The Motivating (and Demotivating) Effects of Learning Others’ Salaries

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Pay inequality is common in most workplaces. You get paid significantly more than your subordinates, your boss gets paid more than you, and your boss’s boss gets even more. In many large organizations, some employees can take home paychecks tens or hundreds of times more than others.

Whether you like it or not, your employees have wondered at some point about your salary — and their peers’. Should you be worried about that? Our recent research sheds light on this question, and our findings may surprise you.

We conducted an experiment with a sample of 2,060 employees from all rungs of a large commercial bank in Asia. The firm is quite representative of most companies around the world across some key dimensions, including its degree of pay inequality and non-disclosure policy around salary.

The first thing we looked at was manager salary. Through an online survey, employees had to guess the salaries of their managers. To make sure they had incentives to be truthful, we offered rewards for accurate guesses. The vast majority of respondents missed the mark by a significant margin (on average, employees tend to underestimate their manager’s salary by 14%). And this is where the action happens: by the flip of a virtual coin, we decided whether to “correct” a respondent’s estimate, by providing accurate information from the firm’s official salary records. So half of the respondents learned how much their boss truly earned — a salary higher than what they initially thought — while the other half did not.

Think about it this way: Let’s say there are two employees (similar in terms of level and experience) who think that their bosses get paid three times as much as them; but in reality, their boss gets paid five times as much. The flip of our coin randomizes which employee will learn that her boss actually gets paid five times more than she does, and which employee will not be corrected. Then we can compare the subsequent behavior of these two similar employees, to see how learning that your boss makes much more than you might affect your productivity.

To measure the behavior of these two groups of employees, we gathered daily timestamp, email, and sales data for the year following our survey. To our surprise, finding out that their managers got paid more seemed to make employees work harder than those who did not find out the true salary. Our estimates suggest that discovering that the boss’s salary is 10% higher than originally thought causes employees to spend 1.5% more hours in the office, send 1.3% more emails, and sell 1.1% more. (The higher the surprise, the larger the effect — finding out the boss earned 50% more led to effects five times larger.)

The evidence suggests that these effects were driven by aspirations. The effect of knowing manager salary was more substantial for employees who learned about the pay of managers who were only a few promotions away, whose shoes they could realistically aspire to fill. We find that, when the boss is fewer than five promotions away, for each 10% increase in the perceived salary of the boss, employees spend 4.3% more hours in the office, send 1.85% more emails, and sell 4.4% more. We also found that, after realizing that these managers get paid more, employees became more optimistic about the salaries they will earn themselves five years in the future. On the other hand, we found no effects on effort, output, or salary expectations when the employees learned about managers several promotions away (e.g., an analyst learning about C-suite salaries).

There is a caveat, though. While employees seemed perfectly capable of handling this vertical inequality, they did not handle horizontal inequality nearly as well.

In our experiment, we also asked employees to guess the average salary among their peers — that is, the other employees with the same position and title, from the same unit. Even though employees did better at guessing the salaries of their peers than that of their managers, most employees still guessed incorrectly. We flipped a second virtual coin to decide whether to “correct” their misperception about the peer salary.

We saw that finding out peers get paid more does have a negative effect on the employee’s effort and performance. Finding out that peers earn on average 10% more than initially thought caused employees to spend 9.4% fewer hours in the office, send 4.3% fewer emails, and sell 7.3% less.

This evidence suggests that it might not be wise to motivate individual employees through raises alone. If you increase the pay of one employee, that employee may work harder but the rest of the peer group could work less hard. You can avoid this by motivating employees through the prospect of a higher salary attached to a promotion. In other words, keep salaries compressed among employees in the same position, but offer them large raises when they get promoted to a higher position.

Our research raises the question: should you increase pay transparency at your company? Though surveys reveal most employees wish their employers were more transparent about salaries, the majority of firms maintain pay secrecy policies. But there is little evidence on how transparency affects the outcomes that managers care about. It is possible that managers choose pay secrecy because they think it is in their best interest when in fact it is not.

You may not need to worry too much if one of your employees catches wind of your salary. Employees in our study tended to underestimate the pay of their managers, and learning the actual amount led them to work harder. This degree of pay transparency seems to have given employees a sense of their earnings potential, driving up motivation. But we need further evidence to better understand how to best leverage transparency to promote productivity and employee satisfaction.

Of course, we must remember that salary information is sensitive, and thus there can be such a thing as too much transparency. For example, the majority of employees participating in our study were in favor of increasing transparency in an anonymous fashion, by reporting average salaries by position. However, when the same employees were asked about increasing transparency in a non-anonymous fashion, meaning their names and salaries would be shared, most of them opposed. And in a follow-up study, we found that most employees were willing to pay significant amounts in order to conceal their own salary from coworkers.

Many U.S. policies promoting pay transparency are mandating complete, non-anonymous salary transparency. For example, some states like California and New York publish online lists with the full names and salaries of every state employee. We think a wiser approach is what our study participants called for: transparency about average pay for a position, without disclosing individual salaries.

We encourage you to start experimenting with transparency at your company.  The first step is to figure out what your employees want. You can find out through anonymous surveys. Just mention some alternatives that you consider viable, and let them voice their preferences. For instance, do your employees feel informed about their salaries five years down the road? Would they want to find out the average pay two or three promotions ahead? Once you look at the survey results, you can decide what information to disclose and how. According to our findings, signals about the enticing paychecks waiting five years in the future is the push they need to be at their best.

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How can an organization can tell whether it’s actually letting data inform its decision making — or if it’s merely using superficial analyses to retroactively justify decisions it has already made?

Traditionally, organizations have used data analytics as a tool of retrospection, as a means of answering questions like, “Did this marketing campaign reach our desired audience?” or “Who were our highest-value customers over the last year?” or “Did engagement peak at regular intervals throughout the day or week?” These answers are typically built around metrics — or key performance indicators (KPIs) — like click-through rates, cost per impression, and gross rating points, which companies all-too-often decide on too late in the process.

These descriptive analytics — that is, analytics that measure what has already happened — are undeniably important. But they’re just a bit player in the far more sprawling drama that is data-driven decision making. Within organizations that are truly data-driven, KPIs aren’t arbitrarily plucked out of thin air, but are generated at the start of a decision-making process. More precisely, it’s not an organization’s KPIs, but the key business questions (KBQs) — of which KPIs are an extension — that serve as the cornerstone of its success.

In their HBR article Big Data: The Management Revolution, Andrew McAfee and Erik Brynjolfsson arrived at a similar conclusion, writing, “Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions.”

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However, arriving at “the right questions” is easier said than done, as any investigation must extend beyond, “What do the data say?” At my agency, our KBQs emerge from a rigorous four-step process that forces us to leverage data throughout the planning phases of our marketing campaigns. Though its specific applicability may vary slightly from industry to industry, our process provides a highly actionable model for deploying data analytics in a proactive, transformational manner; one that guides your decision making instead of justifying it.

Step One: Define your purpose. At the start of every planning cycle, an organization should make a concerted effort to engage stakeholders from every corner of its business in a wide-ranging discussion aimed at defining the campaign’s purpose. This begins with methodically zeroing in on the challenge(s) you’re trying to solve. Are you trying to improve a customer satisfaction rating? Cultivate long-term loyalty among a specific subset of customers? Increase the number of products that ship from a certain warehouse?

Don’t hesitate to interrogate the status quo — and, when appropriate, dismantle it. A history of maximizing pageviews is not itself a compelling reason to set a renewed goal of maximizing pageviews. Take a step back, survey the landscape (both internal and external), and carefully consider whether you’ve defined your purpose in accordance with anything other than the force of habit.

Step Two: Immerse yourself in the data. Once an organization has identified its purpose, it should conduct a comprehensive survey of what it already knows to be true. This is the stage where an organization should answer, “What do the data say?” That said, it should do so with a distinctly forward-looking mindset. At this stage of the process, an organization should take little interest in evaluating — and even less in justifying — past decisions. The totality of its interest should rest with how its data can inform its understanding of what is likely to happen in the future.

Like the previous stage, stage two is highly collaborative. In pursuit of broad-based collaboration, an organization should democratize its data to the greatest extent possible, funneling it into the hands of experts and non-experts alike. Not everyone at your organization is going to have a PhD in mathematics or a professional background in data science, but this doesn’t preclude anyone from getting their hands dirty in your data — after all, one doesn’t need to understand how a tool works to appreciate and take advantage of its utility. Ensuring that stakeholders across your organization come to a mutual understanding not only of the facts, but of their importance, is critical to the success of the rest of the process.

Step Three: Generate key business questions. While the previous stage pushes an organization to the edge of its organizational knowledge, this stage sends it tumbling into the unknown. With a goal and a set of agreed upon assumptions in hand, the organization has everything it needs to start posing KBQs, or lines of inquiry that propel it from “What do we want to achieve?” to “What do we need to know in order to achieve it?”

Using the precise purpose-defining language it established during the initial stage, an organization should now challenge stakeholders to ask as many questions as they can think of, first individually, then as teams. Good questions, bad questions, self-evident questions, unrealistic questions — it matters not. The objective is quantity, not quality.

While no topic or line of inquiry should be off-limits, an organization could start with these:

Can we predict which customers are at the highest risk of switching to a competitor, and design programs to decrease that risk? Can we predict which customers have the highest probability of trying and subsequently adopting our brand, and design cross-channel promotional strategies to reach them most effectively? Can we identify the optimal price point for our brand in order to maximize growth at a certain level of profitability? Can we rethink the way we communicate with our target customers across our portfolio of products by understanding the combinations of products that are most often purchased by the same customers?

In many cases, such unfettered inquisitiveness requires feigning a degree of ignorance; that is, pretending that you don’t know what you know or pretending that your data doesn’t exist. This can be something of a high-wire act, especially for organizations new to data analytics, but it pays immense dividends if executed properly. Creativity and innovation are central to this phase of KBQ generation, and hewing too closely to your existing data is a recipe for the opposite.

To a similar end, it can be valuable to take the KBQs you generate and “invert” them. Just as sketching an object upside down can help an artist more accurately reproduce its likeness, rewriting your KBQs in the negative can produce more “Aha!” moments than would otherwise arise. Consider the following hypothetical progression that a pharmaceutical company might go through:

Purpose: Increase medication adherence among patients who have been prescribed Drug X.

KBQ: Which outreach methods do non-adherent patients respond to most reliably?

Inverted KBQ: Which outreach methods do non-adherent patients not respond to?

This slight shift in perspective can be a game-changer. Like any activity dealing with human behavior, marketing is an inexact science, and the value of strategically constraining your efforts cannot be overstated. Uncertainty is far more palatable — and far less problematic — when you know precisely where it exists than when it pervades your entire operation. In business, known unknowns are preferable to unknown unknowns.

Step Four: Prioritize your key business questions. Only after an organization has compiled an exhaustive list of KBQs should it begin evaluating, critiquing, and prioritizing them. In practice, some KBQs are highly actionable but lack the clear potential for making a business impact, while others have the potential to revolutionize your business but are highly inactionable. Pipe dreams, curiosities, and incremental improvements are all situationally valuable, but focusing on the pursuit of high-value KBQs will ultimately drive meaningful results.

Transforming a defense mechanism into a change agent. It’s tempting to place data analytics at a discrete juncture in your operational processes, but the reality is that data is not something to be used periodically, nor within strict project-based silos.

To drive real results, an organization must use data analytics throughout its business cycle. Today’s descriptive analytics are the foundation of tomorrow’s KBQ-oriented planning processes, which in turn are the foundation for a forward-looking analytics brief that details how an organization is going to answer its high-value KBQs. It’s this cyclical, mutually-informing decision-making architecture that both accelerates organizational transformation and disrupts your fixation on the rear-view mirror.

As Nobel Prize-winning physicist Niels Bohr once quipped, “An expert is a man who has made all the mistakes which can be made in a very narrow field.” Nowhere is this truer than in business. A well-conceived data analytics program empowers organizations to redirect their focus from justifying past decisions to learning from past mistakes. The sooner organizations make this pivot, the sooner they will enjoy the benefits of truly data-driven decision making.

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Research has found that employees are more likely to flourish at work when they have high quality workplace relationships with people from diverse backgrounds. These relationships can contribute to greater trust, facilitate information sharing, and engender positive emotional connections. Taken together, this can lead to increased productivity and reduced turnover.

There’s still a lot we don’t know about these connections, however. How often do they occur? Between whom? And what can better facilitate them? We’re trying to dig deeper, and we need your help. This survey, which takes approximately 10 minutes to complete, is the first step. We’d love to hear from you, and we’ll share what we learn both on this site and via an episode of the new season of HBR’s Women at Work podcast. You can also stay up to date by including your email address in the survey itself. All responses, of course, are anonymous.

We also wanted to let you know that this survey may be a bit different than ones you’re used to coming from HBR. It asks a series of questions about your personal relationships and activities both inside and outside of work, as well as some questions about you. We want to make sure we receive enough feedback from you in order to contribute to the literature on workplace relationships — and to help managers and employees better understand how to cultivate them. Thank you so much for being a part of what we hope is a project that will change work for the better.

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