By Peter Hansen
AVIXA
Good news on a critical topic! ProAV appears to have made progress on the gender pay gap. Our first study, published in 2021, revealed a pay gap of between 79% and 89% (more on why that’s a range in a moment). Our latest analysis now suggests a pay gap between 86% and 94%. Stated more plainly, the new data suggests women earn between 86% and 94% of what men do. These numbers deserve plenty of context—which we’ll get into — but that shouldn’t detract from the overall conclusion: This is positive news for everyone in ProAV.

First, why a pay range? Simply put, reality is too complex for summary by one number. For pay gaps, researchers generally cite three numbers: raw total pay, average weekly pay for full-time workers, and then total hourly pay adjusted by résumé traits like education, experience, and job category.¹ In 2021, those three numbers were 79%, 83%, and 89% respectively. Now, those numbers are 86%, 91%, and 94% (Figure 1)².

Figure 1. U.S. Gender Pay Gap in 2021 vs 2024

Let’s consider the value of these numbers back to front. The third number, which controls for résumé traits including education, experience, and job category — 89% in 2021, 92% now — is, at first glance, the most apples-to-apples comparison. Roughly, you match résumés and gauge difference in total hourly pay. This is valuable because it addresses the narrow question of fairness in assigning compensation, but it’s deceptive because it ignores much of the real world.

Women do not live in a sexism-free utopia until the moment they enter salary negotiations. Instead, the biases in society, education, business and people influence every step along the way. That makes it misleading to focus only on the final step. Context is the strength of the other two numbers: They reveal the impact of all the steps that lead up to the résumé. Those numbers are now 86% for total pay and 91% for weekly pay — up from 79% and 83%. While these numbers show lower gaps than the readings from 2021, they remain substantial and bigger than the similar-résumé stat.

 We Have Improvement! How Good is that Really?

It is good news — there’s no doubt — but close statistical analysis keeps excitement under control. For one, randomness influences statistics. We’ve measured the gap closing by an average of 7 percentage points across the three metrics. That is substantial — but is it big enough that we can be confident it’s a real change rather than random chance? Statistical testing gives us a mixed answer: It tells us the difference is insignificant at the 95% confidence level, but significant at the 90% confidence level. More straightforwardly, it is unlikely the difference is due to randomness, though it is still a real possibility. 

The possibility of randomness should not temper our excitement too much: Even if we decide the difference is pure chance and there’s been no change between 2021 and today, the new information is still good news. Deciding that the change is due to randomness does not mean we throw out the 2024 number. Instead, we adjust our beliefs from the 2021 number to the midpoint between the two numbers, with 2021 a randomly low draw and 2024 a randomly high draw. So, whether you believe there’s been change or not, this should shift your beliefs towards a smaller gender pay gap than our initial research found. The 2024 numbers are positive either way!

Realistically, the shift is likely a combination of the two possibilities, no change and gap closing. The 2021 reading probably was a little more pessimistic than reality, and there’s a decent chance this reading is a little more optimistic than reality. In the meantime, there’s room for a little bit of positive shift. To stress, no matter how you slice it, the new news is good news for the gender pay gap.

What Could Be Helping? And What Can Continue to Help?

If something happened that we would expect to shrink the pay gap, then real change is the more likely interpretation; if nothing happened, it’s likely that the pay gap is unchanged and any difference is random.³ We see a few causes for pay gap shrinking, including efforts at promoting equality and macroeconomic trends.

Let’s talk promotion of equality. First and foremost, the AV industry cares. Past research shows company values are one of the top factors AV pros consider when deciding who to work for (more here). For the specific question of women in pro AV, we see how much the industry cares in the attention paid to research on inclusion, including both page views of our reports and in how trade media picks up and enhances the story (with examples including SCN and rAVe).

People are taking action on their values. Malle Kaas started Women in Live Music to help women on their pathway into the live sound corner of pro AV. Amy DeLouise started #GALSNGEAR to advocate for women in film and tech. At AVIXA, a team of committed volunteers runs the Women’s Council (including local groups around the world), which supports and empowers women who work in technology and AV—and recently drew record attendance to its tenth annual InfoComm breakfast.Economic tides are likely helping as well. Marginalized groups feel exaggerated effects from the business cycle. In plainer language, the booms are extra good, and the recessions are extra bad. Recent years have been on the boom side, as post-pandemic recovery and return to in-person has created rapid AV revenue growth. This created a labor market highly favorable to workers (full report here and highlights here) and likely increased equality in our industry.

Figure 2. High Raises in Recent Years Show How Much the Labor Market Has Favored Workers

Altogether, we see clear reasons why the gender pay gap has closed some, including both efforts focused on equality and broader economic forces. This gives us more confidence that the observed improvement is at least somewhat the product of real, positive change in pay equality.

What Next?

With hopeful news in hand for the gender pay gap — whether improvement over time or just new data revealing a better status quo than previously thought — the focus must turn to what’s next. In the coming months and years, the focus is twofold: shoring up any ground gained and further closing the gap. 

One aspect to consider here is how you approach pay. The modern practice of focusing on the role and experience to establish market value is helpful compared to the outdated practice of basing current pay off past levels. Not only is using past pay information to determine salary illegal in a growing number of jurisdictions, it can also perpetuate past history of pay discrimination. 

An additional critical consideration for the future of the pay gap is the threat of recession: The strong payroll growth of recent years likely promoted equality, and a downturn could threaten that. That threat can be mitigated, and your company can be strengthened by focusing on objective criteria for hiring and firing. Traits like quality and efficiency are better than retreating to what is “known” or the last in, first out approach.

Above all, keep caring. Keep supporting organizations like the AVIXA Women’s Council, Women in Live Music and #GALSNGEAR. Stay attentive to your company’s values and keep promoting a fair, objective workplace in your day-to-day. When we study the gender pay gap again in the future, we want to report good news again!

1. All pay numbers are for the respondents primary AV job, and they include all forms of in-year financial compensation: base pay, bonus, overtime, profit-sharing, etc. ↩︎
2. Data reported in a given year reflect the prior year’s salary. So, the 2021 report reflects 2020 earnings and this report reflects 2023 earnings. ↩︎
3. This is Bayesian analysis. In Bayesian analysis, you use prior information to help determine the most likely hypothesis. Prior information would be something like, “Because X, Y, and Z happened, I expect the gender pay gap to close.” Or the inverse: “Because nothing happened, I expect the gender pay gap to be the same.” Here, we analyze if there is an “X, Y, and Z” that happened and would cause us to expect a change. If so, change is the more likely hypothesis! ↩︎