Insights
How much do height and income shrink the dating pool?
Two filters that look harmless can remove most of a dating pool. These reproducible examples show how fast, country by country, and why the answer is not just a multiplication.
The short answer
For a woman looking for men aged 28 to 38 in Germany, a minimum height of 180 cm keeps about 51% of the men. Adding an income of at least 60,000 EUR a year leaves about 21%. The available singles fall from about 2.6 million to about 476,000, which is 18% of the starting pool. Switzerland (income of 100,000 CHF: 15% of the men) and the United States (100,000 USD: 9%) show the same effect with different thresholds.
The setup
All examples use the same model as the calculator. The searcher is a woman looking for men aged 28 to 38 (and, further down, the reverse). The filters always describe the people being sought: the height and the income are those of the men. “Available singles” are the matching men who are attracted to women and are neither married nor partnered.
The income thresholds are one choice per country, in local currency: 60,000 EUR in Germany, 100,000 CHF in Switzerland and 100,000 USD in the United States. They are meant as “clearly above average” cut-offs, not as a recommendation. Income is individual gross income per year, as in the calculator.
Step by step: a woman looking for men aged 28 to 38
Each country has its own table: no filter, height only, income only, both filters from the model, and for comparison the plain product of the two single shares.
How to read these tables: “Men matching” counts all men aged 28 to 38 who meet the filters, before sexual orientation is taken into account. Only “Available singles” applies orientation and removes the married and the partnered.
| Step | Men matching | Share of men aged 28–38 | Available singles |
|---|---|---|---|
| No filter (all men aged 28–38) | 5,520,000 | 100% | 2,580,000 |
| Height 180 cm or more | 2,810,000 | 51% | 1,320,000 |
| Income 60,000 EUR a year or more | 2,150,000 | 39% | 873,000 |
| Both filters (model) | 1,160,000 | 21% | 476,000 |
| Both filters if independent (product) | 1,090,000 | 20% |
| Step | Men matching | Share of men aged 28–38 | Available singles |
|---|---|---|---|
| No filter (all men aged 28–38) | 622,000 | 100% | 292,000 |
| Height 180 cm or more | 271,000 | 44% | 128,000 |
| Income 100,000 CHF a year or more | 199,000 | 32% | 79,600 |
| Both filters (model) | 94,000 | 15% | 37,900 |
| Both filters if independent (product) | 86,700 | 14% |
| Step | Men matching | Share of men aged 28–38 | Available singles |
|---|---|---|---|
| No filter (all men aged 28–38) | 25,300,000 | 100% | 10,900,000 |
| Height 180 cm or more | 9,040,000 | 36% | 3,920,000 |
| Income 100,000 USD a year or more | 5,910,000 | 23% | 2,070,000 |
| Both filters (model) | 2,350,000 | 9.3% | 834,000 |
| Both filters if independent (product) | 2,110,000 | 8.3% |
Income means individual gross income per year. The “product” row multiplies the two single shares and is shown only for comparison: it is what would come out if the filters had no link to each other.
Why the filters are not simply multiplied
If height and income were unrelated, the share meeting both would be the product of the two shares: 51% × 39% = 20% in Germany. The model gets 21%, which is above that product.
The reason: the calculator does not treat traits that tend to go together as unrelated. Income, net worth, education, home ownership and, weakly, height belong to one group in the model. It therefore assumes a small positive link between height and income, a correlation of about 0.09 (0 would be no link at all, 1 a perfect one). Each filter keeps its exact single share; only the overlap changes.
With stricter or more filters the gap to the plain product can grow, which is why long wish lists do not collapse as fast as a simple multiplication says. How strong the link really is, we do not know precisely: the values in the model are judgement calls, not fitted to individual-level data.
Technical footnote: the method is a Gaussian one-factor copula. Height and income are tied to one shared, unobserved factor with weights of 0.12 and 0.72; the correlation is the product of those two weights.
Are tall high earners as often single?
The model does not assume so. Among men aged 28 to 38 who are attracted to women, 49% are single in Germany; among those who also meet both filters it is 43%. The difference rests on an assumption: men with a higher income are somewhat more often married or partnered than other men of their age (often called the “marriage premium”). We have not measured how strong this is in each country; the model sets it from general survey findings. An income filter therefore removes some extra men as “already taken”, not only through the income cut.
For height the model applies no such tilt. That is also an assumption and not a measurement: we know of no reliable, age-controlled source for a link between height and relationship status, and we do not want to nudge the result with a mere hunch, which would be putting a thumb on the scale.
A few centimetres matter
Height is close to normally distributed, so the upper tail thins out quickly: in Germany 51% of men aged 28 to 38 are at least 180 cm tall, 24% at least 185 cm and 8% at least 190 cm. Moving the cut-off by five centimetres changes the pool noticeably. Heights are whole centimetres in the calculator, and “180 or more” means a measured height of 179.5 cm or more.
| Minimum height | Germany | Switzerland | United States | |||
|---|---|---|---|---|---|---|
| Share of men | Available singles | Share of men | Available singles | Share of men | Available singles | |
| 170 cm | 93% | 2,390,000 | 90% | 262,000 | 85% | 9,240,000 |
| 175 cm | 77% | 1,990,000 | 71% | 208,000 | 63% | 6,880,000 |
| 180 cm | 51% | 1,320,000 | 44% | 128,000 | 36% | 3,920,000 |
| 185 cm | 24% | 639,000 | 19% | 56,200 | 14% | 1,590,000 |
| 190 cm | 8.0% | 210,000 | 5.6% | 16,600 | 4.0% | 440,000 |
The other way round: a man looking for women
The same kind of filter has a different effect for the other side, because the distributions differ. A man looking for women aged 28 to 38 who asks for 170 cm or more (tall for women) keeps about 27% of the women in Germany; an income of at least 60,000 EUR keeps about 23%; both together about 7%. In the country data women have lower median individual incomes and a smaller average height than men, so a cut-off of the same size removes more of them. Available singles fall from about 2.3 million to about 155,000.
How to read these tables: “Women matching” counts all women aged 28 to 38 who meet the filters, before sexual orientation is taken into account. Only “Available singles” applies orientation and removes the married and the partnered.
| Step | Women matching | Share of women aged 28–38 | Available singles |
|---|---|---|---|
| No filter (all women aged 28–38) | 5,370,000 | 100% | 2,310,000 |
| Height 170 cm or more | 1,460,000 | 27% | 637,000 |
| Income 60,000 EUR a year or more | 1,250,000 | 23% | 495,000 |
| Both filters (model) | 384,000 | 7.2% | 155,000 |
| Both filters if independent (product) | 339,000 | 6.3% |
| Step | Women matching | Share of women aged 28–38 | Available singles |
|---|---|---|---|
| No filter (all women aged 28–38) | 630,000 | 100% | 275,000 |
| Height 170 cm or more | 150,000 | 24% | 66,200 |
| Income 100,000 CHF a year or more | 107,000 | 17% | 42,800 |
| Both filters (model) | 29,600 | 4.7% | 12,000 |
| Both filters if independent (product) | 25,400 | 4.0% |
| Step | Women matching | Share of women aged 28–38 | Available singles |
|---|---|---|---|
| No filter (all women aged 28–38) | 25,500,000 | 100% | 9,310,000 |
| Height 170 cm or more | 4,010,000 | 16% | 1,490,000 |
| Income 100,000 USD a year or more | 3,050,000 | 12% | 1,000,000 |
| Both filters (model) | 586,000 | 2.3% | 196,000 |
| Both filters if independent (product) | 480,000 | 1.9% |
Income means individual gross income per year. The “product” row multiplies the two single shares and is shown only for comparison: it is what would come out if the filters had no link to each other.
What these examples cannot tell you
They describe statistical populations, not individuals. Heights come from national survey estimates (NCD-RisC and national surveys), incomes from published medians, Gini coefficients and an assumed shape of the top tail, and the age profile of income is modelled. The uncertainty band of the calculator grows with every filter you add. Expect the right order of magnitude, not the exact count. The method page explains each step: how DateTruth counts.
Try it with your own numbers
These links open the calculator with exactly the search used in the tables above (a woman looking for men aged 28 to 38, height 180 cm or more, the income threshold of the country). Change any filter and watch the funnel.
Germany: 60,000 EUR Switzerland: 100,000 CHF United States: 100,000 USD