The UNECE Statistical Division has launched a new database of the Millennium Development Goals (MDGs) indicators. The database contains official estimates of over a hundred indicators used to monitor MDGs nationally and internationally.
It is designed to benefit users and producers of MDG indicators on both national and international levels and help understand the different estimates that are available from different sources. The database also aims to alleviate the reporting burden of national statistical offices by making the national official data broadly available.
The United Nations Millennium Declaration, unanimously adopted by the General Assembly in 2000, resulted in the largest international development effort. The declaration identified eight time bound goals and set quantified targets.
Monitoring the progress towards these goals is essential as it guides the planning and evaluation of policies and supports accountability of governments to citizens. Internationally, the monitoring results are used as criteria for financial and other assistance. For all these reasons, good and reliable statistics on progress towards MDGs need to be available.
The United Nations Statistics Division coordinates the monitoring process. It regularly updates a defined and internationally agreed set of monitoring indicators. In the context of the global importance of MDGs, the regional specificities in achieving and monitoring them are also widely recognized and UNECE member countries have requested establishment of a regional database to improve the monitoring.
Besides the official internationally agreed indicators, UNECE countries often use indicators by population sub-groups and additional indicators relevant for monitoring MDGs in their specific situation. UNECE includes those indicators in the regional database if they are reported among a wider group of countries.
A key benefit of the UNECE regional database is the presentation of both the international and national official estimates of the monitoring indicators. Frequently, the values of the international and national official estimates for a specific monitoring indicator differ from each other because of different definitions, methods, primary data sources and reference periods. To help users interpret the data and the discrepancies between the estimates, UNECE publishes in the database extensive additional information — metadata.
To access the database, go to http://w3.unece.org/pxweb
One of the main indicators in the MDG framework is the population living below the national poverty line. In the UNECE region, more than 20 different definitions have been used for poverty lines. The definitions are not always clearly stated, but among those reported are, for example, the population living below an absolute daily income of $1.00, $1.25, $2.15, $2.30, $4.00 or $5.00. Each time, the conversion factors to purchasing power parity and the base years may differ. Other examples are relative poverty lines such as population living in households with an income that is less than 40, 50, 60, 70 or 75 percent of the equivalised median disposable income. Commonly used among countries in the UNECE region are also concepts such as the population living below an income needed to buy a food basket necessary to prevent under-nutrition, the share living below an income needed to obtain basic needs, the share living below an income needed to prevent social exclusion, and many more. Many countries have published more than one definition. Additionally, the exact methodologies change over time and are different between countries and base years and purchasing power parity conversion factors are adjusted. For example, household expandable income can be measured through consumptions or through income. The exact questions used in surveys to obtain this information differ. The estimate can also be influenced by the time of conducting the survey or the length of the survey questionnaire. Even a seemingly less trivial poverty line of one dollar is therefore not necessarily comparable. The official international estimates aim at applying the same methodology and therefore tend to be more comparable.