The impact of technological advancement on unemployment
Bibliographic record
Abstract
Endüstri 4.0, aslen imalat sanayinde dijitalleşmenin önünü açmak için Alman hükümeti tarafından başlatılan bir yüksek teknoloji projesi olup icatçılık, inovasyon ve yenilikçiliğin yanında Yapay Zekanın (AI), Nesnelerin İnternetinin (IoT), Büyük Verinin, yeni algoritmaların, sensörlerin, kontrolörlerin, giyilebilir teknolojilerin ve robotların yaygınlaşan kullanımı ile karakterize edilmiştir. Bu çalışma, Yaratıcı Yıkım ve Sektörel Değişim Teorilerini baz alarak Endüstri 4.0 değişkeniyle işsizliği açıklamaya çalışmaktadır. Çalışmada kullanılan veriler WEF (Dünya Ekonomik Forumu), UNIDO (Birleşmiş Milletler Sınai Kalkınma Teşkilatı) ve Dünya Bankasından elde edilmiş olup 2003-2016 zaman aralığını kapsamaktadır. İşsizliği ve sektörel değişimleri tahmin etmek için kullanılan ülkeler Kanada, Fransa, Almanya, İtalya, Güney Kore, Polonya, İspanya, Birleşik Krallık ve Amerika Birleşik Devletleri’dir ve bu ülkeler görece yüksek nüfusa sahip olan Endüstri 4.0 indeksinde ilk sıralarda yer alan OECD ülkeleridir. Ampirik sonuçlar göstermektedir ki Gayri Safi Sabit Sermaye Oluşumu (%GSMH), İmalat Sanayi Katma Değeri (%GSMH) ve “Networked Readiness Index” (Endüstri 4.0 hazırlık indeksi)’inin, beklenenin aksine, işsizlik üzerinde negatif etkisi vardır, yani işsizlik oranını azaltmaktadır. Buna göre, Endüstri 4.0 yeni iş olanakları yaratarak işsizliği düşürmektedir. \n \n-------------------- \nIndustry 4.0 is a term originally used for a high-technology project German government started up, which facilitated computerization of the manufacturing process and is characterized by the promotion of the innovativeness, invention, and innovation as well as the pervasion of usage of Artificial Intelligence (AI), Internet of Things (IoT), Big Data, new algorithms, sensors, controllers, wearable technologies and robots. This study tries to explain the unemployment rate change via Industry 4.0 basing upon two main theories, namely, Creative Destruction Theory and Sectoral Shifts Theory. Data used for this study are obtained from WEF, UNIDO and World Bank with a time range from 2003 to 2016. OECD countries with relatively high population rates, which rank at the top of NRI (Networked Readiness Index) such as Canada, France, Germany, Italy, Korea Republic, Poland, Spain, United Kingdom, and the United States are used to estimate unemployment and sectoral shifts and NRI proposed by World Economic Forum (WEF) is utilized as the technological advancement level. Empirical results show that Gross Capital Formation % of GDP, Manufacturing Value Added % of GDP and Networked Readiness Index (NRI) seem to have a negative and statistically significant impact on Unemployment Rate, which means that in contrary to expectations, \nIndustry 4.0 doesn’t decrease the level of employment, rather it creates new job opportunities decreasing the level of unemployment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".