Purpose - The purpose of this paper is to contribute to the existing disclosure literature by examining the determinants of narrative risk information in the interim reports for a sample of UK non-financial companies.Design methodology approach - This study uses the manual content analysis to measure the level of risk information in interim report narrative sections prepared by 72 UK companies. It also uses the ordinary least squares regression analysis to examine the impact of firm-specific characteristics and corporate governance mechanisms on narrative risk disclosures.Findings - The empirical analysis shows that large firms are more likely to disclose more risk information in the narrative sections of interim reports. In addition, the analysis shows that industry activity type is positively associated with levels of narrative risk disclosure in interim reports. Finally, the analysis shows statistically insignificant impact of other firm-specific characteristics (liquidity, gearing, profitability, and cross-listing) and corporate governance mechanisms on narrative risk disclosure.Practical implications - The study's findings have practical implications. It informs investors about the characteristics of UK companies that disclose risk information in their interim reports. For example, the findings show that narrative risk disclosures are affected by firm size and industry type rather than firms' risk levels (e.g. financing risk measured by the gearing ratio or liquidity risk measured by lower liquidity ratios). Practical implications for managers from these findings are that, in order to keep investors satisfied, companies with high levels of financing and liquidity risks should look at investors' demands for risk disclosure. This will help investors when making their investment decisions.Originality value - The determinants of narrative risk disclosure in interim reports have not been explored so clearly in prior research and, therefore, this paper is the first of its kind to examine this research issue for a sample of UK companies.
Very high energy γ-rays probe the long-standing mystery of the origin of cosmic rays. Produced in the interactions of accelerated particles in astrophysical objects, they can be used to image cosmic particle accelerators. A first sensitive survey of the inner part of the Milky Way with the High Energy Stereoscopic System (HESS) reveals a population of eight previously unknown firmly detected sources of very high energy γ-rays. At least two have no known radio or x-ray counterpart and may be representative of a new class of "dark" nucleonic cosmic ray sources.
RapidIO (http://rapidio.org/) technology is a packet-switched high-performance fabric, which has been under active development since 1997. The technology is used in all 4G/LTE base stations worldwide. RapidIO is also used in embedded systems that require high reliability, low latency, and deterministic operations in a heterogeneous environment. RapidIO has several offloading features in hardware, therefore relieving the CPUs from time- and power-consuming work. Most importantly, it allows for remote direct memory access and thus zero-copy data transfer. In addition, it lends itself readily to integration with field-programmable gate arrays. In this paper, we investigate RapidIO as a technology for high-speed data acquisition (DAQ) networks, in particular the DAQ system of an LHC experiment. We present measurements using a generic multiprotocol event-building emulation tool that was developed for the LHCb experiment. Event building using a local area network, such as the one foreseen for the future LHCb DAQ, puts heavy requirements on the underlying network as all data sources from the collider will want to send to the same destinations at the same time. This may lead to an instantaneous over commitment of the output buffers of the switches. We will present results from implementing an event building cluster based on RapidIO interconnect, focusing on the bandwidth capabilities of the technology as well as its scalability.
The U.S. Bureau of Labor Statistics (BLS) Job Openings and Labor Turnover Survey (JOLTS) collects data for job openings, hires, and separations from sampled establishments. These data are published by industry each month. In September 2018, these data were published in a new format: size of firm. This article first provides background information needed for understanding what firm-based data mean and then explores the new data series. Next, this article compares the new firm size data with the previously published establishment-based data. Last, the JOLTS firm size data are compared with the firm size data produced by the Business Employment Dynamics program, also at BLS.
The Agricultural Act of 2014 shifted farm support payments from mostly fixed amounts to the Agriculture Risk Coverage and Price Loss Coverage programs, which provide income support conditional on market outcomes. The 2018 Farm Bill continued these programs. Since these programs are tied to market outcomes, future payments are uncertain. Using data on county-level yields and macroeconomic variables spanning 1990 through 2017, the approach projects and simulates county-level crop yields across 1,000 draws to estimate the variation in markets and program payments over 10 years, beginning with the 2019/20 crop year.
An analysis of 2015 data comparing average length of stay (ALOS) among states within various U.S. regions finds notable variances associated with this measure.3 In the Far West, the data indicate that ALOS is 18 percent higher in Nevada than the next highest geographic peer.b This high ALOS is associated with average charge per inpatient admission of $66,778, which is 21 percent higher than that of the next highest peer state in the Far West, and 51 percent higher than the national average of $44,072. Rather, hospitals should focus on their own geographic data and circumstances rather than adopting solutions or approaches based on higher-level analyses and results that do not account for regional differences. * Many states collect inpatient admission data from hospitals via organizations such as state agencies, hospital trade associations, and firms that specialize in data collection, and using these data, the states create and make available nonidentifiable data sets for commercial and academic use. Authorization to release this information does not imply endorsement of this study or its findings by either DHCFP or CHIA. c.New York Inpatient Discharge Data source data were provided by the New York State Department of Health, Bureau of Biometrics and Health Statistics, and the Statewide Planning and Research Cooperative System.