In 2023, women's wages as a share of men's were highest in Kanagawa at 76.9% and lowest in Ibaraki at 74.4%. Yet the difference across all seven Kanto prefectures is only 2.5 percentage points. The rankings are close, and every prefecture shares the broader problem of women's wages being roughly three-quarters of men's. This article looks at both ratios and absolute monthly gaps.
Gender wage ratio ranking for the seven Kanto prefectures (2023, all ages, industries, and employer sizes)
| Rank | Prefecture | Men (¥1,000) | Women (¥1,000) | Women ÷ men |
|---|---|---|---|---|
| 1 | Kanagawa | 408.5 | 314.4 | 76.9% |
| 2 | Chiba | 363.1 | 278.3 | 76.7% |
| 3 | Saitama | 372.5 | 284.3 | 76.3% |
| 4 | Tochigi | 375.3 | 285.5 | 76.1% |
| 5 | Tokyo | 426.8 | 321.7 | 75.4% |
| 6 | Gunma | 347.5 | 260.3 | 74.9% |
| 7 | Ibaraki | 365.9 | 272.2 | 74.4% |
More important than the 2.5-point prefectural gap is the fact that every prefecture remains in the 70% range
Women's wage ratios are 76.9% in Kanagawa, 76.7% in Chiba, 76.3% in Saitama, 76.1% in Tochigi, 75.4% in Tokyo, 74.9% in Gunma, and 74.4% in Ibaraki. The difference between first and seventh place is small, showing that this is not a problem unique to one prefecture.
In absolute terms, Tokyo has wages of ¥426,800 for men and ¥321,700 for women, a monthly gap of ¥105,100. Even when a prefecture does not rank last by percentage, a region with higher male wages can still have a large absolute yen gap.
Different industries, but common structural issues in management and occupational segregation
The main industries differ across Kanto: corporate headquarters and finance in Tokyo, R&D in Kanagawa, manufacturing in Tochigi, Ibaraki, and Gunma, and logistics and services in Saitama and Chiba. Yet women's wage ratios still cluster between 74% and 77%. This suggests that structural factors such as management representation, occupational segregation, tenure, and shorter working arrangements are common across the region.
Even Kanagawa, which ranks first, has a monthly gender gap of ¥94,100. Rather than focusing on rank alone, it is more useful to examine why small gaps among younger workers widen from the 30s onward using age- and employer-size data.
How to read these figures
This ranking is based on scheduled monthly wages across all ages, industries, and employer sizes. It therefore cannot isolate differences in industrial composition, management composition, employment status, or tenure between prefectures. Because bonuses and overtime are excluded, the ranking also does not correspond directly to total annual income.
The appropriate way to read this article is as baseline data showing that, in the published 2023 figures, women's wage ratios across the seven Kanto prefectures fall in the 74% to 77% range. Age-, industry-, and employment-status data are also needed to investigate the background factors.
Summary: Even the prefecture with the smallest gap remains below 80%
The key point is not the difference between Kanto prefectures, but that women's wages are below 80% of men's in every one of them. Both ratios and absolute yen gaps should be used when examining management advancement and continued employment across the region.
- Kanagawa is highest at 76.9%
- Ibaraki is lowest at 74.4%
- The full range across prefectures is only 2.5 percentage points
- Tokyo is at 75.4%, but still has an absolute monthly gap of ¥105,100
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 industrial structure, management composition, 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.