A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.
- Topic classification is ongoing.
- Please kindly let me know [mingze.gao@mq.edu.au] in case of any errors.
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22,315 resources
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This paper aims to improve the practical applicability of the classic theory of incentive contracts under moral hazard. We establish conditions under which the information provided by an A/B test of incentive contracts is sufficient for answering the question of how best to improve a status quo incentive contract, given a priori knowledge of the agent's monetary preferences. We assess the empirical relevance of this result using data from DellaVigna and Pope's (2018) study of a variety of incentive contracts. Finally, we discuss how our framework can be extended to incorporate additional considerations beyond those in the classic theory.
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Many of the facts about the extensive margin of trade—which firms export, and how many products are sent to how many destinations—are consistent with a surprisingly large class of trade models because of the sparse nature of trade data. We propose a statistical model to account for sparsity, formalizing the assignment of trade shipments to country, product, and firm categories as balls falling into bins. The balls-and-bins model quantitatively reproduces the pattern of zero product- and firm-level trade flows across export destinations, and the frequency of multiproduct, multidestination exporters. In contrast, balls-and-bins overpredicts the fraction of exporting firms.
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We show that the Armenter and Koren model's firm-product-countryresults rely on the assumption that export shipment size is independent of firm size, and this assumption is contradicted by the data.When actual shipment sizes are used in the balls-and-bins model, itcannot reproduce the data on single product/single country exporters.Beyond just showing that the shipment size assumption mattersto balls-and-bins outcomes, our results highlight the important factthat shipment size is an economic decision, co-determined with otherexport choices. For this reason, we argue that a balls-and-bins modelcannot be a purely statistical benchmark model. (JEL F11, F14, O13,O19, Q37)
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Blum, Claro, and Horstmann (2016) make two statements about theballs-and-bins model of Armenter and Koren (2014). First, that usingfirm-level shipment data changes some of our results. Second, thatthe balls-and-bins model is not an appropriate statistical method.We respond to the first statement and argue that the second statementis unfounded and unrelated to the first. Indeed, the work ofBlum, Claro, and Horstmann (2016) is a perfect example of how touse balls-and-bins in a rich dataset to spot interesting data patterns.(JEL F11, F14)
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Journals
- American Economic Review (10,442)
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