Sharing data on the activities of multinational enterprise groups: Innovations to redesign statistical practices and processes

1.Introduction

For about a century or so, statisticians have engaged internationally to agree on statistical classifications, concepts, definitions and methodologies to ensure global comparability of the statistical information. In a world of closed borders, protective trade barriers, restricted financial markets and mostly national generation of wealth, it was perfectly reasonable to focus the international cooperation in official statistics on developing common standards and leaving all the rest of the statistical production to the domain of national statistical offices.

Unfortunately, the world has been rapidly evolving for about half a century or so. The statisticians’ response to diluting borders, barriers and restrictions – i.e. globalisation – has been to increase the level of detail in statistical standards and develop new guidance. As an example, we can take the first global manual on National Accounts from early 1950’s (A System of National Accounts and Supporting Tables, United Nations, 1953). The book contained some fifty pages only. The most recent version of the System of National Accounts (System of National Accounts 2008, European Commission, IMF, OECD, UN, World Bank, 2009) is more than 700 pages long. We can see same kind of developments in almost any statistical area; consumer price index manuals have grown from a couple of pages in the early 1900’s to close to 800 pages in the most recent version (Consumer Price Index Manual – Concepts and Methods, International Labour Office, International Monetary Fund, Organisation for Economic Co-operation and Development, Statistical Office of the European Union (Eurostat), United Nations Economic Commission for Europe, The World Bank, white cover publication, pre-edited text subject to official editing, January 2020). In addition to core national accounts we nowadays also have the Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6) (350 pages), System of Environmental-Economic Accounting 2012, Central Framework (almost 350 pages), and a number of manuals and practical guidelines on specific statistics.

It is striking that all the increasingly detailed standards statisticians are creating, rely strictly on a national approach to data collection and statistics compilation. We are facing a situation where economic actors – be it MNEs or other enterprises engaged in cross border activities act and think globally, whereas statisticians act and think locally. The situation is not sustainable. Innovative solutions to improve the situation are urgently needed.

2.Innovation and data sharing

Innovation can be defined as non-routine, significant, and discontinuous organizational change that embodies a new idea that is not consistent with the current concept of the organization’s business [1]. Data sharing has the potential of bringing a major change to the traditional business model of official statistics and challenge statistical offices to review and modernize their work. With data sharing (or exchange) we refer to the sharing of data and accompanying metadata among producers of official statistics strictly for statistical purposes only. The data that are shared can be qualitative, quantitative, confidential, non-confidential, aggregated or disaggregated, collected directly or otherwise obtained by statistical authorities from varying sources, or data that are publicly available.

Innovation is a process starting from insight, followed by problem identification and leading to a solution. The Chief Statisticians’ debate in the Bureau of the Conference of European Statisticians (CES) in October 201611 provided this insight to international data sharing for economic statistics, and the process was launched with an in-depth review of data sharing to define the problem. The Chief Statisticians of the CES member states then established a UNECE Task Force to find and propose solutions for statistical authorities. A central question related to innovation is how to translate individual insights and knowledge into collective understanding and capability in the national and global statistical systems.

The Oslo Manual [2] defines innovation as a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method. Data sharing influences all these aspects, as it introduces changes to the product (quality), service (respondent burden), the process of statistical production (data flows) and the customer experience (consistency). The fourth type of innovation, organizational innovation, is defined as changes to the business practices, workplace organization or external relations. Again, data sharing has all the elements of organizational innovation with the potential to fundamentally change official statistics.

MNEs’ data are a critical element for economic statistics. In this article, with the term ‘economic statistics’ we refer to macroeconomic, trade and business statistics. If MNEs’ data are wrong or missing, national statistics will be greatly affected and will not be of sufficient quality to support governments or business decision making.

While globalisation presents new opportunities for businesses that seek more efficient and more profitable ways to manufacture their products, their innovative and agile global production arrangements challenge statisticians, often in unexpected ways. In recent years, the challenge has become even larger due to the increasing intensity and complexity of MNE’s global arrangements, changing intra-group structures and agreements, and the evolving division of intangibles and R&D across countries.

The national activities of MNEs must be viewed in the context of their global operations to ensure the accuracy of both national and international figures. The largest businesses often receive many statistical surveys, perhaps from different statistical authorities and even from different countries, and it may be difficult for the MNE itself to properly determine which activities should be reported in which country. Inconsistent treatment of the few largest MNEs can lead to huge discrepancies and asymmetries in global key statistics.

Statisticians joined forces to figure out how to compile statistics that appropriately reflect globalisation. Global problems require global solutions. Indeed, data sharing emerged in these discussions as a game changer for statistical production.

The global statistical system needs to act decisively and in coordination to develop data sharing to achieve a complete and accurate picture of MNE activities. This calls for building a more consistent and efficient global statistical system that enables the reaping of benefits from shared data and from the available technologies to do so. More focus needs to be put on the consistency of statistics so that they can offer worthwhile insights about national economies, global value chains, economic interlinkages across countries and developments in the global economy.

We need a vision to lead us forward with innovative solutions for effective data sharing in official statistics. So, what kind of a vision would help us thrive? If official statisticians had all the data needed, what could we achieve? We could reconcile the data on MNEs globally and produce economic statistics without statistical asymmetries, gaps or double counting. The same data could only be collected once from an MNE to be used several times for producing different statistics by various statistical authorities across countries. Policy makers, businesses and researchers would be able to base their findings and decisions on more accurate statistics.

Are we there yet? Are we getting close? It seems we are still far from being able to put together all relevant data on MNEs. First, we need to translate this vision, step by step, into practice. A series of questions need to be answered. Where would the MNE data reside – in a global statistical data base? How would the data end up there – through a secure data exchange platform? Who would be responsible for data collection and validation for global MNEs – would there be a single point of MNE data collection or multiple? Who would be responsible for management of the global statistical database? Who would be certified to have access to the global database with confidential MNE data? Who would certify statistical authorities and qualify them for such access? What if there are breaches?

These are tricky questions that cannot be all answered at once. This paper contributes to the process by reflecting on the thinking developed in the work of an internationally composed UNECE Task Force that developed a Guide to Sharing Economic Data.

3.The CES approach to data sharing

The paper is based on the findings of international statistical work carried out by the UNECE Task Force on the exchange and sharing of economic data during the past years. The work was launched as a reaction to the view held by Chief Statisticians in the CES Bureau that in a globalised economy, national official statistics cannot anymore be produced in isolation from the rest of the world if we want to ensure the high quality and the overall relevance of economic and business statistics.

In October 2016, the Chief Statisticians noted that compiling national statistics is becoming increasingly challenging in the globalised world. They recognised that a cultural change is required in the way statistics are produced at national and global levels. The Chief Statisticians called for urgent work to operationalize the exchange of data between national statistical offices. International data exchange between the producers of official statistics for statistical purposes was seen as a prerequisite for statisticians to be able to depict economic reality, profile multinational enterprises and provide accurate and consistent data on their activities.

The Chief Statisticians also asked for guidance to statistical offices as to how to communicate with multinational enterprises in a way that creates trust and ensures transparency. They considered it important to explain clearly why data exchange is necessary for national and global statistics, and to inform multinationals in one voice how confidentiality will be kept in all stages of data exchange and across all parties involved in data exchange.

In 2016, UNECE conducted a survey of countries’ experience in data sharing. National Statistical Offices and entities of National Central Banks (NCBs) from 48 countries replied to the survey. The survey covered the current scope of economic data exchange nationally and internationally, organizational aspects of data sharing, benefits and challenges experienced and suggestions for international activities that might support national capacity development.

The survey showed that data sharing for statistical purposes was quite common among institutions at national level. However, statistical authorities engaged rarely in international data sharing with statistical authorities from other countries. These cases related to the recording of cross-border transactions and to the reduction of bilateral asymmetries between countries. These data exchange cases were either facilitated by international organizations, such as Eurostat, or based on bilateral or multilateral agreements between countries. The European Statistical System (ESS) has a robust legal framework in place enabling international data exchange and an exchange of data between national statistical offices and central banks.

In recent years, the reuse of micro-data has increased nationally and has started to increase also internationally between countries. This was a consequence of the changes in the European statistical law and Eurostat’s single market statistics (SIMSTAT) project that enabled international micro-data sharing between statistical offices of the European Union (EU) member states in the domain of international trade in goods statistics. Since then, many innovative microdata linking and exchange initiatives have been implemented in the EU.

A quarter of responding offices had examined the activities of MNEs with another countries’ statistical authorities and a third of the offices had worked with other producers of official statistics within their own country. However, at the time of the survey, the exchange of data on MNEs was still relatively rare outside the EU.

The CES Bureau carried out an in-depth review of economic data sharing. As a result, the CES Bureau established a UNECE Task Force on exchange and sharing of economic data in March 2017. The Task Force consisted of experts of national accounts, balance of payments, business statistics, foreign trade statistics and other economic statistics from the following countries and international organizations: Canada, Denmark, Finland (Chair), Italy, Ireland, Mexico, Poland, the Netherlands, United Kingdom, United States, European Central Bank (ECB), Eurostat, the International Monetary Fund (IMF), the Organisation for Economic Co-operation and Development (OECD), UNECE and the United Nations Statistics Division (UNSD). UNECE provided the Secretariat for the Task Force.

In the course of its work, the Task Force engaged in regular coordination with several expert groups in statistics and provided input to the work undertaken by the OECD Working Parties on National Accounts and Financial Statistics, and the Advisory Expert Group on National Accounts, the United Nations Expert Group on International Trade and Economic Globalization Statistics (ITEGS), the G20 Data Gaps Initiative, Eurostat’s Integrated Global Accounts (IGA) project, the relevant CES Task Forces and the Data Integration Project under the UNECE High-level Group for the Modernisation of Official Statistics. The Task Force consulted experts and expert groups widely to learn from ongoing work, such as related initiatives of UNSD, Eurostat, OECD, World Trade Organization (WTO) and IMF. The UNECE/Eurostat/OECD Group of Experts on National Accounts and the CES Bureau provided a sounding board for discussing the Task Force’s findings in the course of its work.

4.Old certainties versus new possibilities

The introduction of data sharing must be done respecting the traditions of official statistics and in adherence with the Fundamental Principles of Official Statistics and legal frameworks. March [3] points out that a fundamental tension in organizational learning is balancing the competing goals of “the exploitation of old certainties” and the “exploration of new possibilities”. A good example of a contradiction between old certainties and new possibilities is the relationship of statistical confidentiality and data sharing.

Safeguarding statistical confidentiality is fundamental to official statistics. It cannot be compromised. Nowadays maintaining the trust of business respondents requires increasing attention from statistical authorities, especially due to the use of multiple new data sources, increasing data integration and the release of rich combinations of data to meet user needs. Businesses’ willingness to provide truthful and accurate data in a timely fashion can be greatly influenced by their trust in statistical authorities. Distrust, on the other hand, could threaten the very foundation of official statistics, reliable source data, and, thus, the capacity of official statistics to inform society.

Transparency and effective communication with MNEs about data sharing contribute to building trust. Respondents must be ensured that confidentiality rules apply also when data are shared with statistical authorities in other countries and that data collected or acquired for statistics will not leave the statistical system. Respondents must be informed of the use of their data.

The reuse and sharing of data also provide opportunities for improved respondent service and reduction of response burden. Many statistical offices have invested in good communication and relationship management with MNEs. In addition to technical innovations, data sharing requires cultural change and innovation in the way things are done.

Statistical offices are professional organizations that rely in their operations on internationally agreed statistical standards and recommendations, in particular the Fundamental Principles of Official Statistics and the related European statistics Code of Practice, as relevant. When considering data sharing, the most important of these Fundamental Principles are the following:

Principles 5 and 10 can be considered enablers of data exchange. Principle 5 gives national statistical offices a general mandate to use data collected by other organizations. Principle 10, in turn, urges statistical authorities to collaborate with each other to improve statistics globally.

Principles 2 and 6, however, pose some challenges to be considered carefully in the context of data exchange between statistical organizations. The reasons are the following:

The next review of the Fundamental Principles of Official Statistics should reflect, and promote, the need for data sharing in the global statistical system and collaboration between national statistical systems. The review should include a principle and suite of protocols to encourage international data sharing, data exchange and data reconciliation between countries’ official statistical bodies. This should cover cross-border activities for statistical purposes (not for publication as such) in order to improve the measurement of official statistics on MNEs and related activities as well as addressing asymmetries in terms of consistency, coherency and quality.

Data sharing between statistical authorities must respect “old certainties”. Confidential data can only be used for statistical purposes, not for any administrative purposes or decisions about individual businesses. The statistical authorities with access to data must have a legal framework in place to ensure the full protection of statistical confidentiality. Confidentiality has to be ensured in all phases of data processing, and confidential data can never leave the statistical system.

5.Spring of innovative practices to boost data sharing

The UNECE Guide to Sharing Economic Data (forthcoming) makes recommendations for a phased approach to international data sharing for economic and business statistics. Data sharing can involve the exchange of non-confidential aggregates, and only if needed, the exchange of confidential microdata. It can be either continuous, or one-off exchange to address a particular issue. The sensitivity of the shared information increases in a continuum of sharing aggregated data, sharing publicly available data on individual units, sharing metadata on the treatment of individual units, or sharing confidential individual data.

The following cases are intended to provide examples of the innovative nature of data sharing and promote the application and development of similar solutions to advance data sharing in the future. The cases have been grouped by type of innovation, but several good practice cases could be reported under more than one type of innovation.

6.Innovation in other industries to learn from

Important progress has been made by the OECD/G20 Inclusive Framework on Base Erosion and Profit Shifting (BEPS), where over 135 countries are collaborating to put an end to tax avoidance strategies that exploit gaps and mismatches in tax rules to avoid paying tax. The BEPS initiative has increased transparency by establishing a new reporting mechanism in the form of a dashboard of BEPS indicators and launching a new BEPS Action 13 on country-by-country reporting among tax authorities. Under this action, jurisdictions commit to requesting the largest MNEs, i.e. MNEs with more than €750 million in consolidated revenues to provide the global allocation of their income, taxes and other indicators of the location of economic activity. Information to be compiled by MNEs includes the amount of revenue reported, profit before income tax, income tax paid and accrued, the stated capital, accumulated earnings, number of employees and tangible assets, broken down by jurisdiction. MNEs are required to provide the report annually for each tax jurisdiction in which they do business. Several national statistical offices have already requested access to the country-by-country reporting data held by their local tax authorities. This may provide an important source of information for validating MNE data within and across EU countries.

According to United Nations Office on Drugs and Crime (UNODC) every year criminals launder some $1.6 trillion in illicit funds across the globe. Toughening data privacy regulation, like the EU General Data Protection Regulation (GDPR) impose strict limits on the processing and sharing of personal information, which also poses a barrier to authorities’ efforts at curbing money laundering. Data sharing is key for pattern spotting. Banks have, therefore, embarked on techniques relying to artificial intelligence and machine learning to help identify and thwart illicit transactions. Banks simply cannot rely on their own data only. It would deny them the full picture of potentially suspicious activities. International data sharing using innovative technologies designed to preserve data privacy is key to combatting money laundering. Leading authorities, like the United Kingdom’s Financial Conduct Authority (FCA) have developed new solutions using Privacy-Enhancing Technologies (PETs) to allow such data sharing.

Customs authorities have long engaged in international data sharing to facilitate legitimate border crossing of goods and services by means of secure electronic exchange of information between Customs authorities of other countries. This data exchange is intended to mitigate the many obstacles, costs and difficulties international transport faces at borders. In view of the large volumes of cross-border transport, Customs authorities cannot control every vehicle or container anymore. Instead, they have had to adjust their business model to engage in risk management and identify high risk consignments by exchanging and analysing data available through a common Electronic Data Interchange system. This data exchange also enables the analysis of trade flows, and prevention of illegal activities, such as trade misinvoicing, tariff, duty and revenue offences etc.

For example, in the European Union the exchanged data contains most of the data elements included in the standard customs declarations (SAD) containing commodity classification, commodity description, value and quantity of the goods, customs procedure applied etc. For the Intra-EU trade, a similar sharing of data will be carried out in the near future, the legal act enabling this exchange was adopted in 2019. The contents of intra-EU data exchange are of the same type as for trade with countries outside the EU, with some simplifications relating to the fact that o customs procedures are applied within the EU. Also, the Intra-EU trade data is based on (large) samples, not on totals as in the case of regular customs data. The practical arrangements relating to trade data exchange ensure the confidentiality and data security of the exchanged data. The measures taken are based on data protection- and confidentiality regulations and include measures like formal data security certification of the actors and processes applied.

Private businesses are increasingly aware of the value of data and are striving for innovation to utilise data to improve productive and feed economic growth. For instance, some businesses have extended their activities to new areas just to get access to data on consumer behaviour. Now companies are discussing how to create a common data market place for data exchange, where the owners of the data, i.e. private people, could provide their data for purposes they select, for instance medical or environmental research, and would get a compensation when their data are used.

7.Conclusion

Data can fuel a culture of innovation and growth. Yet, even businesses struggle to use data to the full potential, and many companies need to re-invent their approach to data governance, analysis and evidence-based decision making. The same is true for statistical offices – even professional data experts need to reinvent their ways to ensure their relevance.

The decision to engage in data sharing for statistical purposes will be influenced by the overall pressure to reduce response burden, reuse and manage existing data better and retain the high quality of economic and business statistics in the face of the data challenges posed by globalization. Engaging in data sharing for statistical purposes is likely to require a review of statistical legislation and data sharing agreements to ensure full adherence with statistical confidentiality, possibly a new data sharing policy, the necessary systems enabling secure data sharing and new governance procedures. Each of these activities requires a substantial amount of effort and the consideration of risks.

We need to be innovative in small steps. Successful experiences are probably the best way to demonstrate that data sharing among statistical authorities is the way forward. Being successful in change management requires sufficient resources and the necessary initial investments in technology, process improvements and methodology.

According to the Guide to Sharing Economic Data, international data exchange will only happen if statistical offices are open and willing to:

It will be essential to build trust and enhance cooperation between national statistical offices and MNEs that provide the data that are crucial for the quality of economic and business statistics across countries. The legal framework enabling international data sharing for statistical purposes has been laid down in the Guidance on Modernizing Statistical Legislation [5]. The legal processes and consequences of data breaches should be defined internationally. To enable data sharing for statistical purposes with international organizations, the borders of the global statistical system will need to be defined.

International organizations are key players in promoting cultural change and providing discussion fora to share country experiences. These fora should bring together various statistical authorities in addition to national statistical offices, such as statistical units of central banks, ministries of finance and customs, to discuss and agree upon the practical needs for data sharing and to inform participants of successes and lessons learned.

It will be important to have a communication plan and a set of risk management tools available to ensure that the general public is well-informed of data sharing for statistical purposes and measures to safeguard privacy. The Guide to Sharing Economic Data provides common messages for the communication with MNEs, and further work should be undertaken to develop common tools for communication and risk management in data sharing.

The results of data sharing should be measured in quantitative terms to show the impact of shared data on the reconciliation of statistical asymmetries and the improved quality of statistics as a consequence. Furthermore, respondents’ trust would be easier to achieve if statistical authorities could show a measured decrease in response burden and an increase in the quality of statistics as a result of data sharing for statistics.

Building trust calls for direct collaboration with MNEs. Any changes to the treatment of confidential data needs to be discussed openly and transparently, and be justified by reduced response burden, if expected, and better statistics for decision making.

It remains to be seen how these insights and lessons learned from recent innovations in data sharing will contribute to our collective understanding and help build the capability of the national and global statistical systems to make data sharing a regular part of the production of official statistics.