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Beyond GDP

Measuring What Counts for Economic and Social Performance

image of Beyond GDP

Metrics matter for policy and policy matters for well-being. In this report, the co-chairs of the OECD-hosted High Level Expert Group on the Measurement of Economic Performance and Social Progress, Joseph E. Stiglitz, Jean-Paul Fitoussi and Martine Durand, show how over-reliance on GDP as the yardstick of economic performance misled policy makers who did not see the 2008 crisis coming. When the crisis did hit, concentrating on the wrong indicators meant that governments made inadequate policy choices, with severe and long-lasting consequences for many people. While GDP is the most well-known, and most powerful economic indicator, it can’t tell us everything we need to know about the health of countries and societies. In fact, it can’t even tell us everything we need to know about economic performance. We need to develop dashboards of indicators that reveal who is benefitting from growth, whether that growth is environmentally sustainable, how people feel about their lives, what factors contribute to an individual’s or a country’s success. This book looks at progress made over the past 10 years in collecting well-being data, and in using them to inform policies. An accompanying volume, For Good Measure: Advancing Research on Well-being Metrics Beyond GDP, presents the latest findings from leading economists and statisticians on selected issues within the broader agenda on defining and measuring well-being.

English Also available in: German, Polish

Executive Summary

The High-Level Expert Group on the Measurement of Economic Performance and Social Progress(HLEG) builds on the analyses and recommendations of the 2009 Commission on the Measurement of Economic Performance and Social Progress (the “Stiglitz-Sen-Fitoussi” Commission, SSF) in highlighting the role of well-being metrics in policy and encouraging a more active dialogue between economic theory and statistical practice. The report makes explicit the often-implicit assumptions hidden in statistical practices and their real-world consequences. Its central message is that what we measure affects what we do. If we measure the wrong thing, we will do the wrong thing. If we don’t measure something, it becomes neglected, as if the problem didn’t exist.

English Also available in: German

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