Collective action typologies and their implications for policy targeting: The case of smallholder households from the central region of Kenya

Abstract

Collective action initiatives (CAIs) are important in ameliorating the negative externalities in agriculture to promote social and economic development in Sub-Saharan Africa (SSA). However, the empirical basis exploring the context-specificity underpinning the CAIs is barely established. This study characterized the organizational attributes of the CAIs and their implications on policy targeting. The study employed mixed qualitative and quantitative approaches based on survey data drawn from 532 multistage randomly and proportionately sampled smallholder households, ten focus group discussions and two key informant interviews from the central region of Kenya. Multiple correspondence analysis and cluster analysis were used in exploring the organizational attributes of the CAIs. A multivariate probit model was employed to explore how organizational and household socio-economic characteristics influence participation patterns across the collective action typologies. The results revealed two typologies of collective action namely, efficiency and livelihood that were highly distinct in their level of formalization, membership composition, nature and scope of coordination, level of social capital, level of social networking and internal governance mechanisms. The households’ participation patterns across the efficiency and livelihood typology were differentially influenced by household demographics, farm and farming factors, assets, institutional and infrastructural factors. The findings suggest that a ‘one-size-fits-all’ collective action policy is inadequate and suggests a portfolio of context-specific collective action promotional policies. The policies would confer potential complementarities, synergistic and spillover effects in developing the CAIs to address multiple developmental objectives simultaneously.

Keywords

• Collective action initiatives
• Typology
• Multiple correspondence analysis
• Cluster analysis
• Kenya