Land Reform and Local Agents: The Grassroots Origins of State Capacity in Communist China and Beyond
Bibliographic record
Abstract
How do dictatorships morph from weak ‘paper leviathans’ into powerful institutional machines that command monopolized authority throughout its realm? While previous scholarship has shown how strong central state capacity can contribute to the endurance of authoritarian rule, less attention has been paid to how autocratic regimes construct local state capacity through the recruitment, deployment, and management of agents to enforce their rule at the grassroots level. Yet the selection of local agents for policy implementation also requires regimes to make strategic tradeoffs regarding the centralization and decentralization of their political authority. Using the National Land Reform Campaign (1950-53) under the early years of the Chinese Communist regime as a case study, I address two puzzles: under what conditions do regimes seek to prioritize different types of local agents, and which types of agents deliver better policy performance for regime leaders? The dissertation utilizes a mixed-method approach, by leveraging rare primary archival materials (e.g. internal party communications, work reports, memoirs) and novel quantitative historical datasets at the subnational level to answer these questions. I argue that the Chinese Communist Party (CCP) post-1949 faced a critical centralization dilemma during the Land Reform Campaign: it could select either centrally-deployed agents, who are more likely to be faithful representatives of central political will but are unfamiliar with local governing communities, or locally-embedded agents, who are more likely to adapt to local particularities but are prone to societal capture. However, I also find that the CCP regime was sometimes able to harness a ‘hybrid’ model of local agent selection by maintaining both agent loyalty and governing familiarity, under conditions of deliberate central intervention and the presence of organizational legacies left behind by revolutionary mobilization – which consequently allowed for more effective implementation of land redistribution targets. By revisiting this massive project of socioeconomic reform in 1950s China, which was one of the largest redistributions of land ownership in human history, this dissertation sheds new light on the study of state-led land reform beyond China (including Taiwan and Asia), and offers new insight into the study of principle-agent problems and state capacity in authoritarian regimes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".