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This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The aim of this paper is to learn about some patterns of sectoral and industrial structural change of the Chinese economy over the 1995-2010 period, which also complements a previous paper of the author. The chosen period is about (and conveniently) bounded by two international crises: the Southeast Asian crisis of 1997 and the world crisis that started in 2007/2008.

To such a purpose, this paper set up a quantitative methodology via input-output modelling, which allows us to decompose gross output into some key demand sources or contributions. These are then analyzed over the full period.

It can be shown that the trajectory of the main structural patterns over the period was not smooth and was pretty unbalanced and that they generally responded to both domestic policy and international shocks. Export demand and heavy industry appeared to be the main engines of the economy, which showed massive increases in their share of output, at the expense of domestic demand, services and agriculture. Despite the high growth rates over this period, the Chinese economy seemed to be in need of rebalancing, which seems to have started toward the end of the authors’ period.

The decomposition method has been applied before by the author and others, but the variations in this paper are original, just as original is the application to China (never been done before), which in addition is not confined to two or so snapshots separated by many years, as is the usual use, but to the full year-after-year change of the sectoral and industrial structure over this study’s focus period.

Tourism is a labor-intensive sector with extensive links to other industries and plays a vital role in creating employment. This study aims to propose a new framework to analyze the intrinsic structure of the employment effects of tourism-related sectors and their drivers.

This study uses input–output and structural decomposition analysis (IO-SDA) to quantify the employment effects of tourism-related sectors and their driving mechanisms based on China’s I-O tables of 2002, 2007, 2012 and 2017.

The results show a declining trend in the intensity of direct or indirect employment effects in tourism-related sectors, indicating a decreasing number of jobs directly or indirectly required to create a unit of tourism output. Among tourism-related sectors, catering has the highest intensity of indirect employment effects over the study period. Catering stimulates the indirect employment of agriculture, forestry, animal husbandry, fishery and food and tobacco manufacturing. The decomposition analysis reveals that final demand is the largest contributor to the increase in tourism employment, while technological progress shifts from an employment-creation effect in 2002–2012 to an employment-destruction effect in 2012–2017.

This study proposes a new analytical framework to investigate the structural proportional relationship between the direct and indirect employment effects of various tourism-related sectors and their dynamic changes. Doing so, it provides valuable references for policymakers to promote tourism employment.

旅游相关部门就业效应的驱动因素：以中国为例

摘要

旅游业是一个劳动密集型部门, 与其他国民经济部门有着广泛的联系, 这在创造就业方面发挥着重要作用。本研究旨在建立一个框架, 分析旅游相关部门就业效应的内在结构及其驱动因素。

本研究基于中国2002年、2007年、2012年和2017年的投入产出表, 引入投入产出和结构分解分析（IO-SDA）法量化了旅游相关行业的就业效应及其变化的驱动机制。

旅游相关部门的直接或间接就业强度呈下降趋势, 可见创造一个单位的旅游产出所需的直接或间接工作数量在减少。在旅游相关部门中, 餐饮部门在研究期内的间接就业效应强度最高, 主要带动了农、林、牧、渔业和食品及烟草制造业的间接就业。旅游就业效应变动的驱动因素中, 最终需求是旅游就业效应增加的最大贡献者, 技术效应从2002-2012年期间的就业创造效应转变为2012-2017年期间的就业破坏效应。

本研究建立了一个全新的分析框架, 可以揭示各个旅游相关部门的直接和间接就业效应之间的结构比例关系及其动态变化。对旅游就业效应的驱动因素分析可以为政策制定者提供针对性的建议, 以促进旅游就业。

Factores que impulsan los efectos del empleo en los sectores relacionados con el turismo: El caso de China continental

Resumen

El turismo es un sector intensivo en mano de obra con amplios vínculos con otras industrias y desempeña un papel vital en la creación de empleo. Este estudio propone un nuevo marco para analizar la estructura intrínseca de los efectos en el empleo de los sectores relacionados con el turismo y sus impulsores.

Este estudio utiliza el análisis de entrada-salida (input-output) y de descomposición estructural (structural decomposition) (IO-SDA) para cuantificar los efectos sobre el empleo en los sectores relacionados con el turismo y sus mecanismos impulsores, basándose en las tablas input-output de China de 2002, 2007, 2012 y 2017.

Los resultados muestran una tendencia a la baja en la intensidad de los efectos directos o indirectos del empleo en los sectores relacionados con el turismo, lo que indica un número cada vez menor de puestos de trabajo directos o indirectos necesarios para crear una unidad de producción turística. Entre los sectores relacionados con el turismo, la restauración tiene la mayor intensidad de efectos indirectos sobre el empleo durante el periodo de estudio. La restauración estimula el empleo indirecto de la agricultura, la silvicultura, la ganadería, la pesca y la fabricación de alimentos y tabaco. El análisis de descomposición revela que la demanda final es la que más contribuye al aumento del empleo turístico, mientras que el progreso tecnológico pasa de ser un efecto de creación de empleo en 2002-2012 a un efecto de destrucción de empleo en 2012-2017.

Este estudio propone un nuevo marco analítico para investigar la relación estructural proporcional entre los efectos directos e indirectos del empleo de varios sectores relacionados con el turismo y sus cambios dinámicos. De este modo, proporciona valiosas referencias para que los responsables políticos promuevan el empleo en el sector turístico.

The purpose of this paper is to look at the interplay of technology and social preferences in different stages of economic development.

The authors use a set of input-output tables for 32 different countries, published by OECD. The tables refer to the period 1996-2001 and were consolidated in 48 sectors so that structural comparisons were possible through the use of techniques of decomposition used for comparing different economic structures in the context of partitioned input-output systems.

The authors confirm that, for different levels of per capita GDP, technological change is an important element to drive growth. However, as an economy evolves, the data set also confirm that the composition of final demand, which reveals social preferences in a static way, moves away from agricultural and manufacturing to services activities. Such structural changes favor sectors with stronger value added multipliers, and stronger complexity found in higher income countries generates a force that helps driving income divergence.

Given the chosen methodological approach, the structural features revealed in this study remain to be empirically tested in growth models.

The paper includes implications for the testing of growth models, suggesting there may be an association between movement into service sectors and higher growth, as modern service sectors have important spillovers on and from the manufacturing. Moreover, the way countries engage in global value changes may affect growth.

Using a unique data set, this paper adds to the empirical literature on economic growth that looks closer at the distinction between the role of structural change and changes in composition ode demand.

The purpose of this paper is to examine the effects of several structural shocks in oil prices on the Vietnamese economy and answer three key research questions: Is there a relationship between oil price shocks and macroeconomic indicators in Vietnam? How do different types of oil price impulses affect Vietnamese inflation and economic performance? To what extent do structural shocks in oil prices explain variations in Vietnam’s macroeconomic indicators?

Lower triangular Cholesky decomposition is performed on a short-term impact matrix in a two-block structural vector autoregressive model. The data set is defined monthly, from January 2000 to December 2021. The contributions of structural shocks in oil prices to the domestic variances are analysed using variance decomposition methods. In this study, both forecast error variance decomposition and historical decomposition are used.

The consequences of oil price fluctuations on Vietnamese output and inflation depend on different sources of oil price shocks. In comparison, oil supply shocks have an insignificant effect on both domestic industrial output and consumer price index inflation; however, positive shocks in aggregate and precautionary oil demands increase these domestic indicators substantially and sustainably. An analysis of variance decompositions reveals that supply-side oil shocks have very limited explanatory power for variations in domestic variables. Nevertheless, the contributions of unanticipated demand-side booms to domestic variations in the past and projected forecasts are considerable.

The findings from this research uncover potential risks for Vietnam’s economic prospects if the consequences of oil price shocks are not managed effectively.

Given the lack of economic sensitivity to supply-side oil shocks and the strong response to shifts in oil demands, greater pressure on the domestic economy is likely when Vietnam increases its dependence on oil imports.

The paper aims to develop an efficient data-driven modeling approach for the hydroelastic analysis of a semi-circular pipe conveying fluid with elastic end supports. Besides the structural displacement-dependent unsteady fluid force, the steady one related to structural initial configuration and the variable structural parameters (i.e. the variable support stiffness) are considered in the modeling.

The steady fluid force is treated as a pipe preload, and the elastically supported pipe-fluid model is dealt with as a prestressed hydroelastic system with variable parameters. To avoid repeated numerical simulations caused by parameter variation, structural and hydrodynamic reduced-order models (ROMs) instead of conventional computational structural dynamics (CSD) and computational fluid dynamics (CFD) solvers are utilized to produce data for the update of the structural, hydrodynamic and hydroelastic state-space equations. Radial basis function neural network (RBFNN), autoregressive with exogenous input (ARX) model as well as proper orthogonal decomposition (POD) algorithm are applied to modeling these two ROMs, and a hybrid framework is proposed to incorporate them.

The proposed approach is validated by comparing its predictions with theoretical solutions. When the steady fluid force is absent, the predictions agree well with the “inextensible theory”. The pipe always loses its stability via out-of-plane divergence first, regardless of the support stiffness. However, when steady fluid force is considered, the pipe remains stable throughout as flow speed increases, consistent with the “extensible theory”. These results not only verify the accuracy of the present modeling method but also indicate that the steady fluid force, rather than the extensibility of the pipe, is the leading factor for the differences between the in- and extensible theories.

The steady fluid force and the variable structural parameters are considered in the data-driven modeling of a hydroelastic system. Since there are no special restrictions on structural configuration, steady flow pattern and variable structural parameters, the proposed approach has strong portability and great potential application for other hydroelastic problems.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

This paper aims to clarify the CO₂ emissions of global construction industries under the consideration of different patterns of international trade and thus to draw a comprehensive picture for understanding the international paths of CO₂ transfer to global construction industries.

This research inventories the CO₂ emissions induced by the final demand of 15 economies for construction products and explores the CO₂ intensities of these economies based on a multi-regional input–output model. This paper further decomposes CO₂ emissions into four components based on different patterns of international trade to estimate the roles of four patterns of international trade in shaping the environmental pressures from global construction industries.

The results indicate that the CO₂ intensities of the construction industries in Russia, India and China were higher than those in other economies, and the CO₂ intensities of global construction industries experienced a decline over the years 2000–2014. The decomposition analysis demonstrates that domestic and foreign CO₂ emissions accounted for 42.67 and 54.23%, respectively, of the CO₂ emissions of the construction industries in the 15 economies during the period 2000–2007. Although the major part of the CO₂ emissions of the construction industries come from domestic production systems, the final demand for construction products in the 15 economies caused substantial emissions in other economies. Further decomposition by upstream industrial production source indicates that 58.65% of domestic emissions and 66.53% of foreign emissions can be traced back to the electricity industry.

Although the major patterns of CO₂ emissions of the construction industry have been identified in this paper, the difficulty of understanding the relationship between upstream production industries or countries and the construction industry deserves more attention in the future research.

Previous research on inventorying CO₂ emissions has generally been limited to evaluating the impact of industrial consumption activities on national or global emission accounting, tending to ignore the effects of different international trade patterns on the change in industrial CO₂ emissions. This research is the first attempt to account for and decompose the CO₂ emissions of global construction industries under consideration of the effects of different patterns of international trade on environmental pressures. The decomposition and upstream industrial distributions of different patterns of CO₂ emission provide a comprehensive picture for better understanding of the emission pattern and source of the CO₂ emissions of global construction industries. The research outcomes reveal how the final demand of a country for construction products induces CO₂ emissions in both domestic and foreign systems, thus providing basic information and references for policy adjustment and strategy design in relation to mitigation of climate change and sustainable development.