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Field
Abstract
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Before
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.
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After
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 and selfish 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 arrangements that provide some insurance against income uncertainty may be superior from the perspective of both the gig workers and the platforms acting as intermediaries. Such alternatives may work particularly well if – as many platforms claim – their gig workers are enthusiastic about their work and thus have an intrinsic desire to do a good job.
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 an order bonus - a piece rate based on the number of client consultations independent of final sales or returned items. This provides insurance and an incentive to serve more clients. [Note: the formal model from which we derive our experimental predictions has been uploaded as a supplementary document to this pre-registration].
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) this effect on quality is smaller for gig workers with higher intrinsic motivation [Note: we added this hypothesis as a consequence of developing our theoretical model further. We also added questions on intrinsic motivation to the baseline survey (see below) shortly before entering the field]; (e) for sufficiently risk averse gig workers, platform profits are higher in the treatment group.
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Field
Last Published
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Before
August 30, 2016 11:10 AM
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After
November 17, 2016 08:48 AM
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Field
Intervention (Public)
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Before
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.
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After
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 bonuses 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 an order-based piece-rate 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 for gig workers in the sense that their 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.
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Primary Outcomes (End Points)
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Before
Our primary outcome variables are desired quantity, quality achieved and profits realized. Our secondary outcome variables include individual earnings and job satisfaction.
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After
Our primary outcome variables are desired quantity, quality achieved and platform profits realized. Our secondary outcome variables include individual earnings and job satisfaction.
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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 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.
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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.
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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 are based on information from the baseline survey.
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After
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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