JBNST - Vol. 240/6 - 2020

We propose a method to decompose net lending flows into loan origination and repayments. We show that a boom in loan origination is transmitted to repayments with a very long lag, depressing the growth rate of the stock for many periods. In the euro area, repayments of the mortgage loans granted in the boom preceding the financial crisis have been dragging down net loan growth in recent years. This concealed an increasing dynamism in loan origination, especially during the last wave of ECB’s non-standard measures. Using loan origination instead of net loans has important implications for understanding macroeconomic developments. For instance, the robust developments in loan origination in recent times explain the strengthening in housing markets better than net loans. Moreover, credit supply restrictions during the crisis are estimated to be smaller. Overall, we show that analyses of credit dynamics benefit from putting the focus on loan origination instead of net loans, especially after large booms.

We predict university dropout using random forests based on conditional inference trees and on a broad German data set covering a wide range of aspects of student life and study courses. We model the dropout decision as a binary classification (graduate or dropout) and focus on very early prediction of student dropout by stepwise modeling students’ transition from school (pre-study) over the study-decision phase (decision phase) to the first semesters at university (early study phase). We evaluate how predictive performance changes over the three models, and observe a substantially increased performance when including variables from the first study experiences, resulting in an AUC (area under the curve) of 0.86. Important predictors are the final grade at secondary school, and also determinants associated with student satisfaction and their subjective academic self-concept and self-assessment. A direct outcome of this research is the provision of information to universities wishing to implement early warning systems and more personalized counseling services to support students at risk of dropping out during an early stage of study.

Clinical studies and economic experiments are often conducted with randomized controlled trials. In clinical studies, power calculations are carried out as a standard. But what’s about economic experiments? After describing the basic idea of the calculation procedure in a brief tutorial, I tackle the practice of sample size calculations in the field of experimental economics by considering the publications of 5 economic journals in the period 2000–2018. These are two top-ranked economic journals (Quarterly Journal of Economics and American Economic Review), the leading field journals in the area of experimental economics (Experimental Economics) and behavioral sciences (Journal of Economic Behavior and Organization), and a leading field journal in environmental economics (Environmental and Resource Economics). In contrast to clinical drug trials, sample size calculations have rarely been carried out by experimental economists. But the number of power calculations has slightly increased in recent years, especially in the top-ranked journals of economics. However, this can be partly explained by the fact that field experiments (in which scholars pay more attention to power analyses than in lab experiments these days) play an important role in these journals.