1988), or a binary variable identifying individuals living in a household whose per capita income lies below the national poverty or extreme poverty line. We estimate these models using OLS and probit models with robust standard errors,<sup>5</sup> respectively. _Tdt_<sup>+,</sup><sup>_T −_</sup> _dt_<sup>,</sup><sup>_P_+</sup> _dt_<sup>and</sup><sup>_P_</sup> _dt_<sup>_−_represent</sup> measures of temperature and precipitation shocks in district _d_ at time period _t_ , measured as positive and negative anomalies as explained in 4.1, _Wd_<sup>_′_isavectorofweatherandclimate</sup> variables, namely the historical average temperature and precipitation in district _d_ , and the average temperature and precipitation in the district during time period _t_ ,<sup>6</sup> _Xidt_<sup>_′_isavectorof</sup> household demographic variables, _ηdt_ are department-year dummies,<sup>7</sup> _θm_ are interview-month dummies, and _εidt_ is the error term. In all models, we use individual-level survey weights to obtain population representativeness. After estimating the models, we calculate marginal effects which allow us to obtain estimates of the changes in the expected value of the dependent variable associated with a one-unit change in the independent variable of interest, in this case temperature and precipitation anomalies, while holding other variables constant. To account for differences in incomes between urban and rural areas, and because we expect the impact of weather shocks to be structurally different given the different economic structure of production in rural vs. urban areas, we split the models by area of residence. Since districts are the lowest level of geographical aggregation in the EPH data, our results will capture the effects of an average weather shocks on income or poverty at the district level. In other words, there may be underlying heterogeneity this analysis will not be able to highlight, such as more nuanced differences in the intensity of weather shocks within districts, and may perhaps under-estimate the severity of the impact for highly-affected households. However, most districts in Paraguay are reasonably small, allowing for sufficient variation across units of analysis.

> 5 Ideally, we would cluster standard errors at the primary sampling unit (PSU) level. However, PSU codes vary across years in the EPH, so that it can currently not be used for clustering.

> 6We include those variables to control for differential weather conditions across areas of Paraguay, similar to previous studies like Azzarri and Signorelli (2020), Gao and Mills (2018) or Asfaw and Maggio (2018).

> 7We use the variable for ’representative departments’ in the EPH survey, which groups households into the following areas: Asunción, San Pedro, Caaguazú, Itapúa, Alto Paraná, Central, as well as other urban and other rural areas. ’Other urban and ’other rural’ contains all households who do not live in any of the aforementioned departments, divided by rural or urban area. Only the departments Boquerón and Alto Paraguay are not covered in the EPH due to the small population size living in those departments (1.4% according to the latest census). As a result, our study is representative of the Paraguayan population except for Boquerón and Alto Paraguay. As our weather shock variables are fixed at the district level, we cannot use district-fixed effects but use (representative) department-fixed effects instead.

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