Encadeamentos produtivos e multiplicadores macrossetoriais no Brasil após a abertura comercial

Este artigo analisa o processo de desenvolvimento produtivo setorial agregado da economia Brasileira de 1990 a 2015 aplicando os índices de encadeamento produtivo e os multiplicadores setoriais para a produção e emprego, utilizando uma abordagem de Insumo-Produto e mesoeconomia. A geração de linkage effects para uma economia é importante, pois desencadeia uma série de resultados positivos desde que sua estrutura esteja interligada e seja capaz de causar spillovers. Assim, a pesquisa pretende contribuir para as discussões acerca das temáticas relativas ao crescimento macrossetorial da economia brasileira explorando o processo de crescimento produtivo como uma ferramenta essencial das relações de produção, demanda e crescimento tecnológico. Os resultados apontam que a estrutura produtiva da economia brasileira não evoluiu na direção de criação e desenvolvimento de encadeamentos produtivos consolidados e os multiplicadores de produção e de emprego diminuíram em grande parte dos setores considerados.

An evolution-based high-fidelity method of epistasis measurement: Theory and application to influenza

Linkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the average speed of evolution and the statistics of phylogeny. However, predicting statistically the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult to predict theoretically and detect experimentally in sequence data. A large number of false-positive interactions arises from stochastic linkage effects and indirect interactions, which mask true epistatic interactions. Here we develop a proof-of-principle method to filter out false-positive interactions. We start by demonstrating that the averaging of haplotype frequencies over multiple independent populations is necessary but not sufficient for epistatic detection, because it still leaves high numbers of false-positive interactions. To compensate for the residual stochastic noise, we develop a three-way haplotype method isolating true interactions. The fidelity of the method is confirmed analytically and on simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large sequence database of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure epistatic interactions of any sign from sequence data.

A Study of Linkage Effects and Environmental Impacts on Information and Communications Technology Industry between South Korea and USA: 2006–2015

This study examined the relationship among carbon dioxide emissions and linkage effects using Input–Output (IO) data of the information and communications technology (ICT) industry between South Korea and the USA. As we wanted to find out if the ICT industry, which the world is passionate about, is a sustainable industry. The linkage effects are analyzed to determine the impact of ICT industry on the national economy, and CO₂ emissions of the industry are analyzed to determine how much influence it has on air pollution. In addition, we classify ICT industry by ICT service and manufacturing industries as the key industries in Korea and the US. Data were collected from OECD ranging from 2006 to 2015 in order to quantitatively estimate backward linkage, forward linkage effect, and carbon dioxide emissions. The results indicated that ICT manufacturing industry in Korea has high backward and forward linkage effects. CO₂ emissions from ICT service is more than from ICT manufacturing in both Korea and the US. We wanted to find out if the ICT industry, which the world is passionate about, is a sustainable industry. As a contribution, ICT manufacturing and service industries in Korea and the United States are directly compared, and CO₂ emissions over 10 years are analyzed in a time series.

A New Method for Interindustry Linkage Analysis Based on Demand-Driven and Multisector Input-Output Model and Its Application in China’s Manufacturing and Producer Services

Interindustry linkage analysis is an important interdisciplinary research field of technical economic and complex systems, and the results can be used as critical bases for making strategies and policies of economic development. This study reviews the previous methods for measuring interindustry linkages and their disadvantages and puts forward a new method for interindustry linkage analysis in a complex economic system on the basis of demand-driven and multisector input-output model. Firstly, it makes a further decomposition of the Leontief inverse matrix in the economic sense and decomposes the gross output of one industrial sector or its sub-industries into three components. Then, it analyzes the structural features of output and measures the interindustry linkages between two industrial sectors with three indices: interindustry linkage effect, interindustry linkage contribution, and interindustry linkage coefficient. Compared with the previous measurements, the method in this study has three obvious advantages: it integrates the sectoral internal effect and external linkage effect at the same time; it can not only measure the interindustry linkage effects between two given industrial sectors but also clearly describe the composition ratio of the direct and indirect interindustry linkage effects; and it adopts, respectively, the absolute flow value, relative flow value, and unit relative value to measure the linkages comprehensively. Finally, this study takes China’s input and output in 2017 as an application case to analyze the structural features of output of its manufacturing and producer services and measure the interindustry linkages between them.

Identifying the Sustainable Industry by Input–Output Analysis Combined with CO2 Emissions: A Time Series Study from 2005 to 2015 in South Korea

Preventing global warming caused by increased CO2 emissions is a major global problem. It is necessary to find and cultivate an efficient industry with a small amount of CO2 emissions and a great impact on the national economy. This article used input–output analysis to quantify the linkage effects on the Korean economy by dividing the Korean industries into 36 categories, according to the OECD (Organization for Economic Cooperation and Development) industrial classification criteria. In addition, the total amount of carbon dioxide emitted during the year was described by its criteria to compare how much of one industry emits carbon dioxide. The analysis shows that Korea still has an economic structure centered on traditional manufacturing and the characteristics of these industries include CO2 emissions. According to the result, in the construction industry, the carbon dioxide emissions are considerably high, but the linkage effects of the industry is small. By quantitatively analyzing the impact of an industry on the economy and carbon dioxide emissions generated in the production process, this study aimed to identify Korea’s eco-friendly and highly related industries with other industries and objectively present sustainable development.

Volatility Modelling of Chinese Stock Market Monthly Return and Investor Sentiment Using Multivariate GARCH Models

This article examines the linkage and volatility spillover among Chinese Stock Market Monthly Return and Investor Sentiment, investigating the effect dynamic links of various investor sentiment indicators and Chinese stock market return volatility. Employing the DCC and BEKK GARCH, we find investor sentiment is to some extent linked to the yield fluctuations of the Chinese stock market, but the volatility spillover is relatively weak. In the test period (2005-2020), we observe that several indicators do not explain their linkage effects with CSI 300 index of return fluctuations and volatility spillovers well, with no indicators can reflect both of these effects. Most indicators are linkage with the CSI 300 index, especially consumer confidence index (CCI), new investor account openings last month (NIA) and the volume of transactions last month (TURN) have significant linkage effects with the CSI 300 index. We also find that only the CCI index has a one-way volatility spillover on the CSI 300 index, and the CSI 300 index has no volatility spillover on any indicator.

Volatility Modelling of Chinese Stock Market Monthly Return and Investor Sentiment Using Multivariate GARCH Models

This article examines the linkage and volatility spillover among Chinese Stock Market Monthly Return and Investor Sentiment, investigating the effect dynamic links of various investor sentiment indicators and Chinese stock market return volatility. Employing the DCC and BEKK GARCH, we find investor sentiment is to some extent linked to the yield fluctuations of the Chinese stock market, but the volatility spillover is relatively weak. In the test period (2005-2020), we observe that several indicators do not explain their linkage effects with CSI 300 index of return fluctuations and volatility spillovers well, with no indicators can reflect both of these effects. Most indicators are linkage with the CSI 300 index, especially consumer confidence index (CCI), new investor account openings last month (NIA) and the volume of transactions last month (TURN) have significant linkage effects with the CSI 300 index. We also find that only the CCI index has a one-way volatility spillover on the CSI 300 index, and the CSI 300 index has no volatility spillover on any indicator.