Migration,Friendship Ties,and Cultural Assimilation

We study immigrant assimilation by analyzing whether friendship with natives is a measure of cultural assimilation, and by investigating the formation of social ties. Using the German Socio‐Economic Panel, we find that immigrants with a German friend are more similar to natives than those without German friends, along several important dimensions, including concerns about the economy, an interest in politics, and a host of policy issues. Turning to friendship acquisition, we find that becoming employed, time spent in the host country, the birth of a child, residential mobility, and additional education acquired in the host country are significant drivers of social network variation.     

Machine learning,artificial neural networks and social research

Quality & Quantity - Machine learning (ML), and particularly algorithms based on artificial neural networks (ANNs), constitute a field of research lying at the intersection of different...     

On the determinants of data breaches: A cointegration analysis

Decisions in Economics and Finance - Cyber risks and particularly data breaches constitute one of the new frontiers of risk modeling for insurers across the world. We use the cointegration...     

Sharp IV Bounds on Average Treatment Effects on the Treated and Other Populations Under Endogeneity and Noncompliance

In the presence of an endogenous binary treatment and a valid binary instrument, causal effects are point identified only for the subpopulation of compliers, given that the treatment is monotone in the instrument. With the exception of the entire population, causal inference for further subpopulations has been widely ignored in econometrics. We invoke treatment monotonicity and/or dominance assumptions to derive sharp bounds on the average treatment effects on the treated, as well as on other groups. Furthermore, we use our methods to assess the educational impact of a school voucher program in Colombia and discuss testable implications of our assumptions. Copyright © 2015 John Wiley & Sons, Ltd.     

The complementary nature of technological capabilities: Measurement and robustness issues

The application of composite indicators to complex phenomena in social science has come to remarkable prominence. As a result, they have been widely applied in wide-ranging fields. This paper explores the application of a composite indicator of technological capabilities that can be also valid for composite indicators in other fields. We focus on whether different methodologies to build composite indicators lead to different results. The arithmetic mean to aggregate technological capabilities variables has been widely criticized, as it allows for perfect substitutability between variables, thus disregarding the complementary nature of technological capabilities. We introduce a new aggregation rule, the concave mean, in order to take into account the complementarity argument in a Science-Technology-Innovation context. Using a dataset for 138 countries, we show that the arithmetic mean is biased in principle but robust in practice. This depends upon the fact that technologically more advanced countries tend to have a more complementary structure in their technological capabilities compared to less developed nations.     

The European Monetary Policy Responses During the Pandemic Crisis

This paper uses an event-based analysis to describe how the European Central Bank’s (ECB’s) policy responses to the pandemic crisis have affected the European financial and economic system. The result of our exercise, which is based on the examination of the main measures taken by the ECB during 2020, is that these responses have positively affected the European economic system by improving banks’ lending activity and by indirectly creating room for expansionary fiscal policies in the euro area’s high-debt countries that do not have fiscal capacity.

     

The power of dimensional analysis in production systems design

Dimensional Analysis (DA) is a well-known methodology in physics, chemistry and other traditional engineering areas. In its simplest form, DA is used to check the meaningfulness of a set of equations (dimensional homogeneity). In the last century, the dimensional theory has been profoundly investigated: its highest achievement is the Buckingham theorem (or pi-theorem), which states that any equation modelling a physical problem can be rearranged in terms of dimensionless ratios, thus saving variables to be handled, and especially enriching the inner physical knowledge of the studied phenomenon.In this paper we investigate how DA can be applied to Operations Management (OM) topics and which benefits it can bring to researchers in this area. A literature review is performed to clarify the main operative issues regarding DA application (assumptions and limitations); then existing applications of DA to OM are explored, pointing out that few researchers have tried to apply this methodology in the OM research field.Stemming from this analysis, we applied the pi-theorem to the design of a Flexible Manufacturing System. A complex problem, requiring 13 dimensional quantities to be expressed, is first studied via simulation; then DA is applied, reducing the number of variables to 9 dimensionless ratios. The reduced problem has a suitable size to be analytically explored and a regression model is formulated which, compared with the simulation study, offers the same precision in analysing the FMS behaviour, being more compact and powerful. This application shows the potential of DA in OM research, and will hopefully draw the attention of researches to this powerful, but unfamiliar and therefore neglected, methodology.     

Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.