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>8</sup>

# 4.2. Impact of displacement flows on NTL growth

New population flows settling in the location are likely to impose deviation from the NTL growth, e.g., through settlements’ expansion, growing services needs, or the modification in the economic activity. High inflows of the displaced population are associated with a growing need for basic electricity services such as indoor and street lighting. In addition, they could directly impact settlements’ economic activity, e.g., by modifying the repartition of the labor force throughout the different sectors or through a direct increase in the settlements’ population. As NTL levels are traditionally used to monitor human activities, see e.g., Li et al. (2018), important influxes of persons are expected to impact the evolution of NTL levels. This note therefore does not assume a fixed growth rate of NTL but rather compares growth rates across settlements and provides insights on why some settlements have higher growth rates than others - reflecting an additional inflow of migrants but also increased economic activity.

A linear regression at the settlement level allows us to check for correlation between NTL growth and overall displacement flows between 2018 and 2020.<sup>9</sup> The following regression looks at how the NTL growth across time is affected by _Net Inflow_ , the cumulative net inflow of persons in settlement over the 2018-2020 period. The regression controls for the initial population in 2017, the share of community income derived from agricultural and livestock activities in late 2020, time seasonality ( μ the time fixed effect) and the fixed effect for district _j_ . For visibility’s sake, let’s label _X := Net Inflow_ , the regression is hence: tt jj δδ N = β0 + β1N + β2 + β3 ∗N + β4P + β5A A + μ (3) The NTL growth therefore follows ii,tt ii,tt−1 ii ii ii,tt−1 ii ii jj tt ii,tt LL LL XX XX LL PP2017 AA2020 + δδ + uu The linear regression shows that settlements with higher net inflow of displaced population experience a larger NTL growth (Table 2, column 2). From equation (4), one obtains that β3 is positive, i.e., d NTLi,t is increasing with _Net_ d NTLi,t−1 _inflow._ In other words, an increase in net inflow of displaced population (for inbound areas with _Net Inflow_ positive), or a reduction in net outflows (for outbound areas with _Net Inflow_ negative) would be associated to larger NTL growth , while a marginal increase in outflows drops the NTL levels. Second, a marginal increase in the net inflow of displaced population would raise NTL levels, provided the settlement already recorded some level of human activity. A marginal increase in the net inflow of displaced population increases the NTL levels if equation (5) is positive, that is, if NTLi,t−1 > 0.28. As a reference, all settlements studied recorded an NTL value above 0.28 at least once during the 2018-2020 period.<sup>10</sup> In other words, settlements receiving a higher number of displaced populations experience a higher NTL growth if the Nighttime Light level is not too small to start with.

Table 2. _Regression of NTL on net inflows of persons in settlement (cumulated over 2018-2020)_

|VARIABLES|NT<br>(1)|L level<br>(2)|
|---|---|---|
|β1;Lagged NTL|0.780***|0.744***|
||(0.00738)|(0.00805)|
|β2;Net Inflow<br>||-5.40e-07***<br>(9.38e-08)|

> 8 Equation-3 is positive if and only if N > 1.194/0.534. 9 The average NTL is not available for June 2020. 10 NTL levels around Badghis settlement ranged between 0.03 and 2.215 during the 2028-2020 period (see Table 1). LLii,jj,tt−1

> 10 NTL levels around Badghis settlement ranged between 0.03 and 2.215 during the 2028-2020 period (see Table 1).

9