13 2020-March 2021) and the first Maha season after the introduction of the ban (September 2021March 2022). Rice yields fell in almost all regions, with some spatial heterogeneity in the degree of yield reduction. The fall was more pronounced in Sri Lanka’s northern and western regions, including major rice-producing districts such as Ampara, Anuradhapura, Batticaloa, and Polonnaruwa.

We also examine yields of Sri Lanka’s major export crop, tea. Tea yields suffered a large drop of 28.7% in 2022 (after controlling for weather fluctuations) relative to historical averages, as seen in Appendix C.D. This evidence shows that the fertilizer ban harmed Sri Lankan agriculture substantially, consistent with the importance of fertilizer as an import determinant of agricultural productivity across the world (Donovan, 2021; Gollin et al., 2014).

# **IV.C Fact 3: Agricultural Exports Declined After the Fertilizer Import Ban**

Facts 1 and 2 show that fertilizer imports and rice and tea production declined, and rice imports increased, with the fertilizer import ban. We now show the ban also had adverse downstream effects on agricultural exports.

To assess the ban’s effects on the export performance of agricultural firms, we consider the sample of firms who exported in 2017-2019 (i.e. pre-ban) at least one of the 23 crops with fertilizer intensity data. To understand which firms were more exposed to the ban, we define a firm-level exposure measure capturing how much fertilizer is embodied in the crops each firm exports. This exposure measure is the weighted average of the firm’s exported crops’ fertilizer intensities, where the weights are given by the share of each crop in the firm’s agricultural exports portfolio preban.<sup>33</sup> We rank firms by fertilizer exposure and compute the third quartile of that measure. We define exporting firms to be treated if their fertilizer exposure is above the third quartile. As in Fact 1, we use the De Chaisemartin and d’Haultfoeuille (2022) estimator to allow for the fact that the bans were switched on and off. Our dynamic DID estimator recovers the evolving effects of the ban over time on the log of firm export value at quarter level in the period between January 2019 and September 2022.<sup>34</sup> An advantage of our approach is that the firm’s potential exposure to fertilizers is computed using information on its agricultural exports and on each product’s fertilizer requirements. A disadvantage is that the control group may be imperfect, since all agricultural exporters might have been affected to some degree. If anything, this will work against our finding of any effect of the ban on exports. Appendix B.B provides further details on the firm-level fertilizer

> 33Formally, the exposure measure for firm _f_ is given by: _Uf_ =<sup>�23</sup> _v_ =1<sup>_FICv×_</sup> � _Xfv_<sup>2017</sup><sup>_−_2019</sup> _/_<sup>�</sup> _v_<sup>_′ X_</sup> _fv_<sup>2017</sup><sup>_′−_2019</sup> �, where _v_ indexes crops, _FICv_ is fertilizer intensity of crop _v_ (in kg/ha), and _Xfv_<sup>2017</sup><sup>_−_2019</sup> is the firm’s total exports of crop _v_ in the 2017-2019 period.

> 34The use of a quarterly, rather than a monthly time frequency as in our previous import regressions, has two justifications. First, fertilizer inventories might have been available which would take time to be depleted. Second, the agricultural cultivation cycle takes several months from fertilizer application to harvest and export. Our specification allows export effects to materialize some time after the ban was implemented.