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1. Trade Openness and Domestic Water Use: TRADE AND WATER USE

   https://doi.org/10.1002/2017WR021102  

   Dang, Qian; Konar, Megan (January 2018, Water Resources Research)
2. Networks, Barriers, and Trade

   https://doi.org/10.3982/ECTA17513  

   Baqaee, David Rezza; Farhi, Emmanuel (January 2024, Econometrica)

   We study a flexible class of trade models with international production networks and arbitrary wedge‐like distortions like markups, tariffs, or nominal rigidities. We characterize the general equilibrium response of variables to shocks in terms of microeconomic statistics. Our results are useful for decomposing the sources of real GDP and welfare growth, and for computing counterfactuals. Using the same set of microeconomic sufficient statistics, we also characterize societal losses from increases in tariffs and iceberg trade costs and dissect the qualitative and quantitative importance of accounting for disaggregated details. Our results, which can be used to compute approximate and exact counterfactuals, provide an analytical toolbox for studying large‐scale trade models and help to bridge the gap between computation and theory. 

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3. Trade less Accuracy for Fairness and Trade-off Explanation for GNN

   https://doi.org/10.1109/BigData55660.2022.10020318  

   Liu, Yazheng; Zhang, Xi; Xie, Sihong (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   Graphs are widely found in social network analysis and e-commerce, where Graph Neural Networks (GNNs) are the state-of the-art model. GNNs can be biased due to sensitive attributes and network topology. With existing work that learns a fair node representation or adjacency matrix, achieving a strong guarantee of group fairness while preserving prediction accuracy is still challenging, with the fairness-accuracy trade-off remaining obscure to human decision-makers. We first define and analyze a novel upper bound of group fairness to optimize the adjacency matrix for fairness without significantly h arming prediction accuracy. To understand the nuance of fairness-accuracy tradeoff, we further propose macroscopic and microscopic explanation methods to reveal the trade-offs and the space that one can exploit. The macroscopic explanation method is based on stratified sampling and linear programming to deterministically explain the dynamics of the group fairness and prediction accuracy. Driving down to the microscopic level, we propose a path-based explanation that reveals how network topology leads to the tradeoff. On seven graph datasets, we demonstrate the novel upper bound can achieve more efficient fairness-accuracy trade-offs and the intuitiveness of the explanation methods can clearly pinpoint where the trade-off is improved. 

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4. Agricultural Trade and Environmental Sustainability

   https://doi.org/10.1146/annurev-resource-101420-090453  

   Baylis, Kathy; Heckelei, Thomas; Hertel, Thomas W. (October 2021, Annual Review of Resource Economics)

   null (Ed.)

   Global agriculture consumes substantial resources and produces significant pollution. By shifting its production to new locations, and inducing changes in technology and input use, trade has a substantial impact on environmental sustainability of the world's food systems, but due to suboptimal environmental policy, the exact nature of these impacts is in dispute. We review the literature on agricultural trade and environmental sustainability, highlighting the different approaches taken in ecology versus economics. While useful in identifying environmental costs, much of the ecological literature does not compare these costs to a trade-free counterfactual and can therefore be misleading. Further, by moving production to places with more resources and increasing production efficiency, trade can reduce the environmental impact of food production. On the other hand, trade can also limit the effectiveness of domestic environmental policy because production can be shifted to countries with less stringent regulations. However, recently, consumers are leveraging trade policy to induce exporters to improve environmental sustainability. While such policies are gaining traction in wealthy countries, evidence suggests that such measures will not reach their potential without buy-in from decision makers in the countries where the environmental damages are occurring. Expected final online publication date for the Annual Review of Resource Economics, Volume 13 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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5. Volatility and the Gains From Trade

   https://doi.org/10.3982/ECTA14411  

   Allen, Treb; Atkin, David (January 2022, Econometrica)

   Trade liberalization changes the volatility of returns by reducing the negative correlation between local prices and productivity shocks. In this paper, we explore these second‐moment effects of trade. Using forty years of agricultural micro‐data from India, we show that falling trade costs due to expansions of the Indian highway network reduced the responsiveness of local prices to local yields but increased the responsiveness of local prices to yields elsewhere. In response, farmers shifted their production toward crops with less volatile yields, especially so for those with poor access to risk mitigating technologies such as banks. We then characterize how volatility affects farmers' crop allocation using a portfolio choice framework where returns are determined in general equilibrium by a many‐location, many‐good Ricardian trade model with flexible trade costs. Finally, we structurally estimate the model—recovering farmers' risk‐return preferences from the gradient of the mean‐variance frontier at their observed crop choices—to quantify the second‐moment effects of trade. The simultaneous expansion of both the highway and rural bank networks increased the mean and the variance of farmer real income, with the first‐moment effect dominating such that expected welfare rose 4.4%. But had rural bank access remained unchanged, welfare gains would have been only half as great, as risk mitigating technologies allowed farmers to take advantage of higher‐risk higher‐return allocations. 

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