x

Please fill out this short user survey of only 3 questions in order to help us improve the site. We appreciate your feedback!
Economic effects of Entrepreneurship Training
Last registered on March 22, 2019

Pre-Trial

Trial Information
General Information
Title
Economic effects of Entrepreneurship Training
RCT ID
AEARCTR-0004013
Initial registration date
March 16, 2019
Last updated
March 22, 2019 11:42 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Massachusetts Institute of Technology
Other Primary Investigator(s)
PI Affiliation
Asia School of Business - MIT Sloan
PI Affiliation
Asia School of Business - MIT Sloan
Additional Trial Information
Status
In development
Start date
2019-03-18
End date
2020-03-14
Secondary IDs
Abstract
We will be conducting a randomized controlled trial on the Rapid Youth Success Entrepreneurship Program (RYSE).

The RYSE Program is a research and social outreach project that aims to improve youth unemployment rates by equipping low-income individuals aged 18-24 who currently enrolled at polytechnic institutions in Malaysia with the skills to plan, design, and run their own business or startup from scratch. The RYSE Program builds on ASB and MIT’s strengths in entrepreneurship and innovation as well as relying on the experience of local partners and successful entrepreneurs in Malaysia. The RYSE Program takes in uninitiated participants from “zero” to “entrepreneur”. This intensive (hence “rapid” program), will be held at 4 polytechnics in Klang Valley (i.e. Central Region) involving 420 participants for just over 6 months from mid March-December 2019.
External Link(s)
Registration Citation
Citation
Egana del Sol, Pablo, Samuel Flanders and Melati Nungsari. 2019. "Economic effects of Entrepreneurship Training." AEA RCT Registry. March 22. https://doi.org/10.1257/rct.4013-1.0.
Former Citation
Egana del Sol, Pablo et al. 2019. "Economic effects of Entrepreneurship Training." AEA RCT Registry. March 22. http://www.socialscienceregistry.org/trials/4013/history/43800.
Sponsors & Partners

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
Experimental Details
Interventions
Intervention(s)
The RYSE Program is a research and social outreach project that aims to improve youth unemployment rates by equipping low-income individuals aged 18-24 who currently enrolled at polytechnic institutions in Malaysia with the skills to plan, design, and run their own business or startup from scratch. The RYSE Program builds on ASB and MIT’s strengths in entrepreneurship and innovation as well as relying on the experience of local partners and successful entrepreneurs in Malaysia. The RYSE Program takes in uninitiated participants from “zero” to “entrepreneur”. This intensive (hence “rapid” program), will be held at 4 polytechnics in Klang Valley (i.e. Central Region) involving 420 participants for just over 6 months from mid March-December 2019.



Intervention Start Date
2019-03-25
Intervention End Date
2019-08-23
Primary Outcomes
Primary Outcomes (end points)
Risk Taking behavior and Quality, Impulsiveness, Grit, Creativity, Entrepreneurial Intentions.




Primary Outcomes (explanation)
All measures will be measured using self-reported test.

Creativity will be classified into three dimensions: flexibility, fluidity and originality as described by Egana-delSol (2016).
Fluid Intelligence will be measured using progressive matrices Raven’s test.

See the complete survey attached to this registry.

Secondary Outcomes
Secondary Outcomes (end points)
Matching Intention and quality.
Equity Split intention
Secondary Outcomes (explanation)

Matching
Students will be encouraged to form partnerships via a “speed dating” exercise where the students sequentially meet potential partners to discuss possible collaboration. For each meeting, students will evaluate potential partners’ quality on a Likert scale of very low, low, medium, high, very high:
Personal compatibility
Shared vision for business
Partner’s Ability to execute
Partner’s Resources
Fit between your skills/resources and your partner’s
Overall interest


Empirical Strategy

Equity split Arrangement Game/Exercises and Matching Outcomes

The treatments will be short video lectures presenting one of two perspectives on equity splits in startups. The equal-splits treatment will feature a video advocating the “1/N rule”—that each founder should receive an equal stake in the company and its profits. Students will be provided with the arguments frequently cited for this division rule: that equity is an incentive so small stakes should be avoided, that uncertainty about future roles and productivity make it hard to predict appropriate shares, and that unequal splits go against basic notions of fairness and can sour your relationship with your cofounder from the beginning.

The unequal-splits treatment will feature a video advocating a formal pie slicing approach—that the founders should give a numerical score to their value added along several dimensions—initial idea, business plan development, domain expertise, commitment & risk, and day-to-day responsibilities—assign weights to each dimension, and use the averaged scores as a starting point to discuss an equity split. Students will be provided with the arguments frequently cited for this division rule: that effort and value added will rarely be equal, so equal splits can create friction as asymmetries become clear; that founders often bring capital to the table or work without pay while the other founder keeps their day job, rationalizing a greater share.

The unequal-splits treatment video will illustrate an excel implementation of the pie slicing activity, which the students in this treatment will perform with their partner later in the program.


Finally, we will estimates heterogeneous effects based on race, work-related dimensions and personality (using the big five taxonomy).

Experimental Design
Experimental Design

Random assignment into program based on oversubscription at individual level. We will assign randomly students into classes that provide the RYSE program. In this sense, the randomization is at individual level, but the treatment will be at class level.

The treatment is the participation in the RYSE during 6 months.

We will also subselect a random subsample of participating students to conduct some games related to matching and equity split between potential business partners.
Experimental Design Details

Empirical Strategy

Equity split Arrangement Game/Exercises and Matching Outcomes

The treatments will be short video lectures presenting one of two perspectives on equity splits in startups. The equal-splits treatment will feature a video advocating the “1/N rule”—that each founder should receive an equal stake in the company and its profits. Students will be provided with the arguments frequently cited for this division rule: that equity is an incentive so small stakes should be avoided, that uncertainty about future roles and productivity make it hard to predict appropriate shares, and that unequal splits go against basic notions of fairness and can sour your relationship with your cofounder from the beginning.

The unequal-splits treatment will feature a video advocating a formal pie slicing approach—that the founders should give a numerical score to their value added along several dimensions—initial idea, business plan development, domain expertise, commitment & risk, and day-to-day responsibilities—assign weights to each dimension, and use the averaged scores as a starting point to discuss an equity split. Students will be provided with the arguments frequently cited for this division rule: that effort and value added will rarely be equal, so equal splits can create friction as asymmetries become clear; that founders often bring capital to the table or work without pay while the other founder keeps their day job, rationalizing a greater share.

The unequal-splits treatment video will illustrate an excel implementation of the pie slicing activity, which the students in this treatment will perform with their partner later in the program.

Because classes within the same polytechnic will have different treatments, we will acknowledge that the lesson the students receive represents one school of thought, and that other classes may be presented with different rules.

We will follow a direct difference-in-difference approach, comparing treatment and control group at baseline and a follow up. The treatment will last one semester, and we will conduct a follow up one year after the program start.

We decided to follow this approach given our relatively small number of observations in total, and more importantly, our small number of participants institutions --- vocational colleges, community colleges--- in the program, which undermine our statistical power. Standard error will be clustered at institution level.

As outcomes we will use the outcomes specified previously under II. Skills and Decision-Making and III. Entrepreneurship Outcomes. As control we will use all the dimensions specified in the demographic section.

We will correct our p-values by index using family-wise error correction.
Since we care about mechanisms, we are not correcting for multiple testing between all different types of outcomes (Haushofer and Shapiro, 2017).
We will consider a 10% of attrition in our sample. For attrition we will run the following estimations:
1-. Mean test between control and treatment who “attrited” at baseline.
2-. If we add new subject at follow up, mean test between new control and treatment subjects at follow up.
3-. If the attrition is unbalanced between groups, we will also compare the mean differences among non-attritors at baseline.

In order to limit noise caused by variables with minimal variation, questions for which 95 percent of observations have the same value within the relevant sample will be omitted from the analysis and will not be included in any indicators or hypothesis tests. In the event that omission decisions result in the exclusion of all constituent variables for an indicator, the indicator will be not be calculated.



Data analysis

Random allocation of students to different interventions, and their match to random comparison student’s in control colleges, provides an exogenous variation in treatment status that allow us to estimate effects on relevant outcomes previously describe.

To estimate the impact of each of the interventions, we can use the following OLS specification:

Y_ij=α+β_1 T1_ij+β_2 T2_ij+X_ij+S_j+ϵ_ij

where Y_ij is the measure of an outcome of interest post-treatment for individual i in stratification block j (college and race), T1 is an indicator variable if college of student i was assigned to RYSE activities, T2 indicates whether college of student i was assigned to Potential Shares exercises.

X_ij is a vector of control variables at student level, S_j is a stratification block fixed effect, and ϵ_ij is the error term, clustered at the college-level. From this specification, coefficients β_k represent the causal effect of being offered the participation in the k intervention and corresponds to the Intention to Treat (ITT) estimate under imperfect compliance.

To analyze heterogeneity by race, we will use the previous specification and include interactions between each treatment arm and race.
Finally, we will estimates heterogeneous effects based also on work-related dimensions and personality (using the big five taxonomy).


In addition to the direct analysis of the experiment, we will use data from the speed dating exercise to infer preferences by fitting a frictionless matching model.
Variation in class size induces variation in market thickness for the matching process, which will allow us to study the effect of more partner options on each outcome of interest by adding class size into the above specification. Generally, we predict that thicker markets should yield more efficient assignments, though the degree to which this is true will depend on the preference structure and potential instabilities endemic to stable roommates’ problems.

Additionally, we will study a specification where variations in class size are interacted with treatment. We hypothesize that equal splits will lead to less efficient assignments, but has an indeterminate effect of incentives within the partnership. We hypothesize that the costs of equal splits relative to unequal splits will be greater for larger markets (class sizes) as situations where a non-equal split is optimal but blocked by a less efficient equal split become more likely.
Randomization Method
randomization done in office by a computer,
Randomization Unit
individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
420 students
Sample size: planned number of observations
420 students
Sample size (or number of clusters) by treatment arms
The expected sample size is 420 polytechnic students as follows:

40 students from Politeknik 1
150 students from Politeknik 2
80 students from Politeknik 3
150 students from Politeknik 4
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power Calculations Using previous evidence of Egana-delSol (2016), the expected effect of an intervention aimed at improving creative skills of adolescents participating in a similar program is around 0.13 standard deviations on different indices related to socio-emotional skills. From this data, we also use mean and standard deviations of these dimensions from the control group and correlations between baseline and follow-up. For instance, regarding creativity and assuming power of 0.80, a serial correlation of 0.22 (from Egana-delSol, 2016), and a reliability of 0.95, and individual assignment to treatment (i.e. no clusterization of treatment) we will require a sample with size of 366 students, 122 in the control group and 244 in the treatment group. However, estimating an attrition of 10%, the final sample should be 407 students, following the same proportion of treated and controls. These values are slightly different if we consider cognitive skills using Rave’s Test. Based on data from Egana-delSol (2016), which only considered follow up measurement, we have an estimated sample of 581 students, 194 in the control group and 387 in the treatment group. Considering a 10% of attrition, we would need 645 students in total. According our estimated sample we will be underpowered to estimate impact on Raven. Indeed, our minimum detectable effect for Raven is around .16 standard deviations given our restrictions and sample size.
Supporting Documents and Materials

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is 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