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This analysis is a result of the collaboration between the eScience Insitute Data Science for Social Good Program and Vital Signs. Learn more about this program here.

Existing studies of farmer field schools (FFS) have found that these programs exhibit a sizable positive effect on per-acre crop productivity value among households with lower educational attainment, with negligible effects on higher-education households[1] [2]. In these analyses, we examined whether a similar effect can be observed when examining a broader set of extension services.Agricultural households in Vital Signs landscapes were therefore asked whether they received extension services in the past 12 months. The categories of extension services used in this study were Agricultural Production, Agro-processing, Marketing, and Livestock Production.

In the analyses,we  examined the effects of extension services on crop productivity and if these services were moderated by farmers’ educational attainment. To measure receipt of extension services, we counted the total number of instances in which a given household received advice on any of the topics measured in the Vital Signs dataset. For example, if a household received advice from two sources on Agricultural Production and one source on Agro-processing, we counted that household as having received three extension instances.

For education, previous studies [2] have measured education using educational attainment of the head of the household. However, based on our team’s experience providing and examining extension services in the region, we argue that the maximum individual-level educational attainment represents a better measure. Older household members often receive fewer opportunities to study than their younger counterparts, who may be able to “translate” advice.

Otherwise, to maintain consistency with existing work we attempted to maintain the same set of independent variables as those used in&nbsp [2]. In particular, we controlled for country, total area farmed, household size, age of household head, gender of household head, and median household-level field distance to road and market. We also included variables corresponding to field ownership and shared field usage, discretized into “All Owned”/“Some Owned”/“None Owned” and “All Shared”/“Some Shared”/“None Shared”, respectively.

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