The widespread adoption of robots has transformed firm production and introduced new challenges for domestic service inputs. We measure the domestic service value-added ratio as the share of value added in firms' exports generated by domestic service activities and examine how robots affect this ratio, thereby assessing how technological advancements influence Chinese firms' use of domestic service inputs. The findings indicate that robots significantly reduce the value-added ratio in domestic services, thereby diminishing firms' use of domestic service inputs. Robustness and endogeneity tests confirm that the finding still holds. The negative effect of robots on the domestic service value-added ratio primarily stems from employment substitution, capital dependence, and a squeeze on the production chain. Furthermore, the diffusion effect of industry differs significantly between ordinary and processing trade firms. It broadens the understanding of the relationship between robots and the growth of domestic service sectors, providing guidance for business managers.
This study assesses the short-term impacts of nuclear wastewater discharge on Japan's fishing and fishery product exports and on output changes across countries and sectors using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a Mixed Input-Output (IO) model. A SARIMA model is used to predict export values for Japan's fishing and fishery products without discharge, and the predictions are compared with the observed total export value to derive export changes. These changes are used to analyze shifts in output and final demand within Japan and other countries through a Mixed IO model. The results indicate that nuclear wastewater discharge decreases Japan's fishing and fishery product exports, reducing the output and final demand of Japan's fishing and aqua-culture industry. Furthermore, it negatively affects industrial output in all 75 countries (and the Rest of the World). These findings provide essential insights for policy responses to environmental shocks and for sustainable development.
This paper examines how China's dual circulation strategy affects employment, accounting for heterogeneity in firm ownership. We first use a value-added decomposition framework to quantify the evolution of dual circulation and its associated employment outcomes at the aggregate, industry, and ownership levels. We then employ structural decomposition analysis (SDA) to identify the key drivers of changes in employment. We find that both domestic and complex external circulations - particularly the former - have jointly supported employment growth at multiple levels. Notably, high-tech manufacturing has emerged as a new engine of job creation. Circulation among domestic-owned firms contributes the most to employment, whereas circulation between domestic-owned and foreign-owned firms contributes the least, although it has been rising. SDA results further indicate that the positive effects of intermediate supply and final demand outweigh the negative effects of labor productivity and the value-added rate, yielding a net positive employment effect for dual circulation.
This paper examines how reductions in transportation costs reshape regional economic and environmental outcomes in Morocco. We simulate a reduction in delivered (purchasers') costs via a transport-margin efficiency improvement - implemented as a margin-saving technical change in the transport sector - within a province-level Spatial Computable General Equilibrium (SCGE) framework. Simulating a 1% decline in transport costs, we assess the marginal impacts across regions. Results reveal uneven gains: coastal areas along the Casablanca-Tangier axis and emerging hubs like B & eacute;ni Mellal-Kh & eacute;nifra and Dakhla-Oued Ed Dahab benefit from growth and lower emissions, while traditional centers such as F & egrave;s-Mekn & egrave;s and Marrakech-Safi experience stagnation and rising carbon intensity. The findings highlight a partial decoupling of economic and environmental convergence, driven by both production structure and scale effects. Our analysis stresses the importance of territorially differentiated infrastructure policies that balance spatial equity with sustainability, offering insights for place-based development and green transformation strategies.
Input-output tables provide a useful tool for analyzing economic and environmental impacts. The main purpose of this paper is to evaluate the accuracy of different regionalization methods in estimating input-output tables based on location quotients. Specifically, the paper aims to identify the most accurate methods and to propose a practical procedure to improve their estimation. In doing so, this study compares the accuracy of various methods, using the 2015 Korean multi-regional input-output table as a benchmark. In the analyzed location quotient methods, smoothing adjustments to the estimated quotients depend on the choice of smoothing parameter values. An additional contribution of this study is the proposal of a simple and efficient procedure for estimating these parameters based on commonly available information on road freight transport and goods imports from the rest of the world. The results show that this procedure improves the accuracy of these estimation methods.
The return of inflation in Western economies has reignited the debate over its causes, bringing sector-specific shocks and supply chain bottlenecks to the forefront. Building on inflation studies using the Leontief price model, we develop a method to measure EU member states' vulnerability to sectoral price shocks. Using international input-output data, we identify which sectors have the greatest impact on overall inflation and analyze how price shocks spread across borders. Our findings reveal two critical asymmetries. First, peripheral countries are more exposed to shocks originating in the EU core than vice versa. Second, all EU member states are considerably exposed to price shocks originating from non-EU countries, namely Russia and China. These strategic dependencies pose challenges for price stability and require targeted industrial policy interventions going beyond conventional monetary policy.
Barriers to free trade slow economic growth and reduce the efficient use of resources. Although economic theory shows that trade benefits countries overall, some people mistakenly see it as a zero-sum game in which one country's gain is another's loss. Tariffs are a key obstacle because they raise the price of imported goods to protect domestic industries. This paper uses recent OECD data to examine how higher tariffs affect the entire U.S. economy, both with and without retaliation from trading partners. The analysis uses a nonlinear input-output model to capture how consumers and firms substitute between domestic and imported goods when prices change. It considers both intermediate goods used in production and final goods consumed by households. The model allows for vertical and horizontal substitution across product varieties and distinguishes five trading partners, each supplying different varieties and facing different tariff rates.
This study employs the Average Propagation Length (APL) model to examine carbon emission propagation in China's electricity and heat sector, using a detailed 2020 input-output dataset. By integrating input-output analysis and carbon accounting methods, the research identifies critical emission pathways and quantifies the direct and indirect emissions triggered by final demand shocks. Results highlight structural imbalances, with carbon-intensive upstream sectors significantly influencing downstream emissions through extensive production chains. Notably, the electricity and heat production subsector contributes disproportionately to total industrial emissions, necessitating differentiated carbon mitigation strategies. This research recommends centralized regulation for emission-intensive industries and decentralized, market-driven mechanisms for sectors with dispersed emissions. The findings offer crucial insights into refining carbon trading policies and optimizing industrial energy efficiency, directly supporting China's dual-carbon objectives and providing a robust methodological foundation for future environmental-economic studies.
Informal economic activities account for a large share of employment in the Philippines, yet their contribution to the production system is not fully captured in standard economic measures. This study uses an economy-wide input-output model, disaggregated into formal and informal activities. It estimates how changes in demand affect total output, household income, and production linkages across formal and informal activities. Results show that informality is concentrated in agriculture and key service sectors, while industry sector remains predominantly formal. Despite smaller transaction volumes, informal activities are more strongly connected to domestic supply chains than their formal counterparts, both upstream as input buyers and downstream as suppliers. Informal activities also generate larger household income gains when local demand expands. These findings suggest that policies addressing informality should promote improvements in productivity and fairer market integration of informal producers into domestic production networks, as regulatory constraints may dampen multipliers and income gains.
Environmentally extended multi-regional input-output (EE-MRIO) models playa crucial role in sustainability analysis and policy making, yet the treatment of uncertainty in MRIO modelling remains under-explored. So far, uncertainty analysis has primarily been applied to carbon footprints and methods such as aggregation-based uncertainty analyses and Monte Carlo simulations have been used. In this work, we apply linear error propagation to trace uncertainty from environmental and social extensions to final footprint estimates, with validation through Monte Carlo simulations. We also conduct a sensitivity analysis to identify which footprints are more influenced by extension uncertainty. Our findings suggest that footprints linked to extensions evenly distributed across economic sectors (ex: GHG emissions, nitrogen, and employment) exhibit lower relative standard deviations compared to those tied to sector-specific extensions, such as land use, phosphorous, and water consumption. Sector-specific footprints, in general, demonstrate greater sensitivity to stressor uncertainty. The research further introduces an Outsource Coefficient, revealing that regions with higher outsourcing levels in their consumption-based accounts are less impacted by uncertainty in the underlying extension data. Finally, we provide uncertainty estimates for all EXIOBASE footprints using a 2019 product-by-product EXIOBASE 3 model and assume a uniform relative standard deviation of 0.1 across all extensions.
Recent years have seen an increased interest in consumption-based emissions. To calculate these, global multi-regional input-output (MRIO) data with environmental extensions are needed. However, various MRIO databases exist, each with unique strengths and limitations. This research investigates differences in emission estimates from global MRIO databases: EXIOBASE, FIGARO, GLORIA, and ICIO. We ask how the imported component of a country's environmental footprint differs between these databases, to understand the implications of choosing one of these datasets over another. We find high levels of similarity across the databases for most countries, including for the 10 largest economies. However, differences are stronger for some countries with higher proportions of imported emissions. Here, choice of MRIO data can have a significant impact on policy decisions. Finally, industry-level analyses indicate that priority industries for climate policy are consistent across the datasets, even where absolute emission estimates may differ.
Long-term economic growth arises from the interaction of technological change and structural transformation, evolving consumption patterns, and productivity gains. Historical evidence shows these processes do not affect women and men symmetrically; rather, they often reinforce pre-existing inequalities rooted in unequal positions within economic, social, and institutional systems. This Special Issue addresses a persistent institutional and analytical gap by applying a multi-sectoral modelling perspective to the study of gender differentials. By integrating gender-disaggregated information into input-output tables and models, Social Accounting Matrices, Computable General Equilibrium models and Global Value Chain frameworks, the six papers included reveal gendered impacts of international trade, the macroeconomic effects of wage equality, uneven policy effects and differentiated vulnerabilities to shocks such as COVID-19 and oil price changes. The studies demonstrate the value of gender-aware input-output and multisectoral analysis for understanding how economic growth, globalisation, and structural change affect men and women differently, and for informing the design of more inclusive and effective economic policies.
Multisectoral economic modelling provided insights to policymakers during the COVID-19 pandemic, including the economy-wide impacts of changes in tourism and the design of policy responses. These models embed assumptions about how firms and households respond to adverse shocks, which are linked to the level of economic resilience they exhibit. This paper illustrates how Input-Output and Computable General Equilibrium models, incorporating different behavioural assumptions, can yield different estimates of static resilience, the economy's ability to maintain function when shocked. Using the example of Scotland in 2020 and simulating an Accommodation demand shock, we show that model specification can rule out responses that directly influence the degree of estimated resilience in an economy. By comparing simulation results with observed changes in value added of the Accommodation sector, we demonstrate that appropriately accounting for resilience mechanisms helps close the gap between simulated and observed data.
We adapt the dynamic disequilibrium input-output model of Pichler et al. (2022). [Journal of Economic Dynamics and Control, 144, 104527.] to the Belgian economy and conduct a cross-context validation of the COVID-19 pandemic. Labor supply and export demand shocks are refined using business surveys and observed trade flows, while household demand shocks are calibrated to 115 time series on GDP, revenue, employment, and interindustry transactions. The refined shocks improve the model's ability to reproduce the observed evolution of GDP, revenue, and employment. However, the model systematically underestimates the persistence of interindustry trade, suggesting structural limitations in its ability to represent firms' incentives to sustain trade. Relaxing the Leontief production function based on input criticality improves the model's accuracy, consistent with the original model, though differentiation across degrees of relaxation proved unidentifiable despite a larger dataset. Overall, our results confirm the original model's validity for assessing epidemic-driven economic impacts, thereby strengthening its credibility as a policy tool.
International trade statistics rarely align with the corresponding trade values in national accounts due to trade asymmetries, the national accounts' ownership principle (compared to the cross-border principle applied in trade statistics), international transport and insurance costs, and the possible misclassification of products, among other factors. This paper presents a novel method to resolve discrepancies related to the potential product misclassification. Unlike automatic balancing processes, this approach is designed to be transparent, allowing users to understand how national data can be adjusted to fit within globally balanced trade datasets and inter-country input-output tables. It also aims to improve consistency across various international initiatives such as those of Eurostat (FIGARO), OECD, UN-ECLAC and ADB, among others. The proposed method enables users to develop balanced international trade datasets, thereby substantially reducing discrepancies in national accounts' trade values arising from product misclassification.
Concordance matrices play a crucial role in input-output analysis, for translating between databases expressed in different sector classifications, or translating many misaligned databases into a common format. These matrices are critical tools in enabling the utilisation of all possible primary data sources for compiling input-output tables, even if those data sources are completely misaligned. Until this date, concordance matrices are constructed manually by interpreting pairwise sector labels, resulting in an often labour-intensive process. In this work, we use artificial intelligence (AI) approaches for the first time to estimate concordance matrices for input-output analysis, offering to significantly reduce the time and labour involved in primary data processing. We show that, when applying deep learning techniques to textual sector labels, AI algorithms are able to grasp intricate linguistic relationships and capture semantic nuances, thus bridging the gap between human language and numerical binary relationships. We use a range of performance evaluation measures and demonstrate the ability to predict a wide range of concordance matrices with up to 85% accuracy.
What have been the different channels that contributed to the dynamics of global labor productivity in the 'hyper-globalization' (1995-2009) and 'slowbalization' (2009-2018) periods? To answer this question, this paper identifies four channels contributing to global saving of labor requirements by means of an input-output-based decomposition: within each global value chain (GVC), we distinguish whether a reduction in labor requirements is due to (i) direct labor saving trends or (ii) a geographical/sectoral reorganization of the GVC; (iii) across countries within a global sector, we identify the contribution of changes in countries' final output market shares; and (iv) across global sectors, we identify the contribution of changes in the product composition of global final output. Our results suggest that technological change within GVCs was the main channel for global labor productivity growth, whereas the reallocation of final output between countries exerted a negative effect on productivity.
We use a spatial dynamic computable general equilibrium model incorporating a macroeconomic measure of educational mismatch and endogenous labour participation for three different educational groups. Our objective is to assess the impact of the European Social Fund's investments in labour productivity on both regional macroeconomic educational mismatch and employment. The analysed labour market interventions generate positive long-run effects on GDP and employment in all regions, with interesting implications for educational mismatch. Raising the productivity of the low educated workers reduces mismatch, while targeting the medium educated leads to relatively smaller reductions. Interventions targeting the highly educated result in an increase in mismatch, although they generate larger increases in GDP, implying the existence of a trade-off. Its intensity depends on the initial regional economic conditions as well as on the size of the policy interventions.
