Mapping Global Value Chains at the Product Level (preprint)

Value chain data is crucial to navigate economic disruptions, such as those caused by the COVID-19 pandemic and the war in Ukraine. Yet, despite its importance, publicly available value chain datasets, such as the ``World Input-Output Database'', ``Inter-Country Input-Output Tables'', ``EXIOBASE'' or the ``EORA'', lack detailed information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and rely instead on more aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method based on machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 300+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) and 1200+ products to infer value chain information implicit in their trade patterns. Furthermore, we use proportional allocation to assign the trade flow between regions and countries. This work provides an approximate method to map value chain data at the product level with a relevant trade flow, that should be of interest to people working in logistics, trade, and sustainable development.

Drivers of employment effects in tourism-related sectors: the case of Mainland China

PurposeTourism 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.Design/methodology/approachThis 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.FindingsThe 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.Originality/valueThis 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.

The Energy Sector Amid an Industry 4.0: Issues of Information Support Transformation

The article is focused on the issue of providing information for the energy sector in the modern economy. The purpose of the article is to summarize ways of collecting and analyzing primary data in the energy sector in order to complete the methodology harmonization in accordance with international standards. The methodological basis of the study is the theory of input-output tables, the concept of Industry 4.0, the basic provisions of national accounts;and the methods used by the authors are balance comparisons, economic and structural analysis, as well as methods of peer review. Input data sources for each level of information support have been systematized. The characteristic of the system of indicators and the model for constructing energy balances are generalized, within whose framework the analysis of the production and use of energy resources is carried out taking into account the new opportunities of Industry 4.0. Methodological improvement of the official questionnaires of enterprises, the integration of administrative and alternative sources, their harmonization with regional and departmental data will enhance the information support of fuel and energy balances at various levels. The paper finds out that the final harmonization of the energy statistics system in accordance with the recommendations of international organizations will improve it amid Industry 4.0. The integration of accounting, statistical and tax reporting and the construction of satellite accounts taking into consideration negative environmental trends will promote completing the national methodology transformation. Since, in a market economy, the fuel and energy balance often reveals a mismatch between the social demand for energy and its supply, its social focus can be adjusted by government regulation. The present analytical potential of the FEB forecasts revealed by the authors contribute to improving the economy's energy efficiency and reducing the energy intensity of GDP, overcoming global crises, especially in the context of the Covid-19, further sustainable development of the economy and social sphere of Russia. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

GLOBAL PRODUCTION NETWORKS IN THE REGIONAL ANALYSIS FRAMEWORK: CASE OF THE EU PERIPHERAL AUTOMOTIVE MANUFACTURING

Recent transformations following the global financial crisis of 2009, COVID-19 pandemic, supply chains disruptions and newest shocks have radically reshaped global production landscape and challenged comparative benefits of global production networks (GPN) vs global value chains (GVC) paradigms in international production analysis. The study tests the hypothesis that GPN concept allows for a better identification of structural shifts in international production structures while revealing regional patterns of cooperation. In the first section, the main methodological constraints of GVC paradigm are specified. Additionally, the reasons for the application of network-based approach to international production are outlined. The second section dissects the EU automotive manufacturing to support the theoretical propositions. While comparing GVC and GPN quantitative toolkits, the possible trade-off has been reached which is to calculate network indicators (transitivity, centrality, etc.) on the inter-country input-output tables. As a result, the hypothesis was confirmed. Specifically, betweenness centrality metric suggests that Czechia and Slovakia have immediately favoured a positive effect of the entry into the EU, whereas neither of GVC indicators reveals such a shift. Simultaneously, 2008 crisis is depicted via GVC indicators, whilst network metrics suggest no structural changes in the production system. These results corroborate to our theoretical juxtaposition of GVC/GPN approaches. The methodological cohesion of two sets of indicators further advances the views on European regional core-periphery integration and automotive production networks dynamics. At the same time, the findings may contribute to the reassessment of regional integration developments in Europe, as well as in Latin America and Eurasia. © González G. H., Sapir E. V., Vasilchenko A. D. Text. 2023.

Economic Role of Government Budget Revision in the Presence of COVID-19

Fiscal policies are one of the most important instruments of government to guide the progress of the country's economic development. They find significant use in cases where the economy is experiencing a period of recession, such as the current one caused by COVID-19. This study aims to assess the multiplier effects that budget revision has on the economy for the case of Albania, and more specifically by referring to the initial and revised budget scenario for the year 2020 which is characterized by significant changes caused by the presence of COVID-19. Referring to the multipliers from the input–output tables (IOT) the total effect that the state budget brings to the economy for a certain year is derived. From this paper, it appears that the budget restructuring that takes place during the year does not take into account the multiplier effect in the economy, but is mostly done for specific purposes related to certain government functions. In this context, it is very important that various options during budget revision are evaluated, concluding with the option that has the highest returns for the economy.

Economic forecasting with an agent-based model

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national accounts, sector accounts, input–output tables, government statistics, and census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts (ESA 2010) and includes all economic sectors populated with millions of heterogeneous agents. In addition to being a competitive model framework for forecasts of aggregate variables, the detailed structure of the ABM allows for a breakdown into sector-level forecasts. Using this detailed structure, we demonstrate the ABM by forecasting the medium-run macroeconomic effects of lockdown measures taken in Austria to combat the COVID-19 pandemic. Potential applications of the model include stress-testing and predicting the effects of monetary or fiscal macroeconomic policies. © 2022 The Author(s)

Estimating the loss of economic predictability from aggregating firm-level production networks (preprint)

To estimate the reaction of economies to political interventions or external disturbances, input-output (IO) tables -- constructed by aggregating data into industrial sectors -- are extensively used. However, economic growth, robustness, and resilience crucially depend on the detailed structure of non-aggregated firm-level production networks (FPNs). Due to non-availability of data little is known about how much aggregated sector-based and detailed firm-level-based model-predictions differ. Using a nearly complete nationwide FPN, containing 243,399 Hungarian firms with 1,104,141 supplier-buyer-relations we self-consistently compare production losses on the aggregated industry-level production network (IPN) and the granular FPN. For this we model the propagation of shocks of the same size on both, the IPN and FPN, where the latter captures relevant heterogeneities within industries. In a COVID-19 inspired scenario we model the shock based on detailed firm-level data during the early pandemic. We find that using IPNs instead of FPNs leads to errors up to 37% in the estimation of economic losses, demonstrating a natural limitation of industry-level IO-models in predicting economic outcomes. We ascribe the large discrepancy to the significant heterogeneity of firms within industries: we find that firms within one sector only sell 23.5% to and buy 19.3% from the same industries on average, emphasizing the strong limitations of industrial sectors for representing the firms they include. Similar error-levels are expected when estimating economic growth, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data is key for reasonable predictions of dynamical economic systems.

Examining the Total Factor Productivity Changing Patterns in Kazakhstan: An Input-Output Analysis

The interconnectedness of sectors displays the demand for inputs and supply as a level of output in any economy. This paper addresses the Total Factor Productivity (TFP) in Kazakhstan sectors by using input-output tables during 2012-2017. The change in total sectoral production was separated into two parts: the changes in technical coefficients of intermediate inputs and the change in value-added inputs, respectively. The main findings have identified a changing pattern in sectoral performance. At the same time, the result justified that various sectors such as;petroleum, manufacturing, construction, and food processing sectors have shown increased productivity. The country highly depends on extractive industries but still has better manufacturing value-added performance. The study suggests that to combat challenges like COVID-19 and climate change, it is vital to develop human capital and diversity. With diversification and innovative measures, an economy can attain sustainable economic growth in the long term.

Assessing the importance of risky products in international trade and global value chains.

The COVID-19 pandemic has thrown the world's economy and trade into disarray, putting international reliance in the limelight. This sparked debate on the durability and resilience of global value chains. In this paper, we construct a 'product riskiness indicator' for 4700 globally traded products based on components such as market concentration, clustering tendencies, network centrality of players, or international substitutability to determine which products are vulnerable to trade shocks at the global level - referred to as 'risky' products. In a second step, bilateral risky product imports are matched to multi-country input-output tables, allowing for an examination of the importance of globally supplied risky products by country and industry. Due to the high percentage of dangerous products in high-tech product categories, higher-tech industries are more vulnerable to supply-chain vulnerabilities. Third, we analyse the GDP impact of reshoring using a "partial global extraction method." Assuming that risky product imports from non-EU27 nations are re-shored to EU27 countries, the EU27 GDP might rise by up to 0.5 percent. Non-EU27 countries suffer as a result of such reshoring activity. This implies that ensuring robust or at least resilient supply networks is also in the interest of the supplier countries and sectors.

[The experience of Japan: the application of the intersectoral balance methodology to control health care costs as a factor of mobilization readiness of health care to COVID-19 pandemic].

One of possible reasons for success of Japan in confronting the COVID-19 pandemic (low mortality rates, refusal of hard lock-downs and relatively low fall in economy) is seen in record high (3-4 times higher than in most other developed countries) provision of hospital beds. Its financing was supported during first 2 decades of the XXI century by the policy of relative to GDP advanced growth of public health public expenditures based on assessment of multiplier impact of these expenditures on demand, production and employment in other sectors of the economy using the intersectoral balance method based on "input-output" tables.Purpose of the study is to analyze Japan's economic policy in managing budgetary health care costs.The comprehensive statistical, comparative and retrospective analysis of available data was applied.The study results permit to suggest that high provision of the Japan population with hospital care resources and low mortality rates in 2022 prior to development of vaccines and effective treatment schemes for COVID-19 can be explained, among other things, by long-term policy of managing health care costs using assessment of their effect on production growth, demand and employment in other economy sectors using intersectoral balance method based on regular compilation of "input-output" tables.The data obtained permits to characterize as promising approach of the Japanese government to management of health care costs using assessment of their effect on production growth, demand and employment in other sectors of the economy using intersectoral balance method based on the regular compilation of "input-output" tables. This approach permitted to increase up to 1.5 times health care costs during 2005-2018 in situation of chronic stagnation of the national economy and thus to avoid world-wide trend towards reduction of hospital bed stock and after the start of pandemic severe shortage of hospital beds. The positive experience of Japan is confirmed by encouraging results of 2 pilot projects in the EU countries on applying the intersectoral balance method to assess the multiplier effect of health care costs in 2017-2018. It is considered that using the experience of Japan in managing budgetary health care expenditures through intersectoral balance method is challenging.

Addressing the COVID-19 Shock: The Potential Job Creation in China by the RCEP.

In 2020, coronavirus disease (COVID-19) left around 81% of the global workforce, nearly 2.7 billion workers, affected. Employment in China was the first to be hit by COVID-19. The Regional Comprehensive Economic Partnership (RCEP) is expected to bring dynamism to China's employment market in an era of long COVID-19. This study aims to examine the number of sectoral jobs that the RCEP will create in China, with the number of skilled or unskilled labour employed in each sector. The exogenous shocks to the RCEP can be reflected in the number of jobs created through multipliers based on a social accounting matrix compiled from China's input-output tables in 2017, combined with the employment satellite accounts compiled. The results show that the RCEP is expected to create over 17 million potential jobs in China, with unskilled labour accounting for 10.44 million and skilled labour for 6.77 million. It is even expected that there will be job losses in the metalworking machinery sector. The contribution of this paper can serve as a reference for policies to protect vulnerable sectors, further open up trade markets and strengthen cooperation among RCEP members as important measures to address the employment impact of long COVID-19.

Evaluating economic benefits of the revitalization vouchers: The case of Taiwan's Triple Stimulus Vouchers

This article takes Taiwan’s Triple Stimulus Vouchers (TSVs) launched in the second half of 2020 as an example to evaluate the economic benefits of revitalization vouchers during the COVID‐19 period. We apply scenario settings and input–output tables to evaluate the policy benefits. The main conclusions are as follows: (1) The issuance of TSVs will induce consumers to use revitalized triple vouchers. As the epidemic is under control domestically and border control continues that ruled out non‐citizen consumptions,  issuing the TSVs during summer vacations generates positive economic benefits. (2) Using promotional activities TSVs are indeed helpful to boost consumer confidence. (3) According to estimates, the benefit of TSVs to real GDP is between NT$45.062 and 83.727 billion;the economic growth rate increases by 0.1173–0.2156%. (4) TSVs have a significant impact on the service industry. The department stores benefited a lot from the TSV policy, and supermarkets and hypermarkets saw rising revenues. Merchants, small shops, and even hawkers in the streets or traditional markets also felt the strength of economic recovery from the retail consumption growth of TSVs, which also brings economic benefits expected by the policy.

Evaluating economic benefits of the revitalization

This article takes Taiwan's Triple Stimulus Vouchers (TSVs) launched in the second half of 2020 as an example to evaluate the economic benefits of revitalization vouchers during the COVID-19 period. We apply scenario settings and input-output tables to evaluate the policy benefits. The main conclusions are as follows: (1) The issuance of TSVs will induce consumers to use revitalized triple vouchers. As the epidemic is under control domestically and border control continues that ruled out non-citizen consumptions, issuing the TSVs during summer vacations generates positive economic benefits. (2) Using promotional activities TSVs are indeed helpful to boost consumer confidence. (3) According to estimates, the benefit of TSVs to real GDP is between NT$45.062 and 83.727 billion;the economic growth rate increases by 0.1173-0.2156%. (4) TSVs have a significant impact on the service industry. The department stores benefited a lot from the TSV policy, and supermarkets and hypermarkets saw rising revenues. Merchants, small shops, and even hawkers in the streets or traditional markets also felt the strength of economic recovery from the retail consumption growth of TSVs, which also brings economic benefits expected by the policy.

Economic forecasting with an agent-based model

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national accounts, sector accounts, input–output tables, government statistics, and census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts (ESA 2010) and includes all economic sectors populated with millions of heterogeneous agents. In addition to being a competitive model framework for forecasts of aggregate variables, the detailed structure of the ABM allows for a breakdown into sector-level forecasts. Using this detailed structure, we demonstrate the ABM by forecasting the medium-run macroeconomic effects of lockdown measures taken in Austria to combat the COVID-19 pandemic. Potential applications of the model include stress-testing and predicting the effects of monetary or fiscal macroeconomic policies.

The impact of the COVID-19 pandemic on China's economic structure: An input-output approach.

Recognizing the impact of COVID-19 on economic structure is an urgently required task for the post-pandemic era. However, studies have been hampered in undertaking this task by a lack of current data and the use of inappropriate methods. This paper fills the gap in the literature by applying a network analysis method using the newly released input-output tables of China and evaluating the structural impacts on the economy, including the changes in the sectoral closeness, betweenness, risk condition, and network backbone. The modelling results demonstrate that the pandemic has accelerated the structural transformation process of the Chinese economy: the traditional growth engines, such as the petroleum and finance industries, have lagged, whereas new growth engine sectors, including the digital services and scientific research industries, have expanded rapidly. Accordingly, we propose that the government formulate policies to stabilize old growth engine industries and foster new drivers to promote a sustainable economic recovery in China.

Assessing the "negative effect" and "positive effect" of COVID-19 in China.

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO2 emissions declines into firm climate action.

Epitope-Evaluator: An interactive web application to study predicted T-cell epitopes.

Multiple immunoinformatic tools have been developed to predict T-cell epitopes from protein amino acid sequences for different major histocompatibility complex (MHC) alleles. These prediction tools output hundreds of potential peptide candidates which require further processing; however, these tools are either not graphical or not friendly for non-programming users. We present Epitope-Evaluator, a web tool developed in the Shiny/R framework to interactively analyze predicted T-cell epitopes. Epitope-Evaluator contains six tools providing the distribution of epitopes across a selected set of MHC alleles, the promiscuity and conservation of epitopes, and their density and location within antigens. Epitope-Evaluator requires as input the fasta file of protein sequences and the output prediction file coming out from any predictor. By choosing different cutoffs and parameters, users can produce several interactive plots and tables that can be downloaded as JPG and text files, respectively. Using Epitope-Evaluator, we found the HLA-B*40, HLA-B*27:05 and HLA-B*07:02 recognized fewer epitopes from the SARS-CoV-2 proteome than other MHC Class I alleles. We also identified shared epitopes between Delta, Omicron, and Wuhan Spike variants as well as variant-specific epitopes. In summary, Epitope-Evaluator removes the programming barrier and provides intuitive tools, allowing a straightforward interpretation and graphical representations that facilitate the selection of candidate epitopes for experimental evaluation. The web server Epitope-Evaluator is available at https://fuxmanlab.shinyapps.io/Epitope-Evaluator/.

Controlled Hb and deferral prediction evaluation using a Docker container for every blood establishment

Background: The ability to accurately predict Hb deferral would be useful in avoiding adverse effects of blood donation deferrals: anaemia caused to donors, extra costs to blood services, demotivated donors. To evaluate the performance of various prediction models in an unbiased manner, it would be beneficial to be able to run the same prediction models in exactly same way in different blood services, using their respective data. Aims: Our aim is to develop a software package for Hb deferral prediction that is easy to install and use. To further control the comparison of different models and datasets, we also define the format and preprocessing of the input data. The source code is released with a permissive licence, which enables adaptation and extension by the user. (Table Presented) Methods: The Docker platform (www.docker.com), which is available for all major operating systems, allows creating self-contained software container images without any external software dependencies. Our software is provided as a Docker container, which means it can be used anywhere where Docker is installed. Our prediction methods are based on standard R packages, with each method placed in a separate R notebook (Rmd). The models are fitted, unseen data is predicted and results are visualized by rendering these Rmd files as html and pdf reports, which contain text, tables and figures. The user interface of the software, accessible through any web browser, is based on the standard html, css, javascript and websocket technologies. In addition to the html and pdf reports, the output also includes the raw prediction results as csv files to allow arbitrary postprocessing, for example, pooling the results from different countries. Results: An extensible and easy to use software package was developed that is available both as a source code from GitHub (https:// github.com/FRCBS/Hb-predictorćontainer) and as a ready to use Docker container image from DockerHub (https://hub.docker.com/r/ toivoja/hb-predictor), that can be run anywhere after installing Docker. The container has already been applied in practice in a comparison study between four countries: Finland, the Netherlands, Belgium and South-Africa. Since the software is transferrable, no sharing of private data was needed since each participant country ran the container on their own data using their own Linux or Windows machines. During this international collaboration, the container was extensively tested, streamlined and one additional prediction model was included, making the total number of models four. Summary/Conclusions: The use of the Docker container allows unbiased comparison of performance of several prediction models between blood services, since the implementation, input format and preprocessing are fixed. However, as the container cannot anticipate all scenarios where it will be used, some additional preprocessing, such as selecting an appropriate time-window, may be needed. By subsetting data before analysis, a user can test numerous scenarios, for example: is there a difference in my donation data before and during the COVID19 pandemic;does change in the donation process cause a change in the prediction outcome;are predictors of haemoglobin different on women younger than 30 years in comparison to older women? To enforce data protection, the container can be run in a closed environment without any internet connection, and we have ensured that the produced html and pdf reports contain only summary level data.

Heterogeneity of technological structures between EU countries: An application of complex systems methods to Input-Output Tables

The identification of the channels through which a given shock spreads to the rest of the economy, determining its final impact, is essential to formulate effective policy interventions. Input-output tables (IOTs) are widely used to detect the network of intersectoral relations of a country - i.e., its sectoral technological structure or domestic supply chains - and the role of different sectors in the propagation of a shock. However, the heterogeneity that characterize the technological structures of different countries is inevitably a source of complexity for the development of supranational and timely coordinated policies because it requires to analyse and interpret a large amount of information. This paper proposes a unique problem setting that aims to deal with this complexity by facilitating the analysis and visualization of similarities and differences among the technological structures of countries, relying on the identification of a small number of archetypes and showing how their interpretation could be exploited to support the definition of coordinated policy interventions. Specifically, non-negative matrix factorization is used to extract the archetypal matrices of the technological structures of the 28 European countries from IOTs, revealing dense intersectoral relationships and a low degree of heterogeneity between them. Then, random walk indicators are applied to study shock propagation within these archetypes, uncovering sectoral centralities. Finally, COVID-19 lockdown restrictions are analysed to exemplify the use of the proposed approach for coordinated policy action.

Social distancing and supply disruptions in a pandemic.

We integrate an epidemiological model, augmented with contact and mobility analyses, with a two-sector macroeconomic model, to assess the economic costs of labor supply disruptions in a pandemic. The model is designed to capture key characteristics of the U.S. input-output tables with a core sector that produces intermediate inputs not easily replaceable by the other sectors, possibly subject to minimum-scale requirements. Using epidemiological and mobility data to inform our exercises, we show that the reduction in labor services due to the observed social distancing (spontaneous and mandatory) could explain up to 6-8 percentage points of the roughly 12% U.S. GDP contraction in the second quarter of 2020. We show that public measures designed to protect workers in core industries and occupations with tasks that cannot be performed from home, can flatten the epidemiological curve at reduced economic costs-and contain vulnerabilities to supply disruptions, namely a new surge of infections. Using state-level data for the United States, we provide econometric evidence that spontaneous social distancing was no less costly than mandated social distancing.