Reports & Papers

The potential impact of big data for border security is, in a word, transformative. However, U.S. Customs and Border Protection (CBP) does not leverage much of the large volume of data it collects. If CBP could better integrate these data into operations, it would speed up cross-border trade by helping authorities identify the most at-risk travelers and cargo. Big data can also help policymakers better understand the extent to which the border is secure and improve the allocation of enforcement resources.

Significant barriers remain for CBP to leverage big data, such as information sharing barriers between operational components as well as safeguarding data from breaches. These barriers are caused by a variety of factors. Like much of the U.S. government, CBP has struggled to develop a data culture receptive to changes brought on by the information technology revolution and has significant issues with its data governance standards, technology acquisition, and human capital development processes. This article explores these issues and offers recommendations to address these and other barriers to unlock the potential of big data for U.S. border security.

In this report, we propose a market reform pathway for Yunnan that is both feasible and applicable to address some of these challenges immediately, while aiming for a standard design based on well-documented international experience. Our proposal includes at its heart a pay-for-performance monthly capacity auction that can help cover revenue deficiencies in the energy market. Building on international experience with capacity markets, this approach provides incentives for availability when generation is needed most and is compatible with the adoption of a single energy market for all electricity resources. Out-of-market payments to cover stranded costs of certain firms can thus be minimized. Finally, engaging consumers in both these energy and capacity markets can create high-powered incentives to shift consumption to low-cost months and hours, benefitting the entire province.

This paper explores the spillover of protests across countries using data on nonviolent and spontaneous demonstrations for 200 countries from 2000 to 2020. Using an autoregressive spatial model, the analysis finds strong evidence of “contagious protests,” with a catalyzing role of social media. In particular, social media penetration in the source and destination of protests leads to protest spillovers between countries. There is evidence of parallel learning between streets of nations alongside the already documented learning between governments.  

This paper explores the effect of oil shocks on electoral outcomes. Using a new polling and election data set for 207 elections across 50 democracies, the paper shows that oil price increases systematically lower the odds of reelection for incumbents. The analysis verifies that these shocks—which reduce consumption growth—are associated with worsening performance for incumbents in the runup to reelection and a reversal in the leaning of the political party in power post-election.  

This paper explores the dynamics of media chatter about economic reforms using text analysis from about a billion newspaper articles in 28 languages. The paper shows that the intensity of reform chatter increases during economic downturns. This increase is more significant in democracies. Using instrumental variable techniques, the analysis finds the relationship to be causal. The paper also documents that reform chatter is followed by actual reforms, suggesting that democracies benefit from a “self-correcting” mechanism stemming from changing popular attitudes toward reform. 

Successful reformers know how to recognize a good idea, demonstrate its utility, and follow through on its application.

The Economic Diplomacy Initiative (EDI) at the Belfer Center for Science and International Affairs is presenting a first-of-its-kind analysis of the makeup of the Commerce Department’s Entity List, by country, by sector, and by year of addition. Starting with raw data made available by the Commerce Department, we manually grouped blacklisted sub-entities at the parent level to give a clearer view of the companies targeted by export controls. While  the Entity List does not include industry tags, we used Federal Register announcements and secondary research to manually assign industry sectors to each entity. The analysis provides a quantitative review of the evolving use of the entity list to shed light on shifting aims of US economic and national security policy.

Since 2010, China has significantly expanded its indigenous enrichment capacity to meet the expected rapid increase of enrichment requirements. Meanwhile, China has expanded its plutonium reprocessing and recycling capabilities for “saving uranium.” The purpose of this report is to provide a better understanding of the development of China’s uranium enrichment and plutonium recycling programs.

Differential privacy is a safeguard used to protect an individual’s data privacy. It allows for the collection and publication of data patterns and trends, while protecting the privacy of individuals captured in a dataset. Differential privacy is not a tool or method, but rather a criterion or a property that multiple methods can achieve. More specifically, it is a mathematical definition of privacy that quantifies privacy risk. 

A vaccine platform is a “plug -and-play” physical framework that can be used when developing vaccines for emerging infectious diseases, such as COVID-19. Vaccine platforms use a base carrier or “vehicle,” such as a nucleic acid, viral vector, or liposome, which can be used interchangeably for various diseases. Once designed and licensed for one vaccine, the development of future vaccines using the same platform would simply require substituting the desired antigenic component, or a genetic compound that normally triggers an immune response. This would enable faster and cheaper development, regulatory approval and mass production.