GCRI · Labs
Lab masa depan pekerjaan
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 sistem 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.
Future of work lab on the record.
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.
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.
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.
Institutions mediate change
Collective agreements, training sistem, perlindungan sosial 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.
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.
Stok, aliran, jeda, dan ambang titik balik.
Dua stok, tekanan dan kapasitas adaptif, dihubungkan oleh lingkar pertumbuhan yang saling menguatkan, respons penyeimbang yang tertunda, dan kerusakan ketika tekanan melampaui kapasitas; melewati ambang, sistem dapat berbalik. Model pengajaran dengan nilai ilustratif yang dinyatakan, bukan prediksi.
Kendali
- Pertumbuhan tekanan
- 6,0% per tahun
- Efektivitas kapasitas dalam menurunkan tekanan
- 4,0% per tahun
- Respons investasi terhadap tekanan yang dirasakan
- 20%
- Jeda persepsi
- 4.0 tahun
- Ambang titik balik tekanan
- indeks 60
- Penguatan setelah melewati ambang
- 2.0 kali
- Tahun yang disimulasikan
- 50 tahun
Pembacaan
- Tekanan puncak
- 113 indeks
- Kapasitas di akhir
- indeks 186
- Tahun di atas ambang
- 15.8 tahun
Angka, metode, dan sumber
| Tahun | Tekanan | Kapasitas | Dirasakan |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Metode
- Dinamika sistem Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Transisi kritis dan titik balik Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Diagram lingkaran kausal Sterman (2000), Business Dynamics, McGraw-Hill
- Kipas ketidakpastian (Monte Carlo parameter) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- Sensitivitas satu per satu (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
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.
Di seluruh enam pilar, dipimpin oleh Nexus Consortium
- Eksperimen Labs di Labs
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- Dikodifikasi dalam Data dan Bukti di Standards
- Diterbitkan di Labs di Reports
- Diajarkan di Fakultas Ilmu dan Metode Risiko di Academy
The work lab studies change in each domain’s workforce.
Findings feed the seri Bukti Labs. The ILO’s 2023 working paper sets out exposure measures.
- Workplace study sites.
- Worker accounts with consent.
How future of work lab is modelled, measured and tested.
Model, asumsi, data, dan ketidakpastiannya, dalam bentuk yang akan diperiksa ilmuwan atau insinyur. Strukturnya mengikuti metode yang dipublikasikan. Nilai yang digambar bersifat ilustratif, bukan temuan.
Model, persamaan, dan asumsi
The model structure used for future of work lab, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)bahaya, keterpaparan, kerentanan, dan kapasitas penanggulanganEAL = ∑k pk · Lkatas peristiwa k dengan probabilitas tahunan pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Asumsi | Mengapa ini dibuat | Bagaimana ini diuji |
|---|---|---|
| Bahaya dan eksposur bersifat independen dalam satu peristiwa | Menjaga integral kerugian tetap dapat dihitung | Bandingkan dengan pencocokan kopula pada catatan gabungan |
| Keterkaitan bersifat linear di dekat titik operasi | Membiarkan guncangan merambat melalui A | Uji di luar rentang linear lalu bandingkan |
| Kurva kerentanan dapat dipindahkan antartempat | Kurva lokal jarang tersedia | Validasi terhadap kerugian teramati jika tersedia |
- Masukan
- dataset berversi dengan asal-usul yang tercatat
- Keluaran
- distribusi, bukan angka tunggal
- Kode
- dirilis bersama kartu model dan pengujiannya
- Reproduksi
- data, kode, lingkungan, dan seed yang sama
Apa yang diukur, dan seberapa baik.
Setiap indikator membawa satuan, frekuensi, jenis sumber di baliknya, dan label bukti yang akan dibawa suatu nilai. Tidak ada nilai yang ditampilkan: sebuah program menyediakannya berdasarkan wewenangnya sendiri.
| Indikator | Unit | Kadensi | Jenis sumber | Label |
|---|---|---|---|---|
| Task exposure index | dimensionless index by occupation | tahunan | Task databases mapped to technology capability | DIMODELKAN |
| Firm adoption of the studied technology | percent of firms | tahunan | Enterprise surveys | ESTIMASI |
| Adult skills proficiency | score on the survey scale | per survey cycle | OECD Survey of Adult Skills | TERAMATI |
| Job quality index | composite index | per survey cycle | Working conditions surveys | ESTIMASI |
| Participation in job-related training | percent of adults | tahunan | Labour force and adult education surveys | Statistik terbitan |
| Pre-analysis plans for workplace trials | percent of trials registered before launch | tahunan | AEA RCT Registry | Statistik terbitan |
| Worker representatives consulted | percent of workplace studies | per studi | Study ethics records | DINYATAKAN |
Asal bukti.
Set data publik bernama dan catatan kelembagaan, masing-masing dengan kelas lisensinya. Sumber berdasarkan perjanjian hanya digunakan sesuai ketentuannya dan tidak pernah diterbitkan ulang.
| Sumber | Apa yang diberikannya | Kelas lisensi |
|---|---|---|
| ILOSTAT (International Labour Organization) | Employment, hours, wages and informality by country | Free and open, CC BY |
| OECD Survey of Adult Skills (PIAAC) | Literacy, numeracy and problem solving of adults, with job tasks | OECD terms; microdata by release |
| O*NET (US Department of Labor) | Task, skill and work activity descriptors by occupation | Open, CC BY |
| ESCO (European Commission) | Multilingual classification of skills, competences and occupations | Gratis dan terbuka, dengan atribusi |
| European Working Conditions Survey (Eurofound) | Job quality, autonomy, work intensity and representation | Free for research, end-user licence |
| AEA RCT Registry (American Economic Association) | Registered field experiments and pre-analysis plans in economics | Free access, terms of use |
| National labour force surveys | Employment, occupation and training microdata | Terms per statistical office |
Siapa yang memutuskan, dan apa yang dilayani bukti.
Laboratorium prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Siapa yang memutuskan | Pertanyaannya | Bukti yang dilayani halaman ini |
|---|---|---|
| Labour ministries and public employment services | Which training and transition programmes to fund or scale | Impact evaluations with credible comparison groups, cost data, effects by group |
| Employers and worker representatives | Whether and how to introduce a technology, with what consultation and safeguards | Pilot evaluation, task and job-quality effects, data protection assessment |
| Research ethics committees | Whether a workplace experiment protects voluntariness and personal data | Consent design, opt-out route, representation agreement, data minimisation |
| Penyandang dana penelitian | Which questions on work and technology merit sustained funding | Evidence gaps mapped by strength of design, not by volume of commentary |
Keluaran yang dihasilkan topik ini
- 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
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?
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. Perubahan iklim 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 sistem, labour law, perlindungan sosial and bargaining arrangements.
Evidence and its limits.
- 01
Task and occupation data
Classifications of work activities linked to employment data. They lag behind real change and vary between countries.
- 02
Labour force and administrative records
Surveys and registers tracking employment, earnings and transitions. Access is often restricted and linking sources raises privacy questions.
- 03
Evaluasi dampak
Randomised and quasi-experimental studies of training and matching programmes using inferensi kausal. Results may not transfer across labour markets.
- 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.
Cara hal ini dikaji, dan apa yang tidak dapat dinyatakan metodenya.
- 01Inferensi kausal dan evaluasi dampakUji coba teracak dan peluncuran bertahap · Evaluasi program kuasi-eksperimentalDiterapkan di sini
- 02Probabilitas dan statistikaPerencanaan ukuran sampel dan daya uji · Meta-analisis dengan penilaian biasDiterapkan di sini
- 03Kecerdasan buatan dan pembelajaran mesinPenyaringan berbantuan untuk tinjauan sistematis · Model pengganti untuk memandu eksperimenDiterapkan di sini
- 04Kuantifikasi ketidakpastian dan validasi modelAnggaran ketidakpastian pengukuran · Analisis sensitivitas model simulasiDiterapkan di sini
Where this page’s references lead.
Badan dan dokumen yang menjadi acuan halaman ini. Status dan edisi berubah; setiap tautan menuju badan penerbitnya.
Tanya jawab.
Does exposure to automation mean jobs will be lost?
Why register a pre-analysis plan for a field experiment?
Can employees refuse to take part in a workplace study?
Does the lab predict how many jobs will be lost to AI?
Can an employer run a workplace trial with the lab?
How does this lab differ from the Future of Work and Skills platform?
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