How Workforce Agencies Can Turn Labor Market Information Into Career Guidance and Employer Insights People Actually Use

Every state has a labor market information office, and most produce a remarkable volume of data: employment by industry and occupation, wages by area, long-term and short-term employment projections, unemployment rates, job posting trends, commuting patterns, and more. Much of it is published on state websites in reports, tables, and interactive tools. Meanwhile, in a career center a few miles away, a career advisor helps a laid-off warehouse worker decide whether to pursue training in medical coding, industrial maintenance, or commercial driving. The advisor relies mostly on what she has heard from other participants, a few employer conversations, and her own sense of which programs seem to work. The labor market data that could inform this decision sits unused, not because it is unavailable but because it is not in a form the advisor or the jobseeker can easily use.

This gap between labor market information and the decisions it could inform is one of the persistent challenges of the workforce system. Labor market information, often called LMI, is meant to help people make better choices: jobseekers choosing careers and training, students planning their futures, career advisors guiding participants, employers setting wages and planning recruitment, training providers deciding which programs to offer, and policymakers setting priorities. When LMI reaches these decisions in usable form, it can help jobseekers avoid training for occupations with few openings, help employers understand why they cannot fill positions, and help educators align programs with demand. When it does not, decisions are made on assumptions, anecdotes, and outdated impressions.

The obstacles are not primarily technical. Data exist, and many are accurate and timely. The obstacles are communicative: data are presented in technical formats, organized around statistical categories rather than user questions, buried in long reports, described in jargon, disconnected from the moment of decision, and stripped of the context and caveats people need to interpret them. Users who could benefit from LMI often do not know it exists, do not know how to find it, or cannot interpret it when they do.

This article examines how workforce agencies can turn labor market information into career guidance and employer insights that people actually use. Its central argument is that LMI becomes useful when it is organized around the questions users are asking, translated into plain language, delivered at the moment of decision, combined with local knowledge, presented honestly with its limitations, and carried by trusted intermediaries such as career advisors and business services staff. It addresses what LMI includes, who uses it, how to translate it for jobseekers, advisors, employers, and educators, how to combine data with local knowledge, how to present it honestly, which tools and formats work, and the accessibility and ethical considerations that apply.

The Gap Between Data and Decisions

Labor market information is produced to inform decisions, yet it often fails to reach the decisions it was meant to inform. Understanding why this gap exists helps workforce agencies design communication that closes it.

The first reason is that data are organized around producers rather than users. Statistical agencies organize data by survey, classification system, and geography, which makes sense for data production but not for decisions. A jobseeker wondering whether there are good jobs in her area for someone with her skills must navigate occupational classification codes, industry codes, geographic definitions, and multiple data sources to piece together an answer. Most will not try.

The second reason is jargon. Terms such as location quotient, replacement openings, occupational employment and wage statistics, and annual average job openings are precise for analysts but opaque for most users. Even simpler terms, such as median wage or projected growth rate, may be misunderstood.

The third reason is timing. People make career and training decisions at particular moments, often under pressure, such as after a layoff or when choosing a training program. If LMI is not available at those moments, in the conversations and tools people are using, it will not inform the decision. A comprehensive annual report published in the spring does little for a jobseeker deciding on training in the fall unless someone brings the relevant information to her at the right time.

The fourth reason is volume. LMI producers often publish everything they have, which overwhelms users. A jobseeker does not need data on every occupation; she needs information about the few she is considering. An employer does not need statewide projections; he needs wage information for his positions in his area.

The fifth reason is disconnection from local reality. Published data may not capture local nuances, such as a major employer’s recent expansion, a local skill shortage not yet reflected in surveys, or regional wage differences within a broad area. Users who notice disconnects between data and their experience may dismiss the data entirely.

The sixth reason is lack of interpretation. Data alone do not tell users what to do. A projection that an occupation will grow does not tell a jobseeker whether she should pursue it, given her interests, skills, circumstances, and the training required. Interpretation, connecting data to individual situations, is often what users most need and least receive.

The seventh reason is trust. Users who do not understand where data come from, or who have seen data contradicted by experience, may not trust them. Building trust requires transparency about sources and limitations and consistency with users’ observations.

Closing the gap requires addressing all of these reasons: organizing information around user questions, translating jargon, delivering information at the moment of decision, curating rather than overwhelming, connecting data to local knowledge, providing interpretation, and building trust through transparency. The sections that follow describe how.

What Labor Market Information Includes

Labor market information encompasses many types of data from many sources. Workforce staff who communicate LMI need a working understanding of the main types, their sources, and their strengths and limitations, so they can choose the right information for each question and explain it accurately.

Employment and Industry Data

Data on employment by industry, often drawn from federal and state programs such as the Quarterly Census of Employment and Wages, show how many people work in each industry in an area and how employment has changed. These data are comprehensive for employment covered by unemployment insurance and are useful for understanding the structure of the local economy, but they are released with a lag of several months.

Occupational Employment and Wages

Occupational employment and wage data, from the federal Occupational Employment and Wage Statistics program conducted with states, estimate employment and wages for hundreds of occupations by area. They provide wage distributions, such as median and percentile wages, which are useful for career guidance and employer wage benchmarking. Estimates for small areas or uncommon occupations may be unavailable or less reliable.

Employment Projections

State LMI offices produce long-term occupational and industry projections, typically over a ten-year horizon, and short-term projections, typically over two years, following methods coordinated nationally. Projections estimate expected growth and annual openings, including openings from growth and from workers leaving occupations or the labor force. Projections are informed estimates, not predictions, and depend on assumptions about economic conditions.

Unemployment and Labor Force Data

Data from the Local Area Unemployment Statistics program estimate labor force, employment, and unemployment for states and local areas. These data help describe overall labor market conditions but say little about specific occupations.

Job Posting Data

Online job posting data, often from private vendors, provide near-real-time information about employer demand, including occupations, skills, credentials, and sometimes wages advertised. They are timely and detailed but have limitations: not all jobs are posted online, some postings are duplicates or remain open after positions are filled, and coverage varies by industry and occupation.

Education and Training Data

Data on program completions, credentials awarded, and training provider performance help show the supply of trained workers in particular fields. Data on the eligible training provider list, including program outcomes where available, inform training choices.

Workforce Supply and Demographic Data

Data on population, educational attainment, commuting patterns, and labor force participation describe the available workforce. These data inform regional strategy and employer location decisions.

Qualitative and Local Information

Employer surveys, sector partnership discussions, business services observations, and local news provide qualitative information about skill needs, hiring challenges, and changes not yet visible in statistical data. This information is essential for interpreting statistical data and filling gaps.

Each type of information answers different questions. Effective LMI communication selects the types relevant to each user’s question and explains them accurately, combining sources where appropriate to provide a fuller picture.

Who Uses Labor Market Information and for What

Labor market information serves many users, each with distinct questions and decisions. Designing useful LMI communication begins with understanding these users and what they need.

Jobseekers, including unemployed workers, dislocated workers, and people seeking better jobs, use LMI to decide what kinds of jobs to pursue, whether to seek training, which training to choose, and what wages to expect. Their questions are personal and practical: what jobs are available in my area for someone with my experience, what do they pay, what training would help me qualify for better jobs, and how long would it take.

Students and young people, including high school students, young adults in youth programs, and college students, use LMI to explore careers and plan education. Their questions are exploratory: what careers might fit my interests, what education do they require, what do they pay, and are there jobs in them where I live.

Career advisors and counselors use LMI to guide participants. They need information that helps them have informed conversations about career options, training choices, and job search strategies, and that they can explain to participants with confidence.

Employers use LMI to set wages, plan recruitment, understand competition for talent, decide where to locate, and plan training. Their questions are business-oriented: what do similar positions pay in this area, how many workers with these skills are available, where are they, and how competitive is the market for this talent.

Business services staff use LMI to understand employers’ industries, advise employers on wages and recruitment, and identify employers to engage.

Educators and training providers use LMI to decide which programs to offer, expand, or close, to align curricula with skill demands, and to advise students. Their questions concern demand: which occupations have strong openings, what skills and credentials employers want, and how many graduates the market can absorb.

Workforce boards and policymakers use LMI to set priorities, choose target sectors, allocate training funds, approve training providers, and develop plans. Their questions are strategic: which industries and occupations should we prioritize, where are the gaps between supply and demand, and how are conditions changing.

Economic development organizations use LMI to understand workforce assets and gaps for business attraction and retention.

These users differ in their expertise, the level of detail they need, and the context in which they make decisions. A jobseeker needs simple, personalized information delivered in conversation with an advisor. An employer needs specific wage benchmarks presented concisely. An educator needs supply and demand analysis for specific programs. A board needs strategic overviews with trends. Designing LMI communication for each user group, rather than producing a single product for all, makes information far more usable.

Career advisors and business services staff deserve particular emphasis because they are intermediaries who translate LMI for jobseekers and employers. Equipping these intermediaries to use LMI effectively may do more to close the gap between data and decisions than any public-facing tool.

Translating Labor Market Information for Jobseekers and Students

Jobseekers and students are the users for whom LMI can make the most personal difference, and the users least likely to find and interpret it on their own. Translating LMI for them requires organizing information around their questions, using plain language, personalizing where possible, and connecting information to action.

The first principle is starting from their questions. Jobseekers and students rarely ask about employment projections or location quotients. They ask what jobs are out there, what they pay, what training they need, and whether they can get hired. LMI tools and conversations should be organized around these questions. A career exploration tool that lets users start with their interests or current skills, rather than with occupational codes, meets them where they are.

The second principle is plain language. Technical terms should be translated: annual openings becomes about how many job openings there are each year in this area; median wage becomes the typical pay, with half earning more and half earning less; projected growth becomes whether the number of these jobs is expected to increase. Wages should be presented in familiar terms, such as hourly and annual, rather than only in one form.

The third principle is local relevance. Jobseekers care about jobs where they live or can commute. Statewide data may not reflect local conditions. Presenting data for the local area or region, where reliable, and noting when local data are unavailable, makes information relevant.

The fourth principle is connecting occupations to pathways. Information about an occupation is most useful when it includes what it takes to get there: the education or training required, how long it takes, what it costs, whether training funds are available, and which local programs offer it. Linking occupational information to eligible training provider programs and their outcomes helps jobseekers move from exploration to action.

The fifth principle is showing the range of pay and progression. Entry-level wages may be lower than median wages, and jobseekers should understand what they can expect starting out and how pay may grow with experience. Showing wage ranges, such as entry-level and experienced wages, gives a more realistic picture.

The sixth principle is honesty about uncertainty and competition. Projections are estimates, and many openings come from workers leaving occupations rather than growth. Some occupations with many openings also have many qualified candidates. Jobseekers deserve honest information that helps them understand both opportunity and competition.

The seventh principle is connecting to transferable skills. Dislocated workers and career changers often have skills that transfer to other occupations. Tools and conversations that identify related occupations based on transferable skills, using resources such as the federal Occupational Information Network known as O*NET, help jobseekers see options they might not have considered.

The eighth principle is presenting LMI as one input among several. A jobseeker’s interests, abilities, values, circumstances, and constraints matter as much as labor market data. An occupation with strong demand may not suit a particular person. LMI should inform, not dictate, career decisions.

The ninth principle is delivery through trusted conversations. For many jobseekers, especially those facing barriers, LMI is most useful when delivered through a conversation with a career advisor who can interpret it in light of the jobseeker’s situation. Self-service tools are valuable but rarely sufficient on their own.

Translated well, LMI helps jobseekers and students make informed decisions about their futures, avoid investing in training for occupations with poor prospects, and pursue opportunities they might not have known existed.

Equipping Career Advisors to Use Labor Market Information

Career advisors are the most important channel through which LMI reaches jobseekers. An advisor who understands local labor market conditions, can find relevant data quickly, and can interpret it in conversation with a participant transforms data into guidance. Yet many advisors receive limited training in LMI, lack convenient tools, and rely on informal knowledge. Equipping advisors to use LMI effectively is one of the highest-value investments a workforce agency can make in LMI communication.

The first element is training. Advisors need practical training in the main types of LMI, what each can and cannot tell them, where to find it, and how to interpret it. Training should be grounded in the questions advisors actually face, such as helping a participant choose between training programs or assessing whether an occupation has openings locally. Hands-on practice with the tools advisors will use, using realistic cases, builds confidence. Refresher training as tools and data change keeps skills current.

The second element is convenient tools. Advisors work under time pressure and need information quickly during appointments. Tools that provide occupation profiles with local wages, openings, training requirements, related programs, and outcomes on a single screen, accessible during conversations, are more useful than multiple databases requiring separate searches. Integration with case management systems, where feasible, allows advisors to access LMI within their normal workflow.

The third element is curated local summaries. LMI staff can prepare brief summaries of local labor market conditions for advisors, such as a list of in-demand occupations in the area with typical wages and training requirements, updated regularly. Summaries focused on the occupations most relevant to local participants are more useful than comprehensive data.

The fourth element is conversation guidance. Advisors benefit from guidance on how to discuss LMI with participants: how to present wages and openings in plain language, how to explain uncertainty, how to connect data to participants’ interests and circumstances, and how to avoid steering participants inappropriately. Sample conversation approaches and questions can help.

The fifth element is access to experts. Advisors encounter questions they cannot answer from standard tools, such as how a recent plant closure affects local demand or whether a particular credential is valued by employers. Access to LMI analysts or business services staff who can answer these questions supports advisors.

The sixth element is connection with business services. Business services staff learn directly from employers about hiring needs, skill requirements, and wages. Regular communication between business services and career advisors, such as shared meetings or brief updates on employer needs, enriches advisors’ understanding of local demand beyond what published data show.

The seventh element is feedback to LMI producers. Advisors notice when data do not match local reality, such as when an occupation shown as in-demand has few local openings, or when participants struggle to find jobs in occupations with favorable projections. Channels for advisors to share these observations with LMI staff improve data interpretation and tool design.

The eighth element is integration with eligible training provider information. Advisors helping participants choose training need LMI connected to training program information, including program outcomes. Tools that link occupations to local training programs and their results support informed choices.

The ninth element is leadership support. Supervisors and managers should reinforce the use of LMI in career guidance, through case reviews, training expectations, and recognition of good practice.

Advisors equipped with LMI skills and tools give participants informed guidance that improves their chances of choosing training and careers that lead to good jobs.

Labor Market Insights for Employers

Employers are often unaware that public labor market information could help them, or they assume it is too general to be useful. Yet LMI can answer questions employers face regularly: whether their wages are competitive, how many workers with particular skills are available, where those workers live, how competitive the market for talent is, and how their industry is changing. Delivering LMI to employers as practical business insight, rather than as statistical reports, can strengthen employer relationships and help employers make better decisions.

The first application is wage benchmarking. Employers struggling to fill positions may be offering wages below the local market. Occupational wage data, showing the range of wages for an occupation in the area, can help employers assess whether their offers are competitive. Business services staff can share this information tactfully, framing it as information that may help with recruitment rather than criticism. Wage data should be presented with caveats about how they are collected and what they include.

The second application is talent supply. Employers want to know how many people with relevant skills or credentials are available. Data on employment in related occupations, training completions, unemployment, and commuting patterns can provide a picture of available talent. Job posting data can show how many other employers are seeking similar workers, indicating competition.

The third application is recruitment strategy. LMI can inform where and how employers recruit, such as by identifying areas with concentrations of workers in related occupations, training programs producing relevant graduates, or populations with relevant skills who may be underemployed.

The fourth application is skill requirements. Employers sometimes set requirements, such as degrees or years of experience, that exceed what the work requires and narrow their candidate pool. Data on the education and experience typically held by workers in an occupation, and on requirements in competitors’ postings, can help employers reconsider requirements. Business services staff can raise this topic constructively.

The fifth application is industry trends. Information about employment trends in the employer’s industry, projected openings, and changing skill demands helps employers plan workforce development, such as incumbent worker training or apprenticeship.

The sixth application is location decisions. Employers considering expansion or new locations use workforce data to assess talent availability. Economic development partners often provide this information, and coordination with workforce boards ensures accuracy.

Delivering LMI to employers requires business-oriented formats. Brief, customized summaries focused on the employer’s specific positions and area are more useful than general reports. A one-page wage and supply snapshot for an employer’s hard-to-fill position, discussed during a business services visit, can be more valuable than a comprehensive industry report. Presentations to industry groups or sector partnerships can share LMI with many employers at once.

Business services staff are the natural channel for employer LMI. They need training in interpreting LMI for employers and tools to produce customized summaries efficiently. LMI analysts can support them with more complex requests.

Employers also contribute to LMI. Their input through surveys, sector partnerships, and business services conversations provides qualitative information that enriches statistical data. Sharing LMI with employers and inviting their perspectives creates a two-way exchange that improves both data interpretation and employer relationships.

When employers find LMI useful, they see the workforce system as a source of business insight, not only candidates. This perception strengthens the system’s value proposition and deepens employer engagement.

Labor Market Information for Educators and Training Providers

Educators and training providers make decisions that shape the regional workforce for years: which programs to start, expand, or close; how many students to enroll; and what skills and credentials to teach. These decisions involve significant investment in faculty, equipment, and space, and they are difficult to reverse. LMI can help educators align programs with demand, but only if it is presented in ways that support program decisions.

The first need is supply and demand analysis. Educators want to know not only whether an occupation is growing, but how many openings are expected relative to the number of graduates being produced by all programs in the region. An occupation with many openings may already be well served by existing programs; one with modest openings may be underserved. Supply and demand analyses that compare projected annual openings with completions from regional programs help educators identify gaps. These analyses must be presented with care, since completions data may not capture all sources of supply and openings estimates involve uncertainty.

The second need is skill and credential detail. Educators designing curricula need to know which specific skills, certifications, and tools employers seek. Job posting data can reveal commonly requested skills and credentials, and employer input through advisory committees and sector partnerships adds depth. Combining these sources gives educators a more complete picture than either alone.

The third need is wage and outcome information. Educators want to know whether programs lead to jobs that pay well enough to justify students’ investment. Wage data for target occupations, along with outcome data for program graduates where available, help educators assess program value and communicate it to students.

The fourth need is trend information. Educators planning programs that take years to develop need to understand how demand may change. Long-term projections, industry trends, and employer insights about technological change inform these plans, with appropriate acknowledgment of uncertainty.

The fifth need is regional perspective. Students may commute or relocate, and employers recruit across regions. Program decisions should consider regional demand, not only demand within a single jurisdiction. Regional analyses help educators coordinate with neighboring institutions rather than duplicating programs.

Delivering LMI to educators can take several forms. Workforce boards and LMI offices can produce sector or occupation briefs focused on priority areas, presented at program planning meetings or advisory committees. Interactive tools can allow educators to explore supply and demand for specific occupations. Joint planning sessions bringing together educators, employers, and workforce staff can combine LMI with direct employer input.

The eligible training provider list connects LMI to training decisions directly. Boards and states set criteria for training programs eligible to receive WIOA funds, often including alignment with in-demand occupations. Communicating these criteria and the LMI behind them to providers helps them understand which programs are likely to qualify and why.

Educators also need honest communication about data limitations. Projections for small occupations or areas may be unreliable, job posting data may overstate demand for some occupations, and completions data may miss some training sources. Educators who understand these limits can make better decisions.

When educators and workforce agencies share a common understanding of labor market needs, education and training programs align more closely with demand, and students are more likely to find jobs related to their training.

Labor Market Information for Boards and Policymakers

Workforce boards and policymakers use labor market information to make strategic decisions: which industries to target for sector strategies, which occupations to designate as in-demand for training funds, how to allocate resources across programs and populations, and what to include in local and regional plans. WIOA requires local plans to include an analysis of regional economic conditions, including existing and emerging in-demand industry sectors and occupations and the employment needs of employers, which makes LMI a formal part of planning. Yet board members, many of whom are business leaders and community representatives rather than labor economists, often receive LMI in forms that are difficult to use for decisions.

The first need is strategic synthesis. Board members do not need every data series; they need a clear picture of the regional economy’s structure, its strengths, its growth and decline, and the gaps between employer demand and worker supply. A concise strategic briefing, perhaps a few pages with clear visuals, that synthesizes the most important findings and their implications serves boards far better than a comprehensive data compendium. Detailed analysis can be available as an appendix.

The second need is decision framing. LMI is most useful to boards when it is connected to the specific decisions they face. If the board is choosing target sectors, LMI should be organized around criteria for that choice, such as employment size, growth, wages, career pathways, and alignment with economic development priorities. If the board is approving an in-demand occupation list, it should see the criteria, the data for each occupation, and the reasoning behind the recommendations. Framing data around decisions helps boards act on it.

The third need is explanation of methods and limitations. Board members who will defend decisions to officials and the public need to understand where data come from and how reliable they are. Brief, plain explanations of sources, such as the difference between projections and job postings, and of key limitations, prepare board members to answer questions and avoid overconfidence.

The fourth need is employer and community voice. Board members value hearing directly from employers and community partners about conditions they observe. Presentations that pair data with brief employer perspectives, or that report findings from sector partnerships, give boards a richer basis for decisions.

The fifth need is trend monitoring. Strategic decisions should be revisited as conditions change. Regular, brief updates on key indicators, such as quarterly summaries of employment changes in target sectors, layoffs, and job posting trends, allow boards to notice shifts and adjust. A small set of consistent indicators, tracked over time, is more useful than occasional comprehensive reports.

The sixth need is connection to outcomes. Boards benefit from seeing how labor market conditions relate to participant outcomes, such as whether participants trained in target occupations are finding related jobs at expected wages. This connection helps boards evaluate whether their strategies are working.

Policymakers beyond the board, including chief elected officials and state legislators, have similar needs at a higher level of abstraction. They benefit from short, plain-language summaries of regional labor market conditions, the workforce challenges employers face, and how the board’s strategies respond.

LMI that helps boards and policymakers make sound strategic decisions has a multiplier effect, shaping how training funds are spent, which programs are supported, and which industries receive attention. Investing in clear, decision-focused LMI communication for these audiences strengthens the entire workforce system.

Combining Data With Local Knowledge

Statistical labor market information provides an essential foundation, but it rarely tells the whole story. Data lag behind events, miss local nuances, and cannot capture qualitative factors such as employer preferences, workplace conditions, or emerging skill needs. Combining statistical data with local knowledge from employers, staff, partners, and participants produces a fuller, more accurate, and more credible picture.

The first source of local knowledge is employers. Employers know their own hiring needs, skill gaps, wage pressures, and plans before these show up in data. Business services conversations, employer surveys, sector partnership discussions, and advisory committees provide insights that can confirm, qualify, or challenge statistical data. An occupation shown as growing in projections may face local hiring freezes; an occupation not highlighted in projections may be experiencing acute local shortages.

The second source is career advisors and frontline staff. Advisors observe which occupations participants are finding jobs in, which training programs lead to employment, and where participants struggle. These observations reveal how labor market conditions play out for real jobseekers.

The third source is training providers. Providers know how many students are enrolling, completing, and finding jobs, and what employers tell them about graduates. Their information supplements completions data and outcome data.

The fourth source is participants. Participants’ experiences in the job market, such as which employers are hiring, what wages are offered, and what requirements they encounter, provide grounded information, especially when gathered systematically.

The fifth source is economic development partners and local news. Information about business expansions, closures, new investments, and industry changes helps anticipate shifts in demand before they appear in data.

Combining these sources requires processes. Regular meetings bringing together LMI analysts, business services staff, career advisors, and partners can review data alongside local observations. Employer surveys designed to supplement statistical data can gather information on hiring plans and skill needs. Brief, structured ways for staff to share observations, such as a monthly summary of employer feedback, make local knowledge available to others.

When data and local knowledge conflict, the conflict is informative. It may reveal a data limitation, such as job postings overstating demand, a local exception to a broader trend, or a recent change not yet reflected in data. Investigating conflicts, rather than dismissing one source, improves understanding.

Communicating combined information requires transparency about sources. Users should know which information comes from statistical data, which from employer input, and which from staff observation. This transparency allows users to weigh information appropriately and builds trust.

Local knowledge also helps make LMI credible to users. A jobseeker may doubt a projection but trust an advisor who says she has seen several participants hired in this occupation by local employers recently. An employer may question statewide wage data but accept wage information confirmed by other local employers in a sector partnership. Combining data with local knowledge makes LMI more persuasive as well as more accurate.

Finally, combining sources requires attention to the limits of anecdote. Local observations can be unrepresentative, and a few vivid cases can mislead. The goal is to use local knowledge to interpret and enrich data, not to replace systematic data with anecdote.

Building Partnerships Between Local Boards and State LMI Offices

In most states, labor market information is produced primarily by a state LMI office, often located within the state workforce or labor agency and working in cooperation with federal statistical programs. Local workforce boards, career centers, and business services teams are among the most important users of that information, yet the relationship between producers and local users is often distant. State analysts may not know what questions local advisors face; local staff may not know what analyses the state can provide or how to request them. Building a working partnership between local boards and state LMI offices improves the relevance of LMI and the capacity of local staff to use it.

The first element is mutual understanding. Local staff benefit from understanding what the state LMI office produces, its methods, its release schedules, and the custom analyses it can provide. State analysts benefit from understanding local decisions, users, and conditions. Periodic meetings or presentations, such as a state analyst attending a local board meeting or local staff visiting the LMI office, build this understanding.

The second element is designated contacts. Many state LMI offices assign regional analysts or economists to work with particular areas. Local boards should know who their contact is and how to reach that person. A regional analyst who knows the local economy and the board’s priorities becomes a valuable resource for interpreting data and preparing analyses.

The third element is custom analysis. Local boards often need analyses not available in standard products, such as supply and demand comparisons for specific occupations, wage information for a particular combination of occupations and areas, or profiles of target sectors. Clear processes for requesting custom analyses, with realistic timelines, help boards obtain what they need. Boards should frame requests around the decisions the analysis will inform, which helps analysts design useful products.

The fourth element is feedback on products. Local users are well positioned to tell state offices which products are useful, which are confusing, and what is missing. Channels for structured feedback, such as user surveys, advisory groups of local staff, or periodic review sessions, help state offices improve their products. Feedback from career advisors about how jobseekers respond to occupational information is particularly valuable.

The fifth element is sharing local knowledge. Local boards and business services teams learn about employer expansions, layoffs, and skill needs before these appear in data. Sharing this information with state analysts, where appropriate and with attention to confidentiality, helps analysts interpret data and anticipate changes.

The sixth element is joint training. State LMI offices can offer training for local staff on using LMI tools and interpreting data. Local boards can help design training around the practical questions their staff face. Joint training builds a common understanding across state and local staff.

The seventh element is co-developed products. Some of the most useful LMI products, such as local in-demand occupation guides or sector briefs, can be developed jointly, with state analysts providing data and methods and local staff providing knowledge of users and local conditions. Co-development produces products that are both technically sound and locally relevant.

Partnerships between local boards and state LMI offices strengthen both. State offices gain users who understand and value their work and provide feedback that improves it. Local boards gain access to analytical expertise and data they could not produce themselves. Together, they can close much of the gap between labor market data and the decisions it should inform.

Presenting Labor Market Information Honestly

Labor market information involves estimates, projections, and samples, all of which carry uncertainty. Presenting LMI as more certain or precise than it is can mislead users into decisions they regret, such as investing in training for an occupation that does not grow as projected. Honest presentation acknowledges uncertainty and limitations while still providing useful guidance.

The first principle is distinguishing projections from facts. Employment projections are informed estimates based on assumptions about economic conditions, technology, and other factors. They are not predictions of what will certainly happen. Presenting projections as expected trends, with language such as is projected to or is expected to, and noting that actual outcomes may differ, is more honest than presenting them as facts.

The second principle is acknowledging data limitations. Wage estimates for small areas or uncommon occupations may be imprecise or unavailable. Job posting data may overstate demand due to duplicates and evergreen postings, and may understate demand for occupations that rarely post online. Employment data may miss self-employment and informal work. Users should be informed of significant limitations relevant to their decisions.

The third principle is avoiding false precision. Presenting wages or projections with excessive precision, such as projected growth of a specific decimal percentage, suggests more certainty than exists. Rounding and ranges communicate more honestly.

The fourth principle is explaining openings accurately. Many occupational openings arise from workers leaving occupations, through retirement, career changes, or other reasons, rather than from growth. An occupation with little growth may still have many openings. Conversely, an occupation with high growth from a small base may have few openings. Explaining what openings include helps users interpret them correctly.

The fifth principle is providing context for wages. Median wages may differ from starting wages, and wages vary by experience, employer, and location within an area. Presenting wage ranges and noting that entry-level wages are often lower than medians gives users realistic expectations.

The sixth principle is noting timeliness. Data have reference periods, and conditions may have changed since. Noting when data were collected, and whether more recent information suggests changes, helps users assess relevance.

The seventh principle is acknowledging what LMI does not show. LMI does not show job quality, working conditions, employer practices, or individual fit. Users should understand that LMI is one input among many.

The eighth principle is avoiding overstated claims about in-demand occupations. Labeling occupations as in-demand can encourage many people to pursue them, potentially creating oversupply. Lists of in-demand occupations should be explained, with criteria and limitations, and updated as conditions change.

Honest presentation need not overwhelm users with caveats. For jobseekers, a few plain-language notes, such as these are estimates and actual pay may vary, and many openings come from people leaving the job rather than new jobs, may suffice. For educators and policymakers making larger decisions, more detailed discussion of limitations is appropriate. The level of detail should match the stakes of the decision and the user’s needs.

Honest presentation builds trust. Users who find that LMI matches their experience, and who understand its limitations, are more likely to rely on it for future decisions.

Tools and Formats That Get Used

The format in which LMI is delivered largely determines whether it is used. Comprehensive reports and complex databases serve analysts but rarely reach the decisions of jobseekers, advisors, or employers. Formats designed around users’ questions, time, and context are far more likely to be used.

Several formats have proven useful across different users:

• Occupation profiles that present, on a single page or screen, an occupation’s description, typical local wages, expected openings, education and training requirements, related local training programs, and related occupations, in plain language.

• Career exploration tools that let jobseekers and students start from interests, skills, or current occupations and explore matching careers with local data.

• In-demand occupation lists for the local area, with brief explanations of criteria, typical wages, and training paths, updated regularly and used by advisors in conversations.

• Wage and supply snapshots for employers, customized to specific positions and areas, delivered by business services staff.

• Sector briefs summarizing employment, wages, projections, skill needs, and employer perspectives for priority industries, for boards, educators, and employers.

• Supply and demand analyses comparing projected openings with regional training completions, for educators and boards.

• Interactive dashboards allowing users to explore data by occupation, industry, and area, with plain-language guidance and default views.

• Brief periodic updates, such as a monthly or quarterly labor market summary for staff and partners, highlighting notable changes.

Several design principles apply across formats. Organizing information around user questions, rather than data categories, makes it easier to find answers. Leading with the most important information, such as wages and openings, before details helps users get what they need quickly. Using plain language, with technical terms explained, makes information accessible. Visual elements such as simple charts and icons help users grasp information at a glance. Connecting information to action, such as links to training programs or career center services, helps users take next steps. Mobile-friendly design matters, since many jobseekers access information on phones.

Tools should be tested with users. Observing jobseekers, advisors, and employers using a tool reveals where they get confused, what information they look for, and what they ignore. Iterative improvement based on testing produces tools people actually use.

Tools should be maintained. Outdated data or broken links undermine trust. Assigning responsibility for regular updates, and noting the date of data on each tool, keeps tools reliable.

Tools should be promoted. Many states have useful LMI tools that few people know about. Training advisors and partners to use and recommend tools, featuring them on career center websites, and introducing them in workshops increases use.

Finally, tools are not substitutes for people. Even the best self-service tools are most effective when complemented by advisors and business services staff who help users interpret information and apply it to their situations.

Accessibility and Equity in Labor Market Information

Labor market information can expand opportunity by helping people discover careers and training they might not have considered. It can also reinforce inequity if it reaches only those already well connected, if tools are inaccessible to some users, or if guidance steers people based on assumptions rather than their interests and abilities. Attention to accessibility and equity ensures that LMI benefits everyone.

Accessibility begins with formats. LMI tools and materials should meet accessibility standards so that people with disabilities can use them, including compatibility with screen readers, keyboard navigation, sufficient color contrast, captions for videos, and alternatives to charts, such as data tables or text descriptions. Digital tools from state and local governments are subject to accessibility requirements, including the technical standards established by the Department of Justice’s 2024 rule under Title II of the Americans with Disabilities Act.

Language access is essential. Jobseekers with limited English proficiency need career information in their languages. Key LMI tools and materials, such as occupation profiles and in-demand occupation lists, should be available in the languages most common in the community, and advisors should be able to discuss LMI through interpreters.

Digital access matters. Many LMI tools are online, but some jobseekers lack reliable internet access, devices, or digital skills. Printed materials, in-person assistance at career centers, and access through libraries and partner locations ensure that LMI reaches people without digital access.

Literacy and numeracy considerations matter. Many users find numbers, percentages, and charts difficult to interpret. Presenting information in plain language, with simple numbers and visual aids, and offering conversation with advisors helps users with limited literacy or numeracy.

Equity in career guidance requires particular attention. Career guidance has historically sometimes steered people toward or away from occupations based on gender, race, ethnicity, disability, or other characteristics, rather than individual interests and abilities. Advisors using LMI should present the full range of options to every participant, encourage exploration of occupations where groups are underrepresented, and avoid assumptions about what particular participants can or should do. Training on equitable career guidance supports this.

Equity also involves attention to occupational segregation and wage disparities. LMI can reveal occupations where some groups are underrepresented, and where wage differences exist. Workforce agencies can use this information to design outreach and support that broadens access to higher-wage occupations, such as encouraging women to consider skilled trades or supporting underrepresented groups in technology careers.

Equity extends to employer insights. When sharing LMI with employers, business services staff can highlight talent pools employers may overlook, such as people with disabilities, older workers, people with records, or residents of particular neighborhoods, and can encourage inclusive recruitment and skills-based requirements.

Finally, equity requires reaching people who are not already connected to the workforce system. LMI shared through community organizations, schools, libraries, and other trusted channels reaches people who would not visit a career center or state website.

LMI that is accessible and used equitably can help open career pathways to people who might otherwise be excluded, contributing to a more inclusive labor market.

Ethical Commitments in Using Labor Market Information

Labor market information influences consequential decisions about careers, training investments, wages, and public priorities. Using it responsibly requires ethical commitments that protect users and maintain the credibility of the information.

The first commitment is accuracy. LMI should be presented accurately, without selective use that misrepresents conditions. Agencies should not highlight data that support a preferred conclusion while omitting contrary data, such as promoting an occupation as in-demand while ignoring evidence of oversupply.

The second commitment is honesty about uncertainty, discussed earlier. Users making significant decisions deserve to understand that projections and estimates carry uncertainty.

The third commitment is respecting individual choice. LMI should inform, not dictate, career decisions. Advisors should not pressure participants into occupations solely because they are in demand, nor discourage participants from pursuing interests because data suggest limited demand, without honest discussion. Participants’ interests, abilities, and circumstances are central to good decisions.

The fourth commitment is avoiding steering based on characteristics. As discussed under equity, career guidance should not channel participants toward or away from occupations based on gender, race, ethnicity, disability, age, or other characteristics. LMI should be presented equally to all participants.

The fifth commitment is transparency about sources and methods. Users should be able to learn where data come from, how they were produced, and what they include. Transparency allows users to evaluate information and builds trust.

The sixth commitment concerns the use of private data. Job posting and other private data sources can be valuable, but they have limitations and may be produced by vendors with commercial interests. Agencies using private data should understand its methods and limitations and communicate them to users.

The seventh commitment concerns confidentiality. Some LMI is derived from confidential data, such as employer wage records. Statistical agencies protect confidentiality by suppressing data that could identify individual employers or workers. Workforce staff sharing LMI must respect these protections and should not attempt to identify individual employers from published data or share confidential information obtained through business services.

The eighth commitment is fairness in employer advice. When sharing wage and supply information with employers, staff should present information objectively and should not favor particular employers. Wage information should support employers in making informed decisions, not be used to suppress wages.

The ninth commitment is avoiding overreliance on technology. Tools using algorithms or artificial intelligence to match people with occupations or training may embed biases or produce recommendations users do not understand. Agencies using such tools should understand how they work, test them for bias, explain them to users, and ensure that human judgment remains central.

The tenth commitment is responsiveness to evidence of harm. If LMI guidance appears to be leading participants into training for occupations with poor outcomes, or if tools produce biased results, agencies should investigate and correct the problem.

These commitments ensure that LMI serves the people it is meant to help, maintaining the trust that makes it useful.

Making Labor Market Information Useful in Practice

The following hypothetical example illustrates how a workforce board might make labor market information more useful. It does not describe an actual board or region.

A workforce board notices a pattern: many participants choose training in a small number of familiar occupations, regardless of local demand, and some complete training but struggle to find related jobs. Career advisors acknowledge that they rarely consult LMI during appointments, finding state tools cumbersome and unsure how to interpret projections. Employers in the board’s sector partnerships report wage pressures and skill gaps that advisors are unaware of. Meanwhile, the state LMI office publishes extensive data that few in the local system use.

The board’s executive director convenes a working group including career advisors, business services staff, a state LMI analyst, a community college representative, and two employers. The group identifies the questions each user group most needs answered. Advisors want quick, local information on wages, openings, and training paths for the occupations participants consider most. Employers want wage benchmarks and information on talent supply. The college wants supply and demand comparisons for its programs.

Working with the state LMI analyst, the board develops a local in-demand occupation guide covering about forty occupations relevant to local participants, each with a one-page profile showing typical local wages, entry-level wages, expected annual openings, training required, local training programs and their outcomes, and related occupations. Profiles use plain language and include brief notes on what the numbers mean and their limitations. Employer input from sector partnerships is added to profiles, noting skills employers emphasize and recent hiring trends. The guide is available in print and online, in English and Spanish, and is updated twice a year.

Advisors receive half-day training on using the guide and discussing LMI with participants, including how to explain projections and wages, how to connect data to participants’ interests, and how to avoid steering. Monthly team meetings include a brief labor market update from business services staff, sharing what employers are saying.

Business services staff develop a simple template for employer wage and supply snapshots, which they prepare for employers with hard-to-fill positions. Several employers adjust wages or requirements after reviewing snapshots, and report improved recruitment.

The board and the community college produce an annual supply and demand analysis for priority occupations, which the college uses in program planning. The college expands a program in an occupation where analysis shows a significant gap.

Over the following year, the share of participants choosing training in occupations with strong local demand increases, and the share finding training-related employment improves. Advisors report greater confidence in career conversations. Participants report that the occupation profiles helped them understand their options.

The example illustrates the core principles: identifying user questions, curating local information, translating data into plain language, combining data with employer knowledge, equipping advisors, delivering employer insights through business services, supporting educators with supply and demand analysis, ensuring language access, and testing whether information is used.

Measuring Whether Labor Market Information Is Used

LMI producers often measure output, such as reports published, tools launched, or data series updated. These measures say little about whether LMI is used or whether it improves decisions. Measuring use and influence helps workforce agencies understand whether their LMI communication is working.

The first area is reach. Website analytics show how many people use LMI tools, which tools and pages are most used, and how users navigate. Distribution counts for printed materials, such as occupation guides, provide additional reach information. Tracking which audiences are reached, such as jobseekers, advisors, employers, or educators, reveals gaps.

The second area is use in advising. Surveys or case reviews can assess how often advisors consult LMI in career conversations, which tools they use, and how confident they feel. Observation of advising sessions, with consent, can reveal how LMI is discussed.

The third area is user understanding. Testing whether users understand key concepts, such as what projected openings mean or how to interpret wage ranges, reveals whether translation is effective. Brief user surveys or usability tests can assess understanding.

The fourth area is influence on decisions. Asking participants whether LMI influenced their training or career choices, and asking employers whether wage or supply information influenced their recruitment, shows whether LMI affects decisions. Educators can report whether LMI influenced program decisions.

The fifth area is outcomes. Over time, agencies can examine whether participants’ training choices align more closely with local demand, and whether training-related employment improves. These outcomes are influenced by many factors, but trends can suggest whether LMI guidance is contributing.

The sixth area is user satisfaction and feedback. Users can be asked whether LMI tools and materials are useful, easy to understand, and trustworthy, and what would make them more useful.

Several questions help agencies assess LMI communication:

• How many jobseekers, advisors, employers, and educators use LMI tools and materials, and which ones?

• Do career advisors consult LMI regularly in career conversations and feel confident interpreting it?

• Do users correctly understand key concepts such as openings, projections, and wage ranges?

• Do participants, employers, and educators report that LMI influenced their decisions?

• Are participants’ training choices increasingly aligned with local demand, and is training-related employment improving?

Measurement should inform improvement. Tools that are rarely used may need redesign or promotion. Concepts users misunderstand may need clearer explanation. Advisors who lack confidence may need more training. Feedback from users can guide changes in content, format, and delivery.

Measurement should also inform LMI producers. Sharing information about how LMI is used, and where users struggle, with state LMI offices helps them design products that better serve users.

How Labor Market Information Communication Compares With Other Agency Types

Translating technical data into information people can use for decisions is a challenge shared across many fields. Comparing LMI communication with other examples offers useful lessons.

Weather forecasting is perhaps the most successful example of translating complex, uncertain data into public guidance. Forecasters present probabilities, such as a chance of rain, in simple terms, provide forecasts at local scales, deliver information through many channels at the moment of decision, and have developed ways to communicate uncertainty that most people understand. LMI communicators can learn from forecasting’s attention to local relevance, timing, and plain expression of uncertainty, though labor market projections involve longer time horizons and greater uncertainty.

Public health agencies translate epidemiological data into guidance for individuals, clinicians, and policymakers. They have developed approaches for communicating risk, tailoring information to different audiences, and equipping intermediaries such as clinicians to discuss data with patients. The role of clinicians as trusted interpreters parallels the role of career advisors.

Higher education institutions and federal tools present college outcome data, such as graduation rates and earnings, to help students choose schools and programs. Experience with these tools shows that data alone often have limited influence on student choices unless paired with advising, and that presentation and context strongly affect interpretation.

Agricultural extension services translate research and market data into practical guidance for farmers, often through extension agents who work directly with producers. The extension model, in which trusted local experts interpret data for individual situations, closely parallels the role of career advisors and business services staff in LMI.

Financial regulators and consumer protection agencies present information about financial products in standardized, plain-language formats to help consumers compare options. Their emphasis on standardized disclosure and comparability offers lessons for occupation profiles and training program information.

What distinguishes LMI communication is the combination of many data sources with different strengths and limitations; long-term projections with substantial uncertainty; decisions that are highly personal and depend on individual interests and circumstances; diverse users from jobseekers to educators; and the central role of intermediaries such as career advisors and business services staff. These features make user-centered design, plain language, honest presentation of uncertainty, combination of data with local knowledge, and investment in intermediaries especially important. Workforce agencies can borrow from weather forecasting’s clarity, public health’s attention to intermediaries, and the extension model’s local expertise, adapting each to the workforce context.

Tying It All Together

Labor market information is not a side function of the workforce system. It is meant to inform the most consequential decisions the system supports: which careers jobseekers pursue, which training they choose, what employers pay and how they recruit, which programs educators offer, and which priorities boards set. When LMI reaches these decisions in usable form, it helps people choose well. When it does not, decisions rest on assumptions and anecdotes.

The gap between data and decisions arises from data organized for producers rather than users, jargon, poor timing, overwhelming volume, disconnection from local reality, lack of interpretation, and limited trust. Closing it requires understanding what LMI includes, from employment and wage data to projections, job postings, education data, and qualitative local information, and understanding the distinct questions of jobseekers, students, advisors, employers, business services staff, educators, boards, and economic development partners.

For jobseekers and students, LMI should start from their questions, use plain language, focus on local conditions, connect occupations to pathways, show wage ranges, acknowledge uncertainty and competition, highlight transferable skills, remain one input among many, and arrive through trusted conversations. Career advisors, the most important channel, need training, convenient tools, curated summaries, conversation guidance, access to experts, connection with business services, channels for feedback, integration with training provider information, and leadership support.

For employers, LMI becomes business insight on wages, talent supply, recruitment, requirements, trends, and location, delivered in customized, concise formats through business services staff. For educators, it supports program decisions through supply and demand analysis, skill detail, wage and outcome information, trends, and regional perspective. Combining data with local knowledge from employers, advisors, providers, participants, and partners produces a fuller, more credible picture, with transparency about sources.

Honest presentation distinguishes projections from facts, acknowledges limitations, avoids false precision, explains openings and wages accurately, notes timeliness, acknowledges what LMI cannot show, and avoids overstated in-demand claims. Useful formats, including occupation profiles, career exploration tools, local in-demand guides, employer snapshots, sector briefs, supply and demand analyses, dashboards, and periodic updates, are designed around user questions, tested, maintained, and promoted. Accessibility and equity ensure that LMI reaches and serves everyone without steering, and ethical commitments to accuracy, honesty, individual choice, transparency, confidentiality, fairness, and responsible use of technology maintain trust. Measuring reach, use, understanding, influence, outcomes, and satisfaction shows whether LMI is actually informing decisions.

Strategic Communication Support

Turning labor market information into guidance people use requires collaboration between LMI analysts, career advisors, business services staff, educators, and communicators; user-centered design of tools and materials; plain-language translation that remains accurate; training for intermediaries; and ongoing measurement. Workforce agencies often have access to rich data but limited capacity to redesign how it is communicated. External support can help them bridge the gap between data and decisions.

Stegmeier Consulting Group (SCG) helps public agencies translate technical information into communication people can act on. For workforce agencies, that work can begin with user research: interviews and observation with jobseekers, career advisors, employers, educators, and board members to understand the decisions they make, the questions they ask, the information they currently use, and the barriers they face in using LMI. A review of existing LMI products and tools identifies what is available and how well it serves users.

From that foundation, SCG can work with LMI analysts and workforce staff to design user-centered products, such as local occupation profiles, in-demand occupation guides, employer wage and supply snapshots, sector briefs, and educator supply and demand analyses, written in plain language with honest treatment of uncertainty. SCG can help develop processes for combining statistical data with employer and staff knowledge, and can help ensure accessibility and language access.

Implementation support can include training for career advisors on using and discussing LMI equitably, training for business services staff on delivering employer insights, conversation guides, and templates. SCG can help design usability testing for tools and materials and coordinate with state LMI offices to share user feedback.

Evaluation support helps agencies track reach, use, understanding, influence on decisions, and outcomes, and use findings to improve LMI communication over time. The aim is labor market information that jobseekers, advisors, employers, and educators actually use to make better decisions.

Future Trends

Several established developments are shaping how workforce agencies communicate labor market information. Real-time and alternative data sources continue to expand. Online job posting data, data from professional networking platforms, and other alternative sources provide timely information about employer demand and skill requirements. These sources complement traditional statistical data but have limitations in coverage and reliability. Agencies will increasingly need to combine traditional and alternative data thoughtfully and explain their differences to users.

Skills-based approaches to labor market information are developing. Rather than focusing only on occupations, some tools and analyses emphasize skills, identifying the skills employers seek and the skills workers have, and mapping pathways between occupations based on shared skills. Skills-based LMI can help career changers identify transferable skills and help employers broaden requirements. These approaches are still evolving, and skill taxonomies and data vary in quality.

Artificial intelligence tools are being applied to LMI, including tools that analyze job postings, match jobseekers with occupations or training, generate personalized career recommendations, or produce plain-language summaries of data. These tools may make LMI more accessible and personalized. They also raise concerns about accuracy, transparency, and bias, particularly when recommendations influence significant decisions. Agencies adopting such tools should evaluate them carefully, explain them to users, and ensure human oversight. Their role in public workforce systems is still developing.

Technological change in the labor market itself, including automation and the adoption of artificial intelligence by employers, may change skill demands in some occupations. Projections and guidance will need to account for these changes, though their extent and pace are uncertain and vary by occupation. Agencies should communicate this uncertainty honestly rather than making speculative claims about which jobs will be affected.

Data integration across education and workforce systems is improving in some states, allowing analysis of pathways from education and training to employment and earnings. These linked data can support richer information about training program outcomes and career pathways, with appropriate privacy protections.

Accessibility requirements for digital government content are becoming more concrete, with the Department of Justice’s 2024 rule establishing technical standards for state and local government web content and mobile applications. LMI tools will need to meet these standards.

Finally, the structure and funding of federal workforce and labor market information programs may change. Budget proposals in recent cycles have contemplated changes to federal workforce programs, and federal statistical programs that produce LMI depend on continued funding. Whether and how such changes occur is uncertain. Agencies that have built strong user-centered LMI communication will be better positioned to make the most of whatever data remain available.

Conclusion

The laid-off warehouse worker choosing between medical coding, industrial maintenance, and commercial driving deserves more than her advisor’s best guess. The information that could help her, including what each occupation pays locally at entry and with experience, how many openings arise each year, which local programs prepare people for them and how their graduates fare, and what local employers are saying about hiring, already exists. What is missing is the communication that brings it into her conversation, in words she understands, at the moment she is deciding.

For workforce board leaders, state LMI offices, and the staff who serve jobseekers and employers, the implication is that labor market information should be designed around decisions rather than data categories. That means starting from the questions users actually ask, translating technical terms into plain language, curating local information instead of publishing everything, combining statistics with what employers and advisors observe, presenting uncertainty honestly, and investing in the career advisors and business services staff who carry LMI into real conversations. It also means making tools accessible to everyone and guarding against guidance that steers people based on who they are rather than what they want and can do.

Agencies that take this approach will find that labor market information stops being a report few people read and becomes a resource that shapes better career choices, smarter employer recruitment, better-aligned training programs, and more grounded public priorities.

Stegmeier Consulting Group’s Strategic Approach to Communication Systems

SCG approaches labor market information as a communication system that connects data to the decisions it is meant to inform. That approach begins with the people who make those decisions, including jobseekers, career advisors, employers, educators, and boards, and learns what they are asking, what they currently rely on, and where data fail to reach them. It then helps agencies curate local information, translate technical measures into plain language, combine statistics with employer and staff knowledge, and present projections and estimates honestly.

For workforce agencies, this means occupation profiles and in-demand guides that advisors can use in real conversations, employer snapshots that turn wage and supply data into business insight, supply and demand analyses that help educators plan programs, and tools that are accessible, multilingual, and tested with users. It means training intermediaries to discuss LMI equitably, guarding against steering, and measuring whether information is actually shaping decisions.

Use the form below to connect with our team and explore how strategic communication support can help your workforce agency turn labor market information into career guidance and employer insights people actually use.