Economic effects of Entrepreneurship Training

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.

Introduction

The Citi Foundation found, through its 2017 Global Youth Survey, that 86% of the youth surveyed from emerging markets such as Malaysia agreed with the statement: “My dream is to open my own business.” This agrees with researchers at the Malaysian Institute of Economic Research who have found that there has been an increase in the development of entrepreneurship as both a concept and an activity in Malaysia. In addition, our observation is that many young individuals in Malaysia already have existing skills or interests that could be brought to the market. A significant number of youths take short courses on practical skills through community colleges set up by The Ministry of Higher Education – classes that include baking, woodworking, and tailoring. Many of these individuals would like to start their own business or startup, but lack the practical skills to design, plan, and execute their ideas.

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.



Brief Literature Review

Successful entrepreneurs usually share key characteristics such as a risk-taking attitude, motivation, optimism, personal initiative, and self-efficacy (de Mel et al., 2009, 2010; Schoar, 2010; Gielnik and Frese, 2013; Frese and Gielnik, 2014; Lederman et al., 2014). However, there are many of characteristic showing mixed evidence.
For instance, evidence related to entrepreneurial intention is mixed and hence it would be interesting to see if these hold importance for entrepreneurial success and intent. Moreover, evidence on the internal locus of control as an influence of entrepreneurial intention is mixed. Internal locus of Control (LoC) is a measure of the belief of people in their ability for controlling the environment through their actions. Several studies verify that internal LoC influences entrepreneurial intentions (Brockhaus and Horwitz, 1986; Hansemark, 1998; Mueller and Thomas, 2000; Gürol and Atsan, 2006; Orman, 2009; Karabult, 2016). People who believe that their actions can influence the environment will be willing to take more risks and establish businesses. However, Luca et al. (2011) in studying a sample of Romanian students found internal locus of control decreases the probability of participating in entrepreneurial activities.

Need for achievement as one of the imperative psychological motivations differentiating entrepreneurs to non-entrepreneurs, while many research experts have extensively used the Big five personality Taxonomy to predict entrepreneurial intention. These personality dimensions include extroversion, neuroticism, agreeableness, conscientiousness and openness to experience. Using scale of Big five personality traits, Goldberg (1981), Chem, Jing and Sung (2012), Indarti et al. (2004) and Akranbi (2013) found agreeableness, openness to experience, extraversion and conscientiousness to significantly and positively influence entrepreneurship while neuroticism has significant but negative influence on entrepreneurship.

Over the years, empirical literature has identified cognitive abilities to reflect numerical capabilities and verbal, reasoning, and problem-solving abilities, confidence and optimism and have found these characteristics to be correlated with entrepreneurial intent (De Mel et al., 2010; Djankov, 2006a, 2006b, 2006c; Bux and Honglin, 2015). Other studies like Krueger and colleagues (2000) reported that factors like perceived feasibility and desirability has positive impact on individuals’ intention to start a new business. Attitude - another form of cognitive structure - has also received attention in entrepreneurship research. Some scholars argued that attitude is a better indicator for entrepreneurial behavior than personal traits or demographic variables (Robinson et al., 1991; McCline et al., 2000).


Program Description

The RYSE Program contains 4 modules:

• Module 1: Design Thinking
o Students will first be exposed to Design Thinking classes, in which they learn how to systematize their thinking and ideas surrounding business proposals.
• Module 2: Entrepreneurial Strategy
o Students will be taught entrepreneurial strategy using Professor Scott Stern’s internationally renowned curriculum from the MIT Sloan School of Management. The skills accumulated through this course include opportunity identification, detailed industry analysis, and business research skills.
• Module 3: Business Skills
o Students will be taught practical business skills, which includes how to register one’s business, filing income taxes, hiring strategies, and marketing tools. Here we will also be teaching how to split equity among company founders.
• Module 4: Mentoring and Leadership and Personal Initiative
o Students will be paired up with local business leaders to learn from the best – both in terms of leadership, and also for practical advice on how to advance their business ideas. This Module will also have a component on Personal Initiative as in Campos et al. (2017).

2019-03-25

2019-08-23

Risk Taking behavior and Quality, Impulsiveness, Grit, Creativity, Entrepreneurial Intentions.

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.

Matching Intention and quality.
Equity Split intention

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).

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.

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 done in office by a computer,

individual

No

420 students

420 students

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

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.