18 November 2019:
18 November 2019
(University of California, Berkeley)
Interventions aimed at raising agricultural productivity in developing countries have been a centerpiece in the global fight against poverty. Much of the recent evidence in this space has been based on randomized control trials (RCTs), with the well-known limitation that findings from local interventions generally do not speak to the general equilibrium (GE) effects if the policy were to be scaled up. In this paper, we study these forces through the lens of a quantitative GE model of farm production and trade that we develop to capture several stylized facts in this setting. We propose a new solution approach in this environment that allows us to study high-dimensional GE counterfactuals at the level of individual households in the macroeconomy. We then bring to bear rich administrative microdata to calibrate the model to the roughly 6 million households populating Uganda. We use these building blocks to explore the average and distributional implications of small-scale interventions compared to policies at scale, and quantify the underlying mechanisms.
1st Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH
CEP/STICERD Applications Seminars
18 November 2019
The FDIC resolves insolvent banks using an auction process in which bidding is multidimensional and the rule used to evaluate bids along the different dimensions is proprietary. Uncertainty about the scoring rule leads banks to simultaneously submit multiple differentiated bids. This resolution mechanism typically results in considerable losses for the FDIC—$90 billion during the crisis. Our objective is to see whether the mechanism could be improved. To do so, we propose a methodology for analyzing auction environments where bids are ranked according to multiple attributes chosen by bidders, but where there is uncertainty about the scoring rule used to evaluate the different components of the bids. Using this framework, which extends structural estimation techniques for combinatorial auctions, and FDIC data summarizing bids, we back out the underlying preferences of banks for failed institutions. With these we perform counterfactuals in which we eliminate uncertainty and/or multiple bidding. Our findings suggest that the FDIC could reduce the cost of resolution by around 17% by announcing the scoring rule before bidding begins.
2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH
STICERD Industrial Organisation Seminars
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