Introduction
The Wildlife Economy Investment Index (WEII) methodology applied in Versions 1 and 2 was developed using internationally recognised best practice for composite indices, drawing inspiration from sources such as the OECD Handbook on Constructing Composite Indicators. The methodology was developed through four key stages:
- The first stage established the rationale and conceptual framework for the Index through research, stakeholder engagement, and review of existing literature and global indices. This process identified the need for a comprehensive tool to assess wildlife economy investment potential across Africa.
- The second stage focused on identifying core domains (categories) that encompass the drivers of wildlife economy investment, such as wildlife assets, management, ease of doing business, public sector capacity, and investment safety. These categories were grouped into two overarching themes: Wildlife Status and Investment-enabling Environment, forming sub-indices of the WEII.
- The third stage involved selecting specific indicators to represent each category, with an emphasis on reliable and relevant data sources. These indicators were organised into sub-categories based on commonalities and distinctions, considering data definitions and sources to avoid misclassification and redundancy.
- The final stage applied statistical normalisation and aggregation techniques to calculate country scores, supported by extensive stakeholder consultation and expert review to strengthen the credibility, transparency, and robustness of the Index.
WEII v3, currently under development, will introduce a substantially revised methodology to improve ecological measurement, strengthen governance indicators, broaden representation of wildlife economy activities, and enhance the Index's relevance for policy, investment, and sovereign biodiversity finance discussions.
Index Organisation
The Wildlife Economy Investment Index (WEII) Versions 1 and 2 provide a comprehensive framework for assessing the potential of African countries to attract investment in wildlife-based economic activities. The Index evaluates both the status of wildlife resources and the broader policy, governance, and economic conditions that enable investment. Together, these dimensions provide a multidimensional assessment of wildlife economy performance across the continent.
The WEII is organised into two sub-indices: the Wildlife Status Sub-index and the Investment-enabling Environment Sub-index.
The Wildlife Status Sub-index assesses the breadth, condition, and management of a country's wildlife resources. It comprises indicators relating to wildlife assets, including species richness, endemic species, ecological habitats, protected areas, key biodiversity areas covered by protected areas, wildlife legal frameworks, and wildlife management effectiveness. Together, these indicators provide insight into both the state of a country's biodiversity and the systems in place to conserve and sustainably manage it.
The Investment-enabling Environment Sub-index evaluates the broader conditions that influence wildlife-based investment. It incorporates indicators relating to ease of doing business, business operations, access to finance and markets, anti-corruption, public sector capacity, rule of law, infrastructure, labour markets, social inclusion, investment safety, money growth, property rights, and security and stability. These indicators assess the extent to which a country's institutional and economic environment supports investment in the wildlife economy.
The two Sub-indices are further organised into five categories and 18 sub-categories, drawing on 280 indicators sourced from 34 datasets provided by 24 recognised institutions. Scores are calculated using min-max normalisation and aggregated to produce standardised scores ranging from 0 to 100, enabling meaningful comparison between countries and over time.
Data Sources
The Wildlife Economy Investment Index (WEII) Versions 1 and 2 draw on 280 indicators from 34 datasets provided by 24 recognised institutions. These datasets capture ecological, governance, economic, and investment-related dimensions of the wildlife economy, providing a comprehensive and evidence-based assessment of wildlife economy investment readiness across Africa.
Major contributors to the WEII include the World Economic Forum (WEF), Mo Ibrahim Foundation, World Bank, Bertelsmann Stiftung, United Nations (UN), V-Dem Institute, and the World Justice Project (WJP). Additional data are sourced from a wide range of international organisations, conservation institutions, and research initiatives, ensuring broad coverage across the Index.
The indicators included in the WEII were selected using transparent inclusion and exclusion criteria designed to ensure that the data are relevant, reliable, regularly updated, and comparable across African countries. This approach supports the robustness, consistency, and credibility of the Index while enabling meaningful comparison between countries and over time.
Inclusion Criteria
Relevant to one or more WEII categories or sub-categories.
Published by recognised and credible institutions.
Available for more than 50% of African countries.
Regularly updated to support longitudinal analysis.
Freely accessible and transparent.
Exclusion Criteria
Limited geographic coverage across Africa.
Infrequently updated or outdated datasets.
Duplicate or overlapping indicators measuring the same concept.
Indicators with insufficient variation to distinguish country performance.
WEII Score Calculation
- Data Preparation: Raw data from multiple sources were compiled, cleaned, and standardised. This included checking for missing values, duplicates, country name consistency, and outliers before preparing the dataset for analysis.
- Data Standardisation: Raw indicator values were transformed using min-max normalisation to produce standardised scores ranging from 0 to 100. For indicators where higher values represented poorer performance, a reverse transformation was applied.
- Winsorisation: Extreme values were adjusted using winsorisation to reduce the influence of outliers while retaining all observations in the dataset.
- Weighting: Equal weights were assigned across the two sub-indices, categories, and sub-categories to ensure a balanced assessment of wildlife economy performance.
- Data Aggregation: Indicator scores were aggregated using arithmetic averages to produce sub-category, category, sub-index, and overall WEII scores.
- Country Rankings: Countries were ranked according to their overall and component scores, with rankings calculated only where sufficient data were available to ensure comparability.
Descriptive Analysis
Country performance is analysed across all components of the WEII, highlighting leading and lagging countries, regional trends, and opportunities for policy reform and investment.
Cluster Analysis
Hierarchical clustering groups countries with similar wildlife economy performance profiles, supporting peer learning, comparative analysis, and targeted policy recommendations.
Validation
The WEII methodology was developed using internationally recognised best practice and validated through stakeholder engagement, expert review, and robustness testing during the development of WEII v1.
Limitations
Reliance on secondary data may result in some aspects of the wildlife economy not being fully represented due to limited data availability.
Variation in data quality and statistical capacity across countries can affect the availability, consistency, and comparability of some indicators.
Uneven distribution of indicators across Index components means that some indicators have a greater influence on scores than others.
Transformation of categorical data into numeric values may simplify complex governance and institutional information.
Linear aggregation assumes that strong performance in one area can offset weaker performance in another, which may mask important trade-offs.
About SOWC
The ALU School of Wildlife Conservation is the first of its kind on the continent, dedicated to growing the next generation of world class conservation leaders in Africa.
The continent needs home grown African leaders to spearhead new and innovative approaches in the business of conservation.
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ALU School of Wildlife Conservation
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Phone: +250 784 650 219
sowc@alueducation.com
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