See how every figure is built.
Four steps, from a household survey to a spending forecast for every market, category and cohort, each one tested against what actually happened.
See the data
| Age | Life | Spending perspective |
|---|---|---|
| 10 | At ten | I have never earned anything, and I am already somebody’s customer. Books, clothes, the bus. My parents pay, but the spending is mine. |
| 24 | At twenty-four | I am two years off a first salary, so no payslip has my name on it yet. I still spend every week: rent, groceries, a phone plan on $38 a day. |
| 31 | At thirty-one | A baby, three weeks in. One of our salaries is on pause, so income says this household just shrank. Our spending has never been higher. |
| 48 | At forty-eight | I earn well and I am careful with it. Income overstates me. Only the part I actually spend is a market anyone can compete for. |
| 76 | At seventy-six | I left the payroll sixteen years ago and income stopped recording me. I still travel, still pay for health, still buy for the grandchildren. |
Everyone spends. Not everyone is a consumer.
Spending measures all five of those lives on one scale, but below a certain level it buys only survival: food, fuel, a roof. A consumer begins where choice begins, so we draw a line.
See the data
| Band | Daily spending | Billion people |
|---|---|---|
| Vulnerable and poor | under $13.20 | 3.5 |
| Lower middle | $13.20 to $45 | 2.9 |
| Core middle | $45 to $90 | 1.1 |
| Upper middle | $90 to $130 | 0.33 |
| Rich | $130 and above | 0.32 |
The consumer class is everyone who spends $13.20 a day or more, in 2021 purchasing power parity (PPP) dollars.
We build a complete consumer picture from consumption surveys and additional data sources.
See the data
| Spending category | Weekly spending |
|---|---|
| Food and non-alcoholic beverages | 41.2 |
| Alcohol and tobacco | 6.75 |
| Clothing and footwear | 12.4 |
| Housing, water and fuel | 96 |
| Health | 18.3 |
| Transport | 34.6 |
| Communication | 11.05 |
We turn each survey’s spread of spending into how many people spend how much.
See the data
| Daily spending (PPP dollars) | Density |
|---|---|
| 0.50 | 0.0154 |
| 0.56 | 0.021 |
| 0.63 | 0.0224 |
| 0.71 | 0.0224 |
| 0.80 | 0.0224 |
| 0.90 | 0.0224 |
| 1.01 | 0.0224 |
| 1.13 | 0.0224 |
| 1.28 | 0.0224 |
| 1.43 | 0.0224 |
| 1.61 | 0.0288 |
| 1.81 | 0.0548 |
| 2.04 | 0.0937 |
| 2.29 | 0.1196 |
| 2.58 | 0.1261 |
| 2.90 | 0.1261 |
| 3.25 | 0.1261 |
| 3.66 | 0.1261 |
| 4.11 | 0.133 |
| 4.62 | 0.1609 |
| 5.20 | 0.2027 |
| 5.84 | 0.2306 |
| 6.57 | 0.242 |
| 7.39 | 0.2594 |
| 8.30 | 0.2855 |
| 9.34 | 0.3029 |
| 10.50 | 0.3083 |
| 11.80 | 0.3125 |
| 13.26 | 0.3187 |
| 14.91 | 0.3224 |
| 16.76 | 0.3222 |
| 18.85 | 0.3188 |
| 21.19 | 0.3117 |
| 23.82 | 0.3003 |
| 26.78 | 0.2866 |
| 30.10 | 0.2743 |
| 33.84 | 0.2644 |
| 38.05 | 0.2541 |
| 42.77 | 0.2425 |
| 48.09 | 0.2303 |
| 54.06 | 0.2161 |
| 60.77 | 0.1996 |
| 68.32 | 0.1819 |
| 76.81 | 0.1633 |
| 86.35 | 0.1446 |
| 97.07 | 0.1273 |
| 109.13 | 0.1095 |
| 122.69 | 0.0887 |
| 137.93 | 0.0695 |
| 155.06 | 0.0593 |
| 174.32 | 0.055 |
| 195.97 | 0.0466 |
| 220.31 | 0.0342 |
| 247.68 | 0.0248 |
| 278.44 | 0.0189 |
| 313.03 | 0.013 |
| 351.91 | 0.0091 |
| 395.62 | 0.0082 |
| 444.76 | 0.0077 |
| 500.00 | 0.0056 |
We split the national average into every spending tier and age band.
See the data
| Spending tier | 0-15 | 15-30 | 30-45 | 45-65 | 65+ |
|---|---|---|---|---|---|
| Poor | -0.1% | 0.2% | -0.8% | -0.5% | 1.8% |
| Lower middle | 1.6% | 3.2% | 1.7% | 3.2% | 5.3% |
| Core middle | 3.3% | 4% | 3.1% | 3.7% | 4.9% |
| Upper middle | 4.1% | 4.6% | 3.7% | 4.2% | 6.1% |
| Rich | 5.2% | 5.8% | 5.1% | 5.5% | 7.6% |
We validate our data off these checks.
20103.25 billion in the middle class by 2020
Homi Kharas, our co-founder, put the number in an OECD working paper ten years ahead of it. The outturn, as we later measured it, was 3.4 billion. He forecast their spending at $49 trillion and it came in at $49.5 trillion.
See the data
| Measure | Forecast / China | Outturn / India | Unit |
|---|---|---|---|
| People in the middle class, 2020 | 3.25 | 3.4 | bn |
| What they spent, 2020 | 49 | 49.5 | $T |
2022Half of Asia in the consumer class by 2024
Our founders, Wolfgang Fengler and Homi Kharas, called it at Brookings in June 2022, when Asia-Pacific stood at 48.7%. Our current model has the line crossed in 2023, a year earlier than called, and 2024 at 53.6%.
See the data
| Measure | Forecast / China | Outturn / India | Unit |
|---|---|---|---|
| Year Asia passes half | 2024 | 2023 | year |
2023India adds more consumers than China in 2024
Our World Consumer Outlook for 2024 called India 34 million new consumers against China’s 32 million. Our current model has 40.9 million against 35.5 million, and India ahead again in 2025.
See the data
| Measure | Forecast / China | Outturn / India | Unit |
|---|---|---|---|
| New consumers, 2024: China / India | 35.5 | 40.9 | m |
2025131 million new consumers in 2025
Our 2025 outlook, The Resilient Consumer, called 131 million people joining the consumer class in 2025, 47 million of them in India. Our current model has 128.6 million, and 45.3 million in India.
See the data
| Measure | Forecast / China | Outturn / India | Unit |
|---|---|---|---|
| New consumers worldwide, 2025 | 131 | 128.6 | m |
| New consumers in India, 2025 | 47 | 45.3 | m |
Registered vehicles
See the data
| Exhibit | Meaning |
|---|---|
| Country rows | Model affordability compared with registrations; schematic only |
We check our figures against how many cars are actually registered.
Nightlights
See the data
| Place index | Weight | Longitude | Latitude |
|---|---|---|---|
| 1 | 1 | 77.18 | 12.43 |
| 2 | 1 | 7.39 | 36.04 |
| 3 | 1 | -98.3 | 26.56 |
| 4 | 2 | 115.9 | 40.61 |
| 5 | 1 | 128.07 | 37.02 |
| 6 | 2 | 6.26 | 5.48 |
| 7 | 4 | 47.42 | 30.2 |
| 8 | 2 | 47.57 | 29.69 |
| 9 | 1 | 85.26 | 25.55 |
| 10 | 1 | 130.09 | -1.43 |
| 11 | 1 | 12.98 | -13.79 |
| 12 | 0 | 123.48 | 9.45 |
| 13 | 1 | 112.6 | 33.38 |
| 14 | 1 | 104.62 | 29.76 |
| 15 | 0 | 35.21 | 30.86 |
| 16 | 1 | 32 | 25.61 |
| 17 | 0 | 66.19 | 25.49 |
| 18 | 1 | 121.04 | 13.69 |
| 19 | 2 | 139.13 | 35.91 |
| 20 | 0 | 30.02 | 36.74 |
Brighter places are richer, so we check our figures against how brightly each place shows up in satellite nightlights.
National accounts
See the data
| Country | ISO3 | Consumer-class share, 2026 (%) |
|---|---|---|
| BDI | BDI | 0.14 |
| YEM | YEM | 0.30 |
| CAF | CAF | 0.62 |
| COD | COD | 0.84 |
| SSD | SSD | 1.22 |
| NER | NER | 1.26 |
| SDN | SDN | 1.96 |
| PNG | PNG | 1.94 |
| TCD | TCD | 2.42 |
| MDG | MDG | 2.56 |
| LBR | LBR | 3.12 |
| MOZ | MOZ | 3.48 |
| GMB | GMB | 4.00 |
| GNB | GNB | 4.32 |
| BFA | BFA | 4.34 |
| SLB | SLB | 4.18 |
| MWI | MWI | 4.32 |
| AFG | AFG | 5.70 |
| VUT | VUT | 5.80 |
| TKM | TKM | 6.22 |
| SOM | SOM | 6.36 |
| ZMB | ZMB | 6.60 |
| ERI | ERI | 6.68 |
| UGA | UGA | 8.38 |
| TGO | TGO | 8.60 |
| TLS | TLS | 8.70 |
| TZA | TZA | 10.00 |
| ETH | ETH | 11.50 |
| SLE | SLE | 10.70 |
| RWA | RWA | 12.10 |
| FSM | FSM | 11.36 |
| GIN | GIN | 14.86 |
| MLI | MLI | 12.70 |
| BEN | BEN | 13.76 |
| COM | COM | 13.58 |
| HTI | HTI | 12.68 |
| LSO | LSO | 14.12 |
| SEN | SEN | 14.76 |
| COG | COG | 15.18 |
| SYR | SYR | 16.86 |
| ZWE | ZWE | 16.68 |
| MRT | MRT | 20.76 |
| TJK | TJK | 21.56 |
| KIR | KIR | 21.46 |
| CMR | CMR | 22.72 |
| PSE | PSE | 23.30 |
| TUV | TUV | 24.98 |
| VEN | VEN | 25.22 |
| KHM | KHM | 27.04 |
| MMR | MMR | 26.50 |
| IRQ | IRQ | 27.24 |
| KEN | KEN | 27.26 |
| NGA | NGA | 26.96 |
| STP | STP | 27.24 |
| NPL | NPL | 29.34 |
| SWZ | SWZ | 29.68 |
| AGO | AGO | 29.88 |
| PAK | PAK | 30.78 |
| CIV | CIV | 31.88 |
| GHA | GHA | 32.90 |
| LBY | LBY | 33.72 |
| MHL | MHL | 37.90 |
| DJI | DJI | 37.00 |
| LAO | LAO | 36.90 |
| WSM | WSM | 36.82 |
| NIC | NIC | 38.26 |
| BWA | BWA | 38.00 |
| BGD | BGD | 39.30 |
| MAR | MAR | 40.62 |
| ZAF | ZAF | 39.04 |
| HND | HND | 40.56 |
| CPV | CPV | 41.48 |
| GAB | GAB | 40.98 |
| NAM | NAM | 42.28 |
| GNQ | GNQ | 41.40 |
| IND | IND | 46.44 |
| IDN | IDN | 47.04 |
| PHL | PHL | 48.60 |
| JOR | JOR | 48.36 |
| KGZ | KGZ | 50.62 |
| LKA | LKA | 53.36 |
| SUR | SUR | 59.72 |
| LBN | LBN | 54.16 |
| IRN | IRN | 54.44 |
| GTM | GTM | 57.88 |
| DZA | DZA | 58.42 |
| GRD | GRD | 58.78 |
| ECU | ECU | 58.80 |
| FJI | FJI | 59.86 |
| PER | PER | 60.18 |
| EGY | EGY | 60.70 |
| TON | TON | 60.58 |
| BLZ | BLZ | 60.66 |
| COL | COL | 61.94 |
| SLV | SLV | 62.60 |
| JAM | JAM | 62.28 |
| ARM | ARM | 64.82 |
| GEO | GEO | 64.86 |
| VCT | VCT | 64.82 |
| BOL | BOL | 65.94 |
| ATG | ATG | 67.60 |
| VNM | VNM | 70.00 |
| BRA | BRA | 70.50 |
| TUN | TUN | 70.66 |
| UZB | UZB | 72.86 |
| KSV | KSV | 71.74 |
| LCA | LCA | 72.20 |
| PRY | PRY | 72.74 |
| DMA | DMA | 73.12 |
| CHN | CHN | 74.12 |
| MEX | MEX | 73.64 |
| MDA | MDA | 76.94 |
| MNG | MNG | 76.78 |
| PAN | PAN | 76.72 |
| BTN | BTN | 79.18 |
| MUS | MUS | 78.56 |
| DOM | DOM | 80.14 |
| ALB | ALB | 80.82 |
| THA | THA | 80.38 |
| MKD | MKD | 80.46 |
| ARG | ARG | 81.54 |
| MDV | MDV | 82.42 |
| TTO | TTO | 81.88 |
| BRB | BRB | 82.68 |
| BHS | BHS | 82.54 |
| KNA | KNA | 83.36 |
| CRI | CRI | 84.74 |
| KAZ | KAZ | 85.02 |
| BIH | BIH | 86.76 |
| PLW | PLW | 88.56 |
| ISR | ISR | 88.38 |
| OMN | OMN | 88.54 |
| UKR | UKR | 89.48 |
| SRB | SRB | 89.78 |
| MNE | MNE | 89.78 |
| CHL | CHL | 89.82 |
| TUR | TUR | 90.74 |
| URY | URY | 90.66 |
| ROU | ROU | 90.78 |
| ABW | ABW | 91.90 |
| SYC | SYC | 93.44 |
| AZE | AZE | 94.72 |
| SVK | SVK | 94.44 |
| KWT | KWT | 94.56 |
| BHR | BHR | 94.86 |
| BGR | BGR | 95.44 |
| HUN | HUN | 95.36 |
| PRI | PRI | 95.30 |
| ARE | ARE | 95.38 |
| QAT | QAT | 96.20 |
| JPN | JPN | 96.28 |
| LVA | LVA | 96.56 |
| MYS | MYS | 96.78 |
| BLR | BLR | 96.68 |
| LTU | LTU | 96.80 |
| BRN | BRN | 96.90 |
| GRC | GRC | 96.86 |
| PRT | PRT | 96.94 |
| ESP | ESP | 97.10 |
| HRV | HRV | 97.28 |
| RUS | RUS | 97.24 |
| GUY | GUY | 97.86 |
| ITA | ITA | 97.64 |
| DEU | DEU | 97.74 |
| CZE | CZE | 98.00 |
| EST | EST | 98.02 |
| SAU | SAU | 98.34 |
| SMR | SMR | 98.56 |
| SWE | SWE | 98.54 |
| KOR | KOR | 98.74 |
| AUS | AUS | 98.74 |
| USA | USA | 98.84 |
| AUT | AUT | 98.92 |
| GBR | GBR | 98.94 |
| NZL | NZL | 98.94 |
| MLT | MLT | 99.30 |
| POL | POL | 99.28 |
| SGP | SGP | 99.32 |
| FRA | FRA | 99.42 |
| ISL | ISL | 99.42 |
| TWN | TWN | 99.44 |
| MAC | MAC | 99.50 |
| NLD | NLD | 99.50 |
| CAN | CAN | 99.62 |
| CHE | CHE | 99.60 |
| FIN | FIN | 99.60 |
| CYP | CYP | 99.70 |
| HKG | HKG | 99.72 |
| LUX | LUX | 99.70 |
| NOR | NOR | 99.70 |
| BEL | BEL | 99.80 |
| DNK | DNK | 99.82 |
| IRL | IRL | 99.80 |
| SVN | SVN | 99.82 |
Every figure we publish adds up to the household spending total in that country's national accounts.
Published method
See the data
| Paper | Published | Journal |
|---|---|---|
| Will the Sustainable Development Goals be fulfilled? Assessing present and future global poverty | 20 March 2018 | Humanities and Social Sciences Communications, a Nature Portfolio journal |
We publish the methodology and it is peer-reviewed. Read the paper →
Cities the map is redrawn first
Satellite built-up land and a one-kilometre population grid decide where each of the 9,400+ cities actually ends. Local wealth signals then shift the national curve neighbourhood by neighbourhood, and the cities are re-summed so the country total still holds exactly.
Open the cities data →See the data
| Panel | Meaning |
|---|---|
| Cities | Illustration of the model cut, not quantitative observations |
Households people are regrouped into homes
The model is built person by person, so for household questions we regroup people into homes using each country's recorded household composition. The same spending then reads per person or per household, and market rankings can swap between the two.
Open the households data →See the data
| Panel | Meaning |
|---|---|
| Households | Illustration of the model cut, not quantitative observations |
Generations a diagonal through the age grid
Because the model carries every single year of age for every year of the forecast, a generation is a band of birth years we follow diagonally through that grid as it ages. Spending stays attached to the people, which is how we can say Gen X took the largest share in 2019 and Millennials take it in 2033.
Open the generations data →See the data
| Panel | Meaning |
|---|---|
| Generations | Illustration of the model cut, not quantitative observations |
Categories shares that move with the budget
From the harmonised surveys we fit how each category's share of the wallet moves as budgets grow, one curve per category, 200 of them. Read the curves at any group's spending level and its wallet splits, for a city, an age band, or a year that has not happened yet.
Open the categories data →See the data
| Panel | Meaning |
|---|---|
| Categories | Illustration of the model cut, not quantitative observations |
What every figure carries.
Backcast
2000Rebuilt from surveys and national accounts
Nowcast
2026Measured for this year
Forecast
2050Projected every year
Where the data comes from.
Public and open
Published by statistical offices and international bodies.
- Household surveys
- National accounts
- Macro forecasts
- Population, geography and alternative data
Private and licensed
Licensed from data providers and partners.
- Country surveys in detail
- Company data
Let's talk through your market question.
Tell us the decision you are trying to make, and we will show you the forecasts that bear on it.
