Explaining the Gender Gap in Waiting Times for Scheduled Surgery in the Portuguese National Health Service

Objective: This paper evaluates the gender gap in waiting times for scheduled surgery, using information on 2.6 million surgical episodes in Portuguese National Health Service hospitals covering the period from 2011 to 2015. Methodology: We estimated the gross gender gap, i.e., the differential between the waiting times of men and women, and then add several explanatory variables that can account for this difference to estimate an adjusted gender gap. The variables are added in a way that permits the most flexible parametric specification. Next, we used Gelbach’s decomposition to understand the contribution of each variable to the difference between the gross and the adjusted gender gaps. Results: The gross gender gap of 10% is reduced to a 3% adjusted gender gap after accounting for observable explanatory factors. Gelbach’s decomposition shows that patient priority and hospital-fixed effects are the variables that contribute the most to the explained component of the gap. The analysis suggests that men tend to be ranked with more severe priorities, and that there are hospital specificities that cause men to have shorter waiting times. Conclusions: Overall, we identified a gender bias against women in surgery waiting times, but the size of the bias is smaller than was previously suggested in the literature.

Learning With Heterogeneous Misspecified Models: Characterization and Robustness

This paper develops a general framework to study how misinterpreting information impacts learning. Our main result is a simple criterion to characterize long‐run beliefs based on the underlying form of misspecification. We present this characterization in the context of social learning, then highlight how it applies to other learning environments, including individual learning. A key contribution is that our characterization applies to settings with model heterogeneity and provides conditions for entrenched disagreement. Our characterization can be used to determine whether a representative agent approach is valid in the face of heterogeneity, study how differing levels of bias or unawareness of others' biases impact learning, and explore whether the impact of a bias is sensitive to parametric specification or the source of information. This unified framework synthesizes insights gleaned from previously studied forms of misspecification and provides novel insights in specific applications, as we demonstrate in settings with partisan bias, overreaction, naive learning, and level‐k reasoning.

COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

We consider here an extended SIR model, including several features of the recent COVID-19 outbreak: in particular the infected and recovered individuals can either be detected (+) or undetected (−) and we also integrate an intensive care unit capacity. Our model enables a tractable quantitative analysis of the optimal policy for the control of the epidemic dynamics using both lockdown and detection intervention levers. With parametric specification based on literature on COVID-19, we investigate sensitivity of various quantities on optimal strategies, taking into account the subtle tradeoff between the sanitary and the economic cost of the pandemic, together with the limited capacity level of ICU. We identify the optimal lockdown policy as an intervention structured in 4 successive phases: First a quick and strong lockdown intervention to stop the exponential growth of the contagion; second a short transition phase to reduce the prevalence of the virus; third a long period with full ICU capacity and stable virus prevalence; finally a return to normal social interactions with disappearance of the virus. We also provide optimal intervention measures with increasing ICU capacity, as well as optimization over the effort on detection of infectious and immune individuals.

TESTING A PARAMETRIC TRANSFORMATION MODEL VERSUS A NONPARAMETRIC ALTERNATIVE

Despite an abundance of semiparametric estimators of the transformation model, no procedure has been proposed yet to test the hypothesis that the transformation function belongs to a finite-dimensional parametric family against a nonparametric alternative. In this article, we introduce a bootstrap test based on integrated squared distance between a nonparametric estimator and a parametric null. As a special case, our procedure can be used to test the parametric specification of the integrated baseline hazard in a semiparametric mixed proportional hazard model. We investigate the finite sample performance of our test in a Monte Carlo study. Finally, we apply the proposed test to Kennan’s strike durations data.

COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

An extended SIR model, including several features of the recent COVID-19 outbreak, is considered: the infected and recovered individuals can either be detected or undetected and we also integrate an intensive care unit (ICU) capacity. We identify the optimal policy for controlling the epidemic dynamics using both lockdown and detection intervention levers, and taking into account the trade-off between the sanitary and the socio-economic cost of the pandemic, together with the limited capacity level of ICU. With parametric specification based on the COVID-19 literature, we investigate the sensitivities of various quantities on the optimal strategies. The optimal lockdown policy is structured into 4 phases: First a quick and strong lockdown intervention to stop the exponential growth of the contagion; second a short transition to reduce the prevalence of the virus; third a long period with full ICU capacity and stable virus prevalence; finally a return to normal social interactions with disappearance of the virus. The optimal scenario avoids the second wave of infection, provided the lockdown is released sufficiently slowly. Whenever massive resources are introduced to detect infected individuals, the pressure on social distancing can be released, whereas the impact of detection of immune individuals reveals to be more moderate.

SMT-Based System Verification with DVF

We introduce the <i>Deductive Verificaton Framework</i> (DVF), a language and a tool for verifying properties of transition systems. The language is procedural and the system transitions are a selected subset of procedures. The type system and built-in operations are consistent with SMT-LIB, as are the multisorted first-order logical formulas that may occur in DVF programs as pre- and post-conditions, assumptions, assertions, and goals. A template mechanism allows parametric specification of complex types within the confines of this logic. Verification conditions are generated from specified goals and passed to SMT engine(s). A general assume-guarantee scheme supports a thin layer of interactive proving.

SIGNAL EXTRACTION IN LONG MEMORY STOCHASTIC VOLATILITY

Long memory in stochastic volatility (LMSV) models are flexible tools for the modeling of persistent dynamic volatility, which is a typical characteristic of financial time series. However, their empirical applicability is limited because of the complications inherent in the estimation of the model and in the extraction of the volatility component. This paper proposes a new technique for volatility extraction, based on a semiparametric version of the optimal Wiener–Kolmogorov filter in the frequency domain. Its main characteristics are its simplicity and generality, because no parametric specification is needed for the volatility component and it remains valid for both stationary and nonstationary signals. The applicability of the proposal is shown in a Monte Carlo and in a daily series of returns from the Dow Jones Industrial index.