Mechanisms and evidence of new quality agricultural productive forces empowering comprehensive grain production efficiency: A causal inference based on double machine learning
Xiaoding Tao
Anhui University of Finance and Economics
DOI: https://doi.org/10.59429/bam.v8i3.15442
Keywords: new quality agricultural productive forces; grain production efficiency; double machine learning
Abstract
Do new quality agricultural productive forces actually move the needle on grain production efficiency in China? The old input-heavy growth model has run its course. We tackle this with panel data on 30 regions (2014–2022) and a DML estimator. The composite index lifts both land and labor productivity at 1% significance. Substantive pieces (labor, land, capital) and permeating ones (technology, data) complement, not crowd out. Three channels carry the load — Technological progress, resource reallocation, industrial upgrading. Effects hit hardest in major producing and balance areas, western provinces, and medium-development regions; high-level regions show diminishing returns. IV correction and swapping the learner leave the story unchanged.
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