agricultural activities and report having cultivated at least one plot in a given season (for countries with two cropping seasons, i.e., Malawi and Uganda, we focus our analysis on the main season). Starting with 36,014 household-wave observations in the original dataset, dropping 9,359 observations that report not having engaged in agricultural activities, and then dropping 1,381 observations without cultivated plots, leaves us with a maximum of 25,274 household-wave observations, comprising 11,154 households and 50,478 plot-wave observations. In all of the main regression analyses we work with household fixed effects, which requires two rounds of data. After dropping 748 observations with missing data on the use of agricultural advice, 162 observations with missing data on shocks, and 755 observations with missing data on one of the key household level control variables, the subsample of households that appear in the data for at least two rounds consists of 20,233 household-wave observations.

The rest of this section describes the construction of the key variables used to test the model’s predictions. Additional information on the construction of variables and differences in survey designs across countries can be found in Appendix B, together with a complete list of all variables and basic summary statistics (see Table A1). **Extension services.** In all three countries, the LSMS-ISA surveys collect detailed information on whether households received advice related to farming activities, as well as the number of extension contacts and the sources of advice (e.g., governmental extension service, NGOs, or other farmers). The dummy variable _Advice_ indicates whether a household has received advice related to farming at least once over the past 12 months. The dummy variable _Extension_ considers only advice that was obtained through sources other than media (i.e., sources such as TV, radio, or flyers). The variable _Contacts_ contains the number of extension contacts, which includes visits to the household as well as visits by the household to the source.<sup>23</sup> In addition, the data allow us to separately identify extension contacts that were actively solicited by farmers from extension services for which the household was merely a passive recipient. The former is captured by the variable _Solicited Contacts_ , which is the sum of visits to the household solicited by the farmer, and visits by the household to the source. The dummy variable _Solicited Extension_ equals one if the household had at least one such actively demanded contact in the past 12 months.

Table 1 provides a first impression of farmers’ exposure to extension services in the studied countries. In this table we focus on the variable _Extension_ , i.e., on dimensions

> solicited by farmers from extension services for which households were merely passive recipients. As we are interested in studying farmers’ own demand for extension, we focus on these three countries.

> 23It should be noted that while, in all three countries, the survey questions underlying our extension variables refer exclusively to received advice and information, we cannot exclude the possibility that some farmers maintain contacts with extension services also in order to receive other forms of support (e.g., financial or in-kind support). Note also, that extension services will likely be heterogeneous even within countries.

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