Development Strategy, Optimal Industrial Structure and Economic Growth in Less Developed Countries

In this paper, we develop an endogenous growth model that combines structural change with repeated product improvements. There are two sectors in the present paper, one is traditional sector, and the other is modern sector. The technological progress in the traditional sector takes the form of horizontal innovation based on expanding variety, while the technologies in the modern sector become not only increasingly capital-intensive but also progressively productive over time. The application of the basic model to the less developed economies show that the optimal industrial structure in the less developed countries (LDCs) is endogenously determined by its factor endowments; the firm in the LDCs that enters the capital-intensive, advanced industry in the developed countries (DCs) would be nonviable owing to the relative scarcity of capital in the LDCs factor endowments; whether the industrial structure matches with the factor endowment structure or not is the fundamental cause to explain differences in economic performance among the LDCs.Capital Intensity, Development Strategy, Factor Endowments, Endogenous Growth, Industrial Structure, productivity, technology, Viability

Industrial structure, appropriate technology and economic growth in less developed countries

The authors develop an endogenous growth model that combines structural change with repeated product improvement. That is, the technologies in one sector of the model become not only increasingly capital-intensive, but also progressively productive over time. Application of the basic model to less developed economies shows that the (optimal) industrial structure and the (most) appropriate technologies in less developed economies are endogenously determined by their factor endowments. A firm in a less developed country that enters a capital-intensive, advanced industry in a developed country would be nonviable owing to the relative scarcity of capital in the factor endowments of less developed countries.Economic Theory&Research,Political Economy,Technology Industry,Economic Growth,Inequality

Development Strategy, Viability, and Economic Institutions: The Case of China

development strategy, institution, viability, trinity system

Photonic crystal fiber half-taper probe based refractometer

A compact singlemode - photonic crystal fiber - singlemode fiber tip (SPST) refractive index sensor is demonstrated in this paper. A CO2 laser cleaving technique is utilised to provide a clean-cut fiber tip which is then coated by a layer of gold to increase reflection. An average sensitivity of 39.1 nm/RIU and a resolvable index change of 2.56 x 10-4 are obtained experimentally with a ~3.2 µm diameter SPST. The temperature dependence of this fiber optic sensor probe is presented. The proposed SPST refractometer is also significantly less sensitive to temperature and an experimental demonstration of this reduced sensitivity is presented in the paper. Because of its compactness, ease of fabrication, linear response, low temperature dependency, easy connectivity to other fiberized optical components and low cost, this refractometer could find various applications in chemical and biological sensing

Development strategy, viability, and economic institutions: The case of China

This paper explores the politically determined development objectives and the intrinsic logic of government intervention policies in east developed countries. It is argued that the distorted institutional structure in China and in many least developed countries, after the Second World War, can be largely explained by government adoption of inappropriate development strategies. Motivated by nation building, most least-developed countries, including the socialist countries, adopted a comparative advantage defying strategy to accelerate the growth of capital-intensive, advanced sectors in their countries. In the paper we also statistically measure the evolution of government development strategies and the economic institutions in China from 1950s to 1980s to show the co-existence and coevolution of government adoption of comparative advantage defying strategy and the trinity system

Convergence, financial development, and policy analysis

We study the relationship among inflation, economic growth, and financial development in a Schumpeterian overlapping generations model with credit constraints. In the baseline case, money is super-neutral. When the financial development exceeds some critical level, the economy catches up and then converges to the growth rate of the world technology frontier. Otherwise, the economy converges to a poverty trap with a growth rate lower than the frontier and with inflation decreasing with the level of financial development. We then study efficient allocation and identify the sources of inefficiency in a market equilibrium. We show that a particular combination of monetary and fiscal policies can make a market equilibrium attain the efficient allocation.First author draf

Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.Comment: Accepted at CIKM 201