Labour Productivity and the Chaotic Economic Growth Model： G7

Chaos theory is used to prove that erratic and chaotic fluctuations can indeed arise in completely deterministic models. Chaos theory reveals structure in aperiodic, dynamic systems. The number of nonlinear business cycle models use chaos theory to explain complex motion of the economy. Almost three years after the crisis, the G7 countries continue to be challenged with economic volatility. The global economy has slowed. Growth in the United States has weakened. In Europe, economic instability is generated by the financial and economic imbalances. Europe is gripped with financial strains from the sovereign debt crisis in the euro area periphery. How these G7 economies confront their fiscal challenges will profoundly affect their economic stability. The basic aim of this paper is to provide a relatively simple chaotic economic growth model that is capable of generating stable equilibria, cycles, or chaos. This paper looks in more detail at the GDP growth stability issues in each of the G7 countries in the period 1990-2012 （Retrieved from http：//www, imf.org）. A key hypothesis of this work is based on the idea that the coefficient π =[p（s_p-i-n/pb-p_mb_m）] plays a crucial role in explaining local stability of the gross domestic product growth, where, p---the coefficient of labour productivity; p.,--the coefficient of the marginal labour productivity, sp-private saving rate;i--investment rate; b-percent of the gross domestic product which belongs to budget deficit; bm-marginal budget deficit coefficient; n-net capital outflow rate.

Labour and Multi-factor Productivity Analysis and Their Impact on Operations： A Case Study of a Large Poultry Farm

This paper investigates a large integrated poultry farm in terms of its operations, its labour, and multi-factor productivity based on the operations data received from two processing plants over a period of 15 months. The purpose of this paper is to： （a） identify and classify various types of costs that impact the operational success of the farm; （b） collect data, compute labour productivity, multi-factor productivity, rejects, and losses for the two plants; （c） compare the two processing plants of the company from various perspectives, such as rejection in products, process losses, and different types of costs; and （d） recommend ways to improve the productivity and operations of the processing plants to produce good quality products and reduce wastes during the production.

The Impact of Internal Financing on Productivity-An Empirical Study on Chinese Listed Manufacturing Firms

Using a panel of 1,747 Chinese manufacturing firms over the period of 2001 to 2016, we examine how internalfinancing affects labour productivity. Value-added revenue over total employees and total factor productivity areused as proxy for labour productivity. Different regressions models including OLS, FGLS, and GMM have beenimplemented to conduct the empirical analysis. The results show that internal financing has positive effect on firms＇productivity as a whole. What is more, whether the firms have state-owned property or not differentiates theexplanation of cash-flow boosted productivity improvement as well as equity-boosted productivity improvement.This paper further took firms＇ location and the effect financial crisis into consideration. It discovered that thedegree of regional development, which is measured by nominal GDP, has significant positive impact on labourproductivity, especially for state-owned enterprises （SOEs）. The 2008 financial crisis is found to have a positiveinfluence on after-crisis labour productivity. We believe this paper shed light on firms＇ after-crisis financingmanagement and government policies to improve the financing structure in the manufacturing sector.

Information Models for Forecasting Nonlinear Economic Dynamics in the Digital Era

The aim of this study was to develop an adequate mathematical model for long-term forecasting of technological progress and economic growth in the digital age (2020-2050). In addition, the task was to develop a model for forecast calculations of labor productivity in the symbiosis of “man + intelligent machine”, where an intelligent machine (IM) is understood as a computer or robot equipped with elements of artificial intelligence (AI), as well as in the digital economy as a whole. In the course of the study, it was shown that in order to implement its goals the Schumpeter-Kondratiev innovation and cycle theory on forming long waves (LW) of economic development influenced by a powerful cluster of economic technologies engendered by industrial revolutions is most appropriate for a long-term forecasting of technological progress and economic growth. The Solow neoclassical model of economic growth, synchronized with LW, gives the opportunity to forecast economic dynamics of technologically advanced countries with a greater precision up to 30 years, the time which correlates with the continuation of LW. In the information and digital age, the key role among the main factors of growth (capital, labour and technological progress) is played by the latter. The authors have developed an information model which allows for forecasting technological progress basing on growth rates of endogenous technological information in economics. The main regimes of producing technological information, corresponding to the eras of information and digital economies, are given in the article, as well as the Lagrangians that engender them. The model is verified on the example of the 5th information LW for the US economy (1982-2018) and it has had highly accurate approximation for both technological progress and economic growth. A number of new results were obtained using the developed information models for forecasting technological progress. The forecasting trajectory of economic growth of developed countries (on the example of the USA) on the upward stage of the 6th LW (2018-2042), engendered by the digital technologies of the 4th Industrial Revolution is given. It is also demonstrated that the symbiosis of human and intelligent machine (IM) is the driving force in the digital economy, where man plays the leading role organizing effective and efficient mutual work. Authors suggest a mathematical model for calculating labour productivity in the digital economy, where the symbiosis of “human + IM” is widely used. The calculations carried out with the help of the model show: 1) the symbiosis of “human + IM” from the very beginning lets to realize the possibilities of increasing work performance in the economy with the help of digital technologies;2) the largest labour productivity is achieved in the symbiosis of “human + IM”, where man labour prevails, and the lowest labour productivity is seen where the largest part of the work is performed by IM;3) developed countries may achieve labour productivity of 3% per year by the mid-2020s, which has all the chances to stay up to the 2040s.