DID Analysis on the Impact of Policies on the Rural-Urban Income Disparity in Resource-Dependent Regions: A Case Study of Ordos
Bibliographic record
Abstract
Ordos is the most abundant coal resource city in Inner Mongolia. Its coal resources account for one half of Inner Mongolia's coal resources and one sixth of China's total coal reserves. Abundant coal resources have laid the foundation for Ordos become today’s resource-based city. In 2003, Inner Mongolia issued “the guiding opinions on accelerating the development of key coal enterprises” (hereinafter referred to as “policy”), supporting the development of coal enterprises and providing policy conditions for the rapid economic development of Ordos. However, with the rapid development of economy, the rural-urban income disparity is also getting bigger in Ordos. Based on panel data from 1999 to 2012 and use the DID analysis of “quasi-natural experiment”, the paper finds that the policy has increased the rural-urban income disparity. The policy increases the rural-urban income disparity by promoting GDP growth. Therefore, the role of the policy system in the economic development of a region cannot be ignored. The government supports the development of local resource-based industries and also increase support for the development of upstream and downstream industries. Under the guidance of policy, the mineral resources income should be transformed reasonably. Government should invest the proceeds of mineral resources in material capital and human capital. Government also should invest the proceeds of mineral resources in external industries and projects that require large initial capital or long construction cycles, such as those essential infrastructure sectors: education, health, transportation and energy. In this way, the integration of urban and rural development will be realized and the rural-urban income disparity will be reduced.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".