This story was originally published by Data-Smart City Solutions.
The U.S. currently has an unemployment rate of 4.1 percent. While the lowest since 2000, this number is by no means a floor: with creative tech and policy solutions, there are a number of opportunities to help more individuals find employment.
Bringing unemployment down requires confronting a number of challenges. First, jobs require increasingly modern skillsets. According to the University of Chicago, "despite persistent unemployment in the United States, millions of jobs are hard to fill due to a lack of qualified applicants." Second, the process of job placement is complicated and opaque, often requiring job seekers to jump through hoops to match with an employer. Finally, a wealth of job placement programs exists, but it is difficult to assess which ones are most effective in the long-term and to scale those programs, or course-correct within programs that are less effective.
Governments can deploy—and, in many cases, already are deploying—data to address all three of these challenges. By leveraging computational power to close the skills gap, data-driven platforms to match job seekers and employers, and longitudinal data systems to better assess what is working well and where there are areas for improvement, governments can put more qualified applicants in well-suited jobs.
To help job seekers overcome the challenge of developing increasingly modern skills, the University of Chicago and Argonne National Laboratory created the National Center for Opportunity Engineering & Analysis at the Computation Institute. Launched in late 2016, the Center uses computational and data science tools to help close the skills gap and reduce economic inequality by offering new ways to search for and identify training and career opportunities. The Center focuses on gathering and distributing data related to job experience, education and training, and employment and labor. One aim of the Center is to provide job search sites with data that can help them to better match job seekers with relevant positions and trainings aligned with the evolving job market. With this information, these sites can develop 'dating service'-like matching algorithms that pair qualified applicants with relevant positions.
This work builds on the Skills Cooperative Research Database, for which developers amassed troves of employment data via the Open Skills Project. Cities can leverage resources such as the National Center for Opportunity Engineering & Analysis and Open Skills Project's joint "Data at Work" platform to determine how best to prepare residents for the next generation of jobs. By analyzing such data, cities can more effectively develop trainings that are targeted to skills needed in today's job market. For example, one such training is New York City's Tech Talent Pipeline, which "aims to connect participants with 21st-century skills and the employers in search of those skills" through trainings in a variety of tech-related topics, including information technology (IT), quality assurance (QA), and data.
A number of governments provide digital platforms connecting job seekers to employers. At the state level, Mississippi and Washington offer compelling examples of easy-to-use, data-driven employment services platforms. Launched in February 2014, Mississippi Works ("MS Works") is an interactive website and mobile app that pairs employers and job seekers in Mississippi through a real-time, web-based system. According to the state’s Department of Employment Security, the site and app each serve as a "one-stop shop, allowing job seekers to search for openings based on job type, location or academic degree required and allowing employers to post openings at no cost and make connections with qualified candidates." If the job seeker is looking for further in-person services, the site and platform also contain data on the nearest WIN Job Centers. MS Works offers direct access to the unemployment system via single sign-on as well. That unemployment system is a part of ReEmploy USA, the country's first multi-tenant cloud-based unemployment insurance system, launched in August 2017 by Mississippi, Rhode Island, Maine, and Connecticut.
Released in 2016, WorkSource Washington is a job match website developed by Washington State in conjunction with the job search website Monster Worldwide Inc. It offers job seekers a personalized platform that stores their preferences, alerts them to relevant listings, and connects them with WorkSource specialists who can support them in their search, and can access their past job search activity and skills profile via a case management platform. On the employer end, the platform provides real-time, ranked matches with candidates for each role, making the hiring process faster and easier. The platform has been touted as scalable for other jurisdictions.
While these examples are focused on states, which traditionally are the jurisdictions that administer unemployment insurance and, as a result, much of the employment support, cities could also leverage this model to link their residents to job opportunities in a data-driven, centralized way. In helping city job seekers make decisions more effectively in their job search process, governments could also create (or embed) tools such as Redfin's Opportunity Score, a preliminary tool that helps people find housing that is easily accessible to the types of jobs for which they are looking.
Longitudinal data systems, which track beneficiaries and their related outcomes over multiple years and programs, can help answer important questions about the impact of workforce training programs in job placement, the most effective types of training, and, as a result, how best to invest taxpayer money in this space. Illinois has set up such data systems across different levels of government, and in partnership with research institutions such as Chapin Hall, a research and policy center at the University of Chicago. The Illinois Department of Children and Family services and other human service agencies worked with Chapin Hall to produce the Integrated Database on Child and Family Programs in Illinois. The Integrated Database combines data from different agencies and information systems for analysis. Researchers use the database to assess the use and impact of multiple social service programs on families in Illinois.
Bringing their analytical resources to the City of Chicago, in 2009 Chapin Hall launched CWICstats for the Chicago Workforce Investment Council (CWIC). The purpose of this tool was to better monitor public investment in workforce training, and better serve employer needs. CWICstats produced a quarterly 'dashboard report' for the Council, providing information including labor force trends and industry sector changes and captured data on training participants before, during, and after the publicly-funded trainings. Insights from this data showed that the city needed a better system for customer intake and program management, so, building on CWICstats, the city set up Career Connect as a new intake and information system for workforce customers (including both job seekers and employers).
Florida also developed a longitudinal data system to support more evidence-based workforce training. The Florida Education and Training Placement Information Program (FETPIP) collects follow-up data on workforce training participants' employment and education experiences as well as military enlistment, incarceration, or use of public assistance. According to Mike Switzer, a Vice President with Enterprise Florida's Jobs and Education Partnership, such data collection allows the state to regularly iterate on its workforce training efforts, rather than solely depending on traditional academic evaluations that, while valuable, can take many years to complete. For example, based on preliminary FETPIP data that identified "epicenters of difficulty" in relation to employment, the State Legislature enacted a $25 million job-creation fund, created business expansion incentives and upped support for residents who move to Orlando, Sarasota and other areas with an abundance of jobs.
As cities implement job training services, it is important that they collect and analyze the relevant longitudinal data to evaluate the success of such services. This will enable the cities to take a more outcome-focused approach to their workforce training and job placement programs, and to identify both what is working and where there are areas for improvement.
Data can empower jurisdictions to more effectively confront the challenges of determining what kinds of training job seekers need, matching job seekers and employers in an efficient way, and monitoring and evaluating the effectiveness of workforce training programs. Data can equip jurisdictions to use computational power to help job seekers find training relevant to highly demanded skills, build efficient platforms to match job seekers and employers, and track outcomes related to job training and placement. Implemented together, these interventions can create a comprehensive workforce training and placement program, guiding residents from their initial search for skills training to their ultimate job placement more effectively and with a greater eye toward the needs of a modern workforce. In this way, putting data to work can help cities and other jurisdictions better support their residents in their search for employment opportunities.