The Effect of Notches on Labor Supply - Evidence from an Online Experiment

Last registered on June 03, 2022

Pre-Trial

Trial Information

General Information

Title
The Effect of Notches on Labor Supply - Evidence from an Online Experiment
RCT ID
AEARCTR-0008410
Initial registration date
June 01, 2022

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
June 03, 2022, 8:18 AM EDT

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

Locations

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Primary Investigator

Affiliation
University of Freiburg, Walter Eucken Institute

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Status
In development
Start date
2022-05-02
End date
2022-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This experiment examines the impact of policies aimed at promoting (legal) labor by reducing tax liabilities for taxpayers who earn up to certain thresholds (notches). For instance, marginal earnings thresholds exempt employees from social security contributions and taxes (e.g., in Austria, Germany, Switzerland). Value added tax (VAT) exemptions allow small businesses earning less than a certain limit to refrain from charging VAT. From a political perspective, such notches may be attractive if particular groups can be motivated to change their behavior (Slemrod, 2010). However, economists are traditionally skeptical of their benefit (e.g., Blinder & Rosen, 1984). Since the average tax rate discontinuously jumps when the cutoff is crossed, notches are associated with a region of strictly dominated choice above the cutoff; individuals can increase both consumption and leisure by moving down just below the cutoff (Kleven & Waseem, 2013).

People can react to notches by adapting their labor supply (Kleven, 2016). Questions that remain open are whether the elimination of a tax notch has a different effect on labor adaptation than the introduction of a tax notch and to what extent reactions to a tax increase differ from a tax decrease. The study will be implemented as an online experiment. The advantage of an online experiment is that we can vary the type of tax notch and the direction of the tax reform in a controlled environment with a non-standard sample. Participants will be recruited using Amazon Mechanical Turk.
External Link(s)

Registration Citation

Citation
Feld, Lars, Sarah Necker and Katharina Pfeil. 2022. "The Effect of Notches on Labor Supply - Evidence from an Online Experiment." AEA RCT Registry. June 03. https://doi.org/10.1257/rct.8410
Experimental Details

Interventions

Intervention(s)
We implement four interventions that vary the type of tax reform (introduction vs. elimination) and the type of the tax notch (tax increase vs. tax decrease) in a 2x2 between-subjects design.
Intervention Start Date
2022-06-01
Intervention End Date
2022-07-31

Primary Outcomes

Primary Outcomes (end points)
Chosen gross income
Primary Outcomes (explanation)
The fraction of subjects choosing a lower income than the one allocated (“labor reducers”, coded from 0 to 1)
The fraction of chosen income: ratio of chosen income divided by allocated maximum income (coded from 0 to 1)
The fraction of bunchers (choosing an income at the notch point, coded from 0 to 1)
The fraction of neverdominated (choosing an income that is never in the strictly dominated region, coded from 0 to 1)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In our study, individuals participate in an online laboratory experiment. In each of the rounds, they choose what income they want to earn. After the decisions, one round is randomly chosen to determine the number of text sequences they have to transcribe in a real-effort task. We vary the type of the tax reform and the tax notch in four treatments (between-subjects design). The study will be implemented online using Amazon Mechanical Turk.
Experimental Design Details
Not available
Randomization Method
Randomization is done by our software LIONESS Lab, or more specifically the Math.random() function on JS that generates a floating-point, pseudo-random number in the range 0 (including) to 1 (excluding).
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Treatment is not clustered.
Sample size: planned number of observations
480
Sample size (or number of clusters) by treatment arms
120
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2022-06-01
IRB Approval Number
BFcWXvpD