Referred to as one of the “Fathers of Science Fiction Genre”, Herbert George Wells stated that “[S]tatistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” (Wells, 1903). Encompassing both logical and analytical reasoning, statistical thinking evaluates the “whole” of a problem with its sub-components, including the processes and solutions. Statistical thinking is viewed as a philosophy of learning and action based on the following fundamental principles (Hlavacek, 2008):

• All work occurs in a system of interconnected processes,
• Variation exists in all processes, and
• Understanding and reducing variation are keys to success.

Historically, Al-Kindi is seen as the first scientist to write on statistics. In his book “Risalah fi Istikhraj al-Mu'amma – Manuscript on Deciphering Cryptographic Messages”, he gave detailed description of how to use statistics and frequency analysis to decipher encrypted messages (AL-Kadi, 1992). From the times of Al-Kindi to today, statistics as a science has advanced both in mathematical formalism and implementation fields due to the ever changing social practice. Especially the early nineteenth century witnessed the gilded age of official statistics throughout Europe where people like Ernst Engel, Director of the Royal Prussian Statistical Bureau, strongly believed that “statecraft, namely, the practical application of political science, is a mere sham without a statistical foundation.” (Hacking, 1987).

Official statistics produced by National Statistical Offices (NSO) and international agencies are expected to provide information on all main parts of our daily life. However, as put forward in 1996 by Yves Franchet, Former Director General of Eurostat, that statistics produced by the NSOs are like any other product to compete with all sorts of information from various sources, and timeliness; even at the expense of accuracy, reliability, and relevance; is a vital issue for official statistics to keep its market (Kotz, 2005).

To attain the points mentioned above, the NSOs need to build a statistical capacity on a continuous basis. From the viewpoint of some international organisations, statistical capacity is defined as the ability of countries to meet user needs for good quality official statistics which are produced by governments as a public good (World Bank, 2013).

From this aspect, the Statistical Capacity Indicator (SCI) was developed to measure statistical capacity of countries. Maintained by the World Bank, the SCI for 2012 provides an overview of the national statistical capacities of 146 countries of which 50 of them are OIC countries. The SCI framework is comprised of three dimensions: statistical methodology; source data; and periodicity and timeliness. With a scale ranging from 0 to 100, these dimensions are then averaged to provide the overall SCI score (World Bank, 2012a).

In this OIC Outlook Report, we will analyse, based on the 2009 and 2012 SCI scores from the (World Bank, 2012a), the dimension indicators – that the OIC countries have still a room to perform better – and the possibility to construct country clusters based on the performances in the respective SCI dimensions. Based on the analyses carried out, this Report derives conclusions and policy implications for the OIC Member Countries to improve their statistical capacities.

Online Electronic Version

Current State of Statistical Capacity in OIC Countries (English)