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Capitalizing on a crisis: a computational analysis of all five million British firms during the Covid-19 pandemic.
The Covid-19 pandemic brought unprecedented changes to business ownership in the UK which affects a generation of entrepreneurs and their employees. Nonetheless, the impact remains poorly understood. This is because research on capital accumulation has typically lacked high-quality, individualized, population-level data. We overcome these barriers to examine who benefits from economic crises through a computationally orientated lens of firm creation. Leveraging a comprehensive cache of administrative data on every UK firm and all nine million people running them, combined with probabilistic algorithms, we conduct individual-level analyzis to understand who became Covid entrepreneurs. Using these techniques, we explore characteristics of entrepreneurs-such as age, gender, region, business experience, and industry-which potentially predict Covid entrepreneurship. By employing an automated time series model selection procedure to generate counterfactuals, we show that Covid entrepreneurs were typically aged 35-49 (40.4%), men (73.1%), and had previously held roles in existing firms (59.4%). For most industries, growth was disproportionately concentrated around London. It was therefore existing corporate elites who were most able to capitalize on the Covid crisis and not, as some hypothesized, young entrepreneurs who were setting up their first businesses. In this respect, the pandemic will likely impact future wealth inequalities. Our work offers methodological guidance for future policymakers during economic crises and highlights the long-term consequences for capital and wealth inequality.
The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items.
The deployment of statistical models-such as those used in item response theory-necessitates the use of indices that are informative about the degree to which a given model is appropriate for a specific data context. We introduce the InterModel Vigorish (IMV) as an index that can be used to quantify accuracy for models of dichotomous item responses based on the improvement across two sets of predictions (i.e., predictions from two item response models or predictions from a single such model relative to prediction based on the mean). This index has a range of desirable features: It can be used for the comparison of non-nested models and its values are highly portable and generalizable. We use this fact to compare predictive performance across a variety of simulated data contexts and also demonstrate qualitative differences in behavior between the IMV and other common indices (e.g., the AIC and RMSEA). We also illustrate the utility of the IMV in empirical applications with data from 89 dichotomous item response datasets. These empirical applications help illustrate how the IMV can be used in practice and substantiate our claims regarding various aspects of model performance. These findings indicate that the IMV may be a useful indicator in psychometrics, especially as it allows for easy comparison of predictions across a variety of contexts.
The Role of the Third Sector in Public Health Service Provision: Evidence from 25,338 heterogeneous procurement datasets
We examine the role of non-profits within publicly funded healthcare which runs parallel to private provision in a 'two-tier' system through a unique Big Data pipeline. Scraping tens of thousands of heterogeneous transaction datasets across a commissioning hierarchy, the processed dataset contains over £445Bn worth of transactions across 1.9m+ rows of clean data, spanning 2012-2020. Information includes but is not limited to date of procurement transaction, supplier name, and transaction value. We utilise this dataset to test a range of hypotheses related to the introduction of the Health and Social Care Act of 2012. The proportion of contracts placed with non-profit organisations is relatively low, with limited evidence as to whether the Act increased the involvement of third sector organisations in line with NHS `marketisation'. We analyse the pattern of procurement by corporate category, field of service delivery (ICNPO and SIC codes), and ‘expense area’ to show the unique array of services which voluntary organisations supply. We also analyse the pattern of commissioning across entity class and size distributions of registered charities. We conclude with a consideration of high-value Community Interest Companies, and discuss potential further areas of research within a healthcare context which such government transaction data makes possible.
Publisher Correction: Offshoring emissions through used vehicle exports
Correction to: Nature Climate Changehttps://doi.org/10.1038/s41558-024-01943-1, published online 20 February 2024. In the version of the article initially published, the y-axis label of Fig. 1f, now reading “Age in years”, read “Fuel efficiency (miles per gallon)”. This has now been amended in the HTML and PDF versions of the article.
Offshoring emissions through used vehicle exports
Policies to reduce transport emissions often overlook the international flow of used vehicles. We quantify the rate at which used vehicles generated CO2 and pollution for all used vehicles exported from Great Britain—a globally leading used vehicle exporter—across 2005–2021. Destined for low–middle-income countries, exported vehicles fail roadworthiness standards and, even under extremely optimistic ‘functioning-as-new’ assumptions, generate at least 13–53% more emissions than scrapped or on-road vehicles.
The Rise of Machine Learning in the Academic Social Sciences
This short data visualisation and accompanying perspective explains recent trends and outlines three reasons to be even more optimistic about the future of Machine Learning in the academic Social Sciences.
Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries.
BackgroundVariations in the age patterns and magnitudes of excess deaths, as well as differences in population sizes and age structures, make cross-national comparisons of the cumulative mortality impacts of the COVID-19 pandemic challenging. Life expectancy is a widely used indicator that provides a clear and cross-nationally comparable picture of the population-level impacts of the pandemic on mortality.MethodsLife tables by sex were calculated for 29 countries, including most European countries, Chile and the USA, for 2015-2020. Life expectancy at birth and at age 60 years for 2020 were contextualized against recent trends between 2015 and 2019. Using decomposition techniques, we examined which specific age groups contributed to reductions in life expectancy in 2020 and to what extent reductions were attributable to official COVID-19 deaths.ResultsLife expectancy at birth declined from 2019 to 2020 in 27 out of 29 countries. Males in the USA and Lithuania experienced the largest losses in life expectancy at birth during 2020 (2.2 and 1.7 years, respectively), but reductions of more than an entire year were documented in 11 countries for males and 8 among females. Reductions were mostly attributable to increased mortality above age 60 years and to official COVID-19 deaths.ConclusionsThe COVID-19 pandemic triggered significant mortality increases in 2020 of a magnitude not witnessed since World War II in Western Europe or the breakup of the Soviet Union in Eastern Europe. Females from 15 countries and males from 10 ended up with lower life expectancy at birth in 2020 than in 2015.
Quantifying impacts of the COVID-19 pandemic through life expectancy losses: a population-level study of 29 countries
Variations in the age patterns and magnitudes of excess deaths, as well as differences in population sizes and age structures make cross-national comparisons of the cumulative mortality impacts of the COVID-19 pandemic challenging. Life expectancy is a widely-used indicator that provides a clear and cross-nationally comparable picture of the population-level impacts of the pandemic on mortality. Life tables by sex were calculated for 29 countries, including most European countries, Chile, and the USA for 2015-2020. Life expectancy at birth and at age 60 for 2020 were contextualised against recent trends between 2015-19. Using decomposition techniques, we examined which specific age groups contributed to reductions in life expectancy in 2020 and to what extent reductions were attributable to official COVID-19 deaths. Life expectancy at birth declined from 2019 to 2020 in 27 out of 29 countries. Males in the USA and Lithuania experienced the largest losses in life expectancy at birth during 2020 (2.2 and 1.7 years respectively), but reductions of more than an entire year were documented in eleven countries for males, and eight among females. Reductions were mostly attributable to increased mortality above age 60 and to official COVID-19 deaths. The COVID-19 pandemic triggered significant mortality increases in 2020 of a magnitude not witnessed since WW-II in Western Europe or the breakup of the Soviet Union in Eastern Europe. Females from 15 countries and males from 10 ended up with lower life expectancy at birth in 2020 than in 2015.
Population Studies at 75 years: An empirical review.
Population Studies advances research on fertility, mortality, family, migration, methods, policy, and beyond, yet it lacks a recent, rigorous review. We examine all papers published between 1947 and 2020 (N = 1,901) and their authors, using natural language processing, social network analysis, and mixed methods that combine unsupervised machine learning with qualitative coding. After providing a brief history, we map the evolution in authorship and papers towards shorter, multi-authored papers, also finding that females comprise 33.5 per cent of authorship across the period under study, with varied sex ratios across topics. Most papers examine fertility, mortality, and family, studying groups and change, but topics vary over time. Children are rarely studied, and research on women focuses on family planning, fertility decline, and unions, whereas key domains for research on men are migration, historical demography (war, famine), and employment. Research on Africa and Asia focuses on family planning, with work on fertility decline concentrated on North America and Europe, consistent with theories of demographic transition. Our resulting discussion identifies future directions for demographic research.
Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world.
Social distancing and isolation have been widely introduced to counter the COVID-19 pandemic. Adverse social, psychological and economic consequences of a complete or near-complete lockdown demand the development of more moderate contact-reduction policies. Adopting a social network approach, we evaluate the effectiveness of three distancing strategies designed to keep the curve flat and aid compliance in a post-lockdown world. These are: limiting interaction to a few repeated contacts akin to forming social bubbles; seeking similarity across contacts; and strengthening communities via triadic strategies. We simulate stochastic infection curves incorporating core elements from infection models, ideal-type social network models and statistical relational event models. We demonstrate that a strategic social network-based reduction of contact strongly enhances the effectiveness of social distancing measures while keeping risks lower. We provide scientific evidence for effective social distancing that can be applied in public health messaging and that can mitigate negative consequences of social isolation.
Tools for Transparency in Central Government Spending.
Trust in government, policy effectiveness and the governance agenda has rarely been more important than in the opening decades of the twenty first century. For that reason, we herein present centgovspend, an open source software library which provides functionality to automatically scrape and parse central government spending at the micro level. While the design ideals are internationally applicable to any future data origination pipelines, we specifically tailor it to the United Kingdom, a country which is unique not only in terms of its transparency in procurement, but also one which was subject to a parliamentary expenses scandal, years of austerity, and then a volatile political process regarding a referendum to leave the European Union. The library optionally reconciles suppliers and subsequently analyzes payments made to private entities. Our implementation results in scraping over 4.9m payments worth over £3.5tn in value. As a way of showcasing what such a dataset makes possible, we outline three prototype applications in the fields of public administration (procurement across Standard Industry Classifier), sociology (stratification across those who supply government) and network science (board interlock across suppliers) before presenting suggestions for the future direction of public procurement data origination and analysis.
A scientometric review of genome-wide association studies.
This scientometric review of genome-wide association studies (GWAS) from 2005 to 2018 (3639 studies; 3508 traits) reveals extraordinary increases in sample sizes, rates of discovery and traits studied. A longitudinal examination shows fluctuating ancestral diversity, still predominantly European Ancestry (88% in 2017) with 72% of discoveries from participants recruited from three countries (US, UK, Iceland). US agencies, primarily NIH, fund 85% and women are less often senior authors. We generate a unique GWAS H-Index and reveal a tight social network of prominent authors and frequently used data sets. We conclude with 10 evidence-based policy recommendations for scientists, research bodies, funders, and editors.
Hidden heritability due to heterogeneity across seven populations.
Meta-analyses of genome-wide association studies (GWAS), which dominate genetic discovery are based on data from diverse historical time periods and populations. Genetic scores derived from GWAS explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the 'hidden heritability' puzzle. Using seven sampling populations (N=35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller from across compared to within populations. We show that the hidden heritability varies substantially: from zero (height), to 20% for BMI, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results more likely reflect heterogeneity in phenotypic measurement or gene-environment interaction than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene-environment interaction may be a central challenge for genetic discovery.
The Decline and Persistence of the Old Boy: Private Schools and Elite Recruitment 1897 to 2016
We draw on 120 years of biographical data (N = 120,764) contained within Who’s Who—a unique catalogue of the British elite—to explore the changing relationship between elite schools and elite recruitment. We find that the propulsive power of Britain’s public schools has diminished significantly over time. This is driven in part by the wane of military and religious elites, and the rise of women in the labor force. However, the most dramatic declines followed key educational reforms that increased access to the credentials needed to access elite trajectories, while also standardizing and differentiating them. Notwithstanding these changes, public schools remain extraordinarily powerful channels of elite formation. Even today, the alumni of the nine Clarendon schools are 94 times more likely to reach the British elite than are those who attended any other school. Alumni of elite schools also retain a striking capacity to enter the elite even without passing through other prestigious institutions, such as Oxford, Cambridge, or private members clubs. Our analysis not only points to the dogged persistence of the “old boy,” but also underlines the theoretical importance of reviving and refining the study of elite recruitment.