Financialization and the nonfinancial corporation: an investigation of firm-level investment behavior in the U.S., 1971-2011

Changes in the portfolio and financing behavior of nonfinancial corporations (NFCs) over the post-1970 period point to the financialization of the nonfinancial corporation and raise the question of accompanying changes in fixed investment behavior. Using a firm-level panel, this paper econometrically investigates the relationship between financialization and investment, exploring the implications of changes in financing behavior, increasingly entrenched shareholder value norms, and rising firm-level demand volatility for investment by NFCs in the U.S. between 1971 and 2011. Shareholder value norms and firm-level volatility are, in particular, identified as characteristics of the post-1970 U.S. economy that are associated with a significant decline in NFC investment rates. The analysis also highlights key differences by firm size. In particular, shareholder value norms are found to primarily influence the investment behavior of large NFCs, while rising volatility most substantially impacts small firms

An empirical analysis of Minsky regimes in the US economy

In this paper we analyze Minskian dynamics in the US economy via an empirical application of Minsky’s financing regime classifications to a panel of nonfinancial corporations. First, we map Minsky’s definitions of hedge, speculative and Ponzi finance onto firm-level data to describe the evolution of Minskian regimes. We highlight striking growth in the share of Ponzi firms in the post-1970 US, concentrated among small corporations. This secular growth in the incidence of Ponzi firms is consistent with the possibility of a long wave of increasingly fragile finance in the US economy. Second, we explore the possibility of short-run Minskian dynamics at a business-cycle frequency. Using linear probability models relating firms’ probability of being Ponzi to the aggregate output gap, which captures short-term macroeconomic fluctuations exogenous to individual firms, we find that aggregate downturns are correlated with an al- most zero increased probability that firms are Ponzi. This result is corroborated by quantile regressions using a continuous measure of financial fragility, the interest coverage ratio, which identify almost zero effects of short-term fluctuations on financial fragility across the interest coverage distribution. Together, these results speak to an important question in the theoretical literature on financial fragility regarding the duration of Minskian cycles, and lend support, in particular, to the contention that Minskian dynamics may take the form of long waves, but do not operate at business cycle frequencies

The Empirical Analysis of Minsky Regimes in the U.S. Economy

In this paper we analyze Minskian dynamics in the US economy via an empirical application of Minsky’s financing regime classifications to a panel of nonfinancial corporations. First, we map Minsky’s definitions of hedge, speculative and Ponzi finance onto firm-level data to describe the evolution of Minskian regimes. We highlight striking growth in the share of Ponzi firms in the post-1970 US, concentrated among small corporations. This secular growth in the incidence of Ponzi firms is consistent with the possibility of a long wave of increasingly fragile finance in the US economy. Second, we explore the possibility of short-run Minskian dynamics at a business-cycle frequency. Using linear probability models relating firms’ probability of being Ponzi to the aggregate output gap, which captures short-term macroeconomic fluctuations exogenous to individual firms, we find that aggregate downturns are correlated with an almost zero increased probability that firms are Ponzi. This result is corroborated by quantile regressions using a continuous measure of financial fragility, the interest coverage ratio, which identify almost zero effects of short-term fluctuations on financial fragility across the interest coverage distribution. Together, these results speak to an important question in the theoretical literature on financial fragility regarding the duration of Minskian cycles, and lend support, in particular, to the contention that Minskian dynamics may take the form of long waves, but do not operate at business cycle frequencies

Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Davis, G. E., Baumgartner, M. F., Corkeron, P. J., Bell, J., Berchok, C., Bonnell, J. M., Thornton, J. B., Brault, S., Buchanan, G. A., Cholewiak, D. M., Clark, C. W., Delarue, J., Hatch, L. T., Klinck, H., Kraus, S. D., Martin, B., Mellinger, D. K., Moors-Murphy, H., Nieukirk, S., Nowacek, D. P., Parks, S. E., Parry, D., Pegg, N., Read, A. J., Rice, A. N., Risch, D., Scott, A., Soldevilla, M. S., Stafford, K. M., Stanistreet, J. E., Summers, E., Todd, S., & Van Parijs, S. M. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Global Change Biology, (2020): 1-30, doi:10.1111/gcb.15191.Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate‐driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata ) and North Atlantic right whales (NARW; Eubalaena glacialis ). This study assesses the acoustic presence of humpback (Megaptera novaeangliae ), sei (B. borealis ), fin (B. physalus ), and blue whales (B. musculus ) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottom‐mounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004–2010 and 2011–2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid‐Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution.We thank Chris Pelkie, David Wiley, Michael Thompson, Chris Tessaglia‐Hymes, Eric Matzen, Chris Tremblay, Lance Garrison, Anurag Kumar, John Hildebrand, Lynne Hodge, Russell Charif, Kathleen Dudzinski, and Ann Warde for help with project planning, field work support, and data management. For all the support and advice, thanks to the NEFSC Protected Species Branch, especially the passive acoustics group, Josh Hatch, and Leah Crowe. We thank the field and crew teams on all the ships that helped in the numerous deployments and recoveries. This research was funded and supported by many organizations, specified by projects as follows: data recordings from region 1 were provided by K. Stafford (funding: National Science Foundation #NSF‐ARC 0532611). Region 2 data: D. K. Mellinger and S. Nieukirk, National Oceanic and Atmospheric Administration (NOAA) PMEL contribution #5055 (funding: NOAA and the Office of Naval Research #N00014–03–1–0099, NOAA #NA06OAR4600100, US Navy #N00244‐08‐1‐0029, N00244‐09‐1‐0079, and N00244‐10‐1‐0047). Region 3A data: D. Risch (funding: NOAA and Navy N45 programs). Region 3 data: H. Moors‐Murphy and Fisheries and Oceans Canada (2005–2014 data), and the Whitehead Lab of Dalhousie University (eastern Scotian Shelf data; logistical support by A. Cogswell, J. Bartholette, A. Hartling, and vessel CCGS Hudson crew). Emerald Basin and Roseway Basin Guardbuoy data, deployment, and funding: Akoostix Inc. Region 3 Emerald Bank and Roseway Basin 2004 data: D. K. Mellinger and S. Nieukirk, NOAA PMEL contribution #5055 (funding: NOAA). Region 4 data: S. Parks (funding: NOAA and Cornell University) and E. Summers, S. Todd, J. Bort Thornton, A. N. Rice, and C. W. Clark (funding: Maine Department of Marine Resources, NOAA #NA09NMF4520418, and #NA10NMF4520291). Region 5 data: S. M. Van Parijs, D. Cholewiak, L. Hatch, C. W. Clark, D. Risch, and D. Wiley (funding: National Oceanic Partnership Program (NOPP), NOAA, and Navy N45). Region 6 data: S. M. Van Parijs and D. Cholewiak (funding: Navy N45 and Bureau of Ocean and Energy Management (BOEM) Atlantic Marine Assessment Program for Protected Species [AMAPPS] program). Region 7 data: A. N. Rice, H. Klinck, A. Warde, B. Martin, J. Delarue, and S. Kraus (funding: New York State Department of Environmental Conservation, Massachusetts Clean Energy Center, and BOEM). Region 8 data: G. Buchanan, and K. Dudzinski (funding: New Jersey Department of Environmental Protection and the New Jersey Clean Energy Fund) and A. N. Rice, C. W. Clark, and H. Klinck (funding: Center for Conservation Bioacoustics at Cornell University and BOEM). Region 9 data: J. E. Stanistreet, J. Bell, D. P. Nowacek, A. J. Read, and S. M. Van Parijs (funding: NOAA and US Fleet Forces Command). Region 10 data: L. Garrison, M. Soldevilla, C. W. Clark, R. A. Chariff, A. N. Rice, H. Klinck, J. Bell, D. P. Nowacek, A. J. Read, J. Hildebrand, A. Kumar, L. Hodge, and J. E. Stanistreet (funding: US Fleet Forces Command, BOEM, NOAA, and NOPP). Region 11 data: C. Berchok as part of a collaborative project led by the Fundacion Dominicana de Estudios Marinos, Inc. (Dr. Idelisa Bonnelly de Calventi; funding: The Nature Conservancy [Elianny Dominguez]) and D. Risch (funding: World Wildlife Fund, NOAA, and Dutch Ministry of Economic Affairs)

Rare germline copy number variants (CNVs) and breast cancer risk.

Funder: CIHRGermline copy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. Gene burden tests detected the strongest association for deletions in BRCA1 (P = 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P = 0.0008), ATM (P = 0.002) and BRCA2 (P = 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. To the best of our knowledge, this is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance