Survival advantage of worker buyouts over newly created worker-owned firms

PurposeVarious theories predict that firm buyouts survive longer than newly created firms. The study aims to know whether it is the case for worker-owned firms (WOFs), i.e. firms owned and controlled mostly by their workers.Design/methodology/approachThe author conducted a comparative survival analysis of French WOFs distinguished by their entry mode (i.e. newly created, worker buyouts (WBOs) of sound conventional firms, WBOs of conventional firms in difficulty or WBOs of non-profit organizations).FindingsThe hazard of exit is 32% lower for WBOs of sound conventional firms than newly created WOFs, 18% for WBOs of conventional firms in difficulty and 64% for WBOs of non-profit organizations. The current study confirms that WBOs, even of conventional firms in difficulty, have on average a survival advantage over newly created WOFs. Surprisingly, the author also shows that this survival advantage is similar across sectors with different knowledge intensity but is lower in high capital-intensive sectors than in low capital-intensive ones.Research limitations/implicationsEndogeneity issues limit the scope of the results and should be tackled in future research. Overall, these findings show that WOFs are composed of groups with different survival likelihoods that are obscured if one only looks at the aggregate population.Practical implicationsWith caution, support agencies could foster WBOs of firms in difficulty and of non-profit organizations as viable forms of entrepreneurship.Originality/valueThe current study offers the first survival analysis distinguishing four modes of entry among WOFs.

Benchmarking gambling screens to health-state utility: The PGSI and the SGHS estimate similar levels of population gambling-harm

Background and aims: Both the Problem Gambling Severity Index (PGSI) and the Short Gambling Harms Screen (SGHS) purport to identify individuals harmed by gambling. However, there is dispute as to how much individuals are harmed, conditional on their scores from these instruments. We used an experienced utility framework to estimate the magnitude of implied impacts on health and wellbeing.Methods: We measured health utility using the Short Form Six-Dimension (SF-6D), and used this as a benchmark. All 2603 cases were propensity score weighted, to balance the affected group (i.e., SGHS 1+ or PGSI 1+ vs 0) with a reference group of gamblers with respect to risk factors for gambling harm. Weighted regression models estimated decrements to health utility scores attributable to gambling, whilst controlling for key comorbidities.Results: We found significant attributable decrements to health utility for all non-zero SGHS scores, as well as moderate-risk and problem gamblers, but not for PGSI low-risk gamblers. Applying these coefficients to population data, we find a similar total burden for both instruments, although the SGHS more specifically identified the subpopulation of harmed individuals. For both screens, outcomes on the SF-6D implies that about two-thirds of the ‘burden of harm’ is attributable to gamblers outside of the most severe categories.Discussion and Conclusions: Gambling screens have hitherto provided nominal category membership, and population surveys typically focus on problem gambling prevalence. These results quantify the health utility decrement for each category, allowing for tracking of the aggregate population impact based on all affected gamblers.

Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020

Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.

Proposed U.S. Census Bureau Differential Privacy Method is Biased Against Rural and Non-white Populations

The proposed changes to disclosure avoidance policies of the U.S. Census Bureau, grounded in differential privacy, have faced increasing criticism from demographers and other social scientists. Scholars have found that counts generated via Census-released test data are accurate for aggregate population statistics but introduce considerable error for tabulations of sub-groups. At present, the ramifications of this new approach, and the error it may introduce, remain unclear for rural populations. In this brief, we focus on rural populations and evaluate the ability of the proposed differential privacy data to estimate growth rates from 2000 to 2010 across the rural-urban continuum for the total, non-Hispanic white, non-Hispanic Black, Hispanic or Latino/a, and non-Hispanic American Indian population. We find the method introduces significant error into growth rates at the county level for all groups except the total and non-Hispanic white population. Further, errors increase dramatically as we move from urban to rural. Thus, unless corrected the proposed differential privacy method will introduce significant rural and non-white bias into census tabulations.

What can lifespan variation reveal that life expectancy hides? Comparison of five high-income countries

Objectives In most countries, life expectancy at birth (e0) has improved for many decades. Recently, however, progress has stalled in the UK and Canada, and reversed in the USA. Lifespan variation, a complementary measure of mortality, increased a few years before the reversal in the USA. To assess whether this measure offers additional meaningful insights, we examine what happened in four other high-income countries with differing life expectancy trends. Design We calculated life disparity (a specific measure of lifespan variation) in five countries -- USA, UK, France, Japan and Canada -- using sex- and age specific mortality rates from the Human Mortality Database from 1975 to 2017 for ages 0--100 years. We then examined trends in age-specific mortality to identify the age groups contributing to these changes. Setting USA, UK, France, Japan and Canada Participants aggregate population data of the above nations. Main Outcome Measures Life expectancy at birth, life disparity and age-specific mortality. Results The stalls and falls in life expectancy, for both males and females, seen in the UK, USA and Canada coincided with rising life disparity. These changes may be driven by worsening mortality in middle-age (such as at age 40). France and Japan, in contrast, continue on previous trajectories. Conclusions Life disparity is an additional summary measure of population health providing information beyond that signalled by life expectancy at birth alone.

Recent advances in understanding RNA polymerase II structure and function

More than 50 years after the identification of RNA polymerase II, the enzyme responsible for the transcription of most eukaryotic genes, studies have continued to reveal fresh aspects of its structure and regulation. New technologies, coupled with years of development of a vast catalog of RNA polymerase II accessory proteins and activities, have led to new revelations about the transcription process. The maturation of cryo-electron microscopy as a tool for unraveling the detailed structure of large molecular machines has provided numerous structures of the enzyme and its accessory factors. Advances in biophysical methods have enabled the observation of a single polymerase’s behavior, distinct from work on aggregate population averages. Other recent work has revealed new properties and activities of the general initiation factors that RNA polymerase II employs to accurately initiate transcription, as well as chromatin proteins that control RNA polymerase II’s firing frequency, and elongation factors that facilitate the enzyme’s departure from the promoter and which control sequential steps and obstacles that must be navigated by elongating RNA polymerase II. There has also been a growing appreciation of the physical properties conferred upon many of these proteins by regions of each polypeptide that are of low primary sequence complexity and that are often intrinsically disordered. This peculiar feature of a surprisingly large number of proteins enables a disordered region of the protein to morph into a stable structure and creates an opportunity for pathway participants to dynamically partition into subcompartments of the nucleus. These subcompartments host designated portions of the chemical reactions that lead to mRNA synthesis. This article highlights a selection of recent findings that reveal some of the resolved workings of RNA polymerase II and its ensemble of supporting factors.

Inference of a Mesoscopic Population Model from Population Spike Trains

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

Population health, economics and ethics in the age of COVID-19

Are the steps that have been taken to arrest the spread of COVID-19 justifiable? Specifically, are they likely to have improved public health understood according to widely used aggregate population health measures, such as Quality Adjusted Life Years (QALYs) and Disability Adjusted Life Years (DALYs) as much or more than alternatives? This is a reasonable question, since such measures have been promoted extensively in global and national health policy by influential actors, and they have become almost synonymous with quantification of public health. If the steps taken against COVID-19 did not meet this test, then either the measures or the policies must be re-evaluated. There are indications that policies against COVID-19 may have been unbalanced and therefore not optimal. A balanced approach to protecting population health should be proportionate in its effects across distinct health concerns at a moment, across populations over time and across populations over space. These criteria provide a guide to designing and implementing policies that diminish harm from COVID-19 while also providing due attention to other threats to aggregate population health. They should shape future policies in response to this pandemic and others.

A simulation-based procedure to estimate base rates from Covid-19 antibody test results I: Deterministic test reliabilities

We design a procedure (the complete Python code may be obtained athttps://github.com/abhishta91/antibody_montecarlo) using Monte Carlo (MC) simulation to establish the point estimators described below and confidence intervals for the base rate of occurence of an attribute (e.g., antibodies against Covid-19) in an aggregate population (e.g., medical care workers) based on a test. The requirements for the procedure are the test’s sample size (N) and total number of positives (X), and the data on test’s reliability.The modus is the prior which generates the largest frequency of observations in the MC simulation with precisely the number of test positives (maximum-likelihood estimator). The median is the upper bound of the set of priors accounting for half of the total relevant observations in the MC simulation with numbers of positives identical to the test’s number of positives.Our rather preliminary findings areThe median and the confidence intervals suffice universally.The estimator may be outside of the two-sided 95% confidence interval.Conditions such that the modus, the median and another promising estimator which takes the reliability of the test into account, are quite close.Conditions such that the modus and the latter estimator must be regarded as logically inconsistent.Conditions inducing rankings among various estimators relevant for issues concerning over-or underestimation.JEL-codes: C11, C13, C63

U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways

Spatial population distribution is an important determinant of both drivers of regional environmental change and exposure and vulnerability to it. Spatial projections of population must account for changes in aggregate population, urbanization, and spatial patterns of development, while accounting for uncertainty in each. While an increasing number of projections exist, those carried out at relatively high resolution that account for subnational heterogeneity and can be tailored to represent alternative scenarios of future development are rare. We draw on state-level population projections for the US and a gravity-style spatial downscaling model to design and produce new spatial projections for the U.S. at 1 km resolution consistent with a subset of the Shared Socioeconomic Pathways (SSPs), scenarios of societal change widely used in integrated analyses of global and regional change. We find that the projections successfully capture intended alternative development patterns described in the SSPs, from sprawl to concentrated development and mixed outcomes. Our projected spatial patterns differ more strongly across scenarios than in existing projections, capturing a wider range of the relevant uncertainty introduced by the distinct scenarios. These projections provide an improved basis for integrated environmental analysis that considers uncertainty in demographic outcomes.