29 Feb

Blog Post #08 – Prosperity Indicators: What, Why, How

A bit of history

As (Innes & Booher, 2000, p.173) claim “Indicators and performance measures have become an important element in policy initiatives relating to sustainability and to the re-invention of government”. The idea of employing quantitative indicators in order to evaluate policy implementation goes back to the ‘40s, when the US economy was being evaluated in terms of the Monthly Economic Indicators (Wong, 2006, p.1ff). The idea of exploiting social indicators and developing a theory for defining, using, combining and interpreting them, passed from the US Administration to the large international organisations such as the United Nations (UN, Social and Economic Council) and the Organisation for Economic Co-Operation and Development (OECD).

The wave of interest around prosperity indicators has been significantly motivated by the global questions on environmental matters and has led to a series of approaches, typically associated to the keywords ‘indicators for quality of life’, ‘sustainability indicators’, sometimes combined with other widely used terms in public discourse, such as `economic competitiveness’, etc. (Sawicki, 2002). As an indication of the widespread interest, let us mention that the European Union has issued a set of recommended ‘European Common Indicators’ focusing on ‘monitoring environmental sustainability at the local level’ while, some years earlier, a call for suitable ‘indicators for sustainability’ had been included in Agenda 21 of the Earth Summit Conference (1992, Rio de Janeiro), which marked an avalanche of actions and initiatives.

One of the major concerns in the construction and exploitation of indicators has been the access to the relevant data and the difficulties in the collection and reliability of the data needed in order to calculate and interpret social metrics. The revolution of the WWW and the Open Data Movement, conceived as “the idea that certain data should be freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control”  arguably opens a new arena of experimentation with social indicators. This idea lies at the heart of the Policy Compass approach and thus, in this deliverable we will also provide a quick review of the contemporary situation of Open Data.

What is an indicator?

The term ‘indicator’ is one that people can easily understand. It is regularly conceived as a sort of ‘statistical measure’ that can adequately capture crucial aspects of a (social) phenomenon that should be monitored, in particular when a specific policy measure is enforced to affect it. Perhaps then, the simplest and most general definition is that of (Innes J. E., 1990): an indicator is “a set of rules for gathering and organising data so they can be assigned meaning”. In the policy-making arena, an indicator is conceived as a concrete tool used for justifying and optimizing resource allocation. From the scientific perspective, social indicators can be examined both from the theoretical and the practical viewpoint.

A quick look at the typology of indicators

In the preceding subsection, the (abstract) notion of an indicator has been given some well-known definitions. Yet, we should have in mind that it is usually the quantitative nature of indicators which makes them potentially interesting and useful. At this point, it certainly makes sense to see how indicators are perceived by the people who work on their calculation and exploitation, at least technically.

Aggregate (or summary) indicators: An aggregate or summary indicator concentrates information into a single figure. Examples include Gross Domestic Product (GDP) and the Consumers Price Index (CPI).

Composite (or integrated) indicators: Composite or integrated indicators draw from, or reflect, interaction between different areas such as the environmental, economic and social dimensions. An example would be the Human Development Index (HDI). An aggregate indicator can also be a composite indicator. To use this type of indicator successfully, awareness and acceptance of the assumptions that have gone into its construction are required.

Decoupling indicators: Decoupling is a (desired) outcome, such as having reduced energy consumption along with increased economic growth. The decoupling process can be very complex, so indicators aiming to show whether it is happening need to be developed with care.

Headline indicators: Some indicators may be selected as headline indicators – usually because they describe key issues. They are often supported by a subset of indicators. Usually they form a quick guide or overview and can be used to engage public awareness and focus attention. For instance, the UK sustainable development project has 15 headline indicators that are used to make up a quality-of-life barometer. Headline indicators may include composite indicators or other types of indicators, depending on the reporting focus.

International, National, Regional and Local indicators: Indicators are used at all levels, including international, national and regional and may be referred to as national and regional indicators. Indicators can be produced for lower levels such as community scheme monitoring where local indicators may refer to. For example, data gathered at the subnational level to produce regional indicators, could feed into national or international indicator reporting.

Proxy indicators: Proxy indicators are indicators that measure one aspect of a system that is thought to be reflective of a wider system. For example, lichen species are used as a proxy for air quality, and insect species in waterways may be used as a proxy for water quality.

Sustainability and other topic based indicators: Indicators may belong to a set that builds a picture of a whole system or framework, such as sustainability indicators. Sustainable development integrates development and developmental reporting across the economic, environment, cultural and social domains. Sustainability indicators refer to the monitoring of sustainable development.

On the methodology of defining Social Indicators

The description below draws directly from (Wong, 2006, Chapter 7), a very readable presentation. The steps of the methodology comprise:

  • Step 1: Conceptual consolidation – Clarifying the basic concept to be represented by the analysis
  • Step 2: Analytical structuring – Providing an analytical framework within which indicators will be collated and analysed
  • Step 3: Identification of indicators – Translation of key factors identified in Step 2 into specific measurable indicators
  • Step 4: Synthesis of indicator values – Synthesizing the identified indicators into composite index/indices or into analytical summary

What makes a `good’ indicator?

According to OECD, a well-defined and useful indicator should comprise (UNEP, 2014):

  • Policy relevance: the indicator needs to address issues that are of (actual or potential) public concern relevant to policymaking. In fact, the ultimate test of any single indicator’s relevance is whether it contributes to the policy process.
  • Analytical soundness: ensuring that the indicator is based on the best available science is a key feature to ensure that the indicator can be trusted.
  • Measurability: the need to reflect reality on a timely and accurate basis, and be measurable at a reasonable cost, balancing the long-term nature of some environmental, economic and social effects and the cyclicality of others. Definitions and data need to allow meaningful comparison both across time and countries or regions.

References

Innes, J. E. (1990). Knowledge and Public Policy: The Search of Meaningful Indicators,. New Brunswick: NJ: Transaction Publishers.

Innes, J. E., & Booher, D. E. (2000). Indicators for Sustainable Communities: A Strategy Building on Compexity Theory and Distributed Intelligence. Planning Theory and Practice, 1(2), 173-186.

Sawicki, D. S. (2002). Improving community indicator systems: injecting more social science into the folk movement. Planning Theory & Practice, 3(1), 13-32.

UNEP. (2014). GREEN ECONOMY: Using indicators for green economy policy making. Retrieved from http://www.unep.org/greeneconomy/Portals/88/documents/PAGE/IndicatorsWorkingPaper.pdf

Wong, C. (2006). Indicators for Urban and Regional Planning: the interplay of policy and methods. London and New York: Routledge, Taylor and Francis.

29 Feb

Policy Compass 6th Plenary Meeting @ Madrid, Spain

The 6th Plenary Meeting of the Policy Compass consortium was hosted by ATOS in Madrid, Spain between the 24th and 25th of February, 2015.

The participants had fruitful exchange of ideas on the upcoming public launch of the project’s platform, as well as on the plans for the Policy Compass pilots’ operation.

Past and future dissemination activities, as well as the project’s exploitation plan, were also discussed.

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For future news and insights, stay tuned on our website and the project’s social media!

02 Feb

“Enabling Effective Policy Making” Workshop: Proceedings now available!

Policy Compass, along with other Global Systems Science projects, organised a Workshop in the context of the dual EGOV / ePart 2015 conference, titled “Enabling Effective Policy Making – Coupling the Power of the Data with the Wisdom of the Crowd”.

The main purpose of the Workshop was to disseminate the project’s platform and up to date results to all participating stakeholders, as well as stimulate discussion on all participating projects.

The workshop’s proceedings are now available (open access) though CEUR Workshop Proceedings: http://ceur-ws.org/Vol-1553/

30 Nov

Policy Compass at eChallenges e2015!

eChallenges e-2015 (hosted in Vilnius, Lithuania) attracted participation from senior representatives of leading government, industry and research organisations around the world.

The goals of eChallenges e-2015 were to promote ICT Entrepreneurship and Innovation, facilitate Information Society and Applied ICT related knowledge sharing between government, industry and research stakeholders, raise awareness of the current state of eAdoption in developing countries, stimulate rapid take-up of RTD results by the public and private sectors, and identify opportunities for ICT related research and innovation collaboration under Horizon 2020.

DSSLab NTUA, partners in the Policy Compass project, attended many sessions and presented a scientific paper titled “Lessons Learnt from the Use of Prosperity Indicators in Policy Making: Towards Community-Generated Indicators“.

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The respective presentation can be found here.

30 May

Blog Post #01 – The Policy Compass Methodology

The Policy Compass methodology (reflected in detail in the submitted in April Policy Compass deliverable “D1.2: Policy Compass Methodology and High level user Requirements” – available in the “Deliverables” section of the project’s website) constitutes the first of the core parts of the whole Policy Compass idea and approach.
Policy Compass aims to follow a specific approach in order to cover the maximum set of end users’ needs and possible requests, in a user friendly and comprehensible way. In the following lines the complete process behind the use of Policy Compass is described summarily, without though excluding any of the important parts.
The overall Policy Compass methodology is built around 3 interrelated pillars depicted in the following figure and described below:

Policy Compass Methodology Pillars

Figure 1: Policy Compass Methodology Pillars

  1. Evaluating Performance of Policies (EPP): Motivated by their desire to actually check/verify that a specific policy action, policy directive law etc. has either actually achieved, or failed to achieve the initially met targets and KPIs (or simply aiming to verify that what politicians and public servants claim as actual results), citizens seek for data confirming the understanding they have. Seeking for relevant data and metrics is the first logical step, while casual models that would verify their assumptions in a more scientific way is an even better scenario. In the same time, making connections between facts and specific actions in a visualised manner is more vivid and user friendly than simple text and mathematics. Thus, besides giving the user the ability to engage himself/herself in a formal and scientific procedure, Policy Compass will also offer visualisation capabilities. And, having achieved the initial idea, sharing the findings with the community is the final step of the process; Policy Compass will also support disseminating the results of the procedure through popular social channels.
  2. Building Causal Policy Models (BCPM): Relevant to the previous step, a more advanced user might not be satisfied with simply utilising existing casual and simulation models to verify and/or explain his/her findings. Having collected the necessary data, a user with the relevant background can build a new (or ameliorate an existing) casual model. Turning this model in a more user friendly and easily comprehensible form, i.e. a Fuzzy Cognitive Map, can act as a catalyst for better verification and understanding of the newly developed model. Running simulation through the aforementioned model, in order for example to predict future impacts, is the next logical step, also supported by Policy Compass. And, similar to the previous step, disseminating the findings is also wished for and supported.
  3. Online Deliberation And Argument Mapping (ODAM): Online deliberation can act as a catalyst both a priori and a posteriori of the two previous steps; online discussions can offer valuable input to anyone looking for data relevant to his/her interests (a priori offering) while, in the same time, interlocutors can be engaged for discussing, criticising and verifying the resulting findings. However, non-structured deliberation is not always of actual value. A way to structure new or even existing deliberations should be offered, in order to facilitate easy and effective navigation through the discussions and conclusions’ extraction. This is what Policy Compass will offer through online argument mapping.

The Policy Compass methodology is not strictly structured and static; on the contrary, it constitutes a highly dynamic approach, letting the user follow parts of the integrated workflow in the order he/she wishes. The integrated approach can be found in the following figure:

Interrelations between Policy Compass Main Components

Figure 2: Interrelations between Policy Compass Main Components