Methods and

Processes

Introduction

The Wildlife Economy Investment Index (WEII) methodology drew inspiration from sources like the OECD handbook and other global indexes. It was developed through four key stages

Index Organisation

The Wildlife Economy Investment Index (WEII) is a comprehensive framework that centers on the concept of the wildlife economy, which involves the utilization of indigenous wildlife, including both flora and fauna from marine and terrestrial ecosystems, as an economic asset. This approach aims to create value while also aligning with conservation objectives and fostering sustainable growth and economic development. At its core, the WEII comprises two major sub-indices: Wildlife Status and Investment-enabling Environment.

The Wildlife Status Sub-index dives into the size and status of a country's wildlife, encompassing a range of indicators related to wildlife assets, species richness, endemic species, ecological habitats, protected areas, key biodiversity areas covered by protected areas, wildlife management, and the effectiveness of wildlife management. This sub-index provides a deep insight into a nation's wildlife resources and the efforts in place to protect and manage them.

On the other hand, the Investment-enabling Environment Sub-index evaluates the conditions that facilitate investments in the country. It takes into account a broad spectrum of indicators, including ease of doing business, business operations, access to finances, access to markets, corruption, public sector capacity, rule of law, infrastructure, labor market, social inclusion, investment safety, money growth, property rights, and security and stability. This sub-index assesses the overall business environment in the country and how conducive it is to wildlife-based economic activities.

Within each of these sub-indices, there are further categories and sub-categories, allowing for a more detailed and nuanced evaluation of specific aspects. This structured approach enables a multidimensional analysis of a country's wildlife economy, covering various thematic areas and providing valuable insights into its strengths and areas for improvement. With a specified number of indicators for each category and sub-category, the WEII offers a comprehensive overview of the data points considered in the assessment.

Overall, the WEII serves as a powerful tool for governments, researchers, conservationists, and policymakers to understand and enhance a country's performance in the wildlife economy. By providing a comprehensive and structured framework for evaluation, it promotes the adoption of sustainable and inclusive practices in wildlife-based economic activities, ultimately contributing to the preservation of wildlife and the well-being of communities dependent on these resources.

Data Sources

The Wildlife Economy Investment Index (WEII) is underpinned by a wealth of data from 34 datasets, each contributed by 24 reputable institutions. This collaborative effort ensures a robust and comprehensive evaluation of wildlife economies and investment potential across African countries. Among the prominent contributors are the World Economic Forum (WEF), offering 39 indicators (14%) that enrich the economic perspective, and the Mo Ibrahim Foundation, providing 35 indicators (12%) that emphasize governance and leadership aspects. The World Bank's 34 indicators (12%) contribute a significant economic and developmental dimension, while the Bertelsmann Stiftung's 32 indicators (11%) bring insights into social and political aspects. Other influential contributors include the United Nations (UN), V-Dem Institute, and World Justice Project (WJP), each offering unique perspectives and expertise.

Other contributors include: Convention on International Trade in Endangered Species (CITES), Fraser Institute, Freedom House, Gold Standard, International Labour Organization, International Monetary Fund, International Union for Conservation of Nature (IUCN), joint projects of the African Union Commission, United Nations Economic Commission for Africa, and the African Development Bank, joint project of the Yale Center for Environmental Law & Policy and The Center for International Earth Science Information Network (CIESIN) at Columbia University’s Earth Institute, Ocean Health Index team, Organisation for Economic Cooperation and Development (OECD), Protected Planet, The Fund for Peace, The Heritage Foundation, Transparency International, Verra, and World Resource Institute & African Leadership University, School of Wildlife Conservation.

This diverse set of institutions collectively ensures that the WEII methodology reflects a comprehensive and nuanced understanding of the interplay between wildlife, economy, and sustainable development.

Inclusion Criteria

Stringent criteria ensured data quality, including relevance, validity, reliability, open accessibility, and recency. The focus on regularly updated data and exclusion criteria refined the dataset.​

Exclusion Criteria

Data covering fewer than half of Africa's countries were excluded, maintaining a focus on regularly updated and relevant information. Uniform data across countries and duplicate sources were omitted.

WEII Score Calculation

Descriptive Analysis

The report presents scores and ranks for all countries, facilitating comparisons and recognizing top performers. Regional analysis provides insights into performance trends within Regional Economic Communities and African regions.

Cluster Analysis

Hierarchical agglomerative clustering grouped countries based on similarity, offering insights into relationships and commonalities. This analysis extended to countries' inherent characteristics.

Triangulation

Methodological triangulation using different aggregation methods confirmed the reliability of the arithmetic average method, enhancing the overall credibility of WEII scores.

Limitations

Reliance on secondary data may omit crucial indicators.

Data endorsement by governments poses risks of selective or distorted presentation.

Disparity in statistical capacity among countries can affect data quality.

Increasing sub-categories or indicators may decrease their influence due to equal weighting.

Transformation of categorical data into numeric values can result in information loss.

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