GID Technical Overview
Global Impact Database (GID) quantitatively describes environmental, social and economic impact estimates for countries and sectors in the global economy.
Economic activity causes impacts throughout the entire interconnected economy. GID estimates this impact with input-output analysis based on data on the interconnectedness of industries in various countries and their environmental, social and economic performance. The impact estimates produced are categorised into capitals and provided in comparable monetised units.
The development of the GID is based on multiple internationally recognised frameworks and frameworks published by the Impact Institute. The data quality included in GID is ensured through multiple checking and data cleaning procedures.
Several versions of the GID have been developed and released, this development is still ongoing.
This technical description aims to give an overview of the topics introduced above in the following sections:
Impact Assessment and Monetisation
Impact assessment and monetisation consists of taking a selection of extensions and converting them into impact indicators that are expressed in monetary terms. Conversion from extensions to indicators requires impact factors and conversion to monetary units requires monetisation factors. These impact factors and monetisation factors are further explained below:
- Impact factors – factors that are multiplied by extensions to convert them into a standardised set of GID indicators e.g. kilograms of different green-house gas emission are converted into kilograms of CO2 equivalents.
- Monetisation factors – factors that are multiplied by GID indicators producing a comparable set of monetised GID impacts e.g. kilograms of CO2 equivalents and m3 of scarce water use are converted to comparable $ values.
For more (general) information about impact measurement and valuation at organisational level refer to FIS and IAM Core.
Input Output Analysis
Input-Output Analysis (IOA) is an economic technique that represents the interdependencies between different countries and sectors. It is a well-established method used for academic articles and research papers, that is now also being utilised for sustainability reporting by corporate and financial institutions. The Impact Institute has developed the pioneering ‘chains’ IOA to deliver a unique dataset allowing impact to be attributed, based on value added, across global value chains instantly. It includes upstream and downstream linkages.
IOA is a widely used method, a selection of academic articles and research papers where IOA is the main technique used for estimating environmental and social value chain impacts are listed below:
- Evaluating the environmental impacts of dietary recommendations. Behrens, Paul et al. PNAS. Web. December, 2017
- The Global Resource Footprint of Nations: Carbon, water, land and materials embodied in trade and final consumption calculated with EXIOBASE 2.1. Tukker, Arnold et al. ResearchGate. Web. June, 2014.
- Carbon Footprint of Nations: A Global, Trade-Linked Analysis. Hertwich, Edgar and Glen Peters. ACS Publications. Web. June 15, 2009.
The use of IOA for estimating environmental value chain impacts is known as Environmentally-Extended Input-Output analysis (EEIO). For more information about the EEIO approach refer to the paper below:
The raw data used in GID is described below:
- Input Output (IO) data – trade data describing the interconnectedness of the global economy.
- Extensions – Datasets extending the IO trade data describing social, environmental and economic performance of sectors worldwide. These are provided by IO databases in the form of environmental and socio-economic extensions and derived from public sources such as Wageindicator, ILOStat, and OECD statistics.
- The main data sources used in GID include as GTAP, SHDB, Eora, and Exiobase.
This section explains how the GID works using the data elements and philosophies introduced in the sections above:
GID uses the principals of IO analysis with the monetised indicator data to produce estimates of what the impact of economic stimulation is throughout the economy, the concepts behind this approach are further described below.
Raw GID data has the unit impact per euro economic activity in a specific sector. However, if you source from a sector, you do not only stimulate economic activity in the sector where you source from, but also in sectors where that sector in turn sources from, etc. Similarly, if an organisation (e.g., a bank) stimulates economic activity through provision of loans, it does not only activate the economic sector it directly lends to, but also their value chain.
The principle of ‘value chain responsibility’ as laid down in FIS and IAM Core, state that an organisation should take (co-) responsibility of the impacts in their value chain (such as contribution to climate change), GID helps the user to do that. For every euro of sourcing, it traces what other sectors are stimulated, and what is the cumulative effect of all these sectors on the impacts (such as contribution to climate change). Similarly, for every euro of interest income from business lending, it traces how the loan has stimulated economic activity at the direct business client and beyond – and how much impact results from that.
The impact estimates are represented by impact indicators describing the impact of global value chains, see the section below for a list of impacts per capital. These indicators can be used to easily communicate impact using a variety of frameworks, such as:
- International Integrated Reporting Council’s (IIRC) Integrated Reporting (IR) framework capitals – further described below
- United Nation’s Sustainable Development Goals (SDGs)
Impacts Included in the GID
The 26 impact indicators included in GID are shown in the table, these are shown categorised into 5 of the 6 IIRC’s IR capitals, intellectual being out of scope due to lack of data. The capitals can be further simplified into the Environmental (Natural capital), Social (Social and Human capitals) and Economic (Financial and Manufactured capitals) framework (ESE).
The GID is built using the following frameworks as its foundations:
The International Integrated Reporting Council’s (IIRC’s) Integrated Reporting (IR) Framework is used to define the capitals grouping the impact indicators.
Universal Declaration of Human Rights is the basis of the rights-based method to quantify and monetise externalities for social, human, and natural capital.
UN Guiding Principles for Business and Human Rights and the Principles for True Pricing by True Price are the basis for the remediation cost approach used to develop the monetisation factors.
Framework for Impact Statements (FIS) is followed regarding the principle of value chain responsibility, as well as in how impacts are monetised.
Principles for True Pricing are used for measuring and monetising non-economic impacts on social, human, and natural capital.
Integrated Profit & Loss Assessment Methodology (IAM): Core is followed regarding the principle of value chain responsibility and valuation. In addition, IAM Core provides brief information on how to apply the GID in practical impact measurement through top-down models.
A high level of data quality is ensured in GID through rigorous implementation of approaches developed over multiple GID development cycles including outlier control, data gap filling and validation against third party data sets.
GID development follows a pragmatic approach focused on the needs of the end user by following the methodical and comprehensive lifecycle development steps: requirements analysis, design, development, testing and finally release. The following versions of GID sector have already been released:
- v1.0.0 – First version (early 2019)
- v1.4.0 – Updated attribution method, updated extensions (mid 2019)
- v2.3.0 – Added outlier control, updated financial indicators, updated impact factors and monetization factors (early 2020)
- v2.4.4 – Added GenderWageGap and Forced Labour impacts (mid 2020)
- v3.0.0 – Increased sector granularity for select indicators (early 2021)
- v3.1.0 – Increased sector granularity for select indicators, updated disaggregation/aggregation method, updated monetization factors (end 2021)
We recommend use of the most up to date data. Where users want to directly compare results developed using previous data versions these can be provided.