The effects of health, social, and consumption capital on running-related expenditures in China
Bibliographic record
Abstract
Research question: This study investigates the effects of health, social, and consumption capital on running-related expenditures. It adds to previous research by empirically testing investment in the stock of health on participation-related expenditures.Research methods: Chinese amateur runners (n=6,693) were surveyed on health capital (i.e. health change since taking up running), social capital (i.e. running group participation), consumption capital (i.e. sport profiles), socio-demographics, and running-related expenditures over a one-year period. Two instrumental variables reflecting life domain satisfaction were included to address the endogeneity of health change.Results and findings: Results show that variables capturing health, social, and consumption capital significantly affect total running-related expenditures, whereas the effects on expenditure categories vary. After taking endogeneity into account, the results show that health change since taking up running positively affects total running-related expenditures and sport apparel expenses.Implications: The findings provide empirical support for Downward et al.’s (2009) general economic model of sports consumption by revealing that health, social, and consumption capital are significant drivers of participation-related expenditures. While mass participation, health, and economic objectives may be achieved concurrently, policy makers should carefully balance these objectives. Sport managers and marketers can use mass participant sport events to stimulate continued participation, and this in turn generates health, social, and consumption capital that drives expenditures. Fostering running group participation increases expenditures. Early career runners should be targeted for sport apparel. Cross-promotion among related sports may increase overall sport consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".