experienced at least 1SD above normal rainfall, a smaller proportion is in the _income poor & asset rich_ category (8% vs. 24% for the full sample). There is no equivalent pattern for the asset poor with above normal rainfall, nor for the distribution across poverty categories for those experiencing less than normal rainfall.

In conclusion, the pattern is clearest for the dry areas: higher than normal rainfall during the survey year seems to have a positive effect on household income. In villages that had higher than normal rainfall, a higher share of the asset poor households is above the income poverty line.

# **4.3 Self-reported shocks**

Our third research question shifts the focus from exposure to weather anomalies to exposure to self-reported shocks:<sup>16</sup> _Are self-reported shocks related to lower household income, and how does shock exposure vary across poverty categories?_ We know that there were no catastrophic events during the period of the study (Wunder et al. 2014b). Some of the survey villages experienced floods, while others experienced drought, attacks by wild animals and macroeconomic shocks. These events are considered as shocks, but they are indeed characteristic of rural economies in developing countries.

Shocks were grouped into three categories: _income shocks_ , dominated by crop failures, which is often related to rainfall or temperature below/above normal; _asset shock_ , including theft or death of livestock; and _labor shocks_ , most commonly in the form of illness or death of a productive household member. An asset shock would normally imply a medium term income reduction. A loss of household labor would normally have income implications, although for some the impact might (partly) be offset by higher efforts from other household members. We focus on severe shocks in this section.

Table 6 gives the incidence of self-reported severe shocks during the year covered by the income survey. Overall, close to a quarter (24%) of the households did experience some type of shock during the survey year.

The _income & asset poor_ have higher incidence of income shocks compared to the other households (14% vs. 9%). In other words, _income & asset poor_ have more than 50% ((14-9)/9) higher probability to have experienced a severe income shocks. This difference is robust across wet-dry zones and across regions. The higher exposure to income shocks among the _income & asset poor_ is consistent with a hypothesis that some asset poor might – due to the income loss in the survey year – be in the _income & asset poor_ rather than the _income rich & asset poor_ category. About one fifth of the asset poor are _income rich & asset poor_ , for example, because they enjoyed a bumper harvest. An income shock can make it less likely for income poor to be in this category. The higher prevalence of shocks among the _income & asset poor_ serves to illustrate how shocks can contribute to maintaining poverty.

> 16 The correlation between weather anomalies and self-reported is very low, which might be due to the following reasons. First, the weather anomalies were relatively modest, and households have adaptive and coping mechanisms available. Second, a weather anomaly is not necessarily bad for household income, cf. the finding of higher income if rainfall increase in dry areas. Third, most of the shocks in poor rural economies are household-specific (idiosyncratic) while the weather anomalies are at the village level.

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