35 really a QR tends to lead to overestimation of the welfare losses induced by the policy.

To illustrate this point, we perform two additional counterfactual exercises in which we use different means to reduce the quantity of imported fertilizer by 50%. In the first exercise, this is achieved via QRs, while in the second exercise it is done by increasing the iceberg trade cost of transporting fertilizer from the RoW to Sri Lanka.<sup>90</sup> Results are displayed in Columns (4) and (5) of Table VI. As expected, for all types of agents, welfare losses are less severe under QRs than under iceberg cost increases, with the average welfare effect on mobile workers even switching from negative to positive in the former case.<sup>91</sup>

# **VII.F Model Validation: Evidence from Night Lights**

Our model-driven counterfactual exercise replicates the stylized facts documented in Section IV, with large decreases in fertilizer imports, crop yields, and agricultural exports. To further validate our model, we now show that the decreases in real regional income (and, thus, in regional welfare) that are implied by night light intensity data also align with the predictions of our counterfactual.

A well-established literature (Henderson et al., 2012) has confirmed the validity of night lights as a reliable way to indirectly assess the spatial distribution of real income per capita when direct income metrics with the desired coverage or frequency are unavailable. The elasticity of per capita income to the average night light intensity of a geographical unit is often estimated to be around 0.3. We combine this elasticity with the observed reduction in average night light intensity in each Sri Lankan district between April 2021 (i.e. the last month before the ban was implemented) and January 2022 to estimate districts’ income losses in that period.<sup>92</sup> Estimated income losses are displayed in Figure VIIIa. The decreases in district-level real income predicted by the counterfactual exercise are displayed in Figure VIIIb.<sup>93</sup> A visual comparison of the two figures suggests that the model performs well in terms of replicating the spatial pattern of real income losses during the post-ban period. For example, both maps show that the north and east of Sri Lanka were particularly severely affected relative to the southwest. Moreover, Figure VIIIc shows that these northern and eastern regions are also the ones most economically dependent on fertilizer, thus explaining why they were so affected by the ban.<sup>94</sup>

> 90Note that, regardless of the trade policy instrument, there are no rents to be captured in the extreme cases (such as Sri Lanka’s ban) in which imports are driven down to zero. That is why we perform the comparison exercise between the two instruments using a less restrictive (50%) reduction in the quantity of imported fertilizer.

> 91The mechanism by which welfare losses are attenuated in the QR case is related to the tax rate. In both cases, reduction in fertilizer imports leads to a reduction in fertilizer use in Sri Lanka, which scales down the fertilizer subsidy program, lowering the tax rate that is needed to fund such program. However, the tax rate drops by a greater magnitude in the QR case (0.77 percentage points) than in the iceberg cost case (0.4 percentage points).

> 92For details on our night lights data, see Appendix A.I.

> 93More precisely, Figure VIIIb displays the average EV across agent types (weighted by types’ population shares) in each district, as predicted by our model-generated counterfactual exercise. For details, see Appendix F.

> 94To measure the economic importance of fertilizer for a district, we use the district’s baseline ratio of fertilizer