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Macroeconomic Expectations and Consumption
Last registered on May 13, 2019


Trial Information
General Information
Macroeconomic Expectations and Consumption
Initial registration date
April 29, 2019
Last updated
May 13, 2019 11:26 PM EDT
Primary Investigator
UCLA Anderson
Other Primary Investigator(s)
Additional Trial Information
In development
Start date
End date
Secondary IDs
We are interested in understanding the relationship between individuals' beliefs about the macro-economy and their spending behavior. For example: if one believes that prices will go up in the future, will that increase spending in durable goods? If one believes that the nominal exchange rate will go up in the future, will that increase spending in imported goods?
External Link(s)
Registration Citation
Galashin, Mikhail. 2019. "Macroeconomic Expectations and Consumption." AEA RCT Registry. May 13. https://doi.org/10.1257/rct.3717-1.0.
Former Citation
Galashin, Mikhail. 2019. "Macroeconomic Expectations and Consumption." AEA RCT Registry. May 13. https://www.socialscienceregistry.org/trials/3717/history/46342.
Experimental Details
We will conduct phone surveys with a sample of clients from a large commercial bank. We will be able to link the survey responses back to the administrative data of the bank, which includes real-time information on the behavior of these individuals such as their credit card and debit card transactions.

In the survey, we'll measure two key beliefs that are important in the context of this experiment: the expected inflation rate over the following 12 months, and the expected nominal exchange rate 12 months in the future.

We will start by exploring the association between these beliefs and different forms of behavior. As complementary evidence, we'll embed and information-provision experiment in the survey. That will allow us to obtain experimental estimates of the causal effects of beliefs on behavior.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The expected future spending, according to survey data, and the actual future spending, according to the high-quality administrative records from the bank.
Primary Outcomes (explanation)
We will test a series of predictions from macroeconomic models. For example, if an individual believes that the nominal exchange rate will go up in the next 12 months, that individual may want to buy soon imported goods such as consumer electronics. For these outcomes, we'll have two versions:
(i) Subjective measures of future spending elicited at the end of the survey.
(i) Objective measures of actual future spending obtained from the administrative records of the bank.
For example, we included one subjective question about whether it was a good time to buy consumer electronics. And we can use the bank's transaction data to measure (approximately) the actual spending on consumer electronics. Given that the transaction data is high-frequency data, we can explore effects at different time horizons (e.g., the effects during the first month versus the effect during the first three months).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The first part of the analysis is non-experimental: we want to measure the association between certain beliefs and spending behavior. The second part of the analysis is experimental, for which we use a standard information-provision experiment.
Experimental Design Details
The first part of the analysis is purely observational. For the second part of the analysis, we exploit an information provision experiment -- that is, we randomize different pieces of information to different individuals, and then track the effects of the experiment on subsequent beliefs and behavior. The structure of this experiment is as follows: — We elicit prior beliefs about two macroeconomic variables (inflation and the nominal exchange rate). — We randomly provide them with one of multiple pieces of information about these expert forecasts on these key parameters. — We elicit posterior beliefs, to determine if our feedback shifted their expectations. Note that we are interested in measuring the causal effect of beliefs. We are not interested in measuring the average treatment effect of providing a piece of information -- indeed, this average effect is probably zero, because the same piece of information may make some individuals update upwards and other individuals update downwards. Given that we have multiple outcomes, we are planning to use standard methods for joint hypotheses testing. Additionally, we will also consider more modern machine learning methods. Note also that we will have the administrative data for the year before our intervention as well as the year after our intervention. As a result, we can use pre-treatment outcomes for falsification tests (i.e., did the information affect behavior before the information was provided?). We'll be able to use the pre-treatment outcomes as control variables, which will help us to increase power. The pre-treatment outcomes may also be useful for heterogeneity analysis.
Randomization Method
The randomization will be managed automatically by the Qualtrics survey platform.
Randomization Unit
Individual respondent
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
The final number of respondents will depend on the response rate to the phone surveys. We are aiming to collect responses from 3000 individuals
Sample size: planned number of observations
3000 individuals
Sample size (or number of clusters) by treatment arms
Individuals can receive feedback about the inflation rate, the exchange rate, or both. We'll randomly assign them with equal probability, resulting in one third of the respondents in each treatment group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
UCLA North General IRB
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers