Periodic Reporting for period 2 - BIGPROD (Addressing productivity paradox with big data: implications to policy making)
Okres sprawozdawczy: 2020-12-01 do 2022-04-30
Looking at companies like Google and Amazon innovating new services for consumers or the ability for doctors to detect cancer cells more precisely from through creating massive training data on what is a cancerous cell, we are very much in the cusp of creating a broad utility of “Big data”. This being said, public policy is not at the forefront of utilizing “Big data” in decision making. We know that novel big data and analytics based methods can have a significant impact to public policy-making and BIGPROD addresses the issues by adopting big data measures on understanding the “productivity paradox”. In the communication from the European Commission (26.2.2020) on the 2020 European Semester it is noted that "“[p]roductivity growth remains a challenge, even more so in the light of demographic change…" In addition, "…[t]here are multiple causes for this weak performance…" and "…[p]olicies to foster productivity need to be tailored to national circumstances…”. This highlights the importance of the BIGPROD project.
However, adopting big data measures is not simply a question of technical capability to create novel measures. Big data and analytics comes with new challenges involving reproducibility, complexity, security, and risks to privacy, as well as a need for new technology and human skills. We know that, institutional capacities have a significant role to play in the use of “Big data” in public policy, particularly on what will be the impact “Big data” information on the policy cycle. We need to better explain and make transparent the utility and complementarity of “Big data” driven analysis into the policy cycle. In the case of productivity, “Big data” per se can not solve the productivity challenge. Rather the question at hand is if novel measures can create additional vantage point to understand an important question in our society and if we then can integrate it with the policy cycle.
The objective of the BIGPROD project is to extend existing econometric approaches on productivity with a theoretically sound “Big data” measures that can be operationalized and validated through pilots. In addition we are addressing issues beyond the technical capability of creating new metrics. This is achieved through deep stakeholder consultation mitigating the skills gap, creating transparency, enabling stakeholder influence in sources and tools and enabling policy makers being informed on tools and pilots. The goal of the project is to 1) create tools for utilising “Big data” for innovation and productivity assessment, 2) extended econometric framework for the evaluation of the productivity-innovation link based on “Big data”, 3) build a large-scale data platform, 4) create policy-relevant pilots that measure the impact of proposed changes, and 5), use the most effective tools available to effect stakeholder engagement and co-creation, while simultaneously ensuring the dissemination of the knowledge gained in this process to the wider public.
However, adopting big data measures is not simply a question of technical capability to create novel measures. Big data and analytics comes with new challenges involving reproducibility, complexity, security, and risks to privacy, as well as a need for new technology and human skills. We know that, institutional capacities have a significant role to play in the use of “Big data” in public policy, particularly on what will be the impact “Big data” information on the policy cycle. We need to better explain and make transparent the utility and complementarity of “Big data” driven analysis into the policy cycle. In the case of productivity, “Big data” per se can not solve the productivity challenge. Rather the question at hand is if novel measures can create additional vantage point to understand an important question in our society and if we then can integrate it with the policy cycle.
The objective of the BIGPROD project is to extend existing econometric approaches on productivity with a theoretically sound “Big data” measures that can be operationalized and validated through pilots. In addition we are addressing issues beyond the technical capability of creating new metrics. This is achieved through deep stakeholder consultation mitigating the skills gap, creating transparency, enabling stakeholder influence in sources and tools and enabling policy makers being informed on tools and pilots. The goal of the project is to 1) create tools for utilising “Big data” for innovation and productivity assessment, 2) extended econometric framework for the evaluation of the productivity-innovation link based on “Big data”, 3) build a large-scale data platform, 4) create policy-relevant pilots that measure the impact of proposed changes, and 5), use the most effective tools available to effect stakeholder engagement and co-creation, while simultaneously ensuring the dissemination of the knowledge gained in this process to the wider public.
The project as so far been able to gain better understanding of both the technical capability and usability issues relating to the project scope. The project has engaged with a broad pool of stakeholders, from economists, policy practitioners, researchers and data scientists, in a series of workshops sessions. These sessions have been key to scope and direct the work in the project. In addition, the engagement with different stakeholder groups have created a way to spread information on the project and also the early findings created in the different work packages.
The technical work in the project has focused on understanding the econometric model to be extend and in creating the data platform need to retrieve novel data. During the first reporting period, the project has published the first report on what are the potential avenues to extend the econometrics model used as a foundation of the project. This report was disseminated to stakeholders for feedback and further direction on future work. In addition, the report surveyed economists doing research on productivity to better understand the potential of big data and analytics in productivity analysis. Simultaneously, to gaining a better understanding of the econometric model extension, work started to meet the goal of creating novel metrics for 160 000 - 200 000 companies in European Union and the United Kingdom. During the first reporting period, we have created a sample of over 180 000 companies, which will serve as the core sample for the BIGPROD project. In addition, the data platform (seen in the Figure) to web crawl raw data for the companies have been fully developed, tested and is now operational. The data platform has been reported in a separate deliverable. During the reporting period, the web crawling process has been able to retrieve data for over 11 000 companies.
The technical work in the project has focused on understanding the econometric model to be extend and in creating the data platform need to retrieve novel data. During the first reporting period, the project has published the first report on what are the potential avenues to extend the econometrics model used as a foundation of the project. This report was disseminated to stakeholders for feedback and further direction on future work. In addition, the report surveyed economists doing research on productivity to better understand the potential of big data and analytics in productivity analysis. Simultaneously, to gaining a better understanding of the econometric model extension, work started to meet the goal of creating novel metrics for 160 000 - 200 000 companies in European Union and the United Kingdom. During the first reporting period, we have created a sample of over 180 000 companies, which will serve as the core sample for the BIGPROD project. In addition, the data platform (seen in the Figure) to web crawl raw data for the companies have been fully developed, tested and is now operational. The data platform has been reported in a separate deliverable. During the reporting period, the web crawling process has been able to retrieve data for over 11 000 companies.
At the current stage of the project, the project has progressed beyond the state of the art by creating an operational data platform that can create novel metrics on the industrial activity within European Union and the United Kingdom. The data platform creates information on innovation outcomes, such as products and patents, but extend to also understand more deeper the industrial networks, field of activity, knowledge depth and breadth and use of standards. This progress will create multiple avenues for future research to create completely new vantage points to analysing industrial activity.
The expect results until the end of the project focus on four aspects. First, we develop a framework and data process providing a robust, transparent and reproducible method of providing information on the performance of the research and innovation system. Second, we strive for increased trust with the “Big data” processes underlying the measures is built through creating a transparent architecture for the data processing. Third, the project will deliver a literature review responding in detail to the debate around the productivity standstill. The project will in particular address the relevance of research and innovation on productivity. Finally, the pilots done in the project will in detail look into different vantage point to the productivity issue.
These actions will have a potential impact to scientific work, but also extend to addressing societal issues such as UN SDGs. The project will potentially have implications to the SDG “Decent work and economic growth”, as we try to create new vantage points to how we can build sustainable economic growth through societies creating conditions that stimulate the economy while not harming the environment. We also foresee a potential impact to the SDG “Industry, Innovation and Infrastructure”, where it is highlighted that ”...growth in productivity and incomes, and improvements in health and education outcomes require investment in infrastructure”. We see that the BIGPROD project has a clear impact on creating a deep understanding of how countries are able to create new technologies and support innovation. The web scraped data, containing mission and vision statements from companies, will also shed light on the strategic importance of sustainability in companies.
The expect results until the end of the project focus on four aspects. First, we develop a framework and data process providing a robust, transparent and reproducible method of providing information on the performance of the research and innovation system. Second, we strive for increased trust with the “Big data” processes underlying the measures is built through creating a transparent architecture for the data processing. Third, the project will deliver a literature review responding in detail to the debate around the productivity standstill. The project will in particular address the relevance of research and innovation on productivity. Finally, the pilots done in the project will in detail look into different vantage point to the productivity issue.
These actions will have a potential impact to scientific work, but also extend to addressing societal issues such as UN SDGs. The project will potentially have implications to the SDG “Decent work and economic growth”, as we try to create new vantage points to how we can build sustainable economic growth through societies creating conditions that stimulate the economy while not harming the environment. We also foresee a potential impact to the SDG “Industry, Innovation and Infrastructure”, where it is highlighted that ”...growth in productivity and incomes, and improvements in health and education outcomes require investment in infrastructure”. We see that the BIGPROD project has a clear impact on creating a deep understanding of how countries are able to create new technologies and support innovation. The web scraped data, containing mission and vision statements from companies, will also shed light on the strategic importance of sustainability in companies.