The report Job Creation and Local Economic Development 2024: The Geography of Generative AI examines the health of regional labour markets and provides new estimates on regional labour shortages. In addition, it provides new findings on the impact of Generative AI on different regions and workers. It examines how AI technologies can be leveraged to address critical labour market challenges and boost productivity growth.
Job Creation and Local Economic Development 2024 - Country Notes: Japan
(a) Employment rate for the working age population (15-64 year-olds), 2023
Note: The figure shows the regional values and the national and OECD regional average in the working-age employment rate in 2023. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
In Japan the employment rate in 2023 varies across regions, ranging from a low of 77.4% in Kansai region to 82% in Hokuriku. This represents a difference of 4.6 percentage points, below the average OECD regional dispersion of 10 percentage points. The national employment rate in Japan stands at 79.2%, above the OECD benchmark of 69.4%.
(a) Change in the employment rate, 2019 to 2023
Note: The figure shows the difference between 2019 and 2023 for the employment rate for regions in Japan, as well as the national and OECD regional average. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
By 2023, all of (10 out of 10) of Japanese regions saw their employment recover to at least pre-pandemic levels. Shikoku experienced the greatest recovery for employment rates, surpassing the pre-pandemic level by 3.4 percentage points. Overall, employment rates are 2 percentage points above pre-crisis levels, a stronger recovery than the regional OECD average of 1.5 percentage points.
(a) Change in the gap in the participation rate by age (between 25-64 year-olds and 15-24 year-olds) and gender (between men and women), 2013 to 2023
Note: The figure shows regional values and the national and OECD regional average in the change in the age gap (difference between the prime-age working population (25-64 year-olds) and youth (15-24 year-olds) and the gender gap (difference between men and women) in the participation rate in 2013 and that in 2023. A negative value implies that disparities decreased. The participation rate is defined as the number of employed persons and those looking for work as a share of the population in that subgroup. For gender disparities, it is defined using the working age population of 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Over the past ten years, the gap in participation rates between prime-age and younger workers (age inclusion gap) fell in 7 out of 10 regions in Japan, on average by 0.7 percentage points. The age inclusion gap grew by 1.3 percentage points across OECD regions. The biggest decrease in age disparities occurred in Southern-Kanto at -3.9 percentage points, while the biggest increase was in Chugoku by 2.3 percentage points. Over the same period, the gap in participation rates between male and female workers (gender inclusion gap) fell in 10 out of 10 regions. The gender inclusion gap fell by, on average, 7.7 percentage points. The smallest decrease in gender disparities was in Shikoku by -6.1 percentage points, while the biggest decrease was in Hokkaido at -9.3 percentage points.
(a) Share of youth not in employment, education or training among the youth working-age population, 2023
Note: The figure shows the regional values and the national and OECD regional average in the NEET rate (not in employment, education or training) for the youth working-age population (15-24 year-olds) in 2023.
Source: OECD calculations based on the OECD Regional databases.
In Japan, all (10 out of 10 regions with available data) have youth not in employment, education, or training (NEET) rates below the OECD benchmark of 16.8%, while the regional mean stands at 4%. The highest rate of youth exclusion is observed in Chugoku at 4.6%, while the lowest rate is in Hokuriku at 2.2%. This underscores the uneven opportunities for youth across the country. In Japan, all (10 out of 10 regions with available data) have youth not in employment, education, or training (NEET) rates below the OECD benchmark of 16.8%, while the regional mean stands at 4%. The highest rate of youth exclusion is observed in Kyushu, Okinawa at 4.6%, while the lowest rate is in Hokuriku at 2.2%. This underscores the uneven opportunities for youth across the country.
References
OECD (2024), Job Creation and Local Economic Development 2024: The Geography of Generative AI https://doi.org/10.1787/83325127-en
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