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This paper discusses the factors to consider when designing studies to measure hiring discrimination against transgender job applicants.

The paper builds on academic literature related to hiring discrimination and transgender employment to build a detailed discussion of the numerous factors and issues inherent in hiring discrimination against transgender job applicants. By isolating and describing a number of relevant considerations, the paper aims to act as a guide for future studies to build upon.

Three types of hiring discrimination studies are discussed: correspondence tests, in-person experiments and student cohort experiments. Three main categories of factors relevant to an experiment’s design are then discussed: the legal context, industry/role factors and transgender population-specific factors. A flow-chart detailing the research design decision-making process is provided.

The discussion within this paper will act as a reference and a guide for researchers seeking to address the dearth of empirical studies in the literature. The list is not exhaustive; while a number of factors relevant to transgender-specific studies are identified, there may be more that could affect an experiment's design.

Hiring discrimination against transgender people has been recorded in many surveys, but there is little empirical measurement of this discrimination. To the author's knowledge, this paper is the first to examine the experimental design decisions related to transgender hiring discrimination. In doing so, it provides contributions for two primary audiences: those researching transgender employment issues but who have never conducted a study measuring hiring discrimination; and those who have previously conducted studies on hiring discrimination, but have not done so with reference to transgender job applicants.

The purpose of this paper is to explore how both gender and facial attractiveness affect job candidates’ chances of obtaining interviews in China’s dynamic internet job board labor market. It examines how discrimination based on these attributes varies over occupation, location, and firms’ ownership type and size.

The authors carry out a resume audit (correspondence) study. Resumes of fictitious applicants are first carefully crafted to make candidates appear equally productive in terms of their work histories and educational backgrounds. The authors control gender and facial attractiveness. The authors establish the facial attractiveness of candidate photos via an online survey. In total, 24,192 applications are submitted to 12,096 job postings across four occupations in six Chinese cities. Callbacks are carefully tracked and recorded. Discrimination is estimated by calculating the differences in the rates of callbacks for interviews received by individuals whose applications vary only in terms of facial attractiveness and gender. The authors reuse the same resumes repeatedly through this project such that names and photos of each of the candidates: attractive man, attractive woman, unattractive man, and unattractive woman is attached to each resume hundreds of times for each occupation in each city.

The authors find sizable differences in the interview callback rates of attractive and unattractive job candidates. Job candidates with unattractive faces need to put in 33 percent more applications than their attractive counterparts to obtain the same number of interview callbacks. Women are preferred to men in three of the four occupations. Women, on average need put in only 91 percent as many applications as men to obtain the same number of interview callbacks.

The analysis of this paper focusses on only four different occupations. Its scope is also limited to exploring only the first part of the hiring process – obtaining a job interview. Furthermore, its fictitious applicants are all young people, approximately 25 years old. It would be useful to explore how gender and facial attractiveness affect candidates’ chances of landing a job after getting an interview.

This paper contributes to and expands the literature on hiring through China’s internet job boards. It also contributes to the literature on the role of facial attractiveness in hiring.

Document management is growing in importance proportionate to the growth of unstructured data, and its applications are increasing from process benchmarking to customer relationship management and so on. The purpose of this paper is to improve important components of document management that is keyword extraction and document clustering. It is achieved through knowledge extraction by updating the phrase document matrix. The objective is to manage documents by extending the phrase document matrix and achieve refined clusters. The study achieves consistency in cluster quality in spite of the increasing size of data set. Domain independence of the proposed method is tested and compared with other methods.

In this paper, a synset-based phrase document matrix construction method is proposed where semantically similar phrases are grouped to reduce the dimension curse. When a large collection of documents is to be processed, it includes some documents that are very much related to the topic of interest known as model documents and also the documents that deviate from the topic of interest. These non-relevant documents may affect the cluster quality. The first step in knowledge extraction from the unstructured textual data is converting it into structured form either as term frequency-inverse document frequency matrix or as phrase document matrix. Once in structured form, a range of mining algorithms from classification to clustering can be applied.

In the enhanced approach, the model documents are used to extract key phrases with synset groups, whereas the other documents participate in the construction of the feature matrix. It gives a better feature vector representation and improved cluster quality.

Various applications that require managing of unstructured documents can use this approach by specifically incorporating the domain knowledge with a thesaurus.

Experiment pertaining to the academic domain is presented that categorizes research papers according to the context and topic, and this will help academicians to organize and build knowledge in a better way. The grouping and feature extraction for resume data can facilitate the candidate selection process.

Applications like knowledge management, clustering of search engine results, different recommender systems like hotel recommender, task recommender, and so on, will benefit from this study. Hence, the study contributes to improving document management in business domains or areas of interest of its users from various strata’s of society.

The study proposed an improvement to document management approach that can be applied in various domains. The efficacy of the proposed approach and its enhancement is validated on three different data sets of well-articulated documents from data sets such as biography, resume and research papers. These results can be used for benchmarking further work carried out in these areas.

This chapter reviews the historical and political context of immigration to Norway, patterns of ethnic inequality in the labour market, as well as how ethnic discrimination has been legislated, publically debated and studied in the Norwegian context. Drawing on the findings of a multimethod study of discrimination in the Norwegian labour market, combining a field experiment with employer-interviews, the chapter furthermore clarifies the extent of discrimination in ethnic minority applicants’ access to the labour market and discusses what mechanisms influence the level of ethnic discrimination ‘at work’. The field experiment reveals that young Norwegians of Pakistani heritage – the by far largest group among immigrant descendants in the country – face substantial discrimination when applying for work. However, it also demonstrates striking differences in the scope of discrimination between the public and the private sector, as well as across occupational contexts, indicating that discrimination should not be seen as mere reflections of individual bias, ethnic preferences or statistical uncertainty, but rather that such individual-level dispositions are mediated through factors at the organizational level. This conclusion has important implications for our theoretical understanding of why discrimination occurs, as well as for the further development of anti-discrimination measures.

A surprisingly large proportion of the working population of the USA consists of individuals with felony convictions. Moreover, the issue of employability of these individuals is compounded for minorities. This paper aims to present two experimental studies investigating whether minorities with felony backgrounds have a more difficult time being selected for employment than identically situated white applicants. The authors ground the paper in realistic group conflict theory. Results indicate discrimination is more acute against minorities with felony backgrounds than whites with the same background and shed light on the mechanisms leading to this discrimination. Theoretical and practical implications are discussed.

This paper involves two experimental studies involving working adults engaging with realistic survey situations using mTurk.

Results of both studies indicate discrimination is more acute against minorities with felony backgrounds than whites with the same background, and shed light on the mechanisms leading to this discrimination.

One limitation of the methodology is that the authors used fictional candidates and jobs. This may have led to understating the effects of discrimination on minorities because it allowed applicants to answer in socially desirable ways (e.g. absent of racial bias) without suffering any of the anticipated negative consequences of actually hiring individuals about whom they hold negative stereotypes.

This research has several important implications for practice. First, organizations should be keenly aware of the potential for subtle and unconscious bias to affect the job application process even among well-intentioned hiring managers. Second, as the bias is often triggered by threats, organizations should share with their employees the nature of the threat involved with former felons.

Organizations should deliberately address issues associated with the use of criminal background checks. For many organizations, a felony conviction in an applicant’s background automatically eliminates that person from employment. However, a substantial amount of the workforce now has a felony in their background. Indeed, the Equal Employment Opportunity Commission (2012) has issued guidelines that detail important factors that organizations should consider on a case-by-case basis when considering employment for former felons. Organizations may consider updating any blanket exclusions regarding the hiring of ex-felons – not only because it makes good policy but also because it may help the organization hire the best people.

This research studies an important – and growing – societal problem related to the hiring of convicted felons, and the related issue of racial discrimination that affects black convicted felons particularly hard. There has been very little work in the management area on this topic. Moreover, there has been very little work in all areas that includes experimental methods. The use of such methods is particularly useful to eliminate confounds found in field data.

This article deals with the impact of ethnic origin on individual employability, focussing on the first stage of the hiring process. Deeply, the authors’ goal is to fathom whether there is a preference for native job candidates over immigrants, decomposing the discrimination against minority groups into its statistical and taste-based components by means of a new approach.

The authors built up a data set by means of an ad hoc field experiment, conducted by sending equivalent fictitious CVs in response to 1000 real online job openings in Italy. The authors developed the discrimination decomposition index using first- and second-generation immigrants.

The authors’ main result is that both first- and second-generation immigrants are discriminated compared to Italians. In between the two categories, second-generation candidates are discriminated especially if their ethnicities are morphologically different from those of natives (i.e. Chinese and Moroccans). This last finding is a clear symptom of discrimination connected to taste-based reasons. On the other hand, first-generation immigrants of all nationalities but Germans are preferred for hard-work jobs.

The authors develop the discrimination decomposition index to measure the proportion of the two kinds of discrimination (statistical and taste-based) over the total one and apply a probit model to test the statistical significance of the difference in treatment between the three groups of natives, first-generation and second-generation immigrants.

The purpose of this paper is to present the results of a discrete choice experiment (DCE) on the competencies of potential information technology (IT)-retrainees. The results give insights in the monetary value and relative returns to both soft and hard skills.

The authors apply a DCE in which the authors propose seven pairs of hypothetical candidates to employers based in the municipality of Amsterdam, the Netherlands. These hypothetical candidates differ on six observable skill attributes and have different starting wages. The authors use the inference from the DCE to calculate the marginal rates of substitution (MRS). The MRS gives an indication of the monetary value of each skill attribute.

Employers prefer a candidate who possesses a degree in an exact field over a similar candidate from another discipline. Programming experience from previous jobs is the most highly valued characteristic for an IT-retrainee. Employers would pay a candidate with basic programming experience a 53 percent higher starting wage. The most high-valued soft skill is listening skills, for which employers are willing to pay a 46 percent higher wage. The results of this paper show that both hard and soft skills are important, but not all soft skills are equally important.

The results on the returns to skills provide guidelines to tailor IT training and retraining programs to the needs of the business environment. A key strength of this paper is that the authors have information on the preference orderings for different skills and kinds of experience.

The economics literature on gender has expanded considerably in recent years, fueled in part by new sources of data, including from experimental studies of gender differences in preferences and other traits. At the same time, economists have been developing more realistic models of psychological and social influences on individual choices and the evolution of culture and social norms. Despite these innovations, much of the economics of gender has been left behind, and still employs a reductive framing in which gender gaps in economic outcomes are either due to discrimination or to “choice.” I suggest here that the persistence of this approach is due to several distinctive economic habits of mind – strong priors driven by market bias and gender essentialism, a perspective that views the default economic agent as male, and an oft-noted tendency to avoid complex problems in favor of those that can be modeled simply. I also suggest some paths forward.

Gender inequality in paid work persists, in the form of a gender wage gap, occupational sex segregation and a “glass ceiling” for women, despite substantial institutional change in recent decades. Two classes of explanations that have been offered as partial explanations of persistent gender inequality include economic theories of statistical discrimination and social psychological theories of status-based discrimination. Despite the fact that the two theories offer explanations for the same phenomena, little effort has been made to compare them, and practitioners of one theory are often unfamiliar with the other. In this article, we assess both theories. We argue that the principal difference between the two theories lies in the mechanism by which discrimination takes place: discrimination in statistical models derives from an informational bias, while discrimination in status models derives from a cognitive bias. We also consider empirical assessments of both explanations, and find that while research has generally been more supportive of status theories than statistical theories, statistical theories have been more readily evoked as explanations for gender inequalities in the paid labor market. We argue that status theories could be more readily applied to understanding gender inequality by adopting the broader conception of performance favored by statistical discrimination theories. The goal is to build on the strong empirical base of status characteristic theory, but draw on statistical discrimination theories to extend its ability to explain macro level gender inequalities.

In this chapter, we aim to make the following contributions to the perceived overqualification literature. First, we provide an opportunity-based fairness conceptualization of perceived overqualification, and differentiate it from other justice constructs. Second, we present a multilevel model of perceived overqualification, which enumerates the antecedents and consequences, and explicates the mediators and moderators of the effect of perceived overqualification. Third, we emphasize the importance of considering methodological issues in future research on overqualification. Finally, we offer specific suggestions in studying applicant overqualification and recruiter perceptions.