ADP National Employment Report Definition
The report is prepared by using actual transactional payroll data. It uses an anonymous subset of around 400,000 U.S based private companies working in the nonfarm sector.
A Little More on What is the ADP National Employment Report
The ADP National Employment Report, commonly known as the ADP Jobs Report or ADP Employment Report, was originally conceptualized and maintained by Macroeconomic Advisers, LLC.
Automatic Data Processing Inc. is preparing the report since 2006. Moody’s Analytics is contracted by ADP for developing the report’s methodology and the methodology was revised by Moody’s Analytics in November 2012.
ADP collects data from its client firms and prepares the report based on the payroll and benefits data.
On the first Friday of each month, The Employment Situation Report is published by the Bureau of Statistics. The ADP National Employment Report is released two days prior to this report. It provides a primary idea about the nonfarm private sector’s employment situation, before the more detailed and comprehensive government report. It helps investors and economists understand the market and analyze the situation.
The report is divided into four parts, each part is a separate report. In the first report, the change in the number of private payrolls in the nonfarm sector is provided. This number is then categorized by the size of the business and industry.
The second report provides the details of the small nonfarm businesses. The change in payroll is categorized by size (small and micro) and broad sector (manufacturing and service).
The third report is focused on franchises and changes in employment are categorized by sector (like real estate, restaurant, accommodation, etc.). The fourth report categorizes the employment trends by region. It provides sector and industry-wise breakdown for six states: California, New York, New Jersey, Texas, Florida and Illinois and highlights the changes in these states.
Reference for “ADP National Employment Report”
Academics research on “ADP National Employment Report”
Testing the value of lead information in forecasting monthly changes in employment from the Bureau of Labor Statistics, Gregory, A. W., & Zhu, H. (2014). Testing the value of lead information in forecasting monthly changes in employment from the Bureau of Labor Statistics. Applied Financial Economics, 24(7), 505-514. This article examines the value of lead information by investigating the predictive power the automatic data processing (ADP) report has on nonfarm payroll employment data released by the Bureau of Labor Statistics (BLS) 2 days after the ADP. We find that updating a vector autoregression (VAR) forecast with the ADP data improves the forecast accuracy relative to a standard VAR forecast. However, this informational advantage disappears if real-time comparisons are made with the Bloomberg consensus forecasts of the BLS which are available prior to the ADP. We explore the confounding effects of data revisions and the potential pitfalls in testing the value of lead information based on the accumulated historical data.
Splitting the EB-5 program: A proposal for employment-based immigration reform to better target immigrant entrepreneurs and investors, Lin, A. A. (2014). Splitting the EB-5 program: A proposal for employment-based immigration reform to better target immigrant entrepreneurs and investors. Chap. L. Rev., 18, 527.
Analysis of the Current Employment Statistics program using customer outreach survey results, Chi, J., & Leslie, K. (2015). Analysis of the Current Employment Statistics program using customer outreach survey results. Monthly Lab. Rev., 138, 1.
Small business owner satisfaction with financial performance: A longitudinal study, Gibson, S. G., McDowell, W. C., & Harris, M. L. (2014). Small business owner satisfaction with financial performance: A longitudinal study. New England Journal of Entrepreneurship, 17(1), 15-20.
The ADP National Employment Report, Tasci, M., & Stepanczuk, C. (2007). The ADP National Employment Report. Economic Trends.