Bibliographic record
Abstract
Since the beginning of its economic reform in 1978, China’s extraordinary economic growth has been accompanied by increased migration and rapid structural transformation – the reallocation of economic activity from agriculture to nonagriculture. This significant labor reallocation and migration stemmed from a series of institutional reforms and policies that significantly reduced labor market barriers. My thesis studies the impact of changes in labor market barriers during China’s reform era. Chapter 1 constructs measures of sectoral reallocation and geographical relocation of labor at the provincial level for 1978-2015 resulting from a series of institutional reforms and policies that lowered labor market barriers. I find that the structural transformation process was uneven across provinces. Agriculture-to-nonagriculture worker reallocation began earlier and on a larger scale for coastal provinces. Since 1990, workers moving from inland agriculture to coastal nonagriculture became an important source of nonagricultural labor growth. Chapter 2 quantifies changes in barriers regarding sectoral reallocation and regional migration and studies their impact on China’s economic growth over 1978-2015. I build a two-sector two-region general equilibrium model focusing on labor market barriers both between agricultural and nonagricultural sectors and across coastal and inland regions. I find that the 1982-2015 decline in labor market barriers contributed to an increase in output of 26.5% in 2015. Despite this, there remain considerable gains for future improvement. In particular, eliminating barriers from inland agriculture to coastal nonagriculture in 2015 could further increase output by 12%. Chapter 3 presents the joint work with Ruiqi Sun, Trevor Tombe, and Xiaodong Zhu. Expanding on Chapter 2, we explore the effect of changes in capital market and trade frictions in addition to labor market barriers on resource allocation. Employing a rich spatial general equilibrium model, we quantify the size and impact of migration barrier changes, capital barrier changes, and trade cost changes, to growth, regional income convergence, and structural change in China over 2000-2015. While each contributed meaningfully to growth, migration policy changes were central to China’s structural change and regional income convergence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".