The Economics of Innovation, Investment, and Taxation

The digitalization of economy has been creating profound difficulties in the tax policy and intensive debates among policy makers, economists and entrepreneurs. In this thesis, a series questions relating to innovation and tax policy are explored in three aspects. Chapter 1 evaluates the role of public R&D support on labour productivity and productivity growth using non-parametric matching with up-to-date ONS data. We find that public support for R&D has a negative impact on labour productivity and such impact is insignificant for productivity growth. The significant negative effect on labour productivity and insignificant negative effect on productivity growth are mainly driven by high-tech firms and SMEs. Chapter 2 presents a model of taxation of multinational businesses operating in a competitive international digital economy. The model includes important features of the digital economy, such as the network externality in consumption, significant market power of the providers, and the role of digital technology innovation. In particular, the focus is on the investment in innovation of two types: (i) innovation that reduces production cost, or process innovation, and (ii) innovation that improves the quality of the good and thus boosts the consumer demand, or product innovation. Chapter 3 analyses a potential solution to taxing the digital economy based on the idea of division of the tax base. Firstly, we consider a two-country model in two approaches: a non-cooperative approach and a cooperative approach. The non-cooperative approach means each country decides what proportion of the firm’s profit to tax. We consider a simple case when there is no profit split; next, we introduce profit split and analyse the situation where a firm earns profits from both countries. The cooperative solution is the case when two countries jointly decide how to share the taxable profits. In this case, we first consider a simple case when a firm earns all profits from sales in the source country; next, we relax this assumption and analyse the situation where a firm also earns profits in the residence country. Secondly, we investigate the outcome when there are more than one source countries

ANHYDROUS FLUORIDE SALTS AND REAGENTS AND METHODS FOR THEIR PRODUCTION

Anhydrous organic fluoride salts and reagents prepared by a method comprising the nucleophilic Substitution of a fluorinated aromatic or fluorinated unsaturated organic compound with a salt having the formula: [QnM]x+Ax- in an inert polar, aprotic solvent; wherein M is an atom capable of supporting a formal positive charge, the n groups Q are independently varied organic moieties, n is an integer such that the [QnM] carries at least one formal positive charge, x is an integer defining the number of formal positive charge(s), +, carried by the [QnM], A- is an anionic nucleophile capable of substituting for F in the fluorinated compound and F represents fluorine or a radioisotope thereof

Locality Preserving Projections for Grassmann manifold

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grassmann based algorithms.Comment: Accepted by IJCAI 201