Statistical Forum on Inclusive Growth – Leaving Nothing Out and Nobody Behind
IMF News, December 2, 2016
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Bibliographic details
- Published: December 2, 2016
Forum overview and purpose
- Event: Fourth IMF Statistical Forum on Statistics for Inclusive Growth.
- Location and dates: Held at the IMF headquarters in Washington DC on November 17 and 18.
- Publication date on IMF site: December 2, 2016.
- Objective: Discuss how statistics can contribute to policies aimed at achieving inclusive growth and the increasing demand for such statistics among statisticians, academics, experts, and policymakers.
What is inclusive growth?
- Inclusive growth described as multidimensional, involving:
- income and wealth distribution within economies;
- segmented data by gender, social groups, and geographical area;
- measures that capture developments in a more globalized and digitized world economy.
- Key point: A clear definition of inclusive growth is required to guide compilation of statistics and to develop informative indicators.
- Notable speaker: Ravi Kanbur, Professor of Economics at Cornell University, emphasized that statistics have a political component and that methodological choices (e.g., survey framing, global poverty line construction) can have major policy implications, including for defining Sustainable Development Goals (SDGs) indicators.
Going beyond GDP
- Drivers creating pressure on GDP-based measures:
- increased attention to distributional and environmental issues;
- role of the informal economy;
- rapid technological developments.
- Discussed approaches:
- supplement GDP with alternative metrics combining education, health, and living standard factors to better capture welfare dynamics;
- enhance existing GDP-framework metrics where possible.
- Specific measurement challenges noted:
- new forms of intermediation services (for example, AirBnB and Uber) and Internet-based consumer benefits widen perceived gaps between GDP, productivity, and welfare;
- better measurement of volumes and prices of digital services could improve alignment between output, productivity, and welfare.
Combining statistics
- Integration needs:
- additional statistics on prices, the labor force, and bilateral trade to better measure impacts such as offshoring.
- Use of multiple data sources:
- IMF’s Financial Access Survey shows an increase of access to financial services over the past years, with uneven coverage globally;
- complementary survey data of individuals and firms provide information on segments lacking access and barriers they face.
- Data types emphasized: micro-level data, Big Data, technological innovation, and administrative data can help meet massive compilation challenges for SDG indicators.
The way forward — conclusions and recommendations
- Consensus points from roundtable led by IMF Deputy Managing Director Tao Zhang:
- Inclusiveness is difficult to capture in a single metric due to multiple dimensions.
- Recommend a structured framework with a limited set of clearly defined indicators.
- International organizations should play an important role in establishing and promoting data standards and coordinating efforts across countries.
- Emphasize the need to supplement, improve, and combine existing statistics with new and more granular data.
- Encourage active roles for national and international organizations in developing necessary data and coordinating cross-country efforts.
- Caution: while leveraging new data sources, maintain focus on traditional statistics.
Source: IMF News — Statistical Forum on Inclusive Growth–Leaving Nothing Out and Nobody Behind (December 2, 2016).