countries · sectors · sub-national hubs · trade bodies · FTAs · tools · academy · essays
v129.6 · business hubs
128 tier-1 / financial / business-capital cities. Each city profile carries trade-corridor depth: mandates, FTAs, SOPs, vertical-specific data.
Totality lens · 32 points to ponder · 16 user POV + 16 developer POV · this travelogue hub
User POV — for the traveller, migrant, or researcher navigating the Business hub
Eight dimensions
A business intelligence lens that scores cities on company-formation ease + corporate-tax + personal-tax + payroll-tax + workforce-availability + skill-pool-quality + co-working density + ecosystem-maturity replaces the consultancy-report wall with a transparent decision surface. The possibility is to let founders, investors, and self-employed professionals compare cities the way trade-zone agencies compare countries, but for the city scale where lived experience actually happens.
Plausibility tracks the country-vs-city tension. Tax rules are mostly national; workforce + ecosystem signals are mostly local. The schema acknowledges this by labelling each field with its scale-of-determination (country / city / both). Readers see at a glance which fields they could change by moving cities versus which require changing countries.
On a six-month horizon, business-led search is dominated by two segments: solo-self-employed (high volume, simple needs) and SME-founder (low volume, complex needs). The lens serves both by structuring data layer-by-layer — top of panel is solo-friendly summary, deeper sections are SME-founder territory.
What works is the company-formation cost + time pair, shown as twin metrics with their components decomposed (registration-fee + notary + lawyer + initial-capital). Visitors absorb the formation barrier in five seconds. What works less well is "ecosystem maturity" as a single score; ecosystems are sector-specific and a strong fintech ecosystem says little about a strong biotech ecosystem.
What does not work is treating tax rates as the primary business metric. Tax rates matter, but in many markets the de facto effective rate (after deductions, incentives, treaty benefits) differs significantly from the headline. The schema reports both nominal and effective where data permits.
A common pitfall is using English-language ecosystem reports as proxy for local reality. Many emerging-market cities have stronger ecosystems than English-language coverage suggests because the founders speak local language and operate outside the international VC press. Ground-sourced correspondents in those cities materially improve the schema.
Counter-intuitively, the cities with the lowest corporate tax rates are often the worst for SME founders because they offset tax with high formation costs, complex compliance, or weak rule-of-law. Total-cost-of-doing-business decomposes formation + ongoing compliance + tax to expose this pattern. Readers who optimise on tax alone are often misled.
The highest-leverage move is the formation-feasibility shortlist: input business model + target market + capital + workforce-need + risk-tolerance, output the ranked list of cities where formation is straightforward, tax is reasonable, and the workforce or skill-pool supports the business. The compute is moderate (multi-criteria filter); the user value is high because most existing tools do this only for free zones, not for cities.
Eight user intents
For business-establishing migrants — solo founders looking to incorporate abroad for tax or market reasons, SME founders relocating headquarters, freelancers choosing self-employment-friendly jurisdictions, investors comparing portfolio-company domiciles, executives weighing inter-company-transfer locations, and the entrepreneur-curious sub-group exploring side-projects.
They want decision-grade data about company-formation, tax, workforce, and ecosystem in their candidate cities. Not the consultancy-report (high price, vendor agenda) and not the tourist-guide (no business depth) — the structured middle that supports a real business decision.
When they have validated personal-fit (cost / climate / safety / visa) and are now testing business-fit. The lens is generative for the establishment-shortlist case (which cities support my business model) and gating for the relocation case (does the candidate city support my existing business).
Where they read it: 75 percent desktop because business research is a sit-down task that benefits from multi-tab comparison. The mobile design surfaces headline metrics (formation cost + tax rate) and pushes deeper sections to expandable cards.
Because business-formation-by-city research is genuinely poorly served. International-business literature focuses on countries; local-business literature focuses on neighbourhood. The city-scale, comparable, structured view in the empty middle is what most founders actually need.
Which sub-lens dominates per business model: formation-cost for capital-light startups, tax for income-heavy professionals, workforce + skill-pool for talent-dependent businesses, ecosystem for network-dependent startups, co-working density for solo founders, regulatory-clarity for compliance-sensitive sectors.
Whose business profile shapes the analysis: pre-revenue founders care most about formation cost + co-working; revenue-generating founders care most about tax + workforce; established businesses care most about regulatory stability + ecosystem connectivity. The schema labels each field with the business-stage it most affects.
How they use it: enter via candidate-city panel after personal-fit validation, scan business headlines, drill into the sub-lens most relevant to their business, decide gate / proceed. Comparator-mode for two or three candidates side-by-side is the second most common path.
Developer POV — for the architect, maintainer, future contributor to this hub
Eight dev dimensions
Data architecture: per-city schema with formation-cost-decomposition + formation-time + corporate-tax-rate-nominal-and-effective + personal-tax-rates + payroll-tax + VAT-equivalent + workforce-size + skill-pool-percentiles-by-discipline + co-working-density + ecosystem-maturity-by-sector. Sources: Doing Business reports (where current), national business-registry portals, LinkedIn talent-supply data, ground-sourced ecosystem correspondents.
Schema markup: each city business-panel emits as Place + Organization aggregate (the local government registry as the organization). Formation cost + time emit as Service with offers. Tax rates emit as TaxRate (custom). Workforce + skill-pool emit as JobCategoryAggregate (custom Property). Ecosystem maturity emits as Rating per sector.
Internal linking: business hub → 2,326 city business panels → comparator. Each panel cross-links to country-level tax overview, to sector-specific ecosystem hubs (e.g. /trav/best-cities-fintech-startups/), and to the visa + real-estate lenses (because business migration interlocks with all three).
Page-speed posture: formation + tax data is small (under 10 KB per city). Workforce + ecosystem visualisations are server-side SVG. Comparator is server-rendered. Total business panel weight under 90 KB. PageSpeed-100-v7 layer applies.
Mobile UX: formation cost + corporate tax + workforce-size as a three-card row at the top. Decomposition below in expandable cards. Sticky filter chip-bar at top: "solo-founder" / "SME" / "investor" presets adjust headline metrics.
Accessibility: numeric metrics have aria-labels naming the figure + unit + comparison-percentile. Tax-rate cells distinguish nominal from effective in the announced label. Comparator tables use proper table semantics with row + column headers.
SEO saturation: each city business panel has unique H2 ("Doing business in {city}: {formation-summary}, {tax-summary}, {workforce-summary}"), unique meta-description, FAQPage with the four most-asked business questions per city, BreadcrumbList. Sector-specific ecosystem hubs get ItemList. Speakable on summary lines.
Extensibility: schema is sector-extensible. Adding a new sector (e.g. crypto) means appending to ecosystem-sectors taxonomy + sourcing maturity scores per city + extending the sector-specific demographic-hub list. Next field on deck: digital-services-tax exposure.
Eight dev intents
For the developer maintaining this lens, the workforce + skill-pool data is the most third-party-API-dependent on the platform. LinkedIn talent-supply data is gated behind enterprise pricing; we use proxy signals (job-posting density on free job-boards, government employment statistics) for cities where we lack direct data.
What changes when business data updates: data/shared/business-by-city.php (resolved per-city record) is rewritten by the quarterly cron. Sector-specific demographic-hub rankings re-derive. Formation-feasibility presets recompute. Schema-emitter pre-flight validates.
When the cron runs: quarterly at 06:00 UTC on the 1st for the full sweep. Stagger from real-estate (05:30) and visa (05:00) crons. Annual at 06:00 UTC on Jan 1 for the ecosystem-maturity-by-sector full recompute (slow-moving, doesn't need quarterly refresh).
Where files live: data/shared/business-by-city.php (resolved record), data/shared/business-tax-by-country.php (country-level tax data inherited by cities), data/cache/business-sector-rankings.json (sector-specific orderings). Renderer at includes/lens-business-block.php.
Why country-level + city-level layered: because tax is country-set but workforce is city-set. The schema lets each field declare its scale-of-determination so the renderer can show country-level labels where appropriate ("This tax rate applies nationally") and city-level labels otherwise. Misattribution is a common failure mode in business-content sites.
Which renderer: includes/lens-business-block.php emits the business panel + sector-specific overlays. Accepts $city_slug + optional $sector (defaults to general). Echoes directly. Idempotent.
Whose responsibility: per-country tax data is sourced by the cron operator from OECD + national tax-authority portals. Per-city workforce + ecosystem data is sourced by the cron + correspondent-contributors. Schema validity enforced by pre-flight; tax-data accuracy is the highest-stakes verification because tax errors mislead readers into substantial financial mistakes.
How to add a new sector to the ecosystem taxonomy: (1) declare in ecosystem-sectors.php; (2) source per-city maturity scores from sector-specific signals (sector employment density, sector startup count, sector VC activity); (3) extend lens-business-block render; (4) create sector-specific demographic hub at /trav/best-cities-{sector}/. Total: roughly six hours per sector.
Explore
Every page in the AJG platform cross-links to these primary entities. Click any pill to explore that branch of the knowledge graph.