Friction Appears After the Signature

Most acquirers plan the thesis, model the value, and negotiate the terms with precision. Integration plans look complete on paper. Then the deal closes and the two operations meet for the first time under shared ownership.

Friction appears immediately. Redundant roles surface, workflows collide, and handoffs between the two companies stall because no single person owns the outcome. The acquirer adds hours, headcount, and meetings to solve the confusion. More capacity without ownership only moves the chaos faster.

Tasks duplicate across both teams. Decisions reverse because no one knows who holds final authority. Metric definitions differ between the two companies and reporting becomes a negotiation instead of a diagnostic.

The bottleneck is not effort. The bottleneck is the absence of a structural rule that assigns one owner to every handoff before the integration starts. Firms that succeed at post acquisition integration separate the ownership question from the capacity question.

They name the owner first. Then they decide what resources the owner needs to deliver the combined operation. Strategy breaks when accountability is diffuse.

Ownership Maps Assign One Person Per Handoff

Integration creates dozens of handoffs that did not exist before the deal. Payroll runs on two systems. Customer lists live in separate databases. Vendor contracts carry different terms.

Pricing models diverge. Each difference is a handoff that someone must resolve, standardize, or explicitly leave separate. Without that answer, every handoff becomes a negotiation and the integration stalls.

Theory of constraints applies here. The constraint is not the complexity of the systems. The constraint is the diffused accountability that prevents any single decision from closing. Integration magnifies that break because two organizations bring two sets of assumptions about who decides what.

An ownership map resolves the constraint by naming the decider before the question arises. One person per handoff holds accountability for the outcome and coordinates everyone else. That person answers when the handoff breaks. Clarity protects the team from scrambling and prevents the concealed bottleneck where two people think the other one is responsible.

Staffing Structure Separates Baseline from Surge

Integration creates a temporary spike in operational demand. Two accounting systems must reconcile. Two HR platforms must merge.

Product lines must share a warehouse. That work is real and it requires capacity.

The acquirer makes the mistake of treating surge capacity as permanent. The company hires full-time staff to handle the integration and then carries that overhead after the combined operation stabilizes. Cost never recovers and the team becomes bloated.

Baseline capacity is the payroll the combined business needs permanently to run operations, serve customers, and grow. Surge capacity is the temporary resource that handles the integration work and then leaves. Fixed payroll funds the baseline. Contract resources, fractional operators, and project staff cover the surge.

Firms that run integration well build the baseline team first. They model the combined operation at steady state and staff for that model. Then they layer in the surge resources to handle the integration tasks. Surge resources report to the baseline owners, execute the handoff work, document the new processes, and train the permanent team.

A Fractional COO Holds the Handoffs

A fractional COO works inside the integration as an operator, not as an advisor. The role owns the execution of the combined operation during the surge period. Kamyar Shah holds the handoffs, aligns the departments, and standardizes the processes. The deliverable is a running system, not a recommendation deck.

The first step is to map every handoff between the two companies. Mapping starts with interviews of the department heads, traces the workflows, and identifies where the two operations must connect. Each handoff gets a named owner, a success metric, and a timeline. That map becomes the single source of truth for the integration.

The second step is to standardize the metric set using a balanced scorecard. Both companies arrive with different definitions for revenue recognition, customer count, utilization, and margin. The fractional COO selects one definition per metric, documents the choice, and trains both teams on the standard. Reporting aligns and the combined business becomes measurable.

The third step is to design the shared processes using jobs to be done as the organizing principle. The fractional COO takes the best elements from both companies and builds the process the combined operation will run permanently. Processes cover order to cash, procure to pay, and hire to onboarding. Documentation follows, training completes, and monitoring continues until the process runs without intervention.

Integration Execution Is Not a Document

A post-merger integration consulting engagement produces the plan: the sequencing decision, the owners, and the timeline. The plan is usually correct. The plan does not execute itself.

Someone must hold the handoffs, make the calls when the two companies disagree, and drive the work to completion. That someone is the operator. The operator role is not advisory. The operator role is accountable for the combined operation running.

Integration breaks in execution, not in planning. The acquirer knows what needs to happen. The acquirer does not have the capacity or the structure to make it happen while running the existing business. A fractional COO provides that capacity.

Consider a mid-market brand that acquired a smaller competitor to expand geographic reach. The integration plan was clear. Execution stalled because the leadership team was running the core business and could not dedicate time to the integration.

The acquirer brought in a fractional COO to own the integration. The fractional COO held the handoffs, aligned the operations, and handed a running combined business back to the baseline team.

Surge Ends and Baseline Runs

Integration is not a permanent state. The combined operation reaches a point where the handoffs are standardized, the metrics align, and the processes run without intervention. At that point, the surge capacity should leave. Baseline teams take full ownership and the combined business operates as one entity.

The acquirer makes the mistake of extending the surge resources beyond the integration period. The company keeps the fractional COO on retainer or converts contract staff to full-time employees because the resources are performing well. That decision adds permanent cost without permanent need. The combined operation does not require the surge capacity once the integration completes.

Define the end state before the integration starts. Model the combined operation at steady state and identify the baseline team that will run it permanently. Surge resources know from day one that their engagement is temporary. The fractional COO builds the operation with the goal of handing it off.

Firms that execute integration well measure the handoff completion rate. They track how many handoffs are running without intervention, how many metrics are standardized, and how many processes are documented and trained. When those numbers reach full coverage, the integration is complete. Surge resources exit and the baseline team owns the combined operation.

Owners Decide Before Integration Kicks Off

Bringing in a fractional COO happens before the deal closes or immediately after. Waiting until the integration stalls wastes time and money. Early weeks after close are when the two companies set the patterns that will persist. Patterns that form without clear ownership and structure drift into chaos.

The acquirer should model the combined operation during diligence. Identify the handoffs, estimate the surge demand, and decide who will hold the integration execution. Most mid-market companies do not have a dedicated integration function. For those companies, the choice is between asking the existing management team to absorb the integration or bringing in temporary operational capacity.

Management teams are already running the core business. Adding the integration load creates risk. An interim COO model fits the structure.

The role is temporary, accountable, and operational. Engagement ends when the integration completes.

Match the resource to the demand. Integration is a surge event. Surge events require surge resources.

Permanent staff handle baseline operations. Temporary resources handle temporary spikes. That separation protects the business from cost bloat and keeps the integration pointed at the value the thesis promised.

Combined Operations Run as One System

Post acquisition integration aims to deliver a combined operation that runs as one system. Handoffs are standardized. Metrics align.

Processes are documented and the team knows who owns what. The combined business serves customers, reports one set of numbers, and grows without the friction that marked the first weeks after close.

Measure success by operational coherence. The combined business operates with the same discipline the acquirer brought to the core business before the deal. Reporting is consistent.

Decisions are clear. Accountability is assigned. Leadership teams can diagnose performance using VRIO and make adjustments without debating definitions or chasing down who is responsible.

Every successful integration follows the same pattern. The owner names the deciders before the work starts. The owner separates baseline capacity from surge capacity.

That owner brings in temporary operational resources to handle the integration and then removes those resources when the integration completes. Baseline teams take ownership of the combined business and run it permanently.

Firms that execute integration well protect their teams, serve their customers, and build systems that compound over time. Integration is not a distraction. Integration is the operational work that turns the thesis into a running business.

Discipline required to execute that work is the same discipline required to run any mid-market operation. Integration compresses the timeline and magnifies the consequences of diffused accountability. Ownership maps and surge capacity structures prevent those consequences and deliver the combined operation the acquirer intended to build.

Kamyar Shah sells fractional COO and CMO services.

Two counts of the same year

Two Census programs offer different measures related to job endings and employment contraction at young firms.

The Quarterly Workforce Indicators (QWI) are built from state unemployment insurance wage records. They count jobs, quarter by quarter, and record a separation whenever a job present in one quarter is absent in the next. Census defines it exactly: “A separation is indicated when a job is present in one quarter, but is not present in the following quarter.” It also warns what the count cannot do: “Voluntary separations (retirement, new job) and involuntary separations (layoff, firing) cannot be separately identified.”

The Business Dynamics Statistics (BDS) are the Census program used here for job destruction and jobs lost at closing establishments. They count jobs lost from contracting and closing establishments during the last 12 months. Census defines the measure as “Job destruction is the sum of all employment losses from contracting establishments from year t–1 to year t including establishments shutting down.” The data service labels the subset from closures as “Number of jobs lost from closing establishments during the last 12 months”.

Both programs classify by firm age. In the BDS, “The age of the parent firm is based on the age of the oldest establishment in the firm, in the year the firm came into existence, and does not change in subsequent years as a result of establishment entry or exit.” In the QWI, “Firm age and size are defined at the national level, rather than the state level.” This study takes the youngest group each program offers that lines up with the other: QWI firm age 0 to 1 years, and BDS ages 0 and 1 combined.

The registered side-by-side

All industries, private employers, QWI quarters 1 to 4 of 2023 and BDS data year 2023, covering 48 states and the District of Columbia (every jurisdiction for which both programs returned usable figures, since Alaska and Michigan returned no QWI data).

Sources: U.S. Census Bureau, Quarterly Workforce Indicators, state files, 2023 Q1 to Q4, private ownership, all industries, not seasonally adjusted, firm age groups 1 and 5. Business Dynamics Statistics, state by firm age, 2023, age codes 0 years, 1 year and 11+ years. Both retrieved from the Census data service on October 4, 2026 and archived before the protocol was frozen.

The QWI rate is the year’s separations (5,212,670 at firms aged 0 to 1) divided by the average over the four quarters of jobs held at the start of a quarter (4,027,491). It can exceed 100 without any error in the data, because the numerator is an annual count of separations and the denominator is the average beginning-of-quarter job count. The BDS rate is jobs destroyed (611,207) divided by the Census denominator for the age group (3,625,836), which is the average of employment in the current and prior year. Of those destroyed jobs, 345,091 were at establishments that closed.

Nothing is subtracted. The two rates count different events (a worker-job relationship ending, as against gross year-to-year employment loss at contracting and closing establishments), on different denominators (average beginning-of-quarter employment, as against average current- and prior-year employment), with different timing (the quarter a job ended, as against a year-to-year comparison), and with firm age set on different program schedules (QWI reports its 0 to 1 group for each quarter, while BDS determines firm age annually from the year the firm came into existence, using the age of its oldest establishment), so matching the calendar year, the age labels and the jurisdictions does not make the two observations one cohort. There is also a known asymmetry between them: QWI separations are built from unemployment insurance wage records and can be unobserved for a firm’s final quarter, so observed QWI separations at exiting firms may be understated, while BDS job destruction from deaths is designed to include employment losses from closing establishments. That affects how the two columns should be read beside each other. It does not make them parts of one total, and no remainder between them is reported here.

By sector

The same three measures by sector appear below for firms aged 0 to 1 and, as the prespecified contrast, for firms aged 11 years or more. Each row uses the jurisdictions where QWI returned usable figures for that sector in every quarter and BDS returned the matching annual cells. Sectors with fewer than 40 jurisdictions are marked as limited coverage.

Firms aged 11 years or more, by sector, on the same construction. Where a sector’s jurisdiction count differs between the two age tables (mining 32 against 48, utilities 27 against 49, management of companies 42 against 49), the two age rows rest on different jurisdiction sets and are not comparable with each other across age. The all-industries rows and every sector at the same count in both tables are on the same set.

The sector columns do not move together. Information had one of the lower QWI separation rates, at 96.0 separations per 100 beginning-of-quarter jobs, and the second-highest BDS job destruction rate, at 23.6 jobs destroyed per 100 jobs on the Census annual denominator, behind only management of companies. Finance and insurance had the second-highest QWI separation rate, at 244.0 separations per 100 beginning-of-quarter jobs, and the sixth-highest BDS job destruction rate, at 20.7 jobs destroyed per 100 jobs on the Census annual denominator. A reader looking for the sectors where young firms “churn the most” will get a different list from each column, which is the point of showing both.

What the numbers mean for an operator

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

For an operator, the two 49-jurisdiction aggregate figures identify different things to monitor. QWI records worker-job separations of every kind, which it cannot sort into quits, layoffs, firings or exit-related losses. BDS records gross year-to-year employment losses at establishments that contracted or closed. In the BDS aggregate, job destruction equaled about 17 per 100 jobs on the Census annual denominator, and more than half of that was at establishments that closed. As operating judgment, the first figure points at hiring and retention, which an owner can measure monthly, and the second at survival and sizing, which an owner can also track internally. The data cannot say how much of the first is connected to the second, and this article does not estimate it.

How to handle it: keep two ledgers

The steps below are an operating recommendation. This study did not test them, and the Census Bureau does not endorse them.

1. Track worker-job endings and establishment employment reductions separately. Record worker-job endings throughout the year and, separately, compare establishment employment over a consistent annual period. The first is an internal counterpart to QWI separations, and the second is an internal counterpart to BDS job destruction. Neither internal count is the federal measure, and a company that tracks only period-end headcount sees neither clearly.

2. Use 129.4 as aggregate context, not a company forecast. Across firms aged 0 to 1 in the 49-jurisdiction frame, annual QWI separations equaled 129.4 per 100 average beginning-of-quarter jobs. The aggregate does not show what a particular company, or a firm in its first year alone, should expect. As operating judgment, a budget that assumes a stable team in the first two years is assuming something the aggregate does not show for young firms in general.

3. Read the sector column that matches the decision. For a hiring plan, the separations column. For a question about whether the business model is holding, the job destruction column. The two rank sectors differently, so the right comparison depends on the question.

4. Measure the first ninety days of each job. The QWI count includes jobs that started and ended inside a single quarter. Whether a company’s separations are concentrated there is something its own records can show and the public data cannot. Employment decisions, including terminations and reductions in force, carry legal obligations that vary by jurisdiction, so an owner should take them with qualified legal and HR advice.

What this data does not tell you

• It does not separate quits from layoffs or from jobs lost when firms close (QWI), and it does not say why an establishment contracted or closed (BDS).
• It does not show what share of separations came from job destruction. The two measures cannot be subtracted, and no such figure is reported here.
• Both are national firm-age classifications applied to state records, and the sector tables cover different jurisdiction sets.
• QWI values are protected with noise and carry status flags. Only values flagged 1 (OK) or 6 (calculated from other released measures, no significant distortion) were used, and Census publishes no sampling error for either program.
• QWI separations can be unobserved in a firm’s final quarter, so separations at exiting firms may be undercounted.

Method

Already published by the agency: Census publishes QWI separations and employment by firm age and state every quarter, and BDS job destruction, job destruction from deaths and the denominator by firm age, state and sector every year. New here: the two programs built on matched jurisdiction frames for one year (one 49-jurisdiction frame for all industries, a complete-case frame for each sector) and reported beside each other at firms aged 0 to 1 and 11 or more.

Sources. U.S. Census Bureau, Quarterly Workforce Indicators via the Census data service (qwi/sa), 2023 quarters 1 to 4, private ownership (A05), all industries and each two-digit sector, firm age groups 1 (“0-1 Years”) and 5 (“11+ Years”), not seasonally adjusted, indicators Emp (“Beginning-of-Quarter Employment: Counts”) and Sep (“Separations: Counts”) with their status flags. U.S. Census Bureau, Business Dynamics Statistics via the Census data service, 2023, state by firm age and state by sector by firm age, age codes 010 (“0 years”), 020 (“1 year”) and 075 (“11+ years”), variables JOB_DESTRUCTION, JOB_DESTRUCTION_DEATHS and DENOM. All responses retrieved October 4, 2026 and archived before the protocol was frozen.

Frames. A jurisdiction is in a frame when its four QWI cells for that age group and industry carry status 1 or 6 on both Emp and Sep, and its BDS row(s) carry the three variables. The all-industries frame is 49 jurisdictions: every state except Alaska and Michigan, plus the District of Columbia. Each sector has its own frame, with counts in the table. Sectors at 49 use that same set. For firms aged 0 to 1, agriculture lacks the District of Columbia (48). Mining (32) lacks Connecticut, Delaware, the District of Columbia, Hawaii, Iowa, Maine, Maryland, Massachusetts, Minnesota, Nebraska, New Hampshire, New Jersey, North Carolina, Oregon, Rhode Island, South Dakota and Vermont. Utilities (27) lacks Alabama, Arkansas, Delaware, the District of Columbia, Hawaii, Indiana, Iowa, Kansas, Louisiana, Maine, Mississippi, Nevada, New Hampshire, New Mexico, North Dakota, Rhode Island, South Dakota, Vermont, Washington, West Virginia, Wisconsin and Wyoming. Management of companies (42) lacks Delaware, Hawaii, New Mexico, Rhode Island, South Dakota, Vermont and Wyoming. For firms aged 11 or more, agriculture and mining lack the District of Columbia (48) and every other sector is at 49.

Rates. QWI: the sum of separations over four quarters and frame jurisdictions, divided by the mean over the four quarters of beginning-of-quarter employment summed over the frame, times 100. BDS: job destruction summed over frame jurisdictions and the age codes, divided by the summed denominator, times 100, and the same for job destruction from closures.

Protocol. The age mapping, frame rule, measures and the rule that no difference or ratio between the programs is computed were frozen in a dated protocol on October 4, 2026, after the source responses had been archived with hashes and before the registered quantities were computed. Before freezing, the QWI status flags and the Census API’s structure had been inspected, and national BDS rows by firm age, including 2023 national job destruction rates by firm age, had been seen, but the 49-jurisdiction rates reported here had not been computed. No QWI value had been read. The protocol (wave 3 section) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. A second program, written separately from the registered definitions by a different author from the analysis code and run against the raw files without access to this study’s code or results, recomputed all three claim groups, covering every frame list, count and rate in the tables. Every figure in the tables and text was compared and matched. The programs, archived inputs, manifests and hashes are kept with the protocol record.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

Kamyar Shah sells fractional COO and CMO services.

The ratio the IRS table does not print

For tax year 2023, the Internal Revenue Service’s Statistics of Income division published an income statement for nonfarm sole proprietorships, by industrial sector, built from a sample of Schedule C returns. The table carries a line for “Advertising expenses” and a line for “Business receipts, total [1]”. Dividing one by the other gives the share of the sector’s receipts that went to advertising. That share is the number this article is about. It is simple arithmetic on two published lines, and the IRS does not publish it.

For tax year 2023, across all nonfarm sectors, advertising expenses were 1.18% of aggregate estimated business receipts on sample-weighted sole proprietorship returns, a receipts-weighted ratio in which the largest filers carry most of the weight: about 1.2 cents per aggregate receipts dollar. The table note says what the figures are: “[All figures are estimates based on samples–money amounts are in thousands of dollars]”.

Advertising by sector, and three other bought-in costs

Source: IRS, Statistics of Income Division, Sole Proprietorship Returns 2023, Table 2 “Nonfarm Sole Proprietorships: Income Statements, by Industrial Sectors, Tax Year 2023”, released March 2026, downloaded October 4, 2026. Shares are the sector’s aggregate deduction divided by the sector’s aggregate receipts. The all-industries row includes 521,885 unclassified returns that are not assigned to one of the 18 matched sector rows. Receipts are rounded to the nearest million.

Three other patterns in that table stand beside the advertising column. First, in every sector but retail trade, sole proprietors reported more contract labor than advertising, and in construction the gap is more than twenty to one: 11.89 cents of each receipts dollar to contract labor, 0.51 to advertising. Second, the two sectors with the heaviest wage bills, accommodation and food services (15.73%) and health care (13.13%), sit in the middle of the advertising ranking. Third, legal and professional services ran between 0.50 and 2.57 cents on the dollar, and in six sectors (agriculture, mining, utilities, transportation and warehousing, professional, scientific and technical services, and health care and social assistance) it exceeded the advertising share.

The registered comparison: a near-zero rank correlation

The study fixed one question in the frozen protocol before the IRS advertising values were read and before the registered comparison was computed: across sectors, does the 2023 advertising share rank the way employer-firm job growth ranks? Growth is the Census Bureau’s net job creation rate for 2021 to 2023, from the Business Dynamics Statistics, recomputed as 100 times net job creation summed over those years divided by the Census DHS denominator summed over the same years, for the same 18 sectors. Because both numerator and denominator are sums of three annual values, the result is the denominator-weighted mean of the three annual rates, a rate per year, not a three-year cumulative change. One IRS column, administrative and support and waste services, includes management of companies among its subsectors, so its Census counterpart is sectors 55 and 56 combined. That correction was made after the first result was seen and is recorded as Amendment 1 to the protocol: under the earlier, wrong mapping the coefficient was minus 0.10. The year 2020 was left out, as registered, as a pandemic outlier.

With 2020 excluded as registered, the Spearman rank correlation across 18 sectors between the 2023 advertising share on sole proprietorship tax returns and the 2021 to 2023 net job creation rate at employer firms was minus 0.09, and because the growth window largely precedes the tax year in which advertising is measured, the correlation carries no temporal direction. That is a near-zero descriptive rank correlation: in these matched sector rows, the sectors where sole proprietors put the largest share of receipts into advertising were not, as a group, the sectors where employer firms added jobs faster. No inferential test was run, so this is not a statistical null, and it is not evidence that advertising has no effect. Information had the highest advertising share and a job growth rate of 0.41% a year, the fourth-lowest. Transportation and warehousing had the second-lowest advertising share and the highest job growth, 4.16% a year. Construction, with the third-lowest advertising share, grew at 2.05%.

The advertising measure covers sole proprietorship tax returns, while the growth measure covers jobs at employer firms. The growth window, 2021 to 2023, largely precedes the tax year in which advertising is measured, so the correlation carries no temporal direction, and no causal reading is available either way. The result is reported because the comparison was registered. Two values are reported: minus 0.10 under the crosswalk as originally registered, and minus 0.09 under the crosswalk corrected by Amendment 1 after the first result was seen. The correction rests on a fact visible in the IRS table’s own column headers (the administrative column lists management of companies as a subsector), not on the coefficient, which it barely moved.

Source for the growth column: U.S. Census Bureau, Business Dynamics Statistics, sector by year, variables NET_JOB_CREATION and DENOM, 2021 to 2023, retrieved October 4, 2026.

What the share is, and is not

The advertising share is advertising dollars per dollar of aggregate estimated receipts on sample-weighted returns. It is receipts-weighted, which means the largest sole proprietorships in a sector carry most of the weight. It is not the mean or median return’s share, not a benchmark for a business of any particular size, and the table does not classify returns by whether the business has employees. IRS estimates are based on a sample and may differ from a full count. No coefficient of variation table accompanies this income statement on the IRS site, so no interval is shown and the shares should be read as sector-level descriptions, not precise constants.

Two descriptive companions. The first was registered as the advertising share by sector and subsector, and its extension to the detail tier is recorded in Amendment 1, made after results were seen. The second was registered as written. First, Appendix A, available as a downloadable CSV at Appendix A (CSV), reports the advertising share for every labelled column of the IRS table, 153 rows at the sector, subsector and detail tiers (two detail rows, formal wear and costume rental and video tape and disc rental, are suppressed in the IRS file). Second, computed from the published return counts in the IRS’s Table 1 rather than printed by the IRS, the share of returns with net income:

What the numbers mean for an owner deciding what to spend

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

The useful fact in this table is not the 1.18 cents. It is that aggregate contract labor exceeded aggregate advertising in 17 of the 18 sectors, that the all-industries legal and professional services share was 1.01% against 1.18% for advertising, and that the sectors buying the most advertising per receipts dollar did not rank higher on employer-firm job growth in the registered comparison. An owner setting a marketing budget by asking “what does my sector spend” is asking a question this table can answer only in aggregate, and this study found no rank alignment between that aggregate and employer-firm job growth.

How to handle it: budget from the funnel, not from the sector

The steps below are an operating recommendation. This study did not test them, and the Internal Revenue Service and the Census Bureau do not endorse them.

1. Start from cost per acquired customer, not from a percentage of receipts. A share of receipts is a reporting category. A budget is the number of customers the business needs, times what it has cost to acquire one, measured on the business’s own last twelve months.

2. Put contract labor and advertising on the same page. In most sectors in this table, aggregate contract labor exceeds aggregate advertising. If outside labor is doing work that wins customers (referral partners, subcontracted sales, agency retainers booked as contract labor), the real marketing spend is larger than the advertising line shows, and the budget review should count it.

3. Treat a sector average as a prior, not a target. The information sector’s 3.26 cents and construction’s 0.51 cents describe what those businesses reported in aggregate. Neither number says what a given firm should spend, and the near-zero rank correlation above is a reason not to copy a heavy-advertising sector on the assumption that it grows faster.

4. Review the deduction categories with an accountant before comparing across years. Where a cost is booked (advertising, contract labor, commissions, other expenses) changes the shares. Whether a particular outlay is deductible, and in which category, depends on the taxpayer’s facts and applicable law, so a qualified tax professional should review the classification.

What this data does not tell you

• It does not say what a typical sole proprietor spends on advertising. The shares are aggregate and receipts-weighted.
• The IRS advertising table does not cover corporations or partnerships, and it does not say whether a sole proprietorship return has employees.
• It does not connect advertising to growth: the growth measure is a different population (employer firms) over an earlier window, and the registered comparison found no alignment.
• IRS figures are sample estimates without a published coefficient-of-variation table for this income statement.
• Deduction categories reflect how taxpayers classified costs, not a marketing accounting standard.

Method

Already published by the agency: the IRS publishes advertising expenses and business receipts by sector as dollar totals (Table 2) and return counts with and without net income by sector (Table 1). Census publishes net job creation by sector in the Business Dynamics Statistics. New here: the advertising, contract labor, legal and professional, and wage shares of receipts by sector, the advertising share for every subsector and detail column (Appendix A), the share of returns with net income computed from the published counts, and the registered rank comparison with Census job growth.

Sources. IRS, Statistics of Income, Nonfarm Sole Proprietorship Returns, Tax Year 2023: Table 2 (file 23sp02is.xls) and Table 1 (file 23sp01br.xls), both released March 2026 and downloaded October 4, 2026. U.S. Census Bureau, Business Dynamics Statistics, sector by year, retrieved October 4, 2026 and archived before the protocol was frozen.

Crosswalk. Sixteen of the IRS’s 18 sector columns correspond directly to one NAICS sector each. The IRS nonfarm agriculture column is paired with Census sector 11, but the coverage is not identical: the IRS table is for nonfarm returns and excludes farm proprietorship activity, while Census sector 11 covers the full employer-firm agriculture sector. That row stays in the comparison as registered, with this difference disclosed. The administrative and support and waste services column includes management of companies among its subsectors, so it corresponds to NAICS 55 and 56 together, and its Census rate is computed from the two sectors’ summed counts (Amendment 1, made after the first result was seen). The IRS unclassified establishments column has no counterpart and is excluded from the comparison. For each matched row, the Census rate is 100 times net job creation summed over 2021 to 2023, divided by the Census denominator summed over the same years.

Comparison. Spearman rank correlation across the 18 matched sectors between the 2023 advertising share and the 2021 to 2023 net job creation rate: minus 0.09 (minus 0.10 under the pre-amendment crosswalk). Coefficient and count only, with no probability value and no causal reading.

Protocol. The sources, share formulas, growth window, crosswalk and comparison were frozen in a dated protocol on October 4, 2026, after the source files and responses had been archived with hashes and before the registered comparison was computed. Before freezing, the IRS file’s labels and the Census API’s structure had been inspected, and some Census rows had been seen (national rows by firm age, the all-sector net job creation row, and the agriculture and mining sector rows). No IRS value had been read. The crosswalk was corrected after the first result was seen (Amendment 1, above). The protocol (wave 3 section) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. A second program, written separately from the registered definitions by a different author from the analysis code and run against the raw files without access to this study’s code or results, recomputed the 9 claim groups of the original analysis and, after Amendment 1, all 12 amended claim groups, including every row of Appendix A. Every figure in the tables and text was compared and matched. The programs, archived inputs, manifests and hashes are kept with the protocol record.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

Kamyar Shah sells fractional COO and CMO services.

A person buying a business with a government-guaranteed loan is making two bets at once: on the business, and on the sector it sits in. The Small Business Administration’s loan records show recorded charge-off dates for loans coded as a change of ownership, which this study tests against the registered five-year window. The Census Bureau’s Business Dynamics Statistics show, for the same sectors, how often employer firms exited. Neither agency publishes the two beside each other. This study does, for 13 sectors, and then reports one registered comparison: whether the sector ranking of loan charge-offs matched the sector ranking of firm deaths.

The sector table

Loans are non-cancelled SBA 7(a) records with an approval date from October 1, 2017 through September 30, 2020 (fiscal 2018 to 2020) and a business-age code of “Change of Ownership”. A loan counts as charged off within five years when SBA’s charge-off date falls on or before the fifth anniversary of approval. The firm death rate is the Census count of employer firms that exited, divided by the count of employer firms, averaged over calendar 2019 to 2023.

Sources: U.S. Small Business Administration, 7(a) FOIA loan-level files as of June 30, 2026 (fiscal 2010 to 2019 and fiscal 2020 to present). U.S. Census Bureau, Business Dynamics Statistics, sector by year, retrieved October 4, 2026. The six sectors with fewer than 200 loans are shown for completeness and were excluded from the registered comparison, as fixed in advance. The sold-on-the-secondary-market column matters because the public file may not record later charge-off performance for every sold loan, so a sector’s charge-off rate may be understated, and more so where more loans were sold. No loan had a blank, non-numeric or unresolvable NAICS code, and no sector in the loan file fell outside the Census sector list.

The Census counts behind the last column, by year (firms, then firm deaths, from the Business Dynamics Statistics, with the mean annual death rate calculated as the mean of the five yearly ratios):

Across all 19 sectors the cohort was 14,517 loans and 295 charge-offs, a five-year rate of 2.03%. The loan unit is a loan, not a business, and SBA’s borrowers include firms with no employees. The Census unit is an employer firm. A charge-off is SBA’s accounting event, defined in its data dictionary as “Date SBA charged off loan (if applicable)” for the date, and a firm death is Census’s “Number of firms that exited during the last 12 months”. The two populations overlap without being the same population, and the article never divides one by the other.

The registered comparison

The study fixed one comparison before any loan was counted: a rank correlation across eligible sectors between the five-year charge-off rate and the mean annual firm death rate. Eligible meant at least 200 change-of-ownership loans and complete Census figures for every year from 2019 to 2023. Thirteen sectors qualified.

The descriptive, ecological Spearman rank correlation was 0.68 across those 13 sectors, comparing five-year charge-off rates for SBA change-of-ownership loans, whose borrowers can include firms with no employees, with Census death rates for employer firms. In words: the sectors whose acquisition loans charged off most often were, broadly, the sectors where employer firms exited most often. Transportation and warehousing had the second-highest charge-off rate (3.93%) and the highest firm death rate (10.76%) among eligible sectors. Finance and insurance had the lowest charge-off rate (1.03%), while manufacturing had the lowest firm death rate (6.25%). Health care and social assistance had the second-lowest charge-off rate (1.21%) and the third-lowest firm death rate (6.93%). Arts, entertainment and recreation had the highest charge-off rate of all (4.38%) and the third-highest firm death rate (9.08%) among eligible sectors.

This is a descriptive, ecological result. Thirteen points support a ranking, not an inference, and no probability value is attached. The loans’ five-year windows run from late 2017 to late 2025, while the Census years are 2019 to 2023, so the two measures overlap in time without matching exposure. Nothing here says whether a charge-off happened because of the sector, the deal, the buyer, the lender or the year.

What is in the loan count, and what was set aside

The cohort started from 15,938 change-of-ownership loans approved in the window, after applying the registered rule that BusinessAge had to be exactly “Change of Ownership”. In the same approval window, 1,220 records had a blank BusinessAge value and 21,889 were coded “Unanswered”, so they were outside the cohort by construction. Cancelled loans (1,421) were removed. No record in any sector was inconsistent under the registered rules (a charge-off date before approval, an invalid date, or a charged-off status with no date), so the inconsistent count was 0 and the inconsistent share was 0.0%. No loan was excluded because its fifth anniversary fell after the June 30, 2026 as-of date: the count was 0. One undisbursed loan with status “COMMIT” stayed in the cohort under the registered definition.

A further 262 loans carry a charge-off date after their fifth anniversary. Under the registered endpoint they count as not charged off within five years, because they had not been. Over the whole fiscal 2018 to 2020 cohort, then, 557 loans show a charge-off status in the June 30, 2026 file, of which 295 fall inside the five-year window.

Of the 14,517 loans, 8,174 (56.3%) carry SBA’s indicator for a loan sold on the secondary market, which the dictionary describes as “An indicator if the loan was sold on the secondary market.” The public file may not record later charge-off performance for every sold loan. If unrecorded post-sale charge-offs occurred, the charge-off rates here may be understated, especially in sectors where more loans were sold. The sold count for every sector is in the sector table. Among the 13 eligible sectors, the share sold ranged from 45% in wholesale trade (209 of 462) to 66% in accommodation and food services (1,924 of 2,904).

SBA’s status “EXEMPT”, carried by 5,794 of the loans, means in the dictionary’s words “The status of loans that have been disbursed but have not been cancelled, paid in full, or charged off are exempt from disclosure under FOIA Exemption 4”. Those loans count as not charged off, because they have no charge-off date and no charge-off status.

The five-year rate here is the proportion observed in the complete eligible extract cohort, not an estimate from a probability sample. No sampling interval is shown, and the wave 1 study’s intervals for the same file are not repeated. Variation across loans that might be made in the future is not estimated.

What the numbers mean for a buyer or a seller

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

A charge-off rate of 2.03% over five years, with sector rates from about 1% to about 4% among the large groups, is the backdrop against which a buyer is negotiating. The sector ranking says something a buyer can use without over-reading it: broadly, in sectors where employer firms exited more often, the loans that financed a purchase also charged off more often. That is consistent with sector conditions showing up in both records, and consistent with several other explanations the data cannot separate.

For a seller, the same table supplies historical sector-level context that a buyer may consider when pricing financing risk. It does not measure any particular buyer’s risk.

How to use it: three checks before the letter of intent

The steps below are an operating recommendation. This study did not test them, and the Small Business Administration and the Census Bureau do not endorse them.

1. Price the sector, then the business. Before valuing the specific company, a buyer can read the sector’s employer-firm death rate as sector context, and treat a target whose own history appears better than that context as a claim to be verified in diligence, not accepted from the listing.

2. Do the underwriting as if no one else will. More than half of these loans were sold on the secondary market, which is normal practice and says nothing in this data about any loan’s risk. Whoever ends up holding the loan, this data does not establish who bears the legal, financial or operating consequences of a charge-off. A buyer should review those consequences with qualified legal and lending advisers, and the buyer’s own underwriting of the business has to stand on its own.

3. Separate the deal risk from the sector risk in the plan. The sectors with high charge-off rates in this table also have high employer-firm death rates, and those exits are measured across employer firms generally, not only bought firms. A buyer’s first-year operating plan in such a sector should stress-test customer, staffing and supplier assumptions, rather than treating the purchase as inheriting a stable position. Decisions about loan structure, personal guarantees and tax treatment depend on the buyer’s circumstances and applicable law, and should be reviewed with qualified legal, lending and tax advisers.

What this data does not tell you

• It does not say why any loan charged off, or that the sector caused it.
• A charge-off is a loan outcome, not the failure of the purchased business, and a firm death is an exit from the Census frame, not necessarily a failure.
• The loan unit includes businesses with no employees, while the Census unit is employer firms only.
• The five-year clock starts at approval, and loans charged off later than that are not counted here.
• Charge-off performance may be unobserved for loans sold on the secondary market.
• Thirteen sectors support a ranking, not a statistical test, and no interval or probability value is claimed.

Method

Already published by the agency: SBA publishes industry-level charge-off rates for all 7(a) loans in its portfolio performance reports, without a split by the business-age code. Census publishes firm counts and firm death counts by sector in the Business Dynamics Statistics. The wave 1 study on this site reported the all-sector five-year charge-off rate for change-of-ownership loans against existing-business loans. New here: the change-of-ownership cohort’s five-year charge-off rate by sector, set beside the Census firm death rate, and the registered rank comparison.

Sources. U.S. Small Business Administration, 7(a) FOIA loan-level files, FOIA_7a_FY2010_FY2019_asof_260630.csv and FOIA_7a_FY2020_Present_asof_260630.csv, data as of June 30, 2026. The files are public records. In SBA’s data dictionary, BusinessAge is a “Categorical description of the age of the business” and NaicsCode is the “North American Industry Classification System (NAICS) code”. U.S. Census Bureau, Business Dynamics Statistics, sector by year, variables FIRM (“Number of firms”) and FIRMDEATH_FIRMS (“Number of firms that exited during the last 12 months”), retrieved from the Census data service on October 4, 2026 and archived before the protocol was frozen.

Cohort and outcome. Approval dates were from October 1, 2017 through September 30, 2020, BusinessAge was exactly “Change of Ownership”, and cancelled loans were excluded. The fifth anniversary is the same month and day five years after approval (a February 29 approval maps to February 28). A loan is charged off within five years when its charge-off date is valid, not before approval, and not after that anniversary. Sector is the first two digits of the NAICS code, with 31 to 33, 44 to 45 and 48 to 49 combined as NAICS does. No borrower, lender or franchise brand is named.

Comparison. Spearman rank correlation across sectors with at least 200 eligible loans and complete Census figures for 2019 to 2023, between the five-year charge-off rate and the mean of the five annual firm death rates. No quotient is formed between loan events and firm events.

Protocol. The cohort, outcome, exclusions, eligibility threshold and comparison were frozen in a dated protocol on October 4, 2026, after the source files and responses had been archived with hashes and before the registered comparison was computed. Before freezing, the SBA file’s field list and the Census API’s structure had been inspected, and some national Census rows had been seen. No loan had been counted. The protocol (wave 3 section) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. A second program, written separately from the registered definitions by a different author from the analysis code and run against the raw files without access to this study’s code or results, recomputed all 13 claim groups, including every row of the sector tables. Every figure in the tables and text was compared and matched. The programs, archived inputs, manifests and hashes are kept with the protocol record.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

The Bureau of Labor Statistics measures nonfatal workplace injuries and illnesses through the Survey of Occupational Injuries and Illnesses (SOII). BLS describes its scope this way: “The SOII excludes all work-related fatalities as well as nonfatal work injuries and illnesses to the self-employed; to workers on farms with 10 or fewer employees; to private household workers; to volunteers; and to federal government workers.” Each year, according to BLS, “approximately 200,000 employers report for establishments in private industry and the public sector (state and local government).” This article uses private industry only.

BLS describes SOII as “an establishment-based survey used to estimate incidence rates and counts of workplace injuries and illnesses,” and places each establishment in a size class that is “based on an establishment’s average annual employment.” The unit is therefore the establishment, generally a single workplace, not a company. Rates are cases per 100 full-time equivalent workers. BLS computes them as the number of cases times 200,000, divided by hours worked, and explains: “(The 200,000 hours in the formula represents the equivalent of 100 employees working 40 hours per week, 50 weeks per year, and provides the standard base for the incidence rates.)”

The national pattern, which BLS has already published

Total recordable case rates, private industry, by establishment size (cases per 100 full-time equivalent workers):

Source: U.S. Bureau of Labor Statistics, SOII, quartile table 1 (total recordable cases) for each year, private industry, downloaded October 3, 2026. The 2020 and 2021 rates may reflect pandemic-period changes in employment and reporting.

In every year shown, the 50-to-249 class had the highest rate and the 1-to-10 class the lowest. BLS described the same shape for 2023 in November 2025: “rates range from 1.0 cases per 100 full-time workers among establishments with 1–10 employees to 3.1 cases for establishments with 50–249 employees.” The question this study asks is whether that national shape also holds inside individual industries.

Inside industries: where the highest rate falls

Before any detailed-industry size-class rate was read, the study listed the most detailed private industries in the 2024 table that do not overlap one another: 652 industries. Industries whose injury data BLS takes from mine-safety or railroad regulators were excluded first, because BLS states those records are not comparable with other industries. For each of the 652, the study checked whether BLS published a 2024 mean rate for all five size classes. BLS published all five for 17. In the other 635, at least one size class was not published.

Among those 17 industries, the class with the highest total recordable case rate in 2024:

The study’s single registered result is the count in the two middle classes: 8 of 17, or 47.1%. BLS does not publish standard errors for industry-by-size rates, so no significance is claimed. The 17 industries are not a sample of the economy. They are the 17 industries for which BLS published rates in all five size classes, and they span manufacturing, wholesale and retail trade, restaurants, health care and social assistance, construction, transportation, oil and gas support, and civic organizations (see Appendix A). The result describes them and nothing wider.

The same count for two narrower measures, in the same 652 industries:

The industry-level picture is more spread out than the national one. The national rate peaked at 50 to 249 in every year shown, but within these industries the peak falls in every size class, including the smallest.

Averages and the middle of the distribution

For the larger size classes, BLS also publishes quartiles of establishment rates. In 2024, private industry:

In each class, the published mean is above the median. The class mean and the rate of the establishment in the middle are different summaries, and the mean and median alone do not establish the full shape of the distribution. Quartiles are not published for the two smallest classes.

A known limitation: small establishments are less likely to keep records

Lower rates at small establishments may partly reflect recordkeeping rather than safety. A BLS follow-up survey of SOII respondents found that “small establishments are less likely than midsized and large establishments to keep records” (Elizabeth Rogers, “The Survey of Occupational Injuries and Illnesses Respondent Follow-Up Survey,” Monthly Labor Review, May 2020). That finding concerns recordkeeping practice, and it does not measure how many cases go unrecorded at each size. It is still reason to treat the 1-to-10 figures, nationally and inside industries, with care.

What the numbers mean for an operator

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

The national size pattern is a useful starting point and a poor benchmark for any one establishment. Within the few industries where a comparison is possible, the size class with the highest rate varies, and in four of the seventeen it was the smallest. A growing company in the middle classes should not assume it is in the most dangerous size band, and a small one should not assume it is in the safest. The relevant comparison is each workplace’s own industry and its own establishment size class, where BLS publishes it. A company with several workplaces should compare each one separately.

The gap between mean and median in the larger classes points the same way. When the mean sits far above the median, the class average is higher than the rate of the workplace in the middle, so the two should be read as different summaries. An operator who only checks the class average can be below it and still above the median workplace.

How to handle it: count properly, then compare

The steps below are an operating recommendation. This study did not test them, and BLS does not endorse them.

1. Calculate your own rate the BLS way. For comparison with these figures, use nonfatal recordable injury and illness cases in the year times 200,000, divided by total hours worked by all employees. BLS excludes fatalities from these rates. Use the OSHA recordkeeping rules that apply to the business to decide which cases are recordable, and ask a qualified safety or compliance professional if it is unclear which rules apply. A rate calculated another way cannot be compared with the published figures.

2. Compare against your industry and size, not the national average. For each workplace, find the most detailed industry row that matches it and the size class that matches that establishment’s average annual employment, not the company’s current or total employment. If BLS does not publish that cell, use the industry’s all-sizes rate and say so. Track the comparison every year rather than once.

3. Fix the records before reading the rate. If reliable records of cases and hours are missing or inconsistent, do not read a calculated rate as low. Treat it as unknown until the records are fixed. Where an OSHA log is required, or kept voluntarily, assign one person to own it, review it monthly with supervisors, and record near misses separately so they can be acted on before they become cases.

4. Look for the cases behind the number. Group recorded cases by task, location, shift and length of service. A small number of tasks or a single location often accounts for a large share of cases, and that is where controls pay off first.

5. Expect the rate to move as headcount grows. Moving from 40 to 80 employees usually means new supervisors, new shifts and less direct oversight by the owner. Put the safety routines in writing before that happens, not after the first serious case.

What this data does not tell you

• It does not show why injury and illness rates differ by size, or that any practice lowers them.
• The industry-level result covers only 17 industries where BLS published all five size classes. It does not describe the other 635.
• No standard errors are published for industry-by-size rates, so differences between classes inside an industry may not be meaningful.
• Small-establishment rates may be understated, because small establishments are less likely to keep records.
• Establishment size is the size of one workplace, not the company.

Appendix A: the 17 industries with all five size classes published (2024, total recordable cases)

Appendix B (CSV download), published with this article, lists all 652 industries in the analysis set, including the 635 without all five classes, and marks every size class for which BLS did not publish a rate.

Method

Already published by the agency: BLS publishes rates by industry and establishment size, with quartiles, in its SOII quartile tables, and has published the national size pattern. New here: a count, across the most detailed industries, of which size class has the highest rate, and the same count for two narrower case types.

Source. U.S. Bureau of Labor Statistics, Survey of Occupational Injuries and Illnesses, 2024 quartile tables 1 (total recordable cases), 2 (days away, restriction or transfer) and 3 (days away from work), and quartile table 1 for each year from 2019 to 2023, national, private industry, downloaded October 3, 2026.

Analysis set. Built from industry labels and NAICS codes only, before any detailed-industry size-class rate was read: the 1,020 private-industry rows with a six-digit NAICS code, less the 31 rows BLS footnotes as drawn from mine-safety (MSHA) or railroad (FRA) data, reduced to the 652 lowest-level rows that do not overlap. A size class with no published mean is treated as missing, never as zero. An industry’s highest class counts only if one class is strictly higher than all others. Otherwise the industry is counted as tied.

Protocol. The analysis set, the measure and the counting rule were fixed in a dated protocol before any rate was computed. The protocol (wave 2 section, frozen October 4, 2026) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. All figures were recomputed from the raw files by a separately written program, built without access to this study’s results. The 2023 private-industry size-class rates match those BLS published in November 2025 (1.0, 2.1, 3.1, 2.8, 2.7).

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

The Census Bureau’s Quarterly Workforce Indicators (QWI) are built from state unemployment insurance wage records linked to Census business data. This article uses QWI average monthly earnings of new hires into stable jobs, based on covered UI wages. Census defines the measure as “Average monthly earnings of newly stable employees (i.e., full-quarter employees who were new hires with a firm in the previous quarter).”

Two features of that definition matter. First, it counts only new hires who stayed long enough to work a full quarter. Hires who left quickly are not included, so this is a selected group, and the share of hires who stay may differ by firm age. Second, it is earnings, not a wage rate: it includes no information on hours, and it covers UI-reported wages only, not benefits or other compensation.

Firm age is measured for the whole national firm: “Firm age and size are defined at the national level, rather than the state level,” according to Census, and are reported for private-sector firms only.

Earnings by firm age

Private sector, all industries, both sexes, all worker ages, nominal dollars per month, not seasonally adjusted. The recent-window columns are means of four quarterly values, 2024Q3 to 2025Q2. The last column is the mean of 12 quarterly ratios, 2017 to 2019:

Source: U.S. Census Bureau, LEHD Quarterly Workforce Indicators, national files by firm age, release R2026Q3, downloaded October 3, 2026. Ratios are means of the four quarterly ratios, so they can differ slightly from dividing the dollar means shown.

New-hire earnings rise with firm age in the recent window. Against firms aged 11 or more, the ratio for every younger class was between 78.9% and 85.9%.

The registered comparison

The study set one comparison in advance: for each quarter, new-hire earnings at firms aged 0 to 1 year divided by the same measure at firms aged 11 or more, averaged across the latest four quarters, at or before 2025Q3, in which every value used had a QWI status of 1 or 6.

Dividing the four-quarter dollar averages instead gives the same 78.9%. In 2017 to 2019 the mean ratio was 82.1%.

Census says QWI uses “a system of multiplicative noise infusion, whereby all released data are ‘fuzzed.'” Every value carries a status flag, and this analysis used only values with status 1 (OK) or 6 (calculated from other released measures, no significant distortion). Census publishes no sampling error or other margin of error for QWI, and a covariance-aware interval for this ratio is not available from the public tables. No statistical significance is claimed, and values can be revised in later releases.

New hires against all stable employees

The fourth column of the table compares new hires with all stable employees at firms of the same age. At firms aged 0 to 1, new hires averaged 81.9% of what stable employees earned. At firms aged 11 or more, they averaged 62.1%. Put differently, the earnings gap between young and established firms is wider for stable employees than for new hires: dividing the four-quarter averages, stable employees at firms aged 0 to 1 earned about 60% of what stable employees at firms aged 11 or more earned, against 79% for new hires. Workforces at firms aged 0 to 1 have, by definition, shorter possible tenure with the firm than workforces at firms aged 11 or more. The data does not isolate why the two gaps differ.

What the numbers mean for an owner

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

A young company recruits against established employers whose new hires, on average, earn more. The data cannot say whether that reflects different jobs, different industries, fewer hours or lower pay for comparable work, and the table should not be used as a pay benchmark. What the data show is an aggregate earnings difference between these groups. As operating judgment, a young company should expect some candidates to compare its offer with established employers, and should be ready to compete on the whole offer.

The second pattern is useful in a different way. New hires at the youngest firms earned much closer to what all stable employees there earned (81.9%) than new hires at firms aged 11 or more did (62.1%), a difference that may reflect tenure, seniority, career progression and other factors the data does not isolate. A young company that wants to keep the people it hires will eventually need the same thing: a visible path from entry earnings to something better.

How to handle it: compete on the whole offer, and plan the second year

The steps below are an operating recommendation. This study did not test them, and the Census Bureau does not endorse them.

1. Benchmark pay against the right comparison. National averages across all industries say little about a specific role. Use local pay data for the actual occupation and level, and decide deliberately whether to pay at, above or below that market.

2. Put the whole offer in writing. Base pay, any variable pay, benefits, flexibility, and what the role will lead to. A young company that cannot match an established employer on salary often can on scope, speed of advancement and direct access to leadership, but only if the candidate can see it.

3. Define what the second year looks like. Write down, for each core role, what someone must achieve to earn the next step in pay and responsibility. It turns a modest starting figure into a credible trajectory, and it gives the company a retention tool that costs nothing until it is earned.

4. Track who stays past the first quarter. The Census measure counts people who were new hires in one quarter and full-quarter employees in the next. A young company should define its own checkpoint, such as the share of hires still employed after ninety days, and track it with what those employees earn.

5. Revisit pay when the company’s position changes. As revenue becomes predictable, the case for paying below the market weakens. Set a date, such as the close of each fiscal year, to review pay against the market and against the company’s own results.

What this data does not tell you

• It does not show what any firm pays for a given job, or the hourly rate behind these earnings.
• It covers only new hires who stayed a full quarter, a selected group.
• Earnings are nominal UI-covered wages and exclude benefits and other compensation.
• Differences by firm age reflect industry, occupation, location, hours and worker mix as well as pay practice, and none of these is isolated.
• QWI is noise-protected administrative data with no published sampling error, and values can be revised.

Method

Already published by the agency: Census publishes QWI earnings of new hires into stable jobs and of stable employees by firm age every quarter. New here: four-quarter and pre-pandemic averages across firm-age classes, the registered young-versus-established ratio quarter by quarter, and the comparison of new hires with stable employees within each firm age.

Source. U.S. Census Bureau, LEHD Quarterly Workforce Indicators, national firm-age file, release R2026Q3, data through 2025Q3, downloaded October 3, 2026. Selection: private ownership, all industries, both sexes, all worker ages, not seasonally adjusted.

Window rule. The current window is the latest four quarters, at or before 2025Q3, in which every value used had a status of 1 (OK) or 6 (calculated from other released measures, no significant distortion): 2024Q3 to 2025Q2. Values with any other status are treated as missing, never as zero. The baseline is 2017Q1 to 2019Q4.

Protocol. The comparison, the window rule and the baseline were fixed in a dated protocol before any value was computed. The protocol (wave 2 section, frozen October 4, 2026) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. All figures were recomputed from the raw files by a separately written program, built without access to this study’s results. As a second check, the ratio was rebuilt from state-level values for the 48 states and the District of Columbia that the Census data service returned for these quarters, weighting each state by its number of all new hires, because the Census data service does not serve the count of new stable hires that matches this earnings measure. Alaska and Michigan returned no data. Both limits make this a partial, approximate check. The quarterly ratios from that rebuild (79.8%, 81.7%, 78.5% and 77.5%) agree with the national file to within 0.7 point.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

The Census Bureau’s Quarterly Workforce Indicators (QWI) are built from state unemployment insurance wage records linked to Census business data. They count jobs that start and end each quarter. Census defines a separation as follows: “A separation is indicated when a job is present in one quarter, but is not present in the following quarter.” Census also notes that “Voluntary separations (retirement, new job) and involuntary separations (layoff, firing) cannot be separately identified.” This article therefore says “separations,” not “quits” or “turnover.”

Firm age is a property of the whole firm, measured nationally. Census explains: “Firm age and size are defined at the national level, rather than the state level. A national firm may frequently be larger or older than the part of that firm found in any one state.” Firm age is reported for private-sector firms only.

Separation rates by firm age

Census defines the separation rate as “All separations as a percent of average employment,” where average employment is the average of jobs at the start and end of the quarter. Means of four quarterly rates, private sector, all industries, both sexes, all worker ages, not seasonally adjusted:

Source: U.S. Census Bureau, LEHD Quarterly Workforce Indicators, national files by firm age, release R2026Q3, downloaded October 3, 2026. The 2017 to 2019 column is the mean of 12 quarters. Hire rate is all hires as a percent of average employment.

Separation rates fall at each step of firm age in both periods, and all five were lower in the recent window than before the pandemic.

The registered comparison: firms aged 0 to 1 against firms aged 11 or more

The study set one comparison in advance: the separation rate at firms aged 0 to 1 year minus the rate at firms aged 11 or more, averaged over the latest four quarters, at or before 2025Q3, in which every value used had a QWI status of 1 or 6.

The difference was positive in all four quarters. Over the 12 quarters of 2017 to 2019 it averaged 17.3 percentage points. A pooled version, summing separations and average employment across the four quarters before dividing, gives 30.5% and 14.5%, the same 16.0-point difference.

QWI values are protected with noise. Census describes it this way: “all released data are ‘fuzzed.'” Every value carries a status flag, and this analysis used only values with status 1 (OK) or 6 (calculated from other released measures, no significant distortion). Census publishes no sampling error or other margin of error for QWI, and a covariance-aware interval for this difference is not available from the public tables. No statistical significance is claimed, and QWI values can be revised in later releases.

What the separation measure includes, and what it does not separate

A separation here is any job that ended, whatever the reason. When a firm closes, all of its jobs end, and those jobs are counted with quits and layoffs. QWI does not show how much of any firm-age class’s rate comes from closures. Firms aged 0 to 1 also had a higher hire rate than separation rate in the recent window: 41.4 hires and 30.5 separations per 100 jobs per quarter. The table does not adjust for industry, location or worker mix, so nothing in it says that firm age itself causes the difference.

The rate of separations from stable jobs, those that had lasted at least a full quarter, follows the same order: 13.5% at firms aged 0 to 1, 12.9% at 2 to 3, 12.4% at 4 to 5, 11.6% at 6 to 10, and 8.3% at 11 or more, in the recent window. The gap between the youngest and oldest firms is smaller on this measure: 5.2 percentage points, against 16.0 for all separations. The two rates use different denominators, so this comparison is descriptive and does not show how much of the all-jobs gap comes from jobs that never reached a full quarter.

What the numbers mean for an operator

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

A company aged 0 to 1 year is operating where, on average, almost a third of jobs end every quarter. Some of that is the company finding its shape, and some of it is cost: every job that ends early carries the cost of hiring, training and lost output, and young companies have the least slack to absorb it. The stable-job figures cannot show where the gap comes from, because the two rates use different denominators. As a matter of operating judgment, the first months of employment are still a practical place to start looking.

For an established firm, the useful number is not the national 14.5% but the firm’s own rate measured the same way. The national figure blends every industry and region.

How to handle it: protect the first ninety days

The steps below are an operating recommendation. This study did not test them, and the Census Bureau does not endorse them.

1. Measure separations the same way. Each quarter, count jobs that ended (for any reason) and divide by the average of headcount at the start and end of the quarter. Track jobs that ended within the first ninety days separately. In operating experience, that second number is often where a young company’s losses sit.

2. Write down the job before hiring for it. Early exits often come from a mismatch between what the role was described as and what it turned out to be. A one-page description of the outcomes expected in the first ninety days, agreed before the search, makes the hiring decision and the first review concrete.

3. Give every new hire an onboarding sequence. A written first-week plan, a named person to go to, and check-ins at thirty, sixty and ninety days. Young companies tend to skip this because they are busy, and that is when it matters most.

4. Separate the separations you chose from the ones you did not. Record whether each exit was the company’s decision or the employee’s, and the reason. QWI cannot make that distinction, but a company can, and the two call for different fixes.

5. Hire to a plan, not to a backlog. A young company that hires in bursts when work piles up, and lets people go when it slows, will show high separations whatever else it does. A twelve-month hiring plan tied to revenue is the operating answer to both.

What this data does not tell you

• It does not separate quits from layoffs or from jobs lost when firms close.
• It does not show why separation rates differ by firm age, or that any practice lowers them.
• Firm age is the age of the national firm, not of a local workplace, and not the worker’s age.
• QWI is noise-protected administrative data with no published sampling error, and values can be revised.
• It is a different source from the federal job openings and turnover survey, and this analysis does not compare its figures numerically with that survey.

Method

Already published by the agency: Census publishes QWI separation, hire and stable-separation rates by firm age every quarter. New here: four-quarter and pre-pandemic averages across all firm-age classes, the registered young-versus-established comparison quarter by quarter, and the stable-job comparison.

Source. U.S. Census Bureau, LEHD Quarterly Workforce Indicators, national firm-age files (rates and counts), release R2026Q3, data through 2025Q3, downloaded October 3, 2026. Selection: private ownership, all industries, both sexes, all worker ages, not seasonally adjusted.

Window rule. The current window is the latest four quarters, at or before 2025Q3, in which every value used had a status of 1 (OK) or 6 (calculated from other released measures, no significant distortion). That was 2024Q3 to 2025Q2. Values with any other status are treated as missing, never as zero. The baseline is 2017Q1 to 2019Q4.

Protocol. The comparison, the window rule and the baseline were fixed in a dated protocol before any rate was computed. The protocol (wave 2 section, frozen October 4, 2026) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. All figures were recomputed from the raw files by a separately written program, built without access to this study’s results. As a second check, the same rates were rebuilt by adding up state-level counts for the 48 states and the District of Columbia that the Census data service returned for each of these quarters. Alaska and Michigan returned no data, so this is a partial check rather than a national reconstruction. The quarterly differences from that rebuild (16.4, 15.8, 16.0 and 15.4 points) agree with the national file to within 0.1 point.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

The IRS Statistics of Income (SOI) division publishes detailed tabulations of corporate tax returns each year. This article uses Table 3.2 of the Corporation Complete Report, titled by the IRS “Returns of Active Corporations, Form 1120S, Total Receipts and Deductions, Portfolio Income, Rental Income, and Total Net Income,” which groups S corporation returns by size of business receipts. The unit is the tax return. The figures are estimates from a sample of returns: as the IRS explains in its documentation of the corporate sample (tax year 2015 edition), the estimates “are based on a sample, they may differ from population aggregates resulting from a complete census of all corporate income tax returns.”

How to read these numbers

Except for return counts and differences in points, every percentage below is an aggregate share: the total dollars of one line, summed across all returns in a size band, divided by the total business receipts of the same returns. Larger returns contribute more dollars, so each share is weighted toward the bigger returns in the band. It is not the margin of a typical or median company, and deficits are netted into the IRS line for net income less deficit. The measure is tax-return income, which follows tax rules rather than the financial statements a company prepares for its owners or lenders.

The registered comparison

Net income (less deficit) from a trade or business, as a share of business receipts, tax year 2022:

The IRS does not publish coefficients of variation for these size tables, so no interval is shown and no significance is claimed.

Selected lines as a share of receipts, by size band

Each line as a percentage of business receipts, aggregate across all returns in the band, tax year 2022:

Source: Internal Revenue Service, Statistics of Income, Corporation Complete Report, Table 3.2, tax year 2022 (file 22co32ccr.xlsx), downloaded October 3, 2026. Selected compensation and benefits is officer compensation plus salaries and wages plus employee benefit programs. It is not a measure of every labor-related cost. Net income is the line for net income less deficit from a trade or business. The band under $25,000 is excluded, because with very small receipts any ratio to receipts is unstable. The lines shown are the seven the study’s protocol lists, plus two sums. They are not every line on the return and do not add up to 100. Protocol Amendment 2 records that clarification.

Three patterns run through the table. Cost of goods sold rises steadily with size, from 13.55% in the smallest band shown to 76.78% in the largest. Officer compensation peaks at 15.88% in the $100,000 to under $250,000 band and falls to 0.56% at $250 million or more. Net income as a share of receipts is highest at $100,000 to $250,000 (17.75%) and falls in every larger band after that. Because the table mixes industries, it cannot show how much of the cost-of-goods pattern reflects industry mix rather than receipts size.

Officer compensation and net income should be read together

The IRS line is compensation of officers. Some officers are also shareholders, but the table does not say how much of this compensation went to shareholder-employees. Where an officer is also a shareholder-employee, tax rules affect how payments are split between salary and distributions. The IRS states that “S corporations must pay reasonable compensation to a shareholder-employee in return for services that the employee provides to the corporation before non-wage distributions may be made to the shareholder-employee.” Because salary decisions can move dollars between the two lines, the last column of the table adds them together. On that combined measure, the $1 million to under $2.5 million band reported 16.08% of receipts, against 9.34% for $10 million to under $50 million.

The same comparison, 2014 to 2022

Net income as a share of business receipts for the two registered bands, each year’s tabulation:

The bands are in nominal dollars and were not adjusted for inflation, so a company can move between bands without changing in real size. Each year is a separate set of returns, not the same companies followed over time. The band definitions were checked to be the same in every year shown. The smaller band’s share was the higher of the two in all nine years.

What the numbers mean for an owner

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

The table compares different groups of returns at one point in time. It does not follow companies as they grow. In the aggregate, cost-of-goods shares rise across the bands shown, officer-compensation shares fall after the $100,000 to under $250,000 band, and net income as a share of receipts peaks in that band and falls in every larger one. Officer compensation plus net income falls from about a third of receipts at $100,000 to $250,000 to under a tenth at $10 million to under $50 million.

As operating judgment, the shape of the table matches what owners often experience as they grow: purchasing, staffing and management take a larger role, and the owner’s own work accounts for less of the result. The table does not show that any one company will follow that path, and industry differences are large. It is a reason to plan for the change deliberately rather than to assume the old ratios will hold.

How to handle it: build your own common-size statement and compare it fairly

The steps below are an operating recommendation. This study did not test them, and the IRS does not endorse them.

1. Put your own numbers in the same format. Express each line of the income statement as a percentage of revenue: cost of goods, officer pay, other salaries, benefits, rent, advertising and net income. Do it for at least three years so a single unusual year does not mislead.

2. Add officer pay and net income before comparing. In an S corporation where the owner is also an officer, how the owner is paid shifts dollars between those lines. Comparing the sum avoids reading a salary decision as a change in performance. The salary itself should reflect reasonable compensation for the work, which is a question for the company’s tax adviser.

3. Compare with your industry first, size second. These IRS figures mix all industries. A distributor and a consulting firm of the same size will look nothing alike. Use industry benchmarks for the main comparison and this table to understand how the shape changes with size.

4. Watch the cost-of-goods and labor lines as you grow. As receipts grow, purchasing and staffing become the main levers. Review gross margin and labor cost as a share of revenue every month, and set a target for each before the next stage of growth, not after.

5. Decide what net income is for. Retained profit funds growth, reserves and debt service. An owner who knows how much the business needs to keep, and for what, can set distributions and salary with the company’s tax adviser without starving the business.

What this data does not tell you

• These are aggregate-dollar shares weighted toward larger returns, not the margin of a typical company in the band.
• It mixes all industries, so it cannot separate industry mix from receipts size.
• It reflects tax-return accounting, not financial statements prepared for lenders or buyers.
• Officer compensation and net income are shaped by tax rules and owner choices.
• The IRS publishes no sampling error for these size tables, so small differences between bands or years may not be meaningful.

Method

Already published by the agency: the IRS publishes these dollar amounts and return counts by size of business receipts in Table 3.2 each year. New here: the lines shown converted to shares of business receipts for each band from $25,000 upward, selected compensation and benefits, defined as officer compensation plus salaries and wages plus employee benefit programs, and officer compensation plus net income, and the registered comparison for 2014 to 2022.

Source. Internal Revenue Service, Statistics of Income, Corporation Complete Report, Table 3.2 (Form 1120-S by size of business receipts), tax years 2014 to 2022, downloaded October 3, 2026.

Calculation. Within each band, each line’s aggregate amount is divided by the band’s aggregate business receipts and multiplied by 100. Net income is the line for net income less deficit from a trade or business. The band definitions were compared across years before the series was built. Cells not published would be treated as missing, never as zero.

Protocol. The measure, the two bands and the years were fixed in a dated protocol before any ratio was computed. The protocol (wave 2 section, frozen October 4, 2026) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. All figures, including the return counts for each band, were recomputed from the raw files by a separately written program, built without access to this study’s results.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

The Census Bureau’s Annual Business Survey (ABS) asks the owners of employer firms about themselves and their history in business. Census describes the survey’s coverage as “All domestic, non-farm, for profit businesses with at least one paid employee. Additionally, non-profit organizations (excluding universities) that operate in industries identified as potentially having R&D are sampled.” It notes that “The Annual Business Survey excludes the collection of data from nonemployer businesses.” This article uses the owner tables from the 2024 ABS. Census explains that the survey’s name uses the collection year: “The 2024 ABS, for example, covers reference year 2023.”

The unit throughout is the owner, not the firm. One firm can have several owners, and the survey records a separate answer for each owner. Census also notes that the owner tables “include only businesses that were classifiable by sex, ethnicity, race, and veteran status and responded to at least one owner demographic question.” The counts below are therefore owners of responding employer firms.

The question, and who answers it

The question asks about the status of the previous business the owner started most recently, not including the current one. The form records one answer for each owner. The question is about a business the owner started. A business the owner bought, or a business started before the most recent one, is outside it.

That framing matters for one reason above all. Everyone represented in this table owned an employer firm in reference year 2023. An owner whose previous business sold and who had retired, taken a job, or otherwise left employer-firm ownership before then is not represented. The table describes the history of people who were still owners. It is not a census of how businesses end.

Status of owners’ previous businesses

Census estimates that, of 4,212,412 owners who answered the question, 2,051,500 (48.7%) said the current business is their first. The remaining 2,160,912 reported a previous business they had started. Among those owners:

Source: U.S. Census Bureau, 2024 Annual Business Survey (reference year 2023), Characteristics of Business Owners, file AB2300CSCBO, downloaded October 3, 2026. United States, all sectors, all owners of respondent employer firms. Owners who did not answer the question are excluded, as Census excludes them from its count of owners reporting. Census rounds each published estimate separately, so the five individual answers add to 2,160,913, one more than the 2,160,912 calculated as all reporting owners minus first-time owners.

The study’s single registered comparison is the sold share minus the closed share: 5.5% minus 16.5%, or minus 11.0 percentage points. The shares are this study’s calculations from published counts, with owners reporting a previous business as the denominator. Census publishes standard errors for each answer as a share of all owners who answered, but not for these conditional shares or for the sum of the two “purchased” answers. These figures are point estimates, and no interval is shown. The public table includes an “Other” answer, and this article does not guess what it contains.

By sector

The same shares, calculated for each sector in which Census published every needed count (sector of the owner’s current firm, not of the previous business):

Shares are percentages of owners in that sector who reported a previous business. Differences are calculated from unrounded shares, so they can differ by 0.1 from subtracting the rounded figures. “Industries not classified” could not be calculated because a needed count was not published. In 18 of the 19 sectors shown, the closed share was larger than the sold share. In management of companies and enterprises, the sold share was larger. Sector figures are point estimates with no interval, and small differences between sectors should not be read as meaningful.

Context: owner age and when owners acquired their business

Census publishes the age of owners of respondent employer firms directly, with standard errors:

Adding the two oldest groups gives 51.3% aged 55 or over. That sum is this study’s calculation and has no published standard error. The public tables do not cross owner age with the status of the previous business, so this article does not say how older owners’ previous businesses ended.

When owners acquired their current business: 2.1% before 1980, 5.3% in the 1980s, 10.6% in the 1990s, 19.2% in 2000 to 2009, 33.7% in 2010 to 2019, and 4.3%, 4.9%, 4.3% and 1.3% in 2020, 2021, 2022 and 2023 respectively. 14.4% answered “don’t know.” These are Census’s published shares of owners who answered.

What the numbers mean for an owner

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

Among owners of responding employer firms whose answer concerned the previous business they had started most recently, closure was reported about three times as often as sale. The data cannot say why. It also misses owners who sold or closed and then left ownership altogether, and the direction in which that tilts the comparison is unknown. The table does not estimate the chance that a still-operating business will eventually find a buyer.

The practical reading is about transferability. A business sells when someone other than the founder can run it, can see why it makes money, and can believe the money will keep coming after the founder leaves. Many owner-run companies are built the other way: the owner holds the key customer relationships, prices from memory, approves every exception, and keeps the numbers in a format only the owner understands. Those businesses can be very profitable for the owner and still be hard to sell, because what a buyer would be paying for walks out of the door with the owner.

How to handle it: build a business someone else could buy

The steps below are an operating recommendation. This study did not test them, and Census does not endorse them.

1. Find out what a buyer would be buying. List what makes the business money: customers, contracts, a process, a location, a license, a team. Next to each, write who controls it today. Any line where the answer is the owner alone is a transfer risk a buyer will discount.

2. Move one relationship or decision off the owner each quarter. Pick the largest customer or the most frequent decision that only the owner handles. Hand it to a named person, with a written standard for how it is handled, and review the results monthly. In operating experience, a business that runs for a month without the owner is easier to sell than one that cannot.

3. Make the numbers readable by a stranger. Monthly financial statements closed on a fixed schedule, revenue broken down by customer and by line of business, and owner compensation separated from what the business needs to pay a manager. A buyer’s first questions are about these. An owner who cannot answer them quickly is negotiating from weakness.

4. Get a valuation before you need one. An outside view of what the business is worth, and why, shows the gaps while there is still time to close them. Doing it three to five years before an intended exit leaves room to act on the answer.

5. Decide what happens if no buyer appears. Closing is a legitimate outcome, and among these owners it was reported about three times as often as a sale. Planning for it, including a review with appropriate legal, HR or financial advisers of any obligations to employees, customers and lenders, gives the owner a fallback if a sale does not come together.

What this data does not tell you

• It does not show why previous businesses closed or sold, or what any practice would change.
• It covers only people who owned an employer firm in reference year 2023. Owners who sold or closed and then stopped owning a business are not counted, and that could move either share in either direction.
• It describes the previous business the owner started most recently, not every business the owner ran or bought.
• The unit is the owner. Two owners of the same previous business can each be counted.
• It does not cross owner age with how previous businesses ended.

Method

Already published by the agency: Census publishes the counts and percentages for every answer to this question, as shares of all owners who answered, in the ABS Characteristics of Business Owners tables. New here: the shares restricted to owners who reported a previous business, the sold-minus-closed comparison, and the same calculation by sector.

Source. U.S. Census Bureau, 2024 Annual Business Survey, reference year 2023, Characteristics of Business Owners (AB2300CSCBO), downloaded October 3, 2026. Selection: United States, all owners of respondent firms for sex, ethnicity, race and veteran status, all sectors, and each two-digit sector. Counts flagged by Census as suppressed or not available are treated as missing, never as zero.

Calculation. Owners with a previous business = “Total reporting” minus “This is the owner’s first business.” Sold = “purchased by another individual” plus “purchased by another company.” Each share = count ÷ owners with a previous business × 100. No interval is reported for these shares, because Census does not publish the covariances needed to compute one.

Protocol. The comparison, the denominator and the sector rule were fixed in a dated protocol before any estimate was computed. Before results, the question was confirmed to be single-answer from the Census questionnaire. The protocol (wave 2 section, frozen October 4, 2026) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. All figures were recomputed from the raw file by a separately written program, built without access to this study’s results. The national counts match the Census API (2023 ABS owner characteristics) exactly. The share of owners aged 65 or over, 23.1%, matches the Census release.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

The Census Bureau’s Annual Business Survey (ABS) describes its coverage this way: “All domestic, non-farm, for profit businesses with at least one paid employee. Additionally, non-profit organizations (excluding universities) that operate in industries identified as potentially having R&D are sampled.” The 2024 ABS covers reference year 2023. This article uses the Census table of business characteristics by employment size of firm. The unit is the employer firm, counted once, which is different from the Census owner tables, where each owner is counted.

What family-owned means here

Census asks whether two or more members of one family owned the majority of the business. A firm with one owner cannot answer yes, even if that owner’s family depends on the business. So majority family-owned, in this article, means exactly that: two or more relatives together hold the majority. It does not mean that firms outside that definition have no family involvement.

Family-owned and single-owner shares, by firm size

Shares of employer firms that answered each question, with Census’s published standard errors. The 95% intervals are calculated here as the published percentage ± 1.96 × the published standard error:

Source: U.S. Census Bureau, 2024 Annual Business Survey (reference year 2023), Characteristics of Businesses by employment size of firm, file AB2300CSCB04, downloaded October 3, 2026. United States, all sectors, all firms. Percentages are Census’s published shares of firms that answered each question. Only the mutually exclusive employment-size classes set in the study’s protocol are shown.

The study’s single registered comparison is the family-owned share at 1 to 4 employees minus the share at 250 to 499: 23.1% minus 40.8%, or minus 17.7 percentage points. Each share has its own interval, calculated from its published standard error. The difference does not, because Census does not publish the covariance between size classes, so no interval is shown for it.

The 500-or-more estimate carries a published standard error of 24.4 percentage points on a 39.2% share. An interval built from it would run below zero. The figure is shown for completeness and should not be used.

How the two ownership measures differ by firm size

Among firms with 1 to 4 employees that answered the number-of-owners question, 69.0% are owned by one person. Among firms with 250 to 499 that answered the number-of-owners question, 22.8% are. Under this definition a one-owner firm cannot be majority family-owned, and one-owner firms make up a much larger share of the smallest firms. The two questions are answered separately, so the table cannot show exactly how the two shares overlap. The higher family-owned share in the 250 to 499 class than in the 1 to 4 class is consistent with that, and the public tables do not allow ownership structure and other size-related differences to be separated. This article does not claim that family ownership itself becomes more common as firms grow, or that family firms grow faster.

The family-owned share does not rise at every step. It is 34.8% at 20 to 49 employees and 33.2% at 50 to 99, and the intervals overlap for 20 to 49 and 50 to 99, for 50 to 99 and 100 to 249, and for 100 to 249 and 250 to 499.

What the numbers mean for an owner

These are descriptive aggregates for the stated federal-data population. They do not identify causal effects, diagnose an individual company, or estimate the result of adopting a practice. The guidance that follows is operating judgment, not a finding from the data.

Two facts from the table provide context for transition planning. First, among firms with 1 to 4 employees that answered the number-of-owners question, 69.0% report one owner. That makes operational continuity worth addressing, but the table does not show whether a successor has been identified. Second, among firms with 250 to 499 employees that answered the family-ownership question, 40.8% report majority ownership by two or more members of one family. That makes family relationships relevant to ownership planning, but the table does not show how those firms make or settle decisions.

Census does not measure succession plans, and nothing here says family firms are more or less prepared. The operating observation is narrower: when two or more relatives own a majority, future ownership and management decisions may involve both governance arrangements and family relationships. The rights and approvals required depend on the firm’s governing documents and applicable law, so owners should consult qualified legal and tax professionals.

Census does not measure succession plans, and nothing here says family firms are more or less prepared. The operating observation is narrower: when two or more relatives own the majority, decisions about the business’s future need agreement among people whose relationship does not end when the business changes hands.

How to handle it: separate the ownership plan from the management plan

The steps below are an operating recommendation. This study did not test them, and Census does not endorse them.

1. For a single-owner firm, write down who could run it. One owner means one point of failure. Name the person who would run the business for six months if the owner could not, list what that person would need to know, and start transferring it. This is the same work that makes the business easier to sell.

2. For a family-owned firm, separate three questions. Who will own the business, who will run it, and who will be paid by it are different questions. Family firms often answer them together by default. Answering them separately, in writing, avoids treating ownership as a reward for working in the business, or management as a reward for being family.

3. Put the agreement in the documents. A buy-sell agreement among the owners, with a valuation method and funding, can decide in advance what happens if an owner dies, divorces, becomes disabled, or wants out. Without one, those events are settled under pressure. A lawyer and an accountant should draft and review it.

4. Set a date for the review, not just for the plan. Ownership and family circumstances change. A plan reviewed every year or two, on a fixed date, stays usable. A plan written once tends to be out of date when it is needed.

5. Bring in someone outside the family for the hard conversations. An outside adviser or board member who answers to the business, not to any one relative, can raise the questions family members find hardest to raise with each other.

What this data does not tell you

• It does not measure succession plans, family involvement short of majority ownership, or whether family firms perform differently.
• The family-owned definition excludes single-owner firms, which constrains how the size pattern can be read.
• The unit is the firm. It is not comparable to Census owner tables, which count owners.
• The difference between size classes has no published interval, and the 500-or-more estimate is too imprecise to use.

Method

Already published by the agency: Census publishes the family-ownership and number-of-owners percentages and standard errors by employment size of firm. New here: the two measures laid side by side across size classes, the registered comparison between the smallest and the 250-to-499 class, and the explanation of how the definition constrains the pattern.

Source. U.S. Census Bureau, 2024 Annual Business Survey, reference year 2023, Characteristics of Businesses by employment size of firm (AB2300CSCB04), downloaded October 3, 2026. Selection: United States, all firms for sex, ethnicity, race and veteran status, all sectors, and the mutually exclusive size classes shown above. Cells flagged as suppressed or not available would be treated as missing, never as zero. None of the cells used was flagged.

Calculation. Shares and standard errors are Census’s published values. Intervals are the published share ± 1.96 × the published standard error, rounded to one decimal. Before any family-owned or one-owner estimate was viewed, Census’s denominator for these percentages was confirmed, using a different question, to be the firms that answered.

Protocol. The comparison and the size classes were fixed in a dated protocol before any estimate was computed. The protocol (wave 2 section, frozen October 4, 2026) and its dated amendments are at https://kamyarshah.com/public-data-protocol/

Check. All percentages and standard errors shown were recomputed from the raw file by a separately written program, built without access to this study’s results, and matched. The family-owned percentages and standard errors also match the Census API (2023 ABS business characteristics) exactly. The difference and the intervals were recalculated from those values.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

This study uses SBA 7(a) loans approved in fiscal 2018 to 2020, excluding loans that were cancelled or never disbursed and loans whose business age was blank or recorded as Unanswered. Selling a company and buying one are decisions most people make rarely. The Small Business Administration’s 7(a) loan records offer one way to see how loans coded as a change of ownership performed in their first five years, next to loans in the age codes this study groups as existing businesses. For change-of-ownership loans, the business-age field identifies the loan as a change of ownership rather than recording the purchased business’s age.

The SBA publishes 7(a) loan-level files that include the business’s age category and whether the loan was later charged off. Starting in fiscal 2018, the records include a “Change of Ownership” category. This study followed 7(a) loans approved in fiscal 2018 through 2020 for five years from their approval date, after excluding loans that were cancelled or never disbursed and loans whose business age was blank or recorded as Unanswered. Loans coded “New, Less than 1 Year old” or “New Business or 2 years or less” (4,886 loans) fall in neither comparison group. After the exclusions, 115,884 loans remain: 14,516 change-of-ownership loans, 74,530 existing-business loans, 21,952 startup loans and those 4,886.

The main comparison

Of 14,516 change-of-ownership loans, 295, or 2.03%, were charged off within five years of approval (95% interval 1.82% to 2.27%). Of 74,530 loans in the age codes this study groups as existing businesses, 2,760, or 3.70%, were charged off in the same window (95% interval 3.57% to 3.84%).

Change-of-ownership loans were lower by 1.67 percentage points (loan-level 95% interval: 1.40 to 1.92 points lower). These are all in-scope loans for those years, not a sample. The intervals describe uncertainty for loans of this kind under a simple binomial model, not sampling error for the listed loans. They are not adjusted for lender clustering, which may make them narrower than a clustered interval would be. This was the single primary comparison, set before the group charge-off rates were calculated. The fiscal 2018 to 2019 sensitivity, the sector breakdown, the dollar comparison, the franchise split, and the loan-size bands were also registered in advance. They are descriptive, not further tests. The existing-business group is defined in Method. It is not an SBA published category, it includes age codes for businesses as young as 2 years, and the age field does not show whether owners were changing.

For context only, 1,030 of 21,952 loans to open a new business, or 4.69%, were charged off within five years. Comparisons between acquisition and startup loans have been published before. The comparison with existing businesses is the focus here.

Four industries, described

Four industry sectors had at least 1,000 loans in both groups. This breakdown is descriptive, not a further test. In each, the change-of-ownership point estimate was lower. The intervals for each group are in the table. Overlap of those intervals is not a test, and no sector difference interval was pre-registered.

Five-year charge-off rate, number of loans and 95% interval (source: SBA 7(a) FOIA files FOIA_7a_FY2010_FY2019_asof_260630.csv and FOIA_7a_FY2020_Present_asof_260630.csv, data as of June 30, 2026, downloaded October 3, 2026):

Dollars, loan size, and franchises

SBA’s data dictionary defines the approved amount (GrossApproval) as “Total loan amount” and the charged-off amount (GrossChargeOffAmount) as “Total loan balance charged off (includes guaranteed and non-guaranteed portion of loan)”. Measured as charged-off amount within five years divided by approved amount, the ratio was 0.93% for change-of-ownership loans (bootstrap 95% interval 0.76% to 1.12%) and 1.07% for existing-business loans (0.98% to 1.16%). The approved amount is not the amount disbursed or outstanding, so this ratio is not a loss rate on money actually advanced. This comparison is descriptive. No interval for the difference between the two ratios was registered or computed, so it does not show whether the ratios differ.

In descriptive breakdowns among change-of-ownership loans, five-year charge-off rates by approved loan size were 2.66% for loans under $150,000 (1,878 loans), 3.10% for $150,000 to under $500,000 (5,031), 1.43% for $500,000 to under $1 million (3,071), and 0.99% for $1 million or more (4,536). Change-of-ownership loans with a franchise code had a rate of 1.88% (3,089 loans) and those without one had 2.07% (11,427). This split is descriptive, and no interval for the difference was computed. Loan-size bands differ in industry and lender mix. Differences by loan size reflect both. Nothing here isolates an effect of loan size. Franchise status comes from the SBA franchise code field, which can be incomplete. The registered band labels (under $150,000, $150,000 to $500,000, $500,000 to $1 million, and over $1 million) were ambiguous at the exact dollar boundaries. The analysis code, written before any rate was computed, used half-open bands: under $150,000, $150,000 to under $500,000, $500,000 to under $1 million, and $1 million or more. A dated protocol amendment, made after the results were seen, recorded that clarification. The bands and the calculation did not change.

Limits that matter

A charge-off is a loan outcome, not a business failure. Loans charged off more than five years after approval are not counted, so these are not lifetime loss rates. The clock starts at approval, not at disbursement. The exploratory box below checks whether that matters. Payment relief under Section 1112 of the CARES Act covered many SBA loans in 2020, including loans approved in fiscal 2018 and 2019 that were then in repayment, so no cohort here is free of that relief. A sensitivity check on fiscal 2018 and 2019 loans only was registered in advance. It is reported because it was registered. It is not a remedy for that relief. Excluding fiscal 2020, the rates were 2.20% for change-of-ownership loans (211 of 9,572) and 4.25% for existing-business loans (2,327 of 54,779), the same direction. Loans from those two fiscal years could still have received Section 1112 payments while in repayment.

Business age was blank or recorded as Unanswered for 20,446 approvals, so those loans could not be assigned to either comparison group. If those loans differ in charge-off outcomes, excluding them could affect the reported gap. The exploratory box below describes a selection-risk check for them. These are SBA-guaranteed loans only, not all business purchases. Change-of-ownership loans finance a purchase, while existing-business loans finance other uses such as working capital or equipment, and the two can carry different SBA underwriting requirements. They may also differ in loan size, industry, and lender. The rates here are not adjusted for any of that. This study does not compare loan-size mix, or charge-off rates by size, between change-of-ownership loans and the existing-business group. It cannot say whether size mix accounts for any of the gap. The gap describes how these loans performed. It is not proof that buying a business is safer.

Exploratory, not pre-registered. Reason included: outside reviewers asked whether change-of-ownership loans take longer to disburse, which would give them less time at risk under an approval-based clock. SBA’s FirstDisbursementDate field is filled for 14,511 of 14,516 change-of-ownership loans and 74,520 of 74,530 existing-business loans. The median time from approval to first disbursement was 31 days in both groups. The mean was 42.6 days for change-of-ownership loans and 50.0 days for the existing-business group. Counting five years from first disbursement, for loans first disbursed by June 30, 2021 (14,500 and 74,463 loans), the charge-off rates were 2.11% (95% interval 1.89% to 2.36%) and 3.83% (3.69% to 3.97%). The gap remained in the same direction when the five-year clock started at first disbursement. That does not rule out every timing difference.

The same box also covers the 20,446 loans excluded because business age was blank or recorded as Unanswered. Their five-year charge-off rate, counted from approval, was 4.85% (991 of 20,446 loans, 95% interval 4.56% to 5.15%), higher than either comparison group, and their median approved amount was $50,000. Which group these loans belong to is unknown. As a simple reassignment scenario, if every one of them had been a change-of-ownership loan, that group’s rate would have been 3.68%, about the same as the existing-business group’s 3.70%. This is not a worst case, because group membership among these loans could be related to their outcomes. This box is descriptive.

What the numbers mean for buyers and sellers

This section and everything after it, up to Method, are interpretation and guidance from operating practice. They are not findings of this study.

Among disbursed 7(a) loans with a usable business-age code, change-of-ownership loans were charged off less often within five years than loans in the existing-business group, 2.03% against 3.70%. Measured by dollars, the two ratios were much closer, 0.93% against 1.07%. The figures describe loans, not businesses, and they cannot show that buying a business is safer than running one. The 20,446 loans without a usable age code are a reminder that the answer depends on which records are counted, as the exploratory box above shows.

For a buyer, the honest reading is narrow. Among loans with a usable age code, purchases financed through the 7(a) program did not show a higher five-year charge-off rate in these years than the existing-business group. That is useful context. It is not a reason to relax diligence, because a loan that is repaid can still sit on a business that disappoints its new owner.

For a seller, the figures say something indirect. Buyers can finance purchases through this program, which widens the pool of buyers who can pay a fair price. A business is easier to finance when its earnings and operations can be verified by someone who has never worked in it.

How to handle it: make the business easy to finance and easy to hand over

The steps below are an operating recommendation. This study did not test them, and SBA does not endorse them. Lending terms, eligibility, and tax treatment change, so each step should be confirmed with a lender, an accountant, and transaction counsel.

For sellers, two to three years before a sale:

1. Separate the owner’s role from the business. Write down what the owner does each week and move as much of it as possible into documented procedures and named roles. A business that depends on one person’s memory is harder to value and harder to finance.
2. Clean the financial record. Consistent monthly closes, reconciled accounts, and a clear line between personal and business spending make earnings verifiable. Lenders and buyers discount what they cannot verify.
3. Plan the handover. Decide in advance how long the seller will stay involved after closing and which relationships, with customers, suppliers, and staff, need a personal introduction.

For buyers, before and after closing:

1. Test the earnings, not the story. Ask for the monthly financials, the tax returns, and the customer concentration, and reconcile them to each other. A quality-of-earnings review by an outside accountant is worth its cost on a financed purchase.
2. Map the operation before day one. Identify the routines that keep the business running and who performs them. Anything that lives only in the seller’s head is a transition risk to cover in the purchase agreement and the handover plan.
3. Hold working capital in reserve. A transition usually costs more and takes longer than planned. A buyer who borrows to the limit leaves no room for the first quarter that goes differently than expected.

What this data does not tell you

• It does not show that buying a business is safer than starting or running one, or that any practice reduces charge-off risk.
• It covers SBA-guaranteed 7(a) loans approved in fiscal 2018 to 2020 only, not all business purchases.
• A charge-off is a loan outcome, not a business failure, and loans charged off after five years are not counted.
• Loans without a usable business-age code are excluded, so the comparison depends on which records are counted.

A business sale is an operating event as much as a financial one. About 98 in 100 change-of-ownership loans in this data were not charged off within five years. Building a business that can keep running after its owner changes is the part both sides control.

Method

Already published by the agency: the SBA publishes the loan-level files but no table for this comparison. Private analysts have published acquisition-versus-startup default comparisons. New here: a fixed five-year charge-off window for fiscal 2018 to 2020 loans, comparing change-of-ownership loans with existing-business loans, overall and by sector.

Source. U.S. Small Business Administration, 7(a) FOIA loan-level files for fiscal 2010 to 2019 and fiscal 2020 to present, data as of June 30, 2026, downloaded October 3, 2026. The files are public records. In SBA’s data dictionary, each record is a 7(a) or 504 loan (the Program field is an “Indicator of whether loan was approved under SBA’s 7(a) or 504 loan program”), and BusinessAge is a “Categorical description of the age of the business”. No borrower, lender, or franchise brand is named here.

Population. All 7(a) loans approved in fiscal 2018 through 2020. Loans cancelled or never disbursed (18,229) were excluded. Loans whose business age was blank or recorded as “Unanswered” (20,446) were also excluded. By fiscal year, they were 8,355 loans (13.8% of fiscal 2018 approvals), 8,536 (16.4% of fiscal 2019), and 3,555 (8.4% of fiscal 2020). “Existing business” combines the SBA age codes “Existing or more than 2 years old”, “Existing, 5 or more years”, “Less than 5 years old but at least 4”, “Less than 4 years old but at least 3” and “Less than 3 years old but at least 2”. Startup loans are those coded “Startup, Loan Funds will Open Business”. Other new-business codes are in neither group.

Measure. A loan counts as charged off if its charge-off date falls within 1,826 days of its approval date, which this article calls five years. For loans approved from March 1, 2019 through February 28, 2020, the window crosses two leap days and ends one day before the fifth anniversary of approval. In those records, two existing-business charge-offs fell on that anniversary day and are not counted, which does not change any rate shown at the precision reported. Every loan in the window has at least five years of follow-up as of June 30, 2026, so open and paid loans count as not charged off rather than being dropped. Count rates use Wilson 95% intervals, the difference uses the Newcombe method, and the dollar ratios use a bootstrap with 2,000 draws. Sectors use the first two digits of the NAICS code, combined as NAICS defines them. No multiplicity adjustment was made. Rates are not adjusted for lender clustering.

Protocol. The analysis plan, including the single primary comparison, was fixed in a dated protocol before any group charge-off rate was computed. Later amendments are listed in it: https://kamyarshah.com/public-data-protocol/

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.

The Bureau of Labor Statistics measures quits every month by the size of the establishment in its Job Openings and Labor Turnover Survey (JOLTS). An establishment is a single workplace, not a company: a small workplace can belong to a large company, and one company can have several workplaces. BLS assigns each establishment to a size class under its own survey methodology, which may not match a workplace’s headcount in a given month. BLS describes the survey’s coverage this way: “The JOLTS program covers all private nonfarm establishments, as well as civilian federal, state, and local government entities in the 50 states and the District of Columbia.” This article uses the private nonfarm size-class series (BLS “total private”) only.

Quit rates rise, then fall, with workplace size

Average monthly quits per 100 employees, private nonfarm establishments, all six published BLS size classes, seasonally adjusted (source: U.S. Bureau of Labor Statistics, JOLTS, Table 7 series JTS100000000000001QUR through JTS100000000000006QUR, flat files downloaded October 3, 2026, data through August 2026):

BLS publishes 10 to 49 and 50 to 249 as two separate classes, and no combined rate for them is calculated here. August 2026 is preliminary, and BLS revises seasonally adjusted figures each year, so later releases can differ. The middle-class high and the 5,000-or-more low held month by month, not only on average. In each of the 36 months from September 2023 through August 2026, the highest published quit rate estimate among the six classes was at 10 to 49 or 50 to 249 employees, and the lowest was at 5,000 or more. The same was true in each of the 36 months from January 2017 through December 2019. The smallest class did not have the lowest rate. In the recent window, establishments with 1 to 9 employees (1.81) and with 1,000 to 4,999 (1.83) were close. At the precision of the published monthly rates, the 1-to-9 class was higher in 16 months, lower in 15, and tied in 5. In the earlier window, it was higher in 28 months, lower in 1, and tied in 7.

BLS publishes median standard errors for JOLTS by industry and region, not by establishment size class, so this article makes no claim of statistical significance. It reports averages and how consistently the pattern held from month to month.

How this framing came about. The study’s plan, fixed before any size-class figure was computed, set one comparison: the 1-to-9 class against a pooled group of the two largest classes. Outside reviewers pointed out that this comparison hides the shape shown above. After the results were seen, a dated amendment moved the six published classes to the front. The registered comparison is unchanged and is reported in full in the next section.

The registered comparison: 1 to 9 against a pooled 1,000-or-more group

The study set one comparison in advance: the monthly quit rate at establishments with 1 to 9 employees minus the rate at establishments with 1,000 or more. Over the 36 months, the difference averaged 0.32 quits per 100 employees per month. The 1-to-9 group had the higher rate in 86.1% of the 36 monthly comparisons, and the monthly difference ranged from negative 0.21 to positive 1.01. The 36-month average is the primary figure. The month-by-month sign count and range describe how the monthly difference varied around it.

The registered comparison group pools the two largest published classes. Over the 36 months it averaged 1.49 quits per 100 employees per month. The pooled 1,000-or-more figure is not a BLS seasonally adjusted series. Each published series is seasonally adjusted on its own, so adjusted figures need not add up exactly, and this pooled rate, built from already adjusted levels, is not the rate BLS would publish for one combined class. Each month this study reconstructs employment for each of the two classes as the seasonally adjusted total separations level divided by the seasonally adjusted total separations rate, times 100. It then divides the sum of the two classes’ seasonally adjusted quits levels by the sum of that employment, times 100. Because the published levels and rates are both rounded, this reconstructed employment can differ from the denominator BLS used, and it is not a separately published BLS series. The pooled group combines two published classes with very different quit rates, 1.83 and 0.86, so its result depends heavily on the 5,000-or-more class. As in the rest of this article, no claim of statistical significance is made.

Layoffs, hires and separations

The registered design compared four groups on all four measures. The 250 to 999 class was left out of that design before any size-class estimate was computed, and it appears only in the six-class quits table above.

Seasonally adjusted rates per 100 employees per month, private nonfarm establishments, averaged over each window (source: U.S. Bureau of Labor Statistics, JOLTS, seasonally adjusted total private by establishment size class, Table 7 series JTS100000000000001QUR, JTS100000000000002QUR, JTS100000000000003QUR, JTS100000000000005QUR, JTS100000000000006QUR, with the matching quits, LD, HI and TS level (L) series and LD, HI and TS rate (R) series, flat files downloaded October 3, 2026, data through August 2026. Inputs are the published classes. The 1,000 or more column is not a BLS series and is not in Table 7. It is this study’s pool of the seasonally adjusted 1,000 to 4,999 and 5,000 or more levels and rates):

The 1 to 9, 10 to 49, and 50 to 249 columns are averages of published BLS seasonally adjusted rates. The 1,000 or more column, including the earlier window and the change row, is not a BLS series. It is this study’s pooled calculation from the published 1,000 to 4,999 and 5,000 or more classes (see Method). Table values are rounded to two decimals. Unrounded values are in the study’s reproducibility files. Changes are the later window minus the earlier window, calculated from unrounded window averages and then rounded to two decimals, so they can differ by 0.01 from subtracting the rounded values shown. Total separations also include other separations, such as retirements, transfers and deaths, which is consistent with quits plus layoffs and discharges being less than total separations for every size group in each window.

Quit levels relative to total separations levels

Quits are one category of separation from an establishment’s payroll. Here, the quit-to-separations ratio is the sum of seasonally adjusted quits levels divided by the sum of seasonally adjusted total separations levels over September 2023 to August 2026, so it is not the ratio of the averaged rates in the table. Because seasonally adjusted series are not strictly additive, this ratio is approximate. On that basis, the ratio was 56.0% at establishments with 1 to 9 employees, 64.9% at 10 to 49, 64.5% at 50 to 249, and 54.4% at 1,000 or more.

JOLTS counts events, not people. It does not record worker demographics, the reason for a quit, or the kind of job involved. Size classes differ in industry and occupation mix. Differences by size reflect both. Nothing here isolates an effect of size.

What the numbers mean for an owner

This section and everything after it, up to Method, are interpretation and guidance from operating practice. They are not findings of this study.

In every month of both periods studied, the highest published quit rate among the six classes was in either the 10 to 49 or the 50 to 249 employee class, and the lowest was at 5,000 or more. The data does not explain why, and size classes differ in industry and occupation mix, so the pattern is not proof that size itself drives quits. For an owner, the more useful reading is practical. A workplace in either middle class that sees about two and a half quits per 100 employees a month is close to the national average for its class over September 2023 to August 2026. JOLTS does not show the spread of rates within a class, so closeness to the average is a benchmark, not a verdict.

That changes the question an owner should ask. A rising headcount often brings layers of supervision, more varied roles, and less direct contact with the owner, and the middle classes are where those changes tend to arrive. Whether they cause the higher quit rates is outside this data. Whether a specific workplace is above or below its own class average is something an owner can measure.

How to handle it: measure first, then fix the causes that are visible

The steps below are an operating recommendation. This study did not test them, and BLS does not endorse them.

1. Measure quits consistently with the BLS definition. Count voluntary quits during the month, excluding retirements and transfers to other locations. Divide by employment for the pay period that includes the 12th of the month, then multiply by 100. Track it every month for at least a year. A single month moves in large steps in a small workplace and says little on its own.

2. Compare against the closest class. Use the published establishment size class that best matches the workplace, not the company as a whole and not the largest classes. BLS assigns classes under its own survey methodology, so treat the match as approximate. For private nonfarm establishments with 10 to 49 employees, the seasonally adjusted average from September 2023 to August 2026 was 2.52 quits per 100 employees per month. A workplace well above its class average over a full year has something specific to find.

3. Ask the people who leave, and the people who stay. A short, consistent exit conversation, written down the same way each time, turns individual departures into a record. A brief conversation with people who stay, asking what would make them leave, often surfaces the same issues earlier and at lower cost.

4. Fix the systems the conversations point to. Common findings are unclear expectations in the first weeks, uneven scheduling, and supervisors promoted without guidance on how to manage. Each has a procedural answer: a written onboarding sequence, a published scheduling rule, and a short management standard for new supervisors.

What this data does not tell you

• It does not show why quit rates differ by size class, or that any practice lowers them.
• It counts quits, not people. JOLTS records no demographics and no reason for leaving.
• Figures are national averages across industries for private nonfarm establishments. An establishment is a single workplace, not a company.
• August 2026 data are preliminary, and BLS revises seasonally adjusted figures each year.

As operating guidance rather than a finding: know the workplace’s own rate, compare it to its class, and build the routines that help people stay once they are hired. The national figures are a benchmark for that work. They do not show which practices reduce quits.

Method

Already published by the agency: BLS publishes monthly JOLTS levels and rates by establishment size class in Table 7 of each release (“Job openings, hires, and separations levels and rates by establishment size class, seasonally adjusted”). New here: 36-month averages for all six published size classes in a current and a pre-pandemic window, a month-by-month count of where quits were highest and lowest, the registered pooled comparison, and quit-to-separations ratios.

Source. U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey, flat files (series for total private, seasonally adjusted, by establishment size class), downloaded October 3, 2026. Data run through August 2026, which BLS marks preliminary. BLS notes: “Establishment size class data are produced for the total private sector only.”

Measures. Rates are the number of events during the month as a percent of employment, which BLS states as per 100 employees. The 1,000-or-more group combines the 1,000 to 4,999 and 5,000-or-more classes. For each class and month, implied employment is the total separations level divided by the total separations rate, times 100. The pooled rate for each measure is the sum of the two classes’ levels divided by the sum of their implied employment, times 100. It is not an average of their rates. Because published levels and rates are rounded, the pooled figures cannot be reproduced exactly from rounded table values. Window figures are simple means of the monthly rates. The quit-to-separations ratio is total quits divided by total separations over the window, both in levels. No multiplicity adjustment was made. BLS revises seasonally adjusted data each year, so later downloads can differ slightly.

Protocol. The analysis plan, including the single primary comparison and the windows, was fixed in a dated protocol before any size-class estimate was computed. The six-class framing of this article was adopted after the results were seen, by a dated amendment (Amendment 8) that keeps the registered comparison and reports it in full. All amendments are listed in the protocol: https://kamyarshah.com/public-data-protocol/

Check. The July and August 2026 monthly rates for quits, layoffs and discharges, and hires in the downloaded files matched BLS Table 7 of the September 29, 2026 release for each of the six published size classes. The pooled 1,000-or-more figures are this study’s calculation and do not appear in Table 7.

The agencies did not review or endorse this analysis. Kamyar Shah sells fractional COO and CMO services.

Questions about this analysis can be sent through https://kamyarshah.com/contact/.