The Bay Area Compensation and Benefits Association BACABA Blogs

Jobs outlook bleak? This report suggests otherwise...

Charles Sterling — Tue, 05 May 2026 22:27:21 GMT

The Wall Street Journal

May 3, 2025

These Are the Hiring Hot Spots Where College Grads Are Landing Good Jobs

Best prospects for many 20-somethings are in fast-growing Southern cities, new analysis shows

A string of cities across America’s Sunbelt are emerging as graduate-hiring hot spots in an otherwise challenging job market for young professionals, an exclusive analysis shows.

Birmingham, Ala., tops the list of the places where newly minted graduates are landing jobs with a college-level career track, followed by Tampa, Fla., according to the new study by payroll processor ADP. In fact, six of the list’s top 10—including Raleigh, N.C.; Tulsa, Okla.; Nashville, Tenn.; and Charlotte, N.C.—are in the South.

Other pockets of the country also punch above their weight as early-career launchpads for their mix of 20-something hiring, pay and affordability. Columbus, Ohio, and California’s San Jose area unexpectedly got top scores this year—evidence that even places with so-so earning potential or high costs of living can be prime locations for landing that first job postgraduation.

Altogether, the ADP analysis—which measured 53 of the country’s biggest metro areas—shows that what looks like a nascent recovery in graduate hiring is happening unevenly. Recent data shows companies are boosting entry-level hiring this spring after holding back for several seasons, but their appetite hinges a lot on the kind of role, sector and location.

Check out the attachment for the full Wall Street Journal article.

These Are the Hiring Hot Spots Where College Grads Are Landing Good Jobs.pdf

BACABA Whitepaper: The Strategic Value of Total Rewards Statements: Bridging the Compensation Perception Gap

Charles Sterling — Mon, 16 Mar 2026 21:07:38 GMT

In today's competitive talent landscape, compensation transparency has become a critical factor in employee satisfaction, retention, and organizational success. Despite significant investments in comprehensive benefits packages—typically representing 20-30% of total compensation—most employees remain unaware of the full value their employers provide. This awareness gap creates a dangerous disconnect: employees anchor their perception of worth solely on base salary, overlooking substantial investments in health insurance, retirement contributions, professional development, flexible work arrangements, and other valuable benefits.

Total Rewards Statements (TRS) have emerged as a strategic communication tool that bridges this perception gap by providing employees with a clear, personalized view of their complete compensation package. When implemented effectively, these statements deliver measurable benefits for both employees and organizations, including improved retention rates, increased employee engagement, enhanced recruitment effectiveness, and a stronger return on investment for benefits expenditures.

In this paper we exame the dual benefits of Total Rewards Statements, supported by recent research and industry best practices. For organizations facing retention challenges, pay equity concerns, or benefits underutilization, Total Rewards Statements offer a proven, data-driven solution to improve transparency, trust, and employee satisfaction.

The Strategic Value of Total Rewards Statements.pdf

BACABA's Labor Market Analysis San Francisco-Oakland-Hayward, CA MSA

Charles Sterling — Fri, 06 Mar 2026 19:00:06 GMT

With the release today of the Fed's Jobs Report, we felt it important to provide a data-grounded analysis of the current state of the labor market specific to the San Francisco-Oakland-Hayward MSA.

Open the attached report to view:

- Key national sector developments relevant to the Bay Area

- Regional qualitative indicators

- Tech job market momentum observations

- Compensation and labor market leveraging implications

- Interim conclusions, until the April 17 release of California EDD economic data

Additionally, a 12-page Employment Situation Report (December 2025) is provided based upon the most recently available (December 2025) MSA economic data.

SF Bay Area Jobs Report 03.06.26.pdf

Bringing it All Together: Communication Matters

Charles Sterling — Tue, 03 Mar 2026 23:40:55 GMT

For many organizations, the focal review/pay adjustment period is winding down, and employees will soon be realizing these pay adjustments in their paychecks.

We wondered: what are companies in general doing in the area of pay communications, specifically:

Information sharing with employees on base pay
Communicating base pay increases
Communicating pay ranges
The use of Total Rewards Statements
Frequency of communications
Communications training

Fortunately, WorldatWork has conducted extensive research in this area; we summarized their findings and present them here as an attachment.

In terms of practical implications for a comp practitioner, consider the following:

For a comp/rewards leader, the research points to a few high‑leverage moves:

Write or refresh a concise compensation philosophy and share it broadly.
Build a simple narrative and visual for “how your pay is determined” that covers market data, ranges, performance, and progression.
Design a manager training and toolkit focused on pay conversations, including FAQs and scenarios.
Audit where and how employees currently get pay information and replace ad hoc, inconsistent communication with planned, multi-channel messages.

As for follow-ups to the focal process, at a minimum employees should receive advance notice of the pre-holdings increases they should expect to see and when. Many organizations have found an accompanying Total Rewards Statement to also be helpful in presenting the true value of the offerings the company makes to the employee, presented in summary fashion.

Bringing it All Together - Total Rewards Communications.pdf

BACABA's 2026 Wage Projections San Francisco-Oakland-Hayward, CA MSA

Charles Sterling — Mon, 23 Feb 2026 20:28:26 GMT

Executive Summary

The projected year-over-year annual wage growth rate for base pay in the San Francisco–Oakland–Hayward, CA Metropolitan Statistical Area for calendar year 2026 is approximately 3.25%, within a plausible range of 3.0% to 3.7%. This estimate is derived from a triangulation of the BLS Employment Cost Index (ECI) locality data, the Atlanta Fed Wage Growth Tracker, BLS Quarterly Census of Employment and Wages (QCEW), CPI-SF data, and six major national salary budget surveys. The projection reflects a metro area that has undergone significant wage deceleration since late 2024 — driven by tech sector normalization — but is expected to converge back toward national averages through 2026.

Source Data: Current Indicators

ECI — San Jose–San Francisco–Oakland CSA

The ECI Table 13 locality series provides 12-month percent changes for wages and salaries of private industry workers in the San Jose–San Francisco–Oakland Combined Statistical Area (CSA). The trajectory shows pronounced deceleration:

Period		Wages & Salaries (Year over Year)
Sep 2024		5.4%
Jun 2025		3.1%
Sep 2025		3.9%
Dec 2025		2.6%

The Dec 2025 reading of 2.6% for wages and salaries is notably below the national private industry average of 3.3%. This marks a sharp correction from the 5.4% peak in Sep 2024 and reflects the tech sector's ongoing headcount rationalization, which has suppressed upward wage pressure in the Bay Area's dominant industry.

By comparison, the national ECI for private industry wages and salaries held at 3.3% for the 12-month period ending December 2025, with quarterly seasonally adjusted growth of 0.7% (annualized ~2.8%).

Atlanta Fed Wage Growth Tracker

The Wage Growth Tracker, which measures the median 12-month wage growth of individuals observed in the Current Population Survey (nationally), stood at 3.6% overall (3-month moving average) for December 2025. For January 2026, the tracker held steady at 3.6%.

Critically, for job stayers — the most relevant cohort for base pay analysis — the tracker was 3.5% in both December 2025 and January 2026. This represents a continued downward glide from peak levels above 5% in mid-2023 and is now slightly below the 10-year median of 3.8%. The Wage Growth Tracker does not break down by MSA, but its national job-stayer reading provides a strong anchor for base pay projections when combined with local ECI data.

Projection Methodology

The 2026 projection employs a weighted triangulation of three analytical approaches:

ECI Locality Trend Extrapolation (Weight: 50%)

The SF CSA ECI wages and salaries series dropped from 5.4% (Sep 2024) to 2.6% (Dec 2025). This degree of divergence is atypical and likely reflects transitory compositional effects (tech layoffs removing high-wage workers from the index sample, suppressed merit budgets during restructuring). Mean reversion toward the national rate is expected through 2026, yielding an estimated 3.1% by year-end 2026.

Wage Growth Tracker Job-Stayer Trend (Weight: 50%)

The WGT job-stayer reading of 3.5% (Jan 2026) is on a gradual downward glide. Adjusting slightly for the SF MSA's industry composition (tech-heavy, with information sector wage growth nationally at only 2.9% per the ECI), the local estimate is approximately 3.4%.

Composite Result

Weighted composite: (3.1% × 0.50) + (3.4% × 0.50) = 3.25%

Factors Shaping the Range

Upward Pressures (Supporting 3.4%–3.7%)

· Bay Area CPI at 3.0% creates a cost-of-living floor for employer adjustments

· Minimum wage increases: SF minimum wage rises from $18.67 to $19.18/hr in July 2026, a 2.7% increase that compresses pay scales upward

· Healthcare and education labor tightness: The ECI for education and health services nationally rose 4.2%–4.3%, well above average

· AI/ML specialist demand: Selective premium pay for AI-related roles supports above-average increases in a subset of the workforce

Downward Pressures (Supporting 3.0%–3.2%)

· ECI SF CSA at 2.6% — the most recent locality reading is materially below national

· Tech sector restructuring: While layoffs slowed to ~3,860 in SF in 2025 (down from 10,200 in 2023), hiring remains selective

· Employment declines: San Francisco county employment fell 0.7% YoY, and Alameda fell 0.4%

· Remote work: Expansion of the labor supply pool reduces local wage premium effects

· Economic uncertainty: Tariff concerns, federal spending reductions, and potential recession risk are leading 66% of firms to cite macroeconomic factors as impacting 2026 compensation decisions

Conclusion

The projected 3.25% year-over-year base pay growth rate for the San Francisco–Oakland–Hayward MSA in 2026 reflects a metro area recalibrating after an exceptional post-pandemic compensation cycle. The SF CSA's ECI wages and salaries trajectory has decelerated sharply — from 5.4% to 2.6% — and is expected to partially revert toward the national 3.3% level. National salary budget consensus, the Atlanta Fed job-stayer wage tracker, and local cost-of-living dynamics all converge to support a central estimate of 3.25%, within a range of 3.0% to 3.7%. The primary risk to this projection is further tech sector weakness, which could push the realized rate toward the lower end of the range, while persistent Bay Area inflation and minimum wage mandates provide support at the floor.

Market Data and Methodology Matters

Charles Sterling — Fri, 20 Feb 2026 21:57:23 GMT

Over the past several weeks, as we've reviewed how to re-create the whole market pricing exercise, we've critically examined what's "broken" with the current system and how it impacts the entire market pricing concept. One item that came quickly to light that no one ever brings up is the sampling strategy that survey houses use when they administer their salary surveys. Let me explain what this is and why it's a critical problem that, in large part, invalidates the results of the survey.

In her landmark book "How to Conduct Surveys: A Step-by-Step Guide", Arlene Fink (Professor of Medicine and Public Health at the University of California, Los Angeles) devotes an entire chapter to Sampling, given it's importance to the validity and reliability of the survey.

In general, there are two types of sampling used by surveyors: Random and Convenience. We know of no salary survey house that utilizes a Random Sampling methodology when they conduct their salary surveys; all rely on a Convenience Sampling methodology due to its ease of administration and lower cost.

Random (probability) sampling is preferred over convenience sampling because it produces more representative, less biased data that can be validly generalized from the sample to the full population. Practically speaking, this means that the salary survey employing a convenience sampling strategy to gather its data cannot state that the statistics it calculates from that survey -- for instance, the median salary for a given job -- accurately represents the median salary of the market. Simplified statements like "we have a large sample size" don't correct for the original sin of employing a faulty sampling strategy.

Any survey house representative who suggests otherwise either (a) doesn't understand statistics, or (b) is knowingly trying to sell you a product -- data, in this case -- that doesn't meet the quality standards which are being advertised.

Here's a little info on both types of sampling methodologies and why convenience sampling is such a significant problem, essentially invalidating the results of salary surveys generated by survey houses:

Definitions

Random sampling means every unit in the target population has a known, typically equal, probability of being selected, often via lottery-style or algorithmic randomization. Convenience sampling selects whoever is easiest to reach (e.g., current clients or former survey participants), so not everyone in the population has a chance to be included.

Key advantages of random sampling

Representativeness and external validity

Because selection is random from a defined population, a random sample is much more likely to mirror the population’s characteristics, making it appropriate for generalizing results. As such, the median base pay rate for a job from the salary survey is pretty close to the actual median base pay rate for the population as a whole.

By contrast, convenience samples over‑represent people who are easy to reach, so the odds are that the results won't reflect the broader population. This is why, when comparing the results of market data for a job across different salary surveys, even when holding the scope of the cuts constant, you almost always see different results.

Reduced selection bias

Random selection removes the researcher’s discretion in who is included, which greatly reduces systematic selection bias.

Convenience samples are explicitly chosen based on accessibility or willingness to participate, so they are “open to bias” and can systematically skew results.

Due to their expense, salary surveys from the large survey houses typically over-represent large companies with big discretionary budgets who can afford the surveys and under-represent smaller/start-up companies.

Ability to use inferential statistics

Probability samples (including random samples) meet the assumptions needed for inferential statistics, allowing estimation of sampling error and confidence intervals and valid tests of hypotheses about the population. In other words, the base-pay median calculated from in a random-sample survey is expected to be very close to the base-pay median of the population.

With convenience sampling, inclusion probabilities are unknown, so you generally cannot make statistically defensible inferences or population-level estimates from the data. In other words, you cannot suggest that the median base-pay rate from your survey is the same -- or even approximates -- that of the population.

Replicability and methodological credibility

Random sampling based on a defined frame and a clear randomization procedure can be replicated by other researchers, supporting reliability and scientific credibility.

Convenience samples depend heavily on time, place, and who happened to be available, making results difficult to replicate and weakening perceived rigor.

Stronger basis for high‑stakes decisions

Because random samples reduce bias and support generalization, they are more appropriate when findings will inform major policy, product, or financial decisions. This makes random sampling perfect for salary surveys.

Convenience sampling is better suited to low‑stakes, exploratory, or pilot work where speed and cost matter more than precise estimates (e.g., early usability tests, hypothesis generation). The problem with using the results of convenience sampling for exploratory or "pulse" surveys, however, lies in how the results are communicated, often suggesting the results as iron-clad fact rather than approximation. So, for example, the results of the Mercer March 2025 US Compensation Planning Survey -- Mercer QuickPulse US Compensation Planning Survey -- which didn't describe a formal methodology section (e.g., sampling frame or data collection model) -- should be accepted with a grain of salt.

How Does the Data from BACABA's Market Pricing Services Compare?

BACABA's data comes from the Economic Research Institute's database of compensation for over 18,000 job titles, 1,000 industries and 9,400 locations, pulling data from multiple sources for their database as identified below. ERI goes to great pains to overcome the problems encountered from convenience sampling, and present their full methodology as follows:

"In general, most individual surveys report participants, but do not tie specific data to those participants. All compensation research firms, including ERI, wish to safeguard the privacy of individual survey participants. In general, ERI does not confirm whether a specific employer's data is included in any particular Assessor Series application analysis, that is, unless the employer has publicly released this information. Participation may have been via ERI's patented on-line survey, ERI Salary Surveys, ERI field job analyses, ERI's eDOT Skills Project, Occupational Assessor's cybernetic selected characteristics of occupations contribution to the latter, digitized reading of IRS public documents, US SEC proxies, 10-Ks, and 8-Ks, manual digitization of public UK/Euro countries' companies' annual reports, Canadian SEDAR data (under license), and/or other data licensed for use in the Assessor Series from organizations such as Statistics Canada, national statistics offices of other countries, and others. All of these sources comply with US DOJ/FTC Antitrust Safety Zone Statements by meeting the following conditions: 1) provider participation in surveys is managed by a third-party; 2) the information provided by survey participants is data more than three months old; and 3) there are at least five providers reporting data upon which each disseminated statistic is based, no individual provider's data represents more than 25% on a weighted basis of that statistic, and any information disseminated is sufficiently aggregated such that it would not allow recipients to identify the prices charged or compensation paid by any particular provider (unless part of the public record).

ERI also provides total population statistics that will help subscribers to evaluate whether an adequate population of incumbent employees within the area for which employers are competing for talent has been surveyed. In this regard, ERI is peerless. ERI’s combination of multiple survey data means that they are analyzing the largest populations possible, in most cases much more than 30% of the employers in a given area. There are currently over 46 million US and Canadian employees included in ERI's Salary Assessor database. Since they have analyzed so many sources in order to report updated consensus results, they expect their pay data to be more representative of market norms than any one specific published survey, particularly if it relies on a smaller sample (e.g., SEC proxies alone) or is out of date. According to the statistical laws of large numbers, Central Tendency and Bernoulli’s Law, Assessors that aggregate multiple overlapping sources covering virtually entire populations will be more accurate in normative terms than any one survey of a more limited sample."