Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step GMM/IV type estimation procedure for the parameters of the model. We then establish the limiting distribution of our suggested estimators, and give consistent estimators for their asymptotic variance covariance matrices, utilizing results given in Kelejian and Prucha (2007b). Monte Carlo results are given which suggest that the derived large sample distribution provides a good approximation to the actual small sample distribution of our estimators.JEL Code: C21, C31.

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This paper studies the use of psychometric tests, designed by the Entrepreneurial Finance Lab (EFL), as a tool to screen out high credit risk and potentially increase access to credit for small business owners in Peru. We compare repayment behavior patterns across entrepreneurs who were offered a loan based on the traditional creditscoring method versus the EFL tool. We find that the psychometric test can lower the risk of the loan portfolio when used as a secondary screening mechanism for already banked entrepreneurs-that is, those with a credit history. The EFL tool can also allow lenders to offer credit to some unbanked entrepreneurs-that is, those without a credit history-who were rejected based on their traditional credit scores, without increasing the risk of the portfolio. JEL Classification: D82, G21, G32 I . I N T R O D U C T I O NGiven the important role that small and medium enterprises (SMEs) play in a healthy and dynamic economy, many studies have attempted to understand the factors that affect their creation and performance. 1 These studies show that SMEs face greater financial constraints than large firms and that these constraints could be one of the factors that limit their growth (

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