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Elicit preferences in the entrepreneurial financing process

Last registered on February 07, 2020
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Pre-Trial

Trial Information

General Information

Title
Elicit preferences in the entrepreneurial financing process
RCT ID
AEARCTR-0004982
Initial registration date
February 01, 2020

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
February 07, 2020, 3:48 PM EST

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

Locations

Country
Canada
Region
Include investors from related developed countries, Canada, Australia, etc
Country
China
Region
Include Chinese investors
Country
United Kingdom of Great Britain and Northern Ireland
Region
Include other European Countries if possible
Country
Singapore
Region
Include other area of East Asia
Country
United States of America
Region
Include the whole US investors

Primary Investigator

Name
Ye Zhang
Affiliation
Columbia University
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2020-02-01
End date
2020-11-01
Keywords
Finance & Microfinance, Firms & Productivity, Gender
Additional Keywords
Entrepreneurship
JEL code(s)
C78, C93, D83, G40, G24, J15, J1
Secondary IDs
Abstract
Startups are crucial to the innovation and development of an economy. However, many startups face difficulties in terms of raising funding for their projects. In this project, we use field experiments and unique databases to provide causal empirical evidence for identifying potential frictions in the entrepreneurial financing process.
External Link(s)

Registration Citation

Citation
Zhang, Ye. 2020. "Elicit preferences in the entrepreneurial financing process." AEA RCT Registry. February 07. https://doi.org/10.1257/rct.4982-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Individual-level randomized intervention is implemented.
Intervention Start Date
2020-02-01
Intervention End Date
2020-10-31

Primary Outcomes

Primary Outcomes (end points)
Investors' evaluation, behaviors
Startups' evaluation, feedback
Primary Outcomes (explanation)
Primary outcomes are direct measurements rather than synthetic outcomes.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment design contains a series of field experiments.
Experimental Design Details
Outcomes:
Experiment 1: Email Correspondence Test
Test:
First order effect: gender, race, education, startup comparative advantages (startup quality)
Interaction Effect: gender by race, gender by education, gender by quality, and other interaction effects
Heterogeneous effect: we will replicate Gornall and Strebulav (2019)’s heterogeneous effect. Specifically, heterogeneous effect along investors’ gender, education, fund size, age.

Experiment 2: Investor-side IRR experiment
Test:
First order effect: all the randomized characteristics of the startups will be tested. All the measurements from the four questions asked for each profile will be used as the outcome variable. Specifically, we want to test the effect of gender, race, age, education and comparative advantages (startup quality)
Interaction Effect: gender by race, gender by education, gender by quality, gender by age and other interaction effects
Heterogeneous effect: if the sample size is large enough, we will check heterogeneous effect along all the demographic dimension of the investors

Dictator Game: use the donated money as the main outcome variables to disentangle the taste-based discrimination.

Experiment 3: Startup-side IRR experiment
Test:
First order effect: all the randomized characteristics of the investors will be tested. All the measurements from the four questions asked for each profile will be used as the outcome variable. Specifically, we want to test the effect of gender, race, education and experience (investor quality)
Interaction Effect: gender by race, gender by education, gender by quality, and other interaction effects
Heterogeneous effect: if the sample size is large enough, we will check heterogeneous effect along all the demographic dimension of the investors (i.e gender)

Follow-up experiment: use the rating and satisfaction rate as the main outcome variables to test the homophily channel of startups’ fund seeking behaviors.

Detailed regressions are described in the attached files.
Randomization Method
Individual-level randomization is implemented on the computer by coding.
Randomization Unit
Individual-level randomization is implemented.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
20,000-30,000
Sample size: planned number of observations
experiment 1: 20,000-30,000 experiment 2: 50-100 experiment 3: 900
Sample size (or number of clusters) by treatment arms
balanced, 50% treatment and 50% control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
experiment 1: 20,000-30,000 experiment 2: 400 experiment 3: 900
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Columbia Morningside Campus Institutional Review Board
IRB Approval Date
2020-01-17
IRB Approval Number
AAAS6419; AAAS8362
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