The Impact of Government Budget Data Visualization on Public Financial Literacy and Civic Engagement

Aixin Kang; Chenyu Li; Sisi Meng

doi:10.70393/6a6574626d.333133

Authors

Aixin Kang Georgetown University
Chenyu Li Columbia University
Sisi Meng University of Rochester

DOI:

https://doi.org/10.70393/6a6574626d.333133

ARK:

https://n2t.net/ark:/40704/JETBM.v2n4a01

Disciplines:

Finance

Subjects:

Personal Finance

References:

71

Keywords:

Data Visualization, Public Finance, Civic Engagement, Financial Literacy

Abstract

This study explores the transformative power of data visualization technologies for public understanding and civic engagement in government budget processes. This material is based on a broad-based empirical study using a sample of 1,200 subjects from a variety of demographic groups in which we investigate how interactive visualization tools foster greater financial literacy and civic engagement relative to traditional budget presentation approaches. Our results show that visualization-enhanced budget information improves comprehension by 47% and willingness to be involved in civic activities by 32%. The methodology used in this study is a mixed-method that includes controlled experiments, cognitive evaluation instruments, and longitudinal surveys, and it focuses on the analysis of the relationship between visualization design elements and user engagement outcomes. Analysis shows that visual complexity is significantly related to user attributes and participatory actions. The current study is a part of emerging literature on digital governance and has implications for policymakers interested in promoting greater transparency and democratic participation in financial communication through technology.

Author Biographies

Aixin Kang, Georgetown University

Master of Science in Quantitative Economics, Georgetown University, DC, USA.

Chenyu Li, Columbia University

Applied Analytics, Columbia University, NY, USA.

Sisi Meng, University of Rochester

Accounting, University of Rochester, NY, USA.

References

[1] Siemionek-Ruskań, M., & Siemionek-Lepczyńska, A. (2024). The essence of green participatory budgeting. Journal of Environmental Management and Tourism, 15(4).

[2] Wang, Z., & Chu, Z. (2023). Research on Intelligent Keyframe In-betweening Technology for Character Animation Based on Generative Adversarial Networks. Journal of Advanced Computing Systems, 3(5), 78-89.

[3] Hasan, N., Nanda, S., Agarwal, M. K., & Debnath, S. K. (2024). Evaluating the mediating effect of financial literacy between fintech adoption in microfinance services. International Journal of System Assurance Engineering and Management, 1-12.

[4] Wu, J., Wang, H., Qian, K., & Feng, E. (2023). Optimizing Latency-Sensitive AI Applications Through Edge-Cloud Collaboration. Journal of Advanced Computing Systems, 3(3), 19-33.

[5] Hamzat, L., Abiodun, D., & Joseph, A. (2023). Empowering entrepreneurial growth through data-driven financial literacy, market research, and personalized education tool. World Journal of Advanced Research and Reviews, 19, 1692-1711.

[6] Napu, F., Syaifuddin, D. T., Wawo, A. B., & Zaid, S. (2025). Study of Financial Management Behavior Based on Financial Self-Efficacy and Financial Literacy in Small-Scale Agribusiness Development. Journal of Global Innovations in Agricultural Sciences, 319-331.

[7] Kale, A., Liu, D., Ayala, M. G., Schwab, H., & McNutt, A. (2024). What Can Interactive Visualization do for Participatory Budgeting in Chicago?. IEEE Transactions on Visualization and Computer Graphics.

[8] Asker, E., Brunner, E., & Ross, S. (2024). JUE insight: The impact of school spending on civic engagement: Evidence from school finance reforms. Journal of Urban Economics, 143, 103688.

[9] Treija, S., Bratuškins, U., Koroļova, A., & Lektauers, A. (2021). Smart Governance: An Investigation into Participatory Budgeting Models. Environmental Sciences Proceedings, 11(1), 30.

[10] Wang, H., Wu, J., Ni, C., & Qian, K. (2025). Automated Compliance Monitoring: A Machine Learning Approach for Digital Services Act Adherence in Multi-Product Platforms. Applied and Computational Engineering, 147, 14-25.

[11] Palacin, V., McDonald, S., Aragón, P., & Nelimarkka, M. (2024). Configurations of digital participatory budgeting. ACM Transactions on Computer-Human Interaction, 31(2), 1-28.

[12] Milosavljević, M., Spasenić, Ž., & Krivokapić, J. (2023). Bibliometric review of participatory budgeting: Current status and future research agenda. International Journal of Financial Studies, 11(3), 104.

[13] Schuler, C., Grossi, G., & Fuchs, S. (2023). New development: The role of education in public sector accounting reforms in emerging economies: a socio-material perspective. Public Money & Management, 43(7), 762-768.

[14] Ibrahimy, M. M., Norta, A., Normak, P., & Nowandish, H. (2025). Transforming e-Participatory Budgeting with Blockchain: Boosting Transparency and Citizen Engagement. IEEE Transactions on Engineering Management.

[15] Menendez-Blanco, M., & Bjørn, P. (2022). Designing digital participatory budgeting platforms: Urban biking activism in Madrid. Computer Supported Cooperative Work (CSCW), 31(4), 567-601.

[16] Wang, M., & Zhu, L. (2024). Linguistic Analysis of Verb Tense Usage Patterns in Computer Science Paper Abstracts. Academia Nexus Journal, 3(3).

[17] Forna, M., & Antonie, R. I. (2023). Electronic Participatory Budgeting Assessment (E-PBA). Three Main Criteria. Revista Transilvană de Ştiinţe Administrative, 25(52), 3-13.

[18] Liu, W., Qian, K., & Zhou, S. (2024). Algorithmic Bias Identification and Mitigation Strategies in Machine Learning-Based Credit Risk Assessment for Small and Medium Enterprises. Annals of Applied Sciences, 5(1).

[19] Mo, T., Li, P., & Jiang, Z. (2024). Comparative Analysis of Large Language Models' Performance in Identifying Different Types of Code Defects During Automated Code Review. Annals of Applied Sciences, 5(1).

[20] Wu, J., Ni, C., Wang, H., & Chen, J. (2025). Graph Neural Networks for Efficient Clock Tree Synthesis Optimization in Complex SoC Designs. Applied and Computational Engineering, 150, 101-111.

[21] Xu, S. (2025). Intelligent Optimization Algorithm for Chain Restaurant Spatial Layout Based on Generative Adversarial Networks. Journal of Industrial Engineering and Applied Science, 3(3), 32-41.

[22] Sun, M. (2023). AI-Driven Precision Recruitment Framework: Integrating NLP Screening, Advertisement Targeting, and Personalized Engagement for Ethical Technical Talent Acquisition. Artificial Intelligence and Machine Learning Review, 4(4), 15-28.

[23] Shin, B., Rask, M., & Tuominen, P. (2022). Learning through online participation: A longitudinal analysis of participatory budgeting using Big Data indicators. Information polity, 27(4), 517-538.

[24] Wang, Y., & Wang, X. (2023). FedPrivRec: A Privacy-Preserving Federated Learning Framework for Real-Time E-Commerce Recommendation Systems. Journal of Advanced Computing Systems, 3(5), 63-77.

[25] Feng, Z., Ni, C., & Zhou, S. (2025). Option-Implied Information for Forward-Looking Market Risk Assessment: Evidence from Commodity Derivatives Markets. Spectrum of Research, 5(1).

[26] Feng, Z., Yuan, D., & Zhang, D. (2023). Textual Analysis of Earnings Calls for Predictive Risk Assessment: Evidence from Banking Sector. Journal of Advanced Computing Systems, 3(5), 90-104.

[27] Feng, Z., Zhang, D., & Wang, Y. (2024). Intraday Liquidity Patterns and Their Implications for Market Risk Assessment: Evidence from Global Equity Markets. Artificial Intelligence and Machine Learning Review, 5(4), 83-98.

[28] Luo, X. (2023). Cross-Cultural Adaptation Framework for Enhancing Large Language Model Outputs in Multilingual Contexts. Journal of Advanced Computing Systems, 3(5), 48-62.

[29] Cheng, C., Zhu, L., & Wang, X. (2024). Knowledge-Enhanced Attentive Recommendation: A Graph Neural Network Approach for Context-Aware User Preference Modeling. Annals of Applied Sciences, 5(1).

[30] QUEYROI, Y., & DONY, S. (2024). Real estate austerity management and governance arrangements in local authorities: an analysis through the paradigm of the new public governance. Finance Contrôle Stratégie, (27-4).

[31] Lian, H., Mo, T., & Zhang, C. (2024). Intelligent Data Lifecycle Management in Cloud Storage: An AI-driven Approach to Optimize Cost and Performance. Academia Nexus Journal, 3(3).

[32] Kang, A., Li, Z., & Meng, S. (2023). AI-Enhanced Risk Identification and Intelligence Sharing Framework for Anti-Money Laundering in Cross-Border Income Swap Transactions. Journal of Advanced Computing Systems, 3(5), 34-47.

[33] Liu, W., Rao, G., & Lian, H. (2023). Anomaly Pattern Recognition and Risk Control in High-Frequency Trading Using Reinforcement Learning. Journal of Computing Innovations and Applications, 1(2), 47-58.

[34] Zhang, S., Mo, T., & Zhang, Z. (2024). LightPersML: A Lightweight Machine Learning Pipeline Architecture for Real-Time Personalization in Resource-Constrained E-commerce Businesses. Journal of Advanced Computing Systems, 4(8), 44-56.

[35] Lian, H., Li, P., & Wang, G. (2023). Dynamic Resource Orchestration for Cloud Applications through AI-driven Workload Prediction and Analysis. Artificial Intelligence and Machine Learning Review, 4(4), 1-14.

[36] Eatherton, M. R., Schafer, B. W., Hajjar, J. F., Easterling, W. S., Avellaneda Ramirez, R. E., Wei, G., ... & Coleman, K. Considering ductility in the design of bare deck and concrete on metal deck diaphragms. In The 17th World Conference on Earthquake Engineering, Sendai, Japan.

[37] Wei, G., Koutromanos, I., Murray, T. M., & Eatherton, M. R. (2019). Investigating partial tension field action in gable frame panel zones. Journal of Constructional Steel Research, 162, 105746.

[38] Wei, G., Koutromanos, I., Murray, T. M., & Eatherton, M. R. (2018). Computational Study of Tension Field Action in Gable Frame Panel Zones.

[39] Wu, S., Li, Y., Wang, M., Zhang, D., Zhou, Y., & Wu, Z. (2021, November). More is better: Enhancing open-domain dialogue generation via multi-source heterogeneous knowledge. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 2286-2300).

[40] Wu, S., Wang, M., Li, Y., Zhang, D., & Wu, Z. (2022, February). Improving the applicability of knowledge-enhanced dialogue generation systems by using heterogeneous knowledge from multiple sources. In Proceedings of the fifteenth ACM international conference on WEB search and data mining (pp. 1149-1157).

[41] Wu, S., Wang, M., Zhang, D., Zhou, Y., Li, Y., & Wu, Z. (2021, August). Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network. In IJCAI (pp. 3964-3970).

[42] Foroughi, H., Wei, G., Torabian, S., Eatherton, M. R., & Schafer, B. W. Seismic response predictions from 3D steel braced frame building simulations.

[43] Wang, M., Xue, P., Li, Y., & Wu, Z. (2021). Distilling the documents for relation extraction by topic segmentation. In Document Analysis and Recognition–ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part I 16 (pp. 517-531). Springer International Publishing.

[44] Zhu, L., Yang, H., & Yan, Z. (2017, July). Extracting temporal information from online health communities. In Proceedings of the 2nd International Conference on Crowd Science and Engineering (pp. 50-55).

[45] Zhu, L., Yang, H., & Yan, Z. (2017). Mining medical related temporal information from patients' self-description. International Journal of Crowd Science, 1(2), 110-120.

[46] Zhang, D., & Jiang, X. (2024). Cognitive Collaboration: Understanding Human-AI Complementarity in Supply Chain Decision Processes. Spectrum of Research, 4(1).

[47] Zhang, Z., & Zhu, L. (2024). Intelligent Detection and Defense Against Adversarial Content Evasion: A Multi-dimensional Feature Fusion Approach for Security Compliance. Spectrum of Research, 4(1).

[48] Li, Y., Jiang, X., & Wang, Y. (2023). TRAM-FIN: A Transformer-Based Real-time Assessment Model for Financial Risk Detection in Multinational Corporate Statements. Journal of Advanced Computing Systems, 3(9), 54-67.

[49] Yuan, D., & Zhang, D. (2025). APAC-Sensitive Anomaly Detection: Culturally-Aware AI Models for Enhanced AML in US Securities Trading. Pinnacle Academic Press Proceedings Series, 2, 108-121.

[50] Cheng, Z. (2025). DeepTriage: A Real-Time AI Decision Support System for Emergency Resource Allocation in Mass Casualty Incidents. Pinnacle Academic Press Proceedings Series, 2, 170-182.

[51] Zhu, C., Xin, J., & Trinh, T. K. (2025). Data Quality Challenges and Governance Frameworks for AI Implementation in Supply Chain Management. Pinnacle Academic Press Proceedings Series, 2, 28-43.

[52] Wei, G., Wang, X., & Chu, Z. (2025). Fine-Grained Action Analysis for Automated Skill Assessment and Feedback in Instructional Videos. Pinnacle Academic Press Proceedings Series, 2, 96-107.

[53] Jiang, C., Wang, H., & Qian, K. (2025). AI-Enhanced Cultural Resonance Framework for Player Experience Optimization in AAA Games Localization. Pinnacle Academic Press Proceedings Series, 2, 75-87.

[54] Ju, C., & Rao, G. (2025). Analyzing Foreign Investment Patterns in the US Semiconductor Value Chain Using AI-Enabled Analytics: A Framework for Economic Security. Pinnacle Academic Press Proceedings Series, 2, 60-74.

[55] Ni, C., Wu, J., & Wang, H. (2025). Energy-Aware Edge Computing Optimization for Real-Time Anomaly Detection in IoT Networks. Applied and Computational Engineering, 139, 42-53.

[56] Trinh, T. K., Jia, G., Cheng, C., & Ni, C. (2025). Behavioral Responses to AI Financial Advisors: Trust Dynamics and Decision Quality Among Retail Investors. Applied and Computational Engineering, 144, 69-79.

[57] Wu, Z., Wang, S., Ni, C., & Wu, J. (2024). Adaptive Traffic Signal Timing Optimization Using Deep Reinforcement Learning in Urban Networks. Artificial Intelligence and Machine Learning Review, 5(4), 55-68.

[58] Ju, C., Jiang, X., Wu, J., & Ni, C. (2024). AI-Driven Vulnerability Assessment and Early Warning Mechanism for Semiconductor Supply Chain Resilience. Annals of Applied Sciences, 5(1).

[59] Ni, C., Qian, K., Wu, J., & Wang, H. (2025). Contrastive Time-Series Visualization Techniques for Enhancing AI Model Interpretability in Financial Risk Assessment.

[60] Wang, H., Qian, K., Ni, C., & Wu, J. (2025). Distributed Batch Processing Architecture for Cross-Platform Abuse Detection at Scale. Pinnacle Academic Press Proceedings Series, 2, 12-27.

[61] Zhang, S., Feng, Z., & Dong, B. (2024). LAMDA: Low-Latency Anomaly Detection Architecture for Real-Time Cross-Market Financial Decision Support. Academia Nexus Journal, 3(2).

[62] Zhang, S., Zhu, C., & Xin, J. (2024). CloudScale: A Lightweight AI Framework for Predictive Supply Chain Risk Management in Small and Medium Manufacturing Enterprises. Spectrum of Research, 4(2).

[63] Wei, G., Schafer, B., Seek, M., & Eatherton, M. (2020). Lateral bracing of beams provided by standing seam roof system: concepts and case study.

[64] Fang, Z., Zhang, H., He, J., Qi, Z., & Zheng, H. (2025, March). Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM. In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT) (pp. 1867-1871). IEEE.

[65] Sun, D., He, J., Zhang, H., Qi, Z., Zheng, H., & Wang, X. (2025, March). A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization. In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) (pp. 1527-1531). IEEE.

[66] Zhang, H., Ma, Y., Wang, S., Liu, G., & Zhu, B. (2025). Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning. arXiv preprint arXiv:2504.19583.

[67] He, J., Liu, G., Zhu, B., Zhang, H., Zheng, H., & Wang, X. (2025). Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models. arXiv preprint arXiv:2504.19436.

[68] Wang, X., Liu, G., Zhu, B., He, J., Zheng, H., & Zhang, H. (2025). Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction. arXiv preprint arXiv:2504.04385.

[69] Wei, G., Foroughi, H., Torabian, S., Eatherton, M. R., & Schafer, B. W. (2023). Seismic Design of Diaphragms for Steel Buildings Considering Diaphragm Inelasticity. Journal of Structural Engineering, 149(7), 04023077.

[70] Zheng, H., Wang, Y., Pan, R., Liu, G., Zhu, B., & Zhang, H. (2025). Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models. arXiv preprint arXiv:2506.00726.

[71] Foroughi, H., Wei, G., Torabian, S., Eatherton, M. R., & Schafer, B. W. Seismic Demands on Steel Diaphragms for 3D Archetype Buildings with Concentric Braced Frames.