Indicators. Other variables controlling for household demographics and assets are constructed from the data sets.

The commune surveys collect information about the occurrence of emergencies in the last 3 years listed by type and month in addition to information on population, economic conditions, agriculture and land. These data allow identifying communes exposed to weather events, such as storms, floods and droughts.

Although the data allow estimating nationally‐representative consumption and income estimates and their changes over time, several shortcomings for the purpose of this study are to be noted. First, the study relies on observations from only three years within a five year time period and only a subsample of the included households is observed in several years. This short‐term panel does not allow to understand time‐variant household factors or structural changes over longer time horizons. Second, little information is available about labor allocation, production inputs and returns, which are important to explain income differences over time. The lack of such information can limit the explanatory power of the regression models. Yet many of these variables could be determined by weather conditions and thus create potential endogeneity biases.

# **2.2 Weather data**

In addition to these self‐reported weather events in the commune surveys, this study uses weather data from the CRU of the University of East Anglia to control for rainfall and temperature conditions. From the global CRU TS3.21 data set, monthly time series of rainfall, minimum, mean and maximum temperature from 1961 to 2014 is available at 0.5 x 0.5‐degree grid.<sup>4</sup> This balanced panel of weather data was produced using statistical interpolation based data from 4,000 individual weather stations (Harris et al. 2014).

These data are merged at the commune level to construct current and long‐term weather variables for each month. For each commune, the household interview dates are specified and the current weather variables are defined for the 12 months before that interview date (e.g. if the interview date was in May 2012, the current weather variables cover June 2011‐ May 2012). The long‐term means and standard deviation of monthly rainfall, minimum, mean and maximum temperature are calculated for 30 years before these 12 months (e.g. in the above example June 1981– May 2011). Based on these data several variables measuring current weather conditions are constructed as explained in section 4.

Although gridded data set, such as CRU are commonly used in economic studies as they provide a balanced panel that adjusts for missing data and spatial factors (e.g. elevation), they suffer from some limitations for assessing weather variation at subnational level. Optimally such data would be measured based on ground station data, which, however, is not readily available for all of Vietnam. The precision of the CRU data at the subnational level depends on the interpolation method and the availability of station data for the areas of interest (Dell et al., 2014). Although this is an important concern that deserves further investigation, this is beyond the scope of this paper. Moreover, only monthly average rainfall and temperature data are available from CRU. Given high intra‐annual variations, the distribution of

> 4 http://www.cru.uea.ac.uk/cru/data/hrg/

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