ADP^® National Employment Report

Upcoming reports:

June 04, 2025
July 02, 2025
July 30, 2025
September 04, 2025
October 01, 2025
November 05, 2025
December 03, 2025

The ADP National Employment Report (NER) presents independent measures of the U.S. labor market based on ADP payroll data covering more than half a million companies with more than 25 million employees.

The ADP NER provides a high-frequency, weekly measure of U.S. private-sector employment. In addition, it presents regular measures of wages or earnings for defined samples or segments of the U.S. workforce.

ADP payroll data include payroll transactions data – when a person is paid and how much – as well as administrative data on who is on the company payroll (even if they are not paid in the current pay period), and characteristics of the employer and employee.

We use a business-level database that provides aggregated counts of employment at the level of an ADP Payroll Account. An ADP client company may have one or more Payroll Account. As an approximation, we consider a Payroll Account a business establishment (i.e., a company work location).

We also use a person-level database of payroll transactions that enables us to construct a matched-persons sample to measure changes in wages or earnings over time.

With ADP data, we can measure how many employees are on company payrolls (Payroll Employment) as well as how many employees were paid in a given pay period (Paid Employment). Both measures are of interest, and together provide a richer understanding of the labor market.

Payroll Employment shows how many people have an attachment to an employer in the labor market, while Paid Employment shows how many people are actively working and earning income in the labor market at any given time. The relationship between the two measures may vary across segments of the labor market (e.g., industry, geography, company size) or over time (e.g., recession, pandemic, natural disaster).

Because the underlying ADP payroll databases are continuously updated, we can create high-frequency, near real-time measures of U.S. employment. Also, ADP payroll data at the person level (in addition to the establishment level) enables more detailed, richer analysis.

We use weekly snapshots of ADP payroll data. For Payroll Employment, each week’s snapshot reports the number of employees on payroll at the company that week. For Paid Employment, each week’s snapshot reports on payroll transactions at the company during that week.

Employee pay periods and pay frequency might be weekly, biweekly, semimonthly, or monthly. We have to wait until after the end of the pay period in order to count Paid Employment for the week(s) in the pay period. For example, for employees with monthly pay frequency, we have to wait until after the end of the month before we can produce the Paid Employment count for all weeks during the month.

Using ADP data, for each week, we construct a matched sample of business establishments present in the data in both the current and the previous week. We use data on Payroll Employment to compute the weekly employment growth for each business establishment in the consecutive-week matched sample. For each establishment-week, we calculate employment growth git as

The Quarterly Census of Employment and Wages (QCEW) provides a quarterly count of Paid Employment reported by employers covering more than 95 percent of U.S. jobs. It is the benchmark measure of employment in the U.S., but it is reported with a lag of about five months after the end of the quarter.

The purpose of the ADP NER is to produce a more timely measure of U.S. employment than the QCEW measure of near universe U.S. employment.

To produce a nationally representative measure of employment, we use QCEW data on the national distribution of employment across industries, U.S. states, and business establishment employment size categories to weight the weekly employment growth of establishments in the ADP weekly matched sample.

From QCEW, we obtain counts of employment for industry x U.S. state x establishment size cells, and create weights w_j,t as

where e^QCEW_j,t and e^ADP_j,t are employment in cell j from QCEW and ADP data, respectively. The nationally representative estimate of employment growth is then gt_,weighted given by

where J is the set of industry x U.S. state x establishment size cells, and I_j,t is the set of establishments in cell j in week t. The ADP NER measure of weekly employment growth is used to create an index for U.S. employment, which is then applied to a base period measure of employment level to construct a data series for weekly employment level. We apply the ADP NER weekly employment index to a QCEW base period level of employment to produce the ADP NER measure of weekly employment level.

We use historical ADP payroll data to construct longitudinal data on weekly employment and create a model for seasonal adjustment of weekly employment data. We apply the estimated model to the ADP NER weekly employment data series to create a seasonally adjusted data series for weekly employment.

ADP and BLS both report on jobs (an employee-employer relation), not employed persons; a person may have more than one job.

ADP produces a weekly-frequency data series for jobs in each week, while BLS produces a monthly-frequency data series for jobs in the week that includes the 12th of the month.

Cajner, Tomaz, Leland Crane, Ryan Decker, Adrian Hamins-Puertolas, Christopher Kurz, and Tyler Radler (2018). "Using Payroll Processor Microdata to Measure Aggregate Labor Market Activity," Finance and Economics Discussion Series 2018-005. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2018.005.

Cleveland, William P., and Stuart Scott (2007). “Seasonal Adjustment of Weekly Time Series with Application to Unemployment Insurance Claims and Steel Production,” Journal of Official Statistics, Vol. 23, No. 2, pp. 209–221.

What is the ADP National Employment Report in collaboration with Stanford University?

The ADP National Employment Report is a monthly, independent, high frequency measure of the private-sector labor market. The report is based on the real-world anonymized and aggregated payroll data of more than 25 million U.S. workers.

In 2022, the ADP Research Institute enhanced its U.S. labor-market analysis and partnered with the Stanford Digital Economy Lab to produce new measures of private-sector employment, with a focus on jobs and pay.

The ADP National Employment Report’s fine-grained, high-frequency data on jobs and pay delivers a rich analysis of the labor market.

What is included in the National Employment Report?

The National Employment Report and Pay Insights both rely on ADP’s anonymized and aggregated payroll data to provide a representative picture of the private-sector labor market. Each week, the ADP Research Institute taps actual, real-time payroll transactions to obtain a high-frequency read of U.S. employment. The institute uses that information to build a monthly snapshot of employment based on the reference period, which is the week including the 12th day of the month, and pay, which includes data across the entire month.

ADP’s job report, which is built on the payroll records of more than 25 million workers, measures month-over-month change in private employment and tracks total private employment as measured during the reference period. It also provides weekly historical data dating back to 2010 through the month prior to the release month. Because ADP payroll databases are updated continuously, the report is a near real-time measure of private U.S. employment, one that provides a rich understanding of the labor market. Data is broken out by industry, business establishment size, and U.S. census region at both a monthly and weekly frequency. Weekly data is provided up through the month prior to the release month.

ADP deploys its unique ability to track the pay of more than 14.8 million individual workers to deliver three trackers. Each month, Pay Insights provides the median year-over-year pay change of individual job-stayers, those people who held the same job 12 months ago as they hold in the current month. It also releases the year-over-year pay change of individuals who have changed jobs in the past 12 months. Finally, it provides the median annual pay level for job- stayers and job-changers. Pay Insights also provides median annual pay growth by industry, firm size, state, gender, and age for job-stayers.

Does the ADP National Employment Report forecast the Bureau of Labor Statistics monthly non-farm payroll report?

No. The ADP National Employment Report is an independent measure of private-sector employment. It is not intended to forecast the Bureau of Labor Statistics monthly jobs report.

How does the ADP National Employment Report differ from the BLS non-farm payroll report?

In 2022, ADP retooled the National Employment Report to build a more robust tool for measuring the labor market, one that could better signal the trajectory of economic growth. The NER is based on real-world, real-time ADP payroll data, and thus provides a nationally representative measure of private-sector employment. This approach differs from the report’s previous model-based methodology, which sought to forecast changes in the Current Employment Statistics monthly survey conducted by the Bureau of Labor Statistics. The NER is not intended to forecast the BLS non-farm payroll report. For more information, see our technical note.

What are the advantages of the ADP National Employment Report?

Unlike many other measures of employment, the NER is not a survey. It is based on the real-world, real-time payroll data of millions of workers and hundreds of thousands of business establishments. The ADP Research Institute monitors data at a weekly frequency across industry, establishment size and region. Our monthly NER and Pay Insights data exists independently of government survey data and provides an additional vantage point from which to analyze the labor market.

Why did the ADP Research Institute change the National Employment Report?

ADP’s payroll data provides a comprehensive, nationally representative, and independent measure of U.S. private employment. The NER’s prior model-based methodology sought to forecast changes in the Current Employment Statistics survey conducted by the Bureau of Labor Statistics. The NER provides business leaders, researchers, and policymakers with a reliable read on the economy and the direction of work in near real time.

How is the seasonal adjustment calculated for the ADP National Employment Report?

The seasonality adjustment is calculated at a weekly level, which introduces several complexities. Years can have 52 or 53 weekly periods, and those weeks align differently in different years, in calculating year-over-year change at a weekly frequency. ADP uses the period between 2010 and the present to calculate weekly seasonal components and weights for each category reported in the NER (regions, sizes, and industries). The composition of job gains along these dimensions could affect the aggregate seasonal adjustment. We provide both seasonally adjusted and non-seasonally adjusted data on employment. A look at the difference between weekly seasonally adjusted and non-seasonally adjusted employment can provide additional insight into our seasonal adjustment and how it interacts with the monthly data.

How are prior-month revisions calculated in the ADP National Employment Report?

Employment estimates are based on weekly summaries of anonymized and aggregated ADP client activity. Employers pay individuals on different cadences, including weekly, biweekly, semi-monthly, or monthly. In any given month, a small number of clients might report no activity. These clients are excluded from NER estimates of employment change. In the subsequent month, if and when we receive any client data remaining from the prior month, that information is incorporated into the revision.

What is the release schedule for the ADP National Employment Report?

The ADP National Employment Report is released monthly. A publication calendar is available at www.adpemploymentreport.com.