(This analysis presented in a highly interactive, image-based format on Brookings.edu, which is the where the original analysis was published. The original article can be accessed by clicking here.)

Article and analysis by: Martha Ross and Natalie Holmes of Brookings.edu

Even in the midst of a prolonged economic expansion with a low national unemployment rate, jobs are not always available and not everyone who wants work can find it. Both job availability and demographics vary markedly around the country, yielding diverse local populations wanting and/or needing work.

This analysis aims to deepen understanding of out-of-work Americans, and support local officials in their efforts to help these individuals find jobs. We provide a unique perspective on adults ages 25-64 who are out of work in each of 130 large cities and counties across the United States, using cluster analysis to segment the out-of-work population into distinct groups based on factors such as educational attainment, age, work history, disability, English language proficiency, and family status. We present detailed information on these groups accompanied by information on appropriate and effective workforce development programs in order to help local officials, funders, and other stakeholders develop, strengthen, or diversify strategies to connect their residents to employment.

Defining the out-of-work population

In the 130 study jurisdictions, there are 78.9 million adults ages 25–64 who are civilians and not living in institutional settings such as correctional facilities.

Of this 78.9 million, 4 million are unemployed—people who do not have a job, are available for work, and have actively looked for work in the last four weeks.

An additional 16.2 million are considered not in the labor force—people who are neither working nor looking for work. This is a heterogeneous group with different reasons for not entering the labor force, not all of which are readily observable. Individuals may be devoting time and energy towards other activities such as raising children, taking care of other family members, or going to school. They may be retired or have disabilities that preclude employment. They may be interested in working, but because they have not searched for a job in the past four weeks, they are not counted among the unemployed.

Of the combined unemployed and not-in-the-labor-force populations, our goal is to identify those most likely to be interested in or benefit from workforce development assistance. Therefore, we subtracted the following groups: people receiving retirement and disability benefits, most students, and our best estimate of people who choose to be stay-at-home parents with sufficient earnings from a spouse who works. These subtractions amount to 10 percent of the unemployed and 53 percent of those not in the labor force.

This leaves 11.3 million individuals defined as out-of-work (14 percent of the 25–64 year-old non-institutionalized civilian population.)

Place matters

Cities and counties do not all fare equally in the global economy, nor do their residents. Local conditions and interventions play a pivotal role in connecting job seekers to employment opportunities, and most of the responsibility for executing on this goal rests with local officials and leaders in the public, private, and social sectors.

The 130 jurisdictions included in the analysis collectively account for nearly half (48 percent) of the nation’s population aged 25-64. The study jurisdictions include large cities with populations upwards of 1.5 million, such as Los Angeles, Chicago, Philadelphia, and Phoenix; mid-size cities such as Albuquerque, Milwaukee, Louisville, and Nashville; and high-density counties with populations over one million near the core of large metropolitan areas (Alameda, CA; Fulton County, GA). They also include lower-density counties with populations under one million (Montgomery County, OH; Anne Arundel County, MD), including some with rural characteristics (Lancaster County, PA; Fresno County, CA). Although they all pass the 500,000 population threshold, in other words, they show substantial variation in size and other characteristics.

Segmenting the out-of-work into groups based on shared characteristics

The question of what works best in workforce development is more usefully conceptualized as a narrower question: what works best for whom? While successful programs have common elements, they typically tailor key components—the intensity, length, and specific focus of services—to the needs and circumstances of the people they are serving.

Someone with less than a high school diploma needs a different educational program than someone who enrolled in college but dropped out without a credential. Someone with relatively steady work experience probably does not need an orientation to the culture of work as might someone with a sporadic work history; and factors such as limited English proficiency, child-care responsibilities, and criminal backgrounds are issues that programs must address to help participants successfully increase their skills, find a job, and chart a path to higher earnings.

We used cluster analysis to segment the out-of-work population into groups of individuals with similar attributes in order to better identify what kind of help they might need to find employment. We identified 828 clusters across the 130 study jurisdictions, which together roll up into 7 major groups.

Effective practices to connect out-of-work groups to employment

Broadly speaking, effective workforce programs offer training that aligns with regional labor market needs and provide guidance, counseling, and other appropriate supportive services to participants. We reviewed the research literature and identified eight approaches shown to be effective by formal evaluations, preferably with random assignment techniques in order to identify causality. These approaches disproportionately target people with lower levels of education, reflecting the general orientation of the field of workforce development.

While our focus on formally evaluated programs adds to our confidence in their effectiveness, it also reduces the number of programs we list. Because third-party evaluations are complicated and expensive, they are undertaken by a minority of programs. There are gaps in the research literature about how best to serve specific groups, such as older individuals, the long-term unemployed, and people with very low literacy and English language skills. This is not to say that there is no practical knowledge base about how to serve these groups, but rather that the field would benefit from a stronger emphasis on documenting and disseminating effective practices. In short, our list should be viewed as a point of departure, not a comprehensive inventory.

(This analysis is continued and presented in a highly interactive, image-based format on Brookings.edu. The original article can be accessed by clicking here.)

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