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The purpose of this paper is to propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job-seeker qualifications and skills, against the vacancy provided by employers or job-agents.

The paper presents a framework of bidirectional jobseeker-to-vacancy matching system. Using occupational data from various sources such as the WageIndicator web survey, International Standard Classification of Occupations, European Skills, Competences, Qualifications, and Occupations as well as vacancy data from various open access internet sources and job seekers information from social networking sites, the authors apply machine learning techniques for bidirectional matching of job vacancies and occupational standards to enhance the contents of job vacancies and job seekers profiles. The authors also apply bidirectional matching of job seeker profiles and vacancies, i.e., semantic matching vacancies to job seekers and vice versa in the individual level. Moreover, data from occupational standards and social networks were utilized to enhance the relevance (i.e. degree of similarity) of job vacancies and job seekers, respectively.

The paper provides empirical insights of increase in job vacancy advertisements on the selected jobs – Internet of Things – with respect to other job vacancies, and identifies the evolution of job profiles and its effect on job vacancies announcements in the era of Industry 4.0. In addition, the paper shows the gap between job seeker interests and available jobs in the selected job area.

Due to limited data about jobseekers, the research results may not guarantee high quality of recommendation and maturity of matching results. Therefore, further research is required to test if the proposed system works for other domains as well as more diverse data sets.

The paper demonstrates how online jobseeker-to-vacancy matching can be improved by use of semantic technology and the integration of occupational standards, web survey data, and social networking data into user profile collection and matching.

The purpose of this paper is to overcome the problems that skill mismatch cannot be measured directly and that demand side data are lacking. It relates demand and supply side characteristics by aggregating data from jobs ads and jobholders into occupations. For these occupations skill mismatch is investigated by focussing on demand and supply ratios, attained vis-à-vis required skills and vacancies’ skill requirements in relation to the demand-supply ratios.

Vacancy data from the EURES job portal and jobholder data from WageIndicator web-survey were aggregated by ISCO 4-digit occupations and merged in a database with 279 occupations for Czech Republic, being the only European country with disaggregated occupational data, coded educational data, and sufficient numbers of observations.

One fourth of occupations are in excessive demand and one third in excessive supply. The workforce is overeducated compared to the vacancies’ requirements. A high demand correlates with lower educational requirements. At lower occupational skill levels requirements are more condensed, but attainments less so. At higher skill levels, requirements are less condensed, but attainments more so. Educational requirements are lower for high demand occupations.

Using educational levels is a limited proxy for multidimensional skills. Higher educated jobholders are overrepresented.

In Europe labour market mismatches worry policy makers and Public Employment Services alike.

The authors study is the first for Europe to explore such a granulated approach of skill mismatch.

This study aims to explore the spatial externalities of apartment vacancy rates on housing rent by considering multiple vacancy durations.

This research uses smart meter data to measure unobservable vacant houses. This study made a significant contribution by applying building-level smart meter data to housing market analysis. It examined whether vacancy duration significantly affected apartment rent and whether the relationship between apartment rent and vacancy rate differed depending on the level of housing rent.

The primary finding indicates that there is a significant negative correlation between apartment rent and vacancy duration. Considering the spatial externalities of apartment vacancy rates, the apartment vacancy rates of surrounding buildings did not show any statistical significance. Moreover, quantile regression results indicate that although the bottom 10% of apartment rent levels showed a negative correlation with all vacancy durations, the top 10% showed no statistical significance related to vacancies.

This study measures the extent of spatial externalities that can differentiate taxation based on housing vacancies.

The findings indicate that landlords have asymmetric information about their buildings compared with the surrounding buildings, and the extent to which price adjusts for long-term vacancies differs depending on the level of apartment rent.

The purpose of this paper is to explore the matching process before and after the Great Recession in the Netherlands. The Dutch case is interesting because it is characterised by increasing matching efficiency.

This paper uses data from 2001 to 2014 to study the Dutch labour market matching process accounting for the three labour market states and their heterogeneities.

The elasticity of hires with respect to the short-term employed was significant, positive and countercyclical, while elasticities relating to new entrants were procyclical. The matching function (MF) displays constant returns to scale (CRTS) when using an alternative labour supply (LS) measure that includes the short-term employed as jobseekers. The findings are at odds with the idea of mismatch and a shortage of skills. Search frictions for employers were lower and vacancies were filled faster. This can be related to the fact that in a loose labour market context with increasing short-term employment, employers increase their hiring of employed workers which generates negative externalities on unemployed.

The implications concern the specification of the MF and the CRTS assumption when using unemployment as a LS measure.

Peston points out that the conventional measure of unemployment (the number of persons unemployed) is inadequate as a measure of the social and economic costs of unemployment. He outlines a new approach which consists of weighting the number of unemployed in, say, each occupation, by a relative wage rate. This paper takes up Peston's suggestion and extends it to other measures of the labour market. Attempts are then made to obtain numerical estimates of these measures and compare them with the more conventional measures.

The purpose of this paper is to determine how much the lodging shared economy (LSE) utilizes minimum length of stay (MLOS) controls to maximize revenue and reduce housekeeping expense, since cleaning between guest visits represents a substantial variable cost for each guest’s stay. Hosts in the LSE are becoming increasingly perceptive in maximizing revenues.

Daily data for one year were collected for Vacation Rental by Owner properties in Hilton Head Island, SC and Orlando, FL. The collected data include daily vacancies for two different lengths of stay. Linear regression was used to explore the relationship between relative demand and vacancy length of stay differences.

During high-demand periods, there were few differences between the availability of short-term and longer-term reservation vacancies, which indicated hosts were not encouraging guests to stay longer during each visit. These results reveal differences in vacancies for three-night vs six-night reservations. A host can generate more revenue and decrease expenses by maximizing booked nights per visit.

Due to confidentiality issues, this study does not capture vacation bookings but instead captures vacancies. In addition, Average Daily Rate was not utilized in this study.

LSE hosts can maximize revenues using MLOS controls. Minimizing housekeeping costs boosts a host’s profitability.

Although this research has been conducted for hotel MLOS, there is a gap in the literature regarding LSE hosts’ use of MLOS.

Death by suicide among Black people in the USA have increased by 35.6% within the past decade. Among youth under the age of 24 years old, death by suicide among Black youth have risen substantially. Researchers have found that structural inequities (e.g. educational attainment) and state-specific variables (e.g. minimum wage, incarceration rates) may increase risk for suicide among Black people compared to White people in the USA. Given the limited understanding of how such factors systematically affect Black and White communities differently, this paper aims to examine these relationships across US states using publicly available data from 2015 to 2019.

Data were aggregated from various national sources including the National Center for Education Statistics, the Department of Labor, the FBI’s Crime in the US Reports and the Census Bureau. Four generalized estimating equations (GEE) models were used to examine the impact of state-level variables on suicide rates: Black adults suicide rate, Black youth (24 years and younger) suicide rate, White adult suicide rate and White youth suicide rate. Each model includes state-level hate group rates, minimum wage, violent crime rates, gross vacancy rates, and race-specific state-level poverty rates, incarceration rates and graduation rates.

Across all GEE models, suicide rates rose between 2015–2019 (ß = 1.11 – 2.78; ß = 0.91 – 1.82; ß = 0.52 – 3.09; ß = 0.16 – 1.53). For the Black adult suicide rate, state rates increased as the proportion of Black incarceration rose (ß = 1.14) but fell as the gross housing vacancy rates increased (ß = −1.52). Among Black youth, state suicide rates rose as Black incarcerations increased (ß = 0.93). For the adult White suicide rate, state rates increased as White incarceration (ß = 1.05) and percent uninsured increased (ß = 1.83), but fell as White graduation rates increased (ß = −2.36). Finally, among White youth, state suicide rates increased as the White incarceration rate rose (ß = 0.55) and as the violent crime rate rose (ß = 0.55) but decreased as state minimum wages (ß = −0.61), White poverty rates (ß = −0.40) and graduation rates increased (ß = −0.97).

This work underscores how structural factors are associated with suicide rates, and how such factors differentially impact White and Black communities.

The purpose of this paper is to propose a prediction model to predict the cost of complex products with lack of data. The cost estimating is one of the key elements of arguments around technological economy and investment decision‐making process of complex product.

A complex product has many characteristics, such as complex structure, large investment, high risk and it usually falls into small‐batch‐production category. Its cost estimation samples are small and cost data are very limited. Based on the characteristics of complex product and cost estimating, this paper introduces performance parameters sequence of associated known data, establishes an N‐GM (0, N) model of characteristic sequence with straddle missing data.

On the basis of the known key performance parameter sequence, N‐GM (0, N) model is used to predict the grey interval of overall cost vacancy data. Overall cost vacancy data is whitened by sorting reference sequence and realizing complex product overall cost estimation.

The method introduced in the paper can be used to solve practical problems, especially cost prediction of complex products with poor data. The model is also applied on the overall cost and the key component cost estimation of similar but different complex products. Moreover, it provides potential theoretical support for the development of complex product industry in the future.

In this paper, the complex product, which now plays a strategic industrial role in China, is systematically studied by utilizing a new methodology based on grey systems, especially the cost evaluation of the complex product. The use of grey correlation analysis in screening control key item index of complex product cost, the overall cost sequence of the complex product as related sequence and sorting reference sequence, the paper predicts and whitens vacant key item index, obtaining the key item cost index of complex product.

An acute need exists for a practical quantitative risk management‐based real estate investment underwriting methodology that clearly helps guide decision making and addresses the shortcomings of discounted cash flow (DCF) modeling by evaluating the full range of probable outcomes. This paper seeks to address this issue.

The simulation‐based excess return model (SERM) is an original methodology developed based on an application of Monte Carlo simulation to project risk assessment combined with the widely practiced DCF modeling. A case study is provided where results of the modeling are compared with traditional DCF risk models and with prior projects with known outcomes.

This paper lays out a practical method for stochastic quantitative risk management modeling for real estate development projects and illustrates that for identical projects risk‐adjusted returns derived with the use of SERM may differ significantly from returns provided by traditional discounted cash flow analysis. SERM corrects serious shortcomings in the DCF methodology by incorporating stochastic tools for the measurement of the universe of outcomes. It further serves to condense the results of Monte Carlo simulations into a simplified metric that can guide practitioners and which is easily communicational to decision makers for making project funding decisions.

SERM offers a simple, practical decision‐making method for underwriting projects that addresses the limitations of the prevailing methodologies via: stochastic assessment of the range of outcomes; interdependence of input variables; and objective risk premium metrics.

This paper presents an original methodology for making project‐funding decisions for real estate development projects that is based on Monte Carlo simulation combined with DCF analysis. The methodology presented here will have value for real estate developers, investors, project underwriters, and lenders looking for a practical and objective method for project valuation and risk management than is offered by traditional DCF analysis. A review of literature did not reveal analogous methodologies for risk management.

This study seeks to explore the demand side of the labour market influenced by the digital revolution. It aims at identifying the new composition of skills and their value as implicitly manifested by employers when they look for the new labour force. The authors analyse the returns to computing skills based on text mining techniques applied to the job advertisements.

The methodology is based on the hedonic pricing model with the Heckman correction to overcome the sample selection bias. The empirical part is based on a large data set that includes more than 9m online vacancies on one of the biggest job boards in Russia from 2006 to 2018.

Empirical evidence for both negative and positive returns to computing skills and their monetary values is found. Importantly, the authors also have found both complementary and substitutional effects within and between non-domain (basic) and domain (advanced) subgroups of computing skills.

Apart from the empirical evidence on the value of professional computing skills and their interrelations, this study provides the important methodological contribution on applying the hedonic procedure and text mining to the field of human resource management and labour market research.