Table 2: Topics of advice

|Description<sup>_a_</sup>|Full Sample|Malawi|Nigeria|Uganda|
|---|---|---|---|---|
|Agricultural production and processing|91.3|92.6|80.1|96.3|
|New seed varieties|50.7|51.3|49.2||
|Fertilizer|52.6|50.0|58.9||
|Pest control<br>|29.0|28.3|30.8||
|Composting/manure|41.8|50.3|21.0||
|Irrigation|26.5|34.5|6.9||
|Marketing and crop sales|25.9|17.2|24.6|39.9|
|Livestock production<br>|32.8|20.2|26.7|55.8|
|Animal diseases and vaccination|30.5|18.8|28.1|50.0|
|Fishery|8.1|7.9|1.9|12.4|
|Forestry|15.7|21.3|2.3||
|Access to credit|12.8|15.8|5.5||
|Other|2.1|2.8|0.5||
|Observations (HHs who received advice)|6,877|3,336|1,369|2,172|

_Source:_ Authors’ calculation based on survey data from the LSMS-ISA database (World Bank, 2018). _Notes:_ Numbers are percentages of households who received advice on indicated topic, conditional on having received in-person advice (i.e., excluding advice received through media).<sup>_a_</sup> Exact wording differs between countries.

extension programs seem to be less prevalent, farmers report receiving advice relatively more often from private extension services and from other farmers.

**Shocks.** Data on shocks are available at the household level and include detailed information on whether households have been negatively affected by any shocks over the past year and the type of each shock.<sup>25</sup> The latter is collected based on a list of around 20 different events, which are very similar across countries. To test the predictions of the model, we classify shocks as either directly related to agriculture ( _AgShock_ ) or not directly related to agriculture ( _NonAgShock_ ). Table 3 provides a complete list of the shock items that appear in the data and the assigned categories for our baseline regressions.<sup>26</sup> As can be seen in the table, agricultural shocks include events such as “irregular rains”, “unusual high level of crop pests”, and “livestock disease”. Non-agricultural shocks include events such as “illness of a household member”, “theft of money”, and “end of regular assistance/remittances”.<sup>27</sup> Of course, any classification of the shock items listed in Table 3 into two distinct groups related to agriculture and not directly related to agriculture

> 25As described above, there are several years between panel rounds, and these are inconsistently spaced over time and countries. Therefore, we do not work with lagged shocks, as these are (i) several years, and therefore, in our view, too far in the past, and (ii) irregularly spaced.

> 26The shock items “other” and “increase in the prices for food” are not classified in our baseline regressions since we found it difficult to attribute them to either of the two shock variables. As shown below, our results are robust to including “increase in the prices for food” in _AgShock_ or in _NonAgShock_ .

> 27As can be seen in Table 3, the extent to which households report experiencing negative shocks differs considerably between countries. For example, in Nigeria only 12% of farmers report having been negatively affected by agricultural shocks (on average during one year), while in Malawi and Uganda the corresponding percentages are 82% and 41%, respectively.

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