Research Insights

Do Higher Rewards Buy Better Data? An Incentive Experiment

We fielded the same survey at four reward levels. Higher pay filled quotas dramatically faster - but quality moved less than folk wisdom predicts, in both directions.

T

Tayqun Team

July 1, 2026

8 min read 418

The two folk theories

Researchers hold two contradictory beliefs about incentives. The first: higher rewards attract professional survey-takers and fraud, degrading quality. The second: higher rewards signal respect and buy effort, improving quality. Both are stated as obvious. They cannot both be true, and pricing decisions on the platform depend on which one the data supports.

Design

We fielded an identical 10-minute consumer survey to 4,800 participants across four randomized reward arms: $0.75, $1.50, $3.00, and $6.00. Participants saw only their own arm's reward. We measured fieldwork speed, response quality (composite 0-100 score), fraud-signal prevalence, open-text effort, and the demographic composition of completes. All arms drew from the same eligible pool with identical targeting.

Results

RewardTime to fill quotaMean quality scoreFraud-flag rateMedian open-text length
$0.7568 hours76.83.1%34 chars
$1.5031 hours78.42.9%39 chars
$3.009 hours79.13.0%44 chars
$6.004 hours79.43.3%46 chars

What moved and what did not

  • Speed moved enormously. Doubling the reward from $1.50 to $3.00 filled the quota 3.4× faster. If fieldwork deadlines matter, incentives are the lever.
  • Quality moved a little, upward. +2.6 points from the lowest to highest arm, driven almost entirely by open-text effort (+35% length, higher informativeness ratings). Ratings-based measures barely changed.
  • Fraud did not move. Flag rates were statistically flat across arms. The fraud-magnet theory found no support - plausibly because quality gates make fraud unprofitable at any reward level, so the marginal fraudster does not appear when pay rises.
  • Composition moved subtly. The $0.75 arm skewed toward high-frequency responders (participants completing 20+ surveys/month); higher arms pulled in more occasional participants, mildly improving demographic balance.

Interpretation

Neither folk theory survives contact with the data in strong form. Higher rewards do not poison the well, and they do not transform data quality either - on a platform with response validation, quality is protected by the gates, not the price. What the reward actually buys is speed, open-text effort, and access to respondents who are not survey regulars.

Pay does not buy honesty - the quality system enforces that. Pay buys speed, effort, and people you could not otherwise reach.

- From the discussion section

Practical guidance

  1. Price for your deadline: quota-fill time is hyper-sensitive to reward, quality is not
  2. If open-text answers are the point of your study, pay above the platform median - it is the one quality dimension money reliably improves
  3. Do not underpay to "filter for intrinsically motivated respondents" - what you actually select for is professional speed-runners
  4. Fair pay also compounds platform-wide: it retains the casual, demographically balanced participants every researcher needs

Limitations

One survey topic, one region, four price points. Effects may differ for B2B audiences, very long instruments, or rewards far outside the tested range. The fraud finding is conditional on active quality gating - platforms without validation may well see the fraud-magnet effect we did not.

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