Heterogeneity in financing for development strategies as a hindering factor to achieve a global agreement on the 2030 Agenda

The paper addresses persistent disagreement among donor countries on how best to finance the 2030 Agenda amid a changing aid landscape and post-COVID-19 pressures. It highlights critiques of ODA effectiveness and the emergence of broader financing concepts (Beyond Aid/ODA) and instruments (remittances, philanthropy, policy coherence for development). The research questions are: (1) Are there distinct groups of donor countries sharing similar financing-for-development (FfD) strategies? (2) Could these approaches conflict and hinder a new international consensus on financing the 2030 Agenda? The study’s purpose is to empirically classify donor strategies and assess how heterogeneity complicates coordination, drawing on rationalist IR and strategic-group theory to argue that greater heterogeneity among actors impedes agreement. The contribution is twofold: quantitatively demonstrating diverse national FfD strategies and qualitatively discussing how this diversity obstructs consensus on financing the SDGs.

The theoretical framework builds on coordination problems in international cooperation: as the number and heterogeneity of actors increase, coordination becomes harder, especially when distributional bargaining is limited. The paper situates FfD within a transformed aid system moving beyond ODA, influenced by the 2007–2008 financial crisis and COVID-19. The literature documents: (a) longstanding scrutiny of ODA effectiveness from Global North and South perspectives; (b) a shift toward broader financing concepts (Global Policy Finance, Beyond Aid/ODA); (c) calls to integrate public and private resources and to consider new metrics like TOSSD; and (d) the growing importance of instruments beyond ODA (philanthropy, remittances) and policy coherence for development (PCD). The authors classify FfD instruments along two dimensions—governance (public vs. private) and scope (sectoral vs. global)—yielding four categories. As proxies, they select: ODA (public, sectoral), PCD via CDI score (public, global), philanthropic outflows (private, sectoral), and remittances (private, global). The review underscores data advances (OECD-DAC, World Bank, Global Philanthropy Tracker) yet notes measurement and coverage limitations. The framework supports the expectation of heterogeneous national strategies that can form strategic groups and complicate consensus on a shared FfD agenda.

Design: Quantitative clustering of donor countries based on four FfD instruments to test two hypotheses: (H1) distinct clusters of countries share similar FfD strategic patterns; (H2) clusters differ significantly in the weight of instruments used. Sample: 26 OECD-DAC donor countries with complete data: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Korea, Luxembourg, New Zealand, Norway, Portugal, Slovak Republic, Spain, Sweden, Switzerland, The Netherlands, United Kingdom, United States. Variables and measures: - ODA: ODA as % of GNI (2019, OECD-DAC). - PCD: Commitment to Development Index (CDI) 2018, excluding the aid component to avoid double-counting (Centre for Global Development). - Philanthropy (PHI): Cross-border philanthropic outflows as % of GNI (2018, Global Philanthropy Tracker, Indiana University Lilly Family School of Philanthropy). - Remittances (REM): Bilateral remittance outflows from DAC to non-DAC countries per donor-country resident (US$ per person, 2018, World Bank bilateral flows; population from UN 2018). Preprocessing: Indicators normalized for clustering and visualization. Clustering procedure: Hierarchical cluster analysis using Ward’s method with squared Euclidean distance. Number of clusters selected via NbClust (30 indices), yielding an optimal k=4. Dendrogram used to inspect group formation. Validation and inference: One-way ANOVA to test between-cluster differences for each instrument; Bonferroni post-hoc tests to identify which instrument contrasts drive cluster separations; internal validation via Silhouette Coefficient and Dunn’s Index; robustness check via k-means with k=4 to confirm identical membership. Limitations of data and design: Cross-sectional snapshot; proxies for broader constructs; limited recipient- and sector-level granularity; some series are recent, limiting longitudinal analyses.

- Optimal partition: Four distinct clusters of donor FfD strategies were identified and validated. - Cluster membership and centroids (means): • Cluster 1 “The Underachievers” (Austria, Belgium, Finland, France, Italy, Portugal, Spain): ODA 0.31% of GNI; PCD 5.23; PHI 0.03% of GNI; REM 156.57 US$/person. • Cluster 2 “The Outsiders” (Czech Republic, Greece, Hungary, Japan, Korea, Slovak Republic): ODA 0.18%; PCD 4.60; PHI 0.02%; REM 72.23 US$/person. • Cluster 3 “The Traditional Winners” (Denmark, Germany, Luxembourg, Norway, Sweden, The Netherlands, United Kingdom): ODA 0.82%; PCD 5.27; PHI 0.09%; REM 180.26 US$/person. • Cluster 4 “The Champions of Philanthropic Flows” (Australia, Canada, Ireland, New Zealand, Switzerland, United States): ODA 0.28%; PCD 4.89; PHI 0.13%; REM 364.03 US$/person. - Statistical significance: One-way ANOVA shows significant between-cluster differences for all four instruments (p<0.001 for ODA, PCD, PHI, REM). Bonferroni post-hoc tests indicate that each instrument contributes to differentiating clusters (e.g., PHI differs significantly across most cluster pairs; PCD differs across most pairs; REM and ODA show significant contrasts in specified pairs). - Cluster quality and robustness: Silhouette Coefficient = 0.39 overall (Cluster 1: 0.56; Cluster 2: 0.47; Cluster 3: 0.27; Cluster 4: 0.24). Dunn’s Index = 0.3125. K-means with k=4 reproduced identical memberships, indicating robust structure. - Strategic profiles: • Cluster 1 emphasizes PCD with comparatively lower ODA/PHI/REM. • Cluster 2 exhibits low levels across all instruments. • Cluster 3 prioritizes high ODA (including 0.7% achievers), with moderate PHI/REM and strong PCD. • Cluster 4 emphasizes private instruments (highest PHI and REM), with lower ODA and middling PCD. - Implication of findings: Clear heterogeneity in donors’ FfD mixes helps explain delays and difficulties in reaching a shared financing framework for the 2030 Agenda.

The findings validate that donor countries form distinct strategic groups in how they finance development, directly addressing the research questions. The heterogeneity in instrument mixes (public vs. private; sectoral vs. global) aligns with game-theoretic expectations that coordination becomes more challenging as actors’ preferences diverge. Strategic-group logic further suggests that disparate strategies impede mutual understanding and alignment. Each cluster’s profile implies different advocacy in global negotiations: Cluster 3 likely defends ODA centrality; Cluster 4 promotes private giving and remittances; Cluster 1 gravitates to PCD-driven approaches within EU frameworks; Cluster 2’s low engagement across instruments makes its alignment uncertain, potentially leaning toward investment-oriented approaches. Consequently, efforts to forge a global FfD consensus must acknowledge and bridge these structural differences, possibly by designing a flexible, multi-instrument framework (e.g., via TOSSD) that accommodates diverse national preferences while ensuring complementarity, predictability, and accountability. The study also underscores how post-pandemic fiscal constraints and public attitudes may push some donors toward private instruments, accentuating the divide with ODA-centric countries.

The study empirically demonstrates that donor countries cluster into four distinct FfD strategy groups with statistically significant differences in their reliance on ODA, PCD, philanthropy, and remittances. This strategic heterogeneity likely hinders reaching a unified global financing approach for the 2030 Agenda. Contributions include: (1) a quantitative classification of donor strategies using a novel four-instrument lens and a hierarchical clustering approach; (2) a conceptual discussion of how strategic diversity complicates international coordination. The authors suggest that future FfD agreements should be designed to accommodate plural strategies while improving data, accountability, and complementarity across instruments. Future research avenues include: integrating richer, more recent datasets (e.g., TOSSD), conducting longitudinal analyses to track strategy dynamics and leadership within clusters, linking donor strategies to recipient outcomes and sectoral allocations, and comparing DAC and non-DAC donors.

- Data constraints: Proxy indicators with limited granularity; some instruments lack recipient- or sector-level detail; recent series limit time-series analysis. - Design: Cross-sectional analysis captures a snapshot, not dynamics; normalization and aggregation choices may influence clustering. - Scope: Focus on donor-side strategies executed or promoted through public policy; excludes multilateral-led instruments and direct corporate/transnational flows; does not assess the effectiveness of identified strategies on development outcomes.