Observations 293 R-squared 0.238 District Fixed Effect Yes Standard errors in parenthesis, *** p<0.01, ** p<0.05, * p<0. Note: Regressions control for population in settlement (2017), the share of settlement income derived from agricultural activities (2020), and a constant.

This methodology allows us to predict whether a settlement is an Inbound area from coefficients in Table 3. We thereby construct the variable _Predicted Inbound,_ which equals 1 if the predicted values for _Net Inflow_ are positive, and 0 if they are negative. We then compare the actual _Inbound_ classification with the predicted outcome. If the null hypothesis is H0: outbound settlement, the model has a false positive rate of 40 percent and a false negative rate of 34 percent. Overall, 63.2 percent of settlements are correctly classified. Given limited accessibility, this level of accuracy can inform preliminary assessments and resource mobilization prior, which can then be confirmed with onthe-ground validation when feasible. There is always a tradeoff between false positives and false negatives, in terms of policy preference. With limited resources, a model that minimizes false positives would allow the concentration of resources where they are most needed.

Table 4. _Inference Predicted Inbound using NTL variations_

|||Set<br>0 = Outbound|tlement type<br>1 = Inbound|Total|
|---|---|---|---|---|
|Predicted|0=Outbound|87|51|138|
|Settlement||_True Negative (1-α=.60)_|_False Negative (β=.34)_||
|type|1=Inbound|57|98|155|
|||_False Positive_(α=.40)|_True Positive (1-β=.66)_||
||Total|144|149|293|

Source: Authors’ computation.

Using the reverse engineering method, hence, yields promising results, yet the methodology needs to be improved, which stresses the need to collect high frequency data on displacement and socio-economic outcomes at the disaggregated level. Including other NTL related regressors did not improve the performance of the model, neither did the inclusion of additional controls on economic activities (average settlement income and share of employment in services, in 2020).<sup>11</sup> To date, alternative statistical methodologies do not yield better predictive power. For example, machine learning algorithm such as the least absolute shrinkage and selection operator (Lasso) or the random forest model do not outperform the current analysis, due to the low sample size (294 settlements). This paper works as a proof-of-concept, and calls for an expansion of the analysis to the whole Afghanistan, as a means to increase both statistical power and representativity.

# 6. Conclusion

This work investigated whether geospatial data could be used to proxy for displacement flows and settlement expansion, using NTL data. By focusing on Badghis province it provided a relevant proof of concept for a province that was particularly affected by recent climatic shocks, including the 2018 drought. We find that large displacement movements can indeed be observed by NTL satellite imagery. Settlements classified as high net inflow of displaced persons over the 2018-2020 timespan (high inflow receivers) have larger NTL growth than others. Results remain robust when looking at the net inflow of persons as a share of the existing population. Accounting for population in 2017 as a proxy for initial levels of urbanization does not change our findings. We can use these insights to predict which settlements are experiencing a large inflow of displaced people, with a true positive rate of 66 percent.

This paper provides a first basis for better understanding the drivers and spatial characteristics of settlement growth. Future expansions of the methodology could draw on additional sources of geospatial data (e.g., shifts in vegetation density due to drought). Evidence from the ground suggests that there are regional differences across

> 11 The regressions were run including the average, median, standard deviation, minimum and maximum of NTL in settlements during the year 2018, the year 2020 and the difference between the two.

11