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16 results for “Human capital”
Wittgenstein Center (WIC) Population and Human Capital Projections - 2023
<p>THIS IS VERSION WIC3.004 (Beta) (<strong>internal version V14,</strong> <strong>the last three versions were called V13, V12 and V11). </strong>This is a release version corresponding to the <a href="https://data.ece.iiasa.ac.at/ssp/#/login?redirect=%2Fworkspaces">SSP-Database 3.0</a> (will be updated) release and <a href="https://dataexplorer.wittgensteincentre.org/wcde-v3/">WIC-Data Explorer 2023</a></p> <p>Short Abstract</p> <p>We update the population and human capital components of the Shared Socio-Economic Pathways (SSPs) at the global level, considering the most recent baseline information. While the long-term assumptions based on extensive analysis and expert solicitations remain unchanged, we modify only the trend component. The first set of SSPs was based on demographic data through 2012. The population structures by age, sex, and education of the base year (2010) have since changed for most countries, mainly due to more recent data and reliable information. The mortality situation has improved in many countries affected by HIV/AIDS and among children in countries with higher mortality. The impact of COVID-19 on demographic trends must be addressed. In many countries, fertility rates have fallen faster than expected. International migration has been irregular and volatile as usual. These changes are reflected in the new update, with some improvements in operationalization.</p> <p>Changes in version "V14":</p> <p> We found a bug affecting the education distribution sex (interchanged) in 37 countries (with UN country code): 32 Argentina, 44 Bahamas, 48 Bahrain, 64 Bhutan, 76 Brazil, 108 Burundi, 132 Cabo Verde, 156 China, 158 China, Taiwan Province of China, 170 Colombia, 214 Dominican Republic, 218 Ecuador, 226 Equatorial Guinea, 288 Ghana, 296 Kiribati, 360 Indonesia, 392 Japan, 400 Jordan, 410 Republic of Korea, 414 Kuwait, 496 Mongolia, 528 Netherlands, 558 Nicaragua, 583 Micronesia (Fed. States of), 591 Panama, 626 Timor-Leste, 630 Puerto Rico, 634 Qatar, 643 Russian Federation, 662 Saint Lucia, 702 Singapore, 764 Thailand, 784 United Arab Emirates, 788 Tunisia, 792 Turkey, 840 United States of America, 894 Zambia. The impact on the total population is minimal.</p>
TalentCLEF 2025 corpus: Skill and Job Title Intelligence for Human Capital Management
<blockquote> <p><strong>🚨 Current Status: Submission of Working Notes</strong></p> </blockquote> <p><strong>If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo: </strong></p> <pre><code>@inproceedings{gasco2025overview, title={{Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management}}, author={Gasco, Luis and Fabregat, Hermenegildo and Garc\'{\i}a-Sardi{\~n}a, Laura and Estrella, Paula and Deniz, Daniel and Rodrigo, \'{A}lvaro and Zbib, Rabih}, booktitle={{International Conference of the Cross-Language Evaluation Forum for European Languages}}, year={2025}, publisher={Springer} }</code></pre> <p> </p> <h2><strong><a href="https://talentclef.github.io/talentclef/" target="_blank" rel="noopener">TalentCLEF2025</a> corpus - </strong>Task B Test set release</h2> <h3><strong>Introduction:</strong></h3> <p>The first edition of TalentCLEF aims to develop and evaluate models designed to facilitate three essential tasks:</p> <ol> <li>Finding/ranking candidates for job positions based on their experience and professional skills.</li> <li>Implementing upskilling and reskilling strategies that promote the coninuous development of workers</li> <li>Detecting emerging skills and skills gaps of importance in organizations.</li> </ol> <p>With that aim, the task is divided into two tasks: </p> <ul> <li><strong>Task A - Multilingual Job Title Matching</strong>. This task involves developing systems to identify and rank the job titles most similar to a given one by generating a ranked list of similar titles from a specified knowledge base for each job title in a provided test set.</li> <li><strong>Task B - Job Title-Based Skill Prediction. </strong>Task B requires developing systems that can retrieve relevant skills associated with a specified job title.</li> </ul> <div> <p>This data repository contains the data for these two tasks. The data is being released progressively according to the<a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/schedule/"> task schedule</a>.</p> <p>The task evaluation takes place on Codabench (<a href="https://www.codabench.org/competitions/5842/">Task A</a> and <a href="https://www.codabench.org/competitions/7059/">Task B</a>). Participants must register for the competition through <a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a> to be part of the evaluation campaign.</p> <p> </p> </div> <h3><strong>File structure: </strong></h3> <div> <div> <div> <div> <blockquote> <p>For a detailed description of the data structure, you can refer to the <a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/description_corpus/">TalentCLEF2025 data description page,</a> where it is thoroughly explained.</p> </blockquote> </div> </div> </div> </div> <p>The files is organized into two <code>*.zip</code> files, <code>TaskA.zip</code> and <code>TaskB.zip</code>, each containing training, validation and test folders to support different stages of model development. So far, only the training set for both tasks has been released, but in future releases, as the tasks progress, additional data will be added to the different subfolders for each task.</p> <p><strong>TaskA</strong> includes language-specific subfolders within the training and validation directories, covering English, Spanish, German, and Chinese job title data. The training folders for TaskA contain language-specific .tsv files for each respective language. Validation folders include three essential files—queries, corpus_elements, and q_rels—for evaluating model relevance to search queries. TaskA’s test folder has queries and corpus_elements files for testing retrieval.<br><br></p> <pre><code>TaskA/ │ ├── training/ │ ├── english/ │ │ └── taskA_training_en.tsv │ ├── spanish/ │ │ └── taskA_training_es.tsv │ └── german/ │ └── taskA_training_de.tsv │ ├── validation/ │ ├── english/ │ │ ├── queries │ │ ├── corpus_elements │ │ └── qrels │ ├── spanish/ │ ├── german/ │ └── chinese/ │ └── test/ ├── english/ │ ├── queries │ └── corpus_elements ├── spanish/ ├── german/ └── chinese/ </code></pre> <p><strong>TaskB</strong> follows a similar structure but without language-specific subfolders, providing general .tsv files for training, validation, and testing. This consistent file organization enables efficient data access and structured updates as new data versions are published.</p> <pre><code>TaskB/ │ ├── training/ │ ├── job2skill.tsv │ ├── jobid2terms.json │ └── skillid2terms.json<br>│ ├── validation/ │ ├── queries │ ├── corpus_elements │ └── qrels │ └── test/ ├── queries └── corpus_elements </code></pre> <p><strong>Tutorials:</strong></p> <table> <tbody> <tr> <td><strong>Notebook</strong></td> <td>Link</td> </tr> <tr> <td>Data Download and Load using Python </td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_data_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Development set Baseline generation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskA_DevSet_Baseline.ipynb">Link to Colab</a></td> </tr> <tr> <td>Task B - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskB_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> </tbody> </table> <p><strong>Resources: </strong></p> <ul> <li><a href="https://talentclef.github.io/talentclef/docs/" target="_blank" rel="noopener">Web</a></li> <li><a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a></li> <li><a href="https://www.codabench.org/competitions/5842/">Codabench Task A</a></li> <li><a href="https://www.codabench.org/competitions/7059/">Codabench Task B</a></li> <li><a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/additional_resources/">Additional Resources</a></li> </ul>
Human Capital-weighted population estimates for 185 countries from 1970 to 2100
<p>We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population's productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970-2020 and 2020-2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.</p>
FINANCIAL INSIGHTS INTO HUMAN CAPITAL: ASSESSING EMPLOYEE CONTRIBUTION FROM HR-FINANCE PERSPECTIVE
<p>International Research Journal of <br>Management Sociology & Humanities</p> <p>ISSN 2277 – 9809 (online)<br>ISSN 2348 - 9359 (Print)</p> <p>Volume 15, Issue 9 , page 126-136 </p> <p>Name of the research paper : FINANCIAL INSIGHTS INTO HUMAN CAPITAL: ASSESSING EMPLOYEE CONTRIBUTION FROM HR-FINANCE PERSPECTIVE</p> <p>Authors <br>Suvendu Narayan Roy<br>Director, Knowgen Education Services Private Limited<br>Researcher, Faculty of Finance & Management<br>Orcid id : https://orcid.org/0000-0002-2801-0740<br>Soham Roy<br>Researcher & Partner, E & O Learnet and Learnet Manpower Solutions<br>Orcid id : https://orcid.org/0000-0002-3127-608</p>
Data for: What you have, not who you know: food-enhanced social capital and changes in social behavioural relationships in a non-human primate
<p>Social network position in non-human primates has far-reaching fitness consequences. Critically, social networks are both heterogenous and dynamic, meaning an individual's current network position is likely to change due to both intrinsic and extrinsic factors. However, our understanding of the drivers of changes in social network position is largely confined to opportunistic studies. Experimental research on the consequences of <em>in situ, </em>controlled<em> </em>network perturbations is limited. Here we conducted a food-based experiment in rhesus macaques to assess whether allowing an individual the ability to provide high-quality food to her group changed her social behavioural relationships. We considered both her social network position across five behavioural networks, as well as her dominance and kin interactions. We found that gaining control over a preferential food resource had far-reaching social consequences. There was an increase in both submission and aggression centrality and changes in the socio-demographic characteristics of her agonistic interaction partners. Further, we found that her grooming balance shifted in her favour as she received more grooming than she gave. Together, these results provide a novel, preliminary insight into how <em>in situ</em>, experimental manipulations can modify social network position and point to broader network-level shifts in both social capital and social power.</p>
Replication package for "On the family origins of human capital formation: Evidence from donor children"
<p>The package contains the code necessary to reproduce the tables in Lundborg, Plug, Wurtz-Rasmussen (forthcoming) "On the family origins of human capital formation: Evidence from donor children", Review of Economic Studies. Detailed instructions are also given about accessing the raw data.</p>
Data for: What you have, not who you know: food-enhanced social capital and changes in social behavioural relationships in a non-human primate
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Inheritance taxation and human capital indicators
<p>Data on inheritance taxation and human capital indicators</p>
Metadata for the paper "Technology Boom, Labor Reallocation, and Human Capital Depreciation"
<p>This document lists the confidential datasets used for this study and how to request access to them.</p>
Replication package for: Entrepreneurial Human Capital and Firm Dynamics
<p>This replication package contains publicly available data and code to replicate the figures and tables in:</p> <p>Queiró, Francisco. 2021. Entrepreneurial Human Capital and Firm Dynamics.</p>
Replication package for: Human Capital, Female Employment, and Electricity: Evidence from the Early 20th-Century United States
<p>The package contains all the code necessary to reproduce the figures and tables in Vidart, D. (forthcoming). "Human Capital, Female Employment, and Electricity: Evidence from the Early 20th-Century United States", Review of Economic Studies. Instructions are also given about accessing the raw data.</p>
MODERN STUDENT YOUTH AS A CARRIER OF HUMAN CAPITAL IN A DIGITAL SOCIETY
<p>The article examines the specifics of modern student youth as a special social group in a digital society. The article analyzes the main factors influencing the formation of modern youth, which form the basis of the so-called digital generation, and formulates the thesis about the leading role of student youth as a carrier of modern human capital. Based on the results of the research, the author identifies the main trends in the transformation of the worldview of the younger generation, manifested in the views, values, forms and methods of communication of modern Russian students.</p>
MECHANISMS OF HUMAN CAPITAL UTILIZATION AT SERVICE ENTERPRISES
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THE CONCEPT OF HUMAN CAPITAL IN INDUSTRIAL ENTERPRISES AND ITS METHODOLOGICAL FOUNDATIONS
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Data on Digitalization of Human Capital in Montenegro and Malaysia
<p>The file gathers data from several sources with indicators featuring the internet users, consumer readiness and digital skills of the workforce in MNE and MY. In addition, there is the DSGP index. </p>
The Northwestern University Two-Generation Study of Parent and Child Human Capital Advancement
ClinicalTrials.gov study NCT02743039. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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