# The Metro Target Run, From One Role to a Ranked City Shortlist

*You will convert one job title into a ranked shortlist of five to ten metros, scored on role concentration, market crowding, and what local pay actually buys.*

- Canonical URL: https://www.refolk.ai/candidates/guides/metro-target-run-city-shortlist
- Pillar: Reading the market
- Format: Playbook
- Published: 2026-10-11
- Last reviewed: 2026-10-11
- Reading time: 16 min

This guide is for anyone deciding where to point a job search, or whether to relocate, for one specific role. It converts a single job title into a ranked shortlist of metros, scored on three things a generic "best cities" page never combines: where your role actually concentrates, how crowded each market is, and what the local pay buys after cost of living. By the end you will have an ordered list of five to ten cities and the spreadsheet behind it.

Every ranking page hands you one list for everyone, and every relocation guide is a feelings checklist. Neither takes your occupation code and produces something you can defend. This run does, using public employment data, two named competition indices, and a cost adjustment that regularly reorders the obvious answers.

## Why a generic best-cities list fails your role

A one-size city ranking fails because it ranks on headline job growth or total openings across all occupations, and your role does not move with the aggregate. The number that matters is how a single occupation distributes across metros, and that distribution is sharp.

Take software developers. In Refolk's index of professional profiles, the US entry-band software engineer pool runs to 421,814 people, clustering in San Francisco, Seattle and Sunnyvale. Those are the same metros that top employment in the official data. That overlap is the whole problem with a generic list: it sends everyone to the biggest hubs, which are simultaneously the easiest place to find the job and the hardest place to win it, because supply concentrates exactly where demand does.

**421,814 - US candidate pool for software engineer and developer titles, entry band**

From Refolk's index. The top supply regions are San Francisco, Seattle, Sunnyvale and Palo Alto, which overlap the highest-employment metros, so the busiest markets are also the most contested.

A real metro ranking has to separate three questions that headline lists blend together. Where does the role live? How many other people are chasing it there? And what does the pay actually buy once you subtract rent and groceries? Answer those three, weight them, and you get a shortlist that survives scrutiny.

> A hub is at once the easiest place to find the job and the hardest place to win it.

## The three signals that make a defensible ranking

A defensible metro ranking rests on three independent signals: role concentration, market competition, and cost-adjusted pay. Each comes from a distinct public source, and each lies in a specific way if you read it alone.

#### The three-signal stack

1. **Role concentration** - OEWS employment and location quotient by metro, answering where the role lives
2. **Market competition** - LinkedIn or Indeed tightness index, answering how crowded the market is
3. **Cost-adjusted pay** - OEWS wage divided by BEA Regional Price Parity, answering what pay buys locally

*Each layer narrows the shortlist, and each has its own source and its own failure mode.*

**Role concentration** comes from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, known as OEWS. It covers roughly 530 metropolitan and nonmetropolitan areas across about 830 occupation categories under the 2018 Standard Occupational Classification. Granularity runs to the detailed six-digit code, for example 15-1252 Software Developers. The key measure is the location quotient, or LQ, which compares an occupation's local share against its national share: below 1.0 the role is under-represented locally, above 1.0 it is over-represented.

**Market competition** comes from one of two named indices. LinkedIn's Labor Market Tightness is job openings divided by total active applicants in a month, and it counts everyone searching, employed or not, so it reads looser than a simple vacancy-to-unemployment ratio. Indeed Hiring Lab's Tightness Index distils 13 proprietary measures into four dimensions using principal components analysis, covering job-seeker engagement per posting, employer paid effort, per-seeker search intensity, and market concentration.

**Cost-adjusted pay** comes from BEA Regional Price Parities. RPPs express a metro's price level as a percentage of the national level, which equals 100.0. To compare real pay, divide a nominal wage by the RPP over 100.

> **Rule:** Concentration before pay
>
> Rank role concentration before cost-adjusted pay. A high wage in a thin market is unreachable, so filter for where the role actually exists first, then compare what the pay buys among the survivors.

## Reading location quotient without getting fooled

A location quotient tells you how concentrated your role is in a metro relative to the whole country, and an LQ of 2.0 means the occupation is twice as concentrated locally as nationally. It proves relative pull, but it says nothing about size, which is where it lies.

There is no single official bright line, but a widely used convention marks an LQ above 1.2 as over-represented and below 0.8 as under-represented. That convention is your first filter. The second, non-negotiable, filter is absolute employment. Lightcast's hub test pairs the two: it flags mining in Butte, Montana at an LQ of 5.9 and over 400 workers, where both signals agree. A high LQ on a few hundred jobs is a specialist village, not a market you can run a search in.

Here is the teaching dataset for software developers. Read every row against both columns.

| Metro | Employment | Emp per 1,000 | Location quotient | Annual mean wage |
|---|---|---|---|---|
| San Jose-Sunnyvale-Santa Clara, CA | 96,590 | 84.60 | 7.75 | $199,800 |
| New York-Newark-Jersey City | 119,010 | 12.53 | 1.15 | $152,100 |
| Washington-Arlington-Alexandria | 72,010 | 23.29 | 2.13 | $148,480 |
| Dallas-Fort Worth-Arlington | 59,570 | 15.02 | 1.38 | $128,100 |
| Los Angeles-Long Beach-Anaheim | 58,450 | 9.45 | 0.87 | $151,780 |
| Boston-Cambridge-Nashua | 55,240 | 20.00 | 1.83 | $148,100 |

Source: BLS OEWS, May 2023. Two rows carry the lesson. San Jose has the highest LQ at 7.75, so concentration alone sends you there. New York has the most jobs at 119,010 but an LQ of only 1.15, so headcount alone sends you to New York. Los Angeles is the trap: 58,450 jobs is a large raw count, but at LQ 0.87 the role is below-average there, a big city with weak relative pull. Rank on headcount and you overrate Los Angeles; rank on LQ and you overrate a specialist town. You need both columns to agree before a metro earns a place on the shortlist.

#### The concentration filter

Horizontal axis runs from Low absolute employment to High absolute employment. Vertical axis runs from Low LQ (below 1.2) to High LQ (at or above 1.2).

| Quadrant | What it means |
| --- | --- |
| Specialist village | High LQ, few jobs; drop it, too few openings to run a search |
| True hub | High LQ and high headcount; shortlist it |
| Thin and weak | Skip entirely, no pull and no volume |
| Big but diluted | High headcount, below-average pull; keep only if pay or competition is favorable |

*A metro only clears the filter when both LQ and absolute employment are high; the other three quadrants each fail in a specific way.*

## Turning nominal wages into what the pay buys

A nominal wage is meaningless across cities until you divide it by the local price level, because the same dollar buys different amounts in different metros. BEA Regional Price Parities give you that price level, and the adjustment regularly reorders a pay ranking.

The method is one division. If area A has an RPP of 120, dividing a $12,000 income by 1.20 yields $10,000 of national-equivalent purchasing power. Two incomes are comparable only after each is divided by its own RPP over 100. Metro-level RPPs are published in the same BEA program that publishes the state figures below.

| Area | RPP (national = 100) | Reading |
|---|---|---|
| District of Columbia | 117.7 | ~18% above national |
| Hawaii | 116.2 | ~16% above |
| New York | 115.3 | ~15% above |
| Arkansas | 87.5 | ~12% below |
| Mississippi | 86.8 | ~13% below |

Source: BEA RPP, 2013 states, illustrative; metro-level values are published in the same program. The spread is wide enough to flip rankings. The Tax Foundation's metro map puts the real value of $100 at $84.58 in San Francisco and $86.61 in Los Angeles, so a coastal wage that looks 15 percent higher can evaporate once deflated. Apply this before you rank on pay, never after.

> **Watch out:** The nominal wage mirage
>
> San Jose's $199,800 mean wage tops the table, but divide it by the metro RPP and the lead shrinks. Never rank cities on raw pay; a cost-blind list always overrates expensive coastal hubs.

Refolk does this adjustment for you when it scores how well you fit a posting, pulling the metro wage and the local price level so the number you compare is already in real terms rather than a headline figure.

## The metro target run, step by step

The procedure below takes most people a focused day. Each step produces one artifact, and the whole run ends in an ordered shortlist. Work it in order; the one place guides disagree is whether competition or pay comes first, and this run puts concentration ahead of both because a high wage in a thin market is a number you can never collect.

#### One title to a ranked shortlist

1. **Fix the occupation code** - Map your job title to one detailed six-digit SOC code using the Occupational Outlook Handbook or the OEWS occupation list, for example 15-1252. Done when you have one code and its national employment baseline.
2. **Pull metro employment and LQ** - From the OEWS geographic profile for that code, record each metro's employment, employment per 1,000, and location quotient in a spreadsheet. Done when candidate metros have all three columns.
3. **Score concentration** - Keep metros with LQ at or above roughly 1.2 and meaningful absolute employment, since a hub needs both. Done when specialist villages and diluted big markets are filtered out.
4. **Layer market competition** - For survivors, pull a tightness signal from LinkedIn or Indeed and treat it as a market-wide modifier, not a role-specific one. Done when each metro has a tightness read.
5. **Convert pay to purchasing power** - Divide the OEWS metro mean or median wage by the metro RPP over 100. Done when every metro has an RPP-adjusted wage comparable across cities.
6. **Check remote relevance** - Look up your occupation group's remote share in the Indeed Remote Tracker. Done when you know whether to weight location heavily or let it fade.
7. **Rank the shortlist** - Combine concentration, competition, and cost-adjusted pay into a weighted score and sort. Done when you have an ordered shortlist of five to ten metros.

When you reach the ranking step, a simple weighted score works. The template below is a starting rubric; adjust the weights to your own situation, for example raising the competition weight if you are a career switcher with a thin resume for the role.

**Metro weighted-score rubric**

```
Concentration score (LQ, capped so a specialist town cannot dominate): weight 0.40
Competition score (inverse of tightness crowding; less crowded scores higher): weight 0.25
Cost-adjusted pay score (RPP-deflated wage): weight 0.35
---
Metro total = 0.40*Concentration + 0.25*Competition + 0.35*CostAdjPay
Rule: drop any metro below your absolute-employment floor before scoring, regardless of total.
```

*Normalize each column to 0-100 across your candidate metros, then apply these weights. Higher total ranks higher.*

#### The target run end to end

1. **Fix code** - One SOC code and national baseline
2. **Pull and filter** - Metro LQ and employment, thin markets removed
3. **Score three signals** - Concentration, competition, cost-adjusted pay
4. **Weight and rank** - Ordered shortlist of five to ten metros

*One occupation code enters, an ordered metro shortlist leaves, with a filter and three scores in between.*

## When remote work changes the whole calculation

Remote work weakens metro targeting for some occupations and leaves it decisive for others, so you check your own occupation group before weighting location. The headline figure hides this entirely.

On August 31, 2026, remote or hybrid terms appeared in 8.4 percent of US postings, against 8.2 percent a year earlier, measured as a seven-day trailing average of remote postings over total postings. That all-jobs number is low, but occupational variance is large. Insurance postings now mention remote or hybrid work more often than software development postings do, and in retail and driving the share is close to zero. Reading the 8.4 percent as your number is the remote-share blindspot: it tells you almost nothing about your specific role.

The practical rule follows from the variance. If your occupation group runs high on the Remote Tracker, metro choice matters less, and you can widen your search to the whole country while still using cost-adjusted pay to decide where to physically live. If it runs near zero, location is the dominant variable and the concentration filter does most of the work. Indeed's own 2026 best-jobs index weights remote availability at just 10 percent, a reminder that even where remote exists, it is one factor among many rather than the headline.

## How this goes wrong

The failure modes below are the most valuable part of this run, because each one is a plausible-looking shortcut that produces a confidently wrong ranking. Read them as a pre-flight check before you trust your list.

| Failure mode | What it looks like | The check |
|---|---|---|
| High LQ, tiny market | A town shows LQ 4+ on a few hundred jobs and reads as a "specialist hub" | Pair LQ with absolute employment before shortlisting |
| Big but diluted | Los Angeles at 58,450 jobs, LQ 0.87: large count, weak pull | Read LQ, not just headcount |
| Metro tightness is not your-role tightness | A tight metro overall is loose for your specific occupation | Treat tightness as a market modifier; check role signals separately |
| Nominal wage mirage | San Jose's $199,800 tops the list before adjustment | RPP-adjust every wage before ranking on pay |
| City vs metro confusion | A city-limits figure read off a multi-county metro number | OEWS areas are metro statistical areas, not municipalities |
| Supply counted as demand | A candidate-pool count read as open roles | Refolk's index measures competition, not demand |

Two of these deserve a closer look. **Metro tightness is not your-occupation tightness.** Both the LinkedIn and Indeed indices publish at metro level but not by a single detailed occupation, so they are market-wide signals. Indeed's tightest US market in May 2026 was Lebanon-Claremont NH-VT at index 148.3, followed by Duluth, Minnesota at 143.5, small New England and Upper Midwest areas driven by aggregate signals across all jobs. A reader chasing those for a software role would find almost no postings. Use the index to rank crowding among metros that already cleared your concentration filter, not to discover new targets.

**Supply counted as demand** is the subtler trap. Refolk's index measures the candidate pool, which is your competition, not the number of open roles, which is demand. The two are different directions of the same market. Do not substitute one for the other; use OEWS for demand and a candidate-supply read for competition.

> **Note:** Year mismatch
>
> Keep your numerator and denominator in the same release year. Mixing May 2023 metro employment with a May 2025 national total skews any share calculation, which is why the 21 percent top-five figure for software developers is derived and approximate rather than published.

## Reading competition where supply clusters

Competition is highest exactly where the role concentrates, because candidate supply pools in the same metros as demand. This is the correction a concentration-only ranking most needs, and Refolk's index makes it visible by role and market.

**8.9x - US entry-band software pool vs Canada's software pool**

From Refolk's index, directional because band filters differ. The US pool of 421,814 is roughly 8.9 times Canada's 47,497, so moving across a border changes competition far more than moving between two US metros.

Here is the supply picture by role and market. Read it as competition, not demand, and against the OEWS employment tables above.

| Role / market | Candidate pool | Top supply metro |
|---|---|---|
| US software eng/dev (entry band) | 421,814 | San Francisco |
| US data scientists (all levels) | 28,867 | Seattle |
| Canada software eng/dev (all levels) | 47,497 | Vancouver |

Source: Refolk's index. The US software pool is roughly 14.6 times the US data-scientist pool and 8.9 times the Canada software pool, though the band filters differ, so treat the multiples as directional. The practical read is that cross-border moves change your competition by an order of magnitude, while moves between two US metros shift it far less. One honest limit: a query filtered to the senior band for US software engineers returned no usable sample, so seniority-split supply is unverified here. If seniority matters to your targeting, confirm it locally rather than assuming the entry-band shape holds.

To see the supply side for your own role in a target metro, you can ask Refolk directly instead of inferring it from aggregate counts.

Ask me this: `Senior software engineers in the Seattle metro area open to relocation.` - [run the search](https://www.refolk.ai/start?q=Senior%20software%20engineers%20in%20the%20Seattle%20metro%20area%20open%20to%20relocation.).

*Returns a sense of how deep the local senior pool is and who is already willing to move, which is the competition you would be joining in that metro.*

Refolk is built to turn questions like that into a usable answer, so the supply read that normally takes manual digging arrives as a list you can compare across metros. [Refolk](/candidates) writes your resume from your own history and tailors it to each posting, so once your shortlist is set, applying into these markets is a scored, per-posting exercise rather than a guess.

## Keeping the shortlist current

A metro shortlist is a snapshot, so it decays as the underlying releases update. Re-run the concentration and pay steps when a new OEWS or RPP vintage publishes, usually annually, and re-pull the tightness and remote signals more often since they refresh monthly.

#### Before you trust the shortlist

- [ ] One detailed six-digit SOC code is fixed, not a loose title
- [ ] Every candidate metro has LQ, employment, and employment per 1,000 recorded
- [ ] Thin markets are filtered on both LQ and absolute employment, not one alone
- [ ] Each surviving metro has a tightness read, treated as a market-wide modifier
- [ ] Every wage is divided by its metro RPP before any pay comparison
- [ ] Your occupation group's remote share is checked, not the 8.4% headline
- [ ] Numerator and denominator in any share figure share a release year
- [ ] Candidate-pool counts are read as competition, never as open demand

The run itself does not change; only the inputs do. The discipline is to re-check the mechanism rather than remember a value. When the next OEWS vintage lands, your LA-style traps and San Jose-style mirages will sit in the same places, so the filter and the rubric keep working. Point it at your code, pull fresh numbers, and the shortlist regenerates in under a day.

## Frequently asked questions

### What data source tells me which city has the most jobs for my role?

The US Bureau of Labor Statistics Occupational Employment and Wage Statistics (OEWS) program. It publishes employment counts and wages for roughly 530 metropolitan and nonmetropolitan areas across about 830 occupation codes. Map your title to one detailed six-digit SOC code, then read each metro's employment and location quotient from that code's geographic profile. Note it is an employment-count survey, not a postings count, so it measures where the role lives, not live openings.

### Is location quotient alone enough to pick a city?

No. A small town can show a location quotient above 4 on only a few hundred jobs, which looks like a specialist hub but offers almost no openings. Pair LQ with absolute employment: Lightcast's hub test flags mining in Butte, Montana at LQ 5.9 and over 400 workers, meaning both signals agree. A hub needs high relative concentration and meaningful raw headcount, so filter on both columns before shortlisting.

### Why adjust pay for cost of living when ranking cities?

Because nominal wages mislead. BEA Regional Price Parities express each metro's price level against a national 100.0; $100 is worth $84.58 in San Francisco. To compare, divide the metro wage by its RPP over 100. San Jose's $199,800 software developer mean wage looks unbeatable until you deflate it, which is why cost-blind best-cities lists overrate coastal hubs.

### Does remote work make metro targeting pointless?

Only for some roles. The all-jobs US remote or hybrid posting share was 8.4 percent on August 31, 2026, but occupational variance is large. Insurance postings mention remote more often than software development now, while retail and driving are near zero. Check your own occupation group in the Indeed Remote Tracker; if it runs high, location matters less, and if it is near zero, metro choice is decisive.

### Can I use a market tightness index to judge competition for my specific job?

Treat it as a modifier, not a verdict. LinkedIn's Labor Market Tightness is openings divided by active applicants, and Indeed's index distills 13 measures across more than 800 areas. Both publish at metro level but not by single detailed occupation, so a metro that looks tight overall can be loose for your role. Indeed's tightest US market in May 2026, Lebanon-Claremont at 148.3, has few software postings.

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*From the Refolk guide library. I revise these guides rather than replacing them, so the current version is always at https://www.refolk.ai/candidates/guides/metro-target-run-city-shortlist*