Where has all the dynamism gone? Productivity growth in China’s manufacturing sector, 1998–2013

Predictive modeling of anxiety and depression DALYs in Indonesia before and after the COVID-19 pandemic: Insights from the Global Burden of Disease 2021 study.

The article presents structural analysis of interregional disparities in labor productivity levels and growth rates across Russia. The study aims to uncover structural foundations of spatial differentiation in labor productivity by comparing its static (levels) and dynamic (growth rates) aspects. It examines the roles of industrial specialization, resource allocation efficiency, and intra-industry disparities, and assesses the contribution of these factors to interregional income inequality. The analysis utilizes data on the subject of the Russian Federation for 2010—2023, broken down by economic activity (excluding extractive indust­ ries) and adjusted for regional price differentials. The shift-share analysis is employed to decompose the deviation of regional indicators from the national average into sectoral, localization, and regional components. The results reveal a persistent trend toward convergence in labor productivity levels but a growing divergence in growth rates. The factor of sectoral structure diversity contributed in the least to these processes, often promoting regional convergence. The second most influential factor was the concentration of production in regions, reflecting agglomeration effects: it fostered divergence in productivity levels but countered it in growth rates. The most significant influence came from regional differences in within-industry labor productivity. Combinations of shift-share decomposition components define distinct models of labor productivity level and growth formation in the regions. Simulation-based calculations assess the direction and magnitude of structural components’ impact on labor productivity growth in shaping interregional inequality in per capita GRP.

Lithium-oxygen batteries (LOBs) offer combustion fuel-like energy densities but remain constrained by low efficiency, limited cycle life, and coupled degradation pathways linking the electrochemical growth and decay of the reaction product (Li2O2) and associated generation of reactive oxygen species, electrolyte and electrode instabilities, and lithium dendrite growth. Here, we introduce a hybrid materials-informatics framework that integrates structured query learning with retrieval-augmented generation (RAG) to systematically analyze the full-text corpus of 3134 peer-reviewed articles in LOBs. Unlike conventional artificial intelligence (AI) tools, which learn from unstructured literature and risk factual drift, the present approach forms a relational performance-validated database, enabling evidence-traceable comparison of cathode architectures, catalyst types, electrolytes, redox mediators, and lithium protection strategies. The analysis reveals composition-dependent performance hierarchies and exposes interdependencies among Li2O2 morphology, singlet-oxygen formation, overpotentials, and solid electrolyte interface disruption, as reported under their documented experimental conditions. Using this method, we identified the catalyst-electrolyte-anode configurations capable of reducing charge polarization by 0.3-0.6 V and extending cycling stability to 100-200 cycles under standard cycling conditions reported in the source studies. This data-driven roadmap establishes a quantitative foundation for translating LOBs from laboratory demonstrations to deployable high-energy systems and demonstrates how materials informatics can accelerate electrochemical materials synthesis and device design.

Regulatory burdens, managerial practices, and gender inclusion: determinants of firm performance

Abstract This paper argues that the current UK fiscal framework fails to support growth-enhancing public investment while inadequately restraining debt accumulation. Frequent changes to fiscal rules, their short horizon and incentives that prioritise current spending over long-term investment have undermined economic stability and productivity growth. We propose a reformed framework centred on clear fiscal objectives, enhanced OBR analysis of long-run sustainability and a target for the primary surplus consistent with maintaining stable debt. A supplementary investment rule would ensure adequate public capital formation. Together, these reforms aim to raise productivity, support resilience and improve living standards.

This study investigates how farmers' socioeconomic characteristics and sustainability conditions shape agricultural credit allocation, productivity, and credit utilization across farm-size groups in Pakistan. Using survey data from 1,150 farmers and three complementary econometric approaches multiple linear regression (MLR), structural equation modeling (SEM), and propensity score matching (PSM) the analysis reveals that age, education, farming experience, farm size, and income all exert positive and statistically significant effects on the amount of credit received, with farm size emerging as the strongest determinant of loan volume. Sustainable agriculture indicators, particularly the Crop Production Index and irrigated land share, are positively associated with both credit and productivity, indicating that more productive and sustainability-oriented farmers are more likely to secure higher credit and translate it into yield gains. The structural model shows that farm size and income dominate the latent socioeconomic status construct that drives credit access, while the Crop Production Index simultaneously enhances credit allocation and farm productivity, underscoring the central role of sustainability in the credit productivity linkage. Propensity score matching further demonstrates that borrowers attain substantially higher annual farm income than comparable non-borrowers, confirming a robust income-enhancing effect of agricultural credit after controlling for observable selection. A key novel contribution of the study, reflected in the detailed credit-use patterns by farm size, is that smallholders allocate a larger share of credit to core farm inputs, whereas medium and large farmers divert a non-trivial portion of loans toward non-agricultural expenditures, revealing heterogeneous and sometimes inefficient credit utilization. These results support differentiated, farm-size–sensitive credit policies that prioritize concessional, input-tied credit and simplified procedures for smallholders, link lending conditions to sustainability performance, and strengthen monitoring of off-farm diversion among larger farms, thereby aligning agricultural credit policy with inclusive productivity growth and sustainable, SDG-consistent agricultural transformation in Pakistan.

ABSTRACT This paper examines how financial technology (Fintech) development affects the total factor productivity (TFP) of energy companies in China using panel data from 2010 to 2022. The results show that Fintech significantly improves corporate TFP. Mechanism analyses indicate that Fintech enhances productivity by easing financing constraints, increasing policy support such as financial subsidies and tax incentives, and promoting R&D‐oriented innovation investment. The effect is stronger for mature firms and non–state‐owned enterprises, reflecting clear heterogeneity across firms. In addition, regional marketization and attention to the digital economy serve as important external moderators that reinforce the influence of Fintech on TFP. These findings deepen the understanding of digital financial innovation in the energy industry and provide new evidence on how Fintech contributes to high‐quality and low‐carbon development.

An integrated intelligent model for simulating and optimizing regional water resource sustainability under multiple pressures.

Purpose: The Cameroonian economy has experienced multiple crises since the 1970s, resulting in slow labour productivity growth and persistent income and opportunity disparities. This study investigates productivity movements across the three formal sectors; agriculture, industry, and service, as well as the aggregate economy, between 1970 and 2023. Specifically, it examines the presence of labour productivity convergence or divergence, identifies their sources and evaluates how productivity growth is influenced by key determinants, with the goal of informing policies to reduce poverty and improve living standards particularly for low-income populations. Methodology: A parametric approach employing ordinary least squares techniques are used to test for the presence of labour productivity convergence or divergence, using the Beta and Sigma-convergence tests. A quantile regression approach is conducted to properly reveal the labour productivity dynamics within the various sectors. A Labour productivity decomposition technique is conducted to identify sources of convergences or divergence in productivity in the economy. Findings: The study finds evidence of sigma-convergence between sectors and the aggregate economy, driven mainly by industry and service. No significant Beta-convergence is observed within sectors or at the aggregate level. Industry and services contribute most to labour productivity growth, with an annual convergence speed of 12.2%, reflecting the combined effects of worker reallocation and productivity gains. Estimated times to halve productivity gaps are 8 years for industry, 20 years for services and 42 years for agriculture, highlighting persistent structural imbalances. Unique Contribution to Theory, Policy and Practice: The study emphasizes the need for balanced sectoral development through coordinated policies, improved institutional quality, and substantial investment in human capital. Targeted interventions across agriculture, industry and services are essential to accelerate structural transformation, reduce income disparities, and achieve sustainable economic growth in Cameroon. Keywords: Sectoral Labour Productivity, Economy Growth And Development, Technology, Employment

This study assesses the impact of the Make in India initiative on the performance of India’s Food and Beverage Industry (FBI) using annual time-series data from 2000–01 to 2023–24. To capture both level changes and growth dynamics across the pre- and post-policy periods, this study combines level comparison, compound annual growth rate (CAGR) analysis, and econometric regression techniques to assess changes in output, investment, employment, productivity, and profitability across pre- and post-policy periods. Unit root tests are performed to assess the time-series properties of the variables, and regression analysis is subsequently conducted using stationary series to ensure econometric robustness. The level analysis shows marked improvement in all key indicators after the policy intervention, with significant increases in output, capital stock, labour productivity, and profits, alongside moderate employment growth, indicating rising efficiency and capital intensity. The CAGR analysis reveals mixed growth dynamics: while the growth of factory creation and productivity moderated, investment and employment growth accelerated in the post-policy period. Regression results based on first-differenced data confirm a positive and statistically significant association between the ‘Make in India’ initiative and key industry performance indicators. Overall, the findings suggest that Make in India played a significant role in strengthening the FBI through investment-led and efficiency-enhancing growth.

This paper tests the Fast State governance model, which posits that accelerating financial flows and automating administrative procedures improve macroeconomic stability, institutional quality, and human capital utilization. Using 2010–2025 panel and cross-country data — World Bank World Development Indicators (WDI) and entrepreneurship database, International Monetary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), United Nations E-Government Development Index (UN EGDI), World Bank Worldwide Governance Indicators (WGI) — the study estimates regression models with income controls and documented outlier handling. Results show that higher money velocity associates with lower gross domestic product (GDP) growth volatility, e-government development predicts stronger regulatory quality, and shorter, cheaper start-up procedures relate to higher new business density. These findings cohere with evidence that digital governance curbs corruption and raises effectiveness, and that business registration reforms significantly increase firm entry. The contribution is an integrated, empirically grounded framework linking “velocity” in state and market processes to resilience and entrepreneurship across diverse economies. Policy relevance is immediate: predictable acceleration and process automation can stabilize demand transmission, strengthen governance, and unlock entrepreneurial activity. The paper concludes that adopting the Fast State approach yields measurable gains in stability, trust, and human entrepreneurial capital, with implications for post-shock recovery and long-run development.

Agricultural technology extension reform and productivity growth: Evidence from rural China

In the context of China’s pursuit of high-quality economic development, enhancing agricultural productivity is crucial for ensuring food security and promoting common prosperity. This paper constructs a systematic IV-LP-ACF-SAR econometric framework to analyze agricultural Total Factor Productivity (TFP) growth using panel data from 31 Chinese provinces spanning 2014 to 2023 (n = 341 observations). The framework employs the instrumental variable (IV)-based Levinsohn–Petrin (LP) proxy variable method under the Ackerberg–Caves–Frazer (ACF) system to estimate a Translog production function while addressing endogeneity using multiple spatial weight matrices. TFP growth is decomposed into technical change (TC), technical efficiency (EC), and scale efficiency (SC). A Spatial Autoregressive (SAR) model with Dynamic Common Correlated Effects (DCCE) explores spatial spillover effects and regional heterogeneity. Results show that China’s agricultural TFP remained largely stagnant from 2014 to 2023 with an average annual growth rate of −0.18%, where technical efficiency decline (−0.33% annually) was the main constraint. Technical change remained neutral, while scale efficiency contributed positively (+0.15% annually). Mechanization showed the highest output elasticity (0.99), while fertilizers, pesticides, and labor exhibited negative marginal returns. Spatial analysis revealed significant negative scale efficiency spillovers with regional patterns of “scale synergy in the Northeast/Northwest” and “efficiency synergy in East/North China.” These findings suggest that productivity policy should shift toward a dual-driver model combining efficiency enhancement and optimal scaling, with differentiated regional policies and inter-provincial coordination mechanisms necessary to mitigate negative spillovers and enhance sustainable agricultural growth quality.

Taking the rail transit transfer stations in Qingyang, Wuhou, and Chenghua Districts of Chengdu as the research objects, this study constructs a static-dynamic coupled analytical framework by integrating the input-oriented three-stage super-efficiency SBM model and the output-oriented Malmquist-Luenberger (ML) index to systematically evaluate rail transit transfer efficiency. The findings reveal that the transfer efficiency of Chengdu Metro exhibited a fluctuating growth pattern from 2017 to 2023, with significant variations corresponding to periods of network expansion and operational adjustments. Improvements in technical efficiency and management optimization have been key drivers of overall efficiency gains. The three-stage super-efficiency SBM model effectively filters out the impacts of environmental variables and random noise, uncovering inter-station efficiency disparities and resource redundancy issues. Decomposition of the ML index indicates that both technical efficiency and technological progress jointly drive total factor productivity (TFP) changes. On average, technical efficiency has been the more stable and prominent contributor to productivity growth. However, the reasons for TFP declines at certain stations are varied; some under-performed due to lagging technological progress, while others faced constraints in technical or scale efficiencies. The study confirms that the synergistic application of the three-stage model and the ML index can accurately identify bottlenecks and provide theoretical support and practical pathways for optimizing resource allocation and dynamic management in urban rail transit systems. Findings and methods from Chengdu’s practice provide a replicable paradigm for evaluating, planning and optimizing rail transit transfer hubs in Chinese cities at different development stages, and offer empirical references for advancing urban public transport and sustainable development of comprehensive transportation systems.

ABSTRACT This study investigates the causal relationships between international trade, intraregional trade, and gross domestic product (GDP) growth across Central Asian countries. It aims to determine the direction, significance, and heterogeneity of these interactions, offering insights into how trade performance influences economic development in the region. Empirical findings reveal that international trade has a positive and statistically significant impact on GDP in all Central Asian countries. However, the reverse relationship exhibits a negative and statistically significant effect in Uzbekistan, whereas Tajikistan displays mixed but statistically significant effects. This suggests that, in these countries, economic growth does not necessarily lead to proportional increases in international trade. With regard to intraregional trade, the results are mixed. Intraregional trade positively and significantly contributes to GDP in Kyrgyzstan and Uzbekistan, implying that deeper regional economic integration in these countries enhances domestic economic activity. In contrast, Turkmenistan exhibits negative and statistically significant effects in lags two to three, while the effect turns positive and remains statistically significant at lag four. The impact of GDP on intraregional trade is negative and statistically significant in Kyrgyzstan, Turkmenistan, and Uzbekistan, with mixed but statistically significant effects observed in Tajikistan. This indicates that economic growth in these countries does not necessarily stimulate increased trade within the region. These findings emphasize the need for Central Asian economies to adopt more outward-looking and regionally coordinated trade strategies. Enhancing trade efficiency, diversifying trade partners, and investing in regional trade infrastructure could help convert domestic economic gains into expanded trade flows.

Purpose This study examines the influence of exchange rate movements, measured by both the level and first difference of the exchange rate, on sectoral investment decisions in Tanzania, using sectoral credit allocation as a proxy for investment across five key economic sectors: manufacturing, agriculture, tourism, construction, and transport and communication. Design/methodology/approach The study employs the Autoregressive Distributed Lag (ARDL) model using quarterly time-series data to estimate both short-run and long-run effects of exchange rate movements on sectoral credit, while controlling for foreign direct investment, gross domestic product growth, and lending interest rates. The ARDL framework allows for mixed orders of integration and captures dynamic adjustment processes across sectors. Findings The results indicate that exchange rate depreciation has a significant and positive long-run effect on credit allocation across all sectors, suggesting that a weaker Tanzanian shilling may stimulate investment by enhancing export competitiveness and encouraging lending. In the short run, however, exchange rate movements exert negative effects in some sectors, particularly transport and communication, highlighting the disruptive impact of sudden currency fluctuations on sectoral financing. Originality/value The study provides novel sector-level evidence on the heterogeneous short-run and long-run investment responses to exchange rate movements in Tanzania, an under-researched context. By combining ARDL dynamics with disaggregated sectoral credit data, the paper offers policy-relevant insights for designing sector-specific monetary and exchange rate interventions to support investment in volatile macroeconomic environments.

This study examines the impact of industrial policy reforms on productivity and employment generation across major manufacturing sectors in India using a panel data approach. Drawing on sector-level data from the Annual Survey of Industries (ASI) for the period 2000–01 to 2022–23, the analysis investigates how deregulation, trade liberalization, and investment policies have influenced industrial performance. Fixed effects and random effects models are employed to capture sector-specific heterogeneity. The results indicate that industrial reforms have significantly improved labor productivity across sectors, while employment generation has remained uneven and concentrated in labor-intensive industries. The findings highlight the need for sector-specific industrial policies that balance productivity growth with employment creation.

Introduction From the perspective of sustainable agricultural development, the adoption of artificial intelligence (AI) not only improves factor allocation efficiency but also constitutes a critical economic foundation for efficiency-driven sustainable growth in agriculture by optimizing resource utilization and strengthening risk-management capacity. Methods Using panel data on agricultural firms listed on the Shanghai and Shenzhen A-share markets from 2007 to 2023, this study applies a multidimensional fixed-effects model to estimate the impact of AI on firms’ total factor productivity (TFP). Results The empirical results demonstrate that AI significantly enhances TFP. However, mechanism analysis reveals a structural divergence in transmission pathways: while AI fosters productivity growth mainly by optimizing labor structures and facilitating inter-firm resource sharing, it has yet to significantly promote university-industry collaborative R&D capabilities. Heterogeneity analysis further indicates that these productivity gains are more pronounced among firms in their growth stage and in regions facing higher natural risks. Discussion Overall, the expanding use of AI is reshaping agricultural production systems and has emerged as a key driver of high-quality development in the sector. Within an efficiency-driven sustainability framework, continued advances in AI are expected to play a pivotal role in achieving a dynamic alignment among the objectives of efficiency, environmental performance, and long-term sustainability in agriculture.

This article examines the role of financial support and investment in achieving sustainable economic development in Kazakhstan. The purpose of the article is to identify structural imbalances and factors hindering productivity growth based on a comprehensive analysis of investment processes and financial infrastructure trends in Kazakhstan from 2000 to 2023, and to substantiate strategic investment priorities in agriculture, innovation, and "green" finance to ensure the country's sustainable economic development. Using statistical data and policy documents, a descriptive and comparative analysis of trends over the period 2000–2023 was conducted, revealing a structural imbalance in investment toward the extractive sector. Factor analysis also allowed for the classification of internal and external determinants of productivity. The study identifies key internal and external factors influencing productivity, including financial infrastructure, technological modernization, human capital, and governance quality. Particular attention is given to inefficiencies in agricultural fixed capital investments and the limited participation of commercial banks in long-term lending. The findings reveal that insufficient coordination of investment policy, depreciation of fixed assets, and weak institutional support constrain productivity growth, thereby slowing sustainable development. The paper argues that targeted investment in agriculture, innovation, and green finance, combined with stronger institutional frameworks, can mitigate external shocks, increase productivity, and support balanced economic growth in Kazakhstan.