Ferrante, Ferrante, Sebastian Graves and Matteo Iacoviello (2023). “The Inflationary Effects of Sectoral Reallocation,” International Finance Discussion Papers 1369. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/IFDP.2023.1369.
The COVID-19 pandemic has led to an unprecedented shift of consumption from services to goods. We study this demand reallocation in a multi-sector model featuring sticky prices, input-output linkages, and labor reallocation costs. Reallocation costs hamper the increase in the supply of goods, causing inflationary pressures. These pressures are amplified by the fact that goods prices are more flexible than services prices. We estimate the model allowing for demand reallocation, sectoral productivity, and aggregate labor supply shocks. The demand reallocation shock explains a large portion of the rise in U.S. inflation in the aftermath of the pandemic.
Author(s): Francesco Ferrante, Sebastian Graves and Matteo Iacoviello
Findings of the Association for Computational Linguistics: NAACL 2022, pages 2182 – 2194 July 10-15, 2022
Recent work has shown that deep learning models in NLP are highly sensitive to low-level correlations between simple features and specific output labels, leading to overfitting and lack of generalization. To mitigate this problem, a common practice is to balance datasets by adding new instances or by filtering out “easy” instances (Sakaguchi et al., 2020), culminating in a recent proposal to eliminate single-word correlations altogether (Gardner et al., 2021). In this opinion paper, we identify that despite these efforts, increasingly-powerful models keep exploiting ever-smaller spurious correlations, and as a result even balancing all single-word features is insufficient for mitigating all of these correlations. In parallel, a truly balanced dataset may be bound to “throw the baby out with the bathwater” and miss important signal encoding common sense and world knowledge. We highlight several alternatives to dataset balancing, focusing on enhancing datasets with richer contexts, allowing models to abstain and interact with users, and turning from large-scale fine-tuning to zero- or few-shot setups.
Understanding the mortality impact of COVID-19 requires not only counting the dead, but analyzing how premature the deaths are. We calculate years of life lost (YLL) across 81 countries due to COVID-19 attributable deaths, and also conduct an analysis based on estimated excess deaths. We find that over 20.5 million years of life have been lost to COVID-19 globally. As of January 6, 2021, YLL in heavily affected countries are 2–9 times the average seasonal influenza; three quarters of the YLL result from deaths in ages below 75 and almost a third from deaths below 55; and men have lost 45% more life years than women. The results confirm the large mortality impact of COVID-19 among the elderly. They also call for heightened awareness in devising policies that protect vulnerable demographics losing the largest number of life-years.
Author(s): Héctor Pifarré i Arolas, Enrique Acosta, Guillem López-Casasnovas, Adeline Lo, Catia Nicodemo, Tim Riffe & Mikko Myrskylä
And yet, nothing in the series leads the viewer to the conclusion that the SEC needed a bigger budget to catch Madoff. In fact, outsiders were sounding the alarm without access to government funding or regulatory muscle. In 2001, Barron’s journalist Erin Arvedlund reported that many Wall Street investors were suspicious that Madoff was engaged in foul play.
And the SEC received its first complaint that Madoff was running “an unregistered investment company” “offering ‘100%’ safe investments” in 1992. In 1999, a derivatives expert named Harry Markopolos, who worked at a competing firm, started to alert the SEC that Madoff’s investment returns were virtually impossible. In 2005, Markopolos sent the agency an infamous 25-page memo explaining why “The World’s Largest Hedge Fund is a Fraud.” The SEC opened an investigation in 2006, and then closed it the following year because the “uncovered violations” were “remedied” and “those violations were not so serious as to warrant an enforcement action.”
So how is this tale of epic failure on the part of a government agency the fault of deregulation?
Instead of making lazy allusions to the evils of free market capitalism, to better understand the lessons of the Madoff saga, director Joe Berlinger should have consulted the work of the free market economist George Stigler, who won the Nobel Prize in part for his work on “regulatory capture.”
Author(s): ZACH WEISSMUELLER AND DANIELLE THOMPSON
The CalPERS long-term care fiasco continues, with the board and staff taking a course of action that increases harm to policyholders by continuing to bleed them rather than put the program in bankruptcy.
For those new to this train wreck, the public comment at the February 14 CalPERS board meeting by policy-holder and certified financial planner Lawrence Grossman provides an introduction. A key bit of background is that state legislation allowed CalPERS to jump on the long-term care insurance bandwagon in the 1990s. Most of these insurance plans have gotten into a world of hurt by underestimating the degree to which proper elder care would extend lifepsans of policy-holders and overestimating the lapse rate (lapsed policies mean the premiums paid by dropouts benefit the remaining policyholders). But CalPERS’ recklessness and incompetence were in a league of its own.
CalPERS not only considerably underpriced its policies compared to commercial competitors, but it made matters worse via giving CalPERS policyholders the options of lifetime benefits (as opposed to fixed dollar benefits) and inflation protection. Inflation protection would seem like an incredible promise for any long-term insurance scheme. Yet the policies were advertised as CalPERS policies, not those of a free-standing “CalPERS Long-Term Care Fund,” as in not backed by CalPERS or the state of California.
Four years later and things are going according to CalPERS’ abusive plan. Even though Judge Highberger clearly rejected CalPERS’ position that it can violate policy terms and raise premiums, CalPERS has continued to increase premiums because the court so far has issued only preliminary decisions. Note these increases are vastly in excess of those implemented by commercial carriers.
By way of a few paragraphs inserted into the recently enacted 4,000-page 2023 National Defense Authorization Act, Congress mandated that state and local governments prepare their annual financial statements in a standardized format that is electronically searchable. The provision effectively drags state and local governments kicking and screaming into the 20th century, if not the 21st.
As worthy an accomplishment as this appears to be, it was resisted mightily by the state and local government financial community. Most prominently, they argue, the measure can potentially result in a major transfer of accounting and reporting regulatory authority from states to the federal government, thereby undercutting what many consider a fundamental principle of federalism. Moreover, state and local officials see it as one more costly unfunded mandate imposed upon their governments.
The opposition by state and local governments is understandable. But they have no one to blame but themselves. To this day they are wedded to a technological past. In a perverted way, they may be getting their just desserts. The act requires them to do little more than what they should have done years ago on their own for the benefit of their investors and other stakeholders.
Implicit in the act is that governments will have to prepare their financial statements using XBRL (eXtensible Business Reporting Language) or some comparable reporting framework. This is the format that the Securities and Exchange Commission, which would be charged with implementing the new provision, currently demands corporations use in their financial filings. XBRL requires all entities to classify each of the elements of their financial statements (e.g., assets, liability, revenues and expenses) by identical rules and in machine readable form.