weather station records (Harris et al. 2013). These data are commonly used in economic studies (Aufhammer et al., 2013; Dell et al. 2014).

We use annual means of precipitation and temperatures for the reference period from 1981 to 2010. We chose the last year of the reference period such that it equals the last year of fieldwork for the PEN study. The selected period length of 30 years is covered by most climatologies, and may be reasonably relevant for current household decision-making. The squared climate terms account for nonlinearities in the response of income to climate (Mendelsohn et al. 1994, 2007; Kurukulasuriya et al. 2006, and Section 2).

To obtain the temperature anomalies, we use the mean temperature of the survey year (temp_survey) minus the average temperature of the reference period (temp_mean) and divide the difference by the standard deviation of temperature in the reference period (temp_sd): ����_�����������_���� temp_anomaly � ~~.~~ ����_�� The precipitation anomalies are calculated correspondingly. We define the survey year as the year that starts with the survey period, i.e. three months prior to the first interview round within a village.<sup>9</sup> The anomalies measure therefore deviations from the means in terms of standard deviations (e.g. Lobell et al. (2011).

The soil data are from the Harmonized World Soil Database v 1.2. The data on forest cover are based on MODIS-satellite based estimations of tree canopy cover (in percent) at 250 m spatial resolution globally (Hansen et al. 2010).<sup>10</sup> For estimating the distance to the nearest city, we use ESRI's world cities shape file of the year 2008 that is available online (http://www.baruch.cuny.edu/geoportal/data/esri/esri_intl.htm). The distance was calculated as Euclidian spatial distance between each PEN village centre and the nearest city, expressed in km. Similarly for distance to major roads, we used the world data on major roads shape file available - online (http://www.vdstech.com/world <u>data.aspx). The distance in km is referring to both secondary and</u> primary roads in the datasets.

When the Köppen-Geiger climate classification, according to Peel et al. (2007), is superimposed on the location of the PEN sites, most of the PEN villages are located in the tropics, and subsidiarily in the subtropics, with just a few temperate sites complementing the picture. No PEN site is located in arid, cold or polar climates.

# 3.3 How representative and applicable are the PEN data?

As for the PEN data, what universe is the sample representative of? The sheer number of countries, sites, and villages does obviously not _per se_ safeguard any wider scope of extrapolation, if the sample was not randomly selected, and if in the worst of cases the sample was subject to systematic selection biases. For addressing this concern, we thus first have to look closer at the PEN sampling strategy at multiple levels (Wunder et al. 2014). As a collaborative research effort with a relatively limited budget, PEN was not able to choose its study sites randomly, which were rather determined by the sites PhD students had preselected.

> 9 It starts three month before the first interview round since the households were asked about their incomes in the last three month.

> 10 We are grateful to Martin Herold and his team at Wageningen University for providing to us these data for the PEN villages.

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