Subnational strategies for the development of statistics


Subnational   or local development is an essential dimension of national development. In developing countries, especially those with a large population and territory, communities drive both growth and inequality. While in many countries communities have autonomy in governing their own affairs, they are in reality highly inter-dependent with one another and with national institutions toward shared national development goals. Proximity in terms of geography and environment, economic resources, and culture are among factors that naturally create synergy among local communities. 

Countries are usually subdivided into states, regions, or districts (subnational context) and further subdivided into smaller geo-administrative units lik provinces, cities, municipalities and villages. In many countries, these subdivisions have autonomous mandates and with statistical offices that are not necessarily linked to the national statistical office. In some countries, the geo-administrative units contribute to national data collection through program agencies while in others they do not.The need for subnational statistics  , which includes state, regional and district level statistics and local area statistics   (e.g., provincial, city, town/municipal, and village), is a long-standing concern of development stakeholders and national statistical systems alike. As progress continues and inequality widens, better, quicker, and more granular data is central to achieving the sustainable development goals at the national and subnational/community levels. Improved subnational statistics is vital in finding those who might be left behind.

Subnational statistics is complex in terms of both production and use with many, often inter-twined issues of legal, organizational, and technical nature. Legal mandate for the collection of subnational data is usually not very clear between national and subnational institutions if one exists at all. Allocation of organizational resources and hence statistical capacity to produce good quality data is extremely imbalanced and biased against subnational institutions. As a consequence, data quality issues are far more and diverse at the subnational level. 

Many studies and country reports are in general agreement that the supply of subnational statistics is wanting in many countries especially in the developing and less developed ones. Some of key observations are as follows: 

  • Subnational statistical systems, whether formally defined or not, are not necessarily and systematically coordinated. Subnational statistical systems are highly decentralized (i.e., many institutions at the national and subnational levels collect data) even when the national statistical office has a ‘centralized’ mandate. 
  • Relationship between ministries/agencies and the national statistical office on subnational statistics is generally weak. User-producer communication at the subnational level is also less established than at the national level. 
  • Community-based monitoring systems and local data collection do not necessarily benefit from technical guidance and capacity development from the national statistics office and/or the national statistical system.
  • More often, subnational data does not meet widely-recommended quality standards and user needs. Data, including national survey results, is too aggregated, sparse, and tardy to allow insightful understanding of subnational economic and social dynamics. Further, subnational data is not necessarily linked to local institutions. 

A comprehensive and inclusive NSDS must therefore include strategies to improve the capacity of the national statistical system including the subnational statistical systems in producing subnational/local data. Such strategies should address persistent issues such as capacity development and the need to improve administrative data   systems as well as the challenges and opportunities of the emerging data ecosystems. The subnational statistical system will benefit significantly from exploration of non-traditional data sources (e.g., big data  , sensor and other scientific data, geospatial data, and citizen-generated data  ) to narrow data gaps or expand subnational data.

The national statistical office is crucial in (a) facilitating statistical capacity development of subnational institutions, the local-level units in particular, to produce quality data; (b) establishing and coordinating timely, reliable, and predictable flow of data between national and subnational institutions; (c) managing subnational data quality assurance; and (d) promoting user-producer communication at the subnational levels. 

An effective subnational strategy for the development of statistics will contribute to the sustainable development goal of enhancing “capacity building support to developing countries, including for LDCs and SIDS, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.”

Concrete actions

  • Assess the users of subnational statistics. Step 3.1
    • Identify stakeholders of subnational statistics and analyze their data needs.
  • Assess current subnational statistics in relation to user needs. Step 3.2
    • Identify/define the policy framework for subnational development.
    • Identify key indicators for planning and monitoring development goals.
    • Assess current data in relation to data needed to compile the key indicators.
      • Assess administrative data systems in priority sectors and/or subject-matter areas (e.g., education, health, disability, agriculture, poverty, infrastructure, civil registration, etc.).
      • Explore and assess yet untapped data sources (e.g., big data, sensor and scientific data, geospatial data, citizen-generated data).
  • Assess the existing subnational statistical system. Step 3.3
    • Assess established systems in priority sectors and/or subject-matter areas (e.g., education, health, disability, agriculture, poverty, infrastructure, etc.).
  • Identify strategic goals and key outputs toward improvement of subnational statistical system. Step 4.2 | Step 4.3
  • Identify specific actions and corresponding costs, as well as key risk factors and mitigating measures. Step 5.1 | Step 5.2 | Step 5.3
  • Mobilize resources and implement actions. Step 6.2 | Step 6.3
    • Establish institutional partnership mechanisms between NSO and the ministries/agencies mandated in the selected priority sectors and/or subject-matter areas.
    • Consider partnerships with big data sources such as data science firms, telecommunication companies, and other service providers to share data and/or help develop applications to extract data. 
    • Study and consider existing systems of civil society organizations and non-government organizations (many are location-specific) including citizen-generated data to produce statistics on specific issues.
  • Monitor results of strategies and actions. Step 6.4
    • Identify key results and milestones (e.g., systems/partnerships established/improved, priority sectors and/or subject-matter areas addressed, etc.)
  • Update strategies and action plans based on the annual and mid-term review. Step 6.5 | Step 6.6 | Step 6.7
  • Evaluate key outputs and outcomes. Step 7.1 | Step 7.2 | Step 7.3