Insuring gig workers

Last registered on August 30, 2016

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Pre-Trial

Trial Information

General Information

Title

Insuring gig workers

RCT ID

AEARCTR-0001555

Initial registration date

August 30, 2016

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

August 30, 2016, 11:10 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

Germany

Region

Primary Investigator

Name

Sebastian Butschek

Affiliation

University of Cologne

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Other Primary Investigator(s)

PI Name

Anonymous co-author

PI Affiliation

Partner firm

PI Name

Dirk Sliwka

PI Affiliation

University of Cologne

Contact Investigator

PI Name

Patrick Kampkoetter

PI Affiliation

University of Tuebingen

Contact Investigator

Additional Trial Information

Status

In development

Start date

2016-08-31

End date

2017-08-30

Keywords

Labor

Additional Keywords

Incentives, risk aversion, multitasking, gig workers, on-demand economy

JEL code(s)

M52, J33, D23

Secondary IDs

Abstract

Individuals working on a freelance basis are instrumental to the on-demand economy. These "gig workers" are people tasked with and paid for specific work assignments. Many gig workers offering their services via online platforms are formally independent contractors. To align incentives, platforms typically compensate gig workers based on the profits resulting from their service to a client. It is not clear, however, whether gig workers, though legally entrepreneurs, behave like firms - for instance whether they are best thought of as risk neutral economic actors. While purely commission-based compensation of gig workers creates non-distortionary incentives for both quality and quantity provision, such pay schemes may lead to insufficient insurance against income risks. As a consequence, alternative contractual agreements that provide some insurance against income fluctuations may be superior from the perspective of both the gig workers and the platforms acting as intermediaries.

In this project we collaborate with an online platform to study how introducing some insurance to their compensation affects gig worker behaviour and the profitability of the platform. In our setting, the platform acts as the intermediary between clients and gig workers, whose task it is to provide remote shopping advice to clients. This service may result in the online sale of physical goods, again handled by the platform. Gig workers choose the quantity offered, i.e., the number of clients they want to serve. Their efforts also determine the quality achieved, i.e., the usefulness of their advice affects the sales to each client. At the outset, gig workers' compensation is commission-based, paying them a fraction of net sales to the clients they advised.

We start from a stylized formal model in which agents choose quantity (number of clients served) and average quality (sales per client) and compensation contracts may reward both. We first show that when agents are risk neutral, a pure sales-based commission is optimal. When agents are risk averse, however, this may no longer hold. In this case it may be preferable to introduce a fixed order fee - an unconditional payment per client served. This provides insurance and an incentive to serve more clients.

We implement a natural field experiment (RCT) to test the following predictions: (a) as compared to purely commission-based pay (control group), compensation including costly insurance (treatment) increases gig workers' desired number of jobs; (b) this effect on quantity is larger for more risk averse gig workers; (c) the intervention decreases gig workers' average quality per job; (d) for sufficiently risk averse gig workers, platform profits are higher in the treatment group.

External Link(s)

Registration Citation

Citation

Butschek, Sebastian et al. 2016. "Insuring gig workers." AEA RCT Registry. August 30. https://doi.org/10.1257/rct.1555-1.0

Former Citation

Butschek, Sebastian et al. 2016. "Insuring gig workers." AEA RCT Registry. August 30. https://www.socialscienceregistry.org/trials/1555/history/10468

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Experimental Details

Interventions

Intervention(s)

We create exogenous variation in the way in which the online platform compensates collaborating gig workers. The intervention only involves new gig workers who have no previous experience with the platform and its compensation policies. The intervention randomly varies whether new gig workers are subject to the existing commission-based compensation scheme or to the new compensation scheme that includes order fees as an insurance component.

In the commission-based compensation scheme, earnings are a fraction of the net sales realized as a consequence of the gig worker's consulting service to a client. In the new compensation scheme, this commission is reduced and a fixed component is added, paid out as long as the client places an order and irrespective of final sales or returned items. This insurance is costly in the sense that gig workers' upside from a particularly successful trade is reduced: they receive a smaller share of net sales. The treatment is calibrated such that in expectation - assuming no quality adjustment - earnings per average job are approximately equal under the two compensation schemes.

Intervention Start Date

2016-09-20

Intervention End Date

2017-02-20

Primary Outcomes

Primary Outcomes (end points)

Our primary outcome variables are desired quantity, quality achieved and profits realized. Our secondary outcome variables include individual earnings and job satisfaction.

Primary Outcomes (explanation)

Desired quantity is measured as the average daily number of slots a gig worker makes available in his calendar for client consultations. For quality achieved, we measure sales per order and per consultation; we also use a measure developed by the platform that adjusts net merchandise value for the number of items recommended and the client's expected willingness to spend.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

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 risk aversion (for heterogeneity analysis) and predicted activity level (to increase power). Group 1: control - purely commission-based pay. Group 2: treatment - lower commission plus a fixed order fee.

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 workers on the platform). Treated gig workers know 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. 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.

Experimental Design Details

Randomization Method

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 are based on information from the baseline survey.

Randomization Unit

Individual (gig worker)

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

n/a

Sample size: planned number of observations

200 (280) individuals. [See section on power calculation for details]

Sample size (or number of clusters) by treatment arms

100 (140) individuals control, 100 (140) individuals treatment. [See section on power calculation for details]

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

The online platform expects to recruit around 40 new gig workers per month. Based on power simulations using past gig worker data, we requested that the experiment be left in the field for at least seven months, giving us an expected 280 observations. With 257 observations, our simulated MDE for desired quantity with 77% power is .3 standard deviations. Due to planning constraints, however, the online platform was able to make a commitment for only five months in the field (yielding an expected 200 observations). This reduces power: with 200 observations, our simulated MDE for desired quantity (with 79% power) is .35 standard deviations.

Supporting Documents and Materials

IRB