Do Consumers Believe in an Inverse Phillips Curve? Evidence from the US and South Africa

Last registered on December 01, 2023

Pre-Trial

Trial Information

General Information

Title
Do Consumers Believe in an Inverse Phillips Curve? Evidence from the US and South Africa
RCT ID
AEARCTR-0012206
Initial registration date
November 16, 2023

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
December 01, 2023, 4:19 AM EST

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

Locations

Region
Region

Primary Investigator

Affiliation

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-12-01
End date
2024-01-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Consumer surveys in rich economies reveal a positive correlation between unemployment and inflation expectations, consistent with a supply-side narrative. I explore this correlation further by (a) developing a novel experimental design that allows me to elicit the average elasticity of unemployment expectations in response to inflation expectation changes, (b) measuring how behavior in the labor market changes in response to increasing or decreasing inflation expectations caused by unspecified, supply or demand shocks and (c) measuring whether this response differs between men and women.
External Link(s)

Registration Citation

Citation
Reiche, Lovisa. 2023. "Do Consumers Believe in an Inverse Phillips Curve? Evidence from the US and South Africa." AEA RCT Registry. December 01. https://doi.org/10.1257/rct.12206-1.0
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Experimental Details

Interventions

Intervention(s)
Survey using hypothetical vignettes.
Intervention (Hidden)
The experiment runs in three blocks.
In the first section I assess characteristics of the individual. Initially participants record their age, income (household gross and net, personal gross and net), highest educational qualification, gender and race (Black, Colored, Indian, White), and province, as is standard in household surveys. In the sample recruited via prolific the demographics are not elicited directly and recorded by the provider when participants sign up to the platform. In addition, I include controls for the employment status. In particular, I am interested in whether a respondent is employed, self-employed or unemployed, and if employed, whether they are working in the private or public sector and are members of a workers union. All three additions are important due to their relevance for wage bargaining.

After characteristics are recorded, baseline beliefs about inflation, unemployment and GDP growth are elicited as point forecasts for 12 months ahead. All participants are given a definitions of the three variables. (“Inflation is the rate at which the overall prices for goods and services change over time.”; “The unemployment rate is the percentage of adults who want to work and are capable of working but do not have a job and are looking for one.”; “The GDP growth rate measures by how much a country's economy is getting bigger, i.e. is producing more goods and services, in a given year.”). Prior beliefs are used as reference points. Respondents receive no numerical anchor before their prior beliefs are elicited. Finally, I elicit expected behaviors by inquiring the percentage chance of the respondent or other members of their household to ask for a higher wage, increase hours worked, reduce consumption of goods and services, use up savings, sell larger items or take a loan should the scenario realize.

The third step is the presentations of hypothetical scenarios. Participants are randomly allocated to one of twelve treatment arms, which will be labeled up_1 - up_6 and down_1- down_6. Up indicates the upper arm, in which inflation expectations are increased and down the lower arm, in which reference expectations are decreased. First, participants are asked what they believe will be the most likely reason inflation would be higher/lower than their initial forecast. This type of "big picture" question can capture first order considerations. Then, each participant in up_1 - up_6 randomly draws a factor Y_u ~ U((1,2]) and equivalently each participant in down_1- down_6 randomly draws a factor Y_d ~ U([0,1)). Hence for arms in up the scenario strictly increases expectations by no more than 100% of the initial value while for groups arms in down the scenario strictly reduces them by no more than 100% of the initial value.Due to the asymmetric nature of the intervention around zero, exceptions must be made for those that expect deflation. Individuals in down with deflationary expectations get assigned a Y randomly drawn from Y_d ~ U((1,2]) and in reverse those in up randomly draw from Y_u ~ U([0,1)). Under this correction we maintain our interpretation of the groups as “decreasing” and “increasing” expectations respectively. I expect very few observations with deflationary expectations for which this correction would apply.

The factors are then used in three hypothetical scenarios.
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You said that you believe inflation in South Africa in the next 12 months will be [Prior]%. Assume now that instead, inflation in South Africa over the next 12 months will be [Y_u/d x Prior]\%$.

Scenario 1 (Open) Assume that the cause of this difference is [Reason_u/d].

Scenario 2: (Supply) Assume that the cause of this difference is a(n) decrease/increase in the supply of goods and services. In other words, by firms producing less/more than before.

Scenario 3: (Demand) Assume that the cause of this difference is a(n) increase/decrease in the demand for goods and services. In other words, by households, firms and the government consuming and investing more/less than before.

I will ask you now about your beliefs under such a scenario.
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Each participant answers posteriors for each scenario. Posteriors are the same questions as asked as prior. The order of the scenarios is determined by the treatment group.
Intervention Start Date
2023-12-01
Intervention End Date
2024-01-31

Primary Outcomes

Primary Outcomes (end points)
The elasticity of unemployment expectations and wage bargaining to inflation expectations.
Primary Outcomes (explanation)
This will be computed as coefficient in a regression of percentage deviation in inflation on percentage change in unemployment expectation/percentage chance to bargain.

Secondary Outcomes

Secondary Outcomes (end points)
1. Difference between men and women
2. Difference across different scenarios
Secondary Outcomes (explanation)
1. Is measured as coefficient on the interaction term with female dummy
2. Is measured using within-subject design.

Experimental Design

Experimental Design
The survey uses hypothetical vignettes to estimate the effect of changes in inflation expectations on excpectations about unemployment and growth as well as macroeconomic behaviors. Participants are asked for their prior inflation, unemployment and growth expectations and are then presented with different scenarios that move expectations away from their baseline about inflation. Treatments are in 2x6 factorial design, either moving up or down and then one of 6 treatment arms which present 3 scenarios (potential causes) in all possible order combinations. Expectations are then reelicited.

Experimental Design Details
Treatment arms:
up: increase expectations by a random factor in U((1,2])
down: decrease expectations by a random factor in U([0,1))

1: open - demand - supply
2: open - supply - demand
3: demand - open - supply
4: demand - supply - open
5: supply - open - demand
6: supply - demand - open
Randomization Method
Randomization done by a computer.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
approx. 1200 individuals in each country
Sample size: planned number of observations
approx. 1200 individuals in each country
Sample size (or number of clusters) by treatment arms
approx. 100 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Department’s Research Ethics Committee (DREC), Department of Economics at the University of Oxford
IRB Approval Date
2023-10-13
IRB Approval Number
ECONCIA23-24-03
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials