Job Data API Coverage: Which Countries Each Board Actually Serves

Pick job data sources by where they actually operate, not by brand recognition. The coverage numbers below come from each API’s own country schema, verified against live runs — and they disagree by an order of magnitude: LinkedIn’s job surface spans 98 countries while Naukri serves exactly one. Both are the right choice for different questions, and the wrong one silently returns thin or empty data.

What does the coverage map look like?

The global tier, by declared country count:

• LinkedIn — 98 countries. The widest single-board reach, with a differentiator no other source carries: applicant counts per posting, a direct competition signal.
• Talent.com — 77 countries. Broad aggregation; in our runs the reliably filled fields were title, employer, location and posted date, with salary sparse.
• Jooble — 65 countries. Aggregator with salary present on roughly half of returned rows — usable, if your analysis treats it as “salary where listed”.
• Indeed — 62 countries. Strong on required-skills lists and official application links straight to the employer’s flow.

The regional tier, where depth replaces breadth:

• Glassdoor — 23 countries, and the only source pairing salary ranges with employer ratings on nearly every row.
• Bayt — 17 Middle East markets, the Gulf specialist. Verified caveat: it returns no salary fields at all, so compensation research needs a different source.
• JobStreet — 8 APAC markets on the company-profile side, resolving employer names into industry, locations and live opening counts.
• Glints — 7 Southeast Asian markets, with salary ranges filled on most rows.
• StepStone — Germany, Austria, Belgium, Netherlands. The DACH-Benelux board, with validity dates on postings.
• ZipRecruiter — US, Canada, UK, Ireland, and in our verification the pay range was filled on every returned contract listing.
• Naukri — India only, and the deepest Indian source: required skills and experience ranges on every row we pulled.

Why does a schema list more countries than a board serves?

Declared coverage is a menu, not a warranty. A country appearing in an input schema means the API will accept it — not that the board has meaningful inventory there. An aggregator’s 65th country may carry a handful of listings syndicated from elsewhere; a specialist’s single country carries the local market’s actual depth.

The practical test is the one we run before publishing anything: a smoke query per target country with a common occupation, checking row count and field fill rather than trusting the enum. Ten minutes of runs converts the menu into a map.

How should geography drive source selection?

Three patterns cover most real projects:

Single-market depth. Researching hiring in India, the Gulf, or Southeast Asia? The regional specialist beats the global board on field richness where it lives — Naukri’s skills and experience data, Bayt’s Gulf employer detail, Glints’ salary fill. Use the global source as a supplement, not the spine.

Cross-market comparison. Comparing demand across ten countries needs one schema and one collection method, or the differences you measure are pipeline artifacts. That is the case for a wide aggregator or a multi-source orchestrator, accepting shallower per-row detail as the price of comparability.

Salary-led analysis. Coverage counts mislead here entirely. The question is which sources fill compensation fields: Glassdoor and ZipRecruiter did on nearly every verified row, Jooble on about half, Bayt on none. A salary study built on a no-salary source fails at the last step, after all the collection spend.

What breaks when you ignore the map?

The failure is quiet, which is what makes it expensive. Query a board for a country it technically accepts but barely serves and you get a small, valid-looking result set — no error, no warning. Averages computed over it look precise. Dashboards ship. The numbers describe the board’s syndication overlap, not the labor market.

The tell is asymmetry: if one country returns 400 rows and another returns 9 through the same query, the 9 is usually the board’s edge, not the market’s. Comparing only within a source’s home territory — and stating the source next to every cross-market number — keeps the analysis inside what the data can support.

Where should a new project start?

Name the countries first, then read this map backwards. One target market in a specialist’s territory: start with the specialist, verify field fill with a live sample, done. Five markets across regions: start with the widest aggregator that covers all five, then add one specialist where a decisive field — salary, skills, ratings — is missing. And re-verify quarterly: boards add markets, drop them, and change what fields they expose, so the map above carries its date for a reason.