This is a rejoinder to a comment written by Cutler and Miller on our recent paper, "Public Health Efforts and the Decline in Urban Mortality" (IZA DP No. 11773), which reanalyzes data used by Cutler and Miller to investigate the determinants of the urban mortality decline from 1900 to 1936. Two...

Using data on 25 major American cities for the period 1900-1940, we explore the effects of municipal-level public health efforts that were viewed as critical in the fight against food- and water-borne diseases. In addition to studying interventions such as treating sewage and setting strict...

Pollution is a common byproduct of economic activity. Although policymakers should account for both the benefits and the negative externalities of polluting activities, it is difficult to identify those who are harmed and those who benefit from them. To overcome this challenge, our paper uses a...

The 21st century has been a period of rising inequality in both income and health. In this study, we find that geographic inequality in mortality for midlife Americans increased by about 70 percent from 1992 to 2016. This was not simply because states such as New York or California benefited...

Using a large panel of US banks over the period 2008-2013, this paper proposes an early warning framework to identify bank heading to bankruptcy. We conduct a comparative analysis based on both Canonical Discriminant Analysis and Logit models to examine and to determine the most accurate one....

This research paper shows how the visualization of the GDP (real prices) on a surface is moving in real-time motion effect. We applied an alternative Multi-Dimensional (MD) graphical computational system and Artificial Intelligence (AI) to observe different GDP behaviors simultaneously in...

We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior...

Machine Learning models are often considered to be "black boxes" that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which...

This paper shows that shootings are predictable enough to be preventable. Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, we train a machine learning model to predict the risk of being shot in the next 18 months. We address central concerns...