At ages 20–24, women's wages are around 97% of men's in all seven Kanto prefectures. By ages 25–29, however, the ratio falls into the 90%–92% range. The most important point is that even when pay is nearly equal at a stage close to entry-level hiring, a gap appears only a few years into the career.
Scheduled monthly wages by gender at ages 20–24 in the seven Kanto prefectures (2023)
| Prefecture | Men (¥1,000) | Women (¥1,000) | Women ÷ men |
|---|---|---|---|
| Tokyo | 267.6 | 261.4 | 97.7% |
| Kanagawa | 258.3 | 251.6 | 97.4% |
| Saitama | 247.2 | 241.3 | 97.6% |
| Chiba | 243.8 | 237.9 | 97.6% |
| Ibaraki | 238.5 | 231.4 | 97.0% |
| Tochigi | 241.7 | 235.2 | 97.3% |
| Gunma | 236.4 | 229.8 | 97.2% |
Scheduled monthly wages by gender at ages 25–29 in the seven Kanto prefectures (2023)
| Prefecture | Men (¥1,000) | Women (¥1,000) | Women ÷ men |
|---|---|---|---|
| Tokyo | 322.7 | 299.9 | 92.9% |
| Kanagawa | 310.4 | 285.1 | 91.8% |
| Saitama | 296.3 | 270.8 | 91.4% |
| Chiba | 290.7 | 266.9 | 91.8% |
| Ibaraki | 285.6 | 259.3 | 90.8% |
| Tochigi | 292.1 | 265.7 | 91.0% |
| Gunma | 280.8 | 254.6 | 90.7% |
A gap of about 3% in the early 20s becomes 7%–9% by the late 20s
At ages 20–24, women's wage ratios range from 97.0% to 97.7%, a spread of only 0.7 percentage points across prefectures. At the hiring stage, men's and women's wages are therefore quite close regardless of region.
By ages 25–29, the ratio falls to 92.9% in Tokyo, 91.8% in Kanagawa and Chiba, and 90.7% in Gunma. Tokyo's monthly gap widens from ¥6,200 to ¥22,800, while Kanagawa's increases from ¥6,700 to ¥25,300.
The first divergence in assignment, occupation, overtime, and promotion appears in the late 20s
The late 20s are when career paths begin to diverge: career-track versus clerical roles, sales and technical positions versus office work, regular versus non-regular employment, job changes, marriage, and other choices. If men and women are unevenly represented in higher-paying tracks—such as headquarters positions in Tokyo or technical roles in Kanagawa and northern Kanto—average pay can separate within only a few years even when entry-level wages are similar.
It is also too simplistic to attribute the gap at this age only to childbirth. Assignment, training opportunities, overtime, the first promotion, and occupational choices may already be producing differences. Companies that want to reduce later gender gaps should examine what happens in the late 20s, before employees reach management age.
How to read these figures
These statistics measure scheduled monthly wages and exclude bonuses and overtime pay. The table also cannot identify differences in occupation, position, tenure, or employment status. It is therefore not possible to determine a single cause for the widening gap in the late 20s from these figures alone.
What this article shows is that, in the published 2023 data for the seven Kanto prefectures, the gender gap is larger among workers aged 25–29 than among those aged 20–24. Industry- and employment-status data are also needed to investigate the background.
Summary: The first signs of the gender gap appear in the late 20s
The gender wage gap is small immediately after hiring across Kanto, but clearly widens by the late 20s in every prefecture. Reducing the gap in the 40s requires looking at assignment, occupation, evaluation, and the first promotions starting in the 20s.
- Women's wage ratios are in the 97% range in every prefecture at ages 20–24
- At ages 25–29, they fall to 90.7%–92.9%
- Tokyo's monthly gap widens from ¥6,200 to ¥22,800
- Career divergence in the first few years matters more than entry-level pay alone
Source: Ministry of Health, Labour and Welfare, Basic Survey on Wage Structure (2023)
📊 Analysis notes
- Statistics used: Ministry of Health, Labour and Welfare, Basic Survey on Wage Structure (Table ID: 0003426933, view on e-Stat)
- Area: Seven Kanto prefectures
- Year: 2023
- Data retrieved: June 6, 2026
- Notes: The article primarily organizes published statistical values. Interpreting the background also involves factors outside the table, including occupational mix, management structure, and employment status, so directly observable figures should be distinguished from interpretation.
Figures in charts and tables were independently compiled and visualized from the statistical data above.