For the purposes of exploring drought-driven displacement, we kept the analysis constrained to Badghis Province in Northwestern Afghanistan, but the same approach is easily replicated in other regions or even country wide. The input data used in this analysis were the UN IOM DTM dataset from 2020, and the Nighttime Lights data were monthly composites from January 2018 to December 2020.<sup>2</sup> To replicate the methodology, one may open the Google Earth Engine script at the universal link<sup>3</sup> or download the script directly from GitHub<sup>4</sup> . In the GitHub repository there is a folder titled “NTL,” which contains the monthly NTL script that can be extracted to the buffers, as well as a script for extracting a monthly average of NTL data for the entire area of interest. The user then may update the boundaries of the analysis by simply uploading a new dataset and preserving the object name ’table’ so that the script will apply the same processes on the new area of interest. The user would also update the export location to ensure that the file saves to a location on their personal Google Drive account (or alter the code to save it as an Earth Engine ’asset’, or dataset. The outputs of the methodology are a panel dataset in the form of a CSV (unless otherwise specified in the code) and can be exported from Drive onto one’s local machine for further statistical analysis.

# Appendix C: Quadratic model

## C.1. Benchmark NTL growth (quadratic)

Table 5 displays the linear and quadratic regression of NTL levels on its past values, at the settlement level, controlling for district fixed effects. As seen previously, there are 294 settlements, spread across 7 districts. Let’s define N the average NTL in the 1km buffer around the settlement _i_ in district _j,_ at date _t (_ each month from Jan 2018 to Jan 2020) _._ The following regressions looks at how the NTL levels naturally grow through time in settlements ii,jj,tt within Badghis province, accounting for the initial population in 2017, the share of community income derived from LL agricultural and livestock activities in late 2020, time seasonality ( μ the time fixed effect) and the fixed effect for district _j_ . Equation-1 in the main text assumes a linear evolution of NTL, while equation-2 is quadratic: tt jj 2 δδ N 0 1N 2�N � 3P 4A A + μ (2) The NTL evolution is such that a marginal increase in past NTL value modifies the current NTL by LLii,jj,tt = αα + αα LLii,jj,tt−1 + αα LLii,jj,tt−1 + αα PP2017ii + αα AA2020ii + δδjj tt + uu1 ii,jj,tt 2N . The regression analysis of the growth rate of NTL in Badghis settlement is positive and concave, as settlements with ii,jj,tt−1 higher NTL in period t-1 will experience larger NTL in period t. The linear regression suggests that the NTL growth ββ̂ + 2ββ̂ LL follows a concave shape, that is, settlements with the largest expansion are those with the lowest level of displacement inflow to start with. On average, the natural growth of NTL levels is such that a 1-unit increase in past NTL value in an IOM settlement always raise the current NTL, as long as past NTL values are smaller than 2.2, which is always true in the Badghis sample.<sup>5</sup> Table 5. _Linear and quadratic regression of NTL its lagged value and district fixed effects_

||NTL levels||
|---|---|---|
|VARIABLES|(1)<br>Linear|(2)<br>Quadratic|
|β1;Lagged NTL|0.780***<br>(0.00738)|1.194***<br>(0.0205)|
|β2; (Lagged NTL)<sup>2</sup>||-0.269***<br>(0.0125)|
|Observations|8,497|8,497|

> 2 Due to reflectance issues from collection, NTL from June are unavailable for most of Afghanistan. As such, there was no NTL data used for the month of June 2018-2020.

> 3 Belanger, J. Google Earth Engine Script. <u>https://code.earthengine.google.com/4b069482a54f1fb6041d2e2cff25834d.</u> 4 Stewart, B., Chamorro, A.E., Martine, L. and Belanger, J. World Bank - GEE_Zonal. GitHub Repository. <u>https://github.com/worldbank/GEE_Zonal/.</u>

> 5 Equation-3 is positive if and only if N > 1.194/0.534. ii,jj,tt−1 LL