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Macroeconomic Expectations and Consumption
Last registered on January 06, 2020


Trial Information
General Information
Macroeconomic Expectations and Consumption
Initial registration date
April 29, 2019
Last updated
January 06, 2020 3:47 PM EST
Primary Investigator
UCLA Anderson
Other Primary Investigator(s)
Additional Trial Information
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. 2020. "Macroeconomic Expectations and Consumption." AEA RCT Registry. January 06. https://doi.org/10.1257/rct.3717-1.1.
Former Citation
Galashin, Mikhail. 2020. "Macroeconomic Expectations and Consumption." AEA RCT Registry. January 06. http://www.socialscienceregistry.org/trials/3717/history/60043.
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 nominal exchange rate 12 months in the future and the expected inflation rate over the following 12 months.
We will start by exploring the association between these beliefs and consumption and financing behavior. As complementary evidence, we will embed an information-provision experiment in the survey. In the experiment, we randomly provide expert forecasts on the exchange rate and the inflation rate in 12 months. That will allow us to obtain experimental estimates of the causal effects of beliefs on behavior.

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 three predictions from macroeconomic models. First, we check if inflation expectations impact spending on durables. Second, we ask whether beliefs on exchange rate depreciation affect purchases of the tradable part of durables. Third, we check if inflation expectations affect household debt: specifically, we look at credit card debt and purchases with installments.

For these outcomes, we'll have two versions:
(i) Subjective measures of future spending elicited at the end of the survey.
(ii) Objective measures of actual future spending obtained from the administrative records of the bank.

Subjective measures are composed of a battery of consumption-related questions asked at the end of the survey. Specifically, we ask two questions on general expectations of the respondent’s financial well-being and state of the economy, three questions on expected expenditures and credit card financing, and four questions on the timing of durable goods consumption.
The setup will allow us to observe the analogs of expenditure variables in the administrative data. First, we use the credit and debit card purchases data to test the predictions on the effect of the exchange rate and inflation on durable consumption. We use MCC categories to classify the goods into perishables vs durables and tradables vs non-tradables.
Second, we use the data on credit card debt and installment payments to check if there are effects of inflation on borrowing.
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 exchange rate, the inflation 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, Papers & Other Materials
Relevant Paper(s)