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https://luminawp.wpengine.com/campaign/learn-and-work-ecosystem/
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Sunday, January 26, 2020
Thursday, January 9, 2020
Workday Skills Cloud: A Big Idea With Much More To Come
Workday Skills Cloud: A Big Idea With Much More To Come
BY JOSHBERSIN<https://joshbersin.com/author/joshbersin/> · PUBLISHED JANUARY 2, 2020 · UPDATED JANUARY 3, 2020
https://joshbersin.com/2020/01/workday-skills-cloud-a-big-idea-with-much-more-to-come/
In the Fall of 2018 Workday introduced the Skills Cloud<https://www.globenewswire.com/news-release/2018/10/02/1588881/0/en/Workday-Delivers-Machine-Learning-Powered-Skills-Cloud-to-Unlock-Untapped-Workforce-Potential.html>, a new offering designed to help companies create a "skills ontology" that discovers the skills (and skill gaps) in your workforce. While it sounded like a fascinating concept, we hadn't heard much about it until Workday Rising<https://joshbersin.com/2019/10/workday-fires-a-cannon-into-the-hr-technology-market/> this Fall.
Well, I just spent a day going into details with Workday and I want to give you an update.
Let me summarize by saying this is a very big deal, and it delivers on one of the biggest promises in human capital systems: the move from "systems of record" to "systems of capability." And I think the term "Skills Cloud" understates what this will become.
Creating a Job and Skills Ontology
First, let's start at the core. The Skills Cloud (and related products) are trying to identify thousands of skills and experiences in your workforce and arrange them in an Ontology.
What is an "ontology?" If you look at the word in the dictionary, it is described as a "categorization scheme" for large amounts of data.
[ontology]
In Human Resources, we use ontologies all the time. Why? Because we have to arrange, document, and categorize lots of information about people.
Look at the whole concept of a job. The reason we have "jobs" is because we need to get work done (ie. designing, building, marketing, or selling something), and we want to decompose the work into tasks. Jobs were designed to "bundle" these tasks, and we ended up with what we now call a "job description."
[jobs and work]
Whenever you write a job description, you identify the functional area (sales, marketing, engineering finance), the job role (tech, managerial, support), the level, reporting structure, and details of the job itself. As you write all this down, you end up describing the job tasks and responsibilities, skills needed, certifications required, experience desired, and more. This becomes an ontology.
If it's a job in engineering, for example, you may specify knowledge of the C++ language, an understanding of data structures, and the ability to write and debug code. If it's a job in sales, you may look for candidates that can develop rapport, ask penetrating questions, persuasively propose solutions, and ask for the order. If it's a job as a manager you'd specify abilities to interview people, select candidates, set goals, and so on.
You get the picture. The problem is quite complex. Every job is a little bit different, and the way we describe it can vary.
In accounting, this kind of categorization is easy: there are Generally Accepted Accounting Principles. In HR and with people, however, there is no real standard – so companies build many types of job descriptions and then use many forms of assessment, testing, and interviewing to evaluate people. (The pre-hire assessment industry is over $2Billion in size and tremendously fragmented with tools and types of assessments.)
Some companies use criteria like education, prior work experience, and prior employer; others use technical or professional skills; others evaluate people based on soft-skills (PowerSkills), potential and other attributes. Whatever scheme you use, it's yours to define and you end up creating your own "Ontology" over time.
Competency Models: They've Come and Gone
To try to make this easy, the pioneers of organizational design came up with the idea of competency models<https://www.valamis.com/hub/competency-model>: a detailed breakdown of job tasks, each of which demands skills, competencies, and behavioral strengths. (Often called KSA's, Knowledge, Skills, and Abilities).
In my early days as an analyst, I read books about competency models and found them fascinating. They're developed by evaluating jobs, and then studying what successful people in these jobs do. They are based on the idea that a "job" can be designed by a designer, and we can specify what work each person will do.
In the real world, however, work is much messier. While we usually have a job title, the work we do varies widely from day to day, project to project, and year to year. As the company grows and changes, every job tends to adapt. And today, technology is eliminating more routine work than ever.
So a competency model developed five years ago may be quite out of date today, which is why companies like LinkedIn, Indeed, EMSI, and BurningGlass constantly study the latest skills in demand, by looking at real-world job requisitions being created every day.
To make this even trickier, the hierarchical nature of a job has changed. Rather than work directly for a "boss," many of us now work in teams, on projects, and hold a variety of roles. In service-related industries (almost all industries are now becoming service industries) your job adjusts based on need. So while you may have been hired to "design" or "build" something, you're likely to be listening, adapting, and adjusting your work output all the time. And every year there are new automation and digital tools to learn.
There is also enormous variation in jobs between companies. A sales role at SAP is very different from a sales role at Michelin Tire, for example. And even well-defined jobs in engineering, finance, HR, and IT vary widely based on company culture.
Some companies, for example, value speed over quality – so they expect quick results and look for people with deep domain experience. Others value innovation and creativity, so they want people who can think outside of the box. Still others base success on quality over speed (Boeing is facing up to this right now), so they expect you to focus on ponderous detail. So while an off-the-shelf competency model may seem like a fit, the true drivers of success in one company are quite different in another.
And thanks to technological change, the most basic elements of work keep changing. If you're an engineer and don't keep up on your trade, you fall behind quickly. Studies show that "the half-life of skills is five years<https://eab.com/insights/daily-briefing/workplace/the-half-life-of-professional-skills-is-5-years/>" which is a frightening thought if you think about it. (Half of your years of expertise go out of date in five years?)
My experience shows that today your "ability to learn" is one of the most important elements of a job. Almost every day I find something I need to learn more about, so I feel like I"m "reskilling myself" on a constant basis. So my "job description" has to adapt, and we need HR systems that automatically keep up.
Then there's the word "Skills," which I find misleading. We tend to use it in a sloppy way, often ignoring the granularity of "what a skill really is." Is "creating a pivot table in Excel" a skill? Or is "building a predictive model" a skill?
I like to use the word "capability," which describes how people use skills to solve problems at work. A skill is an atomic item, and skills plus experience create capabilities. So while Workday calls it system the Skills Cloud, I hope it goes well beyond "building a pivot table" and can understand these higher level ideas.
Skills plus experience create capabilities.
In the model we've built for the Josh Bersin Academy<https://bersinacademy.com/>, we describe what I call Full-Stack capabilities<https://joshbersin.com/2019/11/the-full-stack-hr-professional/> – solutions you know how to build and deliver. These capabilities are dependent on many skills, which in turn are related to each other. And among these skills, some are hard and some are soft (which I call Power Skills<https://joshbersin.com/2019/10/lets-stop-talking-about-soft-skills-theyre-power-skills/>), so technical skills are never enough.
We also have to remember that experience is one of the most important skills of all. Nobody learns a skill until you have to use it, so it's your projects, assignments, and roles that define your success. So implicit in a discussion of skills is the fact that we have to measure experience and include this in the ontology too.
When you interview a candidate, for example, it's likely you'll ask the person to "tell you about your experience in this job" or "share a situation where you've done this in the past." This is because real-world experience IS a skill, so we have to consider job history, successes and failures, and the people you've met and known as part of your "ontology" too.
Your Company As A Collection of Capabilities, not Individuals
I've been thinking about this for a long time, and it's a very important topic. If you think about your organization or team – it's not a collection of people, it's a collection of skills, all of which translate into capabilities. It doesn't really matter how many people you have, it matters what they're capable of doing. The Skills Cloud, I would argue, must expand to understand this bigger picture.
[skills and capabilities]
I once asked the head of recruiting for one of the leading energy companies "you do a lot of recruiting, what are the key drivers of success in your candidates?" His answer was surprising: "the single most predictive factor in a new hire's success is the recruiter, nothing else." I had to scratch my head.
"What we've found is that our top recruiters really understand the skills, background, experience, and culture of our company – and they know how to select the right person that will thrive." In other words, the top recruiters are "human Skills Clouds."
Can you imagine a human capital system that understands all of this? That's the whole idea.
What if your HR software could identify the true skills and experiences in your company, identifying the characteristics of the highest performers? This information could be used by managers, individuals, and executives to select people, develop people, and create powerful career paths for everyone.
It's not a new idea: many vendors have gone after this market. Not only have LMS companies tried to build these tools, more than ten years ago I talked with a company that built its own "skills cloud." The platform was a fancy assessment platform that let you self-assess your skills, and then let managers and peers assess them as well. It was elegant and magnificent, but nobody wanted to buy it. It was too much work to implement, and companies just didn't focus on this area. Today self-assessment tools are built into many systems, but most companies tell me they just aren't used enough.
What if we ask managers to create "individual development plans" and tell leaders it's their job to assess and improve skills in their teams. This makes sense too (many books have been written on this), but to be honest, it shows mixed success. Most top managers get promoted for "doing the job well" and they may or may not know why and how they succeed. A sense of self-awareness and the ability to develop others is itself a rare skill, so most companies tell us that fewer than a quarter of their managers<https://blog.betterworks.com/betterworks-research-confirms-managers-feel-talent-management-needs-improvement/> are good at developing people.
How about putting the burden on L&D? Let's ask the Chief Learning Officer to assess the skills in the company and build a set of Capability Academies<https://joshbersin.com/2019/10/the-capability-academy-where-corporate-training-is-going/> to push the company's skills forward. This is what most good companies do (there is almost always a Sales Academy and Leadership Academy). But this is a messy, complex business … can't we just buy a piece of software that does this automatically? After all, AI is so powerful it can tell us which route to drive to work, can't it tell us what skills we need?
Enter Workday Skills Cloud
Enter Workday Skills Cloud, one of the industry's most ambitious attempts to assess and categorize skills through software. Workday, through its acquisition of Identified<https://www.forbes.com/sites/joshbersin/2014/02/27/workday-acquires-identified-a-potential-disruptive-move-in-recruiting/#5f2cd034333d> (in 2014) got interested in Ontologies long ago. After all, every Workday customer has to build the job catalog, so this problem of "creating a job and skills ontology" is going on in every single Workday customer. So why wouldn't Workday try to do this in an intelligent, automated, data-driven way?
If you think about the problem, there are essentially two ways to solve it.
Approach 1: Define an organization, job, and skills model and then use software to "apply it to your company."
This is the approach IBM takes with its Talent Frameworks<https://www.ibm.com/products/watson-talent-frameworks> (an exhaustive set of job descriptions and capabilities developed by IBM). You sit down with your leaders and more or less "design" the skills, experiences, capabilities, and job levels in your company. And organizations do this all the time.
While this is exciting and fun to do, it's quite difficult and takes enormous amounts of time. And as you proceed you realize that the instant you're done it starts to become out of date. So a second approach is starting to take hold.
Approach 2: Develop a system that "self-describes" the work, skills, and capabilities you need to succeed.
What if the system automatically figures out what skills you need in real-time? By reading new job descriptions and understanding feedback and results, this data is floating around in your company right now.
Think about "Waze" vs. "Google Maps." Google Maps navigates the world based on a lot of hard work measuring and photographing roads. It's similar to sitting in the conference room designing jobs and competency models.
Waze, by contrast, "watches" where people go, and can "find out" what roads are open, closed, fast, and slow. Google Maps tells you how the world is supposed to work. Waze tells you how it really is working.
So what companies really want is the Waze of jobs and careers. Just tell me what successful people are doing in the company TODAY, and show me how to get there from here.
And this is what Workday Skills Cloud is setting out to do.
(By the way, companies like Eightfold.ai, PhenomPeople, Degreed, Edcast, and others are working on this too.)
How Does This Work?
The Workday team has been working on this for several years now, and they've built a very interesting system. And you have to think about it as a whole system. It's not an application sitting on top of Workday, it's whole new infrastructure within Workday that considers all aspects of work in the context of skills, capabilities, experiences, and relationships.
Architecturally, the Skills Cloud is a set of powerful search, matching, and AI-driven prediction algorithms. It doesn't really know what a skill is, but it knows that people with "software engineering" roles also have "C++ and Java" associated with their job descriptions, feedback, and other communications. It's somewhat similar to Google Search – it clusters words together based on patterns, relationships, job history, and other measures of "proximity."
Consider, for example, a successful salesperson in your company who over-exceeds their sales quota, manages large accounts, and successfully moves into management. The Skills Cloud may discover that this person went to certain schools, studied certain topics, and spent a lot of time learning, discussing, or writing about account management, consulting, and marketing. This person's "cloud" would identify these skills at a deep level, and others could use this information to improve their own levels of success.
It's a complex problem, one that has been going on in Workday since the acquisition of Identified in 2014. Google for Jobs<https://www.forbes.com/sites/joshbersin/2017/05/26/google-for-jobs-potential-to-disrupt-the-200-billion-recruiting-industry/#4b692d194d1f> and Indeed have been doing this for job search ("find me an engineering job for my level of experience within 10 miles of my house), but the Workday problem is even harder. Not only does Workday want to "fit people to jobs," they want to help people find the new skills they need, the new assignments they should consider, and even the people they should meet.
Once turned on it has amazing potential. With more than 275 customers live, the system is collecting data now. It will soon be used to help search for critical skills, help people improve their capabilities, and help executives understand skill gaps in their organizations.
The system has three main components:
1/ Skills Inference
The first is the engine that finds, categorizes, matches, and evaluates skills. This part of the system reads job descriptions, feedback, and many other sources. It's a little bit like what happens under the covers of LinkedIn. Without you knowing it, LinkedIn recommends you jobs, associates, and learning based on your own experience, conversations, and resume.
[workday skills cloud]
Where does the data come from? It looks at current jobs and job transitions, courses completed, projects and assignments completed, talent and performance reviews, public feedback, and resume and job history. Of course, companies have to "opt-in" to the Skills Cloud, so this won't happen unless you want it turned on.
Degreed, for example, infers your skills from the content you click on. You can also be "tagged" by others (EdCast, LinkedIn, Gloat and other systems do this).
2/ Skills Verification, where users and others can verify the skills on your profile. This includes features that let you and others verify your skills through experiences and feedback.
[workday skills cloud verification]
Users have the ability to view and edit this profile, so while the system may infer that you know a lot about machine learning because you just worked on a machine learning project, you can always "dial down" that it discovered. It will build this evidence through your job history, evaluations, and other data in the system.
While the system does not let you endorse your own skills, it prompts managers and relevant indivisuals (project owners, instructors) to endorse your skills in the context of work (gigs, projects, training, jobs). When I worked at a consulting firm we "endorsed" people informally – with a system like this we could quickly see who the experts really are. (EdCast, Degreed, and other systems do this.)
3/ Skills Strength. If you haven't demonstrated the use of a particular skill in some time, it may decline in value in the system. I don't know how Workday decides the "aging" of skills (in my case I seem to get better at things after I come back to them later, but who knows), but this is an important signal as well.
[workday skills cloud recency]
How Will This All Work?
The Workday Skills Cloud is already live with more than 275 customers. Later this year it will be used by a variety of applications, including the Workday Talent Marketplace, Recruiting, Learning, Career Hub (a career recommendation platform) Planning and Skills Insights (a system similar to LinkedIn Talent Insights), and apps for project skill identification and skill scheduling.
And if this works well, it can be useful for many operational issues. Suppose an employee quits or takes leave and you need a replacement? A manager could quickly search for someone with the required skills, experience, credentials, or interests. This is the real future of a dynamic talent management platform.
This Is A Big Deal: And Competition Is Coming
As I mentioned above, this is a big and important project. Not only could Skills Cloud analyze and categorize vast amounts of data, it will change the fabric of the HCM system. One could say that if Skills Cloud works, Workday is no longer an ERP system, but now a "system of capabilities" – one that can constantly evaluate, measure, and improve the capabilities in your company.
As Google has learned over the last twenty years, categorizing billions of words into an ontology is a serious effort. Workday is essentially doing something similar – finding ways to group and understand skills without any formal direction.
As I talked with the product team it occurred to me that the most useful part of the system may be within a functional area. In sales, for example, are there a set of deep and unknown skills we have in our company that drive high levels of success? How do our manufacturing engineers and supervisors compare to others in our industry? These types of functional models will be extraordinarily valuable over time.
While Workday is a big and amazing company, there is some serious competition work on this too. There may be a "war of the skills clouds" ahead.
First, companies like Degreed, EdCast, Valamis, and others in the LXP space are all working on skills inference. They have millions of data records from their learners' activity, so they can infer skills and skills by looking at skills demand. As Workday Learning becomes more popular the company will be able to do the same – but today these systems are "skills systems of record" too. They don't have the range of data Workday can access, but they're smart companies so they're going to try the same things.
Second, Microsoft and LinkedIn are working on this too. LinkedIn doesn't have internal data for employees, but today with LinkedIn Talent Insights you can already see your company's skills vs. your competitors. Granted their skills data is all through recommendations, but the company is now offering its skills assessments so its database will improve. Products like Microsoft Project Cortex will index this data inside your company also, so I could see Microsoft offering something similar to Workday in the next year or two.
Third, many recruiting and AI companies are building similar products as well. These include Gloat (an AI engine for internal talent matching), Eightfold.ai (an vendor that has more than a billion employee records and has built a "Google for Jobs" you can buy for your own company), Fuel50, PhenomPeople, and Avature. Remember that recruiting vendors have been working on job matching algorithms for years. Each of these companies sees skills matching, internal mobility, and various forms of skills development in their future – and they have been successfully matching people to jobs for a long time. ADP is doing this also, through their embedded AI engine which powers its entire Next-Gen HCM platform.
Fourth, there are a few dark horses like IBM and Google. IBM, through its Watson Career Assistant and other skills inference products, could focus in this area. Today IBM sells its AI tools as recruiting enhancements, but the IBM Watson Career Assistant is essentially a skills cloud of its own – if the company decided to partner with Workday it could be quite interesting. And Google, now that they're trying to "become Oracle" and sell enterprise solutions, could start selling its ontology-building engine to corporations as well.
Ultimately all these "ontology builders" have to work together. I could see a world a few years ago where core systems like the Workday Skills Cloud collect and aggregate ontology information from the recruiting, learning, and other HR systems in the company. But right now each vendor is doing their own thing.
A New World Is Arriving
While Workday Skills Cloud is still new and just getting started, the potential for this technology is game-changing. Imagine a system that intelligently analyzes the capabilities, experiences, and skills of all your people – and what power this could give to your leaders. Yes, there are many ethical and privacy issues to think about here (which I detail in this article<https://joshbersin.com/2019/05/the-ethics-of-ai-and-people-analytics-four-dimensions-of-trust/>), but to me this cat is out of the bag.
Just as Twitter, Google, and Facebook know hundreds of things about your consumer behavior, why wouldn't your company want similar information to help you perform better, find the next job, and decide how to progress in your career.
We're working closely with Workday on this technology and I will keep you informed. And in 2020 we will be launching a new program in the Josh Bersin Academy<https://bersinacademy.com/> to help you understand this topic.
Stay tuned, because this new area of HR tech will be an exciting space in the year ahead.
BY JOSHBERSIN<https://joshbersin.com/author/joshbersin/> · PUBLISHED JANUARY 2, 2020 · UPDATED JANUARY 3, 2020
https://joshbersin.com/2020/01/workday-skills-cloud-a-big-idea-with-much-more-to-come/
In the Fall of 2018 Workday introduced the Skills Cloud<https://www.globenewswire.com/news-release/2018/10/02/1588881/0/en/Workday-Delivers-Machine-Learning-Powered-Skills-Cloud-to-Unlock-Untapped-Workforce-Potential.html>, a new offering designed to help companies create a "skills ontology" that discovers the skills (and skill gaps) in your workforce. While it sounded like a fascinating concept, we hadn't heard much about it until Workday Rising<https://joshbersin.com/2019/10/workday-fires-a-cannon-into-the-hr-technology-market/> this Fall.
Well, I just spent a day going into details with Workday and I want to give you an update.
Let me summarize by saying this is a very big deal, and it delivers on one of the biggest promises in human capital systems: the move from "systems of record" to "systems of capability." And I think the term "Skills Cloud" understates what this will become.
Creating a Job and Skills Ontology
First, let's start at the core. The Skills Cloud (and related products) are trying to identify thousands of skills and experiences in your workforce and arrange them in an Ontology.
What is an "ontology?" If you look at the word in the dictionary, it is described as a "categorization scheme" for large amounts of data.
[ontology]
In Human Resources, we use ontologies all the time. Why? Because we have to arrange, document, and categorize lots of information about people.
Look at the whole concept of a job. The reason we have "jobs" is because we need to get work done (ie. designing, building, marketing, or selling something), and we want to decompose the work into tasks. Jobs were designed to "bundle" these tasks, and we ended up with what we now call a "job description."
[jobs and work]
Whenever you write a job description, you identify the functional area (sales, marketing, engineering finance), the job role (tech, managerial, support), the level, reporting structure, and details of the job itself. As you write all this down, you end up describing the job tasks and responsibilities, skills needed, certifications required, experience desired, and more. This becomes an ontology.
If it's a job in engineering, for example, you may specify knowledge of the C++ language, an understanding of data structures, and the ability to write and debug code. If it's a job in sales, you may look for candidates that can develop rapport, ask penetrating questions, persuasively propose solutions, and ask for the order. If it's a job as a manager you'd specify abilities to interview people, select candidates, set goals, and so on.
You get the picture. The problem is quite complex. Every job is a little bit different, and the way we describe it can vary.
In accounting, this kind of categorization is easy: there are Generally Accepted Accounting Principles. In HR and with people, however, there is no real standard – so companies build many types of job descriptions and then use many forms of assessment, testing, and interviewing to evaluate people. (The pre-hire assessment industry is over $2Billion in size and tremendously fragmented with tools and types of assessments.)
Some companies use criteria like education, prior work experience, and prior employer; others use technical or professional skills; others evaluate people based on soft-skills (PowerSkills), potential and other attributes. Whatever scheme you use, it's yours to define and you end up creating your own "Ontology" over time.
Competency Models: They've Come and Gone
To try to make this easy, the pioneers of organizational design came up with the idea of competency models<https://www.valamis.com/hub/competency-model>: a detailed breakdown of job tasks, each of which demands skills, competencies, and behavioral strengths. (Often called KSA's, Knowledge, Skills, and Abilities).
In my early days as an analyst, I read books about competency models and found them fascinating. They're developed by evaluating jobs, and then studying what successful people in these jobs do. They are based on the idea that a "job" can be designed by a designer, and we can specify what work each person will do.
In the real world, however, work is much messier. While we usually have a job title, the work we do varies widely from day to day, project to project, and year to year. As the company grows and changes, every job tends to adapt. And today, technology is eliminating more routine work than ever.
So a competency model developed five years ago may be quite out of date today, which is why companies like LinkedIn, Indeed, EMSI, and BurningGlass constantly study the latest skills in demand, by looking at real-world job requisitions being created every day.
To make this even trickier, the hierarchical nature of a job has changed. Rather than work directly for a "boss," many of us now work in teams, on projects, and hold a variety of roles. In service-related industries (almost all industries are now becoming service industries) your job adjusts based on need. So while you may have been hired to "design" or "build" something, you're likely to be listening, adapting, and adjusting your work output all the time. And every year there are new automation and digital tools to learn.
There is also enormous variation in jobs between companies. A sales role at SAP is very different from a sales role at Michelin Tire, for example. And even well-defined jobs in engineering, finance, HR, and IT vary widely based on company culture.
Some companies, for example, value speed over quality – so they expect quick results and look for people with deep domain experience. Others value innovation and creativity, so they want people who can think outside of the box. Still others base success on quality over speed (Boeing is facing up to this right now), so they expect you to focus on ponderous detail. So while an off-the-shelf competency model may seem like a fit, the true drivers of success in one company are quite different in another.
And thanks to technological change, the most basic elements of work keep changing. If you're an engineer and don't keep up on your trade, you fall behind quickly. Studies show that "the half-life of skills is five years<https://eab.com/insights/daily-briefing/workplace/the-half-life-of-professional-skills-is-5-years/>" which is a frightening thought if you think about it. (Half of your years of expertise go out of date in five years?)
My experience shows that today your "ability to learn" is one of the most important elements of a job. Almost every day I find something I need to learn more about, so I feel like I"m "reskilling myself" on a constant basis. So my "job description" has to adapt, and we need HR systems that automatically keep up.
Then there's the word "Skills," which I find misleading. We tend to use it in a sloppy way, often ignoring the granularity of "what a skill really is." Is "creating a pivot table in Excel" a skill? Or is "building a predictive model" a skill?
I like to use the word "capability," which describes how people use skills to solve problems at work. A skill is an atomic item, and skills plus experience create capabilities. So while Workday calls it system the Skills Cloud, I hope it goes well beyond "building a pivot table" and can understand these higher level ideas.
Skills plus experience create capabilities.
In the model we've built for the Josh Bersin Academy<https://bersinacademy.com/>, we describe what I call Full-Stack capabilities<https://joshbersin.com/2019/11/the-full-stack-hr-professional/> – solutions you know how to build and deliver. These capabilities are dependent on many skills, which in turn are related to each other. And among these skills, some are hard and some are soft (which I call Power Skills<https://joshbersin.com/2019/10/lets-stop-talking-about-soft-skills-theyre-power-skills/>), so technical skills are never enough.
We also have to remember that experience is one of the most important skills of all. Nobody learns a skill until you have to use it, so it's your projects, assignments, and roles that define your success. So implicit in a discussion of skills is the fact that we have to measure experience and include this in the ontology too.
When you interview a candidate, for example, it's likely you'll ask the person to "tell you about your experience in this job" or "share a situation where you've done this in the past." This is because real-world experience IS a skill, so we have to consider job history, successes and failures, and the people you've met and known as part of your "ontology" too.
Your Company As A Collection of Capabilities, not Individuals
I've been thinking about this for a long time, and it's a very important topic. If you think about your organization or team – it's not a collection of people, it's a collection of skills, all of which translate into capabilities. It doesn't really matter how many people you have, it matters what they're capable of doing. The Skills Cloud, I would argue, must expand to understand this bigger picture.
[skills and capabilities]
I once asked the head of recruiting for one of the leading energy companies "you do a lot of recruiting, what are the key drivers of success in your candidates?" His answer was surprising: "the single most predictive factor in a new hire's success is the recruiter, nothing else." I had to scratch my head.
"What we've found is that our top recruiters really understand the skills, background, experience, and culture of our company – and they know how to select the right person that will thrive." In other words, the top recruiters are "human Skills Clouds."
Can you imagine a human capital system that understands all of this? That's the whole idea.
What if your HR software could identify the true skills and experiences in your company, identifying the characteristics of the highest performers? This information could be used by managers, individuals, and executives to select people, develop people, and create powerful career paths for everyone.
It's not a new idea: many vendors have gone after this market. Not only have LMS companies tried to build these tools, more than ten years ago I talked with a company that built its own "skills cloud." The platform was a fancy assessment platform that let you self-assess your skills, and then let managers and peers assess them as well. It was elegant and magnificent, but nobody wanted to buy it. It was too much work to implement, and companies just didn't focus on this area. Today self-assessment tools are built into many systems, but most companies tell me they just aren't used enough.
What if we ask managers to create "individual development plans" and tell leaders it's their job to assess and improve skills in their teams. This makes sense too (many books have been written on this), but to be honest, it shows mixed success. Most top managers get promoted for "doing the job well" and they may or may not know why and how they succeed. A sense of self-awareness and the ability to develop others is itself a rare skill, so most companies tell us that fewer than a quarter of their managers<https://blog.betterworks.com/betterworks-research-confirms-managers-feel-talent-management-needs-improvement/> are good at developing people.
How about putting the burden on L&D? Let's ask the Chief Learning Officer to assess the skills in the company and build a set of Capability Academies<https://joshbersin.com/2019/10/the-capability-academy-where-corporate-training-is-going/> to push the company's skills forward. This is what most good companies do (there is almost always a Sales Academy and Leadership Academy). But this is a messy, complex business … can't we just buy a piece of software that does this automatically? After all, AI is so powerful it can tell us which route to drive to work, can't it tell us what skills we need?
Enter Workday Skills Cloud
Enter Workday Skills Cloud, one of the industry's most ambitious attempts to assess and categorize skills through software. Workday, through its acquisition of Identified<https://www.forbes.com/sites/joshbersin/2014/02/27/workday-acquires-identified-a-potential-disruptive-move-in-recruiting/#5f2cd034333d> (in 2014) got interested in Ontologies long ago. After all, every Workday customer has to build the job catalog, so this problem of "creating a job and skills ontology" is going on in every single Workday customer. So why wouldn't Workday try to do this in an intelligent, automated, data-driven way?
If you think about the problem, there are essentially two ways to solve it.
Approach 1: Define an organization, job, and skills model and then use software to "apply it to your company."
This is the approach IBM takes with its Talent Frameworks<https://www.ibm.com/products/watson-talent-frameworks> (an exhaustive set of job descriptions and capabilities developed by IBM). You sit down with your leaders and more or less "design" the skills, experiences, capabilities, and job levels in your company. And organizations do this all the time.
While this is exciting and fun to do, it's quite difficult and takes enormous amounts of time. And as you proceed you realize that the instant you're done it starts to become out of date. So a second approach is starting to take hold.
Approach 2: Develop a system that "self-describes" the work, skills, and capabilities you need to succeed.
What if the system automatically figures out what skills you need in real-time? By reading new job descriptions and understanding feedback and results, this data is floating around in your company right now.
Think about "Waze" vs. "Google Maps." Google Maps navigates the world based on a lot of hard work measuring and photographing roads. It's similar to sitting in the conference room designing jobs and competency models.
Waze, by contrast, "watches" where people go, and can "find out" what roads are open, closed, fast, and slow. Google Maps tells you how the world is supposed to work. Waze tells you how it really is working.
So what companies really want is the Waze of jobs and careers. Just tell me what successful people are doing in the company TODAY, and show me how to get there from here.
And this is what Workday Skills Cloud is setting out to do.
(By the way, companies like Eightfold.ai, PhenomPeople, Degreed, Edcast, and others are working on this too.)
How Does This Work?
The Workday team has been working on this for several years now, and they've built a very interesting system. And you have to think about it as a whole system. It's not an application sitting on top of Workday, it's whole new infrastructure within Workday that considers all aspects of work in the context of skills, capabilities, experiences, and relationships.
Architecturally, the Skills Cloud is a set of powerful search, matching, and AI-driven prediction algorithms. It doesn't really know what a skill is, but it knows that people with "software engineering" roles also have "C++ and Java" associated with their job descriptions, feedback, and other communications. It's somewhat similar to Google Search – it clusters words together based on patterns, relationships, job history, and other measures of "proximity."
Consider, for example, a successful salesperson in your company who over-exceeds their sales quota, manages large accounts, and successfully moves into management. The Skills Cloud may discover that this person went to certain schools, studied certain topics, and spent a lot of time learning, discussing, or writing about account management, consulting, and marketing. This person's "cloud" would identify these skills at a deep level, and others could use this information to improve their own levels of success.
It's a complex problem, one that has been going on in Workday since the acquisition of Identified in 2014. Google for Jobs<https://www.forbes.com/sites/joshbersin/2017/05/26/google-for-jobs-potential-to-disrupt-the-200-billion-recruiting-industry/#4b692d194d1f> and Indeed have been doing this for job search ("find me an engineering job for my level of experience within 10 miles of my house), but the Workday problem is even harder. Not only does Workday want to "fit people to jobs," they want to help people find the new skills they need, the new assignments they should consider, and even the people they should meet.
Once turned on it has amazing potential. With more than 275 customers live, the system is collecting data now. It will soon be used to help search for critical skills, help people improve their capabilities, and help executives understand skill gaps in their organizations.
The system has three main components:
1/ Skills Inference
The first is the engine that finds, categorizes, matches, and evaluates skills. This part of the system reads job descriptions, feedback, and many other sources. It's a little bit like what happens under the covers of LinkedIn. Without you knowing it, LinkedIn recommends you jobs, associates, and learning based on your own experience, conversations, and resume.
[workday skills cloud]
Where does the data come from? It looks at current jobs and job transitions, courses completed, projects and assignments completed, talent and performance reviews, public feedback, and resume and job history. Of course, companies have to "opt-in" to the Skills Cloud, so this won't happen unless you want it turned on.
Degreed, for example, infers your skills from the content you click on. You can also be "tagged" by others (EdCast, LinkedIn, Gloat and other systems do this).
2/ Skills Verification, where users and others can verify the skills on your profile. This includes features that let you and others verify your skills through experiences and feedback.
[workday skills cloud verification]
Users have the ability to view and edit this profile, so while the system may infer that you know a lot about machine learning because you just worked on a machine learning project, you can always "dial down" that it discovered. It will build this evidence through your job history, evaluations, and other data in the system.
While the system does not let you endorse your own skills, it prompts managers and relevant indivisuals (project owners, instructors) to endorse your skills in the context of work (gigs, projects, training, jobs). When I worked at a consulting firm we "endorsed" people informally – with a system like this we could quickly see who the experts really are. (EdCast, Degreed, and other systems do this.)
3/ Skills Strength. If you haven't demonstrated the use of a particular skill in some time, it may decline in value in the system. I don't know how Workday decides the "aging" of skills (in my case I seem to get better at things after I come back to them later, but who knows), but this is an important signal as well.
[workday skills cloud recency]
How Will This All Work?
The Workday Skills Cloud is already live with more than 275 customers. Later this year it will be used by a variety of applications, including the Workday Talent Marketplace, Recruiting, Learning, Career Hub (a career recommendation platform) Planning and Skills Insights (a system similar to LinkedIn Talent Insights), and apps for project skill identification and skill scheduling.
And if this works well, it can be useful for many operational issues. Suppose an employee quits or takes leave and you need a replacement? A manager could quickly search for someone with the required skills, experience, credentials, or interests. This is the real future of a dynamic talent management platform.
This Is A Big Deal: And Competition Is Coming
As I mentioned above, this is a big and important project. Not only could Skills Cloud analyze and categorize vast amounts of data, it will change the fabric of the HCM system. One could say that if Skills Cloud works, Workday is no longer an ERP system, but now a "system of capabilities" – one that can constantly evaluate, measure, and improve the capabilities in your company.
As Google has learned over the last twenty years, categorizing billions of words into an ontology is a serious effort. Workday is essentially doing something similar – finding ways to group and understand skills without any formal direction.
As I talked with the product team it occurred to me that the most useful part of the system may be within a functional area. In sales, for example, are there a set of deep and unknown skills we have in our company that drive high levels of success? How do our manufacturing engineers and supervisors compare to others in our industry? These types of functional models will be extraordinarily valuable over time.
While Workday is a big and amazing company, there is some serious competition work on this too. There may be a "war of the skills clouds" ahead.
First, companies like Degreed, EdCast, Valamis, and others in the LXP space are all working on skills inference. They have millions of data records from their learners' activity, so they can infer skills and skills by looking at skills demand. As Workday Learning becomes more popular the company will be able to do the same – but today these systems are "skills systems of record" too. They don't have the range of data Workday can access, but they're smart companies so they're going to try the same things.
Second, Microsoft and LinkedIn are working on this too. LinkedIn doesn't have internal data for employees, but today with LinkedIn Talent Insights you can already see your company's skills vs. your competitors. Granted their skills data is all through recommendations, but the company is now offering its skills assessments so its database will improve. Products like Microsoft Project Cortex will index this data inside your company also, so I could see Microsoft offering something similar to Workday in the next year or two.
Third, many recruiting and AI companies are building similar products as well. These include Gloat (an AI engine for internal talent matching), Eightfold.ai (an vendor that has more than a billion employee records and has built a "Google for Jobs" you can buy for your own company), Fuel50, PhenomPeople, and Avature. Remember that recruiting vendors have been working on job matching algorithms for years. Each of these companies sees skills matching, internal mobility, and various forms of skills development in their future – and they have been successfully matching people to jobs for a long time. ADP is doing this also, through their embedded AI engine which powers its entire Next-Gen HCM platform.
Fourth, there are a few dark horses like IBM and Google. IBM, through its Watson Career Assistant and other skills inference products, could focus in this area. Today IBM sells its AI tools as recruiting enhancements, but the IBM Watson Career Assistant is essentially a skills cloud of its own – if the company decided to partner with Workday it could be quite interesting. And Google, now that they're trying to "become Oracle" and sell enterprise solutions, could start selling its ontology-building engine to corporations as well.
Ultimately all these "ontology builders" have to work together. I could see a world a few years ago where core systems like the Workday Skills Cloud collect and aggregate ontology information from the recruiting, learning, and other HR systems in the company. But right now each vendor is doing their own thing.
A New World Is Arriving
While Workday Skills Cloud is still new and just getting started, the potential for this technology is game-changing. Imagine a system that intelligently analyzes the capabilities, experiences, and skills of all your people – and what power this could give to your leaders. Yes, there are many ethical and privacy issues to think about here (which I detail in this article<https://joshbersin.com/2019/05/the-ethics-of-ai-and-people-analytics-four-dimensions-of-trust/>), but to me this cat is out of the bag.
Just as Twitter, Google, and Facebook know hundreds of things about your consumer behavior, why wouldn't your company want similar information to help you perform better, find the next job, and decide how to progress in your career.
We're working closely with Workday on this technology and I will keep you informed. And in 2020 we will be launching a new program in the Josh Bersin Academy<https://bersinacademy.com/> to help you understand this topic.
Stay tuned, because this new area of HR tech will be an exciting space in the year ahead.
Wednesday, January 8, 2020
White House Proposes U.S. AI Regulatory Principles
White House Proposes U.S. AI Regulatory Principles
Today, the White House is proposing U.S. AI regulatory principles<https://urldefense.com/v3/__https:/www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJR9IHqHF$> to govern the development and use of artificial intelligence (AI) technologies in the private sector. Through these 10 principles, developed as part of the American AI Initiative<https://urldefense.com/v3/__https:/www.whitehouse.gov/articles/accelerating-americas-leadership-in-artificial-intelligence/__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJQRmZ3XD$> launched by President Trump, the United States is taking the lead to advance emerging technology in a way that reflects our values of freedom, human rights, and civil liberties.
Chief Technology Officer of the United States Michael Kratsios authored an op-ed in Bloomberg today explaining the significance of the regulatory principles: "AI That Reflects American Values<https://urldefense.com/v3/__https:/www.bloomberg.com/opinion/articles/2020-01-07/ai-that-reflects-american-values?srnd=opinion__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJUmaZKE_$>."
The U.S. AI regulatory principles are underpinned by three goals designed to drive AI innovation:
* Ensure Public Engagement: Regulators must base technical and policy decisions on scientific evidence and feedback from the American public, industry leaders, the academic community, non-profits, and civil society.
* Limit Regulatory Overreach: Regulators must conduct risk assessment and cost-benefit analyses prior to any regulatory action on AI, with a focus on establishing flexible frameworks rather than one-size-fits-all regulation.
* Promote Trustworthy AI: In deciding regulatory action related to AI, regulators must consider fairness, non-discrimination, openness, transparency, safety, and security.
The principles, to be delivered as a memorandum to federal agencies once finalized, will be open for public comment. When proposing any regulation on AI technologies in the private sector, agencies will have to demonstrate to the White House that the proposed regulations abide by the principles described in the memorandum.
"Building upon this Administration's record of leadership in artificial intelligence, the U.S. AI regulatory principles set the Nation on a path of continued AI innovation and discovery. By reducing regulatory uncertainty for America's innovators, increasing public input on regulatory decisions, and promoting trustworthy AI development, the principles offer the American approach to address the challenging technical and ethical issues that arise with AI technologies," said Michael Kratsios, Chief Technology Officer of the United States.
"A first-of-its-kind document internationally, these principles show the United States leading the way among likeminded nations to shape the evolution of AI technology consistent with our common values of freedom, human rights, and civil liberties. We look forward to further engagement with the AI community, American public, and international partners to ensure the advancement of robust, reliable, and trustworthy AI technologies."
Click here<https://urldefense.com/v3/__https:/www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJR9IHqHF$> to view the memorandum online.
Visit AI.gov<https://urldefense.com/v3/__https:/www.whitehouse.gov/ai/__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJat-nOeG$> to learn more about the Trump Administration's efforts in artificial intelligence.
Today, the White House is proposing U.S. AI regulatory principles<https://urldefense.com/v3/__https:/www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJR9IHqHF$> to govern the development and use of artificial intelligence (AI) technologies in the private sector. Through these 10 principles, developed as part of the American AI Initiative<https://urldefense.com/v3/__https:/www.whitehouse.gov/articles/accelerating-americas-leadership-in-artificial-intelligence/__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJQRmZ3XD$> launched by President Trump, the United States is taking the lead to advance emerging technology in a way that reflects our values of freedom, human rights, and civil liberties.
Chief Technology Officer of the United States Michael Kratsios authored an op-ed in Bloomberg today explaining the significance of the regulatory principles: "AI That Reflects American Values<https://urldefense.com/v3/__https:/www.bloomberg.com/opinion/articles/2020-01-07/ai-that-reflects-american-values?srnd=opinion__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJUmaZKE_$>."
The U.S. AI regulatory principles are underpinned by three goals designed to drive AI innovation:
* Ensure Public Engagement: Regulators must base technical and policy decisions on scientific evidence and feedback from the American public, industry leaders, the academic community, non-profits, and civil society.
* Limit Regulatory Overreach: Regulators must conduct risk assessment and cost-benefit analyses prior to any regulatory action on AI, with a focus on establishing flexible frameworks rather than one-size-fits-all regulation.
* Promote Trustworthy AI: In deciding regulatory action related to AI, regulators must consider fairness, non-discrimination, openness, transparency, safety, and security.
The principles, to be delivered as a memorandum to federal agencies once finalized, will be open for public comment. When proposing any regulation on AI technologies in the private sector, agencies will have to demonstrate to the White House that the proposed regulations abide by the principles described in the memorandum.
"Building upon this Administration's record of leadership in artificial intelligence, the U.S. AI regulatory principles set the Nation on a path of continued AI innovation and discovery. By reducing regulatory uncertainty for America's innovators, increasing public input on regulatory decisions, and promoting trustworthy AI development, the principles offer the American approach to address the challenging technical and ethical issues that arise with AI technologies," said Michael Kratsios, Chief Technology Officer of the United States.
"A first-of-its-kind document internationally, these principles show the United States leading the way among likeminded nations to shape the evolution of AI technology consistent with our common values of freedom, human rights, and civil liberties. We look forward to further engagement with the AI community, American public, and international partners to ensure the advancement of robust, reliable, and trustworthy AI technologies."
Click here<https://urldefense.com/v3/__https:/www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJR9IHqHF$> to view the memorandum online.
Visit AI.gov<https://urldefense.com/v3/__https:/www.whitehouse.gov/ai/__;!!NCZxaNi9jForCP_SxBKJCA!CvWOgRaSAs0ci0NvY-6KtXng_aopJrWwi9L-HfphmJgahtwT6RfcUUfpJat-nOeG$> to learn more about the Trump Administration's efforts in artificial intelligence.
Wednesday, December 18, 2019
Federal Sources of Entrepreneurship Data: A Compendium
Compendium<https://www.dropbox.com/s/kkqqc167muhdcza/Reamer%20Kauffman%20Federal%20Sources%20Entrepreneurship%20Data%2012-16-19%20v3.pdf?dl=0> developed by Andrew Reamer: "The E.M. Kauffman Foundation has asked the George Washington Institute of Public Policy(GWIPP) to prepare a compendium of federal sources of data on self-employment, entrepreneurship, and small business development.
The Foundation believes that the availability of useful, reliable federal data on these topics would enable robust descriptions and explanations of entrepreneurship trends in the United States and so help guide the development of effective entrepreneurship policies.
Achieving these ends first requires the identification and detailed description of available federal datasets,as provided in this compendium.Its contents include:
*
An overview and discussion of 18 datasets from four federal agencies, organized by two categories and five subcategories.
*
Tables providing information on each dataset, including:
*
scope of coverage of self-employed, entrepreneurs, and businesses;
*
data collection methods (nature of data source, periodicity, sampling frame, sample size);
*
dataset variables (owner characteristics, business characteristics and operations, geographic areas);
*
Data release schedule; and
*
Data access by format (including fixed tables, interactive tools, API, FTP download, public use microdata samples [PUMS], and confidential microdata).
For each dataset, examples of studies, if any, that use the datasource to describe and explain trends in entrepreneurship.
The author's aim is for the compendium to facilitate an assessment of the strengths and weaknesses of currently available federal datasets, discussion about how data availability and value can be improved, and implementation of desired improvements…(More<https://www.dropbox.com/s/kkqqc167muhdcza/Reamer%20Kauffman%20Federal%20Sources%20Entrepreneurship%20Data%2012-16-19%20v3.pdf?dl=0>)"
The Foundation believes that the availability of useful, reliable federal data on these topics would enable robust descriptions and explanations of entrepreneurship trends in the United States and so help guide the development of effective entrepreneurship policies.
Achieving these ends first requires the identification and detailed description of available federal datasets,as provided in this compendium.Its contents include:
*
An overview and discussion of 18 datasets from four federal agencies, organized by two categories and five subcategories.
*
Tables providing information on each dataset, including:
*
scope of coverage of self-employed, entrepreneurs, and businesses;
*
data collection methods (nature of data source, periodicity, sampling frame, sample size);
*
dataset variables (owner characteristics, business characteristics and operations, geographic areas);
*
Data release schedule; and
*
Data access by format (including fixed tables, interactive tools, API, FTP download, public use microdata samples [PUMS], and confidential microdata).
For each dataset, examples of studies, if any, that use the datasource to describe and explain trends in entrepreneurship.
The author's aim is for the compendium to facilitate an assessment of the strengths and weaknesses of currently available federal datasets, discussion about how data availability and value can be improved, and implementation of desired improvements…(More<https://www.dropbox.com/s/kkqqc167muhdcza/Reamer%20Kauffman%20Federal%20Sources%20Entrepreneurship%20Data%2012-16-19%20v3.pdf?dl=0>)"
Tuesday, November 19, 2019
Health care jobs will keep the US labor market going — even if there's a recession
Quote: "Health care is by far the largest and healthiest sector of the economy," (?)
https://www.cnn.com/2019/11/18/economy/health-care-jobs-us-labor-market/index.html
New York (CNN Business) America's health care sector is an employment powerhouse that is keeping the US labor market strong. Health care hiring is so robust, the industry would be pretty much immune to a recession or changes in politics.
The population of the United States is aging and living longer. For the health care industry this means more people require care for a longer period of time.
"Health care is by far the largest and healthiest sector of the economy," Glassdoor Chief Economist Dr Andrew Chamberlain told CNN Business.
And as long people continue to get sick and need care, the sector will blossom. That's what's making it immune to outside shocks like a recession or a government efforts to reform the industry, experts say.
Presidential candidates Elizabeth Warren and Bernie Sanders, for example, want to make drastic changes to America's health care industry<https://www.cnn.com/2019/11/01/politics/elizabeth-warren-medicare-for-all-financing-plan/index.html>. Although reforms might change prices and payment structures for care, they won't keep elderly people from needing it.
"I don't worry about [potential] health care reform affecting hiring, which is mostly nurses and elderly care," Chamberlain said.
Over the past decade no sector has added more net jobs to the US economy than health care, analysts at ratings agency Moody's said in emailed comments.
"The growing US health care industry supports the US economy through output, employment and innovation," said the agency's analysts in a 2018 industry report. "The sector has created more jobs than any other industry on a net basis over the past decade — nearly three million — and today employs 16 million people, or 11% of the workforce."
Meanwhile, the US unemployment rate is near a 50-year low<https://www.cnn.com/2019/11/01/economy/october-jobs-report/index.html> and most people who want to work have a job.
Teachers for health subjects in post-secondary education, home health aides, and nurses are in the top 10 fastest-growing jobs until 2028, according to the Bureau of Labor Statistics. Other health care jobs, including medical assistants, nurse practitioners, physical therapists and personal care aides, are expected to be among the top 20 fastest-growing jobs over the next decade, according to BLS data.
The BLS expects health care and social assistance jobs to add 3.4 million jobs by 2028. Moody's estimate is more conservative but still high at 1.7 million jobs.
Aging populations are a global phenomenon. People are living longer and having fewer children in the United States and around the world.
By 2030, the worldwide population over 65 will account for 12% of the total, according to the US Census Bureau. That's up from just 9% in 2015. By 2050, senior citizens will account for for 17% of the world's population.
Europe is currently the "oldest" region in the world, but its followed by North America, which is expected to have more than 20% of people over the age of 65 by 2050. Meanwhile, America's birthrate hit a 32-year low in May<https://www.cnn.com/2019/05/15/health/us-birth-rate-record-low-cdc-study/index.html>.
But the growing costs of care in America are comparatively high and rising. That, in turn, is weighing on the country's growth prospects, according to Moody's.
https://www.cnn.com/2019/11/18/economy/health-care-jobs-us-labor-market/index.html
New York (CNN Business) America's health care sector is an employment powerhouse that is keeping the US labor market strong. Health care hiring is so robust, the industry would be pretty much immune to a recession or changes in politics.
The population of the United States is aging and living longer. For the health care industry this means more people require care for a longer period of time.
"Health care is by far the largest and healthiest sector of the economy," Glassdoor Chief Economist Dr Andrew Chamberlain told CNN Business.
And as long people continue to get sick and need care, the sector will blossom. That's what's making it immune to outside shocks like a recession or a government efforts to reform the industry, experts say.
Presidential candidates Elizabeth Warren and Bernie Sanders, for example, want to make drastic changes to America's health care industry<https://www.cnn.com/2019/11/01/politics/elizabeth-warren-medicare-for-all-financing-plan/index.html>. Although reforms might change prices and payment structures for care, they won't keep elderly people from needing it.
"I don't worry about [potential] health care reform affecting hiring, which is mostly nurses and elderly care," Chamberlain said.
Over the past decade no sector has added more net jobs to the US economy than health care, analysts at ratings agency Moody's said in emailed comments.
"The growing US health care industry supports the US economy through output, employment and innovation," said the agency's analysts in a 2018 industry report. "The sector has created more jobs than any other industry on a net basis over the past decade — nearly three million — and today employs 16 million people, or 11% of the workforce."
Meanwhile, the US unemployment rate is near a 50-year low<https://www.cnn.com/2019/11/01/economy/october-jobs-report/index.html> and most people who want to work have a job.
Teachers for health subjects in post-secondary education, home health aides, and nurses are in the top 10 fastest-growing jobs until 2028, according to the Bureau of Labor Statistics. Other health care jobs, including medical assistants, nurse practitioners, physical therapists and personal care aides, are expected to be among the top 20 fastest-growing jobs over the next decade, according to BLS data.
The BLS expects health care and social assistance jobs to add 3.4 million jobs by 2028. Moody's estimate is more conservative but still high at 1.7 million jobs.
Aging populations are a global phenomenon. People are living longer and having fewer children in the United States and around the world.
By 2030, the worldwide population over 65 will account for 12% of the total, according to the US Census Bureau. That's up from just 9% in 2015. By 2050, senior citizens will account for for 17% of the world's population.
Europe is currently the "oldest" region in the world, but its followed by North America, which is expected to have more than 20% of people over the age of 65 by 2050. Meanwhile, America's birthrate hit a 32-year low in May<https://www.cnn.com/2019/05/15/health/us-birth-rate-record-low-cdc-study/index.html>.
But the growing costs of care in America are comparatively high and rising. That, in turn, is weighing on the country's growth prospects, according to Moody's.
Wednesday, November 13, 2019
Rise
https://schmidtfutures.com/our-work/talent/rise/
Rise is the anchor program of a $1 billion philanthropic commitment by Eric and Wendy Schmidt, founders of Schmidt Futures, to talent serving others.
Read the announcement of Eric and Wendy Schmidt's $1 billion philanthropic commitment to talent here<https://schmidtfutures.com/eric-and-wendy-schmidt-announce-new-1-billion-commitment/>
Read the announcement of the Rise program here<https://schmidtfutures.com/schmidt-futures-and-rhodes-trust-announce-rise/>
Exceptional talent drives the pace and scale of global progress. However the identification and support of that talent is often ad hoc and fragmented across countries, educational institutions, and industries. As a result, some of the world's most talented people never realize their potential for global impact: they go undiscovered, are inefficiently matched to development opportunities, or are limited in their opportunity to work in service of the public benefit as they enter the workforce.
Rise is a new talent program designed to increase opportunity for exceptional young people who need it to do more for others, together, throughout their lives.
This program—an initiative of Schmidt Futures and the Rhodes Trust—will build a network of exceptional young people who are committed to helping others throughout their lives.
The program will be designed to encourage a lifetime of service and learning by providing support that could include scholarships, career services, and funding opportunities to help these leaders serve others for decades to come.
Rise cohort members, who will apply between the ages of 15 and 17, will be eligible for various types of support. They will be invited to attend a residential fellowship before their final year of high school that will support them as they consider how to serve others, how to become leaders, and how to transition to higher education and careers. Participants may also receive scholarships to continue their education, mentorship and other assistance tailored to their specific needs and interests, and a variety of career services as part of the Rise network.
To encourage service, Rise will invite its community members to make service commitments together and develop a platform to match network members with common interests. Among a range of pursuits, we envision that Rise winners will create policy, build new enterprises that benefit the public, catalyze new interdisciplinary areas of study, and develop new solutions to the world's hardest problems.
Rise is the anchor program of a $1 billion philanthropic commitment by Eric and Wendy Schmidt, founders of Schmidt Futures, to talent serving others.
Read the announcement of Eric and Wendy Schmidt's $1 billion philanthropic commitment to talent here<https://schmidtfutures.com/eric-and-wendy-schmidt-announce-new-1-billion-commitment/>
Read the announcement of the Rise program here<https://schmidtfutures.com/schmidt-futures-and-rhodes-trust-announce-rise/>
Exceptional talent drives the pace and scale of global progress. However the identification and support of that talent is often ad hoc and fragmented across countries, educational institutions, and industries. As a result, some of the world's most talented people never realize their potential for global impact: they go undiscovered, are inefficiently matched to development opportunities, or are limited in their opportunity to work in service of the public benefit as they enter the workforce.
Rise is a new talent program designed to increase opportunity for exceptional young people who need it to do more for others, together, throughout their lives.
This program—an initiative of Schmidt Futures and the Rhodes Trust—will build a network of exceptional young people who are committed to helping others throughout their lives.
The program will be designed to encourage a lifetime of service and learning by providing support that could include scholarships, career services, and funding opportunities to help these leaders serve others for decades to come.
Rise cohort members, who will apply between the ages of 15 and 17, will be eligible for various types of support. They will be invited to attend a residential fellowship before their final year of high school that will support them as they consider how to serve others, how to become leaders, and how to transition to higher education and careers. Participants may also receive scholarships to continue their education, mentorship and other assistance tailored to their specific needs and interests, and a variety of career services as part of the Rise network.
To encourage service, Rise will invite its community members to make service commitments together and develop a platform to match network members with common interests. Among a range of pursuits, we envision that Rise winners will create policy, build new enterprises that benefit the public, catalyze new interdisciplinary areas of study, and develop new solutions to the world's hardest problems.
Tuesday, November 12, 2019
Automation, work, and skills: what do we know?
Marcus Casey and Sarah Nzau at Brookings<https://www.brookings.edu/blog/up-front/2019/11/07/automation-work-and-skills-what-do-we-know/>: " This summary was prepared for the inaugural conference on "Automation and the Middle Class" for the Brookings Institution, Future of the Middle Class Initiative.
What do we really know about how technology will impact employment? Which workers will be impacted most, both in terms of class and gender? What role can retraining play? These questions are addressed in a new series of three academic papers on automation published by the Future of the Middle Class Initiative (FMCi)<https://www.brookings.edu/project/future-of-the-middle-class-initiative/> here at Brookings.
Concerns about the impact of technological change on jobs, wages, and the economic security of workers are not new. Most major technological advances cause social disruptions that can often be painful for impacted workers and communities. While the long arc of history has shown that, by and large, past technological change has us wealthier and more productive, it is important to consider both the needs of people potentially harmed in the interim and potential policy choices that can mitigate the harm.
These issues are particularly salient today. Advanced robotics and other automating technologies in concert with the emergence of human-mimicking artificial intelligence (AI) protocols both have the potential to raise the productivity of workers whose skills complement them well, but also to displace workers for whom their skillset competes<https://blogs.wsj.com/experts/2019/05/22/the-demographic-most-vulnerable-to-automation-teens-and-young-adults/>. While almost all jobs are likely to change to at least some degree, with a reorganization of the tasks<https://mitibmwatsonailab.mit.edu/research/publications/paper/?id=The-Future-of-Work-How-New-Technologies-Are-Transforming-Tasks> contained within them, research suggests that concerns about widespread loss of jobs are overblown. But an important caveat is that many middle – skill jobs that pay decent wages and benefits are particularly at risk of being displaced.<https://www.brookings.edu/blog/up-front/2019/07/18/how-much-will-automation-impact-the-middle-class-we-dont-know-yet/>
The new papers, discussed at a recent Brookings private seminar, summarize recent developments in the academic literature, suggesting directions for future research and/or surveying options for policy reform or innovation. We summarize these papers below, focusing in particular on their implications for future work.
AUTOMATION AND THE MIDDLE CLASS
Henry Siu and Nir Jaimovich, in "How Automation and Other Forms of IT Affect the Middle Class: Assessing the Estimates," explore the role of skill-biased technological change. They focus specifically on how advances in the technological capabilities of machinery, equipment, and software are contributing to job polarization, i.e. why employment growth has generally been weighted toward the lower-tail and especially upper-tail of the wage distribution. Siu and Nir Jaimovich discuss existing evidence on the combined contribution of automation, trade, and offshoring on these trends. Their comprehensive review of empirical work shows that job losses to this point have been concentrated in occupations that feature routine tasks, arguing that the occupations least at risk of displacement are those that require human interaction. The Figure 2 from their paper is presented below and highlights the stark differences in job growth across occupations of differing task content.
[Changes in employment by occupation group]
They also push for more quantitative, policy-oriented research on the consequences of automation and AI for the middle class. While the existing empirical literature quantifies the role of technological change on employment, there is too little information on the potential welfare impacts on workers and families affected by these structural changes in the labor market and potential policy options to mitigate potential harms. Quantitative, policy-oriented models would account for macroeconomic factors such as:
*
Changes in the occupational employment structure and types of tasks that workers perform
*
Differing elasticities of substitution among high-, middle-, and low-paying workers in response to automation
*
Underemployment and changes in labor force participation
*
Existing redistribution programs aimed at middle class workers
Future policy-oriented research should focus on the role of interpersonal skills, labor market and retraining programs, and the relative role of globalization and automation in driving these employment dynamics. (Read the full paper here<https://www.brookings.edu/research/how-automation-and-other-forms-of-it-affect-the-middle-class-assessing-the-estimates/>).
MEN NOT AT WORK? GENDER AND AUTOMATION
Patricia Cortes and Jessica Pan, in their paper "Gender, Occupational Segregation, and Automation," note that since tasks vary in terms of their susceptibility to automation, and men and women are typically do different jobs, even within similar occupations, men and women likely face different risks from automation. They study how automation, occupational segregation, and gender gaps in skill acquisition and job transitions interact. A deeper understanding of these trends should enable more directed policy responses aimed at alleviating the distinct challenges that male and female workers may face in a changing economy.
Cortes and Pan propose a new routine-task intensity (RTI) index that measures the susceptibility of an occupation. They use the RTI to investigate how occupational segregation contributed to gender differences in job automation risk between 1980 and 2017. Historically female-dominated occupations typically had a higher risk of automation, but recent changes in the labor market have led to a resorting of women away from those occupations. Figure 2B from their paper, reproduced below, illustrate these trends using their preferred measure. The graph shows that in 1980 occupations in which women were more heavily concentrated were also occupations that ranked high in routine task intensity and, consequently, were at high risk of automation. Since that time, female worker share has dropped substantially in those occupations with high routine task intensity as they have sorted more heavily to occupations in the middle to lower part of the routine task intensity distribution. No similar pattern is observed for men.
[RTI and changes in occupation shares by 1980 female share percentile]
Women were more likely than men to be in occupations with a high automation risk in 1980, but between 1980 and 2017, they disproportionately shifted into management-related and medical occupations, with lower risks.
Educational attainment played a key role here. Women were disproportionately more likely to shift into high-skilled occupations because of higher educational attainment; by contrast, men were disproportionately more likely to move into low-skilled occupations. Other factors such as changes in gender norms also contributed to observed changes in employment distribution.
The authors note that trends reported in this paper are not causal, but conclude that today, male-dominated occupations are more exposed to higher automation risk. This is particularly important given that women are increasingly outpacing men in educational attainment and acquisition of skills required to succeed in the labor market of the future—including interpersonal and social skills. These trends suggest that men may be at a distinct disadvantage to women for success in the technology-driven labor market of the future. (Read the full paper here<https://www.brookings.edu/research/gender-occupational-segregation-and-automation/>).
TRAINING FOR AN AUTOMATED WORLD
In "Employment and Training for Mature Adults: The Current System and Moving Forward," Paul Osterman argues that society should better prepare adults for labor market disruption caused by technological change. In particular, technological change contributes to increased risk of job displacement and occupational skill requirements. Citing the fact that 30 percent of adults now work in jobs that pay less than $15 per hour, he notes that technological change is exacerbating inequality. This problem is particularly acute for displaced older workers, as they tend to have lower educational attainment and face potential employers that are reluctant to invest in their training.
Importantly, Osterman argues that the US lacks a coherent training system for adults: that is, a well-articulated and easily accessible set of programs or opportunities that provide pathways for skill acquisition. Rather, it features a "cafeteria approach" that combines community colleges, work force development initiatives, employer training, on-line programs, and apprenticeship programs. Aside from their lack of coherence, uncertainty remains regarding the efficacy of many existing initiatives.
Retraining workers for the modern economy requires a combination of public and private actors to develop best practices, developing appropriate curricula, and expanding access to a broader population. Osterman proposes policies along the lines of:
*
Increased transparency in job skill requirements
*
Individual training or lifelong learning accounts
*
Increased federal funding for workforce development programs
As well as mitigating economic distress due to earnings losses and job displacements, Osterman argues that such policies will also limit spillover effects in communities facing these shocks. The danger, he warns, of ignoring these issues goes beyond individual households, as the prospective changes to work driven by automation and AI may potentially have implications for social stability. (Read the full paper here<https://www.brookings.edu/research/employment-and-training-for-mature-adults-the-current-system-and-moving-forward/>).
IMPLICATIONS AND FUTURE DIRECTIONS
The key takeaways from this work center on education and skills, and the need to confront changing norms in the labor market. In particular, evidence-based policies aimed at helping workers<https://www.pgpf.org/sites/default/files/Holzer-The-US-Labor-Market-in-2050-Supply-Demand-and-Public-Policy.pdf> train into and transition across jobs are key. In addition, this training should be focused on providing better matches between employers and employees<https://www.brookings.edu/blog/up-front/2019/08/05/policymakers-can-help-businesses-retrain-their-workers/>. However, these policies to help workers and investments in human capital must take occupational segregation into account since men and women will be impacted by automation differently.
Work disruptions from technological change may be inevitable, but we can prepare better. As these three papers show, more quantitative, policy-oriented research on the consequences of technology for middle-wage workers is needed. Watch this space! (And sign up<http://connect.brookings.edu/sign-up-for-class-notes> for our newsletter.)...(More<https://www.brookings.edu/blog/up-front/2019/11/07/automation-work-and-skills-what-do-we-know/>)"
What do we really know about how technology will impact employment? Which workers will be impacted most, both in terms of class and gender? What role can retraining play? These questions are addressed in a new series of three academic papers on automation published by the Future of the Middle Class Initiative (FMCi)<https://www.brookings.edu/project/future-of-the-middle-class-initiative/> here at Brookings.
Concerns about the impact of technological change on jobs, wages, and the economic security of workers are not new. Most major technological advances cause social disruptions that can often be painful for impacted workers and communities. While the long arc of history has shown that, by and large, past technological change has us wealthier and more productive, it is important to consider both the needs of people potentially harmed in the interim and potential policy choices that can mitigate the harm.
These issues are particularly salient today. Advanced robotics and other automating technologies in concert with the emergence of human-mimicking artificial intelligence (AI) protocols both have the potential to raise the productivity of workers whose skills complement them well, but also to displace workers for whom their skillset competes<https://blogs.wsj.com/experts/2019/05/22/the-demographic-most-vulnerable-to-automation-teens-and-young-adults/>. While almost all jobs are likely to change to at least some degree, with a reorganization of the tasks<https://mitibmwatsonailab.mit.edu/research/publications/paper/?id=The-Future-of-Work-How-New-Technologies-Are-Transforming-Tasks> contained within them, research suggests that concerns about widespread loss of jobs are overblown. But an important caveat is that many middle – skill jobs that pay decent wages and benefits are particularly at risk of being displaced.<https://www.brookings.edu/blog/up-front/2019/07/18/how-much-will-automation-impact-the-middle-class-we-dont-know-yet/>
The new papers, discussed at a recent Brookings private seminar, summarize recent developments in the academic literature, suggesting directions for future research and/or surveying options for policy reform or innovation. We summarize these papers below, focusing in particular on their implications for future work.
AUTOMATION AND THE MIDDLE CLASS
Henry Siu and Nir Jaimovich, in "How Automation and Other Forms of IT Affect the Middle Class: Assessing the Estimates," explore the role of skill-biased technological change. They focus specifically on how advances in the technological capabilities of machinery, equipment, and software are contributing to job polarization, i.e. why employment growth has generally been weighted toward the lower-tail and especially upper-tail of the wage distribution. Siu and Nir Jaimovich discuss existing evidence on the combined contribution of automation, trade, and offshoring on these trends. Their comprehensive review of empirical work shows that job losses to this point have been concentrated in occupations that feature routine tasks, arguing that the occupations least at risk of displacement are those that require human interaction. The Figure 2 from their paper is presented below and highlights the stark differences in job growth across occupations of differing task content.
[Changes in employment by occupation group]
They also push for more quantitative, policy-oriented research on the consequences of automation and AI for the middle class. While the existing empirical literature quantifies the role of technological change on employment, there is too little information on the potential welfare impacts on workers and families affected by these structural changes in the labor market and potential policy options to mitigate potential harms. Quantitative, policy-oriented models would account for macroeconomic factors such as:
*
Changes in the occupational employment structure and types of tasks that workers perform
*
Differing elasticities of substitution among high-, middle-, and low-paying workers in response to automation
*
Underemployment and changes in labor force participation
*
Existing redistribution programs aimed at middle class workers
Future policy-oriented research should focus on the role of interpersonal skills, labor market and retraining programs, and the relative role of globalization and automation in driving these employment dynamics. (Read the full paper here<https://www.brookings.edu/research/how-automation-and-other-forms-of-it-affect-the-middle-class-assessing-the-estimates/>).
MEN NOT AT WORK? GENDER AND AUTOMATION
Patricia Cortes and Jessica Pan, in their paper "Gender, Occupational Segregation, and Automation," note that since tasks vary in terms of their susceptibility to automation, and men and women are typically do different jobs, even within similar occupations, men and women likely face different risks from automation. They study how automation, occupational segregation, and gender gaps in skill acquisition and job transitions interact. A deeper understanding of these trends should enable more directed policy responses aimed at alleviating the distinct challenges that male and female workers may face in a changing economy.
Cortes and Pan propose a new routine-task intensity (RTI) index that measures the susceptibility of an occupation. They use the RTI to investigate how occupational segregation contributed to gender differences in job automation risk between 1980 and 2017. Historically female-dominated occupations typically had a higher risk of automation, but recent changes in the labor market have led to a resorting of women away from those occupations. Figure 2B from their paper, reproduced below, illustrate these trends using their preferred measure. The graph shows that in 1980 occupations in which women were more heavily concentrated were also occupations that ranked high in routine task intensity and, consequently, were at high risk of automation. Since that time, female worker share has dropped substantially in those occupations with high routine task intensity as they have sorted more heavily to occupations in the middle to lower part of the routine task intensity distribution. No similar pattern is observed for men.
[RTI and changes in occupation shares by 1980 female share percentile]
Women were more likely than men to be in occupations with a high automation risk in 1980, but between 1980 and 2017, they disproportionately shifted into management-related and medical occupations, with lower risks.
Educational attainment played a key role here. Women were disproportionately more likely to shift into high-skilled occupations because of higher educational attainment; by contrast, men were disproportionately more likely to move into low-skilled occupations. Other factors such as changes in gender norms also contributed to observed changes in employment distribution.
The authors note that trends reported in this paper are not causal, but conclude that today, male-dominated occupations are more exposed to higher automation risk. This is particularly important given that women are increasingly outpacing men in educational attainment and acquisition of skills required to succeed in the labor market of the future—including interpersonal and social skills. These trends suggest that men may be at a distinct disadvantage to women for success in the technology-driven labor market of the future. (Read the full paper here<https://www.brookings.edu/research/gender-occupational-segregation-and-automation/>).
TRAINING FOR AN AUTOMATED WORLD
In "Employment and Training for Mature Adults: The Current System and Moving Forward," Paul Osterman argues that society should better prepare adults for labor market disruption caused by technological change. In particular, technological change contributes to increased risk of job displacement and occupational skill requirements. Citing the fact that 30 percent of adults now work in jobs that pay less than $15 per hour, he notes that technological change is exacerbating inequality. This problem is particularly acute for displaced older workers, as they tend to have lower educational attainment and face potential employers that are reluctant to invest in their training.
Importantly, Osterman argues that the US lacks a coherent training system for adults: that is, a well-articulated and easily accessible set of programs or opportunities that provide pathways for skill acquisition. Rather, it features a "cafeteria approach" that combines community colleges, work force development initiatives, employer training, on-line programs, and apprenticeship programs. Aside from their lack of coherence, uncertainty remains regarding the efficacy of many existing initiatives.
Retraining workers for the modern economy requires a combination of public and private actors to develop best practices, developing appropriate curricula, and expanding access to a broader population. Osterman proposes policies along the lines of:
*
Increased transparency in job skill requirements
*
Individual training or lifelong learning accounts
*
Increased federal funding for workforce development programs
As well as mitigating economic distress due to earnings losses and job displacements, Osterman argues that such policies will also limit spillover effects in communities facing these shocks. The danger, he warns, of ignoring these issues goes beyond individual households, as the prospective changes to work driven by automation and AI may potentially have implications for social stability. (Read the full paper here<https://www.brookings.edu/research/employment-and-training-for-mature-adults-the-current-system-and-moving-forward/>).
IMPLICATIONS AND FUTURE DIRECTIONS
The key takeaways from this work center on education and skills, and the need to confront changing norms in the labor market. In particular, evidence-based policies aimed at helping workers<https://www.pgpf.org/sites/default/files/Holzer-The-US-Labor-Market-in-2050-Supply-Demand-and-Public-Policy.pdf> train into and transition across jobs are key. In addition, this training should be focused on providing better matches between employers and employees<https://www.brookings.edu/blog/up-front/2019/08/05/policymakers-can-help-businesses-retrain-their-workers/>. However, these policies to help workers and investments in human capital must take occupational segregation into account since men and women will be impacted by automation differently.
Work disruptions from technological change may be inevitable, but we can prepare better. As these three papers show, more quantitative, policy-oriented research on the consequences of technology for middle-wage workers is needed. Watch this space! (And sign up<http://connect.brookings.edu/sign-up-for-class-notes> for our newsletter.)...(More<https://www.brookings.edu/blog/up-front/2019/11/07/automation-work-and-skills-what-do-we-know/>)"
Sunday, November 10, 2019
A DATA-DRIVEN ROADMAP FOR CITY-LEVEL INDUSTRY AND WORKFORCE PLANNING
Marcela Escobari, Ian Seyal and Michael J. Meaney at Brookings<https://www.brookings.edu/research/realism-about-reskilling/>: "Every person deserves the opportunity for dignified employment that provides living wages and potential for advancement. But for many in America today caught in a cycle of low-wage work, this is far from reality.
Low-wage workers are struggling—and not for a lack of new jobs. The coming flood of innovation will create new tasks and occupations, and the labor market will demand new skills just as quickly as it will shirk others. Robots may be unlikely to wholly replace America's workers anytime soon, but new technologies will radically displace workers, eliminating jobs in some industries while expanding others.
Policy and company responses have failed to keep pace with this transformation. As the labor market splits into low-wage and high-wage work, lower tier jobs are precarious, marked by unpredictable schedules, reduced benefits, and stagnant wages. While reskilling alone will not be enough to lessen inequality or provide equal opportunity in the face of these trends, it must be an integral part of the solution to support workers without leaving anyone behind.
This new research leverages data to highlight the realities of low-wage work in America and maps realistic pathways to mobility. We examine how labor market dynamics shape the way low-wage workers move within and between occupations and develop a near-term mobility index that estimates whether workers will earn more money by transitioning out of certain occupations. We pair this index with projections of local occupational growth to show how city planners can invest in key industries and design programs that provide workers with realistic opportunities for upward transitions.
WHO ARE AMERICA'S MOST VULNERABLE WORKERS, AND WHAT ARE THEIR PROSPECTS?
An estimated 53 million people—44 percent of all U.S. workers ages 18–64—are low-wage workers. That's more than twice the number of people in the 10 most populous U.S. cities combined. Their median hourly wage is $10.22, and their median annual earnings are $17,950.
Low-wage work spans gender, race, and geography, but, women and members of racial and ethnic minority groups are disproportionately likely to be low-wage workers. A Black worker is 32 percent more likely to earn low wages than a white counterpart—that number jumps to 41 percent for Hispanic workers. Altogether, women are 19 percent more likely than men to be low-wage workers.
Low-wage workers switch jobs most frequently but are more likely to churn within a set of low-wage occupations. Workers in the lowest wage quintile have the highest likelihood to switch into another low paying job. Workers in the second lowest quintile have a 55% chance to remain or move downward, and even those in the middle quintile are more likely to move to a lower wage group than a higher one.
Certain occupations are likelier to lead to higher wages. Our near-term mobility index estimates whether workers departing an occupation are likely to see higher wages. For instance, telemarketers tend to transition to a much higher paid job, compared to cooks or housekeepers. Cleaners, cooks and hairdressers, are the most vulnerable; their occupations pay low wages and offer little opportunity for advancement.
WHERE ARE THE LOCAL OPPORTUNITIES FOR MOBILITY, AND HOW CAN POLICYMAKERS HELP LOW-WAGE WORKERS TRANSITION?
Concentrations of low-wage workers varies from 30-62 percent in cities throughout the nation
Because complex industries can potentially drive growth and bring the unemployed and underemployed into the workforce, city leaders should deliberately cultivate the capabilities needed to host (and attract) strategic industries. Our previous report, Growing cities that work for all<https://www.brookings.edu/research/growing-cities-that-work-for-all-a-capability-based-approach-to-regional-economic-competitiveness/>, describes how cities might identify those strategic industries. But urban policymakers must balance the need to host high-wage industries with efforts to support low-wage workers by increasing living wages, improving job quality, and expanding access to housing, transportation, and upskilling. The two sets of policies—to promote both growth and inclusion—are complementary, but they require distinct efforts.
Comprehensive local strategies must link industrial and workforce development. Workforce development is most promising when tied to specific economic development strategies. Cities facing job losses might combat the path-dependence of industrial trends by making strategic investments in industries that build on regional capabilities and also bring good jobs. Place-specific insights on the growth and decline of occupations, and the opportunities it create for low wage workers, can help policymakers and firms build a reskilling infrastructure that brings opportunity to those who need it the most.
Download the overview of the findings »<https://www.brookings.edu/wp-content/uploads/2019/11/Realism-about-Reskilling_Brookings_Overview-FOR-WEB.pdf>
A DATA-DRIVEN ROADMAP FOR CITY-LEVEL INDUSTRY AND WORKFORCE PLANNING
1. Do local industries have the potential to grow?
Regions can think strategically about the industries they foster to promote growth and inclusion. Cities can build capabilities to host industries that not only drive growth, but also offer good jobs for their workers.
[Boise's strategic and feasible industries, 2017]
2. What are cities' workforce needs?
Global trends drive local workforce needs. But local industry structure also determines the regional demand for talent. Low- and high-wage jobs will be created and lost, and these will vary by city. Policymakers can use place specific occupational projections to help build the human capital that advanced industries seek.
[Job growth bar chart]
3. Which workers are best able to transition and what avenues exist for low-wage workers?
*
[Sankey chart: network and computer adminstrators]
*
[Sankey chart: retail sales people]
*
[Sankey chart: janitor]
*
[Sankey chart: food prep]
*
[Sankey chart: Administratve Assistant]
1 of 5
Using data on actual job to job transitions, firms and reskilling organizations can help low-wage workers move into in-demand jobs, such as Network and computer administrators.
Expand<https://www.brookings.edu/research/realism-about-reskilling/#>
4. How can we help workers reskill?
Skilling, if connected to local opportunity, can be an engine for mobility. To promote mobility, reskilling infrastructure can target destination occupations that are expected to grow, likely to offer higher wages, and are realistic, given a worker's starting occupation or employment history. To be inclusive, skilling programs need to meet workers where they are.
[How can we help workers reskill]
5. How can public-private collaboration support workers?
[How can public-private collaboration support reporters?]
Armed with this information, policymakers can design reskilling programs that tap into local talent pools and facilitate workers' realistic upward transitions into growing occupations.
Findings from this report can be applied to:
• Provide policymakers with a window into the forces driving the local and national proliferation of low-wage work.
• Illuminate upward transitions available to workers and facilitate strategies that link industrial policy and reskilling efforts to drive inclusive growth.
• Assist workforce development practitioners as they design programs to meet workers where they are.
Read the companion report titled "Meet the low wage workforce<https://www.brookings.edu/research/meet-the-low-wage-workforce/>" by Martha Ross and Nicole Bateman.
Low-wage workers are struggling—and not for a lack of new jobs. The coming flood of innovation will create new tasks and occupations, and the labor market will demand new skills just as quickly as it will shirk others. Robots may be unlikely to wholly replace America's workers anytime soon, but new technologies will radically displace workers, eliminating jobs in some industries while expanding others.
Policy and company responses have failed to keep pace with this transformation. As the labor market splits into low-wage and high-wage work, lower tier jobs are precarious, marked by unpredictable schedules, reduced benefits, and stagnant wages. While reskilling alone will not be enough to lessen inequality or provide equal opportunity in the face of these trends, it must be an integral part of the solution to support workers without leaving anyone behind.
This new research leverages data to highlight the realities of low-wage work in America and maps realistic pathways to mobility. We examine how labor market dynamics shape the way low-wage workers move within and between occupations and develop a near-term mobility index that estimates whether workers will earn more money by transitioning out of certain occupations. We pair this index with projections of local occupational growth to show how city planners can invest in key industries and design programs that provide workers with realistic opportunities for upward transitions.
WHO ARE AMERICA'S MOST VULNERABLE WORKERS, AND WHAT ARE THEIR PROSPECTS?
An estimated 53 million people—44 percent of all U.S. workers ages 18–64—are low-wage workers. That's more than twice the number of people in the 10 most populous U.S. cities combined. Their median hourly wage is $10.22, and their median annual earnings are $17,950.
Low-wage work spans gender, race, and geography, but, women and members of racial and ethnic minority groups are disproportionately likely to be low-wage workers. A Black worker is 32 percent more likely to earn low wages than a white counterpart—that number jumps to 41 percent for Hispanic workers. Altogether, women are 19 percent more likely than men to be low-wage workers.
Low-wage workers switch jobs most frequently but are more likely to churn within a set of low-wage occupations. Workers in the lowest wage quintile have the highest likelihood to switch into another low paying job. Workers in the second lowest quintile have a 55% chance to remain or move downward, and even those in the middle quintile are more likely to move to a lower wage group than a higher one.
Certain occupations are likelier to lead to higher wages. Our near-term mobility index estimates whether workers departing an occupation are likely to see higher wages. For instance, telemarketers tend to transition to a much higher paid job, compared to cooks or housekeepers. Cleaners, cooks and hairdressers, are the most vulnerable; their occupations pay low wages and offer little opportunity for advancement.
WHERE ARE THE LOCAL OPPORTUNITIES FOR MOBILITY, AND HOW CAN POLICYMAKERS HELP LOW-WAGE WORKERS TRANSITION?
Concentrations of low-wage workers varies from 30-62 percent in cities throughout the nation
Because complex industries can potentially drive growth and bring the unemployed and underemployed into the workforce, city leaders should deliberately cultivate the capabilities needed to host (and attract) strategic industries. Our previous report, Growing cities that work for all<https://www.brookings.edu/research/growing-cities-that-work-for-all-a-capability-based-approach-to-regional-economic-competitiveness/>, describes how cities might identify those strategic industries. But urban policymakers must balance the need to host high-wage industries with efforts to support low-wage workers by increasing living wages, improving job quality, and expanding access to housing, transportation, and upskilling. The two sets of policies—to promote both growth and inclusion—are complementary, but they require distinct efforts.
Comprehensive local strategies must link industrial and workforce development. Workforce development is most promising when tied to specific economic development strategies. Cities facing job losses might combat the path-dependence of industrial trends by making strategic investments in industries that build on regional capabilities and also bring good jobs. Place-specific insights on the growth and decline of occupations, and the opportunities it create for low wage workers, can help policymakers and firms build a reskilling infrastructure that brings opportunity to those who need it the most.
Download the overview of the findings »<https://www.brookings.edu/wp-content/uploads/2019/11/Realism-about-Reskilling_Brookings_Overview-FOR-WEB.pdf>
A DATA-DRIVEN ROADMAP FOR CITY-LEVEL INDUSTRY AND WORKFORCE PLANNING
1. Do local industries have the potential to grow?
Regions can think strategically about the industries they foster to promote growth and inclusion. Cities can build capabilities to host industries that not only drive growth, but also offer good jobs for their workers.
[Boise's strategic and feasible industries, 2017]
2. What are cities' workforce needs?
Global trends drive local workforce needs. But local industry structure also determines the regional demand for talent. Low- and high-wage jobs will be created and lost, and these will vary by city. Policymakers can use place specific occupational projections to help build the human capital that advanced industries seek.
[Job growth bar chart]
3. Which workers are best able to transition and what avenues exist for low-wage workers?
*
[Sankey chart: network and computer adminstrators]
*
[Sankey chart: retail sales people]
*
[Sankey chart: janitor]
*
[Sankey chart: food prep]
*
[Sankey chart: Administratve Assistant]
1 of 5
Using data on actual job to job transitions, firms and reskilling organizations can help low-wage workers move into in-demand jobs, such as Network and computer administrators.
Expand<https://www.brookings.edu/research/realism-about-reskilling/#>
4. How can we help workers reskill?
Skilling, if connected to local opportunity, can be an engine for mobility. To promote mobility, reskilling infrastructure can target destination occupations that are expected to grow, likely to offer higher wages, and are realistic, given a worker's starting occupation or employment history. To be inclusive, skilling programs need to meet workers where they are.
[How can we help workers reskill]
5. How can public-private collaboration support workers?
[How can public-private collaboration support reporters?]
Armed with this information, policymakers can design reskilling programs that tap into local talent pools and facilitate workers' realistic upward transitions into growing occupations.
Findings from this report can be applied to:
• Provide policymakers with a window into the forces driving the local and national proliferation of low-wage work.
• Illuminate upward transitions available to workers and facilitate strategies that link industrial policy and reskilling efforts to drive inclusive growth.
• Assist workforce development practitioners as they design programs to meet workers where they are.
Read the companion report titled "Meet the low wage workforce<https://www.brookings.edu/research/meet-the-low-wage-workforce/>" by Martha Ross and Nicole Bateman.
Wednesday, November 6, 2019
A Credential Project Gets a Big Shot in the Arm. Here’s Why Some People Are Still Skeptical.
By Goldie Blumenstyk NOVEMBER 06, 2019
https://www.chronicle.com/article/A-Credential-Project-Gets-a/247490
You're reading the latest issue of The Edge, a weekly newsletter by Goldie Blumenstyk. Sign up here<https://www.chronicle.com/page/get-the-edge/712> to get her insights on the people, trends, and ideas that are reshaping higher education.
________________________________
I'm Goldie Blumenstyk, a senior writer at The Chronicle of Higher Education, covering innovation in and around academe. Here's what I'm thinking about this week.
Making sense of credentials, or trying to "boil the ocean?"
By a recent count, colleges and other organizations award more than 738,000 different kinds of degrees, certificates, licenses, and other credentials.<https://credentialengine.org/wp-content/uploads/2019/09/Press-Release_Analysis-of-U.S.-Education-and-Training-Landscape-Identifies-Over-738000-Unique-Credentials_190925.pdf> Imagine a tool that could put detailed data about each one of those into a single repository — and then connect with other tools and apps that could help students and employers make sense out of this dizzying maze of information.
Actually, no need to imagine it. The idea is already in the works. It's a project called Credential Registry, run by a new-ish nonprofit organization called Credential Engine.
In theory, the Credential Registry project is one of the most ambitious and perhaps even visionary efforts ever undertaken in the postsecondary sector — that is, if you believe it's valuable and possible to delineate and describe all the "competencies" taught in degree programs and other educational offerings.
In practice, however, Credential Registry seems to generate a lot of skepticism, and not just from those who question whether its aspirations are just too pie in the sky. (If I've ever heard the "trying to boil the ocean" metaphor more in talking to people about a higher-ed topic, I can't recall it.)
I and my Chronicle colleagues have covered the Credential Registry project efforts now and again (see here<https://www.chronicle.com/blogs/ticker/researchers-plan-credential-registry-to-compare-educational-qualifications/101781> and here<https://www.chronicle.com/article/Credentialing-Summit-/233623> for examples) from some of its earliest incarnations as an academic project.<https://www.ansi.org/news_publications/news_story?menuid=7&articleid=de4e4462-95f0-4bf2-ab7a-a545f8a8270d> Along the way, I've noted the skepticism surrounding this effort<https://www.chronicle.com/article/2-Projects-That-Promote/237823>, mostly from people who questioned the organization's decision to leave out any measures of program quality as part of the registry.
Bigger picture, I'm sure there are also plenty of academics with doubts about the feasibility and utility of describing programs by the competencies taught. I share some of those doubts, too — even though I understand why employers and policymakers would want to know what a degree means in practical terms. I honestly can't imagine that if my college reduced my history degree into a listing of competencies it would adequately reflect all that I learned, and such a list would in no way reflect the rest of my education. But maybe that's a discussion for another day.
This week the American Council on Education, along with a dozen-plus higher-ed groups, came out with a statement endorsing the sort of "credential transparency"<https://credentialengine.org/2019/11/04/major-postsecondary-education-organizations-commit-to-credential-transparency/> that the registry could provide. So it seemed a good moment to check back to see if the skepticism about the project, which has garnered more than $12 million in philanthropic support, still prevails.
Yup, I found that it's still out there. But I also found that when a project has prominent backers, like the Lumina Foundation and JPMorgan Chase & Company, some people get a little hinkey about going public with their criticisms for fear of perhaps alienating potential funders of their own projects.
So a lot of what I'm sharing today comes from people who didn't want to be identified. If that makes you question the substance of their critique, I get it. Still, I believe it's useful to get this information out there because it comes from people who are familiar enough with Credential Registry to be credible. Several of them are really frustrated by what one described as "big promises, but not promises kept." They also want to see this big idea succeed.
The gist of the concern centers on two things: The paucity of information colleges and others are submitting to the registry in their listings and the accuracy of that information. That's on top of the lingering concerns some still have about the organization's decision to be "agnostic" about programs' quality.
While the registry was designed to include the "competencies" of a degree or other program, colleges and other entities aren't required to include them in the listings. Nor are they required to include other information from external bodies that could indicate the quality of a program. They can enter a degree with only the minimum required information, such as name, general description, and what occupational sector it's most closely tied to.
Yet it's exactly the non-required information that will ultimately make the Credential Registry useful, particularly to employers. Once employers visit the site and don't see that information, one person told me, "you're going to lose them." Without the buy-in from educators and employers, the registry isn't all that valuable.
The registry itself<https://credentialfinder.org/> isn't that user friendly. Credential Engine officials are hoping app developers and others will use it as the basis for new search tools designed with the needs of employers and prospective students in mind. Chauncy Lennon, the Lumina Foundation's vice president for the future of learning and work, compares the registry's approach to data to the airline-flight information that feeds sites like Kayak and Expedia. But as I was reminded by several skeptics, you can't create apps if the data's not there.
Most of the skeptics I spoke to told me that Credential Engine's leaders decided to let colleges and others list programs with the minimal amount of information because they decided to prize numbers over quality. Scott Cheney, the executive director, says to a degree, that was the case. But that doesn't mean even the basic data isn't valuable. State policymakers, he said, are already making use of the information. And besides, he added, "We have to start someplace."
Truth be told, even on the "quantity" front, Credential Engine is behind Cheney's own projections. As of Tuesday evening, it had 9,248 credentials listed. In mid-2018, Cheney predicted<https://www.insidehighered.com/news/2018/07/31/credential-engine-seeks-map-credential-landscape> it would have 50,000 by the end of that year and as many as 150,000 by the end of 2019.
Cheney told me that he expects that those credentials now in the registry will be enriched over time and that a "few dozen" app developers have already begun experimenting with the registry data. He also estimated that about 75 percent of the credentials listed have more than the minimum of information.
As for the questions about accuracy, he said organizations that submit credential data stand behind their word, and since the information is all public, that creates a way to check the data once it's published. That's all true. And certainly, it would be all but impossible for Credential Engine to fact-check everything that gets submitted. But clearly the current system leaves plenty of room for shading the truth as well as honest errors. On Tuesday Cheney told me that the organization is now in the process of building the policy and mechanisms to do random, selective audits of entries.
Two more thoughts.
I'm fascinated by the implications of Lumina's big bet on this project. The $5 million it's put toward the project over the years makes this one of the foundation's biggest grant-supported programs.
The foundation is now sponsoring an independent evaluation of Credential Engine, which is being conducted by the Urban Institute. Lennon said such a review was routine for investments of this size and is designed to identify approaches to help the project advance and to remove barriers, not recriminations. I hope it's made public once it's done.
Lennon, who previously oversaw philanthropy projects for JPMorgan, said he still has full faith and big hopes for Credential Registry, even if it takes time to develop. Eventually, he predicted, it will become a "collective resource that everyone is glad to have."
It's also hard to know what to make of the endorsement this week by the higher-ed groups. If signing the letter means that they'll follow up with efforts to encourage their members to participate in the registry, that's probably a measure of progress.
The organizers behind this latest push may have had good motives for their endorsement — one person involved said they saw support for credential transparency as one way to signal that they recognize the public and political concerns higher ed is facing.
Yet, given that the registry still only requires minimal information from participants, enhanced participation by colleges may not mean very much. As one person said to me: "It's a win for the colleges not to have a quality metric in there," but it doesn't help the prospective students or employers. Colleges "dodged what could have been a political nightmare and a market nightmare."
That's a cynical take, to be sure. But it's made possible by the direction Credential Engine has taken.
https://www.chronicle.com/article/A-Credential-Project-Gets-a/247490
You're reading the latest issue of The Edge, a weekly newsletter by Goldie Blumenstyk. Sign up here<https://www.chronicle.com/page/get-the-edge/712> to get her insights on the people, trends, and ideas that are reshaping higher education.
________________________________
I'm Goldie Blumenstyk, a senior writer at The Chronicle of Higher Education, covering innovation in and around academe. Here's what I'm thinking about this week.
Making sense of credentials, or trying to "boil the ocean?"
By a recent count, colleges and other organizations award more than 738,000 different kinds of degrees, certificates, licenses, and other credentials.<https://credentialengine.org/wp-content/uploads/2019/09/Press-Release_Analysis-of-U.S.-Education-and-Training-Landscape-Identifies-Over-738000-Unique-Credentials_190925.pdf> Imagine a tool that could put detailed data about each one of those into a single repository — and then connect with other tools and apps that could help students and employers make sense out of this dizzying maze of information.
Actually, no need to imagine it. The idea is already in the works. It's a project called Credential Registry, run by a new-ish nonprofit organization called Credential Engine.
In theory, the Credential Registry project is one of the most ambitious and perhaps even visionary efforts ever undertaken in the postsecondary sector — that is, if you believe it's valuable and possible to delineate and describe all the "competencies" taught in degree programs and other educational offerings.
In practice, however, Credential Registry seems to generate a lot of skepticism, and not just from those who question whether its aspirations are just too pie in the sky. (If I've ever heard the "trying to boil the ocean" metaphor more in talking to people about a higher-ed topic, I can't recall it.)
I and my Chronicle colleagues have covered the Credential Registry project efforts now and again (see here<https://www.chronicle.com/blogs/ticker/researchers-plan-credential-registry-to-compare-educational-qualifications/101781> and here<https://www.chronicle.com/article/Credentialing-Summit-/233623> for examples) from some of its earliest incarnations as an academic project.<https://www.ansi.org/news_publications/news_story?menuid=7&articleid=de4e4462-95f0-4bf2-ab7a-a545f8a8270d> Along the way, I've noted the skepticism surrounding this effort<https://www.chronicle.com/article/2-Projects-That-Promote/237823>, mostly from people who questioned the organization's decision to leave out any measures of program quality as part of the registry.
Bigger picture, I'm sure there are also plenty of academics with doubts about the feasibility and utility of describing programs by the competencies taught. I share some of those doubts, too — even though I understand why employers and policymakers would want to know what a degree means in practical terms. I honestly can't imagine that if my college reduced my history degree into a listing of competencies it would adequately reflect all that I learned, and such a list would in no way reflect the rest of my education. But maybe that's a discussion for another day.
This week the American Council on Education, along with a dozen-plus higher-ed groups, came out with a statement endorsing the sort of "credential transparency"<https://credentialengine.org/2019/11/04/major-postsecondary-education-organizations-commit-to-credential-transparency/> that the registry could provide. So it seemed a good moment to check back to see if the skepticism about the project, which has garnered more than $12 million in philanthropic support, still prevails.
Yup, I found that it's still out there. But I also found that when a project has prominent backers, like the Lumina Foundation and JPMorgan Chase & Company, some people get a little hinkey about going public with their criticisms for fear of perhaps alienating potential funders of their own projects.
So a lot of what I'm sharing today comes from people who didn't want to be identified. If that makes you question the substance of their critique, I get it. Still, I believe it's useful to get this information out there because it comes from people who are familiar enough with Credential Registry to be credible. Several of them are really frustrated by what one described as "big promises, but not promises kept." They also want to see this big idea succeed.
The gist of the concern centers on two things: The paucity of information colleges and others are submitting to the registry in their listings and the accuracy of that information. That's on top of the lingering concerns some still have about the organization's decision to be "agnostic" about programs' quality.
While the registry was designed to include the "competencies" of a degree or other program, colleges and other entities aren't required to include them in the listings. Nor are they required to include other information from external bodies that could indicate the quality of a program. They can enter a degree with only the minimum required information, such as name, general description, and what occupational sector it's most closely tied to.
Yet it's exactly the non-required information that will ultimately make the Credential Registry useful, particularly to employers. Once employers visit the site and don't see that information, one person told me, "you're going to lose them." Without the buy-in from educators and employers, the registry isn't all that valuable.
The registry itself<https://credentialfinder.org/> isn't that user friendly. Credential Engine officials are hoping app developers and others will use it as the basis for new search tools designed with the needs of employers and prospective students in mind. Chauncy Lennon, the Lumina Foundation's vice president for the future of learning and work, compares the registry's approach to data to the airline-flight information that feeds sites like Kayak and Expedia. But as I was reminded by several skeptics, you can't create apps if the data's not there.
Most of the skeptics I spoke to told me that Credential Engine's leaders decided to let colleges and others list programs with the minimal amount of information because they decided to prize numbers over quality. Scott Cheney, the executive director, says to a degree, that was the case. But that doesn't mean even the basic data isn't valuable. State policymakers, he said, are already making use of the information. And besides, he added, "We have to start someplace."
Truth be told, even on the "quantity" front, Credential Engine is behind Cheney's own projections. As of Tuesday evening, it had 9,248 credentials listed. In mid-2018, Cheney predicted<https://www.insidehighered.com/news/2018/07/31/credential-engine-seeks-map-credential-landscape> it would have 50,000 by the end of that year and as many as 150,000 by the end of 2019.
Cheney told me that he expects that those credentials now in the registry will be enriched over time and that a "few dozen" app developers have already begun experimenting with the registry data. He also estimated that about 75 percent of the credentials listed have more than the minimum of information.
As for the questions about accuracy, he said organizations that submit credential data stand behind their word, and since the information is all public, that creates a way to check the data once it's published. That's all true. And certainly, it would be all but impossible for Credential Engine to fact-check everything that gets submitted. But clearly the current system leaves plenty of room for shading the truth as well as honest errors. On Tuesday Cheney told me that the organization is now in the process of building the policy and mechanisms to do random, selective audits of entries.
Two more thoughts.
I'm fascinated by the implications of Lumina's big bet on this project. The $5 million it's put toward the project over the years makes this one of the foundation's biggest grant-supported programs.
The foundation is now sponsoring an independent evaluation of Credential Engine, which is being conducted by the Urban Institute. Lennon said such a review was routine for investments of this size and is designed to identify approaches to help the project advance and to remove barriers, not recriminations. I hope it's made public once it's done.
Lennon, who previously oversaw philanthropy projects for JPMorgan, said he still has full faith and big hopes for Credential Registry, even if it takes time to develop. Eventually, he predicted, it will become a "collective resource that everyone is glad to have."
It's also hard to know what to make of the endorsement this week by the higher-ed groups. If signing the letter means that they'll follow up with efforts to encourage their members to participate in the registry, that's probably a measure of progress.
The organizers behind this latest push may have had good motives for their endorsement — one person involved said they saw support for credential transparency as one way to signal that they recognize the public and political concerns higher ed is facing.
Yet, given that the registry still only requires minimal information from participants, enhanced participation by colleges may not mean very much. As one person said to me: "It's a win for the colleges not to have a quality metric in there," but it doesn't help the prospective students or employers. Colleges "dodged what could have been a political nightmare and a market nightmare."
That's a cynical take, to be sure. But it's made possible by the direction Credential Engine has taken.
Sunday, October 6, 2019
Emsi Skills
https://skills.emsidata.com/
Mission
Our mission is to use data to drive economic prosperity. To do this, we inform and connect three critical audiences: people (who are looking for good work), employers (who are looking for good people), and educators (who are looking to build good programs and engage students).
Emsi Skills
Language barrier. Communication failure. Information gap. Call it what you will, but when it comes to skills, we speak different languages. Educators struggle to know which skills to include in their programs. Students don't know how to describe their skills and competencies to employers. Employers aren't sure which skills to request on job postings.
This is because the world of employment is constantly evolving, and our traditional data-tracking methods can't keep up. So Emsi is tackling this problem head-on. Welcome to Emsi Skills: a skills language that reflects the real world. A skills language that educators, students, and employers can use to communicate with each other.
How it works
We gather data from hundreds of millions of online job postings, resumes, and profiles in order to find the real-world skills and competencies that people actually have. This information allows us to define jobs by specific skills instead of by a generic title. We also update our skills every two weeks so you can trust they reflect the latest changes in the labor market.
Mission
Our mission is to use data to drive economic prosperity. To do this, we inform and connect three critical audiences: people (who are looking for good work), employers (who are looking for good people), and educators (who are looking to build good programs and engage students).
Emsi Skills
Language barrier. Communication failure. Information gap. Call it what you will, but when it comes to skills, we speak different languages. Educators struggle to know which skills to include in their programs. Students don't know how to describe their skills and competencies to employers. Employers aren't sure which skills to request on job postings.
This is because the world of employment is constantly evolving, and our traditional data-tracking methods can't keep up. So Emsi is tackling this problem head-on. Welcome to Emsi Skills: a skills language that reflects the real world. A skills language that educators, students, and employers can use to communicate with each other.
How it works
We gather data from hundreds of millions of online job postings, resumes, and profiles in order to find the real-world skills and competencies that people actually have. This information allows us to define jobs by specific skills instead of by a generic title. We also update our skills every two weeks so you can trust they reflect the latest changes in the labor market.
Wednesday, September 18, 2019
LinkedIn: Announcing Skill Assessments to Help You Showcase Your Skills
https://blog.linkedin.com/2019/september/17/announcing-skill-assessments-to-help-you-showcase-your-skills
We know it's important to have a way to effectively show the skills you spend time cultivating. In fact, according to new LinkedIn research<https://news.linkedin.com/en-us/2019/January/new-linkedin-research-xxplores-the-shift-toward-skills-based-hir>, 69% of professionals think their skills are more important than college education when job-seeking, and more than 76% wish there was a way for hiring managers to verify their skills so they could stand out amongst other candidates.
* [76 percent of workers wish they could validate their skills]
That's why we're we're excited to roll out LinkedIn Skill Assessments, a new way for you to validate the skills you have. When skills are validated you can showcase your proficiency and become more discoverable to opportunities -- early results show candidates who complete LinkedIn Skill Assessments are significantly more likely (~30%) to get hired.<https://business.linkedin.com/talent-solutions/blog/product-updates/2019/linkedin-skill-assessments-help-streamline-candidate-search>
Each Skill Assessment, whether its Adobe Photoshop to showcase your design skills or Java to land a developer role, is constructed through a rigorous content creation and review process in partnership with LinkedIn Learning industry and subject matter experts. Once candidates have completed an assessment, a badge will be displayed on their profile in LinkedIn Recruiter and LinkedIn Jobs so hirers are able to quickly identify and verify skill proficiency.<https://business.linkedin.com/talent-solutions/blog/product-updates/2019/linkedin-skill-assessments-help-streamline-candidate-search>
Tools that allow me to authentically show people what I can do are valuable. As recruiters sift through applications, I feel my proven skills serve as a point of differentiation and increase my odds of hearing back." LinkedIn member Carolyn Ye<https://www.linkedin.com/in/carolyn-ye-133b1761/>
Show off your skills
Simply scroll to the skill section of your profile and select one of the available Skill Assessments you'd like to take. Any results are kept private to you, and if you pass (in the 70th percentile or above), you will have the option to add a "verified skill" badge to your profile. If you don't pass, you have complete control over the visibility of their results, and can brush up on your skills so you can pass next time.
We know it's important to have a way to effectively show the skills you spend time cultivating. In fact, according to new LinkedIn research<https://news.linkedin.com/en-us/2019/January/new-linkedin-research-xxplores-the-shift-toward-skills-based-hir>, 69% of professionals think their skills are more important than college education when job-seeking, and more than 76% wish there was a way for hiring managers to verify their skills so they could stand out amongst other candidates.
* [76 percent of workers wish they could validate their skills]
That's why we're we're excited to roll out LinkedIn Skill Assessments, a new way for you to validate the skills you have. When skills are validated you can showcase your proficiency and become more discoverable to opportunities -- early results show candidates who complete LinkedIn Skill Assessments are significantly more likely (~30%) to get hired.<https://business.linkedin.com/talent-solutions/blog/product-updates/2019/linkedin-skill-assessments-help-streamline-candidate-search>
Each Skill Assessment, whether its Adobe Photoshop to showcase your design skills or Java to land a developer role, is constructed through a rigorous content creation and review process in partnership with LinkedIn Learning industry and subject matter experts. Once candidates have completed an assessment, a badge will be displayed on their profile in LinkedIn Recruiter and LinkedIn Jobs so hirers are able to quickly identify and verify skill proficiency.<https://business.linkedin.com/talent-solutions/blog/product-updates/2019/linkedin-skill-assessments-help-streamline-candidate-search>
Tools that allow me to authentically show people what I can do are valuable. As recruiters sift through applications, I feel my proven skills serve as a point of differentiation and increase my odds of hearing back." LinkedIn member Carolyn Ye<https://www.linkedin.com/in/carolyn-ye-133b1761/>
Show off your skills
Simply scroll to the skill section of your profile and select one of the available Skill Assessments you'd like to take. Any results are kept private to you, and if you pass (in the 70th percentile or above), you will have the option to add a "verified skill" badge to your profile. If you don't pass, you have complete control over the visibility of their results, and can brush up on your skills so you can pass next time.
Wednesday, July 24, 2019
Governor Cuomo Signs Legislation Creating New State Commission to Study Artificial Intelligence and Robotics
https://www.governor.ny.gov/news/governor-cuomo-signs-legislation-creating-new-state-commission-study-artificial-intelligence
Commission Will Determine How These Technologies Can Be Used to Enhance Public Sector Services and How the State Should Regulate Them in the Best Interest of New Yorkers
Cuomo: "Artificial intelligence and automation are already having a profound impact across many industries and their influence keeps growing, so it's critical that we do everything in our power to understand their capabilities and potential pitfalls. This new commission will look closely at how these rapidly evolving technologies are functioning and report back on how we can optimize use to benefit New Yorkers and our economy."
Governor Andrew M. Cuomo today signed legislation (S.3971B/A.1746C) creating a temporary state commission to study and investigate how to regulate artificial intelligence, robotics and automation. The New York State Artificial Intelligence, Robotics and Automation Commission will look at the latest uses and impacts of these technologies to determine how the State can best utilize and regulate them as necessary.
"Artificial intelligence and automation are already having a profound impact across many industries and their influence keeps growing, so it's critical that we do everything in our power to understand their capabilities and potential pitfalls," Governor Cuomo said. "This new commission will look closely at how these rapidly evolving technologies are functioning and report back on how we can optimize use to benefit New Yorkers and our economy."
The commission will examine how artificial intelligence, robotics and automation:
* affect employment in New York State
* acquire and disclose people's personal information
* affect technology industries
* can be used by the public sector to enhance performance and services
* may be used in unlawful or unsafe ways
The commission will also examine how these innovative technologies have been used and regulated by other states, determine whether current New York laws are effective in regulating them, and make recommendations for how the State can leverage existing uses and, if necessary, update laws to protect industries and residents. This review will include meetings with key stakeholder groups to understand the full landscape of AI, robotics and automation as emerging technologies within the business, nonprofit, academic and governmental sectors.
The commission will consist of 13 members: five appointed by the Governor; two by the temporary president of the Senate; one by the minority leader of the Senate; two by the speaker of the Assembly; one by the minority leader of the Assembly; one by the SUNY chancellor; and one by the CUNY chancellor.
Upon completion of its study, the commission will issue a final report with its findings and recommendations to the Governor and legislative leaders.
Senator Diane J. Savino, Chair of the Senate Committee on Internet and Technology, said, "Artificial intelligence is an essential part of the workforce now. We cannot fear a future in which machines evolve beyond humans, so let's get ahead of the curve and study the issue. We're going to put experts in the field in a situation to help the State and workforce excel. Thank you Governor Cuomo for sharing your concern on this issue and signing this into law."
Assembly Member Clyde Vanel, Chair of the Assembly Subcommittee on Internet and New Technology, said, "It is very important for our state and country to lead in the newest technologies and innovation. We have to make sure that New Yorkers are properly positioned for the jobs and opportunities of tomorrow. While at the same time, ethical, moral and privacy issues must be at the forefront of our efforts."
Contact the Governor's Press Office
Contact us by phone:
Albany: (518) 474 - 8418 <tel:5184748418> New York City: (212) 681 - 4640<tel:2126814640>
Contact us by email:
Commission Will Determine How These Technologies Can Be Used to Enhance Public Sector Services and How the State Should Regulate Them in the Best Interest of New Yorkers
Cuomo: "Artificial intelligence and automation are already having a profound impact across many industries and their influence keeps growing, so it's critical that we do everything in our power to understand their capabilities and potential pitfalls. This new commission will look closely at how these rapidly evolving technologies are functioning and report back on how we can optimize use to benefit New Yorkers and our economy."
Governor Andrew M. Cuomo today signed legislation (S.3971B/A.1746C) creating a temporary state commission to study and investigate how to regulate artificial intelligence, robotics and automation. The New York State Artificial Intelligence, Robotics and Automation Commission will look at the latest uses and impacts of these technologies to determine how the State can best utilize and regulate them as necessary.
"Artificial intelligence and automation are already having a profound impact across many industries and their influence keeps growing, so it's critical that we do everything in our power to understand their capabilities and potential pitfalls," Governor Cuomo said. "This new commission will look closely at how these rapidly evolving technologies are functioning and report back on how we can optimize use to benefit New Yorkers and our economy."
The commission will examine how artificial intelligence, robotics and automation:
* affect employment in New York State
* acquire and disclose people's personal information
* affect technology industries
* can be used by the public sector to enhance performance and services
* may be used in unlawful or unsafe ways
The commission will also examine how these innovative technologies have been used and regulated by other states, determine whether current New York laws are effective in regulating them, and make recommendations for how the State can leverage existing uses and, if necessary, update laws to protect industries and residents. This review will include meetings with key stakeholder groups to understand the full landscape of AI, robotics and automation as emerging technologies within the business, nonprofit, academic and governmental sectors.
The commission will consist of 13 members: five appointed by the Governor; two by the temporary president of the Senate; one by the minority leader of the Senate; two by the speaker of the Assembly; one by the minority leader of the Assembly; one by the SUNY chancellor; and one by the CUNY chancellor.
Upon completion of its study, the commission will issue a final report with its findings and recommendations to the Governor and legislative leaders.
Senator Diane J. Savino, Chair of the Senate Committee on Internet and Technology, said, "Artificial intelligence is an essential part of the workforce now. We cannot fear a future in which machines evolve beyond humans, so let's get ahead of the curve and study the issue. We're going to put experts in the field in a situation to help the State and workforce excel. Thank you Governor Cuomo for sharing your concern on this issue and signing this into law."
Assembly Member Clyde Vanel, Chair of the Assembly Subcommittee on Internet and New Technology, said, "It is very important for our state and country to lead in the newest technologies and innovation. We have to make sure that New Yorkers are properly positioned for the jobs and opportunities of tomorrow. While at the same time, ethical, moral and privacy issues must be at the forefront of our efforts."
Contact the Governor's Press Office
Contact us by phone:
Albany: (518) 474 - 8418 <tel:5184748418> New York City: (212) 681 - 4640<tel:2126814640>
Contact us by email:
Friday, July 12, 2019
The State of Cybersecurity Hiring
Burning Glass report on the state of cybersecurity hiring, by Will Markow, Scott Bittle, and Pang-Cheng Liu.
https://www.burning-glass.com/wp-content/uploads/recruiting_watchers_cybersecurity_hiring.pdf
For each cybersecurity opening, there was a pool of only 2.3 employed cybersecurity workers for employers to recruit. That is almost exactly the same ratio of openings-to-employed workers as in 2015-16. By comparison, there are 5.8 employed workers per job opening across the economy in general. Even with the expansion of cybersecurity programs, supply has not kept up with demand
https://www.burning-glass.com/wp-content/uploads/recruiting_watchers_cybersecurity_hiring.pdf
For each cybersecurity opening, there was a pool of only 2.3 employed cybersecurity workers for employers to recruit. That is almost exactly the same ratio of openings-to-employed workers as in 2015-16. By comparison, there are 5.8 employed workers per job opening across the economy in general. Even with the expansion of cybersecurity programs, supply has not kept up with demand
Thursday, July 11, 2019
Amazon Pledges to Upskill 100,000 U.S. Employees for In-Demand Jobs by 2025
Amazon (NASDAQ: AMZN) today pledged to upskill 100,000 of its employees across the United States, dedicating over $700 million to provide people across its corporate offices, tech hubs, fulfillment centers, retail stores, and transportation network with access to training programs that will help them move into more highly skilled roles within or outside of Amazon.
Amazon's Upskilling 2025 pledge invests in a range of new upskilling programs to serve employees from all backgrounds and Amazon locations. Programs include Amazon Technical Academy, which equips non-technical Amazon employees with the essential skills to transition into, and thrive in, software engineering careers; Associate2Tech, which trains fulfillment center associates to move into technical roles regardless of their previous IT experience; Machine Learning University, offering employees with technical backgrounds the opportunity to access machine learning skills via an on-site training program; Amazon Career Choice, a pre-paid tuition program designed to train fulfillment center associates in high-demand occupations of their choice; Amazon Apprenticeship, a Department of Labor certified program that offers paid intensive classroom training and on-the-job apprenticeships with Amazon; and AWS Training and Certification, which provide employees with courses to build practical AWS Cloud knowledge that is essential to operating in a technical field.
"Through our continued investment in local communities in more than 40 states across the country, we have created tens of thousands of jobs in the U.S. in the past year alone," said Beth Galetti, Senior Vice President, HR. "For us, creating these opportunities is just the beginning. While many of our employees want to build their careers here, for others it might be a stepping stone to different aspirations. We think it's important to invest in our employees, and to help them gain new skills and create more professional options for themselves. With this pledge, we're committing to support 100,000 Amazonians in getting the skills to make the next step in their careers."
The upskilling programs are built by Amazonians, around insights provided by Amazon's fast-growing workforce – which will reach 300,000 employees in the U.S. this year – and experts in the changing jobs landscape. Based on a review of the company's jobs and analysis of hiring data from its U.S. workforce, Amazon's fastest growing highly skilled jobs over the last five years are data mapping specialist (832% growth), data scientist (505%), solutions architect (454%), security engineer (229%) and business analyst (160%). Within customer fulfillment, highly skilled roles have increased over 400%, including jobs like logistics coordinator, process improvement manager and transportation specialist within our customer fulfillment network.
According to the U.S. Bureau of Labor Statistics (BLS), there are now more job openings (7.4 million) than there are unemployed Americans (6 million). In looking at job growth over the next decade, the BLS anticipates some of the fastest growing job areas are increasingly in more skilled areas, including medical assistants, statisticians, software developers, nurse practitioners, and wind turbine service technicians. This provides a huge opportunity for individuals who build additional skills to move into better paying jobs. Through its Upskilling 2025 pledge, Amazon is focused on creating pathways to careers in areas that will continue growing in years to come, including healthcare, machine learning, manufacturing, robotics, computer science, cloud computing, and more.
"The future of work is now and the challenge is not just adapting to new technologies, but adapting to the dynamism of the economy, which will only accelerate," said Jason Tyszko, Vice President at the U.S. Chamber of Commerce Foundation. "Amazon is demonstrating the new role employers must play to counter that challenge, fostering a new relationship with workers where maintaining and growing their skills is an imperative for business success."
As part of Upskilling 2025, Amazon is announcing new training opportunities and building on existing programs for employees across the U.S., including:
* Launching Amazon Technical Academy, a training and job placement program that equips non-technical Amazon employees with the essential skills to transition into, and thrive in, software engineering careers. Combining instructor-led, project-based learning with real-world application, graduates of the program master the most widely used software engineering practices and tools required to thrive into a career at Amazon. This tuition-free program was created by Amazon software engineers for Amazon employees who want to move into the field. Find out more here.<https://cts.businesswire.com/ct/CT?id=smartlink&url=https%3A%2F%2Fblog.aboutamazon.com%2Fworking-at-amazon%2Fskills-today-for-tomorrows-jobs&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=here.&index=1&md5=caf0fb970d0d7e483c3c0f479bd8cf8b>
* Launching Associate2Tech, a program that provides fulfillment center associates the opportunity to move into technical roles, regardless of their previous IT experience, within Amazon's vast operations network. This fully-paid 90 day program is designed to place associates in on-the-job training for IT support technician roles and pays for their A+ Certification test, a widely recognized certification. No existing degree is needed, and participants have paid study time during their work week. Learn more here<https://cts.businesswire.com/ct/CT?id=smartlink&url=https%3A%2F%2Fblog.aboutamazon.com%2Foperations%2Fa-tech-job-without-college&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=here&index=2&md5=d8780da544b82a76d00df21f62648dd0>.
* Launching Machine Learning University (MLU), an initiative that helps Amazonians with a background in technology and coding gain skills in Machine Learning. As machine learning plays an increasingly important role in customer innovation, MLU helps employees learn core skills to propel their career growth – skills that are often taught only in higher education. Divided into six-week modules, the program requires only half to one full day of participation a week. MLU is taught by more than 400 Amazon Machine Learning scientists who are passionate about furthering skills in the field. Originally launched as a small cohort, the program is on course to train thousands of employees.
* Growing Career Choice, Amazon's pre-paid tuition program for fulfillment center associates looking to move into high-demand occupations. Amazon will pay up to 95% of tuition and fees towards a certificate or diploma in qualified fields of study, leading to in-demand jobs. Since launching Career Choice in 2012, over 25,000 Amazonians have received training for high-demand occupations including aircraft mechanics, computer-aided design, machine tool technologies, medical lab technologies, and nursing. The company is investing in expanding the program by building additional classrooms in its fulfillment centers globally, and expects to have over 60 on-site classrooms by the end of 2020.
* Expanding Amazon Apprenticeship, a Department of Labor certified program that offers paid intensive classroom training and on-the-job apprenticeships with Amazon. Providing a combination of immersive learning and on-the-job training, the Amazon Apprenticeship program has already created paths to technical jobs for hundreds of candidates working to break into careers including cloud support associate, data technician and software development engineer.
* Expanding AWS Training and Certification to close the cloud skills gap in the industry. Amazon employees have access to free classroom and digital training to build cloud knowledge, and discounted AWS Certification exams to validate cloud expertise. Cultivating these in-demand skills opens opportunities both within Amazon and in organizations around the world as demand for cloud talent continues to grow.
This pledge furthers Amazon's commitment to supporting its employees. Last year, Amazon raised its minimum wage to $15 for all U.S. employees, adding to the suite of industry-leading benefits offered by the company, including comprehensive healthcare (medical, dental and vision coverage), up to 20 weeks of paid parental leave, 401(k) matching, and more. To learn more about Amazon's upskilling efforts visit www.aboutamazon.com/upskilling<https://cts.businesswire.com/ct/CT?id=smartlink&url=http%3A%2F%2Fwww.aboutamazon.com%2Fupskilling&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=www.aboutamazon.com%2Fupskilling&index=3&md5=f90220b9ba67f2dc3def2a694528c29d>.
With more than 630,000 employees worldwide, Amazon has been recognized on LinkedIn's Top Companies list for the past four years, ranked #2 in the Fortune 2017 and 2018 World's Most Admired Companies<https://cts.businesswire.com/ct/CT?id=smartlink&url=http%3A%2F%2Ffortune.com%2Fworlds-most-admired-companies%2F&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=World%26%238217%3Bs+Most+Admired+Companies&index=4&md5=340a809a13690d82357c4de6119ff0a3>, and ranked #5 in Fast Company's 2018 World's Most Innovative Companies<https://cts.businesswire.com/ct/CT?id=smartlink&url=https%3A%2F%2Fwww.fastcompany.com%2Fmost-innovative-companies%2F2018&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=World%26%238217%3Bs+Most+Innovative+Companies&index=5&md5=aff0b95923056fe3a8348e860d9a36f1>. The company also receives a perfect score from the Human Rights Campaign Corporate Equality Index.
About Amazon
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa are some of the products and services pioneered by Amazon. For more information, visit amazon.com/about<http://amazon.com/about> and follow @AmazonNews.
Amazon's Upskilling 2025 pledge invests in a range of new upskilling programs to serve employees from all backgrounds and Amazon locations. Programs include Amazon Technical Academy, which equips non-technical Amazon employees with the essential skills to transition into, and thrive in, software engineering careers; Associate2Tech, which trains fulfillment center associates to move into technical roles regardless of their previous IT experience; Machine Learning University, offering employees with technical backgrounds the opportunity to access machine learning skills via an on-site training program; Amazon Career Choice, a pre-paid tuition program designed to train fulfillment center associates in high-demand occupations of their choice; Amazon Apprenticeship, a Department of Labor certified program that offers paid intensive classroom training and on-the-job apprenticeships with Amazon; and AWS Training and Certification, which provide employees with courses to build practical AWS Cloud knowledge that is essential to operating in a technical field.
"Through our continued investment in local communities in more than 40 states across the country, we have created tens of thousands of jobs in the U.S. in the past year alone," said Beth Galetti, Senior Vice President, HR. "For us, creating these opportunities is just the beginning. While many of our employees want to build their careers here, for others it might be a stepping stone to different aspirations. We think it's important to invest in our employees, and to help them gain new skills and create more professional options for themselves. With this pledge, we're committing to support 100,000 Amazonians in getting the skills to make the next step in their careers."
The upskilling programs are built by Amazonians, around insights provided by Amazon's fast-growing workforce – which will reach 300,000 employees in the U.S. this year – and experts in the changing jobs landscape. Based on a review of the company's jobs and analysis of hiring data from its U.S. workforce, Amazon's fastest growing highly skilled jobs over the last five years are data mapping specialist (832% growth), data scientist (505%), solutions architect (454%), security engineer (229%) and business analyst (160%). Within customer fulfillment, highly skilled roles have increased over 400%, including jobs like logistics coordinator, process improvement manager and transportation specialist within our customer fulfillment network.
According to the U.S. Bureau of Labor Statistics (BLS), there are now more job openings (7.4 million) than there are unemployed Americans (6 million). In looking at job growth over the next decade, the BLS anticipates some of the fastest growing job areas are increasingly in more skilled areas, including medical assistants, statisticians, software developers, nurse practitioners, and wind turbine service technicians. This provides a huge opportunity for individuals who build additional skills to move into better paying jobs. Through its Upskilling 2025 pledge, Amazon is focused on creating pathways to careers in areas that will continue growing in years to come, including healthcare, machine learning, manufacturing, robotics, computer science, cloud computing, and more.
"The future of work is now and the challenge is not just adapting to new technologies, but adapting to the dynamism of the economy, which will only accelerate," said Jason Tyszko, Vice President at the U.S. Chamber of Commerce Foundation. "Amazon is demonstrating the new role employers must play to counter that challenge, fostering a new relationship with workers where maintaining and growing their skills is an imperative for business success."
As part of Upskilling 2025, Amazon is announcing new training opportunities and building on existing programs for employees across the U.S., including:
* Launching Amazon Technical Academy, a training and job placement program that equips non-technical Amazon employees with the essential skills to transition into, and thrive in, software engineering careers. Combining instructor-led, project-based learning with real-world application, graduates of the program master the most widely used software engineering practices and tools required to thrive into a career at Amazon. This tuition-free program was created by Amazon software engineers for Amazon employees who want to move into the field. Find out more here.<https://cts.businesswire.com/ct/CT?id=smartlink&url=https%3A%2F%2Fblog.aboutamazon.com%2Fworking-at-amazon%2Fskills-today-for-tomorrows-jobs&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=here.&index=1&md5=caf0fb970d0d7e483c3c0f479bd8cf8b>
* Launching Associate2Tech, a program that provides fulfillment center associates the opportunity to move into technical roles, regardless of their previous IT experience, within Amazon's vast operations network. This fully-paid 90 day program is designed to place associates in on-the-job training for IT support technician roles and pays for their A+ Certification test, a widely recognized certification. No existing degree is needed, and participants have paid study time during their work week. Learn more here<https://cts.businesswire.com/ct/CT?id=smartlink&url=https%3A%2F%2Fblog.aboutamazon.com%2Foperations%2Fa-tech-job-without-college&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=here&index=2&md5=d8780da544b82a76d00df21f62648dd0>.
* Launching Machine Learning University (MLU), an initiative that helps Amazonians with a background in technology and coding gain skills in Machine Learning. As machine learning plays an increasingly important role in customer innovation, MLU helps employees learn core skills to propel their career growth – skills that are often taught only in higher education. Divided into six-week modules, the program requires only half to one full day of participation a week. MLU is taught by more than 400 Amazon Machine Learning scientists who are passionate about furthering skills in the field. Originally launched as a small cohort, the program is on course to train thousands of employees.
* Growing Career Choice, Amazon's pre-paid tuition program for fulfillment center associates looking to move into high-demand occupations. Amazon will pay up to 95% of tuition and fees towards a certificate or diploma in qualified fields of study, leading to in-demand jobs. Since launching Career Choice in 2012, over 25,000 Amazonians have received training for high-demand occupations including aircraft mechanics, computer-aided design, machine tool technologies, medical lab technologies, and nursing. The company is investing in expanding the program by building additional classrooms in its fulfillment centers globally, and expects to have over 60 on-site classrooms by the end of 2020.
* Expanding Amazon Apprenticeship, a Department of Labor certified program that offers paid intensive classroom training and on-the-job apprenticeships with Amazon. Providing a combination of immersive learning and on-the-job training, the Amazon Apprenticeship program has already created paths to technical jobs for hundreds of candidates working to break into careers including cloud support associate, data technician and software development engineer.
* Expanding AWS Training and Certification to close the cloud skills gap in the industry. Amazon employees have access to free classroom and digital training to build cloud knowledge, and discounted AWS Certification exams to validate cloud expertise. Cultivating these in-demand skills opens opportunities both within Amazon and in organizations around the world as demand for cloud talent continues to grow.
This pledge furthers Amazon's commitment to supporting its employees. Last year, Amazon raised its minimum wage to $15 for all U.S. employees, adding to the suite of industry-leading benefits offered by the company, including comprehensive healthcare (medical, dental and vision coverage), up to 20 weeks of paid parental leave, 401(k) matching, and more. To learn more about Amazon's upskilling efforts visit www.aboutamazon.com/upskilling<https://cts.businesswire.com/ct/CT?id=smartlink&url=http%3A%2F%2Fwww.aboutamazon.com%2Fupskilling&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=www.aboutamazon.com%2Fupskilling&index=3&md5=f90220b9ba67f2dc3def2a694528c29d>.
With more than 630,000 employees worldwide, Amazon has been recognized on LinkedIn's Top Companies list for the past four years, ranked #2 in the Fortune 2017 and 2018 World's Most Admired Companies<https://cts.businesswire.com/ct/CT?id=smartlink&url=http%3A%2F%2Ffortune.com%2Fworlds-most-admired-companies%2F&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=World%26%238217%3Bs+Most+Admired+Companies&index=4&md5=340a809a13690d82357c4de6119ff0a3>, and ranked #5 in Fast Company's 2018 World's Most Innovative Companies<https://cts.businesswire.com/ct/CT?id=smartlink&url=https%3A%2F%2Fwww.fastcompany.com%2Fmost-innovative-companies%2F2018&esheet=52011660&newsitemid=20190711005341&lan=en-US&anchor=World%26%238217%3Bs+Most+Innovative+Companies&index=5&md5=aff0b95923056fe3a8348e860d9a36f1>. The company also receives a perfect score from the Human Rights Campaign Corporate Equality Index.
About Amazon
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa are some of the products and services pioneered by Amazon. For more information, visit amazon.com/about<http://amazon.com/about> and follow @AmazonNews.
Thursday, June 6, 2019
We should extend EU bank data sharing to all sectors: Open Banking rules are a model for a broader digital solution
CARLOS TORRES VILA<https://www.ft.com/stream/38c26605-af42-4abc-9edd-534d3e55dbdb>
https://www.ft.com/content/0304b078-82c6-11e9-a7f0-77d3101896ec
Data is now driving the global economy — just look at the list of the world's most valuable companies. They collect and exploit the information that users generate through billions of online interactions taking place every day.
But companies are hoarding data too, preventing others, including the users to whom the data relates, from accessing and using it. This is true of traditional groups such as banks, telcos and utilities, as well as the large digital enterprises that rely on "proprietary" data.
Global and national regulators must address this problem by forcing companies to give users an easy way to share their own data, if they so choose. This is the logical consequence of personal data belonging to users. There is also the potential for enormous socio-economic benefits if we can create consent-based free data flows.
We need data-sharing across companies in all sectors in a real time, standardised way — not at a speed and in a format dictated by the companies that stockpile user data. These new rules should apply to all electronic data generated by users, whether provided directly or observed during their online interactions with any provider, across geographic borders and in any sector. This could include everything from geolocation history and electricity consumption to recent web searches, pension information or even most recently played songs.
This won't be easy to achieve in practice, but the good news is that we already have a framework that could be the model for a broader solution. The UK's Open Banking system provides a tantalising glimpse of what may be possible. In Europe, the regulation known as the Payment Services Directive 2 allows banking customers to share data about their transactions with multiple providers via secure, structured IT interfaces. We are already seeing this unlock new business models and drive competition in digital financial services. But these rules do not go far enough — they only apply to payments history, and that isn't enough to push forward a data-driven economic revolution across other sectors of the economy.
We need a global framework with common rules across regions and sectors. This has already happened in financial services: after the 2008 financial crisis, the G20 strengthened global banking standards and created the Financial Stability Board. The rules, while not perfect, have delivered uniformity which has strengthened the system.
We need a similar global push for common rules on the use of data. While it will be difficult to achieve consensus on data, and undoubtedly more difficult still to implement and enforce it, I believe that now is the time to decide what we want. The involvement of the G20 in setting up global standards will be essential to realising the potential that data has to deliver a better world for all of us. There will be complaints about the cost of implementation. I know first hand how expensive it can be to simultaneously open up and protect sensitive core systems.
The alternative is siloed data that holds back innovation. There will also be justified concerns that easier data sharing could lead to new user risks. Security must be a non-negotiable principle in designing intercompany interfaces and protecting access to sensitive data. But Open Banking shows that these challenges are resolvable.
The EU has been at the forefront of setting out clear rules for data and the rapidly growing digital economy. It must continue to champion users, while helping to drive innovation and competition. The next European Commission should prioritise a cross-sector data-sharing regulation and show how open data can boost innovation, benefiting consumers and businesses. A comprehensive regulatory framework on data would boost the EU's overall competitiveness. The next task would be to lead discussions at the G20, aiming at a much needed global consensus on this matter. The writer is group executive chairman of BBVA
https://www.ft.com/content/0304b078-82c6-11e9-a7f0-77d3101896ec
Data is now driving the global economy — just look at the list of the world's most valuable companies. They collect and exploit the information that users generate through billions of online interactions taking place every day.
But companies are hoarding data too, preventing others, including the users to whom the data relates, from accessing and using it. This is true of traditional groups such as banks, telcos and utilities, as well as the large digital enterprises that rely on "proprietary" data.
Global and national regulators must address this problem by forcing companies to give users an easy way to share their own data, if they so choose. This is the logical consequence of personal data belonging to users. There is also the potential for enormous socio-economic benefits if we can create consent-based free data flows.
We need data-sharing across companies in all sectors in a real time, standardised way — not at a speed and in a format dictated by the companies that stockpile user data. These new rules should apply to all electronic data generated by users, whether provided directly or observed during their online interactions with any provider, across geographic borders and in any sector. This could include everything from geolocation history and electricity consumption to recent web searches, pension information or even most recently played songs.
This won't be easy to achieve in practice, but the good news is that we already have a framework that could be the model for a broader solution. The UK's Open Banking system provides a tantalising glimpse of what may be possible. In Europe, the regulation known as the Payment Services Directive 2 allows banking customers to share data about their transactions with multiple providers via secure, structured IT interfaces. We are already seeing this unlock new business models and drive competition in digital financial services. But these rules do not go far enough — they only apply to payments history, and that isn't enough to push forward a data-driven economic revolution across other sectors of the economy.
We need a global framework with common rules across regions and sectors. This has already happened in financial services: after the 2008 financial crisis, the G20 strengthened global banking standards and created the Financial Stability Board. The rules, while not perfect, have delivered uniformity which has strengthened the system.
We need a similar global push for common rules on the use of data. While it will be difficult to achieve consensus on data, and undoubtedly more difficult still to implement and enforce it, I believe that now is the time to decide what we want. The involvement of the G20 in setting up global standards will be essential to realising the potential that data has to deliver a better world for all of us. There will be complaints about the cost of implementation. I know first hand how expensive it can be to simultaneously open up and protect sensitive core systems.
The alternative is siloed data that holds back innovation. There will also be justified concerns that easier data sharing could lead to new user risks. Security must be a non-negotiable principle in designing intercompany interfaces and protecting access to sensitive data. But Open Banking shows that these challenges are resolvable.
The EU has been at the forefront of setting out clear rules for data and the rapidly growing digital economy. It must continue to champion users, while helping to drive innovation and competition. The next European Commission should prioritise a cross-sector data-sharing regulation and show how open data can boost innovation, benefiting consumers and businesses. A comprehensive regulatory framework on data would boost the EU's overall competitiveness. The next task would be to lead discussions at the G20, aiming at a much needed global consensus on this matter. The writer is group executive chairman of BBVA