Project

BIGPROD: Addressing the Productivity Paradox with Big Data

Goal: The goal of the BIGPROD project is to extend existing econometric approaches to productivity, such as the Crepon-Duguet-Mairesse (CDM) model, with theoretically sound ‘Big data’ measures that are operationalised, validated through pilots and communicated to the relevant stakeholders.

Visit our project website at http://www.bigprod.eu

Date: 1 December 2019 - 30 April 2022

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Project log

Ad Notten
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AI-related technologies have become ubiquitous in many business contexts. However, to date empirical accounts of the productivity effects of AI-adoption by firms are scarce. Using Finnish data on job advertisements between 2013 and 2019, we identify job advertisements referring to AI-related skills. Matching this data to productivity data from ORBIS, we estimate the productivity effects of AI related activities in our sample. Our results indicate that AI-adoption increases productivity, with three important qualifications. Firstly, effects are only observable for large firms with more than 499 employees. Secondly, there is evidence that early adopters did not experience productivity increases. This may be interpreted as technological immaturity.Thirdly, we find evidence of delays of least three years between the adoption of AI and ensuing productivity effects (investment delay effect). We argue that our findings on the technological immaturity and the investment delay effect may help explain the so-called AI-related return of the Solow-paradox: I.e. that AI is everywhere except in the productivity statistics.
 
Ad Notten
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This technical note reports on a write-up of three pilot cases done during the BIG-PROD project. Using a novel web scraped company website data of approximately 100 000 companies, the note reports on a comparison of NACE and Microsoft Aca-demic Graph (MAG) based industry classifications, Field of Study (FOS) code based digitalization score and academy-Industry collaboration based on website data. The technical note is intended to open discussion of the potential of the web scraped data based indicators compared to existing innovation measures.
 
Ad Notten
added a research item
This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. The method illustrates the benefit of Natural Language Processing (NLP) in creating links between established economic classification systems with novel and agile constructs that new data sources enable. Therefore, we experimented on the European classification of economic activities (known as NACE) on sectoral and company levels. We established a connection with Microsoft Academic Graph hierarchical topic modeling based on companies' website content. Central to the operationalization of our method are a web scraping process, NLP and a data transformation/linkage procedure. The method contains three main steps: data source identification, raw data retrieval, and data preparation and transformation. These steps are applied to two distinct data sources.
Ad Notten
added an update
BIGPROD has run two successful Webinars in November, 2021, on the process the project employs to add value to the unstructured web-scraped company data, by adding additional data from structured sources as well as transforming the textual data in such a way that it can be used to signal innovative activity at the company level. The goal of this exercise it to arrive at a "Digitalization Score" and "Servitization Score" which can be employed in the updated CDM model we envisage.
 
Arash Hajikhani
added an update
Excited to have an EU-SPRI session presenting our analysis coming from H2020 BIGPROD project. Slides presented in the session are enclosed. The event details were listed in eventbrite:https://www.eventbrite.co.uk/e/eu-spri-webinars-tickets-204958906607#
 
Arho Suominen
added 3 research items
The use of online job advertisement has made them an important source of quantitative information about the innovation system. This data offers significant opportunities to study trends, transitions in the job markets and skill demands. In this study, we have utilized the job ads data of a major Finnish job market platform to investigate the emergence of AI-related jobs. More than 480 000 job advertisements during 2013-2020 was used to create insight on skills transitions, particularly focusing on artificial intelligence related skills. A glossary of AI-related skills was created and applied to the job data to identify the relatedness spectrum of ads to AI using a three-tier system. By incorporating sectoral firm-level information, we explored the variation in AI-related skills demand over time and sectors. Our study presents a systematic way to utilize job advertisement data for detecting demand trends for specific skills.
en The use of big data and data analytics are slowly emerging in public policy-making, and there are calls for systematic reviews and research agendas focusing on the impacts that big data and analytics have on policy processes. This paper examines the nascent field of big data and data analytics in public policy by reviewing the literature with bibliometric and qualitative analyses. The study encompassed scientific publications gathered from SCOPUS (N = 538). Nine bibliographically coupled clusters were identified, with the three largest clusters being big data's impact on the policy cycle, data-based decision-making, and productivity. Through the qualitative coding of the literature, our study highlights the core of the discussions and proposes a research agenda for further studies. 摘要 zh 大数据和数据分析的使用已逐渐出现在公共决策中,对此需要展开系统性综述和研究议程,聚焦大数据和数据分析对政策过程产生的影响。本文通过文献计量分析和定性分析,分析了公共政策中大数据和数据分析这一新兴领域。本研究包括了从SCOPUS中选取的科学刊物(N=538)。识别了9个文献耦合簇,其中最大的三个簇分别为:大数据对政策周期的影响、基于数据的决策、生产率。通过对文献进行定性编码,我们的研究强调了探讨的核心,并提出了进一步的研究议程。 RESUMEN es El uso de macrodatos y análisis de datos está emergiendo lentamente en la formulación de políticas públicas, y hay pedidos de revisiones sistemáticas y agendas de investigación que se centren en los impactos que los macrodatos y el análisis tienen en los procesos de políticas. Este artículo examina el campo naciente del big data y el análisis de datos en las políticas públicas mediante la revisión de la literatura con análisis bibliométricos y cualitativos. El estudio abarcó publicaciones científicas recopiladas de SCOPUS (N = 538). Se identificaron nueve grupos acoplados bibliográficamente, siendo los tres grupos más grandes el impacto de los macrodatos en el ciclo de políticas, la toma de decisiones basada en datos y la productividad. A través de la codificación cualitativa de la literatura, nuestro estudio destaca el núcleo de las discusiones y propone una agenda de investigación para estudios posteriores.
Ad Notten
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By Sajad Ashouri, Arho Suominen, Arash Hajikhani, Lukas Pukelis, Torben Schubert, Serdar Türkeli, Cees Van Beers, and Scott Cunningham
This article presents data on companies' innovative behavior measured at the firm-level based on web scraped firm-level data derived from medium-high and high-technology companies in the European Union and the United Kingdom. The data are retrieved from individual company websites and contains in total data on 96,921 companies. The data provide information on various aspects of innovation, most significantly the research and development orientation of the company at the company and product level, the company’s collaborative activities, company’s products, and use of standards. In addition to the web scraped data, the dataset aggregates a variety firm-level indicators including patenting activities. In total, the dataset includes 28 variables with unique identifiers which enables connecting to other databases such as financial data.
 
Arho Suominen
added 4 research items
Ad Notten
added an update
Please find the second BIGPROD Policy Brief, by Arho Suominen and Arash Hajikhani, on our project website: http://www.bigprod.eu/output/
Synopsis:
Big data and data analytics have been seen as augmenting knowledge, ultimately leading to better
decision- making. Arguments such as that the broad-based use of big data and data analytics will
lead to the end-of-theory speak volumes about our expectations of the transformative power of
technologies. While industry has been leading the way to test big data and analytics, public actors
have been slower to engage (Poel et al., 2018), despite an at least equal opportunity for big data
and data analytics to augment the public policy process.
Utilizing big data and data analytics has become a near necessity due to our increasing capability
for creating and collecting data at an extraordinary rate. The terms ”big data” and ”data analytics”
have been among the buzzwords of recent years, leading to an upsurge of research, industry, and
government applications (Zhou et al., 2014). Scholarly discourse has highlighted case studies and
narratives on the implementation of big data and data analytics in the policy process, but the
literature lacks a systematic view of the currentstate of big data and data analytics in public policy,
and there are clearly identifiable research gaps (Desouza and Jacob, 2017)
 
Ad Notten
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This paper develops the plan for the econometric estimations concerning the relationship between firm productivity and the specifics of the innovation process. The paper consists of three main parts. In the first, we review the relevant literature related to the productivity paradox and its causes. Specific attention will be paid to broad economic trends, in particular the higher importance of intangibles, the increasing importance of knowledge spillovers and servitisation as drivers of the slowdown in productivity growth. In the second part, we introduce a plan for the econometric estimation strategy. Here we propose an extended Crépon-Duguet-Mairesse type of model (CDM), which enriches the original specification by the three influence factors of intangibles, spillovers, and servitisation. This will allow testing the influence of these three factors on productivity at the level of the firm within a unified framework. In the third part, we build on the literature review in order to provide a detailed plan for the data collection procedure including a description of the variables to be collected and the source from which the variables are coming. It should be noted that we will rely partly on structured data (e.g. ORBIS), while many others variables will need to be generated from unstructured sources, in particular the webpages of firms. The use of unstructured data is a particular strength of our proposed data collection procedure because the use of such data is expected to offer novel insights. However, it implies additional risks in terms of data quality or missing data. Our data collection plan explores the maximum potential of variables that will ideally be made available for later econometric treatment. Whether indeed all variables will have sufficient quality to be used in the econometric estimations will be subject to the outcomes of the actual collection efforts.
 
Arash Hajikhani
added an update
The BIGPROD project aims to provide better explanations of the technological and market processes observed in the global economy. Current explanations of these processes are inconsistent or even contradictory. New processes and sources of big data enable the creation of new metrics and economic indicators. BIGPROD will create new indicators that enable better input, and ultimately, better econometric models used for productivity analysis. These models try to explain the changes in economic productivity. However, there is a consensus among economists that the current models do not include enough information on intangible assets (inputs other than capital or fixed assets), the changing nature of innovation towards more open modes, and service delivery trends. Consequently, these models fail to provide a sufficiently thorough understanding of the productivity slowdown which we know as the “productivity paradox”. BIGPROD will focus primarily on the total or multifactor productivity (TFP/MFP) part that accounts for the contributions of firm R&D, innovation and technology.
 
Arash Hajikhani
added an update
Our EU H2020 project consortium (BIGPROD) is organizing a session at EU-SPRI annual conference this year. The session is "New indicators and approaches in STI policy: Beyond data and prediction to impacting the policy cycle".
There is time until 15th February to submit research proposals and abstracts. Find out more -> https://euspri2020.nl/call-for-papers/
 
Ad Notten
added a research item
The BIGPROD project aims to provide better explanations of the technological and market processes observed in the global economy. Current explanations of these processes are inconsistent or even contradictory. New processes and sources of big data enable the creation of new metrics and economic indicators. BIGPROD will create new indicators that enable better input, and ultimately, better econometric models used for productivity analysis. These models try to explain the changes in economic productivity. However, there is a consensus among economists that the current models do not include enough information on intangible assets (inputs other than capital or fixed assets), the changing nature of innovation towards more open modes, and service delivery trends. Consequently, these models fail to provide a sufficiently thorough understanding of the productivity slowdown which we know as the "productivity paradox". BIGPROD will focus primarily on the total or multi factor productivity (TFP/MFP) part that accounts for the contributions of firm R&D, innovation and technology.
Ad Notten
added a project goal
The goal of the BIGPROD project is to extend existing econometric approaches to productivity, such as the Crepon-Duguet-Mairesse (CDM) model, with theoretically sound ‘Big data’ measures that are operationalised, validated through pilots and communicated to the relevant stakeholders.
Visit our project website at http://www.bigprod.eu