(Un)bounded rationality of inequality recognition: Perception through the use of the stratification index and percentiles

Last registered on October 15, 2021

Pre-Trial

Trial Information

General Information

Title
(Un)bounded rationality of inequality recognition: Perception through the use of the stratification index and percentiles
RCT ID
AEARCTR-0008317
Initial registration date
October 13, 2021
Last updated
October 15, 2021, 1:17 PM EDT

Locations

Region

Primary Investigator

Affiliation
The University of Tokyo

Other Primary Investigator(s)

PI Affiliation
The University of Tokyo
PI Affiliation
The University of Tokyo
PI Affiliation
The University of Tokyo

Additional Trial Information

Status
In development
Start date
2021-11-01
End date
2021-11-14
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Our decision-making behaviors toward public policy depend on our perception of society. Preferences for policies that address inequality and poverty rely on how we perceive inequality. Thus, it follows that our preferences could depend on our cognitive capacity to perceive facts. If our capacity is large enough compared to the presented measurement units used to describe society, then such measurement units are irrelevant to our decisions regarding public policy. However, if our cognitive capacity is bounded across the measurement units used to describe society, then our political decision-making in fact begins with the choice of measurement unit used to describe reality prior to making decisions that are recognized to be sensitive to partisanship.

Thus, whether measurement units based on the same fact affect our perceptions or impressions of reality is critical to examine further, i.e., beyond behavioral and experimental economics and extending toward social sciences in general and policy-making.

Income inequality is a keen issue in both developed economies and relatively wealthy emerging economies. We need to address this issue. Thus, understanding how people feel about inequality from different measurement units based on the same fact is critical to policy-making in order to address the issue. To investigate how we perceive income inequality, we compare two measures of inequality, namely, the stratification index (Zhou 2012) and percentiles.

We use data on income distribution in Japan from 1985 and 2018, which were obtained from the National Livelihood Survey conducted by the Ministry of Health, Labour and Welfare of the government of Japan. Using this data, we calculate percentile proportions and the stratification index.

Then, we implement a randomized conjoint experiment on the internet. We show the household income distribution of households with children and households of elderly people of two "societies"; one's income distribution from 1985 to 2018 is described according to percentile proportions and the other one's income distribution from 1985 to 2018 is described according to the stratification index. Both measures actually describe the same Japanese society in 1985 and 2018. Then we ask respondents which society seems to have become more unequal between 1985 and 2018.

If we find different evaluations between two descriptions according to income percentiles and stratification index, that means that our perception of inequality is susceptible to the presentation of statistics of the very same distribution. Otherwise, we would conclude that the human recognition of inequality does not show a significant difference between the percentile measure and stratification index presentations and that we need more trials to investigate whether the irrelevance of measurement units applies to a broader range of measures of inequality.

A policy implication of this study is that if the former result is obtained, then it can be seen that we essentially begin to decide a public policy choice when we calculate a measurement of inequality and present this measurement to the public, which occurs far before citizens and their representatives begin to discuss such policies consciously. Otherwise, citizens who are exposed to different measurements are more likely to achieve a consistent perception of a certain issue and share their recognition of the facts, which sets a better ground for policy-making.

Zhou, Xiang (2012) "A nonparametric index of stratification," Sociological Methodology, 42, 365-389. https://doi.org/10.1177/0081175012452207
External Link(s)

Registration Citation

Citation
Fujihara, Sho et al. 2021. "(Un)bounded rationality of inequality recognition: Perception through the use of the stratification index and percentiles." AEA RCT Registry. October 15. https://doi.org/10.1257/rct.8317-1.0
Experimental Details

Interventions

Intervention(s)
We create two measures of household income inequality using the same distribution of household income data obtained from the National Livelihood Survey conducted by the Ministry of Health, Labour and Welfare of the government of Japan in 1985 and 2018. The measures are percentiles and the stratification index. We randomly cite a point from each measure of 1985 and 2018 and show these points to respondents. We do not indicate that the measures are drawn from the same distribution but rather explain that each point measures the inequality of "a" society. For randomization, we adopt a randomized conjoint experimental design. Looking at the two descriptions according to the percentile and stratification index that are generated by the randomized conjoint design, the respondents are then asked to evaluate which "society" became more unequal between 1985 and 2018. Our intervention is a combination of two samples, each of which is based on the percentiles and the stratification index taken from the same household income distribution. Thus, our intervention is based on a factorial design.
Intervention Start Date
2021-11-01
Intervention End Date
2021-11-14

Primary Outcomes

Primary Outcomes (end points)
Depending on the human cognitive capacity, we predict that measurement units of the same fact will affect human recognition of that fact. Our experiment aims to investigate whether percentiles of income and the stratification of income cause respondents to have different levels of recognition of the same fact.

In our experiment, we describe changes in household income distribution between 1985 and 2018 according to household income percentiles and the stratification index; then we ask the respondents whether either one seems to become more unequal than the other. If either one is evaluated to have become more or less unequal than the other, we interpret this outcome such that human recognition of the inequality is sensitive to measures.

Otherwise, we will conclude that our experiment remains within the rationality of human recognition and that further investigation is needed to judge how broad the range of inequality measurement is to keep the measurement unit from being relevant to the human recognition of inequality.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We create two measures of household income inequality using the data obtained from the National Livelihood Survey conducted by the Ministry of Health, Labour and Welfare in 1985 and 2018; these two measures are the percentiles and the stratification index. We cite 5 points from each measure in both 1985 and 2018. We randomly pair a point from each measure, show them to each respondent (who is not informed that these are from the same society), and then ask the respondent which "society" seems to have become more unequal between 1985 and 2018. For the randomization in regard to pairing the measures, we adopt a randomized conjoint experimental design as a factorial design. We assign each respondent five tasks of choice between the randomly matched pairs of inequality measures.

Along with the experiment, we collect information about the respondents' background characteristics such as age, gender, prefecture, working status, employers' size, job title, education, income, household income, political preferences, partisanship, whether reading a newspaper, Internet news, or books, and self-perception of social class. Considering the possible heterogeneity of intervention effects across the background characteristics, we deploy a generalized random forest algorithm.

Experimental Design Details
Randomization Method
The paring of the two inequality measures is to be conducted by a survey company using a computer.
Randomization Unit
Individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
15,000 individuals.
Sample size: planned number of observations
15,000 individuals X 5 tasks X 2 outcomes (more/less unequal) = 150,000 observations.
Sample size (or number of clusters) by treatment arms
15,000 individuals as our design is a factorial design.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethical Review Board, Institute of Social Science, The University of Tokyo
IRB Approval Date
2021-07-21
IRB Approval Number
21-4

Post-Trial

Post Trial Information

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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
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Relevant Paper(s)

Reports & Other Materials