The Mexican Constitution prohibits any form of discrimination that infringes on human dignity, rights, and freedoms. This is further reinforced by the federal labor law, which outlines discriminatory workplace practices, and the federal law for preventing and eliminating discrimination, which provides preventive measures, equality initiatives, and complaint procedures. Employers in Mexico are legally required to adhere to these regulations. Although no specific legal standards exist for demonstrating reliability and validity in employment selection tests, employers using such tests must demonstrate their job-related relevance. Employers found guilty of discrimination may face fines ranging from 1,400 to 30,000 USD, with penalties determined by intent, severity, and recurrence. Common administrative sanctions include mandatory training for involved staff and internal awareness campaigns. Publicizing these cases has proven effective in raising awareness in human resources professionals, encouraging the elimination of discriminatory practices in the workplace. These legal and administrative measures aim to foster a fair and inclusive work environment in Mexico.

This chapter examines bias and fairness in employment testing in the Netherlands, addressing twenty key questions related to historical and cultural developments, legal frameworks, professional guidelines, and psychometric issues. Although equal treatment is a fundamental legal principle, perceptions of hiring discrimination remain widespread. The chapter explores demographic shifts that have shaped discussions on employment fairness and outlines the Dutch legal framework, focusing on the Equal Treatment Act and the role of the Netherlands Institute for Human Rights in handling discrimination complaints. It also highlights the relatively limited attention given to fairness in professional guidelines for practitioners. Furthermore, the chapter evaluates how psychological tests are assessed for bias, particularly through analyses of score differences, differential item functioning, and measurement invariance, while noting the scarcity of research on predictive bias. Emerging challenges, such as algorithmic bias, are also examined. Finally, the chapter discusses recent legislative efforts to promote fairness in employment testing, including a proposed law that was rejected in 2024.

In this chapter, we examine pay negotiations (collective and individual) and pay communication policies (national policies and firm strategy). Pay negotiation creates substantial differences in pay across individuals, groups and organisations. We begin with an overview of the different wage-setting mechanisms that include collective bargaining, individual negotiation and legal minimum wage (such as Award wages in Australia). We then explore collective pay bargaining, starting from its main concepts and cross-national differences in the industrial relations system. Following this, we delve into the main features and major strategies of collective pay bargaining. We then examine individual pay negotiation, discussing the process of pay negotiation, factors that drive negotiation propensity, and the main strategies and tactics involved. Special attention is paid here to the role of gender in individual pay negotiation. The chapter concludes with an examination of pay transparency and pay communication, including the recent regulatory movement towards pay transparency and the strategic considerations of pay transparency policies.

This chapter examines employment testing bias and fairness in Japan. Japan’s hiring practices are shaped by its historical ethnic homogeneity, employer discretion, and a legal framework emphasizing procedural fairness over outcome equity. Anti-discrimination laws protect women, older workers, and people with disabilities, but issues concerning nationality, race, and minority groups such as the Ainu and Dowa have historically received less attention. Regulatory bodies, including the Ministry of Health, Labor, and Welfare, issue guidelines to ensure fair hiring practices, but these lack effective enforcement. Legal remedies for discriminatory hiring are rare due to the high burden of proof and limited application of disparate impact doctrine. Traditional aptitude and psychological tests remain central in employment selection, with the increasing emergence of artificial intelligence-based hiring practices raising new fairness concerns. In response, some employers are adopting blind procedures, including anonymized resumes. As globalization and labor shortages intensify, Japan’s employment practices face increasing pressure to evolve toward greater inclusion and equity.

This chapter examines employment testing bias and unfairness in Romania, the legal framework, discrimination types, and the role of regulatory bodies. Multiple organizational stakeholders are involved in employment testing, yet the Romanian legal system lacks explicit regulations on test bias and fairness. Although EU directives mandate equal treatment, they do not require validation of employment tests. The National Council for Combating Discrimination oversees enforcement but lacks authority over compensation or reinstatement. Employers, particularly in the private sector, often outsource recruitment and selection. Psychological testing is primarily governed by the Romanian College of Psychologists. Bias detection and disparate impact assessment remain underdeveloped, with limited legal requirements for test validity evidence. Romania’s approach to balancing validity and diversity remains informal, and artificial intelligence-based hiring tools are scarcely used. Legal consequences for discrimination range from fines to criminal penalties, yet selection methods remain largely unregulated. The chapter highlights gaps between advances in psychometrics and their application in employment selection.

Rewarding employees based on their performance is a crucial part of rewards management. Performance-based payments can illicit two behavioural responses, summarised as the incentive effect and the sorting effect. ‘Incentive effect’ refers to employees being more likely to increase their work effort when they believe it will be rewarded with higher pay. ‘Sorting effect’ has to do with the movements of people across organisations: highly capable and competitive employees are attracted to meritocratic organisations, while low performers are driven out of such organisations. This chapter examines the main types of individual and collective incentive plans, focusing on short-term plans (usually one year or less of the performance cycle). The individual performance pay includes merit payments, results-based individual incentives (piece rate, sales commission and goal-based bonus), and employee recognition plans. Collective incentives are profit-sharing, gainsharing, goal-sharing and team incentives. The chapter also discusses how each incentive plan aligns with the firm’s strategies and the implications of implementing a complex pay system with multiple types of performance pay plans.

This chapter presents the answers to the twenty questions on employment bias and fairness in Spain. It describes the national demographic and administrative-political characteristics. In Spain, bias is associated with the notions of equality and non-discrimination, and it is regulated by both Spanish laws and EU regulations. The chapter describes (a) which groups are protected by the law, (b) which are the main regulatory authorities (general and sectorial laws, and EU regulations), (c) which employers are covered, (d) the guidelines used in Spain to guide professional activity, (e) the nine categories of discriminatory actions and situations distinguished in Spain, (f) the 60 percent or three-fifths rule of disparate impact, (g) the burden of proof in the Spanish law, (h) the use of quotas and score adjustment for the categories of age, sex, and disabilities, and (i) issues about the legal consequences of violating the laws, the type of questions that must be excluded from selection procedures, and the statistical tools used to examine bias. It closes with an overview of recent laws affecting the use of artificial intelligence technologies and how the legal environment impacts the practice of industrial, work, and organizational psychology.

The use of tests and assessments in employment-related decision making has the potential to benefit organizations and individuals. However, their use is frequently criticized because of their adverse potential for bias and unfairness. The saliency of and attention to these issues may also vary from one country to another. Therefore, in addition to an overview of the handbook and its objectives, the present chapter presents a synthesis of the twenty-three chapters organized around four themes pertaining to bias and unfairness in employment testing, specifically, (1) historical and/or cultural issues, (2) legal and professional guidelines and issues, (3) psychometric issues, and (4) future- and forward-looking issues. Furthermore, the theory of cultural tightness-looseness is used in an exploratory manner to gain additional insights into patterns, or the lack thereof, across countries as reported in the chapters. The patterns of associations indicated that, relative to tight countries, loose countries were generally more attune to and have in place practices and regulations addressing employment testing bias and unfairness. Finally, some thoughts and suggestions for future research are discussed.

Our central proposition is that developing and maintaining an effective and sustainable approach to performance and reward management requires careful consideration of two key factors: (1) the organisation’s strategic objectives; and (2) employees’ psychological needs, expectations and perceptions. To optimise their effectiveness, performance and reward policies and practices should be compatible with, and support, both of these factors. Misalignment between the two occurs all too readily and is likely to produce suboptimal outcomes for the organisation or its employees, or both. We examine the considerations and challenges involved in achieving strategic alignment and explore the possibilities and complexities of securing psychological engagement. Throughout the chapter, ‘reality check’ boxes encourage you to think through these two themes in practical terms, while the end-of-chapter case study invites you to apply the themes of strategic alignment and psychological engagement to our focal case study organisation, IT Angels.

Employment testing is a key tool for selection and placement in China’s public and private sectors. Rooted in a tradition of rigorous exams and shaped by modern workforce demands, such testing significantly influences access to job opportunities. Yet concerns about bias and fairness persist, driven by cultural norms, legal structures, and changes in the labor market. This chapter examines key issues related to bias and fairness in Chinese employment testing, exploring historical and cultural contexts, legal regulations, professional standards, and enforcement mechanisms. It also addresses measurement bias, challenges to diversity, and the growing influence of machine learning and advanced psychometrics in assessment design. By analyzing these dimensions, the chapter offers a comprehensive view of current challenges and highlights opportunities to improve equity in hiring practices. The discussion provides insights for employers, policymakers, and researchers navigating the complexities of employment testing in China.

The Korean term insamansa – “human resources are everything” –‘captures the deep value the nation places on personnel. Despite long-standing recognition of their importance, formal selection systems have emerged only recently. This chapter examines bias and fairness in Korean personnel selection through historical, legal, and societal lenses. Korea’s transformation from the labor-intensive industries of the 1960s–1980s to a technology-driven economy in the 2000s has reshaped perceptions of employment fairness. Current workplace protections primarily address sex and disability discrimination through laws such as the Equal Employment Opportunity and Work-Family Balance Assistance Act. Although the Fair Hiring Procedure Act aims to reduce biased practices, challenges persist in ensuring the validity and fairness of selection methods. Moreover, the increasing use of artificial intelligence in hiring raises concerns over algorithmic bias. The chapter calls for evidence-based policies and robust statistical methods to improve validity and fairness in Korea’s evolving labor market.

Testing and assessment have a long history in Greece. External hiring in the Greek public sector is carried out by the Supreme Council for the Selection of Personnel, an independent human resource management (HRM) body that currently runs employee selection procedures with the use of employment tests. In the private sector, employee assessment methods are used to a much greater extent than in the public sector. Greece’s entry into the European Union in 1981, as well as the competition from foreign companies, have further challenged HRM practices and methods used in staffing. Hiring processes have been enhanced by the inclusion of additional selection stages, such as semi-structured interviews, group interviews, and initial screening via job boards to augment the level of standardization and reduce incidents of bias. Greece’s entry into the EU has also led to the gradual addition of new laws to the Greek constitution aimed at establishing and enhancing equal opportunities in work, employment, and education. However, there are no specific guidelines implemented by psychological or HRM associations that specifically address bias and fairness in employee recruitment and selection processes.

This chapter explores bias and fairness in employment testing in Türkiye across governmental and private sectors. It distinguishes fairness – equal opportunity, transparency, and uniform outcomes – from bias, especially in relation to predictive validity. The chapter situates these issues within Türkiye’s cultural, ethnic, and socioeconomic landscape, examining how historical and regional factors shape perceptions and practices. Key legal and regulatory frameworks, such as Turkish Labor Law and constitutional mandates, are reviewed to highlight protections for equal treatment. It also evaluates bias detection methods, including differential item functioning, sensitivity reviews, and predictive bias analyses, and discusses challenges from emerging technologies such as the use of artificial intelligence in personnel selection. The chapter underscores the need for strong validity evidence and proactive strategies to promote fair and equitable hiring in Türkiye.

Nigeria’s diverse history and ethnic diversity have shaped the country’s current understanding of bias and fairness, including issues relating to employment. This chapter focuses on employment testing bias and fairness in Nigeria. When making employment decisions, it is a common occurrence, albeit not a legally permissible one, to have factors such as age, sex, political beliefs, religion, ethnicity, and disability taken into account. Nigeria’s discrimination laws cover all employers, third parties, and licensure. However, Nigerian discrimination adjudication has a narrow purview. For instance, there are no clear standards for validity evidence, no rules for demonstrating disparate impact, no shifting of the burden of proof, and no recognition of disproportionate impact. The limited use of professionally designed selection processes also means that bias-related concerns receive little attention. Information about the impact of the legal environment on industrial and organizational psychology is similarly lacking. Nonetheless, there are initiatives aimed at professionalizing psychology in the nation, which should increase the reliability and validity of selection procedures.