Job Market Health score
28/ 100
Weak
Stable · -2
- At least 90% of required inputs and occupation-industry mapping are covered.
Job Market Health scores are derived indicators based on published data, informational only, not a forecast or guarantee.
Interviewers, except eligibility and loan
SOC 43-4111 · 43-0000 · Score reference month August 2026
Interview persons by telephone, mail, in person, or by other means for the purpose of completing forms, applications, or questionnaires. Ask specific questions, record answers, and assist persons with completing form. May sort, classify, and file forms.
In short
The national Job Market Health score is in the Weak band for August 2026.
$45,920Median annual wageOEWS May 2025
13,200Projected annual openingsBLS Employment Projections 2025-2035
-10.4%Projected growth2025–2035
148,060Total employmentOEWS May 2025
Latest interpretation
What stands out for interviewers, except eligibility and loan
The clearest signals from the latest validated score, outlook and metro data.
Current demand is the strongest component
Current demand scores 38/100, compared with 13/100 for occupation fundamentals, a 25-point difference.
JobMarketHealth score componentsLong-term employment is projected to contract
BLS projects 13,200 average annual openings and -10.4% employment growth from 2025 to 2035. Projected openings are a modeled annual average, not vacancies open today.
BLS Employment ProjectionsLargest published metro market
New York-Newark-Jersey City, NY-NJ has the largest published metro employment count for interviewers, except eligibility and loan, at 9,460 jobs. The next-largest published metro is Los Angeles-Long Beach-Anaheim, CA with 8,500 jobs. This measures employment scale, not market quality.
BLS OEWSLocal market context
QCEW industry conditions for this occupation’s employment mix
Choose a state to see how the industries that employ this occupation are performing locally.
Choose a state above to explore its available industry context.
How to read this section
QCEW measures employment and wages by industry, not by occupation. We weight those industry conditions using the published industry mix for this occupation. These figures describe the industries that employ this occupation; they are not direct counts of people working in the occupation or jobs currently open.
Source: BLS QCEW. This section is not part of the national 0 to 100 Job Market Health score. See the methodology.
Compare states for interviewers, except eligibility and loanSide-by-side published industry conditions for two or three states. There is no state-level health score; the 0 to 100 score is national.
How to read this page
The score is a national composite from 0 to 100 that weights near term hiring demand at 50 percent, the occupation's own employment fundamentals at 30 percent, and long-term outlook at 20 percent. Wages and occupation profile details are context shown alongside it, not score inputs. See the full methodology.
Median annual wage and metro employment come from BLS OEWS, which is annual. Projected annual openings and projected growth come from BLS Employment Projections and describe the published projection horizon, so they are not a count of jobs open today. Values shown as not published are suppressed by the source rather than filled in here.
Job Market Health trend
Published composite score · 0–100 · by reference month
Latest published values
- Aug 2628 / 100
View values: Job Market Health history
| Reference month | Score / 100 |
|---|---|
| Jan 25 | 42 |
| Feb 25 | 40 |
| Mar 25 | 37 |
| Apr 25 | 38 |
| May 25 | 38 |
| Jun 25 | 37 |
| Jul 25 | 35 |
| Aug 25 | 31 |
| Sep 25 | 31 |
| Oct 25 | 28 |
| Nov 25 | 29 |
| Dec 25 | 27 |
| Jan 26 | 31 |
| Feb 26 | 29 |
| Mar 26 | 29 |
| Apr 26 | 28 |
| May 26 | 30 |
| Jun 26 | 31 |
| Jul 26 | 30 |
| Aug 26 | 28 |
Score components
The three published inputs behind the composite · each 0–100
Largest metro markets by employment
OEWS employment count, where the jobs are, not a quality ranking
OEWS May 2025. These occupation counts describe the survey reference period.
Linked metro names open overall job-market profiles.
| Metro area | Employment | Jobs per 1,000 | Median annual wage |
|---|---|---|---|
| New York-Newark-Jersey City, NY-NJ | 9,460 | 1.00 | $57,330 |
| Los Angeles-Long Beach-Anaheim, CA | 8,500 | 1.36 | $55,780 |
| Dallas-Fort Worth-Arlington, TX | 4,220 | 1.04 | $47,320 |
| Denver-Aurora-Centennial, CO | 3,100 | 1.93 | $50,180 |
| Houston-Pasadena-The Woodlands, TX | 2,910 | 0.88 | $44,400 |
| San Francisco-Oakland-Fremont, CA | 2,540 | 1.07 | $63,020 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 2,470 | 0.85 | $46,490 |
| Boston-Cambridge-Newton, MA-NH | 2,440 | 0.90 | $50,700 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 2,250 | 0.72 | $49,700 |
| Kansas City, MO-KS | 2,130 | 1.94 | $45,020 |
Education & training
Typical entry requirements · BLS Employment Projections 2025-2035
Top skills
Occupation profile · O*NET 31.0 Database
- Active Listening
- Speaking
- Reading Comprehension
- Social Perceptiveness
- Critical Thinking
- Service Orientation
- Time Management
- Writing
- Complex Problem Solving
- Coordination
Also known as: Admissions Clerk, Admissions Representative, Admitting Representative, Data Collection Assistant, Interviewer, Market Research Interviewer
AI & this occupation
How AI relates to Interviewers, except eligibility and loan work
Across 4 research exposure measures, Interviewers, except eligibility and loan is in the higher technical AI exposure band based on the mean of the available source-specific percentile ranks. Exposure describes how much of the work the studied AI capabilities could technically assist with; it is not a forecast of job loss.
Observed use of Claude for this occupation's tasks is in the higher third of occupations in the Anthropic Economic Index, a dated observed task-exposure benchmark, not conversation share or the share of workers using AI. Its observation window is not recorded in this saved release.
In the task mix derived from the March 27, 2025 Anthropic release, 58.7% followed automation patterns (AI completing a directed task), based on 4 matched tasks.
JobMarketHealth has not measured an AI effect on this occupation's employment or wages, and these figures do not enter its scores; there is no clear evidence of displacement in the data shown here.
Exposure measures (kept separate)
- GPTs are GPTs exposure (β, GPT-4 rated): 0.67 of 1, national percentile 95 (higher third)
- Microsoft AI applicability: 0.23 of 1, national percentile 76 (higher third)
- AIOE (Felten, Raj & Seamans): 1.30 (standardized), national percentile 88 (higher third)
- OECD AI Capability Gap (lower gap = higher potential exposure): gap 1.0 of 23, national percentile 88 (higher third)
OECD capability gap by domain
As published by the OECD: the gap between what the occupation demands (0-5) and current AI capability in each domain. A lower gap means AI is closer to the requirement (higher potential exposure); 0 means AI already meets it or the domain is not relevant.
- Language: gap 0.00 (demand 3.00 of 5)
- Social interaction: gap 1.00 (demand 3.00 of 5)
- Problem solving: gap 1.00 (demand 3.00 of 5)
- Creativity: gap 0.00 (demand 0.95 of 5)
- Metacognition & critical thinking: gap 0.05 (demand 2.05 of 5)
- Knowledge, learning & memory: gap 0.00 (demand 2.10 of 5)
- Vision: gap 0.00 (demand 0.25 of 5)
- Manipulation: gap 0.00 (demand 0.10 of 5)
- Robotic intelligence: gap 0.00 (demand 0.10 of 5)
National context: what businesses used generative AI for
From the Census 2025-26 BTOS AI Supplement (all industries; businesses whose employees used generative AI). Census did not measure Interviewers, except eligibility and loan specifically.
- Writing or editing documents, emails, or communications: 85.4%
- Searching for information or technical help: 49.9%
- Interpreting, analyzing, translating, or summarizing documents: 44.6%
- Information processing, paperwork, or filing: 34.7%
Exposure and observed usage are separate measures and neither equals job loss. Census BTOS figures are business-level surveys and are not occupation-specific. Sources: GPTs are GPTs occupation exposure; Microsoft Working with AI: AI applicability; AI Occupational Exposure (AIOE); OECD AI Exposure Measure (AI Capability Gap); Anthropic Economic Index; Census BTOS Core AI Use; Census BTOS AI Supplement 2025-26. These figures do not affect Job Market Health scores. Explore AI & Jobs · How we use AI data.
Sources and methodology
The datasets behind the figures on this page
Values marked “Not published” are suppressed or not published by the source. We leave them blank rather than estimating them. Pipeline status for every source is on Data Status.
Related occupations
O*NET® lists these occupations as related. Each link opens the same published view of score, wages and outlook.
Evidence record
JobMarketHealth derived indicator; not a government statistic. The composite score is national and occupation level.
- Occupation
- Interviewers, except eligibility and loan
- SOC code
- 43-4111
- Geography
- United States
- Reference month
- August 2026
- Published Job Market Health score
- 28 / 100
- Restated score
- 28 / 100
- Band
- Weak
- Current Demand component
- 38 / 10050% weight
- Occupation Fundamentals component
- 13 / 10030% weight
- Long-Term Outlook component
- 26 / 10020% weight
- Confidence
- HighAt least 90% of required inputs and occupation-industry mapping are covered.
- Methodology version
- v1.0
- Source vintage: Annual
- EP-2025-2035|CPS-25b-2025|OEWS-2020-2025
- Source vintage: Monthly calculated at
- 2026-10-02T15:32:37.772Z
- First published at
- Sep 29, 2026
- Recalculated at
- Oct 2, 2026
- Canonical record URL
- https://jobmarkethealth.com/occupations/interviewers-except-eligibility-and-loan#evidence-record
- Derived by
- JobMarketHealth
MethodologyBLS JOLTSBLS CESBLS Employment ProjectionsHow to citeNational Job Market Health release, August 2026
Keep going
Learn how to judge a market from these numbers in how to tell if a job market is healthy, or why projected growth and annual openings often disagree in projected growth vs projected annual openings. Coverage, revisions and limits are explained on about the data.
The datasets behind these figures, and what each one can and cannot show, are set out on data sources.