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.

Potatoes, possessing the characteristics of being suitable for food crop, vegetable, and fodder use, have become an important supplementary product for ensuring food security and vegetable supply. Their price fluctuations play a significant role in regulating production and consumption. Against the backdrop of establishing a unified national market, studying potato price fluctuations from a spatial perspective is crucial for scientifically and systematically understanding the patterns of China’s potato market. This study employs Ensemble Empirical Mode Decomposition, Spatial autocorrelation and Vector Autoregression models to analyse spatial variations and inter-regional transmission mechanisms in China’s potato price fluctuations, utilising wholesale market price data from January 2014 to December 2024 across diverse regions. Findings indicate distinct spatial patterns in potato price dynamics with significant inter-regional interactions. The Northern Crop Region exhibits predominantly short-term, high-frequency fluctuations, whereas the Central Crop Region, Southern Crop Region, and Southwestern Crop Region are characterized by long-term, low-frequency fluctuations. Potato prices in China exhibit significant spatial heterogeneity, and potato price fluctuations at both national and regional levels are primarily influenced negatively by those in other regions. The degree of interactive influence between potato prices across regions exhibits considerable variation, with the Central China crop region holding a certain degree of dominance in the national market. Based on these findings, policy recommendations are proposed, including strengthening tiered and regional monitoring and analysis of potato prices, standardizing inter-regional transmission pathways for potato prices, and guiding the formation of a complementary regional structure for potato production.

Designs dosing units by baseline composition and measuring the change in another summary of it carry a mean-reversion term. Under a persistence restriction, moments the regression produces bound that term, which we propose reporting relative to the coefficient. In Autor, Dorn and Hanson's China-shock designs the bound exceeds the coefficient without a share control and is small with their manufacturing-share control. Controlling both that share and the timing-consistent share keeps it small and barely moves corrected estimates. Instrumented-side intervals show no evidence that mean reversion drives them, but cannot exclude it. The bound prices a single channel, not total bias.

Authors’ note (October 2026). This version adds a one-page authors’ note before the original text; the original text is unchanged and is kept for the record. Agrifood structural transformation underpins progress toward Sustainable Development Goals, yet whether the state should withdraw, deregulate, or inject this transition remains contested. We evaluate three governance modes across over 2,700 Chinese counties and two decades, applying Causal Forest with double/debiased machine learning to three policy reforms—the 2006 agricultural tax abolition (withdraw), the 2016 supply-side reform (deregulate), and the 2014 targeted poverty alleviation campaign (inject). Governance design, not fiscal magnitude, determines effectiveness, and each mode’s impact is conditional on local institutional context: withdrawal accelerates the shift out of agriculture where pre-reform distortions bind most tightly; deregulation diversifies cropping structures; yet injection produces no significant average effect, masking offsetting heterogeneity along fiscal-capacity lines. Data-driven targeting robustly outperforms administrative allocation in out-of-sample validation while disproportionately retaining the poorest counties. Sustainable agricultural transitions thus depend on the political economy of policy execution, not on spending magnitude.

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.

Agrifood structural transformation underpins progress toward Sustainable Development Goals, Yet whether the state should withdraw, deregulate, or inject this transition remains contested. We evaluate three governance modes across over 2,700 Chinese counties and two decades, applying Causal Forest with double/debiased machine learning to three policy reforms the 2006 agricultural tax abolition (withdraw), the 2016 supply-side reform (deregulate), and the 2014 targeted poverty alleviation campaign (inject). Governance design, not fiscal magnitude, determines effectiveness, and each mode’s impact is conditional on local institutional context: withdrawal accelerates the shift out of agriculture, where pre-reform distortions bind most tightly; deregulation diversifies cropping structures; yet injection produces no significant average effect, masking offsetting heterogeneity along fiscal-capacity lines. Data-driven targeting robustly outperforms administrative allocation in out-of-sample validation while disproportionately retaining the poorest counties. Sustainable agricultural transitions thus depend on the political economy of policy execution, not on spending magnitude.