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Field
Experimental Design (Public)
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Before
The online platform recruits gig workers on a rolling basis. As new gig workers are admitted to the platform, they are randomly allocated to one of two groups, stratifying by predicted activity level (to increase power). Group 1: control - purely commission-based pay. Group 2: treatment - lower commission plus order bonus. [Note: we initially planned to also stratify based on risk aversion elicited from our baseline survey (see below). We realised in a pre-test that our measure of risk aversion in the baseline survey needed to be changed. We were able to make last-minute adjustments to the survey, but could not use the pre-test sample to define risk-aversion strata and thus decided to stratify only on predicted activity level].
The treatment starts with HR informing new gig workers of their compensation rules. For any given new gig worker, the treatment ends after two months. Thereafter, both groups are compensated according to the same scheme (which is used for all other existing gig workers on the platform). Treated gig workers know ex ante that the insurance component their compensation entails is limited to two months.
We collect baseline data using an online survey among all new applicants to the platform. The survey is conducted prior to allocation into treatment or control group. Detailed administrative data on gig workers' activities during and after the treatment period are provided by the platform. We complement this with a post-intervention online survey.
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After
The online platform recruits gig workers on a rolling basis. As new gig workers are admitted to the platform, they are randomly allocated to one of two groups, stratifying by predicted activity level (to increase power). Group 1: control - purely commission-based pay. Group 2: treatment - lower commission plus order bonus. [Note: we initially planned to also stratify based on risk aversion elicited from our baseline survey (see below). We realised in a pre-test that our measure of risk aversion in the baseline survey needed to be changed. We were able to make last-minute adjustments to the survey, but could not use the pre-test sample to define risk-aversion strata and thus decided to stratify only on predicted activity level].
The treatment starts with HR informing new gig workers of their compensation rules. For any given new gig worker, the treatment ends after two months. Thereafter, both groups are compensated according to the same scheme (which is used for all other existing gig workers on the platform). Treated gig workers know ex ante that the insurance component their compensation entails is limited to two months.
We collect baseline data using an online survey among all new applicants to the platform. The survey is conducted prior to allocation into treatment or control group. Detailed administrative data on gig workers' activities during and after the treatment period are provided by the platform. We complement this with a post-intervention online survey. [Note: at the time that this amendment to the preregistration is uploaded, the authors have had no access to or received information about performance or activity data on participants of the field experiment.]
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Field
Randomization Method
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Before
Stratified randomization (by risk aversion and predicted-activity quartiles) is performed in office by tossing a fair coin. (In the medical literature on clinical trials, where sequential randomization is commonplace due to patients trickling in, this method is known as permuted block randomization). Predictions of gig worker activity use information from the baseline survey and predictive regressions estimated on a pre-test sample of existing gig workers.
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After
Stratified randomization (by predicted-activity quantiles) is performed in office by tossing a fair coin. (In the medical literature on clinical trials, where sequential randomization is commonplace due to patients trickling in, this method is known as permuted block randomization). Predictions of gig worker activity use information from the baseline survey and predictive regressions estimated on a pre-test sample of existing gig workers.
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