BIGPROD: Addressing the Productivity Paradox with Big Data

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.

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.

Description of the BIGPROD platform used to web crawl over 180 000 companies

Presentation on the early analysis of BIGPROD results for Data Science consultation.

Presenatation on the BIGPROD data platform

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.