Detailed Occupations Research Articles

Can wage transparency alleviate gender sorting in the labour market?

SUMMARY A large share of the gender wage gap can be attributed to occupation and employer choices. If workers are not well informed about these pay differences, increasing wage transparency might alleviate the gender gap. We test this hypothesis by examining the impact of mandatory wage postings. In 2011, Austria introduced a policy that requires firms to provide a minimum wage offer in job postings. To compare the pay prospects of vacancies before and after the introduction, we predict posted wages using detailed occupation–firm cells, which explain about 75% of the variation in wage postings. While we estimate a substantial gender gap of 15 log points, mandatory wage postings do not affect gender sorting into better-paying occupations and firms.

Allosteric modulation of fluorescence revealed by hydrogen bond dynamics in a genetically encoded maltose biosensor.

Genetically encoded fluorescent biosensors (GEFBs) proved to be reliable tracers for many metabolites and cellular processes. In the simplest case, a fluorescent protein (FP) is genetically fused to a sensing protein which undergoes a conformational change upon ligand binding. This drives a rearrangement in the chromophore environment and changes the spectral properties of the FP. Structural determinants of successful biosensors are revealed only in hindsight when the crystal structures of both ligand-bound and ligand-free forms are available. This makes the development of new biosensors for desired analytes a long trial-and-error process. In the current study, we conducted μs-long all atom molecular dynamics (MD) simulations of a maltose biosensor in both the apo (dark) and holo (bright) forms. We performed detailed hydrogen bond occupancy analyses to shed light on the mechanism of ligand induced conformational change in the sensor protein and its allosteric effect on the chromophore environment. We find that two strong indicators for distinguishing bright and dark states of biosensors are due to substantial changes in hydrogen bond dynamics in the system and solvent accessibility of the chromophore.

Eu2+ doping and Mg2+-coupled Zn2+, Ca2+ and Sr2+ cations induce tunable orange-blue luminescence in cordierite

A series of Mg2Al4Si5O18:xEu2+ (x = 0.2, 0.6, 1.4, 2.8, 6.0mol‰) and Mg2Al4Si5O18:0.05B, 1.4Eu2+ (BZn2+, Ca2+ and Sr2+) phosphors were prepared by high-temperature melt ice-water quenching and heat treatment. In this study, using Eu2+ ions as a structural probe, we investigated the effects of heat treatment temperature, Eu2+ ion concentration, and cation position on the crystal phase and local structure, triggering changes in the luminescent properties of the phosphor and achieving coordinated blue and red luminescence. Through heat treatment analysis, we proposed the formation process of the two phases of cordierite. The doping of different concentrations of Eu ions in cordierite triggers the phase transition of the host lattice. The molar distribution ratio of Eu2+ ions was calculated by Gaussian peak-splitting fitting to identify the detailed occupancy distribution of the Eu2+ ions in μ-cordierite and α-cordierite. In the α-cordierite phase, with the doping of Zn2+, Ca2+, and Sr2+ cationic sites, the PL is modulated from orange to blue emission based on the selective occupancy of Eu2+ ions, which is caused by the difference in cationic radii and structural distortion. The comprehensive luminescence intensity of MASE1.4, MASZ0.05E1.4, MASC0.05E1.4, and MASS0.05E1.4 phosphors were 81.18%, 83.49%, 62.67%, and 76.41% at 150 °C compared to that at 30 °C, indicating the better thermal stability of the prepared phosphors. The luminescence tunable strategy by cation and Eu2+ doping simultaneously modifies the emission and achieves different thermal stability, providing a new design concept for the development of Eu2+-doped silicate phosphors.

The within occupation and industry component of the immigrant-native gap in U.S. self-employment rates

ABSTRACT This paper examines whether the higher self-employment rates among U.S. immigrants, relative to native-born Americans, can be accounted for by immigrants being over-represented in occupations and industries that are commonly pursued through self-employment. After accounting for detailed occupations and industries, approximately 58% of the immigrant-native gap in self-employment rates remains. This finding suggests that the higher self-employment rate among immigrants is driven by factors that influence the likelihood of pursuing self-employment in and of itself, rather than factors that act indirectly by driving immigrants towards occupations and industries that are commonly associated with self-employment.

A detailed occupant activity classification model in a residential environment using building monitoring data: Considering occupant characteristics

In this study, we developed a detailed occupant activity classification model to detect occupant activities using various non-intrusive monitoring data in buildings (i.e., indoor environmental data, energy consumption data, motion·door sensor data, and state of indoor climate control devices). To collect building monitoring data, we conducted experiments with five subjects (Person A to E) in an experimental testbed. In total, ten occupant activities (i.e., Sleeping, Eating, Personal hygiene and care, Working, Cooking, Cleaning up after meals, Indoor cleaning, Exercising, Resting, and Away) were selected to predict occupant activity experimentally. Three classification algorithms, including random forest (RF), K-nearest neighbor, and support vector machine, were employed to develop the detailed occupancy activity classification models. In this study, we found that the key variables influencing each occupant activity classification varied significantly for each individual. The lifestyle and characteristics of each occupant were reflected in the building monitoring data. Accordingly, the performance of the model varied depending on each occupant’s behavior. In the case of Person A, all classification algorithms displayed a high performance when only environmental datasets were considered; the accuracy was 0.98, and the f1-score was also above 0.98. Person B’s classification model required various building monitoring data to ensure optimal performance; the highest accuracy and f1-score was 0.86 using RF. In contrast, Person D’s classification model showed a low performance when all monitoring datasets were used; the accuracy was 0.43, and the f1-score was 0.41 using RF. These results support that occupants’ characteristics affect the feature variables in an activity classification model, and these feature variables determine the performance of the model of each occupant.

Quantifying the impact of Covid-19 on the energy consumption in the low-income housing in Greater London

Covid-19 has caused great challenges to the energy sector, particularly in residential buildings with low-income households. This study investigates the impact of the confinement measures due to the Covid-19 outbreak on the energy demand of seven residential archetype buildings in Greater London. Three levels of confinement for occupant schedules are proposed and compared with the base case before Covid-19. The archetypes, their boundary conditions, and input parameters are set up according to statistics from English Housing Survey (EHS) sample data for low-income housing. The base case scenario (normal life without confinement measures) is validated against the measured data energy consumption from the National Energy Efficiency Data-Framework (NEED) statistics. The results show that electricity consumption is significantly lower than that for heating and hot water for all the archetypes. By comparing the base case scenario with the full Covid-19 lockdown scenario, the results indicate that heating and hot water consumption (kWh) for all the residential archetypes increases, on average, by 10%, and total electricity demand (kWh) increases by 13%. The study highlights the importance of introducing detailed occupancy profiles in multi-zone building energy simulation models during a pandemic that leads to a greater shift towards home working, which may increase the risk of fuel poverty in low-income housing.

Comparative advantages in the digital era–A Heckscher-Ohlin-Vanek approach

This paper revisits the Heckscher-Ohlin-Vanek (HOV) theorem and investigates its fit for digital tasks and ICT capital, which both represent endowment factors that are expected to shape the digital transformation. We use a theory-consistent methodology for calculating the measured net factor content of trade (Trefler and Zhu, . The structure of factor content predictions) and apply it to a unique dataset on digital and non-digital tasks performed in detailed occupations, as well as recent data on ICT capital stocks. Equipped with these data we provide new evidence on the factor-based trade patterns for 25 EU countries and use it to test the HOV theorem. Overall, the performance of the sign test and the rank test is good if not impressive. In 83% of the cases countries are net exporters of those factors with which they are abundantly endowed, with a higher score achieved for digital tasks than for ICT capital. The relevance of digital tasks and ICT capital are corroborated by a gravity model which indicate that higher endowments with these factors foster bilateral exports.

Preliminary validation of the GHBMC average male occupant models and 70YO aged model in far-side impact

The objective of the current study was to perform a preliminary validation of the Global Human Body Models Consortium (GHBMC) average male occupant models, simplified (M50-OS) and detailed (M50-O) and the 70YO aged model in Far-side impacts and compare the head kinematics against the PMHS responses published by Petit et al. (2019). The buck used to simulate the far-side impacts comprised a seat, headrest, center console plate, leg support plate, and footrest plate with rigid material properties.The three occupant models were gravity settled onto the rigid seat and belted with a 3-point seatbelt. Positioning details of the PMHS were followed in the model setup process. A deceleration pulse with ΔV of 8 m/s was applied. The far-side crash simulations were performed with and without the addition of a plexiglass cover around the setup similar to the experimental setup. The head kinematics were extracted from the models for comparison against the PMHS data. Peak head displacements in Y and Z axes from the three models were compared to the PMHS data in addition to the head rotation along X axes. The peak head displacement values for the M50-OS, M50-O, and M50-O 70YO aged models are 594.10 mm, 568.44 mm, and 567.90 mm along Y and 325.21 mm, 402.66 mm, and 375.92 mm respectively along Z when the plexiglass cover is included in the test. The peak head rotation values for the M50-OS, M50-O, and M50-O 70YO aged models are 95.64°, 122.15°, and 129.08° respectively when the plexiglass cover is included in the test. The three occupant models capture the general trend of the PMHS data. The detailed occupant models have higher head rotation compared to the simplified model because of the deformable structure of the spine and intervertebral discs modeled. These three occupant models can be used for further parametric studies in this condition to study the influence of restraint parameters.

International trade and job polarization: Evidence at the worker level

We employ employer-employee matched data from Denmark and utilize plausibly exogenous variation in the rise of import competition due to the dismantling of import quotas as China entered the World Trade Organization to show, first, that rising import competition has led to reduced employment in mid-wage occupations compensated by an increased likelihood of employment in both low-wage and high-wage occupations. Workers with higher education are more likely to move from mid- to high-wage occupations due to trade compared to moving from mid- to low-wage occupations. Employing task content information of detailed occupations, we also show that workers performing manual tasks are the ones most affected by import competition independently of the routine-task intensity of occupations. This implies that the effect of import competition is distinct from that of routine task-replacing technological change.

Occupational COVID-19 Exposures and Illnesses among Workers in California-Analysis of a New Occupational COVID-19 Surveillance System.

Little is known about occupational SARS-CoV-2 exposures and COVID-19 outcomes. We established a Doctor's First Reports of Occupational Injury or Illness (DFR)-based surveillance system to study cases of work-related COVID-19 exposures and disease. The surveillance data included demographics, occupation, industry, exposure, and illness, details including hospitalization and lost work. We classified workers into 'healthcare', non-healthcare 'public-facing', or 'other' worker groups, and rural-urban commuting areas (RUCAs). We describe worker exposures and outcomes overall by worker group and RUCA. We analyzed 2848 COVID-19 DFRs representing workers in 22 detailed occupation groups and 19 industry groups. Most DFRs were for workers in metropolitan RUCAs (89%) and those in healthcare (42%) and public-facing (24%) worker groups. While DFRs were from 382 unique worksites, 52% were from four hospitals and one prison. Among 1063 DFRs with a suspected exposure, 73% suspected exposure to a patient or client. Few DFRs indicated hospitalization (3.9%); however, the proportion hospitalized was higher among nonmetropolitan (7.4%) and public-facing (6.7%) workers. While 56% of DFRs indicated some lost work time, the proportion was highest among public-facing (80%) workers. Healthcare and prison workers were the majority of reported occupational COVID-19 exposures and illnesses. The risk of COVID-19 hospitalization and lost work may be highest among nonmetropolitan and public-facing workers.

Geographies of Frontline Workers: Gender, Race, and Commuting in New York City

The COVID-19 pandemic amplified social, economic, and environmental inequalities in American cities, including inequities in commuting and access to employment. Frontline workers—those who had to work on site during the pandemic—experienced these inequalities in every aspect of their daily lives. We examine the labor force characteristics and commuting of frontline workers in New York City with a focus on gender and race/ethnic disparities in wages and commuting modes and times. Using Census PUMS microdata for a sample of New York City residents in the 2015–2019 period, we identify frontline workers from detailed industry and occupation codes and compare characteristics of frontline workers with those of essential workers who could work remotely. The data highlight wide disparities between frontline and remote workers. Minority men and women are concentrated in the frontline workforce. The residential geographies of frontline and remote workers differ greatly, with the former concentrated in low- and moderate- income areas distant from work sites and with long commute times. Compared to men, women frontline workers rely heavily on public transit to commute and transit dependence is highest among Black and Latina women. Low-wage employment, long commute times, and transit dependence intersected to increase minority women’s economic and social vulnerability during the pandemic.

Occupational Segregation and Gender Wage Gap in Pakistan: Do Occupational Classifications Matter?

Abstract: This paper aims to quantify the relationship between occupational gender segregation and gender wage disparities in Pakistan. Non-parametric matching-based decomposition method and a detailed set of individual and labor market characteristics, especially the detailed occupations at a 3-digit level, are used to determine the gender wage gap for males and females in and out of the common support. The results show that even though the inclusion of a detailed set of human capital, labor market characteristics, and occupational segregation by gender helps in explaining most of the gender wage gap, a large proportion of the gender wage gap remained unexplained and can be attributed to gender discrimination. A comparison of rural and urban labor markets shows a high rate of the gender wage gap in rural areas compared to urban areas of the country.

Consider your origins: Parental social class and preferences for redistribution in the United States from 1977 to 2018

Capitalizing on the newly available and consistent coding of detailed occupations for the General Social Surveys (GSS), this article examines the link between class origins and public support for redistribution in the United States from 1977 through 2018. The findings reveal significant net associations between class origins and preferences for redistribution. Individuals with farming-class or working-class origins are more supportive of government action to reduce inequality than individuals with salariat-class origins. These class-origin differences are associated with individuals’ current socioeconomic characteristics but are not fully accounted for by these factors. In addition, individuals in more privileged class positions have increased their support for redistribution over time. Attitudes toward federal income taxes are also analyzed as an additional measure of redistribution preferences. Overall, the findings suggest a continuing role of class origin in determining support for redistribution.

Can digital skill protect against job displacement risk caused by artificial intelligence? Empirical evidence from 701 detailed occupations.

To identify the role of digital skill in the skill-biased technological changes caused by artificial intelligence, this study estimates the impacts of displacement risk on occupational wage and employment and examines the moderation effects of digital skill through the occupational data from the U.S. Bureau of Labor Statistics through the methods of fixed-effects modeling, heterogeneity analyzing and moderation effect testing. The results highlight three main points that (1) the displacement risk by artificial intelligence has significantly negative effects on occupational wage and employment, (2) the heterogeneous effects across occupational characteristics are significant, and (3) the digital skill exerts a significant moderation effect to protect against displacement risk. The core policy implication is suggested to emphasize digital skill in education and training across occupations to accommodate job requirements in the future.

COVID-19 vaccination rates among healthcare workers by immigrant background: A nation-wide registry study from Norway.

Studies have suggested that some minority groups tend to have lower vaccination rates than the overall population. This study aims to examine COVID-19 vaccination rates among healthcare workers in Norway, according to immigrant background. We used individual-level, nation-wide registry data from Norway to identify all healthcare workers employed full time at 1 December 2020. We examined the relationship between country of birth and COVID-19 vaccination from December 2020 to August 2021, both crude and adjusted for, for example, age, sex, municipality of residence and detailed occupation codes in logistic regression models. Among all healthcare workers in Norway, immigrants had a 9 percentage point lower vaccination rate (85%) than healthcare workers without an immigrant background (94%) at 31 August 2021. The overall vaccination rate varied by country of birth, with immigrants born in Russia (71%), Serbia (72%), Lithuania (72%), Romania (75%), Poland (76%), Eritrea (77%) and Somalia (78%) having the lowest crude vaccination rates. When we adjusted for demographics and detailed occupational codes, immigrant groups that more often worked as healthcare assistants, such as immigrants from Eritrea and Somalia, increased their vaccination rates. Substantial differences in vaccination rates among immigrant groups employed in the healthcare sector in Norway indicate that measures to improve vaccine uptake should focus on specific immigrant groups rather than all immigrants together. Lower vaccination rates in some immigrant groups appear to be largely driven by the occupational composition, suggesting that some of the differences in vaccine rates can be attributed to variation in vaccine access.

Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study

How to mitigate the spread of infectious diseases like COVID-19 in indoor environments remains an important research question. In this study, we propose an agent-based modeling framework to evaluate facility usage policies that aim to lower the probability of outbreaks. The proposed framework is individual-based, spatially-resolved with time resolution of up to 1 s, and takes into detailed account specific floor layouts, occupant schedules and movement. It enables decision makers to compute realistic contact networks and generate risk profiles of their facilities without relying on wearable devices, smartphone tagging or surveillance cameras. Our demonstrative modeling results indicate that not all facility occupants present the same risk of starting an outbreak, where the driver of outbreaks varies with facility layouts as well as individual occupant schedules. Therefore, generic mitigation strategies applied across all facilities should be considered inferior to tailored policies that take into account individual characteristics of the facilities of interest. The proposed modeling framework, implemented in Python and now available to the public in an open-source platform, enables such strategy evaluation.

Enhancing the detection performance of a vision-based occupancy detector for buildings

Occupant behaviour is one of the key parameters that significantly impact the operation of heating, ventilation and air-conditioning (HVAC) systems and the energy performance of buildings. Detailed occupancy information can improve HVAC operation and utilisation of building spaces. Strategies such as vision-based occupancy detection and recognition have recently garnered much interest. This study investigated the performance of a vision-based deep learning detection technique for enhancing building system operations and energy performances. The model used was the Faster region-based convolutional neural network with Inception V2. Two occupancy detection model configurations were developed, tested and evaluated. Both models were analysed based on the application of the detector within a selected case study building, along with an evaluation based on different evaluation metrics. The results suggest the occupancy detector using model 1 provided an overall accuracy of 95.23%, while model 2 (the occupancy activity detector) provided an accuracy of 89.37%. In addition, the F 1 score, which represents the harmonic mean between the precision and recall values, was 0.9756 for model 1 and 0.8298 for model 2. Building energy simulation was used to evaluate and compare the impact of such an approach on indoor occupancy heat gains. The study highlighted the potential of the detection approaches, but further development is necessary, including optimisation of the model, full integration with HVAC controls and further model training and field testing.

Use of outpatient and inpatient health care services by occupation\u2014a register study of employees in Oulu, Finland

BackgroundThe aim of this study was to examine how the use of outpatient and inpatient health services differs by occupational groups, and whether the differences are explained by sociodemographic factors and health status.MethodsWe used register-based data on 25–64-year-old employees living in the city of Oulu, Finland, in 2018 (N = 61,848). Use of outpatient health care services (public, private and occupational health care) among men and women was analysed with negative binomial regression models, and use of inpatient health care with logistic regression models, using two occupational classifications: occupational group (1-digit level) and more detailed occupation (2-digit level). Adjusted covariates were age, education, income, marital status, special reimbursement entitlements for medicines, and sickness absence.ResultsExamined at the level of larger occupational groups, the use of outpatient and inpatient health care was less common than average among managers, professionals and skilled agricultural, forestry and fishery workers; in women also among craft and related trades workers. Controlling for covariates explained only part of the differences, more among women than among men. Analysed at the level of more detailed occupations, the adjusted use of outpatient and inpatient care was more common among health associate professionals and stationary plant and machine operators, both among men and women. Furthermore, the use of outpatient care was common among male personal care workers, protective service workers and metal, machinery and related trades workers as well as among labourers in mining, construction, manufacturing and transport, and female customer services clerks and sales workers.ConclusionThe use of health care services differs by occupation, and the differences are not fully explained by sociodemographic factors and health status. High occupational risks, attitudes and knowledge may explain the more frequent use of health services. Furthermore, explanations may be sought from lack of access to occupational health care or healthier working conditions and behavior.

Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior.

Several contrasting effects are reported in the existing literature concerning the impact assessment of the COVID-19 outbreak on the use of energy in buildings. Following an in-depth literature review, we here propose a GIS-based approach, based on pre-pandemic, partial, and full lockdown scenarios, using a bottom-up engineering model to quantify these impacts. The model has been verified against measured energy data from a total number of 451 buildings in three urban neighborhoods in the Canton of Geneva, Switzerland. The accuracy of the engineering model in predicting the energy demand has been improved by 10%, in terms of the mean absolute percentage error, as a result of adopting a data-driven correction with a random forest algorithm. The obtained results show that the energy demand for space heating and cooling tended to increase by 8% and 17%, respectively, during the partial lockdown, while these numbers rose to 13% and 28% in the case of the full lockdown. The study also reveals that the introduced detailed occupancy scenarios are the key to improving the accuracy of urban building energy models (UBEMs). Finally, it is shown that the proposed GIS-based approach can be used to mitigate the expected impacts of any possible future pandemic in urban neighborhoods.

The Network Structure of Occupations: Fragmentation, Differentiation, and Contagion.

Occupational structure is commonly viewed as either hierarchical or organized around stable classes. Yet, recent studies have proposed to describe occupational structure as a network, where the mobility of workers demarcates boundaries. Moving beyond boundary detection, this article develops occupational networks as a dynamic system in which between-occupation exchange is shaped by occupational similarities and occupational attributes are in turn responsive to mobility patterns. The authors illustrate this perspective with the exchange networks of detailed occupations. Their analysis shows that the U.S. occupational structure has become more fragmented. The division was in part associated with the emerging importance of age composition, as well as of quantitative, creative, and social tasks. The fragmentation reduced wage contagion and therefore contributed to a greater between-occupation wage dispersion. These results indicate that occupational attributes and mobility are coconstitutive and that a network perspective provides a unifying framework for the study of stratification and mobility.