Business reform outcomes: Why so different?

Post-crisis policy making increasingly focuses on doing business reforms. We argue that the effects of those reforms will be different across countries. To understand the reasons for the reform outcome divergence, we advance a novel firm-size distribution (FSDs) argument. At the center of the argument is the fact that FSDs are different across countries and stable over time. Then, if a given doing business reform induces firms of different size to grow differently, this will produce a variety of reform outcomes across countries. To advance the argument, we set up a tractable general equilibrium (GE) model and study how firms of different size grow after a doing business reform. The model predicts that larger firms will grow faster than smaller firms after the reform. The model predictions are tested on the Enterprise Surveys (ES) data, merged with the Doing Business indicators. We confirm that firms of different size grow differently after a Doing Business reform. Thus, based on the notable differences of firm size distributions across countries, identical reforms to start, operate and close a business will produce a variety of reform outcomes across countries.     

Anti‐foreigner Sentiment: State of the Art

This review presents the contributions of anti‐foreigner sentiment research, its theoretical and methodological limitations, and potential solutions for its further development. Six different explanations are proposed to account for the distribution of anti‐foreigner sentiment within and across countries: economic competition, human capital, cultural affinity, social capital, political values, and the institutional environment. In this review, we argue that much of the extant literature heavily emphasizes variables, rather than causal mechanisms, and exhibits three main methodological limitations: (a) variable selection bias; (b) determining causality; and (c) endogeneity. We propose synthesizing prevailing theoretical perspectives around causal mechanisms and reformulating predictive models to strengthen a promising research program.     

Asteroid Risk Assessment: A Probabilistic Approach

Following the 2013 Chelyabinsk event, the risks posed by asteroids attracted renewed interest, from both the scientific and policy‐making communities. It reminded the world that impacts from near‐Earth objects (NEOs), while rare, have the potential to cause great damage to cities and populations. Point estimates of the risk (such as mean numbers of casualties) have been proposed, but because of the low‐probability, high‐consequence nature of asteroid impacts, these averages provide limited actionable information. While more work is needed to further refine its input distributions (e.g., NEO diameters), the probabilistic model presented in this article allows a more complete evaluation of the risk of NEO impacts because the results are distributions that cover the range of potential casualties. This model is based on a modularized simulation that uses probabilistic inputs to estimate probabilistic risk metrics, including those of rare asteroid impacts. Illustrative results of this analysis are presented for a period of 100 years. As part of this demonstration, we assess the effectiveness of civil defense measures in mitigating the risk of human casualties. We find that they are likely to be beneficial but not a panacea. We also compute the probability—but not the consequences—of an impact with global effects (“cataclysm”). We conclude that there is a continued need for NEO observation, and for analyses of the feasibility and risk‐reduction effectiveness of space missions designed to deflect or destroy asteroids that threaten the Earth.     

On the convergence of two sequential Monte Carlo methods for maximum a posteriori sequence estimation and stochastic global optimization

This paper addresses the problem of maximum a posteriori (MAP) sequence estimation in general state-space models. We consider two algorithms based on the sequential Monte Carlo (SMC) methodology (also known as particle filtering). We prove that they produce approximations of the MAP estimator and that they converge almost surely. We also derive a lower bound for the number of particles that are needed to achieve a given approximation accuracy. In the last part of the paper, we investigate the application of particle filtering and MAP estimation to the global optimization of a class of (possibly non-convex and possibly non-differentiable) cost functions. In particular, we show how to convert the cost-minimization problem into one of MAP sequence estimation for a state-space model that is “matched” to the cost of interest. We provide examples that illustrate the application of the methodology as well as numerical results.     

Cross‐Cultural Measurement Invariance of Adolescent Self‐Report on the Pediatric Quality of Life Inventory™ 4.0

This study evaluated the cross‐cultural measurement invariance of the Pediatric Quality of Life Inventory version 4.0 (PedsQL^?) among adolescents sampled from Bulgaria, Croatia, India, Indonesia, Nigeria, Serbia, and Turkey. The multiple‐indicator multiple‐cause (MIMIC) model was used, which allowed controlling of demographic variables (i.e., age, gender, and socioeconomic status). Significant effects of country on scores within the PedsQL^? domains were observed, with up to 17 items showing differential item functioning (DIF) across the countries. We did not find support for cross‐cultural measurement invariance hypotheses for scores on the PedsQL^? adolescent self‐report in this study. Researchers should use caution in making cross‐cultural quality of life comparisons while using the PedsQL.