple relationships defined by the traditional classification systems with yes or no in terms of production process (NAICS) or major profit resource (ICB). Based on the sample firms, the authors proposed a four-layer hierarchical taxonomy framework that quantitatively reveals the inherent contradictory relationships between firms, which the authors defined as competition vs consistency. The proposed taxonomy framework is sufficiently flexible to accommodate complex relationships between firms, and it is also adaptable to new information. Under both the competition and consistency categories in the taxonomy model, more detailed subcategories are further coded into two more layers quantitatively to represent the firms' nuanced relationships.Originality/valueThis study provides a novel atheoretical approach to reveal complex firm relationships utilizing narrative text data gathered from news media. The framework of the firm relationship taxonomy constructed in this study provides an alternative and supplementary approach to the classical industry classification systems that can quantitatively specify comprehensive and dynamic connections between firms.

An artificial intelligence-enabled industry classification and its interpretation

PurposeTo overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).Design/methodology/approachThe authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.FindingsUsing the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.Originality/valueThe authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.