The Economy of People’s Republic of China from 1953
Bibliographic record
Abstract
This paper studies growth and structural transformation of the Chinese economy from 1953 to 2012 through a lens of a two-sector growth model.The main goal of the paper is to provide a systematic analysis of both the pre-1978 reform and post-1978 reform periods in a unified framework.First, we construct a dataset that allows the application of the neoclassical model and computation of wedges, their components, and rates of TFP growth.Second, we determine the key quantitative factors behind growth and structural transformation.The changes in the intersectoral labor wedge play the dominant role in accounting for the change in the share of labor force in agriculture.TFP growth and changes in the intersectoral wedges are the two most significant factors contributing to GDP growth.Further decomposing the effects of reduction in wedges, we find that two components: the production component (the gap between the ratio of the marginal products of labor and relative wages) and the consumption component (the gap between the marginal rate of substitution and the relative prices) play a particularly large role.Third, we use the pre-reform period as a key benchmark to measure the success of the post-1978 reforms.We show that reforms yielded a significant growth and structural transformation differential.GDP growth is 4.2 percentage points higher and the share of the labor force in agriculture is 23.9 percentage points lower compared with the continuation of the pre-1978 policies.We provide extensive historical evidence for the reforms that are consistent with the evolution of the components of the wedges.The decrease in the production component of the intersectoral wedge is consistent with increased competition and demonopolization of the economy.The decrease in the consumption component of the wedge is consistent with the price and housing reforms.Finally, we project the path of the Chinese economy until 2050 and also calculate a lower bound on future growth by projecting pre-reform trends.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".