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The pipes model for latency and throughput analysis
No full textTraditionally, latency in distributed computing pro tocols is expressed as the number of communication rounds or network delays; it does not take into account the amount of data sent or the dependencies among parties sending the data. Moreover, throughput for a protocol is typically only empirically computed. Due to this, the only means of obtaining or comparing the practical latency and throughput of protocols is through expensive implementation and experimentation. In this paper, we present Pipes, a model for analyzing latency and throughput in state machine replication (SMR) protocols. The Pipes model captures the effect of processor bandwidth S, transaction arrival rate D, and the network delay ∆, enabling us to explicitly specify the throughput bottleneck and the latency of a protocol. Using Pipes, we perform an analysis of broadcast primitives such as Best effort Broadcast and Reliable Broadcast, as well as state-of the-art SMR protocols such as DispersedSimplex, Tendermint, HotStuff, and Sailfish. We experimentally validate these results by implementing the Best-effort Broadcast primitives and SMR protocols (DispersedSimplex and Sailfish). Our comparisons show clear trade-offs: single-sender pro tocols that exploit pipelining and erasure coding (e.g., Dis persedSimplex) can achieve substantially lower latency across many regimes but have a lower latency bottleneck by a constant factor; many DAG-based protocols push the bottle neck higher at the cost of higher per-block latency scaling. HotStuff’s leader-relay design, while communication-efficient, yields higher latency than Tendermint in our model due to leader bandwidth bottlenecks
What is "fair" pay for a chief executive?
Get PDFTo many of us, chief executives with lavish luxury lifestyles look overpaid. And why can’t they do their job well without extravagant incentives? Pierre Chaigneau, Alex Edmans and Daniel Gottlieb’s study suggests that bosses are paid handsomely not because they refuse to work hard without the carrot of an extra yacht, but because they wish to be recognised for a job well done
Automation is not hollowing out British factory jobs
Get PDFFears that new technology will wipe out jobs are as old as the factory floor. But new analysis by Aniket Baksy, Daniel Chandler and Peter John Lambert shows that rather than taking jobs away, automation in British factories is linked to higher employment. It is a reminder that new production technologies often expand output and change what people do at work, rather than simply replacing them
Combining qualitative and quantitative methods in behavioural psychology for complex human-environment systems
No full textThis chapter explores the combination of qualitative and quantitative methods in behavioural psychology for complex human-environment systems. Behavioural psychology aims to understand the causes of human behaviour, such as economic contexts, beliefs, attitudes, and individual differences. The discipline employs diverse methodologies, from experimentation and surveys to computational models and large-scale data analysis. In this chapter, we discuss human behaviour within complex contexts, characterised by interdependencies, adaptive behaviours, and feedback loops. These can change how behaviour unfolds qualitatively and quantitatively over time, making it hard to capture with analytic approaches. Agent-based models (ABMs) are a computational tool to simulate individual and collective behaviours within these environments. We emphasise the need for interdisciplinary collaboration and iterative model development, combining qualitative insights with quantitative validation. A case study of the POSEIDON model illustrates the application of ABMs in fisheries management, showcasing the interplay between qualitative interviews and quantitative calibration
Shadow banks on the rise: evidence across market segments
No full textThis paper examines the comparative advantages of shadow banks across market segments. Combining credit bureau data on retail loans in India with weather shocks as a proxy for credit demand, we show that Fintechs respond more in uncollateralized markets. In contrast, non-Fintech shadow banks exhibit stronger responsiveness in collateralized markets. Exploiting geographic heterogeneity in the adoption of digital payments, we identify technology as the key advantage for Fintechs. Leveraging four natural experiments, we document the significance of lower regulation for non-Fintech shadow banks. Our results suggest that the comparative advantage of shadow banks differs across market segments
Bitcoin ETFs and structural decoupling in the cryptocurrency market: evidence from altcoin correlation dynamics
Get PDFThe approval of U.S.-based spot Bitcoin Exchange-Traded Funds (ETFs) in January 2024 marked a key milestone in the institutionalization of digital assets. This study examines how ETF introduction reshaped inter-asset dynamics in the cryptocurrency market. Using daily returns from January 2021 to September 2025 for Bitcoin and 18 major altcoins, we apply a Long Short-Term Memory (LSTM) neural network to capture evolving return correlations. Our analysis reveals a pronounced post-ETF decline in correlations across both short-term (6-month) and long-term (12-month) rolling windows. We interpret this structural decoupling as the effect of ‘independent inflows’, whereby institutional capital enters Bitcoin without proportionate investment in altcoins. The findings suggest that Bitcoin is evolving into a distinct, standalone asset class with weaker integration in the broader cryptocurrency market. Policy and investment implications include reconsidering portfolio diversification strategies, reassessing systemic risk, and designing digital asset financial instruments to account for market segmentation and institutional flows
Modelling global trade with optimal transport
No full textGlobal trade is shaped by a complex mix of factors beyond supply and demand, including tangible variables like transport costs and tariffs, as well as less quantifiable influences such as political and economic relations. Traditionally, economists model trade using gravity models, which rely on explicit covariates that might struggle to capture these subtler drivers of trade. In this work, we employ optimal transport and a deep neural network to learn a time-dependent cost function from data, without imposing a specific functional form. This approach consistently outperforms traditional gravity models in accuracy and has similar performance to three-way gravity models, while providing natural uncertainty quantification. Applying our framework to global food and agricultural trade, we show that low income countries experienced disproportionately higher increases in trade costs due to the war in Ukraine’s impact on wheat markets. We also analyse the effects of free-trade agreements and trade disputes with China, as well as Brexit’s impact on British trade with Europe, uncovering hidden patterns that trade volumes alone cannot reveal
US leadership in trade statecraft: diminishing returns and a diminished role
Get PDFThis article puts current developments in US trade policy in an historical context. It identifies three broad types of trade statecraft the US has pursued since 1945: alliance politics, promoting an international trade order that serves the national interest and unilateralism. It argues that US trade statecraft has exhibited all three types. What has changed has been the relative importance of each in any given period. The article illustrates this in three broad time periods: 1945 to the early 1970s, when US trade statecraft pursued ‘alliance politics’; the early 1970s to the mid 2010s when US statecraft was shaped by two-level trade diplomacy aimed at promoting a trading order that served US interests; and finally, 2016 to the present day and the turn to unilateralism. Unilateralism, always latent in US decision making, has now come to the fore because key policy makers see diminishing political and commercial returns from promoting an international trade order. This trend is likely to result in a diminished US role in world trade
The decline of child stunting in 122 countries: a systematic review of child growth studies since the 19th century
Get PDFIntroduction Child stunting, a measure of malnutrition, is a major global health challenge affecting 148.1 million children in 2022. Global stunting rates have declined from 47.2% in 1985 to 22.3% in 2022; however, trends before the mid-1980s are unclear, including whether child stunting was previously prevalent in current high-income countries (HICs). We conducted a systematic review of child growth studies before 1990 to reconstruct historical rates of child stunting. Methods We included reports of mean height by age and sex for children up to age 10.99 years. We excluded studies that were not representative of the targeted population and data for children under age 2. Stunting rates were computed by converting the means and SDs of height to height-for-age Z-scores (HAZ) using the WHO standard/reference, combining the HAZ distributions for all ages and measuring the share of the combined distribution below the stunting threshold. Results We found 923 child growth studies at the community, regional and national level covering 122 countries from 1814 to 2016. We supplemented these historical studies with stunting estimates from the 1990s onward from the Joint Malnutrition Estimates database. Many current HICs had high levels of child stunting in the early 20th century, similar to low- and middle-income countries (LMICs) today. However, there was heterogeneity: stunting rates were low in Scandinavia, the European settler colonies and in the Caribbean, higher in Western Europe and exceptionally high in Japan and South Korea. Child stunting declined across the 20th century. Conclusion The global child stunting rate was substantially higher in the early 20th century than in 1985, and the reduction of child stunting was a central feature of the health transition. The high stunting rates and subsequent reduction of stunting in HICs suggest that current HICs provide lessons for eradicating child stunting and that all LMICs can eliminate stunting
Economic policy narratives: a taxonomy and application
No full textThis article discusses how economic policy narratives can be framed as part of the study of policy formation based on insights from an emerging literature. We offer a taxonomy that distinguishes between causal and moral policy narratives. We analyse how the political success of policy narratives depends on alignment with the interests of voters and is influenced by motivated reasoning. We then show how large language models can be used to study policy narratives through an application to narratives on the size of government voiced in the UK House of Commons over 1950–2023