Andreas Behr’s research while affiliated with University of Duisburg-Essen and other places

We analyze the effect of local supplier and customer densities on employment growth, using a sample of $19\,275$ 19 275 German firms. We suggest a new approach to estimating firm-specific customer and supply densities through combining input-output data from the German Federal Statistical Office and firm-level data from the Orbis database. Existing empirical analyses are primarily conducted on a regional level, so that findings may depend on the regarded regional level and only provide limited insights into the existence and effects of agglomeration on growth at the firm-level. We use kernel density estimation to avoid the arbitrariness of spatial boundaries and scales, and find that the regional agglomeration patterns vary considerably between different sectors. Our econometric analysis reveals that input supplier and customer densities have a statistically significant effect on firm growth and that the firms’ age plays a crucial role in analyzing agglomeration economies.

The increasing number of students enrolled in higher education institutions and the growing demand in the labour market for university graduates make the analysis of study success and study dropout more and more important. Dropping out from higher education is a complex process and students have very different motives for leaving university without obtaining a degree. We provide a detailed analysis of the different dropout reasons and aim at identifying distinctive types of dropout students using cluster analysis. The most important reasons for leaving university without a degree were in this study observed to be mainly related to interest and expectations concerning study programmes as well as aspects associated with student performance. Using hierarchical cluster analysis, we have found that the dropout decision was based on a variety of reasons, rather than a clear single motive. Our results provide higher education institutions insights into the process of dropping out and thereby a basis for suitable and more specific countermeasures.

Withdrawing from university is a complex decision-making process, during which several conditions and problems from different areas of life and study accumulate and affect each other. This study is based on the National Educational Panel Study (NEPS), which includes a wide range of information on study course and students’ characteristics, and aims at providing an encompassing analysis of determinants influencing students’ dropout decision. Determinants can be categorized into demographic and family background, the financial situation of students, their prior education, institutional determinants, as well as motivation and satisfaction with study. Both, a bivariate analysis, as well as a logistic regression model with LASSO regularization identify many important determinants already known before or at the beginning of the study, such as prior education and satisfaction related variables, allowing early identification of at-risk students and the implementation of prevention programs.

In most countries, immigrant and native students perform differently in the Programme for International Student Assessment (PISA) due to two main reasons: different immigration regimes and differences in their home-country educational systems. While there is sophisticated literature on the reasons for these performance gaps, it is barely considered in the educational efficiency research. Our approach distinguishes between selection effects caused by immigration policies, and the efficiency of educational systems in integrating immigrant students, given their socio–economic background. Accordingly, we split our sample, which consists of 153,374 students in 20 countries, calculate various different efficient frontiers, and ultimately decompose and interpret the resulting efficiency values. We find large differences in educational system efficiency, when controlling for negative selection effects caused by immigration regimes.

This guide accompanies the following article: Behr, A., Giese, M., Teguim K., H. D., Theune, K. Dropping out of university: a literature review, Review of Education, https://doi.org/10.1002/rev3.3202

This study provides a comprehensive review of the phenomenon of students dropping out from tertiary education. Student withdrawal is the result of a long decision‐making process and complex interaction between several determinants. We first provide an overview of definitions, theoretical models and perspectives of dropping out. Referring to previous theoretical and empirical evidence from a wide range of disciplines, we then focus on a detailed discussion of determinants affecting the decisions of students to drop out. There are three main reasons for students to leave the higher education system without a degree. These are 1) the national education system, e.g., the country’s financing policy, 2) the higher education institutions, e.g., the type of institution or teaching quality, and 3) the students themselves, with this last aspect subdivided into a) pre‐study determinants, such as the secondary school type, and b) study‐related aspects, such as working while studying. Based on these findings, we discuss the implications for further research, especially the application of modern data mining techniques on comprehensive data sets covering a wide range of relevant determinants which may lead to new insights into the dropping out process. The results will provide helpful tools for universities wishing to implement early warning systems and to support students at risk, at an early stage of their study.

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.

Using a panel data set covering six European countries and 119700 firms ( 582153 observations) over the period 2008– 2013, we analyse the cash flow sensitivity of investment spending. As most of the firms are not listed at stock exchanges, a balance sheet-based approximation on Tobin’s Q is used to indicate investment opportunities. We analyse internal and external liquidity constraints and their effect on investment decisions. In the literature, external constraints are most often indicated by simple accounting-based items/ratios. As the adequacy of the a priori indicator, reflecting the external constraints, is crucial, we contribute in proposing a more sophisticated approach. We estimate propensities to default using adapted random forests. In our descriptive analysis, we find strong evidence for the u-shape of the investment curve. However, after controlling for investment opportunities we find no increased cash flow sensitivity of investment, neither for a priori externally nor for internally constrained firms. Hence, our results hint for the absence of liquidity constraints. We attribute these towards the rather expansionary monetary policy since the financial crisis.