M52 | Macro Paper Warehouse

Education and the Margins of Cyclical Adjustment in the Labor Market

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Research question. This paper asks how the cyclical sensitivity of wages varies with workers’ educational attainment, what mechanisms drive the differences, and what the welfare consequences are of ignoring this heterogeneity. The starting point is a well-known asymmetry: less-educated workers have much higher and more volatile job separation rates, yet the standard macroeconomic literature has treated wages as roughly acyclical for a representative worker. Doniger asks whether this employment-centric picture is incomplete—and finds that it is, in a direction opposite to what the employment pattern would suggest.

Data and methodology. The paper uses two primary data sources: the National Longitudinal Survey of Youth 1979 (NLSY), which provides detailed job histories enabling identification of current and completed employer tenure, and the Current Population Survey (CPS) from 1995 to 2020, used both for employment flow statistics and, via biennial Job Tenure Supplements, for replication of the main wage findings. The sample is restricted throughout to males with 0–30 years of potential experience, following the conventions of the user-cost-of-labor (UCL) literature (Kudlyak, 2014; Basu and House, 2016). Workers are grouped into three educational categories: less than high school, high school or some college, and bachelor’s degree or more.

A key methodological contribution is a new, more parsimonious estimator for the cyclical sensitivity of the UCL. Rather than the multi-step indicator-variable approach of Kudlyak (2014), the paper recovers the UCL sensitivity from interaction terms between a flexible function of tenure and the cyclical position at the time of hiring, estimated within an augmented Mincer regression. This estimator admits higher-frequency identification, enables transparent inference via the delta method, and facilitates nonparametric impulse response estimation via the Jorda (2005) local projection method. Cyclical position is measured primarily as the deviation of the unemployment rate from an HP-filtered trend (lambda = 100,000), with robustness checks using the Hamilton (2018) filter and GDP-based detrending.

Main findings — employment. Monthly separation rates from the CPS (1995–2020) show that workers with less than a high school degree separate at a rate of 9.4 percent per month, more than twice the 3.4 percent rate for workers with a bachelor’s degree or more, regardless of cyclical position. The volatility of the separation rate (measured by the time-series standard deviation) is also larger for the least educated (1.7) than for the most educated (0.6). All sub-components of separation-to unemployment, to inactivity, and job-to-job transitions-exhibit the same ordering. In response to a 100 basis point monetary policy contraction (Romer and Romer, 2004 shocks), employment of workers with less than a high school education falls significantly, while employment of college graduates or more is statistically unaffected.

Main findings — wages. Using the NLSY, the cyclical sensitivity of the UCL to a 1 percentage point deviation of the unemployment rate from trend is estimated at approximately −15.5 percent for workers with a bachelor’s degree or more, −4.9 percent for high school or some college workers, and −1.4 percent (statistically indistinguishable from zero) for workers without a high school degree. In contrast, average hourly earnings (AHE) show much smaller and more compressed differences across education groups (−1.4, −1.1, and −1.0 percent respectively). The pattern of increasing procyclicality with education holds for new hires’ wages (NHW) as well but is considerably less stark than for the UCL. Replication in the CPS confirms the ordering: UCL sensitivities are −7.0 percent for college graduates, −2.9 percent for high school or some college, and effectively zero for those without a high school degree.

Mechanism. Counterfactual decompositions show that differences in the cyclical sensitivity of the wage-tenure profile—not just differences in job duration (separation rates)-account for the vast majority of the divergence across education groups. When separation rates are held constant across groups, the UCL sensitivity of the college-educated falls from -15.5 to −13.0 percent; when wage-tenure profile sensitivities are held constant, it falls to −6.3 percent, and the ordering across groups largely disappears. This finding is consistent with implicit contracting theory (Thomas and Worrall, 1988): longer expected employment durations for the more educated make it optimal to defer a greater share of the wage response to shocks over time, rendering near-term rigidities functionally less binding and producing more persistent effects of hiring-period conditions on subsequent wages.

Robustness. After controlling for cyclical sorting in match quality using the Hagedorn and Manovskii (2013) proxies (cumulated market tightness during tenure and leading up to the present job), the UCL sensitivity for college graduates falls modestly to −12.4 percent, confirming that match-quality composition effects account for only a minority of the documented pattern. The monetary policy shock analysis (Romer-Romer shocks identified from Greenbook forecast errors) yields a 35 percent decrease in the UCL for the most educated at the two-year horizon following a 100 basis point contraction, with no discernible effect for the least educated.

Welfare consequences. Using a stylized New Keynesian model extended to two labor varieties with heterogeneous wage flexibility, the paper shows that ignoring the documented heterogeneity leads to underestimating the welfare costs of business cycle fluctuations by more than 15 percent under the baseline calibration (unit Frisch elasticity and unit elasticity of intertemporal substitution). Conditional on this model, the welfare loss due to fluctuations for the least educated is more than 15 times larger than for the most educated. The paper explicitly notes this is a conservative lower bound, because the model assumes pooled household consumption, and admitting idiosyncratic consumption risk would disproportionately burden less-educated workers who bear adjustment on the extensive (employment) rather than intensive (wage) margin.

In depth

Q1. What is the user cost of labor (UCL), and why does the paper use it rather than average hourly earnings or new hires’ wages?

The UCL, formalized by Kudlyak (2014), is the present discounted value of wage payments an employer expects to make to a worker over the duration of the employment relationship, net of the continuation value of retaining that worker. It equals the new hire’s wage plus the expected wage wedge—the discounted stream of future wage differences between workers hired in the current period versus workers hired one period later. Unlike average hourly earnings or new hires’ wages, the UCL captures the persistent effects of macroeconomic conditions at the time of hiring on all future remitted wages, making it the appropriate allocative wage concept from a macroeconomic standpoint. The paper documents that AHE understates the cyclicality of wages for all groups but especially for the most educated, because AHE omits the highly cyclically sensitive expected wage wedge that characterizes college-educated employment relationships.

Q2. How does the paper’s new estimator for the cyclical sensitivity of the UCL differ from the existing method, and what does this enable?

The existing Kudlyak (2014)/Basu and House (2016) method recovers the UCL by estimating a very large set of date-of-hire x current-date indicator interactions, constructing a time series of the UCL, and then analyzing that series—a multi-step procedure that loses covariances across steps and makes cross-sectional disaggregation or high-frequency identification impractical. The new method instead estimates the UCL sensitivity directly from coefficients on the interaction between a flexible tenure function and the cyclical position at hiring, estimated within a single augmented Mincer regression. The UCL semi-elasticity is recovered analytically from these coefficients via a formula that sums discounted weighted differences in the tenure-interaction coefficients across the tenure horizon. This single-step approach allows transparent inference via the delta method, enables fully interacted specifications for heterogeneous subgroups, permits the hiring-date frequency (e.g., weekly in NLSY) to differ from the wage observation frequency (annual or biannual), and permits estimation from repeated cross-sections—all of which were infeasible in the prior approach.

Q3. What are the quantitative magnitudes of the education gradient in UCL cyclicality, and how do they compare across wage measures?

Using the NLSY with unemployment deviations from HP-filtered trend as the cyclical indicator: the UCL sensitivity is −15.5 percent (se 3.86) for workers with a bachelor’s degree or more, −4.9 percent (se 1.52) for high school or some college, and −1.4 percent (se 2.48, statistically insignificant) for those without a high school degree. By contrast, new hires’ wages show sensitivities of −3.4, −1.8, and −1.2 percent respectively, and average hourly earnings show −1.4, −1.1, and −1.0 percent. The gradient is largest and most statistically significant for the UCL, indicating that the bulk of the education gap in cyclical wage sensitivity operates through the persistent effect of hiring-period conditions on subsequent wages rather than through the contemporaneous wage alone.

Q4. What mechanism accounts for the UCL gradient — differential job durations or differential sensitivity of the wage-tenure profile?

The paper decomposes the UCL into the new hire’s wage and the expected wage wedge, and performs counterfactual exercises holding either separation rates or wage-tenure profile sensitivities constant across education groups (Table 3). Holding separation rates constant while allowing wage-tenure profiles to differ reduces the college-educated UCL sensitivity only modestly, from -15.5 to −13.0 percent; holding wage-tenure profile sensitivities constant while allowing separation rates to differ reduces the college-educated sensitivity to −6.3 percent and compresses the education gradient substantially. Thus, differential sensitivity of the wage-tenure profile—the degree to which wages continue to respond to hiring-period conditions over the course of the job-is the primary driver of the UCL gradient, with differential separation rates playing a secondary but non-trivial role. This finding confirms the prediction of Thomas and Worrall (1988) that lower separation rates support greater use of deferred payment and intertemporal risk sharing in optimal wage contracts.

Q5. How does the paper rule out cyclical sorting in match quality as the explanation for the UCL gradient?

Workers hired during recessions may be of systematically lower match quality, producing persistently lower wages not because wages are more cyclically sensitive for the same quality match but because recession hires are worse matches. Using the Hagedorn and Manovskii (2013) proxies for match quality - cumulated market tightness during the worker’s tenure on the present job (mjob) and on all prior jobs leading to it (mctj) - the paper augments the wage regression with full interactions between these proxies and the tenure-cyclicality terms. After controlling for match quality, the UCL sensitivity for college graduates falls from -15.5 to −12.4 percent (se 5.56); the point estimate remains large, statistically significant, and well above the estimates for lower-education groups. Figure 4 shows that match-quality adjustment primarily affects the first two years of the wage-tenure profile, after which the bias from cyclical sorting fades, confirming that scarring in remuneration for college graduates hired in recessions persists beyond what sorting can explain.

Q6. What do monetary policy shocks reveal about the education gradient in wage sensitivity?

Monetary policy shocks (identified from Greenbook forecast errors as in Romer and Romer, 2004) subject all labor markets to the same aggregate demand shock simultaneously, providing a cleaner test of differential responsiveness than cyclical regressions that may conflate demand composition and supply factors. Using Jorda (2005) local projections, a 100 basis point monetary policy contraction is associated with a 35 percent decrease in the UCL for workers with a bachelor’s degree or more at the two-year horizon, with statistically insignificant effects on the UCL of workers without a high school degree. The employment results are symmetric: less-educated workers’ employment falls significantly after a monetary contraction, while college-educated workers’ employment is unaffected. This cross-validation using monetary policy shocks supports the main thesis that more-educated workers absorb aggregate demand variation through the wage margin, while less-educated workers absorb it through the employment margin.

Q7. How does acyclical wages for the least educated affect interpretation of the existing macro literature on wage rigidity?

The aggregate finding of Kudlyak (2014) and Basu and House (2016)-that the UCL is more procyclical than new hires’ wages or average hourly earnings, casting doubt on wage rigidity as an amplification mechanism—holds only for educated workers. The paper finds that the UCL for workers without a high school degree is statistically acyclical by all three wage measures. This result restores a potential role for nominal wage rigidity in generating amplification and persistence of shocks for less-educated labor markets, including in the Diamond-Mortensen-Pisarides class of search models criticized by Kudlyak (2014) and in New Keynesian models criticized by Basu and House (2016). The paper therefore reconciles the literature on wage rigidity with the empirical finding of cyclical employment volatility concentrated among the less educated.

Q8. What is the welfare calculation, and what are its key results and limitations?

The welfare exercise uses a parsimonious New Keynesian model with two labor varieties (capturing more- and less-educated workers) and price and wage rigidities. The model is extended to admit heterogeneous wage flexibility, and the welfare costs of fluctuations are evaluated following the second-order approximation method of Gali et al. (2007). Under the baseline calibration (unit Frisch elasticity, unit elasticity of intertemporal substitution), the heterogeneous-worker economy incurs welfare costs of fluctuations that exceed those of the output-gap-equivalent representative agent economy by more than 15 percent. The welfare loss of the least-educated workers is more than 15 times that of the most educated. The paper explicitly characterizes this as a conservative lower bound: the model assumes pooled household consumption (within varieties), which implies equal consumption sensitivity across education groups, whereas in reality less-educated workers face income loss on the extensive margin without the wage smoothing available to the more educated. Relaxing this assumption, as in Krusell et al. (2009), could yield welfare losses an order of magnitude larger.

Q9. What does the CPS replication add, and what are its limitations relative to the NLSY baseline?

The CPS replication (Table 7) confirms the main ordering: UCL sensitivities are −7.0, −2.9, and approximately 0 percent for college graduates, high school or some college, and less than high school respectively. This rules out the concern that the NLSY findings are artifacts of the single aging cohort that characterizes the NLSY 1979. However, the CPS must be treated as a repeated cross-section because the tenure data are only available biennially and individual-level panel linkage across tenure supplement waves is infeasible. As a result, the CPS estimates cannot include individual fixed effects and must rely more heavily on observable controls (industry, occupation) to absorb cyclical variation in workforce composition. The CPS also precludes the match-quality controls of Hagedorn and Manovskii (2013). Despite these limitations, the main qualitative and directional findings replicate.

Q10. What policy implications does the paper draw for monetary policy?

The paper argues that because less-educated workers bear adjustment to aggregate demand shocks disproportionately through the employment margin while their wages are acyclical, welfare assessments that focus on the aggregate output gap underweight the costs borne by less-educated workers. The paper suggests that re-optimizing the monetary policy rule to account for documented heterogeneity would entail placing greater weight on the unemployment rate of the least-educated when measuring the output gap. More broadly, the K-shaped nature of labor market adjustment across education groups — wage scarring for the educated versus employment volatility for the less educated - implies that policies targeting either margin in isolation will miss welfare costs concentrated in the other group.

Key Concepts

User Cost of Labor (UCL). The allocative wage from the employer’s perspective, defined as the present discounted value of expected future wage payments to a worker hired at date t, net of the continuation value of retaining that worker in the next period. Formally, UCL_t = w_{t,t} + E_t[sum beta^j(1-s)^j (w_{t+j,t} - w_{t+j,t+1})], decomposing into the new hire’s wage and the expected wage wedge. In this paper’s usage, the UCL is the appropriate measure of the cyclical impact of shocks on labor costs because it captures persistent effects of hiring-period conditions on the entire subsequent wage sequence, not just the contemporaneous wage.

Expected Wage Wedge (EWW). The component of the UCL beyond the new hire’s wage: the discounted stream of differences between wages a worker hired at date t will receive in future periods and the wages a worker hired one period later would receive in those same future periods. The EWW is non-zero whenever wages are history-dependent - i.e., whenever current macroeconomic conditions at the time of hiring affect future remitted wages. The paper finds that the EWW is larger, more negative, and more persistent for more-educated workers conditional on being hired during a cyclical downturn.

Self-enforcing implicit wage contract. A labor contract in which the sequence of remitted wages is not pinned down period-by-period by spot-market forces but instead reflects an intertemporal risk-sharing arrangement between employer and worker that is sustained by the mutual benefit of the ongoing employment relationship. In this paper’s framework (drawing on Thomas and Worrall, 1988), lower separation rates make longer planning horizons feasible, which in turn expands the scope for deferring wage adjustments across time - effectively allowing more-educated workers and their employers to smooth the effects of cyclical shocks over longer horizons than is possible for less-educated workers with shorter expected job durations.

Cyclical sorting / match quality bias. The compositional concern that workers hired during recessions may be of systematically different (in this context, lower) match quality than those hired during booms, so that the persistent wage depression observed for recession hires could reflect poor match quality rather than cyclically sensitive wages for equivalent-quality matches. The paper uses the Hagedorn and Manovskii (2013) proxies - cumulated labor market tightness during the current job and prior employment history - to control for cyclical variation in match quality and assess the residual sensitivity of the UCL for average-quality matches.

Extensive versus intensive margin of labor market adjustment. The distinction between adjustment through changes in the number of workers employed (extensive margin: hiring and separation) versus adjustment through changes in wages or hours conditional on employment (intensive margin). A central finding of the paper is that less-educated workers bear cyclical adjustment disproportionately on the extensive margin (more volatile separation rates, employment losses following monetary contractions) while their wages are acyclical, whereas more-educated workers exhibit the reverse: stable employment but highly cyclically sensitive wages, especially as measured by the UCL.

Wage scarring. The persistent negative effect of hiring-period macroeconomic conditions on wages throughout the subsequent employment spell, beyond what is explained by contemporaneous market conditions. In this paper’s context, wage scarring is concentrated among more-educated workers: being hired when the unemployment rate is one percentage point above trend is associated with wages that remain depressed for several years, with the depression being larger and more persistent for college-educated workers than for those with less education. This is demonstrated via the expected wage wedge profiles in Figure 3 and is confirmed to survive controls for match-quality sorting.

Output-gap-equivalent representative agent economy. A conceptual benchmark constructed in the paper’s welfare analysis: a single-worker-type New Keynesian economy whose wage and labor supply elasticities are set equal to the output-elasticity-weighted averages of the two labor variety types in the heterogeneous economy. The paper shows that the heterogeneous-worker economy and this representative-agent benchmark produce identical aggregate output gap and price level paths (under Cobb-Douglas production, earnings elasticities are identical across varieties), but welfare diverges because period utility is more volatile for the variety with more rigid wages. The 15 percent excess welfare cost of the heterogeneous economy relative to this benchmark is the paper’s headline welfare result.

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

Mon, 01 Jan 0001 00:00:00 +0000

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.