Understanding the individual labor supply and wages on digital labor platforms: A microworker perspective
Bibliographic record
Abstract
The emergence of crowdsourcing as a new form of work has introduced a paradox among workers who receive small payments for piecemeal microwork yet continue to participate in microwork digital labor platforms (DLPs). To better understand what sustains microworkers’ participation, this study draws upon individual labor supply theory to quantitatively examine the impacts of microworkers’ motivations, perceptions, and preferences on their labor supply and wages. To explore the meaning of monetary rewards for microworkers, a qualitative inquiry explores microworkers’ spending patterns. Based on a survey of 306 microworkers on Amazon Mechanical Turk, our hierarchical regression analysis reveals that while individual motivations for monetary rewards, enjoyment, and microtime structure have some impact on the microwork labor supply and wages, their impact is limited. Our thematic analysis uncovers diverse meanings attached to microwork earnings. The two most noted are meeting subsistence needs and nonessential expenditures, both of which have positive effects on microwork wages. By investigating the elasticity of the microwork labor supply and wages and offering a nuanced understanding of monetary rewards, our study contributes to information management research on DLPs. Moreover, it provides practical insights for various stakeholders, including microworkers, requesters, and DLP operators. © 2024 Elsevier Ltd
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".