How "Greedy Jobs" Influence the Gender Pay Gap & What AI Could Do To Close It.

A "greedy job" is characterized by the expectation of working long hours, being available outside of regular working time, and accommodating sudden changes in schedule or travel without much notice. Sounds familiar?

We know that greedy jobs are more accessible to people with no personal obligations. While factors such as job responsibility, level of experience, and geographical location can influence the extent of the gender pay gap within the life sciences sector, the main cause of the global inequality problem according to the 2023 Economics Nobel Prize winner, Claudia Goldin, whose research suggests that much of the gap between men and women is more properly described as a gap between mothers and non-mothers and how “greedy” is their job.

Suppose you are also the primary caregiver for children, as Rose Laub Coser (the Sociologist who coined the term greedy jobs) observed. In that case, that role is considered a greedy job, arguably more demanding than ever before. Additionally, greedy jobs typically allow for only one such commitment at a time.

There is a need to challenge the assumption that childcare responsibilities should primarily fall on the mother, allowing her spouse to prioritize their demanding job. Moreover, it's essential to question why many jobs still maintain demanding schedules. Goldin compares Lawyers to Pharmacists, noting that law similar to other services like that of Consultancy or Executive Search firms, particularly at the partner level, is inherently demanding and incompatible with being readily available for family emergencies, unlike the more predictable schedule of Pharmacists. The face-to-face relationship between the Service Partner and their client is very different to that of Pharmacists and their shop customers.

But this is where it gets even more complex. In the US, over half of Pharmacists are women, and there is a minimal gender pay gap among them. Yet a 2022 study by the UK's Royal Pharmaceutical Society found a median pay gap of 17.4%, higher than the national median of 14.9%. There are undoubtedly other examples far more complex from the EU and US.

The personal experience of Jennifer Chase, of Chase Partners highlights these complexities. "As a Partner in Executive Search at a large global firm, I relocated from France to the United States, a single mother responsible for two young boys (8 and 12 years old). Given that childcare is not as affordable in the US as in Europe, it became obvious that working a “greedy job” was incompatible with my mothering responsibilities. Setting up my firm, Chase Partners LLC, was the only solution to balancing my financial needs and fulfilling my parenting obligations. With hindsight, I can say with conviction, my mission was accomplished; I maintained a senior level high earning position and my children are well-adjusted independent adults in college. But were the struggles necessary? Shouldn’t Corporations pave the way for women to work earning an equitable salary and raise their children?"

Companies must incorporate diverse fields of expertise, including gender expertise, into the development process of AI as well as include perhaps some of the other social complexities. But even the best of algorithms cannot make up for the fact that World Economic Forum in 2023 found that only 29% of women are employed in STEM (science, technology, engineering, and math) so in the short term this may be challenging.

Ultimately, to address these complex gender disparities, perhaps newly designed AI algorithms have the potential to play a significant role. By incorporating sophisticated data analysis and machine learning techniques, AI can help identify and eliminate biases in recruitment processes, ensuring a more equitable selection of candidates. For instance, AI can be programmed to focus on skills and qualifications rather than on gender, thereby reducing unconscious biases that often affect human recruiters. However, while AI offers promising tools to bridge the gender gap, it is crucial to ensure that these systems are developed and monitored with a deep understanding of gender dynamics. Companies using AI should validate and align the systems with their HR strategy to incentivize candidates to remain in the role. Human intervention will be necessary to refine AI algorithms, address emerging biases, and ensure that qualified women are recognized and given fair opportunities in their respective fields and companies.

Currently, there isn't a mechanism to restrict developers from releasing AI systems before they are deemed ready so extensive validation will be needed to ensure fit for purpose within a specific company environment. So, if you are currently using or considering using an AI system for recruitment, perhaps one should ask how the algorithm was developed to bridge the talent gap? In the end, selecting your system or designing a company-owned-system with your IT experts will require incorporating the complexities of the workforce circumstances. ?Well thought out, this collaborative approach, combining AI technology with human expertise, could be essential in creating a more inclusive and equitable workforce in the life sciences sector. It is up to all of us to make this happen.

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