disasters and environmental shocks.<sup>44–47</sup> These studies have mostly focused on single geographies and stopped short of quantifying the value of disaster related losses in smallholder agriculture. Other studies have relied on macro-data from disaster inventories to assess the impact of disasters on agriculture.<sup>14</sup> Here we offer a cross-country perspective using harmonized survey micro-data from six African countries. We value crop production losses to assess the economic importance of disaster impacts on rural households and African agriculture and the regional economies more broadly. The analysis shows that disaster related crop production losses among African smallholder farmers are widespread and significantly reduce the production of affected farms. In any given year, between 18% and 94% of cropfarming households suffer such crop losses and on average 53% of plots’ harvest potential is lost when they are hit by adverse shocks.

In aggregate, disaster impacts reduce the national crop production by 29% every year on average and by up to 81% in years with large-scale disasters. These results show that crop production losses due to adverse climatic events are significant both for individual farms and for the entire sector and the study countries’ economies.

We show that the EM-DAT disaster inventory misses out on a meaningful share of disaster impacts in the agricultural sector in Africa when compared to the micro-data analysis. The micro-data captures many smaller, more localized disaster events, which are less salient and therefore less likely to be reported on and register in the disaster inventory.<sup>37,48</sup> At the same time, disaster impacts are more likely to be missing entirely in lower-income settings.<sup>30</sup> Our analysis shows that less salient and underreported disaster events still have significant economic impacts.

The findings have implications for policies and interventions aimed at disaster risk reduction and resilience building. For such policies to be effective, it is important to recognize the risks from less salient and underreported adverse events and offer ways to insure households’ livelihoods against their impacts. The findings also have implications for research and measurement. Research and analysis using aggregate global data such as EM-DAT are likely missing some disaster impacts in poorer countries and among poorer people. This concerns also tracking progress towards the SDGs, in particular SDG target 1.5, which seeks to reduce the vulnerability of the poor to extreme weather events, and SDG target 13.1, which seeks to strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.<sup>15</sup> Survey micro-data such as the LSMS-ISA have more limited country and temporal coverage. Inventory and survey data offer complementary perspectives on disaster impacts and combining both sources will likely yield a more complete and nuanced understanding of the issue that will promote more effective policy design. In particular, information on disaster impacts from survey micro-data could be incorporated systematically into disaster inventories. Improving micro-data systems is key to systematically utilizing micro-data for monitoring and reporting of emergency events. More flexible and higher frequency data collection is needed to provide better temporal coverage and account for disaster impacts when they occur. Phone surveys, which have only recently become more widely adopted in lowincome settings, may provide this function, for instance as part of mixed-mode survey systems that combine traditional in-person surveys with data collection over the phone.<sup>49</sup> Integration of geospatial data can improve spatial coverage and facilitate better identification of natural hazards and disaster occurrence.<sup>50,51</sup> Sampling protocols of household surveys can be optimized to better capture disaster impacts and greater harmonization of survey methods and measurement instruments in line with best practices can benefit data quality.

10