Experimental Design
The target group for this experiment are YAIG and their members, rural youth members organized for conducting joint farm and off-farm activities. These YAIGs have been operating for the last five years and they were being supported by the SAfA (Foundations Alliance for Africa) project implemented by the Hanns R. Neumann Stiftung (HRNS). These groups have been working together closely, some of which have joint ventures and businesses, which allow them to know each other. This offers a unique opportunity to test the potential of community-based targeting to identify high-ability youth entrepreneurs.
Our intervention includes targeting processing to identify high-ability entrepreneurs, which ultimately leads to transfer of modest seed capital. The main purpose of the intervention is to test two methods for identifying high-ability youth entrepreneurs while also quantifying the impact of the seed capital on a number of labor market and entrepreneurial outcomes. Our sample includes youth aged 18–35 who are organized across 183 YAIGs, with an average size of 15 members. The targeting process and ultimate transfer of the seed capital follows a three-stage selection process. First, we administer a comprehensive baseline survey that involves community-based (peer) ranking while also capturing comprehensive list of psychometric indicators and entrepreneurial ability measures. The community-based peer rankings are used to identify and rank youth based on perceived entrepreneurial ability and following the criteria given above. Similarly, we aim to use the psychometric and measures of entrepreneurial ability to identify and rank youth according to their predicted entrepreneurial ability and using data-driven (machine learning) methods. Second, YAIGs will be randomly assigned into three equal groups, which vary in terms of the targeting process for selecting the ultimate beneficiaries of the seed capital. Below we describe the three arms and the targeting process followed (see Figure 1). In the final stage, four youth from each of the YAIG selected in the treatment group will receive 20,000 Birr seed capital to support business creation or career progression.
Arm 1 (Community Targeting). YAIGs assigned to this group, a third of the YAIGs, will follow community-based targeting. Each participant privately ranks all eligible youth by perceived entrepreneurial ability. This follows Hussam et al. (2022). The core motivation and rationale builds on the fact that community members hold accurate information about who in their neighborhood is a high-ability entrepreneur — an important information that program implementers cannot easily access. We aim to implement alternative aggregation methods to generate weighted ranking and those top-four ranked youth within each YAIG receive the seed capital to support, establish, expand or strengthen their business. We particularly aim to test alternative weighing of rankings by members, leaders and self-ranks. To facilitate this, we also elicit relationships (and acquaintances) between peers, those ranking and being ranked.
Data-driven (machine learning) method: In another third of the YAIGs we follow a data-driven targeting method building on machine learning methods proposed by McKenzie and Sansone (2019) and Bryan et al. (2024), using psychometric indicators (including measures of big five personality, self-efficacy, grit and locus of control) as well as other measures of entrepreneurial tendencies and talents. This approach follows the predictive-targeting approach of McKenzie and Sansone (2019) and Bryan et al. (2024), through which construct a data-driven score that predicts a youth's entrepreneurial performance using a supervised machine learning model trained entirely on baseline data. The procedure is as follows. We define a baseline performance label as a standardized composite of current entrepreneurial and labor-market outcomes (business ownership, revenue, profit, and income), constructed for every youth. We then train a cross-validated, regularized supervised model that predicts this performance label from the full set of psychometric and enterprising ability and tendency indicators, along with basic demographic and business-experience covariates. We use k-fold cross-validation so that each youth's score is an out-of-fold prediction, eliminating overfitting and in-sample leakage. The estimated model yields a single predicted entrepreneurial-performance score for each youth; within each data-driven YAIG. Similar to the community-based ranking, this method will be utilized to rank youth based on their predicted entrepreneurial potential. Like the first arm, those top-four youth with the highest score within each YAIG receive the seed capital to support, establish, expand or strengthen their business.
Pure Control: A third of the YAIGs will remain as control group and no one in these YAIGs will receive seed capital. However, YAIG leaders and members will administer the community and peer-ranking exercise in the baseline survey. Similarly, we will administer the comprehensive modules used for capturing psychometric and entrepreneurial skills data, which will be used to construct comparable control groups for each of the targeting methods described above. We note that in the baseline survey, all three groups of YAIGs conduct similar surveys and ranking exercises. YAIG members in control clusters are enumerated and surveyed at baseline and endline but receive no grant.