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Lessons Learned: Grahame Johnson
No full textGrahame Johnson was named managing director of financial stability at the Bank of Canada in November 2019, just months before the outbreak of the COVID-19 pandemic, and became adviser to the governor in 2021. The Bank of Canada responded to the economic effect of the pandemic by launching 10 liquidity facilities—nine of them never attempted before—to repurchase securities such as government and corporate bonds, bank acceptances, and commercial paper. The measures were largely successful, and nine of the 10 facilities were unwound by 2022. The tenth, the Government of Canada Bond Purchase Program (GBPP), transitioned into a quantitative easing program and remains in operation as a monetary policy tool. This Lesson Learned is based on an interview held in April 2023
Lessons Learned: Katalin Mérő
No full textBetween 1990 and 2010, Katalin Mérő served in various roles for Hungary’s central bank, Magyar Nemzeti Bank, and the Hungarian Financial Supervisory Authority. She led the Economics, Risk Assessment, and Regulatory Directorate between 2005 and 2019 and was the deputy head of the Financial Stability Department. She was also a member of the Joint Board of Appeal of European Supervisory Authority from 2011 to 2021. Mérő graduated from the Corvinus University of Budapest and received a PhD in 2005 from the Budapest University of Technology and Economics. Since 2016, she has served as an associate professor at Budapest Business School, University of Applied Sciences in Budapest. This Lessons Learned summary is based on an interview held in October 2022
Performing Manly Worship Music: Gender and Timbre at Mars Hill Church
No full textTen years ago, Mars Hill boasted fifteen campuses and 15,000 members. Amid mounting allegations of abuse and mismanagement leveled against founding pastor Mark Driscoll, the church famously collapsed, closing its doors at the end of 2014. Driscoll had built Mars Hill’s core brand around his opposition to the perceived feminization of the Christian mainstream, and appointing himself the “leader of a heterosexual male backlash” in the church and broader culture. While Driscoll’s muscular Christianity stands in a wide historical stream, Mars Hill long led the charge in establishing a youth-oriented, hipster brand of hypermasculine Calvinism. And while rhetoric and theology played a central role, the church’s punk, grunge, and indie rock-derived music ministry also contributed significantly to the establishment of an identifiably “masculine” Christian movement.This paper explores how Mars Hill’s worship leaders intentionally manipulated timbre to reflect and perform prevailing masculine ideals. Theoretically, I draw from Nina Eidsheim\u27s theorization of timbre at intersection with gender studies and performance theory, analyzing instrumentation, effects, vocal technique, and range. Further, this study demonstrates how idealized masculinities morph across time, dependent upon their broader social context. Even though Mars Hill no longer exists, this paper offers perspectives for considering how music performance participates in the production and maintenance of power hierarchies, an important lens for music leaders across Christian contexts
Singing a Wide Heart: Timbre and Sung Prayer in Senegal’s Layène Community
No full textLayène sung prayer (called sikkar) is embedded within the religious practices of the Layène community, a minority Sufi order in Dakar, Senegal. This article explores the functions of Layène sung prayer, focusing especially on its loud, vibrational timbre. Both men and women vocalists in call-and-response sing loudly, using wide vibrato and electronic amplification to ensure that the sikkar reaches the inhabitants—both human and non-human—in the surrounding community. This article demonstrates that the distinctive timbre of Layène sung prayer serves the spiritual functions of binding the community together in spiritual and social cohesion, increasing individual and collective generosity, and demarcating Layène territory in the context of rapid urban growth. Through contextualizing ethnographic research, interviews, and analysis of audio recordings within the community’s hagiography, this article demonstrates threads of connection between community ethics of generosity and hospitality and the timbre of sikkar
Disclosure and the Pace of Drug Development
No full textPolicies that mandate disclosure of innovative project outcomes aim to increase innovation by limiting wasteful duplicative innovation. Yet, such policies change not only the ex-post information environment but also firms\u27 ex-ante innovation incentives. Firms may slow down their own innovation efforts in anticipation of increased disclosure by others. We examine the innovation-related impacts of the 2017 FDA Final Rule amendment, which mandates disclosure of clinical trial results for pharmaceutical firms. We show that the policy hastened and increased disclosure of results for clinical trials post-completion, but also increased the time to completion of clinical trials, the time between early phases of clinical trials, and delays in development-related investments. We provide evidence consistent with mandated disclosure leading firms to wait to learn from their competitors. Our results suggest that mandating disclosure may slow innovation when there is value to waiting
SLIM: Stochastic Learning and Inference in Overidentified Models
No full textWe propose SLIM (Stochastic Learning and Inference in overidentified Models), a scalable stochastic approximation framework for nonlinear GMM. SLIM forms iterative updates from independent mini-batches of moments and their derivatives, producing unbiased directions that ensure almost-sure convergence. It requires neither a consistent initial estimator nor global convexity and accommodates both fixed-sample and random-sampling asymptotics. We further develop an optional second-order refinement and inference procedures based on random scaling and plug-in methods, including plug-in, debiased plug-in, and online versions of the Sargan–Hansen J-test tailored to stochastic learning. In Monte Carlo experiments based on a nonlinear EASI demand system with 576 moment conditions, 380 parameters, and n = 105 , SLIM solves the model in under 1.4 hours, whereas full-sample GMM in Stata on a powerful laptop converges only after 18 hours. The debiased plug-in J-test delivers satisfactory finite-sample inference, and SLIM scales smoothly to n = 106
Lessons Learned: Jesper Berg
Full text linkThe career of Jesper Berg, a Danish economist, has spanned multiple financial crises. He held positions with the Danish central bank, Danmarks Nationalbank, from 2004 to 2010, first as head of market operations and later as head of financial stability. He had served as head of the capital markets and financial structure division at the European Central Bank from 2000 to 2004, and earlier he was an economist at the International Monetary Fund’s Exchange and Trade Relations Department. This Lessons Learned summary is based on an interview with Berg in December 2022, when he was director general of the Danish Financial Supervisory Authorit
Bidding with Budgets: Algorithmic and Data-Driven Bids in Digital Advertising
Full text linkIn digital advertising, the allocation of sponsored search, sponsored product, or display advertisements is mediated by auctions. The generation of bids in these auctions for attention is increasingly supported by auto-bidding algorithms and platform-provided data. We analyze the equilibrium properties of a sequence of increasingly sophisticated auto-bidding algorithms. First, we consider the equilibrium bidding behavior of an individual advertiser who controls the auto-bidding algorithm through the choice of their budget. Second, we examine the interaction when all bidders use budget-controlled bidding algorithms. Finally, we derive the bidding algorithm that maximizes the platform’s revenue while ensuring all advertisers continue to participate
Semiparametric Cointegrating Rank Selection for Curved Cross Section Time Series
Full text linkCointegrating rank selection is studied in a function space reduced rank regression where the data are time series of cross section curves. A semiparametric approach to rank selection is employed using information criteria suitably modified to take account of the function space context, extending the linear cointegrating model to accommodate cross section data under general forms of dependence. A parametric formulation is employed analogous to recent work on cross section curve autoregression and cointegrating regression. Consistent cointegrating rank estimation is developed by the use of information criteria methods that are extended to the curve time series environment. The asymptotic theory involves two parameter Gaussian processes that generalize the standard limit processes involved in cointegrating regressions with conventional multiple time series. Simulations provide evidence of the effectiveness of consistent rank selection by the BIC criterion and the tendency of AIC to overestimate order as it does in standard lag order selection in autoregression as well as in reduced rank regression with multiple time series