# Mechanization-driven farmland consolidation and farm household labor allocation: Evidence from grain producers in Shandong, China

## TL;DR

This study examines how farmland consolidation through mechanization affects labor allocation in Chinese grain farming, finding significant differences based on mechanization methods.

## Contribution

The study identifies causal effects of mechanization-driven consolidation on farm labor decisions using a novel combination of Tobit and propensity-score matching methods.

## Key findings

- Consolidation reduced on-farm labor input by 8.4 percentage points through labor-saving technological substitution.
- Custom mechanization services reduced on-farm labor by 7.5 percentage points, while owning machinery increased it by 5.3 percentage points.
- Labor-saving effects were strongest among aging households, smallholders, and farmers in hilly areas.

## Abstract

Mechanization-driven farmland consolidation has become a key component of China’s efforts to raise grain productivity and optimize rural labor allocation. We used a survey of 630 grain-producing households in Shandong Province, and combined a Tobit model with propensity-score matching to identify the causal effects of consolidation on farm-household labor decisions. Consolidation reduced on-farm labor input by 8.4 percentage points through labor-saving technological substitution, yet the magnitude differed sharply between two mechanization pathways. Where households purchased their own machinery, on-farm labor rose by 5.3 percentage points, consistent with specialization incentives. By contrast, the use of custom mechanization services lowered on-farm labor by 7.5 percentage points. Labor-saving effects were strongest among ageing households, smallholders and farmers in hilly areas, suggesting enhanced overall efficiency in constrained settings. Policy implications include expanding service markets, coupling consolidation with vocational training for off-farm employment, and establishing a long-run monitoring framework to ensure sustainable transformation. However, this study relies on cross-sectional data, which limits its ability to capture dynamic change processes. Future research could conduct longitudinal tracking studies to evaluate the sustained effects and sustainability of policies.