Preferences and a Policy Experiment on Regional Job Choices

Last registered on September 19, 2025
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Pre-Trial

Trial Information

General Information

Title
Preferences and a Policy Experiment on Regional Job Choices
RCT ID
AEARCTR-0016482
Initial registration date
September 15, 2025

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
September 19, 2025, 9:58 AM EDT

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

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Primary Investigator

Name
Syngjoo Choi
Affiliation
Seoul National University
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Other Primary Investigator(s)

PI Name
Ohik Kwon
PI Affiliation
Korea University
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PI Name
Seonghoon Kim
PI Affiliation
Singapore Management University
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PI Name
Seunghak Lee
PI Affiliation
Seoul National University
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Additional Trial Information

Status
On going
Start date
2025-08-01
End date
2027-12-31
Keywords
Behavior, Governance, Labor
Additional Keywords
Local labor markets, Young adult employment, Information treatment, Discrete choice experiment, Regional development, Workplace preferences, Survey experiment
JEL code(s)
C93, J28, J61, R23, R58
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
South Korea faces significant regional inequality, with young adult workers increasingly concentrated in the Seoul metropolitan area despite government efforts to promote balanced regional development. This study investigates the factors influencing young adult workers' (aged 19-39) decisions to work in non-Seoul urban areas, addressing a critical challenge for regional sustainability and economic vitalization. Using a sample of 3,200 young wage workers stratified by age, gender, and region, we conduct two complementary experiments designed to inform evidence-based policy interventions.
First, we implement a discrete choice experiment (DCE) where participants make 16 binary choices between hypothetical job offers (8 in the general module and 8 in the Pohang-specific module). The experiment systematically varies workplace attributes across key dimensions: distance from Seoul (travel time), housing support policies, work flexibility (worktime and workplace flexibility), workplace culture, and regional amenities attributes (education, medical, commercial, transport, and leisure), along with annual income. Income levels are randomly drawn from 70-150% of each participant's current or expected salary to ensure realistic trade-offs. The Pohang-specific module features 8 choices with fixed locations (Seoul metropolitan area versus Pohang) and an additional industry sector attribute, enabling place-specific policy insights.
Second, we conduct a randomized information treatment experiment to test whether providing information about regional living conditions can shift young adult employment preferences. Participants are randomly assigned to view one of three 30-40 seconds long videos: factual housing cost comparisons between Seoul and regional cities (Treatment 1), an AI-generated conversational dialogue discussing regional quality of life and cost advantages (Treatment 2), or a placebo video about tourism (Control). We then measure changes in intentions to work in non-Seoul region areas, sector-specific willingness to work for regional public institutions and large corporations, and corrections in misperceptions about regional housing costs.
By incorporating specific policy attributes in the choice experiment, including various forms of housing support and work arrangements, this study enables pre-implementation evaluation of policy effectiveness. The experimental design allows us to calculate willingness-to-pay estimates for each attribute, revealing the implicit price young workers place on different job and location characteristics. Furthermore, our stratified sampling approach enables analysis of heterogeneous treatment effects across key demographic dimensions including current residence (metropolitan versus regional), prior regional experience, age cohorts, and employment status. This heterogeneity analysis is crucial for identifying which population segments are most responsive to different policy interventions, enabling more targeted and cost-effective policy design.
The study contributes to both academic literature and policy practice by providing causally identified estimates of young adult preferences for regional employment and testing scalable information interventions that could shift these preferences. By linking stated preferences to specific policy instruments, we generate actionable evidence for policymakers seeking to attract young talent to regional areas and promote more balanced spatial development in South Korea.
External Link(s)

Registration Citation

Citation
Choi, Syngjoo et al. 2025. "Preferences and a Policy Experiment on Regional Job Choices." AEA RCT Registry. September 19. https://doi.org/10.1257/rct.16482-1.0
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Experimental Details

Interventions

Intervention(s)
This study implements two distinct experimental interventions to understand young adult employment preferences for regional areas in South Korea.

The first intervention is a discrete choice experiment where participants complete 16 binary choice tasks between hypothetical job offers (8 in the general module and 8 in the Pohang-specific module). Each choice varies systematically across eleven attributes: Seoul accessibility measured by travel time, regional housing support policies including subsidies and settlement funds, work time flexibility options, workplace flexibility, workplace culture from hierarchical to horizontal, local education infrastructure quality, medical facility accessibility, commercial amenity availability, public transportation convenience, cultural and leisure facilities, and annual income. Income levels are randomized between 70-150% of each participant's current or expected salary. Additionally, participants complete a specialized Pohang module with 8 choices comparing fixed locations of Seoul metropolitan area versus Pohang, incorporating an additional industry sector attribute.

The second intervention consists of an information treatment experiment using randomized video assignments. Participants view one of three videos lasting 30-40 seconds: Treatment 1 presents factual housing cost comparisons between Seoul and regional cities, Treatment 2 features AI-generated dialogue discussing regional quality of life and cost advantages, and the Control group views a placebo video about tourism destinations. Following video exposure, we measure changes in regional work intentions and housing cost perceptions.
Intervention Start Date
2025-09-01
Intervention End Date
2025-09-30

Primary Outcomes

Primary Outcomes (end points)
[Discrete Choice Experiment Outcomes]
We collect binary choice indicators from eight paired comparisons in the general module and eight paired comparisons in the Pohang-specific module, recording which workplace option (1 or 2) participants select in each scenario. From these choices, we estimate preference parameters using conditional logit or mixed logit models to determine coefficients for each job and location attribute, calculate willingness-to-pay estimates as ratios of attribute coefficients to the income coefficient, and examine heterogeneous preferences across demographic subgroups including age, current residence, and regional experience.

[Information Treatment Experiment Outcomes]
We measure participants' intention to work and reside in non-metropolitan regions through Question P1, using a 5-point Likert scale ranging from no intention at all to strong intention. This outcome is measured immediately following exposure to the treatment videos.
Primary Outcomes (explanation)
These outcomes directly address our core research questions about young adult workplace preferences and the causal effect of information provision on regional work intentions. The DCE allows us to quantify the relative importance of various job and location attributes in young adult employment decisions, while the information experiment tests whether providing factual or narrative information about regional living conditions can shift intentions to work in regional areas. Together, these outcomes enable evidence-based policy recommendations for attracting young workers to regional cities.

Secondary Outcomes

Secondary Outcomes (end points)
[Sector-Specific Regional Work Intentions]
We measure willingness to work in regional public institutions through Question P2 and regional large corporations through Question P3, using the same 5-point scale as the primary outcome.

[Information Treatment Effects on Housing Cost Perceptions]
We assess changes in perceived housing purchase burden indices for five regional cities (Gyeonggi, Gwangju, Daejeon, Daegu, and Ulsan), calculated as the difference between post-treatment responses in Question P5 and baseline responses in Question A4.

[Information Sharing Intentions]
We examine participants' likelihood of sharing video information with others through Question P4, which uses a 5-point scale to measure the perceived credibility and usefulness of the treatment information.

[Subgroup-Specific Treatment Effects on Regional Work Intentions]
We measure the same regional work intention outcome (Question P1) separately for demographic subgroups including metropolitan versus regional residents, those with and without 6+ months of prior regional residence experience, four age cohorts (19-24, 25-29, 30-34, 35-39), and employed versus non-employed participants.
Secondary Outcomes (explanation)
These secondary outcomes provide crucial insights for policy design and implementation. The sector-specific intentions help identify whether certain types of employers might be more successful in attracting young workers to regional areas. The housing cost perception analysis reveals whether information treatments can correct misperceptions about regional living costs, which may be a barrier to regional employment. The information sharing measure provides insight into the potential viral spread of information campaigns, which has important implications for the scalability and cost-effectiveness of such policy interventions. The subgroup-specific treatment effects are particularly important for policy targeting, as they identify which demographic groups show the strongest response to information interventions, enabling more efficient allocation of resources for regional development initiatives.

Experimental Design

Experimental Design
Our study employs two complementary experimental designs with a sample of 3,200 young wage workers aged 19-39, stratified by age, gender, and region.

[Discrete Choice Experiment]
Participants complete 16 binary choices between pairs of hypothetical job offers (8 in the general module and 8 in the Pohang-specific module). We systematically vary eleven attributes across choices using an efficient design that ensures orthogonality and level balance. The income attribute is personalized for each participant, randomly drawn from 70-150% of their current or expected salary to ensure realistic trade-offs. The Pohang-specific module with 8 choices features fixed location comparisons and an industry sector attribute.

[Information Treatment Experiment]
Following the DCE, participants are randomly assigned with equal probability to one of three video conditions. Random assignment occurs at the individual level through the survey platform's randomization algorithm. We measure treatment effects on regional work intentions and housing cost perceptions immediately after video exposure.

[Pilot Study]
A pilot study with 204 participants was conducted in August 2025. Based on pilot results, we added a workplace flexibility attribute to the general DCE module and expanded the Pohang-specific module from 4 to 8 choices to enable more precise estimation of location-specific preferences. All other instruments and procedures were validated for the main study.
Experimental Design Details
Not available
Randomization Method
Computer-based stratified randomization implemented through the online survey platform. For the information treatment experiment, participants are assigned to one of three video conditions (Treatment 1, Treatment 2, or Control) using a stratified randomization algorithm that ensures balance across key demographic characteristics. The randomization procedure maintains approximately equal distribution of age groups (19-24, 25-29, 30-34, 35-39), gender, and regional residence (metropolitan versus non-metropolitan) across all three experimental conditions. Within each stratum, participants are randomly assigned to treatment ensuring both randomization and demographic balance across treatment groups.
Randomization Unit
Individual participant level with stratification. Each survey respondent is independently randomized to treatment conditions within their demographic stratum defined by age group, gender, and regional residence.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A. individual level randomization
Sample size: planned number of observations
3,200 individual participants aged 19-39 years
Sample size (or number of clusters) by treatment arms
[Information Treatment Experiment]
Approximately 1,067 participants in Treatment 1 (factual housing cost video), 1,067 participants in Treatment 2 (AI dialogue video), and 1,066 participants in Control (tourism placebo video), with balanced representation of age groups, gender, and regional residence across all three arms. All 3,200 participants complete the DCE component. Regional oversample: The sample includes an oversample of 1,000 participants from Daegu and North Gyeongsang Province (approximately 33% of total sample) to ensure robust analysis for this region given Pohang's strategic importance for regional development policy.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
This study aims to achieve 90% statistical power with a 5% significance level (two-tailed test) for detecting treatment effects in the information experiment. With approximately 1,067 participants per treatment arm and three experimental conditions, our design targets the detection of effect sizes that are both statistically significant and policy-relevant. Based on standard power calculation formulas for three-group comparisons with our sample size, we anticipate being able to detect small to medium effect sizes, with precise estimates to be determined following pilot data analysis. The pilot study conducted in August 2025 with 204 participants provided critical empirical data to refine these power calculations. Specifically, the pilot allowed us to estimate the actual variance in our outcome measures, observe baseline response distributions on the 5-point Likert scale for regional work intentions, and assess the initial magnitude of treatment effects. These pilot results enabled us to calculate more precise minimum detectable effect sizes and, if necessary, adjust our main study sample size to ensure adequate statistical power for detecting policy-relevant effects. The stratified randomization design, which ensures balance across age groups, gender, and regional residence, is expected to improve statistical precision by reducing unexplained variance in treatment effect estimates. This design feature may allow for detection of smaller effect sizes than would be possible with simple randomization, though the exact efficiency gains will be quantified following pilot data analysis.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Seoul National University
IRB Approval Date
2025-08-05
IRB Approval Number
IRB No. 2508/004-014
Analysis Plan