The Impact of Tax Incentives on the Growth of Small and Medium-Sized Enterprises: A Comparative Study Based on Industry

Yifei Ding

doi:10.54097/a7xrk348

Authors

Yifei Ding

DOI:

https://doi.org/10.54097/a7xrk348

Keywords:

Small and medium-sized enterprises, Tax incentives, Enterprise growth, Value-added tax

Abstract

Tax incentive policies have been widely used as a crucial tool to promote the development of Small and Medium-sized Enterprises (SMEs), but their effectiveness varies across different industries and policy types. This study utilizes a comprehensive panel dataset from 2015 to 2023 to investigate the impact of Value-Added Tax (VAT) and income tax incentives on the growth of SMEs in China. Through fixed effects model analysis, we find that both VAT and income tax incentives significantly enhance SMEs' sales revenue and total asset growth. However, the magnitude of these effects differs substantially across industries, with high-tech and information technology service industries benefiting the most. Our industry comparison analysis reveals that the effectiveness of tax incentives is closely related to industry-specific characteristics, such as research and development (R&D) intensity and capital requirements. These findings provide key empirical evidence for optimizing SME tax policies and suggest that a more nuanced, industry-specific approach to tax incentives is needed. This study not only contributes to the literature on SME growth and tax policy effectiveness but also offers important insights for policymakers to refine SME support systems in the context of rapid technological change and economic transformation.

Downloads

Download data is not yet available.

References

[1] Zhong, Y., Liu, Y., Gao, E., Wei, C., Wang, Z., & Yan, C. (2024). Deep Learning Solutions for Pneumonia Detection: Performance Comparison of Custom and Transfer Learning Models. medRxiv, 2024-06.

[2] Gu, W., Zhong, Y., Li, S., Wei, C., Dong, L., Wang, Z., & Yan, C. (2024). Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis. arXiv preprint arXiv:2407.16150.

[3] Liu, J., Li, K., Zhu, A., Hong, B., Zhao, P., Dai, S., ... & Su, H. (2024). Application of Deep Learning-Based Natural Language Processing in Multilingual Sentiment Analysis. Mediterranean Journal of Basic and Applied Sciences (MJBAS), 8(2), 243-260.

[4] Xu, Q., Feng, Z., Gong, C., Wu, X., Zhao, H., Ye, Z., ... & Wei, C. (2024). Applications of Explainable AI in Natural Language Processing. Global Academic Frontiers, 2(3), 51-64.

[5] Gao, H., Wang, H., Feng, Z., Fu, M., Ma, C., Pan, H., ... & Li, N. (2016). A novel texture extraction method for the sedimentary structures’ classification of petroleum imaging logging. In Pattern Recognition: 7th Chinese Conference, CCPR 2016, Chengdu, China, November 5-7, 2016, Proceedings, Part II 7 (pp. 161-172). Springer Singapore.

[6] Li, W., Li, H., Gong, A., Ou, Y., & Li, M. (2018, August). An intelligent electronic lock for remote-control system based on the internet of things. In journal of physics: conference series (Vol. 1069, No. 1, p. 012134). IOP Publishing.

[7] Wang, Z., Yan, H., Wei, C., Wang, J., Bo, S., & Xiao, M. (2024). Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning. arXiv preprint arXiv:2408.03084.

[8] Bo, S., Zhang, Y., Huang, J., Liu, S., Chen, Z., & Li, Z. (2024). Attention Mechanism and Context Modeling System for Text Mining Machine Translation. arXiv preprint arXiv:2408.04216.

[9] Zhou, R. (2024). Understanding the Impact of TikTok's Recommendation Algorithm on User Engagement. International Journal of Computer Science and Information Technology, 3(2), 201-208.

[10] Zhou, R. (2024). Advanced Embedding Techniques in Multimodal Retrieval Augmented Generation A Comprehensive Study on Cross Modal AI Applications. Journal of Computing and Electronic Information Management, 13(3), 16-22.

[11] Zhang, Y., & Fan, Z. (2024). Memory and Attention in Deep Learning. Academic Journal of Science and Technology, 10(2), 109-113.

[12] Zhang, Y., & Fan, Z. (2024). Research on Zero knowledge with machine learning. Journal of Computing and Electronic Information Management, 12(2), 105-108.

[13] Xu, T. (2024). Comparative Analysis of Machine Learning Algorithms for Consumer Credit Risk Assessment. Transactions on Computer Science and Intelligent Systems Research, 4, 60-67.

[14] Xu, T. (2024). Credit Risk Assessment Using a Combined Approach of Supervised and Unsupervised Learning. Journal of Computational Methods in Engineering Applications, 1-12.

[15] Wang, J., Zhang, H., Zhong, Y., Liang, Y., Ji, R., & Cang, Y. (2024). Advanced Multimodal Deep Learning Architecture for Image-Text Matching. arXiv preprint arXiv:2406.15306.

[16] Wang, J., Li, X., Jin, Y., Zhong, Y., Zhang, K., & Zhou, C. (2024). Research on image recognition technology based on multimodal deep learning. arXiv preprint arXiv:2405.03091.

[17] Yao, Y. (2024, May). Design of Neural Network-Based Smart City Security Monitoring System. In Proceedings of the 2024 International Conference on Computer and Multimedia Technology (pp. 275-279).

[18] Xia, Y., Liu, S., Yu, Q., Deng, L., Zhang, Y., Su, H., & Zheng, K. (2023). Parameterized Decision-making with Multi-modal Perception for Autonomous Driving. arXiv preprint arXiv:2312.11935.

[19] Qiu, L., & Liu, M. (2024). Innovative Design of Cultural Souvenirs Based on Deep Learning and CAD.

[20] Liu, M., & Li, Y. (2023, October). Numerical analysis and calculation of urban landscape spatial pattern. In 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023) (pp. 113-119). Atlantis Press.

[21] Lin, Y. Discussion on the Development of Artificial Intelligence by Computer Information Technology.

[22] Lin, Y. (2023). Optimization and Use of Cloud Computing in Big Data Science. Computing, Performance and Communication Systems, 7(1), 119-124.

[23] Lin, Y. (2024). Application and Challenges of Computer Networks in Distance Education. Computing, Performance and Communication Systems, 8(1), 17-24.

[24] Lin, Y. (2024). Design of urban road fault detection system based on artificial neural network and deep learning. Frontiers in neuroscience, 18, 1369832.

[25] Lin, Y. (2023). Construction of Computer Network Security System in the Era of Big Data. Advances in Computer and Communication, 4(3).