Aggregate Measures Research Articles

Corporate culture and investment inefficiency

Using an aggregate measure of corporate culture, we find that firms with stronger corporate culture encounter lower investment inefficiency. We show that reducing information asymmetry or engaging in tax avoidance are two potential channels through which corporate culture reduces investment inefficiency. Further analyses reveal that the aforementioned relationship is more pronounced for firms with lower local religiosity, firms with less corporate social responsibility engagement, and financially unconstrained firms. Overall, our findings contribute to the literature stressing the importance of corporate culture for corporate decisions and outcomes, and hence, for adding value.

RNA mis-splicing in children with congenital myotonic dystrophy is associated with physical function.

Dysregulated RNA alternative splicing is the hallmark of myotonic dystrophy type 1 (DM1). However, the association between RNA mis-splicing and physical function in children with the most severe form of disease, congenital myotonic dystrophy (CDM), is unknown. Eighty-two participants (42 adults with DM1 and 40 children with CDM) with muscle biopsies and measures of myotonia, motor function, and strength were combined from five observational studies. Data were normalized and correlated with an aggregate measure of alternative splicing dysregulation, [MBNL]inferred, in skeletal muscle biopsies. Multiple linear regression analysis was performed to predict [MBNL]inferred using clinical outcome measures alone. Similar analyses were performed to predict 12-month physical function using baseline metrics. Myotonia (measured via vHOT) was significantly correlated with RNA mis-splicing in our cross-sectional population of all DM1 individuals; CDM participants alone displayed no myotonia despite a similar range of RNA mis-splicing. Measures of motor performance and muscle strength were significantly associated with [MBNL]inferred in our cohort of all DM1 individuals and when assessing children with CDMindependently. Multiple linear regression analyses yielded two models capable of predicting [MBNL]inferred from select clinical outcome assessments alone in all subjects(adjusted R2 = 0.6723) or exclusively in children with CDM (adjusted R2 = 0.5875). Our findings establish significant correlations between skeletal muscle performance and a composite measure of alternative splicing dysregulation, [MBNL]inferred, in DM1. The strength of these correlations and the development of predictive models will assist in designing efficacious clinical trials for individuals with DM1, particularly CDM.

Assessing Territorial Units’ Contribution to Convergence Beyond Aggregate Measures: The Role of Neighbourhood

AbstractThis paper provides an analytical framework for the study of spatial income dynamics using the classical measures of $$\sigma$$ σ -convergence (reduction in dispersion) and $$\beta$$ β -convergence (poor areas grow more quickly than rich ones) linked by a re-ranking metric and reinterpreted based on the ‘leave no one behind’ principle. Our approach allows identifying the contribution of each territorial unit to each of the three facets of distributional change ($$\sigma$$ σ -convergence, $$\beta$$ β -convergence, and re-ranking), as well as gauging the part of each component of distributional change that corresponds to geographically neighbouring and non-neighbouring units. We illustrate our proposal by examining convergence across the census tracts of Malaga – the sixth most populated city of Spain – before and after the COVID-19 pandemic. We find that while income convergence across Malaga’s census tracts tended to improve over the period 2015–2019, this process was interrupted during the first year of the pandemic, affecting some specific census tracts. We examine our outcomes by grouping the results into deciles and districts made up of specific census tracts. Finally, we analyse the impact of neighbours on regional convergence and each of its components. This spatial decomposition highlights the crucial role of the spatial component in the convergence process.

Regional per capita income inequality and fiscal policy in Mexico, 1989–2021

AbstractIn this paper, we use trends in GDP gaps per capita or relative GDP per capita indicator as an aggregate alternative measure to show regional per capita income inequality between the states of Mexico. The indicator compares each state's gross domestic product per capita (GDPpc) with the annual average of each year and 2019; this allows for evaluating growth performance before and after the COVID-19 pandemic. The econometric method is a spatial dynamic panel, and the convergence–divergence model is conditioned to the behavior of the fiscal policy followed in each state: The fiscal policy indicators are current expenditure and public investment. In general, it is concluded that the amounts of current spending and public investment, in an active fiscal policy context, tend to favor states with higher relative per capita income, which is why an increase in regional inequality is observed. However, it tends to affect low-income regions more, with higher ratios of current spending and investment to GDP, which reduces regional inequality. Therefore, the short-term effects of active fiscal policy to reduce regional disparities can be neutralized.

Predicting protein complexes in protein interaction networks using Mapper and graph convolution networks

Protein complexes are groups of interacting proteins that are central to multiple biological processes. Studying protein complexes can enhance our understanding of cellular functions and malfunctions and thus support the development of effective disease treatments. High-throughput experimental techniques allow the generation of large-scale protein-protein interaction datasets. Accordingly, various computational approaches to predict protein complexes from protein-protein interactions were presented in the literature. They are typically based on networks in which nodes and edges represent proteins and their interactions, respectively. State-of-the-art approaches mainly rely on clustering static networks to identify complexes. However, since protein interactions are highly dynamic in nature, recent approaches seek to model such dynamics by typically integrating gene expression data and identifying protein complexes accordingly. We propose MComplex, a method that utilizes time-series gene expression with interaction data to generate a temporal network which is passed to a generative adversarial network whose generator is a graph convolutional network. This creates embeddings which are then analyzed using a modified graph-based version of the Mapper algorithm to predict corresponding protein complexes. We test our approach on multiple benchmark datasets and compare identified complexes against gold-standard protein complex datasets. Our results show that MComplex outperforms existing methods in several evaluation aspects, namely recall and maximum matching ratio as well as a composite score covering aggregated evaluation measures. The code and data are available for free download from https://github.com/LeonardoDaou/MComplex.

Singularities of Taylor’s power law in the analysis of aggregation measures

Taylor’s law is a well-known power law (TPL) for analysing the scaling behaviour of many fluctuating physical phenomena in nature. The scaling exponent b of this law forms the basis of the aggregation process to which a precise probability density function corresponds. In some phenomena, TPL behaviour with periodic components of the aggregates has been observed for small partitions, especially for physical processes characterised by values of b=1 where fluctuation-related aggregation processes are supported by Poissonian distributions. We intend to show that for values of b very close to unity it is possible to find a trend, in the double logarithmic scale, of the TPL that there are ‘periodic patterns’ (components) between variance and mean. This behaviour is found in other binomial-type distributions, of which the Poissonian is a particular case, with mappings characterised by a variance close to 1.

Serum per- and polyfluoroalkyl substance (PFAS) levels and health-related biomarkers in a pilot study of skiers in New England

BackgroundPer- and polyfluoroalkyl substances (PFAS), synthetic chemicals with adverse health effects, are used extensively in consumer products. Ski waxes, applied to the base of skis, contain up to 100% PFAS by mass, but exposure and health effects are poorly characterized. ObjectivesOur objectives were to quantify serum PFAS concentrations among skiers and explore associations with reported ski wax use and biomarkers of cardiometabolic, thyroid, and immune health. MethodsWe recruited 30 active adult skiers to provide non-fasting blood samples and complete questionnaires. We quantified 18 PFAS using mass spectrometry, and measured serum lipids, thyroid hormones, and immunoglobulins. We explored associations of individual and aggregate measures of serum PFAS with wax use indicators and health biomarkers using multivariable regression models, adjusted for age and gender identity. ResultsNine PFAS (PFBS, PFHpS, PFHxS, Sm-PFOS, n-PFOS, PFDA, PFNA, PFUnDA, n-PFOA) were detected in 100% of participants, and MeFOSAA in 93%. Compared to NHANES, median serum concentrations (ng/ml) were similar, but higher in coaches (e.g., PFOAall: 1.1, PFOAcoaches: 2.7, PFOANHANES: 1.2; PFNAall: 0.5, PFNAcoaches: 1.7, PFNANHANES: 0.4). Factors reflecting wax exposure were positively associated with PFAS: e.g., >10 years as a snow sport athlete, compared to ≤10 years, was associated with 3.2 (95% CI: 0.7, 5.6) ng/ml higher aggregate PFAS, as defined by National Academies of Science, Engineering, and Medicine (NASEM). An IQR (6.3 ng/ml) increase in NASEM PFAS was associated with 32.1 (95% CI: 7.0, 57.2), 35.5 (13.5, 57.5), and 12.8 (0.6, 25.1) mg/dl higher total cholesterol, LDL-C, and sdLDL-C, respectively. DiscussionOur study provides early evidence that recreational skiers, particularly coaches, are exposed to PFAS through ski wax. Several PFAS were associated with higher serum lipids among these physically active adults. Interventions to remove PFAS from fluorinated wax could decrease direct exposure to skiers and reduce PFAS release into the environment.

Mapping between measurement scales in meta-analysis, with application to measures of body mass index in children.

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta-analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. The first is an analytical method based on the mathematical definitions of z-scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual-level relationships. We verify and contrast our methods using simulation studies and trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non-convergence.

Unveiling the mask of Financial Exclusion: A Micro-Level Analysis of Gaddis and Gujjars Tribal Communities Financial Inclusiveness.

Financial exclusion is a pervasive issue that affects millions of individuals worldwide, hindering their economic growth and development. Despite efforts to promote financial inclusion, many individuals remain excluded from formal financial services, perpetuating a cycle of poverty and inequality. This study seeks to unveil the mask of financial exclusion by examining the micro-level factors that influence individual financial inclusiveness. Using a mixed-methods approach, analyze data from a survey of 382 non nomadic tribal community individuals in a developing country, supplemented by in-depth interviews with 90 nomadic tribal respondents. The results of this study reveal that financial exclusion is a complex phenomenon driven by a range of factors, including socioeconomic characteristics, financial literacy, and access to financial infrastructure. This study also find that existing measures of financial inclusion, such as account ownership and credit access, mask the true extent of financial exclusion, as many individuals with formal financial accounts still face significant barriers to using these services. This study contributes to the literature on financial inclusion by providing a nuanced understanding of the micro-level factors that shape individual financial inclusiveness. This research argue that policymakers must move beyond aggregate measures of financial inclusion and instead focus on addressing the specific needs and challenges faced by excluded individuals. By doing so, policymaker can create more effective policies to promote financial inclusion and reduce poverty and inequality

Parametric multi-level modeling of Lean Six Sigma

Lean Six Sigma is a methodology that combines Lean manufacturing and Six Sigma into a single framework for process improvement. This combination merges their tools and techniques to overcome their deficiencies while achieving bottom-line improvement. However, existing literature emphasizes general LSS implementation with very little work on modeling the interaction between Leanness and Six Sigma. This work developed a multi-level model containing leanness and Six Sigma levels to generalize LSS implementation. Three hierarchical levels—Six Sigma (level 3), leanness (level 2), and a fattening level (level 1—Sigma quality level) are defined for two models (parametrized and non-parametrized). Aggregate measures (process efficiency, asset intensity, production time, etc.) are considered at levels 2 and 3 to achieve the desired optimization goal at that level. For testing, three process measures [asset intensity (AI), planned stoppages (PS), wastes and rework (WR)] from the manufacturing data of a global brand spanning 18 months were analyzed. The maximum log-likelihood method was used to estimate the model parameters and they converged to 85.2%, 11.2%, and 8.87% for AI, PS, and WR respectively, with a strong correlation between AI and PS. A metaheuristic algorithm was extended to solve both models. The obtained process yield (0.111111 and 0.4132471), defects per million opportunities (DPMO) (12,426 and 18,046), and sigma level (3.6 and 3.74) for the parametrized and non-parametrized models respectively indicated optimal model performance and a need for the strategic use of LSS tools in improving the production output. Also, further reduction in wastes, stoppages, and downtime was observed upon tuning the parametric variables. This model provides a mathematical approach to analyzing LSS implementation and provides a basis for future adaptation to any organization.

Measuring Price Effects from Disasters Using Public Data: A Case Study of Hurricane Ian

Natural disasters can disrupt housing markets, causing destruction to communities and distress to economic activity. To estimate the effects of disasters on home prices, publicly available data on property damages are often used to classify “treated” properties. However, by design, these data lack precise geospatial information, leading to measurement error in the treatment variable because aggregate measures must be used. We leverage leading difference-in-differences and synthetic control methodologies across various treatments and levels of geography to measure price effects with such data following Hurricane Ian’s unexpected landfall in Southwest Florida during September 2022, coinciding with the state’s initial recovery from the COVID-19 pandemic. Empirical results suggest positive, time-varying price effects, though we place caveats on these results because there may be many mechanisms underway; our results should be interpreted as descriptive correlations and not causal effects for various reasons. Our main contribution is methodological, highlighting the importance of robustness checks, functional form, statistical techniques, and testing across different samples. Additionally, quicker access to high-quality public data could enhance quantitatively informed conversations on natural disaster effects.

Type and Distribution of Gross Motor Activity During Physical Therapy in Young Children With Cerebral Palsy.

Physical therapists routinely deliver and prescribe motor practice to improve function. The ability to select optimal practice regimens is limited by a current lack of detail in the measurement of motor practice. The objective of this study was to quantify the type, amount, and timing of gross motor practice during physical therapist sessions. A secondary video coding analysis of physical therapist sessions from the iMOVE clinical trial (NCT02340026) in young children with cerebral palsy (CP) was conducted. The 37 children who completed the treatment phase were included (mean age = 22.1months). Children could initiate pulling to stand but were unable to walk. Videos of randomly selected therapy sessions were coded for gross motor activity (422 videos total). The 10 gross motor activity codes included lying, sitting, four point, crawling, kneeling, knee walking, standing, walking, transitions between floor postures, and transitions to/from an upright posture. Twenty percent of each video was double coded for reliability. Time per session, number of bouts, and median time per bout were calculated for each gross motor activity and for 2 aggregate measures: movement time and upright time. Participants spent more than half of therapy time in sitting and standing combined (60.3%). Transitions occurred more frequently than any other motor activity (49.3 total transitions per session). Movement time accounted for 16.3% of therapy time. Upright time accounted for 53.3% of therapy time. Critical practice time to gain motor skill is not equivalent to chronological time or time spent in therapy. Toddlers with CP spent a small amount of therapy time moving. Future work should explore the relations between motor practice and rehabilitation outcomes. Physical therapists are ideally suited to detail the content of motor practice and ultimately to prescribe optimal patterns of motor practice. We report the characteristics of gross motor practice during therapy in children with CP.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Scenario Storyline Discovery for Planning in Multi‐Actor Human‐Natural Systems Confronting Change

AbstractScenarios have emerged as valuable tools in managing complex human‐natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced “forensic”): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of scenario storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify drought scenario storylines, that can be used to inform future adaptation planning.

Metabolites Link Intake of a Healthy Diet to Better Insulin and Glucose Homeostasis in the Microbiome and Insulin Longitudinal Evaluation Study

BackgroundDietary quality has been linked to better glycemic control, but the precise molecular mechanisms giving rise to these associations are not fully understood. ObjectivesTo examine the association of metabolites associated with the intake of a healthy diet with measures of insulin/glucose homeostasis. MethodsUsing cross-sectional data from 295 United States adults, the associations between 3 diet pattern scores and metabolome-wide metabolites were estimated via linear regression models, which controlled for demographic factors and health behaviors. Subsequently, the associations between the diet-related metabolites with 6 measures of glucose/insulin homeostasis were examined in similar models. A Bonferroni correction was applied to control the familywise error rate at 5%. ResultsFifty-five metabolites were significantly associated with ≥1 diet score (all P < 1.7∗10–5). When these were summed into each of the 3 diet-specific metabolite summary scores, all 3 aggregate measures showed strong associations with 5 out of 6 measures of glucose/insulin homeostasis (P = 9.7∗10–5–4.1∗10–13). ConclusionsAdherence to a priori-defined “healthy diet” is associated with the plasma metabolites that, in turn, are associated with better glycemia. If the associations between replicated in future studies and examined using large-scale longitudinal data, the identified molecules could yield insights into mechanisms by which diet may support glucose and insulin homeostasis.

Distributed non-disclosive validation of predictive models by a modified ROC-GLM

BackgroundDistributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach.MethodsWe employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _2$$\\end{document} sensitivity Δ2(f^)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta _2(\\hat{f})$$\\end{document}) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations.ResultsIn our simulation scenario, the true and distributed AUC measures differ by ΔAUC<0.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta \ ext {AUC} < 0.01$$\\end{document} depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP.ConclusionsThe applicability of our algorithms depends on the ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _2$$\\end{document} sensitivity Δ2(f^)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta _2(\\hat{f})$$\\end{document} of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high Δ2(f^)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta _2(\\hat{f})$$\\end{document}, the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual’s privacy.

A machine learning analysis of the value-added intellectual coefficient’s effect on firm performance

Purpose Recently, machine learning (ML) methods gained popularity in finance and accounting research as alternatives to econometric analysis. Their success in high-dimensional settings is promising as a cure for the shortcomings of econometric analysis. The purpose of this study is to prove further the relationship between intellectual capital (IC) efficiency and firm performance using ML methods. Design/methodology/approach This study used the double selection, partialing-out and cross-fit partialing-out LASSO estimators to analyze the IC efficiency’s linear and nonlinear effects on firm performance using a sample of 2,581 North American firms from 1999 to 2021. The value-added intellectual capital (VAIC) and its components are used as indicators of IC efficiency. Firm performance is measured by return on equity, return on assets and market-to-book ratio. Findings The findings revealed significant connections between IC measures and firm performance. First, the VAIC, as an aggregate measure, significantly impacts both firm profitability and value. When the VAIC is decomposed into its breakdowns, it is revealed that structural capital efficiency substantially affects firm value, and capital employed efficiency has the same function for firm profitability. In contrast to the prevalent belief in the area, human capital efficiency’s impact is found to be less important than the others. Nonlinearities are also detected in the relationships. Originality/value As ML tools are most recently introduced to the IC literature, only a few studies have used them to expand the current knowledge. However, none of these studies investigated the role of IC as a determinant of firm performance. The present study fills this gap in the literature by investigating the effect of IC efficiency on firm performance using supervised ML methods. It also provides a novel approach by comparing the estimation results of three LASSO estimators. To the best of the author’s knowledge, this is the first study that has used LASSO in IC research.

Out‐of‐area home purchase and U.S. internal migration

AbstractThis study demonstrates that out‐of‐area (OOA) property transactions can serve as a proxy for migration. Using micro‐level transaction data, we document that about 35% of migrants make OOA property purchases. The goodness‐of‐fit between migration and OOA purchases is higher for aggregate migration measures and lower for migration flows between disaggregated areas. Furthermore, in most specifications, a one percent increase in OOA purchases is associated with an approximately one percent increase in migration. We characterize the monthly out‐migration from NYC zip codes to surrounding areas after the outbreak of the COVID‐19 pandemic to demonstrate the high temporal and spatial resolution of OOA transaction data.

Economic Inequality, Life Expectancy, and Interpersonal Violence in London Neighborhoods.

Positive associations between levels of socioeconomic inequality and homicide rates have been reported at various geographical levels (e.g., between countries, states, cities, and neighborhoods within a city). However, the extent to which inequality predicts levels of non-lethal violence has been less frequently studied. The present study was conducted to investigate the association between socioeconomic inequality and levels of non-lethal interpersonal violence across neighborhoods of London during the period 2010 to 2012, using two independent data sources: Metropolitan Police service recorded violent crime and London Ambulance Service recorded assaults. Mean income per person and local life expectancy were included as additional predictors. Following exclusions due to census boundary changes, across 533 London wards, there were positive bivariate associations between both violence measures and a measure of inequality between neighborhoods (census lower layer super output areas [LSOAs]) within a ward. Moreover, there were negative bivariate associations between violence rates and both ward mean income and life expectancy measures for males and females. However, in a regression analysis only inequality and male life expectancy were consistent predictors of rates of interpersonal violence across outcome measures. The results of the present study provide further evidence of an association between levels of economic inequality and rates of interpersonal violence. The findings, for variation in rates of non-lethal violence across small geographical areas (neighborhoods), build on previous research that has mostly focused on rates of lethal violence and has tended to use aggregate measures across larger geographical areas.

Real-Time Observation of Protein Aggregation at Liquid-Liquid Interfaces in a Microfluidic Device.

A droplet microfluidic device to capture in real-time protein aggregation at liquid-liquid interfaces is described. In contrast to conventional methods, typically characterized by a lag time between the application of interfacial stress and the measurement of protein aggregation, here protein adsorption, the formation of a viscoelastic protein layer, aggregation, and shedding of protein particles into solution is simultaneously monitored. The device is applied to analyze the stability of antibodyformulations over a wide range of concentrations (1-180mgmL-1) at the silicone oil (SO)-water interface under controlled mechanical deformation. The adsorption onto oil droplets induces the formation of a viscoelastic protein layer on a subsecond timescale, which progressively restricts the relaxation of the droplets within the chip. Upon mechanical rupture, the protein layer releases particles in solution. The rate of particle formation increases strongly with concentration, similar to the bulk viscosity. Concentrations above 120mgmL-1 lead to aggregation in seconds and drastically decrease the mechanical perturbations required to shed protein particles in solution. These results are important for the development of formulations at high-protein concentrations (>100mgmL-1) and indicate that particular attention should be given to interface-induced particle formation in this concentration range. In this context, low-volume microfluidic platforms allow the assessment of protein physical instabilities early in development and represent attractive tools to optimize antibody stability and formulation design consuming limited amounts ofmaterial.