Fourth Industrial Revolution

How data can help improve the state of the world

People stand in front of a big data analytics logo at the booth of IBM during preparations for the CeBIT trade fair in Hanover, March 9, 2014.

Image: REUTERS/Fabrizio Bensch

Margareta Drzeniek-Hanouz
Deputy Head of Social and Economic Agendas, Member of Executive Committee, World Economic Forum Geneva
Daniel Gomez Gaviria
Head of Competitiveness Research, World Economic Forum
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In a recent article, Harvard University's Ricardo Hausmann raised some interesting points about the value of benchmarking exercises and how best to use them. We would like to explore some of the points raised in the piece and argue that, despite the limitations and challenges, both in terms of new data and new theories, benchmarking, and in particular competitiveness and inclusive growth benchmarking, are important tools to contribute to improving the state of the world.

When the first computers for running statistical exercises became available, economics slowly started shifting from a purely theoretical discipline to a much more empirical discipline. As computer power and data availability have improved, anyone with a reasonably good laptop computer and an internet connection can search hundreds of data sources and run statistical models. Thousands of people can give “learning without theory” a shot.

How can such a process inform decision-making on what needs to be done in order to advance economic progress and inclusive growth? Given the complexity of the world, and the ever increasing availability of data, it is rare, some say impossible, to have a completely a-theoretical inquiry. We have to choose what to look for and where to look for it. These choices are driven by priors: systems of ideas intended to explain the complex world we observe and hypothesis testing with a constant interplay between theory and empirical inquiry.

Can we move beyond the scientific method of inquiry based on observation, theory, hypothesis testing and refutation, to a more purely data-driven and theory-free method of learning? Some think that we can and that we should. The capabilities of machine-learning, big data and artificial intelligence will allow us to randomly discover relationships we had never thought about. However, that path can be dangerous if we do not analyse the relevant causal relationships and understand the economics of these relationships. We could end up concluding that reducing the number of Nicolas Cage films will reduce the number of people drowning in pools! Many think that “the success of economics as a discipline is in no small part due to the rigour that formal theory imposes”.

Making progress in the fundamental question of how to improve the state of the world and how to make countries more competitive, and growth more sustainable and inclusive, certainly requires the best from both the theory world and the world of empiricism. While no comprehensive theory of development exists, many theories have been suggested and many pieces of the puzzle are known based on years of economic research, which started with Adam Smith in the 18th Century. These bits and pieces are what help us identify the factors that help countries drive growth, eliminate poverty and improve living conditions for the many.

In this context, benchmarking tools serve many purposes. The Human Development Index, for example, was very successful at shifting the development debate and associated measures from income to a people-centric approach. It was derived from the utility function. A very theory-heavy approach.

Other tools are intended to motivate change and as a starting point of a debate on what needs to be done to advance on specific issues. The World Economic Forum's Global Competitiveness Report and Inclusive Growth Report draw on what we know about the process of economic development to propose features that characterize the success stories, thus helping speed up the process of inquiry, imitation and long-term agenda formulation. These two reports fully acknowledge the complexity of development and how much we still do not know about what works and what doesn’t work. But they also build on our best understanding of the drivers of successful development processes.

As such these indices are diagnostic tools. They do not tell you what should be done, but rather where further investigation and multistakeholder dialogue are warranted. As starting points of conversations, be it between the public and private sectors, as the World Economic Forum is orchestrating them at regional meetings, or between different parts of society, they help frame the debate. They help media and civil society hold governments accountable for long-term growth and inclusion, and they help governments identify strengths and weaknesses that require further work. Likewise, they can be tools for tracking progress over time.

They do not claim to be a prescriptive set of recommendations, but rather serve the purpose of facilitating evolutionary learning by allowing countries to compare themselves over time and with other countries. Any policy measures should not be based on the ranking alone, but also on additional analysis. Rankings are by their very nature limited in terms of absorbing differences between countries, but this does not mean that these differences do not exist, it just represents the view that there is a value in simplification and structuring of data around clear and easy to understand concepts.

The Inclusive Growth and Development Framework is not an index but rather a scorecard based on the notion that institutions matter for the inclusivity and resilience of growth, for which there is quite a bit of theoretical and empirical support, certainly enough to justify further study and comparison. In absence of a proven single policy mix for the pursuit of inclusive growth and development our work is meant to enlarge the frame of reference of the debate by enabling a deeper examination of the relevance of institutions to this challenge, leaving it up to individual countries to determine what they want to emphasize or work on to a greater or lesser extent.

If these tools are designed and interpreted well, they can be immensely successful in catalysing action and motivating change. Serving as a coordination device between government, the private sector and civil society, aiding in constructing policy agendas around long-term goals for prosperity rather than shorter term goals, the tools have made a positive impact on a range of countries.

In particular, by providing a global information public good and drawing attention, year after year, to long-run determinants of growth and shared prosperity, these reports have a particularly positive influence on developing countries. If it weren’t for them, it is unlikely that these issues would be on the public agenda. New sources of data and ways of learning from this data, such as big data, machine learning, artificial intelligence or satellite data, can certainly enrich these exercises. However, they cannot be substitutes but only complements. We definitely need the lense of theory, and the continued incorporation of new knowledge, to keep the indeces and benchmarking current and useful.

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Related topics:
Fourth Industrial RevolutionEconomic Growth
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