3 geneity in tastes, population, productivity, and crop specialization. It incorporates several structural features and policies that characterize the economy of Sri Lanka and are common across developing economies: land inequality, quantitative restrictions (QRs) on fertilizer imports (as opposed to ad valorem trade costs), a tax-funded fertilizer subsidy program, and non-homothetic preferences that generate a negative relationship between income and the agricultural expenditure share. These features play a key role in shaping the welfare effects of fertilizer access restrictions.<sup>9</sup> The model delivers a closed-form solution for the elasticity of crop yields with respect to fertilizer use as a function of changes in the prices of inputs and outputs, clarifying the distinction between PE adjustments—in which only fertilizer prices change—and GE adjustments—in which wages and output prices also change.

In the third step, we estimate the model’s parameters using a range of empirical strategies and a rich set of novel data sources. Household survey data is used to estimate the income elasticity of food’s expenditure share (Engel elasticity), a policy-relevant parameter widely used to analyze food security concerns in developing countries (Baquedano et al., 2021), but hard to estimate causally. We deploy an instrumental variables (IV) approach relying on unexpected income shocks (lottery winnings and disaster relief). Survey data on crop prices and household expenditures enables us to estimate the elasticity of substitution _across crops_ through another IV approach that leverages the effect of regional crop suitability, exogenously determined by regional geography and measured by the Global Agro-Ecological Zones (FAO-GAEZ) project, on local crop prices. Technology parameters in agricultural production functions are recovered from input cost shares computed from national Input-Output (IO) tables and data on fertilizer requirements and subsidy rates. The (regionspecific) parameters that govern land distributions are estimated using survey data on landholding sizes. Unobserved variables (e.g. productivities and taste shifters) are backed out via model “inversion” by solving the equilibrium system of equations while taking as given the values of observed variables (e.g. populations, wages, land endowments, crop outputs/prices) in 2019, our pre-ban baseline year.

In the fourth and final step, we use the estimated model to quantify the aggregate and distributional impacts of the ban, separate general- from partial-equilibrium mechanisms, examine how trade policies (e.g., import restrictions) interact with domestic policies (e.g., subsidies), and highlight how features of developing economies (e.g., land inequality) shape the welfare costs of trade 9As they link household income and agricultural expenditure share, non-homothetic preferences shape agriculture’s _aggregate_ share of expenditure and employment in equilibrium, and the aggregate size of the agricultural sector (relative to manufacturing) matters for welfare because manufacturing serves as a “buffer” for workers, providing them with an employment alternative during the agricultural downturn induced by the ban. Land inequality also influences the relative size of the agricultural sector: high land inequality leads to high income inequality, which lowers aggregate agricultural consumption because (given non-homothetic preferences) richer households have lower marginal propensity to spend on food. QRs restrict fertilizer imports while driving a wedge between international and domestic fertilizer prices, potentially creating rents that are captured by importers. Subsidies act as a transfer from mobile workers to farmers, so fertilizer import restrictions reduce this transfer, benefiting mobile workers.