Inequality and Preferences for Distribution: Insights from a Large-Scale Experiment

Inequality in various forms -- economic, gender-based, educational, and migratory -- shapes societies profoundly, influencing everything from social cohesion to individual opportunities. Despite widespread attention to these issues, there remains a critical gap in understanding how people perceive different forms of inequality and what drives their willingness to support policies aimed at reducing disparities. Our randomized controlled trial seeks to fill this gap by exploring how individuals react to diverse forms of inequality and how these reactions influence their attitudes and beliefs toward policies that could address these issues. This study involves the presentation of four unique video interventions, each crafted to illustrate one type of inequality. Participants will be randomly assigned to one of these videos, which specifically address economic, gender, educational, or migration inequalities. Each video combines real-life examples, statistics, and narratives to highlight the impact of these disparities on individuals and communities, with the intention of eliciting thoughtful reflection and emotional engagement.
The core objective of this intervention is to assess whether raising awareness through targeted visual storytelling can shift beliefs and increase support for redistributive and inclusive policies. By analyzing the differences in responses across groups, we hope to uncover insights into the specific factors that shape individuals' perceptions and motivations regarding inequality. This research ultimately seeks to inform the design of policies and campaigns that resonate more deeply with public values and priorities, supporting a more inclusive and equitable society.
Our main goal is to capture the responses to each type of inequality and identify how exposure to information about different forms of disparity impacts individuals' support for policies aimed at reducing such inequalities. We believe that a better understanding of these dynamics can lead to more effective advocacy and policymaking, aligning public support with initiatives that address inequality holistically.
Our hypothesis is that targeted awareness-raising interventions can create a measurable change in the way individuals perceive and are willing to act upon different forms of inequality. Specifically, we expect that those exposed to information on economic inequality may show greater support for policies like progressive taxation, while those who view content on gender inequality may become more inclined to support workplace equality measures. This nuanced approach will provide a comprehensive view of how different aspects of inequality are prioritized by individuals and what types of messaging are most effective in promoting social cohesion and equity.

Inequality is a complex and multifaceted issue that persists across societies and manifests in various dimensions, each with distinct social, economic, and political implications. While economic inequality is often the focus of policy discussions, gender, educational, and migration inequalities are equally impactful in shaping individual experiences and societal dynamics. However, perceptions of these inequalities are far from uniform. Different forms of inequality may elicit varying levels of empathy, moral outrage, and support for corrective policies, depending on cultural, socioeconomic, and individual factors.
This randomized controlled trial was conceived to address the critical need for understanding these differences in perception. Our central hypothesis is that by exposing individuals to specific narratives about economic, gender, educational, and migration inequalities, we can influence their beliefs and preferences regarding policy responses. This approach is grounded in the idea that informed awareness -- particularly when presented through emotionally resonant visual media -- has the potential to shift perspectives and foster a more active engagement with issues of social justice.
The intervention comprises four carefully crafted video presentations, each focusing on a distinct type of inequality:
• Economic Inequality: This video delves into the disparities in income and wealth distribution, illustrating the limitations imposed on individuals who lack financial resources. Through statistical insights, it highlights how income gaps affect access to essential services and opportunities for advancement.
• Gender Inequality: This segment explores the barriers faced by women in various spheres, including the workplace and the household. It examines societal norms and systemic biases that limit gender equality, fostering awareness of the need for inclusive policies.
• Educational Disparity: This video addresses how unequal access to education perpetuates cycles of disadvantage, affecting individuals' economic prospects and overall quality of life. By showcasing the stories of individuals affected by educational inequality, the video underscores the long-term impacts of unequal educational opportunities.
• Migration Inequality: The final video focuses on the unique challenges faced by migrants, including restricted access to resources and services and social discrimination. It aims to foster empathy and understanding by presenting personal stories of resilience amidst systemic barriers.
Participants are randomly assigned to one of these treatment groups or to a control group that does not receive any video intervention. After viewing the video, participants complete a comprehensive survey that measures not only their emotional responses but also their beliefs about inequality and their willingness to support redistributive or inclusion-focused policies. By employing a randomized approach, we can isolate the effect of each type of inequality exposure on participants' policy preferences and attitudes.
The primary goal of this study is to identify how exposure to different types of inequality information influences support for donations. This research has the potential to offer deep insights into the factors that shape public opinion on social justice and redistributive policies. By distinguishing between responses to various forms of inequality, we aim to provide policymakers with a relevant understanding of how to frame and advocate for inclusive policies. Additionally, the findings may guide future campaigns by highlighting which types of inequality resonate most strongly with specific segments of the population, ultimately supporting a more targeted and effective approach to reducing inequality and fostering social cohesion.
Our study is motivated by the belief that meaningful societal change requires more than policy shifts; it requires a cultural shift in attitudes towards inequality. By raising awareness through this experimental framework, we hope to uncover not only the levels of support for various policies but also the underlying moral and psychological factors that drive these preferences. This approach will provide a rich, data-driven basis for advocacy and public policy that is responsive to the actual beliefs and values of diverse community members.

2024-11-16

2024-12-14

The primary outcomes of this study is the measurement of individuals' preferences for re-distribution.As literature show, individuals are more likely to favor a progressive tax system where the wealthiest segment of the population (top 1%) contributes a disproportionately higher share of public taxes compared to lower-income groups. Specifically, the perception of fairness in tax distribution will lead respondents to allocate a higher tax burden to the richest groups while reducing the burden for the bottom 50% of the population, reflecting
We aim to examine the impact of awareness-raising videos on participants' preferences for tax distribution across income groups. Participants are initially asked to allocate what they believe to be a fair percentage of the tax burden across various income segments (top 1%, next 9%, next 40%, and bottom 50%). After responding, the treatment group watches videos on inequality, addressing income and gender disparities, while the control group does not view any video. Following this, all participants are asked to reassess their preferred tax distribution. By comparing changes within individuals' responses (pre- and post-video) and across the treatment and control groups, this study seeks to determine whether increased awareness influences willingness to endorse a more progressive tax distribution, where higher-income groups bear a larger share of the tax burden relative to lower-income groups.
The other relevant outcome of of this study is the inclusion of experimental games for measuring individuals' preferences and behaviors regarding altruism, and risk-taking. These outcomes will be assessed through two distinct games designed to reveal participants' willingness to share, reciprocate, and engage in financially risky behavior. These games will be introduced after participants have viewed one of four videos, each addressing different forms of inequality: economic, educational, gender, or migration related.

Tax Burden: Participants are asked to allocate the tax burden across different income groups (top 1%, next 9%, next 40%, and bottom 50%) to reflect what they believe is a fair distribution based on each group's resources.
Game 1: In this game, participants are assigned roles as Participant A or Participant B. Each participant begins with an initial endowment of 10 euros. Participant A can choose to transfer any portion of this amount to Participant B. The transferred amount is then tripled and given to Participant B, who can decide how much, if any, to return to Participant A. This setup allows us to observe and quantify altruism and trust behaviors within the context of financial decisions.
Game 2: In this game, participants are invited to consider an investment proposal. Each participant receives 10 euros and can decide to invest any portion of it in a high-risk, high-reward opportunity, where there is a 50% chance of tripling the investment and a 50% chance of receiving nothing. This game enables us to measure participants' tolerance for financial risk under conditions of uncertainty.
The primary outcomes aim to capture changes in participants' economic behaviors—specifically, their propensity to tax burden, altruism and the take on financial risks—following exposure to information about different dimensions of inequality. By comparing behaviors across the four treatment groups, we seek to understand if and how awareness of inequality influences social and economic decision-making. The underlying hypothesis is that heightened awareness about inequality could alter individuals' views and behaviors regarding fairness, trust, risks-aversion and potentially motivating more prosocial and equitable behavior in economic interactions.

The secondary outcomes assess participants' intentions and beliefs related to charitable giving and social support. After completing the primary games, participants are asked about their willingness to donate to a charity focused on social equity and support. This measure allows us to examine the potential spillover effects of awareness on direct altruistic behaviors.
In addition to donation intentions, secondary outcomes include self-reported measures of attitudes and beliefs towards inequality across economic, educational, gender, and migration domains. These attitudes will be measured using survey items that gauge participants' support for policies or actions that address these types of inequality.

The secondary outcomes are intended to provide a broad understanding of how awareness influences social attitudes and the desire to contribute to social causes. By examining these secondary measures, we aim to understand whether exposure to inequality information not only impacts direct economic behavior but also fosters a broader commitment to social equity. This could indicate that awareness interventions may have a lasting impact on individuals' prosocial motivations beyond immediate financial decisions.

This study aims to examine how different forms of inequality -- economic, educational, gender, and migratory -- are perceived by individuals and influence their attitudes towards redistributive actions. Participants are randomly assigned to one of four treatment groups, each exposed to a video addressing a specific type of inequality. Each video aims to raise awareness and provoke thought on the respective inequality topic. Following the video, participants complete tasks designed to measure their beliefs, intentions, and possible actions related to inequality and redistribution.

The experiment is structured as a randomized controlled trial (RCT) with four treatment groups and a control group. Each treatment group is exposed to a distinct video focusing on one type of inequality: economic, educational, gender, or migratory. The videos are designed to stimulate reflection on inequality issues, prompting participants to consider their beliefs and actions related to inequality and social support.
After viewing the assigned video, participants engage in a series of tasks, including on tax allocation, two behavioral games and a donation task. The experimental game simulates a redistribution decision, where participants are asked to allocate a sum of money between themselves and another participant, observing the impact of tripled transfers. The second game involves an investment decision with a 50% chance of tripling the investment or yielding zero return, aiming to capture participants' risk preferences in light of inequality themes. Finally, participants are asked about their willingness to donate to a charity, assessing altruistic responses influenced by the inequality topics presented.
The primary outcome of the study is to measure changes in beliefs and redistributive intentions across different inequality dimensions, while secondary outcomes focus on behavioral shifts in economic decisions like tax allocation, risk tolerance, and altruistic giving. Data will be analyzed to determine if exposure to specific inequality themes has differential impacts on participants’ preferences and actions related to inequality.

The randomization process for this study was designed to ensure a fair and unbiased distribution of participants across the various treatment and control groups. The agency awarded the contract for this experiment has employed a computer-based random number generator, executed in an office environment under controlled conditions, to allocate participants randomly. Specifically, each participant was assigned a unique identification number upon enrollment. Using specialized software, we then applied a randomization algorithm that assigned each individual to one of the four treatment groups or the control group with equal probability.
This computer-generated approach was chosen to eliminate any potential human bias and to maintain the integrity of the randomization process. By leveraging advanced randomization algorithms, we ensured that each participant had an identical chance of being assigned to any group, enhancing the internal validity of our study. Furthermore, this method allows for replicability and transparency in our methodology, as the computer algorithm used can be independently reviewed and audited if necessary.
To further reinforce the reliability of our randomization procedure, we conducted a series of preliminary checks post-randomization to confirm that key baseline characteristics (e.g., age, gender, socioeconomic background) were evenly balanced across groups. This additional step ensures that any observed effects in our results are more likely attributable to the intervention itself, rather than to underlying differences between groups.
In sum, the use of a computer-based randomization method in this study reflects our commitment to upholding rigorous scientific standards, fostering confidence in the robustness of our findings, and contributing valuable insights to the field.

In this study, the primary unit of randomization is the individual participant. Each participant is assigned to one of the treatment groups or the control group independently, ensuring that each person has an equal chance of being allocated to any of the experimental conditions. This individual-level randomization approach allows us to capture a wide range of personal reactions and preferences to the interventions, thus enhancing the robustness of our findings by controlling for individual variability.
Additionally, to manage logistical aspects of our study design, sessions were organized for small groups where randomization was implemented at the individual level within each session. This approach maintains random assignment integrity while optimizing data collection procedures.
If applicable, any subsequent analyses will account for the session clustering in our statistical models, ensuring accurate inferences about the treatment effects.

Yes

The design of our study prioritizes the formation of diverse clusters to capture a wide spectrum of gender, demographic, and geographic characteristics within the target population. Each cluster will be composed of participants selected to represent specific population segments relevant to our research objectives, allowing us to explore variations in response to the intervention with greater granularity. These clusters will serve as the primary unit of randomization, ensuring that the diversity within each grouping mirrors broader social patterns. By structuring the clusters to capture heterogeneity in terms of age, gender, income, and regional factors, we aim to facilitate a more precise understanding of how various groups may experience or respond differently to the interventions being tested. The organization into clusters not only enhances the robustness and representativeness of the data but also allows for targeted analysis of subgroup effects. This approach ensures that the study outcomes will be both nuanced and reflective of the complex social dynamics at play, providing insights that are generalizable yet sensitive to distinct population characteristics.

Our study aims for a planned number of observations around 20,000 individuals, as specified in our agreement with the implementing agency. This large sample size is systematically divided into clusters, designed to capture a representative cross-section of the target population. Each cluster will consist of a subset of these 20,000 participants, ensuring that our observations cover a broad array of characteristics and allow for a relevant analysis across various segments. The approach of clustering within a substantial sample size enhances the generalizability of our findings and provides a robust framework for identifying patterns, behaviors, and responses that may differ based on factors like region, gender, income, and age.

We have organized the 20,000 participants into four treatment groups and one control group, each containing 4,000 individuals, to examine the distinct effects of interventions focused on different types of inequality: economic, gender, educational, and migration-related. This structure allows us to analyze the impact of each intervention separately, while the control group provides a crucial baseline for comparison. To achieve a representative sample, participants are grouped into clusters, with each cluster assigned to one of the treatment groups or the control. This clustering strategy ensures a diverse representation of demographic and geographic characteristics across the sample, enhancing the validity of our findings. By carefully balancing these factors, we can perform a nuanced analysis of how beliefs and behaviors vary across the different dimensions of inequality, as well as explore potential interactions when multiple inequalities are addressed simultaneously.

The power calculation for the main outcomes is currently designed to detect a minimum effect size of a 2-percentage point increase in awareness levels (compared to the control group) with a 5% significance level. Given the sample structure and clustering across the five arms -- four treatments and one control -- the anticipated standard deviation for our primary outcomes was estimated at 0.2, leading to a detectable effect size of approximately 5% when accounting for clustering. These parameters were carefully set to ensure that we could sensitively detect meaningful differences across treatment groups, particularly in areas like changes in beliefs, behaviors, or awareness related to various dimensions of inequality.