How Endogenous Cultural Institutions May (Not) Shape Farmers’ Climate Adaptation Practices: Learning from Rural Cameroon

Abstract

Climate change is high on the science and policy agenda of sub-Saharan Africa (SSA), requiring context-specific adaptation. Studies on endogenous cultural institutions (ECIs) in climate adaptation are fragmented, impeding policy orientation. This suggests the need for complementary evidence on ECIs to inform adaptation practice. Through a sample of 158 households in rural Cameroon, we contribute to bridge this gap, by: (1) exploring socio-political factors shaping peasant farmers’ compliance with agro-based ECIs, and (2) estimating the effect of ECI compliance on farmers’ adaptation practices. Our estimation revealed the following: While political factors do not sufficiently explain compliance, state influence, mirrored through the application of formal rules negatively affects ECI compliance. Traditional rain forecast significantly explains climate adaptation; however, the pouring of libation does not. The age of peasant farmers also positively affects compliance with ECIs. We conclude that compliance with ECIs manifest in climate adaptation in varying proportions.

Keywords:

• Values
• norms
• compliance
• peasant farming
• adaptation choices
• Cameroon

Acknowledgments

We thank the enumerators who assisted in data collection, and the respondents who agreed to participate in this research. We are also thankful to the anonymous reviewers whose comments enriched this paper.

Notes

1 The poverty rate is $1.9/day. The world’s poverty average is estimated at 10 percent (United Nations 2015).

2 Exogenous institutions here relate to interventions in communities, which are externally induced; they might emanate from the state or international actors. Details are provided in section “Analytical framework on endogenous cultural institutions and their determinants”.

3 By endogenous cultural institutions, we refer to informal rules which are developed within communities and transferred from one generation to the other, through norms, values, beliefs and taboos (Colding, Folke, and Thomas 2003).

4 Apart from the key informant interviews, the design of the survey instrument was also informed by previous studies on endogenous cultural institutions in the Western Highlands of Cameroon (see Kimengsi, Abam, and Forje 2021 and Kimengsi and Mukong 2022 among others).

5 The sample size in our regressions ranges from 95 to 105, depending on the specific variables we are using.

6 Descriptive statistics are in Supplementary Appendix 1.

7 These are also known as soothsayers – they are believed to predict future occurrences in people’s daily lives, including rainfall.

8 There are sites reserved as spiritual sites where human activity is prohibited. Non-encroachment here refers to the tendency of the respondent to respect this cultural norm.

9 The monthly income was derived by calculating the average incomes realized during the major cropping seasons, including the low seasons.

10 We have also captured degree in our data but due to the high correlation with schooling, we chose the latter as our primary variable. The results for degree are comparable and can be obtained from the authors.

11 For robustness, we also computed the same regressions using a probit model and obtained similar results that are available from the authors.

12 Mixed cropping usually encourages farmers to expand their farms. Also, the alteration of planting dates almost invariably causes farmers to adopt new harvesting dates. Finally, local irrigation practices influence the timing of pest control to avoid wash offs from pests.

13 The marginal effects for Supplementary Table 1 can be found in Appendix 2.

14 An additional year of Schooling is associated with a reduced probability of ECI compliance by roughly two to four percentage points.

15 The marginal effects for Supplementary Table 2 are in Appendix 3.

16 The marginal effects for Supplementary Table 3 are in Appendix 4.

17 We are well aware of the drawbacks when employing PCA on binary variables, in particular the fact that linear functions of binary variables are not readily interpretable (Jolliffe 2002). Our main objective, however, is to summarize much of the variation of our original dataset into a smaller set of variables and if all included variables are binary the PCA indeed provides a plausible low-dimensional solution (Gower 1966). To test the robustness of our results we also test a manually generated variable that joins the components of our models. The results are comparable across the different variable specifications and can be obtained from the authors.