What's My Employee Worth? The Effects of Salary Benchmarking

This paper studies how salary benchmarking tools — products that reveal aggregate market pay statistics for specific job titles — affect employee compensation. The research question is whether firms’ access to such tools causally changes how they set salaries, and what this implies about information frictions in labor markets and the policy debate over benchmarking regulation.

The authors collaborated with the largest U.S. payroll processing company (serving 650,000 firms and 20 million workers), exploiting the staggered roll-out of a proprietary Compensation Benchmark Tool. The tool aggregates payroll data into salary benchmarks by standardized job title, with the median base salary as its most prominent statistic. The study draws on three linked administrative datasets: payroll records (January 2017 to July 2021), tool usage logs (September 2019 to August 2021), and historical benchmark snapshots. The main analytical sample covers new hires at 586 treatment firms that gained tool access and 1,419 matched control firms that did not, within a 10-quarter window around each firm’s onboarding date.

The identification strategy is difference-in-differences, exploiting three sources of variation: which firms gain access; the staggered timing of access (driven by the arbitrary order in which sales representatives introduced the tool); and within treatment firms, whether a specific position was actually searched in the tool. New hires are classified into Searched positions (5,266 hires at treatment firms for positions eventually looked up), Non-Searched positions (39,686 hires at treatment firms for positions not looked up), and Non-Searchable positions (156,865 hires at control firms). Event-study analyses confirm flat pre-trends across all groups, supporting causal interpretation.

The primary finding is that benchmark access reduces salary dispersion around the median market benchmark by 25%. Before onboarding, the average absolute deviation of offered salaries from the median benchmark in Searched positions was 19.8 percentage points (pp). After onboarding, this fell to 14.9 pp — a drop of 5.0 pp using Non-Searched positions as control (p-value < 0.001) and 6.2 pp using Non-Searchable positions as control (p-value < 0.001). Compression runs in both directions: firms previously paying above the benchmark reduce salaries toward the median, and firms previously paying below raise salaries toward the median. The probability of setting a salary within 2.5% of the median benchmark nearly doubled, from 11.6% to 22.1% after onboarding.

Effects are heterogeneous by skill level. For low-skill positions (approximately 42% of the sample, e.g., bank teller, receptionist), dispersion falls from 14.5 pp to 8.7 pp — a 40% reduction. For high-skill positions (e.g., software developer), dispersion falls from 24.0 pp to 20.5 pp — a 14.6% reduction. For low-skill positions, compression from below dominates, producing a net average salary increase of +5.0% to +6.7% (p-values 0.014 and 0.001 depending on control group). For high-skill positions, the average salary effect is small and statistically insignificant overall. Twelve-month retention rates for low-skill workers increase by 6.6 to 6.8 pp after benchmarking, and the implied retention elasticity is consistent with prior literature estimates.

The authors propose a theoretical model to rationalize these findings. Firms are assumed uncertain about the wage distribution (aggregate uncertainty), with private information about their own value of filling a position and affiliated valuations across firms. In equilibrium, firms with higher values make higher offers — generating wage dispersion among identical workers without monopsony power, efficiency wages, or amenity differences. When a firm gains benchmark access, it adjusts its offer toward the threshold wage needed to hire, compressing offers from both sides. In the full-information equilibrium where benchmarks are common knowledge, the mean salary is weakly higher than without benchmarks, because the marginal firm had previously underestimated labor market tightness and offered too little, capturing extraordinary profits. Benchmarking eliminates these informational rents, intensifying competition and raising average pay.

The scope of the empirical findings is restricted to new hires at firms in the top quartile of U.S. firm size by employment, across all industries and U.S. states, over 2017–2020. The estimated effect is the incremental causal impact of one additional high-quality benchmarking source, since most firms already had access to some pay information through other channels.

Q: What is the main causal finding of the paper? A: Access to the salary benchmarking tool reduces the absolute deviation of new-hire salaries from the median market benchmark by approximately 25%. Specifically, average dispersion in Searched positions falls from 19.8 pp before onboarding to 14.9 pp after, a drop of 5.0 pp (using Non-Searched controls, p-value < 0.001) or 6.2 pp (using Non-Searchable controls, p-value < 0.001). The two estimates are statistically indistinguishable from each other, and both are robust to a wide range of specification checks.

Q: How does compression operate — does it raise or lower salaries? A: Compression operates in both directions. Firms that would otherwise have paid above the median benchmark reduce salaries toward the median (“compression from above”), and firms that would otherwise have paid below the median benchmark raise salaries toward the median (“compression from below”). The probability of offering a salary within 2.5% of the median benchmark nearly doubled, from 11.6% before onboarding to 22.1% after.

Q: What is the identification strategy, and why is the treatment considered as good as random? A: The authors use a difference-in-differences design with three sources of variation: which firms gain tool access, the staggered timing of access, and whether specific positions were actually searched within a treatment firm. The payroll company introduced the tool through sales representatives contacting clients in an arbitrary order, not in response to firm characteristics or outcomes. This is corroborated by empirical tests: event-study pre-trends for Searched versus Non-Searched (and Non-Searchable) positions are flat and statistically indistinguishable from zero (pre-treatment coefficients of -0.346 and -0.310, p-values 0.749 and 0.604, respectively).

Q: How large are the effects for low-skill versus high-skill positions? A: For low-skill positions (approximately 42% of the sample, e.g., bank teller, receptionist), dispersion drops from 14.5 pp to 8.7 pp — a 40% decline (p-value < 0.001). For high-skill positions (e.g., software developer), dispersion drops from 24.0 pp to 20.5 pp — a 14.6% decline (p-value = 0.021). The larger effect for low-skill positions is consistent with anecdotal accounts from compensation managers, who report treating low-skill candidates as interchangeable and therefore wanting to offer exactly the market rate.

Q: Does benchmarking raise or lower average salaries? A: On average across all skill levels, the effect on mean salary is small and statistically insignificant: -0.2% (p-value = 0.756) using Non-Searched controls and +1.7% (p-value = 0.308) using Non-Searchable controls. For low-skill positions specifically, average salaries increase by +5.0% (p-value = 0.014) using Non-Searched controls and +6.7% (p-value = 0.001) using Non-Searchable controls. This net increase for low-skill workers reflects compression from below dominating compression from above in that subset.

Q: What are the effects on employee retention? A: For low-skill workers, benchmarking increases the probability of remaining employed at the hiring firm 12 months after the hire date by +6.6 pp (p-value = 0.101) using Non-Searched controls and +6.8 pp (p-value = 0.029) using Non-Searchable controls. The implied retention elasticity from the ratio of salary and retention effects is consistent with average estimates in the prior literature (Sokolova and Sorensen, 2021). No retention effects are reported for high-skill positions.

Q: What is the theoretical mechanism through which aggregate uncertainty generates wage dispersion? A: The model assumes a unit mass of firms simultaneously making wage offers to a mass Q < 1 of workers, with only the top Q offers accepted. Firms have private information about their value of filling the position, and values are affiliated (correlated in the sense of Milgrom and Weber, 1982). Because each firm is uncertain about what other firms will offer, higher-value firms rationally form higher beliefs about the prevailing wage distribution and make higher offers. This generates equilibrium wage dispersion among identical workers without monopsony power, efficiency wages, or amenity differences.

Q: What does the model predict about the equilibrium effects of benchmarking when all firms have access? A: When the benchmark is common knowledge, all firms make offers with full information about the wage distribution. The firms with the highest values win workers at a uniform wage that makes the marginal firm indifferent between hiring and not hiring. The model proves that the mean salary is higher in expectation under the benchmark equilibrium than in the no-benchmark equilibrium. The intuition is that without benchmarks, the marginal firm underestimates labor market tightness, offers less than the full-information competitive wage, and thereby captures extraordinary profits; benchmarking eliminates those rents and intensifies competition.

Q: What are the policy implications of the findings regarding antitrust concerns? A: In 2023, the DOJ and FTC rescinded a long-standing antitrust “safety zone” for salary benchmarks due to concerns that they could facilitate wage collusion. A 2021 executive order had mandated that agencies consider procompetitive effects as well. The authors’ model addresses the collusion concern directly: in equilibrium, benchmarking raises (not lowers) average salaries. The empirical evidence is consistent with this — low-skill workers see average salary increases of 5-7% after benchmarking — suggesting a procompetitive justification for the tools.

Q: How robust are the main results? A: The main estimates are robust across a wide range of specification checks, including alternative winsorization levels, log-difference and binary (>10% deviation) dependent variables, heteroskedasticity-robust standard errors, exclusion of controls, inclusion of firm fixed effects, exclusion of tipping positions, restriction to Searched positions only, dropping SOC reweighting, and age restrictions. Two additional pieces of evidence corroborate the quasi-experimental findings: a survey experiment with SHRM HR managers shows that hypothetical benchmarks compress stated salary offers from both above and below; and quasi-random benchmark shocks (when large firms abruptly raise a position’s base salary by 10% or more) cause firms with tool access to converge to the new benchmark faster than firms without access.

Q: What does the survey of HR managers reveal about how firms use benchmarks? A: In a survey of 2,696 HR professionals conducted through SHRM’s research panel, 87.6% of those involved in salary-setting report using salary benchmarks. The vast majority (97.4%) use benchmarks to set pay for new hires. The most popular sources are industry surveys (68.0%) and free online data (58.1%), with payroll data services used by 23.2%. The median salary is ranked the most important benchmark statistic by 56.73% of respondents. Most respondents apply filters by state (84.15%) and industry (87.33%) when using the tool.

Q: What are the main sources of potential attenuation or amplification bias in the estimated effects? A: Attenuation bias may arise because (1) the benchmark tool studied is among the most advanced available, so firms already had some wage information from other sources, meaning the estimates capture only the incremental effect of one additional high-quality source; and (2) not all positions at treatment firms were searched, so the sample is restricted to positions where firms actually engaged with the benchmark. Potential upward bias could arise if firms adopting the tool were also undergoing broader HR system changes, but the flat event-study pre-trends argue against this explanation.

Salary Benchmarking: The practice of using aggregated market pay data — provided by third parties such as payroll processors, consulting firms, or online platforms — to identify typical salaries for specific job titles and set internal pay accordingly. In the paper’s context, this refers specifically to an online tool that allows employers to look up the median and distributional statistics of base salaries for standardized position titles, filtered by industry and state.

Aggregate Uncertainty: The paper’s label for a distinct source of information friction in which firms are uncertain about the distribution of wages offered by other firms in the market — as opposed to uncertainty about individual worker characteristics. This uncertainty is assumed to be the primitive that generates equilibrium wage dispersion in the model, and its resolution through benchmarking is the mechanism driving the empirical results.

Salary Dispersion (around the benchmark): Measured empirically as the average absolute percentage difference between a new hire’s starting base salary and the median market benchmark for that position, expressed in percentage points. This is the paper’s primary outcome variable. Dispersion reflects firms’ deviation from the market rate in either direction.

Compression from Above / Compression from Below: Compression from above refers to the reduction in salaries at firms that would otherwise have paid more than the median benchmark after gaining benchmark access. Compression from below refers to the increase in salaries at firms that would otherwise have paid less than the median benchmark. Both directions of adjustment are documented empirically and are predicted by the model.

Searched / Non-Searched / Non-Searchable Positions: The paper’s classification of new hires into three groups for identification purposes. Searched positions are those at treatment firms for which the firm actually looked up the benchmark. Non-Searched positions are at treatment firms but were not looked up, serving as a within-firm control. Non-Searchable positions are at control firms with no tool access, serving as a cross-firm control.

Affiliation (across firm values): A technical condition borrowed from auction theory (Milgrom and Weber, 1982) used in the paper’s model to characterize the correlation structure of firms’ private valuations of filling a position. Affiliation implies that when one firm has a high value, others are also more likely to have high values, and hence to offer high wages — generating the model’s equilibrium wage dispersion.

Procompetitive Effect of Benchmarking: The paper’s term for the welfare-improving property of salary benchmarks identified in the model: by resolving aggregate uncertainty, benchmarks cause the marginal firm to offer closer to the full-information competitive wage, reducing extraordinary profits that arise from informational rents and raising the mean salary in equilibrium. This is the key concept in the paper’s contribution to the antitrust policy debate.