Announcement

ADP Research Institute (ADPRI) and the Stanford Digital Economy Lab (the “Lab”) announced they will retool the ADP National Employment Report (NER) methodology to provide a more robust, high-frequency view of the labor market and trajectory of economic growth. In preparation for the changeover to the new report and methodology, ADPRI will pause issuing the current report and has targeted August 31, 2022, to reintroduce the ADP National Employment Report in collaboration with the Stanford Digital Economy Lab (the “Lab”). We look forward to providing an even more comprehensive labor market analysis and will be in touch with additional details closer to the re-launch, later this summer.  For more information on this announcement, please visit here.

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June 27, 2022

MainStreet Macro: A Penny Saved

Savings accounts were the unsung heroes of the pandemic recovery. The personal saving rate – the share of a person’s disposable income left after taxes and spending on necessities and everything else – soared to a record of almost 34 percent in April 2020. That means people saved 34 cents for every dollar earned in the early days of the pandemic.
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Latest posts

March 26, 2018

The Business Case for Strengths

It’s the single most important driver of team performance – and yet, less than 2 out of 10 employees strongly agree that they get to use their strengths at work every day.
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December 1, 2017

Data Fluency Series #5: Case Study

Let’s show you what good data looks like, and what we went through to get it to see what happens on the world’s most productive teams.
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November 23, 2017

Data Fluency Series #4: Do Current HR Tools Give Us Good Data?

We know that a good HR tool will have reliability, variation, and validity. Let's look at a few common HR tools and see how they stack up.
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November 14, 2017

Data Fluency Series #3: Variation and Validity

If you are using a tool to measure people data, there are two things it has to do: It has to reveal actual variation in the real world, and that variation has to validly relate to something else.
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November 7, 2017

Data Fluency Series #2: How to Get Reliable Ratings Data

How do we know that the people data we’re collecting is reliable? How do we know that it’s measuring what we say it’s measuring? And can you really measure something like performance in another person?
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October 31, 2017

Data Fluency Series #1: How to Tell Good Data from Bad Data

Bad data is a pervasive problem in our world, and especially in the world of work. People are being promoted and fired because this faulty data that actually tells us nothing about our employees.
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