Technology, Leisure and Growth

This thesis develops models and methods to investigate leisure, technology and growth. Models in chapters two, three and four study the macroeconomic impacts of technology on the consumer side. The models allow for consumer habit formation for a technology good purchased for leisure. However, for the consumption good, habits are irrelevant. A method is introduced to determine the steady state of the technology good sector and consumption good sector in- dependently. These chapters show that the models can contribute to the theoretical and empirical understanding of changes in consumption growth, interest rates, labour income share and wages. Models are constructed in chapters five and six to analyse technology on the production side in the form of job replacement by robots. Chapter five shows that the impact on welfare is ambiguous because leisure in the utility function can mitigate against wage decreases. In chapter six, policy to mitigate job losses from technology/robots is discussed

Energy-biased technical change in the Chinese industrial sector with CES production functions

We develop a theoretical framework to study energy-biased technical change considering capital, labor and energy as inputs. The framework involves a first order condition estimation of elasticity and technical change parameters for a three factor-nested Constant Elasticity of Substitution (CES) function. Technical change parameters, elasticities and time derivatives of marginal products are combined to compute technical change bias. Conceptually, we introduce total bias in order to estimate the direction without requiring a direct comparison with another factor. For Chinese industries from 1990 to 2012, the optimal structure is capital and energy to be combined at the composite level and then with labor to form total output. Technical change is found to be unambiguously energy biased, it increases in every year, and the bias is predominately away from labor. The results show that Chinese industrialization was fuelled by fossil fuels and energy-intensive technologies. Nonetheless, the growth rate of energy-biased technical change decreased during the 2000s that may result from more energy efficient development.financial support provided by the China Natural Science Funding No. 71673134, Qing Lan Project

Energy-biased technical change in the Chinese industrial sector with CES production functions

We develop a theoretical framework to study energy-biased technical change considering capital, labor and energy as inputs. The framework involves a first order condition estimation of elasticity and technical change parameters for a three factor-nested Constant Elasticity of Substitution (CES) function. Technical change parameters, elasticities and time derivatives of marginal products are combined to compute technical change bias. Conceptually, we introduce total bias in order to estimate the direction without requiring a direct comparison with another factor. For Chinese industries from 1990 to 2012, the optimal structure is capital and energy to be combined at the composite level and then with labor to form total output. Technical change is found to be unambiguously energy biased, it increases in every year, and the bias is predominately away from labor. The results show that Chinese industrialization was fuelled by fossil fuels and energy-intensive technologies. Nonetheless, the growth rate of energy-biased technical change decreased during the 2000s that may result from more energy efficient development.We are grateful to the comments from David I. Stern and financial support provided by the China Natural Science Funding No. 71673134, Qing Lan Projec

Energy biased technology change: Focused on Chinese energy-intensive industries

Technical change bias has predominately been measured through two-factor models. Resulting from the rising importance of energy we devised a framework to estimate technical change bias for three input factors. The framework involves an estimation of elasticity and technical change parameters for a constant elasticity of substitution function with capital, labour and energy, which is derived from the elasticities and marginal output. We apply the framework to investigate eleven Chinese energy-intensive industries. The optimal nested structure for eight Of the industries is for capital and energy to be combined first at the composite level and then with labour to form total output. Between 1990 and 2012, six of the industries were energy biased, three were towards capital, one towards labour and one mixed. The results show that recent Chinese energy intensity reduction programs are not sufficient to induce energy efficient development. The policy recommendations target specifically the energy biased industries to achieve desired energy savings in the future.The authors are also grateful to the financial support provided by the China Natural Science Funding No. 71673134, Qing Lan Projec

PS14 Lessons from the rise and fall of Chinese peer-to-peer lending

This paper reviews the development and assesses the future of Peer-to-Peer (P2P) lending in China. Chinese P2P lending has expanded by a factor of 60 over the four years from 2013 and 2017. Consequently, it is now much greater, both in absolute terms and relative to the size of the economy, than in any other country. The industry though has been plagued by problematic often fraudulent business models in what was, until 2015, effectively a regulatory vacuum. A strict new regulatory regime is currently being introduced. However, its introduction, especially the requirements on capital requirements and registration, are substantially reducing the volume of P2P lending. We consider the future of P2P lending concluding that it is facing substantial uncertainties

The future of peer-to-peer lending in China

This paper reviews the development and assesses the future of Peer-to-Peer (P2P) lending in China. Chinese P2P lending has expanded by a factor of 60 over the four years from 2013 and 2017, becoming much greater, both in absolute terms and relative to the size of the economy, than in any other country. The industry though has been plagued by problematic often fraudulent business models in what was, until 2015, effectively a regulatory vacuum. A strict new regulatory regime is now being introduced but its introduction, especially the requirements on capital requirements and registration, are substantially reducing the volume of P2P lending. We consider the future of P2P lending concluding it’s facing substantial uncertainties