CHART OF THE DAY: There are more job openings in the USA than ever

I know I have written a couple of versions of this post in the last year or so, but to me, and as the data referenced in the post title keeps increasing, I think it is worth taking a look at the latest job openings data.

As always courtesy of our pals at the BLS and using the fantastic charting capability from the St. Louis Fed.

Here's the chart showing the total number of non-farm job openings in the US over the last 10 years or so and hen some words of wisdom and whimsy from me as we get ready to head into the weekend.

Three quick takes...

1. It may be hard to see on the chart, but the end of June 2017 data point shows a whopping 6.2 million open jobs in the USA. That is the record high for this measurement since records began to be kept starting in 2000. To give the 6.2 million number a little context, the total US labor force at the end of June is just over 160 million. Said differently, if we could magically fill the 6.2 million openings today, total US employment would jump almost 4%. That is a huge, huge number when talking about this kind of data.

2. Wages, while growing, are not yet, (maybe never?), catch up to the fact that job openings keep increasing and time to fill metrics also continue to climb. I caught a quote from a random Fed official recently, can't remember which one at the moment, that essentally said something like 'If your business has a hiring problem or you think you have a 'skills gap' problem, and you have not taken steps to meaningfully increase wages and benefits you are offering, then I just don't believe you actually have a problem.' Persistent sluggish wage growth has been the most baffling element of the sustained labor market recovery of the last several years.

3. I know this is obvious, and I know I have blogged this bit a few times before when considering the tight labor market, but it bears repeating. More and more power is shifting to employees, candidates, graduates - almost anyone with up to date skills and a desire to succeed. Factor in the myriad ways for people to side hustle, and employers have to continued to raise their game and their value props to have any chance of staying competitive in today's market. I am a 'labor' guy at heart, and more leverage and negotiating power shifting to workers just feels like a decent thing to me.

Have a great weekend all!

PODCAST - #HRHappyHour 259 - Big Data and Innovation at ADP

HR Happy Hour 259 - Big Data and Innovation in HR Tech at ADP

Hosts: Steve Boese, Trish McFarlane

Guest Host: Mollie Lombardi

Guest: Don Weinstein, Chief Strategy Officer, ADP

Listen to the show HERE

This week on the show Steve and guest host Mollie Lombardi were joined by Don Weinstein, Chief Strategy Officer at ADP for a conversation on how the power of huge data sets can help HR leaders and organizational leaders be more informed about their own businesses and make better decisions about people and operations. ADP's Data Cloud and Benchmarking tools are leveraging the aggregated insights from hundreds of thousands of customers and millions of employee records to provide information and insight to HR and business leaders in real-time. 

Additionally, Don provided an update on the one of 2015's HR Tech Products of the Year, the ADP Marketplace, and the continued importance of technology to enable better, faster, and less costly integration of multiple HR and Talent systems.

Finally, Don shared some thoughts on where HR technology and innovation may be heading in the coming years, and how your voice may become the next User Interface technology.

You can listen to the show on the show page here, or by using the widget player below, (email and RSS subscribers will need to click through)

This was a fun and interesting show, and we hope you check it out. 

Many thanks to Don and the entire team from ADP for hosting the HR Happy Hour Show. 

Be sure to subscribe to the HR Happy Hour Show on iTunes, Stitcher Radio, or your favorite podcast app - just search for 'HR Happy Hour' to subscribe and never miss a show.

CHART OF THE DAY: There's almost no one left to fill your open jobs

I am an absolute mark for big picture labor market data. And the best, most interesting regular look at labor market data os the Bureau of Labor Statistics monthly Job Openings and Labor Turnover Survey report, better known as the JOLTS report.

Federal Reserve Chair Janet Yellen has stated that the JOLTS report is one of the most important data sets she relies on when pondering the Fed's decisions on monetary policy, and if the JOLTS is good enough for J-Yell then you had better believe the rest of us should be paying attention to it as well.

For today's Chart of the Day, take a look at what's happening with the ratio of unemployed persons to current job openings - a fixture of the JOLTS data. First the chart, then some comments from me after the data.

Some quick thoughts on the data:

1. When the most recent recession began (December 2007), the number of unemployed persons per job opening was 1.9. The ratio peaked at 6.6 unemployed persons per job opening in July 2009 and has trended downward since. The ratio at the end of July was 1.3 unemployed persons per job opening. This represents the all-time low in the ratio since it has been calculated by the BLS.

2. In addition, the very same JOLTS report shows that the denominator of the ratio, the number of current job openings in the US is also at a record level, hitting 5.9 million at the end of July. 

3. This data reminds us that it is both a great and terrible time to be in recruiting/talent acquisition. Let's start with the terrible part. For lots of jobs and locations there simply are not enough (qualified for sure), candidates to form an adequate pipeline for the roles you need to fill. There are fewer unemployed persons overall, workforce participation rates remain really low by historical standards, (a subject to its own), and lots of people with desirable skills are coming to terms with their power and negotiating leverage in the market. When you have to pry someone away from the job they already have, that gives a little bit of power to the person that in worse economic times they would not enjoy.

The good news is that the same JOLTS report that shows the ratio of unemployed persons per job opening is at an all-time low, also shows that the 'Quits' rate, i.e., the percentage of workers who are voluntarily leaving their jobs continues to trend upward - hitting 2.0% in July, which equates to about 2 million quits. In other words, workers continue to express confidence in the labor market and willingness, (almost at a pre-recession rate), to quit the job they have now, to (in theory), take the job you are trying to fill. If you can make a compelling offer, chances are at least decent you can pry someone out of where they are now to take it. And you may have to as the unemployed/jobs ratio continues to fall, and nothing seems to be significantly moving the needle to entice more people back into the workforce who are currently on the sidelines.

There is plenty more in the report, but I think you get the idea and I will leave it to you to dig in more. The JOLTS report should be your monthly must-read if you are interested at all in what is happening at a macro-level in the US labor market. Bookmark this page and thank me later.

Have a great weekend!

A reminder that even the world's most admired company has hiring challenges

Lots of words are spilled in the HR/Talent/Recruiting space that more or less read something like this - 'Oh sure, that (insert HR/Recruiting/Benefits program of choice here), might work for Google or Apple, but there is no way that applies to us, we don't have a sexy, well-known brand.'

Said differently, it is more or less commonly accepted that companies like Google, Apple, Nike, Goldman Sachs, etc., have incredible advantages in competition for talent by virtue of their brand equity, vast resources, employer brand reputation, and the like. If you are repping one of these companies from Fortune's World's Most Admired Companies list, you would think you pretty much could dial up anyone you need and they would be sold on the opportunity. And that is at least partially, if not mostly true.

But even the World's Most Admired Company for 2015, Apple, faces the occasional recruiting challenge. Yep, I know, hard to believe.  But apparently in the global fight for scarce data science talent, even Apple has some issues attracting talent. From a recent piece on The Stack titled Apple's privacy policies repel the data scientists it needs to create 'predictive' smart phones:

Just for once, it seems that Apple ‘can’t get the staff’. According to a Reuters exclusive, the Cupertino-based global device giant is falling behind in the race to create ‘predictive’ services for smartphones because its privacy policies are too protective of the end-user.

The report has crunched numbers on Apple job openings and talked to various industry insiders, many of whom agree that Apple lacks the best conditions to attract the very limited supply of data scientists necessary to leverage cloud-based services and anticipate the most minute demands of smartphone users.

The reason for the company’s difficulty in challenging the likes of Google, Facebook and Amazon for the brightest and the best new minds in data science and analysis seems to lie with its commitment to protect the privacy of its users. The report notes that data retention policies on user-centric information gathered into its Siri ‘personal assistant’ product is a reasonably generous six months, whilst information retained from the user’s exploration of Apple Maps expires after only 15 minutes

So it looks like the world's best talent in the field of data science doesn't like the fact that Apple keeps comparatively less data around upon which to practice their science. Companies like Google and Facebook in comparison, seem to offer these scientists more of a playground for them to challenge themselves with.

A couple really interesting points I think worth noting in this story, that are probably true for both the World's Most Admired Companies and for your shop as well.

1. The work, then challenge, and the opportunity to be your personal best in your field still trumps the 'Brand' or the reputation of the company in general. Apple might be the #1 company in the world to work for, but for this group of highly scarce and talented folks it is the work that matters more.

2. Often the factors that influence a candidate's decision about joining an organization sit well out of reach of the org's HR/Recruiting leadership. No matter how much influence the HR and Talent organization has at Apple, they are never going to impact Apple's customer data storage policies and practices.

3. For a big company like Apple with lots of resources, acquisition might be the best (and only) way to get the talent that they require. The related Reuters study notes that Apple's 'acquisitions of startups such as podcasting app Swell, social media analytics firm Topsy and personal assistant app Cue have also expanded Apple’s pool of experts in the field.'

Interesting times out there when even the most well-known, most valuable and most admired companies is facing recruiting issues. I guess that sort of makes the rest of us feel good, maybe a little anyway.

Have a great Wednesday!

On trusting algorithms, even when they make mistakes

Some really interesting research from the University of Pennsylvania on our (people's) tendency to lose faith and trust in data forecasting algorithms (or more generally, advanced forms of smart automation), more quickly than we lose faith in other human's capabilities (and our own capabilities), after observing even small errors from the algorithm, and even when seeing evidence that relative to human forecasters, the algorithms are still superior.

From the abstract of Algorithm Aversion: People Erroneously Avoid Algorithms After Seeting Them Err:

Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet, when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.

Let's unpack that some. In the research conducted at Penn, the authors showed that even when given evidence of a statistical algorithm's overall superior performance at predicting a specific outcome (in the paper it was the likelihood of success of MBA program applicants that the humans and the algorithm attempted to predict), most people lost faith and trust in the algorithm, and reverted to their prior, inferior predictive abilities. And in the study, the participants were incentivized to pick the 'best' method of prediction: They were rewarded with a monetary bonus for making the right choice. 

But still, and consistently, the human participants more quickly lost and faith and trust in the algorithm, even when logic suggested they should have selected it over their (and other people's) predictive abilities.

Why is this a problem, this algorithm aversion?

Because while algorithms are proving to be superior at prediction across a wide range of use cases and domains, people can be slow to adopt them. Essentially, whenever prediction errors are likely—as they are in virtually all forecasting tasks—people will be biased against algorithms, because people are more likely to abandon an algorithm than a human judge for making the same mistake.

What might this mean for you in HR/Talent?

As more HR and related processes, functions, and decisions become 'data-driven', it is likely that sometimes, the algorithms we adopt to help make decisions will make mistakes. 

That 'pre-hire' assessment tool will tell you to hire someone who doesn't actually end up beign a good employee.

The 'flight risk' formula will fail to flag an important executive as a risk before they suddenly quit, and head to a competitor.

The statistical model will tell you to raise wages for a subset of workers but after you do, you won't see a corresponding rise in output.

That kind of thing. And once these 'errors' become known, you and your leaders will likely want to stop trusting the data and the algorithms.

What the Penn researchers are saying is that we have much less tolerance for the algorithm's mistakes than we do for our own mistakes. And maintaining that attitude in a world where the algorithms are only getting better, is, well, a mistake in itself.

The study is here, and it is pretty interesting, I recommend it if you are interested in making your organization more data-driven.

Happy Tuesday.