Most organisations — especially nonprofits — lack the resources for professional salary benchmarking. This tool uses World Bank purchasing power data to generate a principled, transparent salary grid you can defend to your board, your donors, and your team.
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Grounded in real data
Calibrated using World Bank Purchasing Power Parity indices — 130+ countries, 2024 data — reflecting what local staff actually spend their salaries on
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Sector-aware
Different defaults for NGOs, UN agencies, social enterprises, and the private sector
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Fully transparent
The exact AI prompt and methodology are published on this page — nothing is hidden
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Takes 3 minutes
Answer a few plain-language questions and get a formatted, printable grid
⚗ Experimental tool — use with care
Building a fair salary structure is one of the most common — and most underfunded — challenges in the nonprofit world. Professional pay and compensation consultants exist, but there are very few of them, and the cost of a full salary benchmarking exercise puts proper benchmarking out of reach for most smaller organisations. At Nonprofit Builder, we believe structured data and AI can help close that gap — making a principled starting point accessible to every organisation, not just the well-funded ones.
This tool is genuinely experimental. Salary figures are calculated from the World Bank Purchasing Power Parity (PPP) index — country-level data that measures what a local basket of goods costs compared to the US. Because it reflects what locally-recruited staff actually spend their money on, it tends to produce more grounded salary ranges for local hires in lower-income countries than expatriate-calibrated indices. The key limitation is that it is country-level — Nairobi, Mombasa, and Kisumu all share Kenya's value. And like any cost-of-living measure, it says nothing about the local labour market or what a given role actually commands today.
Please treat this as a starting point, not a final answer. It is no substitute for professional advice from a pay and compensation specialist, a current sector salary survey, or — where one exists — a collective bargaining framework. For high-stakes decisions, particularly around senior leadership pay, please commission proper expert guidance.
This shapes the grade structure, job titles, and salary anchors in your grid.
Organisation name optional
Used only in the report header. Leave blank if you prefer.
What type of organisation is this?
This determines the typical grade structure, job titles, and how the salary anchor is set.
International NGO
A nonprofit operating across multiple countries — development, humanitarian, human rights, environment, and similar missions.
Local NGO / Social Enterprise
A locally-rooted charity, nonprofit, or social enterprise with a community or national focus.
UN Agency or Multilateral
A United Nations body, World Bank, regional development bank, or similar intergovernmental institution.
Government / Public Sector
A government ministry, agency, or public institution.
Small or Medium Business (SME)
A private company, typically up to ~250 staff. Includes tech startups and social-purpose businesses.
Large Company
A larger private sector organisation with formal HR structures and multiple divisions or business units.
Where is your main office?
Salary levels are calibrated using country-level Purchasing Power Parity data from the World Bank (indicator PA.NUS.PPPC.RF, 2024 data). All cities in the same country share the same value — this is country-level data, not city-specific. Select the city closest to where most of your staff are based.
Other cities where you have staff optional
Add as many locations as you need. Each gets its own column in the results, adjusted using country-level PPP data. Cities in the same country will show the same value.
Step 2 of 3 — Salary level
Where do you sit in the nonprofit market?
Most nonprofits pay below private-sector rates — and that's normal and fine. The positioning options below reflect where you aim to sit within the nonprofit and social sector market, not against a blended average that includes private companies. Being transparent about where you sit helps you attract the right people and have honest conversations about compensation.
Salary positioning
Where do you aim to sit relative to the nonprofit and social sector market? These are positioning targets — P25/P50/P75 refer to where you want to sit in the distribution, not figures from a specific salary survey.
P75 — Competitive
Targeting the upper quartile of the sector. Typical for UN agencies, major multilaterals, large INGOs with strong core funding, and mission-driven organisations competing for specialist talent. Supports talent attraction and retention.
P50 — Sector median
Targeting the midpoint of the nonprofit/social sector market. Typical for established INGOs and NGOs with balanced project funding, and government agencies in higher-income contexts. The most common positioning for mid-sized organisations.
P25 — Budget-conscious
Targeting the lower quartile of the sector. Typical for community-rooted nonprofits, local social enterprises, and government agencies in lower-income contexts where funding is tight. Employees are motivated primarily by mission and local market norms.
Living wage anchor
Anchored to what staff need to live decently in your city — not to market comparisons. For early-stage organisations, volunteer-heavy models, or those with very tight funding. Not a long-term retention strategy but a principled floor.
Currency
All salary figures will be shown in this currency. For international orgs, USD is often used as a common base for internal grids — local payroll is then converted separately.
Step 3 of 3 — Grid shape
Shape your grid
These settings control how many levels the grid has and how much spread there is between the lowest and highest salaries.
Number of grade levels
How many rungs does your organisation have, from the most junior role to the most senior? A small team might use 3–4. A large or complex organisation might need 8–12.
5grades
2 → 12
2 flat team5812 large org
Steps within each grade
The number of pay points within a single grade — usually reflecting progression through experience or performance. Three to five steps is most common. More steps give finer-grained progression paths.
4steps per grade
2 → 15
2 minimal581215 detailed
Step increment
The percentage increase from one step to the next within a grade. Smaller increments give tighter progression; larger increments reward seniority more strongly. 5–7% is most common in the sector.
6% per step
3% → 12%
3% tight5%8%12% steep
Grade spread shape
Should the pay range within each grade be the same width at all levels, or wider for senior grades (more room for negotiation at the top)?
Flat
Same spread at every grade level. Simpler and more transparent.
Widening
Junior grades tight; senior grades wider. Most common in INGOs — reflects negotiation latitude at senior levels.
Narrowing
Wider band (relative to salary) at entry; tighter at top grades. Less common — signals that senior roles have less negotiation room relative to their midpoint.
Pay spread — top to entry ratio
How many times higher is the highest salary in the grid compared to the entry-level minimum? A ratio of 6–8× is typical for nonprofits; UN agencies often reach 12–15×. Note: with many steps and a high step increment, the actual compression achieved may exceed this target — the result is shown on your grid.
8× (top ÷ entry)
4× → 20×
4× flat8×12×20× steep
Employer cost load optional
Add a percentage on top of each salary to show the true cost of employment — pension contributions, health cover, social security, etc. Set to 0 to show base salary only. Typical ranges: 8–15% in Africa/Asia; 15–25% in Western Europe; up to 35% in some countries.
0% employer cost load
0% → 40%
0% base only10%20%40%
Building your salary grid…
Applying World Bank PPP data for your locations
Reading cost-of-living data for your cities
Applying sector positioning and PPP calibration
Calculating grade anchors and compression ratio
Spacing steps within each grade geometrically
Generating titles and typical role examples
Finalising and formatting your grid
⚠ Important — please read before using this grid
This is a principled estimate based on country-level purchasing power data and nonprofit sector norms — not a market salary survey. When we say "market", we mean the nonprofit and social sector market, not a blended average across private sector, government, and NGOs. The grid is a starting point for internal discussion, not a substitute for professional benchmarking or HR advice. It does not account for local labour market dynamics, skills scarcity in specific roles, individual performance differentials, benefits package values, tax structures, or collective bargaining agreements. Salary decisions affect real people's lives — please use this tool responsibly, and supplement it with local expertise and, where possible, a sector-specific salary survey.
A salary grid is not a decision — it's a starting point. Here's how to move from this output to something you can actually use.
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Sense-check the floor and ceiling
Look at Grade 1, Step 1 — the absolute entry salary. Does it feel right for your city and sector? If it's too low, consider moving up one positioning level or adjusting the compression ratio. If it's too high for your budget, the living wage anchor may be more appropriate for now. Then look at the top of the grid: could you realistically pay your most senior leader that amount?
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Map your current team onto the grid
Before sharing externally, plot every current staff member onto it. Note who falls below the grade minimum, who is above the grade maximum, and who is at an unusual step. People below the range floor need a pay rise to bring them into band. People above the ceiling ("red-circled") are a more sensitive conversation — typically their pay is frozen until the grid catches up.
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Check it against one or two real data points
Search one or two roles on a local job board or sector salary resource (Devex, BOND, ReliefWeb, Glassdoor for your sector). Do the step values for those roles feel in the right neighbourhood? You're not looking for an exact match — you're checking for a major mismatch that would suggest the positioning or PPP calibration is off for your specific context.
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Decide on grade assignment criteria
The grid shows ranges — it doesn't tell you which grade a role belongs in. You need a simple job grading approach: typically based on scope of responsibility, level of supervision, decision-making authority, and required qualifications. Even a one-page grading rubric makes this consistent and defensible. Without it, grade assignment becomes arbitrary and breeds resentment.
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Get board sign-off before sharing with staff
If this grid will be used to make actual pay decisions, your board needs to approve it — particularly the top of the range, which constrains executive pay. Boards often have strong views on compression ratios and on whether leadership salaries are appropriate given donor expectations. Get alignment before staff see numbers.
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Most common adjustments people make
After seeing a first grid, organisations most often: (a) increase the compression ratio — the initial ceiling felt too low for senior roles; (b) adjust the step count — either more steps for longer progression paths, or fewer for simplicity; (c) shift the positioning up one level — the P25 floor felt embarrassingly low once they saw real numbers; (d) add a separate scale for a specific role family (e.g. finance or tech) that competes in a different market.
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Questions to bring to an HR advisor or compensation specialist
If you're commissioning professional advice, come prepared with: your current pay distribution, any existing grade structure or job titles, the roles that are hardest to hire and retain, your budget envelope for total salary cost, and whether you operate under any donor restrictions on pay levels. A specialist can validate the structure, advise on grade overlap, and help you design a transition plan for existing staff.
Benefits & employment conditions
Statutory minimums and sector norms for each of your locations, researched from live sources by Perplexity AI. Verify locally before making HR decisions — legislation changes frequently. Confidence level noted on each card.
Sources used by Perplexity
About this tool
Salary Grid Generator is a free tool built for the social sector. It uses World Bank Purchasing Power Parity data and AI-assisted calibration to produce indicative salary structures. Salary figures are calculated deterministically in JavaScript — AI is used only for grade titles, role descriptions, and notes. This tool is provided free of charge with no warranty, express or implied. The authors accept no liability for decisions made on the basis of this output. This tool does not replace professional compensation consultancy. For high-stakes decisions — especially senior leadership and board-approved pay — please commission a qualified HR or total rewards specialist.
Data: World Bank WDI indicator PA.NUS.PPPC.RF, 2024 data — country-level PPP index for 130+ countries. USA = 100. Note: all cities in the same country share the same value. Sector positioning multipliers are the authors' informed estimates. AI model: claude-sonnet-4-6 (Anthropic).
Transparency
About the method
We believe AI tools should be transparent about how they work. This page explains the full methodology — including the exact prompt sent to the AI — so you can judge it for yourself and adapt it if needed.
What this tool does
The generator combines country-level purchasing power data with nonprofit sector norms to produce a mathematically consistent salary grid. It is not a "market salary survey" — it does not pull from job postings or HR databases. When we say "market", we mean the nonprofit and social sector market, not a blended average across private companies, government, and NGOs. The output is a principled estimate that gives you a defensible, internally consistent starting point for internal discussion — not a final answer.
Step 1 — Country calibration (World Bank PPP data)
We use the World Bank indicator PA.NUS.PPPC.RF — "Price level ratio of PPP conversion factor to market exchange rate" — published in the World Development Indicators database. This is real, sourced data: 2024 values for 130+ countries. The US = 100. A value of 32 for Kenya means the Kenyan price level is 32% of the US price level — goods and services are roughly two-thirds cheaper. Because it measures what a local basket of goods costs, it reflects what locally-recruited staff actually spend their salaries on, making it well-suited for organisations hiring locally.
Important limitation: this is country-level data, not city-level. All cities in the same country share the same value — Nairobi, Mombasa, and Kisumu all get Kenya's value of 32. The real difference in costs between cities within a country is not captured. This is stated transparently in the dropdown.
Sample values — World Bank PA.NUS.PPPC.RF, 2024 (USA = 100)
Different types of organisation aim to pay at different points in the market distribution. The tool uses P25, P50, and P75 positioning targets — these are targets, not draws from a specific salary survey. They are mapped to floor salary anchors calibrated to nonprofit and social sector norms, informed by ILO Global Wage Reports, NCVO Pay Survey data, and CHS Alliance guidance.
Positioning
Sector target
Typical for
P75 — Competitive
Upper quartile of the sector
UN agencies, major multilaterals, large INGOs with core funding
P50 — Sector median
Midpoint of the nonprofit market
Established INGOs, NGOs with stable project funding, government in higher-income contexts
P25 — Budget-conscious
Lower quartile of the sector
Community nonprofits, local social enterprises, government in lower-income contexts
Living wage anchor
Decent living floor, not market-relative
Early-stage orgs, volunteer-heavy models, very constrained funding
These are starting points, not gospel. If your organisation has better data — a sector salary survey, a donor-mandated scale, or a board-approved policy — use that instead and treat this tool as a cross-check.
Step 3 — Grade structure and compression
The grid is built around three controls. The compression ratio sets how many times higher the top salary is versus the entry minimum (e.g. 8× means $240,000 top if entry is $30,000). The step increment controls the percentage increase from one step to the next within a grade — typically 5–7% in the sector. The spread shape (flat, widening, or narrowing) determines whether within-grade ranges stay constant or grow with seniority — widening is most common in INGOs, where senior roles carry more negotiation room. All arithmetic is done deterministically in JavaScript — same inputs always produce the same numbers.
Step 4 — AI for qualitative content only
Once the salary numbers are calculated in JavaScript, we send them to Claude (Anthropic) with strict instructions: Claude must not change any figures. Its only job is to assign grade titles, write typical role examples for the org type, and produce the methodology notes paragraph. This means the salary figures are stable and reproducible — AI variability only affects the labels and description text, not the numbers.
What Claude does — and does not do
Does: assigns grade titles (e.g. "Programme Officer"), writes 2–3 typical role examples appropriate for the org type, drafts contextual methodology notes.
Does not: calculate, change, or validate any salary figure. All numbers are pre-calculated before Claude is involved.
AI model used for grade titles, typical role examples, and methodology notes only. Salary figures are calculated deterministically in JavaScript before Claude is involved. Model: claude-sonnet-4-6.
Perplexity AI (sonar model)
Used for the benefits and employment conditions section. Perplexity searches live web sources before responding, so this data is more current than a training-data-only response. Source URLs are cited on each report.
Sector positioning multipliers
The authors' informed estimates based on general knowledge of sector pay norms. Not derived from a specific published dataset — treat as reasonable approximations, not benchmarks.
Known limitations
Country-level data, not city-level
All cities within the same country share the same PPP value. Nairobi, Mombasa, and Kisumu all get Kenya's index of 32. In reality, costs differ meaningfully between a capital city and a secondary city. This is stated transparently in the city dropdown.
Cost of living ≠ labour market rates
A country can be cheap to live in but have a competitive talent market — or vice versa. PPP tells you about the price of a basket of goods; it says nothing about what a data analyst, logistics coordinator, or country director actually commands in that market today.
Sector positioning varies widely
Not all NGOs pay the same. The positioning targets are generalisations — your specific funding mix, brand, sector, and country will affect what you can and should pay.
Tax and benefits not included by default
The grid shows gross base salaries. A $50,000 in New York and $50,000 in Berlin differ significantly in take-home and employer cost. Use the employer cost load setting to add a rough benefits uplift if needed.
Exchange rate risk
For international orgs with multi-currency payrolls, exchange rate movements affect the real value of salaries over time. This tool uses static PPP indices and does not model currency risk.
AI can be wrong on qualitative content
Salary figures are deterministic and reproducible — same inputs, same numbers every time. But Claude generates grade titles, role descriptions, and notes, and can make mistakes on these — especially for unusual org types or niche roles. Always review the output critically before sharing it with your team or board.
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