Aggregate Sector Research Articles

Performance of power generation: A dynamic productivity and efficiency analysis

This study evaluates the performance dynamics of Oman's principal part of electricity generation. Emphasis is placed on capturing the influence of the COVID-19 pandemic on the industry's performance indicators. We utilised the Malmquist Index method to analyse the changes in overall performance indicators over time. This approach allows us to distinguish between efficiency and technology changes. In addition, we employed the analysis of variance approach to test hypotheses related to COVID-19. Data is collected from twelve electricity producers across Oman's power sector. These consist of companies listed on the Stock Exchange Market in Oman, accounting for about 60 % of the total electricity production in the Sultanate. The predominant findings indicate that COVID-19 detrimentally impacted the sector's aggregate performance in 2020 but had a swift recovery in 2021. The analysis of the sample firm's productivity indices verifies that the decline in productivity in 2020 due to the Pandemic is attributable to a decrease in average efficiency indices and a negative shift in the projected frontier. These are probable consequences of the recessionary effects caused by the Pandemic. Despite a considerable decrease in average efficiency scores, a positive change in the frontier has facilitated a rapid recovery in 2021. This recovery can be attributed to the implementation of advanced technical upgrades in particular enterprises, which began as early as 2018/2019, well before the onset of the Pandemic.

The performance of the Martini Index in terms of aggregativity: An application on EU KLEMS data

The study of the properties of index numbers is a crucial point in the analysis of economic aggregates in order to ensure the coherence of the results obtained. In this context, the property of coherence in aggregation appears to be very important when these quantities are divided into sectors and subsectors, to ensure consistency of results at different levels of sectoral aggregation. Index numbers of expenditure ratios satisfy this property by construction; however, they often do not satisfy other properties such as factor reversibility. In this paper, the Martini index was proposed as a viable alternative for calculating Total Factor Productivity (TFP). In the proposed application on EU KLEMS data, the results obtained are very encouraging and confirm the interest in the Martini index.

Augmenting input-output system under the curse of dimensionality: AHP-RAS integration and algorithm simulation

ABSTRACT In the estimation of input-output tables, one frequently encountered challenge is the aggregation of sectors in initial tables, which complicates the estimation of more detailed tables due to missing data—a phenomenon referred to as the “curse of dimensionality” when augmenting from lower to higher dimensions. We propose an AHP-RAS method to address the substantial data deficiencies encountered in the expansion of input-output tables. The method applies the Analytical Hierarchy Process (AHP) to transform the subjective relative importance of sectors into input coefficients using Pairwise Comparison Matrices (PCM) and eigenvector methods. Subsequently, it incorporates the RAS approach to supplement objective data, thereby integrating subjective judgement and objective information to tackle the curse of dimensionality. Unlike traditional AHP methods, this study employs binary integer programming to decompose large-scale matrices into subsets, making the AHP technique applicable to input-output tables with a broader range of sectors. We employ Monte Carlo simulation techniques to evaluate the robustness of the proposed methodology, specifically assessing the impacts of subjective judgement, objective data utilization, and the variation in sector numbers. Overall, the method overcomes the curse of dimensionality, yielding coefficients within an acceptable error margin, thereby demonstrating a significant advancement in the field of input-output analysis.

Sector aggregation effect on embodied carbon emission based on city-centric global multi-region input-output (CCG-MRIO) model

Sector aggregation under the input-output framework may lead to deviation in results and the lack of detailed sectoral information, which hinder the targeted implementation of accurate sectoral adjustment policies. This paper explores the effects of sector aggregation on the embodied carbon emission of the residential consumptions of Beijing and Shanghai based on the city-centric global multi-region input-output (CCG-MRIO) model. Integrating with structural path analysis and structural decomposition analysis, the impacts of sector aggregation on the identifying of critical emission transmission paths and driving forces are also revealed. Three sector aggregation datasets are discussed to track the sector aggregation effect in 2012 and 2017. The findings show that the carbon emission results at 8 sector level better align with the results at the 22 sector level than those at 32 sector level. Moreover, sector aggregation will lead to the absence of some critical carbon transmission paths, some factor contributions on carbon emission also changed their directions at different sector levels. On the basis of clarifying the sector aggregation effect on embodied carbon emission, the research results provide a reference for similar studies to select the appropriate level of sector aggregation, to realize the goal of simplifying the calculation or gaining more detailed sectoral emission information.

THE EFFECTS OF CULTURAL DISTANCE AND INSTITUTIONS ON FOREIGN DIRECT INVESTMENT CHOICES: A RESEARCH OF TURKEY & CHINA

Global changes in social, cultural and economic spheres and advances in technology and communication have made the world a global market where borders no longer exist. The importance of foreign investment has become undeniable with the emergence of the concept of foreign investment for developing economies. Due to its direct impact on the global market and its economy in transition, China is the focus of this research. The research aims to determine the interplay of variable factors influencing foreign direct investors and investment location choices with cultural distance and investment models, and to use the associated evaluation as a guide for investors. In this study, qualitative research methods are used to measure the impact of the variable factors that determine the hypotheses of the study on foreign direct investors and to evaluate the findings. In this study, designed with the technique of phenomenology, in-depth interview is used as the method of data collection and data analysis is done through descriptive analysis. All the research and interviews have revealed that FDI is very responsive to institutions and cultural distance. Moreover, agglomeration is the strongest determining factor for FDI in the Chinese market. The reason these factors encompass the sectoral aggregate are not the strongest factors is that agglomeration is the most important finding. This study is a useful guide for developed and developing countries as well as for the strategic plans of local and national institutions.

Estimating the Distribution of Household Wealth: Methods for Adjusting Survey Data Estimates Using National Accounts and Rich List Data

Income and wealth surveys are usually affected by unit non‐response and reporting errors, which contribute to a mismatch with macroeconomic figures from national accounts. In this paper, we develop a novel allocation method to address these two issues simultaneously, when only limited external information is available. The proposed approach combines information from a power law (Pareto) model with imputation procedures based on calibration. We apply the proposed simultaneous approach to produce distributional indicators for four Euro‐Area countries that are consistent with their national accounts. In particular, we use data from the 2014 Finnish, French, German, and Italian Household Finance and Consumption Survey and rich list data from Forbes or national press sources, along with household sector aggregates from national accounts, as auxiliary sources of information. A bootstrap procedure is also applied to evaluate the precision of the final estimates.

A review of methods to trace material flows into final products in dynamic material flow analysis: From industry shipments in physical units to monetary input–output tables, Part 1

AbstractDynamic material flow analysis (dMFA) is widely used to model stock‐flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy‐wide material consumption to different end‐use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end‐use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end‐use products in inflow‐driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio‐temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy‐wide coverage makes them a valuable source to approximate material end‐uses. We find four distinct MIOT‐based methods: consumption‐based, waste input–output MFA (WIO‐MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re‐defining specific intermediate outputs as final demand. Theoretically, WIO‐MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT‐dMFA system boundaries.

Схема “витрати-випуск” Японії: застосування для системного аналізу та моделювання міжгалузевих зв’язків економіки України

Introduction. The scheme input-output, used for the study of inter-branch reproduction of the economy of Japan, on system analysis and modelling of inter-branch relations of the economy of Ukraine is considered. Problem Statement. The analysis and modelling of inter-branch relations of the Ukrainian economy is considered. The purpose is to prepare aggregated the input-output tables of the Ukrainian economy and conduct a systematic analysis and modelling of inter-branch relations of the Ukrainian economy according to the scheme used by the Government of Japan. Materials and Methods. The basis of the study was the official data of the State Statistics Service of Ukraine – the input-output tables in basic prices for 2013-2019. General scientific and special methods were used: analysis, synthesis, grouping, comparison, balance method, method of economic and mathematical modelling. Results. According to the scheme used in Japan, a scheme of the turnover of the Ukrainian economy has been prepared, which follows from the input-output table for 2019. On the basis of input-output table of Ukraine and national accounts (2013-2019), aggregated input-output tables of the Ukrainian economy of 13 types of economic activity and 3 large sectors of the economy in the form of primary, secondary and tertiary sectors were prepared. The aggregation of sectors of the economy of Ukraine is substantively consistent with the aggregation of industries in the 13 sectoral model of input-output of the Japanese economy. Input-output tables are prepared in a competitive-import version. On the basis of aggregated input-output tables was analysed the development of sectors of the economy of Ukraine during 2013-2019. The classification of industries into key ones, with direct and inverse impact , etc. was carried out. Structural changes in GDP by final costs, production method and income categories were analysed as well as coefficients of value added, imports, influence. A visual assessment of the volatility of the GDP structure was carried out. Conclusions. The analysis and modelling of inter-branch relations of the economy of Ukraine is carried out. A structural analysis of Ukraine's GDP within the framework of the prepared input-output table was carried out. Developments can be used for 1) scientific and educational purposes; 2) modelling of specific economic systems presented by the corresponding input-output tables; 3) conducting a comparative analysis of the economies of Ukraine and Japan within the framework of completely comparable the input-output tables.

Public sector employment and aggregate fluctuations

An important stylized fact about public sector employment is that it predominantly hires skilled and more experienced workers. In this paper, we consider a search and matching model with public sector and on-the-job human capital accumulation that incorporates this stylized fact to study how the public sector employment affects the labor market volatility. In the model, public sector employment affects aggregate fluctuations by changing the composition of workers employed in the private sector. Because workers accumulate human capital and become more productive when employed, the flow of benefits from forming a match are spread over time. In this environment, if the flow into the public sector increases with human capital, then the government hiring policy decreases the firm’s benefit of hiring and the matching surplus, increasing the responsiveness of labor market tightness to shocks. We calibrate the model for the Brazilian economy and show that this mechanism amplifies the effects of public employment on vacancy creation and private sector employment volatility.

Input-output analysis of embodied emissions: Impacts of imports data treatment on emission drivers

Input-output (I-O) analysis has been widely used in embodied emission studies. For country-level analysis, the non-competitive imports assumption is generally preferred. However, national I-O tables with the non-competitive imports assumption are often not available. In empirical studies, the uniform imports share approach is usually adopted to give the estimated tables from the I-O tables with the competitive imports assumption. When data are available for two different years, structural decomposition analysis (SDA) can be applied to study the drivers of the embodied emission changes. We propose a SDA framework with two imports effects, i.e. input imports effect and demand imports effect, to evaluate the impacts of imports data treatment on the drivers. An empirical study using China's latest I-O datasets, for 2017 and 2018, show that the impacts are not significant at the national level but are significant at the sectoral level. For the latter, the impacts on sectoral embodied emission intensity range from −5% to 16%, while on sectoral embodied emissions range from −8% to 35%. Sectoral aggregate embodied intensity (AEI) indicators are found to be more robust to the imports data treatments than sectoral embodied emission intensity. In some cases a change in the sign of the input imports effect or demand imports effect is observed, which leads to opposite outcomes. Implications of the findings on embodied emission studies and the role of imports are discussed.

Quantified impacts of international trade on the United States' carbon intensity.

After the USA reached its carbon emission peak in 2007, the share of carbon emissions from its energy activities showed a downward trend. As a country that accounts for even close to 80% of the tertiary industry, the final demands from other industries are usually met by other countries in the form of international trade. While trade is realizing economic transfer, it also realizes the transfer of carbon emissions to a certain extent, helping the country achieve the peaks. From the perspective of combining carbon emission transfer and economic transfer, are there transfers and impacts? In this study, the quantified impact of international trade on the USA's carbon intensity has been investigated by a novel framework, based on a porposed scenario analysis using MRIO. As it gets lower aggregate carbon intensity value under trade scenario, it concludes that international trade is more conducive to the needs of this country for carbon emission reduction and economic development in general. From the different trade patterns, all of them get lower carbon intensity values under trade than no-trade scenario. From trade partners' perspectives, the positive and negative of the intensity gap cannot be kept uniform for all traders. Sectoral driving factors are decomposed by LMDI method, with the sectoral effect of aggrerate value-added structure and sectoral aggregate embodied carbon intensity. Among obvious carbon-intensive sectors, transport sectors always show a negative effect for the gap, and heavy manufacturing and electricity sectors usually give positive effects. As a major trading country in the world, trade and exchanges with other countries are more conducive to this country with a lower carbon intensity, and it also requires the country to shoulder coresponding responsiblities as a great power while enjoying the benefies. It is conducive to the further strengthening of unity of the international community, to jointly address the challenges of climate change and achieve the subsequent carbon neutral targets.

An Analysis of Backward and Forward Linkages of Pharmaceutical Sector in India Based on an Input- Output Model

Aims: The paper study about the inter-linkages between pharmaceutical sector and other sectors of Indian economy. The paper also measures the effects of growth in pharmaceutical sector on other sector’s growth.
 Study Design: The study is mainly a static analysis based on secondary data concentrated in studying the production structure of Indian pharmaceutical sector. 
 Duration of Study: The time taken for the research to be completed is for a period of four months.
 Methodology: We have used the latest input-output table available for the period of 2015-16 to conduct the analysis. First, we converted the input-output table from 131 sectors structure to a 60 sectors structure by aggregation of most related sectors. Then, technical coefficient matrix and Leontief inverse matrix for the 60 sectors included in the table has been calculated to analyze the forward and backward linkages of the industry. In addition to that we have calculated output multiplier of pharmaceutical industry to know the effects of changes in the growth of pharmaceutical sectors on all other sectors of the economy.
 Results: The input-output analysis reveals that both forward linked indicator and backward linked indicator are greater than one. The forward linked indicator is of value 1.12 and backward linked indicator is of value 1.50. These results indicated that pharmaceutical sector is strongly inter-linked both backwardly and forwardly with other industries in the Indian economy. The value of output multiplier of the industry is 2.34; it indicates positive contribution of the pharmaceutical sectors both directly and indirectly to the Indian economy.
 Conclusion: The study finds that pharmaceutical industry is a key industry in stimulating the production activities of other industries. Thus with proper planning and execution of pharmaceutical industry it can help in promoting other sectors growth.

Driving Forces in Carbon Dioxide Emissions of the Hungarian Transport Sector

This article examines the evolution of the long-lasting trends and factors affecting the transport sector, which is responsible for a significant part of Hungarian carbon dioxide emissions. Empirical evidence suggests that the primary driving forces behind emissions from cars are stock development and carbon intensity. In freight transport, the primary driver of decarbonization is the rate of rail freight in total freights. By 2030, motorization and the size of cars will probably increase, so no significant emission reductions are expected in this area. In contrast, the increasing proportion of rail traffic can be a substantial decarbonization reserve. By 2030, carbon dioxide emissions from passenger cars could increase by 1.5 million tonnes and from freight transport could decrease by 1 million tonnes, which would mean a further increase in the sector's aggregate pollution.

Sectoral Media Focus and Aggregate Fluctuations

We formalize the editorial role of news media in a multisector economy and show that media can be an independent source of business cycle fluctuations, even when they report accurate information. Public reporting about a subset of sectoral developments that are newsworthy but unrepresentative causes firms across all sectors to hire too much or too little labor. We construct historical measures of US sectoral news coverage and use them to calibrate our model. Time-varying media focus generates demand-like fluctuations that are orthogonal to productivity, even in the absence of non-TFP shocks. Presented with historical sectoral productivity, the model reproduces the 2009 Great Recession. (JEL D22, D83, E32, L82)

Connecting emerging industry and regional innovation system: Linkages, effect and paradigm in China

An emerging industry refers to an aggregation of sectors and firms that create disruptive technologies and/or huge market potentials. Emerging industries based on technological innovation have increasingly become an important driving force for industrial competitiveness and economic growth. Although regional innovation systems (RISs) boost regional industries, it remains under-investigated whether and how RISs have differential impacts on different industries. Inspired by Pavitt's taxonomy of sectorial innovation, we propose four paradigms of industrial innovation in the context of emerging industries in China. We also empirically explore the effects of RISs on emerging industries, distinguishing the short-term effects from long-term ones. We present two key findings. First, RISs have generally contributed to the growth of emerging industries in China. Second, different components of RISs have varied impacts on different emerging industries. The results provide both theoretical underpinnings and empirical evidence for policy-makers, suggesting that well-designed policies that leverage RIS formation and regional industry specific characteristics are vitally important for development of emerging industries. Our study makes three important contributions. First, it theorizes a conceptual framework on possible mechanism through which RISs promote emerging industries. Second, this study sheds empirical light on both short-term and long-term effects of individual RIS components on seven emerging industries in the Chinese context. Third, the paper proposes four paradigms of industrial innovation in emerging industries under the RIS framework.

Decomposition and sector aggregation analysis of indirect household carbon emission indicators: a case study of Guangdong Province in China

Decomposition and sector aggregation analysis of indirect household carbon emission indicators: a case study of Guangdong Province in China

The Economic Impacts of Air Safety Rating Downgrade for Malaysia

In 2019, the US Federal Aviation Administration (FAA) downgraded the Civil Aviation Authority of Malaysia (CAAM) from tier one to tier two. Existing research has revealed that downgrading air safety ratings has a detrimental effect on the aviation sector. Although extensive research has been carried out on air safety downgrading, limited studies have delved into the backward and forward linkages and inter-industries framework. By employing a difference-in-differences (DID) panel data econometric and input-output (I-O) analysis to a modified sectoral aggregation of Malaysia’s I-O Table 2015, this study is able to simulate the impact of air safety downgrading. The findings show that, apart from being a “key” industry, air safety downgrades could result in a RM722.5 million loss to Malaysia’s GDP. A more in-depth inspection of the results indicates that the reduction in GDP mainly results from the air transport industry (RM252.0 million), other transportation services (RM107.0 million), and wholesale and retail trade (RM66.2 million). The findings complement earlier related studies that air safety rating downgrades could be a severe threat to sustainable economic growth.

Unraveling the Effects of Tropical Cyclones on Economic Sectors Worldwide: Direct and Indirect Impacts

This paper examines the current, lagged, and indirect effects of tropical cyclones on annual sectoral growth worldwide. The main explanatory variable is a new damage measure for local tropical cyclone intensity based on meteorological data weighted for individual sectoral exposure, which is included in a panel analysis for a maximum of 205 countries over the 1970–2015 period. I find a significantly negative influence of tropical cyclones on two sector aggregates including agriculture, as well as trade and tourism. In subsequent years, tropical cyclones negatively affect the majority of all sectors. However, the Input–Output analysis shows that production processes are sticky and indirect economic effects are limited.

Measuring trade in value added with firm-level data

Global Value Chains have proliferated in economic policy debates. Yet a key concept—trade in value added—is likely mismeasured because of sectoral aggregation bias stemming from reliance on input-output tables. This paper uses comprehensive firm-level data on domestic and international transactions to study this bias. We find that sectoral aggregation leads to overstated trade in value added. The magnitude of the bias varies across countries—at 2–5 p.p. of gross exports for Belgium and 17 p.p. for China. We study how the interplay between within-sector heterogeneities in firms' import and export intensities and size determine the magnitude of the bias.

Import Competition and Firms' Internal Networks

Using administrative data on U.S. multisector firms, we document a cross-sectoral propagation of the import competition from China (“China shock”) through firms’ internal networks: Employment of an establishment in a given industry is negatively affected by China shock that hits establishments in other industries within the same firm. This indirect propagation channel impacts both manufacturing and non-manufacturing establishments, and it operates primarily through the establishment exit. We explore a range of explanations for our findings, highlighting the role of within-firm trade across sectors, scope of production, and establishment size. At the sectoral aggregate level, China shock that propagates through firms’ internal networks has a sizable impact on industry-level employment dynamics.