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GCRI · Labs

仕事の未来ラボ

Work changes when technologies, contracts, skills and institutions change, and research on that change is often loud and weakly identified. This lab studies how automation, platform work, remote work, skills システム and workplace institutions reshape jobs and organisations, with designs that can tell a real effect from a trend. This page sets out the mechanisms, the measures, the data, the methods and the people who decide on the evidence.

02質問

Future of work lab: how the risk develops.

Workforce and institutional change research. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

メカニズム

Tasks change before jobs

Technologies automate or augment specific tasks, not whole occupations. Jobs are rebundled, skill demands shift within roles, and the effect on employment depends on how firms reorganise work around the tool.

メカニズム

Exposure is not impact

Indices of which occupations are exposed to a technology describe potential, not outcome. Adoption, cost, regulation and bargaining decide what happens, so exposure indices need causal evidence before they inform policy.

メカニズム

Institutions mediate change

Collective agreements, training システム, 社会保障 and labour law shape whether change raises productivity and wages or shifts risk onto workers. The same technology can produce different outcomes under different institutions.

メカニズム

Workplace trials carry power

Experiments inside firms involve employees who may not feel free to refuse. Consent, worker representation, data protection and a real right to opt out decide whether a workplace study is ethical and its results credible.

03シミュレーター

ストック、フロー、遅れ、そして転換点の閾値。

二つのストック、圧力と 適応能力という 2 つのストックが、自己強化的な成長ループ、遅れて働く均衡化の反応、圧力が能力を上回ったときの損害で結ばれています。閾値を越えるとシステムは転換しえます。明示された例示値を使う教育用モデルであり、予測ではありません。

管理策

圧力の増大
年 6.0%
圧力を下げる上での能力の効果
年 4.0%
認識された圧力への投資の反応
20%
認知の遅れ
4.0 年
圧力の転換しきい値
60 指数
閾値を越えた後の増幅
2.0 倍
シミュレーションした年数
50 年

読み取り値

最大圧力
113 指数
最後に残る能力
186 指数
閾値を超えた年数
15.8 年
数値、手法、出典
年圧力能力知覚された
020.050.020.0
423.344.921.2
827.242.224.0
1231.742.327.7
1636.845.532.2
2042.351.537.2
2447.960.342.6
2853.271.348.0
3257.884.353.1
3666.498.458.4
4082.7115.969.7
4497.8139.783.8
48109.1169.497.3

手法

  1. システム・ダイナミクス Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
  2. 臨界転移と転換点 Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
  3. 因果ループ図 Sterman (2000), Business Dynamics, McGraw-Hill
  4. 不確実性ファン(パラメータ・モンテカルロ) Spiegelhalter, Pearson and Short (2011)、Visualising uncertainty about the future、Science 333
  5. 一因子ずつの感度分析(トルネード図) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

データ

No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.

ストック、フロー、遅れ、転換点のしきい値。説明用の教材モデルで、実測データではなくコントロールで設定した値に基づいて動きます。手法:システムダイナミクス(Forrester (1961)、Industrial Dynamics、MIT Press、Sterman (2000)、Business Dynamics)、臨界遷移と転換点(Scheffer and others (2009)、Early-warning signals for critical transitions、Nature 461)、因果ループ図(Sterman (2000)、Business Dynamics、McGraw-Hill)。
04In the five domains

The work lab studies change in each domain’s workforce.

05手法とエビデンス

How future of work lab is modelled, measured and tested.

モデル、その仮定、データ、不確実性を、科学者やエンジニアが確認する形で示します。構造は公表された手法に従っています。描かれた値は例示であり、知見ではありません。

モデル、方程式、仮定

The model structure used for future of work lab, the assumptions it makes and how each is tested.

要素 i のリスクRi = f(Hi, Ei, Vi, Ci)ハザード、エクスポージャー、脆弱性、対処能力
年間期待損失EAL = ∑k pk · Lk年間確率 pk
結合システムxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error
仮定作られる理由検証の方法
一つの事象の中では、ハザードとエクスポージャーは独立している損失の積分を扱いやすく保つ同時記録に当てはめたコピュラと比較
結合は動作点の近くで線形であるショックを A を通じて伝播させる線形範囲を超えて摂動を与えて比較する
脆弱性曲線は場所を越えて適用できる地域固有の曲線はめったに得られない観測された損失がある場合はそれに照らして検証
入力
来歴付きのバージョン管理されたデータセット
成果物
分布であり、単一の数値ではない
コード
モデルカードと試験とともに公開
再現
同じデータ、コード、環境、シード
標準的なリスクおよび状態空間の定式化です。挙げた前提はこの手法に典型的なもので、審査のために示しており、結果ではありません。
FAIR 原則・GO FAIR と FORCE11W3C PROV・World Wide Web Consortium不確実性の一貫した扱いに関するガイダンス・IPCCCEOS 解析即応データ・地球観測衛星委員会
06指標

何を、どの程度うまく測定しているか。

各指標には、その単位、更新頻度、背後にある出典の種類、値が持つことになるエビデンスのラベルが付いています。値は表示しません:プログラムが自らの権限のもとで値を提供します。

指標単位周期出典の種類ラベル
Task exposure indexdimensionless index by occupation年次Task databases mapped to technology capabilityモデル化
Firm adoption of the studied technologypercent of firms年次Enterprise surveys推定
Adult skills proficiencyscore on the survey scaleper survey cycleOECD Survey of Adult Skills観測
Job quality indexcomposite indexper survey cycleWorking conditions surveys推定
Participation in job-related trainingpercent of adults年次Labour force and adult education surveys公表統計
Pre-analysis plans for workplace trialspercent of trials registered before launch年次AEA RCT Registry公表統計
Worker representatives consultedpercent of workplace studies研究ごとStudy ethics records申告済み
07出典

エビデンスの出所。

名前の明示された公開データセットと機関の記録。それぞれにライセンス区分があります。合意に基づく出典は、その条件のもとでのみ使用し、再公開することはありません。

出典提供するものライセンス区分
ILOSTAT (International Labour Organization)Employment, hours, wages and informality by countryFree and open, CC BY
OECD Survey of Adult Skills (PIAAC)Literacy, numeracy and problem solving of adults, with job tasksOECD terms; microdata by release
O*NET (US Department of Labor)Task, skill and work activity descriptors by occupationOpen, CC BY
ESCO (European Commission)Multilingual classification of skills, competences and occupations無料かつオープン、帰属表示
European Working Conditions Survey (Eurofound)Job quality, autonomy, work intensity and representationFree for research, end-user licence
AEA RCT Registry (American Economic Association)Registered field experiments and pre-analysis plans in economicsFree access, terms of use
National labour force surveysEmployment, occupation and training microdataTerms per statistical office
08決定

誰が決め、エビデンスが何に役立つか。

ラボ prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

誰が決定するか質問このページが提供するエビデンス
Labour ministries and public employment servicesWhich training and transition programmes to fund or scaleImpact evaluations with credible comparison groups, cost data, effects by group
Employers and worker representativesWhether and how to introduce a technology, with what consultation and safeguardsPilot evaluation, task and job-quality effects, data protection assessment
Research ethics committeesWhether a workplace experiment protects voluntariness and personal dataConsent design, opt-out route, representation agreement, data minimisation
研究資金提供者Which questions on work and technology merit sustained fundingEvidence gaps mapped by strength of design, not by volume of commentary

このトピックが生み出す成果物

  • Pre-analysis plans for workplace and labour market trials
  • Occupational task maps linked to technology capability, with the method stated
  • Evaluations of training and transition programmes with the identification strategy stated
  • Ethics and consultation records for studies inside workplaces
09中核となる問い

What experts ask about changing work.

  • Which tasks within an occupation are changing, rather than which jobs disappear?
  • Who bears the costs of transition, by region, age, sex, sector and skill level?
  • Which training, matching and income-support measures actually change outcomes?
  • How do institutions such as schools, unions, employers and public employment services adapt?
  • How do heat, disasters and supply disruptions affect work and safety?
10メカニズムと要因

The forces reshaping work.

Economists analyse technological change at the level of tasks. A technology may substitute for some tasks, complement others and create new ones; the net effect on an occupation depends on the mix and on how employers reorganise work. Automation and AI tend to affect routine cognitive and manual tasks first, but recent tools also reach into tasks once thought to require judgement.

Other forces act alongside technology. Ageing populations change labour supply and care needs. 気候変動 raises heat exposure for outdoor and industrial workers and shifts employment between sectors during energy transitions. Trade and supply-chain disruptions can concentrate job losses in particular places, where the effects persist for years.

Institutions shape outcomes. The same technology can produce very different results depending on training システム, labour law, 社会保障 and bargaining arrangements.

11手法とデータ

Evidence and its limits.

  1. 01

    Task and occupation data

    Classifications of work activities linked to employment data. They lag behind real change and vary between countries.

  2. 02

    Labour force and administrative records

    Surveys and registers tracking employment, earnings and transitions. Access is often restricted and linking sources raises privacy questions.

  3. 03

    インパクト評価

    Randomised and quasi-experimental studies of training and matching programmes using 因果推論. Results may not transfer across labour markets.

  4. 04

    Workplace experiments

    Controlled trials of new tools or practices inside organisations, under consent and ethics review.

Forecasts of jobs lost or created are highly sensitive to assumptions and are not presented as predictions. The lab reports scenarios with their assumptions.

12手法

どのように研究され、手法が言えないことは何か。

13Where this page’s references

Where this page’s references lead.

このページの読解の基準となる機関と文書。状態と版は変わります。各リンクは発行機関へ移動します。

14質問

質問と回答。

Does exposure to automation mean jobs will be lost?
No. Exposure measures which tasks a technology could perform. Whether employment falls, rises or changes shape depends on adoption, costs, demand and institutions, and needs causal evidence.
Why register a pre-analysis plan for a field experiment?
Labour data offer many outcomes and subgroups. A registered plan fixes which ones test the hypothesis, so a reported effect is not simply the best of many tries.
Can employees refuse to take part in a workplace study?
They should be able to, without consequence. Ethics committees look for genuine voluntariness, independent consent routes and, where relevant, agreement with worker representatives.
Does the lab predict how many jobs will be lost to AI?
No. It studies how tasks change and what helps people adapt. Where scenarios are used, they are presented with their assumptions, not as forecasts.
Can an employer run a workplace trial with the lab?
A trial can be proposed. It proceeds only under a published protocol, with consent from workers and independent ethics review.
How does this lab differ from the Future of Work and Skills platform?
The platform covers the field as a whole; the lab runs experiments and evaluations that feed it.
報告書貢献する

このページからエビデンスを提供する。

報告書 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRIラボLabs のエビデンス手法と技術
ラボ

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