competition | Economy Data Observatory

The Economy Data Observatory is Contesting the EU Datathon 2021 Prize

Fri, 21 May 2021 20:00:00 +0200

Reprex, a Dutch start-up enterprise formed to utilize open source software and open data, is looking for partners in an agile, open collaboration to win at least one of the three EU Datathon Prizes. We are looking for policy partners, academic partners and a consultancy partner. Our project is based on agile, open collaboration with three types of contributors.

With our competing prototypes we want to show that we have a research automation technology that can find open data, process it and validate it into high-quality business, policy or scientific indicators, and release it with daily refreshments in a modern API.

We are looking for institutions to challenge us with their data problems, and sponsors to increase our capacity. Over then next 5 months, we need to find a sustainable business model for a high-quality and open alternative to other public data programs.

The EU Datathon 2021 Challenge

To take part, you should propose the development of an application that links and uses open datasets. - our data curator team
Your application … is also expected to find suitable new approaches and solutions to help Europe achieve important goals set by the European Commission through the use of open data.” - this application is developed by our technology contributors
Your application should showcase opportunities for concrete business models or social enterprises. - our service development team is working to make this happen!
We use open source software and open data. The applications are hosted on the cloud resources of Reprex, an early-stage technology startup currently building a viable, open-source, open-data business model to create reproducible research products.
We are working together with experts in the domain as curators (check out our guidelines if you want to join: Data Curators: Get Inspired!).
Our development team works on an open collaboration basis. Our indicator R packages, and our services are developed together with rOpenGov.

Mission statement

We want to win an EU Datathon prize by processing the vast, already-available governmental and scientific open data made usable for policy-makers, scientific researchers, and business researcher end-users.

“To take part, you should propose the development of an application that links and uses open datasets. Your application should showcase opportunities for concrete business models or social enterprises. It is also expected to find suitable new approaches and solutions to help Europe achieve important goals set by the European Commission through the use of open data.”

We aim to win at least one first prize in the EU Datathon 2021. We are contesting all three challenges, which are related to the EU’s official strategic policies for the coming decade.

Challenge 2: An economy that works for people

Our Economy Data Observatory will focus on competition, small and medium sized enterprizes and robotization.

Challenge 2: An economy that works for people, with a particular focus on the Single market strategy, and particular attention to the strategy’s goals of 1. Modernising our standards system, 2. Consolidating Europe’s intellectual property framework, and 3. Enabling the balanced development of the collaborative economy strategic goals.

Big data and automation create new inequalities and injustices and have the potential to create a jobless growth economy. Our Economy Data Observatory is a fully automated, open source, open data observatory that produces new indicators from open data sources and experimental big data sources, with authoritative copies and a modern API.

Our observatory monitors the European economy to protect consumers and small companies from unfair competition, both from data and knowledge monopolization and robotization. We take a critical Small and Medium-Sized Enterprises (SME)-, intellectual property, and competition policy point of view of automation, robotization, and the AI revolution on the service-oriented European social market economy.

We would like to create early-warning, risk, economic effect, and impact indicators that can be used in scientific, business, and policy contexts for professionals who are working on re-setting the European economy after a devastating pandemic in the age of AI. We are particularly interested in designing indicators that can be early warnings for killer acquisitions, algorithmic and offline discrimination against consumers based on nationality or place of residence, and signs of undermining key economic and competition policy goals. Our goal is to help small and medium-sized enterprises and start-ups to grow, and to furnish data that encourages the financial sector to provide loans and equity funds for their growth.

Other Challenges

Challenge 1: A European Green Deal, with a particular focus on the The European Climate Pact, the Organic Action Plan, and the New European Bauhaus, i.e., mitigation strategies. Our Green Deal Data Observatory is a modern reimagination of existing ‘data observatories’; currently, there are over 70 permanent international data collection and dissemination points. One of our objectives is to understand why the dozens of the EU’s observatories do not use open data and reproducible research. We want to show that open governmental data, open science, and reproducible research can lead to a higher quality and faster data ecosystem that fosters growth for policy, business, and academic data users.
Challenge 3: A Europe fit for the digital age, with a particular focus Artificial Intelligence, the European Data Strategy, the Digital Services Act, Digital Skills and Connectivity. The Digital Music Observatory (DMO) is a fully automated, open source, open data observatory that creates public datasets to provide a comprehensive view of the European music industry. It provides high-quality and timely indicators in all four pillars of the planned official European Music Observatory as a modern, open source and largely open data-based, automated, API-supported alternative solution for this planned observatory. The insight and methodologies we are refining in the DMO are applicable and transferable to about 60 other data observatories funded by the EU which do not currently employ governmental or scientific open data.

Our Product/Market Fit was validated in the world’s 2nd ranked university-backed incubator program, the Yes!Delft AI Validation Lab. We are currently developing this project with the help of the JUMP European Music Market Accelerator program.

Problem Statement

The EU has an 18-year-old open data regime and it makes public taxpayer-funded data in the values of tens of billions of euros per year; the Eurostat program alone handles 20,000 international data products, including at least 5,000 pan-European environmental indicators.

As open science principles gain increased acceptance, scientific researchers are making hundreds of thousands of valuable datasets public and available for replication every year.

The EU, the OECD, and UN institutions run around 100 data collection programs, so-called ‘data observatories’ that more or less avoid touching this data, and buy proprietary data instead. Annually, each observatory spends between 50 thousand and 3 million EUR on collecting untidy and proprietary data of inconsistent quality, while never even considering open data.

Our automated data observatories are modern reimaginations of the existing observatories that do not use open data and research automation.

The problem with the current EU data strategy is that while it produces enormous quantities of valuable open data, in the absence of common basic data science and documentation principles, it seems often cheaper to create new data than to put the existing open data into shape.

This is an absolute waste of resources and efforts. With a few R packages and our deep understanding of advanced data science techniques, we can create valuable datasets from unprocessed open data. In most domains, we are able to repurpose data originally created for other purposes at a historical cost of several billions of euros, converting these unused data assets into valuable datasets that can replace tens of millions’ worth of proprietary data.

What we want to achieve with this project – and we believe such an accomplishment would merit one of the first prizes - is to add value to a significant portion of pre-existing EU open data (for example, available on data.europa.eu/data) by re-processing and integrating them into a modern, tidy database with an API access, and to find a business model that emphasises a triangular use of data in 1. business, 2. science and 3. policy-making. Our mission is to modernize the concept of data observatories.

Competition

Sun, 16 May 2021 00:00:00 +0000

Big data and AI create new inequalities in the world. Data monopolization can potentially lead to product and service market monopolization, too. We are designing new policy indicators that can help competition policy practitioners and researchers to measure and understand these trends.

We are planning to programatically access the merger database of the European Commission’s Merger Cases, which we believe to fall under the scope of the Open Data Directive.
We would like to connect to the EUIPO databases to create indicators of potential killer acquisitions - acquisitions by large companies that may thwart innovation by buying up and killing start-up companiees to take hold of competitive patents.

Mapping antitrust markets

Most market-based analysis uses standardised industry classifications such as the NACE system in the European Union. Whilst these systems are useful to have a standardised view of the component parts of the economy, these markets are frequently very different from product markets that are defined by demand and supply conditions (literature refers to this as antitrust markets). One of the cornerstones of our Data Observatory is automating the construction and updating of a system of antitrust markets. This can be done from available antitrust market decisions. For example Affeldt et al. (2021) manually identified 20,000 product/geographic antitrust markets affected by over 2,000 mergers from DG COMP merger decisions. Automating this process will create a continuously updated source of market definitions that can directly benefit a number of users, such as small businesses caught up in costly merger litigations, academics, policy organisations. This new market classification system could also contribute to the topical debate on increasing market concentration and markups (where most current analysis uses standard industry classification systems). Under the umbrella of our Antitrust Market Classification (AMC) and under a standard industry classification (NACE) we will create three company-level tracker datasets:

Computational Antitrust Project Plan

Merger tracker

We compile a merger database from constructing our antitrust markets. This will be complimented by continuously monitoring markets for M&A transactions that are below the radar of DG COMP. As a first step, we will only have this option for selected markets, until we develop a tool to systematically collect this data on a wider scope. Tracking mergers in both NACE and AMC markets helps trace how concentration evolves over time. Moreover, this is a key building block to our Competition and Innovation Data Observatory.

Innovation tracker

Part of this project is to link the firms that appear in our antitrust market definitions to their historical and current innovation data. For this we will draw from patent data APIs, such as that of the European Patent Office. This would enable the tracking of how innovation develops as market become more/less concentrated. This is important for several reasons. First of all, innovation is an important component of economic productivity, and therefore our tracker will provide vital information to competition authorities and sectoral regulators. Second, there are increasingly vocal concerns about the innovation impact of acquisitions between companies that are either on separate antitrust markets, or include an acquired business that is below the size threshold of antitrust scrutiny (these are frequently referred to as killer acquisitions in the literature). One side of the ongoing debate argues that these acquisitions can hamper innovation, whereas other claim the opposite (it is the prospect of these acquisitions that drives innovation in many small start-up businesses). As such, our tool will be a key indicator of how healthy the environment for innovation for small businesses is.

Ownership tracker

Finally, we will construct and ownership tracker from the shareholder reports of listed companies (from the set of companies identified in our market definition process). Once again, this is key information for gauging how healthy competition is in European markets. It has been repeatedly highlighted that overlaps in the ownership structure (in institutional investors) of the largest businesses can hinder competition. Mapping the ownership structure of European listed companies means that not only we are aware of market concentration concerns in antitrust markets, but also concerns when overlap across corporate governance structures is considered.

Photo credit: Matt Ridley, Unplash licence.