Pinging Entrepreneurial Ecosystems

Last registered on May 05, 2021


Trial Information

General Information

Pinging Entrepreneurial Ecosystems
Initial registration date
May 05, 2021

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
May 05, 2021, 11:23 AM EDT

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



Primary Investigator


Other Primary Investigator(s)

PI Affiliation
Max Planck Institute for Innovation and Competition
PI Affiliation
Max Planck Institute for Innovation and Competition

Additional Trial Information

In development
Start date
End date
Secondary IDs
A successful entrepreneurial ecosystem (EE) shows a strong culture of support for entrepreneurship expressed through culture, leadership and and social networks (Stam & van de Ven 2021). We assess entrepreneurs’ willingness to support different levels of start-up ecosystems through a factorial cluster trial in the field. We hypothesize that entrepreneurs are more willing to support their more local EE; that this willingness varies with EE; and that a common industry also plays a role. We plan to contribute to research by providing an additional quantitative measure of the quality of an EE and by identifying an additional lever for policy to increase start-up activity and economic growth.
External Link(s)

Registration Citation

Defort, Aaron, Dietmar Harhoff and Michael Rose. 2021. "Pinging Entrepreneurial Ecosystems." AEA RCT Registry. May 05.
Experimental Details


We send out slightly varied e-mails asking to participate in a survey. The e-mails we aim to send out will have different texts, triggering different feelings of belonging in the participants. Otherwise the mails will be the same mentioning the purpose of the survey, a GDPR claimer and the executing institutions.
The control and treatment interventions are corresponding to a ever growing higher level of relatedness to the subjects in order to isolate the effect. The changes in text should have an impact on the response rate which we interpret as stronger feeling of belonging.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Click rate on the survey link
Primary Outcomes (explanation)
The click rate will be driven by the e-mail content, which is our primary intervention

Secondary Outcomes

Secondary Outcomes (end points)
Response rate
Secondary Outcomes (explanation)
The response rate to the survey is driven by the e-mail content and the interest subjects take in the survey.

Experimental Design

Experimental Design
The above-mentioned interventions are allocated in two ways. Since we are interested in geographical entities we are clustering the participants according to their location (cluster trial). As discussed above our experiment has four arms in order to isolate the mechanisms (factorial trial). The interventions are allocated randomly inside of the clusters.
Experimental Design Details
Our hypotheses are that all treatments will result in significant changes in the response rate, which we interpret as propensity to support the mentioned group. We hypothesize the following order: C1<T1<T2. For T3, there are competing assumptions: Inkpen and Tsang (2005) would suggest that in industrial districts there is no common goal, or trust that is not process based, which would result in T3<T2, in that case it will be interesting to also see where in the order of treatment and control T3 will find its place. Homophily and a closer identification with similar actors would suggest that T3>T2.

The clustering of the subjects is done via a DBSCAN algorithm. This procedure is necessary in order to allocate subjects that are residing in suburbs to the greater metropolitan area, e.g. Boston and Cambridge were connected, just as well as Silicon Valley Cities like Palo Alto and Mountain View.

Ex-ante analyses will try to explain differences in treatment effects between ecosystems. Some initial hypotheses are, that the differences in effect size can be explained by: (1) Level of diversity in the ecosystems (they support any member of the ecosystem and not only based on homophily); (2) Amount of flagship ventures in the ecosystems (members are inspired by common narrative that creates feeling of belongingness); (3) The size of ecosystems (unclear boundaries result in less attachment); (4) Availability of talent in the ecosystems (a scarce regional resource leads to industry-unspecific competition)
Randomization Method
Randomization was done in an office by a computer. For this we used the Pandas package in Python (command: random) with a seed to ensure replicability.
Randomization Unit
Individual randomization inside of clusters
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
110 Ecosystems, plus ca. 11,500 unclustered subjects
1 Ecosystem will be used as pilot
Sample size: planned number of observations
ca. 58.000 start-up executives (founders, CEO, Partner, etc.) 684 start-up executives will be used in a pilot
Sample size (or number of clusters) by treatment arms
ca. 14.500 subjects per arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
If baseline response rate is 2% and N=29,000 (i.e. half of real sample), we can detect an effect size of 0.49-0.57 p.p. in changes in response rate (i.e. 24,5-28,5% with 0.8-0.9 power). If baseline response rate is 1% and N=29,000 (i.e. half of real sample), we can detect an effect size of 0.35-0.41 p.p. in changes in response rate (i.e. 35-41% with 0.8-0.9 power). Similar trials have found effect sizes of 18.7%-54.4%, which could be confirmed in an early pilot of this project (N=124, effect size: 55%). To validate the assumptions about effect sizes we do a pilot with the full technical setup with N=684.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

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Data Collection Complete
Data Publication

Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials