Privacy-Preserving Redactable Blockchain for Internet of Things

In the traditional blockchain system, data is public and cannot be redacted. With the development of blockchain technology, the problem that the data cannot be altered will be more serious once it is written on the chain. Recently, some redactable blockchain schemes have been proposed. However, most of the schemes are based on the public blockchain, and the users’ identities and transaction data may be disclosed. To solve the problem of privacy disclosure, we propose a privacy-preserving transaction-level redactable blockchain. In the proposed scheme, symmetric encryption and ring signature are used to protect transaction data and the users’ identities, respectively. In order to prove the legality of data redaction, the transaction sender can reveal the invalid users’ identities and transaction data in an anonymous environment. To construct a transaction-level redactable blockchain, the users only need to replace a single transaction to complete the data redaction instead of replacing the entire block. The experimental results show that the proposed scheme saves 20% of the redaction time compared to the previous privacy-preserving blockchains, so the redaction efficiency is higher.

Who Uses the EU's Free Trade Agreements? A Transaction-Level Analysis of the EU–South Korea FTA

The tariff preferences in FTAs do not apply automatically to all imports. Instead, importers can request to use the tariff preferences, but must then show that the imported goods fulfil the formal requirements (e.g. rules of origin) of the FTA. This is costly, which is a likely reason why tariff preferences are not always used. This research note examines preference utilization under the FTA between the EU and South Korea, which was formally ratified in 2015 (but had been provisionally applied from 2011). We use firm and transaction level data for Swedish imports from South Korea during November 2016 to answer the question ‘Who uses the EU's FTAs?’ With information on firm size, product category, import mode (direct imports or customs warehousing), preference margin, potential duty savings, and transaction size, we provide a detailed picture of when firms choose to utilize the tariff preferences. The results suggest that the differences across importers are not primarily related to firm size, as is sometimes suggested in extant literature. We also find that it is the size of the import transaction rather than the size of the preference margin that determines preference utilization.

The Profitability of Revenue-Based Quotas Under Price Negotiation

We study how the presence of a monthly revenue-based quota impacts a retailer’s profits when prices are negotiated by a salesperson. Using transaction level data for refrigerators, we first provide reduced-form evidence that prices are impacted by the quota: the negotiated discounts are approximately 3.8% higher if the salesperson is 10% closer to reaching the quota in the final week of the month. Guided by this result, we specify and estimate a demand model that identifies the impact of the quota through two forces: the effort salespeople expend in order to sell the product and their bargaining position. Results indicate that, as salespeople get closer to reaching their quota, their effort increases regardless of the week, and their bargaining position weakens (i.e., they offer lower prices), but only in the final week of the month. We use these results to analyze the impact of the quota and find that, holding salespeoples’ total compensation fixed, eliminating quotas results in 8% lower profit for the retailer. This decrease stems primarily from the reduction in effort that outweighs any benefit from strengthening the salespeoples’ bargaining position. The change in profit is economically meaningful because eliminating both price negotiation (i.e., moving to fixed pricing) and the quota results in an up to 36% reduction in profit. This paper was accepted by Matthew Shum, marketing.

The Fed's Discount Window in "Normal" Times

We study new transaction-level data of discount window borrowing in the U.S. between 2010 and 2017, merged with quarterly data on bank financial con- ditions (balance sheet and revenue). The objective is to improve our under- standing of the reasons for why banks use the discount window during periods outside financial crises. We also provide a model of the decision of banks to borrow at the window, which is helpful for interpreting the data. We find that decisions to gain access and to borrow at the discount window are meaning- fully correlated with some relevant banks' characteristics and the composition of banks' balance sheets. Banks choose simultaneously to obtain access to the discount window and hold more cash-like liquidity as a proportion of assets. Yet, conditional on access, larger and less liquid banks tend to borrow more from the discount window. In general, our findings suggest that banks could, in principle, adapt their operations to modulate, and possibly reduce, their use of the discount window in "normal" times.

JD.com: Transaction-Level Data for the 2020 MSOM Data Driven Research Challenge

To support the 2020 MSOM Data Driven Research Challenge, JD.com, China’s largest retailer, offers transaction-level data to MSOM members for conducting data-driven research. This article describes the transactional data associated with over 2.5 million customers (457,298 made purchases) and 31,868 stock keeping units (SKUs) over the month of March in 2018. We also present potential research questions suggested by JD.com. Researchers are welcome to develop econometric models or data-driven models using this database to address some of the suggested questions or examine their own research questions.