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When veterans leave the service and reenter the civilian workforce, they are coming from a job that required significant training and tremendous responsibility. As new employees, this wealth of experience and unique set of skills theoretically should allow them to add value and increase productivity for an employer at a rate far faster than their nonveteran counterparts. Unfortunately, veterans’ unemployment, particularly for those under the age of 35, remains stubbornly high. In 2013, the average unemployment rate for veterans between the ages of 25 to 34, who joined the military after September 11, 2001, was 9.5 percent, around 2.2 percentage points higher than their nonveteran counterparts. Moreover, as of August 2014, nearly 15 percent of young veterans ages 20 to 24 were unemployed—a rate 4.2 percentage points higher than their nonveteran counterparts. Given their meaningful labor-market experience, why aren’t veterans, and younger veterans in particular, performing better in the labor market?

Certainly, one reason is the challenge of translating military experience and talents into credentials that employers can easily discern. According to a 2012 survey by the Center for a New American Security, or CNAS, more than 60 percent of employers said they had difficulty interpreting veterans’ skills. As CNAS notes, employers without a military background found it difficult to understand the experiences and skill sets of veterans and determine how military skills matched their business needs. In addition to issues of skill translation, the study also examined additional reasons for veteran unemployment, including negative stereotypes associated with returning veterans, skills mismatches where veterans simply do not have the skills for civilian positions in question, concerns about future deployments, and difficulty finding veterans to employ. According to the U.S. Government Accountability Office, or GAO, more than 1 million service members are expected to leave the active military over the next five years and enter the workforce, a number that may only increase depending on the size of the post-war drawdowns. Now is the time to deliver a stronger and more effective workforce system for our veterans.

One way to better the veteran-to-civilian-employee transition is through the improved access and use of labor-market information, or LMI. Over the past five years, the Obama administration has made great strides in opening data to the public, improving the functionality of that data, and ultimately, using data to spur innovation, savings, and reforms. We have seen the birth of an ecosystem of public and private app developers building better services powered by open data, especially in the health care and clean energy markets. In addition to these efforts, this summer, Vice President Joe Biden released a landmark report on job training that highlighted new tools, initiatives, and leaders in workforce development. At the same time, Congress passed the Workforce Innovation and Opportunity Act, or WIOA, with a specific focus on LMI. Building on this momentum, this report lays out five policies for better utilizing data to accelerate veteran employment in jobs that best leverage their skills.

1. Increase public access to more government data sets. These data sets can help local workforce planners better identify the skills of unemployed veterans and match those skills with veteran-friendly employers and local industry demand.
2. Launch a “JobsData.gov” platform. We recommend extending the Data.gov platform, a central depository for government data sets. The federal government should create a data community focused solely on labor-market information. As proposed, the JobsData.gov portal would focus in particular on veterans’ data and allow for easy interoperability with state data sets as well as easy access for third-party web and app developers.
3. Modernize O*NET. The federal government needs to improve how data is collected for the Occupational Information Network, or O*NET, the current primary database for information on occupations, skills, and related variables. Reforming data-collection techniques will ensure that information on various occupations in the United States is always relevant, correct, and updated in real time.
4. Convene the private sector to standardize skills data. Federal agencies should establish a more uniform standard for presenting online job postings and resumes so that the skills associated with each are more clearly distinguishable and machine readable.
5. Accelerate learning pathways. Increase access to new competency-based learning models, which will allow veterans to quickly identify and master the additional competencies they may need for civilian employment and, more importantly, build on the skills they already have. Such action undertaken by the federal government would accelerate the time it takes for veterans to find employment, take into account the skills they already have, and ultimately reduce some of the existing barriers to employment.

When re-entering the civilian workforce, veterans need employment opportunities where they can quickly put their skills, training, and experience to use and be highly productive from day one. As President Barack Obama said in an August 2014 speech to the American Legion National Convention, “if you’re a medic in a warzone, you shouldn’t have to go take nursing 101 to work in a hospital here in the United States.” The above recommendations, which are detailed below, are steps that policymakers, entrepreneurs, and various workforce stakeholders can take to better understand the skill profile of unemployed veterans in their community, match their skills with high-growth jobs that offer a ladder to higher wages, and ultimately decrease the period between unemployment and full productivity for those who have served us so courageously.

Aneesh Chopra is a Senior Fellow at the Center for American Progress and the executive vice president, co-founder, and a shareholder at Hunch Analytics. Ethan Gurwitz is a Research Assistant with the Economic Policy team at the Center.