{"id":595,"date":"2026-08-31T20:52:40","date_gmt":"2026-08-31T20:52:40","guid":{"rendered":"https:\/\/hiringlab.indeed.com\/jp\/?p=595"},"modified":"2026-09-16T21:19:40","modified_gmt":"2026-09-16T21:19:40","slug":"shrinking-workforce-ai-and-labor-reallocation-will-shape-japans-next-15-years","status":"publish","type":"post","link":"https:\/\/hiringlab.indeed.com\/jp\/blog\/2026\/08\/31\/shrinking-workforce-ai-and-labor-reallocation-will-shape-japans-next-15-years\/","title":{"rendered":"The Great Shortage: How a Shrinking Workforce, AI, and Labor Reallocation Will Shape Japan\u2019s Next 15 Years"},"content":{"rendered":"\n<p><strong>Key points:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Japan&#8217;s workforce is already shrinking, a fact that drives everything else. New Indeed Hiring Lab projections show that employment is likely to stay steady through 2031 before falling about 6% to around 60.6 million by 2040, driven overwhelmingly by demographics.<\/li>\n\n\n\n<li>AI is a secondary force affecting employment, but acting more as a complement than a substitute. Even in aggressive scenarios where AI is more disruptive to employment, it would still account for only about 20% of job losses; its impact is softened by slow adoption and a chronic labor shortage.<\/li>\n\n\n\n<li>AI is most likely to impact jobs in Information &amp; Communications, Finance, Professional Services, and Education. But these sectors are short of workers, and AI mainly eases their hiring pressure. AI offers little assistance to workers in sectors facing the deepest shortages, including Medical &amp; Welfare, Construction, and Transportation.<\/li>\n\n\n\n<li>Unemployment is expected to remain relatively low, rising from 2.5% in 2025 to anywhere from 2.7% to 4.8%, depending on the scenario. The strain will probably show up not as visible joblessness, but instead as unfilled vacancies among white-collar workers who cannot easily move from their current roles into ones in sectors facing the largest anticipated shortages.<\/li>\n\n\n\n<li>Employers, policymakers, and job seekers will need to adopt a multi-dimensional approach to address these challenges, including reskilling, better matching, and finding more flexible ways to increase workforce participation for all groups.<\/li>\n<\/ul>\n\n\n\n<p>Japan\u2019s economy cannot find enough workers, just as AI is becoming more capable of performing a large share of skilled office work. For the past decade, rising labor force participation among women, older workers, and foreign workers has helped to offset the impact of an aging population and underlying demographic decline. But that buffer is now thinning. The defining challenge of the next 15 years will not be a shortage of jobs, nor even AI itself, but rather, how readily workers will be able to move from where they aren\u2019t needed, to where they are.<\/p>\n\n\n\n<p>This work is based on an Indeed Hiring Lab search-and-matching model that projects employment in 16 sectors of the Japanese economy through 2040. The projections rest on a few assumptions, all in line with recent trends: that participation among women and seniors keeps rising gradually; that net inflows of foreign workers continue at close to their recent pace (lifting the foreign share of employment toward 8.5% by 2040); and that AI diffuses more slowly in Japan than in the United States, in line with the relatively low share of Japanese <a href=\"https:\/\/data.indeed.com\/#\/ai\" target=\"_blank\" rel=\"noreferrer noopener\">job postings that currently mention AI<\/a>. The broad picture shows that overall employment stays largely steady into the early 2030s, then falls about 6% to roughly 60.6 million by 2040. AI could evolve benignly and end up creating modestly more jobs, or it could evolve more aggressively and destroy some jobs. In either case, the ultimate downward trajectory will be set by demographics alone.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202135\/en_p00_employment_history_JP2040-1024x585.png\" alt=\"Line chart titled \u201cJapan Employment: A decade of gains gives way to decline\u201d shows Japan\u2019s employment in millions across 16 sectors. The solid line shows actual data from 2005 to 2025 (Labor Force Survey); dashed lines from 2025 to 2040 show model projections by AI scenario. Agriculture, Forestry &amp; Fisheries, Mining, and Unclassified Industries (about 3.3 million workers) are excluded.\" class=\"wp-image-606\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202135\/en_p00_employment_history_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202135\/en_p00_employment_history_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202135\/en_p00_employment_history_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202135\/en_p00_employment_history_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202135\/en_p00_employment_history_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Line chart titled \u201cJapan Employment: A decade of gains gives way to decline\u201d shows Japan\u2019s employment in millions across 16 sectors. The solid line shows actual data from 2005 to 2025 (Labor Force Survey); dashed lines from 2025 to 2040 show model projections by AI scenario. Agriculture, Forestry &amp; Fisheries, Mining, and Unclassified Industries (about 3.3 million workers) are excluded.<\/em><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">The supply shortage is already here<\/h2>\n\n\n\n<p>Japan\u2019s working-age population has been declining since the late 1990s. Nonetheless, total employment rose for years because participation climbed faster than the population fell. Three drivers did the work: Since 2013, women\u2019s employment is up roughly 16%, employment among people aged 65 and over is up about 48%, and the number of foreign workers has grown several-fold (from a low base). Each of these drivers is now decelerating as participation approaches its natural ceilings.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202604\/en_p13_three_drivers_JP2040-1024x585.png\" alt=\"Line chart titled \u201cWomen, seniors, and foreign workers powered a decade of job gains in Japan\u201d shows Japan\u2019s three labor-supply drivers, indexed to 2013 = 100: employment of women, of people aged 65 and over, and of foreign workers. They offset population decline for a decade and are now flattening.\" class=\"wp-image-607\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202604\/en_p13_three_drivers_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202604\/en_p13_three_drivers_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202604\/en_p13_three_drivers_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202604\/en_p13_three_drivers_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202604\/en_p13_three_drivers_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Line chart titled \u201cWomen, seniors, and foreign workers powered a decade of job gains in Japan\u201d shows Japan\u2019s three labor-supply drivers, indexed to 2013 = 100: employment of women, of people aged 65 and over, and of foreign workers. They offset population decline for a decade and are now flattening.<\/em><\/figcaption><\/figure>\n\n\n\n<p>The demographics become more unforgiving the further ahead we look. In every year of our projection, more people leave the labor force through retirement than enter it as new graduates or immigrants, and the gap only widens. The annual net drain grows from about 172,000 in 2026 to roughly 757,000 by 2040, as yearly exits climb to about 1.3 million while new entrants fall below 600,000. The retirement wave of the second-generation baby boomers (called <em>\u201cDankai Junior\u201d <\/em>in Japanese) crests in the late 2030s. Even assuming the participation rate keeps rising, foreign inflows continue, and AI adoption is slower in Japan than elsewhere, the labor force still contracts. Employment will likely peak this year and drift lower through the 2030s.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202727\/en_p01_demographic_flows_JP2040-1024x585.png\" alt=\"Bar chart titled \u201cMore workers will leave the labor force than enter it, every year to 2040\u201d shows the projected annual labor-force flows in millions per year, 2026\u20132040: gross entries versus gross exits, computed from the IPSS 2023 population projection combined with age-specific participation. Exits exceed entries in every year. This is not a sudden break: On cohort arithmetic alone, exits have outweighed entries for years, and the employment gains of the past decade came instead from rising participation among women and seniors, as well as from foreign workers (previous figure). Those forces are not shown here. Rising participation and immigration enter the model separately. They lift employment to a peak around 2026 to 2027 and keep it above the 2025 level into the early 2030s. The model does not impose a single retirement age: exits follow the age profile of participation, concentrated around age 65, where participation falls most sharply, while allowing continued work at older ages.\" class=\"wp-image-608\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202727\/en_p01_demographic_flows_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202727\/en_p01_demographic_flows_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202727\/en_p01_demographic_flows_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202727\/en_p01_demographic_flows_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202727\/en_p01_demographic_flows_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Bar chart titled \u201cMore workers will leave the labor force than enter it, every year to 2040\u201d shows the projected annual labor-force flows in millions per year, 2026\u20132040: gross entries versus gross exits, computed from the IPSS 2023 population projection combined with age-specific participation. Exits exceed entries in every year. This is not a sudden break: On cohort arithmetic alone, exits have outweighed entries for years, and the employment gains of the past decade came instead from rising participation among women and seniors, as well as from foreign workers (previous figure). Those forces are not shown here. Rising participation and immigration enter the model separately. They lift employment to a peak around 2026 to 2027 and keep it above the 2025 level into the early 2030s. The model does not impose a single retirement age: exits follow the age profile of participation, concentrated around age 65, where participation falls most sharply, while allowing continued work at older ages.<\/em><\/figcaption><\/figure>\n\n\n\n<p>Interestingly, an expected decline in exits in 2031 traces to the year of the Fire Horse (<em>\u201cHinoeuma\u201d<\/em>). Largely based on folk superstitions, births in 1966 were about 25% lower than in adjacent years, and that unusually small cohort turns 65 in 2031.<\/p>\n\n\n\n<p>Nor are these exits confined to a few aging industries. Retirement rates are expected to rise across nearly every sector through the late 2030s, climbing most steeply in Construction, Manufacturing, and Transportation \u2014 each an industry where a third or more of workers today are over 55.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202825\/en_p05_retirement_heatmap_JP2040-1024x585.png\" alt=\"Heatmap matrix titled \u201cRetirements accelerate in every sector after 2035\u201d shows the projected annual retirement rates by sector under the AI-baseline scenario, measured as retirements as a share of each sector\u2019s employment (%). Retirement here means permanent exit from the labor force, so post-retirement re-employment is not counted until the final exit; sector rates of roughly 1.0\u20132.6% per year are consistent with the roughly 1.0\u20131.3 million total annual exits implied by the IPSS population projection. No fixed retirement age is imposed; exits follow the age profile of participation, concentrated around age 65. Retirements accelerate economy-wide toward 2035, led by Construction, Manufacturing, and Transportation.\" class=\"wp-image-609\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202825\/en_p05_retirement_heatmap_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202825\/en_p05_retirement_heatmap_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202825\/en_p05_retirement_heatmap_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202825\/en_p05_retirement_heatmap_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31202825\/en_p05_retirement_heatmap_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Heatmap matrix titled \u201cRetirements accelerate in every sector after 2035\u201d shows the projected annual retirement rates by sector under the AI-baseline scenario, measured as retirements as a share of each sector\u2019s employment (%). Retirement here means permanent exit from the labor force, so post-retirement re-employment is not counted until the final exit; sector rates of roughly 1.0\u20132.6% per year are consistent with the roughly 1.0\u20131.3 million total annual exits implied by the IPSS population projection. No fixed retirement age is imposed; exits follow the age profile of participation, concentrated around age 65. Retirements accelerate economy-wide toward 2035, led by Construction, Manufacturing, and Transportation.<\/em><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">AI won\u2019t fill the gap, least of all where it\u2019s needed<\/h2>\n\n\n\n<p>Our model assumes that AI will operate through three channels: It could raise productivity, allowing one worker to accomplish more; it could automate tasks and reduce demand for human labor; and\/or it could boost labor demand by creating new tasks and roles. Our most central scenario is an AI-baseline, in which we assume AI spreads gradually and automates some tasks, while productivity gains and newly created tasks generate as much demand for human labor as automation removes. Our optimistic scenario assumes AI augments human work and keeps demand for human labor high, while our pessimistic AI-replacing scenario assumes some human workers are outright replaced by AI. Demographic shifts and AI adoption rates remain the same under each scenario; what differs is the strength and mix of the three channels. AI-augmenting leans on the productivity and matching channels with little outright task displacement, while AI-replacing dials up task automation, so that a larger share of tasks disappears in the occupations most exposed to generative AI (measured from Indeed\u2019s occupation-level GenAI exposure ratings and each sector\u2019s census occupation mix). Two results stand out:<\/p>\n\n\n\n<p>First, we find that AI\u2019s impact is most likely to be felt in high-wage cognitive sectors: Information &amp; Communications, Finance &amp; Insurance, Professional &amp; Technical Services, and Education. The whole economy is expected to be short of workers, and there is nothing in these results to suggest a current surplus of these workers in particular. Instead, in these sectors, AI is likely to slow hiring and ease some of the unfilled demand. Unfortunately, AI will offer limited relief in sectors expected to face the deepest worker shortages. The labor that AI might free up from office work likely cannot be easily applied to roles in Medical &amp; Welfare, Construction, and Transportation. Put another way, a financial analyst who goes unhired in that sector cannot easily become a nurse, care worker, or builder.&nbsp;<\/p>\n\n\n\n<p>Interestingly, the Semiconductors &amp; Electronics sector moves in the opposite direction. As the industry upstream of AI itself, it is likely to see labor demand rise alongside AI adoption. Our model suggests that employment in this sector could rise by roughly 23% as it absorbs some of the workers reallocating from other sectors.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"655\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203200\/en_p04_employment_by_sector_JP2040-1024x655.png\" alt=\"Panel chart titled \u201cThe AI-replacing and baseline paths diverge most in white-collar-heavy sectors\u201d shows the projected employment by sector in millions, for 16 sectors. Solid black lines show actual data from 2013 to 2025 (the industry-detail series starts in 2013); colored lines from 2025 to 2040 show projections by AI scenario. Semiconductors &amp; Electronics are carved out of Manufacturing at a fixed 7% share, and the Manufacturing panel therefore shows employment excluding semiconductors. AI\u2019s effect concentrates in Information, Finance and Professional services, while physical and care sectors are largely unaffected.\" class=\"wp-image-610\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203200\/en_p04_employment_by_sector_JP2040-1024x655.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203200\/en_p04_employment_by_sector_JP2040-300x192.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203200\/en_p04_employment_by_sector_JP2040-768x492.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203200\/en_p04_employment_by_sector_JP2040-1536x983.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203200\/en_p04_employment_by_sector_JP2040-2048x1311.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Panel chart titled \u201cThe AI-replacing and baseline paths diverge most in white-collar-heavy sectors\u201d shows the projected employment by sector in millions, for 16 sectors. Solid black lines show actual data from 2013 to 2025 (the industry-detail series starts in 2013); colored lines from 2025 to 2040 show projections by AI scenario. Semiconductors &amp; Electronics are carved out of Manufacturing at a fixed 7% share, and the Manufacturing panel therefore shows employment excluding semiconductors. AI\u2019s effect concentrates in Information, Finance and Professional services, while physical and care sectors are largely unaffected.<\/em><\/figcaption><\/figure>\n\n\n\n<p>Second, two forces will serve to contain the aggregate impact of AI, which is likely to be smaller than the public might currently imagine. One is that AI is diffusing slowly in Japan. The share of Japanese job postings mentioning AI is among the lowest of any major economy, and adoption is expected to build gradually through the 2030s rather than arriving as a shock. The other (and more fundamental) is that Japan\u2019s chronic labor shortage itself acts as a buffer against some of the negative potential effects of AI. When AI automates tasks in a sector that is already short of workers, it first eases the shortage rather than creating unemployment. In this sense, AI functions more as a complement to labor than a substitute for it.<\/p>\n\n\n\n<p>The stress test shows that even in Information &amp; Communications, which is the most AI-exposed sector, the outlook under the AI-replacing scenario is gradual rather than disruptive. The unemployment rate there certainly climbs under the AI-replacing scenario to about 10% by 2040, compared with about 4.8% for the economy as a whole. But the rise is slow and orderly rather than disruptive: approximately half a percentage point per year, leveling off in the late 2030s, while employment continues to grow into the early 2030s and never falls below today&#8217;s level.<\/p>\n\n\n\n<p>The conclusion is that demographics, not AI, do most of the work to drive down total employment. Even under the aggressive AI-replacing scenario, 78% of the projected employment decline through 2040 will be attributable to demographics, and just 22% to AI.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203448\/en_p02_decline_decomposition_JP2040-1-1024x585.png\" alt=\"Line chart titled \u201cDemographics, not AI, drive most of the projected decline\u201d shows the projected decline in employment, in millions, across 16 sectors. The solid black line shows actual data from 2010 to 2025; dashed lines from 2025 to 2040 show the model projection. Shading splits the decline versus the 2025 level into a demographic component (the AI-baseline path) and an AI-driven component, shown separately for the AI-replacing and AI-augmenting scenarios. Agriculture, Forestry &amp; Fisheries, Mining, and Unclassified Industries are excluded.\" class=\"wp-image-613\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203448\/en_p02_decline_decomposition_JP2040-1-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203448\/en_p02_decline_decomposition_JP2040-1-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203448\/en_p02_decline_decomposition_JP2040-1-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203448\/en_p02_decline_decomposition_JP2040-1-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203448\/en_p02_decline_decomposition_JP2040-1-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Line chart titled \u201cDemographics, not AI, drive most of the projected decline\u201d shows the projected decline in employment, in millions, across 16 sectors. The solid black line shows actual data from 2010 to 2025; dashed lines from 2025 to 2040 show the model projection. Shading splits the decline versus the 2025 level into a demographic component (the AI-baseline path) and an AI-driven component, shown separately for the AI-replacing and AI-augmenting scenarios. Agriculture, Forestry &amp; Fisheries, Mining, and Unclassified Industries are excluded.<\/em><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Unemployment stays low, but the problem is reallocation\u00a0<\/h2>\n\n\n\n<p>Because Japan is already facing a labor shortage, the unemployment rate is expected to remain low under most scenarios. By 2040, it is projected to rise from about 2.5% in 2025 to about 3.0% under the AI-baseline scenario and to 4.8% under the more-aggressive AI-replacing scenario. But those relatively low headline figures only help conceal the problem. What Japan faces is not mass unemployment, nor genuine pockets of labor surplus. It is a mismatch between where workers are needed and where they are not.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203540\/en_p06_unemployment_by_scenario_JP2040-1024x585.png\" alt=\"Line chart titled \u201cUnemployment rises materially only if AI replaces workers\u201d shows the aggregate unemployment rate across 16 sectors (%), by AI scenario, 2025\u20132040. Only the AI-replacing scenario materially raises unemployment; the labor shortage keeps the other scenarios near the structural floor. Agriculture, Forestry &amp; Fisheries, Mining, and Unclassified Industries are excluded.\" class=\"wp-image-614\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203540\/en_p06_unemployment_by_scenario_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203540\/en_p06_unemployment_by_scenario_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203540\/en_p06_unemployment_by_scenario_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203540\/en_p06_unemployment_by_scenario_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203540\/en_p06_unemployment_by_scenario_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Line chart titled \u201cUnemployment rises materially only if AI replaces workers\u201d shows the aggregate unemployment rate across 16 sectors (%), by AI scenario, 2025\u20132040. Only the AI-replacing scenario materially raises unemployment; the labor shortage keeps the other scenarios near the structural floor. Agriculture, Forestry &amp; Fisheries, Mining, and Unclassified Industries are excluded.<\/em><\/figcaption><\/figure>\n\n\n\n<p>This mismatch does not occur simply because individual workers refuse to move. Japanese job-changers are, in fact, fairly mobile: <a href=\"https:\/\/hiringlab.indeed.com\/jp\/blog\/2025\/09\/17\/%E3%82%AD%E3%83%A3%E3%83%AA%E3%82%A2%E7%A7%BB%E8%A1%8C%E3%81%AE%E5%9C%B0%E5%9B%B3%EF%BC%9Aindeed%E5%B1%A5%E6%AD%B4%E6%9B%B8%E3%83%87%E3%83%BC%E3%82%BF%E3%81%A7%E8%A6%8B%E3%82%8B%E8%81%B7%E7%A8%AE\/\" target=\"_blank\" rel=\"noreferrer noopener\">In Indeed resume data covering job moves made between 2022 and 2024, about 58% of them move to a different occupational category.<\/a><\/p>\n\n\n\n<p>The constraint is that these flows do not reach the sectors where shortages run deepest. Nursing is the clearest case. On average, during 2022-2024, 85% of all moves into Nursing come from Nursing itself, and 81% of nurses who changed jobs stayed in Nursing (with most of the remainder coming from adjacent and\/or low-barrier roles such as Caregiving, Retail, and Administrative Assistance work). Inflows into Nursing from the high-wage cognitive occupations where AI eases hiring, such as Finance and IT, are negligible. Skill and credential barriers, together with <a href=\"https:\/\/www.japantimes.co.jp\/news\/2023\/01\/12\/national\/jobs-membership-work\/\" target=\"_blank\" rel=\"noreferrer noopener\">membership-type employment <\/a>culture and steep seniority-based pay, block movement at scale in the direction it is most needed. As a result, even under the AI-baseline scenario, unemployment drifts up modestly as demographic change forces a reallocation that the market is likely to absorb only in part.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203838\/en_p10_nursing_flows_JP2040-1024x585.png\" alt=\"Sankey diagram titled \u201cNursing is a near-closed pipeline\u201d shows the composition of job moves into Nursing (left) and out of Nursing (right), from Indeed resume data for 2022\u20132024, as shares of total moves. The top five partner categories plus an \u201cother\u201d group are shown, and same-occupation moves are included. 85% of inflows come from Nursing itself, and 81% of leavers stay in Nursing, with almost no inflow from AI-exposed office occupations; pink marks Caregiving. For transitions across the other occupational categories, see our map of job-changers' career paths.\" class=\"wp-image-615\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203838\/en_p10_nursing_flows_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203838\/en_p10_nursing_flows_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203838\/en_p10_nursing_flows_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203838\/en_p10_nursing_flows_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203838\/en_p10_nursing_flows_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Sankey diagram titled \u201cNursing is a near-closed pipeline\u201d shows the composition of job moves into Nursing (left) and out of Nursing (right), from Indeed resume data for 2022\u20132024, as shares of total moves. The top five partner categories plus an \u201cother\u201d group are shown, and same-occupation moves are included. 85% of inflows come from Nursing itself, and 81% of leavers stay in Nursing, with almost no inflow from AI-exposed office occupations; pink marks Caregiving. For transitions across the other occupational categories, see our <\/em><a href=\"https:\/\/hiringlab.indeed.com\/jp\/blog\/2025\/09\/17\/%e3%81%a9%e3%81%93%e3%81%8b%e3%82%89%e6%9d%a5%e3%81%a6%e3%80%81%e3%81%a9%e3%81%93%e3%81%b8%e8%a1%8c%e3%81%8f%e3%81%ae%e3%81%8b%ef%bc%9a%e8%bb%a2%e8%81%b7%e8%80%85%e3%81%ae%e3%82%ad%e3%83%a3%e3%83%aa\/?isid=mwm_wordpress&amp;ikw=mwm_wordpress_jp%2Fblog%2F2025%2F09%2F17%2F%25E3%2582%25AD%25E3%2583%25A3%25E3%2583%25AA%25E3%2582%25A2%25E7%25A7%25BB%25E8%25A1%258C%25E3%2581%25AE%25E5%259C%25B0%25E5%259B%25B3%25EF%25BC%259Aindeed%25E5%25B1%25A5%25E6%25AD%25B4%25E6%259B%25B8%25E3%2583%2587%25E3%2583%25BC%25E3%2582%25BF%25E3%2581%25A7%25E8%25A6%258B%25E3%2582%258B%25E8%2581%25B7%25E7%25A8%25AE%2F_textlink_https%3A%2F%2Fhiringlab.indeed.com%2Fjp%2Fblog%2F2025%2F09%2F17%2F%25e3%2581%25a9%25e3%2581%2593%25e3%2581%258b%25e3%2582%2589%25e6%259d%25a5%25e3%2581%25a6%25e3%2580%2581%25e3%2581%25a9%25e3%2581%2593%25e3%2581%25b8%25e8%25a1%258c%25e3%2581%258f%25e3%2581%25ae%25e3%2581%258b%25ef%25bc%259a%25e8%25bb%25a2%25e8%2581%25b7%25e8%2580%2585%25e3%2581%25ae%25e3%2582%25ad%25e3%2583%25a3%25e3%2583%25aa%2F\" target=\"_blank\" rel=\"noreferrer noopener\"><em>map of job-changers&#8217; career paths.<\/em><\/a><\/figcaption><\/figure>\n\n\n\n<p>How much unemployment does this friction itself generate? Comparing the baseline against an idealized flexible market, in which vacancies adjust immediately, and workers move freely across sectors. Under the AI-replacing scenario, unemployment reaches 4.8% in 2040 with today&#8217;s frictions, but 3.7% in the flexible market. The gap of 1.1 percentage points (about 680 thousand people) is the unemployment caused by reallocation friction. Under the AI-augmenting scenario, the same comparison reads 2.7% against 2.3%, a friction gap of 0.4 points (about 250,000 people).\u00a0<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203940\/en_p11_structural_unemployment_JP2040-1024x585.png\" alt=\"Band chart titled \u201cReallocation friction, amplified by AI, creates structural unemployment\u201d shows the structural unemployment by AI scenario: the unemployment rate under baseline frictions (solid) versus an idealized flexible, fast-clearing market (dashed), with shading marking the gap attributable to reallocation friction. In the flexible counterfactual, vacancies adjust immediately, and workers reallocate freely across sectors.\" class=\"wp-image-616\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203940\/en_p11_structural_unemployment_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203940\/en_p11_structural_unemployment_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203940\/en_p11_structural_unemployment_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203940\/en_p11_structural_unemployment_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31203940\/en_p11_structural_unemployment_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Band chart titled \u201cReallocation friction, amplified by AI, creates structural unemployment\u201d shows the structural unemployment by AI scenario: the unemployment rate under baseline frictions (solid) versus an idealized flexible, fast-clearing market (dashed), with shading marking the gap attributable to reallocation friction. In the flexible counterfactual, vacancies adjust immediately, and workers reallocate freely across sectors.<\/em><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Institutions change AI\u2019s role: Retirement extension raises the stakes<\/h2>\n\n\n\n<p>Finally, we asked how the policy environment itself shifts these results, comparing the benchmark against two counterfactuals. The first is a relaxed dismissal regulation (\u89e3\u96c7\u898f\u5236\u306e\u7de9\u548c), which would theoretically make it easier for employers to let current workers go and, in turn, for those workers to more readily switch into other jobs. This is the lever most often proposed for a rigid labor market. Our research shows that this action might raise gross turnover by about 30%, but net reallocation across sectors would rise by only about 3 percentage points, to 7%. The unemployment rate would edge up rather than down, and the impact of AI barely changes. In a shortage economy, the shortage itself already pulls workers toward growing sectors, so easier dismissal mainly adds churn; the binding constraints lie in matching and reskilling.&nbsp;<\/p>\n\n\n\n<p>The second proposal would be a full retirement extension (\u5b9a\u5e74\u5ef6\u9577) that lifts senior participation further, with 2040 participation targets of 92% for workers ages 55\u201364 and 40% for those 65 and over. Japan starts from a high base: About 53.5% of Japanese workers aged 65 to 69 are employed, compared with 32.4% in the US, 26.7% in the UK, 20.8% in Germany, 16.1% in Italy, and 11.1% in France (<a href=\"https:\/\/www.oecd.org\/en\/publications\/2025\/11\/pensions-at-a-glance-2025_76510fe4\/full-report\/employment-rates-of-older-workers-and-gender-gaps_cb8a2f7b.html\" target=\"_blank\" rel=\"noreferrer noopener\">OECD, 2025<\/a>).<\/p>\n\n\n\n<p>Retirement extension raises total employment by about 2.7 million in 2040. But because it refills the labor shortage, it thins the buffer that had been absorbing any potential AI displacement of workers. Under the AI-replacing scenario, the 2040 unemployment rate reaches 5.7% when we assume a retirement extension, compared with 4.8% in the benchmark, while the AI-baseline rate stays in the low 3% range in either setting (3.0% and 3.3%). In headcount terms, AI-attributable unemployment rises from about 1.1 million to 1.6 million, and AI&#8217;s share of the employment decline jumps from about a fifth to nearly 60%. The same amplification works in reverse: Assuming a retirement extension, the AI-augmenting scenario offsets about five times as much of the decline as the benchmark.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"585\" src=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31204104\/en_p12_policy_ai_interaction_JP2040-1024x585.png\" alt=\"Bar chart titled \u201cHow institutions change the AI-driven unemployment\u201d shows the outcomes in 2040 under three institutional settings (benchmark, relaxed dismissal regulation, retirement extension), by AI scenario. The left panel shows the unemployment rate in percent; the right panel shows AI\u2019s share of the 2025\u201340 employment decline, computed as (AI-baseline employment minus scenario employment) divided by (2025 employment minus scenario employment), where negative values mean AI adds jobs.\" class=\"wp-image-617\" srcset=\"https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31204104\/en_p12_policy_ai_interaction_JP2040-1024x585.png 1024w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31204104\/en_p12_policy_ai_interaction_JP2040-300x171.png 300w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31204104\/en_p12_policy_ai_interaction_JP2040-768x439.png 768w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31204104\/en_p12_policy_ai_interaction_JP2040-1536x878.png 1536w, https:\/\/d341ezm4iqaae0.cloudfront.net\/hiringlaborg\/sites\/8\/2026\/08\/31204104\/en_p12_policy_ai_interaction_JP2040-2048x1170.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Bar chart titled \u201cHow institutions change the AI-driven unemployment\u201d shows the outcomes in 2040 under three institutional settings (benchmark, relaxed dismissal regulation, retirement extension), by AI scenario. The left panel shows the unemployment rate in percent; the right panel shows AI\u2019s share of the 2025\u201340 employment decline, computed as (AI-baseline employment minus scenario employment) divided by (2025 employment minus scenario employment), where negative values mean AI adds jobs.<\/em><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Implications, and looking ahead<\/h2>\n\n\n\n<p>Japan&#8217;s coming labor strain will be quiet. It will show up not as a jump in unemployment, but as persistent unfilled vacancies in care, construction, and technical work, and as thinner hiring in skilled office roles. Our results suggest there are real limits to how much targeted reallocation policies can deliver. Even in a frictionless market, unemployment only improves by about 1.1 percentage points, and the barriers that keep office workers from becoming nurses or builders (licensing, physical requirements, wage structures) are not the kind a training course removes. Where movement does happen is along adjacent, low-barrier corridors, and that is where policy can help, including implementation of potentially stackable credentials between care and construction fields, better matching, and more flexible ways to boost participation overall. None of these is a silver bullet on its own, but even small moves today will compound over 15 years.<\/p>\n\n\n\n<p>At least one implication is clear: Japanese firms need not fear AI adoption. The analysis points consistently in one direction: AI is arriving into a labor shortage that will help absorb at least some potential AI-driven displacement, making it more likely to help ease some hiring pressure than to meaningfully push up unemployment. And under the augmenting scenario, AI helps to add jobs outright. Actual adoption, meanwhile, remains strikingly low. <a href=\"https:\/\/hiringlab.indeed.com\/jp\/blog\/2025\/12\/29\/%E4%BA%8C%E6%A5%B5%E5%8C%96%E3%81%99%E3%82%8B%E5%8A%B4%E5%83%8D%E5%8A%9B%EF%BC%9A%E8%AA%B0%E3%81%8Cai%E3%82%92%E4%BD%BF%E3%81%84%E3%80%81%E8%AA%B0%E3%81%8C%E9%81%85%E3%82%8C%E3%81%A6%E3%81%84%E3%82%8B\/\" target=\"_blank\" rel=\"noreferrer noopener\">Just 18% of Japanese workers use AI at work<\/a>, the lowest of the eight advanced economies surveyed in Indeed&#8217;s 2025 Workforce Insights survey, and the share of Japanese job postings mentioning AI likewise trails other major economies. The same survey suggests the binding constraint to faster AI adoption is not worker resistance but a lack of employer encouragement, which is also weakest in Japan. For the coming decade, the greater risk is not that Japan adopts AI too fast, but rather, too slowly, leaving productivity gains unrealized while the workforce shrinks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What other advanced economies might take from Japan&#8217;s case<\/h2>\n\n\n\n<p>Most other advanced economies are heading where Japan already is. Japan\u2019s working-age population has been falling since the mid-1990s, <a href=\"https:\/\/eastasiaforum.org\/2026\/08\/25\/japans-fertility-decline-and-what-to-do-about-it\/\" target=\"_blank\" rel=\"noreferrer noopener\">its fertility rate hit just 1.14 in 2025<\/a> (a fertility rate slightly above 2 represents the breakeven point where populations stay even), and its shortage runs across the economy rather than being confined to a few sectors. Meanwhile, the fertility rate has already fallen below replacement levels in every OECD country except Israel, with the OECD average standing at 1.40, down from 2.25 in 1980. Japan\u2019s case is, therefore, a good case study for what happens when demographic decline runs far enough.<\/p>\n\n\n\n<p>The first lesson is that the employment effect of AI depends less on what AI can do than on the kind of labor market it arrives into. In Japan, the impact of AI in a shortage economy surfaces first as eased hiring pressure, not as unemployment, which is why even an aggressive replacement scenario leaves the unemployment rate at just 4.8%. Debates about whether AI destroys jobs may be less about the technology itself than about the tightness of the market adopting it.<\/p>\n\n\n\n<p>The second lesson is that expanding the supply of older workers calls for more care than the aggregate numbers suggest. Raising senior employment is a direction most advanced economies will likely explore in some form. Japan, for example, is considering raising its mandatory retirement age, while other countries may adjust the age at which people become eligible for pensions or promote phased retirement arrangements. This is partly because, as populations age, fiscal pressure on pensions and health spending is growing, making it increasingly difficult for countries to avoid such measures.&nbsp;<\/p>\n\n\n\n<p>However, any additional labor supply would, in turn, be exposed to the effects of AI. In our counterfactual, higher participation lifts 2040 employment by about 2.7 million relative to the baseline, so the overall decline from today\u2019s level shrinks. But because the added workers are themselves exposed to automation, AI&#8217;s share of the employment decline jumps from roughly a fifth to nearly 60%. This is not an argument against expanding the supply of workers, but is a clear signal that doing so should be accompanied by a broader set of measures meant to reduce frictions, including reskilling and improving job-matching quality.<\/p>\n\n\n\n<p>A final point concerns timing. Japan&#8217;s employment held up for a decade after its working-age population began to fall because participation among women and older workers rose quickly enough to offset it. That buffer is now close to exhausted. But that lever is still available to some countries that are earlier in the same transition, and it is worth using. <a href=\"https:\/\/www.oecd.org\/en\/publications\/2025\/11\/pensions-at-a-glance-2025_76510fe4\/full-report\/employment-rates-of-older-workers-and-gender-gaps_cb8a2f7b.html\" target=\"_blank\" rel=\"noreferrer noopener\">In 2024, 53.5% of Japanese aged 65 to 69 were in work, compared to an OECD average of 26.4% and far less in the US (32.4%), Germany (20.8%), and France (11.1%).<\/a> There is room for improvement, although Japan\u2019s experience suggests there is a ceiling.<\/p>\n\n\n\n<p>No single lever on its own will be enough to single-handedly solve Japan\u2019s \u2014 and other advanced nations\u2019 \u2014 impending demographic challenges. Instead, we will need to use what time there is in the coming years to pull several at once: boost participation, ramp up widescale reskilling and retaining programs, and embrace more flexible ways to work for the most workers possible.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Methodology<\/h3>\n\n\n\n<p><em>This analysis models 16 JSIC-aligned sectors of the Japanese economy, starting from 2025 employment of about 64.2 million (agriculture, forestry &amp; fisheries, mining, and unclassified industries are excluded due to data limitations). Because semiconductors sit upstream of AI and are affected differently, we carve them out of manufacturing and estimate their employment separately.<\/em><em><br><\/em><em><br><\/em><em>Labor-market dynamics follow a search-and-matching framework \u00e0 la Diamond, Mortensen and Pissarides, adjusted to include cross-industry worker flows, new entrants from education and immigration, endogenous education decisions, gender-specific occupational preferences, AI integration, and Japan-specific institutional features such as the simultaneous recruiting of new graduates. Fixed parameters are calibrated on 2024\u201325 data from \u52b4\u50cd\u529b\u8abf\u67fb, \u96c7\u7528\u52d5\u5411\u8abf\u67fb, \u6bce\u6708\u52e4\u52b4\u7d71\u8a08 and \u56fd\u52e2\u8abf\u67fb, together with Indeed job-posting and resume data; the demographic backbone is the IPSS 2023 population projection combined with age-specific participation. Forward projections assume that participation continues rising gradually toward 2040 (mainly women and seniors), that net immigration stays near its recent pace, and that AI diffuses more slowly than in the US, consistent with the low AI share of Japanese job postings.&nbsp;<\/em><\/p>\n\n\n\n<p><em><br><\/em><em>For AI exposure, we draw on Indeed&#8217;s occupation-level generative-AI ratings and academic measures such as Felten et al. (2021), aggregated to the industry level using the Census industry-by-occupation composition.<\/em><\/p>\n\n\n\n<p><em>The policy counterfactuals are stylized experiments, with their assumptions noted beneath the corresponding figures. Sector-level detail is less robust than the aggregate path, and the model tracks headcount rather than hours or job quality.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Demographics, not AI, will drive most of Japan\u2019s employment decline over the next 15 years, and reallocation alone will not close the resulting shortage.<\/p>\n","protected":false},"author":103,"featured_media":618,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"recruiter-hub-blocks.php","format":"standard","meta":{"_acf_changed":true,"footnotes":""},"categories":[1],"tags":[11],"post_mwm_category":[7],"post_topic":[],"post_content_type":[],"post_duration":[],"post_actions":[],"post_franchise":[],"post_mwm_author":[23],"class_list":["post-595","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-multimedia","post_mwm_category-hiring-lab-reports","post_mwm_author-yusuke-aoki"],"acf":[],"yoast_head":"<!-- This site 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