An Empirical Analysis of the Impact of Fiscal and Tax Incentives on Enterprise Technological Innovation - Taking Listed Companies on GEM as Examples
Bibliographic record
Abstract
Enterprise technological innovation is the backbone of the transformation of economic development mode in China, the optimization of economic structure, and the realization of national innovative development strategy. In order to promote the transformation and upgrading of the economic structure and encourage the the development of technological innovation of enterprises, a series of fiscal and tax policies which encourage technological innovation are introduced in China. Although the fiscal and tax incentives are generally adopted by the governments of the world, the research conclusions of the academia on the implementation effect of fiscal and tax policies are not unified. For this reason, in this paper, based on the data of listed companies on the Growth Enterprise Market from 2011 to 2017, the STATA 14.0-version software is used to analyze the sample data, and the relationship between the current fiscal policies and technological innovation is explored. The study results show that the fiscal and tax incentives positively affect the technological innovation of enterprises, which provides an important theoretical basis for the government to further improve fiscal and tax policies. Finally, based on the previous research contents, the corresponding conclusions are summarized, and relevant suggestions for improving the fiscal and tax incentive policies are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".