Experimental evaluation of a Basic Income Pilot in Germany

Last registered on May 27, 2021


Trial Information

General Information

Experimental evaluation of a Basic Income Pilot in Germany
Initial registration date
May 27, 2021
Last updated
May 27, 2021, 4:47 PM EDT


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Primary Investigator

University of Oxford

Other Primary Investigator(s)

PI Affiliation
University of Cologne
PI Affiliation
Vienna University of Economics and Business
PI Affiliation
German Institute for Economic Research (DIW) Berlin and Freie Universität Berlin
PI Affiliation
German Institute for Economic Research (DIW)

Additional Trial Information

In development
Start date
End date
Secondary IDs
A policy proposal that has received much attention in recent years is the introduction of a (universal) basic income.
Relative to existing forms of social protection and redistribution, the distinguishing feature of a basic income is its lack of conditionality: Income receipt is not contingent on requirements such as a prior work history, demonstrated willingness to work, sickness, or old age.
This lack of conditionality has implications for various dimensions of welfare and behaviour, mediated by channels including the bargaining power of (potential) recipients, increased income security, and increased coverage of populations who are otherwise excluded.

In this study, we will evaluate a basic income pilot program in Germany using a blocked randomized controlled trial design.
Our evaluation will focus on dimensions of behavior and welfare which might be distinctively impacted by a basic income, relative to other forms of social protection.
We will consider, in particular, labor market outcomes, expenditures, time use, and subjective well-being.
External Link(s)

Registration Citation

Bohmann, Sandra et al. 2021. "Experimental evaluation of a Basic Income Pilot in Germany." AEA RCT Registry. May 27. https://doi.org/10.1257/rct.7734-1.0
Experimental Details


We study the effect of a basic income on recipients in a randomized controlled trial (RCT) that will last for 3 years.
The RCT has 1,484 participants, of which 107 are assigned to the treatment group, while 1,377 are assigned to the control group.
Members of the treatment group receive unconditional cash transfers of 1,200.00 EUR, paid monthly, over the course of three years.
Members of the control group do not receive unconditional cash transfers, but instead receive incentives to participate in research-related activities.
Participants in the treatment group are required to participate in at least seven online surveys, one before the start of treatment and, thereafter, every 6 months until the end of the trial.
Participants in the control group receive (at least) 10 EUR for each completed survey, plus (at least) 30 EUR if all surveys were completed.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The exact list of outcomes remains to be determined, but likely include will include the following:

• Expenditures and household finance
• Administrative data on labor supply
• Survey responses related to labor supply
• Psychological concepts related to autonomy • Subjective well-being
• Physical well-being
• Political preferences
• Economic preferences
• Social attitudes

A list of outcomes, and of survey instruments to measure them, will be uploaded separately before the start of data collection.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We use the answers to the baseline survey to sort participants into homogenous blocks. The 28 variables used are listed in column 1 of Table 1 (in the attached pre-analysis plan). Appendix A provides further details on the definition of these variables.
Pairwise distances between observations are calculated using the Mahalanobis distance.4 We construct blocks containing 32 observations each. The blocks are chosen to minimize the total sum of distances between pairs of observations within blocks. We do so using the R package blockTools (Moore and Schnakenberg, 2016). We then discard all blocks with a maximum within- block distance greater than 14 (to avoid poorly matched observations), as well as one block with less than 32 observations.

Random assignment within blocks:
Within each block, treatment is assigned uniformly at random. We assign 2 out of the 32 observations in a block to the treatment group, 26 observations to the control group, and the remaining 4 observations to a “reserve,” which is to be sampled in case of attrition of observations from the treatment or control group.
These numbers are chosen based on the following considerations: We want two treated units per block, in order to be able to calculate standard errors for the sample average treatment effect; cf. Athey and Imbens (2017) and our discussion of inference below. We don’t want more treated units per block, to keep blocks as homogenous as possible. The budget constraints of our implementation partner are furthermore such that we can survey 13 control units for every treated individual.
Lastly, because we have 107 treated individuals in total (an odd number), one additional individual from one block is chosen at random to participate in the treatment.

Weighted sampling of blocks:
This procedure results in 273 blocks, while our project budget allows for 53 blocks. These blocks are furthermore not fully representative for the baseline sample, because not all individuals who were invited to participate in the baseline survey passed eligibility and had non-missing responses in the questions we used for blocking (see above) and because of our discarding of poorly matched blocks.
In order to obtain a representative sample of blocks, we create block level sampling weights. These weights are chosen so as to match the distribution of gender, education groups, and income groups of eligible participants in the screening survey. We then draw a sample of 53 blocks from the 273 available blocks using these sampling weights, to obtain a representative subsample.
This results in 107 individuals assigned to treatment, 1377 assigned to the control group, and 212 individuals assigned to the “reserve,” distributed evenly across 53 blocks.
Experimental Design Details
Not available
Randomization Method
See description of experimental design.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
107 in the treatment group (basic income recipients), 1,377 in the control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
University of Cologne, Ethics Committee of the Wirtschafts- und Sozialwissenschaftliche Fakultät
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre-analysis plan (sampling, treatment assignment, and inference)

MD5: 7751fed23013a3b2ba7375605afc7f5e

SHA1: aa3b6ef67a22d97c4b0bb196cadb9fb769e37cfe

Uploaded At: May 27, 2021