Bibliographic record
Abstract
Abstracts: Regional Differences in the Labour Market Response to Volunteers. Using a national survey on volunteering, this paper establishes that the regional labour markets reward volunteers over and above their non-volunteering counterparts. The earnings differential accorded volunteers differs quite remarkably from region to region: from about 13 % in British Columbia to 1 in the Atlantic provinces. Several factors may explain these differences which rely on the characteristics of regional labour markets and the reasons why volunteers may earn a premium over non-volunteers. ********** One of the many reasons why individuals may volunteer is to enhance employment prospects. Students volunteer in order to bolster their resumes (e.g. Dicken and Blomberg 1988); stay-at-home mothers volunteer when trying to re-enter the labour force (e.g. Mueller 1975); business people volunteer to further their job prospects (e.g. Saloner 1985). Underlying these examples is the presumption that the act of volunteering will improve labour-market outcomes. Indeed, the notion that volunteering enhances employment has basically joined the ranks of well-known fact in the western world. Until recently, however, the relationship between volunteering and the paid labour market had not been empirically investigated. The first paper to test the extent to which the labour market may reward volunteers over and above their non-volunteering counterparts was Day and Devlin (1998), which found that volunteers earned a premium of about 6 % of annual earnings. Using an improved data set, Devlin (2000) confirmed these earlier findings. (1) Using a recent data set, Devlin (2000) determined that, on average, volunteers earn more than 4% higher earnings in comparison to their non-volunteering counterparts. To establish this figure, various earnings equations were estimated which included, among the usual determinants of earnings, dummy variables denoting the individual's region of residence. Moreover, the decision to volunteer was frequently found to be influenced by region of residence. It would appear that the region in which an individual resides can affect both the decision to volunteer, and the labour-market impact of volunteering. Two questions naturally arise: what are the regional differences in the labour market responses to volunteering? And, why should the region in which an individual resides matter? The first question is an empirical one, and is addressed in this paper using the recent National Survey of Giving, Volunteering and Participating (1997). The second question, concerning why the region of residence should matter, is in many ways a much more profound question--and is certainly one that has motivated much of the work on regional issues in Canada. While we offer several reasons why region may matter when it comes to the payoff to volunteering, we cannot determine definitely the answer to this question. At least three explanations exist as to why volunteers may earn more on the paid labour market in comparison to non-volunteers. Volunteers can acquire skills that are valued by the labour market, leading volunteers to have higher earnings compared to non-volunteers. Thus, for instance, volunteering at a women's shelter may help a student of social work find permanent employment. The benefits from volunteering, however, need not be so tangible. The fact that an individual volunteers his or her time may signal potential employers about some unobservable but desirable trait of the individual, again leading to better employment. The volunteer would be chosen by a potential employer over an otherwise comparable non-volunteer. Lastly, volunteering may simply expose individuals to a valuable network of contacts who aid them in furthering employment prospects; volunteering for certain service organisations, like the Rotary Club, has often been associated with beneficial networking. From the point of view of this study, it is important to ascertain whether regional differences exist in the three mechanisms which link the labour market and volunteering. …
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".