Consumption Responses to Income Expectations

Last registered on July 11, 2025
View Trial History

Pre-Trial

Trial Information

General Information

Title
Consumption Responses to Income Expectations
RCT ID
AEARCTR-0016320
Initial registration date
July 09, 2025

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
July 11, 2025, 6:19 AM EDT

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

Locations

Country
China
Region

Primary Investigator

Name
Xiao Yin
Affiliation
UCL
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Richard Blundell
PI Affiliation
UCL

Additional Trial Information

Status
In development
Start date
2025-07-15
End date
2026-07-31
Keywords
Behavior, Labor, Welfare
Additional Keywords
MPC, Expectations
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Consumption-saving decisions lie at the heart of household behavior and macroeconomic dynamics. These decisions depend critically on households' expectations about future income, which guide how they allocate resources intertemporally. While a large literature has estimated the marginal propensity to consume (MPC) out of realized income shocks, much less is known about how consumption responds to expected income—particularly across different future horizons. This project fills that gap by studying the MPC to income expectations at multiple horizons, using experimentally induced belief variation. By distinguishing short- from long-run expectation shifts, it aims to clarify how forward-looking beliefs shape consumption dynamics.
External Link(s)

Registration Citation

Citation
Blundell, Richard and Xiao Yin. 2025. "Consumption Responses to Income Expectations." AEA RCT Registry. July 11. https://doi.org/10.1257/rct.16320-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We have four treatment groups and one control group. For each individual in the treatment groups, we show them the following information

Based on an AI model trained on the financial and employment data of over 10 million urban Chinese households, we have generated income growth forecasts for households like yours.

According to the model, households with similar characteristics to yours are expected to experience an average annual income growth rate of approximately X% over the next k years. This growth reflects general life-cycle income trend, local labor markets, and the macroeconomic situation.

where k∈{0.25,1,3,5} respectively for the four treatment groups. For T1 where k=0.25, we provide information about quarterly earnings. So the bolded part of the information treatment is
Intervention Start Date
2025-08-01
Intervention End Date
2025-08-31

Primary Outcomes

Primary Outcomes (end points)
Income expectations and spending
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We expect to select around 40,000 consumers from 40 cities in China. Then

1. we send out surveys to elicit basic information and income expectations
2. provide information treatments
3. elicit income expectations again
4. send follow-up surveys in 3 and 6 months.
Experimental Design Details
Not available
Randomization Method
a random variable generator using Python
Randomization Unit
randomized at individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
we expect to have 25,000 individuals in the end
Sample size: planned number of observations
we expect to have 25,000 individuals in the end
Sample size (or number of clusters) by treatment arms
we expect to have 5,000 for each group (one control and four treatments)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan