The fragile myth of meritocracy.
Building meritocratic and equitable organizations is a complex but critical undertaking, as many business leaders have understood. It requires effective talent management systems to attract, develop and retain qualified and motivated individuals, key factors for the success of the organization. In my book,The Meritocracy Paradox, I warn that some organizational efforts aimed at promoting meritocracy and excellence in organizations can paradoxically accentuate theinequalitiesand theinjustices. I have presented evidence for three key findings, together with related suggestions, which highlight what I call the 'paradox of meritocracy’.
The first tipis that simply having organizational processes to hire, evaluate and promote the best does not automatically guarantee fairness. Indeed, any talent management process can be subject to biases and inefficiencies, and there is a risk that, rather than promoting excellence and opportunity for all, people-based management systems may actually reinforce or create advantages for some groups over others.
The second tipis that emphasizing meritocracy – implicitly or explicitly – as the foundation of hiring, promotion, and reward practices can backfire on women, racial minorities, immigrants, and other historically disadvantaged groups. When individuals believe their organization is meritocratic, they may be less likely to recognize and correct biases in their decision making. This can lead to unfair treatment of certain individuals or groups and, inadvertently, exclude candidates whose skills and talents deserve to be included.
The third tipis that there is no universal agreement on what merit actually is. Even managers and executives, despite having similar training and experience within the same organization, often have divergent opinions. This lack of consensus on what constitutes merit or talent can ultimately undermine efforts to build a truly meritocratic organization.
The encouraging news is that promoting true meritocracy in the workplace does not require an excessive amount of time or resources, rather a strategic and intentional focus on eliminating bias and improving talent management processes.
Taking action is key. However, the pressure to act often leads companies to implement generic solutions – such as diversity and implicit bias training, blind screening and hiring, changes to the language used in job adverts, and the use of AI-based recruiting tools, all of which have been shown to have limited effectiveness – without first diagnosing the organization's specific challenges or needs. The 'best practice' approach is insufficient because it does not take into account the specific context of an organisation. In this article, taken from my book, I propose a more effective approach: a data-driven talent management strategy that actively addresses biases and inequalities, while ensuring efficient, fair and meritocratic decision-making.
An approach based on talent analysis
My strategic approach, based ontalent analysis, aims to promote meritocracy, i.e. organizational systems that reward and promote individuals solely on the basis of their intelligence, efforts, skills, abilities or demonstrated performance, without taking into account their demographic or personal characteristics. By collecting, coding, and analyzing job-relevant data related to people-related processes and outcomes, meritocratic organizations strive to improve fairness and equal opportunity in hiring, promoting, and rewarding individuals – three key career outcomes that not only benefit individuals, but also contribute to the success of organizations.
Two conditions are essential to realize meritocracy in practice. First, they should be thereequal opportunitiesfor everyoneindividuals at decisive moments related to selection, advancement and rewards. A talent analytics approach can support this first objective by identifying any people-related processes that may limit equal access to opportunities. Second, once equality of opportunity is established, ensuring that individuals can obtain positions and rewards based solely on their merit,a disparity in outcomes may be acceptable in a meritocratic system: The key principle here is that disparities in pay, rewards and promotions should be determined by job-relevant factors rather than demographic or personal characteristics. A talent analytics approach can play a crucial role in this regard, helping organizations assess whether their people processes are working fairly and identifying any areas where biases and other inefficiencies may be present.
It is appropriate to make a clarification, or rather, give a warning. The first condition implies that opportunities for all are win-win situations in which people can benefit equally from the organization's efforts to create opportunities, such as corporate initiatives that offer training and employment benefits. But the second condition recognizes that the distribution of positions and rewards – particularly in the workplace – may be a zero-sum game, as organizations have a limited number of jobs and promotions to offer, as well as fixed budgets for raises and bonuses, and consequently not everyone will 'win'. In such zero-sum situations, and to promote meritocracy, it is therefore vital that organizations ensure everyone has an equal opportunity to compete and succeed.
How to arrive at an effective strategy for talent analysis
Many organizations, even large and successful ones, lack a strategic approach to talent management that identifies and addresses challenges related to meritocracy and equity. I present five key steps to help create and develop this powerful strategic approach to fostering meritocracy in your organization.
Phase 1: Identify, develop and define key criteria. You must follow this process for every primary hiring decision within your organization. When recruiters and managers review application materials, they must be clear about the criteria required for the position, such as: qualifications, experience and skills. Some use the term “skills”to refer to these criteria and the term “competence model” to refer to the process of defining these criteria. You need to be specific (and realistic): without this clarity, biases and social processes could lead recruiters to search for candidates in a limited number of ways or to apply different criteria depending on the candidate, potentially reinforcing prejudices and social barriers.
As part of this first step, you need to evaluate whether you observe themdemographic differencessignificant in achieving the criteria used to select and hire candidates. If so, check whether such criteria should be used, especially if only some groups have had the opportunity to meet them in the past. If these criteria are essential (as they often are), consider providing the necessary resources so that all employees can learn (or improve) these criteria, so that, ultimately, everyone has an equal opportunity to succeed. In this regard, post-hire resources and training can help level the playing field. In fact, many organizations have implemented specific onboarding, training and development programs for this very reason.
But this first step is not always easy. Many jobs require people with specific skills or credentials, which can lead to demographic imbalances in the pool of qualified candidates. Furthermore, simply knowing the criteria required for certain jobs does not guarantee an equal opportunity to obtain them; many existing social barriers are difficult for individual organizations to overcome. Suppose, for example, a person joins a medical organization as a certified nursing assistant, is already an adult with children and family responsibilities, and does not have a college education. In this case, even if he has access to information on how to pursue a career in medicine, he has very little chance of becoming a doctor. In these situations, the challenges are clearly social in nature and difficult for a single organization to address: meritocratic societies and institutions, for example, could provide the opportunities and resources necessary for talented people to attend university and medical school.
That said, sometimes not requiring specific qualifications or credentials can be an effective way to tap into a broader talent pool. For example, in 2023, the state of Pennsylvania stopped requiring college degrees for certain jobs, and other states, such as Utah, Maryland, and Alaska, followed suit. Researchers and workers' rights advocates have urged employers to eliminate college degree requirements for jobs that do not actually require them (Fuller and Raman, 2017; R.C. Booth, 2023). Companies like AT&T, Mastercard, Microsoft, and Southwest Airlines have also created alternative paths to secure suitable and stable jobs for those without college degrees.2
In implementing this first phase, it is advisable to follow a holistic approach in selecting and hiring people, focusing on the employeeas a wholerather than on specific skills and abilities, if possible. Many large companies and undergraduate and graduate programs use this approach to ensure a professionally and demographically diverse talent pool. Candidates are selected not only based on certain skills needed to do the job well currently, but also based on their potential to do a great job in the future.
This holistic approach, therefore, takes a long-term and often strategic perspective to talent management in organizations. It allows leaders to experiment and understand whether specific skills required in the past are still necessary. It can offer organizations the opportunity to discover an untapped candidate pool that competitors are not yet aware of, as they often rely on narrow, rigid and outdated hiring criteria. For example, when recruiters and hiring managers establish selection criteria, such as a certain number of years of work experience or a particular qualification or degree, they may need to revise those criteria after reviewing the candidate pool and subsequent performance of new hires.
The same approach can be applied when making decisionspost-hiringrelating to bonuses, promotions or development opportunities. Many employers, in this regard, could benefit from hiding irrelevant information when granting promotions or bonuses. Others may take a more holistic approach that takes into account the employee's current ability to perform the job and how that employee may develop in the future.
Once again, however, this holistic approach may only work in certain situations, for example in organizations and companies that hire many people for relatively entry-level or mid-level professional positions, or in those that periodically take into account a large number of promotions or bonuses. This approach may not work when hiring people for high-level or senior positions or when specific skills and qualifications are an essential part of the job.
Step 2: Measure key individual characteristics and employment outcomes over time.Measurement and data collection are critical to determining whether biases and social processes influence how you select, hire, compensate, and promote employees within your organization. Such measurement can also help to 'de-bias' such employment processes and outcomes. Without first identifying where biases and barriers may exist, it is difficult to know where to focus your efforts, as well as how to later monitor and evaluate whether those efforts have been successful.
First, it is important to collectindividual data(which often includes demographic or personal information) for both employees and candidates. Obtaining this data is often difficult when it has not been previously collected; often, in some countries, lawyers advise against obtaining such data (on the assumption that if a company does not have the data, it cannot be held legally responsible in cases of discrimination). That said, many institutions, experts and managers have encouraged the collection and reporting of demographic data, particularly for medium and large organizations. To ensure that there is no discrimination against particular groups of employees protected by national, regional and local employment laws and regulations, it is helpful to ensure that you carefully review the legal constraints relating to the data you are permitted to collect, as regulations and rules vary depending on the jurisdiction in which the organization is located (Lewi, 2023). Many of these recommendations even promote the practice of transparency – the publication of such data and analysis in easy-to-understand formats to show the progress organizations are making towards equity and equality. (Slan, 2023).
People may rightly be reluctant to provide demographic information if they have experienced prejudice and unfair treatment in the workplace in the past. However, if an organization demonstrates over time that it is serious about correcting imbalances, addressing obstacles, and providing opportunities for all, people may feel more comfortable sharing that information. While it's difficult to find a comprehensive list of companies where employees feel comfortable sharing their demographic information, some companies have made public their efforts to promote diversity and equal opportunity for all. They may even have put initiatives in place to encourage employees to disclose such information.
Such data can also help address concerns about reverse discrimination, where members of a dominant group may feel treated unfairly in favor of historically disadvantaged groups. This is one of the most common reasons for negative reactions to practicesDiversity Equity e Inclusion(DEI): the argument that many of these practices unfairly favor disadvantaged groups. The approach I propose here can actually help identify situations where this might occur and, consequently, help organizations take action to resolve such tensions. This is a truly meritocratic approach should helpeveryoneto have equal opportunities, eliminating prejudices and improving talent management processes: this approach requires ensuring that no group of individuals is penalized or unfairly favored in the pursuit of meritocracy.
In this regard, it is crucial, once again, to systematically collect and store relevant employment outcome data for candidates, employees and managers (including transfers, promotions, demotions and resignations). Below I describe some results that you should consider monitoring over time:
Recruitment Results:these should include the number of candidates for a given position, their professional and personal background, and their skills, abilities and experience relevant to the position.Results of selection and choice:these should include those progressing through the selection process at the interview, offer and offer acceptance stages.Post-hire results:these should include performance evaluations, promotions, transfers, layoffs, base pay, salary increases, bonuses and benefits.
These are the most common employment-related outcomes to track. However, it is up to each company and professional team to identify key employment-related outcomes and steps that may impact the careers of candidates and employees. This is a key part of this second phase, as it will then allow you to critically examine to what extent the organization is meritocratic and fair in making employment decisions.
Measuring employment-related outcomes is as important as measuring and collecting information oninputand onprocessesunderlying the achievement of these results. For example, it is crucial to measure inputs from job seekers and employees at the point of hiring and beyond to understand their relevant skills and abilities. This task can typically be accomplished by capturing and codifying information contained in resumes and other application materials relevant to the job. In the case of employees, such data can be integrated with training and experience within the organization that improves their contribution and performance. All this information is relevant to Phase 3, which involves analyzing the data to identify any disparities and, if they exist, the reasons behind them, so that we can find the best solutions to the identified challenges.
Additionally, while collecting this information can help companies identify areas for improvement and track progress over time, it is critical to do so in a way that respects employee privacy and ensures that the information is used responsibly and ethically. Once again, you need to check the legal constraints on the data you are allowed to collect based on your organization's location.
These collection activities must also be preceded by employee information and transparency on the reasons why the data is collected, how it will be used and how the results of the analyzes will be reported. Data collection processes, analyzes and findings must also reiterate that privacy will always be protected. (Habtemariam, 2022; Melloy, 2020; Magoon, Robinson, & Kissling, 2022).
Step 3: Analyze the data collected not only on results but also on processes.Once you have collected and stored information over time in a database, you can begin to analyze that data. First, it is possible to explore aggregate patterns in each of the employment outcomes measured by the variables of interest. For example, I've seen companies calculate the percentage of candidates who receive interviews based on gender and race and find important disparities. In the case of base salary, a simple approach is to calculate the average base salary by gender and then test whether the observed differences are statistically significant.
If the sample size allows, multivariable models can be estimated that control for other important individual factors that may explain or influence a particular outcome of interest. For example, you might want to estimate a model that predicts the probability of getting an interview based on an applicant's gender and race after controlling for the relevant skills and experiences needed for the job, i.e.you deserve. If the demographic coefficients for such models remain significant or large in magnitude after controlling for relevant occupational factors, this result may indicate some evidence of bias. It is important that job-relevant control factors are reliable, as they help to hire employees who are suitable for the position. For larger organizations processing significant volumes of applications, you can incorporate additional variables related to who the interviewers or recruiters are and when and how the candidates were identified. You'll be surprised to find that such factors can explain a large variation in who ultimately receives an interview or job offer.
This multivariate modeling strategy ultimately allows you to compare individuals with the same control variables. For example, to analyze who gets a merit-based bonus or promotion, you may want to compare employees in the same job, with equivalent performance levels, and take into account any other factors that could influence the outcome of the reward or promotion. This is why steps 1 and 2 are essential, as they allow you to consider, evaluate and collect data to estimate such models.
The pay gapgender is a commonly cited statistic. According to a Payscale analysis of2024on wages above627 milesindividuals in the United States, women earned83 centsfor every dollar men earned when comparing median wages. This figure was reported by Payscale as a measure of the 'uncontrolled gender pay gap', because this statistic does not take into account different types of jobs or qualifications. The reported 'controlled gender pay gap' statistic reflects that this gap is estimated to be much lower, with women earning99 centsfor every dollar a man earns when controlling for those factors. The key here is to determine which variables you need to incorporate into your analyzes (i.e. control) to accurately calculate the demographic gap in your organization. These variables can be, among others, professional qualification, education, professional requirements, skills and geographical location. It is then possible to decide whether the factors considered essential actually determine the salary and whether, in addition to these factors, demographics continue to play an important role.
Another important aspect of this analysis is to investigate and verify each individualprocessepracticerelating to people within your organisation. You may not have enough information about relevant variables at first, but the more strategically you conduct this investigation, the more you will revisit steps 1 and 2 to refine your data-driven analytic approach. It is critical that these checks occur with some regularity, because processes that were once effective, but have not been adequately updated, can often become distorted or less effective over time. Like a car, the talent management system is constantly influenced by external forces and some of its parts periodically fail under such pressures. In this regard, every single employment-related decision could potentially introduce unwanted and unintended biases and social processes that hinder the organization's progress towards meritocracy, equity and excellence. It is important to regularly focus on key people processes:
Processes used to identify and recruit diverse and talented candidates: cThis can include the recruiting sources you use to attract job seekers, the types of messages and information about your company and jobs that might influence who applies and who doesn't, and the activities and processes used by your recruiters and hiring managers.Processes and criteria used to select candidates for the subsequent stages of the selection process.For example, data about who is screened, who is interviewed, who receives an offer, and who ultimately accepts the offer and becomes a potential hire can provide valuable information. If this is the case, pay particular attention to each stage a selected candidate goes through and the extent to which all candidates have an equal opportunity to advance to the next stage. If some groups of candidates are not progressing, investigate what factors shape the selection process and the extent to which these factors are job-relevant, valid, reliable and useful.Processes aimed at maximizing the number of offers accepted by candidates, typically by improving recruitment and onboarding activities.Many companies ignore this part of the selection process, even though their challenge may be convincing potential employees to join the organization. When particular groups of candidates decide not to join an organisation, even if they are offered a good offer and a good compensation package, the organization should evaluate the extent to which it is an attractive employer for all.Processes underlying onboarding initiatives and other training opportunities.These processes should be designed to ensure that every employee can succeed right from the start. If so, also ensure that all new hires have equal training opportunities.Processes underlying performance measurement and evaluation.These processes should clearly define and establish expected performance standards and objectives that are achievable and relevant to the position.Performance measurement processes used for training and development purposes.Pay particular attention to procedures designed to help develop underperforming employees. These procedures must also be clear, consistent and relevant to the success of the job and the organization.Processes used to reward those who meet or exceed standards, as well as those used to decide career advancement and other professional outcomes of employees.This includes promotions, transfers and layoffs.
When analyzing these key talent management processes, I recommend examining not only how these employment decisions are made, but also who is ultimately responsible for them and how others (including managers and employees) might react. You should also carefully examine the effectiveness of these organizational processes with the data collected to see if they are working as intended.
Phase 4: decide which intervention to adopt.By monitoring staffing decisions and their outcomes, organizations and companies can understand how far they are straying from meritocracy and act accordingly. For example, at one large global company I worked with, we analyzed all merit-based promotion and pay raise decisions based on employee performance tracked over a decade, with the help of a corporate team that put a system in place to collect, clean, and prepare the data for analysis. We quickly identified demographic patterns in both promotion and merit pay decisions. We first calculated promotion rates and average salary increases each year, then we processed this data based on some demographic characteristics to check for significant differences. We found onebig differencebetween male and female employees, aswomen did not receive the same merit-based bonuses as men.
After further analysis of this result, we concluded that the problem likely stemmed from the differencesrequest method. Men were more likely to ask for and ensure they received the merit-based bonus, while women tended to assume that the bonus was already included in their paycheck. To resolve this issue, an HR manager was asked to monitor and confirm that all employees automatically received the bonus once they reached the required performance level, thus eliminating the need to request it. After this simple intervention, gender differences in merit-based bonuses disappeared.
When I assist organizations in evaluating their meritocracies, I often encounter leaders and managers who wish they had collected more employment-relevant data to analyze trends and patterns in the workplace, thereby gaining a deeper understanding and diagnosis of observed outcomes. This stepwise analytical framework becomes dynamic and interactive because discovery allows you to revisit phases 1 and 2 to reassess and gather additional information to better address challenges.
Step 5. Remain constantly vigilant and monitor results regularly.Finally, implement processes that frequently notify you ofpotential future challengesthat could impact the proper functioning of talent management strategies and procedures in your organization. As a result, regularly reevaluate and review each of the above steps. Since success criteria may change over time and since technology, organizational practices and labor markets are highly dynamic, Phase 1 is very important as it allows you to re-evaluate and validate additional competencies, skills, merits or talents needed for hiring and, subsequently, promotions, as well as to fairly reward top performers.
The more you will learnunderstand the decision-making processesand organizational outcomes, the more we will want to improve the collection and analysis of employment-related data to ensure meritocracy and equity in practice. This is the virtuous cycle that truly meritocratic organizations can benefit from when they thoughtfully, strategically manage and carefully evaluate their talent management processes and outcomes. Phases 2 and 3 allow you to further improve the data collection activity. As a result, when you decide which practices to maintain or which solutions to introduce in Phase 4, you will be better informed and likely to be more successful. The more often you follow these steps, the more dynamic and effective you will become at systematically evaluating current people practices and designing and implementing impactful interventions for your organization.
BIBLIOGRAPHY
J.B. Fuller e M. Raman, “Dismissed by Degrees: How Degree Inflation Is Undermining U.S. Competitiveness and Hurting America’s Middle Class”, file Pdf, Accenture, Grads of Life e Harvard Business School, 2017.
R.C. Booth, “Stop Requiring College Degrees for Jobs That Don’t Need Them”, Vox, 19 marzo 2023.
N. Lewis, “Technology Can Be Used to Achieve Pay Equity”, Technology Can Be Used to Achieve Pay Equity”, SHRM, 19 giugno 2023.
Y. Slan, “Viewpoint: A Reflection on Juneteenth, Transparency in Diversity Reporting”, SHRM, 16 giugno 2023.
D. Habtemariam, “3 Must-Dos for Collecting Employee Demographic Data Beyond Race and Gender”, Senior Executive, 21 aprile 2022.
B. Melloy, “What Demographic Question Should You Ask in Surveys?” Culture Amp, updated July 7, 2020.
K. Magoon, M.-J. Robinson, A. Kissling, et al., “Best Practice for Demographic Data Collection & Reporting: Evaluator’s Guide”, file Pdf, Boston: Public Consulting Group, agosto 2022.
Articolo Originale: “A Data-Driven Approach to Advancing Meritocracy”, MIT Sloan Management Review, Fall 2025.