Yi, Jiachen
2024.
Can money buy innovation?
Exploring the effectiveness of government subsidies in
boosting China’s productivity.
PhD Thesis,
Cardiff University.
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Abstract
Considering that rational economic agents make decisions to optimize their outcomes, if governmental policies can influence individual decision-making margins, there is potential for these policies to impact the overall growth rate of the economy. This study aims to examine the effects of specific policies on China’s economic growth in its recent history. Understanding these factors is crucial, as it helps to clarify the drivers of economic growth and whether policy interventions can shape this trajectory. Such insights may prove valuable for policymakers when designing future strategies. The study utilizes an open-economy Real Business Cycle (RBC) model, adapted from L. Minford and Meenagh (2020). The endogenous growth mechanism posits that productivity growth depends on the allocation of time to innovative activities. This framework will be employed to analyse the factors contributing to China’s productivity growth. In the model, the policy variable is represented as a trend-stationary process, which examines how temporary disturbances in policy around its long-term path can lead to lasting effects on productivity levels. Historically, the influence of specific policies on economic growth has typically been assessed through calibrated RBC models, using an informal comparison of individual moments for both simulated and observed variables. This approach helps determine whether the model can replicate certain established patterns. The Indirect Inference method, however, formalises this approach by comparing the collective behaviour of variables, ensuring that cross-moment implications are not overlooked. This method provides a more systematic criterion for evaluating the model’s effectiveness.
Item Type: | Thesis (PhD) |
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Date Type: | Completion |
Status: | Unpublished |
Schools: | Schools > Business (Including Economics) |
Date of First Compliant Deposit: | 16 May 2025 |
Last Modified: | 16 May 2025 14:27 |
URI: | https://orca.cardiff.ac.uk/id/eprint/178306 |
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