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The impact of agricultural extension services on social capital: an application to the Sub-Saharan African Challenge Program in Lake Kivu region

Abstract

Many participatory projects in rural Africa aim indirectly to enhance development by promoting different dimensions of social capital: cooperation in networks (formal or informal), trust, and norms of behavior that encourage mutually beneficial action. However, it is unclear whether these development initiatives can actually influence social capital, especially in the short term. To address this question, we used semi-experimental data to investigate the effects of agricultural research and development (ARD) on various indicators of social capital in the border region of Rwanda, Uganda, and the DRC. Specifically, we focused on the effects of the “Integrated Agricultural Research for Development Approach” (IAR4D) and compared it to conventional ARD efforts. We show that IAR4D has influenced the level of social capital, although not in all dimensions and not consistently for all countries. In the Democratic Republic of Congo (DRC) and Uganda, for example, IAR4D strengthened the networks that link villages to the outside world (bridging social capital), but not in other countries. We also find indications that IAR4D resulted in higher levels of intra-village networks (bonding social capital) in Rwanda and improved trust and norms of cooperation (cognitive social capital) in the DRC. Finally, we showed that traditional agricultural extension (ARD) has been less successful than IAR4D in increasing the level of social capital.

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Notes

  1. 1.

    In practice these distinctions might be less clear-cut. For example social ties can be bonding in some respects whereas they are bridging in another (Bhanderi and Yasunobu 2009). Also networks, norms of cooperation and trust are interconnected (Snijders et al. 2007; Uphoff and Wijayaratna 2000).

  2. 2.

    Previous results using this data were reported by van Rijn et al. (2012).

  3. 3.

    See Farrow et al. (2011) and Thorton et al. (2007) for a detailed discussion on site selection.

  4. 4.

    Missing data is associated with households that have a higher educated household head (0.021), more members (0.008), more assets (0.029), and bigger houses (0.023). On the other hand, less missing data is also associated with lower membership in farmer associations (−0.081) and smaller houses (−0.008). At village level we find similar patterns where less missing data is associated with more access to schools, health centers, and mobile phone networks but also with less access to radio and water.

  5. 5.

    We use a backward selection method with a 20 % significance level (P >  |t|  = <0.20) meaning that the first model includes all variables and variables are deleted one by one if not significant at 20 %. The significance level was chosen to overestimate rather than underestimate the role of the covariates in determining the impact on social capital. Using this cut-off point we include almost all variables that demonstrate significant differences for the specific treatment and control group (results not reported in his paper but available upon request).

  6. 6.

    In almost all cases we were able to calculate the propensity score and fulfil the balancing property by using the logit model as proposed in our method section. This means that all control variables included in the model have equal mean values across our treatment and control group. In two cases, the model for ARD in Rwanda and Uganda, we had to eliminate the availability of health centres as a village characteristic from our estimation to fulfil the balancing property. The models used for calculating the propensity score differ by country and by control group. Pre-treatment variables often included are the age of household head, household assets, number of rooms in the house, access to boreholes/wells, radio coverage, and mobile phone coverage. The sign and significance of the variables do not point at an obvious selection effect. The only exception might be assets, which is consistently higher in IAR4D villages in DRC and Uganda. The potential selection effect is contradicted by the fact that in these same models either the number of rooms or access to certain village resources is consistently lower.

  7. 7.

    These results are robust to different matching methods (kernel and nearest neighbour matching). The results of our main model (Model 1) and the fixed effects model (Model 3) are also robust to estimation on the common support. This means we exclude one household for DRC, 38 for Rwanda, and 29 for Uganda. Results are available upon request.

  8. 8.

    In our pooled model we include interaction terms of our control variables with the IAR4D treatment dummy. Results are not reported here but are available on request.

  9. 9.

    An alternative hypothesis that macro level institutions and (local) social capital are complements rather than supplements (Woolcock 2001) is also possible but not apparent in this context.

  10. 10.

    Also see the recent World Bank report “Localizing Development—Does participation work” by Mansuri and Rao (2012) who reach a similar conclusion based on the review of almost 500 studies on participation and decentralization.

Abbreviations

ARD:

Agricultural Research for Development

DRC:

Democratic Republic of Congo

FARA:

Forum of Agricultural Research in Africa

IAR4D:

Integrated Agricultural Research for Development

IP:

Innovation Platform

LK:

Lake Kivu

PSM:

Propensity Score Matching

SSA CP:

Sub-Saharan Africa Challenge Program

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Acknowledgments

We would like to thank the Sub Saharan African Challenge Program, coordinated by the Forum of Agricultural Research in Africa (FARA), for the data used in this study. We also would like to thank Erwin Bulte, Marrit van den Berg, and an anonymous reviewer for their useful comments and suggestions.

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Correspondence to Fédes van Rijn.

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Appendix

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van Rijn, F., Nkonya, E. & Adekunle, A. The impact of agricultural extension services on social capital: an application to the Sub-Saharan African Challenge Program in Lake Kivu region. Agric Hum Values 32, 597–615 (2015). https://doi.org/10.1007/s10460-014-9580-9

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Keywords

  • Agricultural research and development
  • Social capital
  • Impact assessment
  • East Africa