Individual Preferences for FDI in Developing Countries: Experimental Evidence from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Foreign direct investment (FDI) into developing countries such as India and China is often met with domestic backlash by the citizens of the host country, and backlash in the form of protests and other disruptive behavior has increased the salience of public opinion in FDI policy. As one of the first survey experiments assessing Chinese citizens’ attitudes toward FDI, this paper adopts a novel conjoint design to evaluate the impact, in the present project, of individual respondent characteristics and specific FDI features on respondents’ preferences. Importantly, we find that low-skilled respondents are not necessarily more likely to support labor-intensive FDI, a result that challenges the conventional wisdom that individuals in developing countries abundantly endowed with labor should be more likely to support low-skilled FDI. Instead, citizens are more concerned about FDI projects’ country of origin and impact on the local job market when forming their preferences.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it