rich nationally representative panel data of about 4,000 (rural sample) households in 290 villages (enumeration areas). There are three rounds of this survey: 2011, 2013, and 2015. In the rural sample, attrition rate is 5% between 2011 and 2013, and 2% between 2013 and 2015.<sup>4</sup> This attrition is not correlated with the households’ 2011 characteristics, such as food and non-food consumption, share of land allocated to coffee, and many other variables, but is slightly negatively correlated with household size and land holding. See table A.1, which reports correlation of attrition between 2011 and 2013 on selected household 2011 characteristics.

This dataset includes information on household production and consumption, disaggregated by crops. In particular, the dataset enables me to calculate the fraction of land and labor a household allocates across crops (or plots), as well as each household member’s on-farm and off-farm labor supply. Importantly, the sample locations cover both coffee producing and non-coffee producing districts (see figure 3). A key variable that enables me to construct a measure of households’ exposure to coffee price shocks is the share of farmland allocated to coffee production – households’ exposure to coffee price shocks is proportional to the fraction of their farmland allocated to coffee production. The dataset also includes information on household demographic characteristics and anthropometric measures for children under five years of age.

There are some shortcomings of the dataset worth mentioning. Its main drawback its relatively small sample size and short panel. The second main drawback is that the child anthropometric measures (height and weight) are poorly measured – height and weight measures reported are biologically implausible for about 3% of the children in the dataset,<sup>5</sup> which I exclude from my analysis.

My international coffee price data comes from the _International Coffee Organization_ , which maintains historical statistics on international coffee prices and trade. I use the monthly coffee price index for the variety of coffee known as _Brazilian Naturals_ , which is an Arabica species and one of the most widely traded coffee types, with Ethiopia as one of the major suppliers. Figure 1 plots significant swings in the international coffee prices over the study period, 2006-2016.<sup>6</sup> To shed some light on how domestic prices move in response to international prices, figure 2 plots international prices (adjusted by nominal exchange rate) together with domestic prices at major coffee production centers across Ethiopia, obtained from the Retail Price Survey (RPS). The

> 4 For more information, see `https://microdata.worldbank.org/index.php/catalog/2783/download/48264` .

> 5While this problem is also prevalent in the Demographic and Health Survey (DHS) dataset, the large sample size of DHS data means that one can still have good variation in the data after dropping these outliers.

> 6The period 2006-2016 is the study period because some of under-5 children in ESS 2011 round data are in utero in 2006, and some of the under-5 children in ESS 2015/16 data are in utero in 2016.

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