and the date when the entrepreneur was screened by the EFL tool, as well as the applicant’s age, gender, marital status, business sales, and sector of activity.<sup>13</sup> The EFL scores used for the pilot were initially generated by a model built with data pooled across various African countries where EFL had tested its credit-scoring model (EFL Africa model v2).<sup>14</sup> Once the current implementing bank generated enough observations, EFL adapted the model, giving more weight to the implementing institution’s data. EFL recalculated the scores for our sample using the new model (EFL Global model v1—the first to incorporate non-African data). We use these new scores in Section 5 as an extension of our results.

The implementing institution shared with us the threshold EFL score it used to determine whether or not to offer a loan. For each applicant, the institution also let us know which decision it would have taken based on the score provided by the private credit bureau. Because of confidentiality agreements, the institution could not share the credit bureau score itself. Through EFL, we later obtained access to this score for 57 percent of the entrepreneurs in our sample.

We also obtained credit history data from the public credit registry managed by the SBS. All financial institutions subject to credit risk—including credit unions not authorized to receive deposits—have to provide monthly data to this public credit registry. Each month the SBS reports the maximum number of days in arrears (across all financial institutions), total debt, and classifies debtors in one of five status categories: normal, with potential payment problems, poor payment, doubtful payment, and loss.<sup>15</sup> Only banked entrepreneurs appear in the public credit registry data.

> 13 The questionnaire administered by EFL also gathered data on entrepreneurs’ years of education, number of dependents, family history of entrepreneurship, and psychological profile, as well as the age of their business, number of businesses started, business assets, etc. Because this information is used to calculate the score, EFL restricted our access to it.

> 14 Using pooled data has the advantage of improving predictive power because the samples involved are larger, but it has the disadvantage of combining data across different cultures and across financial institutions serving different market segments via different products. Klinger et al. (2013) find that traits explaining loan defaults and business size are not consistently homogeneous across countries or market segments. However, using data exclusively obtained from the implementing bank is costly and time consuming and may not generate large enough samples to overcome low statistical power and overfitting issues. EFL therefore uses an adaptive model—a Bayesian hierarchical logit model—which assumes that the behavior of covariates varies by country, market segment, and financial institution. The parameters estimated by the model are a weighted combination that uses the pooled data across countries and the data available for a particular country and segment (tailored model); the more data are available for a particular country and segment, the larger the weight placed on the tailored model. Additionally, the more homogeneous the behavior of a covariate across countries, the larger the weight put on the global model for that particular variable.

> 15 Entrepreneurs are identified in the SBS database based on their national ID number or their business tax ID number. Entrepreneurs with consumer credit or microcredit are classified as normal if they are up to 8 days in arrears, as showing potential payment problems if they are between 9 and up to 30 days in arrears, as substandard if they are between 31 and up to 60 days in arrears, as doubtful if they are between 61 and up to 120 days in arrears, and as a loss if they are more than 120 days in arrears.

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