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Taking Stock of the Role of Statistics in Economic Development

Vamsee Kanchi's picture

In today’s data-saturated, highly visual and networked world, statistics are used by policymakers, researchers and journalists for just about everything. However, a veritable mix of government officials, economists and statisticians work – often against overwhelming odds - to produce data sets that are, paradoxically, often taken for granted, but also used as gospel in policy discussions.

Earlier this week, the World Bank celebrated the first ‘World Statistics Day,’ where the successes, challenges and future directions for collecting and analyzing economic development-related data were discussed.

The statistics discipline in the economic development field has seen some breakthroughs in the recent past.

Princeton University's Angus Deaton, a panelist at the event, pointed to the 2005 round of the largest international data collection exercise in the world, called the International Comparison Program, which collects internationally comparable price levels. This data set is critical for comparing living standards between countries.

Deaton who has a keen interest in well-being issues, also praised a recent expansion of data collection from traditional subjects like income to health indicators.

Open data initiatives, such as the World Bank’s, have also been recognized widely as a step in the right direction - not only because they make data available to individuals outside academic circles, but also because they can lead to better quality data as a result of increased scrutiny.

In addition, the Gallup World Poll, which was started in 2006 and consists of identical surveys conducted in 155 countries on self-reported well-being, fills an important gap in data collection.

However, much remains to be done. 

Reliable data on infant mortality and related issues are woefully absent in many countries. Furthermore, there is an excessive reliance on inferences (the World Development Indicators, for example, show infant mortality rates for several countries that have been derived rather than actually gathered). Also, many data sets, such as the results of the Living Standards Measurement Survey, cannot be compared across countries. 

Moreover, the ability to collect and generate data in many countries is minimal (although, new efforts like the World Bank’s Virtual Statistical System are attempting to tackle this). National Statistical Offices, the government agencies typically charged by a nation’s constitution to collect and disclose statistics, have a particularly critical role in this regard.

Dr. Eduardo Sojo, Chief Statistician for Mexico, and a panelist at the event, identified seven challenges confronting National Statistics Offices today:

  • Large amounts of information – in today’s networked world, information is available in the form of anything ranging from charts to geo-referenced data.  How to store such information is a big concern
  • New players in generation of statistical information – there is a need to differentiate between official statistics and those from other sources.  How the standards of NSOs can be maintained while the capabilities of external statisticians are leveraged needs to be determined
  • Global dimensions of statistical information – coordination between NSOs and multilateral organizations on issues such as influenza needs to be strengthened
  •  Increasing role of state and other local governments – local governments are themselves generating and collecting a sizable volume of data
  • Broader range of customers – there is widespread demand for data from all quarters of society.  Social media, mobile and other digital technologies also give rise to new methods by which data are used
  • Heightened concern about protecting personal data

 

Another pressing issue is that of disclosing statistics in a timely manner. Dr. Pronab Sen, former Chief Statistician for India said official statisticians can often be hesitant to share statistics that they otherwise ought to (although, he conceded, they often do so because of unclear guidelines or fear of repercussions from politicians). In this respect, Sen indicated that official statisticians are faced with the need to exercise judgment when providing data, being mindful of potential impacts on society at large as well as the credibility of the statistical office they represent.

Although users of data frequently take a given statistic at face value, data sets themselves are tied in with a broader theory. In this respect, Justin Yifu Lin, Chief Economist for the World Bank and host of this blog, floated a proposal to expand statistical measurements from reflecting income levels as indicators of economic development (for example, GNI per capita) to including metrics that represent economic development as a process of continuous structural transformation.

Structural transformation refers to the notion that, as economies transform from being agriculture to industry to service intensive, the role of the government and the services it provides need to change and adapt as well. For example, if a country is in the process of transforming from being a primarily agriculture-driven society to an industry-driven one, the state may need to provide new financial services that support the emerging economy. 

In such a context, the task of the statistician is to determine, as Lin put it, ‘which [indicator] fits what.’ Currently, for example, when measuring the aggregated real output of a country, services often do not get included. As such, service-related metrics (for example, ratios that gauge the relationship between the cost to provide a service and the effectiveness of that service) would be a much welcomed addition.
 

Comments

Submitted by Thomas Danielewitz on
"Garbage in, garbage out", the old programmer's saying goes; and that's equally true for statistical databases. Few people realize the tremendous amount work and the tens of thousands of people in statistical offices and ministries around the World, who are involved in compiling the data that can now be easily accessed through the World Bank's Open Data Initiative. As the author rightly points out, a lot remains to be done to improve availability and quality of statistical information in developing countries. The VSS is one important piece of the puzzle, as it seeks to bridge the knowledge gap. But significant and sustainable improvement will not come about without a scaling up of investments in statistical systems, and core reforms in areas of statistical legislation and organizations and key policies on HR management and dissemination. To support this, the World Bank has developed specialized lending programs like the STATCAP, and raised funds for statistical capacity building through initiatives like the Statistics for Results Facility (SRF). Hopefully, initiatives like Open Data will put the spotlight on issues of quality and availability of development data, and focus the attention of Governments and development partners alike on the urgent need for investments and reforms of statistical systems in developing countries.