Study of the effectiveness of acceleration mechanisms for start-ups

Last registered on February 24, 2017

Pre-Trial

Trial Information

General Information

Title
Study of the effectiveness of acceleration mechanisms for start-ups
RCT ID
AEARCTR-0001345
Initial registration date
June 14, 2016

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
June 14, 2016, 5:25 PM EDT

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

Last updated
February 24, 2017, 9:31 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
Pontificia Universidad Católica de Chile

Other Primary Investigator(s)

PI Affiliation
Pontificia Universidad Católica de Chile
PI Affiliation
London School of Economics
PI Affiliation
Hong Kong University of Science and Technology

Additional Trial Information

Status
In development
Start date
2017-01-15
End date
2019-07-31
Secondary IDs
Abstract
The design of effective programs for fostering entrepreneurial activity is an important activity for stakeholders across the board. However, limited evidence exists regarding what works and what does not. We build on recent causal evidence on the performance enhancing effects of business accelerators that speculates about the existence of a specific mechanism: structured accountability. This trial seeks to test the effect of structured accountability on entrepreneurial performance. That is, whether encouraging entrepreneurs to articulate specific objectives and following up on their implicit commitment causes entrepreneurs to outperform. Understanding the economic significance of such a programmatic design will help policymakers and investors allocate their resources more effectively, and (assuming a treatment effect) guide entrepreneurs to build a hierarchical structure that they may naturally tend to avoid.
External Link(s)

Registration Citation

Citation
Garg, Sam et al. 2017. "Study of the effectiveness of acceleration mechanisms for start-ups." AEA RCT Registry. February 24. https://doi.org/10.1257/rct.1345-3.0
Former Citation
Garg, Sam et al. 2017. "Study of the effectiveness of acceleration mechanisms for start-ups." AEA RCT Registry. February 24. https://www.socialscienceregistry.org/trials/1345/history/14381
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2017-03-01
Intervention End Date
2019-01-15

Primary Outcomes

Primary Outcomes (end points)
Performance measures as start-up survival, jobs created, key entrepreneurial milestones, capital raised, and sales.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The trial will be a completely randomized design with two treatment levels. The treatments conditions and levels will be: i) No Structured Accountability (control group) and ii) Structured Accountability.

The number of entrepreneurs that participate in Start-Up Chile is approximately 100 in each cohort. Therefore, in two years, 400 participants will be randomly assigned to one of the two treatments. In each cohort half of the participants will be randomly assigned to the Structure Accountability Treatment. Obviously, randomization will occur independently in each of the cohorts.

Approximately on a once a month basis the entrepreneurs will meet with an expert (a mentor, SUP Chile executive or a specific industry expert). A meeting scribe will also be present taking notes in every one of these meetings, both control and treatment groups.

The treated group, those subject to structured accountability, will be asked to articulate the strategic tasks to be completed during the following month, and report about their progress from the previous month. That is, the meeting scribe will make sure that the treatment group entrepreneur gets asked “what would you say are the most important tasks you need to work on during the next month?” The scribe will write down the tasks in a document, which will be revised and discussed during the next monthly meeting. The control group meetings will not have any kind of explicit tracking of their tasks. Regardless, if control group entrepreneurs wish to have a copy of the meeting notes (which will be a summary of the meeting conversation), the scribe will make them available.

Besides taking notes of the meeting, the job of the scribe is to verify the fidelity of the treatment. This means that the only ones getting the structured accountability in each one of their meetings are the entrepreneurs on the treated group. Moreover, the scribe will register the number of tasks committed for the next month, the number of tasks from the last meeting in some state of progress, quality of the tasks, and the time spent on structured accountability. For all (treatment and control) meetings, the scribe will register the total duration of the meeting, and the name of participants and their roles.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
The unit of randomization are the participant start-ups. Half of participant startups on each cohort are randomly assigned to the treatment and the other half are randomly assigned to the control group.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
The treatment is not clustered so the number of observations is 360 start-ups
Sample size: planned number of observations
360 start-ups
Sample size (or number of clusters) by treatment arms
We will conduct the experiment in three cohorts wich will give us a sample size 360 start-ups, with 180 start-ups treated and 180 non-treated
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
A sample size of 360 start-ups (given by four cohorts in two years) and 180 start-ups in each arm of the trial is enough to detect a minimum effect size of 0.33 (Cohen’s D) of our outcome variables. This is assuming an R2 0f 0.06 from our previous study (González-Uribe & Leatherbee 2016) a level of significance of 0.05 and a power of 0.80 on a two-tailed test.
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number