2 to document three stylized facts on the impacts of the ban. To assess whether the ban was binding, we examine its impact on fertilizer imports, combining high-frequency granular customs data covering the universe of import transactions in Sri Lanka with import ban data hand-digitized from regulatory gazettes. Since the ban was ultimately reversed amid widespread social unrest, we employ the dynamic difference-in-differences (DID) estimator of De Chaisemartin and d’Haultfoeuille (2022) that corrects for bias when treatment is reversed within the study period. Our first stylized fact is a near-total collapse in fertilizer imports – driven primarily by quantity reductions – with volumes falling by more than 99% within four months of the ban and remaining at similarly depressed levels thereafter.<sup>6</sup> We then examine the ban’s impacts on agricultural production using government production data, with a focus on Sri Lanka’s primary food crop—rice—and its major export crop—tea— both of which are highly fertilizer-intensive. Our second stylized fact documents a reduction in rice yields of more than 30% between 2021 and 2022.<sup>7</sup> After adjusting for weather abnormalities, we estimate yield declines of 24.4% for rice and 28.7% for tea in 2022 relative to historical norms.<sup>8</sup> To capture the ban’s impact on downstream agricultural exports, we construct a firm-level exposure metric linking customs data on agricultural firms’ pre-ban crop export portfolios with agronomic data on crop-specific fertilizer requirements. We then employ the dynamic DID estimator of De Chaisemartin and d’Haultfoeuille (2022) to compare the export performance of highly exposed firms to less-exposed firms over time. Our third stylized fact is a relative decline of 35% in the exports of exposed firms within four quarters after the ban, even after accounting for seasonality. These stylized facts are robust to potential confounders arising from the COVID-19 shock and the broader economic crisis that unfolded in Sri Lanka.

While these stylized facts demonstrate the ban’s adverse impacts, they do not quantify its full GE effects. GE effects are less relevant in contexts where fertilizer availability changes only for a small subset of farmers or regions, but become critical when access is disrupted nationwide—as occurred during Sri Lanka’s fertilizer import ban but also during the Russia–Ukraine war and other geopolitical shocks. The second step of our analysis is thus to develop a novel quantitative GE model of trade, space, and agriculture with the goal of estimating average welfare losses and their distribution across regions and occupations, elucidating the relative importance of partial equilibrium (PE) and GE channels in driving observed effects, and conducting policy counterfactuals. Our flexible model features three sectors (agriculture, manufacturing, and fertilizer), three types of economic agents (landowning farmers, mobile workers, and immobile estate workers), multiple Sri Lankan districts, cross-crop heterogeneity in fertilizer intensity as well as geographic hetero-

> 6Furthermore, we find minimal evidence of substitution towards organic fertilizer: the organic share of fertilizer never surpassed 4% (for either fertilizer imports or agricultural use).

> 7This reduction was accompanied by a significant increase in rice imports, which were negligible prior to the ban. 8To account for local weather fluctuations, we combine granular weather data with novel pixel-level yield estimates constructed using a satellite-based remote-sensing methodology described in Ozdo ̆gan et al.<sup> ̈</sup> (2025).