The probability and significance of big data have dramatically transformed the economic policymaking matrix into a more robust, resourceful and growth-centered toolbox. This paper aims to understand how data science can drive big data to support the US's economic policies in stabilizing GDP, supporting the recovery of labor markets and decreasing policy feedback time during shocks. The research employs machine learning and econometrics models to address various datasets from government and private and public organizations to assess the effects of data-driven decisions on economic stability and equity.
The study shows that big data analysis improves policy accuracy in targeting fiscal interventions and quick responses to financial shocks. For example, analytical tools helped to predict which industries would prove to be more sustainable and aid in the transition of employees; real-time analytics shrunk response time from months to weeks. However, the study also highlights key issues such as algorithmic bias, data accessibility and diversity, privacy and transparency issues. Recommendations for future research focus on the call for further development of digital resources, including different kinds of data and interdisciplinary cooperation to achieve fair and efficient policies. In turn, big data can become a solution, facilitating the creation of the necessary conditions for developing a new economy that can effectively respond to possible future shocks and crises.
Inhaltsverzeichnis (Table of Contents)
- 1. Introduction
- 1.1 The Digital Transformation of Economic Policymaking
- 1.2 Defining Economic Resilience
- 1.3 Historical Context: The Evolution of Data in Economic Policy
- 1.4 The Role of Data Science in Modern Policymaking
- 1.5 The Economic Potential of Big Data
- 1.6 Challenges in Leveraging Big Data
- 1.7 The Case for a Big Data-Driven Economy
- 1.8 Objectives and Contributions
- 2. Materials and Methods
- 2.1 Research Framework
- 2.2 Data Collection
- 2.3 Data Preprocessing
- 2.4 Analytical Methodology
- 2.5 Evaluation Metrics
- 2.6 Ethical Considerations
- 2.7 Integration of Big Data with Policymaking
- 3. Results and Discussion
- 3.1 Big Data's Role in Stabilizing the US Economy
- 3.2 Labor Market Dynamics and Workforce Resilience
- 3.3 Enhanced Policy Responsiveness Through Big Data
- 3.4 Regional Resilience and Equity
- 3.5 Challenges Encountered
- 3.6 Limitations of the Study
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This paper aims to understand how data science can leverage big data to enhance US economic policies, focusing on stabilizing GDP, supporting labor market recovery, and reducing policy response times during economic shocks. The research utilizes machine learning and econometrics to analyze various datasets, assessing the impact of data-driven decisions on economic stability and equity.
- The role of big data in enhancing economic resilience.
- The application of data science techniques in US economic policymaking.
- The impact of data-driven decisions on economic stability and equity.
- Challenges and limitations in utilizing big data for economic policy.
- Recommendations for future research and policy development.
Zusammenfassung der Kapitel (Chapter Summaries)
1. Introduction: This introductory chapter sets the stage by exploring the digital transformation of economic policymaking, emphasizing the pivotal role of big data. It defines economic resilience in the context of modern global economic fragility, highlighting vulnerabilities exposed by events like the COVID-19 pandemic. The chapter further delves into the historical evolution of data in economic policy, contrasting traditional macroeconomic indicators with the potential of big data analytics. It underscores the need for enhanced data systems capable of providing real-time insights into economic flows and interactions, paving the way for more effective and timely policy responses.
2. Materials and Methods: This chapter outlines the research methodology employed in the study. It details the research framework, data collection processes, data preprocessing techniques, and the analytical methodologies used (including machine learning and econometrics). Specific evaluation metrics are explained, along with a discussion of ethical considerations related to data privacy and algorithmic bias. The chapter also addresses the crucial aspect of integrating big data effectively into the existing policymaking infrastructure.
3. Results and Discussion: This chapter presents the key findings of the study, focusing on the role of big data in stabilizing the US economy. It examines the impact of big data on labor market dynamics and workforce resilience, highlighting the improvement in policy responsiveness facilitated by real-time analytics. Further, it delves into the implications of big data for regional resilience and equity, while acknowledging the challenges encountered and limitations of the study. This comprehensive analysis integrates insights gained from the application of the methodologies described in Chapter 2, providing a detailed and nuanced understanding of the study's outcomes.
Schlüsselwörter (Keywords)
Big Data, Economic Resilience, Data Science, US Economic Policy, Predictive Analytics, Algorithmic Bias, Labor Market Recovery, Policy Responsiveness, Inclusive Growth, Digital Infrastructure.
Frequently asked questions: Language Preview - Academic Use OCR Data
What is the main topic of this language preview?
This language preview focuses on the potential of using big data and data science to enhance US economic policies and improve economic resilience.
What are the key themes explored in this preview?
The key themes include the role of big data in enhancing economic resilience, the application of data science techniques in US economic policymaking, the impact of data-driven decisions on economic stability and equity, challenges and limitations in utilizing big data for economic policy, and recommendations for future research and policy development.
What is the purpose of the "Introduction" chapter?
The "Introduction" chapter sets the stage by exploring the digital transformation of economic policymaking and emphasizing the pivotal role of big data. It defines economic resilience and highlights the need for enhanced data systems for more effective policy responses.
What does the "Materials and Methods" chapter describe?
The "Materials and Methods" chapter outlines the research methodology, detailing the research framework, data collection processes, data preprocessing techniques, analytical methodologies (including machine learning and econometrics), evaluation metrics, ethical considerations, and the integration of big data into policymaking.
What are the main points of the "Results and Discussion" chapter?
The "Results and Discussion" chapter presents the key findings of the study, focusing on the role of big data in stabilizing the US economy. It examines the impact of big data on labor market dynamics, policy responsiveness, regional resilience, and equity, while also acknowledging the challenges and limitations of the study.
What are the keywords associated with this research?
The keywords are: Big Data, Economic Resilience, Data Science, US Economic Policy, Predictive Analytics, Algorithmic Bias, Labor Market Recovery, Policy Responsiveness, Inclusive Growth, Digital Infrastructure.
What is the research trying to understand?
This paper aims to understand how data science can leverage big data to enhance US economic policies, focusing on stabilizing GDP, supporting labor market recovery, and reducing policy response times during economic shocks.
What is the purpose of using OCR in this text?
The text is intended solely for academic use and to analyze themes in a structured and professional manner. The OCR data facilitates text analysis and thematic extraction.
- Citation du texte
- Joeleen Kimbell (Auteur), 2025, Big Data for Economic Resilience: The Role of Data Science in Shaping US Economic Policies and Growth, Munich, GRIN Verlag, https://www.grin.com/document/1554988
