We assemble a rich dataset that maps geocoded microdata on various measures of living standards, point-specific geographical attributes, and remotely-sensed pollution data onto gridded population data to determine whether urbanization yields greater benefits or costs. We focus on developing Asia...

Economic uncertainty is a driver of the business cycle. In a recent study, Claveria (2022) used a fixed-effects model to assess the impact of uncertainty on suicide rates worldwide. Using that same panel, in which global economic uncertainty is linked to the evolution of the suicide rates in 183...

This paper examines the performance of several state-of-the-art deep learning techniques for exchange rate forecasting (deep feedforward network, convolutional network and a long short-term memory). On the one hand, the configuration of the different architectures is clearly detailed, as well as...

This paper examines the evolution of consumer uncertainty about unemployment one year after the irruption of the covid-19 pandemic in European countries. Since uncertainty is not directly observable, we use two alternative methods to directly approximate it. Both approaches are based on...

We apply the two-step machine-learning method proposed by Claveria et al. (2021) to generate country-specific sentiment indicators that provide estimates of year-on-year GDP growth rates. In the first step, by means of genetic programming, business and consumer expectations are evolved to derive...

In this study we present a metric of consensus for Likert-type scales. The measure gives the level of agreement as the percentage of consensus among respondents. The proposed framework allows to design a positional indicator that gives the degree of agreement for each item independently of the...